FleetingBits

sf thinkcat

[FleetingBits @fleetingbits]

some thoughts on anthropic's s-1

1) most of what is in anthropic's s-1 is already known to careful observers of the scene.

2) anthropic had $16b of revenue in h1 2026 with 85% gross margins; it spent $6.6b on r&d compute; the company has compute margins of about 45%.

3) gross margins have increased sharply recently: 36.6% in 2023, −94.8% in 2024, 39.5% in 2025 and 85% in h1 2026. i do not think this is a fully optimized business.

4) something interesting is that anthropic spends relatively little on data vendors compared with its r&d compute spend.

5) and, this share has trended down over time.

6) anthropic's estimated data vendor spending was ~$165m in 2024, ~$330m in 2025 and ~$388m in h1 2026. the trend gives ~$936m for 2026.

7) data vendor spending was equivalent to 9.3% of r&d compute spending in 2024, 7.2% in 2025 and 5.9% in h1 2026; purchased data has become a progressively smaller expense relative to compute.

8) i think this is probably because anthropic has increasingly been able to substitute synthetic data for purchased data; compute can substitute for data.

9) anthropic has also long been rumored to spend less on data than the other major labs: google, meta and openai.

10) i'm not sure if this is a reason to be bearish on the data market; anthropic's estimated data spend will still nearly triple this year relative to last year.

11) but i think it is a good sign that we should generally expect data spend to grow more slowly than compute spend, all things being equal.

12) one complication is that @justjoshinyou13 noticed that anthropic's employee count (~5,300) is much higher than reported elsewhere (~3,500).

13) it is possible that some of this extra ~1,800 employees consists of data labelers who are in-house in some sense, which would complicate the above numbers.

14) also interesting, we learn that anthropic's revenue in h1 2026 was 20% subscription-based and 80% consumption-based; subscriptions include enterprise subscriptions.

15) this suggests that incremental api margins could be higher than 85%, since my understanding is that subscription plans tend to be subsidized.

16) another interesting thing in the s-1 is that we get insight into the division between the cloud service provider sales channel and anthropic's direct sales channel.

17) in h1 2026, 48% of anthropic's revenue came through cloud service providers; this has been trending up over time, from 32% in 2024 and 47% in 2025.

18) this means that roughly half of anthropic's revenue comes through amazon, gcp and azure; it appears that in h1 2026, these platforms took a ~35% cut of that revenue.

19) i think that in the long term, anthropic will prefer for this revenue to be direct; anthropic is currently paying ~17% of its revenue in distribution fees.

20) after those fees, its 85% gross margin becomes a margin of about 70%.

21) i guess a final interesting thing, which i will not be able to fully cover, is the company's complicated governance structure.

22) a majority of anthropic's board seats are appointed by the long-term benefit trust (ltbt) but the founders, employees and common shareholders can vote to undo it.

23) the ltbt looks interesting until you realize that its powers can be revoked if it tries to do something unpopular, like remove dario.

24) if the founders want to undo it, it's in the economic interest of the employees and common shareholders to do so too, so it's hard to imagine it wouldn't pass.

25) this seems to be a very common pattern with the anthropic founders; they like to make loud charitable statements but then always make then non-binding

26) like they pledged of 80% of their shares to charity; but, it turns out that is revocable and legally nonbinding.

27) this is very similar to the anthropic public benefit fund; which holds 5% of anthropic's shares, but which is an llc and is fully controlled by anthropic

28) which means the company still controls how the shares are distributed (if at all)

29) so, some of these things sound like loud costly commitments but which turn out on closer inspection to not be so costly at all, at least as of yet

[Anthropic @AnthropicAI]

Anthropic has filed an S-1 registration statement with the SEC related to its proposed IPO.

Read more: https://t.co/ofhv0DK3Zp

Legal disclaimer: A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. This post does not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.

[FleetingBits @fleetingbits]

@norvid_studies interestingly, such a python class could just be refactored by claude now and, in practice, claude might leave it and just program around it, without a care in the world, as a sign of its virtuosity

[FleetingBits @fleetingbits]

@cherylwoooo how do you determine thefts / like separate them from other transactions

[FleetingBits @fleetingbits]

@norvid_studies https://t.co/DnZsjc1MJB

[FleetingBits @fleetingbits]

@latentmoss it looks incredible, i’ll have to take a watch

[FleetingBits @fleetingbits]

formal verification is one of the interesting “future technologies” being made possible with superhuman ai coders

i think we should think about what is possible now with formal verification, which was once too expensive / difficult / costly for mere humans

go read oleg!

[Oleg Mürk @oleg_murk]

Three days before I was pushed out of OpenAI I posted a Formal Verification Manifesto in a core research Slack channel. It listed OpenAI systems that could benefit from formal verification.

The only person who responded was @sama, who was very supportive. Not sure what the moral of this story is, but it's never too late for OpenAI to invest seriously in software & hardware formal verification!

On a more serious note, @theoremlabs and @AnthropicAI did something amazing. They showed how to verify software at scale without requiring developers to write specifications in Rocq or Lean. Instead, they used existing fuzz harnesses as specifications and LLMs to generate machine-checked proofs.

Of course, fuzz harnesses specify only some correctness properties. In the simplest case, the proofs establish crash-safety, not full functional correctness.

We need community-maintained open-source projects with reusable formal specifications and correctness proofs for major libraries, kernels, compilers, virtual machines and hardware.

Human review of critical specifications and LLM-generated patches is essential. A model could satisfy a specification simply by removing functionality.

As AI writes and rewrites critical infrastructure, formal verification should become a key part of AI control. Critical properties of AI-generated code should be machine-checked rather than taken on faith.

AI is making vulnerabilities easier to find and exploit. Frontier LLMs and reusable formal specifications could finally make formal verification practical and economical at scale, while keeping it understandable to humans.

Kudos to Theorem co-founders @rajashree and @diagram_chaser!

[FleetingBits @fleetingbits]

@jachiam0 actually though, using computer use to solve this stuff is very useful and, at least for some, worth the convenience risk tradeoff

[FleetingBits @fleetingbits]

@rankdim at the end of the day, i don't think anyone is going to want to give up margin to a platform

[FleetingBits @fleetingbits]

@qbolec yes, probably this part of the rl training process for models for certain coding tasks, although the grader is probably the model itself rather than a stronger model

[FleetingBits @fleetingbits]

i think a good benchmark for the future of security is to have models write complex programs from scratch, like web browsers, and then test whether models released later can still find vulnerabilities in them, and how many

basically tracking whether we’re approaching vulnzero

[Jason D. Clinton 🔸 @JasonDClinton]

@RothRottweiler vulnzero. the scaling laws will have driven cyber mythos-class capabilities into small, cheap models and all software dev everywhere will be using models for fractions of pennies on the dollar to stop shipping security bugs permanently.

[FleetingBits @fleetingbits]

@norvid_studies @medjedowo @punished_daniel @abelian_soup @schwarzposter_ so much of my art is japan inspired

[FleetingBits @fleetingbits]

@codetaur this is incredible, how did you do this?

[FleetingBits @fleetingbits]

@norvid_studies @medjedowo @punished_daniel @abelian_soup @schwarzposter_ i have never visited japan; only china : O