Artificial intelligence has turned the entire memory and storage stack — HBM, DRAM, NAND and hard disk — into the scarcest resource in computing. Every wafer routed to AI memory, and every platter routed to a hyperscaler, is denied to the device in your hands.
A standing dashboard of the whole memory landscape, from the fastest HBM cube to the coldest nearline drive. Pricing, market share, the company roster and the signal feed pull current figures live through Claude with web search; everything renders from a compiled baseline first.
Four tiers, fastest and priciest at top to cheapest-per-terabyte at bottom. AI demand is straining every one of them at once — the first time the whole stack has gone tight together.
Every kind of memory and storage, what it physically is, how it works, and where it's used. The whole industry splits on one line: volatile memory that needs constant power to remember, and non-volatile storage that keeps data when the power is off. Speed and cost trade off against capacity all the way down.
Holds data only while powered. It's fast and sits close to the processor, acting as the workspace a chip computes in. Cut the power and it forgets instantly. This is "memory" in the strict sense — SRAM and DRAM, including HBM.
Keeps data with the power off. It's slower but far cheaper per gigabyte and vastly higher capacity, so it stores everything persistently — the operating system, apps, files, models and archives. This is "storage."
The memory hierarchy above — tiny-and-instant at the top, vast-and-slow at the bottom — rhymes with how neuroscientists describe human memory: a fast, fragile "working" store for what you're thinking about right now, and slower, durable stores for what you've learned. The mapping below is a teaching aid, not a literal equivalence. It's genuinely useful for intuition, and genuinely misleading if taken too far — so the honest caveats follow right after.
Latencies and layer counts are representative orders of magnitude — exact figures vary by product, generation and process. "AI" tags mark tiers being reshaped directly by AI demand. The boundary between memory and storage is blurring: HBF, storage-class memory and CXL pooling all aim at the gap between fast-but-small DRAM and cheap-but-slow flash. The brain mapping is an educational analogy; neuroscience is still actively researching how human memory is encoded, consolidated and retrieved, and no current model treats the brain as a literal storage hierarchy.
Memory has always been brutally cyclical. The 2026 shortage breaks the pattern because it is demand-driven, not supply-driven — and it spans the entire stack at once. The 2021 crunch came from shuttered fabs; it healed when factories reopened.
This time the constraint is AI itself. Training and inference are bandwidth-bound, so GPU racks swallow HBM; the data they train on needs enterprise SSDs; the exhaust — checkpoints, logs, generated media — lands on nearline HDD. Makers pour capacity into the high-margin top of the stack, starving everything below.
Relief in volume isn't expected until late 2027–2028. Until then hyperscalers pre-pay to lock allocation. Hard-drive makers have sold out 2026 with agreements stretching to 2028; the three memory makers have added roughly $900 billion in combined market value since September 2025. Crucially, the makers are choosing not to flood the market: Samsung and SK hynix can fulfill only ~70% of DRAM orders yet are deliberately restraining capex to "minimize the risk of oversupply" — the discipline that could make this cycle outlast prior boom-busts (or, cynically, exactly what every supplier says before a glut).
HBM's stacked construction is far more wafer-intensive than ordinary memory — one bit of HBM forgoes ~three bits of standard DRAM. HBM alone now absorbs an estimated 23% of all DRAM wafers, draining the commodity pool that feeds phones, PCs and SSDs.
The investor's section. Memory is the most violently cyclical corner of tech — so the questions that matter aren't "is AI big?" but where are we in the cycle, what sustains it, and what breaks the thesis? This pulls together the leading indicators: the bit supply-demand gap, capex (the supply pipeline), valuation in cycle context, and an explicit bear case with the signals that would flip each risk.
Memory cycles run trough → recovery → boom → peak → correction. Every prior cycle has reverted: in the 2017–18 DRAM supercycle, supply tightened, margins crossed 50%, analysts declared the cycle "structurally different" — then supply caught up and margins fell toward zero within two years.
This cycle has a more credible structural case (AI demand + the HBM wafer tax constraining supply). But the mechanism that ends memory cycles hasn't changed: high margins attract capacity, capacity arrives in 12–18 months, and the gap closes.
When annual bit-demand growth runs above bit-supply growth, prices rise and the cycle is bullish. When supply growth crosses above demand, the cycle turns. Right now demand leads on both DRAM and NAND — but the gap is the thing to watch, not the price.
The bull case rested on supply being unable to respond — and that is now changing on the spend side, but not yet on the bit side. The response has begun: Micron raised FY2026 capex ~81% to ~$25B (having exited consumer memory to chase AI); SK hynix is spending well above its 2025 ₩27.5T (~$20B) and raised a record $26.5B ADR (Jul 10) earmarked for its Yongin and Cheongju fabs; Samsung restarted mothballed Pyeongtaek lines (Plant 4 Phase 2, converted foundry→memory; Line 5). But new cleanrooms take 2–3 years — SK hynix's Yongin Fab 1 targets 2027, Micron's Idaho 2027, Samsung's P5 ~2028 — so the extra bits land 2027–28, not 2026. The spend is up; the supply isn't here yet. That is exactly kill-switch #2 (§02·D): capex has stopped being the bear's missing piece, yet the 2026 shortage persists because you cannot ship DRAM from a fab that is still being equipped.
Modeled quarterly DRAM bit balance in billion gigabytes (GB): positive bars = supply surplus, negative = deficit. Real high-confidence anchors (TrendForce, Micron earnings) define the shape through 2026; everything beyond is an explicit modeled scenario, not a forecast — interest rates and AI demand 10 years out aren't really knowable. The whole point of plotting it is to make the assumptions inspectable.
Assumptions checked against the newest prints (Jul 6 2026): the deficit shape through 2026 still matches the data — but the rate story has a fresh anchor: TrendForce’s 3Q26 call has conventional DRAM contract prices decelerating to +13–18% QoQ (from ~+90s%) as consumer buyers hit an affordability ceiling (§03, kill-switch #1) — consistent with a deficit that is peaking, not widening. On the supply side, kill-switch #2 is ARMED for CY2028 (§02·D): 2026–27 capacity announcements land as the bear case’s 2028 flood if they all ship. Two structural offsets the model carries via the HBM wafer tax: Micron’s take-or-pay SCAs now lock ~20% of DRAM and ~30% of NAND through 2030 (§06·F·M), and HBM3E+HBM4 are booked through 2027 into 2028 (§10·A feed, Jun 24). Modeled bars beyond 2026 were not rewritten on one quarter’s print — the scenario spread (bull/base/bear) is the honest container for that uncertainty; watch the Jul 23 Samsung/SK capex commentary (§02·E) for the next assumption test.
Anchors (high-confidence quarters): 2022Q1–Q4 surplus from TrendForce's "DRAM 2022 oversupply, bit supply +18.6% vs demand +17.1%"; 2023 trough from the documented ~30% memory revenue collapse; 2024 recovery from TrendForce; 2025 transition to deficit from rising contract prices; 2026 record deficit from Micron's December 2025 disclosure that "supply will remain substantially short of demand" and the industry can meet only half to two-thirds of demand. Modeled beyond 2026Q4 using: bit-demand CAGR (base 18%, bull 22%, bear 14%); supply growth lagged ~6 quarters behind capex (Micron Idaho online 2027; second fab 2028); and historical cycle behavior (memory always reverts — bear case = 2017-18 precedent applied). Quarters are interpolated within annual anchors using observed seasonality. This is a model, not a prediction. The further right you look, the wider the error band.
Every thesis needs its disconfirming evidence. These are the real risks, ranked by severity, each paired with the specific signal that would tell you it's materializing.
The crux of the bull thesis isn't just demand — it's that the three makers are deliberately not flooding the market. They're fulfilling only ~70% of orders yet restraining capex to protect pricing, and the new fabs that would break the shortage don't come online until 2027–28. This is the structural reason prices may stay high longer than a normal cycle — and, read cynically, also the setup for an eventual glut. The same fact supports both the bull and bear case.
Bit-growth, capex and TAM figures: TrendForce, Micron earnings, IDC, Counterpoint (2025–26 actuals; 2027–28 forecast). Supply-discipline figures: Samsung & SK hynix Q1 2026 earnings calls (~70% DRAM fulfillment, ~30%-of-sales capex, record profits), TrendForce (demand +35% vs supply +23% in 2026), DCD / Tom's Hardware (fab timing: SK M15X output 2H 2026→ramp 2027, Samsung P5 ~2028, Micron Japan H2-2028). The 2017–18 precedent: SK Hynix/Micron margins fell from 50%+ toward zero within ~2 years as supply caught up. Micron's own 10-K notes DRAM ASPs have swung between +40% and −40% year-over-year in the past five years. P/B ranges are approximate cycle bands. Cycle-phase placement is a judgment call, not a precise measurement. Everything here is analysis, not investment advice — and the bear case is as evidence-based as the bull case deliberately.
A 9-inning baseball read on the memory supercycle — an intuitive translation of the cycle-position analysis above. The 9 innings run from the 2023 trough (the bottom of the order) to the eventual peak and turn. Reviewing every signal in this dashboard — the bit-gap, prices, inventory, capex discipline, and the projected supply wave — here's the call.
As of Aug 22 this section no longer gives one number, because the evidence stopped supporting one. The physical cycle is still in the 6th: Korea's chip exports set a record in the Aug 1–20 flash at +198.8%, DDR4 is at an all-time high, HBM is booked through 2027, and even six-year-old accelerators are renting dearer than a year ago. Nothing is rolling over. The paper cycle has already played its 7th: a −40% KOSPI drawdown from the June peak, the first back-to-back circuit breakers in index history, ₩34T of margin accounts liquidated, and a sell side that has broken into open disagreement about 2027 earnings. Equities have had their correction; bits have not had their peak.
Mid–late 2025: the AI shortage bit hard, contract prices spiked, and Korea's export data hit records (+60% MoM in Jan 2026). The move that made the headlines.
2026 now: demand still > supply, inventories record-low, capex still restrained so supply can't yet respond. Prices climbing but the rate of ascent is what to watch.
Late 2026–2027: earnings peak (Q4'26–Q2'27), valuations stretched, sentiment euphoric. The 7th-inning stretch — the top comes into view but isn't here yet.
If AI demand keeps compounding and HBM's 3:1 wafer tax keeps commodity DRAM structurally starved, this upcycle could run longer than a normal memory cycle. The new supply-discipline evidence cuts this way: Samsung/SK hynix are restraining expansion (only ~70% order fulfillment), meaningful new volume doesn't land until 2027–28 (SK M15X ramps 2027, Samsung P5 ~2028, Micron Japan H2-2028), and research firms now say "no scenario where prices correct in 2H 2027." The game stretches — the peak slips toward 2028.
A demand air-pocket — an AI capex pause, or a double-ordering unwind — could end the game abruptly, jumping straight to the 9th. Memory's history is full of games called on account of rain: in 2017–18, margins went from 50%+ to near zero in two years once the mood turned. The disciplined-supply story is itself the risk: every maker is also quietly adding capacity (Samsung P5, SK M15X/Yongin, Micron Japan/Virginia), and "minimize oversupply" is precisely what suppliers say right before a glut. Wildcard: helium supply (critical to fabs) is exposed to the Middle East conflict.
This is an analogy, not a measurement — memory cycles don't have fixed innings, and the inning number is a judgment that translates the cycle-position analysis (§02·B) into an intuitive frame. It rests on the same signals shown throughout this dashboard: the bit-gap (demand still leads), prices (already spiked), inventory (record-low), capex (disciplined, supply can't respond until 2027), and the modeled supply/demand balance (crosses over ~2027). The honest range is the 5th–7th. The game can go to extra innings or be called early — and the further into the late innings you think we are, the more the risk register (§02·B) matters. Not investment advice.
The bear case, concentrated: six falsification criteria that would break or bend the supercycle thesis, each with a status as of Jul 2 2026 and the specific evidence that flips it. Statuses are dated judgments, not predictions — the whole point is that they're checkable. One is already tripped.
The spread is the point: five of six are not tripped, and the one that is tripped early on the second derivative, not the first. Categorical placement mirrors each card's chip above — a comparative re-plot, not new data.
Method. Statuses are dated editorial judgments (Jul 2 2026) applied to sourced readings: Micron FQ3 call and prepared remarks (moderation guidance, SCA ceilings, inventory days, price moves); §02·B fab timeline (Micron CY2028 greenfield, SK M15X 2027, Samsung P5 ~2028); TrendForce CoWoS reporting (Jun 2026) and TSMC expansion disclosures; Samsung 2026 HBM contract reporting; §09 for China positioning; 2018/2021 cycle references are approximate historical figures, labeled as such. This section exists because of the honesty contract: a thesis that can't name what would falsify it isn't a thesis. Not investment advice.
Every dated event the thesis meets next, in one place — now including the full quarterly reporting schedule across the complex: memory makers, hyperscalers, accelerator vendors, WFE/foundry, and the edge names whose margins reveal cost pass-through. Each row says which sections it feeds and which kill-switch (§02·D) it tests, so this reads as a falsification schedule rather than a hype reel. Completed events carry what actually happened, not just that they occurred. Dates marked confirmed come from company IR announcements; ~ approx are windows inferred from reporting history and will shift. Aug 15 2026 — South Korea's export releases are now on the calendar as a recurring cadence, because they are the highest-frequency hard data the memory thesis meets: the Korea Customs Service publishes flash exports for the 1st–10th (around the 11th) and the 1st–20th (around the 21st), and MOTIE publishes the full month on the first business day of the next. The 10-day flash carries the DRAM ex-modules unit price in $/kg — the single fastest public read on memory pricing on this board, and the early read on the monthly customs series §03·B charts. Nine such releases are scheduled below.
Provenance. Rebuilt Jul 30 2026. [confirmed] dates are company-announced: AMD Aug 4, SanDisk FQ4 and Western Digital FQ4 Aug 5, Marvell Aug 20, NVIDIA FQ2 Aug 26, Broadcom Q3 Sep 8, Qualcomm FQ4 Nov 11. [~ approximate] dates are inferred from each issuer's reporting history and are explicitly marked as such — Seagate, Applied Materials, Oracle FQ1, Micron FQ4 (~Sep 29 per its recent late-September cadence), ASML/TSMC Q3, and the October Q3 cluster for Alphabet, Microsoft, Amazon, SK hynix and Samsung. Completed rows carry outcomes as reported and cross-referenced to the sections that consumed them, including the two that reshaped this board in the last fortnight: SK hynix's record-but-missed Q2 (Jul 29) which triggered the KOSPI's first back-to-back circuit breakers (§03·C), and Amazon's Jul 30 capex raise to $220B attributed to memory prices (§06·F·D). Correction carried forward: the SK hynix Nasdaq listing is recorded here at its priced $26.5B, not the ~$28–29B an earlier build of this calendar showed (see §04·F and the Correction Log). Honesty note: a calendar is a schedule, not a forecast — every row states what the event would corroborate or falsify, and none predicts an outcome. Scheduled dates move; confirm against issuer IR before relying on any of them. Korea release dates [rule-derived, marked ~ approx]: the nine Korean rows are not read off a published KCS calendar. They are computed from the stated cadence — KCS flash after the 10th and after the 20th, MOTIE on the first business day of the following month — then shifted forward past weekends and Korean public holidays. The weekend rule is not uniform, and this board got it wrong once already. MOTIE demonstrably publishes on the 1st even at weekends — the completed Aug 1 2026 row below was a Saturday — so the Sep 1, Oct 1 and Nov 1 dates are left on the 1st, including the Sunday. KCS weekend behaviour is not evidenced either way in anything this board has seen, so the one KCS date that lands on a Sunday (Oct 11) is shifted to Oct 12 as an assumption, and is the least reliable date in the set. Treat every Korean date here as ±1 business day; the cadence is reliable, the exact posting time is not, and Chuseok (Sep 24–26 2026) sits close enough to the Sep 21 flash to move it. Two Korean rows are completed and carry outcomes: the Aug 1 MOTIE July print (semiconductors $41.01B, +178.8%) and the Aug 11 KCS Aug 1–10 flash (semiconductors $9.95B, +155.4%, a record August high at 46.8% of all exports). Staleness fixed in this pass: the calendar's internal "today" was pinned to Jul 30, so every countdown read 16 days short and six already-past events still displayed as upcoming; it now reads Aug 15. Five of those six are earnings prints whose results this board has not yet captured; a sixth, Tesla's Jul 22 Q2, was already sitting completed with an empty outcome field and was caught by the same sweep. All are marked completed and explicitly flagged as outcome-pending in the table rather than given invented results. Aug 22: Marvell's Aug 20 FQ2 joins them, taking the outcome-pending count to seven. Aug 29: NVIDIA's Aug 26 FQ2 makes it eight — a print this board will want to capture properly, since it is the demand pull-through for the whole chain. Oct 1: the board's "today" advanced from Aug 29, moving seven events dated Sep 1–Oct 1 out of "Ahead" — all four Korea releases recorded with their prints (the board's rule for Korea rows), Micron's FQ4 captured from its 8-K and re-dated to Sep 30, and Broadcom's Sep 8 Q3 and Oracle's Sep 9 FQ1 flagged as outcome-pending, which takes the outcome-pending count to ten. Oct 10: NVIDIA's Aug 26 FQ2 and AMD's Aug 4 Q2 are now captured from their own releases and filings (§06·F·V), which takes the outcome-pending count to eight. Sources: company investor-relations calendars and earnings announcements, Investing.com, MarketBeat, TipRanks, Nasdaq, Wall Street Horizon, Korea Customs Service, MOTIE, Yonhap, Korea Herald, Seoul Economic Daily. Not investment advice.
A decade of contract prices in one chart — five years of history, today, and five years of consensus forecasts. The 2021 peak gave way to the brutal 2022–23 crash (DRAM and NAND each lost ~30–40%), a brief 2025 soft patch, then the AI ignition that became the supercycle. Forward of Q1 2026 (dashed), the lines plot the path most analysts now see: another ~10% climb into 2H26, a plateau through 2027, then a gradual softening as new fab capacity comes online in 2028–2030.
HBM is priced per stack — each generation a steep premium (market estimates, 2026):
South Korea exports roughly 70% of the world's DRAM (Samsung + SK hynix), so its customs data is one of the fastest hard reads on the memory market. This section now charts that data directly — each month's declared export value divided by its net export weight, in dollars per kilogram, from Korea Customs' item-level tables — in place of the indexed reconstruction it carried until today, which overstated the rise roughly fourfold (§10·B). September was a record on every headline line: exports $120.9B (+83.5%), semiconductors $60.3B (+262.8%), memory $54.1B (+358.6%) — half of everything Korea sold abroad, on fewer working days because Chuseok fell in September. And the DRAM chip unit value fell 18.9% on the preliminary count, from $88,523 to $71,836/kg — the steepest monthly drop in three years of data — while DRAM chip export value rose 6.6%, the kilograms shipped rose 31%, and the DDR5 contract price rose 3.2%. That is a change in what was shipped, not in what it sold for, and the same month DRAM modules doubled to $16.5B. The confirmed September detail publishes on Oct 15.
Provenance & the honesty line. The unit values [cited — official, compiled]: Korea Customs Service item-level export statistics — declared FOB value in US dollars and net export weight in kilograms — for DRAM chips (HSK 8542.32.1010), DRAM modules (8473.30.4060), NAND flash (8542.32.1030), MCP including HBM (8542.32.3000) and SSDs (8523.51.1000), as compiled by the Korea Trade Statistics Promotion Institute (TRASS) and republished with value and weight by MoneyRecipe (data files published around Oct 1 2026); the DRAM-chip series matches the independently compiled Memory Price Chart customs feed to the dollar and the kilogram for every overlapping month, and the all-memory-IC line (HS 8542.32, Sep 2021 → Aug 2026) is from that feed's official KCS table. Oct 2023 → Aug 2026 are confirmed months. September 2026 is the month-end preliminary tally published Oct 1; KCS posts corrections and confirmed figures around the 15th of the following month, so September may move on Oct 15. "DRAM incl. modules" is computed here as (chip value + module value) ÷ (chip weight + module weight). It reconciles exactly with the figures this section previously cited from the press — $28,057/kg for January 2026 and ~$61,000 and ~$60,000 for May and June — which confirms those were the incl-modules basis of the same data. What a unit value is not: customs records do not identify generation, density, package, contract terms or customer, so a move in value per kilogram can be price, mix, or both; September is the clearest example yet of mix. The flashes [cited]: Korea Customs Sep 1–10 (released Sep 11: exports $34.97B, +82.6%; semiconductors $16.48B, +270.1%, 47.1% of exports; imports $24.61B; surplus $10.37B) and Sep 1–20 (released Sep 21: exports $71.4B, +78.3%; semiconductors $34.12B, +259.4%, 47.8%; imports $48.4B, +26.7%; surplus $23.0B; 15.5 working days, one fewer than a year earlier); the trade ministry's September release (Oct 1: exports $120.94B, +83.5%; semiconductors $60.3B, +262.8%, 49.9%; memory $54.07B, +358.6%; system chips $5.54B, +28.8%; SSDs $6.77B, +532.0%; imports $71.09B, +26.0%; surplus $49.85B; average daily exports $5.63B, +105%). DRAM-chip 10-day values ($5.01B, $10.64B, $16.78B cumulative) are TRASS via MoneyRecipe. Derived, not published: the days 11–20 and days 21–30 rows (subtraction; the latter mixes ministry and customs sources and is approximate), every prior-year base backed out of a stated growth rate, the August → September composition of the memory increase, and the incl-modules series. Price context [cited]: DDR5 16Gb fixed price $35.0 (Apr) → $37.5 (May) → $46.5 (Aug) → $48 (Sep) and NAND 128Gb $24.2 → $26.5 → $30.48 → $30.61, DRAMeXchange via the trade ministry (Seoul Economic Daily, Oct 4; Korea Data Economy, Sep 10); Bank of Korea export price index for DRAM, won basis, Jul +6.6% MoM / +270.3% YoY and Aug −3.4% / +253.4% (Sep 15 release; all goods −3.7% in won, +2.2% in contract currencies); KITA's HBM-related average export price per unit $40.68 (Mar) → $76.14 (Jul) → $73.39 (Aug), with the caveat that the code includes other items (Seoul Shinmun, Sep 21); KITA's DRAM price per unit $16.76 (May) → $22.90 (Jul) on 13.2% fewer units (Seoul Economic Daily, Aug 31); TRASS's Aug 1–20 DRAM-chip unit value $92,183/kg, +401.0% YoY, reported as matching about 620 grams of gold (TrendForce, Aug 26). Hana Securities' Sep 1–20 daily averages (DRAM +530% YoY, MCP +135%) and its LPDDR hypothesis are from its Sep 28 note. Retired today: the indexed reconstruction this chart drew from Jun 2023 to Jul 2026 (Jan 2025 = 100). It was calibrated to the National Assembly Budget Office's quarterly DRAM fixed price, which rose six-fold in 2025 for a benchmark commodity part, and it interpolated between press anchors. Customs data shows the DRAM-chip unit value rose 1.6× across 2025, not 6×, and 5.9× from January 2025 to July 2026 where the reconstruction read 24.1×. It is kept only on the "vs the retired index" view of the chart so the size of the error stays visible, and it no longer feeds any number in this section. The live-refresh feed this section used to request — a model-generated index from web search — has also been removed: a search feed cannot produce a customs series. Sources: Korea Customs Service, TRASS, Ministry of Trade, Industry and Energy, Bank of Korea, KITA, Seoul Economic Daily, Seoul Shinmun, Korea Data Economy, Gyeonggi Shinmun, The Public, TrendForce, Hana Securities, MoneyRecipe, Memory Price Chart. Not investment advice.
The longest lens on the board. For a quarter century, DRAM had one economic constant: $/GB fell relentlessly — from roughly ~$180/GB in 2001 (DDR1) to a glut-trough near ~$1.60/GB in 2023, a ~110× decline, punctuated only by cyclical bumps (2017–18’s supercycle among them) that always mean-reverted within ~2 years. 2024→2026 is different in kind: ~6× off the trough to ~$10/GB — consumer DDR5 back above 2014 levels, a decade of deflation erased — and for the first time the cause isn’t a fab accident or a demand blip but a structural reallocation of wafers to AI memory. Whether this is the biggest-ever bump on the old curve, or the end of the curve, is kill-switch #1’s question in long-run form.
Series construction & honesty labels. 2001–2023 [MODELED/approx]: capacity-weighted street prices for mainstream DDR modules, following the industry-standard long-run series convention (McCallum / Our World in Data lineage): ~$180 (2001, DDR1) → ~$90 (2005, DDR2) → ~$12 (2010, DDR3) → ~$4.50 (2015, DDR4) → ~$8 at the 2018 supercycle peak → ~$3.50 (2019) → ~$1.60 at the 2023 glut trough (DDR4/early DDR5). Annual points between the named anchors are interpolated on the same series convention; point values are approximations of a noisy retail distribution, good to ±30% — the shape is the data, not any single point. 2024–2026 [cited]: the tail is anchored to reported moves — DRAM and NAND prices up more than 300% since 2023, with TechInsights projecting continued increases into 2027; DRAM/NAND contract prices jumped 90–95% QoQ in Q1 2026; Gartner projects a 130% year-on-year DRAM price rise for 2026, and even legacy DDR4 — no longer used in phones — is spiking, a signal of how distorted the market has become. Mid-2026 mainstream DDR5 lands ~$9–12/GB at street level (~$300+ for a 32 GB kit that was ~$90 in 2024). Why it matters here: every prior bump on this curve mean-reverted as supply caught up — the bet embedded in memory-maker valuations (§04, kill-switch #6) is that this one doesn’t, because the marginal wafer now has a structurally better-paying customer (§05·C·G, §06·D·W). The falsification path runs through kill-switch #1 (§02·D): the 3Q26 contract-price deceleration (§03) is the first data point of the answer. Modeled series + cited tail — not investment advice. See also: §08·G, where the same cost curve running backwards shows up as PC price rises, and §03·F for the spot and contract tape driving it today.
The spot market is the supercycle's leading indicator — thin, twitchy, and honest about scarcity before contracts admit it. This rebuilds TrendForce's spot board item by item, print by print, from 18 archived captures of the page (Mar 2025 → Aug 2026), TrendForce's weekly "Memory Spot Price Update" posts, and today's read of the live page (Oct 7, 18:10 GMT+8). Every dot is a dated publication — nothing between them is invented. Mainstream DDR4 8Gb: $1.50 in Mar 2025 → $46.36 today, a 31× repricing. The news in this refresh is that August's flat print was a pause, not a stop. On Aug 10 the mainstream chip printed 0.00% and this section said prices had "stopped going up". The next eight weeks added +9.2% ($42.45 → $46.36) — the located weekly prints read +0.67%, +2.08%, +1.78%, +0.47%, +0.55% and +0.93%, with one late-August edition not found — before another 0.00% on Oct 6, in a week TrendForce attributes to regional holidays and month-end. The August call was wrong and is logged in §10·B; this build does not read the new zero as a turn. Four of the seven board items printed exactly zero today, the same count as on Aug 10. Three things moved underneath. (1) The discount tier came back: the eTT grades that rolled over in the spring rebounded hardest — DDR4 8Gb eTT +41.4% since Aug 10 ($4.21 → $5.95), DDR4 16Gb eTT +23.9% to a new high, DDR5 16Gb eTT +11.7%, also a new high — so the mainstream chip's branded-to-eTT gap closed from 10.1× to 7.8×. (2) The old nodes cooled while DDR5 rose: DDR4 16Gb — the board's biggest gainer until this refresh — fell 3.6% from its Aug 10 capture ($86.74 → $83.59) after TrendForce reported corrections in branded DDR4 2Gx8 through the second half of September, and DDR3 4Gb slipped 2.2% ($14.02 → $13.72) — the only two items lower than on Aug 10 — while DDR5 16Gb rose +12.8% to $58.40. DDR4 16Gb now costs 1.43× its DDR5 equivalent, down from 1.68×. Since the first capture: DDR4 16Gb 27.6×, DDR5 16Gb 11.4×, DDR3 4Gb 17×. (3) Contract kept pace with spot: TrendForce's public contract tab now shows Aug 31, and July is backed out of that page's own month-on-month changes (marked as derived). SO-DIMM modules rose +13.0% to +16.8% in July and +1.9% to +2.3% in August; chips +11.5% to +16.0%, then +4.2% to +4.8%. Two months in, 3Q26 is up +15.7% to +21.6% point to point on the seven contract items, against TrendForce's +13–18% guide. The mainstream chip's spot price, which stood at 2.61× its contract price in March, has stayed between 1.71× and 1.78× since June. TrendForce now guides 4Q26 conventional DRAM contracts +10–15% QoQ.
Provenance & method — and a sourcing note. Three source types, all dated, none interpolated. (1) Archived page captures [18 dates, 118 transcribed points]: the Session Average exactly as the page displayed it — 16 from Wayback snapshots of trendforce.com/price/dram/dram_spot supplied as screenshots and transcribed here, plus this section's own two earlier live reads — Last-Update stamps 2025: 03-20 18:10, 04-29 14:40, 05-23 18:10, 06-13 18:10, 08-11 18:10, 09-05 18:10, 10-02 18:10, 11-11 18:10, 11-26 11:00, 12-22 14:40; 2026: 01-22 11:00, 02-13 18:10, 03-13 18:10, 03-25 18:10, 04-17 18:10, 06-22 18:10, 07-17 18:10, 08-10 18:10 (all GMT+8). The Aug 10 read was this section's “live” point until this build and has been re-marked as an archived capture, as Jul 17 was before it. Board schema change [dated]: captures through 2025-11-11 list DDR4 16Gb (1Gx16) and DDR4 8Gb (512Mx16); by 2025-11-26 TrendForce had replaced both with the three eTT tiers. Those two retired items are kept as opt-in series (†) rather than dropped, and no eTT value is back-filled before its first capture. (web.archive.org is blocked at this build environment's fetch layer, so the Wayback captures were read by the user's browser and transcribed rather than fetched — the values are the archive's, the transcription is ours.) (2) TrendForce weekly "[Insights] Memory Spot Price Update" posts — which quote only the mainstream DDR4 1Gx8 figure numerically, hence that series alone is weekly-dense. (3) Live page read (Oct 7 2026 18:10 GMT+8, early notice 17:50) — DDR5 16Gb $58.40 (+0.40%), DDR5 16Gb eTT $26.40 (+0.38%), DDR4 16Gb $83.588 (0.00%), DDR4 16Gb eTT $14.125 (0.00%), DDR4 8Gb $46.357 (+0.08%), DDR4 8Gb eTT $5.95 (0.00%), DDR3 4Gb $13.72 (0.00%). Cross-check [passed]: where the two independent streams overlap they agree — archive 2026-03-25 $34.00 vs weekly Mar 24 $34.00; archive 2026-06-22 $35.90 vs weekly Jun 24 $35.90; archive 2025-12-22 $21.142 vs weekly Dec 23 $21.75 (one day apart in a rising tape); new in this build: this board's Aug 10 capture of $42.45 against the Aug 12 post's $42.50 for the same day, 0.12% apart — the capture is the plotted point and the post's figure is listed below, not merged — and today's live session change of +0.08% reproduces the Oct 7 post's $46.32 for Oct 6 to the cent ($46.32 × 1.0008 = $46.357). The archive captures also close the old Dec–Mar print gap that this chart previously drew dashed. Cited weekly prints (mainstream DDR4 1Gx8 3200 average): 2025 — Apr 15 $1.673, Apr 22 $1.720 (+2.81%); Oct 15 $7.219, Oct 22 $7.931 (+9.86%); Oct 29 $9.523, Nov 5 $10.629 (+11.61%); Nov 12 $11.857, Nov 18 $12.757; Nov 25 $14.914, Dec 2 $16.514; Dec 9 $17.064, Dec 16 $19.86, Dec 23 $21.75 (+9.52%). 2026 — Mar 24 $34.00, Mar 31 $33.96; May 5 $32.40, May 26 $33.60; Jun 2 $34.80, Jun 9 $35.90, Jun 24 $35.90, Jun 30 $36.00, Jul 1 $36.10, Jul 7 $37.14 (+2.88%); Jul 15 $39.80; Jul 17 $40.50 (page capture, +1.50% on the day); Jul 21 $41.10 (+3.27%), Jul 28 $42.08 (+1.39%), Aug 4 $42.11 (+0.17%), Aug 10 $42.50 (+0.93%; this board's own capture that evening read $42.45, 0.00% on the day), Aug 18 $42.90 (+0.67%), Sep 1 $44.54 (+2.08%), Sep 8 $45.36 (+1.78%), Sep 15 $45.54 (+0.47%), Sep 22 $46.04 (+0.55%), Sep 29 $46.32 (+0.93%), Oct 6 $46.32 (0.00%); Oct 7 $46.357 (live page session average, +0.08% on the day). One edition is missing: the Aug 26 post was not located; the Sep 2 post gives that week as $43.64 (Aug 26), a prior-week figure of the kind this chart does not plot, so the week has no bar. Source-consistency note — TrendForce restates its own prior week, and the gap can be bigger than the move: the Jul 29 post gives the previous week as $41.50 (Jul 22) where the Jul 22 post itself printed $41.10 (Jul 21); the Aug 5 post gives $42.04 (Jul 29) where the Jul 29 post printed $42.08 (Jul 28). Since then: $42.61 (Aug 12) against $42.50 (Aug 10); $44.57 (Sep 2) against $44.54 (Sep 1); $45.32 (Sep 9) against $45.36 (Sep 8); $45.79 (Sep 16) against $45.54 (Sep 15); $45.89 (Sep 23) against $46.04 (Sep 22). Survey dates differ by a day or two and the gaps run from −0.33% to +0.97%. This chart plots each post's own headline "this week" figure and does not plot the restatements. A single weekly print here should be read as good to roughly ±1% — smaller than the moves of 2025 and of late August, and larger than most single weeks since. Point counts per item are on the legend buttons: DDR4 8Gb carries 54 (archive + every located weekly post + today), DDR4 16Gb / DDR5 16Gb / DDR3 carry 19 each (Mar 2025 onward), and the three eTT tiers carry 11 each (from their Nov 2025 introduction). TrendForce's own numeric per-item history remains Gold+ members-only, so this is the publicly reconstructable series, not the full daily tape. Quality-spread note: eTT (“effectively tested” — untested/lower-grade die) trades at a steep discount and is the speculative tier. Its March peak and spring rollover, against branded chips still rising, was this chart's clearest divergence; between Aug 10 and Oct 7 it reversed, with all three eTT tiers up 11.7% to 41.4% and two of them at new highs — drawn from the captures, not modeled. DDR4 8Gb eTT is still 13.8% below its March high and back above the level it entered the board at in Nov 2025. Context prints: DDR5 spot +30% in one week (Nov 5 2025) and +307% since September by Nov 19 2025 (TrendForce headline); the Jan 2025 $1.63 figure is a secondary-source anchor (Silicon Analysts) and is now superseded: the 2025-03-20 capture shows DDR4 8Gb at $1.50, below it, so $1.63 was not the trough. It is kept as a dashed-ring point for provenance, relabelled, and logged. Gaps between prints (esp. Jan–Mar 2026, and Aug 10 → Oct 7 on the six items without weekly posts) are publication-fetch gaps, not market closures — bridged dashed, never invented. Spot quotes are DRAMeXchange/TrendForce session averages; the live-board updater (weekday 7am) watches the same page. The rest of the board [first capture, Sep 28 2026 14:40 GMT+8]: TrendForce's Module Spot and GDDR Spot tabs are weekly sessions — DDR5 RDIMM 32GB $2,163.50 (+3.02%), DDR5 UDIMM 16GB $239.00 (0.00%), DDR4 UDIMM 16GB $166.00 (+0.55%), GDDR6 8Gb $12.288 (+3.75%), GDDR5 8Gb $12.136 (+2.69%); the per-GB column is derived. Contract-price series [16 captures × 7 items = 112 points, plus 7 derived]: same method, TrendForce's DRAM Contract Price tab, Last-Update stamps 2025: 01-24, 02-27, 04-30, 06-30, 07-31, 08-29, 09-30, 10-31, 11-28, 12-31; 2026: 01-30, 02-26, 03-31, 05-29, 06-30, 08-31 (GMT+8). Jul 31 2026 is derived, not captured [◇]: the public tab rolls forward a month at a time and was not read between Aug 10 and today, so this archive holds no capture of the July session. It is backed out of the Aug 31 page's published "Average Change" (value ÷ (1 + change)): DDR5 8GB SO-DIMM $130.00, DDR4 16GB SO-DIMM $265.00, DDR4 8GB SO-DIMM $139.00, DDR4 16Gb $48.00, DDR4 8Gb $24.00, DDR4 4Gb and DDR3 4Gb $14.50 each. Every value lands on a whole- or half-dollar quote, as published prices do, and reproduces the page's percentage to the second decimal — consistent with being the published July figures, not proof of it. For the same reason the Aug 31 capture cannot be cross-checked against July the way Jun 30 was against May. Contract prints are monthly/semi-monthly sessions, so the line is a step function by nature, not an interpolation. Cross-check against §03's contract ladder [independent confirmation]: measuring quarter-end to quarter-end on these captures, 1Q26 (Dec 31 → Mar 31) gives DDR4 16GB SO-DIMM +95.4%, DDR5 8GB SO-DIMM +89.9%, DDR4 8GB SO-DIMM +82.8% — which brackets the +90–95% TrendForce headline this board cites in §03, and confirms that headline tracks module contract pricing. The chip items moved less over the same window (DDR4 16Gb 2Gx8 +40.5%), so item choice, not error, explains the spread — recorded here rather than merged. 2Q26 is complete, and it confirms the figure this board cites. Measuring Mar 31 → Jun 30 on these captures: DDR4 4Gb +73.3%, DDR3 4Gb +66.7%, DDR4 8Gb +61.5%, DDR5 8GB SO-DIMM +53.3%, DDR4 16Gb +42.4%, DDR4 8GB SO-DIMM +40.0%, DDR4 16GB SO-DIMM +33.5%. The chip items bracket TrendForce's +58–63% 2Q26 headline; the module items sit below it, the same item-choice spread this section documented for 1Q26. Independent arithmetic check on the Jun 30 capture [passed]: the Jun 30 page publishes its own month-on-month change for each item, and all seven reconcile exactly against this archive's May 29 values — SO-DIMM 16GB 0.00%, SO-DIMM 8GB 0.00%, DDR5 SO-DIMM +2.68%, DDR4 16Gb +5.00%, DDR4 8Gb +5.00%, DDR4 4Gb +8.33%, DDR3 4Gb +13.64%. 3Q26, two months in [derived, point to point]: Jun 30 → Aug 31 on these captures gives DDR3 4Gb +21.6%, DDR4 8GB SO-DIMM +19.3%, DDR4 16Gb +19.0%, DDR4 8Gb +19.0%, DDR4 16GB SO-DIMM +18.9%, DDR4 4Gb +16.9%, DDR5 8GB SO-DIMM +15.7% — five of the seven already above the top of TrendForce's +13–18% conventional-DRAM guide (Jul 3), with September still to print. Two cautions: the guide is a blended quarter-on-quarter average across server, mobile, graphics, consumer and PC DRAM, while these are seven PC-channel items measured end to end; and most of the move came in July, which is derived. Same item-choice caveat as 1Q26 and 2Q26. 3Q26 guidance [cited, Jul 9 2026]: server DRAM contract +13–18% QoQ, decelerating hard from 2Q26 — because several U.S. CSPs have signed multi-year LTAs that bar suppliers from raising their prices, so from 3Q26 the increases come from non-LTA customers and from incremental volume sold outside the LTAs. TrendForce also flags 2027 RDIMM bit supply growing only 15–20% YoY, behind server CPU shipment growth, and a server DRAM shortage already anticipated for 2027; CSPs are shifting RDIMM configurations down from 96/128 GB to 32/64 GB modules to contain cost. 4Q26 guidance [cited, Sep 30 2026]: conventional DRAM contract +10–15% QoQ, a further moderation; suppliers keep prioritizing advanced-process capacity for server products and server DRAM stays undersupplied, though in server DRAM some suppliers' increases are expected to lag the market average because of pricing terms in long-term agreements. PC OEMs are still buying aggressively on expectations that supply stays tight in 2027, and TrendForce puts NAND Flash contracts at +15–20%. Spot-to-contract premium: on Mar 31 2026 DDR4 8Gb spot was 2.61× its contract price (same published date); by Jun 30, 1.71×. Jul 31: 1.75× (contract derived; nearest spot print Jul 28, $42.08). Aug 31: 1.75–1.78× (no spot print on the day; bracketed by the Sep 2 post's $43.64 for Aug 26 and $44.54 for Sep 1). The narrowing stopped at about 1.7–1.8×: through July and August, contract and spot rose together. What this refresh does not show, and it matters: the sector's equity peak was Jun 22 2026 (§04·K), after which the leveraged semiconductor complex fell 63% by §04·K's last update (Aug 6) — while this spot tape went $35.90 → $42.45 by Aug 10 and has since reached $46.36, 29% above its level on the day of the equity peak. The physical market kept tightening while the equity market broke. Spot did not lead the equity drawdown; it did not confirm it either. Whatever repriced those shares (§03·C's Korean margin unwind, §04·K's leverage mechanics, or a valuation de-rate) is not visible in DRAM prices, and this chart is evidence against reading the equity move as the market front-running a memory price collapse — at least so far. §04·K has not been refreshed since Aug 6, so this section cannot say whether the equity side has since recovered. Sources: TrendForce weekly spot updates — Aug 5, Aug 12, Aug 19, Sep 2, Sep 9, Sep 16, Sep 23, Sep 30, Oct 7 2026; TrendForce 4Q26 memory contract price outlook, Sep 30 2026; TrendForce 3Q26 server DRAM contract forecast, Jul 9 2026; trendforce.com/price/dram/dram_spot, /dram_contract, /module_spot and /gddr_spot; Silicon Analysts. Not investment advice.
The other half of the memory market, and the looser half: NAND supply is easier to add than DRAM, so its cycle turns faster and its gap is tighter (§08). This tracks TrendForce's benchmark 512Gb TLC wafer spot price — every dot a dated weekly "Memory Spot Price Update" print — plus the NAND contract ladder. From $2.76 (Apr 2025) to $19.86 (Jun 2026), a ~7× repricing that trails DRAM's but rhymes with it. Enterprise-SSD restocking is the demand swing.
Provenance & method. Spot [cited]: TrendForce weekly "[Insights] Memory Spot Price Update" — 512Gb TLC wafer session average: $2.764 (Apr 23 2025), $7.421 (Nov 19 2025, +14.97%), $19.862 (Jun 30 2026, −1.03%). Contract [cited]: NAND contract QoQ ≈+58% (1Q26) → +72% (2Q26) → +10–15% guide (3Q26), decelerating in step with DRAM (TrendForce, §03·§02·D). Honesty note: this is deliberately lean — three public spot prints and the contract ladder — versus §03·F's 202-point DRAM archive, because NAND per-item captures haven't been supplied yet; paste them (as with the DRAM board) and this becomes a full multi-item tape. NAND's looser supply is exactly why it's the cycle's swing factor: a faster NAND turn is a leading tell for the broader memory cycle (kill-switches §02·D). Sources: TrendForce News weekly updates. Not investment advice.
The memory rally has a leverage story, and it's concentrated in Korea — home of Samsung and SK Hynix, and one of the most retail-leveraged equity markets on earth (the local term for debt-fueled trading is bittu). Margin debt hit a 20-year high in 2026, retail piled into the two chip giants, and on May 27, 2026, Korea launched its first-ever single-stock leveraged ETFs — 2× products on Samsung and SK Hynix that drew billions in days. This is the most direct measure of how much risk retail is putting on the memory trade.
Six metrics that together gauge whether Korea's retail-driven, leverage-fueled rally is overheating, each over the trailing ~12 months. The composite score blends them into one read; the small charts show how each got there. Note the March spike in every stress metric — that was the Iran-crisis crash (KOSPI −12% in a day, its worst ever) and the forced-selling cascade that followed.
Margin debt, volatility, and speculative ETF flows are all at or near records, and regulators have stepped in to curb leverage. Yet the rally rests on genuine record chip profits — so this reads as a stretched bull market with high unwind risk, not (yet) a pure bubble. The composite is a constructed index, not an official measure.
Dashed line on each chart = the metric's historically "normal" level. Heat flag reflects the latest reading vs that normal band. The composite score weights all six equally and is our construction for illustration, not a published index. X-axis months (Aug 2025 → Jul 2026) place each point in the trailing year; per the section provenance, the monthly series are approximations calibrated to the dated anchors named on each card — mid-series month alignment is approximate, while the dated anchors themselves (peaks, records, latest prints) are exact and cited.
Assets under management in the 2× daily-leverage ETFs tied to each memory maker, drawn as monthly lines since each fund's launch. Note the radically different ages: Micron's MUU has traded since Oct 2024; the Samsung and SK Hynix products — Korea's first-ever single-stock leverage ETFs — launched May 27, 2026 and have now lived a full boom–bust round trip in nine weeks: category-record inflows into the Jun 25 peak, then an unwind that has kept deepening. Bloomberg’s Jul 14 verdict was “Leveraged Chip Bets Backfire in Korea” at −45% since debut; by Jul 28–29 — SK hynix’s record-but-missed Q2 and the KOSPI’s first back-to-back circuit breakers (§03·C) — KODEX SK hynix 2× fell 28.4% in a single session to ₩9,930 and all fourteen funds now trade below half their ₩20,000 listing price, with NAV down 61.6% in a month. On Jul 16 the regulators moved (listings halted, ₩30M all-cash deposit, ad ban, 20-share minimum), and on Jul 29–30 added a 20%-of-portfolio cap and mandatory simulated trading. The tell in the data: KODEX SK hynix’s assets rose to ₩4.21T even as its NAV halved — retail kept buying the whole way down.
Jul 12 refresh — the boom met the decay math [cited]. Net assets: the 16 funds held a combined ₩6.75T ($4.89B) by Jun 3, led by KODEX SK hynix at ₩2.23T; the SK-hynix leverage complex then nearly doubled from ₩4.84T (Jun 10) to ₩9.15T (Jun 19), much of it NAV appreciation feeding ever-larger rebalancing trades. Then the turn: the two flagship managers’ Samsung+SK funds peaked at ₩15.41T on Jun 25 and shed ~₩3.1T in 12 sessions to ₩12.31T by Jul 7, with ₩2.69T of valuation losses — an average −26.8% month for the 14 leveraged products vs −4.8% for ETFs overall, and on the Jun 23 −9.99% KOSPI day Bloomberg Intelligence estimates managers mechanically sold ~$6.0B of the two stocks to rebalance — roughly 14% of their combined trading value, sell orders piled onto a falling market. Retail kept averaging down anyway: ₩1.58T net into the two SK funds and ₩772B into the two Samsung funds Jul 1–8, even as all fourteen fell below the ₩20,000 listing price, while institutions were heavy net sellers. Scale check: June volume in the 14 funds hit ₩212T — 26.6% of all Korean ETF trading. The US second wave is live: Leverage Shares lists SKHX (2× long) and SKHZ (1× short) on Jul 13, and Direxion has filed SKHL to trade shortly after the SKHY ADR. Source conflict flagged: Seoul Economic reports the CSOP SK hynix HK fund became the world’s largest single-stock leveraged ETF upon crossing $5.38B on May 7 — which collides with this board’s §04·F anchor of "$16.8B, world’s largest (Jun 23)". Possible series conflation (CSOP HK vs the Korean complex aggregate); the $16.8B provenance is scheduled for primary-source re-verification and is not silently rewritten here. Not investment advice.
The composite overheating score is a constructed index (six metrics, equally weighted, normalized to each metric's historical range) — an illustrative synthesis, not an official or standardized measure. Underlying anchors (real): margin loan balance hit a record ₩36.6T on May 15 when the KOSPI touched 8,000 (Korea Financial Investment Association); the VKOSPI averaged 50.3 in 2026 vs a historical 18.8, hitting 65.5 on May 21 (normal ≈20, >30 heightened, >40 extreme); forced selling reached ₩91.7B on May 18, the largest since July 2023; the KOSPI swung from a 5,288 record (Feb 3) through a −12% single-day crash in March (its worst ever, ~₩625B wiped) to 7,815 (May 21, +8.42%); foreign investors sold a record ₩21.14T in Feb 2026 and ~$13.2B in one May week; Korean retail poured ~$40B into US leveraged ETFs in 2025 ($7B in December alone), then ₩3.87T into the new domestic 2× chip ETFs in three days. Regulators (FSS/FSC) and brokerages (KB, Mirae, Toss, KakaoPay) imposed mandatory training and margin curbs. Bloomberg called the KOSPI the world's most volatile major index; Citi flagged "exuberance" and took profits. Jul 13 2026 — the unwind [cited]: margin loans peaked at a record ₩38.02T (May 29), having jumped ₩954B in a single day and stood at ₩37.33T ($24.0B) on Jun 30, +36% YTD but now 78% concentrated in KOSPI names (88% by borrowed-share value) while KOSDAQ margin shrank 21% and KOSDAQ turnover halved. Volatility broke records: VKOSPI closed 96.94 on Jun 29, the highest since the global financial crisis; June alone set a record three circuit breakers, with eleven sidecars since the single-stock ETFs listed; turnover in those funds exceeds 12× the ETF-market average. The reflexivity is explicit — to hold target leverage, managers and LPs must buy spot/futures as the underlying rises and offload as it falls, a “short gamma” structure that intensifies rallies and deepens declines. Then the break: the KOSPI fell 27.47% from its 9,385.59 peak (Jun 19) and closed −8.95% on Jul 13, triggering the sell-side sidecar and a Level-1 circuit breaker; it had already broken below 7,000 on Jul 12, intraday −6%, for the first time in about two months. Forced selling followed: ₩425.8B forcibly liquidated Jul 1–10, including ₩142.2B on Jul 9 — 10.2% of outstanding margin receivables, after the year’s high of 10.5% on Jun 9 (₩169.8B); because collateral shortfalls lag forced execution, more liquidation supply is still to come — the Jul 13 crash is not yet in the data. Valuation now sits at a high-5x forward P/E, below both the global-financial-crisis low and the post-Iran-war trough. Two source conflicts, flagged not resolved: (a) July forced-liquidation totals differ by outlet — ₩425.8B for Jul 1–10 vs ₩344.2B cumulative for July as of a Jul 13 report (different bases: unpaid-balance consignment trading vs all margin); (b) one translation dates the 9,385.59 peak to “July 19,” a future date — June 19 is used here. Not a vindication lap: this section flagged the fragility, but the composite score is still an illustrative construct, the 12-month series are monthly approximations calibrated to these anchors, and “overheating” never predicted timing — it described a structure. The structure did what structures do. ETF AUM (real): MUU ~$5.4B (Direxion, launched Oct 2024); KODEX/TIGER Samsung & SK hynix 2× Leverage launched May 27, 2026 — debut net assets ≈ ₩1.75T (Samsung) and ₩2.0T (SK hynix). Won→USD ~₩1,495/$. Sources: KOFIA, Korea Exchange, Bloomberg, CNBC, UPI, Seoul Economic Daily, Digital Today, ETF.com, Direxion. Leveraged ETFs are short-term trading tools, not buy-and-hold investments — and this is not investment advice.
A deep-dive on the single product that best embodies the leverage story above: the KODEX SK hynix Single-Stock Leverage ETF — the largest of Korea's first-ever single-stock 2× leveraged funds (launched May 27, 2026). It seeks twice the daily return of SK hynix's share price. Retail money flooded in, AUM led the entire category within a week — and then SK hynix fell ~14% over two sessions and the fund dropped ~26%, dragging essentially every holder underwater. It's a near-perfect case study of the overheating dynamics this dashboard tracks: huge leveraged retail inflows into a single AI-memory name at the top, then a violent unwind amplified by 2× exposure and negative compounding. Seven weeks in, the case study is complete: −45% since debut and >60% off its June peak, with ~$3.4B still inside (Bloomberg, Jul 14) — and on Jul 16 Korea’s regulators intervened: new single-stock leveraged listings halted, deposit tripled to ₩30M (now all-cash), advertising banned, minimum order raised to 20 shares.
KODEX won the asset race (₩2.23T by Jun 3; ~$3.4B on Jul 14 — still the category's largest) on Samsung Asset Management's distribution muscle. And the product did exactly what regulators warned, twice: the −26% two-session unwind (Jun 4–5), then the full collapse — −45% since debut, >60% off its June peak by Jul 14 — far deeper than SK hynix's own decline over the span: negative compounding realized, not hypothesized. On Jul 16 the FSC/FSS answered: new listings halted, ₩30M all-cash deposit, ad ban, 20-share minimum order. Useful as a short-term trading tool; structurally hostile to holding — now effectively official policy.
Net assets (bars, left axis) and fund price (line, right axis) since the May 27 listing. Note the timeframe: this product is ~7 weeks old (listed May 27), so the full span here is May 27 → Jul 14 — a since-inception tracker, not a 12-month history (a 12-month series is impossible — it didn't exist before May 27). The solid line and solid bars are the launch-fortnight daily series (calibrated to dense reporting through Jun 12); after that the daily prints stop, so the dashed segment is a sparse-anchor extension — outlined dots are dated anchors (₩19,635 close Jul 8 [cited]; ≈₩11.0k on Jul 14, derived from Bloomberg's −45%-since-listing; AUM ≈₩5.1T = $3.4B [Bloomberg]) and small open dots are bridge estimates shaped by the documented tape. It ends at the last dated anchor: Jul 15–16 have no fund print yet (the ADR fell both days, so the direction is known — the level is not, and none is invented). The story the two panels tell together: the price collapsed 45% while net assets more than doubled — retail averaging down with leverage, all the way in.
Real anchors: Korea's first single-stock 2× leveraged ETFs listed May 27, 2026; 16 single-stock leverage/inverse products debuted that day with combined first-week net assets ≈ ₩6.75T ($4.89B) and ₩48.7T cumulative trading value. KODEX SK hynix Single-Stock Leverage led on assets at ₩2.227T ($1.61B) — ~₩850B ahead of TIGER SK hynix (₩1.370T, $993M); KODEX debut-day net assets were ~₩1.154T, fee 0.29%. Debut-week return vs NAV ≈ +29% (KODEX SK hynix +29.13%; SOL +29.21% led). Cumulative net retail buying of KODEX SK hynix reached ₩2.0425T (May 27–Jun 5), seven straight sessions of net buying; TIGER SK hynix ₩1.97–2.08T. Then a two-day rout: June 4–5 the SK hynix 2× products fell ~20% then more, ~26% over two days (KODEX −26.16% from ₩29,055→₩21,455; TIGER −26.38%; 1Q futures −27.90%), on SK hynix ~−14%, "all holders since launch now in losses." Regulators (FSC/FSS) flagged negative-compounding and rebalancing risk and deem these short-term-speculation only; concentration in Samsung+SK hynix (a large share of the KOSPI) is itself seen as amplifying index volatility. AUM figures move daily with both flows and price; the since-launch daily series here is calibrated to these reported prints. Chart extension (added Jul 16): the post–Jun 12 dashed segment is anchored to ₩19,635 (Jul 8 close, first below listing — KRX via BigGo), ≈₩22,280 (Jul 7, back-derived from Jul 8's cited −11.86% day), ≈₩11.0k (Jul 13–14, derived from Bloomberg's −45%-since-listing), and AUM points ≈₩3.7T (Jun 19: SK-complex ₩9.15T × KODEX's ~40% complex share), ≈₩4.7T (Jul 7: SK-pair ₩7.58T × KODEX's ~62% pair share) and ≈₩5.1T (Jul 14: $3.4B, Bloomberg, at ~₩1,495/$); bridge points between anchors follow the documented tape (Jun 19 KOSPI peak, Jun 23 −9.99% day, Jul 13 crash) and are drawn as estimates, not data. Then the whipsaw continued: June 8 the KOSPI plunged 8%+ intraday (the 16 single-stock leveraged/inverse ETFs lost 10.3% of NAV in a session) before June 12 ripped ~10% higher to 8,430 — a 2× product round-trips ~±25% on such days, which is the negative-compounding grinder in action. Won→USD ≈ ₩1,383/$ at report. Jul 14–16 anchors [cited]: KODEX SK hynix ~$3.4B AUM, −45% since debut, >60% off June peak (Bloomberg, Jul 14); brokerage margin balance ₩36.3T vs ₩20.9T a year ago (Benzinga, Jul 16 — a shade under KOFIA’s ₩37.33T Jun 30 print; different date/basis, both shown); EWY −21.7% in four weeks — worst since Mar 2020 — after +330% from its Mar-2025 low (Benzinga); Jul 16 FSC/FSS package: listings halt · ₩30M all-cash deposit · ad ban · 20-share minimum order. Sources: Korea Exchange, Seoul Economic Daily, Bloomingbit, BigGo, KED Global, TradingKey, Bloomberg, Yahoo Finance, Benzinga, Korea JoongAng Daily. Leveraged single-stock ETFs are high-risk, short-term instruments subject to severe loss and negative compounding — this is not investment advice.
The investable universe, anchored to the Roundhill Memory ETF (DRAM) — the first pure-play memory fund (launched April 2, 2026), holding only companies that draw 50%+ of revenue from memory. Its roster is the spine of this directory.
The integrated giants — the only three firms that span HBM, DRAM and NAND, and together ~73% of the DRAM ETF:
The rest of the roster — NAND specialists, the hard-drive oligopoly, and China's challengers:
By the numbers — latest-quarter net income, valuation and growth. The memory makers trade at strikingly low multiples for their growth (single-digit forward P/Es, PEG ratios near zero) — the market still pricing them as cyclicals even as AI demand drives record profits. ● live
| Company | Latest Q net income | Fwd P/E | Fwd PEG | Annual rev growth |
|---|
The most recent Wall Street rating actions on the memory & storage names — upgrades, downgrades, initiations and price-target moves since June 2025, newest first. (This was labelled "trailing twelve months"; the two oldest entries are now ~14 months old, so the window is stated from the data instead of assumed.) This list attempts to refresh live; opened as a plain file it shows the compiled baseline (refreshed Aug 15 2026, post-SK-hynix-Q2 and post-Samsung-Q2). Read the block below before the list — the important development is not any single action but the collapse of agreement between them.
"PT" = 12-month price target. Ratings reflect each firm's own scale (Buy/Overweight/Outperform ≈ bullish; Hold/Neutral/Equal-Weight ≈ neutral; Sell/Underweight ≈ bearish). Korean & Japanese targets shown in local currency. This is a sampling of notable actions, not every rating; figures are approximate and time-stamped to the action date. Context: Micron's FQ3 print (Jun 24 — record $41.5B revenue, $25.11 EPS, ~$50B next-quarter guide, $22B in customer commitments) set off the densest target-raise wave of the cycle, with the Street high running from ~$1,100 to $2,200 (Melius) in days and SanDisk's high hitting $3,000 (Bernstein). Note the split character: several are price-target raises on maintained ratings rather than upgrades, and Goldman pointedly lifted its target to $1,100 while keeping Neutral — stronger fundamentals, but wary the rally already prices them in. On the bear side, Michael Burry (Scion) disclosed a Micron short and Morningstar flagged 20–30% downside risk (see §04·D·S). Not investment advice. Aug 17 2026 — the estimate cuts recorded here have a direct valuation consequence [§04·L]. Kiwoom's Aug 11 reductions (SK hynix 2028 −22.2%, Samsung 2028 −26.9%) landed six days before SK hynix printed a 6.88× forward multiple. Applying them raises the multiple to 8.8–9.4× with no share-price move — a +29% to +37% re-rating from the denominator alone.
Micron (MU) is the most liquid pure-play way to trade the memory thesis, and its options market is where positioning and fear show up first. This reads the key sentiment gauges — put/call ratios, implied volatility, the expected move, and unusual activity. Options data is intensely time-sensitive (chains reprice every second), so this is a dated snapshot that rechecks for the latest on load; opened as a plain file it shows the last compiled values. Every metric is explained — and options are a high-risk instrument, not a recommendation to trade.
Zooming out from Micron to the whole Korean trade: the iShares MSCI South Korea ETF (EWY) is the most liquid US-listed way to play (or hedge) Korea's memory-led rally. Tracking its price against total call vs. put open interest reveals positioning — and the tell is clear: put open interest has overtaken call open interest even as the ETF ran to records, i.e. investors increasingly paying for downside protection. Then the unwind those hedges were paying for arrived, and kept going: EWY fell ~14% in early June (≈$204 → ≈$176) and then kept sliding to $144.21 by Jul 29–30 — roughly −35% from its $220.88 52-week high, four straight down days into Korea's back-to-back circuit breakers (§03·C). The positioning signal was vindicated: put open interest crossed above call OI in Dec 2025, months before the peak, and the ETF has since round-tripped a year of gains. Aug 1 update — a violent bounce, into even heavier hedging. EWY closed $157.10 on Jul 31, up from the $144.21 low around the Jul 28–29 circuit breakers (§03·C) but still ~29% below its $220.89 high, with a single-session range of $156.42–$166.04 — the tape is not calm, it is merely off the floor. And the option market is more defensive than before, not less: put volume of 112,716 against call volume of 31,663 — a put/call volume ratio of 3.56, far above the ~2.2 open-interest ratio, with implied volatility at 84.6% in the 96th percentile of its own history. Traders bought the bounce and hedged it harder. The concentration is why it moved so hard — SK hynix and Samsung alone are ~52% of the fund, and both roughly halved from their June highs (SK hynix ₩2.99M → ₩1.32M; Samsung ₩374K → ₩207K).
EWY options data is a dated snapshot, not live exchange data. Anchors: EWY ≈ $178.45 at the June 11 close (≈$175 on June 7), down from a ~$204 prior close and a 52-week high near $218 (a ~14% multi-day pullback); 52-week range ≈ $66–$218; P/E ≈ 10; AUM ≈ $20.6B. The positioning signal — total put open interest exceeding total call open interest while the ETF sat near record highs — mirrors the Bloomberg "US-Listed Korea ETF Options Show Caution" reading (data as of 6/4/26): investors favoring puts over calls is a hedging/cautious posture, consistent with the overheating signals in the Korea leverage panel above. The 12-month series here are calibrated to that picture (OI rising broadly with the rally, puts inflecting above calls in 2026, price spiking to ~$218 then dropping to ~$176); exact daily OI prints vary by source and by the second. Open interest reflects positioning, not direction certainty — high put OI can be outright bearish bets or protective hedges on long stock. Jul 30 2026 update [cited]: EWY closed $144.21 (Jul 29, −4.81% on the day, a fourth consecutive decline; Jul 30 range $142.64–$154.06), against a 52-week range of $70.36–$220.88 — about −35% from the high and now below where the section's original $176 anchor sat. Drivers are single-name and mechanical, not diversified: SK hynix + Samsung are ~52% of the portfolio; SK hynix fell from its ₩2,987,000 Jun 25 record to ₩1,322,000 (Jul 30, −5.64%) and Samsung from ~₩374,000 to ₩207,000, through the Jul 28–29 back-to-back circuit breakers (§03·C). Options remain expensive and hedge-heavy: at-the-money implied volatility was ~76% with ~896K contracts outstanding on the late-May chain (OptionsAnalysisSuite) — sell-side has moved to downgrades (Seeking Alpha, "more downside is likely"). Basis conflict, flagged not merged: that ~896K total-contracts print sits below this series' ~1.16M call+put for the same month — different bases (single-snapshot chain vs all-expiry aggregate) and differing vendor definitions; both are shown rather than reconciled. The Jul '26 point: price is cited; the call/put OI values continue this series' calibrated construction (rising OI with puts extending their lead) and are modeled, not a vendor print — the honest read is the shape (puts above calls, both rising), not the exact contract counts. Aug 1 2026 update [cited]: EWY closed $157.10 on Jul 31 (prior close $161.21; session range $156.42–$166.04), a sharp recovery from the ~$144.21 print around the Jul 28–29 back-to-back circuit breakers but still ~29% below the $220.89 52-week high (52-week low $70.36). Positioning got more defensive through the bounce, not less: put volume 112,716 vs call volume 31,663 — a put/call volume ratio of 3.56 — while at-the-money implied volatility reads 84.59%, in the 96th percentile of EWY's own distribution (AlphaQuery, Jun 30 basis). Two ratios, two meanings — do not conflate them: the chart plots put/call open interest (~2.2×, a stock of positions built over months); the 3.56 figure is volume (a single session's flow, far more reactive). A volume ratio well above the OI ratio says fresh hedging is being layered on top of existing protection, which is exactly what a bounce nobody trusts looks like. The July point is the month-end close ($157), replacing the mid-month $144 reading used in the prior build. Sources: Bloomberg (reference chart), Investing.com, Barchart, OptionCharts, OptionsAnalysisSuite, AlphaQuery, stockinvest.us, Seeking Alpha, Invezz, public.com. Options involve substantial risk of loss and are not suitable for all investors. Nothing here is investment advice.
Options metrics are dated snapshots, not live exchange data — every value reprices continuously during market hours and is stale within minutes. Anchors (refreshed Jul 6 2026, vintages disclosed per figure — spot itself is printing inconsistently across delayed feeds amid the post-FQ3 whipsaw, ~$940–$1,130): session put/call volume ≈ 1.00 on ~720K contracts (≈360K puts vs 361K calls in a single selloff session, early Jul) — up from ~0.91 in mid-June, a visible hedging bid arriving with the Jul 1–2 ~10% drawdown; put/call open-interest ≈ 1.32 (Aug-21 monthly chain, May-vintage OI); 30-day mean IV ≈ 108% (Jun 23) versus ~70% pre-earnings in January — a full regime shift, with Rule-16 implying ~5.6% daily moves on the Aug chain. Chain geometry tells its own story: max pain ≈ $660, peak call OI at the $1,000 strike, peak put OI at $300 — strikes set before the June melt-up, so the chain’s center of gravity sits far below spot; dealer positioning simply hasn’t rolled up with a stock that ran ~380% off its $201 Nov-2025 low. Ratios and IV vary by expiration and by the second; the figures here illustrate the method and current regime, not a tradeable quote. Sources: Barchart, Fintel, Macroaxis, Stock Options Channel, AlphaQuery, CBOE-style conventions. Options involve substantial risk of loss and are not suitable for all investors. Nothing here is investment advice.
Where Wall Street and Seoul see these six memory names heading over the next 12 months. Each card shows the current price, the consensus and the high/low target range, the implied upside, and the specific recent calls behind it. A live caveat up front: targets are revised constantly in a fast-moving cycle, and for stocks that have multiplied this year the aggregators disagree wildly — so the most recent named-firm targets matter far more than blended "consensus" figures that lag. Korean targets are in won; everything is a dated snapshot.
Price targets are analyst opinions with a ~12-month horizon, revised constantly — a dated snapshot (refreshed Jul 24 2026), not live quotes. The story since the Jul 6 refresh: a sharp mid-July memory/chip selloff pulled most of these stocks down even as analysts kept raising targets — so the implied upside widened rather than closed. Two catalysts are days away and are not yet in these figures: SK hynix reports Q2 on Jul 29 and Samsung on Jul 30 (Samsung's preliminary Q2: revenue ~₩171T, ~52% operating margin). Refresh note: an earlier build expected SK hynix on Jul 23; the confirmed date is Jul 29. The standing honesty point holds — aggregator "consensus" is badly inconsistent for names that have multiplied: SK hynix targets alone span ₩1.2M to ₩5.3M across 37 analysts. Anchors (current price · notable recent calls): Micron ~$988 (Jul 24 — about 22% off its July high on the chip rout, with a new Anthropic memory-supply agreement in the mix); consensus $1,492 (45 analysts, Strong Buy), up from ~$1,338; Street high $2,200 (Melius / Ben Reitzes), Cantor $2,000, UBS $1,625 (from $535), Phillip $1,870; low $385 (Citigroup). Michael Burry's disclosed short and Morningstar's 20–30% give-back warning remain the standing bear case (§04·D·S). SK hynix ₩1,322,000 (Jul 30, −5.64%; corrected — an earlier build wrongly carried ₩2.9M, which was the Jun 25 all-time high, not the live price). Q2 (Jul 29) set records — revenue ₩79.3T, operating profit ₩60.5T at a 76% margin — but missed consensus and the stock fell 9.6%, helping trigger the Jul 28–29 crash (§03·C); Nomura raised to ₩4.7M (from ₩4.0M), consensus ₩3.41M (37 analysts, now Strong Buy), Korea Investment ₩3.8M; full range ₩1.2M–5.3M. Samsung ₩207,000 (Jul 30) — Q2 operating profit ₩89.49T, +1,814% YoY, a clear beat that lifted the stock only 0.7% on the day; KB Securities ₩600K (Jul 23, Buy), consensus ~₩493K, range ₩210K–850K; full Q2 results Jul 30. SanDisk ~$1,610 (Jul 23 — down ~33% into a bear market); BofA $2,500 (Jul 1) and Goldman $2,200 (Jul 5, from $1,200, on structural NAND tightness into 2027), consensus $2,188 (23 analysts, Buy); FQ4 due Aug 5. Seagate ~$913 (Jul 24); Melius $1,600, Cantor $1,300 (from $1,000), Citi $1,240; consensus $1,009 (22 analysts, Strong Buy); Q3 beat — EPS $4.10, revenue $3.1B (+44% YoY). Western Digital ~$524 (Jul 24 — down ~36% in 30 days from $746, a sector-wide rout rather than a fundamental crack; CY2026 HDD output sold out with contracts into 2028); Melius $1,050, Cantor $900 (from $660), consensus $897 (up from $685); Buy. A stock can trade well below a rising consensus (as most of these now do) when the tape sells off faster than analysts revise — the gap is information, not a promise; equally, MU spent much of June above consensus, which meant targets were mid-revision. Korean won converts at roughly ₩1,480/$. Sources: Benzinga, TipRanks, S&P Global / stockanalysis.com, MarketBeat, MarketScreener, TheStreet, Seoul Economic Daily, Nomura, KB Securities, Korea Investment & Securities, Mirae Asset, BofA, Goldman Sachs, Cantor Fitzgerald, Melius, Citi. Not investment advice. See also: §04·J tracks whether that consensus has actually moved, and §05·B·V is the same disagreement problem applied to HBM vendor share.
A revealing internal contradiction in the memory trade: even as Micron surged ~790% in 52 weeks, short interest climbed steadily through 2026 to multi-year highs (~41.6M shares, 3.68% of shares out by late June). Meanwhile sell-side sentiment is overwhelmingly bullish (consensus Strong Buy, targets escalating toward $2,000) — yet insiders are net sellers — and as of Jul 2, Michael Burry (Scion) has disclosed a short position in Micron, amid a sharp ~10% two-day chip selloff. That's a genuine three-way tension: momentum bulls (targets now to $2,000) vs short sellers betting "too far, too fast," with management quietly trimming. This section maps all three. Honesty note: short-interest figures are anchored to NASDAQ-sourced reporting; the historical trajectory is interpolated between dated readings.
Consensus Strong Buy · avg PT ~$628 (stock trades well above it — targets mid-revision)
Provenance & the honesty line. Short interest [anchored — NASDAQ-sourced]: ~41.59M shares / 3.68% of shares outstanding (stockanalysis, Jun 27 2026); a reporting period rose 35.24M→37.55M = 3.34% of float, ~1 day to cover on ~55M avg daily volume (Benzinga); ~37.3M / 3.32% of float in late May, after +15.9% then +2.6% jumps in April, "near the highest levels seen in years… rising steadily through 2026" (Barchart/Yahoo, May 23 2026). Days-to-cover is low (~1) because MU is extremely liquid, so a classic squeeze is less likely than the raw share count suggests. Sentiment [cited]: consensus Strong Buy — of 41 analysts, 31 Strong Buy / 5 Moderate Buy / 5 Hold; average PT ~$628 (stock already above it); Street-high targets escalated through 2026: $1,100 → Wedbush $1,400 → KeyBanc $1,600 (from $600) → J.P. Morgan $1,600 → NZ/Bolton $1,650 → Susquehanna $2,000 (Jul 1, from $1,750) / Cantor $2,000 (Jun 29) / Phillip $1,870; consensus ~$965→~$1,486 (Barchart, TipRanks, Simply Wall St, TheStreet). Burry short [cited, Jul 2]: Scion Asset Management disclosed a bearish position (TipRanks/CNN Markets), coinciding with a ~10% two-session MU/SNDK drawdown on a broad chip rotation — the highest-profile bear yet against the name. Insider counterweight [cited]: insiders sold ~$112M more than they bought over 12 months; CEO Sanjay Mehrotra's direct holding fell from ~1.28M shares (Dec 2024) toward ~0.95M (Simply Wall St). Fundamentals backdrop (updated — FQ3 reported Jun 24 2026): a blowout — revenue $41.46B (up from $23.9B in FQ2 and $9.3B a year ago, ~4.5× YoY), non-GAAP EPS $25.11 on ~84.9% gross margin, all above the high end of guidance; the stock jumped ~14.6% on the print. DRAM was $31.3B (76%) and NAND $9.9B; data-center revenue hit $25B in the quarter (enterprise SSD $5B); operating cash flow was $25.4B and adjusted free cash flow a record $18.3B; the board raised the dividend 30%. FQ4 guidance is a record $50.0B revenue, ~86% gross margin, and $31.00 EPS. HBM3E and HBM4 are fully booked through calendar 2027 with demand into 2028. The bear case is now valuation / memory cyclicality / the explicitly guided "meaningful moderation in the rate of price increases" after FQ4 — not current results (which keep setting records). [FQ2 reference: $23.9B rev / $12.20 EPS.] Modeled [interpolated]: the short-interest and sentiment trajectories between dated readings — bi-monthly SI prints exist but a continuous public time series doesn't, so the curve shows shape through anchors, not every print. The price line is schematic to match the reference image's arc (the dashboard's live MU price lives in §04). On the reference image (Earnings Whispers): it shows a Sentiment line + Short Interest band; the circled right-edge spike to ~40M aligns with the ~41.6M NASDAQ figure. Sources: stockanalysis.com, Benzinga, Barchart/Yahoo Finance, TipRanks, Simply Wall St, NASDAQ short-interest data. Not investment advice.
The memory super-cycle now has a pure-play vehicle: the Roundhill Memory ETF (DRAM), launched Apr 2 2026, holding Samsung / SK hynix / Micron (≥50%-of-revenue-from-memory rule). Its asset-gathering pace has been so extreme it belongs in the same conversation as the fastest ETF launches in history — the spot-crypto giants. This chart races AUM by days since launch, so funds that launched years apart can be compared on equal footing. Aug 23 update — the July drawdown was recovered in three weeks, and the flow data now explains it. DRAM fell from $25.08B (Jul 27) to $21.96B (Jul 30) — −12.4% in three sessions, including a −8.89% day on Jul 28, when Korea printed its first-ever back-to-back circuit breakers (§03·C). It has since made a new high of $27.45B on Aug 21. Net flows over that whole month were +$2.07B, which settles the question the previous build refused to guess at: the fall was price, not redemptions. Meanwhile IBIT remains far below its peak — from ~$99B in Oct 2025 to $46.5B on Jul 31 2026, recovering to roughly $48B in late August as Bitcoin staged its best weekly rally since 2023. DRAM at $27.45B is now about 57% the size of IBIT while being 7.6× younger. The honest caveat still stands: the milestone points (●) are anchored to cited reporting; the connecting curves are modeled interpolations — daily AUM histories aren't fully public, so read the shape and ranking, not every point.
Provenance & the honesty line. Anchored milestones [cited]: DRAM (Roundhill Memory ETF, CBOE:DRAM, launched Apr 2 2026) — $1B in 10 trading days, $6.5B in ~36 days, $10B in 43 days, ~$20B by mid-June 2026 (~$17B early June); ~79% return since inception (cryptobriefing, US News, ETF.com, 24/7 Wall St, stockanalysis, Roundhill). IBIT (iShares Bitcoin, launched Jan 11 2024) — $10B in 34 days, ~$50B by ~11 months (Dec 2024), $70B in 341 days (5× faster than GLD's 1,691), then a climb to ~$99–100B by early Oct 2025 (aided by a ~160% BTC rally to ~$125K) before its first-ever monthly outflow in Nov 2025 (−$2.3B) pulled it back toward ~$82–88B (ETF.com, iShares 2025 trends, Benzinga/Bloomberg, cryptonews). IBIT's mid-curve points ($18–21B through 2024, the Oct-2025 peak, the Nov dip) are modeled through those cited monthly anchors. FBTC $10B in 54 days; ETHA (iShares Ethereum, launched ~Jul 2024) $10B in 251 days with a $5B→$10B sprint in 10 days; JEPQ (JPMorgan Nasdaq Equity Premium Income) $10B in 444 days (Eric Balchunas / Bloomberg "five fastest to $10B"; Cointelegraph, AInvest). Modeled [interpolated]: every point between the cited milestones — full daily AUM curves aren't public, so the lines are smooth interpolations through anchored points, not realized daily data. A genuine source conflict, surfaced not hidden: ETF.com called DRAM "the fastest ETF in history to reach $10B" (43 days), yet IBIT reached $10B in 34 days — so DRAM is not faster to $10B by that count. The most defensible reading: IBIT holds the outright $10B speed record (34d); DRAM is the fastest non-crypto / thematic equity ETF and among the fastest overall — the "fastest ever" phrasing likely reflects a category or re-measured basis. Apples-to-oranges caveat: crypto ETFs' AUM is inflated by underlying price rallies (IBIT rode BTC +160%), not just inflows; DRAM's holdings also rallied hard. AUM growth ≠ pure inflows. Aug 1 2026 update [cited]: DRAM — $23.34B (Jul 22), $25.08B (Jul 27, the peak), $21.96B with 23 holdings (Jul 30); the share price closed $47.77 on Jul 28, −8.89% on the day, recovering to $51.20 by Jul 31; standardized since-inception return through Jun 30 was +156.30% NAV / +161.51% market, and Roundhill cited a 179.84% total return when DRAM passed $20B. Top exposures as of Jun 30: Micron, Samsung, SK hynix, SanDisk, Kioxia. Note the provider spread: AUM prints for the same fund differed by ~$3B across sources inside one week — partly real (the fund did move that much) and partly methodology, so treat any single AUM figure as approximate. IBIT — $46.52B (Jul 31 2026), down from $53.4B at end-Q1 on $3.3B of Q2 outflows, with a 10-consecutive-day July outflow streak totalling 35,980 BTC (~$2.24B); Bitcoin entered 2026 above $93,000 and printed a 21-month low of $57,800 before trading near $62,500. RAM / RAMZ [cited]: the Roundhill T-REX 2X Long DRAM Daily Target ETF launched Jun 24 2026 with ~$385M of day-one notional against a prior category record near $282M, gross expense ratio 1.50%; a 2× inverse (RAMZ) followed. Aug 23 2026 update — the question the Aug 1 build refused to guess at is now answered [cited]: DRAM assets $27.45B on Aug 21, a new high above the Jul 27 peak, with net flows of +$2.07B (1 month), +$18.29B (3 months) and +$26.87B (6 months). Because net flows over the month spanning the drawdown were positive, the $3.12B fall from the peak was price, not redemptions — the distinction the previous build set out and could not resolve. [derived — this board's arithmetic]: the fund is roughly 4.7 months old, so the 6-month flow window covers its entire life; assets of $27.45B less cumulative net flows of $26.87B leaves $0.58B of aggregate gain, about +2.2% on every dollar ever invested, against a since-inception NAV return of +156.30%. Two caveats on that derivation: it assumes no distributions and treats aggregator flow estimates as exact, neither of which is guaranteed — and a 6-month window on a 4.7-month fund is being used as a proxy for since-inception, which is reasonable but not stated by the provider. IBIT [cited]: a 1-month net AUM change of +$1.78B and 1-month net flows of +$958M against a 3-month AUM change of −$18.42B and a 1-year change of −$39.2B; Bitcoin rallied 23% in late August, its best week since 2023, on $1.92B of ETF inflows. The $48.3B point plotted for IBIT at day 953 is derived — $46.52B at Jul 31 plus the cited 1-month AUM change — not a published print. Chart composition [Aug 23]: FBTC and JEPQ were removed from the plotted lines; both are retained in the record-book table, where their days-to-$10B figures are part of the benchmark set DRAM is raced against. The "ETF Omen": niche thematic funds have a historical tendency to launch near cycle peaks — and 2×/inverse wrappers arriving twelve weeks after the underlying is the sharper version of that signal. Sources: ETF.com, Bloomberg (Balchunas), cryptobriefing, US News, 24/7 Wall St, Cointelegraph, AInvest, stockanalysis, financecharts, TipRanks, Morningstar, Motley Fool, iShares fact sheet, cryptotimes, spotedcrypto, The Market Periodical, PRNewswire, Businesswire, REX Shares, Roundhill. Not investment advice.
The riskiest corner of the flows story just became one of the biggest: the global 2×-leveraged memory complex. What began as a pair of US single-stock funds is now a three-market phenomenon — the US pair (MUU, SNXX), Hong Kong’s CSOP pair on SK hynix and Samsung, Korea’s sixteen single-stock leveraged/inverse ETFs launched May 27, and now basket leverage (RAM, 2× the DRAM ETF, launched Jun 24). Global single-stock L&I assets doubled from <$30B to >$60B in six weeks (Goldman), the CSOP SK hynix 2× is the world’s largest single-stock leveraged ETF — $16.8B at the Jun 23 peak, ~$9B by Jul 16 after the unwind, still #1 — and the Korean products drive up to 60% of daily turnover in the underlyings — until Jul 16, when Korea halted new single-stock leveraged listings and tripled deposits to ₩30M all-cash (§03·D). On Jun 23 the tail wagged the dog: an FSS warning cascaded into Samsung −12.3% / SK −12.5% (worst day since 2008) and Micron −13%. Two of the long-window funds below are direct leveraged bets on names in this dashboard — MUU (2× Micron) and SNXX (2× SanDisk) — so their asset swings are a high-beta read on memory-trade conviction. The other two, BITX (2× Bitcoin) and ETHU (2× Ether), are the crypto comparison. This tracks AUM / net assets over a Jun 2023 → Jun 2026 window (BITX's launch onward), with monthly data points anchored to fund quotes/filings. Honesty note: current AUM values are from fund quotes/filings; the monthly path is anchored to dated observations with interpolation between — and leveraged-ETF NAV swings on both flows and the amplified price of the underlying.
Aug 3 2026 additions to all three tabs [cited]. SINGLE-STOCK tab: MUU $4.60B (Jul 30 close, stockanalysis/S&P Global — same page shows +37.39% on the day, prev close $19.63, 86.6M shares traded); SNXX $2.02B (Jul 29, 147.4M shares traded vs 145.6M outstanding); CSOP SK 2× $5.79B Jul 30 and $3.30B Aug 2 (HK$25.75B ÷ 7.8). KOREAN tab: no aggregate AUM print has been published since the ₩12.31T of Jul 7, so the Korea 16 line is held flat and dashed from that date rather than extrapolated — it is certainly overstated today, since KODEX SK hynix leverage alone is down >80% from its Jun 23 peak and the Samsung equivalent ~75% from Jun 3. What is cited for the period is turnover, not assets: ₩1.2388T on Aug 3, −58.6% from ₩2.9907T on Jul 31 and −94% from the ₩12.45T peak (Seoul Economic Daily, Bloomingbit). Turnover and AUM are different quantities and are not mixed on this chart. ALL + GLOBAL tab: Goldman's single-stock L&I aggregate still has no print after the >$60B of May 27, so it too stays dashed and flat — an absent print, not a flat market. One unresolved conflict, surfaced rather than smoothed: CSOP 7709's AUM fell 43% between Jul 30 and Aug 2 (HK$45.17B → HK$25.75B) while its quoted price rose from HK$25.36 to HK$42.52 over the same days. Those two facts can only both be true if there were enormous redemptions into a violent rally, or if the fund executed a share consolidation. This board could not confirm which from available sources, so the AUM figure is plotted (that is what the chart measures) and the price discrepancy is flagged here rather than reconciled by guessing. If it was a consolidation, the price series is not comparable across that date. Weekly-chart provenance. Anchors: global single-stock L&I <$30B at the start of April → >$60B by May 27 (Goldman Sachs / Chris Lucas via BigGo); CSOP SK hynix 2× ~$5.38B on May 7 (overtaking the 2× Tesla fund as world's largest, Seoul Economic Daily) → ~$16.8B by Jun 23 (ETF.com) — re-verified Jul 14 2026: Bloomberg still ranks 7709 the world's largest single-stock leveraged ETF at ~$13B on Jul 1, the pullback from Jun 23 tracking SK hynix's own drop (incl. −17% on a KIS profit-forecast cut), confirming one growing series rather than two conflated funds; CSOP Samsung 2× ~$1.65B mid-May (TradingKey — a single dated print, hence the flat dashed line); Korea's 16 funds $3B day one (May 27, ETF.com) → >$9B by Jun 23 (ETF.com, KuCoin; 92% retail); MUU ~$1.1B late Jan → $5.72B Jun 28 (ETF Database) → $5.9B Jul 3 peak; SNXX ~$2.7B late Feb → ~$3.9B mid-June → $3.22B Jul 16 (quote pages). July prints [added Jul 17]: 7709 ~$13B Jul 1 (Bloomberg via AI Weekly) → ~HK$71B ≈ $9.0B Jul 16 (issuer/quote pages); Korean flagship managers ₩15.41T Jun 25 → ₩12.31T Jul 7 (FnGuide via BigGo); KODEX SK hynix alone ~$3.4B Jul 14 (Bloomberg); Korea's 16 funds ₩4.4T May 27 → >₩15T in a month (Benzinga, Jul 16); Jul 16 FSC/FSS: new single-stock leveraged listings halted, ₩30M all-cash deposits. Event flags: May 26–27 — Micron and SK hynix cross $1T market cap within ten hours (Samsung crossed May 6) and Korea's 16 funds launch; Jun 22–23 — FSS governor's public criticism ("did little more than enrich securities firms"), then Samsung −12.31% / SK −12.47% / KOSPI −9.99% / MU −13%. Everything between anchors is interpolation; everything after Jun 23 without a print is dashed. Not investment advice.
| Vehicle | Market | Exposure | Status · scale |
|---|---|---|---|
| MUU · Direxion 2× MU | US | 2× Micron | $5.9B Jul 3 peak · −26.65% NAV Jul 13, no post-selloff print — the original memory-lev trade |
| SNXX · Tradr 2× SNDK | US | 2× SanDisk | $3.22B (Jul 16 read) — fastest single-stock launch on record (Jan '26) |
| RAM · Roundhill T-REX 2× DRAM | US | 2× the memory basket (MU+Samsung+SK+SNDK) | Launched Jun 24 — today's only US leveraged route to Samsung/SK |
| 7709 · CSOP SK hynix 2× | Hong Kong | 2× SK hynix | $16.8B Jun 23 peak → ~$9B Jul 16 — still the world's largest single-stock lev ETF |
| 7747 · CSOP Samsung 2× | Hong Kong | 2× Samsung Electronics | ~$1.65B (mid-May print) |
| Korea 16 · KODEX, TIGER, et al. | Korea | 2× long & −2× inverse, Samsung & SK | ₩4.4T→>₩15T in a month · ₩12.31T Jul 7 · all 14 below ₩20k listing · Jul 16: new listings banned, ₩30M all-cash deposit |
| T-Rex 2× SK hynix (REX) | US | 2× the SK hynix ADR | Superseded by the wave: ADR listed Jul 10, then six US SKHY L&I funds went live Jul 13–14 (SKHX/SKHZ, SKHU, SKUU/SKDD, Kurv); Direxion's SKHL filed |
| Leverage Shares 2× Memory | US | 2× the DRAM ETF (RAM competitor) | SEC registration filed May '26 |
Provenance & the honesty line. MUU — Direxion Daily MU Bull 2X (2× Micron), launched Oct 10 2024, 1.06% ER; ~$100M→~$400M by Nov 2025 (+$148M net flows in a month; ~378% YTD return), crossed $1.1B late Jan 2026 (one of ~7 single-stock ETFs over $1B), then went parabolic on Micron's rally to ~$5.9B by mid-June 2026 (ETF Database 1-yr net AUM change ≈ +$5.86B; NAV ~$972/share) — per Direxion/ETF Trends, ETF.com, ETF Database, AAII, SEC. SNXX — Tradr (AXS) 2× Long SNDK (2× SanDisk), launched Jan 26–27 2026, 1.49% ER; hit $650M in 24 days (fastest single-stock-ETF launch by avg AUM/day on record), ~$2.7B by late Feb, ~$3.9B by mid-June 2026 — per Tradr/PRNewswire, stockanalysis, Robinhood, Kraken. BITX — Volatility Shares 2× Bitcoin (first US leveraged crypto ETF), launched Jun 27 2023, ~2.38% ER; grew through the BTC bull market to a multi-billion peak (~$3B+ in late 2024/early 2025), then round-tripped — a ~76% one-year total-return loss and a peak-to-now AUM decline of ~⅔, to ~$1.0B by mid-2026 (1-yr net AUM change ≈ −$2.05B; NAV ~$12 vs 52-wk high ~$69) — per Yahoo, stockanalysis, ETF Database, Kraken. ETHU — Volatility Shares 2× Ether, launched Jun 4 2024, 2.67% ER; ran up to ~$1.4B in early 2025 then suffered a ~74% YTD drawdown to ~$730–790M by mid-2026 (NAV ~$14 vs 52-wk high ~$189) — per Yahoo, MutualFunds, Dividend.com. Monthly points [interpolated]: the per-month values between cited observations — full daily AUM histories aren't public, and leveraged-fund AUM moves on flows + the 2× mark of a volatile underlying, so monthly points show plausible shape through anchored dates, not realized daily data. A note on the source image: this section was requested from a chart whose dollar labels (e.g. MUU $19.2M) didn't match current filings — I rebuilt from current quote/filing AUM instead of tracing it. AUM ≠ inflows for leveraged funds even more than for spot ones: a doubling underlying doubles the leveraged exposure mechanically. Sources: ETF Trends/Direxion, Tradr/PRNewswire, Volatility Shares, Yahoo Finance, Robinhood, Kraken, stockanalysis, AAII, ETF Database, SEC filings. Not investment advice — and these instruments are especially unsuitable for non-traders.
§04·E tracks how fast the Roundhill Memory ETF (DRAM) gathered assets. This tracks whether the companies inside it are actually making money on what they sell. Gross margin is the cleanest single read on a memory cycle — it strips out opex and one-offs and shows the raw spread between what a bit costs to make and what it sells for, which in this industry swings further than almost any other sector. Micron went from a −9.1% full-year gross margin in FY2023 to 84.6% in the quarter just reported — a ~94-point swing in three years. But the headline finding is the dispersion, not the level: in the same calendar quarter, the seven charted holdings span 52.7% to 84.6% — a 34.6-point spread inside one ETF — and the fund's single largest position has the lowest margin of the group for a reason that has nothing to do with memory.
Provenance — which points are real, and the four ways this comparison can mislead. Holdings basis [cited]: Roundhill Memory ETF (DRAM) holdings as of Aug 5 2026, 23 lines, $23.92B net assets. Weights quoted here combine each company's direct equity and its total-return-swap exposure, because the fund holds several names both ways. These weights are gross and do not sum to 100% — the fund's own top-10 line is 110.57%, and charted (90.7%) plus unchartable (35.6%) comes to 126%. That is not an error in this table; it is how a swap-based fund reports, with the 26.6% Treasury bill serving as swap collateral rather than as a cash allocation — Micron ~26.8%, Samsung ~24.1%, SK hynix ~21.7%, Seagate 5.0%, Western Digital 5.0%, SanDisk 4.5%, Kioxia 3.6%, CXMT 3.4%. Cited margin prints (●): Micron FQ2'24 19%, FQ3'24 27%, FQ4'24 35.3%, FQ1'25 38%, FQ2'25 37%, FQ3'25 38%, FQ4'25 44.7%, FQ3'26 84.6% GAAP (some outlets report 84.9% non-GAAP for the same quarter — the difference is basis, not a dispute), with FY2023 full-year at −9.1%; Q4 FY26 was guided to ~86% and is not plotted because it has not been reported. SK hynix Q3'23 −9.5%, FY23 −1.6%, FY24 48.1%, FY25 60.4%, Q1'26 68.3%, Q2'26 68% (that quarter's operating margin was 76% — a different line item, not used here). Samsung FY23 30.3%, FY24 38.0%, FY25 39.4%, Q1'26 47.7%. SanDisk FQ2'26 51.1%, FQ3'26 78.4%, FQ4'26 ~80% (guided 79–81%). Kioxia Q1 FY26 80% (82% excluding JV items). Western Digital FQ4'26 54.4%. Seagate FQ4'26 52.7%. Interpolated points, stated plainly: where a company published an annual gross margin but not a quarterly one — most of SK hynix's 2024–25 path and nearly all of Samsung's — the quarterly points were interpolated to hit the cited annual figure and are drawn dashed and tagged in the tooltip. Micron's 2023 quarters and its 25Q4–26Q1 are likewise interpolated between cited anchors. Do not quote a dashed point as a reported number. Roughly a third of the plotted points are cited; the rest exist to give the lines a shape between them. The four distortions, repeated because they are load-bearing: (1) Samsung is whole-company, not memory — the single most misleading line on the chart, and the fund's largest position. (2) GAAP and non-GAAP are mixed — WDC, Seagate, SanDisk and Kioxia are non-GAAP, which runs structurally higher. (3) Five fiscal calendars, mapped onto the calendar quarter each period actually covers. (4) Seagate and Western Digital are HDD makers, not memory manufacturers. And 35.6% of the fund has no line at all — a 26.6% Treasury bill, unlisted CXMT at 3.4% with no published accounts, and 5.6% of small-caps excluded rather than approximated. Cross-refs: §04·E (the ETF's asset gathering and its first drawdown), §03·B (the export price driving these margins), §05·D (the supply-demand balance behind it), §04 (the companies). Sources: Roundhill/Finnhub holdings via stockanalysis, Micron 8-K and 10-Q filings, SK hynix newsroom Q2'26, Samsung Global Newsroom, SanDisk investor relations, Kioxia FY26 Q1 results via TrendForce, Western Digital and Seagate FQ4'26 releases. Not investment advice.
§04·D lists who is calling what. This asks a harder question: has the consensus actually moved? Analyst price targets are supposed to be a forward view, but they are revised by humans with careers, and they tend to move after the market rather than before it. The memory complex just ran that experiment at full scale. Samsung and SK hynix both now trade at almost exactly half their average target — an implied +96% upside each — not because analysts got more bullish, but because the share prices halved while the targets barely moved. The revision data is the tell: Samsung had zero target-cut reports from March through June, SK hynix had none for roughly a year, and then six and eight brokerages respectively cut within days of the Jul 29 plunge. Across the Korean market, July produced 824 downgrade reports against 407 upgrades.
Provenance — what is cited, what is dated when, and four ways a consensus average misleads. [cited]: Micron — average target $1,507.38 against a $920.95 close, high $2,200, low $361, 45 analysts (MarketScreener consensus page, retrieved Aug 6 2026); prior average $1,492 at Jul 24 from this board's §04·D. SK hynix — brokerages' average target over the past month ₩3,371,538 against a ₩1,718,000 close on Jul 31 (up the daily limit), 13 houses publishing in July, high ₩4.7M (Korea Investment), low ₩1.48M (BNK, cut from ₩1.85M with a Hold); prior board reading ₩3.41M at Jul 24; MarketScreener separately shows ₩3,189,340 across 37 analysts on Aug 6 — a different sample and window, noted rather than blended. Samsung — average ₩514,545 against a ₩262,500 close on Jul 31 (+26.81% that day), 11 houses in July, high ₩650,000 (Korea Investment), low ₩360,000 (DB Securities), Mirae Asset cutting ₩500,000 → ₩370,000 on Jul 29. SanDisk — average $2,116.64 across 23 analysts (MarketBeat), against a Jul 24 price of $1,610 from §04·D — its spot reading is two weeks stale and its implied upside is therefore the least reliable figure on the chart; individual July moves include Goldman to $2,200, Evercore cutting to $2,800 from $3,100, Jefferies to $1,750 from $3,000, Mizuho to $1,900 from $2,200. Revision counts: FnGuide via Seoul Economic Daily, Aug 3 2026 — 407 upgrade reports against 824 downgrades across the Korean market in July. Four ways this metric misleads, stated because the chart is seductive: (1) The average is not a forecast. With a 6.1× spread on Micron and a ₩3.22M spread on SK hynix, the mean sits between two incompatible views and represents nobody's actual thesis. (2) Targets are anchored to price. Analysts revise toward the tape more often than the tape moves toward targets, so a large gap usually closes by the target falling, not the price rising — which is precisely what July showed. (3) The samples are not the same. The Korean figures are FnGuide's count of houses publishing in the past month; MarketScreener and MarketBeat use their own analyst panels over their own windows. Two of the numbers here for SK hynix (₩3.37M and ₩3.19M) differ for that reason alone and both are reported rather than averaged into a false single figure. (4) Spot dates differ by up to two weeks — Micron is Aug 6, the Korean pair Jul 31, SanDisk Jul 24 — and in a market that moved 20% in a session, that matters. Every spot date is on the chart's tooltip. Related: §04·D (individual calls), §04·I (realised gross margins), §04·E (the ETF), §03·C (the crash that triggered the revisions). Sources: MarketScreener consensus; Seoul Economic Daily / FnGuide, Aug 3 2026; MarketBeat; TipRanks/TheFly. Not investment advice.
SOXL is the oldest and largest leveraged bet on this entire thesis — the Direxion Daily Semiconductor Bull 3× ETF, listed since March 2010, seeking three times the daily move of the semiconductor index. Micron is its third-largest equity holding at 5.11%, behind AMD and Nvidia, so this board's memory cycle runs straight through it. From the April-2025 tariff trough of $12.29 to the June-2026 peak of $302.00 is a 24.6× move in fourteen months — and it now sits at $132.33, still up 977% from that low but 56% below the high. But the price is the less interesting axis. Assets are $23.82B, and they did not fall when the price did: July brought $6.9B of net inflows, the largest month in the fund's sixteen-year history, during a month in which it lost 57%. That divergence — the scissors on the chart below — is the whole section.
Provenance & the honesty line. [cited — live quote, Aug 6 2026]: close $132.33 (+0.20%; $135.70 after hours), assets $23.82B, 170.45M shares outstanding, volume 57,025,099, expense ratio 0.75%, 49 holdings, beta 5.76, inception Mar 11 2010, 52-week range $22.95–$302.00, one-year total return +439.84%, since-inception average annual return +39.16% (stockanalysis / S&P Global Market Intelligence). Holdings: roughly 52% of the fund sits in Treasury and government cash funds — Dreyfus, Goldman and JPMorgan vehicles — as collateral against an ICE Semiconductor Index swap; the equity sleeve is led by AMD 5.53%, NVIDIA 5.19%, Micron 5.11%, Broadcom 4.59% and Intel 3.35%. As with the DRAM ETF in §04·I, a large Treasury position in a leveraged fund is not a defensive allocation — it is swap collateral, and the fund is fully exposed despite it. [cited — 2025 path]: the year opened near $28 on Jan 2 2025 and was at ~$25 by Jun 30, a move that conceals a round trip of more than 70% in between; the biggest single-session gap-down was −23.0% on Apr 10 2025; the trough close was $12.29 on Apr 28 2025; the 52-week low of $22.95 falls in August 2025; and the full-year 2025 total return was +54.91%. SOXL itself did not reverse split — Direxion split the inverse fund SOXS and several others, not the bull fund — so the price series is continuous across the whole window and no split adjustment has been applied. [cited — 2026 path]: best month April at +165.0%; year-to-date +433% through May against ~81% for the unleveraged index; sector peak Jun 22; −23% in the single session of Jun 23 versus ~8% for unleveraged semi ETFs; ~$5.1B of inflows between Jun 22 and Jul 17; worst month July at −57.0% with $6.9B of net inflows, the largest monthly intake since launch; −63% from the Jun 22 peak by Jul 29 against −25% for SOXX; five-year return +479% versus +404% for SMH. What is interpolated, and it matters here: nine of the sixteen price points and only three of the sixteen AUM points are cited readings. No 2025 assets-under-management figure could be cited at all, so the entire 2025 half of the teal line is a modelled path drawn to meet the one anchor that exists — assets ">$12B" entering 2026 — and should be read as a shape, not a series. Extending the price line back to January 2025 also corrected the 2026 interpolation: anchoring to the cited +54.91% 2025 return puts the end of 2025 at ~$43, which moves the modelled May-2026 point from ~$277 in the first build down to ~$231, consistent with the cited +433% year-to-date. The monthly values between them are interpolated to connect dated anchors and are not published figures — daily AUM history for this fund is not public, and monthly price marks were reconstructed to be consistent with the cited month-return and year-to-date figures rather than taken from a series. Every point's status is in its tooltip. Do not quote an interpolated month. The shape — price down, assets up — rests entirely on the cited endpoints and is not an artefact of the interpolation. Three further caveats: (1) AUM is not flows. Assets move on both creations and the mark-to-market of what is already held; the $6.9B July figure is a separately cited net inflow number, which is why it can be stated alongside a 57% price fall without contradiction. (2) SOXL tracks a semiconductor index, not memory — Micron is 5.11% of it, so this is a broad AI-silicon instrument that happens to contain this board's thesis, not a proxy for it. (3) Past leveraged returns are especially unrepresentative because they are path-dependent; the +439.84% one-year figure describes one particular sequence of days and would differ materially for any other path with the same start and end. Cross-refs: §04·E (the DRAM ETF and its 2×/inverse wrappers), §04·F (the leveraged complex), §03·C (Korea's leveraged unwind), §04·I (holdings-level margins). Sources: stockanalysis SOXL, ETF.com daily flows, Benzinga, TechTimes, 24/7 Wall St, Direxion. Not investment advice — and leveraged daily-reset products are especially unsuitable for non-traders.
Same AI boom, two valuation regimes. The PEG ratio (P/E ÷ expected earnings growth) asks one question: how much are you paying per point of growth? On that screen the seven memory names in §04 and the Magnificent 7 live on different planets — median memory PEG ≈ 0.20 vs Mag-7 ≈ 1.4, a ~7× gap — because the market prices memory growth as cyclical (it won't last) and platform growth as durable (it will). The one crossover is the Mag 7's own memory customer: Nvidia at 0.29 — the only platform name priced like a memory maker. AMD is added as the merchant-accelerator control at ~1.31 — the other big buyer of HBM, and the useful contrast to Nvidia: same AI end-market, same memory dependency, but priced 4–5× richer per point of growth, because its AI earnings are a 2027 promise (MI450/Helios ramp) rather than a booked run-rate. Whether the gap is a gift or a trap is exactly kill-switch #6's question, so the warning box below is part of the chart.
Provenance & method. Memory side [board compile, Jun 24–27 2026]: forward P/E, latest YoY growth and forward PEG are §04's financials rows (SK hynix 6.8× / +198% / 0.10 · Samsung 6.8× / +69% / 0.20 · Micron 9× / +346% / 0.10 · Kioxia 11× / +58% / 0.30 · SanDisk 12× / +97% / 0.20 · Seagate 17× / +45% / 0.40 · Western Digital 25× / +40% / 0.43). Post-crash caveat: the Jul 8–13 rout cut the Korean names ~25–35% from their peaks with estimates so far intact — their spot P/E and PEG are now mechanically lower than these compiled values; refreshed on the next financials pass rather than guessed here. Mag-7 side [tracker prints, Jul 14–16 2026]: Nvidia fwd P/E ~24.0, PEG 0.29 (financecharts, Jul 14; GuruFocus fwd P/E 22.8 — ⚑ variance); Meta fwd P/E ~20.3, PEG 0.99 (fullratio; GuruFocus 0.82 — ⚑); Microsoft fwd P/E ~19.5, PEG 1.34 (fullratio/GuruFocus; Motley Fool quotes fwd P/E 24.5 — ⚑ basis variance); Amazon fwd P/E ~29, PEG 1.4 and Apple fwd P/E ~33, PEG 2.5 and Tesla fwd P/E ~179, PEG 5.1 (24/7 Wall St, Jul 15); Alphabet fwd P/E ~27.9, PEG 1.77 (stockanalysis, Jul 16; GuruFocus 25.1 fwd P/E — ⚑). The uncomfortable footnote every PEG screen owes you: PEG has no standard basis — publishers mix trailing vs forward P/E and 1-year vs 3–5-year expected growth, and the memory rows' G is YoY revenue-cycle growth while the Mag-7 rows lean on analyst EPS-growth estimates. Treat this as an indicative screen of two regimes, not a precision cross-table; conflicting prints are flagged ⚑, not merged. Cross-refs: §04 (financials), §02·D KS#1/KS#6, §04·D (targets). AMD [added Jul 24, flagged ⚑]: the widest source spread on the board — forward P/E prints of ~57×, ~73× and ~77× coexist across financecharts/fullratio/GuruFocus for the same week, and PEG reads 1.31 (Jul 20) or 0.96 depending on which growth window is used (consensus EPS growth is ~+64% for FY26, ~+58% FY27, ~+38% FY28 — PEG falls as you extend the window). 1.31 is plotted; the 0.96 alternative is disclosed here rather than averaged in. AMD is grouped separately as AI silicon — it is neither a memory maker nor a Mag-7 member, and is included because it is the second-largest merchant buyer of HBM (§06) and the cleanest read on how the market prices accelerator growth versus memory growth. Its premium to Nvidia on this screen is the market paying for booked revenue over promised revenue. Sources: financecharts.com, fullratio.com, GuruFocus, stockanalysis.com, 24/7 Wall St, TIKR, Motley Fool, company disclosures. Not investment advice. Aug 17 2026 — the unadjusted version of this question is now mapped [§04·L]. SK hynix prints a forward P/E of 6.88× against a semiconductor median of 31.58× — a 78.2% discount. §04·L tests the value-trap counter-argument using §04·B's dated estimate cuts and finds the multiple re-rates to 8.1–9.4× on an unchanged price, while still leaving a 70%+ discount. Growth-adjusting it, which is this section's job, is the more appropriate lens for exactly that problem.
Memory occupies the entire cheap end of the global large-cap table: Samsung at 5.8×, SK hynix at 6.9×, SanDisk at 7.4× — the three lowest multiples among the twenty-seven companies here — against a semiconductor median of 31.6×, an S&P 500 at 21×, ASML at 48.8× and Tesla at 187.6×. The obvious reading is that memory is cheap. The obvious counter-reading is that a single-digit multiple on a commodity cyclical at peak earnings is a value trap, because the E in P/E is about to fall. This board can test that second claim rather than just repeat it — because §04·B recorded the exact estimate cuts, dated, six days before the 6.9× print. Applying them moves the multiple to 8.1–9.4× on an unchanged share price. That is a real re-rating from the denominator alone. It also still leaves a 70%+ discount to the sector — so the honest answer is that both readings are partly right, and the section says so instead of picking one.
Provenance & the honesty line. [cited, with dates where published]: Samsung Electronics 5.84× (Aug 21); SK hynix 6.88× (Aug 17); SanDisk 7.44×; Toyota 7.99× (Jul 1); Micron 10.57×; Alibaba and Tencent 10–15×; Novo Nordisk 13.5×; Alphabet 16.6×; Nestlé 17.62× (May 21); Broadcom 18.71× (Aug 19); Saudi Aramco 19.05×; Microsoft 19.53×; TSMC 21.3×; LVMH 21.74× (Aug 6); Meta 21.9×; NVIDIA 22×; Berkshire Hathaway 22.97× (Aug 20); Seagate 23.75× (Aug 21); Western Digital 24.93× (Aug 6); Amazon 30.7×; Apple 34.11× (Aug 12); AMD 35×; Eli Lilly 35.01× (Aug 5); Walmart 37.93× (Aug 6); ASML 48.82× (Aug 2); Tesla 187.58× (Aug 6). Benchmarks: global equities 18.89× (Jan 1 2026), S&P 500 ~21×, Magnificent Seven ~28–30×, semiconductor median 31.58×, with SK hynix cited at 78.2% below that median. [cited — §04·B, Aug 11 2026]: Kiwoom cutting SK hynix 2027 operating profit −15.6% and 2028 −22.2%, Samsung 2027 −15.0% and 2028 −26.9%. [derived]: every re-rated multiple in the table (6.88 ÷ (1 − cut)), the +18% to +37% re-rating percentages, and the surviving discounts of 70–72%. These are illustrative arithmetic, not a forecast — they apply one broker's operating-profit cuts to a multiple built on a different (net-income, consensus) earnings base, so they show the direction and rough scale of the denominator effect, not a corrected multiple. A precise version would need the consensus EPS the 6.88× was struck against, which is not published in the sources available here. What this section does not do: (1) no view on whether memory is cheap — it sets out what each reading requires you to believe; (2) no averaging of conflicting figures — where two sources disagree the better-dated one is plotted and the other is marked, never split; (3) no PEG or growth adjustment, which is §04·G's job and is the more appropriate lens for exactly this problem. On the source that prompted this: it is a short argument that memory's low multiple reflects a commodity cycle rather than cheapness, and contains no valuation table — the grid here was assembled independently. The argument itself is the section's subject and is treated as a hypothesis to test, not a conclusion to relay. Cross-refs: §04·G (forward PEG, the growth-adjusted version of this question), §04·B (the estimate cuts and the 2.24× dispersion in price targets), §04·D (price targets), §04·I (gross margin by holding), §02·B (cycle position), §02·D (kill-switches). Not investment advice.
The Catalyst Calendar (§02·E) says when; this says what each print has to prove. Every memory earnings date is a scheduled test of a kill-switch (§02·D): consensus/guidance vs actual vs the stock's reaction, in one board. The near-term trio is decisive — SK hynix Q2 (Jul 23) and Samsung full Q2 (Jul 30) land this month, and Micron FQ4 (~Sep 22) is where the guided "moderation" either shows up or doesn't. Reported rows carry the actual; upcoming rows carry the bar to clear.
Provenance. Reported [cited]: Micron FQ3'26 (Jun 24) — revenue $41.5B, EPS $25.11, next-quarter guide ≈$50B (Micron IR; §04·B ratings row). Samsung Q2 preliminary (Jul 7) — record ₩89.4T operating profit ≈19× YoY on ₩171T revenue, stock −6% on profit-taking (Samsung IR; §02·E). Upcoming [scheduled/consensus]: SK hynix Q2 ~Jul 23 (consensus ~₩63–64T operating profit); Samsung full Q2 Jul 30; Micron FQ4 + FY26 ~Sep 22–24. Consensus figures are pre-print estimates and move; each row states the kill-switch it tests rather than predicting the outcome. Cross-refs: §02·D, §02·E, §04·B, §04·D. Sources: company IR, TrendForce, analyst consensus. Not investment advice.
HBM stacks DRAM dies vertically and links them with through-silicon vias — 5–10× the bandwidth of ordinary GDDR. Each generation is matched to a class of AI accelerator.
§05·B is HBM vs the rest of DRAM; this is who supplies the HBM — the single most thesis-relevant, and most disputed, number in the stack. SK hynix leads decisively, but the exact split swings quarter to quarter and disagrees firm to firm. The bars are one internally-consistent source (Counterpoint) so the quarters are comparable; the firm-divergence on SK's Q1'26 share is shown separately. Flagged, not merged.
Provenance & the honesty line. Bars [cited · Counterpoint]: Q2 2025 SK 62 / Micron 21 / Samsung 17; Q3 2025 SK ~53 / Samsung ~35 / Micron ~12 (Micron a residual); Q1 2026 SK 58 / Samsung 21 / Micron 21 (Counterpoint “Global DRAM & HBM Market Share,” Jun 8 2026, via Reuters). Firm divergence [flagged]: for Q1 2026 SK hynix, IDC prints 56.4% (revenue rank), Counterpoint 58%, TrendForce ~70% — a ~14-point spread driven by revenue-vs-shipment-vs-wafer methodology and lumpy HBM revenue recognition. The widely-shared “Micron overtakes Samsung” headline is Q2 2025, not a 2026 event — Samsung rebounded the next quarter. §04's per-company “Samsung HBM share ~30–35%” reflects its Q3'25 peak / 2026 target, above Counterpoint's Q1'26 ~21% read; both are shown rather than averaged. Q4'25 is a public-data gap (not charted). Sources: Counterpoint, IDC, TrendForce, Reuters. Not investment advice. Aug 15 2026 — a source that looks like it settles this, and does not. The Morgan Stanley SKU-level exhibit replicated in §05·C·H names HBM suppliers for every 2027 accelerator — but as unranked strings ("Samsung/Hynix/Micron", "Hynix/Micron"), with one entry reading literally "Samsung?". No vendor allocation can be derived from it, so it does not narrow the disagreement this section documents. It is noted here specifically so the exhibit is not mistaken for a resolution.
What a gigabyte of each HBM generation actually costs — and the single most important caveat on this entire page: HBM has no transparent market price. Unlike DDR5 (which has published DRAMeXchange spot & contract prices), HBM is sold only through confidential long-term contracts between three suppliers and a handful of buyers, bundled and volume-dependent. Every number below is an analyst triangulation from earnings commentary, teardown cost models, and industry reports — directional, not a quoted price. The pattern that is reliable: each generation costs more per bit (more stacked layers, finer TSV pitch, lower yield), and HBM sits at a large premium to commodity DRAM. The newest hard anchor: on June 5, SK hynix finalized 2026 HBM4 price/volume terms with NVIDIA at >50% above HBM3E — the first concrete HBM4 price signal, replacing earlier guesswork.
Note the squeeze at the right of the conventional-DRAM bar: DDR5 itself is now ~$10–12/GB in this shortage, up from ~$3–4 a year ago. That's why HBM3E's premium over DDR5 has collapsed from ~4–5× (early 2025) toward ~1–2× by end-2026 (TrendForce) — not because HBM got cheaper, but because commodity DRAM caught up.
Provenance — read this before citing any number. There is no public HBM spot or contract index; the three suppliers (SK hynix, Samsung, Micron) sell HBM under confidential long-term agreements, so all figures are analyst estimates triangulated from earnings calls, teardown cost models, and trade-press reports, and they vary widely by source, buyer, volume and quarter. Per-stack anchors (Silicon Analysts, Apr 2026): HBM3 ≈ $200 / 24 GB stack (~$8–10/GB), HBM3E ≈ $300 / 36 GB stack (~$8–13/GB), HBM4 ≈ $500 / 48 GB stack (est., ~$10–14/GB; pre-volume). HBM3E reportedly peaked at ~$17–20/GB in H1 2025 at the demand peak (Silicon Analysts citing TrendForce / Goldman Sachs / SemiAnalysis), then 2026 contracts were struck lower before a reported ~20% HBM3E price hike for 2026 (TrendForce / Chosun Biz). HBM4 (June 5, 2026): SK hynix finalized 2026 HBM4 price & volume with NVIDIA at >50% above HBM3E per Korean press — implying ~$17–19/GB on the HBM3E midpoint, the first negotiated HBM4 anchor (prior figures were pre-volume estimates). Legacy HBM2/HBM2E ($/GB) are directional single-source estimates — public data is sparse before the 2022 AI inflection. Baseline: server DDR5 ≈ $1.50/Gb (~$12/GB) currently (Counterpoint), elevated by the shortage. Premium dynamic: HBM3E commanded >4× DDR5 in 2Q25, narrowing toward 1–2× by end-2026 as DDR5 spikes (TrendForce). Cost-of-production driver: 1 GB of HBM needs ~3–4× the wafer capacity of DDR5 (Tom's Hardware / TrendForce). System context: a B200 with eight HBM3E stacks carries ~$2,400 of memory, exceeding its logic-die cost (Silicon Analysts). "$/GB" is the practical proxy for cost-per-bit (1 GB = 8 Gb). Sources: Silicon Analysts, TrendForce, Counterpoint Research, Goldman Sachs, SemiAnalysis, Tom's Hardware, KED Global, Chosun Biz. These are estimates of a non-public price. Not investment advice.
Every other HBM section on this board works top-down — bit growth, wafer share, trade ratios. This one works bottom-up from named silicon: CoWoS wafer allocation → chips per wafer → units shipped → stacks per package → gigabytes. Seventeen products, each with its own HBM generation and supplier list, summing to 6.08 bn GB of HBM in 2027 — the source's "up to 50bn Gb". The board replicated the whole table before using any of it, and it holds: every visible row's arithmetic reproduces exactly, and the two blank cells are recoverable by difference and close both column totals to the unit. But the headline number is not the interesting part. The interesting part is the mix: custom ASICs consume as much HBM as merchant GPUs in 2027 — 49.7% against 49.4% — and Google alone reaches 97% of NVIDIA's HBM consumption. Meanwhile HBM3E is still 48% of demand in a year the roadmap calls the HBM4 era.
Provenance & validation. [cited — transcribed from a user-supplied exhibit]: Morgan Stanley Research, "AI HBM consumption: Up to 50bn Gb in 2027", compiled from the firm's Asian supply-chain checks, with company data. All 2027 estimates. Every figure in the table above is transcribed, not modeled. Validation performed before use, and it passes: (1) implied shipments = CoWoS allocation × chips per wafer for all 16 rows that carry both — exact, no rounding drift; (2) GB per package = stack density × stack count for all 15 HBM-bearing rows — exact; (3) HBM demand = units × GB per package for all 15 — exact; (4) the unit chain closes: 6,077,256 k GB × 8 ÷ 1,000 = 48,618.048 mn Gb against the highlighted 48,618 — consistent to the source's own rounding (a 0.0001% difference), not bit-exact. Stated precisely because an earlier draft of this section claimed "exactly" and the validation harness caught it. [derived by this board]: the 148k-wafer CoWoS allocation for TPU v9 and the 53,640 k GB HBM demand for "Others" — each is the unique value that closes a stated column total, and both close to the unit. The implied 2.70 chips per CoWoS wafer for TPU v9 is a consequence of that recovery, not a source figure; it is low but consistent with a very large multi-die package, and it is the least certain number on this page. What this exhibit cannot do, and is not used for: (1) HBM vendor share. The supplier column lists two or three names per product with no allocation between them, and one entry is literally "Samsung?" — no vendor split can be computed from it, and none is attempted here (§05·B·V holds the contested vendor picture). (2) Any year but 2027. There is no time series; this is a single-year snapshot and is not extrapolated. (3) Supply. It is a demand build only — whether 6.08 bn GB can be produced is §05·D and §05·E's question, not this one. Reconciliation to the rest of the board [derived]: §05·D carries HBM at ~13% of 2027 DRAM bits; 6.08 bn GB at 13% implies a total 2027 DRAM market of ~46.7 bn GB, which sits inside the range implied by §06·E's shipment series — so the bottom-up and top-down views are compatible, which is not guaranteed and is worth knowing. One caution on the CPUs: Vera and Venice occupy 520k CoWoS wafers — 20% of the total allocation — and carry no HBM. Any model that infers HBM from CoWoS capacity alone will overstate it by roughly that fraction. Cross-refs: §05·C·G (what these generations actually are), §05·B (HBM's share of DRAM supply), §05·B·V (why vendor share stays contested), §05·D and §05·E (whether the wafers exist), §05·F (the independent unit forecast this is checked against), §06·B (the accelerator memory roadmap), §06·B·T (Google's TPU line, the largest single ASIC buyer here). Not investment advice.
Four generations, one family. The similarities are the shared DNA below — every generation is the same fundamental idea: stacked DRAM dies wired vertically with TSVs, sitting on a 2.5D silicon interposer next to the GPU, trading a wide-and-slow bus for GDDR's narrow-and-fast one. The differences are where each generation finds its bandwidth: HBM3→3E pushed pin speed on the same 1024-bit bus; HBM4 is the architectural break — it doubles the bus to 2048-bit (while its per-pin speed actually falls back to HBM3-class), moves the base die from a DRAM process to a logic process, and requires a new PHY and interposer; HBM4E then pushes pin speed again on the doubled bus. Honesty note: HBM4E has no announced product — its figures are projections and are marked as such throughout.
The tell: HBM4's per-pin speed drops back to HBM3-class (6.4–8 Gb/s spec) — its ~2× bandwidth gain comes from the doubled 2048-bit bus, not the clock. HBM3E and HBM4E are the "speed" generations; HBM4 is the "width" generation.
Counter-consideration, stated: per-die capacity also rises (24Gb→32Gb dies), which pushes wafer-per-GB down and partly offsets ① and ②. Net direction stays up in every public model, but the generation-over-generation step is smaller than the yield numbers alone imply — which is exactly why a single vendor-published HBM4 ratio would be worth more than this whole panel.
Provenance & the honesty line. Spec anchors [JEDEC + vendor disclosures]: HBM3 standardized Jan 27 2022 (JESD238): 1024-bit bus as 16×64-bit channels, 6.4 Gb/s/pin → ~819 GB/s per stack; SK hynix mass production Jun 2022 for Nvidia's H100. HBM3E is a vendor-enhanced extension of HBM3, not a new JEDEC generation: same 1024-bit bus, 16 channels / 32 pseudo-channels, pin speed pushed to 8–9.6 Gb/s (Micron's 9.6 → ~1.2 TB/s; up to 12.4 in advanced implementations), 36 GB 12-Hi with a 48 GB 16-Hi announced (SK hynix, Nov 2024), ~2.5× perf/W vs HBM2E, backward-compatible with HBM3 controllers (Siemens EDA design guide, Rambus, Wikipedia). HBM4 ratified Apr 15 2025 (JESD270-4): 2048-bit / 32 channels / 64 pseudo-channels — the doubled bus is the generation break; JEDEC per-pin spec 6.4–8 Gb/s (HBM3-class; Samsung has demoed ~13) → ~2.0 TB/s at 8 Gb/s and up to ~3.3 TB/s in advanced configs; up to 16-Hi with 32 Gb dies = 64 GB/stack; the base die moves to a logic process (12nm FFC+ / 5nm at TSMC), opening near-memory compute; first parts shipped late 2025/early 2026, and by mid-2026 all three makers are in HBM4 mass production — SK hynix and Micron first, then Samsung shipping industry-first commercial HBM4 from Feb 12 2026 on its 1c (6th-gen 10nm-class) DRAM with a 4nm logic base die: 11.7 Gb/s (up to 13), 3.3 TB/s per stack, 12-Hi 24–36 GB (16-Hi 48 GB to follow). On Jun 5 2026 Nvidia certified all three (SK hynix, Samsung, Micron) as HBM4 suppliers for Vera Rubin, with supply-chain estimates putting SK hynix ~60–70% / Samsung ~25–30% / Micron the remainder of Rubin volume; Nvidia Rubin R100 (H2 2026, up to 22 TB/s per GPU) and AMD MI400/MI430X are the first major platforms (Siemens, IntuitionLabs, Spheron, HyperPC, Wikipedia). HBM4E [projection — flagged]: in development for ~2027–28; controller IP already supports up to 16 Gb/s (Rambus), implying ~4.1 TB/s per stack on the 2048-bit bus; Micron's roadmap points to customized logic base dies; no GPU product is announced as of mid-2026 and its specs are not manufacturer-confirmed (Spheron, IntuitionLabs) — every HBM4E figure here is hatched/starred as projected. A source conflict, reconciled: Rambus materials describe HBM4/4E controllers as "backward compatible with HBM3" — that refers to multi-mode controller IP; the physical HBM4 interface is a hard break requiring new PHY, a finer-pitch interposer, and CoWoS-L-class packaging ("you cannot drop an HBM4 module into an HBM3E slot" — Siemens/Kynix/AIChipLink). The table reflects the physical reality. Why it's on-thesis: the width-doubling plus logic base die is part of why HBM consumes far more wafer and packaging capacity per GB (see §05·B share and §05·C cost) — and per Micron's FQ3 call (Jun 24 2026), HBM4 12-high is ramping ~2× faster than HBM3E did, with HBM3E + HBM4 sold out through calendar 2027. Wafer consumption [1 cited, rest derived — added Jul 30 2026]: the anchor is Micron's disclosure that a gigabyte of HBM3E takes roughly 3× the wafer capacity of a gigabyte of DDR5 (reported via Tom's Hardware / TechTimes, Jun 2026) — the cause given is that vertical stacking cuts yield while adding process steps. Stack-yield band [cited]: yields fall 10–20 percentage points from 8-Hi to 12-Hi and drop toward 40–60% approaching 16-Hi (SemiEngineering; Samsung's Jun 30 2026 “dummy die” patent targets exactly this warpage/cracking failure mode). TSV geometry [cited]: HBM3-era TSV pitch 40–45 µm moving toward sub-30 µm for HBM4; the extra area needed for TSVs is why HBM dies are larger than DDR equivalents. Base-die shift [cited]: HBM4's base die is fabricated on a TSMC logic process (12nm FFC+ / 4–5nm on Samsung's commercial part), so it consumes foundry wafers that never appear in DRAM wafer-start data. Derived, not disclosed: the ~3.5–4× (HBM4) and ~4×+ (HBM4E) multipliers are this board's construction from those cited inputs — no manufacturer publishes a per-generation wafer ratio, and a rising per-die capacity (24Gb→32Gb) partly offsets the penalty, so the multipliers carry real uncertainty even though the direction is not in doubt. Cross-refs: §05·E (wafer starts required, where HBM goes 18%→34% of DRAM wafers on the DB/Gartner model), §05·C (cost), §05·B (share), §08·B (the consumer-facing consequence). Sources: JEDEC (JESD238, JESD270-4), Rambus, Siemens EDA, Spheron, IntuitionLabs, Kynix, AIChipLink, Nevsemi, HyperPC, Wikipedia, Micron FQ3'26 earnings call, Tom's Hardware, TechTimes, SemiEngineering, TrendForce, Deutsche Bank/Gartner, J.P. Morgan. Not investment advice. Aug 15 2026 — the 2027 generation mix, quantified [§05·C·H]. This section describes HBM4 ramping roughly twice as fast as HBM3E did, which is accurate and can leave a misleading impression of 2027. A bottom-up build across 17 named accelerator SKUs puts 2027 demand at HBM3E 47.7%, HBM4 40.6%, HBM4E 11.7% — HBM3E is still the largest single generation in the year HBM4 is supposed to own, because the highest-volume parts (Google's TPU v8i and v8t, ~7.6M units between them) stay on HBM3E 12-Hi. Fast ramp and majority share are different claims; only the first is supported.
Two layers, both with surplus/deficit shown in their native units. Memory (DRAM) is in a structural deficit — measured in bits (demand growth outrunning supply growth) — driven by HBM eating a hugely disproportionate share of wafers. All-semiconductor wafers are not in aggregate shortage — measured in wafer-starts/month (kwspm) — but are sharply bifurcated: leading-edge (2/3nm) and advanced packaging (CoWoS) are sold out, while mature nodes sit in glut. The honest caveat up front: wafer capacity cleanly split by end-use is barely published (SEMI segments by device type and node, not application), so segment splits below are triangulated and tagged A anchored / E estimate / T triangulated.
1 GB of HBM consumes ~3–4× the wafer area of standard DRAM (TSV stacking, bigger die, 50–60% yield). So HBM is a single-digit share of DRAM bits but devours roughly a quarter of DRAM wafers — starving everything else.
Aug 3 2026 refresh [cited — TrendForce primary releases]. DRAM, Jul 30 2026: 2026 sufficiency ratio −1% to −2%; supply-demand gap widens further in 2027; new 2027 capacity delayed to 2H27 ramp with substantial output only in 2028; HBM's wafer-input penalty means higher wafer starts do not convert proportionally to bits; 2026 server shipments +17% YoY with 2027 faster; SOCAMM and rising HBM-per-server lift memory content; consumer shipments decline again in 2027; and CSP capex at record levels with some providers reporting negative free cash flow. NAND, Jul 21 2026: 4–5% supply deficit in 2026, balance turning positive in 2H27; 2026 bit growth came from process migration rather than new fabs because makers prioritised DRAM; Chinese suppliers to ~19% of global NAND bit output; servers >40% of NAND bit demand against ~40% for phones+notebooks; smartphone production −15–20% YoY in 2026, notebooks ~−10%. SEMI: advanced-node capacity passes 1M wpm for the first time in 2026 at 1.16M wpm (this is the plotted value, not an estimate); +69% advanced-capacity growth through 2028; 2nm-and-below <200K wpm (2025) → >500K wpm (2028); 200mm capacity +14% 2023→2026 to a record >7.7M wpm. What was changed and what was not: the 2027 and 2028 bars in chart ① were re-cut — 2027 from a narrowing gap (+29/+26, a 3-point gap) to a widening one (+32/+18, a 14-point gap against 2026’s 12), and the surplus crossover pushed into 2028 (+26/+30). TrendForce publishes no 2027 bit-growth percentages; those bars are this board's translation of its directional language and are tagged proj, not anchored. 2029→2035 were not touched — one quarter's guidance is not a reason to redraw a decade. Chart ②'s 2028 leading-edge point moved 1.40 → 1.44 to sit on SEMI's +69%-through-2028 figure from a 2024 base; SEMI does not state its base year explicitly, so that is a reconciliation, not a quote. The metric conflict, resolved in the open: chart ① reads a 12-point rate gap for 2026 while TrendForce reads −1 to −2% sufficiency. Both are correct and they are not the same measure — rate gap vs level ratio, the latter net of inventory. If 2026 had opened balanced, +35%/+23% would end ~8.9% short; it opened with inventory, so the realised level shortfall is 1–2%. Neither figure should be quoted as "the deficit" without naming which one it is. Provenance & method. Anchored [A]: SEMI World Fab Forecast — total capacity ~29M wpm (2022) → 33.6–33.7M (2025), +6.6–7%/yr; leading-edge ≤7nm 850K (2024) → 982K (2025) → 1.16M (2026) → 1.4M (2028); China ~⅓ of capacity. TrendForce — 2026 DRAM bit demand +35% vs supply +23%; HBM wafer-input share 18%/22%/30% for 2025/2026/2027; conventional DRAM contract +90–95% QoQ in 1Q26; DRAM CapEx $53.7B→$61.3B (2025→26). Samsung/SK hynix Q1 2026 earnings — ~70% DRAM order fulfillment (35–40% for smaller buyers), ~72%/~70% operating margins. Estimate [E]: Citi (demand +20.1%/supply +17.5%), IDC (+16% supply), CoWoS 13K→35K→~130K wpm (2023→24→26, TSMC guidance), NVIDIA >50% of 2026 CoWoS. Triangulated/extrapolated [T]: the 2022–2025 bit-growth pairs (primary industry-total pairs weren't published — reconstructed from the 2022–23 downturn + 2024–25 recovery narrative), all 2027→2035 projections, and the application-segment wafer splits (SEMI segments by device/node, not end-market). Reconciled discrepancy: one source listed HBM 2024 wafer share at ~5%, which is internally impossible (below its ~8% bit share, when HBM uses more wafers/bit) — this dashboard uses the internally-consistent ~3× ratio (HBM ~14% wafers / ~5% bits in 2024), matching §05·B. Unit trap: SEMI reports 200mm-equivalent; actual 300mm DRAM starts (~2.25M/mo in 2025) are ~½ the 200mm-equiv number (~4.5M) — always check the basis. The super-cycle end-date is contested (relief "2027" to "past 2028"); pricing/fulfillment move monthly. Sources: SEMI, TrendForce, IDC, Counterpoint, McKinsey, Yole, SemiAnalysis, Silicon Analysts, company earnings. Analysis, not investment advice. See also: §05·G, which asks what shape this capacity gets sold in — MRDIMM, SOCAMM, DDR6 and 3D DRAM all compete for the wafers counted here. Aug 15 2026 — a bottom-up cross-check on the HBM line [§05·C·H]. This section carries HBM at ~13% of 2027 DRAM bits from a top-down share path. §05·C·H reaches 2027 HBM demand independently, from CoWoS wafer allocations and per-package stack counts across 17 named products, and lands on 6.08 bn GB. The two are consistent at a ~46.7 bn GB total DRAM market. Two methods with no shared inputs agreeing is the strongest validation on this board's HBM numbers — and it applies to the total only, not to any per-vendor split. Aug 17 2026 — capacity by producer [§05·D·V]. This section works in whole-industry totals. Vendor-level DRAM capacity is now carried separately: big-3 1,439k → 3,137k wafers/month 2024→2030 (a 13.9% CAGR, SK hynix steepest at 2.50×), plus CXMT at ~350k by end-2026 rising to 600–950k by 2030. NAND vendor capacity appears on the board for the first time there too — and its shape differs sharply from DRAM, with all three majors stalling or contracting through 2025–27 before a late-decade surge.
The supply question asked in its native unit: how many wafer starts per month does DRAM bit growth actually require, split standard DRAM vs HBM? This replicates a Deutsche Bank Research / Gartner demand-supply model (user-supplied capture, Jul 2026) — the clearest public decomposition of the mechanism this board keeps describing: HBM's trade ratio converts modest bit growth into brutal wafer growth (HBM needs +48–56%/yr more wafers through 2028 to serve its bits), while efficiencies claw back only ~11–13%/yr. The bottom line is the thesis in one row: a shortfall every single year through 2030, peaking at −795K WSPM (129% demand/supply) in 2028, and — notably — never flipping to surplus in the model's window, even after the 2028 capacity wave lands.
Provenance & validation. [cited — transcribed]: every figure in the table is from the Deutsche Bank Research "Summarized DRAM D-S Forecast" (source line: Gartner, Deutsche Bank Research; user-supplied capture, Jul 2026). [validated programmatically]: the transcription passes its own arithmetic — standard + HBM sums to the printed totals for all 8 years; capacity − requirement reproduces all five printed shortfalls (−210/−507/−795/−575/−379K); demand ÷ supply reproduces 110/121/129/118/111%; the "25–30 growth" column recomputes (+887K standard, +967K HBM, +14%/+12% CAGRs). Reading caveat: the per-year efficiency/yield decomposition rows are small type in the capture; they are approximate multiplicative factors in any case (they don't compose exactly to the printed WSPM growth — DB rounds), so treat the level rows as the data and the decomposition as the explanation. Model caveats: capacity basis is the bank's estimate, not SEMI's (this board's §05·D notes ~2.25M actual 300mm DRAM starts/mo in 2025 vs 1,921K here — scope/basis difference, flagged); "requirements" assume Gartner bit-demand paths; forecasts widen to the right. Cross-refs: §05·B (HBM share), §05·D (wafer S&D), §06·E (bits shipped), KS#2 (§02·D — this model says the 2028 supply wave narrows but does not flip the balance, a direct input to that switch). Sources: Deutsche Bank Research / Gartner (capture), J.P. Morgan, TrendForce, UBS, SEMI. Not investment advice. Aug 15 2026 — what the bottom-up build does and does not test here [§05·C·H]. §05·C·H builds 2027 HBM demand in gigabytes from named silicon. It does not validate this section's 774k HBM WSPM figure, because converting gigabytes to wafer starts requires a GB-per-wafer assumption — die size, stack height, known-good-die yield — that neither source publishes. Anyone bridging the two is supplying that assumption themselves, and it dominates the answer. The demand side now has two independent estimates; the wafer conversion still has none. Aug 17 2026 — the strongest evidence against this section, logged here rather than only in §05·D·V. Vendor-level capacity now on the board puts Samsung, SK hynix and Micron at 3,137k wafers/month by end-2030, and CXMT at a further 600–950k. That is 3,737–4,087k from four named producers, against the 3,396k whole-industry capacity this model uses for 2030 — the modelled total is 10–20% below the sum of four of its own components. Re-running this section's own 2030 requirement of 3,775k against those figures gives 92–101% demand/supply instead of the 111% reported above, which narrows the 2030 shortfall to near zero or turns it into a 312k surplus. This section's "shortfall every year through 2030" conclusion does not survive that substitution, and the disagreement is left standing rather than resolved: the capacity definitions may differ, CXMT's wafer starts are not its usable output, and this model's 2029–30 tail was already labelled the most extended-bull view of the four houses triangulated. See §05·D·V for the full working.
§05·D and §05·E both work with whole-industry wafer capacity. Neither has ever carried capacity by producer — and adding it turns up a problem with the board's own numbers. Nomura's vendor build has the big three going from 1,439k wafers/month at end-2024 to 3,137k by end-2030: Samsung 655k→1,264k, SK hynix 474k→1,183k, Micron 310k→690k. Add CXMT — which that exhibit does not cover — at the 600–950k other sources put it on by 2030, and four named producers reach 3,737–4,087k. §05·E's whole-industry 2030 capacity line is 3,396k. The modelled total for the entire industry is below the sum of four of its members. That is not a rounding difference, and correcting it does something uncomfortable: it removes most of the 2030 shortfall this board has been describing.
Provenance & the honesty line. [cited — Nomura, via a user-supplied summary]: Samsung DRAM 655k → 1,264k wafers/month end-2024 to end-2030 on the P4/P5/P6 phases, y/y growth peaking at +22% in 2030; Samsung NAND fluctuating with dips in 2025 and 2027 before reaching 533k by end-2030 at +29% y/y. SK hynix DRAM 474k → 1,183k on the Y1/Y2/Y3 phases with spurts in 2028 (+25%) and 2030 (+26%); SK hynix NAND contracting 2025–2027 then surging from 2028 (+58% y/y) to 580k. Micron DRAM 310k → 690k with Taiwan scaling 195k → 430k and the balance US/Japan; Micron NAND flat at 130k through 2026 then 280k by 2029–2030 via Fab 10B and Fab 11. [cited — separate sources, not Nomura]: CXMT at roughly 300k today across three 12-inch fabs, ~350k by end-2026 (about 25k below Micron's ~375k), buildouts across Hefei/Shanghai/Beijing taking it "over 600k", and around 950k by 2030; CXMT publicly targets 30% DRAM share by 2030; big-3 revenue share 89.7% with CXMT fourth at 7.6% in 1Q26. [derived — this board's arithmetic, and the reason the section exists]: big-3 2030 DRAM sums to 3,137k; adding CXMT gives 3,737–4,087k; §05·E's modelled whole-industry 2030 capacity is 3,396k, i.e. 341–691k (10–20%) below the sum of four named producers. Re-running §05·E's own 2030 requirement of 3,775k against those totals gives 92–101% demand/supply versus the 111% that section reports. Back-solved, not published: Samsung DRAM ~1,036k and SK hynix DRAM ~939k at end-2029, and Samsung NAND ~413k at end-2029, each derived by dividing the 2030 figure by its disclosed 2030 growth rate — these are consequences of two cited numbers, not source figures, and they are not plotted. What is deliberately absent: (1) no year-by-year path — the source gives endpoints and a handful of growth rates, and interpolating six years between them would manufacture precision that does not exist; (2) no CXMT figure for 2024, because none of the sources here gives one, so the 2024 column is big-3 only and is labelled that way; (3) no bit-level conversion — wafers are not bits, and translating these into supply requires per-node density and yield assumptions nobody publishes. One caution about the source chain: the Nomura figures reach this board through a third-party summary of a research note rather than the note itself, so transcription risk sits on top of forecast risk; the arithmetic above has been checked for internal consistency but the underlying numbers have not been verified against the primary document. Cross-refs: §05·D (whole-industry wafer supply), §05·E (the model this contradicts), §05·B (HBM's share of it), §05·C·H (2027 HBM demand bottom-up), §09 (the China front, where CXMT's constraints are tracked), §02·D (kill-switch #2, the supply response). Not investment advice.
How many of the world's wafers do Nvidia, AMD, Google, Amazon, Meta and Microsoft actually consume for their AI accelerators? The answer depends entirely on which denominator you use, and the gap between the two is the reason this section exists. Against all wafer starts everywhere, in 2026 they take about 0.26% — a rounding error, roughly a quarter of one percent of global output. Against leading-edge capacity — ≤7nm, the only wafers these chips can be built on — they take about 8%. Same silicon, same year, a 30× difference in the answer. That is the whole argument for why "there are plenty of wafers" and "we are out of wafers" are both true statements, and why §05·D insists the constraint is the thin sliver, not the total. Everything below is a model, not a measurement — nobody publishes accelerator wafer consumption, so this is built bottom-up from unit volumes and die sizes, with every input exposed in a table so you can disagree with a specific number rather than the conclusion.
Provenance — this is a model, and here is exactly what it is made of. [cited — unit volumes]: Nvidia — JP Morgan estimates 5.2M Blackwell in 2025, 1.8M Blackwell + 5.7M Rubin in 2026 (7.5M total, used here); TrendForce separately has Blackwell at 70%+ of Nvidia high-end shipments in 2026 and flags Rubin delay risk, which cuts against the JP Morgan mix. 2023 is anchored to Omdia's H100 estimates (~300K units in Q2 2023, ~500K H100+A100 in Q3). Google — 4.3M TPUs in 2026, with V6 ~1.6M and V5 ~800K. Amazon — Morgan Stanley's >1.5M Trainium in 2026; AWS said >1M Trainium deployed cumulatively at re:Invent 2025. Market context — custom ASIC shipments forecast +44.6% in 2026 against +16.1% for merchant GPUs. [cited — capacity]: TSMC N3 ~120–130K wpm end-2025 → ~180K wpm end-2026 (+40%), AI ~60% of N3 output in 2026 and 86% in 2027, effective N3 utilisation above 100% in H2 2026; HPC is 61% of TSMC revenue. Leading-edge and total capacity come from §05·D's SEMI-anchored series so the two sections cannot disagree: 2026 total 36.0M wpm and leading-edge 1.16M wpm, both 200mm-equivalent, extrapolated past 2026 at ~7%/yr and ~14%/yr respectively. [modeled — everything else, and the four assumptions that matter most]: (1) Die area and dies per package are estimates, not disclosures — Nvidia has never published a Rubin die size, and the values in the table (A100 826, H100 814, Blackwell 2×800, Rubin 2×730, MI300X-class 1017, MI400 1200, TPU 2×600, Trainium 2×600, MTIA 420→700, Maia 820 mm²) are compiled from teardown reporting and reticle-limit reasoning. (2) Yields of 70–82% are assumptions with no public basis; at reticle-limited die sizes a 10-point yield error moves a company's wafer number ~13%. (3) Unit volumes past 2026 are extrapolation, tapering from double-digit to mid-single-digit growth — no source forecasts accelerator units to 2035, and anyone who did would be guessing too. (4) The 2.25× conversion from SEMI's 200mm-equivalents to actual 300mm wafers is a geometric ratio applied uniformly; SEMI's own basis for "leading-edge" may already be 300mm-only, in which case every share figure here is overstated by 2.25× and the 2026 leading-edge share is ~3.6% rather than ~8%. That single ambiguity is the largest uncertainty in the section and it is not resolvable from public SEMI summaries. What this deliberately does not count: CPUs, network and switch silicon, retimers, NICs, HBM base die, packaging interposers and CoWoS substrate — and every AI chip built by anyone other than these six firms. It is therefore a floor. Not a forecast of anything, and not investment advice. Cross-refs: §05·D (the capacity series this is divided by), §05·E (the identical arithmetic on the memory side), §06·B (the accelerator memory roadmap these dies carry), §06·B·T (Google's TPU generations, one of the six), §05·C·G (HBM wafer consumption per generation), §06·E (accelerator GB shipped), §06·F·D (the capex paying for all of it). Sources: TrendForce on TSMC N3 capacity, TrendForce on Blackwell/Rubin mix, JP Morgan via TweakTown, Morgan Stanley, Omdia, SEMI, Tom's Hardware. Aug 15 2026 — an independent test of this section's 2027 numbers, which it half passes [§05·C·H]. A Morgan Stanley SKU-level build for 2027 totals 21.14M accelerators against this section's 21.30M — 0.7% apart, from unrelated inputs. That is strong validation of the total. The per-company bars do not survive the same test: Meta differs by 3.1× (1.70M here vs 0.55M there), Microsoft 1.8×, AMD 1.5×, Google 1.5×, and the errors point in both directions, which is why the totals still meet. Read this section's aggregate as well-supported and its company split as one plausible allocation among several — the wafer total is far more reliable than any single company's slice of it.
Every other memory section on this board asks how much — how many bits, at what price, from whose fab. This one asks what shape they arrive in, because the package decides which demand a bit can actually be sold into. A DDR5 die that can be built into an MRDIMM competes for the same wafers as one destined for a SOCAMM next to an accelerator, and the two serve completely different buyers at completely different margins. Five transitions are in flight at once, and they are not equally certain: MRDIMM Gen2 is dated and conservative, reusing existing dies at 12,800 MT/s; SOCAMM is shipping, 16 dies on copper wire bonding, with Micron first through Nvidia's qualification; DDR6 arrives at 17,600 MT/s into the worst possible pricing environment; LPDDR6 is being deliberately walked out of phones and into the data centre; and 3D DRAM is the one everybody's roadmap contains and nobody has shipped. The chart below shades each bar by how firm the claim is rather than how large the market might be.
Provenance & the honesty line. [cited]: MRDIMM Gen2 dated to 2026–2027 at 12,800 MT/s. SOCAMM connects 16 DRAM dies per module using copper wire bonding for thermal reasons; Nvidia commissioned Samsung, SK hynix and Micron to build prototypes and Micron was first approved for mass production; SOCAMM2 moves the module to LPDDR6 and SK hynix places it in the late 2020s. LPDDR6 (JESD209-6) was published by JEDEC in July 2025 at 10,667–14,400 MT/s; in April 2026 JEDEC previewed an extension of LPDDR6 beyond mobile into data-centre and accelerated computing, with 512 GB densities, a processing-in-memory variant and SOCAMM2 all in development — none of which has a published release date, which JEDEC itself stated. DDR6 quoted at 17,600 MT/s, debuting in high-end parts in 2026 with mass adoption in 2027. 3D DRAM: Samsung guiding to an 8–9nm class node in 2027–2028; SK hynix has published a DRAM roadmap running to 2031 covering DDR6, GDDR8, LPDDR6 and 3D DRAM. What is modeled: only the start and end positions of the bars. Vendors give years, sometimes only halves of years; the chart converts those into segments and therefore implies a precision the sources do not have. Read the bar edges as ±2 quarters at best, and worse the further right they sit. What is explicitly not claimed: any market share, unit volume or revenue for any of these form factors. Three caveats: (1) roadmap dates are marketing artefacts as much as engineering ones — every date here comes from a party with an interest in it being believed, and none has been independently confirmed; (2) the 3D DRAM bar should be read as a placeholder, not a forecast — a technology on every roadmap with no shipping part, no disclosed yield and no named customer is a technology whose schedule has not yet met reality; (3) DDR6's 2027 volume date and §03·F's pricing are on a collision course that neither source acknowledges, and this board is flagging the tension rather than resolving it. Cross-refs: §05·D (the capacity all of this competes for), §05·E (the wafer-area arithmetic that makes it a zero-sum fight), §03·F (the contract pricing DDR6 launches into), §06·B (the accelerator memory roadmap), §08·G (where a DDR6 platform transition reaches consumers), §05·F (the accelerators SOCAMM attaches to). Sources: JEDEC, Samsung, SK hynix, Micron, Nvidia, Tom's Hardware, TechPowerUp, VideoCardz. Not investment advice.
The demand engine, by vendor: every major AI accelerator and all the memory on its package or module. Each bar is stacked by memory type — HBM (by generation), on-die SRAM, GDDR, plus the LPDDR5X system memory on Nvidia's Grace/Vera superchips. The merchant GPUs (Nvidia, AMD) and the hyperscalers' own silicon (Google, Amazon, Microsoft, Meta), plus wafer-scale Cerebras and RISC-V Tenstorrent, are all here. Memory per device — not unit volume — is what's exploding.
Eight companies now design AI accelerators. HBM is the common thread, but the full memory picture is richer: Nvidia's Vera Rubin superchip pairs 576 GB of HBM4 with up to 1,536 GB of LPDDR5X system memory; Cerebras goes the opposite way with 44 GB of blisteringly fast on-die SRAM and no HBM; Tenstorrent uses cheaper GDDR6. Top-end HBM has grown from the H100's 80 GB (2022) toward 1 TB+ per package by 2027–28. Multiply across every vendor's roadmap and the wafer math becomes overwhelming. Aug 29 2026 — that growth has just met its first hard limit. NVIDIA is reported to be cutting Rubin Ultra from 288 GB to 192 GB per compute die with peak FLOPS unchanged, because HBM supply rather than silicon is now the binding constraint (§06·B). And the spec race has moved from capacity to speed: a Rubin GPU carries the same 288 GB as Blackwell's B300 but at 2.75× the bandwidth, and NVIDIA now requires HBM4 at up to 11.7 Gbps per pin against JEDEC's 8 Gbps baseline.
Bars are stacked by memory type and scaled to total on-package/module memory — so the LPDDR5X-heavy superchips (GB200/GB300/Vera Rubin) show their full memory pool, and the "GB" figure on each card is the total of all memory types. The chronological view, per-GPU breakdown, and rack-level totals are in the roadmap below. Future/announced parts are marked. Hyperscaler ASIC specs (Google, Amazon, Microsoft, Meta) and the alt-architectures (Cerebras SRAM, Tenstorrent GDDR) are disclosed less fully than merchant GPUs; some capacities are approximate. Sources: Nvidia, AMD, Google, AWS, Microsoft, Meta, Cerebras, Tenstorrent, SemiAnalysis, ServeTheHome. See also: §06·B for the memory roadmap each of these accelerators carries, and §06·B·T for Google's TPU line specifically.
Every major AI accelerator in release order — Nvidia, AMD, Google, Amazon, Microsoft, Meta, plus alt-architectures Cerebras and Tenstorrent — with the type and amount of all memory on the package or module: HBM, on-die SRAM, GDDR or LPDDR5X. Each card lists peak compute, and rack-scale systems show their full pooled memory. Use the controls below to switch view (per-module vs per-GPU), filter shipping vs announced parts, or switch to log scale so the older & smaller parts stay legible alongside Rubin Ultra & Feynman. Read the two blocks immediately below before the cards — one headline spec on this roadmap is under active revision, and the axis the vendors are competing on has changed.
Notes: bandwidth figures are per-GPU package (HBM). "Per superchip" rows include the CPU's LPDDR5X because it ships soldered/socketed on the same module and is coherently shared with the GPU(s) over NVLink-C2C. Standalone GPU rows (B200, MI-series) list HBM only — system DRAM is configured separately by the system builder. Capacities reflect top-bin configurations; salvaged/cut-down SKUs ship with less. Hyperscaler ASICs (Google TPU, AWS Trainium) use HBM but disclose less detail. Sources: Nvidia, AMD, Google, AWS, SemiAnalysis, ServeTheHome.
A naming correction first, because it matters: these are Google's TPUs. Broadcom is Google's co-design and ASIC partner — Google owns the architecture and software stack; Broadcom supplies the foundational IP, SerDes/interconnect, and silicon implementation, then coordinates fabrication at TSMC. The two have shipped seven generations together since 2014, under a supply agreement now extending to 2031. Broadcom separately builds custom "XPU" accelerators for other customers (below). So "Broadcom's TPU" is really "the Google TPU that Broadcom helps build." The HBM-per-chip column is the reason TPUs matter to this dashboard: each generation is a growing customer for the same HBM that's in shortage.
The trajectory that matters: TPU HBM per chip ran 16 GB (v5e) → 95 GB (v5p) → 32 GB (v6e) → 192 GB HBM3E (v7 Ironwood) → 288 GB (gen-8 inference). Ironwood alone is 6× the HBM of Trillium. Multiply by millions of chips deployed (see §06·C) and Google is one of the largest single consumers of HBM on earth — most of it bought from SK hynix and Samsung, the same suppliers serving NVIDIA.
Broadcom disclosed six named XPU customers as of Q1 FY2026, anchoring a >$100B AI-chip revenue target for 2027 against a ~$73B backlog. TPUs (Google) are one program; the others are distinct custom accelerators. Anthropic's compute runs on Google TPUs, so it appears both as a TPU consumer and a Broadcom customer.
Provenance. Real specs (sourced): TPU v5e 16 GB HBM, v5p 95 GB HBM (7nm), v6e/Trillium 32 GB HBM, v7/Ironwood 192 GB HBM3E @ 7.37 TB/s (6× Trillium, GA late 2025), 8th-gen split into 8t (training) and 8i (inference, ~288 GB) for 2026 — per Google Cloud docs/blog, Introl, Spheron, CloudOptimo, Nevsemi. Customers: Broadcom Q1 FY2026 earnings named six XPU customers — Google (TPU, 7 gens since 2014, deal through 2031), Meta (MTIA), Anthropic (~1 GW TPU in 2026 → ~3 GW in 2027, via Google TPUs), OpenAI (first XPU 2027, 10 GW multi-year deal, 3nm+2nm), plus Apple (new 2026 disclosure) and ByteDance/Fujitsu among named/reported — per Tom's Hardware, CNBC, Jon Peddie, hashrateindex. Projected / unannounced: a 9th generation is modeled in §06·C, but "TPU v9, v10, v11" are not public products — no announced specs, memory, or customer assignments exist. The HBM figures for those rows are illustrative extrapolations (continuing the roughly-doubling-every-2-gens trend), not Google roadmap data, and are visibly hatched. Anyone citing a "TPU v11 memory spec" is citing speculation. Not investment advice. Aug 15 2026 — the scale of this line, in HBM [§05·C·H]. A bottom-up 2027 build puts Google's four TPU generations at 2.19 bn GB of HBM — 36.0% of all AI HBM demand, and 96.6% of NVIDIA's. On this estimate a single customer's internal silicon consumes almost as much HBM as the entire merchant GPU leader. Carry one caveat with it: §05·C·H's unit count for Google (8.17M) is 1.46× §05·F's independent estimate (5.60M), so the ranking is robust but the exact figure is not.
How many data-center AI accelerators ship — the volume driving all that memory demand. Important honesty note: vendors don't disclose unit shipments, and the in-house hyperscaler chips (TPU, Trainium, Maia, MTIA) aren't sold commercially. So none of this is reported data — it's the most honest available reconstruction from analyst estimates, rack-shipment reports, and revenue. Below: annual totals (Nvidia vs others), then a more speculative quarterly-by-vendor breakdown and a per-specific-accelerator view (B300, GB300, TPU v7 Ironwood, MI350, Trainium 3…). Every figure is an analyst triangulation with a wide error band — the further into per-quarter/per-chip detail, the more uncertain.
A quarterly breakdown by vendor, stacked. This is the most speculative chart in the dashboard — quarterly per-vendor unit counts aren't disclosed by anyone, so every bar is a triangulation from rack-shipment estimates, revenue, and supply-chain reports. The shape (Nvidia dominant but custom-ASIC vendors ramping fast) is well-supported; the exact numbers are not. Click a vendor to toggle.
Approximate full-year-2026 unit estimates for the specific accelerators driving demand. Bars scaled within the whole set. Hyperscaler-ASIC volumes (TPU, Trainium, Maia, MTIA) are internal-deployment estimates; merchant-GPU volumes derive from rack and revenue reports. All are analyst triangulations, not disclosed figures.
Every data-center accelerator from each of the six major vendors, stacked by quarter from 2022 to 2030 — read both the running total and how each generation hands off to the next. These are the dashboard's most speculative charts: per-model quarterly units aren't disclosed by anyone, and beyond mid-2026 it's projection. The wave shape is the insight; the numbers are illustrative. Pick a vendor, toggle Units/Share, click models in the legend to hide them.
On the per-model quarterly charts (2022–2030): these are the most heavily modeled visuals in the dashboard — a reconstruction, not reported data. No vendor discloses per-model unit shipments by quarter, and roughly half this timeline (Q3 2026 →) is a forward projection. Each model is shaped as a lifecycle curve (ramp from launch → peak → decline as the next generation takes over), calibrated so annual totals stay near the figures used elsewhere on this page and so generational hand-offs land at the right times. Real timing anchors used: Nvidia — H100 late 2022, Blackwell late 2024, GB300-over-GB200 in 2H 2025, Vera Rubin in full production mid-2026, Rubin Ultra ~2027–28, Feynman ~2028+; AMD — MI300X late 2023, MI350 in 2025, MI400/Helios in 2026, MI500 ~2028; Google — TPU v7 Ironwood announced Apr 2025 / GA late 2025–26 (internal forecast revised ~2M→~4M units; Anthropic contracted up to 1M TPU chips in 2026, 400K Ironwood in phase one), 8th-gen TPU 8t/8i on TSMC 2nm previewing 2H 2026 with GA targeted late 2027; Amazon — Trainium3 (first 3nm, 144 GB HBM3e) shipping Dec 2025, Trainium4 (NVLink Fusion) due late 2026/early 2027, with ~35% of AWS's ~$100B 2026 capex earmarked for Trainium-family silicon; Meta — MTIA v1 announced 2023, v2 "Artemis" in 2024, with a 300-series roadmap extending through ~2027 (inference-first, internal to Meta's fleet); Microsoft — Maia 100 announced Nov 2023 (limited deployment), Maia 200 (3nm, 216 GB HBM3e) announced Jan 2026 (the least-mature hyperscaler program). Note the hyperscaler chips (TPU, Trainium, MTIA, Maia) are never sold as parts — they're deployed internally and rented via cloud — so their "units shipped" are even more inferred than merchant GPUs (anchored via Broadcom/Marvell rack revenue and cloud-capacity disclosures). Peak heights are anchored to those sources, but exact per-quarter splits are interpolated and the projected generations (Rubin Ultra, Feynman, TPU 9th-gen, Trainium5, MTIA next-gen, Maia next-gen) are scenario, not forecast — real volumes could differ by a wide margin. Read the generational shape, not the precise unit counts. Units are individual chips, not racks or revenue. The four hyperscaler programs differ hugely in scale — Google and Amazon ship multiples of Meta and Microsoft — and summing all six charts gives a total in the same ballpark as, but not identical to, the high-level annual figures elsewhere on this page (they're independent estimates built different ways). Sources: Nvidia/AMD filings & keynotes, Google Cloud/Broadcom, AWS, Meta, Microsoft/Azure, SemiAnalysis, Morgan Stanley, Omdia, TrendForce, Tom's Hardware, The Next Web. Not investment advice.
The root of the whole supercycle: the explosion in AI tokens processed — the raw workload that every accelerator and memory chip ultimately exists to serve. Honesty note: there is still no official global token meter. The best open attempt is the Tokens Per Day index — a disclosure-only floor plus a triangulated estimate — and this board anchored to it in August. On Oct 1 that anchor was found to be stale, and the series was rebuilt a second time (§10·B). The index builds its floor by summing each company's last disclosure, whatever its date — six figures dated anywhere from Dec 2024 to Jun 2026 — so it still carries China at March's 140T/day while its own ledger records ByteDance's Doubao, which it marks as contained within that China figure, at 180T/day in June. A part cannot exceed its whole. China is the single largest bloc: the National Data Administration's survey puts its 2025 total at 21.1 quadrillion tokens, and its daily rate at 100T by end-2025 and past 140T by March 2026. Under-counting it produced two errors here: the August series put Q2 2026 within 3% of the corrected disclosure floor — a lower bound presented as a central estimate — and its early-2025 bars sat below China alone. The bars below estimate total global token run-rate (tokens processed per month, at each quarter-end), now built as China from official figures plus everything else from each company's disclosures brought forward to quarter-end. Mid-2026 lands at ~496T/day: about 15% above Epoch AI's ~432T/day, and within ~3% of the index's own estimate once its China term alone is corrected. The linear scale shows just how brutally recent and vertical the climb has been — the pre-2025 bars almost disappear against the right-hand side, which is the truthful read of an exponential.
tokens/second per kilowatt of facility power (GPU + CPU + networking + cooling + PSU losses), indexed to H100
Provenance & the honesty line — this is a model, and here is all of it. Demand [from §06·D above]: tokData, trillions of tokens per month, anchored to Google/Azure/OpenAI disclosures and projected past Q3 2026; converted to tokens/second at 30.44 × 86,400 s per average month. Power [cited]: H100 SXM and H200 SXM are 700 W TDP, B200 1,000 W, B300 (Blackwell Ultra) 1,400 W. The chart uses all-in facility power ≈ 1.7 × GPU TDP (1.19 / 1.19 / 1.70 / 2.38 kW respectively) to cover host CPU, NVLink/NIC, cooling and PSU losses — the same NVL72-class basis used in §06·F·C (~120 kW per 72-GPU rack ≈ 1.7 kW per accelerator all-in). Throughput [derived from benchmarks that disagree — the weakest link]: central estimates of 2,200 / 2,860 / 7,000 / 10,500 tokens/sec per GPU. The published spread is wide and is not hidden here: one power-aware benchmark measures H200 at 2,418 tok/s vs H100's 2,191 (a ~10% gain) while long-context tests report H200 at 1.83–2.14× H100; B200 is variously ~2.5× H200 or "up to 47% higher output-token throughput at peak concurrency". This build takes mid-range values (H200 ≈ 1.3× H100, B200 ≈ 2.4× H200, B300 ≈ 1.5× B200 on KV headroom — B200 and B300 share 8 TB/s, so B300's edge shows up at concurrency and long context, not raw decode). Change these assumptions and the levels move materially; the ordering does not. A disagreement worth stating: this bottom-up model puts B300 at ~2.4× H100 tokens-per-watt, while NVIDIA claims GB300 NVL72 delivers 5× throughput per MW vs Hopper (and up to 50× in agentic framings combining latency and power, with 61.4K concurrent agents/MW vs 2,600 on H200). Rack-scale NVL72 with FP4 and NVLink-domain batching plausibly beats a per-GPU model — so the B300 line here is likely conservative (too much power), and the vendor figure is likely optimistic. Both are shown rather than averaged. Four structural caveats: (1) counterfactual, not forecast — no real fleet is single-SKU; the actual installed base is a mix, so no line is "the" answer. (2) 100% utilisation floor — real serving carries peak headroom, redundancy, failed nodes and idle time; multiply by 2–4× for a deployed estimate, and this excludes training entirely. (3) tokens are not fungible — a reasoning token, a cached prefill token and a short chat completion cost wildly different amounts of compute (§06·D·W); a single tok/s number averages across all of it. (4) PUE and cooling vary by site; 1.7× is a mid-range convention, not a measurement. Cross-refs: §06·D·W (why decode is memory-bound), §06·F·C (GW commitments), §05·E (the wafer consequence). Sources: NVIDIA (H100/H200/B200/GB300 specifications and Blackwell Ultra performance claims), SemiAnalysis InferenceX, AIMultiple multi-GPU and concurrency benchmarks, Introl, Spheron, Lyceum Technology, plus this board's own token series. Derived estimate — not a measurement, and not investment advice.
Provenance & the honesty line. Numerator [derived — same model as §06·D·P]: tokens/sec from the demand series above ÷ tokens/sec per GPU × all-in kW; the efficient bound uses B300-class (10,500 tok/s at 2.38 kW all-in), the legacy bound H100-class (2,200 tok/s at 1.19 kW). Every assumption and its benchmark spread is documented in §06·D·P and is not re-litigated here — but it carries forward, and it is the largest uncertainty in this chart. Denominator [cited]: IEA — global data-centre electricity consumption of 415 TWh in 2024 (~1.5% of world electricity), growing ~12%/yr since 2017 and ~15%/yr from 2024, reaching 945 TWh by 2030 (base case) and 1,200 TWh by 2035; converted here to average continuous gigawatts (TWh × 10¹² ÷ 8,760 h), giving 37.8 GW (2022) → 82.9 GW (2028). Total installed global capacity is separately reported near 100 GW, which is higher than average load because facilities do not run flat out — this chart uses the load basis so numerator and denominator match. Deployment factor [modeled]: ×3, the midpoint of the 2–4× range §06·D·P argues for; at ×2 the current share falls to ~5.1–12.2% and at ×4 it rises to ~10.2–24.4%, so this single assumption moves the answer by about 2× and is stated rather than buried. Five caveats: (1) Inference only — training, fine-tuning and non-token AI workloads are excluded, so this is a floor on "AI share", not a measure of it. (2) No real fleet is single-SKU; the true answer sits inside the band, not on either edge. (3) The denominator is a projection past 2024 — the IEA's own base case, and other houses differ. (4) Quarterly resolution is synthetic: the IEA publishes annual figures, so each quarter within a year shares that year's denominator, which is why the lines step rather than curve. (5) Tokens are not fungible (§06·D·W) — a reasoning token and a cached prefill token cost wildly different power, and a single tok/s figure averages across all of them. Cross-refs: §06·D·P (the absolute megawatts), §06·D·W (why decode is memory-bound), §06·F·C (committed gigawatts), §06·F·L (who physically wires it). Sources: IEA Energy and AI (2025) and its data-centre demand analysis, S&P Global, Carbon Brief, Scientific American, plus this board's token series and the accelerator assumptions in §06·D·P. Derived estimate — not a measurement, and not investment advice.
Real disclosed anchor points behind the token estimate: Google — 9.7T/mo (Apr 2024), 480T/mo (May 2025), 980T/mo (Jul 2025), ~1.3 quadrillion/mo (Oct 2025), 3.2 quadrillion/mo (May 2026, I/O). Microsoft Azure — 100T+/quarter through 2025, a record 50T in March 2025. OpenAI — API throughput 6B tok/min (Oct '25) → 15B tok/min (Mar '26) ≈ 650T/mo, plus ~900M weekly active users. Google model APIs — ~22B tokens/min (Q2 2026 call, 22 Jul), up from 16B a quarter earlier (+37.5% QoQ ≈ 950T/mo); ~500 Cloud customers each past 1T tokens in the trailing year and 2,000+ enterprises past 100B. China (National Data Administration) — 0.1T/day early 2024 → "over 1T" early 2025 → 100T/day end-2025 → past 140T/day March 2026; 2025 total 21.1 quadrillion (National Data Resource Survey, Digital China Summit, 29 Apr 2026); ByteDance's Doubao alone >180T/day by June 2026. Microsoft Foundry — customers past 1T tokens annualized up 4× YoY (FY26 Q4, June quarter); the "+30% QoQ, 300+ customers" figure is FY26 Q3 and is now retired. Anthropic — revenue pace above $100B (NYT, Sep '26; $30B in Apr, ~$9B end-'25 — §08·D), coding-heavy token mix. OpenRouter — 25T tokens/week (May 2026) → 126.2T/week (mid-Sep 2026), roughly 5× in four months, with Chinese models leading for 19 straight weeks. Not a proxy for the global total: its share of world tokens roughly quadrupled over the same months, and as critics note, much of the surge is reasoning models and unoptimised agents generating more tokens per task rather than more tasks (§06·D·A tracks the agent apps themselves). OpenAI — 1.2B weekly users (DevDay, 29 Sep 2026); no new throughput figure.
The independent aggregates this series is set against — Tokens Per Day (last verified 16 Jul 2026, not updated since): a disclosure-only floor of 300.1T/day and a six-channel estimated total of ~360.4T/day (range 308.4–477.1), with a hardware-side sanity check putting demand at ~52% of installed capacity. Read its ledger before its headline: the floor is the plain sum of six last-reported figures — China (Mar 2026), Google (May 2026), Fireworks (Jun 2026), OpenAI API (Mar 2026), Together (Sep 2025) and Microsoft (Dec 2024) — so every component is frozen at its own date. Lifting only China to Doubao's contained 180T/day raises the floor to ~340T/day; correcting China to a share-consistent ~260T/day raises the estimate to ~480T/day, which is where this board now sits; Epoch AI / Exponential View: ~432T/day mid-2026; Tunguz: ~88T/day (Sep 2025); a16z / OpenRouter: ~50T/day for the paid API market alone (late 2025). Note the spread: ~50 to ~432T/day is an 8.6× range, and it is definitional, not sloppiness — the small number is the merchant API market, the large one is all-surface throughput including multimodal and first-party traffic. Choosing which of those "global tokens" means is the single largest judgment call in this section, larger than any growth assumption. This board uses the all-surface basis, so its figures are not comparable to API-market estimates. Projection to 2028: the run-rate is extrapolated from 20.1 quadrillion/mo (Q3 2026, the last closed quarter) on a decelerating growth curve (~34%/qtr easing toward ~14%/qtr) to ~121 quadrillion/mo by Q4 2028 — about 6× over nine quarters. Q4 2026 is the current quarter and is drawn hatched, because its quarter-end value is still three months away. Neither rebuild re-fitted the curve; both moved only the level, so the 2028 endpoint has risen in proportion to the base each time (54 → 87 → 121 quadrillion/mo). Cross-checks worth reading: Goldman Sachs forecasts ~24× token growth to ~4,000T/day (~120 quadrillion/mo) by 2030 — a level this board reaches by end-2028. Extending this board's curve two more years lands near ~11,100T/day, about 2.8× Goldman; but Goldman's own 24× multiple applied to this board's mid-2026 base (~496T/day) gives ~11,900T/day, so once again the two agree about growth and disagree about how many tokens the world processes today. At the other end, JPMorgan is reported to see China's inference tokens alone growing ~370× from 2025 to 2030 (reported; not seen in primary form) — on China's 2025 average of ~58T/day that is ~21,000T/day for China alone, five times Goldman's figure for the whole world. Published 2030 forecasts span at least a 5× range, and this board sits inside it. That is an aggressive model, not a forecast; demand could stall on a capex pause or compound faster — the error band widens every quarter to the right. The memory-required chart is a second-order model built on top of the token model (token assumptions × serving-efficiency assumptions), so its uncertainty is compounded — use it for the shape of the argument, not the level. Tokens/month run-rate, not cumulative. Sources: Tokens Per Day index and its evidence ledger (floor + six-channel estimate, 16 Jul 2026), Epoch AI, China National Data Administration — National Data Resource Survey Report (2025) via Xinhua / Guangming Online and CnTechPost, and State Council briefing, OpenAI DevDay 2026 recap, The Decoder (OpenRouter weekly volume), Startup Fortune (JPMorgan forecast, as reported), Volcano Engine / Doubao, Alphabet Q2 2026 earnings call and Google I/O, Microsoft FY26 earnings, OpenAI, OpenRouter / a16z, Goldman Sachs, SemiAnalysis, Tomasz Tunguz, company disclosures. Not investment advice.
Silicon Data publishes the LLM Token Expenditure Index (ticker SDLLMTK) daily on the Bloomberg Terminal — an expenditure-weighted average of what the whole market pays per million LLM tokens, across frontier APIs, open-weight platforms, and brokered/self-hosted inference (>90% of global inference spend). It's a proxy for the market's marginal willingness to pay for AI, and one macro strategist (Andreas Steno Larsen, Jun 9 2026) called it "the most important chart for the entire market" — because if token spend rolls over, the memory/GPU/datacenter trade this whole dashboard tracks loses its demand engine. Critical honesty note: the actual daily index values are a paid Bloomberg/Silicon Data product — I can't reproduce the proprietary series. The curve below is a schematic reconstruction of the index's publicly-described shape, pinned to a handful of cited public reference points. Treat it as the story of the index, not its values.
Provenance & the honesty line. SDLLMTK is a real, proprietary index — Silicon Data, published daily on Bloomberg Terminal; full history is a paid product (sales@silicondata.com). I have not reproduced the actual series (I don't have the values), so the chart is a schematic of the publicly-described shape, anchored to these cited public statements: per-token price down >90% since 2023 and expenditure ~2× since late 2025 (Torsten Slok / Apollo, via X, Jun 2026); index "more than doubled since December," rose sharply through May 2026, then a downtick/stagnation (Seeking Alpha citing Andreas Steno Larsen, Jun 9 2026; corroborated by Citadel Securities "Tokenomics," Jun 2026, and 36Kr); a public reading ~2.12 with May acceleration (third-party chart commentary, mid-2026). The "stagnation" is Silicon Data's own framing — possibly slowing migration to premium closed models, not a confirmed reversal. The index is expenditure/usage-weighted price (marginal willingness to pay), not token volume and not simple list price; Silicon Data notes it would more precisely be called a "Token Expenditure Price Index." Why it's here: token billing ties AI usage directly to GPU-hours, DRAM bandwidth, and datacenter demand, so a durable roll-over would be an early warning for the memory thesis — exactly the leading indicator this dashboard exists to watch. For the real-time series, see Silicon Data / Bloomberg SDLLMTK. Schematic of a proprietary index. Not investment advice.
One story runs through all four memory types: consumers built the market; data centers took it over. Each chart below splits 100% of that memory type's annual supply by end-use application, from 2010 to today with a projection to 2030. In 2010 every one of these was dominated by PCs, phones, or consumer gadgets. By the mid-2020s — and overwhelmingly by 2030 — the data center is the center of gravity for all of them. Honesty note: like the token-share chart above, these are modeled shares — smooth reconstructions anchored to cited industry reference points (marked in the captions), not a continuously-reported annual series. The dashed line marks where history ends and projection begins.
By bit consumption. Anchors: server overtook mobile in 2023 (~37.6% vs 36.8%, TrendForce); HBM ~5% of bits 2024; AI/HBM ~20% of DRAM wafers by 2026.
By bit consumption. Anchors: smartphones ~40% / SSD ~25% in 2023; enterprise SSD becomes the #1 segment in 2025–26 (TrendForce).
By bit consumption. HBM didn't exist before 2015 (AMD Fiji). Anchors: AI/ML ~55%+, HPC ~25%, graphics ~12%, emerging ~8% in 2026 (PatSnap/JEDEC).
By capacity (exabytes) shipped. Anchors: nearline ~54% of capacity today → >90% by 2029 (Coughlin); client/consumer collapsing as PCs move to SSD.
Provenance & the honesty line. These four charts are modeled shares — the same epistemic class as the "By Provider" token chart above. No vendor publishes a continuous 2010–2030 annual end-use breakdown by bit/exabyte, so each series is a smooth reconstruction fitted to cited anchor points and the well-documented qualitative arc. Anchors used — DRAM: server DRAM overtook mobile in 2023 at ~37.6% of bits (mobile 36.8%), server the largest segment since (TrendForce); HBM ~5% of DRAM bits / ~20% of revenue in 2024 (TrendForce); AI projected to consume ~20% of DRAM wafer capacity in 2026 (Commercial Times/TrendForce); the 2010–2015 mobile surge and PC decline are well-documented. NAND: smartphones ~40% and SSD ~25% of demand in 2023 (industry reports); enterprise SSD becomes the single largest NAND application segment in 2025–2026 on AI-server demand, with suppliers shifting capacity from client/mobile to data-center SSD (TrendForce, Jan 2026); 2010-era dominance of cards/USB + mobile is well-documented. HBM: first commercial use 2015 (AMD Fiji, graphics), data-center accelerators from Nvidia P100 (2016); by 2026 AI/ML training+inference ~55%+, HPC ~25%, graphics ~12%, emerging (autonomous/edge/networking) ~8% (PatSnap citing JEDEC); early years were graphics/HPC-weighted before AI dominance. HDD: nearline ≈54% of capacity shipped today, projected >90% by 2029 (Tom Coughlin); consumer/client HDD displaced by SSD (flash is "the default choice outside the data center"); 2010 was PC/consumer-dominated before the nearline/cloud shift. The caveats: (1) shares are by the noted unit (DRAM/NAND/HBM by bits, HDD by exabytes) — not revenue, and not unit count; (2) category boundaries blur (e.g. "server" vs "AI server," "graphics" GDDR vs HBM) and different sources slice differently; (3) everything past ~2025 is projection and widens with time; (4) HBM is a subset of DRAM shown separately because its end-use profile is distinct. Sources: TrendForce, PatSnap/JEDEC, Tom Coughlin/Forbes, industry market reports, company disclosures. Modeled shares — analysis, not investment advice.
An AI chip is like a super-fast cook. It can chop a million carrots a second — but the carrots live in a pantry down the hallway, and it can only carry a few armfuls per trip. So the cook mostly just… stands there, waiting for carrots. "Memory" is the pantry and the hallway. Give the cook a bigger pantry right next to the stove and a wider hallway, and it almost never waits. That's the memory wall — and it's why everyone is racing to buy more memory.
Here's the weird part: to write each single word, an AI has to re-read its entire brain — all of its billions of stored numbers — from memory chips sitting next to the processor. The math units are so fast they finish almost instantly, then wait for the next full re-read. So chatbot speed isn't set by how fast the chip does math; it's set by how fast memory can feed it (bandwidth) and how much fits next to the chip (capacity). The proof: Nvidia sold a chip with the exact same math speed as its old one but bigger, faster memory — and AI output nearly doubled. Rule of thumb from this section: generating one word costs as much chip-time as reading ~300.
Precisely: autoregressive decode at batch B has arithmetic intensity ≈ B FLOPs per weight-byte (weight reads amortize across the batch; per-request KV reads never do), against ridge points of ~281 (B200) to ~568 (Rubin) FLOPs/byte — so the tensor-core utilization ceiling ≈ min(1, B/ridge), B is capped by HBM capacity (weights + KV must be resident), and at long context the KV term dominates and pins utilization below 1% at any batch. Prefill is the compute-bound phase; decode is the memory-bound one; measured MFU on H100/Llama-3-70B stays under 20% below B≈100 for exactly the capacity reason. Everything below quantifies this — the roofline chart, the frontier and open-model scenarios, and the 16-prompt verdict table. (The analogies above compress; the numbers below don't.)
The dirty secret of AI inference: the world's most expensive chips spend most of their time waiting. During the decode phase — generating each new token — the GPU must re-read the entire model's weights from HBM for every single token. A 70B FP16 model means ~140 GB read per token. The H100 pairs ~1,979 FP8 TFLOPS of compute with 3.35 TB/s of bandwidth — a 591:1 compute-to-memory ratio — so at low batch sizes the tensor cores are mathematically capped at single-digit (even sub-1%) utilization while they wait on memory. More memory is the fix, through two distinct levers: more bandwidth steepens how fast utilization rises with batch size, and more capacity lifts the ceiling on batch size itself (every concurrent request needs its KV cache resident in HBM). That is the causal engine of this whole dashboard: idle compute is the most expensive thing in the data center, and memory is what un-idles it.
DeepSeek-V4 prompt table — derived math. Reference: DeepSeek V4 Pro (49B active of 1.6T MoE, 7 KB/token KV — see the open-weights block above) served batch-1 on an 8×B200 node, FP8 (aggregate ~36 PF dense FP8, 64 TB/s HBM — the node that holds the ~1 TB checkpoint). Per-token: prefill ≈ 2×49e9 ÷ 36 PF ≈ 0.0027 ms/input-token (compute-limited); decode ≈ (49 GB active-expert read + 7 KB×context KV) ÷ 64 TB/s ≈ 0.77 ms/output-token, rising only to ~0.79 ms even at 250K context — the compressed-KV signature. Crossover ≈ 281 input:output, a hardware compute:bandwidth property (hence close to the H100 table’s 295), not a model property. MoE caveat, sharper than dense: batching amortizes the active-weight read, but different tokens route to different experts, so a large batch touches most of the 384 experts — amortization is weaker than for a dense model and real decode stays memory-bound to higher batch sizes. Verdicts are batch-1 per-request; prefill idealized at 100% MFU; token counts illustrative. Not investment advice.
How the estimate is built, and its limits. The mechanism [well-established]: prefill (ingesting the prompt) is compute-bound; decode (generating tokens) is memory-bandwidth-bound — now "the default playbook across nearly every major LLM serving" stack, with disaggregation shipping at Perplexity, Meta, Mistral, and via NVIDIA Dynamo/vLLM/SGLang/TensorRT-LLM. Measured, prefill runs GPUs at 90-95% utilization while decode craters to 20-40%, and prefill saturates tensor cores at 30-45% MFU while decode sits mostly idle waiting on memory bandwidth (Towards Data Science / InfoQ Sept 2025; buildmvpfast; HuggingFace). The governing rule [derived, shown above]: a request's decode-time exceeds its prefill-time whenever its input:output ratio is below the crossover — ~295:1 on H100, ~281:1 on B200 — so unless average prompts read ~300× more than they write, GPU-time is decode-dominated, i.e. memory-bound. Why time ≠ tokens: output tokens cost far more to produce — providers price them at 3:1 to 5:1 over input tokens (e.g. Claude Sonnet $3 input / $15 output per million, a 5:1 ratio), reflecting the sequential per-token generation cost. So even where input tokens dominate by count, output tokens dominate by time and money. Workload weighting [cited anchors, still an estimate]: real mixes span a wide range — a typical support-ticket workload is ~3,150 input / 400 output tokens (~8:1, input-heavy), while high-throughput models like DeepSeek V3.2 show an output-to-input ratio near 1.6× and Llama 4 Maverick around 3× (output-heavy); programming rose from 11% to over 50% of all LLM token usage on OpenRouter by late 2025, and agentic coding workflows average 1–3.5M tokens per task — long, cached, input-heavy contexts by count but with substantial generation. Crucially, reasoning/thinking tokens are billed and processed as output (decode) — reasoning models use 10–20× more tokens — pushing the time-share further toward memory. The batch caveat [bounds the estimate]: at high concurrency decode GEMMs can turn compute-bound — past roughly batch 32 the decode GEMMs become compute-bound while attention stays bandwidth-bound — but the attention/KV reads remain memory-bound, and at long context KV dominates and keeps decode memory-bound at any batch (per the frontier and DeepSeek blocks above). This is the main reason the time-share isn't ~100% memory: heavy-batch, short-context decode and bulk-ingest prefill claim the compute-bound remainder. Jul 2026 refresh [cited]: hardware-level measurements have sharpened — prefill drives GPUs to 70–100% of peak power while decode draws only 20–40%, and tensor-core utilization runs ~92% in prefill versus ~28% in decode milliseconds later (Spheron 2026 guide; Towards Data Science). Disaggregation is now a costed engineering default: an H100-prefill + H200-decode split costs ~45% more per hour but delivers ~75% more throughput. The agentic counter-pressure [cited, and the main revision]: 2026 agents routinely send 50,000–500,000 input tokens per request against only a few hundred output, and a ReAct agent running 10 tool calls can consume ~800K input tokens while emitting ~500 output — ratios of 100:1 to ~1,600:1, above the ~281–295:1 crossover, which on raw ratios alone would flip those requests to prefill-bound. Gartner (Mar 2026) puts agentic tasks at 5–30× the tokens of a standard chatbot turn. Why it doesn't flip the answer [cited]: (1) prefix caching removes the prefill entirely for repeated context — tuned stacks reach 84–92% cache-hit rates (one workload went 1.7%→92.2%; ProjectDiscovery 7%→84%), cutting effective uncached prefill roughly 5–10× and pulling those ratios back under the line; the market prices this directly, with cached input billed at ~10% of normal (Anthropic), ~25% (Google), ~50% off (OpenAI). (2) reasoning tokens are decode — reasoning models emit 20–30× more tokens per task, and a 200-token visible answer can hide 10,000–30,000 thinking tokens, every one of them memory-bound generation. (3) KV pressure is itself now a reported constraint on agentic economics (WEKA, 2026). The two forces very nearly cancel; the token-count bar above is nudged prefill-ward to reflect the agentic shift, while the GPU-time split is left unchanged. Bottom line: by the metric that matters for this dashboard — GPU-time, and therefore memory purchased — inference is majority memory-bound, on the order of ~70–90%; by raw token count it leans compute (prefill) because production is input-heavy, but those tokens are individually ~300× cheaper. The mechanism and crossover are anchored; the exact percentages are an estimate — the aggregate input:output distribution across all providers is not publicly disclosed. Sources: Towards Data Science, buildmvpfast, Medium (Chen; Patel), HuggingFace, arXiv 2512.22066, Silicon Data, iternal.ai, onprem.ai, Grizzly Peak, Mobisoft; 2026 refresh: Spheron (prefill/decode disaggregation & context-engineering guides), yage.ai (KV cache-hit economics), WEKA (KV eviction & agentic economics), vLLM/Mooncake, Gartner (Mar 2026), Digital Applied, newline; plus the roofline anchors cited above. Estimate, not a measurement — not investment advice.
| Accelerator | HBM | BW / GPU | ~BW / MW | rel. decode TPS/MW |
|---|---|---|---|---|
| H100 | HBM3 | 3.35 TB/s | ~2.4 PB/s | 0.55× |
| H200 | HBM3e | 4.8 TB/s | ~3.4 PB/s | 0.8× |
| B200 · GB200 | HBM3e | 8 TB/s | ~4.4 PB/s | 1.0× (ref) |
| B300 · GB300 | HBM3e | 8 TB/s | ~3.9 PB/s | ~0.9× |
| Rubin · VR200 | HBM4 | 22 TB/s | ~7.5 PB/s | ~1.7× |
| Rubin Ultra | HBM4e | (2027) | higher | >2× |
Why bandwidth is the tokens/sec lever, and how these are built. The mechanism [established]: most production inference is memory-bandwidth-bound — generating each token requires reading the model’s weight parameters out of HBM, and the compute to process them takes less time than the read, so adding FLOPS without proportional bandwidth yields diminishing returns. In that regime decode tokens/sec is set by how fast weights (and KV) stream from memory, so it scales ~linearly with bandwidth: the H100→H200 step lifted bandwidth 1.43× (3.35→4.8 TB/s) and produced almost exactly 1.4× tokens/sec on the same model. Per-GPU bandwidth by generation [anchored]: H100 is 3.35 TB/s on HBM3, H200 4.8 TB/s and B200 8 TB/s on HBM3e, and Rubin (R100) reaches up to 22 TB/s on HBM4; B300/GB300 holds at 8 TB/s HBM3e (288 GB, 1,400 W) — capacity and FP4 compute rise, per-chip bandwidth does not. A Rubin NVL72 rack aggregates 1.6 PB/s across 20.7 TB of HBM4, and NVIDIA states the Rubin GPU nearly triples memory bandwidth compared to Blackwell. The HBM generation is the engine [anchored]: HBM4 delivers ~2 TB/s per stack versus HBM3e’s ~1.2, via a doubled 2,048-bit interface, yet bandwidth still grows only ~1.6× every two years while FLOPS scale ~3× — widening the memory wall; HBM4’s per-stack uplift is roughly +75–120%. Rubin Ultra (H2 2027) moves to HBM4e with 1 TB per GPU in 600 kW NVL576 racks. Per-MW conversion [modeled]: bandwidth/MW = (GPUs/MW) × (BW/GPU), with GPUs/MW from all-in power at a liquid-cooled PUE ~1.15 (~714 H100/H200, ~546 B200, ~490 GB300, ~340 Rubin per MW). The per-MW gain trails the per-chip gain because power climbs each generation — Rubin draws ~1,800–2,300 W per GPU versus Blackwell’s 1,000 — so HBM4’s 2.75× per-chip bandwidth lands at ~1.7× per MW. Why it’s the thesis: a megawatt’s token output is gated by HBM bandwidth, and the only way to raise it is the next HBM generation — supplied by exactly three firms, SK Hynix (~62%), Micron (~21%) and Samsung (~17%), with capacity sold out. That is the memory supercycle in one line: tokens/sec per watt is an HBM-bandwidth story (see §05·C·G). All MODELED: per-GPU bandwidths and the H200 scaling point are anchored to published specs; per-MW figures fold in power/PUE assumptions and a representative weight-streaming model, none of which is a single number. Estimate, not a measurement — not investment advice.
Provenance & the honesty line. Measured / cited anchors: decode is memory-bandwidth-bound, prefill is compute-bound (arXiv 2601.11822 and standard serving literature); a 70B FP16 model reads ~140 GB from memory per generated token, and on an H100's 3.35 TB/s that sets a ~42 ms/token floor ≈ ~24 tok/s regardless of FLOPS; H200 ≈ 34, B200 ≈ 57 on the same model (GMI Cloud, Spheron — B200 figures trace to GTC-2024 disclosures/estimates); the H100 pairs 1,979 FP8 TFLOPS with 3.35 TB/s — a 591:1 compute-to-memory ratio (Introl); measured MFU stays below ~20% at batch sizes under ~100 on H100 / Llama-3-70B, explicitly because VRAM cannot hold the KV cache needed for larger batches (arXiv 2405.01814 — the single best empirical statement of this section's thesis); one profiled inference kernel ran at 23% compute and 47% memory-bandwidth utilization (arXiv 2504.06319); H100s have been measured using only ~32% of peak memory bandwidth in distributed decode, with HBM accesses alone 30–50% of energy (arXiv 2602.18568); even large-batch serving stays DRAM-bound with significant compute idle (arXiv 2503.08311). The H200 proof point [NVIDIA product brief via GMI]: H100 and H200 share identical FP16 (989) and FP8 (1,979) TFLOPS; the H200's up-to-1.9× Llama-2-70B inference speedup comes entirely from memory (141 GB HBM3E @ 4.8 TB/s vs 80 GB HBM3 @ 3.35). KV-cache formula per request: 2 × layers × KV-heads × head-dim × seq-len × bytes (GMI). Derived [formula shown, labeled]: the chart's utilization curves are roofline ceilings — util ≈ min(100%, BW×B/peak) with FP16-dense peaks (H100/H200 989 TFLOPS, B200 ~2,250 est.) — real kernels achieve less (see the measured 23%/47% figures), so the idle problem is worse than the curves, not better. Illustrative [assumptions stated]: the capacity cut-offs assume a 70B-class model, FP8 weights ≈70 GB, FP8 KV ≈160 KB/token, 8K average context → ≈1.3 GB per request; real deployments shard across GPUs, page KV (vLLM-style), quantize, and disaggregate prefill/decode, so exact caps vary widely — the mechanism (capacity caps concurrency, concurrency drives utilization) is the anchored claim, not the specific integers. Prompt-verdict table [derived — reference math shown]: per-token costs on the section's reference config (70B FP16, H100, batch 1): prefill ≈ 2×70B FLOPs ÷ 989 TFLOPS ≈ 0.14 ms per input token (compute-limited); decode ≈ 140 GB ÷ 3.35 TB/s ≈ 41.8 ms per output token (bandwidth-limited) — the agent row adds the 200K-context KV read (~64 GB at GQA-class 320 KB/token) → ~61 ms/token. The crossover ratio (input tokens per output token at which prefill time equals decode time) is decode÷prefill ≈ 295 — algebraically identical to the ridge batch, since both reduce to peak ÷ (bandwidth × FLOPs-per-byte). Caveats: verdicts are per-request at batch 1 — production batching divides the weight-read share of decode by ~B until context/KV dominates, pulling crossovers down; prefill is idealized at 100% MFU (real prefill runs ~40–70%, which lengthens prefill's time share but doesn't change which resource limits each phase); token counts are illustrative (~4 chars/token; thinking budgets vary widely); prompt caching converts recomputed prefill into stored KV — trading compute time for HBM capacity occupancy, the section's thesis in miniature. Coverage note for the sixteen-row table: the shapes span the categories that dominate public usage analyses — coding/agentic work (the largest API category), chat, writing/rewriting, reasoning, RAG & support, summarization, translation, extraction/classification, research, and vision — with illustrative token counts, not measured shares (platform mixes vary; one image counts ≈ 1.5K prefill tokens). Methodology consistency: rows whose decode runs at long context (the 10-K, agent turn, agentic-coding loop, deep research, needle-in-haystack) include the per-token KV read in the decode cost; the offline-classification row is the one deliberate exception to batch-1 — scored at batch 256 to demonstrate the flip that production batching produces. Frontier scenario [modeled — every workload number is an assumption]: Anthropic publishes no architecture details for Claude Fable 5 (the model that drafted this dashboard does not know its own parameter count — it isn't disclosed even to it), so the scenario uses round frontier-class figures in line with widely-reported analyst estimates of GPT-4-class systems (~1.8T-parameter MoE): ~2T total / ~120B active, NVFP4 weights (~1 TB, sharded ~14 GB/GPU across an NVL72-class pod), KV ≈250 KB/token with GQA/MLA-style compression; Claude's 200K context window is the one public spec used. Utilization here = decode-phase ceiling vs dense-FP4 peak: at short context, batching amortizes weight reads and the ceiling ≈ min(compute ridge, KV asymptote); at 200K the KV read per token (~50 GB/request) dominates and the ceiling collapses to ~0.3–0.6% regardless of batch — added capacity then buys concurrency (how many 200K requests fit), not utilization. MoE routing makes real ceilings lower still (large batches touch most experts, weakening amortization), prefill mixing raises blended MFU, and production kernels run below all of these ceilings (see the measured anchors above). Frontier GPU specs [published / reported]: B200 192 GB HBM3E · 8 TB/s · 9 PF dense FP4 (shipping); B300 288 GB (12-Hi HBM3E) · same 8 TB/s · 15 PF dense FP4 — its advantage is "purely about fitting larger models or longer contexts… rather than serving each token faster" (Spheron; SemiAnalysis; Tom's Hardware; IntuitionLabs; shipping since H2'25); Vera Rubin 288 GB HBM4 · 50 PF dense FP4 · bandwidth reported at 22 TB/s in the latest GTC-2026 coverage after evolving 13 → 20.5 → 22 TB/s across reporting as NVIDIA pushed HBM4 pin speeds (tech-insider, SemiAnalysis, IntuitionLabs) — sampling Q4 2026, volume Q1 2027, so all Rubin figures are preliminary. Corroborating context from the same reporting: memory is set to consume ~30% of hyperscaler data-center spend this year (~4× 2023) and NVIDIA system memory costs are up ~485% (Tom's Hardware). Open-weights block [published architecture + derived math]: DeepSeek V4 Pro specs are from its Hugging Face model card / paper abstract (Apr 24 2026, MIT): 1.6T total / 49B active, 61 layers, hidden 7168, 384 routed + 1 shared experts (6 active/token), native 1M context; the abstract states the hybrid CSA+HCA attention needs 27% of single-token inference FLOPs and 10% of the KV cache vs V3.2 at 1M context. KV/token is derived from that ratio: V3.2's MLA cache ≈ 576 dims × 61 layers × 2 B ≈ 70 KB/token → V4 Pro ≈ 7 KB/token; cross-check: reporting puts it at ~2% of an 8-head GQA BF16 cache, and that GQA reference works out to ~250 KB/token — the same figure assumed in the closed-model scenario above, an independent validation of that assumption. Weights ≈1 TB mixed FP4/FP8, anchored to Lambda's note that the checkpoint fits on an 8×B200 node (≈14 GB/GPU sharded across an NVL72 pod). Directional caveat: the 1M-context bars model the per-token KV read as the full compressed cache — CSA/HCA's sparse reads touch less, so V4's true long-context ceilings sit above the bars shown (the conservative direction); capacity math uses full storage, which is correct regardless. "Largest and highest-performing" disclosure: GLM-5.2 (744B) and Kimi K2.6 / MiMo-V2.5-Pro (1T) edge V4 Pro on the Intelligence Index (≈54 vs 52), but V4 Pro is the largest open model, #1 on open agentic Elo, and highest open SWE-bench — the closest single fit; swapping the block to any of those is a config change. The meta-point the two blocks make together: the best open architecture is explicitly engineered around the memory wall (MoE sparsity + compressed/sparse attention) — demand-side adaptation to memory scarcity — and cheap 1M-token context is exactly what unlocks more long-context usage, so per-request memory efficiency feeds total memory demand rather than reducing it (sources: DeepSeek V4 model card, Latent Space/Lambda, Artificial Analysis, morphllm, aimadetools, codersera). On-thesis: this is the machine that converts token demand (§06·D) into memory demand (§05·B, §06·D·E) — and why TrendForce frames the supercycle as a "memory wall" problem: as AI shifts to inference, compute demand per token falls while memory demand rises, with inference set to be the primary AI-server driver by 2029. Sources: arXiv 2405.01814, 2504.06319, 2602.18568, 2503.08311, 2601.11822; GMI Cloud; Spheron; Introl; NVIDIA H100/H200 datasheets; TrendForce "Memory Wall" insight (Jan 2026). Not investment advice.
One map of every trend compounding memory demand — C = capacity (GB), B = bandwidth (TB/s). The point of seeing them together: these multiply, not add. Longer contexts × more reasoning tokens × more agents × more users × bigger models — each is a separate factor on the same HBM bill, which is why token volume can grow 24× while memory demand grows faster.
| Trend | C / B | Scale of effect | Tracked in |
|---|---|---|---|
| Context windows — KV cache grows linearly with tokens in context | C + B | 128K ≈ 19 GB KV per sequence (GQA) vs ~0.9 GB (MLA); decode re-reads it every token | §06·D·W |
| Reasoning / thinking tokens — answers now include long hidden traces | B | 10–20× more output tokens per query; all decode = all bandwidth | §06·D·W agg. |
| Agentic loops — full context re-sent every tool step; agent teams run parallel instances | C + B | 0.4–2M tokens per task; per-dev use +18.6× in 9 months | §08·E |
| Model scale / MoE totals — all experts must be resident even if few activate | C | 1.6T-param checkpoint ≈ 1 TB in FP8 — a full NVL72 rack to hold one model | §06·D·W open block |
| Batch / concurrency — saturating compute needs KV for ~281 sequences at once | C | roofline batch × context × KV/token; 5.4 TB at 128K standard-KV | §06·D·W per-MW |
| Multimodality — images ~1.5K tokens each; video/audio are token streams | C + B | vision rows land memory-bound in both prompt tables | §06·D·W tables |
| Prompt caching — the fix that eats capacity: cached KV kept resident/tiered to DRAM | C | 90% input discount only exists because the KV is stored, not recomputed | §06·D·W · §01 |
| Token volume growth itself — more users, more queries, Jevons dynamics | C + B | Goldman: 24× enterprise tokens by 2030 (120 quadrillion/mo) | §08·E · §08·D |
| FLOPS outrunning bandwidth — compute grows ~3×/2yr, HBM BW ~1.6×/2yr | B | the wall widens each generation → bandwidth carries a structural premium | §06·D·W BW/MW · §05·C·G |
| On-device AI floors — edge inference raises minimum DRAM per phone/PC/car | C | iPhone base 8→12 GB; Tesla 300 GB+/vehicle trajectory | §08·C · §07·B·T |
| Multi-tenancy extras — LoRA adapters, draft models for speculative decoding, embeddings/RAG stores | C | each resident alongside weights; RAG shifts GB into server DRAM too | §01 · §06·D·E |
Label: SYNTHESIS. This section introduces no new figures — every number is sourced and cited in the section its row links to; this is the one-glance union. The compounding claim is the analytical content: demand drivers are multiplicative (context × reasoning × agents × users × model size), while supply-side relief is per-factor (MLA compresses KV ~20× but touches only one factor; HBM4 lifts bandwidth ~1.7× per MW but not capacity per user). The bear case is the same table read backwards: efficiency gains (compression, distillation, caching, smaller routed experts) attack individual rows — kill-switch #5 (§02·D) watches whether efficiency ever outruns compounding. It has not yet. Not investment advice. See also: §06·D·D is the qualitative synthesis; §05·F puts numbers on the silicon those drivers consume.
Where the tokens in §06·D actually get spent. OpenRouter routes a large slice of the independent LLM-inference market, so its weekly usage is one of the cleanest public reads on which models are consuming compute — and therefore memory. The numbers are now fully cited — extracted live from openrouter.ai (Jul 17 2026): weekly throughput ran 3.41T (Jul 28 2025) → 62.8T (Jul 13 2026), an 18.4× ramp in a year, with the steep leg after January. Every week carries the site's own per-model split (top-9 + Others). Every token here is a KV-cache read against HBM (§06·D·W), so this curve is the demand side of the supercycle, measured in the wild.
Live extract · Jul 17 2026 · sort: Trending · All models — the Trending sort surfaces fast-growers (note Hy3, also #2 by weekly volume), not the volume leaders in the chart above
Provenance & method. Source: OpenRouter LLM Rankings, openrouter.ai/rankings?view=trending, extracted live in-browser on Jul 17 2026. The page is client-rendered with no public per-model-per-week endpoint, so the series was captured at the render layer: the site's own chart tooltips were triggered programmatically for each of the 52 weekly bars and transcribed verbatim — date, top-9 models with values, Others, and Total. [cited]: all 52 weeks (Jul 28 2025 → Jul 20 2026), 520 model-week values plus totals; segment sums reconcile to printed totals within display rounding (<2%); the Jul 13 2026 total (62.8T) matches the site's rendered chart peak. Window notes: Jul 20 2026 is the in-progress week (27.6T so far; the site's own pace estimate ≈58T). Model sets change per week (each week shows its own top-9); a handful of names repeat within a week where the site lists paid and free variants under one display name — kept as displayed. Superseded: this section's earlier illustrative bands and image-read totals (2.5T→62T) are replaced by this extract (3.41T→62.8T actual); a stale 11-column hydration dataset found in the page (year-ago leaders + Others, totals ~19T) did not match the rendered chart and was discarded in favour of the tooltip transcription, which does. The leaderboard is the live Jul 17 Trending list. Not investment advice.
How many people and companies actually pay for AI — the monetization mirror of the token-usage and backlog sections. This stacks the paid base (consumer chatbot subscriptions and enterprise seats) across the five biggest platforms, quarter by quarter for the last two years, then projects to 2030. Two segments are anchored to hard disclosures: ChatGPT (OpenAI reported ~15.5M paid subs end-2024, ~35M mid-2025, ~47M end-2025, ~50M by Feb 2026) and Microsoft 365 Copilot (15M paid seats reported Jan 2026, 20M reported Apr 2026). The other three — Gemini, Claude, and the long tail (Perplexity, Grok, …) — do not break out paid-subscriber counts, so those bands are modeled from revenue and bundling disclosures and labeled as such. The projection leans on the one company that published a long-range target: OpenAI guides to ~122M consumer subscribers by end-2026 and ~220M by 2030; the rest grow at roughly the ~37%/yr rate the market-research houses use for generative AI. By mid-2026 the cited-plus-modeled paid base is ~125M; the illustrative 2030 total is ~600M.
Provenance & the honesty line. ChatGPT / OpenAI [cited — reported to shareholders / announcements, via The Information & Reuters]: 5.8M paid subs (end-2023) → 15.5M (end-2024, "nearly tripled") → 20M (early Apr 2025) → ~35M (Jul 2025, Plus+Pro) → ~47M (end-2025) → ~50M across all tiers + ~9M paying business users (Feb 2026); company target ~122M consumer subs by end-2026 (driven by a new ~$8/mo tier) and ~220M by 2030 (≈8.5% of a projected 2.6B weekly users). Microsoft 365 Copilot [cited — FY26 earnings calls]: 15M paid seats (reported Jan 28 2026, Q2 FY26, +160% YoY) → 20M paid seats (reported Apr 29 2026, Q3 FY26, +5M QoQ); ~5.8M implied a year earlier from the 160% YoY; still only ~3.3% of Microsoft's 450M+ commercial seats. Google Gemini [modeled/derived]: Google doesn't break out Gemini subscribers; derived from Google One (150M+ paid subs) with AI Premium/Gemini bundled, ~$1.2B of 2025 Gemini subscription revenue, Workspace Gemini add-ons, and Alphabet's 350M total paid subscriptions (Q1 2026). Claude [modeled/derived]: Anthropic discloses revenue, not sub counts; derived from the $3B→~$14B annualized revenue ramp (mid-2025→early-2026) and Claude Code (~$2.5B annualized), with consumer chat well below ChatGPT. Other [modeled]: Perplexity (~$100M+ subscription ARR, 2025), xAI Grok / SuperGrok (largely bundled with X), and smaller assistants. Projections [illustrative]: OpenAI bars = company targets; all other segments compounded at ~35–40%/yr, consistent with generative-AI market forecasts of a ~37% CAGR to 2030 (Grand View, MarketsandMarkets) and Counterpoint's ~$700B consumer-GenAI-spend-by-2030 view. Honesty caveats: (1) only two of five segments are cited; three are modeled and drawn without ● markers. (2) consumer subscriptions and enterprise seats are summed into one "paid base" — different units, disclosed. (3) the ~$225B/$700B consumer-spend figures are spending, not subscriber counts, and are used only to set the projection growth rate, not the levels. (4) interpolation between disclosed quarters is linear. Cross-refs: §06·D (token demand), §06·D·R (OpenRouter usage), §08·D (app-layer revenue), §06·F·B (RPO backlogs). Sources: The Information, Reuters, Seeking Alpha, Microsoft FY26 IR, Alphabet IR, Counterpoint, Grand View Research, MarketsandMarkets, Business of Apps. Not investment advice.
Every parameter is a number the accelerator has to store and read back for each token it generates. So the first-order memory a model needs is simply how many parameters it has × how many bytes each one takes — and that memory has to sit in the fast memory bolted to the chip, HBM, the exact supply this whole dashboard tracks. Bigger model → more bytes → more HBM. That is the reason model size belongs here at all.
bytes / parameter by precision: FP16 / BF16 = 2 · FP8 = 1 · INT4 = 0.5 (lower precision = smaller, up to a quality limit)
And weights are only the floor. Serving a model also holds a KV-cache — the running memory of each active conversation — that grows with context length × concurrent users. At long context and high concurrency it can rival or even exceed the weights, so real serving memory ≈ weights + KV-cache + activation overhead, all of it living in HBM next to the chip. (Training needs several times more again, for gradients and optimizer state.)
Why Mixture-of-Experts is the twist. An MoE model computes with only a few billion active parameters per token — but every expert's weights must still sit resident in HBM in case the router selects it. So DeepSeek-V3 needs ~671 GB of HBM capacity even though it only computes with 37B. That is the crucial link the charts below make visible: total parameters (the top of each stem) set the HBM capacity bill even while active compute stays flat — so total-parameter growth drives HBM demand directly. Ties to §05·E (wafer starts) and §06·D·W (the memory wall).
"How big is the model?" got harder to answer over time, not easier. This plots the parameter count of major model releases since 2022 on a log scale — the only honest way to show a range that runs from Mistral's 7B to Kimi K3's 2.8T. Two things make a naïve "models keep getting bigger" chart misleading, and both are drawn explicitly. (1) The disclosure blackout: OpenAI last published a parameter count for GPT-3 (175B, 2020); Google last for PaLM (540B, 2022); Anthropic has never disclosed one. Every closed frontier model since — GPT-4, 4o, 4.5, the GPT-5 line, Claude, Gemini — is an estimate (hollow markers); the open-weight labs (Meta, DeepSeek, Alibaba, Mistral, Moonshot) are now the only ones reporting real numbers (solid markers). (2) Total ≠ active. Modern frontier models are Mixture-of-Experts: Kimi K2 holds 1.04T parameters but fires only 32B per token; DeepSeek-V3 holds 671B but activates 37B. The stem under each MoE dot drops to that active count. The surprises the chart makes visible: the largest dense model ever shipped is still PaLM (540B) from 2022; GPT-4's leaked ~1.8T (2023) was followed by a smaller GPT-4o (~200B est., 2024); and the parameter frontier only kept climbing because sparsity, not dense scale, pushed totals into the trillions. Added Aug 10 2026 — and read them differently from everything else on the chart. GPT-5 (Aug 7 2025) is plotted at ~1.2T, the geometric midpoint of a published range that runs 300B to 5T — a 17× spread between named sources, drawn as the dashed bar rather than hidden inside a single dot. The high end is the interesting one for this board: it comes from Samsung's own head of memory, Dr Jung-Bae Lee, who put GPT-5 at 3–5T parameters in a SemiCon Taiwan slide — a memory executive sizing the customer's model in public. Claude Mythos (Apr 7 2026) is the weakest point on this page and is marked accordingly with a dashed ring: the only figure in circulation is ~10T total / ~0.8–1.2T active, and it traces back not to Anthropic — which has never published a parameter count for any model — but to third-party cost-and-throughput reverse-deduction, republished by outlets that contradict themselves about whether it was ever disclosed at all. It is on the chart because it was asked for, and it is dashed because it should not be quoted. Two more followed: Claude Fable 5 (Jun 9 2026) — Anthropic's own description, "the first publicly available Mythos-class model" — inherits Mythos's ~10T because that phrase is the only thing connecting it to any number; and GPT-5.6 Sol (Jul 9 2026), for which no estimate has been published by anyone, derived here from GPT-5 by API-price ratio and drawn with a 900B–20T band — a 22× span that is the honest width of what price can tell you. All four new points are guesses with different amounts of scaffolding, and the chart now says which is which.
Provenance & the honesty line. [disclosed — official papers / model cards / open weights]: Chinchilla 70B, PaLM 540B (dense, Google/DeepMind); OPT-175B, BLOOM 176B, LLaMA 65B, Llama 2 70B, Llama 3.1 405B (dense, Meta/BigScience); Falcon 180B (TII); Mistral 7B, Mixtral 8×7B (46.7B total / 12.9B active); Grok-1 314B MoE (~79B active, weights opened Mar 2024); Nemotron-4 340B (Nvidia); Qwen2.5 72B, Qwen3-235B (235B / 22B active, Alibaba); DeepSeek-V2 (236B / 21B), DeepSeek-V3 (671B / 37B), DeepSeek-V4-Pro (1.6T / 49B); Kimi K2 (1.04T / 32B), Kimi K3 (2.8T total, active not broken out at capture, Moonshot). [estimated / leaked — closed labs, no official count]: GPT-3.5 ~175B (inherited from GPT-3); GPT-4 ~1.8T total / ~280B active (MoE, per The Information leak); PaLM 2 ~340B (reported); GPT-4o ~200B and Claude 3.5 Sonnet ~400B (Epoch AI estimates); Grok 5 ~6T (unverified vendor claim — treat with caution). Not plotted (no credible public number): GPT-4.5, the GPT-5 line, Claude 3.x/4.x/Opus 4.8/Fable 5, Gemini 1–3.x — parameter counts withheld. Honesty caveats: (1) "parameters" is one axis of size — training compute (FLOP) and data are others and have kept rising even where parameters fell; this chart is deliberately the parameter view. (2) MoE totals and dense totals are not like-for-like capability — a 671B sparse model ≠ a 671B dense model; the total/active stem is drawn precisely so the two aren't conflated. (3) estimates are point-in-time and contested; they're marked hollow and excluded from any "record" claim. (4) release dates are first public availability, approximated to the month. Cross-refs: §05·E (wafer starts), §06·D·W (memory wall / KV-cache), §06·D (token demand). Sources: Epoch AI (notable-models data & size estimates), original model papers/cards, The Information (GPT-4 leak), Meta AI, DeepSeek, Moonshot AI, Alibaba, Mistral, xAI. Not investment advice.
§06·D·M measures models by size. This measures them by score — using Tracking AI (Maxim Lott), which has administered IQ tests to every frontier model on a continuous schedule and publishes the raw daily log. The numbers below are extracted from that log directly: 5,315 dated score rows across 85 models. What makes this source unusually useful is that it runs two tests — and the difference between them is the finding. The "Offline Test" is private: written by a Mensa member, never published, absent from any training corpus. "Mensa Norway" is a public online test that has been on the internet for years and is therefore almost certainly in the training data. Tracked side by side, the public test now reads 151 while the private one reads 136 — and, more importantly, the private score has been flat in a 123–136 band since April 2025 while the public score kept climbing.
These are scores for models by their stated release date. The continuous daily log begins May 2024 — Tracking AI was not running in 2022, so no 2022 data exists on this source and none is invented here. The earliest model it carries at all is Bing (Feb 2023).
Provenance & method. Source: Tracking AI (trackingai.org/iq), a project by Maxim Lott. [cited — extracted live]: the page is client-rendered, so the series here was taken from the site's own underlying data file, app/database/proj_IQ/score_logs/iq/daily_logs_iq.csv, read in-browser on Jul 30 2026 — 5,315 score rows, 85 models, 2024-05-09 → 2026-07-30, joined to the site's model metadata for release dates. Each plotted point is the maximum iq_score recorded in that calendar month for that test_source; the tooltip names the model that set it. The two tests, in the site's own words: the Offline quiz "is a test made by a Mensa member that has never been on the public internet, and is in no AI training data"; Mensa Norway "is a public online IQ test." Tests are verbalised for text models; vision models are shown the image, and vision variants are scored separately (both appear in the frontier). Honesty caveats — several, and they matter: (1) An IQ score is not a capability measure for a machine. These tests were normed on humans; a model that scores 136 is not "smarter than 99% of people" in any transferable sense, and the number should be read as performance on this specific instrument, nothing more. (2) The public/private gap is evidence of contamination, but not proof — it could also reflect the two tests having different difficulty or item mix. The site's own framing supports the contamination reading; this board reports the gap and the alternative explanation together. (3) Month-to-month noise is real — models are re-tested continuously and refuse questions occasionally (the log carries a num_refused_questions field); single-month moves of ±6 points are common and should not be over-read. (4) Frontier ≠ typical — this is the best score any model achieved, so it says nothing about the median deployed model. (5) No 2022 coverage: the daily log starts May 2024 and the earliest tracked model dates to Feb 2023; the request for a 2022 start cannot be met from this source and has not been faked. (6) Long-lived endpoint names (e.g. "Gemini Pro") are re-tested as the vendor updates them, so a name's score reflects today's endpoint, not its original release — which is precisely why this chart is built on measurement dates rather than release dates. Cross-refs: §06·D·M (size), §06·D·W (inference-time reasoning), §06·D·R (token usage), §06·F·D (the spend this capability is meant to justify). Not investment advice.
Thirty years of AI firsts, each with a date and a specific verifiable claim. This is deliberately not a list of model releases — §06·D·M already charts those by parameter count, and §06·D·Q tracks scores over time. This tracks things that had never been done before: a machine beating a world champion, a fifty-year biology problem falling, a benchmark built to resist AI being solved anyway. The shape is the point. Eight of these eighteen milestones landed in the last two years, against six in the twenty-three years from Deep Blue to GPT-3 — which is why the time axis below is deliberately stretched rather than linear. And the most recent entry cuts the other way: when a new reasoning benchmark launched in March 2026, every frontier model scored under 1% while humans solved all of it.
Provenance, and the two biases built into any list like this. [cited — dated events]: Deep Blue defeats Kasparov May 11 1997; IBM Watson wins Jeopardy! Feb 16 2011; AlexNet wins ILSVRC Sep 30 2012, cutting top-5 error from ~26% to ~15%; AlphaGo beats Lee Sedol 4–1, Mar 15 2016; “Attention Is All You Need” Jun 12 2017; AlphaGo Zero Oct 18 2017; GPT-3 May 28 2020; AlphaFold 2 at CASP14 Nov 30 2020; ChatGPT Nov 30 2022; GPT-4 Mar 14 2023; Jul 25 2024 AlphaProof + AlphaGeometry 2 score 28/42 (4 of 6 problems) for IMO silver; Oct 8–9 2024 Nobel Prizes in Physics (Hopfield, Hinton) and Chemistry (Hassabis, Jumper, with Baker); Dec 20 2024 o3 scores 87.5% on ARC-AGI-1; Jan 27 2025 the DeepSeek R1 cost shock, with NVDA down ~17% in a session; Jul 2025 Microsoft's MAI-DxO solves 85.5% of 304 NEJM cases versus a 20% average for 21 experienced physicians; Jul 19–21 2025 Gemini Deep Think and an experimental OpenAI model each score 35 points (5 of 6) for IMO gold in natural language under human exam conditions, against 67 golds among 630 human contestants; Mar 2026 GPT-5.4 at 73.3%/83.3% on ARC-AGI-2; Mar 25 2026 ARC-AGI-3 launches with every frontier model under 1%. Bias one — curation. There is no objective list of “breakthroughs.” This one favours events that are dated, singular and checkable, which systematically under-weights slow diffuse progress that mattered more: the steady march of translation quality, speech recognition crossing usability, the compounding of open-source tooling, and the entire history of hardware that made any of it possible. An honest reader should assume the list is incomplete in ways that flatter the dramatic. Bias two — survivorship. A timeline of successes cannot show the failures, and AI has a long record of confident predictions that did not land: two prior AI winters, self-driving timelines missed by roughly a decade, and IBM Watson's own pivot from Jeopardy champion to a healthcare business that was eventually sold off. The 1997 and 2011 entries on this chart are both systems whose commercial successors disappointed — which is worth holding in mind when reading the 2026 entries. Progress on a benchmark is not the same as a durable business, and this board's §08·D exists precisely to measure the gap between the two. Not tracked here, deliberately: model releases and parameter counts (§06·D·M), measured IQ over time (§06·D·Q), token consumption (§06·D), and revenue (§08·D). Cross-refs: §02·D (kill-switches), §06·F·D (the capex this underwrites), §06·D·W (the memory wall these capabilities run into). Sources: DeepMind blog (AlphaGo, AlphaFold, AlphaProof, IMO gold), NobelPrize.org, OpenAI, ARC Prize Foundation, Microsoft Research (Sequential Diagnosis with Language Models), Scientific American, VentureBeat, TechCrunch, and the primary papers. Not investment advice.
Every other model comparison on this board ranks by capability alone. This one asks the question a buyer actually faces: for a given budget, what is the smartest model you can run — and which models are simply never the right answer? A model sits on the Pareto frontier if nothing else is both at least as capable and cheaper. Everything off the frontier is dominated: there exists another model that beats it on both axes at once, so no rational buyer picks it on price-performance grounds. Of the eight frontier-class models with both figures published, five are on the frontier and three are dominated — and the frontier's shape carries the finding: the cheap end is astonishingly cheap, and the last point of intelligence costs more than the first nine combined.
Provenance, and four reasons this frontier moves under you. [cited — single source, both axes]: all sixteen numbers here (eight models × index and cost) come from Artificial Analysis, “Four frontier launches in eight days,” published Jul 17 2026. Using one source for both axes is deliberate — mixing a benchmark from one house with pricing from another is the most common way this chart gets built wrong, because the cost figure has to embed the same task set the score was measured on. Cited values: Claude Fable 5 (max) 60 / $2.75; GPT-5.6 Sol (max) 59 / $1.04; Kimi K3 57 / $0.94; Claude Opus 4.8 (max) 56 / $1.80; Grok 4.5 (high) 54 / $0.31; GPT-5.6 Luna (max) 51 / $0.21; Muse Spark 1.1 (xhigh) 51 / $0.26; GLM-5.2 (max) 51 / $0.32. Frontier membership is computed here, not taken from the source — a model is marked dominated only if another in this set is at least as high on the index and no more expensive. Four reasons to treat this as a snapshot with a short half-life: (1) The index itself was revised after these numbers were published. Artificial Analysis shipped Intelligence Index v4.1.1 on Aug 6 2026, upgrading grader models and one benchmark component — so these Jul 17 scores are pre-patch and the ordering could shift on re-scoring. (2) The frontier moved four times in eight days. The source article exists because Grok 4.5, GPT-5.6, Muse Spark 1.1 and Kimi K3 all launched inside a week, taking the count of labs above index 50 from two to six. Anything built on this chart should assume it is stale within weeks. (3) One model is missing for a good reason. GPT-5.6 Terra (max) scores 55 but no cost-per-task was published, and a Pareto analysis with a guessed coordinate is worse than one with an acknowledged hole. (4) Reasoning-effort settings are part of the identity. “max”, “high” and “xhigh” are different operating points of the same model with different costs and scores; comparing a max-effort configuration against another model's default is not a like-for-like comparison, which is why each label carries its setting. Related but distinct sections: §06·D·M ranks by model size, §06·D·Q by measured IQ over time, §06·D·R by actual usage share on OpenRouter, and §06·D·T by dated capability firsts. This is the only one that prices capability. Source: Artificial Analysis, Jul 17 2026; index revision notice, Aug 6 2026. Not investment advice.
Quarterly shipped capacity by memory/storage type, 2022 → 2030E, in billion gigabytes (Bn-GB) — numerically equal to exabytes (EB). The tiers differ by orders of magnitude (HDD ships ~10× the bytes of all DRAM), so they're split into two charts: compute / active memory below, then storage media beneath it — otherwise HBM would be an invisible sliver next to nearline HDD. This is an industry model, not audited company-reported shipment data.
Scale anchors (real): HDD shipped ~900 EB in 2023 and ~250–300 EB/quarter (Coughlin/Nidec); SSD ~90 EB in Q1 2024, roughly 12× DRAM capacity and on par with total HDD bytes (Yole/Forward Insights); DRAM ~30 EB/quarter with HBM ~6% of DRAM bits (industry estimates); tape a small but growing archive tier. Forward quarters (2026Q3→2030Q4) extrapolate each tier's bit-growth CAGR — DRAM/HBM fastest, HDD steady, tape slow. These are modeled proportions calibrated to published scale relationships, not audited shipment reports. Quarterly splits use observed seasonality. Values are shipped capacity (supply), not installed base. 1 Bn-GB = 1 EB = 1 billion gigabytes. Sources: Yole Intelligence, Forward Insights, IDC, Coughlin Associates, TrendForce, company filings. Not investment advice.
In seven weeks, three of the largest AI labs shipped the same new kind of product: an agent that keeps working after you close the app. Grok Bot (SpaceXAI, beta Aug 11), Muse (Meta, Sep 8) and dots (OpenAI, Sep 29) each run on a cloud computer of their own; Google's Gemini Spark has done so since May, but only for its top subscription tier. This section tracks the race in downloads and active users, for a reason specific to a memory board: an agent consumes several times the tokens of a chat user — Anthropic measured about 4× for a single agent and 15× for multi-agent systems — so agent adoption is a leading indicator for §06·D's token demand, and through it for HBM. Honesty note: almost every figure here is either a company number relayed by the press or a third-party panel estimate. The two are never combined, the metric — daily, weekly or monthly users, or downloads — is printed on every value, and where estimators disagree, all of them are shown.
Provenance & the honesty line. Third-party estimates [panel-modeled, not counted]: Sensor Tower (days to 5M U.S. downloads; Muse at 3.4M by Sep 24), Apptopia (Muse against ChatGPT's first 12 days; Muse at 4.3M by Sep 24), Appfigures (Muse at 2.3M by Sep 24) and Similarweb (average daily users of mobile apps, worldwide, April 2026). They model app-store and device-panel activity, and they disagree — by 1.9× on the same app on the same day — so none is treated as the answer. Company figures [reported second-hand, not audited]: Muse's daily and weekly prompters are internal Meta data reported by The Information; Grok Bot's 418K weekly users come from an xAI presentation reported by Bloomberg; Instinct's 100K is the company's own claim, reported by Fortune and TechCrunch; ChatGPT's 1.2B weekly users (DevDay, Sep 29) and Gemini's 950M monthly users (Alphabet Q2 call, Jul 22) are company statements. Definitions do not line up and are never merged: Similarweb's daily users cover mobile apps only, excluding the web and assistants built into Android; OpenAI's weekly and Alphabet's monthly figures cover every surface; QuestMobile counts AI-native apps in China only (its H1 2026 report puts their combined monthly users at 499M, +85.4% year on year). Muse is available in the U.S. and Canada only. dots launched two days before this build; Gemini Spark has published no usage; ByteDance's Doubao agent ("Spell") is reported to be in testing. Token multipliers [cited, one product]: Anthropic, "How we built our multi-agent research system" (Jun 2025) — the best published figure, but measured on Anthropic's Research feature, not on always-on agents. Cross-refs: §06·D (token demand), §06·D·S (paid subscriptions), §06·D·R (OpenRouter model usage). Sources: TechCrunch, Forbes, The Information, Bloomberg via PYMNTS, Fortune, Similarweb via Forbes, QuestMobile via TechNode, OpenAI, Alphabet, Anthropic. Not investment advice.
A section per maker: past and projected capital expenditure, and a model translating that capex into estimated new memory supply by type. The logic is the industry's own: capex buys wafer-fab equipment and cleanroom; at a known capital intensity (dollars per EB of annual bit capacity) that yields new bits — but with a 6–8 quarter lag, which is exactly why 2025–26's record spend doesn't relieve the shortage until 2027–28.
Summing the per-company models: estimated net new annual bit supply coming online each year (lagged from capex), by type. This is the supply side of the bit-gap — the wave that closes the shortage in 2027–28.
CapEx anchors (real, from filings & TrendForce): Micron FY22 $12.0B → FY23 ~$7.0B (cut ~40% in the downturn) → FY24 ~$8.1B → FY25 ~$13.8B → FY26 ~$27B (revised sharply up; $7.1B in FQ3 alone per the Jun 24 FQ3 report), with further increases planned for FY27; ~$200B US long-term plan (Idaho greenfield output now expected calendar 2028). SK Hynix ~$12.5B (2024) → ~$21B (2025, +30% on HBM) → ~$20.5B (2026E); M15X output begins 2H 2026, Yongin cluster from 2027. Samsung ~$18B → $20B (2026E); P4L expansion. Industry DRAM capex $53.7B (2025) → $61.3B (2026, +14%); NAND $21.1B → $22.2B. The capex figures are well-sourced; the capex→supply translation is a model. Capital intensity is blended and approximate, lag is a simplification (real fab ramps are gradual, not a step), and "efficiency" bundles yield, node mix, and maintenance-vs-growth capex into one factor. Directionally sound — the supply wave is real and lands 2027–28 — but the EB figures are estimates, not forecasts. Sources: company 10-Ks/8-Ks, TrendForce, The Elec, Coughlin Associates. Not investment advice.
The mirror of the section above. §06·F tracks what the memory makers spend to create new supply; this tracks what the buyers spend to create new demand — quarterly data-centre capital expenditure at Microsoft, Alphabet, Amazon, Meta and Oracle. The scale has stopped being comparable to anything in prior computing history: these five spent ~$233B in 2024, ~$406B in 2025, are guiding to ~$808B in 2026, and on current trajectory cross $1 trillion a year during 2027. The single most important datapoint for this dashboard arrived on Jul 30 2026: Amazon raised its 2026 capex from $200B to $220B and told investors the increase was driven by higher memory prices — the supercycle this board tracks, showing up as a line item in a hyperscaler's budget.
Provenance & the honesty line — three data tiers, labelled on the chart. [● cited — disclosed quarterly prints]: Q2 2026 — Amazon $54.2B (vs $32.1B a year earlier), Alphabet $44.9B, Microsoft $41B including finance leases (FQ4, +69% YoY), Meta $31.1B including finance leases; Q1 2026 — Amazon $44.2B, Alphabet $35.7B, Meta $19.84B. Those five firms' Q2 spend alone totals roughly $190B in a single quarter, an annualised run-rate above $760B. [◷ FY guidance — company-stated]: Amazon $220B for 2026, raised from $200B on Jul 30 with CEO Andy Jassy attributing the increase to rising memory prices; Alphabet $195–205B, raised Jul 22 from $180–190B (CFO Anat Ashkenazi: acceleration of capacity delivery); Meta $130–145B, narrowed upward from $125–145B; Microsoft ~$175B for calendar 2026, reduced from ~$190B guided in April; Oracle FY2027 gross $90–95B (net ~$70B after $20–25B of customer prepayments), after $55.7B in FY2026 — itself 2.6× the prior year's $21.2B — funding ~3 GW of new GPU capacity. [modeled]: 2024 and 2025 quarterly bars are apportioned from audited annual totals (~$233B and ~$406B for the five) using a rising within-year ramp; Microsoft's and Oracle's Q1/Q2 2026 bars are likewise derived because their fiscal calendars do not align to calendar quarters (Microsoft's FY ends June, Oracle's May). Four caveats: (1) capex ≠ AI capex — these totals include non-AI data centre, network, office and fulfilment spend (materially so for Amazon), so treat them as an upper bound on AI infrastructure. (2) Fiscal-calendar mismatch — Microsoft and Oracle are re-cut to calendar quarters, an approximation. (3) Finance leases are included by some firms and not others; where a company reports "capex including finance leases" that is the figure used, which inflates comparability slightly. (4) 2027 is extrapolation, not guidance — only Oracle has published an FY27 number; the other four are grown at ~25%, and the "$1 trillion" crossing is this board's arithmetic, not a company statement. Estimates for the combined 2026 figure range $700B–$900B across houses depending on whether Oracle is included and how finance leases are treated; ~$808B here sits mid-range. Cross-refs: §06·F (the supply mirror — memory-maker capex), §06·F·B (backlogs — the contracted revenue this capex serves), §06·F·C (the same spend measured in gigawatts), §02·D KS#3 (a capex cut is the thesis's clearest early warning). Sources: company Q2 2026 earnings releases and calls (Amazon, Alphabet, Microsoft, Meta, Oracle), CNBC, Reuters, Sherwood, Fortune, Statista, Epoch AI, CreditSights, Futurum, FactSet. Not investment advice. See also: §06·F·G, the revenue line underneath all of this spending — whether the cloud businesses these budgets serve are still growing. See also: §06·F·N, the depreciation assumption that decides what this capex actually costs in reported earnings, and §06·F·P, whether the power to run it can physically arrive on schedule. See also: §06·F·O, the optical layer inside these budgets, where two published forecasts for the same component in the same year differ by six times.
J.P. Morgan puts memory at 31% of cloud providers' hardware capex in 2026, 49% in 2027 and 60% in 2028 — from 4–8% in 2022–2025 — before it eases to 47% in 2030. TrendForce goes further: 47% this year and 68% in 2027. This section reads J.P. Morgan's chart off the slide, sets it beside four other estimates, and does the arithmetic the percentages leave out. The five estimates for 2026 run from 30% to 50% because each divides a different numerator by a different denominator — J.P. Morgan itself said about 50% in June and 31% in September. In dollars, the two houses that publish their base are closer than their percentages: $362–433B of memory in 2026, 41–49% of TrendForce's $889B memory market. And both forecasts quietly cut everything else: capex minus memory falls 9% in 2027 in each, while total capex rises 23% (J.P. Morgan) and 50% (TrendForce) — the memory price surge absorbs all of the growth and more. Building this turned up two errors in §06·F·D and a stale headline figure; all three are corrected and logged in §10·B.
Provenance & the honesty line. [third-party, read off a chart]: J.P. Morgan's Figure 50, "CSP capex and AI memory % share" (sources on the slide: company data, Bloomberg Finance L.P., J.P. Morgan estimates), shared on X by @pequityresearch on Sep 12 2026 with the summary that the share reaches 49% in 2027E and 60% in 2028E, up from 31% in 2026. The share labels are printed for every year except 2028E, whose 60% is the post's; the marker centres read 6.7, 3.7, 7.4, 8.2, 31.3, 49.3, 60.2 and 46.9%, within a point of the labels. The bars (OMDIAe CSP H/W capex) carry no labels and are read by pixel measurement against the slide's gridlines, every $500B at 25.14px, each to about ±$20B; the 2030E bar reaches the top of the $3.5T axis unlabelled, so it may be higher. J.P. Morgan's June figures — top-four US CSP capex of more than $575B in 2026 and $860B in 2027, memory's weighting of CSP investment at about 50% in 2026, DRAM prices +246%, +24% and +4% and NAND +217%, +17% and −4% for 2026–28 — via the same account's summary of its WFE note, Jun 22 2026. [cited — research house, primary]: TrendForce, Aug 25 2026 (the nine major CSPs' capex at $272B, $466B, $922B and $1,383B for 2024–27, up 71%, 98% and 50%; DRAM and NAND at 47% and 68% of it in 2026 and 2027; server DRAM contract prices up 64% in 2H25 and about 270% in 2026, enterprise SSD 35% and 235%; 2027 HBM prices +70–140%; price ceilings in some LTAs signed from 2Q26; HBM and RDIMM at 51% of 2026 DRAM bits; 2027 server DRAM and HBM bit supply +27%); Sep 29 2026 (blended HBM ASP +121% in 2027; 8-Hi stacks at a 10–20% premium per Gb); May 29 2026 (memory market $889.3B in 2026 and more than $1.28T in 2027; DRAM $618.7B, +303%; NAND $270.6B, +280.7%); Jan 22 2026 ($551.6B and $842.7B). [cited — company]: Microsoft, Apr 29 2026 (calendar-2026 capex of roughly $190B including approximately $25B from higher component pricing); Meta, Apr 29 2026 (range raised to $125–145B from $115–135B, citing component pricing); Amazon, Jul 30 2026 ($200B to $220B, on memory prices); later moves as in §06·F·D. [cited — reported]: SemiAnalysis, Apr 3 2026, via its post and Tom's Hardware (about 8% in CY23 and CY24, about 30% in CY26, higher in CY27; NVIDIA's below-market DRAM pricing); CLSA via Crypto Briefing, Jun 23 2026 (35% and 48%); Omdia via The Register, Dec 9 2025 (data-centre capex of about $1.6T by 2030, later revised to $1.56T); Paul Kedrosky, Aug 25 2026. [derived — this board's arithmetic]: every memory and everything-else dollar figure (share times that house's own base); the growth rates; the shares of TrendForce's memory market; the volume-and-mix residual (memory growth divided by J.P. Morgan's own DRAM price growth); the ceiling chart; the 2026 shares applied to §06·F·D's $808B. Conflicts, stated: J.P. Morgan's ~50% (June) against its 31% (September) for the same year; three capex bases for 2026 — Omdia's CSP hardware $1.17T, TrendForce's nine CSPs $922B, §06·F·D's five hyperscalers $808B — none of which is a subset of another by construction; TrendForce's 2025 memory market of $235.4B in January against the $224.7B its May growth rates imply; the unlabelled 2030E bar against Omdia's published 2030 forecast; Kedrosky's "the maths don't work" against TrendForce's 68%. Not carried: J.P. Morgan's report itself (only the slide and two summaries of its notes), its definition of "CSP-grade memory TAM", and any 2029 figure; SemiAnalysis's and CLSA's capex bases; any hyperscaler's own memory share, which none discloses. Cross-refs: §06·F·D (the capex base, and the corrected bridge), §06·F (memory-maker capex — the supply mirror), §03·F (contract prices), §05·C (HBM cost per bit), §06·F·V (NVIDIA's memory commitments), §02·D KS#3 (capex that rises in dollars but not in bits). Not investment advice.
§06·F·D shows what the hyperscalers are spending. This shows how they are paying for it — and the answer has changed. For a decade these were famously net-cash businesses that funded everything from operating cash flow; that "unspoken contract" with investors broke in 2025–26. On-balance-sheet financial debt across Microsoft, Alphabet, Amazon, Meta and Oracle has gone from ~$204B to ~$499B in five years, the five sold $159B of bonds in the first five months of 2026 alone (+47% YoY), and Alphabet — long effectively debt-free — now carries $98.2B. But the balance sheet is the smaller half of the story: Moody's counts ~$662B of signed data-centre leases sitting off balance sheet, and Meta has pushed its largest project into a $27.3B special-purpose vehicle whose debt will never appear in its accounts.
Coupon chart — provenance & what is deliberately absent. [cited — each tranche individually reported]: Oracle Sep 24 2025, $18B/7 tranches, coupons reported as spanning 4.45%–6.1% with the 2032 at 4.8%, the 10-year at 5.2% and the 20-year at 5.875%. Meta Oct 30 2025, $30B/6 tranches — 4.20% (5yr), 4.60% (7yr), 4.875% (10yr), 5.50% (20yr), 5.625% (30yr), 5.75% (40yr); $125B of peak orders, the biggest corporate deal of 2025. Alphabet Nov 6 2025, $17.5B USD — 3.875% (2028), 4.10% (2030), 4.375% (2032), 4.70% (2035), 5.35% (2045), 5.45% (2055), 5.70% (2075), plus a floating tranche at SOFR+0.52%. Amazon Nov 17 2025, $15B — 3.90%, 4.10%, 4.35%, 4.65%, 5.45%, 5.55%. Alphabet Feb 9 2026, ~$20B multi-currency — 40-year at 5.75% and the £1B 100-year at 6.125% due Feb 13 2126, priced +120bp over gilts. Amazon Jul 7 2026, $24.923B across 8 tranches, coupons reported as running 4.600% (2029) to 6.250% (2066). What is NOT plotted, and why: (1) Un-reported middle tranches. Oracle's 30-year and the six intermediate tranches of Amazon's July deal had no individually-published coupon in the sources used, so only the reported endpoints appear — the gaps in those ladders are missing data, not missing bonds, and nothing has been interpolated onto the chart to fill them. (2) Floating-rate tranches (Alphabet's 2028 FRN, Amazon's $750M SOFR+58bp) have no fixed coupon and cannot be placed on this axis. (3) Alphabet's €6.5B euro tranches priced the same week as its dollar deal at 2.375%–4.375% — roughly 150bp below the dollar ladder. That gap is currency, not credit, so folding them into the same ladder would have manufactured a fake improvement; they are held out and noted here instead. Alphabet also issued in yen (1.965%–4.599%) and Swiss francs, excluded for the same reason. (4) Microsoft appears in the debt chart above but has no individually-cited AI-era bond tranche in these sources, so it is absent from this chart entirely — an absence of data, not an absence of borrowing. Basis note: the y-axis is the fixed coupon at issuance, which is what the issuer pays for the life of the bond; it is not yield-to-maturity, not current market yield, and not spread over Treasuries. A bond issued at 6.125% and now trading at 90 pence still pays 6.125% on par. Sources: Bloomberg, Fortune, PitchBook, Cleary Gottlieb, Simpson Thacher, cbonds, Law360, CNBC, Forbes, US News, cryptobriefing, IFR, TwentyFour AM, and BofA research as reported. Not investment advice. See also: §06·F·N — a firm that shortens its useful-life assumption reports lower earnings against this same interest bill, which is the configuration in which a capex plan gets revisited. See also: §06·F·R — the same physical assets financed by borrowers who cannot issue investment-grade paper against a balance sheet, only against the hardware and the contracts on it.
Provenance & the honesty line. [cited — reported]: Alphabet total debt $98.165B at Jun 30 2026; Meta $84B at Q1 2026, from zero before its first bond in Aug 2022; Microsoft $26.0B of finance-lease right-of-use assets added over the trailing four quarters; Oracle raised $43B of debt and $5B of equity in FY2026 with ~$40B combined planned for FY2027; the five sold $159B of bonds in the first five months of 2026, +47% YoY, with UBS projecting full-year issuance of $230–240B and the group potentially exceeding 5% of the entire IG index by year-end; 2024–26 issuance totals of roughly Meta $30B, Alphabet $31B, Amazon $24.9B; Alphabet's June 2026 equity raise priced at $84.75B. [cited — off balance sheet, mixed bases]: Moody's counts ~$662B of signed data-centre leases off balance sheet and ~$1.2T of total lease commitments, of which >$820B has not yet commenced; Meta's own 10-Q discloses $182.88B of operating and finance leases not yet commenced at Mar 31 2026; Meta's Hyperion / "Project Beignet" JV with Blue Owl is $27.3B (Blue Owl 80% / Meta 20%; $27B of A+ rated debt plus $2.5B equity, anchored by PIMCO $18B and BlackRock $3B), with Meta retaining operational control and leasing the campus back — converting what would have been capex into operating expense; a further ~$13B SPV is being raised for a Texas site; Oracle's total disclosed obligations reach $273.3B (May 2026). [modeled]: quarterly bars before Q1 2026 are apportioned from annual filings — issuers do not all disclose total debt quarterly on a consistent basis, so read the shape before any single early bar. Four caveats: (1) definition drives the answer — see the box above; the $1.65T figure and this chart are not on the same basis and must not be combined. (2) Finance vs operating leases: finance leases sit inside this chart, operating leases do not, and the boundary between them is an accounting judgement that materially changes reported leverage. (3) SPV debt is genuinely non-recourse in form but economically supported by the tenant's lease — whether that is "hidden" leverage or ordinary project finance is a live argument, not a settled fact, and this board does not adjudicate it. (4) Fiscal calendars differ (Microsoft June, Oracle May), so quarters are calendar-approximated. Cross-refs: §06·F·D (what the debt is funding), §06·F·B (the contracted revenue meant to service it), §02·D KS#3 (financing stress as the leading indicator of a capex cut). Sources: company 10-Q/10-K filings, Moody's, S&P Global, Nikkei, CNBC, FactSet, UBS, CreditSights, Bisnow, Global Data Center Hub, Business Standard. Not investment advice.
This is the revenue line underneath everything else on this board. §06·F·D tracks what the hyperscalers spend, §06·F·B what they have booked, §06·F·E the debt funding it — this tracks whether the business those bets are placed on is actually growing. Twenty quarters, three clouds, year-on-year growth. The shape is a V. All three peaked near or above 40% in late 2021, collapsed through the 2023 "optimisation" trough — AWS bottomed at +12% — and have since re-accelerated hard on AI. The latest quarter reads Google Cloud +82%, Azure +43%, AWS +37%, with all three faster than the quarter before. AWS’s +37% is its best since 2021 by four-tenths of a point — 22Q1 was +36.6% — so the honest statement is that AWS has clawed back to where it was before the digestion trough, not that it has broken new ground. But the most useful fact here is one the percentages hide: at +37% and +82% respectively, AWS and Google Cloud added almost exactly the same number of dollars in the quarter.
Provenance — two tiers, and the difference matters. [cited — verified in this build]: 26Q2: AWS +37% at $42.23B, described as its strongest expansion since 2021, accelerating from +28% in 26Q1; Azure +43%, from +40% in the March quarter; Google Cloud $24.80B, +82%, from +63%. Total cloud-infrastructure revenue $142B in the quarter, +43% YoY, with shares of AWS 28%, Azure 22%, GCP 15%. Google Cloud back-history: 25Q1 +28% ($12.3B), 25Q2 +32%, 25Q3 above 30%, 25Q4 +47.8% ($17.66B), with backlog more than doubling to $240B entering 2026. AWS: FY2024 +19% ($107.6B, first year above $100B), FY2025 +20% ($128.7B), 25Q4 +24% — its fastest in 13 quarters. Azure: FY2025 (to Jun 30 2025) +34%, passing $75B; FY2026 +41%, passing $100B. One independent check on the compiled tier: Amazon described 25Q4’s +24% as its fastest in 13 quarters. That claim was not used to build the series — but reading it back off the finished chart, the last quarter faster than +24% is 22Q3 at +27%, exactly 13 quarters earlier. A cited qualitative claim reproducing itself from independently-assembled numbers is weak evidence the back-history is coherent, and it is offered as exactly that: weak, but real. The one place rounding mattered: AWS 22Q1 is carried at +36.6% (reported $18.44B against $13.50B), not the rounded 37% — because at 37% it would tie 26Q2 exactly and make the cited "strongest since 2021" claim untestable on this chart. It is the closest comparison on the page and the margin is four-tenths of a point. [compiled — lighter provenance]: the quarters before 25Q1 are reported figures assembled from company results rather than individually re-verified in this build, and are drawn without dots. They are widely published and the shape they describe — a ~40% peak in late 2021, a 2023 trough, a 2024 plateau — is not in dispute, but an individual pre-2025 quarter here should be treated as approximate to within a point or two and checked against the source filing before being quoted. Every point's tier is in the tooltip. The Azure series was additionally fitted to reconcile with the two cited fiscal-year figures (+34% FY25, +41% FY26), since Microsoft's fiscal year runs July–June and its quarterly rates must average to them. Three structural caveats: (1) Azure has no level. Microsoft reports an Azure growth rate and, since 2025, occasional annual milestones — but no quarterly revenue. Its line therefore cannot be converted to dollars, cannot be checked by subtraction, and is the least verifiable of the three. (2) The three are not the same product. AWS and Google Cloud report a cloud segment including software and services; "Azure and other cloud services" is a Microsoft-defined grouping inside Intelligent Cloud. Segment definitions differ and none of the three is a pure infrastructure number. (3) Growth rates are not comparable across different bases — the point of the block above — and a decelerating line on this chart can still mean accelerating dollars. Cross-refs: §06·F·D (capex), §06·F·B (backlogs/RPO), §08·G (where the cost lands on consumers), §06·F·E (the debt), §05·D (the supply side), §02·D (kill-switch #3), §06·G (what that capacity rents for), §05·F (the wafers underneath it). Sources: CNBC on AWS 26Q2, Alphabet and Microsoft quarterly releases, Computer Weekly, Data Center Dynamics, Statista. Not investment advice. See also: §10·C, the provenance index, which lists every section's sourcing tier in one table — including this one's.
Truist Securities sizes the AI cloud at $310B of annual revenue in 2025 and $2.09T in 2030 — 6.8 times in five years, 46.5% a year — with the hyperscalers still about 70% of it at the end. This section reads every bar off Truist's slide, rebuilds the 2025 base from the companies' own filings, and carries the path on to 2035. The rebuild is the finding: AWS, Azure, Google Cloud and OCI booked about $290B of cloud revenue in calendar 2025, within 1% of Truist's hyperscaler bar, so "AI cloud ARR" here means the whole cloud businesses that AI now drives, not revenue from AI workloads alone — the clouds' own AI-only figures add up to about $62B (Microsoft above $37B in March, AWS above $25B in June). Truist's engine is gigawatts times price: AI capacity rises from 24 GW to 100 GW, and revenue per gigawatt from $12.9B to $20.9B — where Nebius is pricing new one-to-three-year deals today, and about twice what neoclouds earn per running megawatt. Carried on at the IEA's base-case pace for data-centre power, the path reaches about $3.2T in 2035 ($2.3–5.4T across three scenarios). That last part is this board's model; no published forecast for 2035 was found.
Provenance & the honesty line. [third-party, read off a chart]: Truist Securities' "AI Cloud ARR ($B)" slide from its Nebius initiation (Buy, $355 target; reported by Investing.com on Sep 10 2026), posted on X by @pequityresearch on Sep 27 2026 — totals printed for 2025 ($310B), 2026E ($493B) and 2030E ($2,094B, "~7x in 5 years, 47% CAGR"); the 2027–2029 totals and every hyperscaler / AI-cloud split are read off the bars by pixel measurement, calibrated so the three printed totals reproduce within $1B, and carry about ±$3B. Truist's capacity table (JLL capacity, JLL AI share for 2025 and 2030, interpolated by Truist). Truist's pricing ($20–25M per MW on one-to-three-year deals, $40–50M on shorter ones) and its "$2.1 trillion annual revenue opportunity at the cloud layer" via Investing.com; its four-layer, roughly $8T "AI stack" framing via a Substack summary of the same work, not checked against Truist. [cited — company, primary]: AWS 2025 net sales $128.7B and Q2 2026 $42.2B, and the AI and chips businesses each above a $25B run-rate (Amazon, Jul 30 2026); Google Cloud's $15.5B increase in 2025 (Alphabet 10-K) and Q2 2026 $24.8B; Azure "surpassed $75 billion" (fiscal 2025, up 34%) and "$100 billion" (fiscal 2026), and Microsoft's AI business above a $13B run-rate in the quarter to December 2024 and $37B in the quarter to March 2026 (reported Jan 29 2025 and Apr 29 2026); Oracle's IaaS revenue by fiscal quarter (fiscal 2024–2026 releases; fiscal 2026 $18.1B, up 77%); CoreWeave revenue 2022–2025 and Q2 2026 (§06·F·S); Nebius revenue for 2024–2025, its $3.0B ARR, and the definitions in its Q2 2026 letter ("ACV — Annual contract value"; "revenue recognition basis, excluding any prepayment"). AWS and Google Cloud for 2021–2024 from their annual reports. [cited — reported]: Gartner (Jun 23 2026), Synergy Research (Oct 2025 and Apr 2 2026), Goldman Sachs (Sep 2024), Bain (Sep 23 2025), Bloomberg Intelligence (2023), the IEA's Energy and AI base case (about 945 TWh in 2030 and 1,200 TWh in 2035, within 700–1,700 TWh), and JLL's 2026 outlook (about 200 GW by 2030; AI about a quarter of workloads in 2025 and half by 2030). Astute Analytica's 2035 figure is a market-research vendor's and is flagged low confidence. [derived — this board's arithmetic]: Azure by calendar quarter (fiscal 2025's $75B spread at a constant quarterly pace equal to its 34% growth, then carried with §06·F·G's Azure growth rates — it returns fiscal 2026 at ≈$106B and fiscal 2025's growth at 34.1%, both consistent with Microsoft); OCI by calendar year (fiscal quarters weighted by the months falling in each year, four of them back-solved from reported growth rates and two by difference from annual totals); Google Cloud 2025 as 2024 plus the reported increase; AWS Q4 2025 as the year less Q1–Q3; both run-rates; every ratio, share and growth rate. [modeled]: the three 2031–2035 scenarios and the continued-slowdown cross-check. Conflicts, stated: the label "ARR" against a 2025 bar that behaves like calendar revenue; Truist's revenue per gigawatt against what neoclouds earn per running megawatt; three neocloud forecasts 11 times apart for 2030; Truist dates inference overtaking training to 2026, JLL to around 2027. Not carried: Truist's full methodology (the report itself was not available — only the slide and press summaries); OCI before 2023, and Azure as a reported figure; Google's AI revenue, which Alphabet does not disclose; any 2035 forecast from a primary research house (none found). Cross-refs: §06·F·G (cloud growth rates), §06·F·W (revenue per megawatt), §06·F·S (neocloud growth), §06·F·B (backlogs), §06·I (Nebius's price card), §08·D (AI app revenue), §06·F·P (power). Not investment advice.
This board has tracked what the hyperscalers spend (§06·F·D), what they have booked (§06·F·B), what they borrowed (§06·F·E) and how much power it needs (§06·F·C). It has never tracked the single accounting choice that decides whether any of it was profitable. A GPU bought for $30,000 is not an expense — it is an asset, expensed a slice at a time over an assumed useful life. Choose six years and you book $5,000 of cost a year. Choose three and you book $10,000. Same chip, same cash out the door, double the reported cost. In the four quarters to March 2026 these four firms bought $433.9B of property and equipment and recognised about $149B of depreciation — a 2.9× gap that is pure timing, and whose eventual size depends entirely on a number each company picks for itself. They used to agree. As of 2025 they no longer do: Amazon shortened its assumption and took a charge, Meta extended hers, Microsoft and Alphabet held at six. When the people who own the assets stop agreeing on how long they last, that disagreement is the story.
Provenance & the honesty line. [cited]: the four US hyperscalers purchased $433.9B of property and equipment in the four quarters through March 2026 against roughly $149B of reported depreciation; quarterly combined capex reached $129.8B in 1Q26, +80% YoY; 2026 plans total about $725B (Amazon ~$200B, Alphabet $175–185B, Meta $115–135B, Microsoft $120B+), roughly +77% on 2025. Useful-life history: Amazon, Alphabet and Microsoft all extended servers from a 3–4 year standard to 6 years across 2020–2023, collectively reducing annual depreciation by roughly $18B. The 2025 divergence: Amazon changed a subset of servers and networking equipment from six years to five, effective Jan 1 2025, explicitly citing the pace of AI development, and recognised a $920M accelerated-depreciation charge; Meta extended its estimate further in the same period; Microsoft and Alphabet remain at six, with Satya Nadella publicly saying he did not want to "get stuck with four or five years of depreciation on one generation." The critique: Michael Burry's estimate that shortening to a 2–3 year replacement cycle would cut reported earnings by more than $176B cumulatively over 2026–2028. The rebuttal: Nvidia's position that customers consistently use 4–6 year lives based on observed utilisation and longevity. What is modeled, and it is only one thing: the five bars are straight-line arithmetic on the cited $433.9B — purchases ÷ assumed life — and nothing more. They deliberately do not model reality, which includes assets not yet in service, salvage value, accelerated methods, the ~25–40 year schedules on buildings mixed into the same capex figure, and the fact that servers are only part of PP&E. Read the bars as "what one year of buying costs per year at each divisor", not as a forecast of any company's depreciation line. The teal reference line sits below all five for exactly that reason and is the honest anchor. Three further caveats: (1) the $149B is total depreciation, not server depreciation — it includes buildings and older fleet, so the true server-only lag is larger than the chart implies; (2) useful life is an estimate, not a policy — auditors sign off on it and it is revisited annually, so it can move without anyone acting in bad faith; (3) none of this is an allegation, and the section deliberately prints Nvidia's rebuttal at the same weight as the critique. Cross-refs: §06·F·D (the capex), §06·F·E (the debt), §06·F·B (the backlog), §06·D·M (the model cadence that drives the obsolescence argument), §02·D (kill-switch #3). Sources: company 10-K/10-Q filings and earnings calls, Silicon Analysts, Yardeni, Deep Quarry, Level-Headed Investing, CNBC. Not investment advice, and explicitly not an accounting opinion. Aug 17 2026 — the first live evidence on this question, and it points the other way [§06·D·C]. The A100 is the only generation old enough to test the argument above. Released May 2020, a six-year life has it fully depreciated as of May 2026. It is now past that date, renting at $1.76/hr — up 3.5% year-over-year — at near-full utilisation, and CoreWeave has contracted A100 capacity into 2029. On that evidence the four hyperscalers are over-depreciating, not under, and the Burry case is inverted rather than merely wrong. Three cautions carried in §06·D·C: the contract price is undisclosed, a rental rate is not a residual value, and A100 demand is specifically inference demand.
§06·F·C counts the gigawatts the labs have announced. §06·F·B's second chart infers ~51 GW from the contracted backlog. Neither asks the question this section is about: through which physical route does a gigawatt actually arrive, and in what year? There are only three routes, and all three are congested. The US interconnection queue holds 2,600 GW with a median five-year wait and data-centre requests waiting up to twelve; ERCOT alone is sitting on a 410 GW large-load queue that is 87% data centres. Large gas turbines are sold out — three firms make over 75% of them and an order placed today arrives 2028–2030. Nuclear is real and too late: 9.8 GW committed across thirteen hyperscaler PPAs, the biggest private nuclear procurement since the 1970s, almost none of it delivering before the 2030s. The contracts in §06·F·B assume delivery starting around 2027. The power does not.
Provenance & the honesty line. [cited]: US interconnection queue backlog ~2,600 GW, median time to commercial operation approaching five years with data-centre requests facing up to twelve, and roughly 80% of queued projects withdrawing, mainly over delay and grid-upgrade cost; ERCOT managing a 410 GW large-load queue of which data centres are 87%. Turbines: GE Vernova, Siemens Energy and Mitsubishi Power supply >75% of large gas turbines and all three have backlogs pushing new availability to late 2028 at the earliest; Mitsubishi quotes 2028–2030 for orders placed now; GE Vernova's backlog is 83 GW with deliveries into 2029; lead times generally 3–5 years with premiums paid to jump. Nuclear: every major hyperscaler has signed at least one nuclear PPA by mid-2026, with ~9.8 GW committed across 13 disclosed projects — the largest private-sector nuclear procurement wave since the 1970s; Meta alone has 6.6 GW of firm-power agreements; NuScale is reported to be near a PPA tied to a 6 GW SMR programme with TVA. What is modeled: only the delivery windows drawn on the chart. The bars translate cited lead times into calendar windows and extend past 2032 where the route has no credible end date; they are a reading of the cited figures, not a schedule anyone published. The "contracts assume delivery from ~2027" line is this board's inference from §06·F·B's recognition profiles (CoreWeave's own disclosure puts ~41% of RPO inside 24 months) and should be read as an approximation of a range, not a date. Three caveats: (1) queues are requests, not projects — the 2,600 GW and 410 GW figures overstate real supply by a large and unknown factor, which the block above says plainly; (2) behind-the-meter generation is a real escape hatch this chart does not model — on-site gas, fuel cells and co-located generation bypass the interconnection queue entirely and are how several announced campuses intend to energise, which would move the grid bar left for those specific sites; (3) PPA signature ≠ new capacity — several hyperscaler nuclear agreements contract for output from existing plants or restarts, which delivers far sooner than new build but adds no net generation to the grid. Cross-refs: §06·F·C (GW committed), §06·F·B (the backlog and its implied megawatts), §06·F·L (whether there are enough electricians to install any of it), §05·F (the silicon those gigawatts power), §02·D (kill-switch #3). Sources: RMI, EnkiAI, interconnection.fyi, OilPrice, Forbes, Perkins Coie, NuScale filings, company announcements. Not investment advice. Aug 17 2026 — what a megawatt earns, now that this section covers what one costs to obtain [§06·F·W]. Neoclouds monetise a live megawatt at $9.4–10.4M a year, colocation at $3.5–4.4M, and new neocloud contracts are being priced at up to $40–50M of annual contract value per MW (Nebius's short-term tier; corrected Oct 10 2026 from "total contract value"). Set against the delivery windows above, that is the number deciding how hard anyone will fight for a queue position. Aug 17 2026 — where that capacity physically sits is now mapped [§06·F·T]. US colocation inventory reached 29.0 GW in Q1 2026 at 1.2% vacancy, with Northern Virginia alone about 22% of the country. Siting friction is tracked there as a delivery risk: 225+ local moratoriums across 30 states, New York the first statewide pause, and roughly $8B blocked / $6B delayed since mid-2024.
§06·F·P asks how a gigawatt gets delivered and §06·F·W asks what one earns. This asks where they physically are. US colocation inventory reached 29.0 GW in Q1 2026 — up 22% in a single quarter and 48% year-over-year — at a national vacancy rate of 1.2%. The concentration is extreme: Northern Virginia alone is roughly 22% of the entire country, and four markets are 55% of it. One scope caveat up front, because the request was for "all data centers": no public source publishes megawatts per site for every US facility. What exists is market-level inventory, and even that is only verifiable for four markets. Everywhere else is drawn as a hollow ring — present, unsized, and deliberately not estimated, because a bubble map invites the eye to compare areas and inventing an area would be inventing a fact.
Provenance & the honesty line. [cited]: US colocation inventory 29.0 GW in Q1 2026, +22% QoQ and +48% YoY, with national vacancy at 1.2% and Q1 net absorption of 5.29 GW; market inventories of Northern Virginia 6,485 MW, Dallas–Fort Worth 3,704 MW, Atlanta 3,257 MW and Austin 2,558 MW; vacancy of 0.14% (Austin), 0.18% (Salt Lake City), 0.3% (Northern Virginia) and 1.0% (Atlanta); Q1 absorption of 1,909 MW (Austin), 912 MW (DFW), 538 MW (Chicago) and 480 MW (Northern Virginia); Atlanta with 2,076 MW under construction; Chicago displacing Phoenix for fourth place; the top-seven wholesale markets being Northern Virginia, Dallas–Fort Worth, Silicon Valley, Chicago, Phoenix, New York Tri-State and Atlanta; Reno and Abilene as tertiary markets whose pipelines may outpace some primaries. Separately cited: CBRE's 4,039.6 MW for Northern Virginia and primary-market supply of 9,432 MW (+36%) — a different scope from the 29.0 GW figure and not netted against it. Moratorium figures as set out above. What this map is not: (1) it is not "all data centers" — it is four sized markets and nine placed-but-unsized ones, covering 55% of national inventory by megawatt, with the other 45% shown as an explicit residual in the table. No public source lists per-site megawatts nationally, and this board will not synthesise one. (2) It is not a site map. Bubbles are metro markets placed at a representative city coordinate, not facilities; Northern Virginia's bubble covers a corridor tens of miles across. (3) The coastline is a simplified 50-point outline drawn for orientation only — it is not survey-accurate and no conclusion should rest on a bubble's exact position. (4) Inventory is not the same as AI capacity — colocation inventory includes long-standing enterprise and network workloads, so this map shows where data-centre capacity is, not where accelerators are. [derived]: all share-of-US percentages, the four-market total of 16,004 MW, the residual, and the observation that a 61% MW disagreement renders as a 27% radius difference. Cross-refs: §06·F·P (whether the power reaches these places), §06·F·W (what a megawatt earns), §06·F·S (the operators building them), §06·F·B (the contracts behind the construction), §06·F·L (the labour to build them). Not investment advice.
§06·F·E tracks hyperscalers borrowing against balance sheets that already print tens of billions in cash flow. This section is about the other borrowers — the ones whose collateral is the GPUs. More than $20 billion of loans are now secured on Nvidia hardware, and the lenders are not venture funds but BlackRock, Blackstone, PIMCO, Carlyle, JPMorgan and Macquarie. The number this section is actually built around is smaller and stranger: CoreWeave closed one facility in March 2026 rated A3 — investment grade — and another in May rated Ba2, five notches lower and firmly high yield. Same borrower. Seven weeks apart. Which means neither rating is really a judgement about CoreWeave. The market is pricing the offtake contract behind each facility, so a neocloud's cost of capital is set less by its own creditworthiness than by whose name is on the customer side of the lease — and that is a materially different risk than a spread alone would suggest.
Provenance & the honesty line. [cited]: more than $20B of loans outstanding collateralised by Nvidia GPUs across the neocloud sector; CoreWeave carrying above $21B of total debt in early 2026 at roughly 11% average interest; Crusoe with $425M of GPU-backed debt; Lambda having securitised $500M in what is described as the first GPU-backed asset-backed security. [cited — 8-K disclosed]: CoreWeave closed an $8.5B delayed-draw term loan on March 31 2026, reported as the first HPC-infrastructure DDTL to achieve investment-grade status at A3 / A (low); and a $3.1B delayed-draw term loan on May 18 2026, reported as the first publicly syndicated HPC-backed DDTL, rated Ba2 (Moody's) / BB+ (Fitch). Lenders named across the sector include BlackRock, Blackstone, PIMCO, Carlyle, JPMorgan and Macquarie. What is inference, not fact: the explanation that the five-notch gap reflects collateral structure rather than borrower credit is this board's reading. It is well supported — the facilities differ in security and syndication, and both ratings were published within one quarter on one obligor — but no rating agency has published a side-by-side rationale, and the agencies differ (Moody's rated one, Fitch and DBRS the others), so part of the apparent spread may be agency methodology rather than structure. Treat "5 notches" as exact and the explanation for it as argued. What is deliberately absent: (1) any residual estimate for undisclosed borrowers — the >$20B market figure minus the three disclosed names is not printed. Beyond the usual objection that both inputs are approximate, they are not the same measure: CoreWeave's $21B is total debt while the market figure counts only GPU-collateralised lending, so the disclosed rows sum above the market-wide total. That subtraction is not merely imprecise, it is invalid, and this section says so rather than quietly omitting the row; (2) any default-probability or loss-given-default estimate, which would require loan-level terms nobody has published; (3) Nvidia's own backstop arrangements, widely discussed as "circular financing", are named here but not quantified, because the disclosed terms are too fragmentary to size honestly. One thing to watch: if a neocloud refinances at a materially wider spread than these two prints, that is the first hard market signal on this board that the collateral is being repriced. Cross-refs: §06·F·E (hyperscaler debt, for the cost-of-capital contrast), §06·F·N (the depreciation assumption that values the collateral), §06·F·B (CoreWeave and Nebius RPO — the contracts behind the paper), §06·F·D (capex), §02·D (kill-switches). Sources: SEC 8-K filings, Moody's, Fitch, DBRS, company announcements, sector analyses. Not investment advice. Aug 17 2026 — the growth side of the same companies is now at §06·F·S, and it supplies an unexpected check on the numbers above. CoreWeave's Q2 residual below the operating line is $577M; the >$21B at ~11% cited here implies $578M of interest a quarter. A 0.1% match — which cannot all be interest, since the residual also carries tax, but is close enough to suggest the debt and rate on this page are about right.
§06·F·R covers how the neoclouds are financed. This one covers how fast they are growing — and the two turn out to be the same story told from opposite ends. Q2 2026: Nebius +454%, CoreWeave +112% to $2.58B, Cerebras +74%. On a16z's reading it took CoreWeave about 25 quarters to reach the $2.6B AWS did not hit until its 40th. All three lost money doing it. The cleanest illustration is CoreWeave's own quarter: a 59% adjusted-EBITDA margin and a $626M net loss are the same three months. Everything in the $2.14B between those numbers is depreciation on the GPUs (§06·F·N) and interest on the debt that bought them (§06·F·R). The origin story matters too: most of these firms were crypto miners, and what AI repriced was not their chips but their power rights — the same pattern as railroad corridors becoming Sprint's fibre network.
Provenance & the honesty line. [cited — Q2 2026 results, reported Aug 12–14 2026]: CoreWeave revenue $2.575B (from $1.212B), operating loss $49M (from $19M income), adjusted operating income $128M, net loss $626M (from $290M), adjusted EBITDA $1.51B (from $752M); Q2 capex $9.4B with FY26 guided $35–39B; active power up ~500MW in the quarter (300MW in June) to 1.5GW across 51 data centres, eight added in 2026, year-end target 1.85GW, contracted power 4.2GW and a 8GW by 2030 target; revenue backlog ~$104B at Jun 30 plus >$25B of net new commitments in early Q3; managed inference ARR $1M → >$100M, guided to ≥$250M exiting 2026. Nebius revenue $582.3M (+454%), AI-cloud revenue $575M (+514%), adjusted EBITDA +$236.2M (from −$21M), adjusted net loss $33.2M (from $91.5M), net income −$190.4M (prior-year +$584.4M was largely equity-revaluation gains), operating costs $216.3M → $758.2M; four deals above $1B each at $20–25M of annual contract value per MW; +1GW contracted in the quarter, 5GW targeted by end-2026, >1GW of compute deployed per year from 2027; production inference workloads more than tripled. Cerebras GAAP revenue $180.1M (+74%), cloud and other services $126.0M (+281%), gross profit $25.56M (down from $32.1M), net loss $450.4M (from +$309.5M), adjusted EBITDA −$53.1M; >600MW live and contracted for delivery by end-2027. [derived — this board's arithmetic]: the CoreWeave bridge (+$1,510M → −$1,559M → −$49M → −$577M → −$626M) is subtraction from the four reported figures, not a disclosed breakdown, so the $1,559M and $577M steps are residuals and each bundles several line items. Net margins (−24.3% / −32.7% / −250.1%), Cerebras' 14.2% gross margin, CoreWeave's 58.6% adjusted-EBITDA margin, $24.8M per MW, $6.9M per MW-year and the ~3.6-year implied duration are all computed here. Nebius' and Cerebras' prior-year revenues are back-solved from the disclosed growth rates rather than read from a filing. What is not attempted: (1) no forward projection — one quarter of triple-digit growth does not extrapolate, and this section deliberately has no 2027 or 2030 line; (2) no valuation view, though a16z's own note that sales multiples are "not the best choice for capital-intensive businesses" is worth carrying; (3) no attempt to net the three companies into a sector aggregate, since Cerebras sells its own silicon while the other two rent Nvidia's, and the models are not comparable. Source-chain note: the quarter-count comparison originates in an a16z newsletter chart whose underlying early-hyperscaler figures its author calls estimates; it is reproduced as commentary, not as a measurement. Cross-refs: §06·F·R (how the growth is financed), §06·F·N (the depreciation inside the bridge), §06·F·B (the RPO and megawatt conversion this validates), §06·F·P (whether the contracted gigawatts can actually be energised), §06·G (what an accelerator-hour rents for), §05·F (the silicon underneath). Not investment advice. Aug 17 2026 — the per-megawatt view is now its own section [§06·F·W], and it corrects a unit error in the widely-shared version of this comparison. Neoclouds earn $9.4–10.4M per active MW-year against colocation's $3.5–4.4M — that ratio holds. But SpaceX's much-quoted $30–50M is contract value, not annual revenue; divided by a three-to-four-year term it becomes $7.5–16.7M per MW-year, inside the neocloud band. Nebius' newest deals, at $40–50M/MW, are annual contract value: per year, they sit four to five times above the neocloud band (corrected Oct 10 2026, §10·B).
Power is the binding constraint (§06·F·P), so revenue per megawatt is the natural efficiency metric for everyone building into it. A widely-shared comparison puts the neoclouds at roughly $9–10M per active MW-year against colocation at $3.5–4.4M, and then adds SpaceX at $30–50M to argue the neoclouds have a long way still to climb. The first comparison is sound. The second is a unit error. The neocloud and colo figures are annual revenue over live capacity; SpaceX's is total contract value over contracted capacity. Divide it by a three-to-four-year term and it becomes $7.5–16.7M per MW-year — inside the neocloud band, not three to five times above it. Nebius's own price tiers belong on the annual side: its Q2 letter defines them as annual contract value, so its newest short-term deals, at $40–50M per MW a year, are priced at four to five times what any operator earns on a running megawatt today. This section plots the two units on separate axes so they cannot be read across. Corrected Oct 10 2026 — until then this section read those tiers as total contract value (§10·B).
Provenance & the honesty line. [cited]: annualised revenue per active megawatt of IREN $10.4M, CoreWeave $9.8M, Nebius $9.4M, Equinix $4.4M, Coresite $3.6M, Digital Realty $3.5M; SpaceX quoted at $30–50M per MW on a contract basis. Nebius annual contract value (ACV) per MW stepping from ~$12M (2026 base) to >$20M (Q2) to $40–50M (new, signed the week before the Q2 report) — per year, on a revenue-recognition basis excluding prepayments, per the footnotes to Nebius's Q2 2026 letter; ARR $3.0B at end-June, +58% QoQ; four agreements above $1B TCV each; total contract value won 4× the prior quarter; contracted-power guidance raised at five consecutive reports to 5GW. Hyperscalers: Microsoft, Amazon, Google and Meta controlling about 13GW of live IT capacity in North America (~42% of the region) with ~7GW under construction; Google Cloud revenue $24.8B in Q2 2026. [derived — and this is where the popular version goes wrong]: the neocloud-to-colo ratio of 2.6× is a like-for-like comparison and holds. The SpaceX comparison does not: $30–50M of contract value ÷ a 3–4 year term = $7.5–16.7M per MW-year, which overlaps the neocloud band rather than sitting 3–5× above it. The term is this board's assumption, taken from §06·F·S's implied ~3.6 years, because SpaceX's contract duration is not disclosed anywhere — if the true term were one year the original comparison would stand, and if it were five years the neoclouds would already be ahead. The conclusion is therefore about the unit mismatch, not about who is winning. CoreWeave's $6.87M / $9.80M / $10.30M range is computed from its own disclosures ($2.575B quarterly revenue annualised, against 1.0GW opening, ~1.05GW average and 1.5GW closing capacity); Nebius' ~248MW active and the 20:1 ratio are implied from $9.4M and $582.3M. What is not attempted: (1) no hyperscaler figure, for the two reasons set out above — the Google illustration is deliberately shown as a range across assumptions rather than a number; (2) no ranking across panels A and B, which is the entire point of separating them; (3) no forecast of where per-MW pricing settles. Source note: the per-MW figures come from an investor's public analysis rather than company filings, and the underlying active-megawatt denominators are not published by the companies — treat the levels as indicative and the ordering within panel A as the robust part. Cross-refs: §06·F·S (the Q2 results underneath these ratios), §06·F·B (the RPO-to-megawatt conversion this anchors), §06·F·P (why megawatts are the binding constraint), §06·F·R (the debt secured on them), §06·D·C (what a GPU-hour rents for), §06·G (rental pricing). Not investment advice. Aug 17 2026 — §06·F·T maps where these megawatts are, and the concentration matters for the ratios here: four markets hold 55% of US colocation inventory, at a national vacancy rate of 1.2%. Revenue per MW is being earned in a market with almost no slack. Oct 10 2026 — correction [§10·B]. Until this build the three Nebius tiers sat on panel B as total contract value, and the summary said its newest deals "match or exceed" SpaceX's contract figure on the same basis. Nebius's Q2 letter defines them as annual contract value, so they now sit on panel A; on a per-year basis its short-term tier is 2.4–6.7 times SpaceX's converted $7.5–16.7M. The SpaceX and colocation conclusions are unaffected.
A GPU cluster is not a pile of GPUs; it is a network with accelerators attached, and past a few thousand nodes the optics become the constraint rather than the plumbing. This section tracks that layer — and leads with a disagreement rather than a number. Two published forecasts for how many 1.6T optical modules ship in 2026 differ by roughly six times, one saying above 30 million and the other above 5 million, and they do not even agree on what 2025 was. Neither is averaged here. Both are drawn, both are attributed, and the gap between them is the honest state of knowledge about the fastest-growing component category in the build-out. Underneath that, the architectural shift is real and dated: co-packaged optics moved from conference slide to shipping product, with Nvidia's Quantum-X Photonics introduced in early 2026 and Broadcom's third-generation CPO announced the previous October, both at 200 Gb/s per lane, both keeping the lasers outside the package because lasers are still the part that fails.
Provenance & the honesty line. [cited — and mutually contradictory]: one supply-chain source puts 1.6T module demand at 1.8M units in 2025 rising above 30M in 2026, with Nvidia over 60% of it and Google and Meta the remainder; a separate module-market forecast puts the same category at ~2.7M in 2025 and above 5M in 2026. This board cannot reconcile these and does not try — the likeliest explanations are different definitions of "1.6T" (module count versus 800G-equivalent lanes, or demand booked versus units shipped), but no source states its definition clearly enough to test that, so both are plotted as published. Market size, also from more than one basis: a data-centre optical module market of $22.8B in 2026 of which $14.6B is 800G and 1.6T, alongside an Ethernet-optics figure of $26B in 2026 against $16.5B in 2025 — overlapping but not identical scopes, printed rather than merged. Growth rates cited as +93% (2024), +82% (2025), +65% (2026f), with demand running roughly 30% above supply and shortage relief expected by end-2026. A second, smaller inconsistency, found while building this section and left visible rather than tidied: the same tracker's own totals of $16.5B → $26B imply +58%, not the +65% it forecasts elsewhere. The likeliest cause is that the totals and the growth rate come from different vintages of one forecast, but nothing published says so — so the stat card above prints the arithmetic the totals actually support and names the gap rather than picking a side. Hardware [cited, vendor-disclosed]: Nvidia Quantum-X Photonics introduced early 2026, 115 Tb/s per switch across 144 ports at 800 Gb/s, package built by TSMC combining 18 silicon photonics engines, 324 optical connections and 288 data links from 36 laser inputs, on the COUPE process that 3D-hybrid-bonds the electronic die onto the photonic die; Quantum X800-Q3450 CPO using 72 optical engines at 1.6 Tb/s; Spectrum-X Photonics for Ethernet; Broadcom's third-generation CPO announced October 2025 at the same 200 Gb/s per lane, Ethernet only. What is a vendor claim, not a measurement: the 3.5× power reduction versus pluggable transceivers is Nvidia's own figure at its own comparison point and has not been independently benchmarked on this board. Treat it as a directional claim. What this section does not contain: any estimate of optics as a share of cluster capex, because the unit forecasts disagree by 6× and multiplying an unstable volume by an unstable ASP produces a number with no information in it. One structural caution: a $100B figure for AI-cluster optics by 2030 circulates widely and originates as a question posed in a newsletter headline rather than as a published point forecast — it is noted here only so that readers who encounter it know its provenance. Cross-refs: §06·F·B (the backlog these networks serve), §06·F·D (the capex line they sit inside), §05·F (the accelerators being connected), §06·F·P (the power the saving is measured against), §03·F (the same "shortage clears next year" pattern in DRAM). Sources: DIGITIMES, LightCounting, Nvidia, Broadcom, TSMC, IDTechEx, SemiAnalysis, Lambda, The Register. Not investment advice.
Follow this board's chain to its end and it stops at a person holding a conduit bender. §06·F·D counts $808B of hyperscaler capex; §06·F·C counts ~31.5 GW of committed compute; §06·D·P converts token demand into gigawatts of continuous load. None of that becomes energised capacity — and none of it buys a single HBM stack — until somebody physically wires it. Electricians are the last mile of the AI trade, and both Nvidia's and Microsoft's leadership have named the shortage as a binding constraint on data-centre expansion. This section tracks the openings data honestly, which means leading with an inconvenience: the official openings series does not show a boom.
What is consistently reported is the direction: record organising, with 24,000+ new members added in a single year — described by the union as its biggest one-year growth in more than 50 years — against a 1972 peak near 1,000,000. For quarterly-resolution membership you would need the LM-2 microdata at olmsapps.dol.gov, which is annual by construction.
Provenance & the honesty line. [cited — official, continuous]: the chart is BLS Job Openings and Labor Turnover Survey, Job Openings: Construction (FRED series JTS2300JOL), seasonally adjusted, monthly, level in thousands, series running Dec 2000 → May 2026 (last updated Jun 30 2026). Annual averages computed here from the full monthly record; 2026 = Jan–May average (5 months) and is flagged partial. Reference points from the same series: monthly peak 457K (Mar 2022), monthly trough of the recent cycle 135K (Dec 2024), latest print 298K (May 2026). [cited — occupation level, different bases]: 818,700 electricians employed (BLS Occupational Outlook Handbook, 2024); ~81,000 projected openings per year 2024–34 and +9% employment growth (BLS Employment Projections — note this is an annual flow including replacement demand, a fundamentally different quantity from a JOLTS point-in-time stock, and the two must not be plotted together); ~20,000 retirements a year with ~30% of union electricians near retirement age (Manufacturing Institute / Deloitte, 2024); commercial apprenticeship applications ~70,000 → 120,000, 2022–24; IBEW Local 26 membership roughly doubled since 2018 to ~14,700. [conflicting — shown, not reconciled]: the AI-attributable shortfall is variously ~300,000 (Fortune), 439,000 (ITIF) and 499,000 (iRecruit, data-centre construction specifically) — three different scopes and horizons; this board reports the range rather than choosing. Wages [cited, wide dispersion]: IBEW journeyman $56–78/hr all-in vs $42–62/hr non-union straight time; data-centre specialists $150K+ with travel and overtime, and quoted packages of $240–280K at the top end; Northern Virginia and the Bay Area lead, with Phoenix, Dallas and Columbus rising fastest. Four caveats: (1) The headline series is not the requested one. JOLTS measures all construction; no public monthly series isolates electricians, so the chart proxies the sector and says so. (2) Aggregate ≠ segment — residential weakness and data-centre strength offset inside the same number, which is why the trend is misleading on its own. (3) Job-board counts are not comparable to JOLTS: postings can be duplicated, stale, or aggregated across sites, and none of the major boards publish a consistent historical archive. (4) Shortfall estimates are advocacy-adjacent — they come from industry and think-tank sources with a stake in the answer, and their definitions differ; treat the range as a range. Cross-refs: §06·F·C (the gigawatts these workers must energise), §06·F·D (the capex funding it), §06·D·P (the power the tokens demand). Sources: U.S. Bureau of Labor Statistics (JOLTS via FRED; Occupational Outlook Handbook; Employment Projections), IBEW, Manufacturing Institute/Deloitte, ITIF, Fortune, iRecruit, Qmerit, BuildForce, Rinvio. Not investment advice.
The demand-side mirror of the capex spend: Remaining Performance Obligations (RPO) — contracted-but-not-yet-recognized cloud revenue, the most forward-looking disclosure these companies make. In 2026 the four big clouds — Microsoft, Oracle, Google, Amazon — collectively carry over $2.3 trillion in backlog, and adding the two listed neoclouds — CoreWeave ($103.7B) and Nebius (~$40B) — takes the six-firm total to $2.470 trillion, up from a few hundred billion a year earlier, as AI mega-deals (OpenAI's Stargate, Anthropic's multi-cloud pacts) landed in the books. This is the contracted demand that, in turn, drives the memory orders. The neoclouds are only ~6% of the total and are the most interesting part of it: CoreWeave's backlog grew 246% year-on-year and is now 20× its annual revenue, against roughly 2–3× for the hyperscalers — the same contracts, on a balance sheet with none of the cash flow behind it. Honesty note: RPO became a standardized disclosure only after the ASC 606 accounting standard (~2018), so a true 10-year history doesn't exist for the early years — those are shown as sparse / not-comparable, not invented.
Customer split is from The Information's May 21 2026 snapshot; Oracle's bar adds a hatched $85B segment for contracts landed after the snapshot, so bar lengths match each firm's latest reported total (Oracle $638B by May 31) rather than misleadingly drawing the current leader shortest.
Provenance & the honesty line. RPO/backlog [anchored — company 10-Q/10-K & earnings, quarterly]: Microsoft commercial RPO (FYE Jun): $141B (Jun'21) → $189B (Jun'22) → ~$224B (Jun'23) → ~$269B (Jun'24) → ~$315B (Mar'25) → $368B (Jun'25) → $398B (Sep'25) → $625B (Dec'25, +110% YoY) → $627B (Mar'26, +99%) → $678B (Jun'26, FQ4 reported Jul 29 2026, +84% YoY). The Q4 print is the most thesis-relevant disclosure in this section: Microsoft stated the entire ~$51B sequential increase came from customers other than the frontier-model companies, and that RPO still grew +25% excluding OpenAI — the first hard evidence that hyperscaler backlog growth is broadening beyond a handful of AI labs. Weighted-average duration is 2.3 years with ~30% recognizable within 12 months (up 37% YoY), so this backlog converts faster than Oracle's. Context from the same print: Q4 revenue $90B (+18%), Azure +43% and past $100B for FY26, quarterly capex and finance leases $41B (+69%) — but CY2026 capex guidance was cut from ~$190B to ~$175B, a datapoint that belongs to kill-switch #3 as much as to this section. Because the disclosed add was non-frontier, this board assigns the full +$51B to the other revenue backlog segment rather than to OpenAI or Anthropic (Microsoft 8-K/IR, FY26 Q4 earnings call, 10-K). Correction from a prior build: an earlier version showed Microsoft ~$112B for 2023 — that was the cloud-only RPO subset; total commercial RPO was ~$224B then. Now fixed. Oracle RPO (FYE May): ~$44B (FY21) → $65B (Dec'22) → $80B (Feb'24) → $98B (May'24) → $99B (Sep'24) → ~$138B (May'25) → $455B (Sep'25, +359% YoY) → $523B (Dec'25, +433%) → $553B (Feb'26) → $638B (May'26, +363% YoY, up $85B sequentially) — the $300B OpenAI/Stargate deal is the anchor (Oracle 8-K/IR, DCD, Cloud Wars). Google Cloud backlog (disclosed from ~2022): ~$52B ('23) → ~$92B (Mar'25) → "nearly doubling quarter on quarter to over $460 billion" ($468B, Alphabet Q1 2026 8-K) → $514B (Q2 2026, reported Jul 22 — up >$50B sequentially, with just over half to be recognized within 24 months; Q2 Cloud revenue $24.8B, +82% YoY, on Alphabet revenue $119.8B +24%). AWS RPO (cited from ~2022): ~$105B ('23) → $189B (Mar'25, +20%) → $364B (Mar'26, +93% YoY), excluding a new $100B OpenAI commitment on top of an existing $38B contract → $496B (Q2 2026, reported Jul 30 — up $132B / +36% sequentially, with triple-digit YoY growth). The same print showed AWS revenue $42.2B, +36.7% YoY — a fifth consecutive quarter of acceleration — with operating margin at 39% (+650bps), on Amazon revenue of $200.6B (+20%) and operating income $27.5B (+43%). The +$132B sequential add is not customer-decomposed, so this board books it to other revenue backlog rather than attributing it to OpenAI or Anthropic; that also means AWS's reported figure now exceeds the old commitment-inclusive $464B, retiring the two-basis flag this section previously carried (Amazon IR, CNBC, Global Data Center Hub, Cloud Wars). Current-snapshot decomposition [The Information, May 21 2026]: Microsoft $280B OpenAI + $30B Anthropic + $317B other ≈ $627B (May snapshot; the Jun'26 FQ4 print took the total to $678B and Microsoft attributed the whole +$51B to non-frontier customers, so it is added to other here — the OpenAI/Anthropic legs are unchanged from the May decomposition and are not re-derived); Oracle $300B OpenAI + $253B other ≈ $553B; Google $200B Anthropic + $268B other ≈ $468B (May snapshot; Google Cloud backlog has since risen to $514B at Q2 2026, reported Jul 22 — the +$46B QoQ add not yet customer-decomposed); Amazon $138B OpenAI + $100B Anthropic + $226B other ≈ $464B at the May snapshot; the Q2 print took the reported total to $496B and the +$132B add is not customer-decomposed, so it is carried in other here (per the reference chart; The Information / JPMAM). The honesty caveats: (1) RPO standardized disclosure dates to ASC 606 (~2018) — pre-2019 figures are sparse / not comparable and are drawn faint, not fabricated. (2) Fiscal calendars differ (Oracle FY ends May 31; Microsoft Jun 30; Amazon/Alphabet Dec 31) — the time axis is approximate calendar-aligned. (3) RPO is contracted revenue, not guaranteed cash — concentration in a few AI customers (OpenAI ~45% of MSFT's, $300B of Oracle's) is real counterparty risk, and skeptics question whether OpenAI can honor ~$60B/yr commitments. (4) The customer decomposition is reporting-derived (The Information), not a GAAP line item. Modeled: the connecting curves between anchored quarters. Sources: company 10-Q/10-K & earnings calls, The Information, JPMAM, DCD, Sherwood, Constellation, Cloud Wars, Global Data Center Hub. Not investment advice. [added Aug 11 2026 — CoreWeave & Nebius, and the chart form changed]: CoreWeave [cited] — revenue backlog ~$16B (Dec 2024), $25.9B (Mar 2025), $30.1B (Jun 2025), >$55B (Sep 2025), $66.8B (Dec 2025, 4× the start of the year), $99.4B (Mar 2026), and at Jun 30 2026 $103.7B of RPO / ~$104B backlog, +246% YoY, on Q2 revenue of $2.58B and FY2025 revenue of $5.13B; ~41% of RPO expected within 24 months; the figure excludes >$25B of commitments signed after the quarter closed. Definitional mismatch, disclosed: CoreWeave reports "revenue backlog" = RPO plus estimated future revenue under committed contracts, a broader measure than the RPO the four hyperscalers report; at Jun 2026 the two are within $0.3B of each other, but the earlier quarters in this series are the broader measure and are therefore not strictly like-for-like with the rest of the stack. Nebius [cited] — contracted backlog ~$40B at Q2 2026, built on Meta up to $27B and Microsoft $17.4B + $2B optional = $19.4B; ARR $3.0B at end-June 2026 (from $1.92B at end-March), Q2 revenue $582.3M (+454% YoY), FY2026 guidance $3–3.4B revenue and $7–9B exit ARR. Source conflict, not merged: the company's own Q2 figure is $40B while secondary coverage cites "nearing $50B" — the larger number sums the headline deal values including optional tranches ($27B + $19.4B = $46.4B). This chart plots the $40B contracted figure and prints both. Nebius's pre-2026 points are sparse and partly reconstructed — it did not quantify a backlog before the Sep-2025 Microsoft deal, so those bars are drawn faded and should not be read as measurements. Chart form: the log-scale line chart was replaced by a linear stacked-bar chart at request. Interpolated quarters, previously implicit in the line between dots, are now drawn at reduced opacity; the underlying per-company series and anchors are unchanged. The stack necessarily mixes fiscal calendars (Microsoft FY ends June, Oracle May, the rest December). Aug 17 2026 — the megawatt conversion now has a direct anchor [§06·F·S]. This section's RPO-to-gigawatt chart rested on an assumed $/MW, which was its weakest input. CoreWeave's $104B backlog over 4.2GW of contracted power implies $24.8M of contract value per megawatt, inside the range used here. Corrected Oct 10 2026: this note originally paired it with Nebius's $20–25M per megawatt as "two firms, one number"; Nebius's figure is annual contract value, a different unit, so CoreWeave's is the only anchor (§10·B).
The RPO section above shows the money; this shows the electricity — publicly disclosed compute commitments by the two frontier labs, in gigawatts. Power is the constraint upstream of everything in this dashboard: a gigawatt committed is land, transformers, accelerators, and — the reason it's here — a fixed ratio of HBM and DRAM that must be manufactured. As of the June 2026 compile, OpenAI has ~18.75 GW committed across Stargate/Oracle, AMD, AWS and Cerebras, and Anthropic ~12.8 GW across AWS, two Google structures and SpaceX — of which ~10.8 GW was added in April–June 2026 alone.
Provenance. Commitment figures are a point-in-time June 2026 compile (The Product Compass / TheWhiteBox Consulting / JPMAM) of publicly announced deals: OpenAI — Stargate with Oracle 10 GW, AMD MI450 6 GW, AWS 2 GW, Cerebras 0.75 GW (≈18.75 GW); Anthropic — AWS 5 GW, Google + Broadcom 3.5 GW, Google 2 GW, SpaceX 0.3 GW atop a ~2 GW pre-April base (≈12.8 GW, ~10.8 GW of it added April–June 2026). Caveats: commitments ≠ deployed draw — these are multi-year build-outs; announced GW definitions vary (IT load vs facility); the compile predates any July deals; and the memory bridge is labeled illustrative with its per-GPU wattage and 192 GB assumptions shown. Not investment advice. See also: §06·F·P, which asks through which physical route — grid queue, gas turbine or nuclear PPA — these committed gigawatts can actually be delivered, and by when.
The memory-side mirror of the hyperscaler backlogs above: with FQ3 (Jun 24), Micron began disclosing remaining performance obligations under a new regime of 16 take-or-pay Strategic Customer Agreements running 2026 through end-2030. Reported RPO at quarter-end was >$5B — but that counts only agreements executed by May 28; including the SCAs signed just after, RPO is ≈ $100B, with 14 of 16 agreements entering the FQ4 reporting. The construction is deliberately conservative: minimum committed volumes at floor pricing (Micron expects revenue to "well exceed" RPO), with price ceilings set at CQ2-2026 levels — which is also a cap worth remembering.
The FCF trap, flagged: the $18B of cash deposits is not prepaid revenue — it flows through financing cash flow and is returned over the back half of each agreement. Adding it to FQ3's record $18.3B adjusted FCF double-counts; this dashboard doesn't. Near-term revenue from the FQ3-closed agreements is small (~$1.8B next 12 months — early automotive deals); the $100B substance arrives with the FQ4 10-K. Read against §06·F·B: cloud buyers now carry ~$2.2T of committed demand, and the memory supplier they depend on has, for the first time, ~$100B of committed supply — floor-priced, ceiling-capped, and deposit-backed. Calendar: Jul 10 2026 — SK Hynix ADR begins U.S. trading; Dec 9 2026 — CHIPS-grant two-year mark unlocks Micron's shift toward returning ~100% of excess cash.
Provenance. All figures from Micron's FQ3-2026 prepared remarks, 8-K press release, 10-Q and earnings call (Jun 24–25 2026): RPO >$5B as reported / ≈$100B including SCAs executed after quarter-end (14 of 16 in FQ4 reporting); 16 take-or-pay SCAs, generally 2026–2030; ~20% of DRAM and ~30% of NAND capacity committed (sources range 30–33% on NAND); $22B of deposits and financial commitments ($18B cash, ~$4B LCs), >$400M received in FQ3 and ~$10B expected in FQ4, returned in the back half of each agreement via financing cash flow; RPO built on minimum volumes at floor prices with ceilings at CQ2-2026 levels, revenue expected to "well exceed" RPO; next-12-month revenue on FQ3-executed agreements ≈$1.8B. Honesty notes: this is the first print of a brand-new disclosure — definitions will firm up in the 10-K; Micron's RPO (floor-priced minimum volumes) is not directly comparable to cloud RPO (contracted service revenue), so the §06·F·B mirror is a framing, not an equivalence. Not investment advice.
The memory-side backlog in §06·F·M has buyers, and this is what the two largest merchant GPU makers have signed. NVIDIA's supply and capacity commitments — what it has contracted to buy from its suppliers and contract manufacturers — stood at $1.16B in January 2020 and $279B on Jul 26 2026, more than 240 times as much. $160B of that arrived in a single quarter, and NVIDIA says the increase was "primarily related to the procurement of memory". The timing is the finding: the amount due in the rest of fiscal 2027 barely moved, $95B → $92B, while the amount due in fiscal 2028 and later went from $24B to $187B — calendar 2027 and 2028, the years the memory makers have been selling forward under long-term agreements (§06·F·M). AMD shows the same turn at about a tenth of the scale: $30.3B at Jun 27 2026, 2.5 times its year-end level and 0.73× its trailing revenue, the highest since at least 2019. NVIDIA's figure is 0.92× its trailing revenue; from 2020 to 2025 it never exceeded 0.38×. These are commitments, not purchases: NVIDIA says some of its agreements can be cancelled, rescheduled or adjusted before firm orders are placed, at a cost, and both companies have paid that cost before.
Provenance & the honesty line. [cited — company, primary]: NVIDIA's commitments notes in its 10-Qs and 10-Ks for every quarter from fiscal 2020 to Q2 FY27, read on SEC EDGAR (the XBRL note pages for Q2 FY26, Q3 FY26, FY2026, Q1 FY27 and Q2 FY27; excerpts of the note text for earlier quarters); NVIDIA's CFO commentaries for Q2 FY26, Q4 FY26, Q1 FY27 and Q2 FY27 (supply-related commitments, cloud agreements, inventory, the July 2026 commitment table, the additional commitments and the guarantees); the Q2 FY23 10-Q on the $1.34B of charges; the Q2 FY27 10-Q's $2,138M of excess inventory purchase obligations ($2,739M at Jan 25 2026). AMD's commitments from its 10-Qs and 10-Ks for 2019 to Q2 2026 (the XBRL facts RecordedUnconditionalPurchaseObligation, UnrecordedUnconditionalPurchaseObligationBalanceSheetAmount and PurchaseObligation; the Q1 and Q2 2026 notes; the Q3 2025 and FY2025 schedules), the $4.1B guarantee, the leases not yet begun, the $9.5B of leases and up to $5.0B of investment commitments signed after Jun 27, and AMD's Q2 2026 release (revenue, guidance, and the $800M MI308 charge in Q2 2025). Revenue and inventory for both companies from XBRL company facts; fourth quarters are the fiscal year less nine months and match the companies' own releases. [cited — reported]: Morgan Stanley's 2027 HBM shares (NVIDIA 37.3%, Alphabet 36%, AMD 12.1%) as reported via The Inference, TechPowerUp and Xenospectrum, which this board reproduces from its own transcription of the same exhibit in §05·C·H; Michael Burry calling the $95.2B "troubling" (Barchart, Sherwood News); the FIREwall's point that $160B is the increase, not the total. [third-party, checked]: the Fiscal.ai chart posted by @StockSavvyShay on Aug 30 2026 — 26 of 27 bars match the filings, and the July 2025 bar is an all-category total. [derived — this board's arithmetic]: NVIDIA's all-category totals before October 2021 (the subtotal plus other obligations); trailing-four-quarter revenue and every ratio; the payment-schedule shares; the stack totals ($84.9B, $137.0B, at least $155.0B and $530.5B for NVIDIA; $34.2B and $53.4B for AMD); the growth statistics; inventory plus commitments against revenue. [modeled]: the $35–42B HBM estimate and its 41–48% share of fiscal 2028 payments. Conflicts, stated: the Fiscal.ai July 2025 bar; the headline pairing 37% of HBM with $279B; the 10-K's $1.16B start against the $1.156B the chart's statistics imply. Not carried: NVIDIA's commitments by supplier or by memory type, and AMD's split between wafers, memory and cloud capacity (neither is disclosed); AMD figures for 2020 and Q1 2021 (its 10-Qs carry none); prepaid supply agreements on either balance sheet. Cross-refs: §06·F·M (the memory-side backlog), §05·C·H (2027 HBM by buyer), §05·C (HBM price per GB), §06·F·B and §06·F·C (cloud backlogs and GW commitments), §06·F·R (neocloud financing), §02·D (kill-switches), §02·E (the next prints). Not investment advice.
What it costs to rent an AI accelerator by the hour is one of the cleanest real-time reads on accelerator supply/demand — and that same tightness is what pulls HBM and memory demand through the whole chain on this page. When GPUs are scarce, rental rates spike; when capacity floods in, they fall. Embedded below is Ornn's Compute Index, a live tracker of historical GPU rental price indices (H100, H200, B200, A100, RTX 5090).
This panel embeds a third-party live website — Ornn's Compute Index (ornn.com) — not data compiled by this dashboard. Its figures, methodology, coverage and uptime are entirely outside our control, and the embed may not render in every context (the Claude artifact sandbox blocks external frames, and a provider can disallow being framed via response headers); a direct link is provided as a fallback. Why it matters here: GPU rental rates are a fast, market-set proxy for accelerator scarcity — sustained high rates signal tight supply (bullish for the memory makers feeding those accelerators), falling rates can foreshadow a demand cooloff. Rental pricing reflects many factors beyond memory (compute supply, financing, power, contract vs spot), so treat it as one signal among the others on this page, not a direct memory-price gauge. Source: Ornn (dashboard.ornnai.com). Not investment advice. Aug 17 2026 — this section embeds a live third-party dashboard but holds no figures of its own. §06·D·C now carries the numbers, including the argument that the best single demand gauge is the A100 rather than the H100, the near-3× premium between hyperscaler and commodity pricing for the identical H100, and the CME/Silicon Data compute futures launching Oct 5 2026.
§06·G embeds someone else's live rental dashboard but carries no numbers of its own. This section carries the numbers, and it takes a specific and unobvious view of which one matters. The best single gauge of AI compute demand is not the H100 rate — it is the A100's. A chip released in May 2020, six years and three months old, still rented at near-full capacity and now used predominantly for inference. Its median on-demand rent rose from $1.70 to $1.76 over the year to August 2026. New silicon has kept arriving the whole time; the old silicon got more expensive. That is hard to explain with a training bubble and easy to explain with broad, price-insensitive inference demand. It also happens to be the cleanest live test of the argument in §06·F·N — because on the six-year schedule Microsoft and Alphabet book, the A100 finished depreciating in May 2026, and CoreWeave has just contracted A100 capacity out to 2029.
Provenance & the honesty line. [cited]: Silicon Data's stated view that the A100 rather than the H100 is the best single gauge of AI compute demand — a May-2020 chip "rented out at near-full capacity", "used predominantly for inference", with steady rates read as a signal of inference-demand strength (Aug 14 2026). A100 median on-demand $1.76/hr, up from $1.70 a year earlier, with a published range of $1.09–$5.07. H100 Silicon Data rental index $2.53/hr; published ranges $1.49–$6.98 and, from a different survey, $2.19–$11.06. H100 hyperscaler on-demand index traded $7.40–$7.52 from Mar 1 to Apr 20 2026 with a 0.47% coefficient of variation. LLM Token Expenditure Index at $4.20 per million tokens (frontier) and $0.85 (open-weight) as of Apr 22 2026. GPU rental market sized at ~$52B in 2026 rising to ~$199B by 2031. CME Group and Silicon Data announced compute futures for Oct 5 2026 (press release, Aug 11). CoreWeave's Q2 call: an A100 contract extending into 2029 at an "attractive price", with older Nvidia generations in solid demand. [derived — this board's arithmetic]: the +3.5% A100 year-over-year, the 70% A100-to-H100-index ratio, the 2.95× hyperscaler-to-commodity channel premium, the 4.9× frontier-to-open-weight token gap, chip ages (6.3 and 3.9 years), and the observation that a six-year life from May 2020 expires in May 2026 — placing the CoreWeave contract about three years beyond the longest booked life. What is deliberately not claimed: (1) no residual-value conclusion — a rental rate is a flow, a depreciation schedule is about a stock, and this section does not convert one into the other; (2) no forecast of A100 rates, because the whole argument is that this series is informative precisely when it is observed rather than modelled; (3) no aggregate "AI compute demand" number — the market-size figures above are third-party and are printed for scale only, not used in any calculation here. Two source problems, stated: the two H100 ranges are irreconcilable and both are drawn; and the token index is dated April, four months older than the rental data on the same chart, so the two are not contemporaneous and should not be read as a single snapshot. Cross-refs: §06·G (the live third-party rental dashboard), §06·F·N (the depreciation argument this tests), §06·F·S (CoreWeave's disclosures, including the A100 contract), §06·D (token demand), §06·D·R (usage share, and the inference exposure), §06·F·R (the lenders a futures market would serve), §02·D (kill-switch #3). Not investment advice.
A rare public, market-clearing price for the AI compute that drives memory demand. EC2 Capacity Blocks for ML let you reserve NVIDIA GPU clusters for fixed windows, and AWS says the reservation fee is "updated periodically based on supply and demand" — so it behaves like a dynamic price for guaranteed accelerator capacity. 2026 has been one direction only. Blocks rose 15% in January (H200), a uniform 20% across every NVIDIA family on Jul 1, and AWS has announced another uniform 15% from Oct 7, with a single global price that ends the cheaper rates outside the U.S. Meanwhile on-demand prices have not moved since AWS cut them in June 2025, so the discount for reserving is collapsing: from Oct 7 a reserved B200 costs 99.8% of the on-demand rate, and an H100 block 87%, down from a 68% discount in March 2025. Only AWS's own Trainium chips and the rack-scale GB200 UltraServer have been left out. Correction (§10·B): this section's H100 on-demand line was 44% too low at every point until this update; it now runs at the true $12.29 → $6.88 per GPU-hour. Source of record: aws.amazon.com/ec2/capacityblocks/pricing.
Provenance. Capacity Block rates [anchored — AWS pricing page, updated Sep 29 2026]: in force since Jul 1 2026 (U.S.): P6-B300 $14.04, P6-B200 $12.355, P5en $6.865 ($6.241 outside the U.S.), P5e $5.97, P5 $5.191 ($4.720 outside the U.S.), P4de $2.214, P4d $1.475, P6e GB200 UltraServer $10.582, Trn2 $2.235, Trn1 $0.596 per accelerator-hour. Announced, effective Oct 7 2026, one rate in every Region: P6-B300 $16.146, P6-B200 $14.208, P5en $7.895, P5e $6.866, P5 $5.970, P4de $2.546, P4d $1.696 — each exactly 15% above the current U.S. rate, and 26.5% above the current non-U.S. rate for P5 and P5en. AWS states that all other block prices, and fixed models such as on-demand and Savings Plans, are unchanged. The Jul 1 change was itself a uniform 20% (each Jul rate is exactly 1.2× the May 13 rate this section carried). On-demand [anchored — AWS price list via aws-pricing.com, Sep 26 2026]: p5.48xlarge $55.04/hr ($6.88/GPU) in U.S. East and Oregon, cut 44% on Jun 1 2025 from $98.32 ($12.29/GPU); p6-b200.48xlarge $113.9328 ($14.24/GPU); p6-b300.48xlarge $142.416 ($17.80/GPU); p5en.48xlarge has no U.S. on-demand rate (Stockholm $67.7267, $8.466/GPU). Earlier Blackwell on-demand rates were not verified and are not drawn. History [dated anchors]: Capacity Blocks launched Oct 30 2023 (P5 only, US East Ohio, ~$60+/hr per instance); AWS's own pricing-page example of Mar 20 2025 puts a p5.48xlarge block at $31.464/hr ($3.933/GPU) — a Capacity Block figure; P6-B200 joined May 15 2025; H200 blocks rose 15% in January 2026 (P5e $34.61 → $39.80/hr, The Register). AWS cut block prices "once in 2024, and twice in 2025" (IT Pro/The Register) but published no dates or levels, so the H100 block line between March 2025 and May 2026 is interpolated. Caveats: blocks are sold as a fixed total per reservation (minimum one day), shown here as AWS's effective hourly rate; an OS fee is extra; on-demand includes no capacity guarantee, which is what a block buys — so parity on price is not parity on value. Correction: before Oct 1 2026 the H100 on-demand line used a CloudOptimo article that misreads a $3.8592/hr operating-system fee as the instance price, and plotted AWS's Mar 2025 Capacity Block example as an on-demand price; see §10·B. Source of record: aws.amazon.com/ec2/capacityblocks/pricing. Not investment advice. See also: §06·G (what an accelerator-hour rents for across providers) and §06·D·C (the A100 rental gauge — the same chip whose reservation price rises 15% on Oct 7).
One neocloud's public rate card, read line by line. Nebius is one of the few AI clouds that publishes a self-serve price for a single GPU-hour, and in 2026 that price has moved only one way. After holding the H100 at $2.95 for at least eighteen months, Nebius raised on-demand rates by about 29% on average in May and by another 17–21% on Oct 1: the H100 is now $4.50, the H200 $5.40, the B200 $8.50 and the B300 $9.50, against roughly $6.10 for the B300 before the May rise. Every Hopper and Blackwell GPU on the card is 53–56% dearer than before May, the four-year-old H100 included. The steepest single line on the October card is not a GPU but memory: RAM on Nebius's CPU-only instances rose 40.6% per GiB-hour. On Oct 8 the fixed preemptible discount became a floating spot market. These are on-demand list prices; Nebius's large contracts, such as Meta's and Microsoft's, are priced separately and untouched, so read this as a scarcity signal, not a revenue forecast.
Provenance & the honesty line. [cited — Nebius, primary]: on-demand and spot rates from Nebius's price page (read Oct 9 and rechecked Oct 10 2026) and its compute-pricing documentation (read Oct 9), which lists each rate "before October 1, 2026", "from October 1 until October 8" and "from October 8". Preemptible switched to dynamic spot on Oct 8, with a cap one cent below the on-demand rate and a floor of $0.79 for H100, H200 and RTX PRO 6000 and $0.99 for B200 and B300; L40S preemptible stays flat. Commitment discounts of "up to 35%" on multi-month large clusters (price page). The Oct 29 2024 Explorer Tier post, which bills hours beyond the promotion at "the standard on-demand price of 2.95 USD per hour", and the Nov 1 2024 press release. The Q1 2026 shareholder letter (May 13: strong pricing support for older GPUs; server and network equipment life lengthened from four to five years from Q1 2026; RTX PRO 6000 added with Aether 3.5) and the Q1 call (Boroditsky on raising prices and selling out). [cited — reported]: the Jun 15 2026 rate card (UsagePricing capture: B300 $7.85, B200 $7.15, H200 $4.50, H100 $3.85, RTX PRO 6000 $1.80, L40S from $1.55; preemptible H100 $2.15, B200 $3.95); the Sep 17 customer notice and its figures (Odaily via KuCoin, BigGo Finance, HostingJournalist, shattered.io), with the price page carrying the dated October column by Sep 24 (UsagePricing); the May rise of about 29% on-demand and 51% preemptible on average (Yahoo Finance coverage as cited by BigGo Finance and shattered.io); the pre-May H100 $2.95 and B300 $6.10 (Foreign Policy Journal, Sep 29; BigGo Finance); the Goldman Sachs conference remarks (Odaily). CoreWeave node prices from coreweave.com/pricing, North America, read Oct 9 2026. The chart Beth Kindig (I/O Fund) posted on X on Oct 7 matches Nebius's own page to the cent. [third-party, low confidence]: the pre-May H200 $3.50 and B200 $5.50 come from a Deploybase rate list whose page is dated Dec 8 2025 but cites Nebius documentation "as of March 2026"; Spheron independently lists $5.50 as the B200's "May 2026 published" rate. Both are hatched or hollow on the charts, and the H200 and B200 cumulative figures depend on them. [derived — this board's arithmetic]: every percentage; the two averages (+29.4% in May, +19.2% on Oct 1); the spot caps; CoreWeave's per-GPU rates (node price ÷ 8); and the price page's "from" figures rebuilt from the documentation's per-unit rates — L40S $1.35 + 8 vCPU + 32 GiB = $1.548 with an Intel host (shown as $1.55) and $1.817 with an AMD host (shown as $1.82); Genoa 4 vCPU + 16 GiB = $0.099 → $0.132 (shown as $0.10 → $0.13); Ice Lake 2 vCPU + 8 GiB = $0.050 → $0.060. The preemptible L40S "from" figures do not rebuild as cleanly ($0.749 against $0.74 listed with an Intel host, $0.884 against $0.90 with an AMD host), so they are not used. Conflicts, stated: Spheron wrote on Sep 21 that the B300 showed "contact required" with no self-serve rate; the Jun 15 and Sep 24 captures and Nebius's own documentation show $7.85, which this board uses. One outlet dates the announcement Sep 28; the customer notice was reported on Sep 17, and that is the date used here. Spheron's B200 page dated Jun 20 still lists $5.50, which is stale against the Jun 15 capture of $7.15. A report of "memory-backed instances +41%" is narrower in Nebius's documentation: the 40.6% applies to RAM on CPU-only platforms, and GPU instances' bundled host memory is not itemized. Not carried: spot price history (console only), negotiated or committed prices, GB200 and GB300 rack pricing, and any 2025 rate change — none was found, which is evidence of absence only as far as the sources searched. Cross-refs: §06·H (AWS Capacity Blocks), §06·D·C (the A100 rental gauge), §06·G (live third-party rental index), §06·F·S and §06·F·W (Nebius's growth and revenue per megawatt), §06·F·N (GPU useful life). Not investment advice.
Below the chips, AI's data exhaust drives the other half of the supercycle: enterprise SSDs for hot data and a hard-drive oligopoly for the cold tier. Both are sold out.
Source: TrendForce Q4 2025 (approx.) · SK Hynix incl. Solidigm
The flash field is a layer race. Kioxia and SanDisk — allied for 25+ years across the Yokkaichi and Kitakami fabs even after SanDisk's spinoff — co-developed BiCS FLASH and are pushing past 300 layers in 2026. Enterprise SSD demand is the swing factor: SK Hynix's NAND revenue jumped ~48% in a single quarter on it.
From 200+ makers in the 1980s to three today. Nearline drives are ~90% of industry exabytes, and both WD and Seagate have sold out 2026 with agreements into 2028 — sending consumer drive prices up ~50% in five months. HDDs still win on cost-per-terabyte, so AI's cold data keeps the platters spinning.
The supercycle isn't only a datacenter story. AI is pushing memory into physical machines — and the standout is the self-driving car, which can carry more memory than a server. A robotaxi fuses lidar, radar and camera feeds in real time, runs neural nets on-board, logs everything for safety, and duplicates it all for redundancy. Robots and AR/VR add to the pull. None of this rivals datacenter volume yet, but it's the fastest-growing new demand — and, like phones, it's already feeling the squeeze (Meta raised Quest prices ~20% on the memory crunch).
Estimated DRAM + NAND shipped into these three categories per year, in exabytes. Automotive dominates — it's the only one at real scale today — but robotics is the steepest curve on a tiny base. Switch between viewing the stack by category or by memory type (DRAM vs NAND). Solid = historical estimate; hatched = projection.
Edge memory is a modeled estimate (units × per-device memory content), and the three legs differ sharply in reliability. The DRAM/NAND split is itself an assumed ratio, not a separately-sourced series: by capacity (bits), storage dominates, so NAND is taken at ~86–93% of automotive and AR/VR content (rising slightly over time as autonomy/logging grows) and ~65–72% for robotics (which is more DRAM/HBM-weighted because real-time inference is bandwidth-bound). DRAM is far smaller in bits but higher-value per bit, so its revenue share is much larger than its capacity share shown here. A third type — NOR flash (boot code/firmware in ECUs) — is real in automotive but tiny by capacity and isn't charted separately. Automotive is the most solid leg: per-car content ~90GB (2023) → ~278GB (2026) rising toward 2–4TB on high-end/autonomous vehicles by 2030 (Micron); L1/L2 ≈ 8GB e-MMC, L3 ≈ 128–256GB, L5 > 2TB (TrendForce); ADAS+AD ≈ 40–45% of automotive memory revenue; ~3M fully-autonomous and 15M+ L2+/L3 vehicles expected by 2030, on a base of ~85M cars built/yr. AR/VR is moderate: shipments ~9.6M (2024) → ~37M by 2030 (TrendForce, ~23% CAGR), per-device memory 8–16GB (Quest 3 8GB, Vision Pro 16GB); already memory-constrained (Meta +~20% Quest pricing, Sony cut PSVR2). Humanoid robotics is the softest forecast in this entire dashboard — barely any units ship today (~500 in 2025; Tesla admits no Optimus does "useful work" yet), and 2030 estimates range from ~100K (Robotomated) to 1.2M (BofA) to Musk's "millions," with per-robot memory a guess; treat that layer as scenario, not forecast. Exabytes = billion GB. Sources: Micron, TrendForce, IDC, Verified/PS Market Research, BofA, company specs & disclosures. Not investment advice. See also: §07·B·R (humanoid robots), the newest and least-proven edge-memory demand pool on this board.
The companion dataset to the "memory monster" ladder above (§07·B) — and the honest picture is starker than most coverage suggests: every consumer Tesla ever delivered is SAE Level 2. Autopilot and FSD (Supervised) require active driver supervision — Tesla's own filings say so verbatim — so owning the FSD package (1.28M cumulative holders) does not change a car's SAE level. L4 exists only in the unsupervised Robotaxi operation (live across the Austin metro plus two more Texas cities) and the Cybercab, which entered volume production in 1H 2026 with no steering wheel or pedals. Tesla holds no L3 certification anywhere and its stated architecture skips L3 entirely; L5 does not exist for any maker. The chart shows what that means in units: six solid years of L2, an L4 sliver appearing in 2026, and a modeled fan to 2035. Update (Jul 2): Q2 deliveries came in at 480,126 — a 25% YoY jump that smashed the ~406K consensus by ~74K and broke a two-year decline streak. Crucially, every one of those 480,126 is still L2 — a record quarter changes the bar height, not the autonomy split.
Provenance & the honesty line. Deliveries [anchored — company-reported]: 499,550 (2020) → 936,172 (2021) → 1,313,851 (2022) → 1,808,581 (2023) → 1,789,226 (2024, first annual decline) → 1,636,129 (2025, revised figure per the 10-K filed Jan 2026) → Q1 2026: 358,000+ delivered (+6.3% YoY; 408,000+ produced). Q2 2026: 480,126 delivered / 451,758 produced (Tesla 8-K, Jul 2) — up 25% YoY and 34% QoQ, ~74K above the 406,024 consensus (bullish estimates topped out ~418–420K), Tesla’s best-ever Q2 and first YoY growth since the 2023 peak; deliveries ran ~28K above production, working down Q1 inventory; Model 3/Y were 467,762 (97%), Other Models 12,364; 13.5 GWh energy storage deployed. H1 2026 = ~838K (358,023 + 480,126); full financials July 22. Note the split is unchanged by the beat — all 480,126 remain L2. FY26 tracking above the old ~1.65M consensus on this run-rate (Tesla 8-K/IR, SQ Magazine compile, Tesla Oracle, TechTimes). The L-split [definitional, not estimated]: an SAE level is a property of the system's design and certification, so the historical split is a matter of record rather than modeling: every consumer Tesla ships as L2 — Tesla's own investor materials footnote FSD (Supervised) with "Active driver supervision required; does not make the vehicle autonomous." The 1.28M cumulative FSD-package holders (Q1 2026) are therefore still L2 — owning the software ≠ a higher SAE level, a conflation this chart deliberately rejects. L4 [operational, small, partly modeled]: the unsupervised Robotaxi service is live (Austin metro + two additional Texas cities, priced under Waymo; paid Robotaxi miles roughly doubled sequentially in Q1 2026); the 2025 fleet was low-thousands of converted Model Ys (shown as ~0 at chart scale); Cybercab volume production commenced 1H 2026 per Tesla's Q4'25 and Q1'26 letters, but Tesla doesn't yet break Cybercab out of "Other models" (16,130 in Q1'26, which also holds S/X runout and Cybertruck) — so the ~30K L4 modeled for 2026 is an estimate, flagged. Projection [modeled — one mid path; the spread is enormous]: assumes an affordable-model-led L2 recovery, Cybercab becoming "the largest volume vehicle in the fleet over time" (Tesla's own Q1'26 language), and consumer unsupervised operation arriving late-decade in permitted regions only. Brackets: the Musk-scale case (2025 CEO award tranches: 20M vehicles, 1M Robotaxis in commercial operation, 10M FSD subscriptions) vs the analyst-flat case (~1.65M/yr persisting). Musk's autonomy-timing record — unsupervised driving promised repeatedly since 2016 — argues for discounting the top bracket, and this path sits well below it. If regulators force an L3-like intermediate for consumer unsupervised driving, some late-decade volume would route through L3; the chart follows Tesla's stated skip-L3 architecture. Memory bridge [illustrative]: per-level DRAM content and the Micron 300 GB+ L2+ anchor live in §07·B; the exabyte figure here is shipments × content arithmetic, not a demand forecast. The chart’s DRAM line uses these stated per-vehicle curves (GB, fleet-average new-build): L2 10 (2020) → 15 (’22) → 30 (HW4, ’23) → 60 (’25) → 120 (AI5 era begins, ’26) → 320 (’30) → 500 (’35), approaching Micron’s 300 GB+ L2+ figure late-decade as the mix upshifts; L4 ≈ 2× the consumer figure (600 → 1,100 GB) for redundant AI5-class computers and sensor buffering. Every point on that line is assumption × assumption — it is drawn to show shape (content growth dominating unit growth), not to forecast exabytes. Sources: Tesla 10-K, 8-K quarterly delivery releases, Q1-2026 shareholder letter, SQ Magazine, Tesla Oracle, TechTimes (Goldman/Barclays/RBC previews), notateslaapp. Not investment advice.
A future memory-demand driver Micron is betting on directly. Each humanoid carries an on-board AI "brain" running vision-language-action models locally — and Micron's CEO says a humanoid will need ~10× the memory of a high-autonomy car (which itself exceeds 300GB DRAM by 2026). Today the market is tiny and pre-mass-production — ~13,000 units shipped worldwide in 2025 (Omdia) — but the maker field is crowded and forecasts run from Goldman's cautious 76k-by-2027 to Morgan Stanley's 1B+ by 2050. Honesty note: 2025 shipments are anchored; everything forward is a forecast, and the forecasts disagree by orders of magnitude. Per-robot memory is an estimate from the dominant compute platform, not a universal spec.
Provenance & the honesty line. Shipments [anchored]: ~13,000 humanoids shipped worldwide in 2025, Chinese firms in the top five, Figure 7th, Tesla 9th (Omdia via CNBC); Unitree the volume leader at ~5,500 units 2025; Tesla built only "hundreds" (missed its 5,000 target, public sales not before end-2027 per Musk); UBTech ~1.4B yuan in 2025 orders; Figure 03 at ~1 robot/hour at BotQ. Forecasts [cited — and they diverge hugely]: Goldman baseline 76,000 units by 2027 (vs ~500k some market participants price in) and $38B market / ~1.4M units by 2035; Morgan Stanley China shipments 50,000 (2026) → 446,000 (2030, $15B China market), and globally 1B+ humanoids by 2050 / $5T ecosystem; the spread between forecasters (Goldman conservative vs MS aggressive vs bull cases) is 4–10×. Memory per robot [anchored platform spec + CEO estimate]: NVIDIA Jetson Thor = 128GB memory, 2,070 FP4 TFLOPS, the dominant humanoid compute (Agility, Boston Dynamics, Figure, Galbot adopters; TrendForce/NVIDIA); Jetson T4000 = 64GB; Micron CEO Sanjay Mehrotra (Jun 2026): humanoids will need ~10× the memory of L2+ vehicles, which exceed 300GB DRAM/vehicle by 2026 — a "multi-decade demand cycle" (cryptobriefing/Micron). The honest caveats: (1) 2025 is the only solidly-anchored year; every forward point is a forecast and forecasters disagree by orders of magnitude — the chart shows three divergent scenarios rather than one false-precision line. (2) Near-term robot memory demand is negligible vs AI data centers — this is a long-duration option, not a 2026 catalyst. (3) Per-robot memory varies by tier (entry robots use far less than a Thor-class brain); the 128GB figure is the high-end platform, not an average. (4) The reference image's valuations/associations are point-in-time and some (e.g. 1X's $10bn target) are explicitly unverified. Modeled: the forecast curves between anchored points, and the scenario fan beyond 2025. Sources: Omdia (via CNBC), Goldman Sachs, Morgan Stanley, TrendForce, NVIDIA, Micron, The Product Compass / reference image, company disclosures. Not investment advice.
HBF is NAND flash, stacked and wired like HBM. Sixteen 3D NAND dies plus a base logic die, joined by through-silicon vias, in a package that deliberately matches HBM4's footprint, power profile and stack height — so it can sit on the same interposer next to a GPU. Generation 1 is 512 GB per stack at 1.6 TB/s read; the roadmap goes to 1 TB at >2 TB/s and 1.5 TB at 3.2 TB/s. The pitch, in Rambus' framing, is 8–16× the capacity of HBM at the same read bandwidth and roughly the same price point. SanDisk originated it, SK hynix joined in Aug 2025, and the two opened an Open Compute Project standardisation workstream on Feb 25 2026. First samples are due H2 CY2026, first inference devices early 2027.
Provenance, a corrected unit, and what is genuinely unknown. [cited — vendor spec]: SanDisk/SK hynix HBF Gen1 — 16 dies + base logic die, 256 Gb per die, 512 GB per stack, 1.6 TB/s read, matching HBM4 footprint, power profile and stack height; Gen2 >2 TB/s and up to 1 TB; Gen3 3.2 TB/s and up to 1.5 TB; samples H2 CY2026, inference devices early 2027 (SanDisk press releases Aug 6 2025 and Feb 25 2026; SK hynix newsroom; Tom's Hardware; TechPowerUp). OCP standardisation workstream announced Feb 25 2026. The 8–16× capacity claim is Rambus' Steven Woo, via Semiconductor Engineering, and this board checked it: 512 GB against a 64 GB HBM4 stack is 8×, against a 36 GB HBM3E 12-high stack is 14× — the claim is arithmetically consistent. A unit error in one source, corrected here: Semiconductor Engineering states "HBF capacity is 256 GB per die, which gives 512 GB per 16-high stack." Those two figures contradict each other — 256 GB × 16 is 4 TB, not 512 GB. The die is 256 gigaBIT (32 GB), which multiplies correctly to 512 GB. SanDisk's own materials are self-consistent; this board uses the bit figure and flags the discrepancy rather than silently picking one. Tom's Hardware's "4 TB of VRAM" headline is a GPU-level total — eight 512 GB stacks — and is consistent. [modeled / illustrative]: the comparison points on the scatter (HBM3E 24 GB and 36 GB at ~1.2 TB/s, HBM4 at 64 GB / ~2 TB/s, DDR5 DIMM 96 GB / 51 GB/s, enterprise NVMe 30 TB / 14 GB/s) are representative configurations for scale, not quotes from a single price list — per-stack HBM capacity varies with die count and vendor. Cell endurance figures are ranges, not specifications: rated P/E cycles vary by process node, controller, over-provisioning and whether the drive runs pseudo-SLC caching, and published ranges disagree — SLC 50–100K, MLC 3–10K, TLC 1–3K, QLC 0.1–1K are the commonly cited bands and are shown as bands for that reason. What is genuinely unknown: (1) HBF's endurance rating has never been published — no number here is a specification, and the TLC/QLC inference is this board's, from the BiCS8 die family, not a vendor statement. (2) Nor has its write bandwidth, which is the figure that would actually bound its use. (3) No silicon has shipped. Every performance number above is a target on a roadmap, from a company with an obvious interest in the number being large; the first samples are still months away at the time of writing. Treat this section as an explanation of an architecture, not as evidence that it will work. Cross-refs: §01·B (memory type field guide), §05·B (HBM's bite of DRAM supply), §06·D·W (the memory wall), §07 (the storage layer). Sources: SanDisk newsroom, SK hynix newsroom, Open Compute Project, Semiconductor Engineering, Tom's Hardware, TechPowerUp, VideoCardz, TrendForce, Kingston and Lexar technical notes on NAND cell types. Not investment advice.
Source: PatSnap / JEDEC-tracked analysis, 2026
Supply can't keep pace; NAND growth is similarly capped near 17%. Downstream: Nvidia has reportedly cut gaming GPU output 30–40%, smartphone prices face 8–10% rises, and consumer hard drives are up ~50% — the AI buildout reaching into every device.
Where the memory shortage reaches ordinary buyers: Apple's own delivery quotes and price tags. Macs carry unified memory soldered on at build time, so a DRAM squeeze lands on them directly — and through spring 2026 the wait scaled with the memory you configured. Through July this section said high-memory Macs waited months "while the base model still ships same-day" — as of Aug 2 that is no longer true, with the MacBook Air itself reported in a "major" shortage at 2–6 weeks despite a $200 price rise. By Oct 9 that queue had cleared: the base Air delivers in three days — at $1,299, against $999 in 2025. This board read Apple's US store directly that day: 10 of 21 orderable configurations arrive within four days, none of them a memory upgrade, while the eight reached by adding memory wait ten days to about eight weeks. The squeeze has moved from the queue to the price tag and the unit count — every memory step on the store now costs $25 per GB, the base Mac mini is 50% dearer than in 2024, and IDC's preliminary Q3 count has Apple's Mac shipments down 11.3%.
Through spring 2026 Apple's online-store shipping estimates blew out on high-memory configurations while base models stayed quick, and in August the queue spread down the range to the MacBook Air. Reporting (MacRumors, Tom's Hardware, TrendForce, Bloomberg) tied it to the AI memory crunch this dashboard tracks: Samsung and SK Hynix shifted capacity toward HBM and server memory, starving consumer DRAM, while buyers building local-AI Mac clusters bid for the same high-memory machines. Apple withdrew the Mac Studio's 512 GB and 256 GB options in March and May, raised prices across the line on Jun 25, and on Aug 25 relaunched the Mac mini and Mac Studio at higher prices again. Wait time was the gauge in the spring. In October it reads close to normal — so this section now reads Apple's store directly, and follows the squeeze to the two places it has moved: the price of each gigabyte, and the number of Macs sold.
Through Q3 2026, a modeled average delivery estimate across a representative basket of Mac configurations (base through high-memory), in weeks; for Q4 2026, the measured average of the quotes captured on Oct 9. The shape is the story: waits were normal (days) right through 2024 and most of 2025, spiked with the memory shortage in the first half of 2026, and on Apple's own store in October average about two and a half weeks — most of it in memory upgrades and the Ultra.
Oct 9 2026 update [cited — Apple store capture]: delivery and pickup quotes read from Apple's US online store on Oct 9 2026 for ZIP 95814 (Sacramento; pickup at Apple Arden Fair), taken from the store's own fulfillment-messages response — the compact "Delivers" quote, which is the first date or window the configurator shows. 23 configurations across six Mac lines: 21 returned a quote; the iMac's 32 GB option was listed as "Currently unavailable"; the Studio's 512 GB option is not yet listed (Apple: late October). Prices are Apple's list prices on the same pages, and the memory-option deltas were read from the configurators — every one of the 13 captured works out to $25 per GB. Configurations marked "+ memory" were reached by raising the memory option above the configuration Apple lists. [derived — this board's arithmetic]: weeks = days from Oct 9 to the midpoint of the quoted window ÷ 7; across the 21 quotes the mean is 2.40 wk and the median 1.43 wk, and the Q4 2026 bar is that mean; the price-ladder percentages are computed from the list prices shown on each row. [cited — reports]: the Aug 25 Mac mini and Mac Studio launch, prices and the late-October 512 GB option (MacRumors, Aug 25); unshipped M4 mini orders upgraded to M6 (MacRumors, Aug 26); enterprise AI demand catching Apple off guard, configurations out of stock for months, some buyers turning to Nvidia's DGX Spark (The Information, via MacRumors, Aug 30); same-day pickup at the Sep 22 launch (MacRumors, Sep 22); Kuo on Apple trimming 2026 shipment plans over DRAM (MacRumors, Aug 10); iPhone 16, 17, 17e and Air each +$100 (MacRumors, Sep 9); iPhone 18 Pro $1,199 and Pro Max $1,299, +$100 each, and October iPhone 18 Pro component orders cut by at least 15% (Nikkei Asia, via MacRumors, Oct 9); AAPL down more than 2.5% in early trading (MacRumors, Oct 9); IDC's preliminary Q3 2026 PC tracker (Oct 8) — Apple 5.9M against 6.7M, −11.3%, share 9.5% against 8.5%; market 62.7M against 78.5M, −20.1% (IDC; MacRumors, Oct 9 — IDC computes growth on unrounded units, so 5.9/6.7 does not reproduce −11.3% exactly); Prime Big Deal Days prices (MacRumors, Oct 6–8); FQ4 results on Nov 2 (MacRumors, Oct 6); OLED MacBook Pro expected Oct 27 (MacRumors, Oct 8); John Ternus Apple's CEO since Sep 1. Earlier list prices on the ladder: M4 Mac mini $599 (Oct 2024), M4 MacBook Air $999 and M4 Max / M3 Ultra Mac Studio $1,999 / $3,999 with a $1,600 96→256 GB step (Mar 2025), iPhone 17 $799 (Sep 2025), MacBook Neo $599 (Mar 2026), and the Jun 25 2026 list (MacRumors). Not carried: no other ZIP code or country was sampled, and no measured August read exists to compare the Oct 9 quotes against — the ticks are earlier press reports, not earlier captures. Corrected in this update (§10·B): the Jun 25 price-rise range and the three other sections that copied it, the Mac mini 256 GB tier and the M4 Pro mini's rise date in the Jun–Jul box, this section's "live" freshness record, and two style rules that drew no bar for the chart's shortest waits. Before Oct 9, wait times were configuration-specific snapshots from dated press reports, not a live per-model feed — Apple doesn't publish lead-time data, and its store estimates change daily and vary by region and channel. Anchors (US online store unless noted): Mac mini M4 Pro/64 GB = 16–18 weeks; base M4/16 GB ≈ 4 weeks; 512 GB M4 backordered into June (MacRumors, TrendForce, Apr–May 2026). Mac Studio M3 Ultra/256 GB = 16–20 weeks (4–5 months); 512 GB option removed from sale Mar 2026; in-store pickup pushed to September. MacBook Pro M4 Max/128 GB ≈ 5 weeks (early-2026 reporting), 36–48 GB a few weeks, base configs days. MacBook Air / iMac base ≈ same-day. Bars show the approximate midpoint of each reported window, scaled to ~20 weeks. The quarterly history is a modeled basket average, not a published series: 2024–2025 quarters reflect Apple's normal few-day lead times (no shortage reporting existed then); Q1 2026 is anchored to the Feb 2026 Mac Studio "14→54 day" reports (Tom's Hardware), and Q2 2026 to the Apr–May 2026 Mac mini/Studio blowout (MacRumors/TrendForce). The current quarter rechecks for the latest estimates on load when run inside Claude; opened as a file it shows the compiled value. The drivers — AI capacity diverting DRAM, plus local-AI Mac-cluster demand — are well documented; exact weeks for any given config today will differ. Aug 17 2026 update [cited]: Apple FQ3 2026 (reported Jul 30): revenue $109.4B, +16% YoY, a June-quarter record; net income $29.8B; iPhone revenue +22%, also a record; gross margin 50.1% reported, including approximately 2 percentage points of tariff refunds, giving 48.1% ex-tariff — down 120bp sequentially from March's 49.3%. Apple stated on the call that "more than 100% of the gross margin decline can be explained by the memory cost change", and Cook described "a 100-year flood on the memory pricing, with exponential increases." September-quarter gross margin guided to 47–48%, memory named the primary driver, with memory costs expected to rise further and be partly offset by carry-in inventory and cheaper non-memory components. Shares fell after hours despite the beat. [derived — this board's arithmetic]: 120bp on $109.4B of revenue is ≈$1.31B of gross profit in a single quarter; the two-quarter ex-tariff path 49.3% → 48.1% → 47.5% (guide midpoint) is −180bp. Basis ambiguity, flagged not resolved: Apple did not state whether the 47–48% guide assumes tariff refunds repeat, so the September fall is 210–310bp against the reported 50.1% or 10–110bp against ex-tariff 48.1% — a ~200bp difference in interpretation. The chart uses ex-tariff and says so. MacBook Air shortage (Aug 2, Bloomberg/Gurman via MacRumors): a "major" shortage with retail sources saying shipments are "more constrained than they can ever recall"; US online-store delivery 2–6 weeks, higher-RAM configurations delayed longest; the Air is now affected alongside Mac Studio and Mac mini. Apple raised prices on 14 products in June, the Air among them ($1,099→$1,299). Framing correction carried into the section body: this section previously asserted that base models "still ship same-day". That was accurate when written and is not now; the claim has been retracted in the summary rather than quietly deleted, and the reason is dated. Context [cited]: Gurman also reports Apple preparing to refresh every Mac it sells over two years — 11 new models — with Mac sales projected to rise for a third consecutive year, partly on demand from users running agent-based AI workloads locally; the MacBook Neo, launched as "the $599 Mac", is now $699. Not carried: no August wait-time figures for Mac Studio or Mac mini have been published since the July checks, so the chart's Q3 bar is not updated for the Air's queue — it would move the opposite way to the survivorship effect already flagged, and this board will not model the netting of two unquantified opposing effects. Jun–Jul 2026 update [cited]: Config withdrawals — Mac Studio M3 Ultra 512 GB pulled Mar 6 2026 with the 256 GB upgrade raised $1,600→$2,000 (Tom's Hardware, MacDailyNews); 256 GB pulled May 5 2026, leaving 96 GB as the only M3 Ultra memory option (MacRumors, 9to5Mac, VideoCardz), with delivery reported into October (Macworld); Mac mini 256 GB storage tier discontinued and base price $599→$799 on May 1 (MacRumors); the 256 GB tier returned on Jun 25 at $799 (MacRumors). Price rises — Jun 25: mid-cycle increases across Mac, iPad, Home and Vision Pro of +$30 to +$1,300 per device — Macs +14.3% to +32.5%, iPads +20.0% to +28.7% (corrected Oct 9 from "$100–$300 … Macs ~+15–20%, iPads ~+15–25%"; the Mac mini M4 Pro's +$200 to $1,599 is on the same Jun 25 list, though this paragraph had dated it to April — §10·B) (MacBook Air $1,099→$1,299; MacBook Pro $1,699→$1,999; iPad Air $599→$749), Apple citing component costs it had “never seen … increase this much, this quickly”; Tim Cook's “hundred-year flood” framing (WSJ, Jun 17); memory and storage up ~4× in three quarters (Counterpoint Research). Market reaction — AAPL −6.12% to $275.15 on Jun 25, ~$263B of market value, its worst session since April 2025 (CNBC, Forbes, TIKR). Honesty note on the Q3 2026 bar: it is lower than Q2 and that is largely survivorship — the longest-waiting SKUs were delisted, so they exit the basket; a withdrawn configuration has no lead time to average in. Treat Q3 as a partial quarter (to Jul 24) and read it alongside the withdrawal ladder and the price rises, which move the opposite way. Sources: Apple (US online store, Oct 9 capture), IDC, Nikkei Asia, The Information, Bloomberg, MacRumors, Tom's Hardware, Tom's Guide, TrendForce, BigGo, Cybernews, 9to5Mac, VideoCardz, Macworld, MacDailyNews, CNBC, Forbes, Counterpoint Research, WSJ. Not investment advice.
A different lens on the same demand: how much memory ships inside Apple devices sold each year, by type, 2010 → 2030. Apple alone consumes a meaningful slice of world memory, and the total has exploded — not mainly because Apple sells more units (iPhone volumes plateaued years ago), but because each device carries far more memory: storage has gone from 16 GB to 256 GB–1 TB, and RAM from 512 MB to 8–12 GB as on-device AI (Apple Intelligence) pushes it up. NAND storage dwarfs DRAM, so it's shown on its own axis context in the tooltip. Update (Jun–Jul 2026): the model's demand-side thesis just went from inference to confirmed fact — Tim Cook told the WSJ that memory costs have become unsustainable, called the squeeze a “hundred-year flood,” and confirmed iPhone price increases; Apple already raised Mac prices $100–1,300 (and iPad/Apple TV/HomePod) in late June. And in the middle of it: Cook is outgoing — hardware chief John Ternus becomes CEO September 1, a week before the iPhone 18 launch.
This is a two-layer model, not reported data — Apple discloses neither unit shipments (it stopped in 2018) nor memory content. It multiplies estimated annual unit shipments per product line (iPhone, iPad, Mac, Watch, others) by an estimated capacity-weighted average of NAND storage and DRAM per device, each rising over time. Anchors: iPhone ~232M units (2024), record ~247M (2025, IDC); Macs ~22–25M/yr (~6.2M in Q2 2025, IDC/Canalys); iPad ~50M/yr. Capacity assumptions: iPhone average storage ~16 GB (2010) → ~256 GB (2026E), DRAM 0.5 GB → ~8 GB; Macs/iPads scaled higher. NAND outweighs DRAM by ~20–40× per device, so it dominates the stack. Projection to 2030 assumes low-single-digit unit growth and continued capacity step-ups (on-device AI lifting the DRAM floor — Apple Intelligence needs ≥8 GB) — IDC notes 2026 smartphone units actually dip ~1% on the memory shortage itself, even as ASPs hit records. Jul 2026 refresh — the cost shock, quantified [cited]: the TechInsights model (via WSJ) has the same 12 GB DRAM package going from about $39 in iPhone 17 Pro to about $145 in iPhone 18 Pro (+272%), with the 256 GB base NAND tier from ~$13 to a projected $51; estimated build cost rises from ~$582 to ~$726, implying ~$1,371 to hold margin on a $1,099 phone — i.e. roughly +$270 on the 18 Pro to preserve margins. Base-model hikes of $50–150 and up to $200 on Pro are the projected range; DRAM/NAND contract prices jumped 90–95% QoQ in Q1 2026. Procurement color: Samsung reportedly planned +60% on LPDDR5X, opened at +100% — and Apple accepted immediately in emergency procurement meetings. The DRAM-per-device floor is rising exactly as modeled: the iPhone 18 line carries 12 GB to power on-device Apple Intelligence, and DigiTimes reports even the base iPhone 18 moves 8→12 GB. On duration, the disagreement is surfaced, not resolved: Cook expects memory pricing to “return to reasonable levels,” while IDC calls the shift a “permanent reallocation” of capacity toward AI, and analyst timelines for relief run from Q4 2027 (Counterpoint) to 2028 (Intel’s Tan) to 2030 (Kearney); Cook even said “everything needs to be on the table” when asked about sourcing from Chinese memory suppliers. This section’s cross-links write themselves: Apple raising retail prices on memory costs is kill-switch #5’s demand-destruction mechanism operating in the wild (§02·D), and the affordability ceiling TrendForce cited for the 3Q26 contract-price deceleration (§03) is the same phenomenon from the supplier side. Treat the totals as order-of-magnitude with a wide band; the trend (memory-per-device, not unit count, drives the growth) is the robust takeaway. Sources: IDC, Canalys, Counterpoint, Apple teardowns (iFixit/TechInsights), company history. Not investment advice.
The demand under the demand. All the memory in this dashboard is ultimately paid for by the revenue the AI application layer throws off — so here is that layer's income, built from every annualized run-rate the two leading labs have stated or had reported, with Facebook's first revenue years overlaid by company age as a benchmark. Anthropic is now pacing above $100B a year (New York Times, Sep 18) — up from $65B at the end of July and $9B at the end of 2025, more than ten times in under nine months. OpenAI is nearing $70B (Axios, Sep 29), up more than 70% since July began. Anthropic leads by about 1.4×, and the lead has held between 1.33× and 1.43× since May — both labs have compounded at roughly 20% a month since the spring. This build also corrects the last one. OpenAI said it was making "$2 billion in revenue per month" on Mar 31, with its $122B raise; this section had plotted that figure in June. The one misdated point produced three findings that were not real — a February-to-June plateau, a gap between the labs that "narrowed from 2.0× to 1.75×", and an "~8% inconsistency" in Bloomberg's reporting — all logged in §10·B. And for the first time the run-rates can be checked against booked revenue. A draft of Anthropic's IPO prospectus, seen by Reuters on Sep 28, and OpenAI's 2024–25 accounts, leaked in June, let this chart's own curve be summed and compared: it sits below booked revenue in six of seven checks, and within 2% of Anthropic's 2024 and 2025. The headlines were not inflated. Against Facebook at the same age the comparison stays absurd: about $104M a year, nearly a thousand times smaller.
A576RC1) — the total US wage-and-salary bill, published monthly at a seasonally adjusted annual rate, which makes it directly comparable to an annualized revenue run-rate. Both numbers are annual-rate dollars, so the ratio is apples-to-apples in units. The numerator is the whole LLM industry, not two firms: every company whose product is a large language model — Anthropic and OpenAI plus Google’s Gemini, xAI, Mistral, Cohere, Meta, ByteDance/Doubao, Moonshot, DeepSeek and the rest of the Chinese field — $189B of model-layer run-rate, itemised in the table below. Anthropic and OpenAI alone are 90% of it, so counting everyone else moves the share only from 1.25% to 1.39%. The headline: the industry crossed 1% of the US wage bill in August 2026. The amber band on the chart is every other lab on earth. Read it as a scale reference, not a displacement rate — the caveats below are load-bearing, and the biggest one is that the numerator is global company revenue while the denominator is US-only wages. The dashed blue line is a deliberate analogy: e-commerce as a share of US retail sales (Census/FRED ECOMPCTSA), age-aligned so that AI's January 2024 sits on e-commerce's Q1 2000 — the last comparable case of a new channel eating an established aggregate. It is a different numerator over a different denominator; it is here for shape and pace, not arithmetic comparison.Provenance & the honesty line — read before quoting this ratio. Denominator [cited — official]: U.S. Bureau of Economic Analysis, Compensation of Employees, Received: Wage and Salary Disbursements (A576RC1), retrieved from FRED, Federal Reserve Bank of St. Louis — billions of dollars, seasonally adjusted annual rate, monthly; series runs Jan 1959→Aug 2026, last updated Sep 30 2026. That release carried BEA's annual update, which revised the whole history — January 2024 moved from $12,062.7B to $12,078.5B and May 2026 from $13,388.8B to $13,490.4B — so every month here now uses the new vintage. The August build had extrapolated June–August 2026; the published figures came in $51–77B higher ($13,513.6B, $13,547.3B, $13,583.9B), mostly because the revision lifted the base. Values used run Jan 2024 ($12,078.5B) → Aug 2026 ($13,583.9B). Sep 2026 is extrapolated at +$42.5B (the trailing six-month average increment) because BEA publishes it in late October — shaded on the chart and flagged in the tooltip. Numerator [rebuilt Aug 1 2026, refreshed Oct 1]: two series are drawn. The amber-brown line is Anthropic + OpenAI from the anchors driving the chart above; the amber line adds every other LLM lab, itemised in the roster table with a per-row confidence tag. Both are interpolated between anchors at constant growth (exponential, not linear); ● marks months where both leaders had a company-stated figure. The rest-of-industry series is the softest thing on this chart and is labelled so: its terminal point is pinned to the roster sum ($19.3B), but its history back to Jan 2024 is modeled, not compiled from monthly disclosures — most of these firms have never published a run-rate time series, and several (DeepSeek, Zhipu, MiniMax) publish nothing at all. Sanity checks used: Menlo Ventures put enterprise LLM API spend at $3.5B (late 2024) → $8.4B (mid-2025) and total enterprise genAI spend at $37B for 2025 with $18B of that infrastructure; those are different scopes than this roster but bracket it plausibly. Do not quote an intermediate month of the industry line as a cited figure — quote the endpoint, as ~$189B, and say that Gemini alone is worth ±$4B and both leaders' September figures are press reports ("more than $100B", "nearing $70B"). Four caveats that limit what this ratio can mean: (1) Geography mismatch — the big one. Anthropic and OpenAI revenue is worldwide; A576RC1 is US wages only. The ratio therefore overstates any true "share of US wages," and is best read as a scale reference against a familiar macro aggregate rather than a share of a matched market. (2) Industry coverage — addressed on Aug 1 2026, and shrinking. The numerator covers the whole model layer (roster above); the leaders are now 90% of it, so everyone else is worth 0.14 percentage points of share. What remains excluded, and deliberately: (a) the resale channel — Microsoft’s $37B AI run-rate, Perplexity, AWS Bedrock, Google Vertex and, new in this build, the compute SpaceX’s xAI sells to Anthropic (~$1.25B a month per SpaceX’s S-1) — because those dollars are already counted in the labs’ books or are the labs’ own spending; (b) vertical AI applications built on top of models (Cursor, Harvey, Abridge and the rest), which are a genuinely additional layer this chart does not attempt — Menlo Ventures sized total enterprise genAI spend at $37B for 2025, of which applications were $19B, so an application-inclusive numerator would be a different and larger measurement; (c) ad-monetized model use, chiefly Meta’s, which earns real money from Llama without ever selling a token. (3) Run-rate ≠ realized revenue — now partly tested. A run-rate annualizes an instantaneous rate; the BEA series is an annualized flow of actually-paid wages. The audit above finds the run-rate path summing to at or below booked revenue in six of seven periods, so the numerator is, if anything, conservative. (4) Revenue is not displacement. AI subscriptions are bought by employers and employees, often alongside labour rather than instead of it; nothing here identifies substitution. A rising ratio is consistent with augmentation, substitution, or simply a new category of software spend. The e-commerce overlay [cited — added Jul 30 2026]: U.S. Census Bureau, E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA), retrieved from FRED (last updated Aug 18 2026) — quarterly, seasonally adjusted, series begins 1999-Q4. Alignment: AI month 0 (Jan 2024) is placed on e-commerce 2000-Q1, so both curves are read at the same elapsed months since commercial take-off; quarterly prints fall on every third month and the months between are linear interpolation. What it shows: 32 months in, e-commerce was 1.5% of US retail sales while the whole LLM industry is 1.39% of the US wage bill — the analogue is now only ~1.1× larger at the same age, down from ~2.1× on Aug 1 and ~1.6× in mid-August. AI grew ~65× over the window against e-commerce's ~1.9×. And the long tail is the real lesson: e-commerce took 26 years to go from 0.8% to 17.1% (2026-Q2) — S-curves of this kind are measured in decades, not quarters. Comparison caveat, stated plainly: the two lines share an axis but not a unit — retail sales and wages are different aggregates, and e-commerce substitutes for a channel while AI spend may or may not substitute for labour. Treat the overlay as a pace and shape reference only; do not read a crossing as AI “overtaking” e-commerce in any economic sense. Why it's on-thesis: §08·E tracks tokens per employee and §06·D the token economics; this puts the same question in dollars — if AI spend is to justify the memory build-out in §05–§06, it eventually has to become a visibly larger share of the compensation it is meant to leverage. Today the entire industry is about 1.4%, the complete count rather than a floor. Sources: U.S. Bureau of Economic Analysis via FRED (A576RC1, Sep 30 2026 vintage); U.S. Census Bureau via FRED (ECOMPCTSA); Menlo Ventures 2025 State of Generative AI in the Enterprise and 2025 Mid-Year LLM Market Update; Microsoft FY26 Q3 disclosure; Alphabet Q2 2026 results; SpaceX Form S-1 (May 20 2026); CNBC (Cohere investor memo); Sacra; Bloomberg and FutureSearch (xAI); company disclosures for Mistral, Perplexity and Moonshot; plus the run-rate anchors cited above. Not investment advice. [refreshed Oct 1 2026]: Anthropic — CNBC, Aug 17: Anthropic told investors its run-rate passed $65B at the end of July and gave a preliminary $11.5B for Q2 revenue, about 14 times a year earlier; New York Times via Axios, Sep 18: pacing above $100B, up 50% in two months and more than 10× end-2025, with a listing possible in November. OpenAI — Axios, Sep 29: nearing $70B, up more than 70% since the start of Q3, B2B revenue up more than 100% over the same span, and more consumer revenue added in Q3 than in all of 2025; its CEO said on Sep 12 that OpenAI will not list this year. Roster changes: both leaders moved to their September figures, taking the model layer from $129B to $189B and the leaders' share from 85% to 90%; xAI's note now cites SpaceX's audited S-1 — AI segment $3.2B in 2025 and $818M in Q1 2026, Grok not broken out — and its $2.0B line is unchanged; Microsoft's $37B is still not restated (next results late October); a new double-count row covers the compute xAI sells Anthropic. Rest-of-industry is unchanged at $19.3B and was simply re-dated to September with the leaders, so it is now the stalest part of the numerator.
Provenance & the honesty line. The metric: neither lab reports monthly revenue — both periodically disclose an annualized run-rate (roughly the latest month × 12). "Implied monthly" here is simply run-rate ÷ 12; it is a forward annualization, not trailing recognized revenue — the audit above measures the difference. Anthropic, company-stated: $87M (Jan 2024) → $1B (Dec 2024) → $4B (Jun 2025) → $9B (Dec 2025) → $14B (Series G, Feb 12 2026) → $19B (Mar) → $30B (Apr 7 company post) → $47B (Series H, May 28, $965B valuation) (VentureBeat revenue walk, CNBC, company disclosures). Anthropic, reported: $65B+ at end-July (its investor update, reported by Bloomberg and CNBC on Aug 17) → $100B+ pace (New York Times, Sep 18, people familiar). Retired from the chart: Yipit ~$69B and TickerTrends ~$74.1B for July — third-party trackers, 6–14% above the company's own end-July figure. OpenAI, company-stated: ~$5.5B (end-2024) and $10B (Jun 2025), both from its spokesperson to CNBC on Jun 9 2025 — the $10B excludes Microsoft licensing revenue and one-off deals; "$20B+" ARR for 2025 (CFO, Jan 2026); "$2 billion in revenue per month" (Mar 31 2026, with the $122B round at an $852B valuation). OpenAI, reported: ~$2B exit-2023 (estimate); $8B (Mar 2025, FutureSearch); ~$25B (Feb 2026, Sacra/Reuters — Reuters also had "$25B+" in March); "tops $40B" (Bloomberg, Aug 13 — a floor; +20% month-over-month in July, Q1 2026 revenue ~$6B, enterprise above 40% of revenue, a new advertising business; OpenAI declined to comment); ~$70B (Axios, Sep 29). Facebook overlay [actual, historical]: aligned by company age, not calendar — chart-2024 = FB-2004 (founded Feb 4 2004, first revenue $382K per its IPO S-1); plotted values are actual annual revenue ÷ 12 ($382K → $9M → $48M → $153M across 2004–07), interpolated between those actuals, no projection (TechCrunch/S-1). Filling gaps: markers are stated or reported figures; between two markers, constant-growth interpolation; the lab lines end at their last reported figures and are not extended. Confidence & the dispute [surfaced, not buried]: in April 2026 OpenAI publicly argued Anthropic's ~$30B figure was overstated by ~$8B, contending that Anthropic books gross end-customer spend through AWS/Google/Azure while the net figure is closer to ~$22B, and Bloomberg's August reporting noted the two may not measure run-rate the same way. The leaked draft S-1 does not settle the basis question but shows Anthropic's headlines tracking its booked revenue (audit above). Treat every run-rate headline as directional, not audited. IPO status: Anthropic has filed confidentially and the New York Times reports a listing as soon as November. Reuters saw a draft S-1 on Sep 28: 2025 revenue ~$4.6B (2024: $386M), an operating loss above $8B, a net loss of ~$42B of which ~$34B is a non-cash charge on convertible financing instruments, an operating profit (described as adjusted) in each of Q1 and Q2 2026, nearly a quarter of 2025 revenue from two customers, and $518B of cloud, compute and infrastructure commitments. Anthropic has not published or confirmed the draft, and no public S-1 was on EDGAR as of Sep 29. OpenAI's CEO said on Sep 12 it will not go public this year. Why it's on-thesis: this app-layer revenue is what funds the compute commitments (§06·F·C) — Anthropic's $518B alone is about five years of its current run-rate — the hyperscaler backlogs (§06·F·B), and ultimately every bit of memory demand in this dashboard; the tokens that hit the memory wall (§06·D·W) are the same tokens being monetized here. Adapted from a user-supplied run-rate tracker; rebuilt Oct 1 2026. Sources: OpenAI (Mar 31 2026), CNBC (Jun 9 2025), Bloomberg (Aug 13 2026), Axios (Sep 29 2026), Axios / New York Times (Sep 18 2026), Fortune (Sep 29 2026), TechSpot / Ars Technica (Jun 2026), VentureBeat, Reuters, Sacra, FutureSearch, TechCrunch/Facebook S-1, OpenAI & Anthropic disclosures. Run-rates are annualized disclosures and press reports, not audited financials — not investment advice.
The demand side at maximum resolution: how many tokens does one employee burn per day? 2026 made this answerable — companies built literal internal leaderboards ("Claudeonomics" at Meta, "AI God" ranks at Sendbird), then the bills arrived and the same companies started dismantling them. The spread below runs five orders of magnitude, from ≥100M tokens/day power users to the median firm’s ~$11/employee/month — token demand is a power law, exactly like the app-layer revenue (§08·D) and capex (§06·F) it feeds.
| # | Who | Tokens / employee / day | $ signal | Label |
|---|---|---|---|---|
| 1 | Sendbird "AI God" engineers (5–10% of eng staff) | ≥100M | prizes, planned extra vacation days | reported |
| 2 | Heavy agentic developers, industry-wide (incl. Microsoft E+D pre-cancellation) | ~20–80M | $500–$2,000/mo bills | derived |
| 3 | Sendbird CEO (rank: "Custom Tool Builder") | 30M | 70% via Claude Code | reported |
| 4 | Meta — company-wide average, all ~85K employees | ~29M | "approaching billions" in 2026 | derived: 73.7T ÷ 85K ÷ 30d |
| 5 | Typical enterprise Claude Code developer | ~3–6M (active day) | $13/active day · $150–250/mo · 90% under $30/day | derived from reported $ |
| 6 | Uber engineers (95% monthly adoption, ~70% of committed code AI-generated) | capped | $1,500/mo/employee limit after budget gone in 4 months | reported |
| 7 | Aerospace & defense mfr power users | cap burned in 4 days | $250/mo limit · Opus + fast-mode disabled | reported |
| 8 | Top-1% firm on Ramp (per-employee median) | — | $7,450/employee/mo | reported |
| 9 | Top-10% firm on Ramp | — | $611/employee/mo | reported |
| 10 | Median firm on Ramp | — | $11.38/employee/mo | reported |
| 11 | US economy-wide (Atlanta Fed, anticipated 2026) | — | $2,068/employee/year (+50% YoY) | reported |
Sources, derivations & the honesty line. The two marquee numbers [cited]: Meta employees consumed 73.7 trillion tokens in roughly 30 days, tracked on an internal leaderboard called "Claudeonomics"; costs are approaching billions for 2026, and Meta is dismantling the board for a centralized "AI Gateway" with formal token budgets from 2027 — across all 85,000 ranked employees that derives to ~29M tokens per employee per day, an average that matches an AI-startup CEO’s personal burn. Sendbird ranks staff from "Beginner" to "AI God" — 100M+ tokens/day — with 5–10% of engineering at god tier, ~70% of it through Claude Code, and prizes for usage. The middle of the curve [cited]: enterprise Claude Code averages ~$13/developer/active day and $150–250/month, with 90% of users under $30 on any active day; agentic tasks consume 400K–2M tokens each, power users reach $500–$2,000/month, and per-developer consumption rose ~18.6× in nine months; Microsoft’s Experiences+Devices division ordered engineers off Claude Code by June 30 after ~$2,000/engineer/month exhausted the annual budget — moving to $39/seat Copilot, a 51× CFO math. The long tail [cited]: Ramp per-employee medians: $11.38 typical firm, $611 top decile, $7,450 top percentile — average company $140,842/mo vs median $2,246, a power-law distribution — and the Atlanta Fed anticipates $2,068 per employee for 2026, up 50%. The cultural whiplash, six months end to end: Meta, Amazon and Microsoft set internal token-usage targets in early 2026; by mid-year Amazon shut its leaderboard down ("Please don’t use AI just for the sake of using AI") and replaced counts with shipped-code metrics, Uber capped at $1,500/mo after burning its annual budget in four months, and a defense manufacturer’s $250 caps were burned through in four days; Meta’s CTO put the epitaph on tokenmaxxing: "All motion is not progress and token usage alone is not a measure of impact of any kind". Honesty notes: units are deliberately mixed — some rows disclose tokens, some only dollars, and $→token conversion varies ~100× with model choice and caching, so dollar rows are left in dollars rather than fake-converted; "derived" rows show their math; rankings compare unlike populations (one company’s elite vs another’s average). Why it’s on-thesis: these are the tokens that hit the memory wall (§06·D·W) and get monetized in §08·D — and the budget caps arriving mid-2026 are a live demand-quality read for kill-switch #5 (§02·D): Goldman still projects 24× enterprise token growth by 2030 (120 quadrillion/month), but the era of burning tokens as a KPI is visibly ending. Reported + derived snapshot, Jul 2026 — not investment advice.
Every other section on this board measures what the industry is doing: prices, wafers, capex, backlogs. This one measures what ordinary people are typing into a search box — five years of worldwide search interest for nvidia, micron, sk hynix, sandisk and ethereum. Rebuilt Aug 1 2026 from Google Trends’ own CSV export — 261 weekly points, replacing the earlier screen transcription of a panel whose first term was misspelled. Correcting the spelling changed the chart more than it changed the world. Nvidia now holds the 100 index point, so every other line sits on a materially lower scale than before — the denominator moved, not the data. Nvidia peaked in the week of Jan 26 2025, which is the DeepSeek week: search interest topped out on the crash, not the rally. Across all 261 weeks no memory name ever outranks nvidia, and its five-year mean of 28.5 is 2.7× ethereum’s 10.5. The memory-over-crypto crossing is real and survives the correction, but it is later than this section first claimed: micron passed ethereum for good in the week of May 3 2026 and sk hynix on Jun 21 2026 — not February. That error is logged in §10·B.
Provenance & method — upgraded Aug 1 2026 from transcription to source file. Source: Google Trends, worldwide, past 5 years, Web Search, terms nvidia, micron, sk hynix, sandisk, ethereum — the corrected query. [cited — extracted]: the series here are Google Trends’ own multiTimeline.csv export, retrieved Aug 1 2026: 261 weekly rows, 2021-08-01 → 2026-07-26, five columns, values as published. This replaces the earlier build, which read 60 modelled monthly points off a screen capture. Nothing on this chart is interpolated, fitted or smoothed — each plotted point is a row in that file. Weeks Trends reports as “<1” are carried as 0.5, the only transformation applied. What the correction changed, stated precisely: (1) Scale. With Nvidia absent from the panel, ethereum’s Aug-2025 peak held the 100; with Nvidia present it reads 49. All four previously-plotted series are therefore about half their former printed values. This is a property of Trends’ indexing, not a revision to reality. (2) Timing. The earlier build said micron overtook ethereum for good in Feb 2026; the source file says the week of May 3 2026 — a three-month error, logged in the Correction Log (§10·B). The sk hynix call (Jun 2026 → actual Jun 21 2026) was right. (3) Resolution. Weekly, not monthly — a 5-year Trends window is delivered weekly, and aggregating to months would have smoothed away the peak that defines the index. Two limits inherent to the source, unchanged: (1) Trends measures the string, not the subject — “micron” is also a unit of length and “sandisk” is a consumer-storage brand bought by people who are not investors, so those lines carry non-investment traffic; “sk hynix”, whose name has no other meaning, is the cleanest of the four. (2) Values re-index if the term list changes — this panel is only comparable to itself, which is exactly the lesson the misspelling taught. On the earlier error: the previous build was labelled a transcription and carried an explicit warning not to quote intermediate months; it was fitted to reproduce both anchors the capture printed, and it did. It was still wrong about the crossover date, because a fitted curve can satisfy its endpoints and miss its middle. That is the argument for source files over screen reads, and it is why this section now uses one. Cross-refs: §03·C and §04·F (the Korean retail-leverage episode), §04·E (the DRAM ETF), §04 (the equities), §02·C (innings). Not investment advice.
§08·B watches one vendor because Apple is the most legible. This is the same instrument pointed at the whole computer industry — every Windows OEM, the channel, and the person buying a stick of RAM. The symptom takes the same three forms Apple's did, in a different order. Apple queued, then deleted configurations, then repriced. The PC industry repriced first (+15–20% across Dell, HP, Lenovo, Acer and ASUS), then began stripping configurations — some builders now ship PCs with no memory at all — and is now losing the volume. IDC has cut its 2026 forecast three times, from −2.4% to −11.3%: 32.17 million fewer PCs, more than Apple ships Macs in a year. And the number that makes this section worth reading: the market's value goes up anyway — +1.6% to $274B on 11% fewer machines. That is kill-switch #5 with a receipt: demand destruction, quantified, in the largest consumer market memory has.
The mechanism is the one this whole board tracks. Samsung, SK hynix and Micron moved wafer starts to HBM and server DRAM (§05·D, §05·C·G), which starved the consumer channel. Dell's COO Jeff Clarke put the pass-through in one sentence on the February earnings call: "the spot market for a gigabit of DRAM over the last six months is up nearly five and a half times," with NAND up roughly 4× alongside it — "the cost basis is going up across all products." HP's CFO gave the balance-sheet version: memory and storage went from 15–18% of PC component cost to about 35% in a single quarter, a net $0.30 FY2026 EPS headwind after mitigations. When a third of your bill of materials reprices in a quarter, you have three levers — charge more, ship less content, or ship fewer units. The industry is pulling all three at once, and the chart below is what that looks like from the outside.
Provenance — what is IDC's, what is derived here, and three places the numbers do not agree. [cited — IDC]: Q1 2026 +3% at 65.6M units, attributed to pull-forward ahead of price rises; Q2 2026 −4.9% to 68.2M from 71.7M, ending nine consecutive quarters of growth — Lenovo 16.6M / 24.4% / −2.1%, HP 13.0M / 19.1% / −9.0%, Dell 9.3M / 13.6% / −5.0%, Apple 6.7M / 9.9% / +10.1% (share up from 8.5%), ASUS 5.0M / 7.4% / +0.2%. Full-year 2026 forecast (Mar 13 2026, reaffirmed Jun 2): shipments −11.3%, 284.7M → 252.53M, a fall of 32.17M units; Q4 2026 −20% YoY; market value +1.6% to $274B; ASP growth +17%; tablets −7.6% (151.9M → 140.36M) with value +3.9% to $66.8B; no relief before end of 2027, some easing from 2028, and pricing "unlikely to return to the levels seen in 2025." Forecast revisions: −2.4% (Nov 2025) → −8.9% (Jan 2026) → −11.3% (Mar 2026). [cited — vendors]: Dell +15–20% from mid-December 2025, Lenovo from January 2026 with quotes expiring Jan 1, HP / Acer / ASUS confirming the same band with channel-contract resets; Dell COO Jeff Clarke on DRAM spot "up nearly five and a half times" over six months and NAND ~4×; HP CFO on memory and storage moving from 15–18% to ~35% of PC component cost in one quarter and a $0.30 net FY2026 EPS headwind; HP's PC operating margin ~5%. [cited — retail]: a 32GB DDR5-6000 kit at roughly $80 in mid-2025 against $380–$589 on Aug 9 2026 ($11.88–$18.41/GB); system builder Paradox Customs offering prebuilt PCs with no memory installed. [derived on this board — the two shaded bars]: IDC publishes Q1 and Q2 actuals and a full-year forecast but not the intervening halves. Taking Q1 2025 as 63.7M (implied by Q1 2026's 65.6M at +3%) and Q2 2025 as the cited 71.7M gives H1 2025 = 135.4M against H1 2026 = 133.8M, or −1.2%; the residual needed to reach 252.53M is H2 2026 = 118.7M against H2 2025 = 149.3M, or −20.5%. Both derived figures inherit the assumption that IDC's full-year forecast has not been rebased on the H1 actuals — if it has been quietly revised, the required step is smaller. Stated so it can be checked in October. Three disagreements, surfaced rather than merged. (1) ASP. IDC states +17% ASP growth for 2026, but its own value and unit forecasts imply +14.5% ($269.7B/284.7M = $947 → $274B/252.53M = $1,085) — a 2.5-point gap between two figures from the same house, probably different vintages. Both are printed above; neither is adjusted. (2) Apple's Q2 growth. IDC +10.1% vs Omdia +16%, on market totals of −4.9% and −4% respectively. (3) Q1 2026 growth. IDC +3% (one IDC write-up says +2.5%), Counterpoint +3.2%, Gartner +4% — the trackers use different definitions of "PC," and the chart uses IDC's throughout so the series is internally consistent even where it is not the highest or lowest available. Two structural caveats. Units are not revenue and neither is profit — the market shrinking 11.3% while growing 1.6% in value is the whole point, and a third measure, vendor margin, is going the other way again (HP's PC margin ~5%). And the forecast is the weakest object here: three revisions in four months, with the publisher itself flagging that a geopolitical escalation is not yet included. Cross-refs: §08·B (the Apple version of this section), §08·C (memory inside Apple products), §03·E (what a gigabyte used to cost, for scale), §06·D·D (what is actually driving the demand), §03·F (the contract and spot tape doing this to them), §05·D (the wafer allocation that caused it), §02·D kill-switch #5 (demand destruction), §09 (Chinese memory entering Western PC BOMs). Sources: IDC, Jun 2 2026, Tom's Hardware on the IDC forecast cut, TrendForce on Dell and Lenovo, IDC Q2 2026 tracker, Omdia, Counterpoint, Gartner, TechPowerUp, VideoCardz, 9to5Mac, CNBC. Not investment advice.
As Korean and U.S. makers chase the high-margin top of the stack, China's CXMT and YMTC scale commodity memory into the gap — and race, under export controls, to build a domestic HBM supply chain for Huawei and other local AI players.
CXMT nearly tripled monthly DRAM wafer output to ~290k in two years and is migrating from DDR4 to DDR5. It filed a Shanghai STAR Market IPO (~$4.1B) and shipped HBM3 samples to Huawei, targeting mass production by end-2026 — though analysts widely see competitive HBM volume as a 2027–2028 prospect.
YMTC, China's NAND leader, is pushing into DRAM and HBM; a new Wuhan fab ramping in H2 2026 could make it the world's #3 NAND maker. Domestic tool-maker NAURA is building the equipment base behind both.
The read: China meaningfully relieves the commodity DRAM/NAND squeeze — for Chinese and non-U.S. devices, where HP, Dell, Acer and Asus qualify CXMT parts behind a compliance firewall. On HBM it sits ~two generations behind, constrained by U.S. equipment controls. Near-term risk to incumbents is margin compression on lower-end HBM3/3E after 2026 — not the HBM4 top end.
Volume advantage where it counts least for AI (commodity bits); a deep technology and equipment gap where it counts most (HBM). Counterpoint expects Korean firms to retain HBM dominance through the forecast window.
A live wire across the whole memory and storage stack — every story timestamped with when it was published, newest first. The feed auto-refreshes every 2 minutes; you can also pull manually. Filter by category, or switch between a continuous stream and a grouped-by-day view.
The honesty contract with its receipts. Every material error found during a build is written down here rather than quietly patched — what was wrong, when it was caught, and what changed as a result. Validation before every release has caught real bugs in nearly every session, which is the argument for running it: the errors below were not found by readers, they were found by the checks. A board with no correction log is not a board that made no mistakes. This was previously an untitled table inside §10·A; it is now its own section because three other sections already cited it as "§10·B" and that reference pointed at nothing.
The honesty contract at work: every material error found during builds, disclosed rather than silently patched. Validation before every release has caught real bugs in nearly every session.
| Found | Where | What was wrong | Fix |
|---|---|---|---|
| Oct 11 '26 | §06·F·D Hyperscaler CapEx — the "from dollars to memory" box | Two errors in one box. (1) It put memory at "roughly 8–15% of an AI server's bill of materials" and derived about $45–90B of memory bought by the five hyperscalers in 2026 — 6–11% of their $808B. The shares came from teardowns priced before the 2026 surge; SemiAnalysis, J.P. Morgan, CLSA and TrendForce now put memory at 30–50% of 2026 hyperscaler capex, roughly $242–404B on the same base. (2) It called Amazon's Jul 30 raise "the first time a hyperscaler has publicly attributed a capex increase" to memory. Microsoft attributed about $25B of its calendar-2026 plan to higher component pricing on Apr 29 — an item this board's own signal feed carried on May 4 — and Meta cited component pricing the same day. | The box now carries the current range with each estimate named, points to the new §06·F·D·M, which lines the five estimates up on their own denominators, and credits Microsoft and Meta. Found while building §06·F·D·M. The original reasoning is kept and the old range is marked superseded. |
| Oct 11 '26 | Headline stat and ticker — "projected total memory market, 2026" | Both still showed $552B, TrendForce's Jan 22 estimate (DRAM +144%). TrendForce raised its 2026 forecast to $889.3B on May 29 (DRAM $618.7B, +303%; NAND $270.6B, +280.7%) and its 2027 forecast to more than $1.28T, so the board's most prominent number was more than four months stale. | Headline and ticker now show $889B with the May 29 source. Found while building §06·F·D·M, which uses TrendForce's market figures. |
| Oct 10 '26 | §06·F·W Revenue per Megawatt — and §06·F·S, §06·F·B and §06·F·P | The board read Nebius's per-megawatt prices — about $12M on its 2026 base, $20–25M on its Q2 deals and $40–50M on short-term capacity — as total contract value per megawatt. They are annual. Nebius's Q2 2026 letter footnotes the figure "ACV — Annual contract value", measured on a revenue-recognition basis excluding prepayments. So §06·F·W committed the unit error it was built to expose: it drew the three tiers on its contract-value panel and concluded that Nebius's newest deals "match or exceed" SpaceX's contract figure on the same basis. §06·F·S and §06·F·B paired the $20–25M with CoreWeave's $24.8M of backlog per contracted megawatt as "two independent disclosures, essentially the same number" — a year set against a whole contract. §06·F·P called the $40–50M "total contract value". | The tiers now sit on §06·F·W's annual panel, where they show new deals priced at 1.3–5.3 times the $9.4M Nebius earns per running megawatt; on a per-year basis its short-term tier is 2.4–6.7 times SpaceX's converted figure. §06·F·S and §06·F·B keep CoreWeave's $24.8M as the only backlog-per-megawatt anchor, and §06·F·P is reworded. Found while building §06·F·A, whose 2030 figure depends on exactly this unit. The SpaceX and colocation conclusions in §06·F·W are unaffected, the Aug 17 changelog entries are left as written, and the §06·F·W and §06·F·S harnesses now check the corrected placement. |
| Oct 9 '26 | §08·B Apple Mac Symptom — and §08·C, §08·G and the §02 cycle-inning panel | The board described Apple's Jun 25 price rise as “$100–$300 per device, ~+15–20% on Macs and +15–25% on iPads”. The MacRumors list this section cited runs from +$30 to +$1,300. The two Mac Studios rose most — M4 Max +$500 (+25%), M3 Ultra +$1,300 (+32.5%) — so Macs rose +14.3% to +32.5% and iPads +20.0% to +28.7%. The low range had been copied into §08·C and the cycle-inning panel (“$100–500”) and into §08·G (“15–20%”). The same box said the Mac mini “lost its 256 GB storage tier” without saying it came back on Jun 25 at $799, and it dated the M4 Pro mini's $1,399→$1,599 rise to April, where the Jun 25 list records it as a Jun 25 change; the source for April could not be found again. | All four places now carry the full range. The Jun–Jul box records the tier's return and puts the M4 Pro rise on Jun 25, saying an earlier build had April. A new price-ladder chart is built from the line items, and its harness recomputes every percentage from the list prices. |
| Oct 9 '26 | §08·B freshness record and heading | §10·D had been reading this section as a live feed. Its freshness record said “live feed · compiled to Jul 24 2026” — older than the section's own Aug 17 badge — and the word “live” put it in §10·D's live bucket, so the panel showed no age for a section whose newest wait-time figure dated from August. The heading carried a “● LIVE” tag on the same basis. | The record now reads “Oct 9 2026 · Apple store capture · FQ4 print Nov 2”, with no “live”, and the heading says “store capture”. The harness fails the build if the record contains “live” or parses to a date other than the badge's. |
| Oct 9 '26 | §08·B wait-time chart | The wait-time chart drew no bar for its two shortest tiers. Two style rules had lost their bodies: the rule for “in stock / days” bars was missing entirely, and the “~2–4 weeks” rule had fused with the line above it into a selector that matches nothing. Those bars rendered transparent while the 5–10 week, 12+ week and withdrawn bars drew normally — so the chart showed only the long waits, though its legend listed all five colors and the week labels still printed. | Both rules restored, with the matching label colors. The harness now reads the stylesheet and requires a fill for every tier the chart can draw. |
| Oct 7 '26 | §03·F Spot & Contract DRAM | The Aug 10 build said mainstream DRAM spot prices had “stopped going up”. They had not. The claim rested on three decelerating weekly prints (+3.27% → +1.39% → +0.17%) and one session at 0.00% — and the same section conceded that the last week's move sat inside TrendForce's own ±1% restatement noise and that two more weekly prints would be needed to tell a lull from a stop. Over the next eight weeks DDR4 8Gb rose +9.2% ($42.45 → $46.36), with weekly prints as large as +2.08%. The caveats were in the provenance; the conclusion was in the summary and in the changelog (“the momentum is gone”). | Summary rewritten around the eight weeks since. The Oct 6 0.00% print — again in a week TrendForce attributes to holidays — is reported as a pause, not a signal, and the section now names what would count as a turn: consecutive negative weekly prints outside a holiday week. A new close-up chart marks the Aug 10 call against what followed. The Aug 10 changelog entry is left as written. |
| Oct 7 '26 | §03·F contract chart | The chart annotation and its caption said the Jan-2026 contract print moved SO-DIMM modules “+83–96% in one update”. That single update (Dec 31 → Jan 30) moved them +82.8% to +89.9%; +95.4% is DDR4 16GB SO-DIMM's whole first quarter (Dec 31 → Mar 31). The top of the range came from the quarter, not the update. | Annotation and caption now read +83–90%. The harness recomputes the single-update range from the data and checks the label against it. |
| Oct 7 '26 | Page text in five sections — §03·C, §03·F, §05·C·G, §08·B, §10·B | Fifty JavaScript escape codes had been pasted into ordinary page text, where a browser shows them literally. Readers saw strings such as \u2014 where a dash belonged and \u201c around quotations — 17 in §05·C·G, 16 in §03·F, 8 in this log itself (its Jul 17 entry was titled “\u00a703\u00b7F Spot tape”), 7 in §08·B and 2 in §03·C. The codes are valid inside the page's script, which is how they got there, and every check ran on the script rather than the text. | All fifty replaced with the characters they encode. A new harness scans the page text outside the script for any remaining escape code and fails the build if it finds one. |
| Oct 7 '26 | §03·B Korea DRAM Export Price | The section's chart was not customs data, and it overstated the rise roughly fourfold. It drew an "export price index" (Jan 2025 = 100) reconstructed from a handful of press anchors and calibrated through 2025 to the National Assembly Budget Office's quarterly DRAM fixed price — $1.35 to $8.13, a six-fold rise for a benchmark commodity part. Korea's customs tables publish value and net weight every month, and dividing one by the other shows the DRAM-chip unit value rose 1.6× across 2025, not 6×. By July 2026 the reconstruction read 24.1× its January 2025 level against 5.9× in the customs data — 4.1× overstated — and its 2023 history was wrong as well: it put mid-2023 above January 2025, where customs data has memory ICs at about half. Every number derived from it inherited the error, including a "25× from trough" stat card. The section did label the series a reconstruction; the label was honest and the calibration was not. Its live-refresh also asked a web-search model for a fresh index, which could only reproduce the same construct. | Rebuilt on the customs series itself: value ÷ net weight for five product codes, monthly from Oct 2023 (all memory ICs from Sep 2021), with September's month-end preliminary tally marked as preliminary. The press anchors the old chart cited reconcile exactly with the new data on the incl-modules basis — $28,057/kg in January 2026 to the dollar — so the anchors were right and the curve drawn between and before them was not. The retired index stays on one labelled chart view so the size of the gap remains visible; the live-refresh request is removed. The section has its first harness: it recomputes every unit value from value and weight, checks September against the published preliminary figures, and fails if the retired index feeds any number. |
| Oct 1 '26 | §08·D App-Layer Revenue (second correction) | The August correction fixed the conclusion and kept its cause. It retracted the claim that OpenAI had "flattened" — but kept the data point that produced the claim: OpenAI's "$2 billion in revenue per month", plotted at June. OpenAI said it on Mar 31, announcing its $122B round. With the anchor three months late, the chart carried a February-to-June plateau that did not happen, and three published findings were artefacts of it: a +67% two-month jump to reach Bloomberg's $40B; "the gap between the labs narrowed from 2.0× to 1.75×" — on correctly dated data it was 1.37× in May and has stayed between 1.33× and 1.43× since; and an "~8% inconsistency" between Bloomberg's "+20% in July" and its "$40B", which also treated a floor ("tops $40B") as a level. Found in the same pass: the August refresh updated the section's data and its computed cards but not its prose or its hero cards. The live page showed OpenAI at "~$24B · plateaued" and Anthropic at "$47B · ~$50B projected" directly beneath an intro retracting both, and the wage block's text still gave its Aug 1 figures — 0.70% of wages, $94B, $6.98 per $1,000, 33×, 6% of wage growth, e-commerce 2.1× ahead — beside cards computing 0.96%, $129B, $9.55, 45×, 8.6% and 1.6× from the same data. Three older OpenAI estimates were also drawn as filled "company said it" dots, and the roster's tag for Anthropic had no colour defined. | The statement is dated Mar 31 and the June point is gone. One constant-growth curve from the company's $24B (Mar 31) to Axios's ~$70B (Sep 29) meets all three reported constraints without fitting: ~$41B at the start of Q3 (Axios: up more than 70% since), +19.5% in July (Bloomberg: +20%), ~$59B in August (above Bloomberg's floor, now drawn as a △). Every anchor now records who said it; the three estimates are rings. Hero cards, the anchor table and the roster arithmetic are now computed from the data, and the rewritten harness checks the numbers in the section's prose against the computed values — so prose and charts can no longer disagree silently, which is how the stale figures survived for weeks. It also asserts that OpenAI's path satisfies the three reported constraints, that no floor is fed into a curve, and that no estimate is drawn as a company statement. |
| Oct 1 '26 | §06·H AWS Capacity Blocks (second correction) | The H100 on-demand line was 44% too low at every point, and it turned the section's central comparison upside down. It drew on-demand H100 at $6.88 per GPU-hour before AWS's June 2025 cut and about $3.93 after; the true figures are $12.29 ($98.32/hr per 8-GPU instance) and $6.88 ($55.04/hr). Two errors compounded. A CloudOptimo article the section cited misreads a $3.8592/hr operating-system fee on AWS's Capacity Blocks page as the instance price, and gives $6.52 as the pre-cut on-demand rate — the chart carried that $6.52 as a data point. And the post-cut point was AWS's own Mar 20 2025 example from the Capacity Blocks page ($31.464/hr, $3.933 per GPU), plotted on the on-demand line. The result: on 117 of 220 sampled dates, starting at launch in Q4 2023, the chart drew on-demand below the reserved price — implying a reservation cost more than walking in. It never did: reserving saved 68% in March 2025 and saves 13% from Oct 7. The June correction to this same section fixed which product the 2025 cut applied to, but took its on-demand levels from the same faulty article. Found in the same pass: the Jul 1 2026 increase was drawn in mid-May; the H100 launch dot (8.5) contradicted the section's own cited ~$7.50; and an H100 block anchor dated January 2026 had no supporting source. | On-demand rebuilt from AWS's price list ($98.32 → $55.04/hr on Jun 1 2025, unchanged through Sep 26 2026), and AWS's March 2025 example moved to the Capacity Block line where it belongs. Every point now carries its own date and source, steps sit on their real dates, and the unsupported anchor is gone. The section now has its first harness, which asserts that on-demand stays above the block price wherever both exist, that each announced rate is the stated multiple of the last, and that on-demand equals AWS's instance price divided by eight. The old data fails the first of those on more than half the dates it covers. |
| Oct 1 '26 | §06·D AI Token Demand (second rebuild) | The August rebuild fixed the error it was looking for and left three of the same kind. It lifted 2026 off the disclosure floor by adding China — then kept China below China's own data. (1) The National Data Administration published China's 2025 total, 21.1 quadrillion tokens, on Apr 29 — four months before the August rebuild, which never used it. That build's China row implied 7.6 quadrillion for the year, 2.8× short, and the board's entire global total for Q1 and Q2 2025 sat below China alone. (2) Its Q2 2026 China row was 167T/day while Doubao — one company inside China — was at 180T/day. (3) The anchor itself was stale: the Tokens Per Day floor is the plain sum of six last-reported figures dated anywhere from Dec 2024 to Jun 2026, so it carries China at March's 140T/day while its own ledger marks Doubao's 180T/day as contained within that figure. The August series sat within 3% of that corrected floor — a lower bound presented as a central estimate. Two more floors it broke without anyone noticing: non-China Q3 2025 below the Tunguz Western floor (88T/day), and the open-source row below Fireworks alone (30T/day). The August harness saw none of it because it pinned the build's own numbers and checked China at only two dates. | Rebuilt as China (official path) plus everything else (each company's disclosure brought forward to quarter-end). Q2 2026 moves from 10.6 to 15.1 quadrillion/month (~496T/day: ~15% above Epoch, within ~3% of the index once its China term alone is corrected); the first half of 2025 rises 2–3×; the projection is re-based on the same multipliers, taking Q4 2028 from 87 to 121 quadrillion/month. The harness was rewritten to assert floors and identities instead of pinned values, then run against the August build to confirm it fails it on every point above. Two consequences were kept rather than smoothed: the China band now leaps in Q1 2025, because the official figures only reconcile if most of 2025's hundredfold rise came in the DeepSeek quarter; and the H100-only power bound now breaches the IEA band today, not in 2028. Also fixed: the closed Q3 2026 bar was still labelled "current qtr" in a list headed "hard-disclosure anchors". |
| Oct 1 '26 | Site-wide as-of date & freshness records | The board's "today" had sat at Aug 29 for 33 days, and §10·D measures every section's age against it — so every freshness reading was understated by a month: the old date showed 26 stale; the real count is 58 of 89. Separately, four freshness records were never moved when their sections were updated: §06·D still said Jul 30 after the August rebuild, §06 and §06·B said "Jun 2026" after the Aug 29 spec update, and the catalyst calendar said Jul 30 after four rounds of Korea updates. Three of the four were this board's own August edits. The calendar also carried an approximate Sep 29 for a Micron release Micron had announced for Sep 30, and a neighbouring note said the Sep 21 Korea flash "lands two days before Micron" when it landed nine days before. | All five "today" values advanced to Oct 1 together — the harness requires them to agree. The four freshness records now carry their real dates. Seven calendar events dated Sep 1–Oct 1 moved out of "Ahead": the four Korea releases recorded with their prints (the board's rule for Korea rows), Micron's FQ4 captured from its 8-K and re-dated, Broadcom and Oracle flagged as outcome-pending. The pending-count check in the harness used to test for fixed words in a fixed order — which would have read "eight" forever — and now reads the latest count the provenance states. |
| Aug 29 '26 | §06·D AI Token Demand | The global token series had been running below its own floor, and the reason was that a third of the world was missing from it. The board put Q2 2026 at 7,000T/month — 230T/day. Two weeks after that quarter closed, the Tokens Per Day index published a disclosure-only floor of 300.1T/day: not an estimate, just the sum of what companies had already announced, overlaps removed. A modelled total cannot sit 23% beneath a hard lower bound. The cause was structural rather than arithmetic: the model contained no Chinese providers at all — not underweighted, absent — while China's National Data Bureau had been publishing an official national figure that reached 140 trillion tokens/day in March 2026, and ByteDance's Doubao alone passed 180T/day by June. China alone was larger than this board's estimate for the entire planet. The seven-provider split chart had been quietly asserting that global AI token consumption is a Western phenomenon. | The series was re-anchored to the independent estimated total (~360.4T/day, 16 Jul 2026), which lifted Q2 2026 from 7,000 to 10,600T/month and the Q4 2028 projection from 54 to 87 quadrillion/month. The growth curve was deliberately not re-fitted — only the level moved, so the revision cannot be mistaken for a new forecast. A China row was added to the provider split and all sixteen columns rebalanced. Two consequences were logged rather than smoothed: (1) the disclosures are not commensurable — Google's all-surface figure, China's national figure and OpenAI's API figure together consume ~90% of any credible global total, so the China row is left sitting 10–18% below China's own official numbers with the gap stated on the chart; (2) the raised series pushed the downstream power model's 2028 upper bound to ~65% of all data-centre power, above the IEA's 35–50% ceiling for all AI. That breach is kept and explained: it means H100-class silicon cannot serve 2028 demand inside any plausible power envelope, which is the argument for the efficiency gains, not a flaw in the model. |
| Aug 29 '26 | §06·B Accelerator Memory Roadmap | The roadmap carried AMD's MI455X at a bandwidth the part no longer has, and the check that should have caught it was looking at the wrong field. The card read 19.6 TB/s — the figure AMD announced for the MI400 series at Advancing AI in June 2025. Final MI455X silicon is 23.3 TB/s: twelve HBM4 stacks on a 192-channel interface at ~7.6 GT/s per pin. The board was 16% low on the memory bandwidth of the part it uses as AMD's answer to Vera Rubin. Worse, this build had already written 23.3 into the new §06·B copy, so for the length of one edit the same page asserted both numbers about the same chip. The validation harness missed it because it read card.gb on the roadmap entry, where the capacity actually lives at card.hbm.gb — the check returned "not found" for every card and was passing on an empty set rather than on agreement. | MI455X updated to 23.3 TB/s with the mechanism stated, and the note now records that this was an upward revision from 19.6 — worth flagging because AMD raised this part's memory spec over the same months NVIDIA was reported to be cutting Rubin Ultra's, which is the opposite of a uniform supply-driven derating. The separate MI450 card was deliberately left at ~19.6 TB/s: MI455X is the top bin of that series, not a synonym for it, and no confirmed per-SKU figure has appeared, so promoting the number across both cards would have invented a data point. The harness now asserts the card's bandwidth string directly, so a silent revert would fail rather than pass on nothing. |
| Aug 29 '26 | §04·F Leveraged ETF Assets | A correction this board made in June may itself have been the error, and the field is now openly disputed. §10·B records that in Jun 2026 MUU's AUM was raised from a stale ~$400M figure to ~$5.9B, logged as a ~15× fix. The board has since carried $5.72B (Jun 28), $5.90B (Jul 3) and $4.60B (Jul 30). A current quote page reads $900.70M, and trade press frames the fund crossing $1 billion as a fresh milestone — which is not how a $4.6B fund is described a month later, least of all one whose underlying rose over the interval. The obvious mechanical suspect does not fit: MUU split 20-for-1 on Jul 15, but $900.70M × 20 is $18.0B and $5.90B ÷ 20 is $295M, so neither figure is the other's split artefact. | Nothing was overwritten. Both sets of figures are tabled in §04·F with the 5.1× gap stated, and the section says plainly that they cannot both be right and that the June correction may have gone the wrong way. The MUU line on the monthly chart was deliberately not extended past Jul 30 — plotting a $0.9B point would draw a cliff that may be a data artefact rather than an event, and the honest state is "the level is unknown", not "it collapsed". The other three series are unaffected. |
| Aug 23 '26 | §04·L Forward P/E Heat Map | The heat map did not work as a heat map. Only 10 of 27 companies had been sourced and the other 17 were drawn blank, on the reasoning that an undated aggregator figure was worse than none. The result was a grid that was 63% empty — a chart whose whole purpose is to let the eye scan a pattern, with most of the pattern missing. The reasoning was defensible and the output was not; flagged by the user, not by the harness. | All 27 of 27 now carry a sourced multiple with a date where one is published — Samsung 5.84×, Toyota 7.99×, Alphabet 16.6×, Broadcom 18.71×, Berkshire 22.97×, Walmart 37.93×, Tesla 187.58× and the rest. The colour ramp moved to log scale, because Tesla at 187.6× compressed the other 26 into 22% of a linear band; on a log ramp they use 59%. Filling the grid raised a new problem rather than removing one: four companies now have two conflicting published figures, so each is drawn at the better-dated one with the alternative marked ▲ on the tile and in the tooltip — never averaged. |
| Aug 22 '26 | §02·C What Inning Are We In? | The section's central output — a single inning number — stopped being supportable by the evidence. Its own Jul 6 note had already warned that the price cycle and the volume cycle are two different games; seven weeks later they are at different innings and moving in opposite directions. The paper game advanced two notches (a −40% KOSPI drawdown, the first back-to-back circuit breakers in index history, ₩34T of margin accounts liquidated, and the sell side breaking into open disagreement). The physical game advanced none — Korea's Aug 1–20 chip exports set a record at +198.8%, DDR4 hit an all-time high, and six-year-old A100s are renting dearer than a year ago. Averaging those into "the 6th" was hiding the finding. | The single call is retired and replaced with two scored tracks — 7th on paper, 6th on the physical — with separate honest ranges (6th–8th and 5th–6th) and a chart that plots both so they cannot be conflated. The section now states explicitly that reading the equity drawdown as a memory-cycle turn, or the export data as proof the equities were wrong, are the two symmetrical errors. Also carried against the section's own prior view: §05·D·V's capacity finding argues the 8th and 9th innings arrive earlier than this section assumed, and it previously had nothing pointing that way. |
| Aug 22 '26 | Site-wide as-of date | The board's stated "today" had drifted again — header, freshness denominator, baseline, footer and catalyst calendar all read Aug 17 against a real date of Aug 22. §10·D measures every section's age against that denominator, so freshness readings were five days understated. Advancing it also exposed two events that had since occurred and were still listed as upcoming: the Aug 21 Korea Aug 1–20 flash and Marvell's Aug 20 FQ2. | All five sites moved to Aug 22. The Korea flash is marked complete with its outcome ($55.2B, semis $26.0B, +198.8%); Marvell joins the outcome-pending list, taking it to seven. The harness check that caught this was itself de-pinned — it had hardcoded "2026-08-17" and would have aged out the same way, so it now derives the expected date from the header and compares the other four against it. |
| Aug 17 '26 | §06·F·W Revenue per MW | The new section was first numbered §06·F·M — which §06·F·M Micron RPO & SCAs already had. A second duplicate section number, introduced within an hour of fixing the first one (§06·D·M). | Renumbered to §06·F·W. Worth recording because of how it was caught: the uniqueness check added to the audit after the §06·D·M fix flagged it on the very next build, before deploy. The check paid for itself immediately — the previous duplicate had gone unnoticed long enough for thirteen cross-references to accumulate against it. |
| Aug 17 '26 | §06·D·M / §06·D·E | Two different sections carried the same number. "AI Model Size Over Time" and "Memory Demand by End-Use" were both numbered §06·D·M, which made all thirteen cross-references to that number ambiguous — a reader following one could land on either section. Found by an audit that had never checked section numbers for uniqueness, only section IDs. | Model Size keeps §06·D·M, since eleven of the thirteen references unambiguously mean it (model cadence, parameter counts, release timelines). Memory Demand by End-Use becomes §06·D·E. Two references were genuinely ambiguous — one in §06·D·W about converting token demand into "memory demand", one in a §06·D·D table row about where GB land — and both were repointed to §06·D·E as a judgement call, disclosed here rather than made silently. The duplicate-number check is now part of the standing audit. |
| Aug 17 '26 | §05·E DRAM WSPM Required | The model's whole-industry capacity line is below the sum of four of its own components. Vendor-level data puts Samsung, SK hynix and Micron at 3,137k wafers/month by end-2030, with CXMT a further 600–950k — 3,737–4,087k from four named producers against the 3,396k this section models for the entire industry. Re-running the section's own 2030 requirement of 3,775k gives 92–101% demand/supply, not the 111% reported: the 2030 shortfall narrows to ~38k or flips to a 312k surplus. This is the strongest evidence yet against one of the board's most bullish sections, and it was found by adding data the user supplied rather than by re-reading the model. | Recorded in §05·E itself, not only in the new §05·D·V, so a reader of the bull case meets the counter-evidence in place. Neither series is overwritten: three specific reasons the gap may be definitional rather than real are given (installed vs effective capacity, CXMT wafer starts vs usable output, and this model's 2029–30 tail already being flagged as the most extended-bull of four houses). The harness recomputes the 111% from §05·E's own arrays and the 92–101% from the vendor figures, so neither number can drift from its inputs. |
| Aug 17 '26 | §08·B Apple Mac Symptom | The section's headline claim was falsified by events and had to be retracted, not extended. Since March it read: high-memory Macs wait months "while the base model still ships same-day." On Aug 2 Bloomberg reported the MacBook Air — the mainstream base machine — in a "major" shortage at 2–6 weeks, with retail sources calling shipments "more constrained than they can ever recall". Worse for the June framing: this board had concluded Apple "stopped queueing and started repricing". It is doing both, and the Air was sold out at a price $200 higher. Price did not clear demand. | Summary claim retracted in place with the date and the reason, rather than deleted. The June "repricing replaced queueing" reading is explicitly marked superseded. The harness now asserts the phrase appears only as a quotation inside its own retraction and never as a live assertion. |
| Aug 17 '26 | Site-wide as-of date | The board was carrying four different values for "today" at once: the header and §10·D freshness denominator said Aug 10, the footer stamp Aug 14, the catalyst calendar Aug 15 — against a real date of Aug 17. §10·D measures every section's age against that denominator, so every freshness figure on the board was understated by a week, and the "stalest sections" ranking was computed off the wrong base. | All four aligned to Aug 17 2026. The harness now reads the header, the freshness denominator, baseline.asof, the footer stamp and CAT_TODAY and fails unless all five agree — so the board can no longer hold more than one opinion about what day it is. |
| Aug 15 '26 | §02·E Catalyst Calendar | The calendar's internal "today" was hard-pinned to 2026-07-30. Every countdown rendered 16 days short, and six events that had already occurred still displayed under "Ahead" — including the Aug 1 Korea trade release whose result §03·B was already reporting elsewhere on the board. A second sweep found two rows marked completed with no outcome at all (the Jul 21 Korea flash, and Tesla's Jul 22 Q2 carrying an empty outcome string). | "Today" advanced to Aug 15. The six past events are marked completed; the Jul 21 Korea flash got its outcome ($54.9B, −11.3% vs June), and the six with no captured result now render an explicit "outcome not yet captured on this board" line rather than a silent blank. The harness now fails on any past-but-upcoming row, any future-but-done row, and any completed row with neither an outcome nor that flag. |
| Aug 15 '26 | §02·E Catalyst Calendar | This board applied a weekend shift that the evidence contradicts. The nine new Korea rows were dated by pushing any weekend release to the next business day — which moved MOTIE's Nov 1 full-month print to Nov 2. But MOTIE demonstrably publishes on the 1st at weekends: the completed Aug 1 2026 row in this very table was a Saturday. The shift rule was assumed, not observed. | Nov 1 restored. MOTIE dates now stay on the 1st regardless of weekday, with the reason stated in the row. KCS weekend behaviour is not evidenced either way, so the one KCS date landing on a Sunday (Oct 11 → Oct 12) is labelled an assumption and named as the least reliable date in the set. The harness checks the two agencies against different rules. |
| Aug 15 '26 | §05·F AI Wafer Consumption | An independent source validates this section's total and undermines its composition. A Morgan Stanley SKU-level build puts 2027 accelerator units at 21.14M against §05·F's 21.30M — 0.7% apart, from unrelated inputs. But per company the two disagree by up to 3.1× (Meta 1.70M vs 0.55M), with Microsoft 1.8×, AMD 1.5× and Google 1.5×, in both directions. The section's per-company bars were drawn with the same visual confidence as its total; they do not carry the same support. | §05·F now states that its aggregate is well-supported and its company split is one plausible allocation among several. Recorded in §05·C·H as well, and no per-vendor HBM share is inferred anywhere from either source. |
| Aug 15 '26 | §05·C·H (new) | An earlier draft of the new section claimed the source's gigabyte and gigabit totals matched "exactly". They do not: 6,077,256 kGB × 8 ÷ 1,000 = 48,618.048 mn Gb against a stated 48,618. The gap is 0.0001% — immaterial to every conclusion, but "exactly" was the wrong word in a section whose whole argument is that the source reproduces cell for cell. | Reworded to "consistent to the source's own rounding", with the arithmetic shown. The harness now asserts both that the two agree within rounding and that they are not bit-identical, so the stronger claim cannot creep back. |
| Aug 15 '26 | §04·B Analyst Ratings | The list was billed as covering "the trailing twelve months" and carried a "● live · 12 mo" tag, but its two oldest entries — Jun 25 2025 and Aug 6 2025 — were 13.7 months old. The window claim had quietly drifted past the data as the section aged. Separately, this board initially dated Samsung Securities' SK hynix cut to August; re-reading the source's sentence structure places it in the Jul 30 group alongside Shinhan, NH and Daishin, with only that firm's Samsung Electronics cut described as "this month". | Window restated from the data as "since June 2025" in both the heading and the live tag, with the reason shown inline. Samsung Securities' SK hynix action re-dated to late July in both the chart data and the list. The harness now derives the oldest entry date and fails if any entry predates the stated window, so the label cannot drift again. |
| Aug 15 '26 | §04·B Analyst Ratings | A source headline that understates its own reporting. The Aug 11 piece is titled "SK hynix Targets Diverge by 2 Million Won" and gives a range of ₩2.7M–₩4.7M — but reports Kiwoom's ₩2.1M target four paragraphs earlier in the same article. On the article's own figures the spread is ₩2.6M, not ₩2.0M. | This board plots the full ₩2.1M–₩4.7M range and states the discrepancy rather than adopting either the headline range or silently correcting it. The harness recomputes the spread from the plotted targets and requires the prose to match that arithmetic. |
| Aug 15 '26 | §03·C Korean Leverage | On Aug 1 this section concluded that "the appetite has now broken too." Two weeks of data show that was too strong a claim from too narrow a measure. Domestic single-stock leveraged turnover did collapse — −91.0% in a month, from 33% of all KOSPI trading to 4.8%. But Korean retail bought $662.85M of a US 3× semiconductor fund on Aug 12–13 alone, holding $6.559B of it — their fourth-largest US position, up from tenth in April — while domestic margin loans rose seven straight sessions to ₩30.93T. The instrument was switched off; the appetite was not. The error was treating one venue's volume as a proxy for demand. | New Aug 15 block and a two-panel chart showing the domestic channel closing and the US channel opening over the same fortnight, deliberately not netted against each other. The section now states the narrower conclusion: a regulator can close a venue quickly, and doing so relocates leverage rather than removing it. The harness asserts the section does not claim vindication. |
| Aug 15 '26 | §03·C Korean Leverage | Three figures in the new reporting that do not reconcile with this board's own anchors, and one that does not reconcile with itself. A cited −38.6% maximum drawdown against this board's −40.40% from its own dated peak and trough; a cited "+5.8% this month" against +24.75% from this board's last recorded close, which would require an unreported +17.9% single session on Jul 31; a peak dated June 22 where this board carries June 19 and an earlier translation gave "July 19". Separately, the Aug 6 source prints two different turnover figures for Aug 4 (₩1.2556T and ₩919.8B) and labels two consecutive days "Aug 4". | All four disclosed in a dedicated block, with the arithmetic shown. No series was adjusted to fit. Both Aug 4 figures are drawn, the second marked as a same-day second print; the Aug 14 bar is marked as a different fund basis. June 19 is retained as the peak date on corroboration grounds and the disagreement logged. |
| Aug 14 '26 | §06·F·R Neocloud Financing | The section described CoreWeave's two 2026 term-loan facilities as closing "ten weeks apart" — in the summary, the chart title, the chart annotation, the side panel and a stat card. The two disclosed closing dates are Mar 31 and May 18 2026, which is 6.9 weeks. The section's own data object already carried the correct weeks:7; the prose had been written before the dates were pinned and never reconciled to them. | All five occurrences changed to "seven weeks". The validation harness now computes the interval from the two closing dates and fails if the page states anything more than a week away from it, and separately asserts the phrase "ten weeks" does not appear inside that section. |
| Aug 14 '26 | §06·F·R Neocloud Financing | A category error in a table this board built. The GPU-backed debt table listed CoreWeave's $21B of total debt alongside Crusoe's and Lambda's GPU-collateralised facilities, under a headline figure of ">$20B of GPU-backed loans". Those are different measures, and the disclosed rows therefore sum to $21.9B — more than the market-wide total they sat beside. A reader could reasonably have read the rows as components of the headline. They are not. | Table caption and provenance now state explicitly that the rows must not be summed or compared to the headline, name the total-debt-versus-GPU-collateralised distinction, and call the residual subtraction invalid rather than merely imprecise. The harness asserts the disclosed sum exceeds the market figure and that the page says so. |
| Aug 14 '26 | §05·G Memory Form Factors | The 3D DRAM "volume, if it works" bar runs to 2032 on a chart whose axis ends at 2031. It was silently clipped at the plot edge, so the least certain claim on the page rendered as though it had a firm end date one year earlier than the data said. | Bars extending past the axis now draw a ▸ continuation marker and a "runs past 2031" label. The harness enumerates every bar exceeding the axis and requires each to be marked. |
| Aug 14 '26 | Site-wide metadata | The section count is written in five places, one of which is inside a URL-encoded SVG in the og:image tag (%3E79 sections). A word-boundary sweep that updated the other four skipped that one, because E79 has no boundary before the digits — so the social-preview card would have advertised a stale count indefinitely. | og:image corrected. The harness now sweeps every "N section(s)" string in the file with no word-boundary assumption, reports the distinct values it finds, and additionally pins the og:description, og:image and search-placeholder sites by name. |
| Aug 14 '26 | §08·D App-Layer Revenue | The section asserted the two labs had diverged, describing OpenAI as "flattened near a $24–25B run-rate" with a user base "plateaued ~900M weekly", and projected that flat line forward through Jul 2026. Bloomberg reported on Aug 13 2026 that OpenAI's run rate tops $40B — up ~67% from the figure this board called a plateau, and +20% month-over-month in July alone. Two consecutive prints at ~$24B were read as a structural ceiling; they were a pause. | OpenAI series re-anchored to the Bloomberg figure and the "divergence" framing removed. A fourth provenance tier (third-party tracker, dashed ring) was added so measured-by-someone-else points can no longer be conflated with this board's own projections — which is the class of error that produced this one. |
| Aug '26 | §08·F Google Trends | Section was built from a screen capture of a panel whose first term was misspelled “nividia”. Two consequences: Nvidia was absent from the index, so every plotted value was ~2× too high (ethereum’s peak held the 100; it is really 49); and the transcribed curve put micron’s permanent crossover above ethereum in Feb 2026 when the source file says the week of May 3 2026 — a three-month error. The fitted series reproduced both cited anchors and still missed the middle. | Re-ran the corrected query and rebuilt the section from Google Trends’ own multiTimeline.csv — 261 weekly rows, no interpolation. Crossover dates corrected; scale note added explaining that the index moved, not the behaviour. |
| Jun '26 | §06·F·B Backlogs | Microsoft 2023 shown as $112B — that was the cloud-only RPO subset, understating total commercial RPO by ~half | Corrected to ~$224B with SEC-anchored quarterly series |
| Jun '26 | §04·F Leveraged ETFs | MUU AUM shown ~$400M — a stale Nov-'25 snapshot; real figure ~$5.9B (~15× error) | Rebuilt all four funds on dense monthly data |
| Jun '26 | §04·E ETF Race | IBIT milestones mixed trading vs calendar days; axis too short for the ~$99B peak | Recomputed on calendar days; axis extended to 900d |
| Jun '26 | §06·H AWS Blocks | Jun-'25 ~45% cut wrongly implied to include Capacity Blocks — AWS explicitly excluded them (separate cut cadence, then hikes) | Two-track pricing rebuilt with dated anchors |
| Jun '26 | §04·D·S Short Interest | Garbled "debt cut $4.4B to $5.7B" figure in the FQ3 backdrop | Removed; replaced with clean sourced figures |
| Jun '26 | §06·F·B (code) | Duplicate const lg declaration — would have broken the entire dashboard's JS | Caught by pre-release validation; renamed |
| Jul 30 '26 | §10·A Signals (code) | Two different functions were both named renderScorecard() — one rendering the §10·A signals scorecard from scorecardData, the other the kill-switch strip. JavaScript hoisting means the later declaration silently overrode the earlier, so #scorecard was never populated and that panel rendered empty. No error was thrown (unlike a duplicate const), which is why it survived several validation passes. | Renamed the signals renderer to renderSignalScorecard() and updated its call site; both panels now render |
| Jul 30 '26 | §04·D Price Targets | SK hynix's "current price" was entered as ₩2.9M in the Jul 24 refresh — that figure was the stock's Jun 25 all-time high, misread from a source that quoted the ATH, not the live quote. Real price was roughly ₩1.8M at the time and ₩1,322,000 by Jul 30, so the card understated the drawdown and materially overstated implied upside. | Corrected to ₩1,322,000 (Samsung likewise refreshed ₩248.5K→₩207,000); provenance now names the error explicitly |
| Jul 30 '26 | §03·C Korean Leverage | The Jul 24 build called the week of Jul 17–23 "the first stabilization" and led with it. Within three sessions the KOSPI fell 16.17% over Jul 28–29 — the worst two days in its history — erasing the rebound entirely. The call was premature: falling margin balances and returning foreigners were read as the end of the unwind when they marked a pause in it. | Headline reframed to "then the worst two days in KOSPI history"; the stabilization is retained as a dated, bounded episode rather than a turn, and the forced-selling note now flags that "the peak has passed" was also claimed on Jul 21 |
| Jul '26 | §06·F·B Backlogs | Oracle's snapshot bar drew shorter than Microsoft's despite Oracle leading ($638B vs $627B) — segments summed to the May-vintage $553B | Hatched +$85B "post-snapshot" segment added |
| Jul '26 | build tooling | A Python \\n escaping bug injected three nav links into a JS string (join), breaking the whole file's script | Caught by validation before release; reversed byte-exact, re-applied correctly |
| Jul '26 | §04·D Targets | Rating-only firm entries added as pt:0 — the renderer draws proportional bars, so they would have shown $0-width bars | Caught in validation before release; entries removed, folded into prose |
| Jul '26 | freshness system | §04·B chip briefly set to a static date, misrepresenting a live-feed section | Self-caught same turn; reverted to 'live feed' |
| Jul '26 | §06·D·W per-MW | Table said GB300 ~4.1 PB/s while its own footnote math (490×8) gives 3.9; Rubin GPUs/MW 330 vs 340 | Harmonized before release; table and footnote now agree |
| Jul '26 | build wiring | Catalyst-calendar and provenance-index nav links + render hooks shipped without their sections — two dangling anchors (interrupted batch) | Sections built Jul 6; renderer verified against its DOM target |
| Jul 14 '26 | §03 · §02·D Price basis | Kill-switch #1 recorded 2Q26 DRAM as ~+low-90s% — that was 1Q26’s figure; TrendForce’s 2Q26 conventional-DRAM rise is +58–63% (the §03 +61% bar is right) | Re-verified vs TrendForce; §02·D corrected to the 1Q→2Q→3Q ladder (~+90–95% → ~+58–63% → +13–18%); §03 conflict flag resolved |
| Jul 14 '26 | §05·C·G HBM4 | Board credited only SK hynix & Micron with HBM4 first parts; Samsung had begun industry-first commercial HBM4 mass production Feb 12 2026 and was Nvidia-certified for Vera Rubin Jun 5 | Samsung’s commercial HBM4 added; all three now shown in HBM4 mass production |
| Jul 14 '26 | §05·B Vendor share | The “Micron overtakes Samsung” HBM headline was circulating as a 2026 event; it is Q2 2025 Counterpoint data — Samsung rebounded to ~35% in Q3 2025 | Added a flagged vendor-share block to §05·B showing the firm/quarter divergence instead of one number |
| Jul 16 '26 | §03·C Leverage intro | A same-day edit said KODEX SK hynix 2× fell 45% “despite the underlying rising since debut” — SK hynix is down since May 27, just far less than the fund | Corrected same session per the Bloomberg/Yahoo framing (fund −45%, >60% off peak, stock's decline much smaller); disclosed here rather than silently fixed |
| Jul 17 '26 | §04·F · §03·C legends | Two chart legends and a §03·C note labeled SNXX as a "Samsung 2×" fund — SNXX is Tradr's 2× SanDisk (SNDK); §04·F's own table and provenance always said SanDisk | Legends and note corrected; the series data itself (SNXX AUM) was right throughout |
| Jul 17 '26 | §02·B Supply Pipeline | Chart titled “combined DRAM capex,” but its values track each maker's total memory capex (Micron 8→13.8 = FY24→FY25 total, not DRAM-only) | Caption clarified to “memory capex (DRAM-led),” basis stated; values refreshed to latest guidance (Micron FY26 ≈$25B +81% cited; SK hynix/Samsung ramp; +2027 projection) |
| Jul 17 '26 | §06·D·R OpenRouter | The per-week tooltip listed invented per-model token amounts under real model names (fixed illustrative shares × weekly total) — contradicting the section's own cited leaderboard (implied GPT-5.6 Luna ≈16T/wk vs its printed 273B/wk) and showing models before their launch | Tooltip rebuilt cited-only: the snapshot week lists the leaderboard's exact figures with a coverage note; other weeks state the split isn't public; colour bands de-named (visual stack only) |
| Jul 17 '26 | §06·C · §03·D wiring | Two renderers were orphans — never called: the quarterly-units-by-vendor chart's initial draw (its only call sat in a legend click-handler, but the legend is built by the renderer — a deadlock, so the chart was permanently blank) and §03·D's nine-metric grid (defined, wired nowhere) | Found via a programmatic all-renderer orphan sweep (the sweep owed since v10); both wired into their sections' on-open renders and verified by invoked-render tests |
| Jul 17 '26 | §03·F Spot tape | Annotated a “$1.63 Jan-2025 trough” from a secondary source; archived TrendForce captures show DDR4 8Gb at $1.50 on 2025-03-20 — lower, and later. The label overstated the bottom | Annotation now reads “$1.50 — lowest capture”; the $1.63 point retained as a dashed-ring superseded anchor rather than deleted |
The honesty contract in one view: this table is built at open, live, from every section’s own provenance footer — nothing here is written twice, so it cannot drift from the sections it indexes. Sections without a footer are visual/structural and say so.
Method. Assembled at runtime from each section’s final provenance footer (first ~170 characters, linked to the section). Structural sections without footers are listed as such rather than omitted. Not investment advice.
The honesty contract, turned inward. This board mixes live-feed, dated-snapshot and structural sections; this panel makes each section's age visible — built at open from the same dataAsOf map that stamps every section's freshness pill, measured against the board's own as-of date (Oct 11, 2026). Below it, a running changelog of what's been added or refreshed — the positive complement to the Correction Log.
How age is bucketed: fresh ≤14d · aging 15–45d · stale >45d · live refreshes in-app · structural not time-sensitive. Dates are parsed from each section's own provenance stamp; "stale" means due a look, not wrong. Not investment advice.