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Anthropic's S-1: three investors, three reasons to read it
Sarah Guo shared the SEC filing with the line "read for yourself (not investment advice)." She wants to see whether a company with a strong mission changes how it operates under quarterly public scrutiny, and whether public opinion shifts once retail investors own part of the upside. Her first observations were an inflection in operating profit, a strong emphasis on novel discovery and bio, a mismatch between consumption-based revenue and long-term compute obligations, and limited shareholder influence. She singled out the "20 pages of chosen case studies starting 220."
Jason Lemkin said: "Read the filing, not the headlines." He linked his SaaStr write-up, 5 Learnings from Anthropic's S-1, which highlights 900% NRR, $6.6M in revenue per employee and "$41M to $16.3B in half a year." The write-up is his own material.
Aaron Levie pointed to Ben Bajarin's post on the filing's Fortune 500 data. According to Bajarin, Anthropic's revenue from those customers averages less than 0.1% of their expenses. Levie called this "the key stat" and sees no reason it can't grow 10–50X. He argues the big jump will come as enterprises rewire their processes for agents. His takeaway: the S-1 shows "the strength of the frontier lab business model" and "how early we are in the build out of AI."
Patrick O'Shaughnessy called it a "good day to rewatch" his conversation with Anthropic CFO Krishna Rao. It was Rao's first podcast appearance. Topics include how he allocates compute across Trainium, TPUs and GPUs, what investors misunderstand about model companies, and platform vs. application strategy.
Amjad Masad: Marc Andreessen on chaotic technology adoption
Masad called Andreessen's Replit and the dawn of the self-driving company "a fantastic piece placing this shift in the history of computing." The essay argues that a chaotic entrance is one of the best signs a technology will change the world. Andreessen cites Andy Grove's question of who will "let chaos reign, and then rein in the chaos." He compares agents to PCs, which people brought to work whether IT allowed it or not. Masad has a stake here: the essay praises Replit's own "Agent Habitat."
OpenAI's claimed refutation of ETH
Ryan Williams posted a direct link to OpenAI's preprint. It claims 3SAT on n variables can be solved in about exp(n/(log n)^{1/2+o(1)}) time. Tim Sweeney shared it with "Wow!" Martin Casado called ETH being false "a stunning result." It is still a claim, not a peer-reviewed result.
Shorter mentions
- Clem Delangue shared Liza Dwoskin's Washington Post story. It reports that ex-DOGE cyber researchers used AI agents to get into Anthropic's systems in under 12 hours, including Slack channels where its most sensitive models are discussed. His takeaway: anyone who thought "concentrating AI behind closed doors is safe" should "update your priors."
- Ryan Hoover framed YC's interview with Farza as a modern-day version of Bezos's "working backwards" press release: start from the launch and ask what the Instagram caption says. In the interview, Farza tells YC's Aaron Epstein why founders should launch constantly and how he scripts videos that still feel off the cuff.
- Balaji Srinivasan: on a Farcaster stream, the host read out Balaji's prize brief for a 59-second video on civility, "starting with Norbert Elias's book, The Civilizing Process." The brief's argument is that people who are polite to each other can cooperate at larger scale and build more valuable things.
Balaji’s brief for a 59-second civility explainer names Norbert Elias’s The Civilizing Process as its starting book. The brief asks creators to show how civility developed and enabled large-scale cooperation; it argues that respectful behavior lets people cooperate at greater scale and build more valuable things together.
@saranormous shared a Washington Post article about cybersecurity startup Cathedral getting inside Anthropic, commenting, “savage timing from the cathedral team.”
Jack Mallers recommends looking up Stratum V2 and other decentralized-pool work in the context of mining-pool centralization; he highlights letting miners build their own block templates instead of relying on a pool’s template.
Jason Lemkin linked a SaaStr post/video while arguing that a well-trained agent can outperform a junior human SDR at qualifying inbound leads . The video’s example: a Base44 agent recalled that the CEO had attended SaaStr and Wix had bought Base44—details the human forgot—and Amelia Lerutte says the inbound lead booked a meeting within ten minutes .
Patrick O’Shaughnessy recommended rewatching his podcast conversation with Anthropic CFO Krishna Rao (episode post); it was Rao’s first podcast appearance. O’Shaughnessy said the appeal was hearing what it is like inside a consequential company at a pivotal moment; topics included compute allocation, investor misunderstandings about model companies, and Anthropic’s platform-versus-application strategy.
@bhorowitz endorsed Ice Cube’s video tribute to D.O.C. as “super thoughtful.” The tribute was shared ahead of D.O.C.’s Paid in Full Award, and @Passionweiss described it as one of the most powerful things they had heard in a long time. Video
Sarah Guo (@saranormous) recommended reading Anthropic’s S-1 filing (SEC filing); she said she was curious whether a strong mission changes under quarterly public scrutiny and whether retail ownership changes public opinion. She flagged an operating-profit inflection, emphasis on novel discovery and bio, a mismatch between consumption-based revenue and long-term compute obligations, limited shareholder influence, and many case studies. She also called 20 pages of chosen case studies starting at page 220 interesting.
Patrick O'Shaughnessy recommends a video podcast conversation with Anthropic CFO Krishna Rao, his first podcast appearance, as a chance to hear what it is like inside Anthropic at a pivotal moment. They discuss compute allocation, what investors misunderstand about model companies, returns to frontier intelligence, platform-versus-application strategy, and Anthropic's internal use of Claude. Episode post.
Vinod Khosla endorsed @hawkblogger’s X post, which says the Seahawks locker room erupted with cheers, calling it a “True message to the doubters.” His takeaway: teams have ups and downs, but great coaching and player excellence “will come thru.”
Aaron Levie said the S-1 discussed alongside data about Anthropic’s Fortune 500 customers is useful for understanding the strength of the frontier-lab business model despite competition and how early AI’s build-out remains.
Jason Lemkin urges readers to “Read the filing, not the headlines,” recommending Anthropic’s S-1 . He links a SaaStr post, “5 Learnings from Anthropic’s S-1”, which highlights 900% NRR, $6.6M in revenue per employee, and “$41M to $16.3B in half a year” .
Elon Musk quote-posted Tahreem57’s “Waymo vs Tesla solving autonomy” post, adding “🎯😂”—an apparent endorsement of the comparison, with no stated rationale.
Elon Musk called @Geiger_Capital’s X post an “Interesting question” and linked to it (post). The post asks which Brazilian voting machines stayed open and why they were not locked at 5pm, saying a Supreme Court extension was relayed minutes after polls were supposed to close.
Ryan Hoover linked YC’s post featuring an interview with Farza and YC’s Aaron Epstein about launch videos, framing its “work backwards” approach as a modern-day Bezos-style press release: start from the launch and work back to the Instagram caption.
Elon Musk endorsed Devon Eriksen’s post with “Yes,” linking to it; Musk gave no further explanation. Eriksen’s post . Eriksen argues that tribal belonging is a fundamental human need and that denying young men a tribe or homeland risks driving radicalization; he calls for immigration-law changes and action on the 14th Amendment.
Amjad Masad recommends Marc Andreessen’s “Replit and the dawn of the self-driving company”, calling it a “fantastic piece.” Masad says it places the shift toward self-driving companies in computing history, where major revolutions first enter businesses chaotically and are later harnessed.
@amasad (Amjad Masad) shared his article The Rise of the Self-Driving Company, introducing it with “Software ate the world, now it’s learning to run it.” The article argues that AI agents will increasingly handle execution and coordination while people set goals and exercise judgment, enabling “Self-Driving Companies.”
Elon Musk endorsed an X post by @Rothmus about Lula’s appointments to Brazil’s Supreme Court and their votes to convict Jair Bolsonaro, replying “True” and linking to the post.
Elon Musk endorsed Gad Saad’s X post with “Good point,” linking to it; Saad argued that dire predictions about Trump’s first term and warnings that his second term would end democracy or impose martial law had not come true.
Clem Delangue pointed readers to Liza Dwoskin’s report (gift link), which said ex-DOGE cyber researchers used AI agents to penetrate Anthropic’s systems—including Slack channels where sensitive models are discussed—in under 12 hours; he offered it as a reason to update priors that closed-door AI is safe.
The Rise of the Self-Driving Company

In 1937, British Nobel Prize-winning economist Ronald Coase asked, “Why do firms exist?” His answer was transaction costs. It’s often cheaper to organize economic activity inside a company than to coordinate it through markets.
But companies have their own coordination costs: management, meetings, departments, reporting, and bureaucracy.
For more than a century, we’ve accepted these costs as the price of doing business.
In 2011, Marc Andreessen wrote that software was eating the world (opens in new tab).
In 2016, we built Replit around the belief that everyone should be able to create it.
Software transformed entire industries, but companies still depend on people to operate that software and coordinate work across teams.
AI promises an even bigger shift. As agents take on more of the execution and coordination, companies can work more efficiently, and their people can pursue more ambitious goals. Realizing that potential will require companies to rethink how they organize and make decisions.
Companies built around this new way of working will be Self-Driving Companies. At Replit, that shift is already underway (opens in new tab).
By the end of 2030, every company in the Fortune 500 will be a Self-Driving Company.

The Rise of the Self-Driving Company
In Self-Driving Companies, people define the goals, exercise judgment and taste, and remain accountable. Agents use the company’s knowledge and systems to execute toward those goals, with clear boundaries for when to act and when to seek approval.
Companies that become self-driving will force an existential choice on their competition: change how you work or watch competitors do what you cannot.
How Work is Changing
We’re seeing this shift at Replit.
It started in engineering (opens in new tab). Engineers nearly tripled their code output in the first half of 2026 while review times, reversions, and product incidents stayed flat.
Then people across the company began putting agents to work, building software and solutions that took repetitive work off their plates so they could spend more time on problems that needed their expertise. Designers turned ideas into working product changes, and support used agents to investigate customer issues.
The nature of work itself began to change. People could act on problems they once had to hand off. They could try an approach, share something tangible, and build it together. More ideas and feedback surfaced, and the people raising them were emboldened to help build the solution.
People spent more time at whiteboards, deciding what to build. Engineers worked on hardening the systems agents depend on so they could delegate more work. Work became less repetitive, more creative, and more collaborative.
Alongside the productivity gains, more people experienced the satisfaction of making something useful, solving difficult problems, and seeing their ideas become real. People had more freedom to shape their work and more confidence in what they could accomplish. They don’t feel like they’re being replaced. They feel like they’re being promoted.
Replit has become a Self-Driving Company.
What a Self-Driving Company Requires
Today, many companies face a false choice.
Keep AI in a cage, where its reach is limited but so is its usefulness. Or let it loose in the wild, where agents can go rogue: accessing sensitive data, changing production systems, or sending information outside the company without the right permissions or approvals. A mistake can become a security incident that reaches beyond the company itself, as the Hugging Face incident (opens in new tab) demonstrated.
One path limits capability. The other sacrifices control. Neither enables a company to become self-driving.
A Self-Driving Company will need a foundation that lets it embrace the power of AI without the risk.
Self-Driving Companies will make agents useful to everyone. The people closest to the problem should be able to put them to work without becoming engineers. A company cannot become self-driving if only its technical teams can participate.
Today, most agents live somewhere chaotic. They run in chat windows, personal accounts, and one-off scripts, holding whatever credentials and data someone handed them. Nobody decided what they could touch, and nobody can see what they did.
As agents take on more work, they need an Agent Habitat: an environment where they can use the company’s knowledge and systems, coordinate with people and other agents, and act within the boundaries the company controls.
The habitat must control access, define when human approval is required, and limit the impact of mistakes. As AI makes it easier to discover and exploit vulnerabilities (opens in new tab), companies will also need agents to find weaknesses and strengthen their defenses. Security must scale with autonomy.
Governance of the habitat will also help the company become smarter. When someone corrects an agent, that correction should be reviewed and made available to improve future work. When one team develops a useful workflow, others should be able to build on it. The habitat becomes a shared body of knowledge, shaped by the people who work within it, helping agents act with more of the company’s expertise, taste, and judgment.
Self-Driving Companies must also remain free to change their models while retaining control of their knowledge, systems, and decisions. As Satya Nadella puts it (opens in new tab), “we need to separate the supply of intelligence from the authority over it.”
Making Every Company a Self-Driving Company
With Replit for Business (opens in new tab), we’re building the foundation to bring what we have learned inside our own company, and help every company become a Self-Driving one. The ambition is to enable everyone to turn their expertise into working software and completed work.
We’re seeing large companies move faster with Replit. At Sumitomo Mitsui Finance and Leasing (opens in new tab) (SMFL), “a development process that previously required six people working for six months was completed by just one person in one week,” says Yoshiyuki Fukuma, GM of Digital Transformation. “A project originally estimated at approximately JPY 3 billion for the full development lifecycle is now expected to be completed for less than JPY 1 billion,” says Takeshi Fujiwara, Corporate Officer and Head of Digital Lab.
Ronald Coase explained the firm through the economics of organizing work. As AI changes those economics, we have an opportunity to rethink the organization itself: how much a small team can accomplish, how far an individual’s expertise can reach, and what a company can attempt.
We believe the biggest economic impact of AI will come from this reinvention of the firm. Self-Driving Companies will organize around people setting direction and exercising judgment, with agents carrying more of that intent through to execution.
Software ate the world. Now it’s learning to run it.
@amasad (Amjad Masad) shared his article The Rise of the Self-Driving Company, introducing it with “Software ate the world, now it’s learning to run it.” The article argues that AI agents will increasingly handle execution and coordination while people set goals and exercise judgment, enabling “Self-Driving Companies.”