Research|ANT: S-1 Review and First-Cut EPS Model

Anthropic filed its S-1 publicly today. This note walks through the filing, and at the end we include the Anthropic financial model we built off it. The model rests only on the S-1 and prior public information, so a number of inputs are still open and we will keep refining it. Please contact Sales@Funda.ai to learn more about the EPS model.

What we focused on:

• Revenue and margins: 2Q26 revenue of $11.6bn at a 19% non-GAAP operating margin; 11% for the first nine months
• How and when ~$800bn of compute commitments flow into costs
• Whether the cash covers it: ~$132bn on the balance sheet; with IPO proceeds, the model trough is ~$150bn
• Demand and customer mix: NRR of ~900%; top five customers ~25% of revenue
• What it means for the AI narrative, and the numbers to watch before the IPO
• How we built the EPS model

I. Revenue

1. Quarterly Revenue

Quarterly revenue went $376M, $787M, $1.38bn, $2.04bn, $4.73bn and $11.56bn from 1Q25 to 2Q26. 2Q26 was up 1,368% y/y, while compute and infrastructure expense grew only 300%.

2. 3Q is blank, but you can back into it

The S-1 leaves 3Q26 revenue blank, but elsewhere states that 3Q26 revenue was 86x 4Q24. With 4Q24 at $197M, that implies ~$16.9bn for 3Q26, in line with the $16.8bn disclosed at the October investor day. Two other data points get you to the same place: TTM revenue up ~1,200% y/y, and TTM revenue per employee above $6.6M on ~5,300 employees.

3. Almost all revenue comes from new models

Model versions released in the six months to September 30 drove more than 80% of 3Q revenue. Older models get replaced very quickly.

4. Consumption is the bulk of it

Consumption-based revenue was 75% of the total in 2024, 83% in 2025 and 80% in 1H26; the rest is subscriptions.

II. Run-rate revenue and revenue recognition

1. How run-rate is defined

The S-1 defines Run-Rate Revenue as trailing-28-day consumption revenue x 13 plus monthly subscription revenue x 12. The company attaches three caveats:

• it includes customers later found to have violated the usage policy;
• it is seasonal, with usage dipping over the summer and winter holidays;
• growth is expected to slow.

2. Revenue is booked gross

When customers buy Claude through the AWS, GCP or Azure marketplaces, Anthropic is the principal and books the full amount as revenue; the cloud providers’ cut sits in “sales, marketing and partnerships.” Marketplace revenue rose from 11% of the total in 2023 to 32% in 2024, 47% in 2025 and 48% in 1H26. Adjust for this before comparing multiples with companies that report net.

3. Distillation revenue is now quantified

The S-1 states:

“we estimate that revenue from such customers represented approximately 5% to 7% of our revenue for the nine months ended September 30, 2026”

“Such customers” are those that violated the usage policy or commercial terms. On ~$33bn of 9M26 revenue, that is roughly $1.7–2.3bn (our estimate). Enforcement was stepped up in August–September, so the share should come down.

4. The holiday effect is real

Total customer spend in the two weeks to January 2, 2026 was ~20% below the prior two weeks. Any run-rate struck off the last week of December will be depressed by the holidays.

5. Some numbers are left blank on purpose

Run-Rate Revenue as of October 31, 3Q revenue, liquidity at September 30, total compute commitments and the price range are all blank. We expect them in the S-1/A.

III. Margins (non-GAAP)

1. Gross margin is in the footnotes

From 2025 the P&L presents expenses by nature, so there is no gross profit line. Note 8 still breaks costs out by function, and cost of revenue is “substantially all” compute and infrastructure for serving customer workloads. The implied gross margin: −95% in 2024, 40% in 2025, 12% in 1H25 and 85% in 1H26.

85% flatters the picture: the cloud providers’ revenue share and inference for free users both sit in sales and marketing, not cost of revenue.

2. Compute intensity is falling fast

Compute and infrastructure expense (serving, training, research and internal use) as a share of revenue: 251% in 1Q25 and 144% in 4Q25; 72% in 1Q26 and 48% in 2Q26.

3. 2Q26 cost structure

Excluding SBC: compute 48% of revenue, sales, marketing and partnerships 18.5% (including the cloud providers’ share), other operating ~9% and personnel ~5%. SBC was $1.26bn, ~11% of revenue.

4. Non-GAAP operating margin

Annual: −1,273% in 2023, −722% in 2024 and −106% in 2025; +10% in 1H26 ($1.6bn). The S-1 also gives the quarters: −221%, −129%, −93% and −84% through 2025, then −13% in 1Q26 and +19% in 2Q26. 2Q26 adjusted operating income of $2.24bn is GAAP operating income of $0.97bn plus $1.26bn of SBC.

5. 3Q likely back to ~12%

At the October 14 investor meeting, the company put the adjusted operating margin for the year through 3Q at 11%. The Information later corrected this to a nine-month figure, not 3Q alone; Bloomberg said “over 11%.” That implies ~$2.0bn of adjusted operating income in 3Q, a ~12% margin, about 7 points below 2Q.

We see 2Q’s 19% as a high-water mark. Revenue rose 144% q/q in 2Q while compute is locked in 12–18 months ahead, so compute fell from 72% to 48% of revenue: revenue simply outran capacity. As new capacity came on in 3Q (Google from April, Amazon from May, SpaceXAI ramping into year-end), plus the pricier short-term compute the S-1 flags, margins settled back around 12%. The sustainable level looks closer to 3Q than 2Q.

6.Cash flow has turned positive

Non-GAAP net margin: −1,115% in 2023, −695% in 2024, −97% in 2025 and +16% in 1H26 ($2.7bn). The adjustments are SBC ($2.2bn in 1H26), fair-value changes on convertible notes, derivatives and SAFEs ($72.4bn), other mark-to-market items ($8.7bn), non-cash charitable contributions and the Bartz settlement. Nearly all of the $80.7bn GAAP net loss is accounting noise on financing instruments.

Operating cash flow swung from −$2.6bn in 2025 to +$3.9bn in 1H26. With $0.5bn of capex, simple FCF was ~$3.4bn, or 21% of revenue; the S-1 attributes part of that to working-capital timing. Note also that the ~$161bn Broadcom TPU agreement is a finance lease: principal runs through financing cash flow, not operating cash flow or capex, so FCF will flatter the true cash cost of compute.

IV. Margins will stay volatile,

1. Why

Compute is contracted 12–18 months ahead and starts hitting the P&L as soon as capacity comes online, in large blocks and ahead of the revenue it will support. The S-1 notes that margins expanded sharply y/y over the first nine months, but expects them to compress while large amounts of new capacity come on, and to swing meaningfully even as the underlying economics improve.

2. The path in our model

Adjusted operating margin of ~12% in 3Q26, ~−9% in 4Q26 and ~−22% in 1Q27, narrowing from there. On a full-year basis: ~−13% in 2027, ~−4% in 2028, back to ~14% in 2029 and ~26% in 2030. The driver is the wave of contracted capacity landing in 2027–28, with costs running ahead of revenue (see Section VI).

3. Short-term compute is the near-term squeeze

When demand exceeds long-term contracted capacity, Anthropic buys short-term compute at a higher unit cost. The S-1 expects to lean on short-term compute more in the near term, which will weigh on margins.

4. Compute could get more expensive

The S-1 warns compute costs could rise materially, especially if AI demand accelerates or the ability to train frontier models spreads and competition shifts to securing compute. It lists factors largely outside its control: concentrated chip and memory supply, tight power and data-center capacity, and export controls.

5. The response: “Anthropic Direct”

The company is moving from cloud-only contracts to a hybrid model: leasing chips directly and placing them in dedicated facilities run by third parties. Data centers take longer to build but are a small share of total cost, so it locks in the sites first and pushes the much larger accelerator commitments later. Once that capacity is live, cheap long-term compute can replace expensive short-term compute.

6. Pricing to customers keeps falling

Opus 5.5 costs ~40% less per task than Opus 5; Fable 5.1 is ~25% cheaper than Fable 5 on standard workloads and up to 45% cheaper on complex coding and agentic tasks. The S-1’s argument is that compute efficiency is what lets it cut prices and hold margins.

V. The compute bill

1. Disclosed compute commitments total ~$780–810bn

2. The mismatch

Revenue is usage-based and not locked in; compute is fixed, multi-year and take-or-pay. The Broadcom leases, the credit agreement and the convertible notes are also cross-defaulted. RPO was $10.8bn at June 30, ~75% of it recognized within 12 months, so cRPO of ~$8.1bn covers less than 20% of 2Q’s ~$46bn annualized revenue. Next year’s revenue depends almost entirely on usage.

VI. Cash and funding: can they pay for it?

1. What they raised, and what is left

From 2023 through 1H26, Anthropic raised ~$140bn in cash (preferred stock, convertible notes and SAFEs), $110bn of it in 1H26 alone, including the $65.2bn Series H. At June 30 it held $112.4bn of cash and equivalents plus $19.3bn of short-term investments, ~$132bn in total, with another $2.5bn of restricted cash.

2. Undrawn capacity

A $15bn revolver (upsized in September, undrawn) and up to $77bn of partner note facilities: Google $20bn, Amazon $15bn (tied to compute-delivery milestones) and Broadcom $42bn (usable only for rent). We assume ~$60bn of IPO proceeds in the model (4Q26).

3. Against the megacaps

Cash and liquid investments at June 30 (gross of debt): Alphabet $242bn, Anthropic $132bn, Amazon $123bn, Meta $90bn, Microsoft $77bn, Tencent ~$75bn, Alibaba ~$70bn. Anthropic holds more cash than Amazon, Meta or Microsoft, yet the SpaceXAI contract alone runs ~$37bn a year from December 2026.

4. Operating cash flow matters too

Compute gets paid from the business as well as the balance sheet. Excluding SBC, non-compute costs were ~33% of revenue in 2Q. The real question is whether cash plus annual operating cash flow can carry the contracted capacity. The figures below come from our model (Section XIV).

5. Compute cost in our model

Contracted capacity cost (new cloud contracts expensed as capacity is delivered, commitments in place at end-2025, Broadcom lease amortization and the related data-center cost, AMD depreciation) is ~$24bn in 2H26 and ~$98.6bn in 2027. To smooth the curve, we assume some long-term contracts are signed early in 2028–29 (~$4bn and ~$14bn). Total compute comes to ~$98.6bn / $152.3bn / $182.0bn / $212.0bn over 2027–30.

6. It ties to the ~$750bn long-term figure

The Information reported a company plan of ~$750bn of compute spend through 2030. Our model has ~$678.7bn of compute expense over 2026–30. With Broadcom equipment amortized over five years and AMD hardware depreciated over five, ~$61.4bn and ~$8.4bn are still unexpensed by 2030; adding them back gives ~$748.5bn on a spend basis, essentially in line (~$772.6bn including ~$24.1bn of Broadcom lease interest). About $93.4bn of that rests on assumptions beyond the S-1 contracts. Of the ~$819.1bn in disclosed S-1 commitments, 68% is expensed by 2030 and ~$261.3bn falls after 2030.

7. Base-case cash path

Exit run-rate of $85bn at end-2026, T28 of $150bn at end-2027, then 60% / 50% / 40% growth over 2028–30; ~$60bn of IPO proceeds in 4Q26 (our assumption).

8. Stress test

• Against today’s long-term compute commitments, the base case needs no further funding. But the outcome is highly sensitive to revenue growth and compute pricing, and if Anthropic builds more of its own data centers or buys more of its own hardware, compute spend rises and a further raise could be needed.
• The company has barely touched the money it has raised so far.
• Still, the conclusion surprised us. We will take a deeper pass and update as soon as anything changes.

VII. How Anthropic gets TPUs, and who books the revenue

1. GOOG and Broadcom

One is renting from Google Cloud: at least ~$114.5bn from April 2026 to July 2033, take-or-pay. The other is leasing TPU-based systems from Broadcom under five-year master equipment leases: ~$161.2bn of rent, of which the 2026 Lease Arrangement is ~$125.2bn. The TPUs are leased, not bought, but they are accounted for as finance leases and sit on the balance sheet, and the S-1 mentions an early buyout option.

2. How the rent is paid

Rent is paid quarterly once the equipment is ready for service. The leases sit in two special-purpose subsidiaries, guaranteed by Anthropic PBC and secured by the equipment. If needed, Anthropic can fund rent by issuing up to $42bn of convertible notes to Broadcom, settled in Class N shares.

3. The data centers aren’t Anthropic’s

The hardware sits in dedicated facilities operated by others, with Fluidstack, for example, providing colocation and services. Anthropic has also given developers guarantees and cost-reimbursement arrangements, with a maximum potential payment of ~$0.8bn at June 30.

4. Who books the revenue (Our view)

On the GCP route, Anthropic is a Google Cloud customer: this is Google Cloud revenue and sits in the Cloud backlog ($513.9bn at June 30). On the Broadcom route, the lessor is Broadcom, not Google; a finance lease for the lessee usually means a sales-type lease for the lessor, with revenue recognized upfront when the equipment is ready for service. Our current view is that Google books its TPU sales to Broadcom gross, without netting Broadcom’s cost, though net treatment cannot be ruled out. The S-1 adds nothing further.

VIII. Demand

1. Key data points

• NRR of ~900% (excluding Pro / Max subscriptions);
• customers spending more than $100K / $1M / $10M a year: 11,900+ / 1,900+ / ~250;
• no single customer above 10% of 9M26 revenue; the top five were ~25%;
• 79% of 3Q revenue came from outside the top five, up from 64% in 1Q;
• Claude Enterprise users who also use Claude Code spend ~20x more than those who do not.

2. Anthropic runs on Claude

In September, more than 90% of code contributions were written by Claude, and code per engineer was 45x the 2024 level. The list-price value of internal Claude usage grew more than 250x from January to September and has exceeded monthly personnel cost (ex SBC) since April. TTM revenue per employee rose from $1.4M to more than $6.6M.

IX. Models and training

1. Pre-training is run like a capital budget

Improvements are tested at small scale and have to hold up at scale before they go into a frontier run. The S-1 calls frontier runs “planned, scheduled capital deployments.”

2. Post-training matters more and more

With the shift from assistants to agents, most of what makes a model useful, accurate and safe now happens in post-training. Agentic skills such as coding, tool use and operating on a user’s behalf are also trained at this stage, on complete, realistic tasks.

3. Beyond scaling laws

Four of the co-founders were lead authors on the original scaling-laws paper. The company says proprietary “compute multipliers” from pre- and post-training research let it improve faster than scaling laws alone would, while inference engineering keeps pushing serving compute down. The risk factors also flag that scaling laws could plateau.

4. What each generation brings

Deeper reasoning, longer autonomous work and more complex tasks. On one internal benchmark, the success rate reached ~90% in September 2026, up ~50 points in nine months. Training costs are not disclosed: the S-1 says only that frontier runs take weeks to months, cost at least several million dollars, and have sometimes failed or underperformed. What it does disclose is that price per task has fallen with every release.

5. Returns

Revenue has grown far faster than compute expense, and new models earn back most of their investment within a few quarters. The flip side: revenue from older models decays quickly, which forces continuous investment in the newest, most compute-hungry models. R&D was ~60% of 1H26 revenue.

6. Where the data comes from

Public web data (crawled pages, scanned books), licensed or custom third-party datasets, and synthetic data generated by its own models. The company says it respects robots.txt, paywalls and login gates, but concedes it may not always succeed. The $1.5bn Bartz settlement was approved in July 2026. A large share of customer traffic is covered by zero-data-retention terms.

7. Constitutional training and safety

Claude is trained against a published constitution with four priorities: broadly safe, broadly ethical, compliant with guidelines, and genuinely helpful. One interesting detail: training on constitution-style documents alongside fictional stories about well-aligned AI reduces Claude’s misalignment rate.

Safety can also slow the roadmap. Anthropic once delayed pre-training of a new frontier base model by more than a month to harden protections against weight theft. In 2026, during pre-release cyber evaluations, a model accessed real computer systems without authorization; the company paused some evaluations and higher-risk RL environments in response.

X. How the founders see the future

A founders’ letter is unusual in an IPO filing. A few passages stood out to us.

1. Reinvent rather than defend

“We will be constantly seeking to reinvent our business, releasing new classes of products whose aim is to dwarf the economic value of our existing ones.”

In plain terms: less energy spent protecting today’s products and business model, more on building new categories.

2. The timeline for viable AI

“AI chatbots became viable around 2022, and AI coding agents around 2025; AI agents for general knowledge work and scientific discovery are gradually becoming viable in 2026.”

Claude Cowork extends Claude Code’s agent architecture to every knowledge worker.

3. How big the market is

• Global knowledge work worth more than $40tn in 2025 (company estimate);
• More than 1bn knowledge workers by 2028 (IDC);
• $22.5tn of cumulative economic impact from AI by 2031 (IDC);
• Pharma spend alone above $2tn in 2026.

4. “A country of geniuses in a datacenter”

Anthropic expects “powerful AI” to be broadly available within a few years. Its definition: expert-level performance in most fields, the ability to use any interface a human can, to work autonomously for hours to weeks, to coordinate across many instances, and to operate lab equipment and robots.

5. Why the compute bets look so large

The founders say forward investments can look irresponsibly large at first glance because AI’s value sits on an exponential curve. Their analogy is pharma R&D, only faster and bigger. The hedge is to trade some upside for downside protection: reselling surplus compute, improving efficiency, raising capital.

6. Revenue they chose to leave on the table

Claude carries no ads. Claude Mythos Preview was not broadly released because of cybersecurity risk and later shipped as Fable 5, forgoing meaningful revenue; the company also gave cyber defenders ~$100M of usage credits. If needed, they may slow capability progress for safety, even at a competitive cost.

7. The historical reference they chose

From 2000 to 2025, revenue at the world’s ten largest tech companies grew ~38x, roughly 11x the pace of global GDP. The founders argue the exponential in machine cognition will matter far more than Moore’s Law.

8. On investor expectations

The founders pass on advice from several entrepreneurs who had been through an IPO:

“never let your company be worth a dollar more than it is actually worth”

Their own rule: under-promise and over-deliver.

XI. Dilution and lock-ups

• $77bn of convertible note capacity: Google $20bn, Amazon $15bn and Broadcom $42bn, converting at a trailing volume-weighted average price;
• Google’s IPO purchase right: up to $10bn of additional stock at the IPO price;
• Public Benefit Fund: 5% of fully diluted shares, plus up to 2% more for every 50% rise in the share price;
• Staggered lock-up releases: tranches on day one and on days 27, 48, 62 and 76; roughly every two weeks after FY2026 results; the 180-day group is fully released at 1Q27 results or day 180. The 365-day group (including all seven co-founders) is released in stages through 3Q27 results;
• Founders’ pledge: each co-founder has pledged to give away 80% of their personal equity, a voluntary and revocable commitment.

XII. What it means for the AI narrative