Spending a trillion is the easy part.
Announced capex becomes depreciation on somebody's income statement for years afterwards. The question is not who can spend it, but who can carry it.
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Hyperscaler capital expenditures came in on trend in Q1 2026, continuing the trajectory that projects them spending $770 billion this year and over a trillion dollars in 2027.
Epoch AI ·
Inspired by Epoch AI · Basket by Thesis
The basket and call are our interpretation. No author endorsement.
The time-bound call
Fixed at publicationThis basket will outperform the S&P 500 over 90 days.
- Started
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How the call is scored
A fixed model basket versus SPYx, each initially quoted from $150 USDC. The original raw token quantities never rebalance. Returns compare their USDC sell quotes with the starting sell quotes. The call succeeds only if the basket's return is strictly higher. Equal returns are a tie. Resolution uses the first complete quote observation started on or after the deadline, within 48 hours. If that window has no valid observation, the result is unresolved. Quotes include route price impact but exclude wallet execution and network costs. These are model returns, not your investment results.
Prices come from live Jupiter quotes and refresh once a day. A price that looks unreliable is skipped rather than guessed.
The deadline only scores the idea. It never sells your holdings and pays no prize. A new version of the thesis starts a new call.
Quote source: Jupiter ↗The argument
Why this idea. Why these businesses.
Epoch AI measures the spending trajectory, not the returns on it. Our reading is that at this scale the binding constraint stops being access to capital and becomes the ability to absorb depreciation without the earnings line breaking, which favours businesses with large, unrelated profit pools to bury it in. Epoch AI published the measurement; the portfolio interpretation and the timed prediction are ours alone.
Inside the basket
- 45%
Microsoft
The largest established software earnings base to absorb depreciation against, plus a rental business that converts the assets into revenue directly.
The tradeoff. Also the largest committed spender, so it has the most to absorb.
About this tokenized stock
Underlying: MSFT. Issuer: Backed Finance. Backed Finance issues these tokens. The issuer can move tokens out of any wallet (permanent delegate) and can freeze all transfers (pausable). Balances rebase for dividends and splits, so your share count can change without a trade.
Issuer terms (opens in a new tab) - 35%
Alphabet
Search advertising is a profit pool almost unrelated to the buildout, which is precisely the cushion this claim is about.
The tradeoff. That independence cuts both ways: the holding can move on advertising news that says nothing about this thesis.
About this tokenized stock
Underlying: GOOGL. Issuer: Backed Finance. Backed Finance issues these tokens. The issuer can move tokens out of any wallet (permanent delegate) and can freeze all transfers (pausable). Balances rebase for dividends and splits, so your share count can change without a trade.
Issuer terms (opens in a new tab) - 20%
Amazon.com
Included deliberately as the thinnest-margin operator, which is where this claim would fail first if it is wrong.
The tradeoff. Retail earnings dominate and can swamp the signal entirely.
About this tokenized stock
Underlying: AMZN. Issuer: Backed Finance. Backed Finance issues these tokens. The issuer can move tokens out of any wallet (permanent delegate) and can freeze all transfers (pausable). Balances rebase for dividends and splits, so your share count can change without a trade.
Issuer terms (opens in a new tab)
Why these weights. Weighted by depth of unrelated profit, because that is the thing the claim says is scarce. It was evenly weighted and therefore identical to another basket in this catalogue, which made two different arguments into one purchase.
The strongest case against
This is an argument for size, and size is already priced. If the assets have shorter useful lives than assumed, a large profit pool delays the damage rather than preventing it — and every holding here is exposed to exactly the same mistake at the same time.
What would change the thesis?
A major operator shortening the assumed useful life of its AI hardware, or writing down datacenter assets.
Read the evidence. Make up your mind.
Sources supporting the idea, including the ones that challenge it.
- Hyperscaler capex has quadrupled since GPT-4's release (opens in a new tab)
Epoch AI
Measures combined capex at Alphabet, Amazon, Meta, Microsoft and Oracle growing about 72% a year since Q2 2023. Epoch states its own limits: finance leases may be understated, operating leases are excluded, companies define capex differently, and the share that is specifically AI is not disclosed. It establishes that the spending is large and rising; it does not establish who profits from it.
- Hyperscaler capex on track to overtake operating cash flow (opens in a new tab)
Epoch AI
Projects aggregate cash capex overtaking operating cash flow around Q3 2026, with capex growing about 70% a year against 23% for cash flow. Epoch calls these simple extrapolations, notes the crossover moves between Q2 and Q4 2026 depending on the fitting window, and does not model whether the returns justify the spending. This is the strongest published case against both baskets: spending that outruns cash generation is the mechanism by which the buildout stops.
- Epoch AI: hyperscaler capex on trend for $770B in 2026 (opens in a new tab)
Epoch AI on X
The measurement this basket reasons from. Epoch AI publishes the capex trajectory; the portfolio interpretation and the timed prediction are ours.
Updates and version history
An update appends dated evidence to this argument. A new version changes the argument or the allocation itself. Neither one touches a position you already hold.
No updates since publication. When the author adds one, it appears here with its date and its sources — it never changes what is written above.
- Version 1
44b3ea7967Current
Written by the Thesis team. The team holds no position in these companies or their tokenized shares, and is paid nothing by any of them.
Tracking begins at publication. This thesis has no established performance history. Tokenized stocks carry issuer and market risk.