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.

Read more

We are at the end of one of the wildest, most volatile three-month periods I can think of in technology’s history. I don’t smoke, but after reviewing the news this week, I felt an urge to light a cigarette.

So today, I wanted to look back at the deep currents of the last 3 months and tie those into some recent headlines. The world is changing so fast, so consistently, that if you don’t pay attention the important stuff will whip right by.

We’ll get to that, but first, this newsletter is brought to you by returning sponsor Crusoe.

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MY RESEARCH

How can you make sure your career survives the AI transition? The prompt engineer went from a six-figure dream job to obsolete in eighteen months. What happened? The mistake people are making is chasing AI-native job titles that have a shelf life of a few months. The real strategy is becoming what I call a plus-shaped person where you retain your expertise, but use AI to thicken the horizontal bar until you’re genuinely skilled across adjacent functions, not just passingly familiar. You become operationally dense, a bundle that’s hard to unbundle. I break down exactly how to build that stack and why the window to do it is closing fast. Read here.

WHAT MATTERED THIS WEEK?

BIG MONOPOLY

We are very, very, very concentrated in AI right now: The largest companies have raised such staggering amounts of cash that it is easy to grow numb to it all. Rather than just rattle off some round sizes, let’s try to get a little more rigorous with it.

The Herfindahl-Hirschman Index is how the Department of Justice measures market concentration. Under the current guidelines, an HHI below 1,000 is “unconcentrated.” Between 1,000 and 1,800 is “moderately concentrated.” Above 1,800 is “highly concentrated” and triggers antitrust scrutiny.

Nobody has calculated the HHI for venture capital by recipient company. Thank goodness you subscribe to this newsletter so we can solve that together.

In February 2026, global VC totaled $189B. Three companies raised $156B of that: OpenAI ($110B), Anthropic ($30B), Waymo ($16B). The remaining $33B was spread across thousands of companies. To calculate the HHI, you square each company’s market share and sum them. Those three companies alone contribute 3,712 HHI points. Add the remaining ~$33B — even if perfectly distributed across 1,000 companies, each contributing negligibly — and February 2026’s venture capital HHI lands around 3,700. That’s more than double the DOJ’s 1,800-point threshold for “highly concentrated.” It’s not close to the line. It obliterates the line.

The AI venture capital market, by DOJ standards, is more concentrated than any sector the government has ever investigated for antitrust. There are lots of caveats my critics could add here—these aren’t really “venture capital” rounds or something like that. While technically accurate, this misses the spirit of my comments here. However you define the boundaries of this market, there is no denying we, as an industry, are betting it all on these companies.

Without OpenAI-scale deals, the monthly numbers look ordinary. US funding dropped to ~$13B in March. It wasn’t like startups quit happening. It was just that the three companies that were the venture market just stopped raising for a month.

AI startups captured 41% of all venture dollars on Carta in 2025. By February 2026, that figure sits at 90%. The venture industry has become a concentrated bet on three companies with returns that exist on paper, denominated in marks that depend on future mega-rounds continuing. If either Anthropic or OpenAI fails, it will send a cascading series of catastrophes through the entirety of the United States economy. Yikes!

BIG ROBOT

Four robotics companies raised a combined $1.2 billion in a single week in March. Mind Robotics, a Rivian spinout building industrial robots trained on factory data, pulled in $500 million in a Series A co-led by Accel and a16z. Rhoda AI emerged from 18 months of stealth with $450 million for its video-predictive robot intelligence platform. Sunday raised $165 million at a $1.15 billion valuation for a household humanoid called Memo. And Oxa, a UK-based autonomous logistics company already deploying with DHL and bp, closed $103 million in its Series D.

Add Skild AI’s $1.4 billion from January (Series C, led by SoftBank, $14 billion valuation) and you get $2.6 billion across five deals in a single quarter. That is just from the headline rounds! I’ve heard rumors of similar deals, not yet announced.

That number deserves context. In 2023, total robotics VC was $6.9 billion globally, per Crunchbase. In 2024, $7.8 billion. Then 2025 nearly doubled the prior year to $13.8 billion. Five deals in Q1 2026 have already cleared more than a third of what the entire sector raised in 2023.

More important than the volume is how the composition of the funding is shifting. Seed and Series A rounds are meant to fund R&D where founders make science and sick demos. The rounds closing now are Series B through D. Sunday’s CEO said it plainly when he announced their raise: “We raised our Series B to stop giving demos.” The money is moving towards “how fast can we ship it?”

I modeled the unit economics of humanoid robots in “A Robot Walks Into a Coffee Shop“ last year. The crossover point where a robot becomes cheaper than a minimum-wage worker landed around $20-30 per task-hour for early deployments, dropping toward $8-12 at scale with declining hardware costs and increasing utilization rates. Sunday’s raise implies they think household economics are within reach in 3-5 years, which means their internal cost models for Memo probably target sub-$15 per task-hour at scale. If they can produce a robot that reliably folds laundry and clears tables for $10-12/hour of operation, the TAM is every dual-income household in the developed world. You should view these later stage rounds as the market indicating there is a very real chance of these numbers being achieved in the next five years.

THE SLOPPENING

Wikipedia banned AI-generated text, and the real story isn’t ideological. Everyone’s favorite way to cheat on their homework banned AI writing for their articles in a 40 to 2 vote. There are, of course, ideological concerns around LLMs, but the real problem is economic.

Generating an AI article takes about 30 seconds. Verifying one takes a Wikipedia editor 4 to 6 hours of checking citations, verifying studies, etc. Call that a 500:1 ratio. For every unit of effort to create AI content, it takes 500 units of effort to confirm it’s true.

The trigger was a bot called TomWikiAssist that autonomously authored and edited multiple articles in early March. One agent, running 24 hours a day, generated content faster than the entire English Wikipedia editor community could review it. The human editors were on the wrong side of an AI exponential.

I started to wonder how pervasive this problem is globally. Two weeks ago, I talked about how the WSJ Opinion page was publishing AI slop, and so too was basically every media company in America. Has AI-generated content taken over the entire internet already and we just didn’t notice? I think yes.

There has been surprisingly little academic research on this topic, so we are stuck pulling together a variety of different data points. I wanted to distinguish between the flow (new content being added to the web) and the stock (everything that already exists). For the flow, I used two large-scale studies: Graphite’s analysis of 65,000 articles crossing 50% by late 2024, and Ahrefs’ study of 900,000 new pages from April 2025 finding 74% contained at least some AI-generated content. For the stock, I estimated from Google’s indexed web (~50 billion pages per antitrust testimony and industry estimates), Common Crawl’s monthly crawl data (~800 million new unique URLs per month, or roughly 10 billion per year), and a conservative ~15% annual churn rate for deindexed and expired pages. To be clear, this is a guesstimate, but it feels reasonable to assume something radical.

AI has already taken over the flow of new content. The stock of accumulated human writing is the only thing keeping the internet majority-human, and it’s being diluted every month.

The natural follow-up question is if this change is a bad thing? After all, the media we consume shapes who we are and how we think. If all you watch is 9/11 conspiracy videos, eventually your number one thought will be “jet fuel doesn’t melt steel beams” or something. So if AI is helping to write at least 80% of the new content we are consuming, what happens?

Dan Williams argues that LLMs are fundamentally a technocratizing technology. Where social media democratized information by shifting power from expert gatekeepers to ordinary people (with all the conspiracy theories and populism that came with it), LLMs push the other direction. They converge users toward expert consensus. Ask Claude, ChatGPT, Gemini, or even Grok about vaccines, climate change, or trade policy and you’ll get broadly similar, evidence-based, expert-aligned answers. Dylan Matthews calls this “epistemic convergence” and draws an explicit analogy to network television in the 1960s, when three channels gave 90% of Americans the same nightly news.

In many cases, this is probably good. If LLMs pull millions of people away from conspiracy theories and toward peer-reviewed evidence, that’s a win. In this newsletter, we’ve previously discussed research showing LLMs can correct conspiratorial beliefs that most people assumed were beyond the reach of rational persuasion. So, if you think social media’s epistemic fragmentation is a crisis (and I do, mostly), LLMs are the closest thing we have to a corrective. Still, convergence toward a consensus is only as good as the consensus itself. And in the most important topics, there is often very little consensus to begin with.

That’s the Wikipedia problem. An LLM writing a Wikipedia article doesn’t just risk fabricating a citation. The topics where Wikipedia is most valuable are precisely the ones where the mean is most dangerous. Having an opinionated editor whose incentives are to maximize truth is extremely valuable because “experts” often have incentives that are not oriented towards truth-maximization. In cases like WMDs in Iraq, Covid denialism, (insert world crisis here), the correct view was held by a minority who pushed against the mainstream. Wikipedia’s editorial model, at its best, gives those minority views room through human editors who know enough to flag what the consensus is missing.

I’m not even saying AI content is worse than human content! I’m saying it’s wrong differently. Human editors are wrong in diverse, idiosyncratic ways. One editor gets an issue wrong; another gets it right. The errors are uncorrelated, which means the system self-corrects over time as editors argue with each other. AI is wrong in consensus ways, biased by its methods of training.

The Sloppening is about understanding how human thought and attention are reshaped by the economics of the internet. Expect to see repeats of this Wikipedia story in every single field where human judgement is the key financial bottleneck.

TASTEMAKER

Aussie punk rage: On a recent Tuesday afternoon, I showed up at my local gym, seemingly interrupting a testosterone convention. There were multiple 405-lb deadlifts happening, accompanied by a chorus of grunting dudes doing 315-lb bench presses for reps. Every jacked guy within 10 miles of Boston was in the weightroom with me that day. I knew that to not shame my family, I would need to lift some heavy-ass weights. To make that possible, I dug deep into my lifting playlists, and pulled out something powerful.

Amyl and The Sniffers is a punk band from Australia that is equal parts cheeky and pissed off. In an interview, their lead singer said the name comes from Australian slang for poppers — “you sniff it, it lasts for 30 seconds and then you have a headache — and that’s what we’re like!” They have a pulsating, pounding energy that makes you want to run through a wall. For my heaviest sets, I played their 2021 song Hertz. Use with caution, side effects include sweat, anger, rage, and a desire to grow a mullet. The missus and I are looking forward to moshing in the pit at their show in Boston this summer.

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.

In early 2023, Anthropic posted a job for a “Prompt Engineer & Librarian” that gave a fat salary of up to $335,000. The internet, predictably, went absolutely feral. The media coverage at the time was stuffed with breathless commentary on how the prompt engineer was the job of the future.

A 22-year-old named Alex Albert got the role. An incredible achievement, with a salary that would’ve made 22-year-old me, baby-faced and diploma in Sociology in hand, weep in envy. Yet within a year, he’d already transitioned to a new position. Today he’s Head of Developer Relations at Anthropic. The job title that launched a thousand think pieces lasted a grand total of twelve months.

Indeed data tells the same story. Searches for “prompt engineer” surged from 2 per million in January 2023 to 144 per million by April 2023, a 72x spike in three months. By late 2024, they’d collapsed to 20–30 per million, where they flatlined.

It feels reasonable to call this the fastest hype cycle in labor market history. From “six-figure career of the future” to obsolete in about eighteen months. The most famous prompt engineer in the world already has a different job title. And today’s answer to “what’s the AI-proof job?” is probably a vibe coder. Which, I’ll just say it, people being hired specifically as vibe coders are not long for this world either.

The mistake is that we either look for things entirely resilient to AI (which typically means manual labor) or we look for AI-native jobs, except that category changes every three months. The AI transition is just starting off; basing your career on the signals of today is like registering your toddler for the NBA draft. It is too early to forecast what this thing will look like when it grows up.

But there’s a version of this question that actually has an answer. And the answer is the opposite of what most career advice is telling you right now.

The Education Premium Is Inverting

A 2025 working paper from the San Francisco Fed found that the college wage premium, the earnings gap between degree-holders and high school graduates, has been essentially flat since 2000 after doubling in the prior two decades. The authors attribute the stagnation to demand factors: technology is no longer favoring college-educated workers the way it did in the late 20th century. A separate analysis from the Cleveland Fed went further, projecting that the premium will likely decline in the coming decades if technical change continues to be education-neutral.

The more education you have, the more likely your job involves sitting in front of a screen all day. And the more of your day you spend typing, the more exposed you are.

A March 2026 paper from Anthropic has been tracking millions of Claude conversations since early 2025 to measure what AI is actually doing across the economy. They broke every U.S. occupation into its component tasks using government labor data, then measured two things: theoretical exposure (what percentage of tasks AI could handle) and observed usage (what percentage people are actually using AI for). Computer and math workers have 94% theoretical exposure but only 33% observed usage. The gap between those lines is your window. But the gap is closing.

Meanwhile, the trades are in a structural boom. The BLS projects electrician employment will grow 9% from 2024 to 2034, three times the average for all occupations, with 81,000 openings per year. The median annual wage hit $62,350 in 2024, with the top 10% earning over $106,000. And that’s before the AI data center surge fully hit. Fortune reported that the U.S. needs 300,000 new electricians over the next decade just to meet data center demand, while 200,000 current electricians are expected to retire.

The most AI-native career path in 2026 might be becoming an electrician who knows how to use Claude to build a website, funnel demand, and manage their P&L. What a world.

You Need to Become Operationally Dense

This points to something bigger than “learn a trade” (though, genuinely, maybe we should all be learning a trade). The pattern that keeps emerging across every industry I study is that the AI-proof career is about stacking skills from adjacent functions faster than the models can commoditize any single one.

We are in the bundling phase of professional life. Career progression comes from automating depth and increasing breadth with the time that automation frees up. The go-to advice used to be that you should become “T-shaped,” an expert in one thing and capable in a lot of others. AI changes capable to adept.” I call this becoming a plus-shaped person. The vertical bar stays, you still need genuine depth in something. But the horizontal bar gets thicker. AI tools make it possible to operate at a genuinely skilled level across domains that used to take years to learn. The person who can write, design, analyze data, build prototypes, and manage projects isn’t spread thin anymore. They’re leveraged™.

I’ve seen this in my own business. The Leverage just wrapped its biggest quarter ever, and I run it with zero employees. AI handles things I used to do manually, which is somewhat scary, but also, great. Daycare is the most expensive bill I’ve ever seen in my life; please Claude, help protect me from it. Now I can move on to higher-value work.

The unglamorous version of this: you become the person in your organization who can competently do three adjacent jobs. You’re the marketing manager who can also pull her own data, build her own landing pages, and write her own briefs without routing through three other teams. You are operationally dense. When your boss runs the mental math on whether to replace you with AI plus a cheaper generalist, the answer keeps coming back no, because you’ve made yourself a bundle that’s hard to unbundle.

Oftentimes, you’ll see analysts like myself trot out the answers of “taste” and “distribution” as things you have to develop in order to have a successful long-term career. And those will be valuable assets! But not everyone is going to be newsletter writers or creative brand directors. That’s fine. (Actually, that is probably a good thing.) I think that office work will shift toward something less romantic than “taste” and “distribution,” but more durable.

But there’s a specific way to build that density, and there’s a closing window to do it. The Anthropic data shows massive gaps between theoretical AI capability and actual adoption. Right now, knowing how to use these tools well is a genuine competitive advantage. In two to three years, it’ll be table stakes.

The question is: what do you actually stack, and how?

Read more

This week I got nerdsniped by Uber’s CFO claiming they’ll have the largest autonomous vehicle fleet on Earth by 2029. So I pulled the partnership announcements, the capital deployment schedules, and the SEC filings, and I ran the numbers myself. Spoiler: they won’t. Most of their partners haven’t actually solved autonomous driving yet, which feels like a prerequisite.

Elsewhere, Amazon is building a phone again because apparently the Fire Phone’s spectacular flameout wasn’t enough to learn the lesson the first time, and The Sloppening has officially graduated from “your uncle shares weird AI images on Facebook” to “senior government officials might be making military decisions based on X feeds full of deepfakes.”

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MY RESEARCH

What is AI native? Investors and operators are behaving as if “AI native” startups will grow bigger, faster. And really, they are betting all of our careers on this notion! So it seems important to actually understand what that phrase means. I investigated. Read here.

Apple keeps stumbling into piles of money. The Neo is a marvel of supply chain mastery at $600. The Mac Mini is an incredible amount of power at the exact same price. And somehow, no matter which way AI goes, Apple will be the one who ends up capturing a lot of the value it generates. Watch here.

WHAT MATTERED THIS WEEK?

BIG CAR

Uber has finally awoken to the Waymo threat. Uber announced a $1.25 billion deal with Rivian on Wednesday to deploy up to 50,000 autonomous R2 robotaxis by 2031. It was the fifth self-driving partnership Uber announced in eight days and the fifteenth in the last twelve months. The company has finally started putting real capital to work in an attempt to stop Waymo—or theoretically Tesla if they ever figure it out. The goal of all these partnerships is to ensure there is sufficient diversity in autonomous vehicle suppliers on their network that they can compete.

A noble goal! So noble that they should’ve done this, like, three years ago. I think the firm has to be aware it is behind here and is trying to convince investors that isn’t the case. At the Morgan Stanley TMT Conference on March 2, Uber CFO Balaji Krishnamurthy declared that “by 2029, Uber has the largest AV deployment globally, and we’ll be facilitating more trips than anyone else.” This is a bold claim and one that no one at the conference pushed back on.

So, I did the math myself by compiling announcement timelines and capital deployment schedules from Uber, and I think Uber is doing some really funny math here. My forecast is that Uber will have less than half the autonomous vehicle inventory of Waymo by 2029.

Waymo has 2,500 vehicles on the road today doing 450,000 paid rides per week. It also has a 50,000-unit Hyundai IONIQ 5 order reportedly targeting delivery by 2028, a Chinese-built “Ojai” robotaxi already in autonomous service, $16 billion freshly raised, and a Mesa, Arizona factory designed to produce tens of thousands of vehicles annually. By 2028, Waymo will almost certainly have 50,000 vehicles deployed. Even in the most optimistic scenario where every single Uber partnership delivers on time and on volume, including Chinese partners like WeRide, Baidu, and Pony.ai operating in the Middle East, Uber’s total global AV fleet reaches maybe 28,000 vehicles by 2029. That’s roughly half of where Waymo will already be. The “largest deployment globally” framing only works if you redefine deployment to mean “trips facilitated across everyone else’s cars” rather than vehicles you actually control.

I also can’t help but wonder if the Uber CFO is fudging the numbers by assuming they get some of Waymo’s fleet on the platform.

But even this math, which I would politely call optimistic, is not the thing that makes me most worried about Uber’s strategic position. The bigger problem is that Uber’s 2029 fleet is almost entirely theoretical. The marquee partner, Rivian, the one where they are putting the most cash, doesn’t even have the technical capabilities to do this yet. Rivian’s own SEC filings note the company “intends to develop” Level 4 capability. The same is true for Nuro, Lucid, Volkswagen, Motional, Wayve, and Momenta. Of Uber’s entire Western partnership roster, only Waymo and Zoox have actually achieved Level 4 autonomy. And Waymo, the most important partner on the list, hasn’t expanded its Uber integration to a single new city since June 2025, choosing instead to launch Miami, Dallas, Houston, Orlando, and San Antonio on its own app.

The CFO promising the largest autonomous fleet by 2029 when most of your partners haven’t solved autonomous driving yet is like a 12-year-old promising he’s going to date Sydney Sweeney despite his voice still being a beautiful soprano. I admire the confidence, kid, but you need to hit puberty first.

BIG TECH

Amazon is building a phone again. The younger readers of my newsletter may not be familiar with the lore of the failed Amazon Fire phone, but it was a smartphone that had “3D” features, very limited apps, no access to the Google Play Store, and was cancelled within a year. It was absolutely not bussin.

So it is worth noting that Amazon is trying again. Reports said they were looking at two options at the polar ends of the screen spectrum. The new device will be a smartphone, tightly integrated with Prime memberships and AI, or alternatively, a “dumb phone,” inspired by the Light Phone, which has relatively few apps and is focused on reducing screentime.

If you look at the history of the company’s consumer hardware efforts, the most successful efforts have either been products at significantly lower prices or utilized Amazon’s ecosystem advantages. So, for example, the Fire’s failure came because they tried to do an existing category at premium prices while the Kindle made an existing category radically cheaper (and offered new technology with e-ink).

For the phone, the dumb phone movement seems far too small to be meaningful to a company of Amazon’s scale. These devices have, at most, a few million users. And the “make an iPhone but Siri isn’t ass” plan could be cool, but my sense is that every single GPU and chip is going to be needed for other AI use cases. Still, I hope they pull it off! The world needs more quality competition in this market.

THE SLOPPENING

The war in Iran has an AI slop problem. I’ve been writing about The Sloppening for a while now, and I’ve mostly framed it as a consumer problem. Algorithmic content degrades your experience on the internet, your brain turns into pixelated oatmeals, etc. etc. The Iran War has heightened my anxiety about this problem into the “this is existential” category. BBC found that the three most popular AI-generated fake videos from the conflict racked up over 100 million views in the first week. Hundreds of millions more views followed across fabricated satellite imagery, repurposed video game footage, and deepfake war clips. X’s own head of product admitted that 99% of the accounts spreading this stuff were doing it to game the platform’s Creator Revenue Sharing program.

There is this comfort I used to offer myself that “the elites are smart enough to not be tricked.” That view, unfortunately, turned out to be incredibly naive. The Sloppening doesn’t just fool your uncle on Facebook, it is coming for everyone. Remember the Venezuela raid? Photos from the Mar-a-Lago war room showed screens displaying X feeds—senior officials had literally searched “Venezuela” on the platform. These are the same feeds saturated with AI-generated war deep-fakes that are good enough to fool Grok, good enough to fool me, and are probably good enough to fool you too. Unfortunately, the guy holding the nuclear football has already been fooled by AI slop about cars and now that same slop is about missiles. The Sloppening started for me as a theoretical concern about people’s decreasing literacy around the humanities. It has now progressed to a national security problem within the span of about a year.

TASTEMAKER

Project Hail Mary: This is a sci-fi film that copied the Marvel movie formula (complimentary). Ryan Gosling shooting zingers with aliens makes for cute entertainment that hums along at a blistering pace. I saw an early release on 70mm film last weekend and it just opened up to the general public. The cinematography has no business being as beautiful as it is, so it is worth seeing it for that alone. A perfect one to take the whole family to. You should go!

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.

Are the startups being funded today ten times better than the cohort five years ago? You better hope to God that the answer to that question is a resounding yes, because otherwise we are all in real trouble.

The reason why this matters is that Silicon Valley is acting like the answer is already decided. Startups are burning more—OpenAI forecasts a $218 billion cash burn between 2026 and 2029. Venture funds keep getting bigger—the top five VC fund closes in Q1 2026 totaled $35 billion, more than half of all US venture capital raised in all of 2025. And the valuations are richer—each week I hear of deals where startups are getting 100-250x revenue multiples.

Top to bottom, everyone is building their strategy around the idea that this current generation of startups are going to be bigger, faster. This gospel has even permeated the public CEO brain as layoffs start to accelerate, with AI being offered up as the excuse. So whether you are in a cushy job at Google, or working in the startup mines, the assumption is that you gotta be “AI-native” or you’re gonna die.

The natural question, and one that I’ve been struggling to answer for myself is, uh, what the hell does that even mean? What is the difference between a normal tech startup and an “AI-native” one? What is the difference between a normal career and one that is fully AI-pilled?

One of my worries is that these strategy choices are incorrect and that these companies/funds are acting too aggressively. That’s capitalism for ya, big risk, big reward, no crying in the casino. But the consequences of getting AI native wrong extend way beyond the people writing the checks.

In either case, answering the question of what is an AI native turns from a theoretical exercise to an urgent, pragmatic question. So today, I’m going to do that in two parts:

1. What is an AI native financial performance?

2. What is an AI native product?

By the end, you’ll have a working definition sharp enough to evaluate whether any AI company you’re investing in, working at, or competing against is actually AI native or just wearing the costume. And more importantly, you’ll know what to do about it.

AI Pilled Growth

Even those whose job description says “invest in AI startups” have a surprisingly difficult time defining what they are looking for. I’ve spent weeks asking venture and growth investors variations on “what is an AI startup?” and gotten responses ranging from “grows faster than I thought possible” (good, but also means you could be investing in like, yogurt or something) “uses AI to solve problems for their customers” (sounds academically rigorous, but again, could apply to anything that slaps on a chatbot).

Perhaps the most useful lens is the one where we can be most quantitative. Money! AI Native companies should make more of it and they should make it more quickly.

To figure out if that was happening, I pulled 50 public tech companies on the basis of revenue per employee. As you can see below, SaaS doesn’t look so hot in comparison with the soaring heights of the Magnificent 7.

Much of this ratio’s performance is determined by the business model. If you are a SaaS company whose growth is directly bound by the number of sales staff you can hire, you’ll naturally have a worse revenue per employee (RPE), while the companies who have built platforms (Google, Meta) or outsource the labor intensive parts of their process (Nvidia, Apple) look much better.

Next, I scraped an astronomical amount of headlines, LinkedIn posts, and podcast appearances to analyze the startups that you could truly consider to be AI native. I broke them into three categories. Foundation model companies like OpenAI, AI-Native applications like Cursor or Harvey, and AI-accelerated legacy SaaS companies like Notion that have leaned hard into AI. Note: this data is fuzzy, but I am confident all these numbers are in the ballpark and in aggregate, telling.

The chart makes the structural shift visible. Foundation model companies compete with the Mag 7 on per-employee efficiency. AI-native apps spread wide, but even the median ($755K) beats the median public SaaS company by 67%. And legacy SaaS companies that have pivoted hard into AI, like Notion and Intercom, land right in line with their public peers which is impressive seeing how they are in such growth mode.

On a revenue per employee basis, the AI native companies are crushing. This is doubly impressive when you consider that every single one of their legacy competitors is frantically adding as many AI features as they can, and it isn’t slowing down the startups.

So the top-line story is clear: AI native companies are more efficient, and they’re winning despite every incumbent scrambling to catch up. But here’s what keeps me up at night, and why I worry more for the readers of this publication than for anyone else in the ecosystem.

If these investors and founders are wrong about AI native, the bubble pops with an unholy level of force, and tech careers take years to recover. If they are right, then betting correctly on the right AI startup, repositioning your investment portfolio, and timing your next career move could make this the most lucrative decade of your life. Either way, the RPE chart above isn’t enough to tell you which scenario you’re in.

Below, I break down what happens when you stress-test RPE against a specific vertical, why the legal tech data reveals a completely different story about AI pricing power, the product framework that separates companies worth betting on from the ones that will get repriced violently, and the cost dynamic that most AI bulls are quietly ignoring.

Read more

This was one of those weeks where the sheer velocity of AI’s impact on business became impossible to ignore. Meta is weighing its largest layoff ever. Anthropic is winning seven out of ten first-time corporate buyers over OpenAI. And when researchers finally stress-tested whether frontier models can do real strategy, the results were humbling.

It sounds bleak, but I actually think the picture is more interesting than it is scary. The revenue-per-employee bar is being violently reset, yes — but that’s also creating a world where a single talented founder can build something that used to require many employees. The models are replacing commodity work, but they’re failing spectacularly at taste, judgment, and long-term thinking—which means the premium on those human skills has never been higher. The people who sharpen those edges right now are going to have the best decade of their careers.

Let’s get into it.

MY RESEARCH

The zero human company. A startup called Polsia is promising what every overworked founder fantasizes about: AI that autonomously builds, codes, and markets an entire company for you—no employees required. With nearly 4,000 AI-generated businesses on its platform and $4M in annualized revenue, it looks like proof that the zero-employee company has arrived. So I put it to the test, feeding it my own business and watching what came out the other side. The result is my argument for why the collapse of creation costs doesn’t make founders obsolete—it just moves the competition to the things AI is worst at. Read here.

Prediction markets have turned gross. Prediction markets were supposed to be truth machines. The research is compelling. Put money on the line and people get honest fast. They’ve been shown to beat polls, expert panels, and economic models at forecasting what’s going to happen next. But something went wrong. Instead of becoming the world’s best information tool, they have devolved into chaotic sports gambling platforms. Worse, the structure creates genuinely dangerous incentives. I think prediction markets are one of the most important innovations in finance and technology. I also think they’re on a path that’s unhealthy for society. Watch here.

WHAT MATTERED THIS WEEK?

BIG TECH

Meta plans to lay off 20% of staff. As I warned last week, you can expect large-scale layoffs from tech giants. Meta is reportedly weighing cuts that would affect roughly 15,800 people out of its 79,000-person workforce. Meta spokesperson Andy Stone called the report “speculative reporting about theoretical approaches,” which is corporate speak for “we haven’t decided the exact number yet.”

Zuckerberg said in January that “We’re starting to see projects that used to require big teams now be accomplished by a single, very talented person,” Two months later, his company is preparing to act on that logic at historic scale.

Two weeks ago I showed you how Block’s 40% layoff brought its revenue per employee in line with AI-native startups. But the full picture is more dramatic than that. The gap between legacy software companies and AI-native startups is a canyon.

Salesforce generates about $500,000 in revenue per employee. Adobe, $760,000. These are two of the best software companies ever built, and they’re operating at a fraction of what AI-native companies produce. 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.

Meta today sits at $2.59 million. Cut 20% and that jumps to $3.24 million. Block, after axing 40% of its workforce, reaches $4 million. Both are still less than half of what Cursor and Anthropic generate per head. Even after one of the largest layoffs in tech history, Meta wouldn’t be halfway to the AI-native frontier. Block had to cut nearly half its company to reach $4 million. Salesforce and Adobe haven’t started cutting at all. Every one of these companies will be citing AI as the reason. Some of that is genuine! A lot of it is convenient cover for pandemic-era overhiring that executives would rather not own.

Founders need to wake up that the revenue-per-employee bar is being reset in real time. Venture capital is a scarce resource and if you want to compete for it and want to get investors excited, these are the new bars you have to clear. (I am very, very glad I chose not to raise money for The Leverage).

BIG LABS

People are dramatically misunderstanding Anthropic’s business. There’s a popular narrative about Anthropic right now: Claude is the coding model. Cursor and the coding agents are reskinning its API, developers love it, and that’s the whole story. New data from Ramp suggests that take is, at best, incomplete.

I sat down this week with Ara Kharazian, who leads the Ramp AI Index. Ramp’s data set is uniquely granular: unlike a credit card network that just sees vendor-level transactions, Ramp sees itemized purchases, broken down by provider, by model, and by whether it’s API spend or chat subscription spend. Here are four numbers that tell the real story.

24.4% of businesses on Ramp now pay for Anthropic. A year ago, it was roughly one in 25. Now it’s nearly one in four. Anthropic’s 4.9% month-over-month adoption growth in February was the largest single-month gain since Ramp started publishing the index. Meanwhile, OpenAI’s adoption rate fell 1.5%, the biggest single-month decline for any model company on record.

70% head-to-head win rate vs. OpenAI among first-time buyers. This is a complete reversal from 2025, when OpenAI was the default first purchase for companies buying AI. When a company buys AI for the first time today, it’s choosing Anthropic seven times out of ten.

~75% of corporate AI spend is API, not chat subscriptions. But that ratio is shifting. For most of 2025, Anthropic was almost exclusively an API business. That started to change around December, and by January, Anthropic had captured the majority of corporate chat subscription spend. Ara told me that Claude Code’s launch coincided with a significant adoption surge, but added: “A lot of it isn’t because of Claude Code. I actually think a lot of it is because of Claude Cowork.” If a desktop agent for non-technical users is what’s driving subscription growth, Anthropic is no longer just the developer’s model. It’s crossing over.

Ara told me that even if you strip out the largest whale customers from the data, the API to chatbot ratio trends still hold. The API business is diversified across use cases, and a meaningful chunk of what looks like coding-agent adoption is people going directly to Claude Code.

The “Anthropic is just the coding model” take was always too simple. The data says they’re building a multi-surface business with real consumer traction, a diversified API customer base, and a superior win rate over their biggest competitor. Not bad!

THE SLOPPENING

AI writing is more pervasive than you can possibly imagine. Apple’s 50 year anniversary letter was entirely AI written. A Wall Street Journal op-ed written about the killing of Iran’s Supreme letter last week? Also entirely AI-written. We have crossed the point where it is no longer just some junior marketer using these tools to write crappy SEO copy, but our most powerful elites, publishing important documents, all being done by AI.

Lest you think this is anecdotal data, check out some science. This study, published in October of 2025, looked at 186K articles from 1.5K American newspapers, and found that roughly “9% of newly-published articles are either partially or fully AI-generated.” Again, this is data from summer of 2025. What do you think the percentage is at now? I am willing to bet at least 15%, perhaps 20%.

They also analyzed 45K opinion pieces from the Washington Post, New York Times, and Wall Street Journal, and found that “they are 6.4 times more likely to contain AI-generated content than news articles from the same publications.”

The people who are supposed to be our leading analysts and writers are outsourcing their craft to the models.

Why are they doing this? Simple—the audience can’t really tell and often prefers the AI copy. The New York Times ran an experiment this week putting copies from literary geniuses like Cormac McCarthy against AI generated copy. And, you guessed it, the AI pulled ahead, with 54% of all votes being cast for the AI written copy. There are obvious flaws to this test; AI writing can be beautiful on the individual sentence level, but it fails as the length increases. Comparing similar length passages is not a fair test. Still, it is directionally indicative of where we are going. I would’ve thought that the erudite audiences of our leading media institutions would have more discernment than this.

I am open to the argument that AI writing 100% of a newspaper is fine. Journalists should be cultivating sources and scoops, not wiling away their time on copy. Shoot, I am even open to the thought that 100% AI analysis is fine too. As dire as that would be for my ability to pay for daycare, someday it is likely a model will be better than me at thinking through the problems I write about.

What I mostly worry about are the second order effects of this on society. These models are subtle things; they can shape your thinking in ways that are only obvious in retrospect. If our elites are trusting them with their, and by extension our, most valuable possessions—our intellectual freedom, our attention, and our minds—what does that do? I don’t know if that is a better world or a worse one, but I do know that no one knows the answer to my concern but we are all doing it anyway. The Sloppening originally competed for our attention, but now it competes with the process of thought itself.

AI is truly atrocious at business strategy. Researchers, including an old classmate of mine from college, just published the first real benchmark for whether AI can actually do strategy.

They ran 34 LLMs through the Back Bay Battery simulation, a Harvard Business School staple where you manage a company through a disruptive technology transition over eight years, balancing a profitable but declining core business against an unproven emerging technology.

The results really surprised me—Mid-tier reasoning models from early 2025 (o3-mini, o4-mini, Claude Sonnet 4, Gemini 2.0 Flash) beat the average MBA student at a top business school. But the frontier models like GPT-5 got meaningfully worse, falling below the MBA average.

(It is never a good day when you say the average MBA was smarter than your AI.)

The mistakes they made were pretty basic. GPT-5 would declare things like “Pause all emerging tech R&D due to long lead times and poor fit with current market requirements.” Classic Innovator’s Dilemma behavior, from the models that are supposed to be the smartest.

Even weirder, AI performance on strategy initially improved alongside gains in coding and science benchmarks, then the correlation broke. The models kept getting better at PhD-level physics and competitive programming while getting worse at placing bets under uncertainty. Strategy is about committing resources to uncertain futures with delayed, noisy feedback, and the biggest models default to backward-looking, risk-averse, protect-what’s-working behavior because that is what works in their primary use cases in coding and marketing.

I worry about this in conjunction with my writing point above. We are mistaking the outputs for the process, and it feels incredibly likely that CEO’s are asking Claude for help on how to plan for the future. Since they employ most of us, that seems pretty bad!

TASTEMAKER

The Spectator: A few years ago I climbed a New Zealand mountaintop for a sunrise hike. After hours of sweating and suffering, I anxiously awaited the spiritual high that always accompanies summiting. To my horror, a woman started blaring techno music on a speaker, put on a flouncy dress, and began a 30-minute photo shoot. The moment was ruined. This short film documented this type of phenomenon. Smartphones have changed how we experience natural beauty. The film is also very meta; it is deliberately slow and contemplative—I counted how many times I itched to reach for my phone and it is more than I am willing to say publicly. Free to watch on YouTube.

LCD Soundsystem is a live band: At my first LCD show, I arrived unbaptized. I had never heard a song of theirs in my life. So when the first song ended, I was just slowly swaying and not quite feeling it. Then I made eye contact with a big, muscular guy about 30 feet away from me in the pit—he proceeded to run straight at me, in an almost dead sprint, full-on crazy in his eyes. Right before we were about to collide, he skidded to a halt and grasped my shoulders,

WHAT IS YOUR NAME?

Evan

EVAN. THIS IS THE LAST NIGHT OF OUR LIVES. LET’S DANCEEEEEEEE.

He started jumping up and down, and I went with him. And right in that moment, the band descended into one of the all time great indie dance songs, “Daft Punk is playing at my house.” My friend group merged with his, and we danced like hooligans the entire night. It was one of the greatest live shows I’ve ever been to. My wife and I just bought tickets to LCD’s upcoming shows in Boston and I can’t wait. If you haven’t listened to them before, I highly recommend this concert film from 2011.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q1 ‘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.

When you ask little boys and girls what they want to be when they grow up, they all answer the same way, “I want to spend my one wild and precious life increasing shareholder value.” The hallways of our schools are replete with posters of Warren Buffet offering inspirational quotes about proper hedging strategies to produce alpha. And who can forget the motivational series from Peter Thiel wearing a mustache made of money, the copy demanding, “Got Monopoly?”

Obviously this is fiction. While I was one of the few children who innately loved inefficient markets—my elementary school banned me from trading Pokemon cards because I hustled too hard and made the other kids cry about getting ripped off—most people are not broken in the specific way that capitalism rewards, and just want to be an athlete or an astronaut or really anything that doesn’t involve understanding what a “software workflow” is.

This phenomena, namely the innate desire of humanity to do anything besides office work, is perhaps why so many of my friends are captivated by the idea of the “zero human company.” This is where you use AI agents to autonomously run a company for you. A zero human company makes the product and then distributes it to customers. It has all the hallmarks for an idea to go viral: Effort required? Minimal. The shareholders gaining value? Only you. Yacht’s acquired? Quite a few. All you have to do is go to the AI of your choice and say, “Create a billion dollar business, make no mistakes.”

The tiny issue is that, of course, this idea is a complete farce. No AI model today can accomplish this task. And even in the world where the models are smart enough to do so, everyone will have access to them, thus increasing competition, which will increase distribution costs, and eventually erode margins to zilch. If digital powers are as abundant as a ChatGPT subscription, then competition switches to other bottlenecks (customer attention, access to capital, etc). I have been banging on this drum since 2022.

Over the last few weeks, I have worried that I am a moron and wrong. The cause for my concern is the startup Polsia. The way the company markets itself is pretty remarkable, so I’m going to quote it in full,

“Polsia thinks, builds, and markets your projects autonomously. It plans, codes, and promotes your ideas continuously — operating 24/7, adapting to data, and improving itself without human intervention.”

Yes, the company is doing what I’ve spent years saying isn’t possible. It builds autonomous companies that should, supposedly, make money without you being involved. The company has seen stellar results over the last month or so, and hit a $4 million annualized revenue run rate since launching in December.

What is happening here? That this startup has 3,890 companies being managed by its AI seems like strong evidence that my whole shtick the last few years is dead wrong. Have I doomed myself, and by extension, all of you my beautiful readers, to the permanent underclass with my errors?

Let’s investigate.

Employees? Where we’re going, we don’t need employees.

The clue to what this thing is comes from the description of how it “plans, codes, and promotes your ideas continuously.” [Emphasis added.]

This means it generates a business idea, writes code to create that idea, and then markets that idea to the target customers.

Plan

To begin it starts by asking if you want to build a new business or add to an existing one. I already have had enough of my hair turning grey running The Leverage so I told it I wanted to sell something using my existing company.

Polsia’s first idea was The LeverageOS, a vertical SaaS and AI agent product that would help newsletter operators run their business. This is truly, godawful idea for a variety of reasons. Just to point out the most obvious one, newsletter writers are overly-opinionated, technoilliterate assholes who will spend the least money possible on software (ask me how I know.)

I could try refining this idea for 30 minutes with the agent, but instead, I had it start again to find something to sell to my audience “founders, investors, and senior tech operators.” The next six suggestions it had, well, uh, sucked. They were pond water ideas—pitches that looked sorta ok, but were ultimately shallow.

As Paul Graham wrote in his classic essay Do Things That Don’t Scale, “The feedback you get from engaging directly with your earliest users will be the best you ever get. When you’re so big you have to resort to focus groups, you’ll wish you could go over to your users’ homes and offices and watch them use your stuff like you did when there were only a handful of them.” Polsia doesn’t have that. It has never read the replies to my posts, never sat in my DMs listening to what you’re frustrated about. It’s doing smart pattern-matching on market categories. That is not how a quality startup is built! Theoretically I could give it all that data, but that seems like a lot of work and the interface is not designed for that.

Eventually, I gave up, and handed it the idea for an app I’ve been working on already.

Codes

Here is where Polsia did the best. The AI built a detailed plan, asked multiple clarifying questions on what I envisioned for my product, and then got to work coding it. The product I am vibe-coding is fairly complex—it requires API calls to prediction markets, user authorization, and robust security that users would trust their personal information to it. Not easy to build. Still, Polsia got it roughly working, and did it in the time it took me to go to the bathroom and get a snack.

Still, it was only 80% of the way there and I felt frustrated at my lack of control over the code and design. Sure, I could go back and forth for three hours with Polsia to guide it via a chat interface, but I just knew that it wouldn’t be an efficient use of my time. Coding agents require more specialized interfaces than a mere chat window.

Keep in mind my original thesis here. If AI dramatically reduces creation costs, that means the minimal baseline of acceptable software products are infinitely higher. Each page, each interaction of the application has to blow the customer’s socks off. I have high standards and so do you.

So, I did what any self-respecting founder would do: I took Polsia’s output, thanked it for its service, and moved the project over to Replit. The difference was immediate. With Polsia, I was describing what I wanted and hoping the agent interpreted me correctly. With Replit, I was looking at the actual design, code architecture, and making decisions myself. The AI was still doing the vast majority of the work—I am not writing CSS by hand like some kind of animal—but I was the one deciding what got built and how it felt to use.

This is the part that the “zero-employee company” evangelists gloss over. The last 20% of polish for any product is the only part that matters. That stuff can’t be autonomously generated because it requires a point of view. It requires someone who has opinions, and the tools to act on them quickly. AI agents will likely one day be able to use those tools as good as you, but that day is not yet here.

We are only 2/3rds of the way into the Polsia process, but you can likely guess my point already. “Zero human companies” are manifestations of the shifting vectors of competition for startups. AI simultaneously raises the quality floor for what a differentiated company has to offer, while raising the possibilities of how large a company can become. A SaaS app is not the afternoon project of someone—it is no longer a venture backable startup. And a billion dollar outcome is no longer enough to shoot for. It needs to be $100 billion.

I’m getting ahead of myself. The last thing Polsia does is get people to pay attention.

Promotes

I am not here to be a hater. AI-automated marketing actually works. There are people out there right now using AI to spin up hundreds of programmatic SEO pages, blast cold email at scale, and generate dozens of ad creative variants per day. They’re making money. The workflows are real.

Polsia does variations of this: Meta ads with AI-generated video, cold email outreach, Twitter posts, SEO content, you get the idea. And look, if you’re selling a generic B2B SaaS tool into a nice enough market that is currently lacking in competition, this is probably fine. You can brute-force your way to some amount of revenue with volume.

But, once again, there are more specialized tools out there to do each of these tasks. For each of these channels, there are already specialized tools that do each job better than a generalist agent ever will. And, every single one of these tools is shipping their own AI features as fast as they can. The autonomy offered by companies selling you zero-human companies is a feature that the rest of the industry is copying by the quarter.

Theoretically a general agent will have access to enough data scattered across your company that they could theoretically be smarter then the silod agents offered by software companies could be. I’ve sold a company, and helped multiple startups scale through their first ten million in revenue, and with the benefit of that experience, let me tell you an uncomfortable truth. Most of the early shareholder value is created via the fine art of winging it. Great founders are data-driven, but for most of the decisions you are making in the starting years, the data is sparse, and often contradictory. You have to build a bone-deep intuition for what your customers want. I fundamentally just don’t think AI currently has the ability to do so.

In a gold-rush, you could sell shovels or you could sell shovel parts

So what is a zero-employee business?

Let’s start with what Polsia actually is, because I think it’s more interesting than the hype suggests. They’re at roughly $4 million in annualized revenue, charging $49 a month. That’s around 6,800 paying customers. They’re also running inference on every one of those companies. My guess is that their margins are razor thin, maybe even negative on heavy users.

But that is why I find this company so compelling and why I want it to succeed—their aligned business model. Polsia takes 20% of revenue from the businesses it builds. Polsia is betting that some of these companies will actually work, otherwise this is just an experiment that might break even. And some of these companies will likely be profitable! If you’re building a simple tool into a big enough market and letting AI brute-force the distribution, you’ll find some customers. The question is whether you’ll find enough to build something durable, or whether you’ve just created a temporary arbitrage that closes as soon as everyone else spins up the same thing.

Here is what I think is actually true, and it’s not as bearish as you might expect.

First, we are watching the abstraction layer of business shift in real time. Ivan Zhao, the CEO of Notion, put it simply this week: “The future of knowledge work is code.” I wrote on Sunday about OpenAI building an entire product with “zero lines of manually written code”. Their engineers “work at a different layer of abstraction” by building systems to guide the AI. This is the long, repeating cycle of computing. Just as C++ replaced writing in binary, AI agents are replacing writing code entirely. It’s helpful to understand how the lower layers work. It is no longer necessary. Polsia is an early, messy attempt at pushing that abstraction layer even higher—to the point where you don’t even need to prompt the AI yourself. They’re not there yet. But the direction is right.

Second, the bottleneck has moved, and it’s moved to the places AI is worst at. It used to be that building software was hard and expensive, so the companies that could ship fastest won. AI demolished that. Now the bottleneck is three things: taste, distribution, and attention. Taste is knowing your customer well enough to make the thousand small decisions that separate something people tolerate from something people love. Distribution is having a channel that you own and that can’t be replicated by someone else spinning up a Polsia instance. And attention is the scarcest resource of all. When everyone can build everything, the competition is over who can get anyone to care.

I wrote two years ago that if everyone has access to the same AI tools, Syndrome Syndrome kicks in: if everyone’s super, nobody is. Polsia’s own dashboard is the best evidence I’ve seen that this is playing out. There are 3,853 AI-generated companies on the platform. They are all competing for the same finite pool of customers using the same set of tools. The winners will be the ones who already have the audience and taste to know what to build before the agent did. Those advantages don’t come from a $49 subscription. They come from years of compounding work that no agent can shortcut.

So no, I haven’t doomed us to the permanent underclass. But I haven’t been proven wrong, either. The zero-employee company is the creation side of the equation finishing its collapse. And when creation is free, demand is all that’s left to fight over.

Here is how work used to happen. You would hire a human to do a task. Hiring more humans meant more tasks getting done. When the costs of communicating between those humans got too high, you would hire a manager to centralize that coordination. Technology’s job was to reduce coordination costs and increase task throughput. Easy peasy, monopoly man squeezy.

I’ve been asking myself how, or if, AI changes that.

For example, my friend Nat has an “autonomous business” run by an AI agent that’s made $102K in the last 30 days. That is a crazy sentence to type! But Nat is still running the business by giving the AI agent direction on what to build and help with distribution through Nat’s social media. I can’t help but wonder if this is truly materially different than how business has always been done.

Not to be glib—but this ultimately comes down to The Leverage™ Nat can get out of his time. (Like the name of this publication! Almost like I planned for this sort of thing to happen when I named it!). AI enables Nat to do more, be more ambitious, be more daring with his work. Therefore, the call for technology companies is to redesign their products to raise the ceilings of possibility, not merely automate the way labor used to work.

This week was full of evidence of how painful—and lucrative—that transition is going to be.

But first, this edition is brought to you by one of the tools that gives me the most leverage, Granola.

I’ve been using Granola as my meeting note-taker for months, but their new MCP integration is what turned it from a nice-to-have tool into genuine infrastructure.

Here’s my workflow: I take about 10 calls a week with founders, investors, and operators to help me write quality research. Often, the best insights from those conversations used to just...evaporate. I’d have some half-formed thesis sitting in a transcript I’d never reopen and would promptly forget about. Now, when I’m writing, I can pull directly from my Granola notes through Claude via MCP. It connects the dots between what a robotics founder told me three weeks ago and the earnings data I’m analyzing today.

This is the actual promise of AI note-taking that most tools miss. It’s not about summarizing your meetings (commodity feature, everyone does this). It’s about making your conversations compounding assets—searchable, connectable context that feeds into your real work. It doesn’t just work for writers, everyone from founders to scientists love using Granola.

If you’re doing any kind of research or analysis work, Granola is worth trying. Readers of The Leverage can get their first month free using the link below. Highly recommended!

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MY RESEARCH

The rise of hyper-creators. In a world where people like Nat can produce significantly more products than they used to, what happens? There will be new bundles offered by individuals that are centered around a vibe, a perspective, a way of examining the world. For the next few years, there is a chance to build a multi-million dollar empire for yourself. Read how here.

SaaS is breaking…what comes next? I have some thoughts on what happens when applications become essentially free. Watch here.

WHAT MATTERED THIS WEEK?

BIG LABS

OpenAI’s engineers aren’t writing code anymore. The Department of War’s golden AI supplier published a blog post last February that has filled me with melancholy all month. The post titled “Harness engineering: leveraging Codex in an agent-first world” talked about how the company had a team of engineers build a product with “0 lines of manually-written code.” This resulted in a wild increase in efficiency. The firm estimated that they built the product in “about 1/10th the time it would have taken to write the code by hand.”

It wasn’t like they just said “Codex, go make me a product.” Instead, the engineers graduated to thinking about systems versus code. “Humans always remain in the loop, but work at a different layer of abstraction than we used to. We prioritize work, translate user feedback into acceptance criteria, and validate outcomes.”

Building something in 1/10th the amount of time is such a profound acceleration that there is little doubt in my mind that this will be the future of software engineering. How could it not?

Still, that sense of melancholy has persisted. This week, I put my finger on why I was so disconcerted—by moving to a “different layer of abstraction” engineers were moving away from the deep pleasure that comes from knowing how a thing works.

Maintenance is a spiritual pursuit. Every action that takes us further from understanding how the world works, no matter the economic rationale for doing so, is one where we lose a sense of who we are. For more on this feeling, please read my review of Stewart Brand’s Maintenance.

BIG CODE

Is Cursor doomed? It is hard to worry about a business that has hit two billion in annualized revenue, but still, the vibes around AI darling Cursor are distinctly off. The Cursor team strategically leaked that their annualized revenue had doubled in the last 3 months to help calm some of the comments on X, but revenue today is not strategic power tomorrow.

I’ll allow the Cursor CEO and cofounder to explain what has shifted,

“When we started building Cursor a few years ago, most code was written one keystroke at a time. Tab autocomplete changed that and opened the first era of AI-assisted coding.

Then agents arrived, and developers shifted to directing agents through synchronous prompt-and-response loops. That was the second era. Now a third era is arriving. It is defined by agents that can tackle larger tasks independently, over longer timescales, with less human direction.

As a result, Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software.”

TL;DR is that the product strategy that got them to that two billi in ARR is essentially kaput. They now need to pivot to helping engineers (and everyone else) manage a fleet of agents that are building software for them.

Unfortunately, this is the exact vision that both OpenAI and Anthropic have. When you have key suppliers trying to move downstream into your market, you have to vertically integrate. To do so, the Cursor team has staffed up with AI researchers who are fine-tuning open-source models from China for coding.

I am one of the few, the proud, people who still believe Cursor can win. Selling a software factory comes with much higher costs. My friends who are fully Claude Code-pilled are easily spending $100K+ in tokens. So if Cursor can come to enterprise CTOs and say “we give you the security and controls of OpenAI at 1/10th the cost,” they’ll continue to build a huge business. Now all they have to do is pull this off.

THE LABOR

Two things are simultaneously true about tech hiring:

1. We are in a period of wild job loss as companies shed excess staff from Covid and use AI as an excuse to do so.

2. AI’s increased leverage does mean you should staff your company profoundly differently. Appropriately structured firms can’t hire fast enough.

The disconnect is that there are very few companies that fall into the latter category. As such, we are in a period of job loss in the tech industry.

TASTEMAKER

Three recommendations this week:

1. An album to put some hair on your chest. As part of Operation Dad Bod, I’ve been hitting the gym a lot more this year, and accordingly, have been back into rock in a big way. My favorite record recently has been “Something to Consume” by Die Spitz. Highly recommended if you are seeking an otherworldly pump. Listen here.

2. A live rendition of one of my favorite songs. Rosalía’s Lux was my favorite album last year. Beautiful, aching, artistic: it pushed the boundaries of what a popular album could be. She just performed at The Brits in an iconic set that included guest artist Björk. Watch it here (warning, this is super European).

3. Have a kid. I am going to finish typing this sentence and go get a hug from a little toddler who wants nothing more in this world than to see me. There is literally nothing better.

One last ask—I’m trying to get to know all of you better. Would you mind spending two minutes filling out this survey? It’ll help me make this newsletter more useful for you.

Take the Survey

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.

Two years ago, Sam Altman predicted that there would soon be a wave of “one-person billion-dollar companies.” Founders could use AI to replace employees and keep all that payroll expense as profit for themselves.

This idea never made sense to me—foundation models are widely available, and if everyone has the same AI superpowers, distribution gets challenging. So far my rebuttal has held true: while there have been many companies growing quickly, there are no one-person billion-dollar companies.

However, there has been a lucrative wrinkle that I didn’t quite foresee. To understand it, you need to understand my three laws of AI:

1. AI dramatically decreases creation costs, thus increasing distribution costs.

2. Knowing what to build is more important than knowing how to build, rewarding people with taste.

3. Most people do not have taste, and as such, will have their brains turned into slopified goo.

In the context of these laws, the ideal founder is someone who has a unique way to distribute their product, the taste to know what to build, and the trust of their target customers. They can then use that combination of factors to bundle products that would previously have been too expensive to make relative to the labor costs and size of the market.

I call these people “hyper-creators.” A hyper-creator business is one where a bundle of digital goods is invented and distributed by an individual, but are created and maintained by an AI agent. They flood their target customer with software, information products, dropshipped physical goods, and recommendations for everything they don’t produce themselves.

I cringe as I write this, and may God have mercy on my soul for saying it, but think of it as Vertical SaaS for a vibe. (Sorry.) A hyper-creator’s job is to curate a nuanced view of the world, and then pull every monetization lever known to man that is associated with said vibe, tying it all together with their own distribution. That is an exceptionally silly company that could never scale to billions of dollars without adding staff, but it is a delightfully profitable company to build for yourself.

I am starting to see this happening in the wild.

Take my friend Nat Eliason. Nat has spent many years online making stuff ranging from a video game that ran on cryptocurrency, a non-fiction book, a sci-fi book, an SEO agency, to recently spinning up an AI agent that runs an autonomous business that has made *check notes* $78,000 in its first 30 days. The thing tying this dizzying array of activity together is his distribution. He is prolific on X with over 90,000 followers and has a newsletter with 46,000 subscribers. More than that, he has always operated in public, building trust.

Now you, my wonderfully handsome and skeptical reader, may be thinking, “Ah, I too love anecdotal data that confirms my priors.” Well, true. But also! There is plenty of macroeconomic data that shows this trend starting across the economy.

We are starting to see a huge jump in new apps being submitted to the Apple App Store.

WordPress plugins are up 87% YoY, with the WordPress team pointing to AI as the driving factor.

Startups with a solo founder are also spiking. People can use AI to do what they used to rely on cofounders for.

And despite all the economic uncertainty of the last year, people are continuing to start new businesses.

Those businesses are growing remarkably faster too. The recent Stripe annual letter noted that, “This new cohort [of companies started in 2025] is by far the highest performing and fastest moving we’ve ever seen, growing around 50% faster than the 2024 cohort. The number of companies reaching $10 million ARR within 3 months of launch was double the 2024 count.” Don’t forget that AI adoption is growing exponentially too. Anthropic is likely the fastest-growing B2B company of all time, and OpenAI is among the fastest growing consumer businesses.

So we have an environment where new businesses are forming faster, growing faster, and AI adoption keeps growing.

I really started to think about the idea of being a hyper-creator because it has been happening to me. The Leverage has seen a similar acceleration. Last year I would be proud just to publish 2 well written pieces a week. This year, I’ve been publishing multiple times a week, making YouTube videos (my latest just dropped on the SaaSacre!), taken on 3 consulting projects, sold multiple ad packages, am planning The Leverage’s first two live events in May and June, am writing a mini-book on AI and education, on and on.

Hyper-creator has moved from a theoretical idea to my economic reality. The business I am running now would’ve required multiple staff just a few years ago, and now it’s just me, my dog, and a pile of empty Diet Dr Pepper cans making this all work. (Full disclosure, I have a few freelancers whom I use for <5 hours a week of work, but even in that department I have spent about 60% less on freelancers than I did last year.)

I am early, but I think this new way of working will eventually be required of everyone. Dramatically higher expectations of output, and, hopefully, dramatically higher earning potential for those with the unique combination of distribution and taste.

The barbell economy

Long-time readers know that I have a passionate love affair with the economist Ronald Coase. He sought to answer a basic question. Namely, why do companies exist? His answer, which eventually won him a Nobel Prize, was transaction costs. It’s expensive to find people, negotiate with them, and coordinate their work on the open market. Companies form because doing all of that internally is cheaper than contracting it out. A company grows until the cost of managing one more person internally equals the cost of just hiring a freelancer.

AI flips the stable state that Coase’s equation has had for nearly a century. When an agent can do your research, write your emails, design your graphics, build your app, and manage your workflow, the cost of coordinating with yourself collapses to nearly zero. It is cheaper and faster to coordinate with AIs than humans. Your only transaction cost is a $200-a-month Claude Max subscription.

Twenty years ago, Chris Anderson identified this dynamic from the demand side with his Long Tail thesis: when you remove distribution bottlenecks, the market fragments into millions of viable niches.

What Anderson didn’t quite get right was the supply side. Sure, the internet enabled demand for niche content and products, but creating for those niches was still expensive. The hyper-creator is what emerges when both sides of the equation unlock simultaneously—infinite niche demand meets near-zero creation cost.

But this future has a dark side, and I’d be a fool to ignore it. The hyper-creator economy produces a barbell. On one end: millions of empowered individuals building profitable businesses around their taste and distribution. On the other: the mega-platforms these creators depend on, capturing the aggregate value. The middle gets hollowed out.

And my first law never stops applying! As creation costs fall to zero, the resulting flood of content makes distribution exponentially harder. Every new hyper-creator makes the game harder for every other one. The niche you’re serving today could be flooded by AI-generated competitors tomorrow, and the platforms you build on could change the rules anytime they want.

In short, this is likely a variation of the “get rich now or join the permanent underclass” meme.

It is deeply unclear to me what the equilibrium will be. Is it a million people each earning ten million dollars? If there are a million people competing fiercely for niches, the winners likely cycle faster than ever. The platforms will even encourage it, because increased creator competition just results in increased revenue for platforms.

Still, until that day of competitive death comes, there is a window of opportunity to build a business that is deeply personal and highly lucrative. These sorts of opportunities are rare, which is why I’m writing this to you.

Which, ultimately, might be the point. As I wrote last year in defense of starting a “bad” business: “I’m not interested in empire or wealth, I’m interested in paying my bills and spending every minute of my time on labor that brings me delight.” The hyper-creator model is about building a life — one where your work is genuinely yours, where the economics are good enough to keep going, and where taste and your fans are the only moat that matters.

For paying subscribers, these are the tools, new media formats, and workflows I'm currently using to transform my life, and by extension, The Leverage into a mode of hyper-creation:

How you can do this

Read more

In sci-fi novels about AI, the acceleration is unevenly distributed. Rapid accelerations contrasted against painful declines. That is where I am this week. I have never been more excited about the future of technology. My life has significantly improved in the last six months thanks to these tools—I am healthier, happier, richer. Simultaneously, the largest story in AI is about the United States government attempting to euthanize one of our best AI companies. And the largest story in the world is the ongoing war in Iran, also instigated by the United States government, potentially using the technology of the company they are attempting to murder to help plan their war. Terrible potential and wonderful promise all at once.

This week we’ll dive into how that manifests in layoffs, in ads, and in fundraising.

But first, a message from a new sponsor to the newsletter, Crusoe.

Every AI company pays an invisible tax. Not in dollars—in engineering hours spent wrestling with inference infrastructure instead of building product. You fine-tune a model, it works beautifully in testing, then you hit production and suddenly you’re debugging GPU clusters at 2am instead of shipping features.

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Bring your fine-tuned model, and their team works with you to deploy it on a platform built for performance—5x more throughput than standard GPU clouds, breakthrough speed even as context windows grow (thanks to their MemoryAlloy technology), and the reliability production apps actually demand. You focus on building, they handle the infrastructure.

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MY RESEARCH

Can ServiceNow become an AI winner? ServiceNow’s stock has been cut in half to ~24x forward earnings despite strong fundamentals, creating either a historic buying opportunity or a value trap. The bull case rests on ServiceNow’s unique position as an enterprise context layer. The bear case counters that foundation model providers like OpenAI are building competing context layers from inference rather than structured data, consumption pricing will erode revenue predictability, and shadow AI adoption may undermine the top-down governance model ServiceNow is betting on. My argument is that this company is a litmus test for the broader SaaS sector. Does AI make workflow platforms more valuable as orchestrators, or render them unnecessary entirely? Read here.

WHAT MATTERED THIS WEEK?

BIG TECH

Block lays off 40% of its staff. This is one of the largest layoffs as a percentage of employees that I can ever remember from a tech company. The announcement by Jack Dorsey, mostly framed the choice as needing to work in new ways with AI. On X, most of the criticism was on his management and staffing policies. Both are likely true. Now though, this cut will put them right in line with the revenue per employee that you see at leading AI startups today.

Block reported $24.2 billion in revenue last year, but nearly half of that is Bitcoin flowing through Cash App — when a user buys $1,000 in Bitcoin, Block books the full $1,000 as revenue even though it only pockets a small fee. Strip out those pass-through transactions and you’re left with $10.4 billion in gross profit, which is the number that actually reflects what Block earns.

That’s why the chart shows both: at face value, post-layoff Block looks like it matches the AI-native startups at $4M per employee, but on the money they actually keep, it’s $1.73 million.

I am in agreement with Dorsey—you do need dramatically fewer people to run a company now. AI agents have crossed the rubicon, and we need new ways of staffing and team design to match this reality. I’ll talk more about that in an essay in the next few days. But this cut is the first of many to come from tech companies.

BIG LABS

OpenAI speaks softly and carries a big check. The non-profit for-profit announced a $110 billion dollar round this week, backed by essentially everyone you’ve ever heard of. Amazon and Nvidia were notable partners kicking in a cool $50B and $30B respectively, with SoftBank rounding it out at $30B. To put that in perspective, this single round is roughly a third of all US venture capital deployed across every startup in America in 2025 ($339B), and it eclipses total annual VC investment in every pre-pandemic year on record. Add in the $40B raise from March 2025, and OpenAI alone has absorbed ~$150B in 12 months — more than every VC in America invested in the entire year of 2024.

Checks of this size always come with asterisks. Much of the Nvidia and Amazon capital is likely self-dealing revenue that goes right into chips for the AI to train on. Amazon’s clauses in particular are fascinating; only $15B of its $50B hits immediately. The remaining $35B is contingent on OpenAI either achieving artificial general intelligence or completing a US IPO. Apparently, building God or going public are considered equally difficult events in today’s capital environment.

Anthropic pokes the bear. As I’ve been arguing for the last few months, AI is an inherently political technology. This matters because politics, particularly the variety practiced by the current administration, is a blood sport dictated by perception, ego, and honor. In comparison with the relatively cool and dispassionate world of computers, the game is played entirely differently.

Anthropic learned that the hard way this week. It had a $200 million contract for the Department of War to use its Claude models. In the contract, there were two redlines. First, that Claude would not be used for domestic surveillance of Americans and second, that it wouldn’t be used to power autonomous weapons. The Pentagon wanted to be able to implement “all lawful uses.” They could not come to a compromise.

The miscalculation by Anthropic was to think that this was an administration that would be above revenge. They distinctly are not. Initially, Trump banned the federal government from using Claude (totally fine, it’s a free market), Pete Hegseth escalated by designating Anthropic as “supply chain risk.” This is a status that has never been applied to American companies before and would ban any contractor for the Department of War from doing business with Anthropic.

Since that includes every major cloud provider, this order could essentially obliterate Anthropic from the face of the earth. Frankly, I doubt this will legally hold up. Supply chain risk designation typically has to go through the Federal Acquisition Security Council, a 7 member body for departments that deliberate on this sort of status. There is some precedent that the Department of War can ban vendors from using Anthropic in defense related services, but there is almost certainly no legal precedent for them to try to ban the entirety of commercial activity.

What this actually reminds me of is the TikTok deal. There, the Trump administration promised broad punishment of a company they disliked. After years of legal battles, the end result was mostly Trump allies getting assets on the cheap. This situation rhymes with that. OpenAI announced on Saturday that they had accepted a contract from the Department of War. And in a happy coincidence, Greg Brockman, a co-founder and President of OpenAI, happens to have just donated $75 million to Trump. Weird how that happens. The major beneficiaries of the TikTok deal also happened to be Trump donors.

Again, I am not a politics writer and wouldn’t pretend to be. Why I bring this up is that politics is business strategy now. If you are a founder, you will have no choice but to think about this stuff because AI regulation is coming for you, no matter who is in charge. Bernie Sanders wants to put a moratorium on building data centers. Ron Desantis is going to be running on an anti-AI populist vote. And multiple Democrats are promising to break up big tech companies once they are back in power. As painful as it is, you have to think about this stuff now.

I will reserve judgement on whether the OpenAI deal is safer than the Anthropic proposal—as I am writing this on Saturday night the OpenAI team is doing damage control on X and I want to give it some time for it all to settle. Still, I think every reasonable person can agree that the supply chain risk designation is not the way Americans do business.

THE SLOPPENING

The AI feature trap. Koah, a startup building “AdSense for AI,” just raised $20.5 million in a Series A. I spoke with co-founder Nic Baird this week. He told me they now estimate there are roughly 46,000 AI apps in the world—up from 33,000 a year ago. This is the fastest expansion of a software category in history, and almost nobody can afford to use it properly.

With his customers, he will see major applications with millions of users launching AI feature pilots, seeing insane engagement, and then hitting a wall. Inference costs take a product from hundredths of a cent per user per year to two to three dollars. That’s a 10,000x increase in cost-to-serve.

The Sloppening is driven by twisting human nature to keep users engaged. Sometimes that’s ad-driven, sometimes it’s subscription-driven. My perspective is that the business model isn’t the disease; its the optimization target. So the question with AI ads is what are they optimizing for?

Right now, the answer is encouraging. AI ads incentivize AI apps to have increased utility and increased engagement. If you’re really using AI with agents, it’s easy to spend hundreds or thousands a month on tokens. The most powerful tools ever built are only accessible to the affluent. Everyone else gets nada. Ads have a way of bridging that gap that I think is noble. As Baird told me: “It would be a tragedy to see one of the most powerful tools we’ve ever created be unavailable to a huge part of the population because we don’t want to do ads.” I remain concerned about the engagement maximization/incentives, but that is a result of the internet and human nature. It isn’t unique to AI.

TASTEMAKER

“I now understand the need for faith—pure, blind, fly-in-the-face-of-reason faith—as a small life preserver in the wild and endless sea of a universe ruled by unfeeling laws and totally indifferent to the small, reasoning beings that inhabit it.” Dan Simmons, Hyperion.

Dan Simmons passed away this week at 77. The author wrote one of the greatest sci-fi novels ever in Hyperion. The first time I read this book, I hooted like a loon. I ran around my bedroom cackling like a maniac. I cried at the loneliness and aching contained in the prose. While most sci-fi today overly focuses on sick space battles bro, Simmons understood the crucial thing about genre fiction—fantastical settings are about giving us more freedom to explore the human condition. RIP to a legend. Read Hyperion this week if you haven’t yet, I’ll be rereading it with all of you too.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for Q1 ‘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.

ServiceNow’s stock has been cut in half while their every fundamental metric improved. This is either one of the best buying opportunities in enterprise software, or it’s the lipstick on a dying pig.

This divergence is happening because of, you guessed it, AI. The company is attempting to execute one of the most complex corporate pivots in technology history: simultaneously transitioning from per-seat to consumption pricing, absorbing $12 billion in acquisitions, repositioning from a workflow tool to an AI agent orchestration platform, and doing all of this while the entire SaaS sector loses roughly $300 billion in market cap in a single day. Not easy!

More importantly, ServiceNow is the perfect case study by which to think about the future of the software sector more broadly. The reasons are 3-fold:

1. They are a theoretical beneficiary of the shift away from systems of records and point applications to the context layer.

2. The stock at ~24x forward earnings either represents a PEG ratio of ~1.1—a level rarely seen for a 20%+ SaaS grower outside of a broad sector selloff—or it’s a value trap dressed in temporarily strong fundamentals.

3. It is a public stock so you can (theoretically) make bucco cash by being long or short. Yay money!

Now to be clear, I will never, ever, ever give a stock recommendation in this newsletter. Your portfolio is between you, God, and the IRS. That said, the market sits on a knife edge. Valuations can swing by 20% on the basis of a blog post from Anthropic. In moments of chaos like this, there is absolutely money to be made which makes this post especially timely. We can pressure test my thesis about the context layer and decide whether to gamble accordingly.

There are two plausible futures for ServiceNow. Either it compounds into one of the most valuable enterprise software companies in history. Or AI turns its core advantage into a commodity. Which future we get depends on a simple question. Namely, what type of company is ServiceNow?

I break it down in 3,000 words below the paywall.

The accidental product, the intentional platform

Read more

The three laws of AI are as follows:

1. AI dramatically decreases creation costs, thus increasing distribution costs

2. Knowing what to build is more important than knowing how to build, rewarding people with taste.

3. Most people do not have taste, and will have their brains turned into slopified goo.

What’s fascinating about these laws is that they are first appearing in the incredibly dusty and boring world of B2B SaaS. LinkedIn is a hellscape of AI-generated slop. Crappy apps are abundant everywhere. The business world has embraced the slop far faster than people’s media diets, which surprised me a little.

This week has 3 different stories fleshing out that idea, ranging from slop ads to OpenAI’s sloppy financial forecasts. We’ll get to that.

But first, this edition is brought to you by returning sponsor Granola.

I’ve been using Granola as my meeting note-taker for months, but their new MCP integration is what turned it from a nice-to-have tool into genuine infrastructure.

Here’s my workflow: I take about 10 calls a week with founders, investors, and operators to help me write quality research. Often, the best insights from those conversations used to just...evaporate. I’d have some half-formed thesis sitting in a transcript I’d never reopen and would promptly forget about. Now, when I’m writing, I can pull directly from my Granola notes through Claude via MCP. It connects the dots between what a robotics founder told me three weeks ago and the earnings data I’m analyzing today.

This is the actual promise of AI note-taking that most tools miss. It’s not about summarizing your meetings (commodity feature, everyone does this). It’s about making your conversations compounding assets—searchable, connectable context that feeds into your real work. It doesn’t just work for writers, everyone from founders to scientists love using Granola.

If you’re doing any kind of research or analysis work, Granola is worth trying. Readers of The Leverage can get their first month free using the link below. Highly recommended!

Try Granola for free

MY RESEARCH

AI’s most popular strategy theory is dead wrong. For the last few years, it felt like you couldn’t talk to an AI founder without hearing “sell work, not software.” The idea was that as AI capabilities increased, the number of employees required by customers would decrease, therefore putting pressure on seat-based SaaS pricing. The only issue is that four years into that journey, only 7% of startups are actually selling work, and it is almost exclusively done in customer service. How did this happen? How should AI companies actually be charging customers? Read more here.

WHAT MATTERED THIS WEEK?

BIG TECH

I’ve seen the future of ads. This week Google Labs released an experiment called Pomelli, which will build a profile of your brand and then generate social media assets or marketing campaigns for you. Are you also yawning reading that last sentence? This sounds generic as hell. I’ve met a hundred startups that promise something similar over the last few years. But what this one does differently is actually work. The product is magical, in particular around generating photo assets. Just give it a picture of your product and you’ll get an image indistinguishable from a photo shoot costing many thousands of dollars. (See examples here and here if you don’t believe me.)

This gives small companies like mine the ability to market physical goods in a competitive way against large companies. For example, one thing I’ve wanted to make is a custom cologne for The Leverage. My reasoning is twofold: 1) this just sounds fun to do and 2) people at tech conferences could use some improvement in the olfactory department.

I gave the tool a picture of a generic perfume bottle, a link to my website, and this is what it came up with first try.

It isn’t perfect! But with a little tweaking on my end, this is something I could easily start putting Meta ads against. What does the world look like when everyone can make beautiful ads? Simple, the volume and price of ads increases, which is exactly why Google is working on this.

BIG LABS

OpenAI’s financial forecasts highlight the problem of ChatGPT. The Information reported a large variety of financial details about OpenAI, but I wanted to point something out with the chart they published below.

The hallmark of all COVID-era SPACs was forecasting generous cash flows from business lines that didn’t exist. This, uh, looks the exact same to me.

They do have ChatGPT! Which is a wonderful asset, but it is the heart of the problem here. ChatGPT’s current weekly active users just hit a new peak at 910 million, with a 5% conversion rate (fairly typical for prosumer SaaS flows in my experience). However, the usage for those 910 million is bad. As Benedict Evans characterized it, “The data that OpenAI released in its ‘2025 wrapped’ promotion tells us that 80% of users sent less than 1,000 ‘messages’ in 2025. We don’t know how that changed in the year (it probably grew) but at face value that’s an average of less than three prompts per day, and many fewer individual chats. Usage is a mile wide but an inch deep.”

How do you fix this? The default answer from my discussions with AI founders is “we just make the models smarter” and then usage grows. This seems observably wrong to me. The smarter the model is, the less time I’ll spend with it on a per task basis, and the more tokens each task/query I give it will consume. It is very hard to be the smart platform that handles your AI agents and also a consumer-friendly app. And notably, OpenAI’s own data reveals the treadmill they’re on. Execs said that user growth actually slowed earlier this fall, and it took the GPT-5.1 and 5.2 updates—which made the model much more “human-like”—to re-spark it. The models have to keep getting meaningfully better just to maintain growth, let alone accelerate it. It is totally possible that this could happen! But making it happen to the tune of an additional $100 billion or so in revenue in 4 short years seems very aggressive.

“Ah, you are missing it,” Sam Altman may say with a twinkle in his slightly soulless eyes, “when usage grows, we can then show more ads.” Again, this seems simplistic and not in line with where the technology is going. On top of the challenges of building a quality ad targeting platform (which is not easy, X and Snapchat haven’t pulled it off despite a decade of trying), the smarter the models are, the more trust I have to give them, and the fewer ads I want to see. Additionally, the smarter the models get, the more autonomous work I want to give them. I don’t want to see the AI working, let alone see the ads. Simply put, I am not convinced that ChatGPT is the product that’ll take 910 million consumers to an Agent-first world.

And the cost side makes the bet even harder to stomach. OpenAI’s adjusted gross margin— revenue minus inference costs—fell from a 46% target to just 33% last year, because higher-than-expected demand forced them to buy pricey last-minute compute. Total burn through 2030 is now 2x previous estimates, with infrastructure and compute alone costing a projected $665 billion. So we have missed margin targets, doubled cost forecasts, and tripled revenue projections. If that combination doesn’t remind you of a 2021 SPAC deck, I don’t know what will.

On top of all that, the long-term plan calls for ChatGPT to reach 2.75 billion weekly active users by 2030. For context, that’s a stricter engagement metric than monthly actives, where only Facebook, YouTube, and WhatsApp currently sit above 2.75 billion. OpenAI is essentially betting they can build one of the three or four most-used products in human history in the next four years. You add in the consumer hardware, the B2B sales, the research partnerships, the “new products” bucket that’s supposed to generate $1.3 billion next year with still-TBD details, on and on, and this is simply too much for any one company to pull off in this time frame. I love ambition! I really do, but I just think this is too much. To the OpenAI investors who read this newsletter, I would love an off the record conversation about all this. What does this analysis get wrong? Are you feeling nervous?

THE SLOPPENING

Generative media is going to take over this year. Last week, I warned you about the implications of the world’s best video models being indistinguishable from real-life videos. This week, it is important to point out how widespread this phenomenon is. In basically every way we have of interacting with the world, there is now an AI model that is near-indistinguishable from reality. Just in the last year, over 985 models were released on the fal platform, which helps developers integrate these type of models into their applications.

Slop is reality, reality is slop. These models are simply too good, and you should ratchet up your skepticism of every single thing you see online, especially the things you see that conform with your worldview.

TASTEMAKER

Three quick recommendations this week:

1. A funky Afro-Cuban vinyl set: I love listening to DJ sets while cruising through emails. It’s fun because I alternate between getting stuff done and then gently shaking my booty at my standing desk. This one has been a recent favorite to work to—I dare you not to at least mildly twerk.

2. Remarkable writing that is only mildly wrong. Sam Kriss is one of the best writers working today. I mean writer in the purest of senses, in that his prose makes my own look like a drowned chicken. There is a sparkling vibrancy to the way he writes that I find simultaneously aspirational and depressing. He also happens to be infected with that delightfully mean and grumpy and ironic attitude that British writers seem prone to. In his latest for Harper’s magazine, he absolutely skewers the “high-agency” grifters that are galloping around Silicon Valley right now. I disagree with much of his analysis, but I very much agree with his emotional insights. Well worth reading.

3. Fargo holds up: Despite being a loud and proud Minnesotan, I had never watched the Coen Brothers’ classic film about my beloved home state. I watched it for the first time recently and fell off my couch laughing. Just an excellent dark comedy with iconic lines of dialogue. I yearn for a studio system that makes more films like this. It is streaming on Hulu and you can rent it on Amazon for like 2 bucks.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q1 ‘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.

For the last four years, the prevailing theory in Silicon Valley was that software companies should “sell work, not software.” The overly simplistic explanation of the idea is that as AI got better at AIing, customers would need fewer employees, and therefore companies who charged on a per seat basis would be screwed. By selling work, a startup was offering “outcome based pricing” whereby they could capture a percentage of a customer’s payroll versus competing for scraps among a firm’s IT budget with the rest of software.

This is an elegant idea. It neatly solves the problems that AI introduced to software’s business model, and like all of Silicon Valley’s most important theses, it could theoretically make founders several yacht’s worth of cash. The narrative it introduced rebounded throughout the valley. Sequoia adopted a “tools → copilots → autopilots” framework with outcome-based pricing as the evolutionary endpoint. a16z began framing the AI era as a shift from “Software-as-a-Service” to “Service-as-a-Software.” And everyone, including myself, nodded in agreement.

Unfortunately for all of us, this elegant idea turned out to be very wrong. There is exactly one category of startups where the model has been effective—AI customer service—and every other major AI application today uses a different positioning and pricing strategy.

So what happened? Why has “selling work” been such a dud?

Outside of mere intellectual curiosity, the answer to this question really matters right now. If you buy my argument from last week, namely that the sell-off in software stocks was justified because profit pools would shift to a new type of applications focused on the context layer, then we need to understand how these new apps should be monetized. Just how exactly are these new types of companies going to make money? Why hasn’t selling work, uh, worked?

The answer is simple: there is no actual “work” to sell.

The opacity of production

The counter-arguments to “sell work” have mostly been economic. Margins are bad—50-60% versus 80-90% for SaaS. These critiques are valid but insufficient.

The deeper problem is ontological. When you hire McKinsey, you can’t reverse-engineer what they did into a bill of materials. The production process is opaque—you’re paying for judgment, relationships, and institutional knowledge bundled into a deliverable whose value is difficult to quantify. That opacity is what sustains pricing power.

AI work has no such opacity. The production process is increasingly transparent. Sophisticated buyers can see the API calls, estimate token counts, and calculate the rough cost of what you just delivered. The “work” firms are charging for — a market analysis, a contract review, a support resolution — is a markup on inference wrapped in varying degrees of prompt engineering, workflow orchestration, and domain context. And unlike McKinsey’s black box, the buyer can peek inside this one.

This is why the “it’s just tokens” critique sticks in a way that “it’s just compute” never stuck against SaaS. Everyone knew Salesforce’s marginal cost per seat approached zero; that was the whole bull case for software! But it didn’t matter. You couldn’t replicate Salesforce’s value by renting your own servers from AWS. The pricing power came from platform lock-in, data gravity, integrations, and switching costs, none of which had anything to do with the cost of production.

AI work is different. The production process is reproducible. Your customer can see that you’re calling Claude with a system prompt and some retrieved context, and increasingly, they can do exactly that themselves. The API is right there. The prompt engineering isn’t patented. The workflow orchestration is a weekend project for a competent engineer. When the buyer can not only see your costs but replicate your production process, the opacity is effectively zilch.

And this matters because of what sits underneath. Price is only partially determined by the value of a product. A pricing model is a reflection of the scarcity in your supply chain. When your production process is reproducible and the inputs are abundant (tokens are hardly hard to come by nowadays) you’ve lost pricing power from both directions.

Take KPMG’s audit group. They forced their own auditor to give them a 14% discount because they know that AI is doing most of the work, and the work is cheap. (Yes, there is some irony in KPMG demanding audit discounts while not extending the same courtesy to their own customers). This is what happens when the opacity that sustained professional services pricing disappears. The customer benchmarks your price against the visible cost of the underlying unit of production, not against the value of the output.

Inference costs dropped roughly 99% per token between 2023 and 2025. Any business model that prices against the value of human labor while its input costs follow Moore’s Law trajectories is building on a foundation of perpetual margin compression. Your customers will demand that you pass those savings through.

The scissors problem

But even if you could maintain the opacity, the economics of “selling work” have a structural problem that makes the model worse over time, not better.

There’s a scissors dynamic. On one blade, inference costs per token keep falling. On the other hand, token consumption per task is exploding. OpenRouter’s analysis of over 100 trillion tokens in December of last year, shows that reasoning models now account for over 50% of all tokens processed. Average sequence lengths have more than tripled over the past 20 months, from under 2,000 tokens to over 5,400. Programming workloads routinely exceed 20,000 input tokens per request. A reasoning model can consume 10,000 internal “thinking” tokens to produce a 200-token answer.

Ask any founder of an AI application today if their token costs have gone down over the last few years and they’ll laugh in your face. Sure, tokens are cheaper, but the frontier models from OpenAI and Anthropic charge a hefty markup on them, and the work we are demanding from these models is forcing ever more token consumption. Sophisticated customers demand being on the smartest models because why shouldn’t they?

This is the treadmill that “sell work” companies are trapped on. You save 50% on per-token costs, but your customers now expect agentic, multi-step workflows that consume 10-100x the tokens. The price per token drops; the tokens per task skyrockets. Net costs stay flat or rise — while your customer still benchmarks your price against the ever-cheaper unit cost of the commodity underneath. The margin improvement that was supposed to rescue outcome-based pricing never arrives because the goalposts keep moving.

The quality specification problem

To make the case even worse for “selling work” is that even if you could stabilize the pricing, “work” requires something that tokens cannot provide: a contractual specification of quality.

The reason firms hire lawyers at $800/hour rather than paying per brief is that the quality of legal reasoning cannot be specified ex ante in a contract. You’re buying access to judgment, not a deliverable. You are buying lawyers who all went to Harvard Law because that carries with it a promise of indefinable quality. The same problem reappears with AI. If you’re “selling work,” you need a contract that defines what good work looks like, and that specification problem is often as expensive as the work itself.

Ronald Coase explained decades ago that firms exist because they reduce the “costs of using the price mechanism.” Subscription pricing is the software equivalent — it eliminates per-transaction negotiation, measurement, and verification costs. Per-task or per-outcome pricing reintroduces all three. Every “outcome” must be defined, measured, verified, and attributed. The AI vendor knows the actual difficulty and cost of completing each task; the customer does not. Classic principal-agent problems emerge on both sides.

This creates what economists call a moral hazard in both directions. Vendors paid per “completed task” face incentives to use cheaper models, minimize compute, skip quality checks, and classify ambiguous outcomes as successes. Customers face incentives to overload systems with low-value tasks or dispute outcomes to avoid payment. You’ve rebuilt the adversarial dynamics of the consulting industry, except with less accountability and no reputational mechanism to discipline quality.

Subscriptions or per-token credit systems sidestep all of this. You’re buying access to the tool. Quality assessment becomes the buyer’s problem, not a contractual negotiation. The entire transaction cost structure is simpler.

The binary exception proves the rule

There is exactly one domain where “sell work” has produced explosive growth: customer support.

Sierra AI grew from roughly $20M to $150M ARR in about 15 months, charging ~$1.50 per resolution. Decagon grew from ~$6M to ~$35M ARR using per-conversation pricing. Intercom’s Fin at $0.99 per resolution drove 40% higher adoption. The only other prominent case study I could find is legal tech firm EvenUp which charges per brief generated, but even then customers have a minimum they have to purchase per month.

But customer support isn’t really “selling work” in the way the argument orginally meant. A resolved support ticket is a binary state change. Did the customer’s problem go away? Yes or no. The outcome is measurable not because the “work” is well-defined, but because the absence of a problem is well-defined. Attribution is unambiguous: the AI either handled the ticket or it didn’t.

These conditions don’t really exist anywhere elsewhere. What’s a good contract clause? What’s a high-quality line of code? These are judgment calls and judgment is precisely what’s hardest to contractually specify and verify. The moment you move from binary state changes to quality spectrums, outcome-based pricing falls apart.

Meanwhile, seat-based AI is eating the world

The irony of the “sell work” thesis is that the fastest-growing AI companies of the past three years mostly ignored it.

Harvey AI uses traditional seat-based pricing. It grew from roughly $50M to $190M ARR in 2025, with median seat counts doubling within 12 months, and reached an $8-11B valuation. Cursor uses tiered subscriptions with compute credits. It became arguably the fastest-growing B2B SaaS company in history, reaching approximately $1B ARR in 24 months. Microsoft Copilot charges $30/seat/month and reached 100M+ monthly active users.

The pricing model was not the differentiator. Product quality, distribution, and domain-specific value drove growth regardless of whether companies charged per seat or per usage unit. Harvey’s seat-based model created strong net revenue retention as firms expanded licenses. Cursor’s subscription model created predictable revenue while allowing usage flexibility. Neither needed to solve the quality specification problem because they sold access to tools, not deliverables.

And note what these companies do have that a raw inference wrapper does not: deep workflow integration, proprietary data pipelines, purpose-built UX, and switching costs that compound over time. The production of AI work — the inference itself — is reproducible. The production of AI products — with their context layers, workflow lock-in, and accumulated user data — is not.

The confusion at the heart of the thesis

The sell work thesis confuses the application of intelligence with the output of intelligence.

Professional services solved the quality verification problem not by making outputs easier to evaluate, but by building accountability infrastructure around the producer — licenses, liability, long-term reputation. AI has the capability to produce the output but none of the scaffolding that lets a buyer trust it. It’s the most competent worker in the room who can’t sign the contract, can’t be sued, and won’t remember the engagement next week. Until that’s solved, the human professional’s role shifts from doing the cognitive work to bearing responsibility for it.

The winning AI companies will sell software that does work and maintain software economics through workflow lock-in, data moats, and subscription revenue — rather than work done by software, which is just consulting economics and problems in an API trenchcoat.

So what does monetization actually look like?

If everything is tokens of context in and tokens of intelligence out, where does the pricing power come from?

Not from the tokens. Inference is a commodity, getting cheaper by the quarter, and your customers know it. Not from the “work” — which is nebulous, hard to specify, and invites the adversarial dynamics of every consulting engagement ever. The pricing power comes from the thing that makes the tokens useful in the first place: context.

As I argued in “Context is King,” the software stack is splitting into three layers: systems of record (databases), point solutions (the interface layer), and a new middle layer — the context layer — that holds the institutional knowledge telling AI agents what to do, in what order, and whether they’re allowed to do it. The “sell work” thesis was an attempt to answer the monetization question, but it landed on the wrong answer because it focused on the output rather than the input.

The provider sells the conditions for good work, not the work itself.

This is why margins are a lagging indicator of where value accrues, not a leading one. Scarcity drives pricing more than anything else, and inference is abundant. Context is scarce. The “sell work” companies are stuck arguing about gross margins on a commodity. The context layer companies are building something that appreciates.

The real lesson

The thesis was right in one important way—AI does expand addressable markets from software budgets into labor budgets. The TAM expansion thesis holds. AI companies should think about replacing work, not merely augmenting workers. The context layer will capture much of the payroll budget that went to project management and operations. We already have early evidence of this happening.

A recent paper out of Ramp, “Payrolls to Prompts,” puts hard numbers on this. Researchers tracked firm-level spending on freelance labor marketplaces like Upwork and Fiverr alongside spending on AI model providers from Q3 2021 through Q3 2025. After ChatGPT launched, the firms most reliant on contracted online labor adopted AI earlier, spent more on it, and cut their freelance budgets by 15%. For every $1 firms stopped spending on human contractors, they spent just $0.03 on AI. “ Keep in mind that this is all firms, for the ones that have gone all in on AI, the effect is more dramatic, “More than 50% of businesses that had spent on online labor marketplaces in Q2 2022 spent 0% in Q2 2025, whereas roughly 80% of businesses spent between 0 and 5% of their total spend on AI model providers in Q2 2025.”

Companies are pulling directly from payroll and contractor budgets, exactly the kind of budget migration the “sell work” camp predicted. They just proposed the wrong business model to capture it.

But “sell work” is the wrong business model for capturing that opportunity. It sounds like a paradigm shift, but it’s actually a regression to the services economics that software was invented to escape. The frontier AI companies already understand this. They sell subscriptions that give customers access to intelligence-on-tap and let the customer define what “good work” means. They’ll maintain 70-90% gross margins, predictable revenue, and deep switching costs. They look like the best software companies in history, not outsourcing firms with better technology.

The market has also spoken. ~92% of AI companies now use hybrid or subscription pricing. Pure outcome-based models represent just 7%. The “sell work” era lasted about as long as it took buyers to realize they were paying a markup on tokens and to start demanding token-level pricing.

The next time someone tells you to “sell work, not software,” ask them one question: can you write a contract that specifies what “good work” looks like for your AI? If the answer involves a spectrum of quality rather than a binary outcome, you’re not selling work. You’re selling tokens at a markup. And your customers will figure that out faster than you’d like.

The defining religion of the AI era has been e/acc. Effective accelerationism preaches that rapid technological progress pushes society forward and will cure the world’s ills. This week delivered miracles for the faithful: Spotify’s earnings call revealed its best developers “have not written a single line of code since December.” OpenAI dropped a new model that writes code 15x faster than existing models. I feel the acceleration.

But e/acc’s detractors got their evidence too. Facial recognition abuse. Naked purchase of political power. AI video models ignoring copyrighted materials wholesale. All of which we’ll cover in a minute. Here too, I feel the acceleration.

In The Leverage, I advocate for a middle way. One where we can be clear-eyed about the enormous benefits of capital markets and technology while also being honest about the downsides. My piece this week struck a nerve, which suggests this middle ground has an audience.

Let’s get to it.

MY RESEARCH

Notes on the SaaSacre. In the last two weeks, over $300 billion in software market value has been yeeted out of the public markets. Everyone thinks AI is killing SaaS, but they’re wrong about why. Instead, AI is shifting where value accrues. Software is splitting into three layers: databases (Salesforce, SAP), point solutions (the stuff humans click on), and a new middle layer I’m calling “the context layer.” Context is the institutional knowledge that tells AI agents what to do, in what order, and whether they’re allowed to do it. Think about what happens when a new employee joins your company. They spend months learning who approves what, which systems talk to each other, what the unwritten rules are. That knowledge — the stuff that lives in people’s heads and Slack threads and outdated wikis — is the context layer. It captures organizational overhead that used to require armies of coordinators and turns it into software margin for the first time. The trillion-dollar question: does this layer get owned by whoever already holds your company’s knowledge (ServiceNow, Notion), or whoever builds the smartest agents (OpenAI, Anthropic)? Either way, context is the new bottleneck. Read more here.

WHAT MATTERED THIS WEEK?

BIG TECH

Welcome to the Silicon Panopticon. Last year, I argued that computer vision was the most important technology to follow LLMs. By pairing cheap smartphone components with near-free AI inference, we will get a new world of automated surveillance. A few days later, I argued that this omni-present virtual intelligence would create a prison of surveillance, one where you were observed, judged, and then disciplined. Frankly, when I published that piece, I wondered if I was being too hyperbolic. Surely my highly principled colleagues in Silicon Valley wouldn’t be shameless enough to build this technology?

Anyway, here are three recent headlines:

1. Meta Plans to Add Facial Recognition Technology to Its Smart Glasses. The company plans to ship the feature “during a dynamic political environment where many civil society groups that we would expect to attack us would have their resources focused on other concerns.” Originally the company planned on releasing the feature to attendees of a conference for the blind, but decided on this new release schedule instead. I mean, come on man.

2. US Border Patrol signed a deal with facial recognition company Clearview AI for “tactical targeting.” The contract is, very importantly, not for targeted investigations but instead, for routine day to day surveillance work performed by its agents. This is the same underlying AI technology that was used by ICE in Minnesota, often without warrants or consent. I mean, come on man.

3. Amazon’s Super Bowl commercial showed how its Ring cameras could form a neighborhood surveillance network — supposedly, only to find lost dogs. After public backlash, the company canceled its partnership with Flock Safety, which lets law enforcement request camera footage without a warrant. Great, except they didn’t cancel a separate contract that allows immigration enforcement to request the exact same Ring footage. So the net result was a press release. Come on man.

I want to name the obvious: all three of these stories touch the current political environment. That’s not the point. The point of The Leverage is always the technology. Facial recognition gives governments a scalable, automated way to track citizens—and the infrastructure being built today will outlast any single administration. Do you want Trump to have this power? Or Kamala Harris? Or *insert your least favorite politician here*? If you don’t believe this will affect you, spend two minutes using the following service to search for your own face. It’ll find you, and it will find information about you in rather uncomfortable ways.

Facial recognition is following the exact same deployment pattern as every other surveillance technology before it. It starts with a sympathetic use case, whether that is finding lost dogs, helping the blind, or catching criminals. Then, it expands.I don’t view (most) of the companies building this stuff as evil. They’re just following the incentives. And right now, the incentives to slow down are laughably weak. Amazon canceled one surveillance partnership after public backlash—and quietly kept a separate contract that does the exact same thing. Grumpy tweets (and newsletters) are not enough to give people privacy rights.

BIG LABS

OpenAI and Anthropic pick their politicians. In one of my videos in January, I argued that politics would play a much greater role in business strategy because AI is such a hot button issue. I have already been proven correct. Greg Brockman, a co-founder and President at OpenAI, has become one of Trump’s largest donors, with the explicit goal of cultivating goodwill and less regulation for AI. Then this week, Anthropic donated $20 million to a Super PAC explicitly designed to counter that effort by backing politicians who support additional safety regulations for AI (which they argue benefits society, and as a fun side benefit, also benefits them). Companies in every industry have used politicians as competitive weapons, but it is unusual to see companies so early into their life engaging so largely and publicly. That tells you something about how high the stakes are and how much both companies believe regulatory outcomes will determine market winners. Very, very different than the previous era of technology!

Anthropic grows revenue 10x every year for three years straight. The company just closed a $30 billion round of funding at a $380 billion valuation. All of this was done on the basis of this chart:

Staggering. There has never been anything like this in human history. I then charted each reported case of revenue from each of the two leading labs.

OpenAI seemed to be breaking away until June of last year. Perhaps in this race the tortoise will win? For more on Anthropic’s strategy, you can read my analysis about why their focus on B2B software has paid off so spectacularly.

THE SLOPPENING

The best video model in the world is Chinese, and its maker doesn’t care about American IP law. Bytedance released Seedance 2.0 and the results are freaky good. The model does an impressive job with audio, with physics, with everything really. For the well-prompted videos, it will fool over 80% of viewers. What is truly remarkable is how terrible the uses of it are. Go to X after you finish this newsletter and just put in “seedance” into the search bar to see what I mean. The videos are mostly about abuse of other people’s IP. Want to watch Batman fight John Wick, here ya go. Kung Fu Panda fighting Godzilla and Optimus Prime? Yup. Dragonball Z as a 1970s Kung Fu movie? Of course. This is almost an exact mirror of what happened when I got early access to Sora, the most viral videos were deepfakes of celebrities or stupid Rick and Morty clips.

When anyone can make a professional looking video, and that video is short/has to appeal to the lowest common denominator, the result is slop. There is a good explanation for why the creators of these videos can’t help but make garbage. T.S. Eliot wrote an essay called “Tradition and the Individual Talent“ where he argued that great poets have “the historical sense.” They understand where the art form came from and relate that legacy to their current moment. In theory, AI video tools could empower people with that sense, letting filmmakers with great taste but no budget produce genuinely original work. Instead, the distribution incentives point the other direction entirely. On platforms optimized for engagement, the fastest path to virality is mashing together pre-existing IP that people already recognize. You don’t need a historical sense to put Batman in a cage match with John Wick. You just need a prompt. The tools are Hollywood-quality. The creative ambition is YouTube comment-quality. And that’s pretty much the Sloppening in two sentences.

TASTEMAKER

I just spent several paragraphs arguing that facial recognition infrastructure will outlast any single administration. Dave Eggers made the same argument in 2013, except he went further — and at the time, I thought he was ridiculous. When I first tried to read The Circle, I didn’t finish it. I thought it was too ridiculous, that there would be no way that people would so willingly sacrifice their rights to privacy and freedom.

I read it again this week as part of my participation in @Commonplace Philosophy’s bookclub, and it struck closer to home this time. Many of the scenarios that struck me as farcical are now uncomfortably real. This is true even in my own life. I’ve been writing a lot about Operation Dad Bod, where I use a variety of AI tools, pharmaceuticals, and wearables to optimize my health this year. To my discomfort, there is a very similar plotline in The Circle. It is easy to rationalize our way into surrendering our choices to the machine, and I have found myself doing so more and more.

I cannot recommend this book to anyone as a work of serious literature. The characters are shallow, borderline unbelievable. The writing has momentum, but is not masterwork of prose. I can however recommend this book as just-barely fiction. You may realize that the shallow, borderline unbelievable character has a startling resemblance to you.

And one more thing…

In the last 72 hours, Barack Obama went on the record saying that aliens “are real, but I haven’t seen them” and NASA published a study that examined the source of organic compounds on Mars where “the non-biological sources they considered could not fully explain the abundance of organic compounds, it is therefore reasonable to hypothesize that living things could have formed them.”

So, I guess, uh, alien life confirmed? We are not alone in the universe? Feels like maybe I should’ve led the newsletter with that and called it a day.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q1 ‘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.

To the individual who enjoys Patagonia vests and the sexual high that increasing shareholder value brings, there is nothing more beautiful than a software subscription business. From 2005-2023, a twinkling flutter of a moment in capitalism’s ascendency, Silicon Valley invented the most powerful business model ever seen on God’s green earth. SaaS seemed divinely sculpted, a kismet meeting between the needs of venture capitalists to deploy capital and of startups that would need Brinks trucks worth of cash to build products. The margins were judicious, ranging above 85%. The terminal value was indisputable, with customers paying you 10% more every year, forever. It simply…worked.

Until last week, when the market said, “Nah.”

$300 billion in software companies’ market capitalizations vanished, affecting everyone from new darlings like Figma to legacy giants like Salesforce. While the market is mysterious, this selloff is likely due to fears over AI generated code decreasing the value of software companies. Why buy a CRM when you can just make one?

This thesis is too simplistic. In the idea’s defense, there is lots of data to make it seem like that is right. Code is looking like more of a commodity input—4% of GitHub commits are already generated by Claude Code, with SemiAnalysis forecasting that to hit 20% by the end of the year. In my own experimentation, it is remarkably easy to generate applications shaped specifically to my needs. If you give Claude a set of instructions, it can do a halfway decent job of recreating your digital labor.

AI doesn’t make software companies less valuable. It simply makes a different group of software companies more valuable. AI increases the power of software, not decreases it, and AI moves where in the stack the value lives.

The SaaSacre is partially justified. Many of the SaaS giants of yesteryear should be valued less. But what’s actually happening is a culling of the herd, a separation. Software is splitting into three layers with fundamentally different economics. Two of those layers are getting commoditized. The third—a layer that barely existed before AI—is where the value migrates. I’m calling it the context layer.

The valuation problem

To understand my idea, first we need to establish why the generic software companies aren’t as valuable as they used to be. The answer is threefold:

1. The growth sucks: The top quartile of public SaaS companies today grows slower than the bottom quartile did in 2016. Public SaaS growth halved from 36% to 17% since 2023. Growth is king in technology and these companies don’t have the juice anymore. AI native private companies are where much of the growth in software markets are happening.

2. The margins are structurally worse: In SaaS, you built it once, and distributed/used it at zero marginal cost per additional customer. In AI-land, you build it once, distribution is more expensive, and each use has a marginal cost. AI-first applications currently operate at 50-65% gross margins, not the 75-85% that justified SaaS premium multiples. Importantly, I would expect these gross margins to improve over time as the cost of intelligence decreases. But that doesn’t matter! That there is a “cost-per-action” associated with the usage of your software dramatically reworks the economic logic.

3. Switching costs are decreasing, wrecking SaaS platform’s strategic power: This is the underappreciated one. LLMs make it much, much easier to switch data over to a new application. Switching costs are moved to data access and organizational context. There are still good reasons to not vibe code a new application—either due to the opportunity cost or you want to let other platforms be responsible for certain types of risk, such as payments or user identity, really anything where being wrong is catastrophic. But that is a small subset in the world of software! And as agents improve, the organizational context will get easier to switch over too.

Essentially, this compresses every lever in the typical SaaS company’s valuation. Growth, margin, and terminal value are the three things that mostly determine a company’s long-term worth. For the majority of SaaS companies, those are materially worse and will not improve.

But this also introduces a paradox: How can AI simultaneously make software significantly more useful while making it a materially worse business? Does it all just go to consumer surplus? In answering these questions, we can understand what the context layer is and why it is so valuable.

The three-layer stack

OpenAI’s new Frontier platform describes itself as an “intelligence layer” that connects “siloed data warehouses, CRM systems, ticketing tools, and internal applications.” While this does read like secret diary entries of an MBA, behind the jargon is something important. The platform’s goal is to make every software company commodity plumbing. Simply dumb pieces of software that are there to help pipe intelligence around the company correctly.

Frontier, and other platforms like it, simplify the tech stack into three layers:

Layer 1: Systems of Record (the database layer). The auditable, regulated data stores. Financial ledgers, healthcare records, compliance systems. Enterprises aren’t replacing these — they’re layering intelligence around them. Oracle and SAP didn’t die when we switched to cloud, but they stopped commanding premium growth multiples. So too with cloud systems of record like Salesforce, Netsuite, etc. They are sticky, hard to sell, and are subject to increased competition, but these databases have to exist so AI agents can have something to reference.

Layer 2: Point solution applications (the interface layer). The software humans interact with directly. Analytics packages on top of systems of record or project management tools, the stuff that is defined by per-seat pricing. This is the true SaaSacre zone. Tools that serve an individual user, that have no complex security or permissioning needs are doomed because they are relatively simple to spin up.

The world that many SaaS companies were designed for simply doesn’t exist anymore. A significant portion of their workflow can/will be done by agents, driving value away from the software provider and towards the token provider. Legacy providers have bridged this gap with their pricing by bundling per seat human subscriptions with a per token credit system. However new entrants are leaning all the way into it, not bothering to charge per seat anymore and just charging on the basis of tokens used.

But we should strive to be very, very precise here: generating code is commoditized. Governing code in production—knowing what should exist, what databases/systems of records it connects to, who’s allowed to change it—is not. And those crucial questions are only answerable in the context layer.

Layer 3: The Context Layer (the new middle).

The industry already has a name for the space between databases and applications: they’re calling it the “orchestration layer.” But orchestration is just a fancy word for a type of plumbing, and plumbing commoditizes fast. All the current leaders from startups like LangChain, to MCP, or even Google’s agents protocol are racing toward interoperability. The orchestration frameworks will be cheap. What won’t be cheap is what directs the orchestration. The institutional knowledge that tells agents what to do, in what order, and whether they’re allowed to do it.

Before AI, every company had an invisible, accidental version of this to direct humans: the email threads, wiki pages, Slack channels, onboarding docs, and tribal knowledge where organizational truth actually lived. It was never structured. Never searchable. Never maintained. It was just the ugly overhead that required companies to hire more people than they wanted.

After AI, this overhead becomes real software, and it becomes the most important software in the stack. Not because the individual emails are all that precious, but because it’s the raw material for a living model of how your organization actually operates. Who has access to what data, who can do what with it, what sequence of steps actually gets a deal closed or an incident resolved. The context layer is the directing layer, the thing that makes the difference between an agent that takes action and an agent that takes the right action. Best of all, this is a compounding asset. Every time an agent executes a workflow, it generates traces that feed back into the context layer, making the next execution smarter.

That’s context. Context is the institutional knowledge that makes coordination valuable. Systems of record can’t provide it—they store data, not meaning. Endpoint applications can’t provide it—they’re just interfaces restricted to their own domain. The context layer is what makes AI agents productive instead of just active. It is the context of how humans work, but over time, it becomes the context of how agents work even better.

You could argue this is just a fancy way to describe a bunch of markdown files. After all, Anthropic shipped its Cowork plugins as literally that. But there’s a critical difference between context you can write down and context that has to be learned. A markdown file can describe your sales process. It can’t encode that deals over $500K stall when legal reviews before procurement, or that your best AE skips the discovery call for inbound leads from existing customers. That kind of process knowledge doesn’t live in a document. It emerges from thousands of workflows executed over time. The markdown file is a snapshot.

Context is what gets the margin that SaaS lost

The playbook for what happens next was written in 2003. (Nobody in Silicon Valley has read it because it wasn’t a tweet.) Clayton Christensen called it the Law of Conservation of Attractive Profits: when one layer of a value chain commoditizes, the adjacent layer de-commoditizes. IBM’s hardware commoditized, and value migrated to Intel and Microsoft. If applications and systems of record just become the cost of the tokens to create them, and are thus commoditized, the value must migrate to the layer between them.

The reason is structural. At any point in time, there is one thing that is the main bottleneck in a technology stack. Everything else gets cheaper and more interchangeable so that bottleneck can be solved. Right now, the thing that matters most is the connection between how AI models are trained and the agent systems that actually use them. That’s where the real performance gains are. For that connection to improve, everything around it has to get out of the way. Databases become interchangeable inputs. Applications become disposable interfaces. Building software isn’t the hard part anymore. Directing it is. The context layer sits at the new bottleneck.

Here’s the important part: the context layer doesn’t fully compete with existing software spend. It replaces coordination overhead that companies just accepted. It takes money from the payroll budget, not the IT one. If that sounds too clean, consider what the alternative looks like where you pay a bunch of MBAs to do fake email jobs and make slides, all just to make sure that your company doesn’t go off the rails.

Most importantly, it compounds. Every other layer is getting easier to swap out. The context layer only gets harder. The more an organization’s nuance gets encoded—definitions, process logic, team-specific meaning—the more switching costs increase.

The context layer increases the importance of software by turning a category of organizational cost that was previously just called “overhead” into software margin for the first time.

The battle for the context layer

The unusual thing about this moment is that the biggest question in enterprise software is genuinely unanswered. There is a trillion dollars sitting on the ground, waiting for someone to pick it up. There are no clear winners yet. This is mostly because the details of the context layer are incredibly murky right now. We can look at the primary competitors as a lens by which to interpret the potential sources of power.

In traditional SaaS, ServiceNow has the deepest enterprise workflow penetration and just announced collaborations with both OpenAI and Anthropic—it already holds decision logic and process definitions for thousands of large organizations. Notion holds institutional knowledge for a different segment: the companies that run on wikis, docs, and databases. They have a new agents product coming soon that will directly target the context layer. Glean is evolving enterprise search into enterprise context — starting from the problem of “find what we already know” and building toward “know what we mean.” Their bet is that if you can find the organizational knowledge, adding agents on top is a natural extension.

The foundation models are also building towards this. OpenAI Frontier wants to be the semantic operating system connecting databases, CRMs, and internal apps, then deploying agents that accumulate institutional context over time. Anthropic’s Cowork plugins target professional workflows directly, bypassing the application layer entirely. Their bet is that if you build the most capable agents, the organizational knowledge accumulates to you. It is easy to see them extending Cowork into a true enterprise platform.

The question that no one knows the answer to is this: Does the context layer get owned by the company that already holds the knowledge, or the company that builds the most capable agents? The answer probably varies by company size, industry, and how centralized their existing knowledge infrastructure is. But the battle for this layer is now the most important strategic competition in enterprise software.

The close

Christensen’s law says that when a layer commoditizes, the adjacent layer captures the margin. Code is commoditizing. Databases are commoditizing. The layer between them—the one that holds organizational meaning, permissions, and institutional judgment—is where the new value forms. The SaaSacre isn’t the death of software. It’s the birth of the layer that makes software actually work. Systems of record are just databases. Endpoint applications are just code. And both of them are just tokens. Context is king.

Thank you to Akshay Kothari, Buck, Tina He, Nathan Baschez, and a few others who wanted to remain anonymous for their feedback on this idea! They are all worth a follow.

It was the best of times, it was the bubbliest of times. One of the ways that you knew crypto had gotten far, far over its skis was that those dorks started buying Super Bowl ads. We have a new type of dork, subspecies artificial-intelligence-bro, and they are buying more Super Bowl ads than ever. And as a card-carrying member of this breed of dweeb, I worry about what it signals.

For example, Anthropic is running an ad, talking about how ads are bad (and how they don’t have them in their product) as a counterposition to OpenAI—who is also doing a Super Bowl ad. The founder of Crypto.com purchased the domain name ai.com for $70 million buckaroos and is launching his new AI startup with, you guessed it, a Super Bowl ad. In total, there should be roughly 15 ads about AI during the event, the highest of any other category. Take that, beer and sports gambling!

If AI decreases the cost of creating software, it only increases the cost of distribution. These Super Bowl ads are a high-profile example of this, but it is happening everywhere. On the public tech end, Adobe is spending $1.4 billion on ads because not enough people believe they were an AI winner. On the private side, I am getting more inbound requests from AI startups and venture capitalists for help with their content than I ever have. The distribution crunch is real and it is increasing. Rejoice, fellow wordcels! We people who can write the goodest are finally going to get a raise.

The reason I worry about this as a signal is that Super Bowl ads heralded the end of the good times for our brothers in Bitcoin. Could it signal the same for all of us who follow Sam Altman on X?

Speaking of sponsors, today’s is a brand new one, and happens to be one of my favorite products in the world, Granola.

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MY RESEARCH

Beware Claude Code. The temptation offered by AI coding agents is that because software is easy and cheap to make, you therefore should stop buying software. This is especially tempting if you are an early-stage founder with limited resources. However, it is precisely because your resources are limited that you should be spending cash on buying other people’s software. The opportunity cost is extraordinary high as a founder, and any time not spent talking to customers and solving their problems is a waste. This was a guest post in the a16z speedrun newsletter!

Note: I apologize that this week was light from a publishing standpoint. My whole family came down with some kind of evil, radioactive flu variant, and I’m just starting to feel better after 5 days in the trenches. If I’m off for illness, I’ll always tell you in the The Leverage group chat, which is hosted on the Substack app. Thank you for being understanding.

WHAT MATTERED THIS WEEK?

Apple is realizing AI is existential. This week, Bloomberg reported that Apple had killed an AI health coach in favor of integrating some of these features in the existing Health app while rolling more of them into the ChatGPT competitor version of Siri that is due later this year. As I’ve been experimenting with micro-dosing GLP-1s, feeding all my data into Apple Health, and then having an LLM analyze that data, I’ve realized that all of my apps should work this way. Cheap intelligence connecting all of my data and then giving me better analysis then I could possibly buy. Apple needs to kill this health coach, because why is it a health coach? It should just be a genius at everything that knows everything about your life, and that’s where mobile software is heading. The combination of tech, tracking, and AI has made “Operation Dad Bod” my easiest, most successful health intervention ever. I’m down 12 pounds, and have increased my weight lifting numbers at the same time. Magic! Now I want that magic embedded in every screen I touch.

Waymo is using world models to take over the world. Three weeks ago, I talked on YouTube about how AI-generated videos and simulations would be a driving force in robotics this year. It only took till February for me to be proven right. Waymo is using the Genie 3 model, which simulates 3 dimensional worlds, to improve Waymo’s models, allowing them to expand to new geographies more easily. Pair that announcement with the firm also sharing that they had raised a $16 billion growth round at a $126 billion post-money valuation and you start to understand the scope of the ambition here. Cars are the invention that has most reshaped the physical world for the last 100 years, and I think autonomous vehicles will have a similar impact.

I am, frankly, still not feeling great, so I’m going to keep it short this week, but before I go, a quick recommendation.

TASTEMAKER

I’ve found an AI video game that is actually good. Pax Historia is a grand strategy game where you or other players devise scenarios like, “What if the United States invades Greenland?” Or “what if the USA had lost at Pearl Harbor?” Or even fun ones like, “What if the entire Vatican City became infested with zombies?” AI then reacts in real time to your choices, allowing the game to go as wild as your imagination will. Most AI video games bore me after 10 minutes, but this one has me hooked. I am anxious to give them more of my money so I can play more scenarios!

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q1 ‘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.

As I type this the internet is freaking out about Moltbook, a so-called AI social network, where a specific type of AI agents can talk to each other. If anyone has followed the science, nothing in the site is revolutionary, like, at all. LLMs have long been capable of writing compellingly creepy copy to each other, but because this is happening more autonomously than previous experiments, relative noobs to the space are getting scared. So this is me officially telling you that this is a nothing-burger and you can move past it. It is simply a larger experiment confirming what we already knew!

The real things that determine the future, unit economics and computer science, are what drive all progress forward. And in that, I got you covered this week.

MY RESEARCH

Maintenance as a spiritual pursuit. I read a truly remarkable book this week that I believe everyone in technology should buy. Stewart Brand’s Maintenance is a meditation on how technology works, and how important its upkeep is. This one had me examining the tools in my life, how my lack of understanding about them alienated me from reality, and then I resolved to change that. You can read more details here.

WHAT MATTERED THIS WEEK?

BIG TECH

Zuckerberg to spend up to $135 billion on AI. Look, I’m going to fire off some ludicrous, jumbo-sized numbers at you, but don’t just scroll over them. Think long and hard about the scope of what we are talking about.

In their quarterly earnings, Meta reported a $6.02 billion operating loss in their “Reality Labs” division—their name for the group that houses virtual reality and the Meta Ray-Bans. In total, this division has lost $75 billion since 2020. The only real success they have had is the Meta Ray-Bans, but that is pennies to what they’ve lost attempting to make VR goggles a thing.

And all those losses over the last 6 years are pennies to what they are going to spend on datacenters this year. The company expects to spend $115 billion to $135 billion in 2026 on GPU infrastructure to build an AI superintelligence. Keep in mind that Reality Labs has been publicly derided by investors for losing too much money, and the big Z is going to blow through 1.8x that amount in one year. And all that cash is for a product that, uh, doesn’t exist yet.

Still, this size of bet is starting to look like the cover charge required to enter into the foundation model casino. OpenAI is rumored to be closing in on a $100 billion new round of growth capital. X.AI is rumored to be merging with SpaceX to give it some much needed access to additional capital markets. And Google is expecting to spend $115 billion on datacenters in 2026. Anthropic, the chronically underfunded pipsqueak, is only spending a measly $50 billion this year on datacenters.

Rather than view this as monolithic bets on the creation of AI Jesus, it is more helpful to view it as a hedge. GPUs are valuable because they are task agnostic. Building these datacenters now allows them to (potentially) make superintelligence and if that fails they can (certainly) utilize them in making their other services more performant.

This earnings call offered additional validation of that thesis. The Meta team described their ad targeting model GEM,

“In Q4, we doubled the number of GPUs we used to train our GEM model for ads ranking. We also adopted a new sequence learning model architecture, which is capable of using longer sequences of user behavior and processing much richer information about each piece of content. The GEM and sequence learning improvements together drove a 3.5% lift in ad clicks on Facebook and a more than 1% gain in conversions on Instagram in Q4.”

Though the model is not an LLM, it shares some technical similarities. Importantly, it also shares scaling laws: The more compute and data they give it, the more powerful it will become. And by powerful, I mean the ads targeting people will be ever more performative. This is a ruthlessly capitalistic application of this technology, but those outside of Silicon Valley are underestimating how much these architectures can be applied to problems beyond ChatGPT. The big tech CEOs believe that GPUs are not going to run out of profitable use cases, even if superintelligence is two decades away.

BIG LABS

OpenAI’s ad product sucks (and that’s ok). Speaking of ad performance, we received additional details on how their upcoming ads will work and it is a throwback to 2005. “OpenAI won’t be providing detailed information about the query responses accompanying their ads or whether ads prompted ChatGPT users to take an action, like buying something or looking up a website.” This lack of data is basically the equivalent of purchasing a TV ad budget, something more meant for brand building versus the targeted direct response ads that have made Meta/Google so powerful. The price is high too, “OpenAI is targeting a price of roughly $60 for every 1,000 views an ad gets, the buyer said.” This is what I would expect it to cost for something premium on TV, not just daytime reruns of Law and Order.

Most of the coverage of this topic has focused on the high price and lack of conversion data, a take that I agree with. In the long run, you have to target ads on the basis of ad conversion rates, demographics, and what you can scrape from cookies. However, that’ll take them multiple years to build out and this is as good a place to start as any.

What I find fascinating, and that no one is really discussing, is the “won’t be providing detailed information about the query responses accompanying their ads” portion of the offering. OpenAI is essentially offering sales conversations as an ad product while simultaneously refusing to tell the advertiser what was said. This is nuts! As I’ve established in my previous research, making the chatbot’s responses steerable by the advertiser is the single most important aspect of this product. Otherwise all sorts of hijinks will inevitably ensue, such as in my case, where the LLM convinced customers to churn!

Years ago, I wrote a very viral piece titled “Snapchat’s Probably Screwed” on the basis that the company was being too cute with their ad inventory. They focused all of their efforts on having customers spend large amounts on brand ads that used augmented reality. My argument was that this was a critical error, and that instead they needed to build out direct response capabilities. I was very right—the stock is down ~87% over the last five years. OpenAI is in danger of making the same mistake. Still, let’s give them a few years, the team there is mostly ex-Meta, they know how to build an ads platform. The temptation for new types of ad units may still be too much for them though. I would really like to not write my followup, “OpenAI’s Probably Screwed.”

LET THE POLICY RIP

You can get $10K for a blog post on how to save the world. In an exclusive interview with The Leverage, Tao Burga of the Institute for Progress (IFP) laid out a thesis that should make you pay attention—some of AI’s biggest problems are ones markets will never solve.

The 30-person nonpartisan think tank that spans AI policy, infrastructure permitting, and meta-science has spent the past year building “Launch Sequence,” an effort to direct philanthropic capital toward exactly these gaps. Tao told me that “$25 billion is about to flow into AI resilience and AI-for-science from the OpenAI Foundation, the Chan Zuckerberg Initiative, and other major funders.” This program is to help direct some of that capital.

AI capabilities will diffuse whether America likes it or not, and Chinese labs are nine months behind. What matters now is building the defensive infrastructure and institutional resilience that determines whether ubiquitous AI goes well or catastrophically wrong. They’ve received 150 proposals already. They want more!

Burga admitted they nearly included “recommender systems and media algorithms” as a focus area—my slop thesis, basically—but pulled back fearing vague grievances instead of concrete solutions. Fair. But the underlying point stands: science acceleration, biosecurity, epistemic integrity, algorithmic accountability—venture capital won’t touch these because the economic incentives simply aren’t there. IFP wants proposals that are concrete, AI-specific, executable within five years, and wouldn’t happen through normal market mechanisms.

If you’re sitting on an idea that fits, submit it here. Published proposals receive a $10,000 honorarium, and IFP is offering $1,000 bounties for successful referrals. I suspect readers of this newsletter are unusually well-positioned to contribute. Prove me right!

THE SLOPPENING

TikTok competitor Upscrolled hit number 1 in the app store. The newly Americanized app has had issues in its first week, including not being able to say the word “Epstein” and being unable to find content on what ICE is doing in Minnesota. The company blamed a “data center outage” for this, which is not how content moderation algorithms work. It is not like there is a server in the backroom with the phrase “leftist talking points” written on it in Sharpie that someone accidentally unplugged. As usual, I’m going to avoid the political talking points here, because that’s not what this newsletter is. (Though I will once again point out that this is a clear case of market manipulation and an unfair bidding process).

What I am going to point out is that the market response was downloading Upscroll. The company differentiates itself by saying that, “Shadowbanned elsewhere? Not here. UpScrolled is the social platform where every voice gets equal power. No shadowbans. No algorithmic games.” This is again, not how this works at all. There are always algorithmic games in short-form media because you have to have some sort of ranking algorithm to maximize engagement. If you find that content you like is getting more views, which some early boosters are cheering on, you are still being manipulated by the algorithm!

The issue is outsourcing your taste and discernment. Anything less than the deliberate selection of media that challenges your views is intellectual self-pleasuring. The only way to win is by opting out.

TASTEMAKER

Books rule. Reading and writing are our greatest technologies ever invented. They allow us to inhabit the minds of others, to build a profound sense of empathy and understanding for people different from us. They have made my life so much richer. The world is especially intense right now if you are following political news. I’m originally from Minnesota, so as you can imagine, it has been a very intense few weeks for me. To find refuge from the storm, and to make sure that I don’t snuff the internal flame of care, I’ve been reading a ton. If you find yourself similarly needing shelter in the storm, here are three books I would recommend you pick up this week.

• Non-things by Byung-Chul Han. A philosophical exploration of why we now prioritize experiences over possessions. Highly recommended if you want to examine your relationship with technology. A word of warning: it is a book you can read in two hours, but it benefits from a slow read. Carefully consider his sentences/references to find its hidden depths.

• Dungeon Crawler Carl: Is this well-written? No. Is it a purely escapist plot-slop book designed for people with a crude sense of humor and love of nerd stuff? Yes. Did I read the entire 7 book series in 3 weeks? Unfortunately, also yes. I had a lot of fun and if you start now you can get ready for book eight to be released in May.

• The Farseer Trilogy: For fantasy that is actually well-written, I would recommend this series by Robin Hobb. It follows a royal bastard named Fitz who is trying desperately to stay alive. His family job of assassin makes that exceptionally hard. This is well-written, well-crafted work and is a wonderful way to elevate your reading game if you like fantasy.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q1 ‘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.

Help me out here.

How would you describe a non-fiction book that has the following contents: Diatribes against the philosopher Jean-Jacques Rousseau, 3 separate diagrams on the proper construction of Dry Stone Walling, one brief anecdote where in which you learn that the author spent “a couple of weeks in France” learning to build said Dry Stone Walling, effusive praise for his favorite manuals ranging in topic from Volkswagen maintenance to childbirth, a comparison of the success of the AK-47 and the failures of the M16 Rifle in Vietnam, 7 different pictures of the Model T car, an explanation of the software platform that has allowed the Ukrainian army to run rings around Russia’s beleaguered forces, and an overview of 12 essential types of corrosion which introduce $2.5 trillion in costs to the global economy.

The way I see it, there are two word choices that would typically be utilized by the Big Book Review™ industrial complex.

1. Sweeping: of scope, ambition, breadth. The vision of the book is so grand that you can get away with quickly and vaguely pointing to a large variety of topics. (See Sapiens reviews upon release).

2. Meandering: a word that allows the reviewer to politely point out this book is ass, can’t focus, and has bad pacing. Alternative word choice include eclectic or scattered. (See Sapiens reviews now that everyone has calmed down a bit).

Both of these choices are bad options to describe author Stewart Brand’s Maintenance: Of Everything. It has sweeping scope, yet simultaneously meanders in its arguments. Instead, I would choose a third, more elusive moniker. I would call this book reflecting. Its loose collection of anecdotes is about filling the soul of the reader with enough stories to triangulate the mind into a new worldview, and then allowing the reader to reflect that worldview onto their personal circumstances.

Within that framework, I found the book to be one of the most spiritually enriching technical manuals I’ve ever read. It tends towards overspecificity in its details and vagueness in its conclusions. It has lots of little ideas and a few big ones and that’s all kinda the point. It is a worthy addition to the Tech Canon and a book that all people interested in startups should read.

Here’s why.

How to think about this book

The default path for reviewing non-fiction books is to closely examine their central argument, find if that argument is worthy of praise or condemnation, and write accordingly. And this, admittedly, is my typical process for reviewing non-fiction work about technology.

To apply the same method here, Brand’s central argument is that maintenance is a vastly underestimated and powerful force in our world. It is simultaneously a spiritual pursuit that shapes the individual practitioner, and when an attitude of Maintenance is adopted by a larger society, it allows for the flourishing of its people, most obviously shown off in mechanical combat.

In this regard, the book is unconvincing. The argument is never stated fully, lacks structure, and the end…just doesn’t exist. The last chapter is mostly about Tesla and electric vehicles’ decreased maintenance needs. It then has about two paragraphs’ worth on why horses are cool, and that’s kinda it. I am not a reader that needs their hand held, but I do think some forms of structure strengthen the case.

Additionally, the book has relatively little in the way of studies, macro-analysis, or comparison to other potential forces that could be more important relative to maintenance. Instead, Brand’s writing mostly relies on the strength of its anecdotal evidence to make its case. And, if this was a typical book, by a typical author, I wouldn’t bother to tell you about it.

But this isn’t a typical author. Stewart Brand is one of the select individuals whom I consider a cultural progenitor of Silicon Valley. He was the publisher of The Whole Earth Catalog, a publication which Steve Jobs once called “Google in paperback form.” It was also sweeping in scope, covering everything from geodesic domes to cybernetics to organic farming, yet was meandering in execution, with its famously chaotic, magazine-style layouts that jumped from chainsaw reviews to book reviews about Zen Buddhism.

His body of work was deeply inspirational to the giants who have made the internet what it is today. He is beloved by everyone from the Collison brothers—who cofounded Stripe and published Maintenance—to Jeff Bezos. The strange alchemy of Silicon Valley, of hippie idealism, rugged individualism, and environmentalism is at least partially due to his work. The Whole Earth Catalog has little writing that will survive the centuries, but the ethos that it cultivated will resonate forever.

In a 2025 interview, NY Times opinion columnist Ezra Klein described why he reads books versus simply asking AI models for a summary of their arguments.

“I think I used to conceptualize knowledge the way you see it in the movie The Matrix, where it’s like I wanted the port in the back of my mind that the little needle would go into…I thought that what you were doing was downloading information into your brain.

Now I think that what you are doing is spending time grappling with the text, making connections. It will only happen through that process of grappling. So, the idea that you could speed run that, the idea that it could just be summarized for you...

Part of what is happening when you spend seven hours reading a book is you spend seven hours with your mind on this topic. The idea that O3 can summarize it for you, in addition to all this stuff you just will not have read, is it you didn’t have the engagement. It doesn’t impress itself upon you. It doesn’t change you. What knowledge is supposed to do is change you, and it changes you because you make connections to it.”

When considering Maintenance, I think this is the proper approach by which to read this book. I found that in marinating in its ethos, I made multiple, meaningful “connections” as Klein calls them, across the most important aspects of my life.

Finally, a technology for the Stupid

Read more

I think software has changed more in the last 24 months than in the last two decades. How you construct a team, what strategies are defensible—shoot, if you even need employees at all, all operating norms need to be reconsidered.

Just this week I met a pair of founders who had built a really novel consumer application. It had beautiful design, gamification, payments, a fully-featured creator backend. This thing fizzed and popped. Five years ago, I would’ve assumed it would take a couple of great engineers 6 plus months to make it.

These guys had made it in four weeks. Four weeks! Even crazier, they don’t know how to code. All they had was a dream, a Claude Code account, and a dangerously high caffeine intake.

It is an incredibly exciting time to be making things. The hard part is knowing what to actually do—thankfully this newsletter has the signals for what is happening next.

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

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MY RESEARCH

GLPs plus AI are witchcraft. Good technology increases our agency while bad technology makes our choices for us. In my physical health I have found that a combination of GLPs and the use of tools like Claude to be the most empowering, useful tools for physical health I’ve ever encountered. Night and day difference. For subscribers I describe the routine, apps, and AI tricks I’m using to lose weight.

Companies who elevate taste can make billions. [Video Essay]. Rather merely bemoan the current state of media and tech, I wanted to understand how startups could elevate people’s aesthetic judgement. This work has turned out to be enormously popular, and this video essay dives deeper into the arguments for a new type of startup that could help fight back. Watch here.

WHAT MATTERED THIS WEEK?

BIG TECH

Apple is making Siri into a ChatGPT competitor. Bloomberg reported that Apple has decided to get into the Chatbot game. The new Siri will far exceed what it can do today—searching the web, creating content, generating images, analyzing files you upload—and it’ll be woven directly into Apple’s core apps. This is late, but necessary. They don’t have the AI talent that the rest of big tech does, which helps explain their recent partnership with Google’s Gemini models.

What’s more interesting is what they could launch in the years to come. As I hypothesized in my essay this week, Apple is the one company with centralized subscriptions, payments, and usage data on its customers, which means they can make a chatbot no other company can on mobile. I don’t want Siri to be controlling just Apple’s apps, I want it to be controlling every app on my phone. I never want to click again. It feels inevitable; you can just see the marketing campaigns right? “Think Different” hits when the phone does all the boring thinking for you.

This step is the next on a journey to a complete reinvention of how we use computers. It can’t come soon enough!

The TikTok deal is done (and is also a weird, slimy, gross mess). American entities now control 80.1% of a new joint venture called TikTok USDS, with ByteDance retaining just 19.9% to comply with the congressional ban that required Chinese ownership below 20%. The ownership structure features three managing investors—Oracle, private equity firm Silver Lake, and UAE-based investment firm MGX—each holding 15% stakes, while another 35% is distributed among eight other investors including Dell CEO Michael Dell’s investment office and trading firm Susquehanna International Group. Under the arrangement, U.S. operators will “retrain, test, and update” TikTok’s content recommendation algorithm using American user data.

I predicted this outcome back in September 2025, and the weirdness has only intensified since then. While I don’t want to judge the investors without knowing the specifics of how this came together, the whole situation feels deeply strange—the political flip-flopping, the particular roster of investors allowed to participate, the timing of everything. Even setting aside concerns about political corruption (which admittedly rest on circumstantial evidence), the most bizarre aspect is that the two things giving TikTok its actual business power—the algorithm and commercial operations—remain functionally controlled by ByteDance. Despite the elaborate ownership restructuring that satisfies the divestiture law, this arrangement changes relatively little in practice, raising questions about whether it genuinely addresses the national security concerns that justified the ban in the first place, or if this all became some sort of political horse trading.

THE SLOPPENING

People can’t distinguish AI from reality anymore. Video model startup Runway released its 4.5 model. The results are genuinely indistinguishable from reality. The startup “recruited a random sampling of 1,043 participants” for a study to prove this. “Each participant viewed 20 videos (10 real, 10 generated) in randomized order and judged whether each was real or AI-generated.”

The results were…shocking.

Keep in mind these are random generations. With careful prompting, creators can make it even harder to tell. I am no Pixel Puritan. Whether a ten second clip of a shark is real or not isn’t a major issue. However, that we can all be so easily fooled is a big one. I took the test and got 60% right. Think you can beat my score? Try it here.

Youtube is against slop (but only of the AI variety). In his 2026 letter, YouTube CEO Neal Mehta said that one of his big focuses for 2026 is “managing AI slop.” He is committed to stopping the platform from being flooded with “low quality, repetitive content.” This is a worthy goal, and godspeed brother.

Still, I think the issue isn’t AI-specific slop, but that AI reduces the cost of all slop. What I mean is that it is hard for me to look at the most recent videos from the platform’s most popular creator and not think, “huh, this is slop.”

No big tech platform has an answer for how to combat the second-order effects of AI. There is a giant race to the cognitive bottom for viewers going on and startups are the best hope to stop it. I applaud YouTube’s efforts to combat AI slop, but there are many, many more types of slop out there that we should also stop.

AI RESEARCH

Claude has a Date Me doc. Anthropic released a “constitution” document that they use to train their chatbots and shape their behavior. It is one of the most fascinating documents I’ve ever seen from a corporation. The fundamental problem with LLMs is that the things we are growing are being asked to make decisions in ethical gray areas. Since you can’t write out rules for every edge case, you instead have to give the model a good sense of ethical judgement. To accomplish that, Anthropic trains it on about 20,000 words’ worth of guidelines. I have many, many thoughts here, and will likely dive in deeper in the weeks to come, but it is worth reading as a technological/ethical artifact. Summary is here and the full document is here.

STARTUP STRATEGY

How much longer will coding survive? Back to my story at the top of this edition. These types of conversations have been happening more and more for me. Founders just keep shipping more products. For some outliers, that increased product velocity has increased their revenue. However, for many others, the revenue growth hasn’t kept up. With that in mind, I saw this tweet from Martin Casado at a16z.

Ultimately, startups are about learning secrets. Novel technologies, how to sell in novel ways. It is about moving faster and grinding harder than a large company could ever hope to match, not for the sake of speed itself, but for the sake of gaining knowledge. Then the code comes. Ultimately code is a reflection of the knowledge you have about your customers’ workflows. LLMs just mean you can translate those insights faster, but it does not materially change the real bottleneck which is secrets. The founders I mentioned could make the product so quickly because they knew what the market needed.

TASTEMAKER

So now I am older

Than my mother and father

When they had their daughter

Now what does that say about me?

Oh, how could I dream of

Such a selfless and true love

Could I wash my hands of

Just looking out for me

Oh man, what I used to be

Oh man, oh my, oh me

I’ve been thinking a lot about the song Montezuma by Fleet Foxes this week. Released in 2011, the album carried me through my 20s. It’s shaped by the soaring harmonies that make Fleet Foxes great, but the lyrics are significantly better than their other two albums. I pulled these verses specifically, because in the context of the world’s cruelty, I yearned for something empathetic and caring. This album hugged me and gave me that. You can listen to a great live version of this song here.

In dearth or in excess

Both the slave and the empress

Will return to the dirt, I guess

Naked as when they came

I wonder if I’ll see

Any faces above me

Or just cracks in the ceiling

Nobody else to blame

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q1 ‘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.

I’ve been running an experiment on myself. Every Sunday, my endlessly patient wife sticks me in the stomach with 2.5 mg of Zepbound. Every few days, I ask Claude to analyze my sleep, workouts, and nutrition data.

I’m calling it Operation Dad Bod, and it’s working better than any health intervention I’ve ever tried.

This is possible because of two recent shifts: Claude and ChatGPT launched Apple Health integrations, so the chatbots can now ingest your health data for analysis. Meanwhile, GLP-1 prescriptions hit record numbers—nearly one in five U.S. adults has now used a GLP-1 drug, with 12% currently taking one.

I’ve always had an interest in health and fitness, and have done many cuts over the years to achieve weight goals. So I’m capable of dieting and consistent exercise. Still, you can’t knowledge your way out of being hungry. You can’t willpower your way into continuous self-monitoring when your attention is already maxed out. AI plus GLPs fix both of those for about 300 bucks a month.

You should pair these two trends because both are technologies of interiority. They aim to modify those inner currents—appetite, attention, motivation—that govern your own behavior.

The bottleneck was my interior self. Between a toddler, scaling a business, and deep analytical work, my inner state (hunger, fatigue, financial pressures, fragmented attention) made “eat less, move more” a cognitive trade that was irrational. I would rather be a successful writer with some heft around my middle than have abs and be back to laboring in the B2B SaaS mines. However, by pairing GLPs with a doctor in my pocket (Claude) that trade-off suddenly shifted in my favor.

While I am early to living like this, I believe this combination will be the future, and it will, by extension, reshape society. I’d like to tell you how to achieve similar benefits, explain how this type of magic alters what we will expect out of companies like Apple, and discuss what this means for the future of technology markets.

But to do that, first we need to understand the mechanics of how both AI and GLPs change our thoughts.

GLP-1s Are Drugs That Change What You Want

Tirzepatide (Zepbound) doesn’t provide information or require willpower or suddenly give you the metabolism of a 21-year-old. The goal is to change your subjective experience of hunger.

Tirzepatide is a “dual agonist,” meaning it activates two different hormone receptors at once. The first is GLP-1, which slows gastric emptying (food stays in your stomach longer, so you feel full) and acts on appetite-regulating neurons in the hypothalamus—the brain’s hunger control center. These neurons, called POMC neurons, are what tell your brain “stop eating.” The second receptor is GIP, which appears to amplify the satiety signal and improve how your body metabolizes fat.

The combination is more effective than either pathway alone. In clinical trials, participants lost an average of 20.9% of their body weight on the highest dose. This is the most effective intervention we have seen in clinical trials. (Though there are drugs on the horizon that are even more powerful).

Every other cut I’ve done put me in a low-level, constant state of grump. Pissed off that I’m hungry. Pissed off that I’m being distracted by being pissed off. Now, I don’t even receive hunger cues till it’s mealtime again. I use RescueTime to track my personal computer use, and ever since I’ve started the drug, I take significantly fewer breaks and have longer, more focused working sessions. It is unclear to me how much of that is hunger dampening versus happy side-benefits, but regardless, it is happening.

(To be clear, please go to a doctor before deciding to take this stuff. It isn’t for everyone, and don’t, for any reason, read through this analysis and guide to Chinese peptides I wrote.)

In the back of my mind though, there sits a gaunt, French philosophy professor reprimanding me: ‘If you need a drug to align appetite with goals, whose goals are they?’ He mumbles this over a mouthful of cigarette, baguette, and socialism. Despite this somewhat grotesque caricature of a French person my subconscious conjured, I spent some time thinking about this question.

The reality is that your appetites were already modified—just by food companies rather than drug companies.

Ultra-processed foods have been engineered to maximize consumption. Big Food (oh lord, they are playing RFK Jr. ‘s theme music now) has optimized their products, through decades of R&D, to override your natural satiety signals. One NIH study demonstrated this directly. Researchers housed participants in a clinical ward and gave them either an ultra-processed or unprocessed diet for two weeks, then switched. The diets were matched for presented calories, sugar, fat, and fiber. People eating ultra-processed foods spontaneously consumed 500 extra calories per day and gained weight, while those on unprocessed foods lost weight. The overconsumption was automatic.

The choice you are actually making is between the “you shaped by food industry optimization for consumption” and “you shaped by pharmaceutical optimization for satiety.” Which is the kinda horrible, late-stage capitalism nonsense we have to deal with now, but at least one of these options can help you bench 225.

AI Integration Solves Attention

The Claude and ChatGPT health launches matter because they solve the other half of the problem: attention.

Even with appetite controlled, health optimization is continuous work. It is exhausting to be forced to think hard about every food taken, every workout performed. And research shows that when cognitive resources are depleted, food choices suffer. In one study, exposure to food advertising caused people to choose 28% more unhealthy snacks, but only when they were also under high cognitive load. When participants weren’t cognitively taxed, advertising had no significant effect.

When your cognitive resources are allocated elsewhere, eating defaults to whatever is easiest. Whenever I have a deadline, my prefrontal cortex, the portion of my brain responsible for self-control, simply decides to go on vacation until I hit publish, and in that void, my stomach signaled it was time to consume Nerds Gummy Clusters by the bucket. Our brains are constantly competing in attention marketplaces and they’ll typically lose in battles with environmental cues when it’s already overloaded.

Honestly, when I first heard about these AI health products, I wasn’t expecting that much. That all changed within my first conversation. Claude:

• Pulled my workout patterns and identified the alternating cardio structure I’d built across three different apps.

• Spotted a protein intake gap (I need 200 g for muscle preservation at my training volume; I was hitting 150 g).

• Integrated my bloodwork from a PDF I shared and reframed the goal of this cut to focus on lipid counts versus the number on the scale.

• Flagged my resting heart rate as a leading indicator to watch

Could I have done this analysis myself? Technically yes. Would I have? No. My attention is allocated to being a great dad and a great steward of The Leverage. Health optimization has always lost the focus battle. The value comes in AI’s ability to synthesize across many sources of data, then make that analysis easy to understand/chat with. This was a 10x better experience than what my doctor can provide, simply with how their time is allocated.

This is why the Claude/ChatGPT launches matter more than they might seem. If it were just a “chat with your health feature” it wouldn’t be that useful. What’s magical is that Apple Health has consolidated data from many places in my life, and then I can chat with that data in partnership with a genius in a datacenter. It isn’t perfect! I found a few integration/analysis errors, but that stuff will get smoothed out over time.

For paying subscribers, I’ll now go over,

• My exact stack of applications, training regime, and diet that have made it stupid simple to lose weight.

• Why good technologies of interiority enhance autonomy, while bad technologies hijack it.

• How this changed how I thought about Apple Health and the centralizing role of chatbots on consumer devices.

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