Research · The Fragility Brief
The 2026 AI-compute cycle is usually argued on valuation. The more decision-relevant question is structural fragility — and it is legible in the data these companies file themselves. This brief reads six independent indicators directly from the filings, then sets a market signal against a ground-truth signal in a single divergence gauge. Every figure is computed from the source filings.
The dataset
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.
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
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.
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 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.
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
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
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 26× committed compute on a funded-cash basis, easing to ~5× 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 21× — nearer 18× if the two undated Microsoft commitments are 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 $16.8B (Microsoft +$4.5B, Amazon +$12.3B) — earnings recognized on the appreciation of the firms one funds. Six 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 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
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
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
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.