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

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

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

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

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

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

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

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

Why this has happened before

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

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

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

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

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

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

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

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

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

The theory

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

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

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

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

1. A brief writer who defines what should exist

2. A translator who turns the brief into a spec

3. A producer who executes the spec

4. A reviewer who catches errors

5. A judge who decides what ships

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

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

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

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

Read more

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

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

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

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

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

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

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

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

Try Hapax

MY RESEARCH

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

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

WHAT MATTERED THIS WEEK?

BIG MODEL

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

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

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

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

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

BIG ACQUISITION

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

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

This deal makes good strategic sense for both parties.

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

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

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

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

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

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

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

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

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

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

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

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

The IPO target is $1.75T.

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

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

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

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

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

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

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

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

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

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

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

TASTEMAKER

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

Go and be kind this week,

Evan

Sponsorships

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

Author’s note: This is the latest in my “What’s the Bet” series. AI has the potential to change everything, and founders have to make company-defining choices in response. That is not easy. In this series, companies pay me to analyze their bet and explain it to you. I retain editorial independence. Today’s bet is brought to you by Pangram, a startup that has built a Chrome extension that labels AI-generated content in your feeds in real time. If you have any feedback, just reply to this email.

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

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

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

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

Why this bet, why now

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

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

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

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

The technical advantage

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

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

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

What I tried, what I found

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

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

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

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

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

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

So how could Pangram’s bet succeed or fail?

What would prove the bet right or wrong

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

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

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

The simple promise

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

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

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

Try Pangram

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

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

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

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

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

Let’s get into it.

Slop works. Like really, really works.

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

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

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

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

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

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

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

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

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

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

My 6 months in hell

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

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

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

A few things made this possible:

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

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

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

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

Quality is all that matters

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

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

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

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

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

Year two

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

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

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

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

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

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

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

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

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

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MY RESEARCH

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

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

WHAT MATTERED THIS WEEK?

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

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

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

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

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

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

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

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

THE SAASACRE

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

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

1. The Margin Trap

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

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

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

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

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

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

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

2. Model Taste Is Real

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

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

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

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

3. The Duopoly Tax

Figma’s API dependency is also a geopolitical problem.

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

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

4. None of This May Matter

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

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

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

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

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

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

TASTEMAKER

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

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

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

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

Go and be kind this week,

Evan

Sponsorships

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

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

AI products are eliminating the designer from both sides simultaneously.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How Do You Manage What AI Makes?

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

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

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

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

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

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

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

Designers as Cops

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

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

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

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

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

Still, this essay is not really about designers.

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

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

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

What do knowledge workers of the future do?

Read more

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

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

Every enterprise has the same problem with AI-vibe coding. Marketing will build a dashboard in an AI tool or product prototypes some feature. Leadership loves it! Ship ship ship. Only then does engineering actually get to see the code. Surprise! It has problems.

And sure, you can export the code. But it’s built on made-up components that don’t exist in your codebase. The “design system” is just six hex codes and a font. Not a bit of it maps to the components the engineering team actually ships with. So the whole thing gets rebuilt from scratch and the time saved by using AI just got spent twice.

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MY RESEARCH

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

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

WHAT MATTERED THIS WEEK?

BIG LAYOFFS

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

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

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

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

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

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

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

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

BIG DREAMS

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

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

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

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

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

THE GOD IN THE MACHINE

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

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

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

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

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

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

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

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

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

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

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

THE SLOPPENING

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

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

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

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

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

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

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

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

TASTEMAKER

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

Go and be kind this week,

Evan

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

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

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

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

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

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

Instead, I want to argue the following:

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

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

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

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

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

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

Performance power

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

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

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

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

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

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

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

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

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

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

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

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

That is not necessarily a good thing.

Better and More Dangerous at the Same Time

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Read more

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

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

Let’s get into it.

MY RESEARCH

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

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

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

WHAT MATTERED THIS WEEK?

BIG AI NATIVE

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

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

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

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

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

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

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

BIG MOVES FOR LITTLE MODELS

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

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

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

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

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

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

BIG MULTIPLE

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

Here’s the key slide:

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

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

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

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

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

Question 2: Is a 41x EBITDA multiple defensible?

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

Question 3: What’s the bear case?

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

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

THE SLOPPENING

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

The numbers are grim.

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

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

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

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

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

TASTEMAKER

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

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

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

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

Go and be kind this week,

Evan

Sponsorships

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

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

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

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

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

The data is piling up fast:

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

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

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

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

The Blame Chain

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

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

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

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

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

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

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?

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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.

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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

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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

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