Every instrument on one desk. The needle row is the live reading; pick a switch or
walk the rack — boards read the engine on cadence, and every figure traces to the data spine.
Nothing here is a frozen snapshot.
source: ai_fragility engine · data as of 2026-07-14
GAUGES · LIVE · AS OF 2026-07-14
AI Fragility Index
Composite read over 68 firms
49composite / 100 · 2026 Q2
Moderate, and holding — four quarters of composite stress across the 68, none of it priced.
Compare firms · L1–L5
loading the universe…
Pick firms to read their engine scores side by side — composite + all six indicators, live from watchlist.json.
How to read it: D(t) = M(t) − G(t), in standard deviations. A positive, widening D means the story is running ahead of the receipts — recomputed live by the engine, stamped with the as-of date.
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EXPLORERS · INTERACTIVE
The Scaling Timeline
Four vectors of AI scaling · log scale
Four scaling vectors on one log scale — three race upward, one collapses.
Deployed everywhere, visible almost nowhere — the payoff scoreboard by industry.
The payoff scoreboard reads industry adoption against demonstrated productivity, from the verticals’ own filings — deployed everywhere, visible almost nowhere. Each bar is a vertical; the tally in the needle row is how many are paying off today. The chart is live from the engine; the Industries board carries the per-vertical receipts.
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DISCIPLINE · LIVE · AS OF 2026-07-14
The Instrument
How the desk measures D(t)
How the desk measures D(t) — the method, stated in advance.
We instrument ourselves to index the future. Capex Watch reads the financing runway from the outside. This reads the productivity lag from the inside — by measuring, in real time, how much cognitive work AI absorbs inside our own research desk. The result is D(t): the divergence between market narrative M(t) and filings ground-truth G(t).
D(t) · divergence+4.06M(t) minus G(t) — narrative ahead of the filings · 2026Q2
M(t) · market narrative+2.83standardized narrative read from calls and releases
Infrastructure friction, tooling latency, context-switching cost — the invisible tax on every cognitive operation. Logged as tool-call latency distributions and model-switch frequency.
F_cap
The Brain
capability signal · human moat
How much of the analytical lift is genuinely AI-generated versus human-directed synthesis — output lineage tagged in session logs: AI-drafted, editor-revised, fully original.
F_dir
The Steering Wheel
direction signal · human moat
Editorial agency — how often the human overrides, redirects, or discards AI output. High F_dir means the desk still steers. Override rate, prompt-revision frequency, final-pass edit distance.
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DISCIPLINE · LIVE · AS OF 2026-07-14
The Fragility Brief
Printable · the Index's summary brief
The Index's printable summary brief — six indicators, reproducible.
The dataset
Filing-sourced and reproducible
Six filing-sourced indicator tables sit underneath this brief, each computed from the source filings: an accounting table of useful-life changes, a capex-versus-demand table, the insider Form 4 record, the financing graph of the compute complex, the disclosed energy commitments, and a ground-truth deterioration series. The brief is built to be reproducible — every figure derives from those filing-sourced tables.
Download the tables · CSV, primary-source notes inline
Each table carries its 10-K / Form 4 accession numbers inline, and shows blanks (NOT_SOURCED) rather than imputing. The circular-financing edge ledger (indicator 04) is published above — revised 2026-07-02 with the Amazon–OpenAI equity legs and a funded_usd column; the energy indicator (05) rests on qualitative disclosures and is discussed in its section. Cite as: The Desk, “AI Fragility” dataset (2026), /brief.
One discipline runs through all of it: where a value cannot be sourced cleanly from a filing, it is shown blank rather than imputed. The point is to read the cycle in the numbers the companies publish themselves, not in estimates layered on top of them.
Indicator 01 · Depreciation integrity
Are the assets aging faster than the books admit?
Has a firm extended the useful life of its depreciable assets — converting paper income without a matching dollar of cash? A life shortened scores zero, regardless of size.
The first indicator asks a narrow accounting question with a wide reach. When a firm extends the useful life of its servers, the same hardware cost is spread over more years, annual depreciation falls, and reported operating income rises — on paper alone, with no extra cash, no new customer.
# §2.1 — paper benefit from a useful-life extension delta_dna = ppe_depreciable × ( 1/life_old − 1/life_new ) # hard rule: a life shortened scores 0, regardless of size
The direction of travel is uniform: every firm that touched a useful life lengthened it, and four did so while running the largest AI-capex programs on record. Amazon is the control — it moved the same lever the other way, six years to five, and absorbed a $1.4B charge against income, which is why it scores zero here despite carrying the heaviest depreciation line ($41.86B) in the set.
The signal is not the size of depreciation; it is the choice to make it smaller while everyone's assets are aging faster.
Indicator 02 · Capex vs demand gap
Is the spending outrunning the demand?
Is AI capital spending outpacing the revenue that would justify it? The break-even hurdle is set generously, so the firm gets credit for all segment revenue, not just AI lines.
# §2.2 — required revenue per $1 of capex per year factor = ( CoC + 1/L ) / m = ( 0.10 + 1/6 ) / 0.30 = 0.889 # fail when FY2025 segment revenue < capex × 0.889
One firm fails the break-even test on full segment revenue: Alphabet, where Google Cloud's $58.7B sits $22.6B below the $81.3B the capex requires — a 28% shortfall. Capex is also growing roughly 2–4× faster than the revenue lines it funds across the cohort, even where the level test still clears.
Firm
Capex / revenue growth
Meta
3.95×
Amazon
3.25×
Alphabet
2.07×
At the system level the aggregate gap widens from $78B to $90B over four quarters. Spending is being committed ahead of the demand — and the test is built to flatter the firms, not to indict them.
Indicator 03 · Insider selling intensity
What are the people who know most actually doing?
Two kinds of insider selling look identical on a tape and mean opposite things. Pre-scheduled 10b5-1 plan sales score low; the signal is discretionary selling — a sale an officer chose to make, in a window when they held material non-public information, with no 10b5-1 footnote on the Form 4.
The three compute leaders divide cleanly. The discretionary cluster — not the headline dollar — is what scores, which is why the largest sellers by dollar (both on 10b5-1 plans) are discounted while smaller discretionary clusters rate higher.
Firm
Discretionary
10b5-1 plan
Largest single seller
NVDA
$0.93B
$1.57B
Dir. Mark Stevens $802M discretionary
AVGO
$0.50B
—
Co-founder Samueli $749M plan
AMD
$0.02B
$0.29B
CEO Su plan
NVDA's $0.93B discretionary is led by director Mark Stevens at $802M with no detected plan, against $1.57B run through confirmed 10b5-1 plans — including CEO Huang's $1.05B, under 1% of his stake. AVGO's $0.50B discretionary is spread across the entire C-suite — CEO Tan, the CLO, the CFO, and two more officers, none with a detected plan. AMD is the quiet one.
Discretionary selling is not a one-quarter event. The universe-level Form 4 total rises every quarter across the window — from $0.85B in 2025Q3 to $1.10B in 2026Q2, a 29% increase — while the same names were guiding investors toward accelerating AI demand.
Indicator 04 · Circular financing
Is the money going in a circle?
The structure is a loop: an investor funds a lab, the lab commits to buy compute from the investor's cloud, that cloud revenue underwrites the investor's capex, and the capex buys the investor's own chips through the lab it funded.
The financing graph of the AI-compute complex is a directed multigraph over twelve principals and four edge types — invests · buys_compute · supplies · marks_up. The recycling ratio measures the loop's leverage: compute committed out of the core labs (OpenAI, Anthropic, xAI) divided by equity put in, across three provenance tiers.
The same dollar of disclosed equity supports roughly 15.5× committed compute on a funded-cash basis (revised 2026-07-02 from 26× — Amazon's Q1 2026 $15B funded OpenAI stake widened the equity base), easing to ~3.6× only when every reported secondary round is admitted as equity. Present-valued at 10% over each commitment's disclosed horizon, the funded-cash ratio is about 13× — nearer 11× if the undated Microsoft commitment is discounted over a typical cloud term. Provenance, not arithmetic, moves the number; stock or flow, discounted or not, the loop turns far above any arm's-length benchmark.
Recycling ratio by equity tier — funded cash → filed → +reported → PV-adjusted.
Two destinations carry the loop: of the labs' committed compute — the same $540B universe as the ratio — Microsoft and Amazon receive 96% (98% on the filing-grade subset). Mark-to-model gains booked on those same customer stakes total $18.2B (Microsoft +$5.9B — primarily the OpenAI recapitalization dilution gain — Amazon +$12.3B) — earnings recognized on the appreciation of the firms one funds. Eight directed cycles run through the cash-flow subgraph, and the largest single commitment — Nvidia's $6.3B backstop to CoreWeave — surfaced only in a September 2025 8-K (accession 0001769628), absent from the March 2025 IPO prospectus that first sold the relationship.
Indicator 05 · Energy & diminishing returns
Are physical limits starting to bind?
Are power, cooling, and chip economics beginning to cap capability gains? This is the thinnest-data indicator in the framework and carries the lowest weight (0.10) — we will not present estimate as measurement.
The firm-level cost-per-capability curve is largely proprietary, so this indicator does not try to measure it. What the filings do record, unambiguously, is the scale of power being committed — the appearance of gigawatt-scale capacity figures inside the same compute-purchase agreements that drive the circular-financing loop. The build stops being denominated in dollars and starts being denominated in power.
Power commitment
Capacity
Provenance
OpenAI → AMD
6 GW
Filing 8-K EX-99.1, 2025-10-06
Anthropic → Amazon
5 GW
Media not yet filed
Anthropic → Google
>1 GW
Media not yet filed
Three edges carry an explicit gigawatt figure — 12 GW in aggregate — but exactly one is filing-sourced. By the methodology's own rule, that single filing item is the floor under any elevated read: the indicator is directionally supportive, not independently load-bearing, and is flagged as such. The cost-per-capability curve that would let it stand on its own is deferred to Phase 2.
Indicator 06 · Organic end-user demand
Is the revenue real, or recycled?
Does reported AI revenue reflect genuine paid adoption by independent end-users — or is it recycled through the same ecosystem that funds the build, or rebranded from existing product lines?
The test is anchored on the MIT NANDA finding that roughly 95% of enterprise GenAI pilots show no measurable P&L impact (Fortune, August 2025). Headline growth in the 30–50%+ band scores well only when paired with demonstrated paid retention and pilot-to-production conversion above 50%; growth sourced from ecosystem participants scores worse, not better. The indicator scores the source of the growth, not its rate.
Revenue growth alone clears the headline band for most of the complex — CoreWeave at 168%, Broadcom at 64%, four firms clustered at 32–36%. CoreWeave is the limiting case: 67% of its FY2025 revenue is a single counterparty — Microsoft, "Customer A" in its 10-K — with the remainder committed by OpenAI, Meta, and Nvidia. Every named buyer is an investor in, or a lab funded by, the same circular structure.
That is growth from ecosystem participants rather than demonstrated independent end-user retention — the band the rubric reserves for recycled demand, and exactly what the NANDA anchor predicts: an "AI revenue" label growing fastest where the demand is most recycled, not where paid conversion is most proven.
The synthesis · Divergence gauge
The tape versus the filings
D(t) = M(t) − G(t) sets a market signal against a ground-truth signal. The market term M(t) is the equal-weight mean of three full-window z-scored components of SOXX price behaviour — 63-day momentum, price-to-trend overextension, and 20-day annualized instability. The ground-truth term G(t) is the negative mean of three deterioration z-scores — AI-layoff share, discretionary insider selling, and the capex gap. The gauge widens when momentum and overextension climb while the fundamentals erode.
Toggle between the composite (M, G, D) and the three ground-truth signals underneath G(t). Source: SOXX + ground-truth series.
Through 2025Q1 the two signals track close and D(t) sits below zero — price had not yet detached from fundamentals. In 2026Q2 the gap inverts hard: M(t) jumps to +2.83 as SOXX closes at 639.45 (63-day momentum +88.0%, instability +0.74 annualized) while G(t) falls to −1.23, dragged by the AI-layoff share and discretionary insider selling both reaching their window highs.
D(t) widens from −1.80 to +4.06, a +5.86 swing — the strongest move in this four-quarter series so far (n=4: descriptive, not a long-run signal).
Method & limitations
What would prove this wrong
This brief is built to be reproducible: every figure derives only from filing-sourced inputs. Each indicator is computed only from filing-sourced inputs; where a value cannot be sourced cleanly it is shown blank rather than imputed.
Two Phase-1 simplifications are stated plainly. The divergence gauge standardizes its components over the full window — it is descriptive, not real-time: it carries look-ahead bias and is not a tradeable signal, and an expanding-window version is deferred. It also weights its three market components equally; empirical calibration is future work. Indicator 05 (energy) rests on the thinnest data in the set and is weighted accordingly — directionally supportive, not independently load-bearing.
The falsifier is built in: if the ground-truth signal turns back up — demand converting, the capex gap closing, insider selling normalizing — the divergence closes and the boom earns its price. We publish the number either way.
Hyperscaler free cash flow turns negative and AI debt refinances at higher ratesconfirms fragility · The Race
Amazon FCF expected negative in 2026; $400B+ AI debt issuance forecast
Watching
Average AI usage deepens well past ~1.5 hrs/week per knowledge workerweakens fragility · The Lag Index
~1.5 hrs/week (executive survey) — logins, not workflow redesign
Not triggered
When a reading crosses its threshold, the call moves with it — and the change is logged below. Sources are on each linked instrument.
The ledger · revise in public
What we've changed, and why
2026-07-02
Recycling ratio revised 26× → 15.5× — a missed Amazon–OpenAI equity edge, added
A line-item re-verification against EDGAR filing text found our edge ledger missing Amazon's Q1 2026 $15.0B funded OpenAI Series C investment and $35.0B commitment letter (accession 0001018724-26-000014). Both were added; the funded-cash denominator widened ~$21B → ~$35B and the headline ratio compressed to 15.5× (~13× present-valued). Why: the ratio must carry every filed edge, including the ones that cut our own headline. Note the falsifier distinction: this equity is intra-ring (a top cloud funding the lab committed to it), not the arm's-length capital that would weaken the thesis — the multiple fell while the circularity tightened.
2026-07-02
Microsoft's OpenAI gain relabeled: $5.9B investment gains, not $4.5B
The Q3 FY2026 10-Q (accession 0001193125-26-191507) reports $5.9B of net gains from OpenAI investments for the nine months — primarily the dilution gain from the OpenAI recapitalization. The $4.5B we carried was the same filing's after-tax net-income impact ($0.60 diluted EPS), mislabeled as the markup. Mark-to-model total is now $18.2B. Why: pre-tax investment gains and their after-tax income effect are different lines; the ledger should quote the one it names.
2026-07-02
Amazon's $920M one-time charge re-attributed to the FY2024 10-K
The ~$920M accelerated-depreciation charge (Q4 2024 early retirements) is verbatim in the FY2024 10-K filed 2025-02-07; we had cited it to the FY2025 10-K. The ~$1.4B actual-2025 depreciation step-up remains correctly cited to the FY2025 10-K. Why: filing-of-record accuracy — the quote must point at the document that contains it.
2026-06-26
Divergence gauge relabeled as a short-series, directional read
Flagged the +4.06 divergence as a four-quarter (n=4) reading — directional, not a long-run signal — on Capex Watch, the Brief, and the homepage. Why: a four-point series can't support two-decimal confidence; the precision implied more than the data holds.
2026-06-26
Amazon depreciation figure reconciled and sourced
Unified to a $1.4B run-rate depreciation step-up (6→5yr policy) plus a separate $920M one-time write-off, cited to Note 1 of the FY2025 10-K, across Capex Watch and the Brief. Why: the two pages had carried non-reconciling figures.
2026-06-26
Reproducibility wording corrected, and the tables published
The indicator pipeline is computed in Python, not "live through DuckDB"; we corrected the wording and published the underlying tables at /data with their accession numbers. Why: the original phrasing overstated the mechanism; the fix is to show the data.
2026-06-26
"Both clocks" claim softened to the defensible version
From "no one else is timing both clocks" to "others time one clock or the other; we hold both on a single scoreboard — with explicit falsifiers, revised in public." Why: the absolute claim contradicted our own sourcing.
This ledger is append-only. Each entry is a place we were less right than we wanted to be, fixed in the open. More on how we work