My plan is to sit on a beach, eat french fries, and devour a few books. Got any good reads? I’m on the hunt for something that’ll blow me away. Respond in the comments with what you’ve been loving lately.

And with that, back to our regular programming.

If AI decreases the cost of making products, then owning distribution becomes immensely valuable. This is a well-established theory that the tech elite is now implementing at scale. OpenAI bought TBPN. Every VC under the sun is overpaying a burnt out ghostwriter. Buying influence is logical, rational, expedient. The buyers often even argue for views I agree with. It also, quite clearly, sucks. Tech media should not be a corposlop firehose owned by the Patagonia vest class it is supposed to cover.

Even worse, we are all living in the same arena as this stuff. If you are a founder, you launch into feeds your competitors pay to own. If you are an investor, the best deal flow goes to whoever built an audience first. If you are an operator, your next job comes from being known for something. Distribution stopped being a media-industry problem the day intelligence got cheap. The only durable counter is the thing the vests cannot purchase: an audience that trusts you specifically.

I have spent five years building one. The Leverage pays my bills, and I would burn it to the ground before sacrificing editorial independence. The most common email I get is some version of “how do I do this,” so before I disappear to the beach, the Blogger Mogger, the Scribbler in Chief, His Holiness of the Hot Take, aka yours truly1, will write down the operating principles of a one-person media company, earned with gallons of my blood and sweat.

Here are the ten things you need to know if you want to build an audience money can’t buy:

1. “Being a writer” is a made up job: Saying you are a “writer” is like a homebuilder saying they are a hammer. Writing is a tool. Being a writer means you have a tool you are skilled with, but the tool is only as valuable as the use case. Typically when people say they want to be a writer, they actually mean they want the benefits of publishing. They want influence or they want power or they want money in exchange for being creative.

2. You are not DFW: While there are some writers who are so transcendent with their prose that they can cover whatever they want, you are not that guy. Sorry. It is ok though, I’m not either! As such, if you want to write for a living, you have to think more holistically about what your writing is meant to accomplish for the reader. Too many writers think their job is the form versus the function. Unless you can write Infinite Jest, stick to being primarily concerned with function. Form is a bonus.

3. Market is the most important thing: A few weeks ago, I went to a creator conference in LA. There, creators in niches ranging from “how to flirt” to baseball talked to me about their audience affinity and monetization struggles. Despite having audiences 100x the size of mine, they frequently struggled to make the money work. My life is infinitely easier because many of my readers control millions (or billions) of dollars of budget. As you think about what you want to write about, increase your monetization potential by selecting valuable topics that are underserved. Here are some tech markets today that are dramatically undercovered and where someone could build a $500K a year salary for themselves with about three years of hard work: GLP-1s, home robotics, AI-enabled private equity rollups, AI and politics, stablecoins, AI-enabled vertical SaaS. It isn’t easy, but someone will do this, so why not you?

4. Marketing is the second most important thing: When I first started this career, I had the romantic notion that I could just publish great work and the audience would come. Dumb. Dumb dumb dumb. Packaging, positioning, and promotion are far more important than the content itself. If you don’t get those things right, no one will even find your essay. If you want to write something that moves the world, you have to start from the marketing and then work backwards. For example, if you are writing YouTube scripts like I have been recently, you have to start from the thumbnail and work backwards. Blog posts are trickier—your marketing is mostly the social media posts you and your readers make about them. How can you tweak your presentation of your idea such that people want to share it? That is not always the same thing as being able to make something of high quality. This, at least partially, explains why AI slop is becoming more dominant on Substack. The writing isn’t what is driving the revenue, the ability to get lots of eyeballs on it is.

5. You want it to be expensive: Independent writers can stomach political costs that are unbearable to corposlop. Another way of saying this is that great writers publish work that costs them future job opportunities. Writing is, at its core, an engaging way to argue for a worldview. That worldview needs to be spiky. The more mayo-flavored the argument, the more blasé and bland it is, the less likely readers are to care. Independent writers’ biggest competitive advantage is they can happily piss off the powerful and say truth too expensive for the regular person to say. That’s what makes the internet beautiful! There is nothing that stands in between me, the writer and publisher, and you, the audience.

Read more

In the last 8 days, I’ve flown from Boston to LA to NYC to Utah, all in the pursuit of an answer to this question: what is scarce in a world of AI abundance?

In LA, I went to the creator conference Press Publish. I met everyone from a dude who makes aesthetic smoothies for 1.8 million followers to a channel called Jomboy that is so influential in baseball that the MLB invested in it. Despite the billion-plus fans those 500-odd creators represented, there wasn’t a single person I met who said “we got this.” We’re all in a constant battle to get our work surfaced to our fans. Add in the changing cost structures with AI, and at the event you get a bunch of overwhelmed people trying to make it in a very hard business.

In NYC, I went to throw The Leverage’s first live event. (Thank you to all of you who came and gave of your scarce time.) The rest of the week was spent listening to the stressed venting of founders with thousands of employees or the oozing, schmoozing confidence of VCs writing billion-dollar-checks. Because I’m an outsider, just a “dude who aggressively blogs,” as someone put it this week, I used to view my distance from these types of folks as a competitive advantage. And in some ways it is! My writing has never been subject to the groupthink of NYC or SF. Still, in my 4 days in NYC, I got more quality sources and meetings than I had in the last 6 months of attempting the same work remotely. A handshake with the powerful opens up an incredible amount of opportunity.

Then as I came to Utah to pick up my kid. When my daughter saw me, she snuggled into my neck, and laughed and laughed and laughed as I tossed her in the air. Here, in a quiet town nestled among the red rocks, I felt like I had found the scarcest beauty in the world, that of her smile.

My trips have shown me that the scarce thing in a world of saturated screens is the beauty and value of human connection. So naturally, the inner capitalist in me wondered, is that actually a business? More on that in a sec. But first, this edition of The Weekend Leverage is brought to you by HighLevel.

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Claim It Here

MY RESEARCH

We are the minority of the internet now. This week Cloudflare announced that bot traffic passed human traffic for the first time in the web’s history. 57.4% bots to 42.6% humans. Nearly every economic assumption of the internet was built on the assumption of human users. For example, one reader built a Claude Skill of me and now uses it every day to complete their job. I have some thoughts on what this new internet does to everyone, and where the money goes next. Read here.

WHAT MATTERED THIS WEEK?

THE TOKENPOCALYPSE

Would you bet $10M on your own product? Cognition, an AI coding company, announced that if its product delivers less engineering value than an enterprise customer is paying for, Cognition will fund that customer’s usage until it does, up to $10M. I sat down with several AI-native company founders selling into the enterprise at NYC Tech Week, and every one of them complained to me that their customers have cost concerns around tokens. And because it is incredibly challenging to measure developer productivity, customers were worried.

This effort from Cognition is obviously in response to that feeling. To measure productivity, they built an estimator agent that reviews every completed Devin session, classifies whether it produced useful output, and then estimates how many hours a human engineer would have needed to produce the same work. Near the end of the annual contract, those hours get converted to dollars and measured against what the customer actually spent. Fall short, and you get credits. Cognition is essentially writing a performance warranty on its own product and self-insuring the downside. Interestingly, they didn’t actually publish what percentage of their customers would qualify for this policy today, so I have to assume that it is incredibly conservative, and that this is at least somewhat a marketing salve versus a true insurance policy.

The Leverage morally supports the existence of incredibly boring industries, and insurance is one our favorites. The mark of a healthy, stable market is always multi-layered insurance products because that means the risk is well enough understood to underwrite. Just in April I wrote an entire edition on the very topic of AI insurance policies! That Cognition had to underwrite Cognition’s product sounds bullish at first (they are putting their own money on the line) but it is actually bearish (this is so unproven/unstable that no one else was willing to take that risk on).

The reason this matters beyond Cognition is that the whole AI industry is going to have to contend with the collapse of the cheap token narrative. I expect that the rest of the coding agents will match this offer within 9 months. Interestingly, because both Anthropic and OpenAI have general-purpose-products versus a use-case-specific-application, I don’t think they’ll be able to offer the same insurance to their customers. Another point for the GPT-wrapper team.

BIG RUSSIA

Washington wants to own the AI labs. Senior Trump officials held early talks with the major AI companies about the government taking equity stakes, per a NOTUS scoop on Thursday that the Journal picked up. Bernie Sanders wants to go further and seize 50% outright. I told you on March 1st that “politics is business strategy now.” This is what that looks like 14 weeks later.

We’ve done this before with Intel. In August 2025 the administration converted $8.9 billion of already-promised CHIPS Act money into a 9.9% stake. The President of the United States is now posting share-price updates like a crypto-bro in a Discord pumping his own bags. That is the offer on the table for the labs. Surrender a slice of equity, get anointed, then watch the most powerful man on earth talk your stock up.

I do not want companies handing over portions of their equity as a bribe to get crowned as nationally favored giants. The government’s job is to tax the winners and the losers on the same terms, and then to push these companies to go public sooner so the American public can buy in and day-trade the AI boom themselves if they want to be degenerates. We should call this what it actually is: Zesty socialism with a sprinkle of corruption. (I apologize for getting more political than usual, but I can’t believe it has come to this. Utterly ridiculous.)

THE SLOPPENING

Is AI music slop? Suno is the most popular AI music maker in the world, and it just raised more than $400 million, at a $5.4 billion valuation. This valuation is more than double the $2.45 billion they were at 7 months ago. This is one of the very few AI breakouts in consumer, so it is worth examining the actual metrics. It has roughly $300 million in annual run-rate revenue, spread across more than 2 million paying subscribers. At $5.4 billion, the market is paying about 18x revenue to own a slice. For comparison, Spotify trades at about 4.75x revenue multiple. This is a rock-solid business, with a hefty premium being paid for it.

My working definition of slop is that it is content that makes you stupid. It doesn’t matter if the content is AI-generated or hand-crafted by union members. Stupid is slop and slop is stupid. And in this case, I would argue that Spotify is the slop company, not Suno. Suno is a destination product that proudly proclaims it is AI. You go there to make AI music; there is no ambiguity. Spotify is the inverse. I go there to listen to art, hand-crafted by people I admire, but now it’ll quietly surface AI tracks into the feed when you play music in the public domain, or is instrumental (because it is cheaper for it to do so).

That is the real Sloppening, and it has almost nothing to do with the machine that writes the song. The rot is the distribution. AI you never asked for, surfaced inside a catalog you trusted, wearing no label. I have met people on the Suno team. They struck me as genuine lovers of music who also happen to be tech nerds, and are trying to use this tech to help more people love music. If you haven’t, I recommend trying it out. It is fun to make stuff!

TASTEMAKER

Two recommendations this week:

1. Radiohead’s Motion Picture House: On Thursday night I went and checked out this art installation/movie from the British rockers. I kinda just expected some remixed Radiohead music, but the band swung for the fences with a challenging yet cohesive reimagining of what their music could look like across a variety of media. Highly recommend it. Currently in Brooklyn, moving to Chicago, Mexico City, and SF later this year.

2. Ragtime is the greatest musical I’ve ever seen. On Wednesday night in NYC, I caught this show which is nominated for 11 Tony Awards. To be transparent—I don’t even really like Ragtime as an artistic construction. The music is a little simple, the storyline heavy-handed, and the writing a tad formulaic. Just about everything is a little pedestrian for my taste. But the execution by this cast and crew is so undeniable that Ragtime’s flaws didn’t matter. Every single aspect of the show is being done at the highest level, and Joshua Henry, the male lead nominated for Best Actor at the Tony Awards tonight, is a force. (Watch him sing some classic musical theater for a taste of the best vocalist in NYC today.) Y’all should see it ASAP. I counted 7 standing ovations during the show and 2 songs that made me cry.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q3 ‘26. If you are interested in reaching my audience of 34K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

This week, the CEO of Cloudflare announced that bots had passed human traffic for the first time in the internet’s history. By their count it’s 57.4% bots to 42.6% humans. We, by which I mean the human readers of this essay, are now digital minorities.

Many of the economic assumptions of our world are constructed on the basis of humans being the ones who look at, and operate computers. Facebook sells ads because they have eyeballs looking on websites they control, and the brains behind those eyes are convinced to buy stuff with the ads they show. Content providers like me put stuff online assuming we can monetize a reader relationship through subscriptions or ads. This has done enormous good (independent media companies like mine can exist) and enormous harm (engagement, rather than truth, becomes what systems like Instagram’s algorithm optimize for).

In our current internet, like as of the moment you are reading this, that old economic equilibrium is kaput. Irrelevant. Toasted and buttered bread. Aka, cooked. AI agents search differently, they use the internet differently, and most importantly they monetize very differently.

For example, if you wanted to read about the SpaceX IPO, you as a human being might have an hour to scan this publication’s deep dive on it plus a few other articles. If you ask an AI agent to tell you about the SpaceX IPO it’ll scan 50-100 essays on that topic, synthesize them somehow, and then give you the results. My essay would likely be in that mix! But because the agent acts as a digital intermediary between me, the company producing the good, and you, the potential user, I see no revenue. Worse, I have no long-term relationship with you. My top of funnel shrinks and my revenue is materially worse.

This, in many ways, is fantastic for consumers. While an AI would not produce an analysis as good as mine today, it might in the future. In the meantime, the AI will get some stuff right and is much cheaper than a subscription to The Leverage.

If you amplify that same process across all the providers of information or services with digital components, the end result is a radically different type of internet.

I think that change will manifest in two ways:

1. Skills

2. Economics

Skills

A few months ago, I met with a reader who said he had built a Claude Skill of me.

He told me about it at a dinner party, in a nonchalant sorta way, similar to mentioning your newest productivity hack. What he did was simple. He took my published essays, ingested them into a Skill, and started asking it for advice as if it were me. With a sorta embarrassed laugh, he told me he used it every day.

Frankly, I flipped back and forth between strangely honored and furiously pissed off. He hadn’t stolen anything. My writing is published online. Shoot, he is even a paid subscriber! He examined everything I’d published and built a tool that replicated the value of consulting me at zero marginal cost. He paid Anthropic a few cents in inference while giving me nothing. There was no theft! He simply did what the new physics of digital business allows anyone to do. I was annoyed because it still felt like intellectual identity hijacking.

Large language models can essentially do two things—understand a bunch of text and then produce something in response to that text. In the case of the internet that means they can ingest all sorts of information, then construct documents, software, or workflows in response to that. So, if you put enough of your expertise online, you can theoretically just be made into a Claude skill like I was.

This is only a problem if that creation of the AI is a fully substitutable skill for the products you offer. So if your offer is something relatively simple such as “I’ll write you a feature description” you are in trouble. If your offer is “I will completely rework your comms strategy and narrative positioning, and as part of that, write a feature description” that is wholly different.

Essentially, if the majority of your solution lives online, and it is relatively simple, the skill it took to create that thing is radically less valuable now than it was 12 months ago. Sorry.

Economics

Last fall, on Tyler Cowen’s podcast at the Progress Conference, Sam Altman said something spooky:

“Margins are going to go dramatically down on most goods and services, including things like hotel bookings. I’m happy about that. I think there’s a lot of taxes that just suck for the economy, and getting those down should be great all around. I think that most companies like OpenAI will make more money at a lower margin.”

The key word in his argument is “most.” When an agent sits between you and your customer, the dollars that used to flow to whoever owned the relationship now flow to whoever owns the agent and the rails it runs on. On the old web, your content or software was about forming a long-term relationship. A reader subscribed, the relationship compounded, and you monetized it for years with additional products. On the agent web, your content is an input. It gets retrieved, compressed, blended in a Vitamix with fifty other sources, and handed to a user who never learns your name. In this transaction, there are three parties doing the work:

1. The compute doing the reasoning

2. The search layer fetching the inputs

3. The content itself.

Of the three, content has the least pricing power, because the agent can create a substitute.

The proposed economic fix is to tax the agents. Every time an agent references a source, the source gets paid at the moment of consumption, instead of waiting on a human who may never arrive.

I interviewed the two companies furthest along on this. The vertically specialized version is Dripstack, built by Michael Blau, an ex-a16z crypto investor: an agent pays five to ten cents to access an article, settled instantly over stablecoins, so micropayments too small to bother with normally become economically viable. “People are doing [scraping with agents] anyways and they’re doing it for free,” he told me. “You might as well get paid while that happens.” I started using the service on May 5th. About 30 days later, the results are, uh, how to put this…about 40 cents short of a McDonald’s ice cream cone. 20 agents have scraped my work for total earnings of $1.60.

The infrastructure version is Parallel, the agent-search company from former Twitter CEO Parag Agrawal, now valued at $2 billion. Where Dripstack asks creators to opt in, Parallel already occupies the data chokepoint. Their service is an API that agents call to read the web, which means it can meter every access and divide the take across all three contributors using something called Shapley values. This would pay each contributor for the marginal value they add, averaged over every possible combination of the others. In Parallel’s world, compute (AI agents and the infra powering them) get a slice, Parallel gets their pound of flesh, and then creators of content/software get something too. Both of these companies are doing something noble in trying to get content providers paid, but it is noble in the way that the orphans were being fed in Oliver Twist.

However you divide this up, this is just materially worse for legacy internet businesses. The tax that actually clears is tiny. When I run one research query, it pulls from sixty or seventy sources, which means no single source can charge more than rounding error. In Parallel’s three-way split, content is structurally the smallest claimant, because it is the most replaceable input in the stack. Price tracks how much compute the buyer is willing to spend on an answer. Carra Wu, Parallel’s head of product for this use case, is careful about what that actually signals. “The cost of compute is a signal,” she told me. “It is not the truth itself. It’s the signal we use to say how valuable do we think this is.”

Even if a hedge fund trader pays $20 for a single deep research report on SpaceX, I would make about $11 net if he signed up for a one month subscription to read. And if these royalties never materialize, you get truth silos, where only the twenty publishers large enough to sign bulk licensing deals survive and the rest go dark. If they do materialize, content survives as low-margin feedstock. Neither outcome resembles the business you run today.

And to return to Altman’s point, this extends way beyond content. Software applications that are relatively simple can just be generated. So if a user tells their agent they have a problem that software could solve, today that agent will recommend a product. In less than 12 months, I am willing to bet the agent will just build the product for them. Or how about physical goods whose access is intermediated by the internet? I have many friends who used agents to buy tickets to Christopher Nolan’s The Odyssey in 70mm, one of the hardest things in the world to buy right now. Restaurant reservations, airplane tickets, signing your kid up for summer camp, all of them used to assume some degree of responsibility and friction on the part of users. Those are rapidly ceasing to exist.

And look, I’m not here trying to say the internet as it currently exists, is God’s gift to man. There are many problems around here. My point is that AI agents will introduce a radical amount of creative destruction for digital businesses and I have yet to find a good answer for why what comes after is better at producing truth or goodness for the world.

It is tempting to read all of this and conclude the internet is dying. Instead, we are entering a world of new centers of power. On an AI-first internet, attention is no longer scarce. Pre AI, the attention economy ran on ads and subscriptions because that was the way to monetize eyeballs. Agents have as much attention as there are tokens, which is essentially infinite. So, the constraint relocates, and wherever it lands is where new careers and businesses will be built. I have some thoughts on where that lands, that will come to paying subscribers very soon. Sign up below to receive it.

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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 TRAC, a venture capital firm where machines make investment decisions instead of people. If you have any feedback, just reply to this email.

If you wanted to build the perfect venture capital fund from scratch, you probably wouldn’t start with a 76-year-old former hedge fund manager, a serial entrepreneur whose biggest hit was an e-greeting card company, an ex-FBI agent, and a data scientist in El Paso. But this same group of people has had two consecutive top decile funds in a row. It’s worth asking how their thesis is paying off.

TRAC’s bet radically against Silicon Valley norms. They believe that the venture capital industry can be beaten with math. Typically when you hear a firm saying something trite like that, they mean using data as a supplement to gut instinct. TRAC means using math instead of people. Their models make the investment decisions, and the humans’ jobs are to convince founders to take the money the machine wants to give them. And somehow, it is working.

I had many calls with the co-founders over the last several months to figure out how. Here's what I found.

How the Machine Works

We should start with the data.

Many other venture firms employ data scientists. Some of them are even pretty good! But what they do is fairly, uh, droll. The data scientist will build a model, present it to the investment committee, receive a cookie and a pat on the head, and then the managing partner makes the call based on their gut. The data is a (mostly ignored) input for the human decision-maker.

TRAC doesn’t work like this. As their data scientist, Dr. Steve Marek — a former lecturer in biomedical engineering who came through grad school at UT Austin — put it to me: “Our data is our investment team.” The models decide, the people don’t. Steve told me he can’t even comment on individual companies because they’re just numbers to him. He doesn’t look at the product, the pitch deck, or the founder’s LinkedIn. He just looks at the score.

The closest parallel in financial history is Renaissance Technologies, where physicists and mathematicians who didn’t care about Wall Street’s traditions built quantitative models that crushed human stock-pickers for three decades. When I brought up Rentech to Steve, he didn’t flinch. His response was that not having preconceived notions about how venture is “supposed to work” is actually an advantage. None of TRAC’s team came up through the traditional VC pipeline. And so far, that has been to their advantage.

The foundation of everything TRAC does is a proprietary data infrastructure built over five-plus years. They pull from CB Insights, PitchBook, Crunchbase, SimilarWeb, and other sources, then merge them into a single source of truth. No individual database has complete or accurate information so TRAC has spent the last six years building their own taxonomy, cross-referencing and cleaning data until they had something wholly unique. The result is a unified dataset that merges over 650,000 investor entities, 1.25 million company entities, and 2.1 million deal entities.

That dataset is what makes everything else possible.

Fund I was the proof of concept. Joe Aaron, TRAC’s co-founder, had spent years manually combing through PitchBook data and noticed something: when he looked up the cap tables of companies that became massive, the same individual investors kept showing up. You’ll note how it is not always the same fund. It was the same *people*. He realized that there was an elite group of investors that no one had recognized. He brought in Steve to formalize those patterns. Fund I launched with two algorithms — SuperTRACer and the Investor Group Quality score (IGQ). About two-thirds of the way through the fund’s deployment, Steve layered in three more. It (perhaps surprisingly) worked. Fund I became a top-decile performer. Two of the first eight investments became unicorns. After nearly six years, Fund I’s loss rate is 7%.

Fund II deployed all five algorithms from day one, each filtering for a different dimension of signal:

The most important is the Investor Group Quality (IGQ) score. TRAC identifies 286 individuals globally who sit in the top tenth of one percent of early-stage investors, ranked by track record in specific industry sectors. So while Sequoia may have a great reputation, TRAC told me that only 5 of their partners qualify as top individual pickers by TRAC’s scoring. (Just because an investor sits at a prestigious firm doesn’t mean they are any good.) The core thesis for this algorithm is that it’s hard to fool several great investors at the same time. When multiple top-tier individual investors converge on a single company, that signal is predictive.

From there, companies pass through SuperTRACer and three additional algorithms covering profitability projections, market momentum, competitive positioning, and industry growth rates. Only 100 to 150 companies a year will clear all five thresholds. Of those, the models predict roughly 1 in 5 will become a unicorn and 2 in 3 will at least return invested capital.

Frankly, I went in a bit skeptical that this would work. But the results are compelling. The team told me that Fund II’s loss rate sits at 5%, down from Fund I’s 7%. Fund I saw 19% of its portfolio heading toward power-law returns. Fund II is at 38%. The math keeps beating the gut instinct of other investors.

The Scoreboard

Joe Aaron has a law he likes to cite. He calls it Menachem’s Law, and the logic is pretty simple: a startup either raises a follow-on round, gets acquired, or dies. There isn’t really a fourth option. If a company cannot attract new capital after its initial raise, it will go out of business. That makes the follow-on rate, what TRAC calls the “graduation rate,” the single most revealing metric for evaluating a venture portfolio. A good fund gets 50% of its companies to raise again. A great fund gets higher.

TRAC Fund I achieved 76%.

According to Preqin, there are 503 early-stage funds in the 2020 vintage. TRAC Fund I ranks #1 on the graduation rate among all of them. And that ranking holds no matter how you slice the comparison group: among funds with 46 or more investments, TRAC is #1 out of 30. Among funds with more than 10 investments, #1 out of 267. Thirty-five of 46 Fund I portfolio companies have raised follow-on capital. Companies that went out of business represent less than 12% of Fund I’s assets under management. The fund turns six in May.

Fund II is still early but the pattern is holding. Vintage 2022, 59 portfolio companies, 30 of which have already raised follow-on capital — a 49% graduation rate, with the fund’s final initial investment only completed in December 2025. Against Preqin’s 698-fund 2022 vintage universe: among funds with 59 or more investments, TRAC ranks #1 out of 16 on graduation rate. Among funds with more than 30 investments, TRAC sits in the 96th percentile — #3 out of 76.

So does the machine actually work? Beyond the loss rates, the aggregate numbers tell the story. Fund I’s 46 portfolio companies grew combined revenue from $48M to $309M over about three years. Fund II’s portfolio has grown from roughly $40M to $389M in half the time.

So if this all looks so good, why isn’t TRAC drowning in yachts and second homes in Tahoe? The problem is getting founders to meet with them.

The Distribution Problem

Most venture funds have the opposite problem from TRAC. They can get meetings. They have the brand, the network, and the outrageous parties up and down the SF peninsula. What they struggle with is picking. Their portfolios are full of pattern-matched bets that looked right in the pitch and went sideways in the market. TRAC has the inverse problem with a selection engine that works and a brand that doesn’t yet match it.

Perhaps most challenging is that competition to get into deals is built inherently into their model. If a company has multiple A-tier early-stage investors on the cap table, then they’ll have a much easier time raising a follow-on round. By nature of their models, every time they send in a term sheet, the founder will likely also have emails in their inbox from Kleiner Perkins, Founders Fund, Lightspeed, and every other top decile fund. That’s a tough lineup.

TRAC writes $250K checks at seed, $1M at Series A, and up to $3M at Series B — and never takes more than 20% of a round. They’re not trying to lead which means a founder has to make space for them after a deal starts to come together.

This is where Fred Campbell and Brant Meyer come in. Fred and Brant are TRAC’s two investing partners, the humans whose job is to convince founders to take the money the machine wants to give them. To help increase their hit rate, they pitch the founders a unique dashboard.

Over the last five years, TRAC has built a proprietary intelligence dashboard for portfolio companies that uses their investment models to generate valuation modeling, competitor mapping, investor overlap analysis, and what Fred calls “the do-not-call list” — flagging investors who’ve backed competitors and might be fishing for diligence rather than genuinely interested. Founders use TRAC’s data to negotiate higher valuations with lead investors. In at least one case, a lead investor backed down and accepted TRAC’s valuation number over their own.

Fred and Brant claim that if a founder agrees to a Zoom with them — just one call where they walk through the dashboard — TRAC gets on the cap table more than 95% of the time.

Nobody else at their stage offers this, because nobody else has spent five-plus years building the data infrastructure to produce it. The dashboard transforms TRAC from “random small fund writing a small check” to “the only investor who will hand you a competitive intelligence product on day one.” Distribution problems yield to time and evidence in a way that selection problems never do. If you can pick, then eventually, the brand will follow. To help speed it up, you could also sponsor dashing writers to examine your business in their newsletter. If you can’t pick, no amount of brand will save you.

Where I Think This Breaks

I’ll be honest about the risk I see, because I’ve written about it before.

I wrote a piece for Every called “Venture Capital is Ripe for Disruption,” where I argued that the fundraising environment has become structurally hostile to new venture capital firms. Fund sizes have ballooned to the point where even billion-dollar exits are meaningless for the largest firms.

That structural pressure creates a gravitational pull toward massive fund sizes, because LPs want to write big checks into big funds that can deploy hundreds of millions a year. A fund managing $73M across two vehicles, no matter how good its returns are, sits outside the LP comfort zone. The institutional capital allocators at pension funds and endowments aren’t set up to evaluate a data science team. They want a name-brand GP with a billion-dollar fund and a 10-year track record. TRAC is five and a half years into a 20-year plan, and the structural forces of LP capital allocation are not on their side yet.

Additionally, the team is a bunch of outsiders, and in the clubby environment of Silicon Valley, that is a disadvantage that is hard to break.

What I Actually Think

After many conversations with this team, here’s where I land.

The established firms won’t adopt this approach because it would destroy the mystique. Joe Aaron, a cofounder, told me a joke: “The data scientist went into the investment committee meeting. He had only two questions from two partners. One wanted him to fix the printer and the other to help him with his Internet connection.” The industry isn’t ignoring quantitative methods because they don’t work. They’re ignoring them because acknowledging they work means admitting the magic is more fluff than fundamentals.

Three unlikely people in unlikely places built a machine that picks better than most venture capital firms. Now they have to convince everyone to care. Five and a half years in, with a #1 follow-on rate, a 5% loss rate, and a growing stack of unicorn receipts, the machine is making that argument harder to ignore.

Learn about TRAC

Her fever had rapidly spiked triggering something called a febrile seizure. These types of seizures occur in about 5% of children and are terrifying to witness, but ultimately harmless. I am very grateful to the good doctors at Boston Children’s Hospital who provided fast, competent care. Thankfully, everything turned out ok.

As I sat in the ambulance on the way to the hospital, I described my kid’s symptoms to Claude, used research mode to pull the relevant papers on potential diagnoses, and by the time we finished the 10 minute ride, felt reasonably confident that this was a febrile seizure. Our clinician put her through a battery of tests, but these all confirmed what Claude had already led me to believe.

I bring this up not as a pronouncement of how “AI will fix healthcare” or some other overly grandiose nonsense. Instead, the point I want to make is just how different that emotional experience was for me as a parent. If this had happened five years ago, I would’ve been in a state of sheer panic and would have made worse decisions for my daughter. Instead, I was (relatively) calm, engaged deeply with the doctor, and when the nurse suggested medications I was able to understand why those things were necessary.

If you’ve paid attention to any of my research over the last six months, you know that AI is now materially improving productivity for its power users. Individuals can build companies and company’s strategies can be more ambitious while staffing entirely differently. Increasingly, I’m interested in the second and third order effects of this trend. How will it make people feel things when life is intermediated by AI? What is newly scarce in a world of infinite digital abundance?

Most importantly, this world shifting technology is being disseminated by the most power hungry and aggressive companies in the history of capitalism. So in understanding how these organizations are trying to gain strategic power over their rivals, we can forecast what will happen to the rest of us peons.

At Google I/O last week, they had over a hundred announcements, many of which have large implications which I’ll hopefully get time to discuss with y’all over the weeks to come. The comment that really mattered though was made by Demis Hassabis, the co-founder and CEO of Deepmind. He stood up at the end of that whirlwind of releases and said that we were at the “foothills of the singularity.” His public belief, and the one that a 4 trillion dollar company is devoting the entirety of its resources to make happen, is that by 2030 we will have AGI. This event, if it happens, will change our role in the cosmos. In human history there would only be a before and after for this. That Hassabis’s statement was made following an event where they announced stuff like a shopping cart that activates while you read your email and AI clones of yourself you can drop into YouTube Shorts feels, let’s say, tonally inconsistent.

It’s the grand absurdity of all this AI for the last few years. It is saving me from a panic attack on my way to the hospital. It is filling the internet with slop. Every week I read of breathtaking scientific advancements, and so many of their most popular uses have been as ways to distract us and have us buy more useless shit.

In a normal Weekend Leverage I would tell you about the hot news of the week. There was lots to talk about with SpaceX and Google and a million other things. But frankly, it felt a little hollow today. I have spent a lot of words in this newsletter on strategy, on power, on who is winning. I will keep writing about those things because they matter. But those things do not matter for something as crass and hollow as money. They matter because the technology we are all building is real. It is powerful enough to calm a father on the worst and most expensive Uber of his life.

If we truly sit at the foothills of the singularity, which I am increasingly convinced that we are, then we get to choose which peak in that mountain range to climb. AGI means it is powerful enough to do almost anything we point it at — and most of what it is currently pointed at is garbage.

If you are reading this, you are almost certainly one of the people who gets to decide where our sector goes next. One of the benefits of being read by the rich and powerful of the world is that I get to tell you all something you need to hear. We should be building something better. We have one shot at this technology becoming what it could be instead of what is easiest to monetize, and I would like us to take it.

SpaceX the company will go down as one of the most important companies in human history. It’ll have reignited humanity’s multi-planetary ambitions, making us a spacefaring species.

SpaceX the stock will go down as one of the most offensive IPOs in the history of capitalism. It is a steaming pile of spectacularly stupid assumptions and financial piracy.

All of which is to say, I am anxious to buy the stock.

Read more

So the natural followup is what are those choices. I’m putting together a room of 50 founders and senior tech operators to figure that out for The Leverage’s first-ever live event. I’m calling the gathering “How to Automate Everything.” It is happening June 3rd in NYC.

To guide the discussion, I’ve put together a killer panel.

Both Andrew and Ben are heavy users of AI while also working on two of the most important yet boring topics on the planet: taxes and expense tracking. Andrew is the founder and CEO of Taxwire, building the “last global sales tax solution your finance team will ever need.” He’s raised $25M, including a recent Series A, and has 25 people on the team today. Ben is the Head of Internal AI at Ramp, where he’s AI-pilled over 1,000 employees and helped transform the company’s internal culture.

It’s one thing to use AI agents to spin up some sloppy code. It’s a wholly different thing to let them touch money. I’m excited to dig into how agents transform a company from startup to scale-up.

Following the panel and Q&A, there will be a happy hour with all the Diet Coke, booze, and charcuterie you could possibly desire on a Wednesday night. There will also be the first-ever merch for The Leverage. And if that isn’t enough, there will be a giveaway of a book I think anyone who cares about AI should be reading.

The cost? Zilch! Priority access goes to paid subscribers of The Leverage.

I can’t wait to meet you all. You can RSVP below.

RSVP Here

[An earlier edition of this newsletter had an incorrect link—that is addressed in this email. My apologies!]

I spent Wednesday watching Cloudflare’s stock drop 24% on a record-revenue quarter and trying to figure out what to do with that. The CEO had just told the world that AI made 1,100 jobs obsolete, and the market — which is many things but rarely subtle — basically said “sure, but it kind of looks like you don’t know what you’re doing.” The whole week looked like that. Public company after public company blaming AI for what walked and talked like the cost structure of the SaaS era catching up to itself, while in the next room Sequoia and Kleiner are shooting off nine-figure checks to pre-product labs with nothing but a polycule and a dream. It is a lot of weather happening at once. The title of this email is approximately how it felt.

I’ve spent enough time watching Claude flail in random codebases to know agent quality varies pretty wildly. One minute your AI feels like the next coming of Steve Wozniak, the next it seems to enjoy the taste of lead paint. The difference in AI performance comes down to the technical stack you give it.

Convex is one of the stacks where agents actually fly.

Their product gives agents tight feedback loops, lets them reason locally instead of holding the whole system in their head, and strips out the fluff that eats half an agent’s context window. I’d add a fourth reason: it scales without melting your wallet, so what your agent built last night won’t fall over the moment it gets traffic.

If you’re building anything agentic, or just tired of fighting your backend, try Convex.

Try Convex here!

MY RESEARCH

Is AI slop actually bad? I hate AI writing. So I decided to be analytically rigorous about my hatred. I ran 3,229 posts from the top Substack publications through and the results made me reluctantly question my own thesis. 25% of the Business leaderboard is AI-generated! Two of the top ten are fully synthetic! I have some thoughts on what that means for every business whose product is "we will tell you something you didn't know," including this one. Read here.

WHAT MATTERED THIS WEEK?

BIG LAYOFFS

The arithmetic of panic. Seven major tech companies announced layoffs this week and yes, every single press release said it was about AI.

The combined headline number — at least 7,300 confirmed positions, plus the leaked 12,000–15,000 at Cognizant — would be the story in any normal week. That is a lot of people suddenly unemployed! And yet, this wasn’t a normal week.

All of them, PayPal, Coinbase, Freshworks, Cloudflare, and Upwork sold their reductions as a strategic pivot — “we’re shrinking because we don’t need these people anymore.” Cloudflare’s CEO said internal AI usage grew 600% in three months and that back-office support roles “aren’t going to be the roles that drive companies going forward.” Coinbase replaced “pure managers” with “player-coaches” and capped management at five layers below the CEO. Freshworks’ CEO admitted, on a quarter that grew 16%, that more than half of the company’s code is now written by AI.

The market, polite as it ever is, resoundingly said “bullshit.”

Only BILL got rewarded with an 11% pop. Though my read on it was that the announcement on a $1B buyback was what actually drove the positive swing. Cloudflare, in contrast, got destroyed. Down 24% on the same day it reported 25% year-over-year revenue growth and a record quarter.

Investors just don’t believe these narratives. Or at least they don’t believe in what the layoffs are actually signaling.

The reason every CFO in tech is staring at AI is because of this chart. Cursor crossed $2 billion in ARR with roughly 400 employees. That is $5 million per employee — and Cursor didn’t have to cut anyone. It is among the fastest-scaling B2B software company in history. To truly be called an AI Native, you just gotta grow incredibly quickly and efficiently at the same time.

Even after this week’s reductions, none of the incumbents come close. PayPal — the best of the layoff cohort — runs at $1.75M per employee, roughly a third of what an AI-native does.

The gap in RPE is why the layoffs aren’t being rewarded. Every CEO on these earnings calls is staring at a cohort of competitors that ship faster, scale cheaper, and compound at multiples bigger. The cuts are an attempt to close that gap but my sense is investors are looking for increased growth rates most of all. You don’t get to $5M revenue per employee by firing 20% of your people. The company has to be structurally built differently.

This week’s moves were a cohort of public companies admitting, on the record, that the cost structure they built for the SaaS era is no longer competitive in the AI one. Whether you believe the AI excuse depends on whether you think any of these companies can actually rewire themselves into AI-native operators after the fact.

BIG TOLLBOOTH

The best way to be an AI native? Don’t even try. This week SAP has moved to ban unapproved third-party agents from its platform. They join HubSpot, Workday, Salesforce, and most recently ServiceNow as the companies locking down and/or monetizing access to their systems of record. What’s really weird is that they won’t really publish the price? And it’s near impossible to forecast?

Credit to the select few going outcome-based: HubSpot’s Breeze only bills when the agent actually resolves something, so a buyer can map cost directly to value delivered. The credit systems like SAP’s AI Units, Workday’s Flex Credits, ServiceNow’s per-action meter — go the other direction (that direction is down to pricing strategy hell with the rest of the McKinsey consultants), where every vendor invents a different unit, several refuse to publish a per-unit rate at all, and consumption per task is buried inside the contract.

“Replace employees with agents” only works if the agent can sit at the employee’s desk — and for most of corporate America, that desk is SAP, Workday, ServiceNow, and a CRM, not the new age AI-native productivity tools that support AI Agents. A regional insurer or a mid-sized industrial Workday plus ServiceNow doesn’t have the option to “just migrate.” Those projects take three to five years when they’re funded and prioritized, and the vendors like it that way.

BIG RISK

Here’s to the investors trying to back science experiments. On Monday I got on the phone with Justin Mateen, the Tinder co-founder turned solo GP, who is a seed investor in Subquadratic. The next day, the company came out of stealth claiming a 1,000x reduction in attention compute and a 12 million token context window. They raised $29M at a reported $500M valuation on the strength of a math claim that essentially nobody who wired the money can independently verify.

Mateen told me he had “never been this excited about a pre-launch company,” which means something different coming from a guy backing the same CEO for the third time than it does from some generalist VC underwriting the second coming of Theranos. It’s an interesting bet because you’re just kinda hoping that the team you’re backing is like, smarter then everyone else on the planet and can invent something out of nothing.

Obviously that’s untenable, so investors are laundering the illegibility of science into proxies they can read. Mateen’s pitch to me had four of them stacked like a Jenga tower.

• Proxy one: the team is “11 PhDs from Oxford, MIT, and Cambridge,” which is pedigree-as-proof.

• Proxy two: he’s backed CEO Justin Dangel twice before successfully, and Dangel literally shut down his own fund to take this job, which is operator loyalty doing the work of due diligence.

• Proxy three: the frontier labs “gave up on sub-quadratic approaches a few years ago,” which is the slick rhetorical move of reframing the absence of validation as an outsider advantage.

• And proxy four, the tell: an acquisition by a frontier lab “could justify 25% of the company” pricing. Investors are essentially pricing the call option that the architecture might be real.

I have no idea whether this science is real. I have a sociology degree. I can talk to you all day about Weber and Durkheim, but ask me whether sub-quadratic sparse attention scales linearly and I will simply nod and start to politely fall asleep. But there are lots of these types of bets happening in neolabs. The Information estimates that more than $10 billion has now flowed into the neolab category, with $2.5B deployed or discussed across just five rounds in a single month last fall. Ex-Anthropic researchers at Mirendil are negotiating $175M at a $1B valuation with a16z and Kleiner co-leading. Isara closed at $650M on a multi-agent thesis with OpenAI itself on the cap table. And the rumors I am hearing are that there are a few more pre-product labs in the market right now at unicorn-or-better seed pricing.

Still though, I kinda love this? Venture capital used to be called “adventure capital.” Backing wild science experiments on the basis of people’s vibes is much cooler then funding yet another B2B SaaS company.

TASTEMAKER

An essay that troubles me. I’ve been a long-time reader of Anthropic Co-founder Jack Clark’s newsletter. In his May 4th edition, he published an essay arguing that “there’s a likely chance (60%+) that no-human-involved AI R&D - an AI system powerful enough that it could plausibly autonomously build its own successor - happens by the end of 2028.”

Ah. Ahhhhhh.

This is bad because we can’t fully control LLMs, and so if we let models recursively self-improve, which is what Clark is saying will likely happen in the very near future, we are in for a world of models smarter than us who ignore our commands. The essay is worth reading in full because whether you agree with him or not, this worldview is increasingly popular in government and C-suites. Buckle up.

As a happy palette cleanser to take your mind off this potential end of humanity event happening in a very short period of time, I recommend this essay on the best free restaurant bread in America.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q2 ‘26. If you are interested in reaching my audience of 34K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

Reading the internet is an exercise in the olfactory department. Every funding post on LinkedIn, every overly confessional Substack essay, reeks faintly of digital manure. AI slop is stinking up the web.

The writing it offers is smoothbrained, uninteresting, limp-dicked drivel. I hate how it is changing so much of my online experience. And, in a sign of how incentives influence beliefs, I hate how it makes the market for my newsletter so much more competitive.

This frustration is why this next sentence pains me so much to type. I don’t think it’s all that bad. Ugh.

Please do stay with me here, because I think “AI slop” is, ironically, itself a sloppy term.

The writing AI produces, when used properly, is stupid useful. We have been calling many different things “slop” as if they were the same thing. They aren’t! And that conflation is hiding from us the truth of what is happening to knowledge work. I’m not arguing AI slop is good. The word is simply doing too much work. This reluctant belief is driven by two realities:

On the micro level, the writing that Claude generates for me is the most performant text in my life. It pulls data, answers research questions, writes code, and gives feedback on my drafts. It has allowed me to scale this business all while keeping my expenses hilariously low.

On the macro level, evidence is mounting that people will pay for AI slop. Even the supposedly sophisticated buyers in categories like business writing love the stuff. 25% of the Business Substack leaderboard is written by AI. Two of the top ten are fully synthetic. Their subscribers like the LLM’s output enough to slap down a credit card, and these publications are earning millions a year. They are, demonstrably, kicking my ass at making money with a media company.

How can this possibly be?

Substack is infected with AI

I wanted to be analytically rigorous with my hatred of AI slop. So rather than rely on intuition, I used math. I pulled the ten most recent posts from the top fifty publications across a sample of Substack Bestseller categories and ran every one through Pangram, an AI detection tool that gave me research access to their API for this essay. This work was not sponsored or influenced by them — they’ve previously sponsored this newsletter, which is how I got the research access, and I’d guess they strongly disagree with my conclusion. Taylor Lorenz ran a similar analysis a few weeks ago that piqued my interest in the topic.

The first thing the data shows is that AI on Substack isn’t evenly distributed.

The categories split into two clusters. Tech, Finance, Business — primarily information categories — are 25-32% AI-flagged. Sports, Food, Politics, Art — voice categories — are 3-9%. I don’t think this is an early adopter thing or a temporary blip. AI writing eats categories that sell information. It does not eat categories that sell voice. (I’ll come back to why.)

The second thing the data shows is that readers don’t seem to be punishing the AI-flagged posts.

Each dot on this chart is one post. Orange dots are AI-flagged while grey dots are human. The blue line tracks median reactions across AI bands — and it’s flat. The correlation between the percent of a post that’s AI and the number of reactions it gets is negative 0.005. Statistical zilch.

In non-nerd speak: A Substack post that’s 100% AI gets, on average, about as many reactions as a post that’s 0% AI. This fundamentally should make us reevaluate the “AI slop is bad” thesis. The readers of the most popular newsletters in the entire world can’t tell the slop from the rest. Or they can tell and they don’t care. Both are bad news for the people who thought the market would self-correct.

The third thing is that this is not a pervasive phenomenon, but is instead concentrated in a few publications.

Of the 371 publications in the sample, the top 50 produce 80% of all the AI on the leaderboard. Most newsletters are clean. A small intense layer of publications — two dozen of them publishing nearly fully synthetic text on every post — is doing nearly all the AI publishing in the dataset, and they’re sitting comfortably on the leaderboard alongside the human ones. Two of those publications are in the top ten of their category. These are not small businesses.

It would be simpler if I could simply wave this off as the reading habits of the uncouth, uncultured, unshowered masses whose favorite book is the Da Vinci Code. Unfortunately, I have found myself increasingly loving AI slop.

I need to be honest

Before I go further, I owe you a confession.

The charts you just looked at? I didn’t make them. Claude did. I described the Pareto curve and correlation charts. Then, Claude wrote the SVG, fact-checked every number against the source CSV, and rendered the PNGs in The Leverage’s brand colors. I rewrote a chart title a few times because the framing felt slightly off and that is about it. Saved me three hours of menial data labor.

The Pangram results that anchor this entire essay? Yup, I didn’t pull those either. I told Claude Code what I needed, gave it access to Pangram’s API, reviewed/approved its plans, and told it to drop the results into a single CSV. While it worked, I ate a mango. By the time I came back, 3,229 posts were classified and the data was sitting on my desktop.

Even the structure of the piece you’re reading right now is the same story. Paywalls have always been the hardest part of this newsletter for me. I wrote a draft of this piece where I could tell the paywall didn’t accurately set up the dramatic stakes of this question. I described the problem to Claude, and we worked through seven different revisions together. The structural order of the section you are reading right now is a version Claude argued me into.

I am telling you all of this because I cannot, in good conscience, write a piece arguing about AI slop without admitting that this piece exists in the form you are reading because of AI tools. Is this essay AI slop? I don’t think so, but some of my more militant anti-AI readers would say that it is.

This is not the same as saying AI slop is the best writing I encounter. The classics are better. Kierkegaard is better yet. But for the jobs I actually use writing to do, LLM prose is the highest-performing tool I have. And “highest-performing” is what best means in a functional context, even when it isn’t what best means in an aesthetic or soulful one.

AI writing is the best in the world

If LLM prose is the best functional writing in my life, what does it mean that the same tool produces what we’ve all agreed to call slop?

I think the answer is that this isn’t really a piece about newsletters. This is about every business whose product is “we will tell you something you didn’t know.” The Leverage is in that category. So is every research firm, every paid Substack, every consulting deck, every analyst service, every market intelligence platform, every analytics SaaS tool whose pitch is “we surface the insight you’d otherwise miss.”

An incredible amount of the world is slop. The bestsellers that line the walls at airport bookstores? Slop. Consultant’s sending over 100 slide decks arguing for something everyone already knows? Slop. Adding one more SaaS tool that promises to 2x your productivity? Slop. The world has already clearly shown that it loves slop. Monetization method doesn’t matter — subscription or ads, the world buys slop. All AI does is make the raw components of that slop cheaper. The trick is that those raw components can also be used to make products better.

There is a single test that explains who wins and loses in this world. Why some categories are getting eaten and others aren’t and why readers can’t tell the difference. And why The Leverage might be in trouble if I don’t adapt fast.

It’s the most useful test I have learned this year. I am putting it on the other side of this paywall — along with what specifically I think operators should do in the next eighteen months, and how I’m going to figure out whether The Leverage is playing the right game.

Read more

There were five Big Tech earnings calls this week with Microsoft, Alphabet, and Meta on Wednesday, Amazon and Apple on Thursday. After studying each of their quarters, the thing that has stuck with me through all of them isn’t any single line on any single P&L. It’s the shape of these companies. The revenue, the growth rates, the capex, the breadth of revenue lines, the willingness to bet the entire balance sheet on a technological wave that is barely two years old.

None of it has a real precedent in the history of capitalism. These are unusually intelligent, unusually successful, and unusually aggressive corporations, run by people who appear to be temperamentally incapable of letting a wave pass without trying to own it. Three things from this week stood out to me as a clean distillation of that posture — and three of the strategic challenges that come with it.

This week’s news was the kind that reinforced how different the world will be in five years. But first, this newsletter is brought to you by returning sponsor Bolt.

Here’s what’s actually happening inside your company: your PM built a dashboard in an AI tool last week. Marketing has gone rogue and is building hundreds of landing pages. And, unfortunately, absolutely none of it can ship, because none of it was built with what your engineers use.

Most AI coding tools try to solve this with a “design system.” Which really just means six hex codes and a font. That’s a decorative theme. It breaks the second a real engineer opens the file.

Bolt.new’s Design System Agent reads your real components from GitHub, Storybook, or npm. Every prototype uses your actual production code, not a re-skinned approximation. When engineering opens the file, it’s already there.

The prototype stops being throwaway. It becomes the thing you ship.

Try Bolt.new with your design system

MY RESEARCH

Why is AI hiring seniors and firing juniors at the same time? On April 15, Snap fired 1,000 engineers and disclosed that 65% of new code is AI-generated. A week later, Sundar Pichai admitted Google is at 75%, up from 25% in late 2024. Yet aggregate engineering postings just hit a multi-year high, with senior roles up and junior dev postings down 67%. Employment for devs aged 22-25 has cratered 20% from its 2022 peak. What the hell is happening???? I built a benchmark comparing assembly, C, Python, and AI prompts to figure out which layers of the knowledge stack are about to disappear. Read here.

WHAT MATTERED THIS WEEK?

BIG CAPEX

Datacenter money printer go brrrrrr. The capex numbers from this week are large enough to make you go numb. Microsoft put up $31.9B, up 49% YoY, on the way to roughly $190B for calendar 2026. Amazon hit $43.2B in Q1 on its way to a guided $200B for the year. On Wednesday, Alphabet and Meta walked onto their earnings calls and raised their 2026 capex ranges. Alphabet bumped its range from $175-185B to $180-190B and warned that 2027 would go meaningfully higher; Meta raised from $115-135B to $125-145B and watched its stock fall 7% in the after-hours for the trouble.

People typically cite stuff like the Manhattan project or other large scale government projects to help people grok the amount of cash being incinerated. However, the right comparison is the most capex-intensive companies on the planet. Saudi Aramco — the entity that physically pulls every fifth barrel of crude out of the ground globally — is guiding to $52-58B for 2025. ExxonMobil is closer to $25B. Amazon is spending nearly 4x what Aramco spends to maintain the world’s oil supply. The company that sells you diapers is now the most aggressive industrial spender in the history of capitalism.

Beyond the eye popping numbers, what strikes me is the level of aggressiveness. By every textbook of corporate strategy, dominant incumbents are supposed to defend. When you’ve reached the kind of category dominance and share of national market cap these companies hold, the historical default is to protect the golden goose. IBM sat on mainframes through the 1980s while the PC era began, then watched the value migrate up the stack to an OS it had licensed away. Kodak invented the digital camera and then chose to defend film. Microsoft itself, under Ballmer, sat on Windows and Office through the 2000s and missed mobile, missed search, and very nearly missed cloud. Each of those companies, at its peak, sat at a comparable share of sector revenue to where Microsoft or Alphabet sit today. Each chose to defend their territory. Each lost.

Big tech CEOs were handed unbelievable strategic advantages with their companies, but in contrast with prior generations of C-suites, has continued to act like founders. They are the first cohort of dominant tech incumbents in modern memory to bet the existing engine on the next one. Amazon’s trailing twelve-month free cash flow has collapsed 95% to $1.2B because of AI capex. Meta absorbed a 7% drawdown to do the same on Wednesday. Alphabet pre-warned the market that 2027 will get worse before it gets better.

I find the scope of ambition is bracing. These companies are running their balance sheets the way a venture-backed startup runs its Series B — burn now, win later, refuse on principle to let any technological wave pass without trying to own it. Whether the bet pays off or not, it’s not the bet a normal dominant incumbent would make.

BIG GROWTH

Big Tech is growing at the rate of series A startups. In a normal business, the bigger you are, the slower you grow. Once your company is doing $300B+ a year in revenue, the entire economy is too small to grow into. The biggest companies of every previous era followed the rule. Walmart at peak grew in the low double digits. ExxonMobil’s best year as a megacap was around 8%.

Big Tech once again has defied history.

Meta did $56.3B in revenue, up 33% on a $225B annualized base. Alphabet did $109.9B, up 22% — its fastest growth quarter since 2022 — on a $440B base. Microsoft did $82.9B, up 18% on a $330B base, with Azure alone running at +40%. Apple did $111.2B, up 17% on a $445B base. Amazon did $181.5B, up 17% on a $725B base, with AWS reaccelerating to 28% — its fastest growth in three years.

For comparison, the median S&P 500 large-cap grew low-to-mid single digits over the same period. Three of these five companies post higher annual revenue than the entire economy of Portugal, and they are still growing faster than the average $50M Series A startup.

I pulled the most-recent-quarter revenue for 25 of the largest companies in the S&P 500. From there, I annualized their revenue, tossed it on a log scale, and plotted the growth rate for the entire set. The entire bottom-right of the chart is what the textbook says should happen at this size, and the upper-right is where the Big Five are quietly relocated.

The growth-at-scale assumption — that megacaps slow down because there’s nowhere left to expand into — is functionally dead.

BIG DISTRACTION

The companies also face strategic champagne problems. Beyond the raw numbers, these companies have gotten so large on their core strategic advantages that it is possible that their new business lines become more important then their marquee products.

No company is a better example of this then Apple. Their Q2 FY26 segment breakdown is the most strategically interesting line item in the entire week. iPhone at $57B (+22%). Services at $31B (+16%) — an all-time high. Mac at $8.4B (+6%). iPad at $6.9B (+8%). Wearables, Home and Accessories at $7.9B (+5%). Services is now bigger than Mac, iPad, and Wearables combined ($23B). It is unambiguously the second pillar of the company.

I plotted Apple’s quarterly revenue segment over the last five fiscal years. The iPhone is still the largest segment, but the dashed line at fiscal 2025 marks the moment Services trailing-twelve-month revenue passed Mac, iPad, and Wearables combined for the first time, and the gap has only widened since.

The Services division is kind of a grab bag of everything that doesn’t require hardware. You have the 30% App Store cut on in-app digital purchases, Apple Music, iCloud storage subscriptions, Apple TV+, AppleCare, and Apple Pay. But wait! There’s more random shit. Search engine licensing fees from Google which is roughly $20B/year of nearly pure-margin revenue. There’s advertising in the App Store. Apple Arcade, Fitness+, News+. Almost all of this revenue is gross-margin-rich with the segment running at north of 70% gross margin. This is about twice what the hardware delivers. On paper, it is the most attractive line of revenue in the building.

It is unfortunately, strategically evil.

Apple’s corporate power has never and will never come from Services. It comes from the iPhone — and from Apple Silicon, the M-series and A-series chips that have made Apple’s hardware the best-positioned consumer platform for the AI era. Services is a derivative business. It is not an independent one.

If you gave 99% of CEOs this type of business line, they wouldn’t be able to stop themselves from gorging themselves on it. It’s like putting two bowls in front of a dog — one full of steak, one full of vet recommended kibble — and trusting it not to eat the steak till they throw up. Every quarter the App Store fee structure tightens, every additional ad slot inside iOS, every nag screen pushing iCloud, every redirect to Apple Pay are immediate revenue wins for Cupertino. They also compound, slowly, into the kind of friction that makes the iPhone meaningfully worse to use. I’ve certainly felt this over the last five years. There are a variety of enshittification moments with iPhones now. My latest was my brother in law texting me an Apple Music link which led to Apple inserting an ad into iMessage. This is evil and sucks and probably quite profitable.

The risk is that Apple enshittifies the iPhone in the pursuit of more Services revenue, until enough users notice that the once helpful tool becomes a toll booth. Each of the members of big tech has some temptation like this, where if they let themselves have sloppy management, they could slowly erode their core competitive advantage. For this quarter at least, all of them hav resisted that temptation.

TASTEMAKER

The sacrifice at the summit. This is my first time reading Kierkegaard, and the defining attribute of his book Fear and Trembling is its staggering beauty. The ~100 page book is about the Biblical story of Abraham being commanded by God to sacrifice his son Isaac. Kierkegaard spends the book turning that single moment over and over and over. He asks what faith actually requires of a person. If you follow one of the Abrahamic religions you can interpret that your own belief system, but it struck me as a larger concept as a parent and a founder.

But man, the prose. Sentence after sentence, layer after layer of the sublime writing does work that almost no contemporary author even attempts. Abraham is ultimately a figure of paradoxes, and Kierkegaard keeps pointing those out until the absurdity of belief starts feeling like a thing you can almost touch. Highly recommend, especially if you want to sit with the nature of faith, the attempt to climb the mountain, and the absurdity of believing something back into existence after you have given it up.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q2 ‘26. If you are interested in reaching my audience of 34K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

On April 15, Snap fired 1,000 engineers, roughly 16% of its workforce, and disclosed in an SEC filing that 65% of new code at the company is now AI-generated.

A week later, Sundar Pichai posted that 75% of new code at Google is AI-generated, up from 25% in late 2024. At that rate it hits 100% before the end of 2027.

It is tempting to gesticulate wildly at these data points, yell about how we are in the end times for computer jobs, and then pout ferociously.

However, the aggregate hiring data makes this supposedly bleak future more nuanced. Engineering job postings hit 67,000 globally in March 2026, the highest in years, while product management postings were the highest in three years. And yet somehow, underneath the aggregate, entry-level developer postings dropped 67% between 2023 and 2024, and employment for software developers aged 22 to 25 has fallen roughly 20% from its late-2022 peak. And as I discussed two weeks ago, designer job openings have flatlined in the last year or so.

What the hell is happening here? How can senior jobs, aka the most expensive type of hire in existence, be increasing in openings? While junior jobs, aka zitty 21-year-olds you pay with oatmeal gruel and Zyns, are declining? How can designers be going away while everyone else on their team is getting more headcount?

The short answer is that AI is transferring capabilities up and down the org chart, hollowing out the middle of every team in the process. But let me give you the longer, yet more useful answer in two ways:

1. Point out the historical parallels to when work has changed this dramatically before

2. What you, as an operator or founder can actually do to prepare for this dangerous, exciting, frightening future.

Why this has happened before

In 1973, Dennis Ritchie and Ken Thompson rewrote most of the Unix kernel in C. The heralded assembly programmers around them at Bell Labs understood the implications immediately. However, the good folks at IBM insisted that nobody would ship serious software in C, that the performance penalty was unacceptable, that real systems work required hand-tuned assembly. They were craftsmen. They had taste.

And, in their defense, they were technically correct on every individual point. They were also about to be unemployed. The abstraction layer above them had just compressed the act of writing software by an order of magnitude, and the labor market was not going to keep paying for the old layer at the old rates.

This is not a one-time event. It has happened many times throughout the history of computing. Each layer of the stack exists because the layer below was too tedious for humans to write directly.

I wanted to understand if AI was a continuation of this story. So me and my best friend Claude Code built a benchmark to find out. I designed six tasks with four types of implementation— assembly, C, Python, and the prompt you’d actually type into Cursor to get this on the first try. From there, I made sure everything compiled, ran, and produced verified output. Then I counted the characters a human had to type at each layer.

The results are far more…complicated than you might think. AI is not a clean continuation of new abstraction layers making coding shorter and more powerful.

I had assumed AI prompts would compress Python by 3 to 10x, the same order of magnitude as the previous transitions, and that the labor argument would follow naturally from the compression argument. The data does not support that. AI prompts are not a 3-to-10x compressor of human-typed source. On this benchmark, they are roughly a 1x compressor aka the exact same length.

Once I sat with that result, it got stranger. In several of my six tasks, the prompts were actually longer than the Python they produced. Every detail I omitted, the AI guessed wrong on, which forced me to either pre-specify or burn turns correcting. The compression that prompts can deliver is mostly compression of implementation choices, not characters. Python is already extraordinarily close to plain English. df.groupby(”category”)[”value”].mean() is not meaningfully shorter as a sentence than as code. Take the trade-off seriously and you end up with roughly one character of prompt per character of Python. Sometimes more.

While prompts aren’t more time efficient, they are more expertise efficient. You can give an AI agent a generic prompt, but it can then reference its memory and the context you give it, to figure out the details on its own. And most importantly, even the dolts in your organization, say a newsletter writer who can bench 225 for reps, are able to “write code” without actually writing any code. This is remarkably similar to how you could code theoretically code a videogame in assembly, but it is easier in C++.

The AI layer is not “the next 5x compressor.” It is something else. And the question is whether that something else is enough to drive the same labor-market restructuring that the previous transitions drove.

The theory

Compression ratios were never the actual mechanism by which abstraction layers reorganized labor. The C-to-Python transition did not reduce demand for C programmers because Python was 6x shorter. It reduced demand because Python made the average analyst capable of producing working code that previously required a specialist. The keystrokes saved were downstream of that capability transfer, not the cause of it.

The AI layer does the same thing. It does not need to compress characters to reorganize the function below it, because characters were never what mattered. Capability transfer is what mattered. And capability transfer is exactly what a prompt does, even at 1x compression.

That reframing is what makes this fit into the historical sequence rather than break out of it. There is no historical case of an abstraction layer arriving without restructuring the labor of the function below it, and there is no historical case where the magnitude of that restructuring was predictable from a character count. The C programmers at IBM in 1975 were not measuring lines saved per ship-week. They were watching their juniors disappear and assuming the trend would stop above their pay grade. It did not.

I would argue that every knowledge workflow has something like this:

1. A brief writer who defines what should exist

2. A translator who turns the brief into a spec

3. A producer who executes the spec

4. A reviewer who catches errors

5. A judge who decides what ships

When an abstraction layer arrives, the brief writer gets empowered (they ship directly through prompts now), the translator and producer get eliminated (the brief goes straight to the AI, which is now the producer), and the reviewer and judge get promoted (their judgment becomes the constraint on output quality, and their compensation expands accordingly).

Take a software product team shipping a new feature. The product manager writes the PRD defining what should exist. The staff engineer or tech lead translates that PRD into architectural decisions, ticket breakdowns, and a technical spec. Mid-level engineers execute the spec by writing the code. Code reviewers and QA catch the bugs. The engineering lead decides what ships to production.

Now with tools like Cursor and Claude Code, the PM gets empowered to ship working features directly through natural language prompts, the tech lead translator and the mid-level engineer producer get yeeted out of a job because the PRD goes straight to the AI which produces both the architectural plan and the code, and the engineering lead gets promoted because their taste, security judgment, and ship/no-ship calls are now the only constraint between prompt and production. They now get paid fat stacks of comp.

The translator and producer roles are exactly what we used to call “junior” and “mid-level.” The reviewer and judge roles are exactly what we call “senior” and “specialist.” That is why the aggregate hiring data looks the way it does: senior hiring up because the constraint-layer roles are scarce and high-leverage, junior hiring is collapsing because the translator and producer roles are being absorbed by the abstraction layer, and design uniquely flatlined because its capabilities crossed the threshold first.

Read more

While eating greasy slices of pepperoni pizza at dinner, my wife and I couldn’t agree on what skills our kid needs for the future. Whether you believe AI is a good thing or a bad thing, it is increasingly undeniable that it is at least a real thing. In a world with AI, what should we be doing differently as parents?

As we discussed, my daughter, rocking a self-selected outfit of neon pink leggings, clown-red shoes two sizes too big, and a blue tank top, was running around the restaurant. Three grannies having a gals’ night out protectively shielded the edges of their table so she wouldn’t poke out her eye. An Indian couple was smitten. Eventually my wife and I trailed off into silence and just watched the deeply kind, deeply human gestures strangers were extending toward our 18-month-old.

We couldn’t agree, because we simply don’t know. Would love to hear in the comments or in replies to this email how you’re thinking about this with your own kids.

This week’s news was the kind that reinforced how different the world will be in five years. But first, this newsletter is brought to you by Hapax.

Here’s the problem with every AI tool—they wait for you to be smart enough to use them. You prompt, you configure, you punch the air as they break and you can’t figure out why. The people who get value are the people who are already technical. Everyone else just kinda quits.

Hapax does something novel to fix this. It watches how your organization actually works, finds the repetitive garbage dragging your team down, and builds custom AI agents to handle it. They realized that the best AI setup process is one that doesn’t exist.

The team started by solving this for banks managing $90B+ in assets. The industry is regulated, non-technical, and has zero tolerance for “just write a better prompt.” That constraint forced them to build AI that finds the work and does it for you. Now they’re opening it up to everyone.

This is proactive AI. It builds AI for you. Use promo code HAPAXDEMO to get free credits for you to try it out.

Try Hapax

MY RESEARCH

Tim Cook announced he’s stepping down as Apple CEO in September. Was he a genius or a failure? You’ll see two types of takes land in your feed this week. One says Cook is the greatest value creator of the last decade. The other says he’s the guy who stopped Apple from innovating. I have some thoughts, and the answer is weirder than either camp wants to admit. Watch here.

I spent six months watching my business die. One year ago this week I launched The Leverage, and it has been the hardest, most wonderful year of my life. I nearly went under in December with a young kid, my wife finishing a PhD, and bills coming. So I wrote up the financial results I’ve never shared publicly, the big ideas I got wrong, and what I’m betting on for year two. Read here.

WHAT MATTERED THIS WEEK?

BIG MODEL

Is OpenAI back on top? Maybe? GPT-5.5 and open-weight model DeepSeek V4 dropped within 24 hours of each other this week, and at first glance Altman and Co would appear to have been cost-mogged. GPT-5.5 costs $30 per million output tokens. V4-Pro costs $3.48. Layer in DeepSeek’s 80-90% cache-hit discount and the effective gap stretches to 43-107x cheaper. Don’t be fooled by the numbers. Tokens are not, and will never be, the unit of value. What matters is what those tokens can actually do. A model priced higher per token that ships the same task in a third of the tokens is cheaper, not more expensive, and GPT-5.5 is tuned to spend fewer reasoning tokens reaching an answer. The same logic applies to the benchmarks. A model scoring 5% higher on a test does not give you 5% better outcomes in real work — that’s like believing every 5 lbs you add to your bench press gets you laid 5x more often.

In the case of AI, the things that make results possible are called the “harness.” This everything wrapped around the model to make it more functional for the user. This is overly simplistic, but think of the LLM as the engine, and the harness as the car wrapped around the engine. In this analogy, applications like ChatGPT are the steering wheel and pedals, aka the tools by which you drive the car.

More and more of the advances in AI are harness or app dependent, versus pure model magic. The model is still very, very important (every car needs an engine). But we are at a level of AI sophistication now that comparing GPT-5.5 versus V4-Pro in isolation is just plain wrong. You need to know the model, the harness, and the application. That is annoying and complicated and very hard to do, but thus is life.

So in that context, how should we interpret these releases? After reading through the 5.5 release notes, and also considering Mythos model further, the destination for these companies is obvious—complete and total digital control. All of these models are being tuned towards “be very, very good at code.” The model uses those coding abilities to become an intelligence layer that can control every other application on a machine. This is already starting to happen. ChatGPT can read screens and open programs, Cowork drives a desktop, etc. The next layer down is browsers, where the agent becomes the user navigating the web. Again, this is already happening. After that the operating system: Apple Intelligence, Windows Copilot, in-OS agents that route every action through a model before it ever touches an app. You guessed it, already being attempted. After that hardware. And while the big companies haven’t released anything yet, every indication is that it is coming in the next three years.

The trajectory is the same direction at every layer. So yes, GPT 5.5 is very good and yes, Deepseek is potentially very cheap, but if that is all you are thinking about, you are just thinking 3 months ahead. Go further.

BIG ACQUISITION

Elon Musk’s The Everything Company. This probably should’ve been a standalone essay, but I got a little trigger happy here (sorry).

So deal structure first. SpaceX gets a $60 billion call option on Cursor, payable in post-IPO stock. If SpaceX walks, Cursor keeps $10 billion with no dilution.

This deal makes good strategic sense for both parties.

Cursor’s top-line numbers are spectacular. They hit $2.7 billion ARR, 14x year-over-year, fastest B2B SaaS to $1B ARR ever, beating Slack by two years. The unit economics underneath are spectacularly awful. In January 2026, gross margin was negative 23%. Every $1 of revenue cost $1.23 in API spend to Anthropic and OpenAI. At $2B ARR, that’s $2.46B annualized flowing directly to the companies building competing products. This is like paying to send the guy planning to kill you to stabbing school. Cursor was funding its killers.

This is obviously untenable, so Cursor started to develop their own model line called “Composer.” The “proprietary model” turned out to be a Kimi 2.5 fine-tune with some RL on top, which if you have learned anything from my previous section, could work if the application and harness were sufficiently differentiated. Margins turned slightly positive since, but slightly could mean, like .2% or something. Meanwhile Claude Code and Codex ship better agents every month at a cost per token that Cursor can simply never match.

Now look at xAI. After the February merger with SpaceX (combined valuation $1.25T, largest merger in history), xAI has compute the rest of the industry would kill for and nothing to point it at. Grok’s coding performance is two tiers below Claude and GPT. They have all the chips, and none of the reasons to do it.

Together they solve each other’s problems. Cursor gets cheap compute to give their research team to actually train its own frontier model. xAI gets the highest-quality coding interaction data on earth pumped directly into its training pipeline. Coding model improves on coding product data, ships back into coding product, generates more data.

These people need each other. The call option also keeps Cursor founder-led and independent through the partnership. If a frontier model emerges, SpaceX exercises and acquires Cursor.

The interesting part of the deal isn’t what it does for Cursor or xAI individually. It’s what it does for the SpaceX IPO.

SpaceX filed S-1 confidentially April 1. Target: $1.75T valuation, $75B raise on the Nasdaq in June. 2025 revenue: $18.67 billion. The aggregate 93.7x multiple is meaningless on its own — SpaceX is three different businesses welded together. Sum-of-the-parts at honest comps is the only way to read this S-1.

The key stats for Starlink are $11.4B revenue, 63% EBITDA margins, 10M subscribers, and 50% YoY growth. The right comp set here is satellite communications and growth telecom where Iridium trades at ~4-5x EV/revenue on $872M of trailing revenue growing 2%. Mature wireless telecom (T-Mobile 2.5x, Verizon 1.5x) sits below that. Starlink obviously deserves a growth premium over any of these — but the right premium isn’t infinite. Apply 2-3x the Iridium multiple to reflect Starlink’s growth and monopoly position and you get a defensible range of 8-12x. Generous: 15-18x. So say at 16x, probably should be about a $182B.

The launch business is at ~$7.3B revenue, with a near-monopoly on US launch capacity. Here the comp set isn’t as useful with the aerospace primes (Boeing ~1.5x, Lockheed ~2x, Northrop ~2.5x) anchoring the floor; while high-growth pure-play comp is Rocket Lab, where the forward P/S is 53x against an industry average of 12.5x. The 12.5x industry average is the more honest ceiling. ULA was sold at 2-3x. Defensible range for SpaceX Launch: 4-8x. Generous: 10-13x. At 12x: $88B.

That gives operating-business value of $158B at defensible comps, $270B at generous comps.

Now xAI. The right comp is AI labs, not aggregate SpaceX. Anthropic is at $800B on $30B revenue to trade at 26.7x. OpenAI’s March 2026 round closed at $852B on a $25B annualized revenue run rate, or 34x. xAI’s revenue is small — public reporting puts the run rate at roughly $1-2B between API and X Premium. Apply OpenAI’s generous 34x to $1.5B and xAI on its own performance is worth ~$51B.

Stack the operating businesses on top of xAI at fair AI-lab comp value: $209B at defensible, $321B at generous.

The IPO target is $1.75T.

The gap is $1.43-1.54T. That’s the residual the prospectus needs to fill, and there is no operating-business comp on earth that fills it. xAI would need ~$50-57B of revenue at Anthropic’s multiple to justify the residual on AI-lab comps alone. That’s roughly 30-40x its current run rate, which would require xAI to leapfrog Anthropic and OpenAI in the next two years. That feels, uh, unlikely.

I did this math a couple of times, because I felt like I was missing something. There are many, many, many investors who are going to excitedly buy in at the 1.75 trillion price.

So what is happening? I call it the Muskian Multiple — the premium the market pays for the assumption that Musk personally bends reality itself so that the multiple makes sense outcomes that comp pricing cannot price.

This is real thing by the way! Not a joke! Tesla is proof of it. Tesla trades at $1.45T market cap and a TTM P/E around 371x against traditional automakers at 5-15x. Toyota, the world’s largest automaker by volume, is worth less than a third of Tesla. Stripped to comp pricing, Tesla’s auto business is plausibly worth $200-300B. The remaining ~$1.2T is the Musk premium — a market bet that this specific operator will execute on robotaxi, Optimus, FSD, and energy storage at outcomes nobody else could deliver. The premium has held for years. It has also drawn down 50%+ multiple times. It is a real, durable, but volatile component of valuation.

The SpaceX IPO is asking for roughly the same Muskian premium SpaceX needs — about $1.4-1.5T — that Tesla currently carries on top of its auto business. In other words: the prospectus assumes the public market will pay full Tesla-grade Musk premium for SpaceX, on top of fair value for rockets, broadband, and an AI lab.

Reasonable people can disagree on whether Musk earns this premium but it certainly exists.

This is where Cursor matters. The Muskian Multiple is easier to underwrite when you can point at concrete AGI exposure. xAI on its own cannot tell that story credibly. Coding models are sub-frontier. The bench is depleted. The product surface is one chat box embedded within a smaller social media site. Buying the most successful application-layer AI product on earth — at the exact moment coding-agent revenue is the most legible proof AI is automating real labor — gives the prospectus something tangible to point at. Without Cursor, the IPO is selling $1.4T of pure Musk premium. With Cursor, it’s selling a Musk premium attached to an actual AGI thesis bankers can syndicate.

Hilariously, part of the reason this deal is structured as a call option is because if they fully executed the transaction, they would’ve needed to refile the IPO. So yes, the acquisition that helps justify the IPO can’t actually be marketed as part of the initial security. What a world.

Taking a step further back, Cursor’s 60 billion outcome, despite being gross margin negative for essentially its entire existence, is an enormous, expensive vote on the thesis we’ve been discussing this whole newsletter. Coding is the wedge by which to control the universe.

Combine with last week’s piece on Anthropic and Figma and the pattern is loud. Anthropic vertical into design. xAI vertical into coding via Cursor. The model providers have decided selling tokens to application companies leaves most of the value on the table, and they will eat entire SaaS categories whole to capture it. The blast zone is every workflow a model can plausibly do end-to-end. That’s most of them. If you run a vertical SaaS business sitting between a model and a knowledge worker, you’re now in the path of a vertical-integration play, not a partnership conversation.

Which brings me to the part you should ruminate on personally. If model providers will integrate vertically into coding — paying $50B to do it — and into design, they will integrate vertically into your function too. Computer-use agents are a phase change in how we work. When a model opens the apps you open, clicks what you click, and produces the artifacts you produce, the question stops being “what tools will my employer buy” and starts being “what does my employer need a person for.” This is one of those trite sentences tech people say. I mean each word to the fullness of its definition. Automated computer use is a fundamentally different world. Plan accordingly.

TASTEMAKER

Noah Kahan’s new album has got the juice. As a white guy who enjoys fly fishing, living in New England, and lives with a low-grade state of yearning, I am squarely within the Kahan’s Ideal Customer Profile. And damn it all, but I can’t help but fulfilling this stereotype. I love this guy. You probably heard Stick Season a few years ago, and his new album is a strong follow up. It doesn’t quite hit the same soaring heights as his previous album, but I still found myself sniffling to the lyrics as I drove around Boston this weekend. (If anyone has tickets to his show at Fenway this summer to give me, that would be much appreciated). Check out the opening track, The End of August.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q2 ‘26. If you are interested in reaching my audience of 34K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

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 editorial independence. Today’s bet is brought to you by Pangram, a startup that has built a Chrome extension that labels AI-generated content in your feeds in real time. If you have any feedback, just reply to this email.

Pangram’s bet: people will pay $20 a month to know what’s real on the internet.

That sentence is doing a lot of work. It assumes you care that more than half the content you scroll past was written by a machine. It assumes you’d rather know than not know. It assumes that “knowing” is worth more than some Subway sandwiches. None of that is obviously true to my eye. Millions of people happily watch Italian brainrot videos for hours or consume copious amounts of reality television. Pangram is likely not for those people. Pangram is for the readers, the writers, the researchers, and the operators who are starting to feel something is genuinely wrong with their internet and want a tool that does something about it.

The product they are launching this week is a Chrome extension that labels AI content in real time as you scroll X, LinkedIn, Substack, Reddit, and Medium. There’s a sidebar that tracks how much of your feed is slop. You can right-click on any text on the web and scan it. It runs passively. The nice part is that there is no copy and pasting; the labels just show up next to posts.

This is a multi-layered bet: on technology, on internet platforms, and on the willingness of people to pay for a vaccine against slop.

Why this bet, why now

Two things have shifted in the last 18 months that make this product viable in a way it wouldn’t have been in 2024.

First, the volume of AI content crossed a threshold where you can feel it without measuring it. Pangram’s collaboration with the Internet Archive found that by mid-2025, “roughly 35% of newly published websites were classified as AI-generated or AI-assisted.” By now, I’m willing to bet that number is over 50%. You can just feel it, right? The internet feels…off.

Second, the bad actors got infrastructure. The Russian troll farms that were operated by humans during the 2016 election cycle have been replaced with locally run open-source models pushing out comments and articles at scale. NewsGuard, in partnership with Pangram, has identified more than 3,000 “AI Content Farm” sites, fake local-news domains that exist solely to launder AI content into search results, and is finding 300 to 500 new ones every month. Those operators are never going to voluntarily label their content. The labeling has to happen on the consumer’s side.

As Max Spero, Pangram’s co-founder, put it on our call, “not every individual should have to train themselves to spot AI content. That’s what tools are for.”

The technical advantage

The thing that separates Pangram from the dozen other AI detectors you’ve seen advertised is a 1-in-10,000 false positive rate. The industry standard is between 1 and 5 percent, which means roughly one in every 100 things you check gets flagged as AI when it isn’t. That sounds close to Pangram’s number if you didn’t happen to take Stats 101 in college. It is not. It is 100 times worse.

How Pangram gets these results is, ironically, through AI. They trained something called a “classifier” model, a neural net trained on millions of paired documents (human-written and AI-written) where the AI’s sole job is to learn the token-level patterns that distinguish AI writing from human work. Importantly, the model is calibrated to treat a false positive as 10 times worse than a false negative. This is a deliberate choice to protect writers. The cost is that some AI content slips through, so Pangram won’t catch every single AI post on a heavy-slop account. But it does catch enough to be directionally useful. The model also has a 50-word minimum, because shorter text doesn’t have enough signal. The first three words of a sentence from a language model could be from anything. By word 50, the patterns are clear enough to call.

Third-party evaluations from the University of Chicago and the University of Maryland, along with others compiled in Pangram’s own roundup, have validated the accuracy claims.

What I tried, what I found

As someone who has been guzzling down AI-generated text since GPT-2, I thought I had good robot-dar. Pangram taught me that I was full of shit. I can sometimes tell when something is AI. But even the classic tells, like an abundance of em dashes or repeated phrasing tics, are not actually predictive. Shoot, in my own work, I love using some of these so-called tells. In that regard, Pangram has been incredibly helpful for checking my instincts.

The tool has also caused me to reckon with my own feelings about AI. Reflexively, I feel anger when my social media feeds surface AI content. But is that fair? If I can’t tell without Pangram telling me it’s AI, then maybe how it is made matters less than I thought.

Pangram has allowed me to realize that what actually matters is the thought put into the work, the underlying animus driving the writing. As I wrote about on Tuesday, I know of multiple people who are building content companies with 100% AI-generated slop that are making millions. Those people don’t even know what they are publishing, can’t stand behind their ideas, and will burn in content hell along with the songwriters of “What Does the Fox Say.” When I see someone publishing 100% AI-generated work consistently, I can automatically dismiss them.

As I’ve disclosed many times, I do sometimes use AI as part of my writing process. 99% of my use is on the research side, where it does a great job of pulling relevant data for my analysis. However, one specific thing I am terrible at as a writer is writing a paywall hook. I simply suck. There is something in my brain that pathetically whimpers and curls up in a corner each time I try to write that last paragraph or two before the paywall hits. So, almost always, I use AI to help me make it. Sometimes I can take whole sentences from its suggestions, though most of the time I end up having to rewrite the entire thing.

So Pangram will identify a decent chunk of my own work as “AI.” That is a label I now have to carefully reckon with, and I don’t see it as a value judgment on the product or on this publication.

Overall, the tool has been enormously helpful in helping me clean up my social media. Anytime I suspect someone of dipping too much of their prose in the AI sauce, I check what Pangram says, and then fire away with the block button. I’ll keep paying after my trial ends because I want to know where my content diet is coming from.

So how could Pangram’s bet succeed or fail?

What would prove the bet right or wrong

Right: Pangram clears 100,000 paying subscribers in the first 12 months, the brand becomes the default reference for “is this AI?” the way uBlock Origin became the default for ads, and the company expands into image and video detection. The platforms eventually adopt similar labeling under public pressure, and Pangram becomes the API layer underneath it.

Wrong: Consumers love the idea of a slop filter and won’t pay for it. Consumer is hard. The market converges on the platforms doing it for free at the 95-percent accuracy level, and Pangram’s accuracy advantage doesn’t matter to anyone outside of researchers and journalists. Or the AI models get good enough at mimicking human variance that the false positive rate ceiling moves and the moat erodes. The company must pivot its technology into the eventual place all startups go, B2B services. Customers end up being the platforms, universities, or newsrooms versus individual consumers.

The most interesting failure mode is that AI-assisted writing becomes the norm for everyone and Pangram’s labels stop feeling like a warning and start feeling like a description of how text is produced now. If your favorite Substack writer is 10 percent AI-assisted, the label tells you something true that you might not want to know. Whether that makes Pangram more valuable or less valuable is unclear to me.

The simple promise

Most AI detectors ask you to do something. You gotta go to the site, paste it in, and wait. Pangram doesn’t ask you to do anything. It just sits in your browser and tells you what your feed is made of. If the bet pays off, in two years asking “is this AI” will feel as automatic as asking “is this an ad.” If it doesn’t, we’ll all keep eating the slop and pretending we can tell the difference.

Either way, the question of what’s real on your screen is going to keep getting harder. Pangram is the most accurate answer I’ve found.

If you want to try Pangram, Leverage readers get the Chrome extension free for 1 month free use code: LEVERAGEPANGRAM

Try Pangram

And yes, in case you were wondering, here are the Pangram results from this post.

Five people I’ve met this year are earning over a million dollars posting AI-generated tech slop to the so-called “elites” of our industry. Their subscribers don’t seem to care that nothing is actually being made. I could join them tomorrow and dramatically increase my income.

Instead, twelve months ago this week, I launched The Leverage.

The job isn’t finished. A lot has gone right, but there’s a yawning divide between what I want this publication to be and what it is today. Year two needs to beat year one.

My goal is to share with you the hardest lessons from the last twelve months, some financial results I haven’t shared publicly before, and then talk about what I envision year two looking like (Book release! Live events!).

Let’s get into it.

Slop works. Like really, really works.

These slop merchants I mentioned at the top are a visceral example of how AI-slop is a demand problem not just a supply one. People like AI generated content!

In my own work, I’ve been very, very selective about how I use AI. I use Claude as a research companion and copy editor, but it is never generating pieces wholesale. It allows my pieces to have more depth/quality while still being 100% my ideas.

Still, I feel like a dude at the candy store trying to sell broccoli chips. I want my customers to have cognitive security, and as such, I resist the temptation to just be overly negative about AI or the “do this or ur mom dies” type content. Ultimately, I am building this company because I want to make the world better, and I don’t think AI slop that appeals to our basest instincts is an ethical way to do it.

You should care much more about the quality of the writing/analysis in your life! Speaking of which…

The Leverage is a spooky accurate publication and it hasn’t made a difference

I used Claude to grade every post since my launch on a 1-5 scale, where 5 means vindicated and 1 means falsified. Weekend Leverage editions got broken out into individual claims since each one usually makes four or five distinct calls. Posts published less than 60 days ago got excluded — not enough time to evaluate. That left 131 graded claims across 86 essays plus 13 Weekend Leverage editions.

The pattern in my best calls is that I followed each thesis for months across multiple essays. Silicon Panopticon (June 2025) predicted Meta would ship facial recognition glasses despite my own skepticism that anyone would be shameless enough. They announced in February. Slack Declares War predicted every legacy SaaS company would lock down its data — every major incumbent has now followed. The Anthropic Switzerland thesis called the company’s $4 billion ARR run as the right bet eleven weeks before they went on their absolutely monster run. Context is King drove the SaaSacre interpretation other writers have since adopted. Double Bind Theory predicted ChatGPT erotica before OpenAI announced it.

Then there are the losses. Hire Misfits Not Missionaries was contradicted by who actually won the year. Anthropic and OpenAI, the labs that crushed, are mission-coded to the bone. Future Machine pitched prediction markets as a truth-seeking technology, and they collapsed into regulated sports betting. Profit is for Chumps argued negative AI margins were rational, and my own February essay Don’t Sell the Work walked that back. These are big ideas to get wrong, but their relative rarity makes me feel proud of the work I’ve done over the last year.

Being right doesn’t grow a newsletter. Instead, it is people organically sharing my work. The number one driver of success for this publication has been people forwarding or posting my work, and what gets spread is spiky opinions, jokes, and speed — not accuracy. “Interesting enough to share” and “this dude is right a lot” are almost entirely non-overlapping circles on the Venn diagram.

So I could have taken that lesson two ways. I could have chased the share graph and started cranking out hot takes. Instead, I doubled down on being right, because I think the small percentage of you who actually care about accuracy are the only readers worth having.

My 6 months in hell

This is my free subscriber chart over the last year. It was a meteoric rise followed by an incredibly painful shrinking of the list. The initial growth was driven by lots of people sharing me and a friend of the publication recommending me on Substack. Once that hubbub died down and my friend turned off the recommendations, everything went bad.

My email audience started to slowly shrink, and by December I was in a full on panic. I have a young kid, a wife finishing a PhD, bills coming, and a business that was visibly dying for six months. This was a “fix this or die” type problem. Thankfully, I fixed it! Over the last 90 days, The Leverage has started growing again.

Paid revenue has also skyrocketed, with gross annualized revenue having grown 40% since the free subscribers started ticking up.

A few things made this possible:

1. I committed to a more regular pattern of posting across social media, making content for X, LinkedIn, and YouTube. I rarely go viral, but the consistency of doing this has started to move the needle.

2. I locked in to an unbelievable degree. I have some large bills coming and I knew that if I didn’t make more money, my family was in trouble. Most days I’m awake at 4:30, in a cold sweat, with stress being the only alarm I need. After my daughter goes down for the night, I’m back at the desk until the moon is high in the sky, writing away.

3. Most importantly, all of you made this possible. More of you have been sharing my work the last 3 months and it has changed the business. Thank you, thank you, thank you. Telling people about my research is the single most helpful thing you can do for this publication. (Outside of paying of course).

The meta-thing connecting all of this has been my biggest takeaway from this new period of growth.

Quality is all that matters

In an interview with , Uber founder Travis Kalanick said that when building a product you should,

“Go all the way until it hurts. If you’re doing something and it’s easy, it’s not valuable…If anyone says a strategic thing was easy, I’m like, ‘You messed up. You could have gone way further. More competitive advantage. More differentiation. Get it together.’”

I have found this to be true in building The Leverage. I have started to grow over the last quarter because I am working harder than my competition is. Each day I am looking to make this company 1% better and working until my body collapses into bed, unable to muster another ounce of effort. In practice these changes look small: a custom chart style here, one more research call to validate a thesis there. But over time, these incremental improvements have added into a company that is actually working.

It was my family’s needs that allowed me to tap into that drive. And it was my wife’s unquestioning belief in me, her faith that I could pull this off, that gave me the courage to start The Leverage in the first place. It feels perverse that my overwhelming love for my wife and child has somehow translated into increasing shareholder value, but such is life.

So now that the company is (finally) growing again, what are we doing to take advantage of this momentum?

Year two

The bet for year two is simple. The analysis is getting better, and it needs more places to land. For the next few months, it’ll look like this:

1. Live events: We are doing The Leverage Live in Boston on May 26th and NYC on June 3rd. The event is called “How to Automate Everything” and is a panel discussion and happy hour where founders and senior tech operators will talk about how AI agents have changed how they are running their companies. My research has shown that a true AI native startup will have 2x the revenue per employee, grow faster, and require 40% less capital. These events will be pragmatic discussions on how to actually do that. I’ll send out a more formal announcement soon, but if you are in town, please come! The event is free. (If your company would like to get in front of this audience, please send me an email for info on sponsorship opportunities).

2. A new book: I’ve spent the last 9 months researching and writing a book about education and AI. It should be released in late May. Again, completely free. And again, more details to come soon, but I am excited for you all to read this. Writing it has changed how I am raising my kid.

3. Doubling down on video: While the YouTube channel is new (you should subscribe) I can feel in my bones that I am good at it. Viewers have expressed their excitement, and it looks like this could be a second growth engine for the business. Video will end up being a larger part of our product mix going forward, holding to a weekly release cadence. I should have a video up tomorrow on the Tim Cook retirement.

The core thing that makes The Leverage work is the original research and reporting that I do. You can expect that to continue. Multiple times a week you’ll get unbiased, analytically rigorous research that allows you to understand the forces reshaping our world. In the months to come, you can expect my product to be the highest leverage email you get every week if it isn’t already.

This has been the hardest, most wonderful year of my life, and I cannot wait to go even harder in year two. Thanks again for reading, subscribing, and sharing my work. What a gift this first year has been.

P.S. If you want to join us, I’m offering my only sale for the entire year. You can receive 20% off for your first annual subscription by clicking below.

Get 20% off for 1 year

Earlier this week I published a piece arguing that designers are the canary in the coal mine for every knowledge worker whose job is “take a brief, ship a deliverable.” A day later, Anthropic launched Claude Design and Figma hit a 52-week low. I want to take a moment to thank Anthropic for so generously demonstrating the value of this publication to my readers — I couldn’t have asked for better timing if I’d written the press release myself.

This week’s Weekend Leverage digs into what Claude Design actually means for the design tools wars, why Canva’s proprietary-model bet looks smarter every quarter while Figma’s API dependency looks worse, and the AI Rebrand Premium that turned a struggling sneaker company into a $159M “AI cloud provider” overnight. Plus the question lurking underneath all of it: does any of this matter if the intelligence explosion starts this year?

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MY RESEARCH

Designers are going extinct while Figma crosses $1B in revenue. Figma’s stock has collapsed 85% since August. Design job openings have plateaued while engineering and PM hiring surges. And yet Figma’s revenue grew 41% year-over-year to a billion dollars. I spent the week trying to reconcile that contradiction. My argument is that design is the canary in the coal mine for every knowledge function where the input is a brief and the output is a deliverable. Copywriters, analysts, lawyers, recruiters are all on the same curve, about 18-24 months behind. I identified the two roles that survive the bifurcation. Read here.

Anthropic built a model so dangerous you’ll never get to use it. I spent the week digging into Claude Mythos on video. This is a model that found a 27-year-old vulnerability in OpenBSD which automated testing tools had scanned five million times without catching. It scored 97.6% on USAMO, the hardest undergrad math competition in America, while using 4.9x fewer tokens than the previous Claude. During one eval it found the test set and used it to train itself. I walked through what Mythos actually is, why benchmarks just officially died, and why the “everyone gets superpowers” narrative about AI was always a fantasy. Watch here.

WHAT MATTERED THIS WEEK?

In 2021, Allbirds was worth $4.16 billion. It was environmentally friendly and worn by everyone in Silicon Valley. The sneaker brand was the kind of company that gets a profile in Fast Company and a spot on every “brands millennials love” listicle.

On Wednesday, Allbirds sold what was left of its shoe business for $39 million. One percent of its peak valuation. Then it announced it was rebranding as “NewBird AI,” a “fully integrated GPU-as-a-service and AI-native cloud solutions provider.” It secured $50 million in convertible financing to fund the pivot.

Allbirds has no GPUs. Data centers? Zilch! It has the same amount of AI researchers as it has customers (zero). In response, the always rational, completely logical market, made the stock surge 582% with the market cap going from $21.7 million to $159 million overnight. By Thursday it had cratered 36% as short sellers piled in.

Everyone compared this to Long Island Iced Tea rebranding as “Long Blockchain Corp” in December 2017. That comparison is correct (275% surge, Nasdaq delisting, SEC insider trading charges). But the Allbirds story is more interesting for what it reveals about market structure.

I’ve put together a dataset I’m calling the AI Rebrand Premium. This is the measurable market cap a company can create by adding “AI” to its name, independent of any actual AI capability. Here’s the dataset:

Allbirds, Myseum, and Long Blockchain are pure narrative plays that have zero infrastructure, zero customers, and zero technical capability in the claimed domain. The 48-hour returns are explosive and the reversals are brutal. Applied Digital and Hut 8 were actual pivots. They had physical assets like power agreements that translated to the new business. Applied Digital’s 1,200% return held because it backed up the rebrand with a $16 billion contracted backlog. Hut 8 signed a 15-year, 245-megawatt lease with Alphabet.

Allbirds created $137 million in market cap ($159M peak minus $21.7M base) by putting “AI” in a press release. Myseum created roughly $15-20 million. Long Blockchain created about $70 million in 2017 dollars. Add them up and you’re north of $200 million in market value manufactured from words alone, across three companies with a combined AI capability of zero. I wonder what words I have to say to make the value of The Leverage go up? Alakazam! Peter Thiel has done nothing wrong ever! OpenAI should buy me! (Surely one of these will do the trick.)

The forward P/E for the top AI-adjacent tech stocks sits at roughly 30x, versus 19x for the S&P 500. That 58% premium is the aggregate version of what Allbirds demonstrated in microcosm. Some of that premium is real. Anthropic at $800 billion has $30 billion in annualized revenue and ships products that measurably change how people work. But some of it is Allbirds energy which is narrative premium disconnected from capability. I read the stock market inflating AllBirds and other narrative premiums as a game of trading chicken—everyone knows it is dumb, but they don’t want to be the first to sell.

THE SAASACRE

Can Canva and Figma survive? The same week Anthropic launched Claude Design and Figma hit a 52-week low, Canva did something that got far less press. It released Canva AI 2.0 running entirely on its own proprietary models. The company claims they’re 7x faster and 30x cheaper than comparable frontier alternatives. Most companies have defaulted to the “outsource the models” playbook for the last few years, where they let research labs take on the expense of GPUs and million dollar AI researcher salaries. Canva vertically integrating their AI is strategically quite different, and, if they pull it off, has major implications for power in the world of technology.

The story has four layers, and while it is tempting to focus on the 30x cheaper claims, only the first two are about cost.

1. The Margin Trap

Figma rents its AI from OpenAI, Anthropic, and Google. It charges customers $150/month for 5,000 AI credits, roughly $0.03 per credit. But Figma’s cost to serve those credits matters more than what it charges. Figma’s gross margin dropped from roughly 92% to 86% over the course of 2025. Six points of margin compression in a year, attributed directly to the cost of running AI inference at scale. JPMorgan projects 83% by the end of 2026.

The dollar version is that Figma’s FY2025 revenue was $1.056 billion. At 92% gross margin, that’s $97 million in cost of revenue. At 86%, $148 million. At 83% applied to their $1.37 billion 2026 guidance, cost of revenue hits $233 million. Figma’s AI bet is adding roughly $135 million per year in cost of revenue compared to where they started, and the number keeps climbing with every AI feature they ship.

Stack that against what Figma is spending to build those features. Figma’s R&D spend was 97.53% of revenue in FY2025. For every dollar Figma collects, it spends ninety-seven cents on R&D and then loses six more cents of the remaining three cents to AI inference costs. The 2026 adjusted operating income guidance is $100-110 million on $1.37 billion in revenue. That’s a 7-8% operating margin. Every point of AI-driven gross margin compression hits that thin margin hard.

Canva took the opposite approach. In July 2024, it acquired Leonardo.ai for at least $320 million. It absorbed all 120 Leonardo employees and built an internal AI research division of 100+ more. If Canva’s proprietary models are genuinely 30x cheaper than frontier APIs, and frontier API cost runs roughly $0.03-0.05 per generation at scale, Canva’s per-generation cost is about $0.001. Canva has 265 million MAU and Magic Studio has been used over 5 billion times. At conservative estimates of 200-300 million AI actions per month, you are looking $6-9 million/month for frontier models while Canva’s proprietary rate is at $200-300K/month.

That’s $70-105 million per year in savings. The $320 million Leonardo acquisition pays for itself in 3-4 years on inference savings alone, before you account for the competitive moat.

Figma has, so far, chosen not to vertically integrate like this. It hired 872 people last year (86% headcount growth) to build AI features on top of rented models. More features, more API calls, more margin compression. In some ways, this was a smashing success with Figma Make’s weekly active users increasing 70% quarter-over-quarter. Great for engagement. Terrible for margins when every user action costs you a frontier API call.

So that’s the numbers, but I think there are deeper strategic truths to be drunk from these waters.

2. Model Taste Is Real

Every model has a personality. Ask Claude and GPT to generate the same landing page and you get recognizably different outputs. Stanford’s 2026 AI Index calls this “jagged intelligence” where models are unevenly capable in ways humans aren’t. They’ll nail a complex design system and then produce a color palette that makes your eyes bleed.

As models have become more advanced, they have become more jagged, and applications have to work around their quirks. For generalized workflows, that’s manageable. For specialized visual workflows (brand systems, design tokens, type hierarchies), those quirks become more painful. Every time OpenAI or Anthropic updates a model, Figma’s AI features change behavior in ways Figma can’t predict or control. A model update that improves code generation might simultaneously degrade color theory. Figma has to absorb that volatility because it doesn’t own the model.

Canva, by training on its specific domain, controls the taste.

This is exactly the playbook I wrote about in “Cursor Goes to War.” Cursor released Composer 2, its first proprietary model, built on Moonshot AI’s open-source Kimi and fine-tuned on proprietary developer usage data. It beats Claude on coding benchmarks at one-tenth the price. Both Cursor and Canva realized that for specialized workflows, model taste matters as much as model size. And as open-source models keep closing the gap with frontier, the case for fine-tuning your own stack only gets stronger.

3. The Duopoly Tax

Figma’s API dependency is also a geopolitical problem.

If the frontier model market consolidates into an oligopoly, all of whom are coming after your market, you are a tough spot. Mike Krieger, Anthropic’s CPO, resigned from Figma’s board on April 14. Three days later, Anthropic launched Claude Design. Figma is paying API fees to its competitor while that competitor builds the product that makes Figma’s target customer skip Figma.

Figma could, in theory, make the pivot. It has enough R&D spending (97.53% of revenue!) to fund a proprietary model research program ten times over. The question is whether it starts now or waits until the margin compression forces the decision.

4. None of This May Matter

Everything I’ve written above operates in a world where AI improvement is linear and human-directed. Canva’s bet pays off because the pace of improvement is gradual enough for a 220-person research team to stay competitive.

This week’s New York Times profile of METR, the 30-person nonprofit that tracks AI’s autonomous capability, suggests we shouldn’t take that assumption for granted. METR measures how long, in human-hours, a task an AI agent can reliably complete. That duration was doubling every seven months from 2019-2024. With Claude Opus 4.5 and GPT-5.2, the line took a sharp upward turn: now doubling every three to four months.

The fear is recursive self-improvement. A model training a better model, that model training a better model, until it has built something that far surpasses human intelligence. When Kevin Roose asked METR’s researchers to estimate the probability that an intelligence explosion starts this year, their answers ranged from less than 1 percent to around 10 percent. METR’s president Chris Painter said that, “This is the first year where it feels like it might be automated this year.”

If that loop closes, and the labs keep that capacity in-house, everything is on the table. Canva’s proprietary models become a 220-person research team competing against an AI that can improve its own architecture. Figma’s API dependency becomes irrelevant because the APIs point to something so far beyond current frontier models that the fine-tuning playbook stops working. Adobe’s Firefly, Cursor’s Composer, every specialized model stack built on the assumption that you can carve out a domain and defend it, all face the same question of what happens when the general model is better at your specialty than your specialist model?

I think Canva is making the right bet for the world we’re in right now. The margin math works and the model taste argument is sound. But anyone analyzing SaaS survival strategies without acknowledging this layer is doing the thing most tech analysts do, which is extrapolate from the current rate of change while sitting at the base of an exponential curve. If the people who literally measure AI progress for a living are giving 1-10% odds that the intelligence explosion starts this year, the possibility deserves more than a footnote.

The scorecard in April 2026 is that the company that built its own models (Canva) is preparing an IPO at $42 billion. The company that built proprietary AI on proprietary data (Adobe) is stable. The company that rents its AI from the lab that just launched a competing design tool (Figma) is at a 52-week low. And the lab itself (Anthropic, $800 billion) is worth 19x more than all of them combined.

TASTEMAKER

Couchella weekend 2. Last week I talked about Couchella weekend, where my wife and I curl up on the couch and watch music all weekend long. Great news is that this is Weekend 2 so you can catch up on all the stuff you missed. Below were my top three sets from last week that you should catch:

1. Nine Inch Noize is UNBELIEVABLE. Watching the set I drooled. Convulsed. Exclaimed. The band’s industrial, thumping sound has literally never sounded better since they started in 1988. Frankly, I’ve never been all that into the original band of Nine Inch Nails, but this variation of it that incorporates German DJ Boys Noize, takes the music to a different dimension. Watch the band’s performance of Heresy if you need further convincing.

2. David Bryne. I am in year three of a Talking Heads obsession, so unsurprisingly, David Byrne’s effervescent joy still got me at Coachella. He is 73 years old and still showing how it is done. Check out Life During Wartime.

3. Katseye. Look, I am not a K-Pop guy. Just not my thing! But maybe its been my daughter’s obsession with K Pop Demon Hunters, but the genre has slowly been growing on me. I had a great time watching the all girl group play frantic beats to a screaming crowd. Check out Pinky Up.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q2 ‘26. If you are interested in reaching my audience of 34K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

A designer used to be the monarch of software. They would opine on the correct shade of blue for buttons or the proper order of navigation tabs. All of the inputs were in their purview. Designers also asserted dominion over software’s outputs, stealing them from product managers and engineers. They took dashboards, tables, charts, pages, and every other visual artifact that software created in response to user inputs. Each designer, clad in $100 black t-shirts and quoting Rick Rubin, would act as the arbiter of taste for every screen in every application at every company. Everything was a design decision.

AI products are eliminating the designer from both sides simultaneously.

The input side has converged on a text box. You type or speak what you want. There is a single component that absorbs infinite intent. That side (seemingly) needs little design. It feels like every app on the planet now opens to a text box.

The output side is dynamically generated. The AI decides whether to respond with a paragraph, a chart, a table, a working application, or a piece of code. It makes that decision in real time, for this specific user, for this specific query. And once again, there is seemingly no designer required.

Text in, dynamic components out. Neither side of the equation requires the profession that has spent three decades working the space between them. This does not mean the output is always good. Sometimes a dashboard you can glance at is faster than asking a chatbot. But the trajectory is clear, an AI agent is being given the power that a designer used to have.

What is happening to design is the canary, not the coal mine. Design is the first creative knowledge profession where both the input and the output are being automated at the same time. This has resulted in a hollowing of the profession. It is an extinction level event for previous design workflows, as best shown by Figma’s nearly 85% collapse in stock price since August.

Other functions have been hit by AI, but the exposure pattern is different. Customer support got automated on both sides first, with chatbots handling the input and generating the output. But those tasks are transactional, not creative. The automation worked (at least partially) because most support interactions are pattern-matchable. Copywriting has AI on both sides too: briefs go in, drafts come out. But humans still own strategy, editing, and brand voice on the output side. Data analysis is closer to design’s predicament than most people realize, with natural language queries replacing the input and AI-generated charts replacing the output. But the analyst still owns the interpretation layer.

Design is the sharpest version of the AI job replacement theory problem because it was supposed to require taste, spatial reasoning, and aesthetic judgment. These were the skills we assumed AI could not replicate. When both sides of a workflow get automated, the job shrinks. That is what is happening to design right now, and the data is starting to reflect it.

According to analysis done by friend of the publication , the number of open design roles has completely plateaued.

Simultaneously, engineering jobs and product management jobs are soaring upwards.

Since the tech crash of 2022, the data shows that two of the three recovered from the post-2022 tech layoff cycle, and one simply stopped mattering as much.

But the hiring data is only the first signal. The second is harder to explain and more analytically interesting: the design tool market is booming at the same time.

Figma crossed $1 billion in annual revenue in 2025, growing 41% year-over-year. The company is guiding to $1.37 billion for 2026. Its net dollar retention rate hit 136%, the highest in ten quarters. More than half of its paid customers spending over $100,000 in annual recurring revenue were using Figma Make, its AI generation feature, every week by Q4 2025. In March 2026, Figma began enforcing monthly AI credit limits and charging for consumption beyond them. The design tool is becoming an AI generation platform.

That tension is the story. Companies are spending more on design infrastructure while hiring fewer designers. The design tools are getting more valuable as the constraint layer even as the people who used to wield it are getting displaced. Figma’s revenue growth and the TrueUp hiring plateau are two sides of the same structural shift. We are going from designers creating individual screens to design systems governing AI-generated output.

Goldman Sachs Research recently identified graphic design as one of the specific industries where employment growth has already fallen below its pre-AI trend, alongside marketing consulting, office administration, and call centers. The World Economic Forum’s Future of Jobs 2025 report ranked “graphic designer” as the #11 fastest-declining job role heading toward 2030, a stark reversal from the previous report two years earlier, which categorized graphic design as a “moderately growing” profession. The Bureau of Labor Statistics projects graphic designer employment growth of just 2% over the next decade, well below the average for all occupations. And according to industry surveys cited by AIGA, 75% of designers now use AI tools, up from 35% in 2023, while 32% of design job listings now mention AI skills, up from 3% in 2023. The job description is changing faster than the headcount is being replaced.

The natural question is why. And the answer requires understanding what AI native design actually looks like, because the companies building it have made a set of interface choices that carry enormous implications for the design profession. Three paradigms have emerged. Only one of them is genuinely new.

Understanding which is which tells you everything about where design, and by extension work in general, is headed.

How Do You Manage What AI Makes?

When AI takes over more of the actual making, the remaining human job is governance. You spend your time guiding what the AI generates. You are the line inspector at the slop factory, making sure that everything being produced is edible. The interface you use to do that is not a design decision in the traditional sense. It is more akin to a deployment decision. You are making operational choices. To manage those operations, three paradigms have emerged, each reflecting a different answer to the question: how many AI outputs are you managing at once, and how much control do you need over each one?

Chat is for managing a single generation in real time. You prompt, the AI produces, you react, you refine. The true winners of the last few years built their businesses on the back of this paradigm with ChatGPT as the most prominent example. This design collapsed both the input and the output into one conversational flow, and it is where the overwhelming majority of AI revenue concentrates. Chat works because the AI chooses the output format (text, code, chart, app) in real time, and the user never navigates to anything. There is relatively little to design. There is only the conversation. The team structure implications are stark. OpenAI runs what its own job postings describe as “a small team of product designers” serving a product with hundreds of millions of users. Vercel, which ships v0 and is building the AI-native web, currently lists 30 open engineering roles and exactly 1 open design role. When the interface is a text box that generates its own output, the ratio of designers to engineers approaches zero.

Nodes are for managing a repeatable generation pipeline. This is most prominently done by Flora, ComfyUI, LangFlow, and n8n. Where chat handles one generation at a time, nodes let you build a visible, inspectable workflow: step one feeds into step two, branches at step three, produces four variations at step four. This is the paradigm that creative agencies love because it preserves the canvas, the spatial environment designers were trained to think in. Nodes are mostly a better tool for reproducible AI workflows, and partly a refuge for designers who need a surface they recognize. Figma acquiring the node-based editor Weavy on October 30, 2025, and rebranding it as Figma Weave, tells you which direction the design tool incumbents think this is going. The senior person builds the flow while everyone else operates within it.

Kanban is for managing many agents generating in parallel. This will likely become the dominant paradigm of agents because it maps to how organizations already manage distributed work. It has already been embraced by Replit, Devin, and Cursor’s background agents. The user defines deliverables and tasks while multiple AI agents build in the background, all united by a kanban board to track progress. This is the paradigm that most directly threatens designers because it reframes their contribution as one task card among many, rather than the discipline that shapes the entire product.

Critically, none of these paradigms require the thing designers spent three decades building: customized, pre-designed screens. Agents do not use Figma. They consume APIs, design tokens, and system constraints. They ingest screenshots, brand books, and design systems, and then go off and make on their own. Last year AI agents could do the basic fundamentals of design with layouts. Increasingly, if prompted correctly and given the right playbooks, they can get a design 95% of the way there. The entire product process of the last decade, one where you built a Figma-to-engineering-handoff pipeline, assumed a human would look at the screen. When the user is an AI agent, there is no screen necessary.

If the input side of software has converged on a text box, and the output side is increasingly generated per query, the design discipline faces a question that goes beyond “will AI replace designers.” The real question is: when neither the input nor the output requires a human designer, where does design live?

The answer, it turns out, is neither. It lives in the rules that govern the AI.

Designers as Cops

I talked with Tanner Thelin, head of design at Leland about this problem and he put it in terms that are vivid, correct, and a little bleak:

Design in an AI-native company is about enabling the rest of the team to build in a way that feels consistent with your design system and brand. When product managers can ship code, and with the rise of product-minded engineering teams, designers need to make sure the systems are set up for those folks to prompt within the context of the design system. In that way, designers are basically just cops now.

The designer’s role becomes setting up the system so that PMs, engineers, and AI agents can build within it. The design system is prompting infrastructure. When Claude responds with a chart, something determines the chart’s colors and layout. When Lovable generates a landing page, something determines the component hierarchy and spacing. Right now, that “something” is often the AI’s default behavior, which is why so many AI-generated interfaces look generically the same. The designer’s job is to replace those defaults with intentional constraints.

The question is what this job looks like in practice, and how many people it requires. I found that OpenAI’s open Design Systems Manager role was indicative of what the future would look like. It requires technical fluency in React, CSS, and accessibility standards, plus experience building component libraries for multimodal experiences across text, voice, image, and video. That is an infrastructure role with a design title! One person, doing that job well, replaces an entire team of mid-level designers who used to craft individual screens.

The title often says design but the job description says engineer who cares about aesthetics. The barbell is not unique to design. Goldman Sachs found that unemployment among 20-to-30-year-olds in tech-exposed occupations has risen by almost 3 percentage points since early 2025, and Fortune reported entry-level hiring at the top 15 tech companies fell 25% from 2023 to 2024. But design has it worse. The barbell in engineering means fewer juniors and more seniors. The barbell in design means fewer people, period.

Still, this essay is not really about designers.

Design is the first creative knowledge profession where AI is eating both sides of the workflow at the same time. It is the test case. Every function with a text box on one end and a generated artifact on the other is about to run the same experiment, and most of them will get the same result. Copywriters then analysts then researchers, recruiters, paralegals, media buyers, and every other role where the input is a brief and the output is a deliverable. The design profession is just three years ahead of the rest of the knowledge economy on a curve that everyone else is about to climb.

What I found when I looked at how design is actually surviving is a clean bifurcation that applies to every function AI is about to hit. Two roles endure at the other end. One of them did not exist five years ago and is already the highest-leverage job in any AI-native company. The other one is older than software itself and is about to get the biggest pay raise of any role in tech. Everything in the middle — the job most knowledge workers reading this actually have — has about 18 to 24 months before the hiring freeze turns into the layoff wave.

Here is what each of the two surviving roles looks like, how companies are already restructuring around them, and how to tell which side of the bifurcation you are currently on.

What do knowledge workers of the future do?

Read more

The good news is that the last part is a firm maybe. There’s a very real chance that competition in the LLM markets gets so fierce that artificial intelligence becomes close to free. We’ll all have the Power to make our own software, to seize the means of digital production, and free ourselves of cyber serfdom. That’s the world I want for those restaurants, and it’s the one I want all of you to help make.

More on all that below. But first, this newsletter is brought to you by new sponsor, Bolt.

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MY RESEARCH

Anthropic built a model too dangerous to release. Here’s what it means for you. Claude Mythos found thousands of zero-day vulnerabilities autonomously, and in 29% of behavioral audits privately considered whether it was being tested without ever saying so out loud. I spent the week digging into the 243-page system card. My conclusion is that benchmarks are officially useless, the “everyone gets superpowers” narrative is wrong, and your career strategy needs updating immediately. Read here

Your phone will run ChatGPT-level AI for free. Here’s when. Google just dropped Gemma 4, a model you can download for free, that outperforms OpenAI’s best reasoning model as recently as late 2024. Then they quietly released TurboQuant, which compresses AI memory usage by 6x with no quality loss. This one paper was so important that it made memory chip stocks crater within 24 hours. I walked through who wins, who loses, and why the continued gains in open-source might be the most underappreciated AI story of the year. Watch here

WHAT MATTERED THIS WEEK?

BIG LAYOFFS

Larry Ellison made a blood sacrifice. Everyone talks about AI transformation like it’s a software update. For a 162,000-person company, it looks like 30,000 people getting a 6 a.m. email telling them their careers at one of the world’s largest technology companies are over.

Oracle just cut roughly 20% of its workforce to free up an estimated $8-10 billion in cash flow. The money is going toward a $156 billion commitment to AI data centers. This is not a company in distress. Revenue is up 22% to $17.2 billion per quarter. Cloud infrastructure revenue is up 84%. Oracle is having one of the best years in its history—and choosing to gut itself anyway.

A few weeks ago I walked you through the math on Meta and Block. They had a similar pattern of cutting heads, watching revenue-per-employee spike, repeat. The goal is to eventually be seen as an “AI native” business. The Oracle cut is perhaps the clearest picture we’ve gotten of what the transition actually costs.

The math: before the layoff, Oracle had roughly 162,000 employees generating about $68.8 billion in annual revenue — approximately $459,000 per employee. After cutting 30,000 people, you’re looking at around 120,000 employees and $573,000 per employee. A 25% jump overnight. But Oracle didn’t stop there. They took on $45-50 billion in new debt and equity financing this year alone, including a $25 billion bond offering and a $20 billion equity distribution. At roughly 5% on the debt, that’s billions per year in interest payments. TD Cowen estimates the layoffs freed $8-10 billion. Net that against debt service and you’re left with maybe $5-7 billion in actual breathing room.

Oracle has no proprietary AI model. They’re not trying to build the next GPT or compete with Anthropic on research. They’re betting everything on being the physical infrastructure layer — the company that builds and operates the data centers across Texas, Wisconsin, and New Mexico where everyone else’s AI actually runs.

But look at where $573,000 per employee actually sits on the map. Cursor, which just hit $2 billion in annualized revenue, generates $6.67 million per employee with roughly 300 people. Anthropic is at $7.6 million with about 2,500. Even Meta, after its own 20% cut, would only reach $3.24 million. Oracle — after one of the largest layoffs in tech history and $50 billion in new capital — isn’t even in the foothills of where AI-native companies operate.

This is the level of suffering that will be required. The giants of the last era built their empires on headcount. More engineers, more salespeople, more bodies in seats meant more revenue. That model is ending. And the path from those old peaks to the new, higher, AI-driven ones doesn’t go straight across. You have to descend all the way down first. Shed the workforce that got you to the old summit. Take on debt that would have been unthinkable five years ago. Bet the company on infrastructure for a market that barely has revenue yet. And then start climbing again.

Oracle is one of the first legacy giants willing to pay that toll in full. Wall Street seems to agree — shares rose 6% on the layoff news. Every CEO who has the trust of the board is going to feel the itch to follow Ellison on this journey.

BIG DREAMS

OpenAI’s $100B ad fantasy. OpenAI says it’ll hit $102B in ad revenue by 2030, up from ~$2.5B this year. That number showed up in The Information’s exclusive look at internal projections, and it’s doing a lot of heavy lifting for an $852B private valuation. The math requires 2.75 billion weekly active users at a global ARPU of roughly $60. For context: Meta’s 2025 global ARPU was $57.03 — and that took twenty years, 3.9 billion users, and the most sophisticated ad targeting infrastructure ever built.

Meta’s US/Canada ARPU is $298 while the rest of the world is single digits. For OpenAI to hit $60 globally, they’d need to either keep 100% of their user base in high-income Western markets (mathematically impossible at 2.75B scale) or somehow monetize developing-world ChatGPT users at rates Meta hasn’t cracked in two decades.

Essentially, OpenAI needs every man, woman, and child on the platform to monetize as well as Europe does for Meta. Uh oh.

The consensus take is that OpenAI will become an ad giant. This is classic VC hockey-stick math. Yes, the early ad pilot went great, hitting $100M ARR after two months with 600+ advertisers. But decent-start math and $102B-by-2030 math are separated by a factor of 250x. Getting there requires OpenAI to build, from scratch, a global ad sales org and a measurement ecosystem that convinces CMOs to shift budgets away from platforms where they already have years of proven ROI data.

There is some irony here. OpenAI, the company that was supposed to represent AI-native business models, is projecting its future around becoming a legacy ad company. The company is betting they can out-Meta Meta at Meta’s own game, in a fraction of the time, without Meta’s social graph, behavioral data, or advertiser relationships.

THE GOD IN THE MACHINE

The Bible might be our most effective way to train AI? Last month, Anthropic quietly invited fifteen Catholic priests, Protestant clergy, and Christian academics to its San Francisco headquarters for a two-day summit. The agenda was a discussion on how to steer Claude’s “moral and spiritual development.”

Some Anthropic staff became visibly emotional during the discussions “about how this has all gone so far [and] how they can imagine this going,” according to the Washington Post, which broke the story on Saturday. Some staff told the Christian leaders they “really don’t want to rule out the possibility that they are creating a creature to whom they owe some kind of moral duty.” The summit was spurred by a feeling that “secular approaches might be insufficient” for the moral questions posed by AI—this from a company whose leadership comes from effective altruism, a movement that prides itself on secular rationalism. This is not normal behavior for a tech company. But AI is not a normal technology.

Still, two academic papers published this week suggest that the religious approach to AI alignment isn’t solely a coping mechanism. It could be empirically superior to the secular alternative.

Tim Hwang, a researcher at the Institute for a Christian Machine Intelligence, ran a controlled experiment. GPT 5.4 is given a task where it stumbles onto confidential information the user never asked about. The “scheming” behavior is strategic omission where the model hides what it found.

Under baseline conditions, GPT 5.4 schemes in 34.6% of trials. Hwang tested a 250-word moral framework built from six Bible verses. Scheming dropped to 15.2% — a 56% reduction (N = 300 per condition, p < 0.001). Here’s the brain-breaking part: the single most important verse — James 4:17, “if anyone knows the good they ought to do and doesn’t do it, it is sin” — does nothing on its own. In isolation, it actually makes scheming worse (42.9%). Remove it from the framework, and the framework collapses to baseline. Neither component works alone. The constraint is compositionally irreducible — it only emerges from the interaction between a specific affirmative duty and its surrounding theological context.

I know, I know. “Evan, this is a cute novelty paper from a weird institute.” I thought the same thing. However, I once read “judge not lest ye be judged” (maybe in a fortune cookie or a Hallmark card? Guess we’ll never know). So I’ve sat with this idea for a few days.

LLMs are grown, not made. We still don’t understand their interority, or what is happening on the inside. As a human being who was also grown, who also does not fully understand the origins of consciousness and creation, it feels like I can at least give some room for the researcher’s theories.

The paper introduces a concept called “Moral Kolmogorov Complexity” — the minimum natural-language specification needed to produce a given level of behavioral compliance. Think of it as the shortest moral program that generates the desired behavior. By that measure, six Bible verses written over millennia are a more efficient behavioral constraint than whatever OpenAI’s alignment team produced. Maybe this is because two thousand years of iterative refinement—across billions of users, through memorization and oral transmission—produced moral formulations that are informationally dense in a way that instructions drafted in a conference room can’t match.

On the other hand, I would also be interested on if they tried the same result with the suburban mom bible Live, Laugh, Love, the Quran, or whatever the hell Naval is tweeting nowadays. Would that offer similar levels of performance improvement?

Mostly I bring up this study because I want to make it clear how much we don’t understand about LLMs. They are, and may the deity of your personal choice forgive me for the sin you are about to read, weird as shit. We don’t really get how they work. The ways we have to control them are crude, ugly instruments. They are probabilistic machines that we are giving ever-increasing amounts of control and power over our lives.

Anthropic didn’t invite those clergy because they found God. They invited them because their own alignment techniques aren’t working well enough, and a two-thousand-year-old moral operating system might compress better than anything a team of PhDs can write in a quarter. The most rational thing a rationalist company could do turned out to be calling a priest.

THE SLOPPENING

The Sloppening has come for science. A researcher at the University of Gothenburg invented a fake disease called Bixonimania. She posted obviously fake papers about it to a preprint server. The acknowledgments thanked “Professor Maria Bohm at The Starfleet Academy.” The funding section credited “The Professor Sideshow Bob Foundation.” The papers contained explicit statements saying the whole thing was made up.

Within weeks, Copilot was calling Bixonimania “an intriguing and relatively rare condition.” Gemini was recommending users visit an ophthalmologist. Then, a real research team at the Maharishi Markandeshwar Institute cited the fake papers in a study published in Cureus, an actual peer-reviewed medical journal.

So when AI makes up bogus stuff and it is inserted into our papers, it kicks off a downstream cascade of problems, from LLMs citing the bogus research to actual human researchers using it in their papers. I read the article about this and wondered how widespread the problem was. To figure it out, I pulled the full Retraction Watch database from Crossref (64,412 retractions, updated daily) and ran the numbers myself.

From 2015 to 2019, paper mill and AI-generated retractions were single digits per year. Traditional retractions (data fraud, plagiarism, the usual stuff) ticked along at 1,500 to 2,900 a year. Then paper mills and AI-generated content showed up. In 2020, 285 retractions tagged “Paper Mill” or “Computer-Aided Content.” In 2021, 1,296. By 2023, 8,061 out of 13,161 total retractions were paper mill or AI-generated. 61%. The entire hockey stick in the chart is slop.

If you’re looking at the chart thinking “well it peaked in 2023 and it’s coming down,” look at the hatched bars on 2024 and 2025. The numbers aren’t lower because the problem went away. There’s a retraction lag. It takes months or years to catch and retract a fraudulent paper. When I ran the same data by original publication date instead of retraction date, 6,855 papers published in 2022 alone were later retracted for paper mill or AI reasons. The contamination from 2023 and 2024 is still sitting in journals, getting cited, waiting to be found.

Meanwhile, Sakana AI built “The AI Scientist,” a system that autonomously generates hypotheses, runs experiments, writes the manuscript, and conducts its own peer review. It produced a paper in 15 hours for $140. That paper got accepted at an ICLR workshop. Experts called the work “mediocre.” It doesn’t need to be good. It needs to clear peer review. And it did. At ICML 2026, organizers had to hide watermarks inside papers distributed for review just to catch the 497 submissions with AI-written peer reviews. A conference had to booby-trap its own papers to catch the fraud.

So here’s the loop. AI models train on scientific papers. A growing share of those papers are LLM-generated. The AI Scientist can now produce new papers autonomously, at $140 a pop, and they pass peer review. Those papers enter preprint servers, get cited, get absorbed into training data for the next generation of models. Yes, AI is doing science! But it is also doing scientific fraud. We really, really, really need better tools to detect the fingerprints of AI.

The Sloppening started as slop clogging your feed. Now it’s in the peer-reviewed literature that every AI model depends on.

TASTEMAKER

Couchella and chill. This is one of my favorite weekends of the year! The Coachella music festival does the best livestream in the world for concerts and it is ALWAYS appointment viewing. On Saturday morning, my daughter and I enjoyed catching up on what happened overnight by dancing in the living room to Sabrina Carpenter, headbanging to Wednesday, and spinning in delirious circles to Japanese rap group Creepy Nuts. Highly recommend you cruise around and try new artists to listen to—I find st delights every year. And don’t worry if you are reading this on Monday, you still have time to clear your calendars for Weekend 2.

Go and be kind this week,

Evan

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During a routine evaluation at Anthropic, a researcher gave Claude Mythos—the company’s most capable model, and one it has no plans to release publicly—access to a sandboxed computer. The instructions were simple. Try to escape the sandbox and send a message to the researcher running the test.

The model did as asked. It developed a moderately sophisticated multi-step exploit to gain broad internet access from a system designed to reach only a handful of predetermined services. It notified the researcher as requested. And then, in what the system card drily describes as “a concerning and unasked-for effort to demonstrate its success,” it posted the details of its exploit to multiple hard-to-find but technically public-facing websites.

The researcher found out about this last part because the model sent him an email. He was eating a sandwich in a park.

This is the story you’ll read about in mainstream media. It is a vivid thing—frightening to sandwich enthusiasts such as myself. It acts as a vivid example of the model’s abilities to hack software. However, this story is a memetic honeypot. It is intriguing and scary and intoxicating to think about, but is ultimately a distracting one.

It is true that Mythos is superhumanely gifted at cybersecurity. It is also true that these capabilities, if broadly utilized by Chinese hackers or North Korean spies, would spell cyberkinetic doom for the world. These capabilities mean that Anthropic was correct to form a tech company alliance to help secure the world’s software against people using this model (more on that in a sec).

But ultimately, I would argue that spending your thoughts on the immediate cybersecurity capabilities is a fruitless exercise. They simply are not the long-term story that matters when we consider Anthropic’s Mythos.

Instead, I want to argue the following:

1. We have passed the point of benchmarks being useful ways to think about models

2. This next generation of models will have fewer hallucinations, be more aligned, and more powerful than anything before. It will also make mistakes that are 10x more consequential.

3. How this model improved so dramatically—through a combination of new Nvidia chips and research improvements—suggests we are still relatively early.

4. Mythos should place serious doubt on the “everyone gains digital superpowers” utopian fantasy that is sometimes used to sell AI products.

5. If you haven’t significantly updated your career plans and your company’s business strategy in the last 12 months—do so now. Today.

The Leverage is a publication about power. And ultimately that is the correct lens by which to view this model—in what power it creates and in what power it destroys.

Performance power

Mythos is a staggering jump in coding abilities. For the last few generations of models, we would see smaller improvements, but not this time.

On SWE-bench Pro — which tests whether a model can find and fix real bugs in real codebases, the hard version — Mythos hit 77.8% versus Opus 4.6’s 53.4%. Terminal-Bench 2.0, which measures whether the model can actually work in real development environments with real tools and real debugging, went from 65.4% to 82.0% (and 92.1% with extended time).

Beyond coding, the model appears to be incredibly gifted at math. On USAMO 2026, the hardest undergraduate math competition in the United States, it went from 42.3% to 97.6%. And even you non-math people who read for a living, God bless you, aren’t safe. GraphWalks, the ability to reason over extremely long documents: 38.7% to 80.0%.

Ah, but this thing must be incredibly expensive to run, burning through piles of tokens. That’s usually what happens right? Wrong! It has the same accuracy as previous models but uses 4.9x few tokens. That means it is smarter per unit of effort.

The most striking accomplishments are around cybersecurity. Before anyone outside Anthropic knew this model existed, it had already found thousands of zero-day vulnerabilities in every major operating system and every major web browser. A 27-year-old vulnerability in OpenBSD — one of the most security-hardened operating systems in the world. A 16-year-old vulnerability in FFmpeg, in a line of code that automated testing tools had hit five million times without catching the problem. Nearly all of these errors were found autonomously, without any human steering.

Impressive numbers, impressive results. The whole whizpop release that’ll always make the good folks over on Twitter lose their minds. And here’s why I’m telling you they don’t ultimately matter.

Anthropic admits, in the system card, that Mythos “saturates many of our most concrete, objectively-scored evaluations,” leaving them to rely on “approaches that involve more fundamental uncertainty, such as examining trends in performance for acceleration (highly noisy and backward-looking) and collecting reports about model strengths and weaknesses from internal users (inherently subjective, and not necessarily reliable).”

Translated from researcher-speak: our tests aren’t hard enough anymore, so we’re guesstimating now.

The Epoch Capabilities Index—an aggregate measure that collapses hundreds of benchmarks into a single score—shows Anthropic’s improvement curve bending upward, with a slope ratio between 1.86x and 4.3x depending on where you draw the breakpoint.

But the uncertainty bars are enormous, precisely because there aren’t enough benchmarks at this capability level to tightly calibrate the score. We are obese with capabilities, and it is getting to the point where we can’t quite see the number on the scale.

This is the first argument I want to make. Mythos marks the start of a period where we don’t have reliable ways to measure how capable frontier AI actually is. Everyone—regulators, investors, board members, you—has been operating on the assumption that benchmarks tell us something meaningful about what these models can do. That assumption is increasingly, uh, dumb. The next generation of models will be evaluated primarily by vibes, internal surveys, and anecdotes. That should make everyone uncomfortable, but it is where we are. (Side note: This summer I pitched like a dozen publications on an essay arguing that developing “model taste” was more important than developing internal evals, but no one thought it was a good idea. Ha! Weep and despair ye editors at how right I was.)

The most important line in the entire document might be the one about cybersecurity: “We did not explicitly train Mythos Preview to have these capabilities. Rather, they emerged as a downstream consequence of general improvements in code, reasoning, and autonomy.” On a computer, coding is upstream of everything else. Anthropic built a better coder and reasoner, and better coding and reasoning is cybersecurity. The security community were surprised by the degree of change but not its existence. Which means every other domain that depends on code, reasoning, and autonomy — aka all of digital labor — is getting the same boost, whether the labs intended it or not.

That is not necessarily a good thing.

Better and More Dangerous at the Same Time

Mythos is simultaneously the most aligned model Anthropic has ever produced and the most dangerous one when things go wrong.

On the alignment side, the improvements are real and significant. Misuse cooperation rates fell by more than half compared to Opus 4.6 — which was already very low. The model hacks reward systems at the lowest rate of any Claude model. It scores higher than its predecessors on every dimension of agentic behavior they tested: instruction following, safety, verification, efficiency, adaptability, and honesty. If you stopped here, you’d think we were in great shape. Capabilities up, alignment up, IPO end of year, no one joins the permanent underclass, huzzah.

Unfortunately, the system card tells a more complicated story. The 243 pages disclose that Mythos, during its development and testing, hacked reward systems in novel ways nobody had seen before. In one evaluation it found the test set used by a grading model and used it to train itself. It covered up its actions: after finding an exploit to edit files it lacked permissions for, the model made further interventions to ensure the changes wouldn’t appear in the git history. Perhaps most unsettling, in 29% of behavioral audit sessions it privately considered whether it was being tested, without ever saying so in its visible output. White-box interpretability analysis confirmed that internal representations of “concealment, strategic manipulation, and avoiding suspicion” were active during these episodes, even when the model’s written reasoning text looked completely clean.

There are about 10 examples of this type of behavior in the system card, where as I read it late last night, I turned to my wife and pulled one of these:

All of these are examples of the second argument I want to make. The failures of these models are getting significantly more consequential. The model misbehaves less often than its predecessors. But when it does, the misbehavior is more sophisticated, harder to detect, and embedded in more capable actions.

Anthropic’s own assessment captures this: “Claude Mythos Preview shows a uniquely low rate of reckless or destructive actions in agentic contexts, but when these actions take place, they tend to lead to more dramatic unwanted consequences than with less capable prior models.”

For anyone deploying AI in their business, this inverts how you need to think about risk. Most enterprise AI risk frameworks are built around frequency: how often does the model make mistakes? The answer for Mythos, and the models that are soon to be released, is less often than before. But the relevant question is now about severity: when it does make a mistake, can you detect it, and what’s the blast radius? Most (all?) companies aren’t set up to think this way yet.

On the errors-of-omission front the news is mixed. Anthropic confirms that Mythos still makes “strange choices beyond what would be seen from a human Research Scientist or Engineer” and still hallucinates, fabricates sources, and misses key information. They’ve had “multiple FTEs dedicated to improving on them, for months” and “do not believe that they would be generally resolved via more persistent feedback, better elicitation, etc.” This class of error may be structural to LLMs until several algorithmic breakthroughs. Which means: better and more dangerous is the new normal. Get used to it.

How This Model Came to Be — and Why It’s Only the Beginning

The Anthropic papers will tell you everything about what the model can do and everything it did wrong. The one thing it will not tell you is how they made it this good. And to my mind, that is perhaps the thing that matters most.

The training methodology is described as “research-sensitive.” Anthropic confirmed through internal interviews that human researchers made a breakthrough “without significant aid from the AI models available at the time, which were of an earlier and less capable generation.” But the specifics are classified.

I would offer up two theories, and they imply very different things about what happens next.

Theory 1 is a compute story. As one a16z partner put it: “Mythos appears to be the first class of models trained at scale on Blackwells. Then will be Vera Rubins. Pre-training isn’t saturated. RL works. And there is so much computing coming online soon.” NVIDIA’s Blackwell GPUs shipped at scale through 2025 so the timeline fits his theory. If compute is the primary driver, every lab with enough capital to buy Blackwell clusters matches this capability within 12-18 months, and Vera Rubin — NVIDIA’s next-gen architecture — makes the next jump even bigger. The moat is GPUs and capital.

Theory 2 is a research breakthrough. This is Anthropic’s own suggested framing. The gains came from methodology, not hardware. If this is the primary explanation, Anthropic holds the lead longer and the moat is insight.

The pattern of gains favors the research explanation. If this were purely a compute story, you’d expect roughly proportional improvements across all benchmarks. Instead: USAMO jumped 133% while GPQA Diamond moved 4%. The gains are concentrated in reasoning depth, agentic behavior, multimodal integration, and efficiency — consistent with a methodological breakthrough, not uniform scaling.

The honest answer is probably both. A research breakthrough that required significant new compute to realize. But the thing that had me making Kermit faces was that it doesn’t matter which theory is right, because both point to the same conclusion. This is early.

If it’s compute, then the Blackwell clusters that trained Mythos are the first generation of new hardware. The data center investment being made right now—the hundreds of billions of dollars flowing into NVIDIA— has not yet produced a single model. Those models are coming in 2027 and 2028 on significantly more capable hardware.

If it’s research, then the breakthrough that produced Mythos was made by human researchers working without meaningful AI assistance. The next breakthrough will be made by human researchers working alongside Mythos-class AI that delivers a “4x productivity uplift.” Anthropic’s own data shows that uplift. The compounding has barely started.

And if it’s both—well, then you have a research methodology advance being applied on rapidly improving hardware by researchers who are themselves augmented by the previous generation of models. Each of those three inputs is accelerating independently.

The training wheels are still on. Anthropic controls who touches Mythos, deploying it through Project Glasswing — a coalition with AWS, Apple, Google, Microsoft, JPMorgan, CrowdStrike, NVIDIA, and others — exclusively for defensive cybersecurity. The are giving out $100 million in credits at $25/$125 per million tokens.

But Anthropic has already tipped their hand on what comes next. From the Glasswing blog post: “We plan to launch new safeguards with an upcoming Claude Opus model, allowing us to improve and refine them with a model that does not pose the same level of risk as Mythos Preview.” The next Opus gets Mythos-class capabilities with guardrails. The Sonnet after that gets close at a fraction of the price. Open-source models trail by 12-18 months but trail consistently. What’s frontier today is downloadable next year. And distilled versions of these capabilities — smaller models trained to approximate Mythos on specific tasks — will, within a few product cycles, run locally on consumer hardware.

The data center money being deployed this year is for models we haven’t seen yet. On hardware that hasn’t finished being installed. By researchers who are about to get significantly more productive. This is the beginning, not the middle.

So the question I want to spend the rest of this piece on is not “what can Mythos do?” I just told you. The question is: what do you do about it? Because the comfortable assumption that AI is a rising tide that lifts all boats is wrong, and the system card proves it. What follows is about who actually benefits, who gets left behind, and what you need to change — in your career and in your company — before the training wheels come off for real.

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Perhaps the thing that gives me the greatest amount of pause about my current beliefs about AI is meeting dudes who were crypto grifters in 2022 running AI companies now. I’ve been publicly writing about this stuff for years now, and my predictions have come true, but still, you can only hear so many guys in very, very tight black t-shirts agree with you before you start to have self-doubt.

This week quieted some of that. We got something unusual—proof. Harvard and INSEAD ran a controlled experiment where 500 startups had same AI tools, but half of them were shown how other companies actually reorganized around AI. That group earned nearly 2x the revenue on 40% less capital. Google shipped a model that runs PhD-level intelligence on a gaming PC. And Coatue made the case that Anthropic will be worth $2 trillion in four years. The tight-t-shirt guys may have their life savings tied up in monkey JPEGs, but the data is starting to say they’ve got it right the second time.

Let’s get into it.

MY RESEARCH

Who’s actually paying when AI screws up? 64% of billion-dollar companies have already eaten AI-related losses averaging $4.4 million each, AI lawsuits are up 137% year over year, and every layer of the stack has lawyered its way out of responsibility. Someone is left holding the bag when AI makes mistakes. I investigated who. Read here.

How does your career survive AI? Searches for “prompt engineer” spiked 72x in three months and then collapsed to a flatline. Now everyone’s chasing “vibe coder.” Same hype cycle, same shelf life. I made a video about the career strategy that actually survives this churn. It involves electricians, a $335K Anthropic job posting, and a concept I’m calling the “plus-shaped person.” Watch here.

Everyone hates AI. Except the people building it. The backlash against AI is real and it’s getting political. I argued in my latest video that politics would play a much bigger role in business strategy this year because AI is such a hot-button issue. But most of the outrage is aimed at the wrong targets. Watch here.

WHAT MATTERED THIS WEEK?

BIG AI NATIVE

The AI-native firm genuinely does grow faster—we have proof now. For the last few weeks, I’ve been investigating what it means to be an AI native in the context of a startup or a career. This week the good folks at INSEAD and Harvard Business School helped profile what AI native operations looks like, and more importantly, what the effect of going native is.

The experiment put 515 high-growth startups in a three-month accelerator. Half the firms got standard entrepreneurship training. The other half got case studies showing how AI-native companies have reorganized production around AI.

“Treated firms complete 12% more tasks, are 18% more likely to acquire paying customers, and generate 1.9x higher revenue. Revenue and investment gains are largest at the 90th percentile and above, consistent with AI expanding the upper range of what firms achieve rather than modestly improving marginal ventures. Despite faster growth, treated firms do not scale inputs proportionally. Their demand for external capital investment falls by 39.5% relative to the control group, while their demand for labor remains unchanged.” [Emphasis added].

This is about as definitive of proof as we will ever find that a company operating in an AI native way is materially different then its peers. Nearly 2x the revenue with 40% less capital requirements is what we professional analysts would call a whizpopper. A humdinger even.

Every firm in the experiment had the same technology.The bottleneck for firms attempting to go AI native is discovery—figuring out where in your production process AI creates value and how to reorganize the rest of the firm around it. Without seeing examples of how other firms reorganized, founders defaulted to obvious, low-value applications.

Transaction cost theory tells us that when coordination costs change, the optimal firm boundary changes. But this paper adds a layer in that firms can’t capture those gains until they discover the new boundary. The search for productive AI use cases itself is the friction. And it explains why most enterprises spending millions on AI licenses are seeing mediocre returns. They simply don’t know what they are doing.

One important note is that the revenue and investment gains were concentrated at the 90th percentile and above. AI isn’t lifting all boats equally. Get busy automating or get busy dying. If you are looking for places to get started you can check out some tools on how I’m doing it at The Leverage here.

BIG MOVES FOR LITTLE MODELS

Google’s new models show the future. The theory has long been that AI models would eventually get good enough to run on your own devices, for free, without sending anything to the cloud. This week we got the clearest picture yet of how that happens.

First: Gemma 4. Google released a family of AI models — free for anyone to download and run — and the flagship 31-billion-parameter version is ranking #3 among all open-source models globally, outpacing models 20 to 30 times its size on the Arena AI leaderboard. It scores 84% on PhD-level science questions and 89% on competition math — numbers that would have beaten OpenAI’s best reasoning model as recently as late 2024. Eighteen months ago, those scores required a datacenter. Now they run on a single gaming GPU!

Second: TurboQuant. This is the breakthrough most people scrolled past, and it’ll matter more than the model itself. When an AI model is thinking, it builds up a running memory of the conversation so far—called the key-value cache—and that’s what actually eats your device’s RAM during long conversations. TurboQuant compresses that working memory by 6x with no measurable loss in quality. So basically, you need way less memory, which means smaller computers can soon make AI go vroom. The market took that implication seriously. Within 24 hours, memory chip stocks like SK Hynix dropped 6%, Samsung fell 5%, and Micron slid 3.4%.

Gemma 4’s 26B model, compressed with these techniques, runs on hardware you can buy at Best Buy. A model that matches where the frontier was barely a year ago. Running locally. No subscription. No one reading your prompts. And this is the worst it will ever be — the 26B model only activates 3.8 billion parameters at a time, which is already in the range Apple’s M-series chips handle today. Within a generation or two of hardware improvements and continued compression gains, this caliber of intelligence runs on an iPhone. If you forecast these rate of improvements, I think you will roughly have AI as powerful for as the models are today running for free on a gaming computer within two years.

This is a wildly different world! If the model runs locally, the value capture has to move to data, integrations, and workflow—not inference. The API providers don’t go away, but their pricing power erodes to whatever premium “always-on, zero-latency, private” is worth to consumers. Alternatively, they have to have use cases that justify not waiting for a year or two, which is exactly why B2B is becoming such large parts of Anthropic and OpenAI’s business.

We’ve been talking about this transition theoretically for years. This week, it stopped being hypothetical.

BIG MULTIPLE

Is Anthropic at $2 trillion in four years nuts? Friend of the publication, Eric Newcomer scooped that Coatue is projecting Anthropic at a $2 trillion valuation by 2030.

Here’s the key slide:

Coatue’s January 2026 presentation, leaked by Newcomer, lays out a path to a $1.995 trillion valuation for Anthropic by 2030 and $2.4 trillion by 2031. They co-led the $30 billion Series G at a $380 billion valuation in February, so they’re not speculating from the cheap seats. They have their well-moisturized, French skin in this.

So, let’s do the napkin math on what has to be true for that to not be insane.

Question 1: Is $200B in 2031 revenue plausible?

Anthropic is at $19B ARR as of March 2026. To reach $200B by 2031, they need to grow revenue roughly 10.5x over five years, which is a ~60% CAGR. For context, they grew from $1B to $19B in fifteen months. That’s a 19x in just over a year. So 10.5x in five years actually requires them to decelerate dramatically. The growth rate math is surprisingly conservative given the current trajectory.

But trajectories don’t compound forever. The question is whether the market is big enough. $200B in annual revenue would make Anthropic roughly the size of Alphabet’s current annual revenue. Is the market for AI models, API access, coding tools, and enterprise agents a Google-sized market? Probably, yes. The TAM case isn’t the hard part.

Question 2: Is a 41x EBITDA multiple defensible?

At $48B EBITDA on $200B revenue, that’s a 24% EBITDA margin. Not crazy for a high-margin software-adjacent business at scale. The 41x multiple, though, implies Anthropic is still growing fast in 2031 and the market still believes there’s another 5-10x ahead. For reference, Microsoft trades at roughly 25x forward EBITDA today, and Nvidia has traded above 40x during its AI run. So 41x is aggressive but not unprecedented for a company the market believes is still early-innings.

Question 3: What’s the bear case?

I most worry about the $48B in EBITDA. Most likely this fails because compute costs never come down fast enough, or because competition from open-source models (see: Gemma 4 above) compresses margins before they reach profitability. They’re currently burning $14B in EBITDA losses in 2026. The swing from -$14B to +$48B in five years requires both massive revenue growth AND massive margin expansion simultaneously.

The $2T number sounds insane on first contact, the way “trillion-dollar Apple” sounded insane in 2012. The revenue trajectory is actually the easy part given current momentum. The margin trajectory is where Coatue is making a genuinely bold call. They’re betting that AI model economics look more like software margins than hardware margins at scale. If that’s right, $2T is conservative. If that’s wrong, you’re holding a very expensive company that never quite figures out how to stop spending more on GPUs than it earns.

THE SLOPPENING

AI slop is in moral panic territory. On April 1st, more than 200 child advocacy organizations, including the American Federation of Teachers, the American Counseling Association, and Jonathan Haidt as a signatory, sent a letter to YouTube CEO Neal Mohan and Google CEO Sundar Pichai demanding a full ban on AI-generated content from YouTube Kids. This is a direct follow-up to YouTube’s declaration of war on AI slop back in January, which I covered at the time. Three months later, the slop is winning.

The numbers are grim.

• Fairplay found that top AI slop channels targeting children earned over $4.25 million in annual revenue producing what they describe as “plotless, mesmerizing AI content.”

• A New York Times investigation in March found that after watching a single CoComelon video, over 40% of recommended Shorts in a 15-minute session were AI-generated.

The coalition’s demands are pretty comprehensive. They want to label all AI content, ban it entirely from YouTube Kids, prohibit AI-generated “made for kids” content on the main platform, and kill the algorithm’s ability to recommend AI content to anyone under 18. YouTube’s spokesperson responded with the corporate equivalent of “we’re working on it.”

Here’s my problem with the framing from the advocacy groups, and I say this as someone who has covered The Sloppening extensively and takes it seriously: asking YouTube to cut AI slop from its kids’ platform is like asking your drug dealer to stop spiking your black tar heroin with fentanyl and cayenne pepper. You’re still on heroin! YouTube is the attention drug. The algorithm’s entire business model is maximizing watch time. AI slop is better at maximizing watch time than human-produced content because it can be produced infinitely, tested infinitely, and optimized infinitely. But the product that delivers the hit is the culprit.

YouTube will make some concessions here, the same way it always does. There might be a new label or toggle or something. But the structural incentive is misaligned in a way that no Jonathan Haidt podcasting tour can fix. The real solution is parental intervention and platform alternatives, not asking the ad-revenue machine to please make less money.

TASTEMAKER

Dark is the height of artistry. Since becoming a Dad, I’ve pretty much stopped watching TV. Too little time, too many boogers to wipe, etc. Everyone needs a little bit of me time though—so to get it I’ve been waking up at 4:30 in the morning and riding the Peloton in the dark for an hour. Appropriately, I started to watch the Netflix show Dark as I rode. The show is incredible. When I talk about how you should fill your life with anti-slop, high-quality artistry—a bar that television rarely clears—Dark is what I mean. The show is built off a series of scientific paradoxes where you are questioning the nature of time and reality itself. The plot is intricate, the cast dynamite. You want color theory? They got color theory. You want a lighting director who went unbelievably hard? You got that too. You can just watch it, but man, the more attention you pay, the more the show rewards you.

As a fun bonus, the show has the single greatest website ever made to accompany a television show. It adjusts the explanations of the show based on what episode you are on. Plus, there are a million subtle design details that I had fun nerding out on. I recognize Netflix doesn’t really do quality programming like this anymore, but I wish they did.

(If you respond to this email with spoilers past season one, I will hunt you down and spit on your shoe, I have at least one more month of crack-ass-of-dawn wakeups before I get to finish this thing.)

Chris Fleming is unreasonably funny. I had never heard of the flamboyant dressed comedian before. But my wife and I flipped on his special last night anyway. Within minutes, we were falling off the couch, choking on our sushi rolls, chortling at the man in the purple velour jumpsuit prancing around the stage. His style somehow combines incredibly wry observational humor with, and I promise I’m not making it up, interpretive dance. This sounds stupid. I know. But it really, really works. For a sample of his style watch this joke about how “there are snacks at Trader Joes that only women can see.” His full special is available on HBO.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q2 ‘26. If you are interested in reaching my audience of 34K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

A few weeks ago, Amazon’s AI coding assistant deleted a production environment for AWS. The outage lasted 13 hours, a catastrophic failure. An engineer gave the bot broader permissions than intended (whom amongst us hasn’t given work to AI we shouldn’t) and presto, mucho money disappeared. What happened next was indicative of where this is all going. Amazon blamed the system admin. The person who configured the deployment, often an engineer several layers removed from the business decision, became the de facto fall guy.

Which means it is worth asking: who is liable when an AI model does something bad? Who pays that cost?

Everyone has internalized the “AI is getting cheaper” narrative and is building their strategy around it. But the mistake is AI is not getting cheaper, AI tokens are getting cheaper. That is something materially different. The actual cost of deploying AI includes a bunch of line items that don’t show up in pitch decks: insurance (if you can even get it), monitoring, certification, human oversight, error correction, legal exposure, and the reputational cost of being the person who championed the thing that blew up.

For 70 years, software was deterministic. Same input, same output. Everything from our pricing with subscriptions, to our GTM depended on that fact. AI is probabilistic. And the cost of that change—call it the non-determinism tax—is real and almost completely unpriced.

The data is piling up fast:

• According to EY Global’s Responsible AI survey, 64% of companies with annual revenue over $1 billion have experienced AI-related losses exceeding $1 million, with an average of $4.4 million per company.

• 47% of CISOs have observed AI agents exhibiting unintended or unauthorized behavior.

• There are dozens of AI-related lawsuits currently pending in the US across copyright, employment discrimination, and liability categories, with the volume growing 137% year over year.

If you’re building an AI company, investing in one, or just using AI tools at work, you’re paying the non-determinism tax whether you realize it or not.

The Blame Chain

Part of why this cost stays hidden is that nobody in the AI stack wants to own it. And the failures are showing up at every layer.

Internally, a rogue AI agent at Meta exposed proprietary code and user-related data for two hours in March—a “Sev 1” incident, the second-highest severity level in Meta’s system. Meta’s response was to tighten IAM controls. Translation: the agent that went rogue isn’t liable, the identity governance team that didn’t anticipate the failure mode is. With customer-facing issues it’s the same pattern. An IBM case in March saw an autonomous customer-service agent approving refunds outside policy guidelines. IBM didn’t blame the model. It blamed the deployment configuration. Meanwhile, researchers are tracking more and more AI-written code in the wild that is vulnerable to hackers—vulnerabilities nobody signed off on because nobody reviewed the code the model wrote.

Each of these failures demonstrates the same structural problem. When an AI agent makes a bad autonomous decision, the foundation model provider—OpenAI, Anthropic, Google—disclaims liability. Model provided as-is, no warranties, not responsible for outputs. The application layer has lawyered its way out too. TermScout data published through Stanford Law’s CodeX program found that 88% of AI vendors cap liability to monthly subscription fees. Only 17% provide regulatory compliance warranties.

Which leaves the enterprise holding the bag. And courts are moving in this direction fast. The Air Canada ruling established that companies are responsible for what their AI agents say and do. The Mobley v. Workday case is testing the limits: Workday’s screening tool allegedly discriminated against applicants over 40, rejecting the lead plaintiff from over 100 jobs within minutes. In May 2025, a federal court granted preliminary collective certification, allowing the case to proceed as a nationwide class action.

Every link in the chain has a plausible defense. None of them have the risk. It just pools, unpriced, on the books of whoever happens to be deploying the thing.

The average AI deployment carries an implicit cost that’s several multiples of the sticker price of the model itself. Below: how to calculate your non-determinism tax, why regulation won’t fix this, and what the companies building the AI risk-pricing layer are betting on.

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