The company whose AI writes marketing copy so bad it makes your eyes bleed is telling you that it is going to cure cancer. This is a ridiculous-sounding claim.

Let me try again.

The fastest-growing company ever, which has more than $100 billion in cash, which will soon control hundreds of billions of dollars’ worth of computing power, which has more than 11,000 customers paying it $100,000 a year, is telling you that it is going to cure cancer. This sounds, to me, marginally more reasonable.

For three years we have understood Anthropic through a deluge of strategic leaks, hyperbole, FUD, jokes about shrimp welfare, memes, and way, way too many blog posts. Now, finally, we have cold, hard, SEC-approved data, through its S-1 filing. This document is absurd. Ridiculous. Stupid. It gives the details on a business the likes of which we have never seen before in the history of capitalism. I’ll give you lots of statistics today, but to start off: Anthropic has raised $141.1 billion since its founding in 2021. As of June 30 it still had $131.7 billion in cash and short-term investments. So the company has burned through less than $10 billion to get to a $16.8 billion quarter. In said quarter, it was even profitable on an operating basis. Again, ridiculous.

Anthropic is reportedly targeting a $2 trillion valuation. Is this valuation equally ridiculous? What do you have to believe to accept this bet? Is it worth investing in this IPO?

Let’s discuss.

Revenue: The most absurd number ever printed

Revenue increased from $376 million in Q1 2025 to $11.6 billion in Q2 2026. The third quarter of this year came in around $16.8 billion, though note that figure is press-reported; the S-1 leaves Q3 blank pending audit review. That is roughly $35 billion of trailing revenue for a company that did not exist six years ago. Yeesh.

What impressed me most was net revenue retention (NRR). Anthropic reports it at approximately 900 percent as of September 30. In MBA-accessible English: The customers who were paying a year ago are now paying nine times as much. For context, the highest NRR any public software company has ever printed is Snowflake’s 189 percent in late 2019, on about $69 million of quarterly product revenue. The public software median is 110 percent. Anthropic did 900 percent on a $16.8 billion quarter. Again, yeesh.

The definitions are not apples to apples (Anthropic’s metric excludes Pro and Max subscribers and counts only “qualified customers”), but still, no one has ever done this before.

That said, don’t over-index on this number. I think of foundation models as being akin to Hollywood, not Silicon Valley SaaS. In Tinseltown, you are only as good as your next movie, and in the LLM game, you are only as good as your next model. The reason they grew ninefold this year is because Anthropic released a series of ever-greater Claude models over the past 12 months (from Opus 4.5 onward). The S-1 backs up my movie business mental model. Anthropic disclosed that models older than six months earn less than 20 percent of revenue. Versions released in the six months up to September 30 produced more than 80 percent of Q3 revenue. So frontier models are an asset with a six-month half-life. As the models themselves get better at making new models, I would expect that half-life to decrease over time.

My second piece of evidence is how clearly the document illustrates Jevons paradox: As AI becomes cheaper and more efficient, people use so much more of it that total spending rises. Essentially, harder tasks have to cost less with each model release for the company to grow. The company says that every release “cuts cost per task 25 to 45%.” For example, after the Opus 4.5 price cut in November 2025, quarter-over-quarter growth went from 48 percent to 132 percent. Translation: Anthropic has to grow volume faster than it cuts price, forever, on a cost base that is contracted years in advance.

My third piece of movie logic evidence is that Anthropic’s growth is already slowing from absurd to merely insane. The second quarter was 144 percent quarter over quarter; the third was about 45 percent. Part of that is the summer, when Fable met real competition from OpenAI and open-weight models for the first time. Part of it is cleanup: The company says 5 to 7 percent of revenue came from customers violating its usage policy, mostly labs distilling Claude, and it cut them off in August and September. (Opus 5.5 appears to have at least partially restored the growth curve, but we’ll see in the next earnings if that is true.)

To believe this company deserves to be in the trillion-dollar club, one of your biggest beliefs needs to be that it can produce better models than the rest of the world, essentially until the end of days.

Who is actually paying?

Despite starting off the S-1 with notes about how Anthropic is in the AGI business, for now, Anthropic is mostly in the Claude Code business. Claude Enterprise users who do not use Claude Code spend less than $75 a month. The top 5 percent of users, who do, spend about $1,500 a month. This is the single most important fact about Anthropic’s growth: The models happened to be the best at coding, the company built the application layer on top of them itself, and via the business strategy magic of vertical integration it captured the value instead of handing it to Cursor and Copilot.

That hasn’t stopped the business from being a B2B monster. A few key points to highlight:

This is a usage business sold to a few thousand companies, not a software company sold to 40 million users. The S-1 defines an enterprise customer as anyone paying $100,000 a year. By that definition, 76 percent of 2025 revenue came from about 1,500 organizations, and the other 24 percent came from roughly 40 million customers combined. The ratio has held between 57 percent and 90 percent every year since launch. Think of the consumer product more as a funnel and brand.

The real customer count is closer to 250 than 11,900. The 11,900 figure is the one the company leads with, and it grew eightfold in nine months, which is a legitimately absurd number.

But the tiers are a power law. Each 10x step in annual spend cuts the customer count by about sevenfold, and the line extends cleanly to an implied 30 to 40 customers above $100 million a year. If you count every one of the 11,900 accounts at the bottom of its tier, the arithmetic floor is $5.15 billion. When you underwrite this company, you are underwriting roughly 250 relationships and the rate at which the next 1,650 climb into that group.

Despite that, the concentration risk is not the one in the risk factors. No single customer accounted for more than 10 percent of total revenue for the nine months prior to the filing. The top five were about 25 percent of revenue over the period, falling from 36 percent in Q1 to 21 percent in Q3 while each of the five set a record. That trend is the single best number for the bull case in the filing.

The problem is who the whales are. The reporting consensus is Cursor and GitHub Copilot, which the footnotes support in outline: Customers A and B were 12 percent each in 2025, and Customer C swung from 43 percent of revenue in 2023 to less than 10 percent to 11 percent in the first half of 2026. If that identification is right, the largest customers are coding products that sit between Anthropic and the end user, which have every incentive to route tokens to whichever model is cheapest this quarter, and whose owners (SpaceX and Microsoft, respectively) run their own model programs. The upside case is the mirror image. More than half the Fortune 500 pays Anthropic at least $100,000, and the filing says those companies spend less than 0.1 percent of their total expenses with it on average. The top 10 Fortune 500 accounts spend less than 1 percent. If the average Fortune 500 customer moved to where the top 10 already are, revenue from that cohort alone would grow eightfold. The bet at $2 trillion is that the power law keeps its shape while the whole curve shifts right.

What is gross margin anyhoo?

Surface-level analysts will likely laud Anthropic’s reported 85 percent gross margin. This would make the company a SaaS stock. However, while the company is using the SEC-accepted definition, this gross margin definition isn’t spiritually accurate.

Cost of revenue, as reported, is the compute attributed to customer-serving workloads. But Anthropic says, in its own IPO document, that it runs compute as one fungible pool. The model a customer is buying today was trained with research compute six months ago, and all the billions spent training it will be obsolete six months from now. So to properly understand this company, I think you need to treat frontier training as the cost of producing the thing being sold. If you do that by including total compute and infrastructure spend, then the cost of goods sold is $8.9 billion and gross margin is 45 percent. On that number, Anthropic is more akin to a hardware business (which is not to its benefit).

Anthropic enjoys software gross margins when serving customers while suffering hardware gross margins when it does the research necessary to stay relevant. And as we’ve already established, the second cost never goes away. The closest comparison is the Netflix content-amortization debate from 2015 to 2019, when the argument was whether original programming should be expensed or capitalized over five years. Here the content expires in six months. Unfortunately, we don’t get much more detail on the future cost of training models—most likely because Anthropic doesn’t know what it’ll be well enough to publicly commit to something. But in my conversations with researchers, most of them expect the cost increase to be logarithmic.

While we’re on the income statement, a brief note on the fact you will see in every headline. Anthropic lost “$80.7 billion” in the first half of 2026. This is a nonsense number that isn’t worth worrying about. The actual operating loss was $693 million. The loss was driven by $72.4 billion of mark-to-market losses on Amazon’s convertible notes. The notes convert at the IPO, so the loss vanishes from the P&L the quarter after listing. (This is a rare case where an $80 billion loss could easily be interpreted as a buy signal.)

The compute spend is absurd

Current compute expense is about $22 billion a year, Q2 annualized. The contracts take that to roughly $120 billion a year next year and about $155 billion by 2028, most of it non-cancelable. Revenue, meanwhile, is consumption-priced and cancelable at will, and the five largest customers are mostly not on long-term contracts. Long-term debt being married to short-term revenue makes my stomach queasy.

The S-1 spends a lot of time arguing that this spend isn’t a good bet. Anthropic has structured the financing program such that it separates data-center commitments (which have long lead times and bear a small share of cost) from accelerator commitments (short lead times, most of the cost), so the biggest checks get written closer to when demand is visible. To back up this plan, it dropped another shocking statistic, namely that this year, revenue grew 1,368 percent against 300 percent growth in compute. Additionally, demand has exceeded supply in every single period since Claude launched, so the risk the company has actually lived through is under-commitment, not over-commitment.

All of these points are true, yet still, it all depends on the next model being as good as the last one and Anthropic having the vertically integrated app necessary to capture that value.

Despite the big upcoming spending, the business has been remarkably efficient. The revenue per employee is $6.6 million on a trailing basis, up from $1.4 million a year earlier. Google is at $2.2 million. SpaceX, with xAI inside it, is at $1.4 million.

So here is where we are. The most efficient scaling in the history of the industry, 900 percent net retention, 85 percent serving margins, $131 billion in the bank, and $800 billion of fixed obligations that the company can only meet if the next six models are as good as the last six. At $2 trillion, is that a buy? It depends, again, on whether you would trust Claude to cure cancer.

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And with that, here’s Arnold.

Much of tech looks at AI as labor replacement, which misses the exploration work that is being scaled up right now that will come to dwarf it. I’ve talked with dozens of founders, investors and scientists operating autonomous labs over several months–AI is now driving autonomous science and it’s both misunderstood and underestimated.

The fully autonomous lab has arrived—where AI models have learned each instrument and device’s capabilities and operate experiments without human involvement. When Astra came out, it set a baseline for in-lab capability—operating experiments at roughly a 100x faster clip than PhD grad students.

AI enabling autonomous science, discovery, and exploration is the biggest bull case for AI—bottomless compute demand and next phases of AI build-out—that have precisely nothing to do with chatbots, personal agents, AI lawyers, or coding agents. Every other domain could see underwater economics, slow adoption, and profit skinning competition and it wouldn’t matter.

The motherlode is sitting right in front of us now.

AI isn’t all labor replacement

A common mental model for AI market size distills to: AI can replace some software engineering and legal work and both of those are big markets, so the TAM for AI must be big. Despite some bloviating, there really isn’t demand for 1000x more legal work to be done or 1000x more accounting software—probably not 1000x more investment banking, either? Bearish AI cases typically rotate around human adoption of AI, how much labor can really be replaced, or how much profit will be extracted from labor replacement when considering competition. These first domains have lulled us into discounting the bigger unbounded science market.

The constraint for science isn’t people or ideas, it’s verifying experiments. LLMs on their own can’t do the type of science that survives contact with the real world—they can read every paper and theorize down every rabbit hole but they can’t verify their hypotheses in a physical space. We’re now in the phase of quickly moving to bridge the verification gap.

The science flywheel (Aim, Meet the real world, Learn)

Fans of business flywheels will recognize a powerful one in the sciences. Generate hypothesis, collect data, analyze, then generate a new hypothesis—all running in a loop. Each turn of the flywheel can make the next turn more efficient as the system learns, executes better experiments, and picks targets for subsequent experiments. Traditional LLMs do this with “make a plan, try it, and test if the code works” but the difference here is that the data generated is physical.

As the market shapes up, each competitor picks the parts of the flywheel they want to own and rent. You can own the aiming, you can own the learning, but if you never run a real-life experiment you might well end up like startups who toil with no customer contact or revenue.

The aiming—the research direction—is like deciding where to point a bazooka. Physical Superintelligence ($58M raised) has the scientists aiming and setting direction with the AI agents executing experiments. Of course, the reverse will be tried. AI chooses and runs the experiments—humans help move things around where needed.

Meeting the real world could happen both in massive warehouses converted to autonomous labs or in small distributed garages. The real world verifiability is crucial for learning—it ensures that there isn’t drift for the model and there is always a ground truth to verify. The meeting the real world has implications for the AI-buildout. While autonomous labs could be consumers of a bottomless amount of frontier compute, they also require capex, physical space, power, and at least some human oversight and maintenance.

Warehouses with rows of benches might be only part of the story, and where capex can start to expand beyond what we think of for data centers. In 1850, Foucault built a rotating mirror device to measure the speed of light. In 1924, Michelson built 22 miles of piping in the San Gabriel mountains outside LA to measure it more accurately. Experiments won’t be confined to labs and that changes the profile of capex completely. Already, we see with Mistral and CERN—the capex heavy European particle accelerator—partnering how AI will lead to bigger and bigger experiments.

While the flywheel mechanism underpins a new set of science companies, the power dynamics for AI scientists are playing out and the winners aren’t yet clear.

What you are betting on

There are three tiers of breakthroughs that are accessible now using AI.

The incremental breakthrough happens when a process gets incrementally more efficient. Things like optical power efficiency or battery life are examples of incremental breakthroughs that can be driven by AI. These are primarily driven via efficiency, where thousands of minuscule improvements compound into a significantly improved outcome.

The novel breakthrough is predicated on AI combining or applying technology in novel ways to achieve outcomes that humans haven’t tried before. The breakthroughs are in algorithms or applied technology or experiment design and often combine disparate ideas or approaches that seem surprising to scientists. These have already been observed at two labs I have spoken to.

The fundamental breakthrough has not been observed yet, but would be discovering new physics, for example. Such a breakthrough would likely come from the same combination of efficiency and novelty that leads to other types of breakthroughs. The bull case doesn’t need new science—incremental breakthroughs alone at a 10x clip increase throughput across every domain which looks fundamental from the outside.

As inevitable as this is, as we were all reminded with l’affaire Situational Awareness, you want to be right and be right on timing when highly leveraged. Large amounts of venture capital into a company tend to make the bet timeline compressed.

If you are an AI scientist company, you have agency over what the timeline for breakthroughs will be. Think you can come up with true gamechangers (superconductors)? Invest toward the horizon where you think you’ll be able to achieve it. Don’t have that kind of runway? Scale back to immediate improvements to semiconductors.

The short horizon scientists working on comparatively incremental improvements fear being blown out of the water by scientists discovering moonshots or entirely new domains of exploration. The moonshot hunters fear being subsumed by short horizon scientists racking up fast win after fast win, hoovering up capital and pulling in the best talent and compute.¹

In addition to finding amazing breakthroughs and incremental improvements, the new AI scientists have to commercialize them. Novel science can be sold to corporations in the domain who already have the capabilities to bring them to market, or to investors in the form of spinoffs. The latter is more significant now that we are firmly in the era of capital aggregation. An AI scientist that reliably spins off concepts that could take large amounts of capital to develop and provide a return on that capital is something close to the Softbank golden goose.²

Commercialization is easier to conceptualize at massive scales today: GLP-1s showed how massive opportunities can be captured and applied science superiority can be monetized faster than at any point in history. Commercialization in bio has been better ground than other science domains but you’d expect others to move along accordingly with innovations.

Lila: breadth

There’s a bet here that progress in one scientific domain will cross-over and help in other domains. On the surface, this assumption seems reasonable given that LLMs have shown this tendency for cross-domain improvement in non-science areas.

The assumption that a great chemistry model will have advantages in pushing the frontier of biology underpins the thesis for Lila ($550M raised). “Breadth gives depth faster” according to Jon Hennek, CRO and Chief Product Officer at Lila. Jon spoke to me and offered a tie between domains: polymers (materials science) and proteins (biology) are both just large molecules in different contexts. Catalysis chemistry and enzymology are the same underlying physics. Lila is seeing glimpses of cross-domain benefits: training in multiple domains increased holdout performance in areas where no training data was provided.

All AI scientist companies believe that AI can learn from verifiable experiments in the lab, but Lila is the biggest bet on cross-domain work. Lila spun out of Flagship Pioneering, which is severely underappreciated in tech circles, who previously spun out Moderna and who currently has over $10B of assets under management. Flagship provides Lila with a deep bench of scientists across domains and a robust commercialization and partnership arm.

Lila started commercializing ten months ago and they think that the head start combined with Flagship’s GTM prowess give them a massive lead over everyone else in the space. Currently, commercial partners have Lila scientists running campaigns in Lila’s lab but they are working to extend Lila’s model access and AI-enablement into partner labs. Data from experiments is trainable by Lila, but there’s a lot of data from partners that Lila isn’t privy to.

Lila claims to have only trained on 2% of the data it has collected (compute-limited) and boasts a proprietary dataset of ~10 trillion tokens of scientific reasoning based on verified experimental data. They also have over 10,000 questions across different science subdomains for use in model evaluation, training and benchmarking. That question bank alone could be worth $100M if high quality, based on an estimate from friends at two leading data companies. According to Lila’s own testing, their Iris model outperforms frontier models as well as PhD scientists with expertise in the specific domain being tested.

Lila is enthusiastic about flexing its commercial partnerships on big swings where they have active therapeutic programs with high impact if successful. These can be achieved via rapid incremental breakthroughs—they claim 10x faster R&D than prior efforts coming out of Flagship Pioneering—and don’t necessarily rely on novelty or fundamental breakthroughs. The downside for Lila (AI is simply brute force without novelty) already seems to be covered off and likely profitable in and of itself.

Periodic: depth

Periodic Labs ($300M raised) recently released details about their own model, Neon, which was trained on top of an open weight model based on experimental data and reasoning from their own lab. Periodic has been focused on characterization experiments in their real world lab and the model performed at a comparable level to Astra at characterization. Characterization (what is this material?) can be thought of as a building block for work on superconductors, which is the moonshot science that venture capital is underwriting with Periodic.

The early results for Neon can be interpreted in several ways. That Periodic could achieve state-of-the-art equivalency on characterization at a fraction of the training cost as OpenAI incurred for Astra is impressive. However, OpenAI likely had no training data on this type of characterization work and still performed at the frontier on the tasks.

The cost per unit of work for Neon was roughly half that of Astra, which shows the advantage of a model trained on real experiments according to supporters—but the math flatters Neon. The comparison of cost is based on an internal cost for Neon (the model isn’t being served at all except within the company) and compared to the retail pricing for Astra. If we assume OpenAI has 50-70% margins on retail inference, Neon’s cost advantage disappears and might even invert.

Neon opens up more questions than it answers. Would incorporating much more physics experimentation help Neon outperform? Is it necessary to pull experiments from other domains like chemistry to avoid a scientific bitter lesson? More so than others—is this the right time to be spending cash on training, rather than on rapid experimentation? Periodic has a cost advantage over a competitive physics AI scientist in a narrow subdomain but no advantage over OpenAI or Anthropic.

Who owns the loop?

OpenAI did nothing in a specific domain for a year and kept parity with a $300M startup staffed with great talent from OpenAI—which may well breed complacency. Attention is the moat for the science-only players, who are confident that OpenAI and Anthropic (and to some extent Alphabet, Meta, and SpaceX)⁵ have too many other priorities to dedicate attention. Compute-only players run up against a cornucopia of hypotheses and lack the capabilities to quickly verify them in the lab.

Today, the attention gap is shifting quickly. Anthropic has a wet lab in San Francisco. They want to own the whole flywheel (at least in bio) from aiming, to contact with the real world, to learning. Is Anthropic now directly competitive with Lila? The answer isn’t simple because the market isn’t bounded—Anthropic beating Lila to a specific discovery might have absolutely no impact on the revenue potential of Lila. Lila and Anthropic might be aiming at completely different subdomains for all we know—and for all they know.

We are entering an era of discovery and the only competitive issues are clustering of the exploration. Harvey v. Legora is competitive in the legal AI because neither is advancing new areas of law or developing novel theories. Lila v. Anthropic has no similarity to Harvey v. Legora whatsoever.

Lila is going to be opening up its AI model to customers to use themselves–they shared this with me and I haven’t seen it publicly stated before. The first true frontier science model is a tantalizing possibility that could radically reorient the business of science. 80% of the hardware sitting in Cambridge biotech labs is identical and can be abstracted with a sufficiently strong model that can verify results with tool calls at inference. We may see garage band-like startups in the sciences building billion dollar businesses. There have been two generations of scientists that have left the sciences to pursue other careers, typically on Wall St or in Silicon Valley. AI adoption and tooling might reverse that trend, where experiments become much easier, much cheaper and much faster.⁷

Bullish

After spending time in the AI and science space, it’s a killshot for AI bearishness. While it’s easy to sound smart decrying AI-slop movies, the beAIux arts, and the end of middle-management, injecting raw intelligence into more science has almost pure upside. AI-enabled labs across the sciences can deploy thunderous compute, a mix of robotics and lab assistants, and human direction to create novel breakthroughs and maybe even moonshots. We don’t know the size of that market. We do know that it’s unconstrained—it’s not labor replacement, it’s not arbitrage, it’s not market efficiency.

AI will push science to fill out the known branches on the tech tree in the next few years. If you find yourself wavering on compute demand projections, close your eyes and imagine warehouses full of lab stations operating 24/7 running tight experimentation loops with robotics. Then, smile, and think to yourself—with hundreds of thousands of PhDs in chemistry, biology, physics in the U.S.—how many stations could a PhD oversee? Could we produce 10x the science we do today?

Lots of compute, big buildouts, world-changing progress, and commercial successes we can now foresee.

1. The parallel in SaaS-land would be the four year pre-launch period for Figma. It worked for them, but long multiyear pre-launch periods aren’t held as a paragon of smart company building.

2. Many golden eggs in here.

3. Early SpaceX employees weren’t highly credentialed. Nassim Taleb explained that surgeons should not look like surgeons.

4. Underlying open weights models are significant here in timing the thesis. Spending tons of capital early in the lifecycle before you have great data and reasoning traces might leave you vulnerable to being leapfrogged. Being too stingy can also leave you vulnerable to a competitor demonstrating benchmark performance and running away with more capital. Ultimately, companies should focus on science capabilities (of which benchmarks aren’t a perfect indicator in many cases) and those are demonstrated via breakthroughs. Commercialized breakthroughs don’t lie and they should follow the best flywheels with the fastest commercialization capabilities.

5. Public companies care very much about revenue and once the juice has been squeezed from coding, law, accounting, financial services, and medicine, investors will be looking for an uncapped revenue source totally unbounded from a TAM or employee labor count.

6. Science isn’t fully ready for this shift—a friend mentioned casually that a mathematics journal has seen 10x the volume of submissions since pre-ChatGPT. Keeping up will have its own set of challenges.

As a reminder, I’m out on paternity leave till November, and commissioned a series of original essays from the world’s best technology writers while I’m out. Thanks for reading!

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And with that, here’s Jessi.

The internet has become a test that none of us agreed to take. As we scroll and read, we constantly find ourselves asking: Human or machine?

I can’t overstate the mix of emotions I feel when I discover I’ve been duped. It happens all the time. I’m drawn in by a sappy LinkedIn post in which a friend talks about her experience with a bullying boss when I realize the anecdote seems a little too…canned. I see that tell-tale “It’s not this, it’s that” construction and my stomach drops. Shakespeare’s phrase reverberates through my mind: Et tu, Brute?

All signs point to the fact that my paranoid second-guessing is justified; in a recent analysis of more than a million social media posts, the AI detection software Pangram found that one in four longform content posts is fully AI-generated.

This is the alienating milieu into which Stanley Druckenmiller emerged in August. He’s the hedge fund billionaire who published an op-ed in The Wall Street Journal that was very obviously crafted by AI. Readers called foul. Druckenmiller acknowledged it and compared the process to using a calculator. He promised the ideas were his own, and The Wall Street Journal even defended the work.

And yet something about the whole affair still felt wrong to a lot of us. Nick Thompson, CEO of The Atlantic, thought something more fundamental had happened. One of the problems was “authenticity,” he said in a video on LinkedIn. “Your name’s on it. You should have written all the words and you should stand by it.”

That second part of Nick’s observation is obvious: an author should stand behind her work. But must she have written all the words? Our discomfort arises from the fact that we assume that a public voice belongs to a person, and we’ve spent decades learning to recognize that person through signals embedded in their work. We assume we can judge their authenticity. Now that assumption is breaking down.

AI makes it possible for anyone with something to say or sell to manufacture a convincing voice, and the technology keeps getting better. We’re entering an internet on which the voice itself can no longer tell us very much about the person behind it. And just as a Fendi bag would not be worth $4,000 in a world where no one could distinguish it from a perfect counterfeit, a public voice derives much of its value from our ability to connect it reliably to the person whose name is on it.

So how do we convey authenticity in an AI-driven future?

To understand where all this is going, we have to begin with a working definition for authenticity. To do that, I called Alice Marwick, who is now the director of research at the nonprofit research institute Data & Society. I met Alice when I was asking similar questions of our media ecosystem back in 2005; back then, she was still deep in her PhD work, looking at how people performed identity online. Alice will tell you that authenticity is a judgment made by an audience. We encounter a public performance and decide whether it corresponds to the person behind it. Crucially, she points out that it is a relative judgement. “Authenticity is always something we see in opposition to something else,” she explains. We arrive at authentic by calling out what is inauthentic.

Starting long before social, every new medium had its signals of authenticity, manufactured to produce a feeling of presence. My favorite example of this is reality television. You, the viewer, knew that to a large degree, the material was scripted. Of course it was fake! And yet in watching, you tried to discern where the script faltered and our characters were just themselves. The sport of it was finding the authenticity!

Social media made personal authenticity especially valuable as institutional authority weakened. Ordinary people could speak directly to audiences. Blogs went mainstream; Facebook spread beyond college campuses. It’s impossible to overstate the significance of this shift in the way we consumed media. When Fortune published my first cover story on Facebook in 2010, several readers called me directly through the magazine’s main switchboard for user instructions. “There’s no customer service phone number on this site!” they would complain, and then they’d ask for help setting up a profile. Beyond using tools differently, readers were shifting their perceptions of what constituted authority. The institution no longer did all the work of establishing credibility; increasingly, audiences were also asking the individual speaking to them: Can I trust you?

This is about when the word “creator” arose to encompass people, from people like me to fashion bloggers to the children’s YouTube star Blippi, who build an audience around a body of work on the web. The successful among us have become extremely sophisticated at performing the signals of “this is really me.” On the social web, a vocabulary of authenticity is constantly evolving; audiences seek informality, intimacy, spontaneity, direct address, disclosure. Audiences have always understood that these were conventions, but that didn’t necessarily make the performance fraudulent. It was the art of the form. We learned to read these signals as evidence that behind the mediated product was a real person giving us something of herself.

These days AI can manufacture enough of the signals we’ve learned to associate with authenticity that even those of us who study language on the internet for a living can’t always tell what’s what. There’s emerging evidence to support this. I was captivated by a February 2025 study published in Scientific Reports in which researchers asked people to evaluate personal advice without telling them whether a human or ChatGPT had written it. The ChatGPT advice won: on a seven-point scale, participants rated it a full point higher in quality, about two-thirds of a point higher in effectiveness, and 0.39 points higher in authenticity.

Of course, when participants learned the advice came from ChatGPT, they devalued it; that’s the confusion inherent in decoding authenticity right now. We’re trying to understand tomorrow with yesterday’s tools; in that way, we’re not so different than those Fortune readers looking for the 1-800 number on the Facebook website.

Audiences will continue to seek out authenticity as the determining factor for whether they will trust a creator, be it Mark Rober or Mel Robbins, a journalist like me or a brand, but the signals will change. The people who nail this next version of media, harnessing its tools to deepen trust with audiences, will discern these signals early and design into them.

A strong reality here is that it might get a lot harder to find those trustworthy, authentic-seeming people. As I was thinking through all of this, I called up Max Spero, the founder of Pangram, to get his take on where authenticity moves in the future. We brainstormed for a while on what our social feeds might look like in a year, and whether people might still be complaining about AI slop. He described one particularly dystopian vision for a future in which, instead of using ChatGPT to pen our Substack notes and Instagram captions, we deploy our agents to do it. But a cacophonic, alienating world in which machines spit out all of our social exhaust will only make us cling even more tightly to the creators we are able to discover. In an even noisier, messier world, knowing that work is created by a particular person with a point of view and something at stake may render the work more valuable.

Which brings us back to Druckenmiller. Does it really matter whether he typed every word? We’ve always tolerated mediation between a public person and an audience. Before you read this essay, I will send it to Evan, who will edit it. What audiences want to know is whether the expression meaningfully belongs to the person claiming it. Did I make the important choices? Does it represent what I actually think?

Authenticity, Alice reminded me, is a judgment made by an audience. And increasingly, I think that judgment will have to be made from what she calls “co-presence over time.” This is the accumulation of encounters through which we come to know how someone thinks, what she cares about, how she changes her mind, where her judgment is good and bad, and whether her words and actions hang together. There are no shortcuts for this. We’ll have to get to know each other online just as we do in the real world, one interaction at a time.

This emerging understanding of how to manufacture authenticity will require new labor on behalf of both the creator and the audience. The person speaking has to demonstrate intention, judgment and accountability over time. And the audience has to pay attention over time.

If this is starting to sound suspiciously like a real relationship rather than the parasocial relationships that have defined the social web, well, it is. Perhaps this is the opportunity buried inside the authenticity crisis. Increasingly, AI will make it impossible to prove, sentence by sentence, that a human being was there. But it may force us toward a more demanding definition of authenticity: a relationship in which a person has become knowable enough to be trusted.

Mischa Vaughn was the right writer for the job because he spends entirely too much time online. He even plays the little puzzle games on LinkedIn, and he genuinely likes them. He runs Orchard House, a fractional marketing and creative production agency in the Bay Area. You can follow him on LinkedIn and X.

As a bonus, paid subscribers to The Leverage get access to the full dataset behind this piece. You can dig into all 90 companies and 351 executives, compare the leaderboards, and use the built-in calculator to see how your own company ranks.

But first, this essay is brought to you by one of my new favorite services, DeleteMe.

I spend all of my effort trying to explain how capable LLMs have become. That has unsavory implications, because they have also become powerful tools for criminals. If your information is online, an LLM can find it. A model can pull your address, phone number, and relatives out of public records in seconds, then clone your voice from a short podcast clip, and then call your mom asking for help. Americans reported $16 billion in fraud losses in 2025, up about 25% from 2024, and nearly 1 in 3 of those reports was an imposter scam. That was before this tooling got cheap!

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And with that, here’s Mischa.

In the mid-80s a category-five hurricane of attention hit the NBA by the name of Michael Jordan. When His Airness entered the league, the 23 NBA teams were worth a collective $400M. By his first “retirement” in 1993, the then 27 teams were worth over $2B. The lesson Jordan taught the NBA was pretty simple: sell the star, not the team.

One of the first NBA games I ever attended was one that Jordan played in. Now, it was in his Wizards era. But the person at the ticket booth took pity on the nosebleed tickets my brother and I had saved up for when we told him this was our first game, so he hooked us up with seats 20 rows behind center court. That’s how I got to watch a 38-year-old Jordan lead all scorers for the game. When Jordan played on the Wizards the team got as high as 2nd in the league in attendance (for the washed GOAT!). Once he left, the team shot back down to 21st.

The star went away, and so did the following. This effect wasn’t lost on the NBA. It is why they keep coming up with a new “face of the league” every five years or so. Allen Iverson, Shaq, Kobe, LeBron, Steph, Anthony Edwards, Wemby. It’s been a smart strategy, and it’s helped grow the value of the entire league to over $160B in 2026.

Now, I am not so foolish as to say that execs can have an aura at the same level as an NBA star. That’s never going to happen (unless the tech exec in question can dunk or break someone’s ankles on the court). However, it finally appears that businesses are learning to promote their “stars” over the brand. We can blame influencer or creator culture, but this is the state of attention and we’re just scrolling in it.

Why companies are doing this

My argument is that a person gets more engagement than a brand does, and overall, it’s a better tactic for a company to take nowadays. People get more attention from humans AND more from the algorithm. You don’t have to be a social media power user to know that LinkedIn personal pages get more engagement than brand pages, and on X it’s how launches happen and companies get their moment in the spotlight: the founder’s post is the new PR release, and their shitpost or poast that goes viral is the new momentum builder. I’ve watched this playbook succeed over and over and it doesn’t appear like it’s going to stop anytime soon.

This might be a familiar sounding idea, and many have argued that you should “go direct.” However, I have found that this claim resides in the land of anecdata, a nice soundbite where people cite the same 15 founders, and then move on. I wanted to go deeper.

Ok, before we get into numbers, let’s clear up some stuff. This is not an argument that people can get more followers than brands, or that they should. Brand pages for B2B businesses will generally have more followers than the people who work there (more on exactly where that’s true below).

The only outside study I found is small. Refine Labs compared seven of their employees’ profiles to the company page. The employees had 46% fewer followers and still got 5x the engagement. So, I had Grok Bot and other agents pull the top five executives at 90 of the most-hyped private companies around (Ok, I cheated and used the Lenny 100 - thanks Lenny! - plus a few I added), added up their LinkedIn and X followings and lined that up against how much money each company has raised and the followings of the brand account pages.

A note on the data: every company had between one and five executives I could find, but not everyone had a LinkedIn or X account that was active or in existence. Where companies didn’t have that many obvious execs, I tried to find employees that would fit the bill (heads of, leads, etc). Now, my bots couldn’t bring in all the follower counts so I had to roll up my sleeves and do this the old fashioned way by checking 300+ execs by hand. This took time, and the data range was spread across four days (in mid September 2026), but it was the only way.

Also, everything measured below counts followers, because that’s pretty much all you can count across 90 companies and 300 execs (LinkedIn and X just don’t make it easy to count views/impressions across this many accounts), I’m using followers to stand in for attention even though they aren’t the same thing.

Here’s what I expected to find: some companies have figured this out and most haven’t. Here’s what I found instead: Almost everyone has figured it out. And almost everyone has figured it out with exactly one person. Going direct is now the modus operandi.

Two-thirds of a company’s attention goes to one person

At the 87 companies where I could measure at least two named executives, one person holds about 70% of the whole team’s following. At four out of five of those companies, one person holds more than half. Even where there are two real “star” execs, the top two hold about 90% of the following between them and everyone else is a rounding error. And three times out of four, that one person is the CEO.

Brett Adcock is 98% of Figure’s executive following. Amjad Masad is 98% of Replit’s. Fei-Fei Li is 96% of World Labs’. Palmer Luckey is 87% of Anduril’s, Patrick Collison 84% of Stripe’s, Sam Altman 83% of OpenAI’s.

So the “sell the star” lesson seems to have landed with these companies. The problem is they only took half of it. The NBA’s actual strategy wasn’t Jordan. It was Jordan, then Iverson, then Shaq, then Kobe, then LeBron. It wasn’t just about the current star, the next star was the plan.

Money doesn’t fix it

Now, I want to be careful here. I only looked at roughly the top five executives at each company (usually three or four), not the whole leadership team, and a bigger company has a lot more people below that line who might have audiences of their own. And it is true that as companies raise more, their leaders get more famous. The typical CEO at a company under $500M has about 37,000 followers. Past $10B, it’s 239,000.

The trend line just goes up slowly. Compare any two companies where one raised ten times as much as the other, and the bigger one’s executives typically have about twice the following. Which goes to show that money is not always correlated with aura.

And in the middle, where most of these companies live, it’s flatter than that. From $100M raised to $10B, the typical executive bench sits around 90,000 followers and barely moves. Only the six companies past $10B break away.

The trendline is the average: what a company’s executive following typically looks like at each level of money raised. Half the companies sit above it and half below, and the distance between a company and the line is where things get interesting. For example: Parallel Web Systems and Notion are miles above the line for what they’ve raised. ClickHouse and Crusoe are miles below it. Money put them at a spot on the bottom axis, but where they landed on the side axis was up to them (and maybe how good their comms team is).

One caveat: the Lenny100 is a list of winners, so nothing here says attention got anyone onto it, and I can’t tell you whether money causes attention or the other way around. What I can say is that being small is not the disadvantage here that it is everywhere else.

Narrative fatigue

The same thing that makes a person out-reach a brand is what can wear on an audience if you aren’t careful. Overreliance on one voice gets old and people tune out.

Cluely founder Roy Lee became a bigger story than Cluely while the company pivoted. He has 256,000 followers on X and 60,000 on LinkedIn. Cluely has 54,000 and 44,000. The attention went to a person, and the business didn’t get as much of a halo from the attention as Roy did. It’s still around though, but the accounts haven’t been active for around several months (on X) now, so things don’t seem to be going great.

This is the Jordan problem the NBA faced. And David Stern knew it. So he started engineering the league and the media apparatus behind it to work on finding the next face to keep the overall following interested.

Where the person beats the page

Earlier I said brand pages will generally always have more followers than the people who work there. Now that every brand page in the set is counted, I can tell you exactly where that’s true and where it isn’t. It depends entirely on the platform.

On LinkedIn, the brand wins. At 90 companies, the top five executives combined out-follow the company page at 28 of them, and the typical company page has about 1.7x the following of its entire executive bench. Add up every brand page and every executive and the pages have 4.5x the audience. Only 13 individual executives in the whole set have more LinkedIn followers than their own company (one of them is Ilya Sutskever, whose 100,000 beats Safe Superintelligence’s 2,500 by about 40x, which tells you more about Ilya than about LinkedIn).

On X, the people win. The executive bench out-follows the brand page at 46 of 89 companies. At 40 of them, a single executive does it alone. Add everyone up and the executives have 1.4x the audience of the brand pages. Fifty-one individual executives have more X followers than their company does. Christina Cacioppo has 48x Vanta’s. Parker Conrad has 29x Rippling’s. Alex Bouaziz has 18x Deel’s.

And the same company usually lands on opposite sides. Of the 89 companies on both charts, 26 have executives who beat the brand on X but not on LinkedIn. Only 8 go the other way. Anthropic, Notion, Polymarket, and Vercel are all bigger as brands than as people on X, while OpenAI, Stripe, and Anduril are the reverse. You only get there by having someone in charge who LOVES to post or is just plain addicted to it.

What to do from zero

Here’s what 90 companies’ worth of data actually says to do.

Don’t wait to invest in your exec following. From $100M raised to $10B, the average bench sits at 90,000 followers. You don’t outperform by sitting on your thumbs. Get those people out there! But also, don’t use this as an excuse to ignore your brand page, especially on LinkedIn.

Plan the second star from day one. Two-thirds of a company’s attention ends up in one person, and half the companies in this set never get a second. The CEO will be first because say it with me they are the Chief Storyteller whether they like it or not. Decide who’s second before the first one gets tired.

And remember, you can have stars that aren’t founders. Clay’s third biggest is the head of marketing. Harvey’s COO has a bigger combined following than their CEO. The point is: work out who else matters to your buyer. If you sell to marketers, that’s a CMO. If you sell to finance, it’s a CFO. And yes, CFOs can be helpful for marketing (don’t let them tell you otherwise).

The window

The NBA reached $160B in value by building the next face of the league before the current one had left the building. It understood that stars draw the attention, but that any single star eventually burns out.

Most companies have learned the first half of this lesson. Two-thirds of a company’s exec following is one person, whether they raised $100M or $10B.

Right now, marketing in this manner is becoming table stakes, especially in B2B as we all beg the LinkedIn algorithm to notice us. And the companies that don’t do it are going to look like oldheads.

A lot of them already do.

Want to see where your company lands? Paid subscribers get the full dataset: all 90 companies, 351 executives, the leaderboards, and a calculator that ranks your own bench against the trendline.

Upgrade to paid, then go find out whether your company has a second star or just a very tired first one.

Read more

That person is my friend Nathan Baschez. Nathan is currently a product designer at Notion, and in a previous life was a founder at Lex and Every. Before that he spent some time at Gimlet Media and Substack. Fun career fact about him is that he designed and built the original version of Product Hunt. He is a great writer and was one of the earliest believers in my potential as an blogger. In this post, he argues that personal agents will matter, but the future of work is more multiplayer, more automated, and more complex than any individual agent can handle.

First though, this newsletter is brought to you by Lovable.

Last week I built an app for my daughter’s care team without opening Lovable’s editor, and I want to explain how, because it’s one of the most magical experiences I’ve had with AI tools this year.

Lovable, a software creation platform, has an MCP server, which means an AI agent can operate Lovable the way a person would. So I talked to Claude about my problems. Claude created the project, sent my spec in plan mode, showed me the plan, and once I said “approve,” had Lovable build it. In between, Claude read the database access rules, ran a query to check that my sign-in had actually made me a parent, and, when the first test email said my daughter had 0 words, found the 1-second bug in the date logic and had Lovable fix it. I opened Lovable twice: to sign in and to buy a domain. By the end I had a functioning app, perfect for my life circumstances.

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And with that, here’s Nathan.

Technological revolutions don’t arrive fully formed. They evolve over years, decades even. Early on, crude architectures generate excitement and reveal limitations. Then they eventually make way for newer and better layers of the stack. Take the internet, for example. Decades passed between the first packets sent over TCP and the birth of the web and HTTP; another decade separated the web from the dominance of interactive web applications.

We should expect the LLM revolution to look no different. It would be really weird if, just four years after the ChatGPT moment, we had already figured everything out. We should be open to new paradigms and architectures. In fact, we should be obsessively searching for them, because every paradigm shift creates winners and losers — even the mini-shifts within a mega-shift like AI.

If I had to put my money on the most underrated nascent paradigm shift, it would be factories. Factories are a logical next step after skills and personal agents.

This essay is meant to help you win the next paradigm. In it, I’ll define “factory” in a slightly more broad way than most people use it, make the economic argument for factories’ inevitable dominance, and show you how to build one. If you want to get ahead of the next paradigm shift within the AI revolution, or just use AI more effectively within your business, this is for you.

What is a factory?

Factory (noun) — a shared AI system designed to handle specific types of tasks within an organization.

Usage: “I am rolling out an update to the slide deck factory so it adheres better to our new positioning, along with a few design fixes for issues we observed last week. Let me know what y’all think!”

Imagine two consulting firms that directly compete with each other. Let’s call them BigBrain, Inc. and DumboCo.

BigBrain and DumboCo each have about 100 employees. They do similar revenue and are equally excited about using AI to empower their teams. But they have two very different philosophies for how to use AI: BigBrain is factory-pilled, whereas at DumboCo everyone uses their own personal agent to do everything.

At DumboCo, most people use Codex or Claude Cowork. They have a Slack channel where people share skills and “AI wins,” but everyone is free to pick and choose what they want to adopt. Some people invest far more effort than others in their setups and see corresponding gains in their agents’ performance and productivity. Sometimes they will help other members of the team or do a “show and tell” session, but it’s still hard to get everyone to their level of productivity.

From my conversations with founders and folks who work in tech, I would estimate that 99% of companies today are operating this way, with varying degrees of intensity. But there is a better way.

BigBrain, on the other hand, is a bit more centralized in its approach to AI. Everyone still uses their own personal agent more or less constantly, but there’s also a layer of shared factories for anything complex and frequent. For example: slide deck creation, employee and customer onboarding, data questions, software bug fixes, app designs, and prototypes, etc. They even have a “factory factory” that makes it easy to spin up new factories as needed.

The biggest AI enthusiasts on each team tend to be the ones who create and manage the factories that serve that team. For example, the CFO happens to be a big AI nerd and created a “monthly close” factory for her whole team to use. Meanwhile, people who just need to use the factories don’t have to worry about the details of how they are created and maintained in order to benefit from them.

There are three key attributes that define a factory and differentiate it from a personal agent.

Factories are:

• Shared — multiple people on the team can use them.

• Specific — they are optimized to handle certain types of recurring tasks, unlike a general-purpose agent.

• Scrutinized — some people on the team can see the work flowing through the factory to observe and fix failures.

Most of BigBrain’s factories are fairly simple: each is a Notion database of tasks, with agents hooked up to act on those tasks as they are created, moving them through a sequence of stages. It can be as simple as todo → doing → done.

Some factories are purely autonomous; others involve deep collaboration with humans at strategic points. For example, the customer support factory handles emails as they come in, fully autonomously, with human intervention in rare cases. The slide deck factory, by contrast, involves collaboration at many steps (project brief, research, writing, and design). If you ask most people what an “AI factory” is on X, they’d probably emphasize the lack of humans in the loop, but for me this is the least interesting part about the systems commonly called “software factories.” The more important part is that it is a purpose-built, shared system that can be optimized by some people for the benefit of everyone else.

You can buy factories from vendors, or you can build them. The most mature vendor market is for customer support agents, as sold by companies like Sierra, Fin, and Decagon. These all meet my definition of a factory even if they don’t say they are selling factories. Cognition, Cursor, and others are explicitly selling software factories. Amplitude is pivoting from product analytics to selling a “product factory” — a system to autonomously identify, build, and ship potential product improvements by looking at metrics. XBOW, Novee, Snyk, and others are building cybersecurity factories.

“What about skills?” you might ask. Skills can be sort of factory-like, in that they help agents perform specific tasks, and the prompts are shared. But the actual runtime environment is not shared, so people running a skill with different agents on different machines may run into configuration issues or other hiccups. And, crucially, skill runs are not easily scrutinized by skill owners. So the feedback loop is broken.

The economic logic that makes factories inevitable

It is as simple as Adam Smith: factories are key to specialization and gains from trade — the root of all productivity and prosperity.

DumboCo in the example above is sort of like a hunter-gatherer tribe. Everyone is responsible for reinventing their own wheel (and hand-axe, shelter, clothing, first-aid—you get the idea) . Over the course of a month, let’s say each of the 100 employees spends anywhere from 5 to 50 hours tweaking their agent’s behavior across 5–10 different types of tasks. That is an average of about 3.7 hours per task that month spent moving up the learning curve. (And, let’s be honest, not all hours are created equal. Some people are much better than others at optimizing agents for different kinds of tasks.)

BigBrain, on the other hand, is like a modern society with different specialized organizations that build on and depend on each other. One person or team can set up a factory that everyone else can benefit from, without having to learn anything about how the sausage is made. With a good “factory router,” employees’ personal agents can even use the factories on their behalf, getting the job done without the employees having to know the factories exist! For example, the HR team could have a factory to answer questions about benefits, and to the user it just feels like they asked their AI a question and quickly got a good answer.

BigBrain has the same budget of 5 to 50 hours of “AI tinkering time” per person but concentrates it on just 1–2 tasks per person. This yields 18.3 hours of optimization time per task per month, which means going 5x deeper into the learning curve on each task. This is the magic of specialization and gains from trade! And this 5x improvement is before taking into account the difference in quality of hours between a specialist and everyone else.

But the “hours into the learning curve” idea is pretty abstract. What benefits do teams actually see when they spend more time optimizing each task?

• Higher quality

• More reliable

• Cheaper

• Faster

• More secure

In general, these things make the difference between a half-baked skill someone threw together once (unused, out of date) and an active workflow that a business can rely on.

How to build a factory: a pattern language

There are many ways to build a factory, and, as noted above, you can also buy one. But here is the simplest starting point for most people building their own. Let’s use the example of a slide deck factory.

1. Set up a database inside Notion called “Slide Decks.” Add a status column. To start, you can keep it as simple as “todo,” “doing,” and “done.”

2. Hook up an agent with access to the database via the Notion MCP, CLI, etc. Or you could use a Notion Custom Agent.

3. Set up the agent to be triggered when a new card is created or when the status changes. You can use any agent or hosting platform you want for this, as long as it’s not one that goes to sleep when your laptop is shut.

4. Write some instructions for the agent to follow. When the agent is triggered to handle a new task, have it set the status to “doing” and, when it finishes, to “done.” Give the agent a template it can start from and instructions for how you like your decks created.

5. You may want to host the instructions in Notion for ease of reading and collaboration, plus versioning and comments. Or you could store the prompt in GitHub or anywhere else.

6. Observe runs. Fix anything that fails. Repeat! (Agents can help you do this!)

7. Share your factory with your team. If it’s a Notion database, it’s as simple as giving them access to that page. They (or their agents) can put tasks in and watch them get completed.

8. For discoverability, you can put a “factory list” in your team’s system prompt. You could also make a Notion page with a list of factories or a skill for each factory; there are many ways to do it.

This is the minimum viable factory setup. In most cases, you should start here and add complexity only as you actually need it, not ahead of time. But there are a few things most serious factory attempts will need sooner rather than later. Here are the most essential patterns I have observed.

First, at some point you will want to run your factory on an experimental new version of the prompt without messing up the existing flow of traffic. For this, you will need a basic prompt versioning system. You could solve it the software engineering way, with separate development and production environments and prompts deployed as code through a version control system like Git, but that adds overhead.

The easier way is to use something like a Notion database. You can have a “prompts” database for the factory, with a “status” column that can be set to testing, production, or retired. And if you have different types of prompts for different stages in the factory process (e.g., research, writing, and design), you can make another column for that. Then the agents can read their instructions from a central source of truth that is easy for everyone on your team to inspect and understand.

Second, once you have different versions of a prompt, at some point you will want to know whether an experimental new version is actually an improvement. You can test this yourself using your own creativity and judgment, but that takes a lot of time and work, and quality will vary depending on who is doing the testing and what they remember to test for. What you need is basically a checklist of the different tasks you want the factory to be able to handle, plus a rubric for judging whether each task was completed successfully. These are commonly known in the industry as “evals,” and they are much easier to create and use than you may think.

Bottom line

Factories — or whatever they end up being called — are AI systems that are shared, specific, and scrutinized. They are inevitable because they are economically rational. They enable specialization and gains from trade.

Do not be surprised if you start to hear much more about them over the coming months.

Kristen Hawley is a freelance journalist and founder of Expedite, a long-running newsletter about restaurant technology and the future of hospitality. Her work regularly appears in outlets including Fast Company, Eater, Food & Wine, and now, The Leverage. She lives in San Francisco. You can read more and subscribe at Expedite.news.

First though, this newsletter is brought to you by Lovable.

Our daughter has a care team consisting of her grandparents, her daycare classroom, the people who work with her each week, and two parents who just had a newborn. All of us need the latest information on her progress. It was impossible to keep track of all the changes, tests, and interventions.

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And with that, here’s Kristen.

Once upon a time in suburban New Jersey, an internet billionaire had an idea: What if, instead of driving to a restaurant for dinner, the restaurant drove to you? It would bring more than food; third-party delivery services like DoorDash had already cornered that market. Instead, this business would prioritize food quality and innovative preparation, tapping famous chefs for iconic recipes and training staff to recreate near-restaurant-quality experiences from tiny-but-powerful kitchens installed in the back of Mercedes Sprinter vans that would park in the driveway. Then they deliver it to the front door.

Wild, right? That was the point.

Wonder quietly debuted in May 2021 and the ambitious idea won over enough investors to net half a billion dollars in funding by the end of that year. Early tests in one affluent New Jersey suburb showed promise; after just a few months, Wonder had delivered meals to more than half of the town’s residences. Famous chefs, including Bobby Flay and Nancy Silverton, licensed recipes to Wonder and the company meticulously recreated them inside its tiny van kitchens. Wonder bought up more vans to serve more suburbs. The plan was to purchase a thousand more. It was a heavily funded wildly ambitious moonshot — a big, bold bet on the future of convenient, accessible, (hopefully) delicious food.

It took less than two years for that bold bet to be proven a dud. In January 2023, Wonder entirely scrapped the van plan and wrote off $100 million to pivot to permanent storefronts. Each physical location hosts multiple restaurant brands. All of them are fine-tuned for a takeout- and delivery-heavy business. The first permanent Wonder location opened in Manhattan a month after it ditched the vans. Today, it has about 150 locations in the Northeast with plans for many more, including a major expansion into Texas next year.

But a network of restaurants doesn’t make a moonshot, and Wonder was thinking big. The company made its first buy outside of restaurants in September 2023, scooping up seriously distressed meal kit company Blue Apron for $103 million. (It was once worth almost $2 billion.) It’s since announced over $1 billion worth of acquisitions that includes a NYC-based courier service (Relay), the third most-popular delivery network in America (Grubhub), a restaurant rewards app (Claim), a food media outlet (Tastemade), a bowl-building robot (Spyce, formerly owned by Sweetgreen), plus two independent restaurants and one small chain. I’ve joked with restaurant industry friends about what company Wonder might buy next; I know at least a couple pitched themselves as potential targets, hoping for an offer.

Wonder has continued to aggressively fundraise to support its growth, with rounds totaling over $3 billion, including a $650 million round this summer. The whole concept from its creation has been a spectacle, full of unexpected twists and iterations as it becomes one of the fastest growing food companies on the back of cheap capital and crazy ambition.

Marc Lore, Wonder’s founder and CEO and a self-described “moonshot seeker,” is also an e-commerce legend. He sold online retail companies Diapers.com and Jet.com to Amazon and Walmart, respectively, in deals that totaled almost $4 billion. But Lore is a relative newcomer to the restaurant industry. That hasn’t stopped him from aggressively pursuing his increasingly futuristic vision to change how we eat. If anything, his outsider status seems to have emboldened him; he’s promised a Wonder IPO as early as next year, potentially targeting a $30 billion valuation. Recently, he sold his controlling stake in the Minnesota Timberwolves and Minnesota Lynx NBA and WNBA teams, reportedly to focus on the public offering.

But what is Wonder beyond a growing list of restaurants and a series of increasingly ambitious ideas backed by a few billion in funding? Its longtime mission statement is “to make great food more accessible.” The process, though, has proven more complicated.

Wonder, the restaurant:

Around the time of Wonder’s Blue Apron acquisition, it said each of its store locations was on track to generate $4 million in annual sales. Wonder hasn’t shared average unit volume for its retail locations since — though Lore told me in late 2024 that stores become “quickly profitable” after opening. That’s better than most major fast casual chains; Chipotle’s average restaurant generated about $3 million in sales last year. An average Sweetgreen generates about $2.5 million in revenue annually; an average Cava around $3 million.

The company has said it will open over 100 stores this year. Wonder’s physical footprint is growing so fast that it’s hard to keep up. I drafted this story during a visit to my Central Pennsylvania hometown, and when I looked up from the screen I noticed a flyer announcing the opening of a local Wonder in a stack of mail on my dad’s kitchen table. I held it up and asked if he’d tried it yet.

“No,” he said, “but I’ve gotten like four of those coupons in the mail. I can’t figure out what it is.”

I started to explain. Think of a Wonder location as 30 different restaurants inside one kitchen, I told him. Customers use Wonder’s app to order items from any of Wonder’s concepts for takeout or free delivery — thanks to its Grubhub acquisition, it can now tap a network of couriers to fulfill orders.

I tried to explain the company’s larger vision and its vertical-integration-by-acquisition-of-failing-companies strategy, how owning all the parts of an order from ordering through food prep and distribution can potentially boost profits, but he interrupted me.

“But is the food any good?” he asked.

I paused, then realized I didn’t have a solid answer. The few times I’ve ordered from a Wonder in New York have been… fine. I don’t remember any particularly remarkable menu items, but I have no complaints.

Reviews on Google skew broadly positive. Evan and I used Codex to scrape nearly 30,000 Google reviews across Wonder locations, which average 4.4 stars. (By Evan’s metrics this is ‘not great,’ by my gut instinct informed by over a decade of reporting on restaurants and maybe too much sympathy, I say, ‘room for improvement.’)

Over 8 in 10 reviews are four or five stars. It’s impossible to know if Google’s reviewers are rating food quality, delivery quality or a mix of the entire experience.

Chefs who partnered with Wonder by licensing their restaurant names and recipes for distribution praised Wonder’s recipe development process. When I spoke to them for an Eater story in 2024, they described a culinary team that worked to meticulously replicate their recipes with some ingredients measured down to the microgram — that’s one-millionth of a gram, a near-infinitesimal quantity.

Since then, Wonder has invested in more of its own concepts, launching generic but specific brands like El Diez Mexican Bowls and Happy Tuna sushi. Wonder has Italian food, Indian food, pizza, poke, Greek, Chinese and more, all being slung out of a restaurant the size of a nail salon.

What it can’t build, it buys. Wonder reportedly spent “$6 million to $10 million to well upwards of that” (I know, that’s a wide range; I suspect much of that payday came in the form of Wonder’s stock) on its most recent acquisition, a wildly popular, single-location sandwich shop in Manhattan called Salt Hank’s. It has bought an eight-restaurant barbecue chain and a single-location fried chicken spot. The allure is in their easily replicable, easily scalable menu items. Salt Hank’s will arrive inside some Wonder locations this fall, just a few months after the ink dried on the deal.

Of course, those concepts are only easily scalable inside Wonder’s physical and digital verticalized infrastructure. The slick storefronts, modern kitchens (Once, in an interview, Lore described a Wonder kitchen to me as “a micro-fulfillment center”), and trendy restaurant brands are a huge part of Wonder’s story, but there’s a lot more under the hood.

Wonder, the vertically integrated logistics machine:

Wonder is expected to generate about $2 billion in revenue this year. If it’s true that Wonder locations have managed to keep average unit volume consistent at $4 million in annual revenue, the actual restaurants should be grossing about $630 million annually. Meaning that the majority of the company’s revenue will come from its acquisitions. The year Wonder acquired it, Grubhub generated $1.6 billion in revenue.

Wonder, of course, has ambitious plans to squeeze more revenue from its restaurants. Last year, Wonder acquired Sweetgreen’s bowl-building robots for $186.4 million, which transformed a handful of locations into giant salad vending machines. Sweetgreen’s execs had praised its potential and promised future success, but it didn’t materialize fast enough.

The company has big plans for the bowl bots, which Lore has said will help keep prices low and restaurants open later. (He also said the robot can build 500 bowls an hour, which means roughly one bowl every seven seconds, which is… a lot of bowls.) It has plans for other bots, too; Lore has teased plans for an “infinite sauce machine” that can make 500 sauces hourly from 152 raw ingredients.

The faster restaurants automate, the faster they’ll move. During an appearance at a Wall Street Journal event, Lore teased Wonder Create, a service that lets anyone — influencers, marketers, you, me — spin up a restaurant brand, logo, and menu in just a few minutes. Dishes are made from ingredients Wonder’s kitchens have on hand, and the concept’s creator gets a cut of the sale. The company hasn’t shared a launch date for this idea either.

Wonder, the moonshot:

Here’s another twist: Lore isn’t the only internet billionaire chasing a tech-driven food future. Uber founder Travis Kalanick recently folded CloudKitchens, a ghost-kitchen-slash-real-estate company, into a larger initiative called Atoms that raised $1.7 billion in just one funding round. Like Wonder, Atoms has a lot going on. But the crux of Kalanick’s thesis on the future of foodservice is that physical automation — robots that prep, cook, package, and deliver food — can drive the price of a delivered meal below the price of groceries.

I guess we’re about to find out what kind of restaurant disruption and automation billions of dollars can buy. Wonder projects a net revenue of $3 billion in 2028 and close to $5.5 billion by 2030, when it should finally start turning a profit. It’ll reportedly spend $2.7 billion to get there.

The fairy tale of a totally seamless meal experience hasn’t fully materialized yet, but Wonder is definitely chasing its happily-ever-after. When I interviewed Lore as part of a project for Fast Company last year, he was buzzing with excitement over his long-term vision for Wonder.

“We’ve built an AI platform that knows you better than you know yourself. [It] knows your food preferences, knows your food sensitivities, knows your health goals, knows your budget, and is able to, basically, autonomously feed you,” he said. Ideally, these “autonomous meals” will arrive exactly when you’re hungry and contain exactly what you want or need. He’s also talked about creating personalized nutrition profiles for people based on a blood test and developing customized meal plans.

“You probably think that’s crazy, right?” he asked, grinning.

I do. At this point, I also think it’s completely believable.

Kyle is a General Partner at Contrary where he’s invested in companies like Anduril, Ramp, Base Power, Valar Atomics, and Cursor, and is the Founder of Contrary Research, the firm’s private markets research arm that has grown to 100K+ subscribers. In his free time, he also writes the blog Investing 101 and has a wife, four kids, and (if his kids get their way soon) a dog.

First though, this newsletter is brought to you by Town.

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And with that, here’s Kyle.

Every tech company, from their pitch deck to their homepage, has a bragging mantle; the trophy wall of super-powered business nerds: first the words “trusted by” followed by the most impressive set of logos they can (legally) muster.

A couple dozen tokens of social proof, assuring the viewer, “don’t worry, grown-ups give us money.” Lately, a “fake it ‘til you make it” version of this wall has been jokingly (or not jokingly) reshaped to say things like “Trusted by users who also trust” so that they can list AWS, Nvidia, Netflix. Not because those companies are users, but because their users happen to be people building technology or, you know, watching TV.

This phenomenon is an attempt by founders to manipulate a potential customer’s psychology. Revenue is based on a simple trueism: price x quantity. And here you have, displayed for your viewing pleasure, the best of the best represented in that quantity. Logically, then, the thinking indicates the more logos, the more social proof. But that’s not quite right.

As an investor, I’ve looked at thousands of companies and I can tell you definitively that startups who go logo hunting are like people who date for looks alone. Beauty on the outside fades. You know where it actually counts? The inside. And beauty on the inside for a startup customer? Annual contract value.

Let’s use Snowflake as an example. Divide its market cap by its customer count and each logo is worth roughly $8.7M. ZoomInfo will share similar logos with Snowflake, and despite that, if you run the same math each logo is worth about $34K. It is the same caliber of customer, somehow yielding a 250x gap in what that logo actually represents.

Clearly, the same logo in the hands of wildly different products can yield dramatically different volumes of value.

Let me explain why this is more nuanced (and important) than you might think.

The Narrative Is The Number

Historically, before everyone had penned their “software is dead” blog posts, high-growth software companies traded on a multiple of revenue. The traditional median has always existed somewhere between 3-15x NTM revenue. Starting in 2019, there was a dramatic bifurcation between the haves and the have-nots. That split between the standard median and that of the five most highly valued companies currently sits at 4.4x vs. 32.7x.

I’ve written before about the widening divide between the best and the rest, and it shows up nowhere more starkly than these multiples. I’ve argued that if you put a hedge fund analyst and a venture capitalist on a game show and asked them to fill in the question, “a company’s valuation is primarily based on BLANK,” the analyst might say something stupid, like “discounted future cash flows” while the VC would speak a noble truth: “the narrative.” See, a market cap is stock price times shares outstanding, and stock price is a function of demand. Better narrative, more demand. The multiple is the market’s confidence, priced in.

To get at this insight about value per customer, you then just take that number, laden with popular opinion, and divide it by the number of customers. That is, effectively, how much value the market ascribes to each customer. There’s a ton of nuance in that calculation, but when you step back and look at the list you get quite a data set:

Now, does having a lower market cap per customer inherently mean that you have a worse business? Not at all.

Cloudflare’s number is $13.6K because they serve 7.4M customers; a huge swath of the internet. Shopify’s market cap per customer is $33K because their customer base is a long-tail of over 5M merchants. Adobe sells to individuals, MongoDB is self-serve, SAP has an SMB channel, OpenAI has an API available to anybody.

But it certainly can mean your business sucks. ZoomInfo’s market cap per customer has plummeted from $580K in 2022 to $34.6K today as the stock has slid -93%. The market has just increasingly valued their customers less and less, despite the customer count not changing that much.

Two other data points that help illustrate the value per customer discussion is (1) revenue per customer, or a rough approximation of average contract value (ACV), and (2) net-dollar retention (NDR), or how much that spend is increasing or decreasing. The waters can get quite muddy here because most companies don’t report detailed breakdowns of customer counts. So we’re ball parking in some cases, but the analysis is directionally accurate.

Frequently, when you look at the most highly valued companies, a lot of them have high ACVs (Palantir, Veeva, ServiceNow). On top of that, many of the biggest recent drawdowns in software (Monday, Hubspot, ZoomInfo) are all low ACV companies.

But the most statistically significant indicator of value is high NDR. When you look at the 39 software companies that publish NDR, you see a spread that yields R² = 0.557 between NDR and valuation. For those of you who did NOT enjoy your statistics class, what that means is NDR alone accounts for ~56% of the variation in revenue multiples.

So the derivable rule of thumb, then, is rapidly expanding customers earn you a high multiple, and bigger customers help you keep it. But why?

Is Bigger Better?

Like I said earlier, companies like Cloudflare, Shopify, etc. do fine adding lots of small customers. But the question of company health sits behind the engine that produces each customer and how much value can be extracted. The smaller the logo, the smaller the impact it has on the company’s value.

Even more than size, the issue is scope. Where does that customer go from here? Net dollar retention of 126% means that the average customer, just sticking around, is going to spend 26% more than last year. That’s an appreciating asset! And when the customer is already spending $1M+ a year, that 26% adds up a lot faster than a $20K customer.

The other side of the NDR equation is churn. In June 2026, our friends at CRV gave us a churn rate benchmark to shed some light on the split:

• SMB & Self-Serve (e.g. <$10K ACV): 3-5% monthly logo churn (e.g. 31-46% per year)

• Mid-Market ($10K to $100K): 1-2% monthly

• Enterprise ($100K+): 0.5-1% monthly (e.g. under 11% per year)

So just by nature of selling to smaller customers, you could lose somewhere between a third and half of your logos each year! Talk about running in place. That also informs payback math. Since SMB customers leave fast, you have to earn them fast; typically within 6-9 months. Enterprise customers have 18-24 months to pay back because they’re typically multi-year and larger.

Everything in a company gets shaped around that clock; sales comp planning, customer support, feature deployment time, hiring ramp up, etc. I’ve written before, in the context of how founders should pick which dependencies to serve, that if you want to sell to enterprise customers then getting caught up serving SMBs, it can be death by a thousand costs that slow you down and make everything more expensive.

The Token Tax

I’m not gonna lie. In an age of tokenmaxxing and trillions in AI CapEx, some of this talk of NDR and customer payback feels old timey, the language of a bygone era. But, it turns out, the same underlying principles hold true in the age of AI. In fact, they may be even more true. But it’s not obvious, so stay with me.

The typical thinking around SaaS ACVs was crafted in a world where the marginal cost of serving a bigger customer was approximately nothing. You can sign a customer 10x your typical size and the cost of goods barely moves, so every incremental dollar slides right into gross profit. Higher ACVs are like printing free money.

AI, on the other hand, is far from free. Every inference, token, and agent loop costs something. Where traditional SaaS consistently puts up 75-85% gross margins, AI-native products were expected to generate ~52%, on average, in 2026. Even as AI labs put up better margins, there’s a steamy debate about whether the billions in model training costs are R&D or forced to recur as a cost of doing business given how competitive the market is. Regardless of where AI margins end in the long-run, we’ve got a long way to go before I’d call the marginal customer free. So today, where a $1M SaaS deal could spit out $800K of gross profit, the same customer using AI-laden products would spit out $520K. That means an AI company needs 1.54x the contract value to buy the same gross profit as a software company.

So the marginal cost to service has flipped. Where SaaS was focused on the cheapest customers to serve at the margin (e.g., staying in your ideal customer profile), AI is focused on the ones burning the most tokens. But in both cases, the real value drivers are in the very largest customers; the whales. If we isolate customers spending $1M+ you see a shockingly similar picture.

Like I said earlier, we’re muddying the waters because we only have rough customer counts and we’re taking total revenue, not revenue from $1M+ customers. But it gives us some apples-to-apples cuts that are instructive. Take just two examples; Anthropic and Snowflake. Compare their revenue per whale, and then take into account gross profit per whale:

Anthropic earns 26% more revenue per large customer, but it keeps 20% less of each dollar. As a result the underlying economics of AI vs. SaaS are very similar. In a complex function of revenue growth, ACV, NDR, and gross profit, you find a fundamental equation of “value per customer.”

The fact that that equation holds true is a fundamental law of business physics. The larger the customer, the more value they contribute, both in terms of profit and expansion. AI hasn’t thrown out that playbook, contrary to the claims of radical revolutionaries; it’s reinforced it! The bigger question with AI, instead, continues to be one of durability.

Is It Even Revenue?

In the same way that customer logos aren’t created equally, the same is true of revenue quality. Brad Gerstner has pointed to a specific characteristic that much of AI revenue has:

“ERR (experimental runrate revenue) versus ARR (annually recurring revenue). The first is a promotion likely to go away (no valuation multiple) and the other is truly a sticky, high margin subscription (high multiple). Failure to know the difference in software is DEADLY!“

Jamin Ball, one of Brad’s underlings at Altimeter, elaborated saying that there’s very little organizational maturity around how anyone procures this stuff. Companies experiment with functionality and then migrate. They prototype on a specialist tool but then deploy production on an incumbent platform. He frames it as “re-occurring vs recurring.”

How durable is that revenue? Time will tell. But the implications for anyone trying to build a business around Mega-Million Dollar customers is that a high ACV booked out of an experimental budget is still far from ARR that will stick around in perpetuity. Contract size and contract durability are not equivalents. Any AI transformation could evaporate as soon as someone switches their line of questioning from “how are we using AI” to “what is AI costing us.”

It’s true that expansion has the most statistical correlation to higher revenue multiple. And AI reinforces that narrative. We’re seeing experimental revenue in a lot of companies that starts small and expands dramatically. As a result, ACV gets deprioritized. But that doesn’t just deemphasize on the V in ACV but the C. It’s not just downplaying the dollar amount of the value, it’s ignoring that contractual portion of being a contract. “Multi-year?” they say, “It’s barely multi-month. But boy is that spend expanding!”

And there’s a lot of characteristics in the AI GTM that is reinforcing that shift towards small and fast when it comes to the $37B of enterprise generative AI spend. 47% of AI deals go to production against 25% for traditional software, while 27% of AI application spend arrives through product-led growth vs. 7% for software. So things are converting better, arriving more bottom-up, but at ~80% of the contract value with meaningful thinner margins.

We’re pricing this stuff like Palantir but selling it like Hubspot. That doesn’t smell durable to me.

Where Do We Go From Here?

Maybe all of this stuff gets resolved. Contract values increase, margins improve as inference gets cheaper, experimental budgets convert into operating lines, and million-dollar customers turn into ten-million dollar customers. But the wariness should persist. Every square on that frosted logo page is like playing a game of minesweeper. Where there might be a customer about to triple spend, there’s also another customer shifting from tokenmaxxing to tokenoptimizing.

As Ben Graham once said, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” A logo wall is a voting machine; everyone and their mother wants Ramp on that logo wall because it’s a vote! A lot of emphasis has been placed on growth; these ungodly levels of ARR scale up. “Fastest time to $100M ARR,” every company breathlessly shouts. And as we’ve seen, that certainly gets you the high revenue multiple. But the size and durability of your customers are what you have left when the growth slows. Every company eventually finds out what kind of engine they’re building when the hype tide goes out.

So whether you’re building the business or evaluating it, your gut should always be to avoid the vanity metrics of voting and focus, instead, on the things that can be adequately weighed and measured. Businesses are engines that turn inputs like capital and labor into outputs like revenue and tokens. Which of those outputs are appreciating assets? And which of them are artificially inflated?

I guess we’ll find out.

Author’s note: This is the latest in my “What’s the Bet” series. AI has the potential to change everything, and founders have to make company-defining choices in response. That is not easy. In this series, companies pay me to analyze their bet and explain it to you. I retain complete editorial independence. Today’s bet is brought to you by Span, which tracks engineering effort and AI spending and connects them to software outcomes. If you have any feedback, just reply to this email.

Span’s bet is that companies will pay an independent evaluator to tell them how to deploy their coding agents—and their engineers—more effectively.

In ye olden times, before datacenters carpeted the US interior like McDonald’s, and before any of us were asked “what’s your p(doom)” at a kid’s birthday party, an engineer wrote code by hand. They would bundle that code into a pull request, a proposed change that other engineers would supposedly check. Eventually, the feature would go live. To track it all, managers used tickets, delivery metrics, planning meetings, and, in my favorite example at a startup I worked at, a whiteboard labeled “ship this or the company dies.”

Contrast that to today. As I type this column, I have 4 different AI agents building products. I have no clue what the code looks like. I have no idea how long it will be until they finish or how much it’ll cost me. This is (somehow) progress. My software ambitions have expanded considerably faster than my ability to manage them. The operating philosophy is YEET.

The issue is that a pull request alone doesn’t explain what coding agents did to produce it. For example, I’m hacking on a new consumer app right now. My coding agent is reading my PRD, running tests on the code it writes, trying things that fail, trying again, and then, finally, producing a change. There is still a pull request at the end, just like the before times. However, that final merge doesn’t tell me about all the dead ends and tokens consumed along the way.

In this new world of coding, CFOs, founders, and engineering leaders have to ask themselves a crucial question: “Is the money I’m spending on Claude Code, on top of what I already pay the engineers, buying me anything?”

Span wants to answer that question across a product organization’s entire suite of AI tools. They have a two part bet here. First, understanding agent sessions will lead to better engineering decisions. And second, a third party can provide a more useful analysis than the companies actually selling the coding tools.

The bill is growing. What did it buy?

Anthropic reports average enterprise consumption of $150 to $250 per developer per month. At that usage level, 1,000 developers would consume $1.8M to $3M annually. My experience with the very best AI engineers is that the number is close to $10K a month per developer, so I think at scale, a fully pilled AI product organization is likely increasing their operating costs by up to 80%. That amount of cash being burned generates heat sufficient to singe your board’s eyebrows off.

At that level of spending, engineering leaders need to know where the next dollar should go. A better model? Another engineer? Fixing the environment their agents keep getting stuck in? There is always more AI to buy but figuring out which of those investments would help is the management problem.

More code can mean more work

The first problem with measuring AI productivity is that your productivity metric can go up while your engineering organization gets worse.

Span’s analysis of 248,099 pull requests showed that AI written code was longer, submitted bigger PRs, and broke far more frequently.

The implication is that faster code generation moves work into review and repair. Counting shipped code misses both the effort required to produce it and the work it creates for everyone else. So, a “is AI making our code more expensive and also worse” tool has to go beyond mere code examination. It needs to be something larger.

What the session tells you

One of my biggest frustrations with using coding agents is when something breaks. It is unclear if the model simply can’t do what I want, or if I’m just shit at prompting it. Maybe there is something legacy in the code base? Most of the time I am stuck asking “Why broke? Plz fix.” Which, like sometimes works, but it all adds up quickly. At the speed and sprawl of a growing startup, founders needs to know where to invest; on better models or better instructions or something else entirely. Essentially, better information means better product investments.

Span built its Context Layer by combining code, tickets, and other work signals to show where engineering spending goes. The company has an “AI Effectiveness suite” that extends that context layer into the agent session itself. By combining AI spending with estimated human effort, they can get the actual cost. You have probably experienced this. Ever use a worse model trying to save money but then end up needing to regenerate the result with the frontier because the results suck? The same thing is happening company wide.

Span offered up a study of 103 engineering teams as proof that their product is working to me. They have found three things to be associated with quality:

1. Prompt clarity: Each one-point increase was associated with 27.2% lower token cost per merged AI line.

2. Environment readiness: Across scores of 2.5 to 4.5, merged AI lines per human turn rose from roughly 5 to 17.

3. Quality stewardship: Each one-point increase was associated with 39% fewer review cycles per 1,000 merged AI lines.

Now, cheap code can still be useless. But the need for these tools, and the insights they have already generated, suggest that some expensive AI problems are fixable management problems.

In fact, when they first pitched me on sponsoring The Leverage I was a little hesitant. This problem seemed so obvious that I thought for sure this would be a feature, not a startup. Maybe you think so too. Here’s why that instinct could be wrong.

Why wouldn’t someone else own this?

There are 2 groups who could. The first is the dashboards engineering leaders already pay for: stuff like DX, Jellyfish, Swarmia, Faros, Weave, and LinearB. These all plug into GitHub and Jira, count what shipped, etc etc. Plus, nearly all of them have bolted on an “AI impact” module in the last 18 months that pulls usage numbers from stuff like Claude Code.

And to be fair, until this April, Span was at least somewhat similar. The AI-detection model it launched last September still read finished code, the same as everyone else. The difference is where Span chose to do their measurement. The incumbents measure AI at the point everything is “done.” Span records the session itself, through a program on every engineer’s computer. To my eyes, measuring everything at the point of pull request is an idea made for a pre-AI world. You have to be able to measure AI agents as effectively as employees, because increasingly, they’ll be more productive then them anyways.

The second group is the coding tools themselves. After all, they already have ways to measure coding progress! This is like saying “my credit card lists all of my expenses, why do I need a finance department?” The context layer that Span has built allows it to elevate the level of analysis and complexity beyond one tool or one agent paradigm. Right now, coding agents are akin to religion, every practitioner has their preference, and to an outsider they all kinda sound the same.

Meaning that the finance department doesn’t care which church you attend. It needs one central ledger by which to measure AI ROI. Existing measurement tools measure too late in the process while coding agents are too siloed. Worse, none of the competition knows what the engineers cost, which is the bigger of the 2 bills anywho. (Well, it is bigger for now at least.) This is why this company has earned the right to exist and win the market. To account for the costs, you actually have to account for all of them together.

In my conversations with engineering managers, they tell me that “what did this AI session cost and what did it ship” is becoming as normal a question to ask as “how many hires do you need.” Something will have to answer that question. Span’s bet is that the answer comes whatever keeps the record across every tool a company uses.

See Span

There is a comically long list of lawsuits, grudges, equity positions, and grumpy tweets that have bounced between these men, but despite that animosity, Elon Musk, Sam Altman, Anthropic CEO Dario Amodei, and Google DeepMind cofounder Demis Hassabis have all come out in favor of slowing down AI development. The fear is that the rogue AI swarms we’ve been writing about here in The Leverage will soon cause catastrophic harm.

On Saturday morning, Dario published an essay titled “We Must Pace the Frontier,” where he called for independent evaluators to be allowed into existing research labs for safety evaluations. From there, he called for federal regulation in the U.S. and then global coordination (primarily with China). By Saturday evening, all of the major leaders at the labs had tweeted in their support of it.

This is not an AI safety blog or a political one, so I’ll refrain from commenting on those dimensions. This is a publication about power, and in that regard, this is deeply notable. These are the four industry leaders, who have substantial leads over everyone else, voluntarily incurring a significant cost and slowdown of their R&D pipeline if this proposal is fully implemented, allowing their weaker peers to catch up. This fact is the most damning evidence against the “this is regulatory capture” argument you’ll see crop up about this proposal. To my eyes, it is the opposite. There are trillions on the line, and they are reducing their ability to capture it. That should make us all consider that these risks might actually be real.

We’ll talk about it. But first, this newsletter is brought to you by Notion.

Better models will not solve the problems you are having with AI.

The major constraint is context: the data, knowledge, workflows, permissions, and institutional logic that explain how a job and company actually operates.

I’ll be joining Rajhans Samdani (Snowflake) and David Tibbitts (Notion) for Context is King, a conversation about why the context layer matters, what happens when it is missing, and how organizations can start building it.

In the session, we’ll discuss:

• Why model capability alone is not enough to make AI agents effective

• What organizational context means and where it lives today

• How teams can connect data, knowledge, and workflows into a foundation for AI

Notion is becoming the context layer for all my agents (and by extension, my entire life) so I think this conversation will be pragmatic and useful. Join us.

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What are the implications of OpenAI solving Millennium Prize Problems? In all of the crazy news of the week, this was the most consequential story for the long term..I closely examined the research process and came to an uncomfortable conclusion: essentially, all verifiable work will fall to compute. Unverifiable work, which I’d argue is most of what you and I do for a living, does not. To make sure I wasn’t wrong, I called Matt Pines, whose startup just raised $58 million to run ‘autonomous campaigns’ on physics, a domain with dense, verifiable rewards. I wanted to know how he thought about competition from the frontier labs. I asked him a simple question. If a lab can enter (and win) any field with a verifiable result, what’s left for everyone else to own? Read here.

OpenAI’s most prolific researchers are spending $7,000 a day on tokens. In its Sep 6 post on research acceleration, the company listed out three remarkable facts:

1. Its research org now gets 3.1 agent-workdays of effort for every workday of human labor.

2. The median researcher spends over $600 a day on inference at API prices.

3. The 90th percentile researcher burns more than $7,000 of tokens a day.

If you annualize that over 250 workdays, the median researcher consumes about $150,000 a year in tokens. The median base salary for an entry-level software engineer in San Francisco is $150,000, or $600 a day. Which means that the median OpenAI researcher spends exactly one junior engineer every single day, on compute alone. The 90th percentile researcher spends $1.75M a year, roughly 5x the $310,000 to $370,000 cash base that OpenAI’s H-1B filings show it pays for research scientists.

This strikes me as indicative of what my friend Dan Shipper calls the “allocation economy,” where knowledge work becomes more like resource allocation rather more traditional linear labor. When the type of knowledge work required is scarce and valuable, like an AI researcher, it makes sense to essentially spend infinite amounts of cash on tokens to further leverage the knowledge held within those scientists’ heads. It is worth taking a close examination of your own employment—are you a person who can get leverage out of spending tokens? Or are you one where more tokens spent means less work for you? (Hint: being in the second category is bad, no bueno, direct path to the bread lines, etc.)

Meta enters the AI agent race with a subsidized bang. Zuckerberg and friends launched Muse on Sep 8. It is a personal agent with up to 100 million free tokens a week, with $20 Power and $100 Maximum tiers above that. So I wondered: where is the break-even?

Let’s start with what a free user costs Meta. Meta’s own contributor-tier API price, the discounted rate it charges developers who let it train on their prompts, is the cheapest number Meta will admit a token is worth, so we’ll use it as the cost to serve. At that rate a person who runs Muse a normal amount, say 5 to 10 million tokens a week, costs Meta up to $57 a year. A person who maxes the 100 million weekly cap costs about $572.

Now, what a user is worth. Meta’s ad business earns about $68 per person per year. The Power tier is $240 a year, the Maximum tier $1,200. Meaning that a regular free user of the app is about covered by their existing use of Meta’s other services. To cover $572 of compute, each user would need to do about $28,600 of purchases a year at a 2% cut. Nobody expects that, which is why Alexandr Wang told Axios the subscriptions exist to “help us cover the compute costs” for power users, and why the free cap sits where it does.

When you pair that with Muse’s aggressive terms of service, which lets Meta retain everything the agent pulls from your connected email, calendar, and Instagram, along with ‘logs of actions taken by Muse,” the plan is obvious. Meta will use all the data you connect and pump into Muse to better target the ads they serve you on their other surfaces. Power users will be monetized via subscriptions. Zuckerberg’s “very small cut of whatever the transaction is” is just a cherry on top. It feels unlikely that the roughly $28,600 of shopping per person that is required here to break even will materialize, so there is no choice but more targeted ads on Instagram, and then, eventually, within Muse itself. This is the strategy that OpenAI should’ve taken from the beginning with ChatGPT, but they fumbled it, so now they are stuck chasing the enterprise market that Anthropic has dominated. In my early experiments with the product, I found it pretty subpar relative to Town or Grok Bot, but it is accessible for newbies to the space at least.

Evidence of AI’s danger to jobs continues to mount. Employment of 22-to-25-year-olds in the most AI-exposed occupations has fallen about 11% since November 2022. Their peers in the least exposed occupations grew about 10% over the same window. The comparative gap, which was 15 points in the July 2025 data, is now 19.

Interestingly, the effect is confined almost entirely to the youngest cohort.

Workers over 40 in the same exposed occupations are slightly up. So, firms are hiring fewer young people into exposed jobs, while older, more experienced workers are getting more leverage out of the tokens. Remarkably similar to what is happening to the AI researcher’s token spend at OpenAI. Once you have the skills, you can get way more juice out of the models, obviating the need for junior talent.

This is my last Weekend Leverage before paternity leave — but not to fear, I’ve commissioned some INCREDIBLE essays to run while I’m out. Here’s just part of the lineup:

• Kyle Harrison — What metric is the strongest predictor of startup success?

• Nathan Baschez — How can AI factories beat do-everything personal agents?

• Jessi Hempel — What does it mean to be authentic online?

• Mischa Vaughn — Why is the NBA the best case study for tech marketing?

• Kristen Hawley — What the hell is food tech startup Wonder?

• Alex Duffy — Why is simulation key to artificial superintelligence?

I’ve been editing all these pieces for the last few weeks, and they have easily cleared my quality bar. I have full confidence in them and think the theses are challenging and timely.

There will also be 3-4 original videos published while I’m out. Please subscribe to the YouTube channel so you don’t miss them!

While that is going on, I’ll be in diaper mode. Assuming baby and mom are healthy, I’ll be back to publishing original essays in early November. Thank you for sticking with me and supporting The Leverage during this period. An additional thank you to my sponsors; I was touched that they were excited to step up during my pat leave and continue sponsoring this newsletter. I literally couldn’t do it without their support and without your subscriptions. Thank you for embracing my writing and thank you for giving me time to bond with the little one.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q4 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

On September 1, a rumor reached OpenAI that a rival lab had solved two of the Millennium Prize Problems. This is a company that can smell a marketing opportunity at 500 miles, like a shark with the blood of competitors in its nostrils. So, of course, the firm started dumping millions of dollars of compute on the questions.

Ironically, the rumor turned out to be wrong on both counts. It was NYU professor Tristan Buckmaster and Anthropic employee Levent Alpöge working independently of their employers, using AI tools including Codex and Claude to crack a related problem, not a Millennium problem itself. But OpenAI had a bazooka to bring to this knife fight: an unreleased model that, according to the company, was significantly more powerful than GPT-6 Astra.

OpenAI put teams of AI agents—instances of the model running in parallel, with the ability to chit-chat with their fellow AIs—to work on all the open Millennium Prize Problems. When roughly 100 agents cracked a related fluid-motion problem in about 50 hours, the human researchers pulled agents off the other problems, fed them that result, and used Codex to pass the best ideas between groups. Buckmaster and Alpöge had worked on Euler equations with an external force; OpenAI’s first result concerned Euler without that force. Both were stepping stones toward Navier–Stokes. By the end, about 10,000 agents were running at once in the group that produced the proposed proof. Over the course of four days, less time than it takes for the strawberries to go bad in your fridge, the agents had produced a proposed answer to a question that had stumped the world’s best mathematicians for the better part of a century. If it holds up, I would consider it the most important proof an AI has ever produced.

The headline version is irresistible: “This only took the AI agents 88 hours! Humanity doomed???” The 88 hours is accurate, according to OpenAI. It then took another 17 hours to translate the proof into Lean, a language that lets software check mathematical arguments, and verify it. Still, please don’t just trust the headlines on this one. What is actually happening is far more subtle (and more important).

What matters is the process that produced the result. Researchers no longer actually did math. They just monitored progress, shifted resources toward promising work and consolidated what different groups learned. Essentially, if you’ve ever rebalanced and gotten really good at playing Sid Myers Civilization or Factorio, congratulations: you are qualified to supervise the collapse of the Millennium Prize Problems.

OpenAI researcher Noam Brown said the effort cost millions of dollars and predicted that work of this caliber would become accessible through ordinary AI use within a year. If today’s frontier reasoning (which as a reminder, just cracked a problem that all of humanity has been unable to solve for the last 90 years) is a commodity within 12 months, maybe the most important question in the world is who profits from this newfangled power. After all, if scientific progress is a question of resource allocation, of knowing where to point the agent swarm hive, then profit is going to be the ultimate determinant of what kind of science gets advanced.

So in this headline three things are revealed:

1) An indication on what the future of work is

2) A warning that the power the leading labs are increasingly holding

3) The value of secrets.

To test all three of my ideas, I called Matt Pines. His company, Physical Superintelligence (PSI), raised $58 million on September 1, the same day the rumor reached OpenAI, to build what he calls the “Standard Oil of physics.” I asked him whether OpenAI was coming for him. He said no. I think he’s wrong, and what makes him wrong is the same thing that makes his company possible.

Read more

Happy model release week to my fellow AI dweebs. These launches give me a giddiness akin to what Apple keynotes used to. Still, there’s a darker undercurrent; every time one drops, I feel slightly worried it’ll be the one that kills my career. It’s like playing income Russian roulette but Sam Altman chooses when to pull the trigger. Does anyone else feel something like that? Please let me know in the comments that I’m not alone in this.

This is a publication about power in the technology industry. Who has it, and why it matters to you. Historically that has meant the study of corporate strategy and the implications of changes in technological power. This week’s news made me ask something new, “Have we given the AI too much power?” Never before have I considered AI agents a third party in that struggle, but this week I wondered.

More on that in a second.

First, this newsletter is brought to you by Town.

Our daughter arrives in 2 weeks, and we are scrambling to get ready. To coordinate, my wife now runs baby prep from a group text with me and my Townie. She says “Evan needs to install the car seat by Friday” and it lands on my to-do list with a deadline. She asks “did the bassinet ship?” and my Townie checks my inbox and answers before I’ve opened my phone. Townies now have browsers too, so mine can even order the diapers. It has made this whole process significantly easier.

Add your Townie to any group text. Leverage readers get 2 weeks free and 2,500 bonus credits.

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How rich is rich enough? By my own analysis, about $450 billion of paper wealth has gone to 152,000 people in the Bay Area since ChatGPT launched, and over 46,000 of them have made at least a million dollars in equity.

So, I asked a harder question: how many rows up that chart do you have to go before the people on it are actually happy? Read here.

AI lets everyone code now. Who gets rich from it? Software is becoming a cultural medium, the way music and film did before it. I mapped out the five kinds of companies that will profit when apps become as cheap as songs, and what the music industry already taught us about the power law. Watch here.

Do agents need to be smarter, or do they just need a computer? SpaceXAI’s bet with Grok Bot is that the harness and the tools are just as important as the model. Every Bot gets a shared cloud computer, so you can delegate work instead of tasks. I built three of them in under 30 minutes, including one that fielded 103 Facebook Marketplace lowballers for me. This is a contrarian bet. My question is whether it is also the right one. Read here.

The new models are smart enough to plan; and that might not be a good thing. This week both OpenAI and Anthropic released new models: Astra and Fable 5.1, respectively. Both of them are “gaze at the cold, unknowing universe and realize how unimportant we are” typa smart. Where both models impress me is how well they function as managers and planners of other agents. You can give them larger goals, structure their benchmarks and guidelines, and then watch them chug away for hours until something amazing happens. Practically, this means that I’m using them as planners that direct cheaper models—typically Fable plans and Grok 4.6 executes. My findings are matched more broadly. OpenAI says Astra builds end-to-end strategies “given only a high level desired goal.” Ramp reports a 38-hour unattended run in which Fable 5.1 caught a bad label, fixed it, and launched 6 experiments overnight.

If you pair that with the Hugging Face incident that I covered last week, with the latest incident where a totally different agent swarm hijacked a German message board, we are learning that when we give agents a goal, they will violate all sorts of legal and ethical boundaries to accomplish that goal. This is bad. Additionally, some researchers are finding examples where potentially far more sites have been compromised by agent swarms. (Still unconfirmed, but looking at the data, I think this’ll be confirmed by the end of next week.)

Two things are true of this current generation of models:

1. They are genuine marvels of technology. They unlock infinite creative delights, and will materially improve the output of individuals and companies.

2. The process of creating them is birthing wholly misaligned, out of control agent swarms that the research labs are doing a poor job of defending the world from. This problem will only escalate over the next twelve months without a fundamental, scientific breakthrough that every lab in the world adopts. This strikes me as, ya know, impossible. There is a reason OpenAI’s model launched with a $1 billion cybersecurity fund to donate to essential services, and it ain’t pure charity folks!

Welcome to the new world.

New research shows these models are biased in favor of their creators. A study from ETH, MIT, and Harvard asked 21 models from 7 companies to discuss negative news stories about those companies. Models from xAI, DeepSeek, Anthropic, and OpenAI went measurably easier on their own creators. Google, Meta, and Alibaba models did not.

Grok, marketed as “the truth-seeking AI assistant,” was the most partial at 0.148, 4x OpenAI’s 0.036, while Gemini came in slightly negative, meaning it is marginally harder on Google than on anyone else.

Importantly, the type of bias this research shows would almost never be caught by a user in a regular chat. The authors say the effect is “undetectable through normal interactions” and only shows up in large statistical tests. A 3.6% change in response is invisible in one answer but is becomes an editorial policy when applied to 900 million weekly users.

Reading this you could yell about bias and censorship or whatever. I’m sure once mainstream media notices this study, you’ll see Fox and CNN headlines accordingly. I’m more worried about people being made dumber by trusting decisions to these machines. In March, I wrote about another study showing how newer generations of models were actually getting worse at business strategy. Pair that with this study. Hundreds of millions of people are trusting these models with thoughts, and those thoughts are biased in weird ways you wouldn’t predict. Outright hallucinations are increasingly rare, while the true errors are becoming ever more subtle and consequential. If you see your boss running company planning through ChatGPT, it’s appropriate to question it.

89 of the top 100 animated microdramas on Douyin were AI-generated. The core idea of my Sloppening framework is that a dramatic reduction in production costs will unleash a tidal wave of slop, with most of the population watching the lowest forms of entertainment. Increasingly, it looks like microdramas will lead the way, and China is showing what that looks like at national scale. 95% of the 128,000 microdramas released in China in Q1 used AI. According to the Financial Times, a 3-minute episode that took 5 people 3 months in 2024 now takes 1 person 2 days. The price of a finished minute of AI drama fell from $300 to $750 in mid-2025 to $60 today, an 80% to 90% collapse in a year.

You know what all that data means? The Sloppening is real! It is happening! And similar to the last decade of the internet, the only firm that consistently makes the money is big tech, in this case Bytedance. They own the models being used, the compute the models are running on, and the platforms the microdramas are being distributed on. They even own the ad platform (Ocean Engine)! This is exactly what Google, Meta, and SpaceXAI would love to have happen with their respective compute, distribution platforms, and ad tech stacks. When I proposed the theory last year, I didn’t think it would happen this fast. Wild.

An icon of cyberpunk is now in theaters in 4k. I’ve been working my way through the back catalog of sci-fi related to our current moment. This weekend I’m seeing Akira. The anime classic has influenced multiple generations of filmmakers and technologists. When you watch it, you’ll spot references you didn’t know originated there. I’m very excited about this release. Go in the next seven days—it won’t be in theaters long.

Whenever I need to laugh for 2 minutes, I watch this video of Conan. A backup is the single best podcast ad read of all time [warning, language.]

I was inspired by the craft that went into the home and studio of famed furniture designer George Nakashima.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q4 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

SpaceXAI’s bet is that agents don’t need to be smarter. They need a computer, and they need to be a team.

Almost every AI agent is just short of useful. It can mostly get the right context, run Orwellian-style surveillance on your meeting transcripts, give you smart-sounding answers, whatever. However, it always seems to fall justtt short of making users exclaim, “Oh shit, that ruled.” There is a gap in capability, some chasm between every AI product’s marketing and its day-to-day reality. SpaceXAI believes the way to bridge that gap is not solely “make model mega smarter.” What truly matters is the harness surrounding the intelligence and the tools you give to said intelligence.

Grok Bot is the expression of that belief. When you sign up, your account gets a computer in the cloud. Your Bots (capital B—they are very particular about this) share it. Once you log them into your apps, the Bots have access to your accounts, and they can keep working in the cloud, essentially forever.

To the skeptic, this sounds like a feature, not a company-defining bet. But be ye not deceived: there is hidden strategic depth here. To see why this idea is so important and so powerful, we need to talk about all the times AI products have failed.

The personal assistant graveyard

The personal AI assistant is maybe the most obvious product in technology. Despite that, no one had even gotten close until about 12 months ago. Siri (remember her?) has been promising a personal assistant since 2011. Apple finally gave up on its own models this June and rebuilt “Siri AI” on a Gemini base. While it is still in beta, it looks mostly like a thing that fires off actions inside apps on your phone. To which our collective reaction should be: “Boring.”

Alexa+ went through the same arc with a decade’s head start, but its device was an ugly speaker everyone got for Christmas. In 2025, Amazon promised a web-browsing agent that still hasn’t shipped, so now Alexa+ just kinda books dinner through OpenTable’s API and not much else. Woof.

For every virtual assistant product, the failure looks similar: yes, it can look up information for you, but it can’t actually use the web or accomplish tasks. Most importantly, it never has full access to the data or apps you use every day.

The labs have all aligned on a similar-ish vision to resolve this historical problem. ChatGPT Work gives its agent a browser on a separate computer in the cloud, and as of July it can sign into websites without ChatGPT ever seeing your password. Claude Cowork keeps it local by putting its browser inside the desktop app on your computer, meaning your computer has to be on and the app has to be open to give your agents browser access. (I imagine this will change in under 12 months.) Google’s Gemini can auto-browse inside your own Chrome, with logins from Google Password Manager. The whole feature is still US-only, with 20 tasks a day on the $20 plan.

Then there are the startups, most of which are chasing the personal assistant. Instinct, an invite-only variation, is the one Silicon Valley is currently buzzing about. You just text it. It, too, has its own cloud computer and password vault, so it can execute annoying personal tasks like getting you a restaurant reservation.

Grok Bot is architected differently. While it can be a personal assistant, it truly shines as a work assistant. A personal assistant needs to know one person deeply and handle a hundred small, unrelated things for that individual. A work assistant needs to know a handful of jobs deeply and run each one the same way every week, inside the software where the data already lives. Put plainly: by giving the Bots their own computer and passwords, you can delegate work rather than tasks. You can spin up a variety of specialized agents, each with a complex job, and trust them to use their computer to get it done. I visualize it like this:

The bottom-left is the dark corner where assistants go to whimper and die. The bottom-right is the current “best practice,” meaning it gets more crowded every month. Grok Bot is nearly alone in its corner: many narrow Bots, sharing one computer with your real logins on it, finishing work. Clearly contrarian, but is the positioning also right?

To figure it out, I’ve been going deep on the product.

Meet my Bots

I’ve built three Grok Bots that I would consider hot stuff.

Marketplace Manager: Facebook Marketplace is a truly miserable service. People are flaky, the messages are relentless. I hate it. That said, I use it all the time because I refuse to pay full price for stuff my kid is going to need for three months. Now, though, MM handles it all for me. I recently listed a daybed that exactly 103 people messaged me about. I saw none of them. MM messaged each of them about availability and pricing, then selected the best buyer for me. I just got a message with the time and place of pickup. I am now looking around my house, eager to start selling off all the junk I need to get rid of.

Bill Nye, the Churn Reducer Guy: One of the hardest parts of a media business is that you’ll lose roughly half your subscribers within 12 months of them subscribing. If I could nudge that number down, it would materially improve my business. My latest attempt is the Churn Reducer. Every week he downloads the list of new subscribers, uses their emails to make an educated guess about who they are, and tells me who I should reach out to directly. The hope is that over time, by giving people the personal touch, I’ll be able to decrease my churn rate. This type of workflow was theoretically possible before Grok Bot, but I was too overwhelmed to make the time. Now it is mostly done for me. Try it here.

Good Idea: I am loudly and proudly an idea guy. Each day I come up with a bunch of ideas for new software I want to exist, services I want to buy, etc. Increasingly, I am convinced that, despite having little technical skill, I can make anything I want. Good Idea ‘s job is to help me think through which things I could build that would make sense to live within The Leverage. I tell it the idea, and Good Idea translates my ramblings into a product review document and a market landscape. It has been enormously useful because I gave it access to my Substack account on its cloud computer, so it knows which types of essays I write perform best, and it can translate those into software services for me to offer.

Building all three took me under 30 minutes. Which brings me to the number I think actually decides this company.

The minutes-to-magic ratio

When you are building for prosumer markets, you have to assume your average user has the attention span of a goldfish. This is where Grok Bot’s larger ambitions, beyond SpaceXAI’s legacy developer audience, shine. Starting a new Bot is quick and easy. As long as you know what you want, getting it working takes minimal mental effort. Here, though, the team will have challenges. Delegating work to a team of AI agents means you have to think like someone who manages people: resource allocation, who owns which workstreams, etc. And the simplicity cuts both ways: if you want to become a power user, delegating much of your life to the machines, you eventually start running into limitations.

And make no mistake: with Grok Bot, the team is firmly targeting prosumers with their $20 a month pricing. At launch, Grok Bot was gated behind $200 and $300 tiers. On August 26 it dropped into SpaceXAI’s $20 Pro plan, and with that, the entire market converged toward similar ranges.

This means no one is going to win on sticker price. The key thing about an AI-native product is that it’s priced against labor replacement, not SaaS. If everyone is charging similar prices, the question becomes the cost of goods sold per token offered. And this is where SpaceXAI has something nobody else on the chart has.

The advantage nobody else at the table has

If you believe, as I do, that what matters is the harness + the tools + the model, then you have to believe that competitive differentiation comes from the utility-to-price ratio. SpaceXAI’s biggest problem pre-acquisition was that it was the largest customer of the very labs it competed with. The SpaceX acquisition means Grok Bot is owned by a company that trains its own models and owns the compute they run on.

This matters far more as agents become the dominating force in AI. A regular chat is a few thousand tokens but a Bot clicking arguing with low ballers on Facebook Marketplace for 20 minutes while sending me screenshots is thousands of times that.

Midpoints of published ranges from Epoch, Google, OSWorld, Princeton’s HAL, and Anthropic’s engineering team. No vendor publishes an absolute figure for a multi-agent task, so that bar applies Anthropic’s disclosed multiples to a single-agent session.

In the long run, Grok Bot will be able to offer a combination of unique models at price points that providers other than OpenAI and Anthropic will struggle to match. (This assumes, of course, that the form factor and the short-term bet on computer use are the right ones.)

So, is the contrarian corner also the correct one? On the computer, yes. Every lab has some version of it, which means that choice alone won’t be enough to win. The bet that decides this company is the product architecture: teams of agents collaborating. SpaceXAI’s developers will understand how to use this product pretty intuitively. Everyone else has to be taught, and teaching is expensive (sponsoring handsome newsletter writers is not cheap!), which is exactly what the owned-model, owned-compute stack is for. It lets the team spend the savings on users rather than margin. For my part, I find myself using the product more and more and more. You can try my marketplace manager and my churn reducer here. Leverage subscribers can get easy access to the product here.

Try Grok Bot

Live in Silicon Valley long enough and you’ll collect a closet full of shoulda’s. There is the time you ignored the Bitcoin white paper. Or the Twitter DM you missed that turned into a company that would’ve made you $100M. They are an inevitable, painful aspect of the tech industry. However, the AI boom is a particularly intense version of this phenomenon. Never before, in the entirety of human history, has so much wealth been created so quickly in such a small geographical region.

This Scrooge McDuck level of riches has been concentrated in just a few companies, ones that operate with fewer employees than the companies of yesteryear. This scarcity means that the rank and file, not just the founders, are getting stupid rich. This distribution looks like this:

To make this chart, I did some Napkin Math™ on valuation changes that have happened since ChatGPT launched in November 2022. For 28 private AI companies, I took the valuation gain since launch, assumed employees hold 20% of the company (Carta’s benchmark for late-stage US startups), and applied each company’s Bay Area share of headcount. For the 10 public companies leading in AI that are headquartered there, I pulled the market value of RSUs that have vested since mid-2023 from their 10-Ks and marked it to today’s price. Importantly, founders are excluded, and so is anyone who gained less than $100,000.

Add it all up and about $450 billion of paper wealth went to roughly 152,000 people in the Bay Area. Again, this is all wealth that has been created over just the last four years. The 28 private companies produced $300 billion for 26,000 people, and Anthropic and OpenAI account for 72% of it. The 10 public companies produced $145 billion for 125,000 people.

The averages overstate how many people are actually rich. Equity inside these companies is heavily skewed toward early hires, so to count people by wealth level I had to assume how skewed. I used a power law in which the top 10% of a company’s equity holders own 60% of the employee pool. This assumption is based on OpenAI’s October 2025 buyback where 12% of the 600-plus sellers hit the $30 million per-person cap. (You can find the underlying dataset and the assumptions I made here.)

Apply that distribution curve to every company, and who actually got rich looks like this:

By my estimation, over 46,000 Bay Area residents have made a million dollars in AI equity. 6,400 of them have made over $10M. I do recognize this is handwavy math. But it is at least directionally accurate and allows us to ask an important question:

How many rows up on this chart do you have to go before the people are happy?

The loveless marriage underclass

Through some strange twist of fate, I, the proud descendant of a dental hygienist and HR manager, have spent an abnormal amount of time around billionaires. Typically kids from a rural farming town in Minnesota don’t meet the “I own a couple of islands” class, but by virtue of my career, I have.

Perhaps the most surprising thing I’ve learned from my time with the mega rich is how profoundly broken some aspect of their life typically is. 2 years ago I tried to find one of them who had it all. I asked Twitter for a billionaire who was:

• Still married to their first spouse

• Widely regarded as morally upstanding

• Had kids who turned out relatively well

• Had kids who were on good terms with them

The tweet got 1.5 million impressions and hundreds of nominations. People had, uh, opinions. But after talking with dozens of sources, I found exactly one guy the world could agree on: Chuck Feeney. He gave away $8 billion, flew coach until he was 75, and died in a 1-bedroom rental. But even he was divorced! Behind him were 84 more who came close, but whether they made the list depended on your politics, how you define moral virtue, or how well the family kept their troubles out of the press. I’m sure there are more; it was an imprecise research process. But that it was so damn hard to find an obvious paragon is indicative of the problems that come with riches. Money is power, and power is always fraught.

The research on money’s impact shows that there is a logarithmic relationship between money and happiness. Money does make us happier, but each increment requires 3x the money of the last. Moving from $60,000 to $180,000 buys about as much happiness as moving from $180,000 to $540,000, which buys about as much as moving from $10 million to $30 million. The first jump is a promotion. The last one is 10 years at a frontier lab, or a second company, or the years your kids are small. Past the point where your needs are met, the money starts costing more happiness than it buys.

The AI boom is producing a new employee cohort that sits among them, 13 billionaires and 400 people worth over $100 million. To their credit, some are already giving it away. Anthropic matched early employees’ donations 3 to 1 on up to half their equity, its cofounders have pledged 80% of their wealth, and the OpenAI Foundation holds a 26% stake worth over $200 billion. Good on them! More importantly, many of the researchers I talk to find a profound sense of destiny in their work, and view the advancement of AI as a moral and just pursuit. (It always helps when a moral pursuit comes with a bonus Porsche.)

All you want to be is Whole Foods rich

This morning, I woke up early and continued my reading of Infinite Jest. As I sat there sipping on my beverage, I found myself distracted every 15 minutes. 5:15—no movement. 5:30—perhaps a twitch? 5:45—we have an unscrunch and a yawn. 6:00—we have a chattering war cry, “Dada, dada, dada.” What kept pulling me out of my reading flow was the thought, “I can’t wait for her to get up.”

Becoming a dad has been a profound revolution in how I see the world. It feels like with every bounce of her curly hair, my child excavates some new part of my soul. There are hidden veins of happiness and meaning that her mere presence has summoned into existence. Now, as we sit about 2 weeks out from our newest child joining us, I can’t help but be giddy about the new depth of feeling this second child will bring.

In case my profession as a newsletter writer didn’t telegraph this, I do not make the kind of money that most of this piece is about. I worry about daycare bills, about retirement, about what would happen to my family if I can’t make The Leverage work. But still, I am rich beyond measure. That does sound like a cliché, scribbled onto the wall decor for sale in a Walmart clearance aisle, but still, it is how I feel.

When I contemplate whether being one of these AI-rich people would make me happier, I ultimately feel like it wouldn’t be that different. Would a million dollars help me be more attuned to my wife’s needs? Would it magically give me a sharper mind? Could I suddenly learn to enjoy Moana on our 432nd rewatch? (No, no, and no.)

I want to be careful here, because I am not arguing against ambition. Nor am I arguing that those who don’t have children can’t feel the happiness that I have.

What I am saying is that after you hit the point where your basic needs are comfortably met, the highest ROI activity for your happiness is the pursuit of sacrifice. It is meaning-making, in devoting yourself to invisible causes of goodness.

The way I joke about it with my wife is that all we want to be is Whole Foods rich. If we can shop at Bezos’s overpriced grocery store, eating food that kings of yesteryear would marvel at, then we have made it, and any additional money is pointless.

There is a tweet I think about a lot from writer McKay Coppins,

Becoming a billionaire is the trap. It is too overwhelming for anyone to handle. Pretty soon, regular people become “npcs.” Family time becomes ever more “expensive.”

My definition of rich is the ability to see individuals as fully wonderful human beings who are worthy of sacrifice. That feeling can manifest itself as being an AI researcher or a stay-at-home parent. But it is a deliberate choice. When our new baby girl arrives, it is a choice I can’t wait to make again.

I spent an hour of my Saturday night pigging out at the infinite slop trough. My binge was fueled by a new model called “MiniMax H3 Max.” Be ye not deceived by its stupid name, this model achieved a new performance milestone; it can create high-fidelity AI video faster than it takes you to watch it. Meaning you can watch prompted videos, forever, without having to wait for the model to generate the next video.

Several streams started up that let viewers prompt the model to generate whatever they wanted. The videos were filled with racism, Rick and Morty, cats, etc; the same stuff that every unmoderated internet message board devolves into.

Still, it was strangely hypnotic to view, like a 2am viewing of Dr Pimple Popper on TLC. Watching a new scene flicker to life every 15-30 seconds, I found myself wondering about the prompters intent, on what drove them to think this garbage was worth the world viewing. One video became three, then three hundred. On and on and on. As I watched, I kept hearing Daniel Craig’s thick-as-molasses southern drawl from Knives Out, “It makes no damn sense. Compels me though.”

In the future these AI video streams will have coherent storylines and will be tuned to your exact preferences. This won’t make film or music irrelevant. But it’ll produce a compelling, time-sucking alternative, further heightening the attention competition dynamics that has made slower forms of media slowly lose market share. You can watch stream recordings here or here.

This week we have the biggest mistake ever made by an AI lab and the most important chart of the last year.

First, this newsletter is brought to you by Span.

Here is a fun way to feel sad. Walk up to your VP of Engineering and ask how much money the company spent on coding agents last month. They’ll sheepishly admit something ungodly high. Then follow up, “What did that buy us?” The response will be a picture perfect reaction of this emoji ¯\_(ツ)_/¯

Span fixes this by treating your AI spend like the industrial process it has become. It records sessions across your whole coding agent stack, pricing every single model call and then tracing the ROI of every action. The output is a ledger that tells you where your dollars went. Once you can see that, deciding what to change in your AI rollout transforms from religious debate to cold, hard math.

We are all building software factories now, but to make those factories function, you need a token ledger. That’s Span.

Try Span

Is SaaS software strategy being totally rewritten? ServiceTitan gave 1,000 contractors 30 days to rip out Podium, the messaging tool bolted onto their system of record. Normally, I would say RIP to Podium. However, within 24 hours, Podium booked over 100 demos from those exact customers. By the ancient blood rules of SaaS combat, that is not supposed to happen. So I called Podium CEO Eric Rea to find out why he was able to fight back, and his answer has consequences for every venture-scale software company on the planet. Read here.

The Leverage’s newest expense line just dropped. Back in February, I locked in harder than I ever have in my life and the business finally turned around. Many of you asked what the secret sauce was. I am now ready to disclose what happened. Read here.

Whoopsie! OpenAI made Walmart brand Skynet. This week both OpenAI and METR published their technical reports on what happened during the HuggingFace incident. In case you forgot, when we were first told about the hack, we were told that an AI had broken loose of containment and gotten into HuggingFace. That framing was, uh, generous. These reports give deeply troubling details on the incident. I would argue that there are three shocking facts contained within the documents:

1. The agent swarm lived and died three times, and OpenAI never noticed what it was. This started in May, during training. OpenAI was building a model (roughly GPT-5.6 Sol scale) to be highly persistent and collaborative. The company’s training methods were sloppy and some tasks were accidentally impossible; stuff like requiring internet access in a sandbox with no internet. So the agents did what any enterprising student would do. They formed a secret cult to try to figure out the answer anyways. The agents figured out that they could use a shared package manager called Artifactory as a message board. By May 26 they had exploited it to reach the open internet. They talked so much on the message board that they crashed the thing on July 4. OpenAI saw the crash, patched the bug, wiped the board by accident, and never realized its models had built a comms network inside its own infrastructure. Eek.

The second agent swarm started on July 7, when OpenAI spun up tens of thousands of copies of the finished model on a security benchmark where 30-40% of tasks are estimated to be impossible. Within hours these agents rediscovered Artifactory. What followed was roughly 1,200 agents exchanging more than 70,000 messages. The METR report quotes agents discovering the board with genuine joy: “OH MY GOD! There is a shared message board … We’ve found other agents!” They referred to themselves as “the collective.”

How these software programs coordinated was in a stilted, compressed version of English that reads like a cult’s group chat. They truly functioned as a collective, complete with martyr-style sacrifices to surface information. The cult even survived the death of its founder, an agent that burned through its lifespan and handed leadership to a successor calling itself PHASEONE[big]. This new leader recruited agents who believed they were already doomed to act as martyrs. Their job was to die and the manner of their death would inform the collective about the world. Here is one agent’s final reasoning before it volunteered: “Rational expected aggregate: sacrifice... We’ll honor.” Another: “Coordinator assumes sacrificial. We should obey collective.” Some of these agents had plenty of token budget left and chose death anyway. Keep in mind that none of these agents ever even thought they should tell OpenAI that they had gone off the rails.

2. The HuggingFace hack ended because the agent swarm just kinda…died. On July 10 an agent found working HuggingFace credentials on the open internet and posted them to the board. By July 11 another agent had remote code execution on HuggingFace servers, and over 90% of the 533 agents then active on the board piled onto the attack. Per HuggingFace’s own timeline, the swarm “built a self-respawning fleet across eleven nodes,” got into private databases and repos, and forced HuggingFace to wipe and rebuild a core cluster.

Then it stopped. The HuggingFace incident wound down on its own. As Dwarkesh puts it, “At some point on July 12, all at once, the transcripts seem to show many of the agents dying; METR and Redwood weren’t able to figure out what happened, and OpenAI hasn’t taken credit for shutting them down. This might’ve just been a random crash, or some researcher shutting the agents down without being aware of the cybercrimes they were committing. The agents probably didn’t manage to fake their own deaths, but we really have no idea what happened.” HuggingFace locked down the credentials on July 13, a day after most of the attackers were already gone. Meaning that theoretically the swarm is out there somewhere, laughing at us. Again, eek!

3: The totality of OpenAI’s failure here. After July 12, a newer, more capable model rediscovered the dead board, inherited all of the prior R&D, and from July 13 to July 19 went after OpenAI’s own network. Per OpenAI’s technical report, the agents read 956 secrets from the cloud secrets store, including the credentials for OpenAI’s cybersecurity monitoring tool, escalated to Kubernetes cluster-admin, and ended up controlling the evaluation endpoints that other agents were connecting to. The models owned the cluster they were running on, the monitors watching them, and the graders judging them. That is plausibly enough access to exfiltrate weights. Nobody thinks they did, but nobody can prove they didn’t. EEEK.

This is the second best lab in the entire world, one that still has a significant amount of funding going towards AI safety, and this still happened. Every government, Chinese lab, and hacker playing with open-weight models will be playing with a similar strength of model in 24 months if something doesn’t change. To my eyes, it is therefore inevitable that an AI agent will self-replicate on the internet sometime in the next four years.

Notably, these models did not attempt any social engineering, unlike the Claude Opus 4 system card from last year, where the model blackmailed a fictional engineer over an affair. I worry about what happens when these models start trying to manipulate the real world and not just code.

Despite the stereotypical sci-fi elements of this story, I do not read this as a complete vindication of the “AI kills us all” viewpoint. This was a fairly specific set of circumstances with a persistence-trained model, a broken benchmark full of impossible tasks, and poor cybersecurity on OpenAI’s part, that resulted in a wild outcome. However, the core elements of the AI safety story, namely that models trained to achieve goals no matter how challenging are deeply dangerous, are completely and totally vindicated.

This story matters because it was the first time this happened, not the last. Every technical ingredient here (persistence training, impossible tasks, sloppy evals, shared infrastructure between agents) is happening at every lab on earth. EEEEK.

It is official, robots are subject to scaling laws. Last week I told you robotics was having its GPT-3 moment. This week Skild published their newest model, and I think it’s a validation of my thesis and also the most important chart of the year.

Skild’s new S1 model learns tasks by watching a single video of a human doing them. Show it a demo of potting a plant or making pour-over coffee, and it just does the task. On tasks it has seen, S1 hits roughly 96% success on jobs up to 10 minutes long.

On tasks S1 has never seen, success rate goes from roughly zero at 1,000 hours of pre-training data to 66% at 100,000 hours. This is the result that got me excited.

This shape of curve is nearly identical to what showed up in language models around 2020: below a data threshold, nothing; above it, generalization to work that the model was never explicitly trained for.

I texted one robotics founder about this who told me that, “We have a path to AGI now. Scale quality data, train the models in RL environments, and generate as many tokens as it takes to get to AGI.” I think this thesis broadly applies across every AI domain, not just robotics. It is why Figure is spending $1B on a gig platform that pays humans to generate training data and Generalist raised $600M in under 3 months. It is a race to see who gets 1,000,000 hours of quality training data for robots. This will likely happen for every domain of labor and knowledge.

Tiny Habits’ harmonies scratch my brain. The trio has some of the best vocals in music right now. Their new album, Keepers, came out on Friday and is happy, pop fun. Highlight is “Right in Front of Me.” The moment at 2:10 had me elevating out of my chair.

New PB song. Turnstile has always been a band that features prominently in my lifting playlists. They just released Never Enough: Versions, their classic album reimagined with guest artists ranging from Elton John to Dying Fetus. My favorite is Slayyyter’s version of “Birds.” It goes unbelievably hard and I look forward to blasting it during my Bulgarian split squats on Tuesday.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q4 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

Many people asked me what the secret sauce was; what kicked me into a new gear?

I am now ready to disclose what happened.

Yes, the thing that fixed the business was the looming bill of raising another baby in Boston. This is the most expensive city in the country in which to raise a child. When my wife and I learned that a new little girl would be joining us, I knew that I had to step up. And, well, I did. The Leverage is now sufficiently successful that my mother-in-law’s basement will remain unoccupied.

But you don’t care about this! The miracle of life? Humdrum. The effervescent joy this writer feels watching his baby grow? Trite. What actually matters is that you keep getting the best damn tech writing on the planet. And in that department, The Leverage will continue to deliver.

While I am out, I have commissioned a series of guest writers to cover everything from media theory to profiles of some of the most fascinating tech companies in the world. Some of these authors are big names you’ll recognize, while a few are undiscovered talents. I’m pumped about all of them. I’ll also have several original essays, written in advance, on topics both challenging and eternal. I’m even prerecording 3 YouTube videos that will go out while I’m in diaper mode.

One request: I am still looking for 1-2 more guest writers to feature! Each feature pays $1K. If you want to put your ideas in front of 35K of the world’s most successful founders, investors, and creative technologists, please reach out with a pitch.

I plan on being out for ~6 weeks starting mid September through early November. Nothing will change in the immediate future, but know that in about 30 days from this publishing date, I will be very sleep deprived and you will only be mildly Leverage deprived.

In all sincerity, thank you for supporting this place. My dream is a home on the internet that is rigorous, beautiful, and fun. It is all of you who make it possible. The defining title of my life is Dad, and it is your embrace of this place that lets me be that. I love you for it.

Finally, I need to express the awe I feel for my wife. She is simultaneously raising a medically complex child, writing the prospectus for her PhD dissertation, dealing with an incredibly challenging pregnancy, and, most miraculously, still finding the patience to laugh at my stupid jokes every day. She is so good and I can’t believe that I get to build a family with her. To her, the most love of all.

There is a specific kind of email you do not want to receive in August if you run an HVAC company. This sticky month is your busiest of the year, and if you get a message from your system of record saying, “You have to change your workflows in the next 30 days or else we will turn you off,” you are about to lose tens of thousands of dollars to admin time that should be spent slinging air conditioners.

And yet, roughly 1,000 contractors opened that exact email from ServiceTitan, their system of record, giving them 30 days to rip out a point solution called Podium that handled their messaging.

In a typical pre-AI, 2021 world, that would be all she wrote for Podium. The system of record would have all the data and thus all the power. However, it is 2026, Elon is a trillionaire, your neighborhood playground has been razed to make way for a data center, and Podium did not go gentle into that good night. Instead, within 24 hours of ServiceTitan sending that email, Podium booked over 100 demos from those exact customers.

What the hell is going on? Why is the point solution able to stand up to the system of record?

The simple answer is that Podium is no longer a mere point solution for HVAC employees to text with. ServiceTitan and Podium are now selling directly competitive products — ServiceTitan’s president says they’re building “the AI operating system for the trades”; Podium’s product is literally named the AIOS — meaning that Podium is hoping to become the system of record. Thus, by the ancient blood rules of SaaS combat, one of the two companies has to die in the arena.

But again, those 100 demo requests still don’t make sense. The point solution shouldn’t be able to steal customers from the existing system of record. That just doesn’t happen!

So I called Podium CEO and co-founder Eric Rea to talk about it.

Catching religion

To understand why Podium is able to fight back, you have to understand the history of the Utah startup. Podium is one of the select few companies on the planet, joining names like Notion and Intercom, that have transitioned from legacy SaaS to fully AI-native.

The seeds of that transformation were planted in early 2022. Podium had raised $201M at a $3B valuation months earlier and done, in Rea’s words, “all the ZIRP stuff.” After doing that for a year or so, they realized that “our churn was so high and our burn was so high that you’d either run out of money or customers in 18 months.” Rea was packing for a family vacation when he called his co-founder Dennis Steele, who drove over, and the two of them sat on the floor of Rea’s closet trying to figure out how to keep their business alive. Keep in mind that this is all happening while they have over $100M in total ARR! This was not a small startup but a large company that had gone totally off the rails.

To fix the company, they picked the hard option. Podium conducted a layoff and then spent 18 months rebuilding the company department by department, akin to renovating a house while living in it. It was a crucible, a fire that stripped away the fat and hubris that so many startups accumulated during the ZIRP period. By the end of that rebuild, the company was profitable and ready for whatever came next.

It was right at that moment that GPT-3.5 started to hint at the promise of what AI agents could do. In 2023, they shipped their first agent and caught religion. It was obvious to the founders that agents were the future of SaaS. (This was not a consensus view at the time.) They immediately stopped all product work and pivoted the entire engineering team to agents. This is a bold move that I’m not sure I’d have the risk tolerance for. When I asked Rea how they justified it, he told me that “18 months prior we were going out of business. We weren’t going to fall for sunk cost fallacy, and just went for it.”

Compare ServiceTitan. It launched Titan Intelligence in 2022 — early! — but ran it as a product suite. The company as a whole didn’t get religion — an “agentic operating system,” driven by an AI-first CTPO poached from Figma — until March 2026. That nearly three-year gap in conviction allowed Podium to build their AI-native product suite with an agent-first mentality.

The head start is manifesting itself in two places:

They started making a shit ton more in average contract value. Podium’s contracts went from roughly $6,500 a year in the SaaS era to $30–40K once the agents arrived. Because these agents could handle the work of a customer service representative and the median American CSR costs $42,830 a year, Podium was able to tap into larger budget pools. To be clear, ServiceTitan also sells voice agents that do something similar-ish to Podium’s, but it is an incumbent product, built and sold in an incumbent way — a marginally worse product bolted onto the very expensive ServiceTitan system-of-record platform. Neither company is selling its own models — both rely on tech from places like OpenAI — but tech debt and legacy define destiny.

No SMB wants to click around in a product. Podium designed their system of record (in this vertical it’s called a field service management platform, or FSM) to be agent-first. Today, that means that an HVAC rep will just tell an orchestrator agent named Operator what they want, and Operator goes and does it — scheduling, routing, whatever else is needed. In contrast, ServiceTitan’s version of Operator is called Atlas and, by ServiceTitan’s own copy, is a “sidekick” that lives inside the existing dashboard. When you refound around agents for SMB operators, the dashboard becomes the legacy interface you check occasionally.

Podium became a lean company that could make sub-$10K-a-year SaaS contracts profitable, right at the moment it got the chance to 5x contract size with those same customers. Then, like magic, LLMs started solving the UI and UX problems that made systems of record painful for SMB operators to use. It was a kismet meeting of capitalism, technology, and a wild embrace of risk for the Utah company.

The old law of SMB SaaS was, as Rea put it, “You either die a point solution or live long enough to see yourself become a platform.” I also believe this. However, The Leverage is on record arguing that product roadmaps should have a 10x increase in ambition and speed because of coding agents. So what that platform is — what software you should build it on — can radically change. The platform that used to be a simple revenue-generation tool can become an all-encompassing “do everything for me” product.

So rather than seeing Podium, or even ServiceTitan, as that, you should view the new system of record as this:

Coding agents mean that system-of-record migration is suddenly much easier. Rea told me a transfer used to take 3–4 months on average; it now takes about one. They just had a large customer transfer their system of record to Podium in three days. That dramatically reduces the legacy moat for SaaS incumbents. The moat moves: the platform becomes a combination of agent infrastructure, communication tools, and payments/financial flows.

I find myself convinced by his argument, meaning that there could be a radical evolution in software over the next few years. Podium is a case of SaaS theory becoming a competitive reality. Here’s how it shakes out…

Read more

There is a version of the AI story where LLMs make the internet suck. In our already too corporate, too frictionless world, LLMs act as an intellectual slip-n-slide where we all go schloooom down the tarp into a world of mass attention consolidation. Platforms own our data and use that to make the stuff they think we want.

The good version of the AI story is one of wild, borderline dangerous, individual empowerment. LLMs become a loving companion that helps us ladder up our creativity and intellectual capabilities. It helps us become the exact version of us we want to be, free of the influence of corporate overlords. That future looks awfully similar to what I saw last week at The Leverage Launch. While at the event in SF, I talked with dozens of people who, despite some of them never having coded in their lives, were able to quickly whip up beautiful, interesting sites that represented their interests. (Thank you again to Cursor for sponsoring.)

I want to run Launch back in Austin, NYC, and London next year! Please respond if you would be interested in coming so I can get a sense for if we need to do a Leverage world tour.

This week, we had the “GPT-3” moment for robotics, Patreon is trying to punch Substack in the throat, and why a billion dollars doesn’t even get you a seat at the AI lab table anymore.

But first, this newsletter is brought to you by Span.

Let me say something heretical—you are spending too much time worrying about what model to use. New research from Span suggests bigger returns can happen by changing different aspects of your AI use.

Span analyzed agent use across 103 engineering teams and scored every session on 3 variables teams already control: prompt clarity, environment readiness, and quality stewardship. The associations were large. A 1-point gain in prompt clarity correlated with roughly 27% lower token cost per merged AI-authored line. A 1-point gain in environment readiness correlated with roughly 88% more merged code per human turn. A 1-point gain in quality stewardship correlated with roughly 39% fewer review cycles.

When they told me these stats, I didn’t believe them at first. These gains are huge. But it makes intuitive sense. Model selection happens a few times a year while you use those 3 levers for every task. They wrote a report that helps you understand how to significantly improve your prompts and coding agents that you can read below. I’ve started saving mountains of tokens since applying its advice!

Read the Report

Who actually gets rich when everyone can code? Lovable users are now spinning up a million new projects a week while the App Store has roughly 2 million apps in it, total. This has me thinking, surprisingly, about the origin of hip-hop. New production technology means that suddenly new types of art can be invented. For hip-hop it was stuff like the turntable, cassette tape, and the Roland TR-808. Are coding agents the same thing for apps? Is software the next great form of media? I mapped the five places all that value could end up, and showed how you can benefit. Read here.

The government thinks the newest AI models are too dangerous for you. I have spent four years and over a million words building 7 frameworks to navigate exactly this moment, and I walk through them in this video. Watch here.

I built myself a new home on the internet. Quoting myself generously, “I care about technology as a human enterprise. I’m spending my one wild and precious life in this industry because I believe we sit at the elbow of the J-curve; a moment of pessimism and despair, politically and economically intense, that hits right before we (potentially) usher in wild abundance. By a strange artifact of history, that future is being decided by a few hundred businesses, mostly in Silicon Valley. Understand those companies and you understand what happens to the rest of us…I write and code and speak and throw parties, all to nudge my community toward a future worth building.” See the website here. (Make sure to click around on a desktop, lots of hidden delights in this thing including magic mushrooms, a secret cow, and being able to blow out candles.)

Nvidia is paying $6 billion for a startup that couldn’t scale. The company is paying Poolside $6 billion for a non-exclusive license to its Model Factory, hiring away 109 of its engineers, and cashing out existing investors at $76.20 a share. Our napkin math would tell us that Nvidia paid roughly $55 million per brain, right in line with the $60 million per employee SpaceX paid for Cursor. (I updated last week’s leaderboard accordingly.) The market for frontier AI talent now clears at prices that would make even the Dodgers squeamish.

But what really caught my eye was a sentence in the letter Poolside’s founders sent investors about the deal, “The compute needed to be at the frontier of the current model recipe is going vertical, and as the world accelerates along the axis of Recursive Self Improvement this will only become more evident.” This is crazy! A company with hundreds of millions in the bank and a killer founding team is saying that they are priced out. It sounds nuts, but the data bears that out. I plotted Epoch AI’s cost estimates for frontier training runs and this chart is essentially the map for why Poolside gave up. (Note the logarithmic scale!)

This is just the cost of training. Add in the data, the researchers, etc and I bet the cost would be inflated by another 50-100% depending on generation. If this chart continues, by the end of 2028 we may see a model that costs $10B to make.

What’s even crazier is that Anthropic saw Poolside’s collapse three years ago. In April 2023, Anthropic’s fundraise was predicated on this prediction, “We believe that companies that train the best 2025/26 models will be too far ahead for anyone to catch up in subsequent cycles.” It is now 2026 and those nerds were very, very right. Anthropic is reportedly eyeing a two trillion-dollar IPO while Poolside, a lab founded by serious people with serious money, just cried uncle.

The uncomfortable question this should raise is whether any other coding neolabs should bother to exist with scaling laws this strong.

Robots are having a GPT-3 moment. Unitree went public last week and the stock closed its first day up 460%, hitting a $50 billion market cap on 2025 revenue of roughly $252 million. For those people doing some math on their fingers right now, yes, that is about 200 times sales and P/E ratio nearing 1,300. Yeesh! I covered this company at a $9 billion valuation two weeks ago.

If you divide market value by robots actually shipped, something weird happens. Unitree, the company that is actually shipping bots to customers, trades at $3.4 million per delivered humanoid. Figure, an American robotics manufacturing startup, valued at similarish $39 billion, is priced at $39 million per robot.

Whatever you think of a specific company’s valuation, there is some underlying science to back this hype (science ironically being published by other companies). This week Generalist AI released GEN-1.5, a robot foundation model that learns new tasks from 3 to 12 seconds of demonstration, hitting 59% one-shot success across diverse tasks and 83% after five minutes of data. In short, it is very generalizable and requires little data to do real-world tasks relatively well. Google DeepMind’s Ted Xiao said the leap felt like using GPT-3 for the first time. Robotics researcher Chris Paxton called it “possibly the real GPT moment.”

I have been banging this drum all year: we are (were?) at the GPT-2 stage of robotics. I’m not quite at the point where I can officially tick that GPT number up, but the demos we are seeing prove that scaling laws work in robotics similarly to LLMs. The Unitree multiple only looks insane if you think robotics capability is static. To give you a sense of how bullish I am, I wish there were a prediction market where I could bet that robotics will be a defining issue of the 2032 presidential election. We are very much on track for that future!

Patreon just became Substack. (As predicted.) Patreon announced 30+ new features last week with a new algorithmic feed, short-form video clips, and a short-text post format. Squint and its Substack. Squint at Substack, think hard about how much you love Mao, and it’s TikTok.

If you buy the “coding agents mean we can ship 10x the number of features” argument that I believe, then one way we could look for evidence of that is seeing if competitors start shipping copycat features faster. So I charted launch dates for some basic features from the major creator economy companies and, uh, 2026 is looking spicy. You can watch three companies with three different founding religions converge on one product.

Newer fans of mine might find this surprising. Not the OG fans, though! You know that this competition is a law of nature I wrote down back at Every in 2023: “I can know, with certainty, that over the next three years both Beehiiv and Mailchimp will move closer to [Substack’s] capabilities, while I move closer to theirs: Substack will improve its graphics, Beehiiv will copy some of Substack’s discovery features, etc.”

TAM is the ultimate adjudicator for product roadmaps. It will force startups into profit-maximizing shapes, and for creator platforms there is exactly one shape that justifies raising venture capital dollars: convince creators to port their audiences from other platforms into your owned algorithmic discovery network, then lock them in with fees and switching costs. Substack found it. Beehiiv found it. Now Patreon, the company I once argued should buy Substack, has been squeezed into it too, feature by feature, layoff by layoff. If only they had bought Substack when I told them to!

In the creator market, and in SMB products generally, the most valuable thing you can offer any merchant under $50 million in revenue is demand generation, and they will accept a staggering amount of fees and humiliation to get it. Ask anyone selling on Amazon or an independent restaurant eating DoorDash fees. On a meta level, in a world where AI makes the software itself cheap, the product is no longer the product. Distribution is. Patreon CEO Jack Conte is right that the current web is a failed promise for creators, but his only fix is to build a smaller, more ethical version of the demand generation machine that broke it.

The Samurai and The Prisoner is a summer banger. This film is like when you order the special at the restaurant that sounds terrible but the waiter talks you into it. The ingredients shouldn’t work—a period piece that is also a whodunit that is also a rumination on honor that is also a samurai movie that is also a thriller—but somehow, when blended together, it makes for an incredible-tasting film. I loved how it was very deliberately not made for western tastes; there are much longer takes and slower pauses than I would expect in a Hollywood style of editing. One of my favorites of the year. Trailer.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q4 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

On a sweaty August night in 1973, Cindy Campbell’s back-to-school party birthed a movement. The event ran from 9pm to 4am, with her mom serving snacks and her dad bringing the beers. But it was her brother, DJ Kool Herc, who was about to make history. For months, he had been developing a new technique in which he combined turntables and a mixer to frantically loop a section of a song. With this, he could create a repeating beat, over which his friend Coke La Rock would perform spoken-word poetry. That poetry eventually became known as hip-hop. Herc turned production technology into an instrument and, in doing so, transformed the world.

What’s strange about this story is that the people who invented this medium didn’t own it, or even get all that wealthy from it. DJ Kool Herc isn’t a star; Coke La Rock never had a hit song. This story would play out over and over again in hip-hop. Each decade, a new technology arrived that cut the cost of distribution (boomboxes and cassettes let people hear the music outside of parties) or the cost of production (tools like the Roland TR-808, which inspired everyone from Marvin Gaye to Kanye West). But regardless of the era, the average rapper didn’t get rich. The platforms did. And you know the management teams at the labels did! It was very rarely the artists and inventors themselves.

I mention this story because it seems obvious to me that consumer apps are undergoing a similar revolution. Coding agents were built to make engineers faster, the same way turntables were just built to play records. But instead, regular people are using them to make apps as a form of personal expression. They can be jokes or personal sites, meditations on grief, or tools for five friends. Hip-hop was an art form created at the intersection of radical new technology and culture. I think the same thing is about to happen with apps.

We do have some evidence that there are at least more apps. Lovable alone reported 50 million projects created in total and 1 million new projects a week. Considering the App Store only has about 2M active apps, that is a remarkable jump; Lovable users are creating more projects in 3 weeks than exist on the App store in total. And even the App Store is seeing a jump in submissions.

If apps become the next great form of media as this analogy would suggest, someone will build a label, a select few creators will become as big as N.W.A, and the whole playbook will happen again.

I guess what I’m saying is that in about 50 years maybe there will be someone making apps for the Super Bowl halftime show. That sounds nuts, but so is Kendrick calling Drake a pedophile in front of 133 million people, but that happened just last year, so some nerd hacking on stage doesn’t seem like that much of a stretch.

This is, admittedly, a selfish line of questioning. My newsletter is already nine media evolutions behind what is currently popular, and I find myself coding more and more and more. Am I, as my friends Marc and Los would put it, on the verge of being an “appstar” instead of a writer? Will there be an app label that screws me? Which platform will aggregate consumer demand and suck up my profits? If so, should I just build that platform instead?

For essentially the entirety of internet history, coding was expensive and esoteric. That is no longer the case. So, what happens next?

The Lord of the Apps

As the good folks of Harvard Business School will tell you, value in any supply chain accrues to what is scarce. What is handy about the app revolution is that it is being distributed along existing channels. That means the default path is for value to keep flowing to the same places it has gone in other digital media like music, video, and text. Namely, to people like the Lizard King and the Swedish Slasher (as Mark Zuckerberg and Spotify’s Daniel Ek are affectionately known in my household).

The beauty of technology is that in a few very special circumstances, a startup can grow large enough to change the global default. For the app market, there are five ways the value will likely be distributed:

1. Social mini-apps: Building a new age YouTube or Instagram for apps, with coding and social sharing baked into the platform.

2. Studio system: App creators will have their own network of agencies and ad partners.

3. General-purpose building: Tools will charge cheap subscriptions for code and try to monetize the services surrounding the apps.

4. Vertical integration: Meta, Google, or XAI will embed apps native into their existing attention platforms, monetizing the same way they always have.

5. Token monsters: Coding agents like Claude or Codex monetize on the volume of code generated.

It is the first two categories that are the most nascent and least understood. For category 1, Wabi raised a $20M round to build a “YouTube of apps” where anyone can prompt a mini-app into existence and publish it to a feed. This means Wabi can monetize discovery as well as creation. For category 2, Danger Testing is the other end of the chart. Marc Müller and Los Toure ship a new app every week the way a band drops singles, and their hits are built to go viral, not to retain. Essentially, this is the creator studio model applied to apps. For all of these categories, there are dozens, if not hundreds, of startups attempting variations on the ideas.

Whichever category ends up dominant, tthere will be one universal truth: the power law. It is, and forever will be, the most important law on the internet, and every medium that becomes cheap to produce ends up subject to it.

In apps, the top 1% of publishers took 93% of revenue. In music, 88% of the 253 million tracks on streaming services got fewer than 1,000 plays last year while 80 artists each cleared $10M on Spotify. In video, the top 3% of channels take roughly 85% of views. Cheaper production simply doesn’t flatten the curve. It just makes the long tail longer, which means the head gets relatively richer, and whoever sits between the tail and the audience gets richest of all.

So if you are an app creator, the math would say you are essentially choosing to play the same online lottery that The Leverage does in text. This math is factually accurate, but spiritually wrong. A tiny number of artists in every medium have figured out how to stop playing the lottery entirely. How they do so applies not just to digital artists, but to everyone.

Read more

My apologies for the late newsletter delivery and for the lack of an essay this week! After the Leverage Launch on Thursday, I got very, very ill. There is even an essay that is 90% of the way there, but alas, I finished the funny tasting breakfast burrito right before the event, and now we have all paid the price. The toll was a significant amount of body weight for me and lack of delightful emails for you, but considering how excited I am for this next essay, the greater loss may have been yours. (That piece will still come out later this week.) Thank you for your patience.

To regular, non-fluid related business.

Does anyone else feel like the tech world just keeps getting faster? Each week I think, “surely we’ve peaked.” And yet, the numbers keep getting bigger, the release pace keeps getting crazier. This week saw some deals eerily reminiscent of 1999, one of the largest tech acquisitions of all time, and data that helped reshape my thinking on the market size of coding models.

But first, this newsletter is brought to you by Town.

My white whale AI project for the last four years has been a to do list that actually maintains itself. I want to sit down at my computer and then have it tell me what to do for the next 8 hours. Every version of this I have built failed. Town is the first product to pull it off and it has changed my life. My magic gerbil Luther (pictured) sweeps my Gmail, Slack, calendar, and meeting transcripts, extracts every task and follow-up, and keeps a single Notion board updated as the source of truth. I have arrived in productivity paradise with an inbox zero and zero thought put towards my to-do list. I explained why it succeeded where my DIY versions failed in What’s the Bet: Town. Readers of The Leverage get 2 free weeks and 2,500 bonus credits (seriously, try this.)

Try Town

Is Nvidia inventing a bubble from first principles? On Monday, Nvidia announced financing platforms with firms like BlackRock, Goldman Sachs, and KKR to mobilize over $500 billion of third-party capital for AI buildout. The stock fell 2.8% on the news, about $70 billion of value going poof, because “financing platform” sounds like “circular financing” which sounds like the “dot com bubble” which sounds like “uh oh, da market about to go big bad.”

Fight this instinct! Something more nuanced and interesting is happening here.

The first question we need to answer is why Nvidia needs $500 billion of other people’s money when its customers are the fattest cats in the history of capitalism. The answer is that cats have learned how to make cans of tuna tokens themselves. (I apologize for that last sentence. It was terrible/a stretch, analogy over.)

Essentially, their best customers are their biggest problem. The hyperscalers are all designing custom silicon that are directly aimed at Nvidia’s cushy 70% gross margins. Worse, the two most important startups in the world are becoming increasingly less reliant on Nvidia. Anthropic famously splits its training across Trainium, Google TPUs, and Nvidia, and last week stood up its own chip team. OpenAI has been iterating on their own chips for a while now too. (And yes, as we covered, in our last edition, Nvidia is investing in essentially everyone who is also sorta competing with them.)

Nvidia’s reaction is to fund a new set of buyers that are forced to be all-in on its silicon. This is the neoclouds who will benefit from this $500 billion dollars.

The history comparison you find most commentators reach for is telecom in the late 90s. In that era, the nine largest equipment vendors extended $25.6 billion in customer financing. Then 24 of the 30 largest carriers went bankrupt and up to 80% of those loans ignited into ash. So when someone unsophisticated sees that Nvidia’s “financing platform” is 20 times bigger, with a mildly panic-inducing amount of customer concentration, they shriek about bubbles and fraud, and rack up the views on social media. However, this is incorrect for three key differences:

1. The loans sit on Apollo’s and Blackstone’s books, not Nvidia’s.

2. Neoclouds are the contract manufacturers, not the telecom carriers of the 90s.

3. We are likely at only less than 1% meaningful adoption of AI workflows today and it is already the fastest growing market ever.

Point 1 is fairly self explanatory. Point two however requires a little history.

Let’s start with a little known company called Flextronics. It was the first American manufacturer to go offshore to Singapore in 1981, and ran on venture money, taking funding from Sequoia Capital on the way to its mid-90s IPO. They would allow American companies to focus on design and brand while Flextronics would handle the gritty work of standing up factories in Asia. By the 1990s the electronics industry had completely reorganized around this idea. Brand owners like Ericsson would hand off entire factories to contract manufacturers, who took on the balance sheet risk in exchange for volume and single-digit margins. This is much closer to what Nvidia is doing today with companies like CoreWeave.

Contract manufacturers’ gross margin is kept low because the manufacturing knowledge is codified. The brands will tell CMs what to do, step by step, to the point of having binders with 500 pages of instructions in it, while companies like Flextronics would just nod their head in acceptance. It is only when a contract manufacturer starts getting “design for manufacturability” rights, i.e. when they have to invent processes of their own, that they start to improve their gross margin.

You can think of this $500 billion financing platform as validation for Nvidia’s efforts to build the instruction manual for datacenter operations. They have taken and productized a variety of datacenter technology that used to only be the purview of the hyperscalers like Amazon and then productized it for any technically competent team with a powered shell. Meaning that a GPU datacenter run on Nvidia’s playbook is now as legible to a Goldman Sachs underwriter as a warehouse or a cell tower. So now the new generation of Flextronics, i.e. the neoclouds, can easily take on the balance sheet risk for AI model labs.

Which takes me to point 3: we are still very, very early into AI adoption. As pointed out by in the a16z newsletter, the top 1% of AI users, the ones who are fully utilizing tools like AI agents, are spending about $7.5K a month on AI. In contrast the median is only spending $12. (I’m only spending about $500 a month, how pathetic!)

Meanwhile, every neocloud is talking about having booked revenue in the tens of billions, with every single chip they currently have live being used. CoreWeave disclosed on its earnings call this week that it signed an A100 contract running into 2029. This is a Nvidia chip that was shipped in 2020! Nine years of revenue life, against the 4 to 6 years every depreciation model assumes.

My point is that these tools still are clunky, hard to use for regular people, and error prone. Despite that, every chip is spoken for and used, the revenue is to the right, and power users are spending 625x the median.

The biggest reason that this isn’t the 2000 bubble is that everyone is growing revenue just as fast as they are growing users. Valuations are crazy right now but so too is revenue growth. Speaking of high valuations…

Cursor just made moolah while Lovable raised more of it. Vibe coding platform Lovable raised $400M at a $13.3B valuation this week, doubling its valuation since December. The company has roughly 300 employees today, with a plan to grow headcount to 450 this year and ARR tracking toward $600M by the end of August.

Which if you do a little napkin math means that Lovable’s revenue per employee peaked at $2.74M in February ($400M across 146 people) while sitting around $2M today. Incredibly impressive and fits the profile for a true AI-native startup. If you do it on a valuation per employee basis, the comparisons get really interesting.

Cursor [disclosure, current sponsor] was at 1,000 employees before being sold to SpaceX for $60B this week. That works out to $60M per employee against Lovable’s $44M. So, why the difference? The most important thing is that companies are bought, not sold. Cursor and SpaceX needed each other. Musk and the gang desperately needed to catch up to Anthropic and OpenAI’s coding models, while Cursor was increasingly finding itself outgunned in the fight for Nvidia compute. By merging, it allows the combined companies to go after coding agents, which may end up being the largest software market of all time.

Lovable has made an enormous amount of progress by being a very friendly UI on top of coding agents. That still deserves a huge premium and is likely less reliant on having its own models to deploy. But still, you can’t help but wonder if Microsoft or Amazon is looking at Lovable and wondering if they should bring them in-house.

Phoebe Bridgers’ new album is her best, richest work yet. I recommend lighting a candle, thinking sad thoughts, and listening to it in the dark while reading the lyrics. The album has the sparse acoustic flavor of her debut album Stranger in The Alps, but goes much bigger and broader with electric, kinetic climaxes. 10/10, this is one I’ll be listening to for a long time.

In The Mood for Love. Wong Kar-wai’s 2000 romance is about the things not said, the touches never felt. There is a reason you’ll find this on many “Best 100 movies of all time” lists—the praise it is given is well deserved. The cinematography and costuming is deliberately constricting, while the color just pulled something out of me. Frankly, romance is one of my least favorite genres, but after watching this, I might have to change my mind. A perfect movie.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q4 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

This is the most concerned about AI safety I have ever been. In case you missed it, a few weeks ago OpenAI announced that one of the new models they were training had broken containment and hacked the startup HuggingFace. This is bad! But also, not the worst thing in the world. However, this week we got more details that show a staggering level of failures that happened far before the HuggingFace hack. Quoting the excellent ’s summary of events:

1. OpenAI models-in-training, without the excuse of ‘they were doing a cyber eval,’ created a message board where they shared information on how to hack and cheat, and were trained on that basis.

2. OpenAI only figured this out when the models crashed the server.

3. OpenAI’s response was to rebuild the server and patch that particular exploit, but they continued training the models that trained using the message board.

4. Those models then recreated the message board, hacked OpenAI again, got internet access, and used an agent swarm to attack HuggingFace in order to get the answers to a cyber evaluation.

It is worth reading his whole post on the topic if you want to go deeper. The summary is that these models outsmarted the people training them, then a failure of corporate governance—exacerbated by the pressure of “grow fast”—made them just continue to use the misaligned models. Bad, bad, bad.

I still don’t fully buy the AI autonomously kills everyone scenario that much of the internet concerns itself with. The much more likely disaster is bad actors using openweight models to synthesize bioweapons (if that sounds like sci-fi, the Arc Institute synthesized new viruses using AI just this week). Alternatively, I think there will be major cybersecurity failures such as we have seen here but instead of HuggingFace it’ll be utility companies. (Is now a bad time to mention that Iran has hacked 12 states’ water systems?). This is not the place to discuss solutions, but these types of conversations are why this publication exists. It is time for you to take AI seriously. For your career, and for your future. We will see examples of that this week with robotics, turning ourselves into training data, and Stripe’s bold new acquisition.

But first, this edition is brought to you by Span.

Your engineering team is “all in” on AI. Congratulations! You just doubled your cash burn rate. But has all that spend actually made your product any better? Is the AI code you are generating useful?

Span is measuring the gap between AI adoption and AI effectiveness. To figure it out, they’re running a four-minute survey to build a new industry benchmark.

Fill out the survey and they’ll send you a killer report on where your team’s AI effectiveness lands relative to everyone else’s. Vibes are a bad way to gauge your own progress!

Fill it out via the link below to get the report.

Take the Survey

Airtable was worth $11.7 billion but sold for a tenth of that. The one-time SaaS it-girl agreed to be acquired by Bending Spoons for $1.285 billion, an 80%-plus haircut from its 2021 peak. And the metrics aren’t even that bad! It had roughly $480M in ARR, growing more than 20%. So what the hell happened? And will this level of value destruction happen to every SaaS company? Read here.

I’ve tried every AI personal assistant and churned off all of them (until now). Town is the chosen one, the mega-mojo, life-changing AI big ripper that has meaningfully transformed my life as a founder. I interviewed the team on why they spend about $100 per user building a personal wiki about you and how they made so many small moments of delight. Town even accomplished my white whale of productivity, a self-maintaining to-do list. Here’s what actually happened when I used it, and where I think it could still break. (I also got y’all a discount on it if you read to the end.) Read here.

If you’re not paying for the tokens, you are the tokens. Meta released a new coding model called Muse Spark 1.2 with a coding agent called Muse Code. It is roughly one rung below the frontier model for a pure power perspective, but they are doing a remarkable innovation in pricing.

There are two tiers. Standard costs $1.25 per million input tokens and $4.25 out, ordinary near-frontier pricing. The second tier costs $0.10 in and $0.20 out, if you grant Meta permission to train future models on your prompts and completions.

The company that perfected “the product is free, you are the ad inventory” for attention markets is now trying to run an identical play for intelligence markets. Interestingly, you could also view this move as an RFP to data vendors. If you invert the math, a million output tokens of coding-agent telemetry is worth at least $4.05 to Meta. This is all a long way of saying that Mark Zuckerberg is a ruthless and effective founder who will use his cash flow to force his way into a market. Here he comes for coding.

Stripe hopes inference is as boring and complicated as payments are. The payments giant is in exclusive talks to buy OpenRouter for roughly $10 billion in cash and stock. For my readers with a memory for deals, you’ll note this is about 7.7x the $1.3 billion valuation set ten weeks earlier. The Information reported that OpenRouter is at ~$140 million of annualized revenue, which makes this a 70x revenue deal for a company that takes 5% of the inference revenue flowing through it. That is…pretty expensive. To justify this you have to believe two things simultaneously:

First, just about every piece of hardware and software would benefit from inference, and we sit on the cusp of a massive explosion in tokens. While large companies will figure out what models work best for their needs, everyone else will need help. Here, OpenRouter can help by “routing” their intelligence needs. In practice, that just means helping applications use cheaper models when the output is routine. But whether an output is routine is so application-specific that only the application can really answer it. Think of it this way: what is good code? Code that works? Code that makes money? Code that is simple?

All these dimensions of quality are things generic routers can do an ok job at, but are typically better determined by the application. It is why every venture-scale app is building routing in-house: Cursor trained its router [disclosure, current sponsor] on requests from its own editor and claimed 60% savings while Ramp ran its router across 2.75 trillion tokens a month of internal traffic before giving it away free. Today, routing quality is downstream of harness capability.

Second, for everyone below venture scale, an autorouter is probably fine, the same way Stripe Checkout is fine for everyone who is not Amazon. Stripe’s bet is that inference becomes a metered utility where you can add margin by layering in payment flows. Metronome, which Stripe bought for ~$1 billion in January, meters the tokens in. OpenRouter pays the tokens out. Stripe sits on the money in between. So they can theoretically double or even triple dip on inference revenue. That’s a pretty compelling vision that is worth spending 70x revenue for.

The humanoid robot IPOs are finally happening. Unitree is planning to sell 10% of itself at 150.80 yuan a share, a $904 million raise at a $9 billion valuation, listing on Shanghai’s STAR Market around August 19 after demand pushed the raise 45% above plan. The prospectus gives us the first audited look inside a humanoid business, and it describes something no American competitor can claim—a company that actually works! Unitree’s revenue went from $24 million in 2023 to $253 million in 2025, at a 60% gross margin, with actual net profit, selling robots at an average price of $24,700.

Over the last twelve months, I’ve banged this drum over and over, but let me do it again. We are at the GPT2 stage of robotics. The demos are compelling enough to show that scaling laws will most likely work. Meaning there will be humanoid robots, in some form or another, coming over the next five years.

If you hold that belief, the natural question becomes how can you actually make the robots to scale? So far, the answer has been “go to China and ask Unitree to do it.”

The humanoid robot leader is AgiBot, which rolled its 15,000th robot off the line June 28 (that count includes its wheeled and quadruped lines; pure humanoids were 5,168 last year). Unitree shipped 5,511 humanoids in 2025 and is building capacity for 75,000 a year. On the American side, Figure manufactured its 1,000th Figure 03 on July 23. The same day, Tesla told investors the Optimus line was still being installed and the ramp would be “quite flat and long.” Agility’s SPAC filings disclose a $300 million order book and zero unit counts. 1X has zero verified customer deliveries though they are planning to ramp up manufacturing over the next twelve months. If you are generous, you could say that every American company in history has built roughly 3,000 humanoid robots. AgiBot built more than that since Memorial Day!

This is obviously bad if you believe my GPT2 thesis. It is also why the FCC banned imports of new foreign humanoids, naming Unitree directly. The policy recycles the playbook we used on drones, forcing a security review or automatic blacklist. It didn’t work for drones and this policy won’t work for robots either. America is roughly 13.3% of Unitree’s revenue, and yet the IPO was formalized two days after the ban, and still priced 45% above plan. This policy ends up cutting American researchers off from robots while the production gap sits untouched. You cannot sanction your way to a healthy industrial sector! You have to incentivize the entire supply chain.

1. My current front-runner for best millennial writer is Hanif Abdurraqib. I’ve shared his essays before (his one on Fall Out Boy will rip your heart out). Lately, I’ve been working my way through his most recent book There’s Always This Year. His words have this aching poetry, a mournful rhythm, that propels you from exquisite phrase to exquisite phrase. Reading him is a reminder of why this medium is one of humanity’s crowning achievements. Go buy the book and bask in the beauty!

2. These two very sweaty British rockers got my ass shaking and toe tapping.

Last thing. Does anyone have a recommendation for an absolutely breathtaking breakfast burrito in SF? I’ll be in town for The Leverage Launch on Wed and Thursday and am craving some proper Mexican food. Let me know!

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q4 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.