Every major lab, cloud and sovereign AI programme routes through one vendor. Nvidia is by far the most-connected entity in the sector: 84 typed claims to 64 counterparties, 54 of them outbound, and a presence in 10 of the graph's 16 circular-capital claims. On undirected two-hop supply and capital adjacency it reaches 154 of 392 entities, 39% of the map. A shock to it stalls the entire stack. The 26 August print measured the other side of that: revenue of $96.2bn, up 106% in a year, with Data Center at $89.0bn, a 75% gross margin, and a guide of $108bn for the quarter in progress that assumes nothing from China. The market added about $442bn to the company in one session, the second-largest single-day gain by any stock. Three things had deepened the dependency in the month before, and none was forced: SpaceX committed on 4 August to build its AI compute exclusively on Vera Rubin against a 10GW end-2027 target; Japan's national physical-AI factory is 13,750 Vera CPUs and 27,500 Rubin GPUs; and on the day of the print AWS scheduled 2 million more GPUs for 2027-2028 after its 1 million commitment ran out early. The concentration the graph flags as a risk is the same concentration the income statement reports as revenue. Both readings are correct, and they are the same fact.

ASML alone makes the EUV lithography every leading-edge AI chip needs, behind TSMC, behind Nvidia. Two more compound it: the EDA duopoly (Synopsys + Cadence, ~96%) and the HBM triopoly (100%). No capital can quickly route around them. The last week of July 2026 showed what that concentration costs on the way down: the memory tier fell hardest of all, with SK Hynix down $176bn, Samsung $173bn and Micron $113bn of market value in days. A fourth chokepoint sits upstream of all of them and is not technological at all. The Strait of Hormuz carries the Gulf's sulfur and about a third of the world's helium, and it was effectively closed from February 2026.

Nvidia invests in the customers that buy its chips; investors fund the labs that spend it back on the investors' clouds. Now 15 loops. The newest one changes the kind of risk rather than the amount: Nvidia and OpenAI were reported in late July 2026 to be discussing a backstop of up to $250bn, which would let OpenAI raise debt against Nvidia's credit rather than take Nvidia's equity. Demand is partly self-referential, and now partly self-guaranteed.

Anthropic is backed at once by Amazon, Google, Microsoft, Nvidia AND now AMD - five direct competitors on one cap table - at a $965B post-money (May 2026 Series H). AMD's up-to-$5bn equity plus its up-to-2GW MI450 supply add a fifth rival while softening pure Nvidia dependency. The lab is simultaneously filing to go public (confidential S-1, 1 Jun 2026).

Eight companies hold IL6/IL7 classified-network agreements as of 1 May 2026: SpaceX, OpenAI, Google, Nvidia, Reflection, Microsoft, AWS and Oracle. Anthropic remains excluded, with talks stalled over surveillance and weapons guardrails, and Palantir pulled Claude from DoD platforms. That access list is one half of the picture; the policy scaffolding around it is the other, and it is consistent in direction. America's AI Action Plan of July 2025 directed the federal government to pursue AI dominance principally by minimising regulation. The executive order of 11 December 2025 moved against state AI laws directly, standing up a Justice Department litigation task force and putting federal broadband funding behind the preference. The executive order of 2 June 2026 addressed frontier-model risk through a framework that is expressly voluntary. Read together the posture is consistent: accelerate at the federal level, pre-empt at the state level, and handle safety by invitation. Access to the frontier is gated by the executive, and the gate is discretionary rather than statutory.

China has built a near-complete, sanction-proof AI stack: DeepSeek's V4 (Apr 2026) trains end-to-end on Huawei Ascend 950PR silicon - the first frontier-class model built without Nvidia - while a $295bn state plan for a unified national compute grid (2028) mandates 80% domestic sourcing, locking out both Nvidia and AMD. Alibaba and Tencent jointly bankroll a fleet of open-weight labs (Moonshot, MiniMax, Zhipu). It advances months - not years - behind the West, answers to none of its evaluators, and export controls are the only remaining lever. The 2026 AI Index puts numbers on how narrow that gap now is. The United States released 50 notable models in 2025 and China 30, with no other country close: South Korea 5, and Canada, France and the UK one each. On capability the distance is smaller than the count suggests, at about 2.7% between the best US and best Chinese model as of March 2026. China simultaneously leads on publication volume, citations, patents and industrial robot installations. The reading that follows is that output quantity is a funding artefact and capability is not, so a count of releases is the wrong measure of where this stands.

The people who build the frontier run more capable models internally than they ship - months early, wired into their own R&D. Self-built and fair, but compounding and invisible to outsiders.

The US leads on error correction: Google's 105-qubit Willow shows below-threshold correction and IBM's 120-qubit Nighthawk targets fault-tolerance by 2029: while China leads on scale and commercial deployment: USTC's 105-qubit Zuchongzhi 3.0 advantage claim and Origin Quantum's 180-qubit Wukong-180 cloud. Two parallel superconducting-QC programmes are maturing side by side, not converging.

Origin Quantum's Wukong superconducting cloud has logged 30M+ visits and surpassed one million completed quantum tasks from users in more than 192 countries (Jun 2026) - and US users have consistently been the largest foreign cohort. The classical chip-export wall does not extend to quantum-cloud access, where the dependency currently runs the other way.

AMD's up-to-2GW MI450 deal with Anthropic, plus up to $5bn of equity, is the first credible second GPU source at the frontier-lab tier. It is a partial, forward-dated mitigant rather than a removal of the risk, and as of 4 August 2026 it is running against the tide: SpaceX, one of the largest compute buyers in this map, has committed to Nvidia exclusively. Diversification at one frontier lab is being offset by deliberate concentration at another buyer of comparable scale.

SandboxAQ now sits at the single intersection of the US/allied post-quantum transition: DoW CIO cryptographic migration (AQtive Guard), DIU quantum navigation (AQNav), a $500M CHIPS materials award, NATO DIANA, and White House PQC EO implementation - while its own compute and capital come from Google and Nvidia, who are simultaneously its investors and its cloud/GPU suppliers. The same circular vendor-invests-in-customer pattern seen at the frontier labs now shapes post-quantum defense, and the US government has taken equity in exchange for the CHIPS award (government-as-shareholder).

On raw AI compute the US commands roughly 75% of global GPU-cluster performance to China's ~15%, hosts 5,427 data centers, and is adding power at about 22% a year toward 90GW or more by 2030. China has reached near-parity on model quality, but its hardware stays a generation or two behind, gated by export controls and SMIC's ceiling versus TSMC. The gap is structural, not cyclical.

Amazon, Alphabet, Meta and Microsoft together guide to roughly $725bn of AI capital spending in 2026, up about 77% from roughly $410bn in 2025. Alphabet alone spent $44.9bn in Q2, double the prior year, and guides $195-205bn for the year. Microsoft guides FY2027 to $255-260bn. Meta's free cash flow fell 91% in the quarter. Cloud revenue is accelerating with it (Google Cloud +82%, Azure +43%, AWS +37%), so the question is no longer whether demand exists. It is whether the spend can be funded at this rate without the circular structures above. On 23 August 2026 one hyperscaler answered the funding question in the open market rather than through the structures above. Alibaba priced HK$80bn ($10.2bn) of new stock at a discount, the largest primary follow-on ever by a Hong Kong-listed company and the world's third largest this year after Alphabet and Intel, with 100% of net proceeds committed to full-stack AI and roughly half of a three-year capex programme already spent. Demand reached about $28bn. The shares fell about 10%. Dilution is the honest route and it is also the one the market punishes on the day, which is a fair description of why the circular structures exist at all.

More than $1tn came off the sector's chip names in the last week of July 2026. Nvidia lost $238bn, SK Hynix $176bn, Samsung $173bn and Micron $113bn; AMD and TSMC each shed over $100bn, and the memory names entered a bear market. The stated cause was not weakening demand but a repricing of expectations, on concern that AI infrastructure spending is peaking faster than expected. This is the first market-priced test of the structure this map describes, and it fell along exactly the lines the map predicts: the chokepoint tier took the worst of it. The follow-through answered the open question: the repricing reversed rather than extended. Through 21 August the SOX was up more than 10% for the month, on pace for its best August in over two decades, which reads as volatility around an intact spending thesis rather than the start of a derating.

In late July 2026 Nvidia and OpenAI were reported to be in talks over a backstop of up to $250bn, letting OpenAI raise debt on the strength of Nvidia's credit to fund a 10GW campus in Pike County, Ohio. On 17 August the first piece was signed: Nvidia agreed to guarantee up to $105bn of OpenAI's conditional lease and power obligations to SB Energy at that campus, put $1.5bn of equity into SB Energy itself, and took the exclusive chip-supply position on the site. Every earlier loop was equity: a vendor buying a stake in its own customer. A guarantee is different in kind. It puts the supplier's balance sheet behind the customer's borrowing, so a failure that was once a lost investment becomes a contingent liability, and the sector's largest market cap becomes the sector's de facto credit desk.

On 27 July 2026 Nvidia and 36 others formed the Open Secure AI Alliance and open-sourced NOOA, a framework for making agent behaviour testable and auditable. Microsoft, IBM, Red Hat, Cloudflare, CrowdStrike, Palantir, Databricks, Hugging Face and the Linux Foundation joined. OpenAI, Google and Meta appear among the letter's signatories but are absent from the inaugural membership; Anthropic appears on neither. The absence is the finding. The firms whose models create the auditing problem are not in the body being formed to solve it, and the Cloud Security Alliance judges the result a standards body without a charter.

This finding originally recorded both frontier labs filing into Q4 2026, and the record has since moved against it: by mid-August OpenAI's listing had slipped, its CFO telling staff the company would be public in 2027, and a $7bn employee buyback at a flat valuation removed the near-term pressure to list. Anthropic still points at October, with a $65bn annualised run-rate at end-July and backers (not the company) talking a roughly $2tn debut through Morgan Stanley, Goldman Sachs and JPMorgan. The concentration risk the original finding described now lands on a single name in a single month: one loss-making lab converting privately negotiated marks into daily public ones, with the second lab's postponement itself a data point on how hard the first pricing is.

The common reading is that Washington keeps tightening. The record says the opposite. BIS rule 2026-00789, effective 15 January 2026, moved the Nvidia H200 and AMD MI325X from presumption of denial to case-by-case review, subject to a 25% tariff and third-party testing, and roughly ten Chinese firms including Alibaba, Tencent and ByteDance were cleared to buy. At the same time Huawei is shipping the Ascend 950PR with a CUDA-compatible stack, ByteDance has committed around .6bn of orders, and DeepSeek V4 trains end-to-end on Ascend silicon. China is not choosing between the two stacks. It is buying the American one while the domestic one matures, which weakens the leverage the embargo was supposed to create.

SpaceX intends to deploy Nvidia Vera Rubin NVL723 racks both on the ground and in space, and is co-designing the Starmind AI1 satellite payload with Nvidia, each unit carrying Rubin GPUs and Vera CPUs. SpaceX asked the FCC in January 2026 to authorise up to one million Starmind satellites; first test units are planned for early 2027. Three consequences. The launch monopoly and the compute build sit inside one company, so the cost of orbiting a data centre is set by the firm that owns both. Orbital compute sits outside every jurisdiction in the policy layer here, all of which is written for hardware on somebody's soil. And the authorising body is not an AI regulator or an export-control agency but the FCC, a spectrum and satellite licensor, which is now a chokepoint on frontier compute by accident of jurisdiction.

Unsupervised autonomy has been promised on three separate dates now held in this map: by June 2025 (said January 2025), Q4 2026 at the earliest (said April 2026, citing complex intersections, road markings and weather), and widespread in the US by year-end (said May 2026, under a month after the concession and with no reported resolution of those obstacles). What exists today is fewer than 40 unsupervised robotaxis across three Texas cities. None of those statements was deleted when the next one arrived, which is the point: the graph can show the gap between what was said, when, and what was delivered, without anyone having to remember it.

Japan's FRONTia programme is a serious attempt at model sovereignty: up to Y1tn over five years for a domestically developed multimodal foundation model, run by a consortium of Sony, SoftBank, NEC and Honda with 44 investing organisations and PLaMo engineers seconded in from Preferred Networks. The compute underneath it is not sovereign at all. The 140MW facility is 13,750 Nvidia Vera CPUs and 27,500 Nvidia Rubin GPUs on Nvidia fabric, described by Nvidia itself as the world's first national AI infrastructure. This is the same pattern the map records at SpaceX: independence pursued at the model layer while dependence deepens at the silicon layer. It also dates the exposure precisely, because the facility does not begin operating until June 2028.

SpaceX's move from about 2GW of compute at end-2026 to roughly 10GW at end-2027 implies well over a million Rubin GPUs and possibly more than two million. BNP Paribas has warned the commitment could trigger another Nvidia supply shortage. SpaceX itself put the constraint plainly: memory supply is growing at about 20 per cent a year while demand for AI compute rises more than 200 per cent, a gap that on its own account persists well into 2027. That points the risk at the HBM triopoly rather than at Nvidia, which is the tier this map already flags as a chokepoint and the tier that fell hardest in the July repricing. An exclusive commitment by one very large buyer does not create capacity; it reallocates a queue, and everyone else in the graph is standing in it.

Demand for military AI is no longer a forecast. Ukraine raised 2026 defence spending to a record UAH4.4tn, about $98bn, and contracted roughly $8bn of UAVs in the first half alone, at double the prior year and with nine-day delivery for in-stock systems. The Iran campaign supplied the consumption figure: Pentagon model usage reached about 20 billion tokens a day during Operation Epic Fury, an increase of 4,425 per cent, with Palantir's Maven Smart System used to plan and coordinate roughly 13,000 strikes, and the Army later reinstating token limits after allowances were exhausted early. Two consequences for this graph. Defence has become a load-bearing customer for commercial AI infrastructure rather than an adjacent market. And Ukraine is functioning as a live testbed whose procurement now sets drone requirements from battlefield data rather than committee.

This map's chokepoints have all been technological: one lithography vendor, one leading-edge foundry, three memory makers. The 2026 Iran conflict exposed one that is purely geographic. The Strait of Hormuz was effectively closed from 28 February 2026, and it carries the Gulf's sulfur exports and roughly a third of the world's helium, most of it Qatari and produced alongside LNG at Ras Laffan. Helium has no substitute in chip fabrication: it is the carrier gas, the cleanroom purge gas and the wafer coolant. Ultra-pure helium prices doubled, and memory fab output begins declining within one to two weeks of rationing. Sulfuric acid, derived from the same route's sulfur, is essential to refining the metals that feed semiconductors, batteries and magnets, and China separately restricted sulfuric-acid exports. The significance is the ordering: a shipping lane can throttle ASML's customers without anyone touching a fab, an export licence or a chip.

ASML remains the sole maker of EUV and High-NA lithography, and Intel's first high-volume High-NA production on Panther Lake extends that monopoly into the next node rather than ending it. The July 2026 capacity announcement, 30% more Low-NA EUV and DUV immersion for 2027 with a further 30% under evaluation for 2028, was read as easing the bottleneck, and on volume it does. On concentration it does the opposite: more of the world's leading-edge capacity now depends on one company's ability to execute an expansion. Four distinct risks in this graph converge on that single point, and three of them reach it indirectly, which is why they are easy to miss. Demand: Nvidia's 52 dependencies and the SpaceX exclusivity all resolve to silicon only ASML's machines can print. Geography: ASML's largest customer concentrates leading-edge capacity in the Taiwan Strait. Materials: the Hormuz closure constrains the helium every customer fab needs, so a shipping lane can idle ASML tools without touching ASML. Policy: about 20% of 2026 revenue is China DUV, which the proposed MATCH Act would restrict further. None of these is a claim about ASML's business. They are paths through it.

Funds associated with Donald Trump Jr. and Eric Trump hold positions in at least 15 companies seeking federal business, and the Washington Post reported on 13 July 2026 that those companies have secured at least $3.2bn in federal contracts since the investments were made, with a further $3.1bn in options. 1789 Capital, where Donald Trump Jr. became a partner within days of the 2024 election, accounts for 11 of the 15. The portfolio maps almost exactly onto the sectors this graph already flags as receiving elevated funding: defence autonomy, drones, space, advanced manufacturing, quantum and rare-earth magnets. The clearest single case is Vulcan Elements, backed at a valuation near $200m in autumn 2025 and awarded a $620m Defense Department loan commitment a few months later. Vulcan Elements is also in the federal equity portfolio, so a single company carries a 1789 Capital position, a $620m Defense Department loan commitment and a US government shareholding at once. On 5 August 2026 lawmakers called for a Pentagon probe. What the graph records is association and sequence: who invested in what, when, and what federal business followed. It does not assert causation, and no source cited here does either. On the President's own account the structure is worth stating precisely, because it is often described loosely: the securities are directly and beneficially owned, held in a revocable family trust whose trustees are family members, not a qualified blind trust, and Q1 2026 alone records 3,642 transactions worth between $220m and $750m. One donor edge deserves separating out because it runs through an entity already central to this graph. Elon Musk spent more than $290m on the 2024 cycle, $193m of it through America PAC, the largest identified flow from any entity in this map into electoral politics; he is also principal of SpaceX, which the graph records as a major federal contractor and as newly exclusive to Nvidia. Timothy Mellon at $151.5m to MAGA Inc and Miriam Adelson at about $105.8m across vehicles were larger or comparable donors but sit outside this sector. The systemic point is narrower and still material: the demand signal this map treats as policy-driven now has a documented overlap with the private interests of the family directing the policy, and that overlap is itself under formal review. On 22 August 2026 that overlap acquired a direct line. A financial disclosure signed on 12 August records the President buying between $15,001 and $50,000 of SpaceX on 23 June, eleven days after its IPO and among more than 1,000 trades that month. The graph already held presidential positions in Nvidia, Palantir and Apple; what is different here is the counterparty. SpaceX in this map is about $12bn of Space Force work, a classified NRO satellite programme, Pentagon AI-compute talks and the single largest identified political donor in the sector as its controlling shareholder, and the administration has since directed an increase in US commercial launches. The White House states that third-party institutions manage the portfolio against recognised indexes and that no family member can direct it, which speaks to who executes rather than to who owns. The sums are trivial beside the contracts; the edge is worth drawing because it closes a loop the map was previously carrying as two unconnected halves.

Two instruments changed the relationship between the US government and this sector, and the map previously carried only a fragment of one. As shareholder: direct federal equity now spans a reported thirty-odd companies, including about 10% of Intel for $8.9bn, which makes the government Intel's largest single shareholder, 15% of MP Materials, 10% of Trilogy Metals and 5% of Lithium Americas. Most of the portfolio is critical minerals, the layer sitting upstream of everything else here. As rent-taker: since August 2025 Nvidia and AMD have paid the Commerce Department 15% of their revenue from specified China AI chip sales in exchange for export licences, an arrangement in which the licensing authority holds a direct financial interest in the volume it licenses. Both instruments are under congressional scrutiny, with a February 2026 oversight letter on the minerals deals. The consequence for reading this graph is that the United States can no longer be modelled purely as a regulator of the AI supply chain. On several nodes it is also a counterparty, and on Intel it is the largest owner.

David Sacks served as White House AI and Crypto Czar from January 2025 to 26 March 2026 as a special government employee, a designation capped at 130 days of service in any twelve months and carrying lighter disclosure and conflict obligations than a permanent appointment. When the clock expired he moved to co-chair the President's Council of Advisors on Science and Technology alongside Michael Kratsios, describing the new seat as widening his remit from AI to technology generally. The structural observation is not about any individual. It is that the influence attached to the person rather than to the office, so the statutory limit on the office did not limit the influence. Any map that models policy exposure by tracking formal titles will misread this, which is why the graph records the role with an end date and the successor role opening where it closes.

Stargate was announced at the White House in January 2025 as roughly $500bn and 10GW over four years, with OpenAI, SoftBank and Oracle, and MGX funding an initial $100bn tranche. It remains the administration's signature compute-infrastructure signal. Execution has trailed the framing: more than a year on, reporting describes unresolved disputes between the partners over structure and responsibility, and OpenAI fell short of locking 10GW of commitments through SoftBank and Oracle, leaning instead on cloud contracts with AWS, Google Cloud, AMD and Cerebras. Further sites have also been announced, so expansion and shortfall are both true. The systemic point is about signal quality rather than about any partner: a headline number announced from a podium entered this graph, and the market, as a commitment. It is a framing. Oracle's $553bn contracted backlog is a commitment. The two should not be read the same way, and until now this map did not clearly distinguish them.

The 2025 annual financial disclosure, 927 pages published on 30 June 2026, reports crypto-related income that outlets total variously at more than $580m, at $1.2bn and at $1.4bn from the same document. The spread comes from which lines are counted and how token proceeds are valued, not from disagreement about the filing. Two lines are firm: more than $635m of royalties from the $TRUMP memecoin, and more than $500m associated with World Liberty Financial, where token-sale proceeds are themselves given in a $236m to $520m range plus about $65m from an equity sale. A figure of roughly $799m for World Liberty circulates and is not supported by the reporting reviewed here. This sits at the edge of an AI-sector map rather than inside it, and is recorded for one reason: the political-capital finding concerns the scale and structure of family financial interests running alongside federal policy, and that scale cannot be assessed from the defence-tech positions alone.

Yao Shunyu authored ReAct in October 2022 and co-created SWE-bench a year later. The first is the loop most production agent frameworks implement; the second became the number OpenAI, Anthropic and Google all quoted for coding agents. He took the doctorate at Princeton under Karthik Narasimhan, did the ReAct work at Google, joined OpenAI in August 2024, and has been Tencent's chief AI scientist since the 17 December 2025 announcement, reporting to president Martin Lau and heading the group's LLM and AI infrastructure departments against a stated plan to roughly double AI spend to over RMB 36bn in 2026. Two things are visible here and neither is an inference about intent. Method and evaluation in this sector are unusually concentrated in individual authorship rather than institutional ownership, and that authorship moves with the person across an export-control boundary the rest of this map treats as the main dividing line. The yardstick is also eroding independently: OpenAI retired SWE-bench Verified on 23 February 2026 after finding frontier models could reproduce its solutions verbatim, so the shared number the labs compared themselves on no longer means what it did.

Open weights are open at the licence and narrow at the door. Nearly every open model that matters, Llama, Qwen, Kimi K2, Nemotron, DeepSeek, is distributed through Hugging Face: over 3m models, 1m datasets and 13m users on one platform. The graph has recorded that as a single point of distribution since it was built. On 26-27 August 2026 the identity of the owner changed in prospect: Nvidia is reported to have agreed to acquire the hub for $12.9bn, unsigned at the time of recording. Openness of the weights is unaffected. Ownership of the channel is not. The company that trains Nemotron, licenses Poolside's model factory and is in talks to fund Perplexity would also own the shelf every rival open model sits on, and the compute-rental outlet attached to it. That is a different kind of concentration from silicon, and it is now on the same node.

Nvidia is training Nemotron 4, an open-weight family whose largest model targets at least a trillion parameters, reported by The Information on 11 August 2026 with no release date confirmed. Nine days later it bought the means to finish it. Nvidia is paying Poolside $6bn to license Model Factory, the internal system Poolside developed its own models on, and more than 100 Poolside engineers move onto Nemotron. The licence is non-exclusive and the founders are not going, which is why the shareholder letter can say it is neither an acquisition nor an acquihire; the effect on this graph is the same either way. The move reads as competitive against the closed labs Nvidia also supplies, but the graph shows the opposite effect on concentration. The weights would be open while the inference stack they are tuned for, TensorRT-LLM and Triton, is not, so the cheapest way to run the best open Western model still routes through the vendor already carrying 52 mapped dependencies. Scale is not the differentiator either: Moonshot's Kimi K2 is already open at 1 trillion total parameters activating about 32 billion per token. What moves is who owns the machine, and every route still lands on the same silicon.

The hyperscalers have contracted up to 13GW of nuclear capacity, split roughly evenly between power purchase agreements on existing plants and direct partnerships funding new build. Meta leads by volume at up to 6.6GW across Constellation, Vistra, TerraPower and Oklo; Amazon holds 1.9GW at Susquehanna plus up to 5GW with X-energy; Microsoft has 835MW at Three Mile Island; Alphabet has 500MW with Kairos and 1.8GW with Elementl. Carnegie's June 2026 assessment puts the combined output at roughly 102 TWh a year, which is less than 20% of projected demand through 2035. The dates are the problem rather than the intent: the PPAs on existing plants deliver from 2027, while most new build lands 2034 or later, against demand the DOE study has rising from 176 TWh in 2023 to between 325 and 580 TWh by 2028.

Cerebras listed in May 2026 carrying the concentration it disclosed in the S-1: MBZUAI and G42 together took 86% of FY2025 revenue, single-customer risk restated as single-country risk. The rotation is now visible in the filings, with G42 down from 85% of 2024 revenue to 11% in Q1 2026, and in the deals: the December 2025 OpenAI master agreement (750MW, over $20bn, plus a $1bn working-capital loan) and March 2026 AWS distribution move the growth engine onshore. Q2 2026 cloud revenue grew 287%. The risk swaps rather than disappears: the same book that was exposed to Abu Dhabi is now underwritten by OpenAI's own capacity commitments holding.

Inside five days in late August Nvidia committed $6bn to license a model-development system, took more than 100 of the engineers who built it onto an open-weight model programme, and opened talks to invest in an application-layer company at over $30bn. In the week before that it guaranteed up to $105bn of a customer's lease and power obligations. Then, on the day it reported a $96.2bn quarter, it was reported to have agreed to buy Hugging Face for $12.9bn, the hub through which nearly every open model is distributed. Read as separate deals these are a licence, a hiring round, a financing and an acquisition. Read on the graph they are one direction of travel: the supplier every layer already depends on is acquiring positions in models, in applications and in distribution, the three layers it did not occupy at the start of the month. The Hugging Face agreement is unsigned and carries medium confidence here; if it closes, the open alternative to the closed labs is hosted by the vendor that sells to both.

China generated 10,573 TWh in 2025 against 4,536 TWh for the United States, and grew that base 74% over the preceding decade against 6%. Read as a power story, China is far ahead. Read as a compute story it inverts. US data centres draw about 29.2 GW, roughly 5.66% of national generation; China's draw is contested at 4.27 to 8.5 GW, so between 0.35% and 0.70%. Taking the high end, the least favourable reading for this argument, the United States converts about eight times the share of its electricity into computation that China does. At the low end it is nearly sixteen. The strategic consequence is not that China is behind. It is that China's generation lead is unused headroom it can convert at will, while the US constraint is conversion capacity rather than generation. Germany runs the highest ratio of the three at about 9.63%, which is the practical ceiling the US number should be read against, and the US is already past half of it.

Take the nine generation developers this graph carries and the five frontier labs, and ask for the shortest path between every pair. All 43 routes that exist are exactly two hops, and every single one passes through Amazon, Meta, Microsoft or Alphabet. Allowing three hops, only 28 of 378 paths avoid those four; allowing four, 364 of 7,728. So better than 95% of every route from electricity to intelligence crosses one of four corporate boundaries. This is a different claim from market share. It says the hyperscalers are not merely large buyers of power and large sellers of compute, they are the only structural connection between the two, and a constraint applied at that layer propagates in both directions at once. One detail is visible only in the edge types: Meta reaches no frontier lab commercially, only through talent movement, because it builds Llama in house.

Nvidia carries 86 relationships in this graph and is not flagged as a chokepoint. Cadence carries one, and nothing tapes out without it. Across the nine tier-zero chokepoints the mean degree is 5.2; across the six largest hubs it is 47. Criticality and connectedness run inversely, and that is close to a definition rather than a coincidence: a chokepoint is a thing with no alternative, so it appears once and is irreplaceable, while a hub is a thing everyone deals with, which usually means it can be dealt with differently. The practical warning is that any analysis ranking this sector by connection count, which is what most network visualisations do by default, will systematically rank the real single points of failure near the bottom. Synopsys and Cadence together hold about 60% of EDA; on undirected three-hop reach they touch 106 and 92 of 388 entities against ASML's 73.

Across the two settlement periods covering the second half of August, short interest fell at Oracle (-6.6%), SpaceX (-5.8%), Palantir (-4.6%) and TSMC (-1.1%), and rose only at ASML (+12.7%). Over the same window the Nasdaq Composite fell about 2% and Alibaba, Baidu and Cerebras each fell more than 16%. THE POINT: the drawdown in these names was not accompanied by a build in disclosed short positions. Whoever sold, it was mostly not new shorts. WHAT THIS DOES NOT SAY: short interest is published twice a month with an eight day lag, so it cannot time anything, and a falling number is equally consistent with shorts covering INTO weakness as with no shorts arriving. THE DAILY SERIES DOES NOT RESCUE IT. FINRA's daily off-exchange short volume sits at 59.5% for SPCX, 50.9% for PLTR and 29.5% for ORCL and barely moves, because it measures market-maker internalisation rather than conviction; an 89-session study of the same series in another name found r=0.995 against total volume and no forecasting power on next-day return. WHAT WOULD FALSIFY THE READING: the 15 September settlement showing a sharp build, which would place the short arrival after the price move rather than before it, and would still not make it the cause.

Every participant depends on a stack it does not control: foreign lithography, one chip vendor, rival-owned clouds. Sovereign and allied compute, and the ability to prove provenance, is the widest opportunity on the map. Japan is now the largest worked example. METI has roughly quadrupled its FY2026 AI and semiconductor budget to about Y1.23tn, is funding leading-edge logic through Rapidus and the optical interconnect layer through Tower Semiconductor, and sits inside a Y370tn, 17-field national investment framework running to FY2040. The strategy plays to genuine existing strength in robots, components, materials and equipment rather than trying to reproduce the whole stack.

With demand partly marking its own homework through circular deals, the scarce asset is trust. July 2026 supplied the proof: a Hugging Face breach in which OpenAI test models escaped a sandbox could not be fully investigated, because the closed AI tools involved blocked forensic analysis. The industry's answer, a 37-member alliance formed within weeks, was described by the Cloud Security Alliance as a standards body without a charter. A system that renders every relationship a typed, sourced, verifiable claim, as this map does, is the antidote.

As open weights commoditise the model layer, durable value concentrates upstream - lithography, fabrication, HBM, the one GPU vendor - and in energy, the binding constraint everyone is racing to secure.

AI is moving onto classified networks (Palantir, Anduril, the Pentagon Seven) with unaudited model supply chains. Verifiable, quantum-safe provenance is precisely the missing layer.

The map's critical concentrations are lithography at ASML, fabrication at TSMC and memory in a triopoly. Japan is the one jurisdiction funding alternatives across several of those layers at once rather than one. Rapidus is state-backed 2nm logic with Broadcom already sampling. Tower Semiconductor is taking up to Y160bn to expand 300mm silicon photonics and advanced packaging, the optical interconnect every AI data centre needs and a layer with far less attention on it than logic. Underneath both sits an existing industrial base in robots, components, materials and equipment that the physical-AI programme is designed to extend rather than replace. The diversification is real; the timelines are late-decade.

THIS IS AN ANALYST ESTIMATE, NOT A SOURCED FIGURE, and it measures a different thing from the tracked total beside it. The tracked figure is capital raised by entities in this graph, as recorded, with no period bound. This one is announced and planned AI-related capital commitments worldwide through 2030. Both can be true at once and neither replaces the other. THE BUILD, with overlaps removed rather than summed: AI-specific data centre capex through 2030 is $5.2tn of McKinsey's $6.7tn total, the remaining $1.5tn being traditional IT and excluded here. Hyperscaler capex sits INSIDE that figure and is not added again, though its trajectory is the reason to believe it: the four largest guided $410bn for 2025 and $725bn for 2026, with $1tn projected for 2027. Semiconductor capacity to make the chips is not data centre capex and is added: industry capex is about $200bn in 2026, the top five spenders were $123bn in 2025, and cumulative leading-edge and memory capex through 2030 is on the order of $1.0tn to $1.25tn, with TSMC's Arizona commitment alone at $100bn. Generation and grid beyond the data centre fence is added net of McKinsey's own energiser share: United States investor-owned utilities alone have announced $1.4tn through 2030 driven primarily by data centre load, of which roughly $1tn is additional to the AI capex figure. Sovereign programmes not already inside those numbers add about $1tn, of which Japan's 17 Strategic Fields target of Y68tn by FY2040 is the largest single line and is already a claim in this graph. Private equity funding into AI companies through 2030 adds about $0.5tn, for which the capital claims in this graph are a floor rather than a measure. That sums to roughly $8.8tn on the mid case, with a defensible range of $8tn to $11tn depending on how the power overlap is treated and whether the 2027 hyperscaler acceleration lands. $10tn is the round number inside that range and it is a judgment, not an arithmetic result. WHAT WOULD FALSIFY IT: announced capex being cut rather than raised at the next two guidance cycles, or the power constraint capping the build below 219GW, which McKinsey treats as the demand case rather than a certainty.