A dozen companies are building the AI economy by investing in each other and then buying each other's products. Nvidia funds OpenAI, which buys Nvidia chips. AMD hands OpenAI a slice of itself in exchange for orders. Microsoft, Amazon and Google pour billions into model labs that pour it back into their clouds. This is a map of that loop: the capital, the hardware, the energy it burns, and the question everyone is asking: flywheel, or hall of mirrors?
Blue is investment: equity, warrants, capital flowing in. Teal is spending: chips, cloud and compute flowing back. When the two close a circle, you are looking at a loop. Dashed lines are letters of intent; dotted claret lines have stalled.
Strip away the noise and the same shape keeps repeating: capital goes out, product orders come back, and both sides book bigger numbers.
Nvidia signalled it could put up to $100B into OpenAI as data centers come online. OpenAI commits to 10+ GW of Nvidia systems. The chipmaker funds its own biggest customer.
AMD handed OpenAI a warrant for up to ~10% of itself at a cent a share, vesting only as OpenAI deploys 6 GW of AMD chips and AMD's stock climbs. It later struck the same deal with Meta.
Amazon and Google invested billions in Anthropic; Anthropic commits $100B+ back to AWS and $30B to Azure. The twist: Alphabet booked a $36.9B paper gain just marking up its own stake.
The circular financing looks like dangerous self-dealing to one side and the first innings of the largest infrastructure build in history to the other. Here is the honest version of each case.
Warned the risk of a sharp correction has risen, called AI valuations particularly stretched, and noted the top five S&P 500 names are a bigger share of the index than at any point in 50 years.
Disclosed puts against Nvidia and Palantir and accused hyperscalers of understating depreciation, stretching chip lifespans on paper to flatter earnings despite an 18–24 month product cycle.
Argue circular funding means the same capital appears several times across different balance sheets, creating hidden fragility, and that the real danger arrives if bank credit enters the loop.
Told markets to buckle up and drew explicit dot-com parallels, flagging that financing is quietly shifting toward debt and private credit.
Runs the depreciation math: one year of AI capex needs roughly $480B of revenue to earn a fair return, against $15–20B realized today. A ten-fold gap that closes only if growth stays vertical.
Found that about 95% of enterprise AI pilots show no measurable return yet, with only ~5% extracting real value. If that holds, the revenue to justify the buildout never shows up.
Rejects the bubble framing (we see something very different), calling it the largest infrastructure build-out in human history, with the world only a few hundred billion into the trillions required.
Calls it a 1996 moment, not 1999, and estimates every $1 of AI hardware spend generates $8–$10 of downstream ecosystem value.
Projects roughly $1.15T of hyperscaler capex across 2025–27 and argues cash flow plus investment-grade credit could fund up to ~$1.4T, with markets rewarding a clear capex-to-revenue link.
Concedes a bust is possible but notes the boom is largely equity-financed, so unlike the dot-com era a correction does not necessarily transmit to the broader financial system.
"Jevons paradox strikes again." As inference costs fall roughly ten-fold a year, and DeepSeek's R1 undercut comparable models by ~96%, cheaper intelligence expands demand far faster than it deflates revenue.
Paid, fast and real: Anthropic's run-rate went from ~$1B to ~$47B in about 18 months, and it now holds ~40% of the enterprise LLM market against OpenAI's 27%. Demand, not vapor.
Bear: the gap keeps widening. Bull: revenue compounds faster than capex.
Bear: stuck at 30–50%. Bull: marches toward 70%+ as inference deflates.
Bear: down-rounds, vendor financing, junk-rated debt. Bull: up-rounds, investment-grade issuance.
Bear: stranded or idle capacity. Bull: demand outruns supply and rate limits bite.
Bear: a hyperscaler shortens useful life or writes down. Bull: useful lives hold and a value cascade appears.
Bear: megawatts gate revenue while capex is sunk. Bull: turbines, grid and nuclear keep pace.
Bear: the 95%-no-return persists. Bull: documented P&L gains broaden beyond tech.
Circular economy also means the real thing: reusing the hardware, recycling the water, and powering it all. The hardest constraint is no longer chips, it is power, and this loop runs behind the buildout it has to keep up with.
Hyperscalers run tight hardware loops. Oracle reports 99.6% reuse, Google has resold 44M+ components since 2015, and refurbishing can save up to 85% of manufacturing energy.
The binding constraint moved from chips to electricity. Gas-turbine slots are booked into 2029–30, transformers are scarce, and interconnection queues run years. Every major lab has also gone nuclear: Microsoft is restarting Three Mile Island and Meta has lined up ~5–6 GW of reactors.
A big data center can draw up to 5M gallons a day. Texas centers were on track for ~49B gallons in 2025, potentially ~399B by 2030. New zero-water cooling designs aim to cut that sharply.
Prices are snapshots from late June 2026 and move constantly, so treat them as a sense of scale, not a quote. Private valuations reflect the latest funding rounds.
| Company | Ticker | Category | ~Price | ~Mkt cap | Role in the loop |
|---|
Private · OpenAI ~$852B (IPO filed) · Anthropic ~$965B · xAI ~$230B · Stargate >$400B committed · SoftBank a leveraged proxy for OpenAI
The same dozen names sit at the top of the index funds and pensions almost everyone holds, which is exactly what the central banks worry about.
Seven companies now make up a third of the entire S&P 500. The other 493 share what's left.
A standard S&P 500 or Nasdaq-100 (QQQ) fund now embeds roughly a one-third AI-concentration bet. Sector ETFs like SMH and SOXX do not diversify that bet. They multiply it. Equal-weight funds such as RSP are one of the few ways to dilute it.
OpenAI, Anthropic and SpaceX are private, but their largest backers are public mega-caps almost everyone holds. Each stake is a low single-digit to mid-single-digit slice of the parent, so the optionality rides nearly free on top of a real, cash-generative core.
Plus a $250B Azure backlog. The revenue share is now capped at $38B and ends at AGI, and the model license is no longer exclusive.
Together roughly 4.5–5% of Alphabet's cap, on top of Search, YouTube and Cloud. The cleanest single proxy for both.
Higher torque, more complex structure. Amazon booked $9.5B and then $16.8B in gains as Anthropic's mark climbed.
This is desk research, not original reporting: public company announcements, SEC filings, central-bank notes and major-outlet coverage, assembled into one picture. Every deal below links to its source.
Confirmed is a signed, disclosed deal. Reported is credible reporting, not formally confirmed. Letter of intent is a non-binding commitment. Stalled means announced, then reportedly paused. The label reflects a reading of the latest reporting, not the linked source alone.
Share prices and market caps are snapshots as of June 2026 and move constantly, so they are dated rather than separately sourced. Private valuations come from the latest funding rounds. Forward-looking figures are flagged as estimates. The optional supply-chain layer ("All companies") shows well-established structural relationships, such as who fabricates whose chips, as context rather than individually sourced deals.
| Flow | Detail | Status | Verified | Source |
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