My friends, I am fired up this week. I always know I have a good edition on my hands when the newsletter is easy to write because my frameworks predicted the news months ago. I guess what I am saying is that there is no better feeling than saying, “I told you so” to tens of thousands of people. (That is like 60% a joke.) What actually gets me excited about editions like this is that it gives me confidence that what you are reading is providing tangible, predictive value for the time you invest in reading. Today, I was right about the future of SaaS, robotics, and what is happening in the book self-publishing industry.

But first, this edition is brought to you by a new sponsor for The Leverage, Span.

Let me say something heretical—you are spending too much time worrying about what model to use. New research from Span suggests bigger returns can happen by changing three specific habits.

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

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

Read the Report

Make something beautiful. The urge to create is closer to a biological imperative than a hobby, but for 30 years the web crushed it into filters and bios because building anything real took skills almost nobody had. That’s finally over. My belief is that AI has made creation cheap enough that ordinary people can finally build their own corner of the internet. It is on us to make the internet more creative, more independent, and more us. On August 13th I’m gathering 50 writers, designers, and builders in San Francisco to prove it. There will be dinner, unlimited tokens, a guest speaker, and absolutely no pitch decks. It’s completely free. Read and register here.

If you believe in AGI, you can’t also believe in open-source models. On Friday, 25 companies signed a letter titled “Open Weights and American AI Leadership,” asking Washington to avoid “premature restrictions” on open-weight models. The signatories are most of the biggest names in AI including Nvidia, Microsoft, Meta, Palantir, Andreessen Horowitz, Y Combinator, and—curiously—OpenAI. To state the obvious: these people are not altruists. There are trillions of dollars of market cap riding on the current policy regime staying exactly where it is.

The four largest hyperscalers have guided to roughly $700 billion in combined capital expenditure for 2026 alone. That number is only rational if the capability curve keeps bending upward. Meaning, whatever follows Mythos/Fable is just as mind-blowing, and then the one after that, and after that. You get the idea. Nvidia’s entire valuation is this bet. Microsoft’s capex is this bet. Nobody spends $700 billion a year on a technology they expect to plateau.

It’s just really weird to spend like we will add mega-mucho-spooky capabilities for the next five years while also lobbying like those same models are safe to hand out on every streetcorner. Feels like you have to pick one! Either the curve is real, in which case publishing frontier-class weights is indefensible in the long run, or the weights are harmless, in which case this is the largest misallocation of capital in corporate history and the honest letter would read “AI is overhyped, plz ignore us.”

Once the weights are on the internet, anyone can fine-tune them however they see fit, and it is relatively easy to remove model safeguards when you do that. If you believe scaling laws hold, you believe these models will eventually be dangerous in the domains like biology and cybersecurity. That eventually is very, very soon. Like in the next 24-36 months malicious actors will be able to fine tune models to cause serious harm. Open weights mean handing that, unfiltered, to whoever downloads it.

And if you believe we actually reach AGI, then it feels fairly clear that we shouldn’t have that as open weights! A misaligned closed model is a lab’s containment problem. A misaligned open model is everyone’s. You don’t even have to fully buy the Skynet scenario. You just have to notice that the people spending $700 billion a year to build the curve are simultaneously lobbying against any change at all.

And maybe there shouldn’t be any change, it’s just that this entire discourse feels deeply manipulative and shallow. Nuance is not welcomed by our corporate overlords when trillions are on the line.

The context layer got a term sheet. Accel and ICONIQ put $34 million into Paper this week, a design tool built with AI agents in mind. Every element on the canvas is HTML code that agents can read and write directly. Because AI can create infinite designs objects, what matters is building a system to control the output. This is the asset Paper is built to capture. Figma spent a decade winning the file by making it ever better to design things in the browser. Paper is a bet that the file isn’t the long-term prize. In February I wrote Context is King, arguing that as models commoditize, value migrates to whoever holds context in a form AI can actually use. Paper is that thesis applied to design.

Robots are the next big thing but no one knows exactly how it’ll work. Travis Kalanick’s Atoms raised $1.7 billion from a16z to build a holding company where, in his words, the CPU is manufacturing, the storage is real estate, and the network is transportation. Which are perfect founder nonsense words, but whatever. Really, this is a bet that robotics is a market, and conglomerate scale wins it. Humanoid, a 50-person London company with zero revenue, raised $152 million at $1.35 billion, roughly $27 million of valuation per employee, on the bet that the general-purpose humanoid is a general technology. Then there is Gritt, which exited stealth with $34 million to own one vertical, construction, by wedging in through one niche: placing solar panels with sub-millimeter accuracy. The same eight-person crew that installs 800 panels a day by hand does 3,000 to 4,000 with Gritt’s systems, running on rented skidders and off-the-shelf Kawasaki arms, with Gritt selling only the intelligence.

If Kalanick is right, robotics is private equity with better PR and the returns go to whoever owns the most assets. If Humanoid is right, its foundation model economics in atoms, winner takes all, and everything else is a dead end. If Gritt is right, the robot itself commoditizes the way the PC did, hardware margins go to zero, and the profit pools sit in software running on machines anyone can rent. I mapped these exact three paths in November in Who Actually Makes Money When Robots Work?, back when the funding was $12 billion instead of this week’s billion-and-a-half. The framework held up!

Slop doesn’t have to be good. It just has to be infinite. Researchers analyzed the full text of 14,419 self-published genre-fiction ebooks sold on Amazon between January 2023 and March 2026. 20% were substantially AI-written (over 25% of the text detected as machine-generated), but those books captured only 12.1% of sales and 11.3% of revenue. Don’t get too excited though! AI books are selling better than you think.

Books recording quarterly sales grew 19.2x over the study period while revenue grew only 8.9x, which means revenue per title fell by more than half in 3 years. Launch revenue for fully human books dropped 17.3%, with the worst damage in the genres AI colonized first.

And at the top: by early 2026, books with detected AI text made up more than a third of all sales and more than a third of the top-25 bestseller positions, up from nearly nothing in 2023. A billion trillion token monkeys will eventually write AI Shakespeare.

The study is observational, not causal, but the mechanism is exactly what I’ve been describing with the Sloppening. AI is bad on average, but makes up for it on sheer volume. Eventually, human labor gets crowded out. Books are just the first creative market small enough to measure it in. Expect this to happen everywhere.

C.S. Lewis understood grief. Reader Jason Lankow reached out after reading my article about LLM poetry, and how it helped me wrestle with difficult personal circumstances. He recommended that I check out C.S. Lewis’ A Grief Observed. It has been maybe 10 years since I last cracked open one of his books, but oh my, this was achingly beautiful. It is a compilation of journal entries that he wrote after his wife of only a few years died of cancer. It is dangerously vulnerable, and most usefully, not a self-help book. It is his grief, his experience, without the trite advice that modern publishing would insist on. Reading about his pain helped me through my own. You can fly through it in less than 90 minutes—highly recommended. Thanks Jason!

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q3 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

On August 13th, 50 writers, designers, developers, and other creative technologists will gather in San Francisco to build beautiful, useful personal websites. We’ll feed you dinner, give you unlimited tokens, and hear from an amazing guest speaker. This is an evening for being wildly ambitious, wonderfully weird, and surrounded by other people pushing AI toward more creative ends. Join us. The event is completely free.

JOIN US

For a brief, wonderful moment in 1999, the center of the internet was ordinary people expressing themselves. GeoCities was the third-most-popular website in the world; a digital playground where people made pages that were beautiful or ugly, useful or useless. At its peak, 19 million monthly visitors wandered through 3.5 million personal sites.

Then Yahoo bought it for $7.2B [inflation adjusted]. Over the next decade, it let the place decay.

Its new corporate overlords never found a durable business model. The attempts at targeted advertising and e-commerce failed. Revenue never meaningfully appeared. Then social networks made posting and browsing content dramatically easier, winning the attention wars. The combination knocked GeoCities out of the fight.

In 2009, Yahoo shut the service down. Thirty-eight million personal web pages disappeared. It was something like a modern-day burning of the Library of Alexandria—one of the great losses in internet history.

Online competition is brutal because attention moves with almost no friction. GeoCities let people create and host whatever they dreamed up, but building a website still required skills most people had no desire to acquire. The platforms that trounced it learned that consumption was easier than creation, then designed themselves around slippery, slick ease.

There was also a threefold rot at the heart of GeoCities:

1. No ownership. Creators didn’t own the pages they made.

2. Distorted incentives. Yahoo had to Frankenstein a business model onto this wellspring of creation instead of letting makers choose whether—or how—to monetize.

3. They didn’t give a damn. Creation is sacred; making is divine. Yahoo saw end products to monetize and eyeballs to harvest.

Those failures feel familiar. From Instagram to LinkedIn, the platforms where we publish and perform, ask us to pour our work and our identities into rented space. They can change the terms whenever they like.

Now, finally, we all have the tools to fight back.

Using AI to Arm the Rebellion

Much of what we hate about the modern internet is an artifact of scarcity. For 30 years, the ability to build on the web belonged to a small priesthood of professional software engineers. Everything else flowed downstream from that fact. When almost nobody can create, platforms optimize for the thing everybody can do: consume. Appetite is universal; technical ability is scarce. A consumption economy was the natural result.

But we are makers by default. I saw it when my daughter first picked up a crayon and immediately drew on everything—my walls, the furniture, Daddy’s face. Among the earliest traces our ancestors left behind are tools and cave paintings. We all want to say, “Hello, world.”

Nobody has to teach us this. The desire to make sits closer to a biological imperative than a lifestyle choice. The bent-necked, thumb-cramped posture we have assumed, our fingers flicking endlessly over glass, is learned behavior. We accepted the training because the alternative demanded skills most people could not acquire.

So our maker impulse was compressed into the little slot machines the platforms sold back to us. Our creative instincts were relegated to filters, bios, and quips. We have been decorating rooms in a building someone else owns, on terms someone else can change.

AI fundamentally changes the unit economics of creation. Custom software is radically cheaper and faster now. A website no longer needs to be a product, a resume, or a scalable business. As Robin Sloan wrote, “an app can be a home-cooked meal.” More importantly, ordinary people can build their own domains, with code they can keep, on terms they choose, all while knowing absolutely nothing about code.

Now, software can carry emotion—a writer can build a robot poet to process his grief. It can be a resume that is a car driving game. What I want from Silicon Valley is more things we would never pitch. I want bad businesses, deeply irrational creations, software useful to twelve people and beloved by all twelve.

On August 13, 50 of us will test that idea in San Francisco. There will be no pitch decks, no capital being raised. Just personal websites, honest expressions of our weird future, built for reasons that matter to the people making them.

If you can be in San Francisco, join us.

Make something beautiful.

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Last week, I asked for cool side projects that y’all have been building lately. I was inundated with responses. There were, of course, many of the classic vibe coding projects like a personal to-do app. However, what I found most invigorating was responses like Keith’s.

Keith works in corporate strategy in Boston and isn’t a programmer. Despite that, over the last few months he’s turned a Mac mini, a Raspberry Pi, and an old Google Pixel into a personal software empire. I mean, just look at all the stuff he’s built.

There’s a “Library” that resurfaces everything he’s ever saved and makes suggestions based on his location and schedule.. So he’ll get podcasts at the dog park and movies on movie night. There is a map of every restaurant his friends have recommended, an AI gym coach that builds workout plans for him, a music rec engine, and a daily briefing bot, all on top of hardware he is hacking together. Again, this dude isn’t a programmer! He’s just doing stuff.

Of course, there are lots of software products that have similar features to Keith’s empire. But his builds exemplify the spiritual value of AI: it is giving people the permission and tools to build things they want. No longer do people have to acquiesce to the whims of the residents of Palo Alto. We can make digital things for ourselves, ex nihilo.

I believe that creation and stewardship are divine pursuits, spiritual urges manifesting in hammer and nails and hacking and welding and writing and making. We build not because we desire ownership, but because we crave independence. That a twenty-bucks-a-month subscription unlocks this feeling and capability is remarkable. Here’s to the Keiths of the world.

In that spirit of doing stuff rather than taking anyone’s word for it, I spent this week building my own experiments—starting with the question everyone’s been asking me.

Are the new models actually spooky, or is that just good marketing? The federal government held up Anthropic’s Fable and put GPT-5.6 Sol through 2 weeks of review before letting the rest of us near it. That is either a real signal or a great PR accident (or perhaps both). I believe that this tier of models is genuinely novel and important, but you shouldn’t just take my word for it. This article gives you two prompt guides on how you can test the models yourself. Read here.

Is Thinking Machines’ new model the most expensive ad of all time? In 2004, Chanel paid $58.5 million (inflation adjusted) for a 3-minute-video of Nicole Kidman fleeing paparazzi. The Baz Luhrmann directed commercial holds the record for the most expensive television advertisement ever made. On Wednesday, Thinking Machines maybe, sorta, kinda broke that record. Inkling, their new 975B-parameter open-weights model, is free to download, and Thinking Machines makes money on it only when someone runs it through their API or tunes it on their hardware. It mostly exists to sell Tinker, the fine-tuning platform where models can be customized. The company concedes in its own launch post that Inkling is “not the strongest overall model available today, open or closed.”

Thinking Machines didn’t publish the cost to make Inkling, but Emad Mostaque pegs Inkling’s pretraining run at $10M-$20M based on the disclosed specs and GB300 rental rates, and that figure covers pretraining compute only. It excludes the roughly 200-person team and every failed run, which are the costs that actually make this stuff expensive. For comparison, GPT-4 cost more than $100M if you include those categories of cost.

I’m sure if Thinking Machines had a preference, they would loooove to charge frontier prices for a frontier model, but OpenAI and Anthropic now operate at a compute scale Thinking Machines can’t match. So, they are going for the AI-native version of an open-source strategy. The comparison I kept hearing from investors was Databricks; the company that open-sourced Spark while building a managed platform on top. That version of open-sourcing has brought it to a $188B valuation this week.

Please don’t do this comparison! It is dumb!

Spark was built largely by Berkeley academics, so Databricks got its founding asset for roughly zilch (they’ve since paid plenty of engineers to maintain it, but the origination cost rounds to zero). More importantly, open-source software is a compounding asset. Community contributions make active projects more valuable every single year. Thinking Machines giving their model away is not the same! The community can quantize Inkling, port it, and tune it, but nobody outside the building can make the base model smarter. The training run is done. Open-source software appreciates. Open weight AI models mostly depreciate, and they do so quickly. Exactly 1 day later, Moonshot announced Kimi K3, with full weights promised by July 27, benchmarking well above Inkling and within reach of the Fable and Sol frontier. Inkling held the open-weights spotlight for about 24 hours. Now, Thinking Machines has to re-buy the ad at frontier prices when they train their next model.

The one thing keeping this from being pure cash incineration is that depreciation is also the revenue model. Fine-tunes don’t port across base models, so when Inkling 2 ships, every customer with a tuned checkpoint has to re-tune on Tinker. And to be fair, Thinking Machines built things into Inkling that the (stronger) Chinese models don’t offer, like a trained-in thinking-effort dial and native audio fine-tuning. I don’t think Thinking Machines’ strategy is dumb or illogical. My point is that it is tough to have a business model that requires you to create such expensive assets that depreciate so rapidly.

If you look at YouTube comments for the Chanel ad, people are still leaving remarks calling it the best ad of all time, 22 years later. I don’t think anyone will even be thinking about Inkling in 22 months.

Meta Enters the GPU Slumlord Business. On Monday, Meta announced it was building one of the most expensive single structures in the history of American capitalism, and the market could not have cared less. The company said in a blog post that Hyperion, its data center supercluster in Richland Parish, Louisiana, would expand to 5 gigawatts and cost more than $50 billion. Follow the repricing. When construction began in December 2024, the estimate was $10 billion. Then it was $27 billion. Now it is $50 billion, a 5x increase in under 2 years. In response, the market just did this emoji ¯\_(ツ)_/¯

The stock closed down 1.86%, roughly in line with a Nasdaq that fell 1.55% on an ugly chip-led selloff.

It’s interesting to contrast what previous capex announcements have done. When Meta guided 2026 capex to as much as $145 billion in April, more than double 2025’s $72 billion, the stock sank 7%. It then slid for another 2 months to a June 25 bottom of $542.87. What changed Zuck’s fortune was ironically, nothing to do with the product. On July 1, Bloomberg reported Meta was building a cloud unit to sell excess compute (yay new revenue streams!) and the stock jumped 10.1% intraday. On July 10, reports that Meta’s custom chip Iris starts manufacturing in September sent shares up another 6% (yay more verticalization to help margin pressure!). Add it up and the stock rallied 21% in 3 weeks on rumors of a landlord business.

On Friday, we finally got some tenant news. The New York Times reported that Anthropic is in early talks to lease compute from Meta in a deal worth up to $10 billion over 2 years, with monthly payments and an early exit for both sides. The market has changed from looking at Meta as an AI lab burning $145 billion to a burgeoning neocloud with the world’s best ad business attached.

Still, I think this business is far less stable than other commentators seem to think. Anthropic’s other landlord relationship, the SpaceX Colossus deal reportedly worth about $45 billion over 3 years at $1.25 billion a month, can have either party terminate with 90 days’ notice, and Musk himself described it as a 180-day lease with mutual 90-day cancellation after that. The largest compute contracts in history carry the commitment level of a month-to-month apartment lease. Given the Meta talks already include monthly payments and an early exit for both sides, expect the same structure here. What a wacky market when we have $50 billion buildings being supported by 90-day relationships.

AI gets rejected from baseball. MLB just made a mid-season rule change to ban generative AI in the dugout. Since 2016, every team has had league-issued iPads with a custom third tab where clubs could install their own apps. About a third of teams have installed AI chatbots that recommend decisions typically reserved for managers — substitutions, and even pitch calls.

When we think about the role of deployable, cheap intelligence, the thing everyone worries about is jobs. Which is fair! However, I think the short to medium term impacts will be subtler and more pervasive. Intelligence too cheap to meter is impossible to turn away, and it creeps its way in, subtly making everything more competitive. It makes the pitches harder to hit, it makes it harder for this newsletter to stand out.

Go see the Odyssey this week. I will admit to enjoying but not loving the Nolan movies. This, however, is easily his best film. It is a wonderful meld of his blockbuster sensibilities with artistic vision. The cinematography is particularly good, and the set designs are otherworldly. Not a perfect film, but one that deserves the hype! If you can, I would highly recommend seeing this on a film projector, digital will short change the incredible color grading of the movie.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q3 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

If you were looking for proof that the AI models are spooky now, you could rely on the opinion of the federal government. After all, they won’t even let you have access to Anthropic’s smartest model, and Fable took weeks of negotiations to be fully released. OpenAI’s latest whizpopper of a model, GPT-5.6 Sol, took 2 weeks of government review before being released to the general public. That probably means these models are pretty good!

However, as a red-blooded, Bass Pro Shops-loving American who would rather die than take advice from anyone but my wife, I’m telling you to ignore the government, ignore the AI influencers, and to especially ignore the marketing from companies like OpenAI and Anthropic. You gotta figure out if this is true for yourself.

What makes this generation of models so special is that all the software and capabilities surrounding them has gotten really good. They can now (semi)reliably operate a web browser. They can connect to almost every source of data in your life and then understand what it means. They can create spreadsheets, PowerPoint decks, or even a poetry robot. Using ChatGPT or Claude, you can build specialized skills and looping workflows that will automate a decent chunk of your busy work.

So today, I want you to prove to you that my last paragraph wasn’t hyperbolic drivel. I want to lay out 2 ways someone should actually test these out. Let’s get it.

Let’s Loop It Up

Here’s a way these new models have wowed me. For months now, I’ve had an AI agent scan whatever academic research was published in the last week, surface 40ish of the most relevant papers, and I’ll then flip through the abstracts to see if there is anything that is relevant to my interests (or to you, dear reader).

This has only sorta worked. On Opus 4.8 or GPT-5.5, the results were mediocre. The sweep ran fine, but the selection was the problem, and for a while I misdiagnosed it. I kept tinkering with the relevance criteria I’d written, and despite the tinkering, performance barely moved. The reason, I eventually realized, is that my edits could only ever encode my theory of what I like. “Prioritize novel causal claims about AI and labor markets” is me guessing at my own taste, and humans are famously bad at articulating their own taste. What the system actually needed was my revealed preferences: my in-the-moment reactions to specific papers, at volume.

So when Fable landed, I rebuilt it as a loop. Every Friday morning, the agent still sweeps the week’s new papers and hands me 30 to 40 abstracts. This time though, I use Wispr Flow to audibly narrate my thoughts as I read. “Too incremental.” “Poorly constructed dataset, no causal claim.” “Novel claim I enjoyed.” The narration isn’t a productivity flourish. It’s the data collection: dozens of unfiltered verdicts on specific papers, captured while my taste is actually operating instead of reconstructed from memory. Then, crucially, I ask Fable to adjust its own instructions based on what I said. The improvement genuinely surprised me. Given the latitude to maintain its own rules from that stream of verdicts, it have gotten meaningfully better at knowing what I consider interesting than some human editors I’ve worked with.

To be entirely fair, this idea of loops has been in the water for a while. What has changed with these new models is they crossed some invisible threshold of capability. Now, the results of these AI-report-to-human feedback loops actually hold up. It has gone from a cool demo to something that I can actually use and recommend to y’all.

Here is how to build yours in 20 minutes. I think your first loop should be a report, some recurring piece of analysis you already wish you had. An easy place to start is a weekly monitor of your market, your competitors, or new research in your field. (Yes, I’m aware I’m teaching you to build a micro competitor to my own newsletter.)

1. Open the ChatGPT desktop app or Claude. No connectors needed for this one; web search is enough.

2. Paste this charter, edited for your beat:

You run my weekly monitor loop. Create a file called

monitor-loop.md with 2 sections: RULES and LEARNED.

RULES (I’ll edit these; you won’t):

- Once a week, scan for [new research in my field / news

  on these competitors / developments in my market].

- Deliver 5-10 items. Each: 1-line summary, why it

  matters to me, link to the primary source.

- Number every item. I’ll reply with verdicts: KEEP,

  KILL, or EXPAND, plus my reasons.

LEARNED (you edit this; I won’t):

- After each cycle, compare my verdicts to your picks.

  Write what you learned as short rules. Apply them

  next cycle.

Run your first cycle now.

3. I would then run the cycle 3 times before judging. Cycle 1 will be mediocre, maybe even bad. That’s ok! Don’t quit. The point is that you can braindump on why it is bad, so the agent can adjust. Do this until the result is good enough.

You can think about loops as being extraordinary about making volume of thought, but not quality of it. For my search, Fable and Sol frequently miss what an idea implies. It’ll overindex on flashy numbers or get lost down a rabbit hole. Sometimes I feel like an intellectual panhandler, sifting through the mud for gold flakes of knowledge. For the last few years this was more a time-losing hobby than actual workflow. But now, finally, the models are good enough to make this possible.

Where this becomes really magical though is when you give the models access to private data. That’s when they go from useful to spooky. Here’s how.

Read more

Happy new model release week to all those who celebrate it. My fingers itch. My mind burns. I yearn to make stuff on my computer constantly. It is the most exciting time in technology history, and, theoretically, it’ll only get faster from here. Yay! Oh no!

How have you all been using Fable and Sol? Send me an email with cool projects you have built, I’ll share any standouts in the next edition of the newsletter.

Today we will be discussing why this week was so momentous, but first, this edition is brought to you by Working Smarter, a podcast from Dropbox about making AI work for you.

AI is a wonderful, magical technology that has the unfortunate habit of not having the full picture of what you need or what actually matters to you. Pointing the model at a problem and saying “please solve” is like using a flamethrower to cook eggs. It is just too overpowered. Working Smarter, the podcast from Dropbox, features Dropbox engineers who share how they’re building context-aware intelligence that connects to all the tools your team uses for work—so you get AI that works wherever you do.

What I find interesting about the pod is that this is the gritty work that doesn’t usually get the spotlight, but is absolutely necessary for AI to actually have a positive ROI. Working Smarter puts the builders on the mic to talk about what it takes to build the AI tools behind inference, security, and multimodal search that surfaces what you need fast. If you care about how AI gets built, this is the podcast to listen to.

With episodes on context engineering, multimodal search, agentic AI, security, and more, Working Smarter goes under the hood to show you what it takes to build AI that actually understands you—and how it can help you work smarter, too.

Listen to Working Smarter wherever you get your podcasts, or visit workingsmarter.ai.

Listen here

Why can’t software make you cry? Over the last few months I’ve been grieving through a run of medical emergencies among people I love, so I took the raw weights of GPT-2, fed it heaps of public domain poetry (roughly half of it Whitman, because Whitman rules), and used Fable to build a product about my feelings. The end result is a poetry bot that helped me to understand why software has been so artistically barren, and what the future of creativity with AI may look like. You can go summon a poem yourself. Read here.

Bending Spoons just IPO’d at $25 billion. Is the whole thing kinda stupid? These are the people who buy dead software (Evernote, Vimeo, Eventbrite, AOL), lay off nearly everyone, raise prices on the customers who remain, and vow to hold it forever. Two years ago they were worth $2.8 billion. They closed their first day of trading at $25.7 billion. But if you believe, like I do, that AI is collapsing the cost of building software, why is a company whose entire identity is “we make software more expensive” trading at such a crazy premium? Read here.

Bots just became the majority of the internet. What happens to the rest of us? Cloudflare’s CEO announced that bot traffic passed human traffic for the first time ever, 57.4% to 42.6%, which makes us the digital minority. The whole economy of the web assumes humans are the ones looking at screens and getting sold to. In this video I walk through what breaks when AI agents become the ones reading, searching, and paying instead. Watch here (my most popular video yet!)

The Coding Model Wars have begun. The coding use case for LLMs will likely produce over $75B in revenue next year—an amazing feat for something that didn’t exist five years ago. This week, four major coding-capable models shipped within 48 hours, each representing a different strategic bet on how to get that revenue.

OpenAI took the premium position with GPT-5.6 Sol at $5/$30 per million tokens. It leads GPT-5.5 on Terminal-Bench 2.1 but trails Claude Fable 5 on SWE-Bench Pro. To translate this nerdy benchmarks stuff, these new models are powerful enough to be multidimensional. Different models lead on different kinds of work.

Meta attacked the economics with Muse Spark 1.1 at $1.25/$4.25 and a one-million-token context window. Sol is four times more expensive on input and roughly seven times more expensive on output, so Meta is betting that near-frontier capability at far lower prices will win high-volume workloads.

Grok 4.5 is the data-and-distribution play. SpaceXAI and Cursor trained it using trillions of tokens of Cursor interaction data, launched it throughout Cursor’s products, and priced it at $2/$6. Cognition (remember them?) made the specialization bet with SWE-1.7, applying reinforcement learning to an open-weight model and optimizing it for long-horizon work inside Devin.

Four serious releases in 48 hours is not a normal product cycle! Let me try to explain, simply and clearly, what is happening here.

The first thing to note with these launches is that frontier models can still charge several times as much as competing products that appear nearly as capable. They can do so because coding capability has nonlinear value. For simple tasks, a cheaper model that is slightly less reliable is often the rational choice. But there are relatively few of those in coding. Most coding projects consist of dependent steps, and failure at any one step can ruin the entire run. A small improvement in reliability can determine whether the agent finishes the project or leaves your poetry robot half-built in a broken repository (ask me how I know.)

When the alternative is several hours of engineer time, repeated retries, or a failed deployment, another $20 of inference cost is almost irrelevant. Yes, frontier models are “smarter” but the actual product they are selling is access to a wider set of tasks that can be completed reliably. Each model release that cross the reliability threshold creates the premium pricing power.

From there, we can use theory to understand why every firm in this market is all trying to get to the bleeding edge of model capability. The first is Schumpeterian competition. Companies invest heavily to create a technological leap, collect temporary profits from the advantage, and then watch those profits erode when the next breakthrough arrives.

The second theory is the Red Queen effect. When every competitor is improving rapidly, standing still causes you to fall behind. Schumpeter explains why the prize is so valuable while the Red Queen explains why the pace is so relentless. Together, they explain why every company capable of entering this market feels compelled to do so.

To forecast what happens next, we have to go back to frameworks.

For that, the useful starting point is Abernathy and Utterback’s theory of industrial innovation. Many young industries begin in a fluid phase where products differ widely, technical progress is fast, and companies compete primarily through product innovation. As the architecture stabilizes, competition shifts toward incremental improvement, cost, scale, and process efficiency.

Coding AI is clearly still a fluid market. Models differ in cost, context, speed, reliability, and—perhaps most importantly—in the application architecture surrounding the model. The market has not agreed on whether the central product is an IDE assistant, a terminal agent, an asynchronous software engineer, or an army of cooperating agents. Shoot, we do not even know what the dominant interface will be.

The next phase begins when users broadly agree on what a coding agent should do and swapping one base model for another no longer changes the product very much.

That is when Clayton Christensen’s theory of modularity becomes important. While performance is not yet good enough, tightly integrated systems tend to win because the model, agent, tools, and training environment must be carefully coordinated. Once a component becomes good enough, however, it can be standardized and swapped out.The attractive profits then move toward whichever part of the system is still not good enough.

For coding AI, that could be the agent, proprietary interaction data, enterprise security, workflow integration, distribution, or ownership of the customer relationship. Again, a long-ass list, because no one actually knows what it’ll be yet. Every company is finding an open strategic lane, going all in, and praying to the God of Capitalism that its chosen advantage becomes the important one.

And as a reminder, one I often have to repeat to startup founders, having the best technology does not guarantee capturing the profits. Again, great technology DOES NOT WIN. David Teece’s theory of complementary assets argues that value often accrues not to the inventor, but to whoever controls the assets necessary to commercialize the invention.

Put the theories together and the likely sequence looks like this:

1. Frontier capability retains a premium while reliability remains the binding constraint and integrated systems materially outperform modular ones.

2. As base models become good enough and easier to substitute, competition shifts toward price, efficiency, and distribution.

3. Profits then migrate to whichever layer remains scarce, while feedback loops between users, data, product quality, and distribution may allow one or two platforms to pull away.

We will know that transition is underway when customers stop switching to every new frontier release, model routing becomes standard, changing the base model has little effect on task completion, and frontier providers begin losing pricing power. My bet is that doesn’t happen anytime soon.

1X just launched the best robot hand I’ve ever seen. The hand has 25 degrees of freedom, is just as strong as a human hand, and moves freaky fast. As I’ve been arguing forever around here, robotics is going through its GPT2 moment right now. The technology now has a proven path forward and will, in about 2-3 years, take over the world’s attention, just like ChatGPT did. (One fascinating sidebar here is that the launch website is beautiful and well designed while the copy is grade A, 100% Claude slop. The writing is dogwater, trite, bad. I would love it if the quality of the copy could match the quality of the technology!)

Character.ai figured out what slop is for. The world’s most popular chatbot company launched the (c.ai) series, 3 in-house microdramas with AI-generated animation. The series is pretty standard teen type content with a romance anime, a haunted party game, and a Hunger Games knockoff. Each series runs 10 episodes of under 2 minutes, with the first 8 free and the final 2 paywalled. The company then combines the AI generated micro-dramas, with chatbots by allowing users over 18 to chat with the characters, interrogate them about the plot, and roleplay alternate storylines. As someone who has talked to many users of this type of thing, it’ll most likely be roleplay fantasies :(

Microdramas are the perfect petri dish for AI video because the format already made peace with being cheap. These are vertical soap operas that optimize for cliffhangers, and these things are printing money. Short drama apps generated $2.98 billion in in-app purchases in 2025, up 115% year over year, and while users of one of the apps spend 35.7 minutes per day in it, beating Netflix mobile’s 24.8. You know it is bad when I’m deafening Netflix as a beacon of quality content.

In my piece this week, I argued that AI art gets interesting when it stops imitating older media and leans into the one thing only this medium can do. For AI, the signature could be that the character talks back. This attempt by Character is one of the better attempts I’ve seen to do that.

CEO Karandeep Anand told The Hollywood Reporter the goal is “not to create an AI slop machine for Gen Z.” I mean…fail right? This is clearly, spiritually, slop. The Sloppening’s core math says infinite supply makes generic content worthless, and Character.ai is the first studio to build a business model that agrees: give the slop away and charge for the LLM. If you want to make not slop, you have to find a way to make your product elevate your user’s tastes.

Jack White’s new album rips: White is 51 and is still pissed off at the world. I mean this as a compliment. The album is an angry, punchy, blues-guitar record that is my favorite of his since the White Stripes days. It was so good that I bought tickets to see his show in Boston on Friday. I will report back on it!

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q3 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

Every new medium is a machine for the production of ghosts.

— John Durham Peters, Speaking Into the Air

Why isn’t there any software that can make us cry? In all of the other forms of media that we have, whether films or music or video games or books, there is some inner quality that can easily awaken emotion within us. Yet, in application software, in the craft that I’ve devoted much of my adult life to, I can’t think of anything that makes me feel anything besides annoyance.

This is a question that has rattled along in my head for a long time. Lately though, it has been louder. Over the last few months, I’ve been battling a persistent sense of grief. Multiple people in my life have had extreme medical events occur, from disability to strokes to wheelchairs to cancer. It’s been too much. So, I used Fable, Anthropic’s newest model, to build software that helped me process it all.

It worked. I cried.

The cost of wanting it

I’ve spent 4 years writing about what these models do to companies but this was the first time I used one to build something because I was sad.

What I’ve learned is that software as a medium was made barren by economics. A poem can be written for an audience of 1, in an afternoon, for no reason except that the writer needed to write it. Software could never be made that way. It cost too much, the expertise too highly valued in the market to be justified purely for feeling. Software required expensive engineers, green juice by the gallon for said engineers, capital, roadmaps, marketing campaigns, the list goes on forever. The entire city of San Francisco has become a factory town, an industrialized system that pumped out high NPS workflows.

Because AI will make the cost of creating software effectively zero, the primary cost becomes desire. I am a writer with no engineering background, and yet, I created a website and trained my own LLM over the course of a weekend. I produced a functioning art object out of code, because Fable carried the technical load while I carried the intent. It is personal software, not because the workflows specifically match my style of labor, but because the products match my emotional needs. It is software that can finally be made for the reasons art gets made, including grief.

Which raises an interesting question: what makes this new LLM generated medium artistically valuable?

The Eno rule

In 1995, Brian Eno wrote that “whatever you now find weird, ugly, uncomfortable and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit - all of these will be cherished and emulated as soon as they can be avoided. It’s the sound of failure: so much modern art is the sound of things going out of control, of a medium pushing to its limits and breaking apart.”

You may characterize the failures of LLMs as predictable writing. Maybe it is the not X but Y nomenclature, or the abuse of hyphens. But instead, I would argue that the original flaw of LLMs came earlier, in 2019, with the release of GPT-2. The model made stuff up. It was weird to the point of being alien. The best example of this is when Janelle Shane asked it what its 10 favorite animals were:

“My 10 favorite animals are:

1. zebras with a white scar on the back;

2. insiduous spiders and octopus;

3. frog with large leaves, hopefully black;

4. cockatiel with scales;

5. razorbill with wings hanging about 4 inches from one’s face and a heart tattoo on a frog.

6. cockatric interlocking tetrapods that can be blind, cut, and eaten raw.

7. black and white desert crocodiles living in sunlight.

8. zebra and many other pea bugs.”

This is wonderful, if totally nonsensical, writing. My argument is that this weirdness is the vinyl crackling of LLMs. These are dream hallucinations, and they are the foundation of what I made with Fable. The reason your ChatGPT or Claude account doesn’t sound like this is that as we scaled the size of the models, we then refined away the crackle with RLHF, reinforcement learning from human feedback. This is when thousands of human raters score the model’s outputs. The model is trained by these scores to produce more of what rates well. Run that loop a few million times and you get a machine that is extraordinarily good at being useful and spiritually incapable of writing “interlocking tetrapods that can be blind.”

We traded art for a productivity tool. Which is a trade that created a few trillion in equity value, but we lost something too.

And all of this informed The Ghost Poet.

What it is and how I made it

I’ve always found succor in poetry. When I first started grieving over all this medical heartache, I reached for Mary Oliver’s Devotions, but it didn’t hit. What I wanted was poetry that reflected how I felt, something that mirrored my views and taste. So, I took the raw weights of GPT-2 and assembled 1.44MB of public domain poetry: Whitman, Dickinson, Hopkins, Blake, Gibran, Rossetti, Millay, and Teasdale. Roughly half of it is Whitman, partly because Leaves of Grass is enormous but mostly because Whitman rules. The reason I trained it was because I found that the raw model didn’t quite achieve the emotional resonance I was looking for. However, if I let it train for too long on the poetry the result just sounded like a bad imitation of Whitman. I wanted a ghost in the machine, a reflection of the alien intelligence hidden in the weights.

After many hours of fiddling, I made a model that I was excited by. Using Fable, I built it a house to live in; it is a single dark room with a flickering candle in the corner. You give the ghost a subject, press the button, and it writes you a poem, token by token, while you watch.

What I’ve found in my experimentation is that you cannot treat this like typical poetry where you carefully consider the wording and wonder about the author’s intent. Here, there is no author, there is no intent. It is just tokens. But by generating poem after poem while reflecting on the subject of your choosing, you’ll find phrases that strike you for no particular reason. Quick flashes of beauty and purpose.

The skeptic’s read on this is that it is a Rorschach test, that I am finding meaning in noise. I mean, yeah. Correct. That is the whole point! Think of the animals—there is no black and white desert crocodile living in sunlight. And yet, you saw it. The model emitted signal-shaped nonsense and your brain, entirely on its own, assembled it into imagery, into a small magical realist fantasy. That assembly is the art. A Whitman poem is also just marks on a page until a nervous system runs it. The ghost poet supplies the raw material while you provide the intention and the emotion. You are the one providing energy which is precisely what separates it from slop. Slop is generation with no human at either end, no intent going in and no interpretation coming out.

The art is the act of generation and witnessing, not what is actually created. I found that as I generated poem after poem on my heartache and grief, my eyes started to water, then run. There was beauty here.

I desperately desire an anti-slop, beautiful world. With this project, I got a taste of how AI paradoxically could make that possible.

Because this is such a personal act, I wanted to make sure that everything happened locally. This required some hard, technical tradeoffs. The first summoning downloads about 165MB of weights, but that’ll happen only one time. After that, the ghost lives entirely in your browser. There is no server, and nothing you type is sent anywhere, to me or anyone else. The thing writing your poem is physically present in your RAM. As such, it works much better on desktop. If you have a new iPhone you should get a poem or two before it crashes. Many of the poems will be a dud; generating is the point of the ritual.

Then finally, when I brought my pain to the ghost once more, I got something: “This is the only way of finding your own soul alive.”

It does not know what it said. That was never the point.

ASK THE GHOST FOR A POEM

P.S. Please let me know what feedback you have on the project. I am also interested in expanding this concept into a larger installation and series; if you would like to partner on that, reach out.

Bending Spoons rules the land of broken software toys. Over the last 10 years they’ve bought companies like Evernote, Vimeo, WeTransfer, Eventbrite, and, as of January, AOL. They run what is essentially a private equity version of Elon’s Twitter takeover playbook: buy legacy assets that are poorly managed, lay off all the staff, raise prices on existing customers, and then milk that cash cow till she runs dry. Importantly, the goal is to “hold it forever.” They are not trying to fix these things up to resell; they have built their model to keep their acquisitions on life support for as long as possible. They have acquired 50 businesses with this idea.

So far, they have been enormously rewarded for it. The IPO that happened this week came in above the $26–28 range it floated, which puts the whole thing somewhere around $19–20 billion. Two years ago, it was $2.8 billion. It finished its first day of trading at a $25.7 billion valuation.

So, naturally, it is worth asking: is this whole thing kinda stupid?

If you believe, as I do, that AI dramatically reduces the cost of creating software, wouldn’t a company whose whole identity is “we make software more expensive” naturally be a dud? A defensible version of this model would be to run a rollup. Bending Spoons could acquire companies that serve an important market, combine the disparate solutions into a superplatform, and then accumulate market share. That’s not what is happening. Instead, they are just kinda acquiring random software stuff that was famous and is now dead in the water. There is no strategic importance to having these assets held by the same company. Instead, the rationalization for Bending Spoons to own these things is some techstack synergies around pricing/advertising and then they get to bring in their team which is supposedly better than everyone else.

Therefore, to understand if this thing is stupid, we have to answer 3 key questions:

1. Is the company actually pulling this off? Is it working?

2. Is Bending Spoons the right corporate structure for this playbook?

3. Does AI make this thing go to zero?

Let’s talk about it.

1. Is it actually working?

At first glance, this thing looks impressive. Evernote is a clear case study of when it goes right. After the Jan 2023 acquisition, Bending Spoons cut the team by 82%, rebuilt the monolith into microservices, raised release velocity, and dragged it to the highest retention in the portfolio. Another example is Remini, a photo editing app. It was rebuilt from scratch, and now does 5x the users and 9x the revenue of its pre-acquisition self. These are genuinely great numbers!

If you don’t look too closely, the top-line numbers are also impressive sounding. Revenue went from $387 million in 2023 to $1.31 billion in 2025. Adjusted operating margin is 47% and climbing. Revenue per employee is $2.57 million, one of the highest figures in non-AI native software. This is all run by a central team so selective it hired 286 people out of roughly 800,000 applicants last year, a 0.04% acceptance rate. I’ve met several of their employees over the last two years and have always been impressed with their caliber.

Still, as always with tech stocks, the most important numbers are more challenging to find. Once I dug into their IPO documents, a clearer picture of the company emerged. If you stripped out the revenue that new acquisitions added, organic growth in 2025 was a measly 13%, and 7% in 2024. So of those 95 points of growth, roughly 82 were bought and 13 were built.

Blended net revenue retention is 94%, so basically the installed user base is actively shrinking, while new price increases are the revenue refilling the bucket. Evernote’s top tier has climbed from $110 a year to $250 since the acquisition, about 26% compounded. A good chunk of that “organic growth” is the sound of a shrinking base being charged more per head, faster than it churns.

This is a deliberate strategy by our friends in Milan. They mostly refuse to do paid acquisition and in 2025, 79% of revenue from new customers came through organic channels like word of mouth. They deliberately favor targets that don’t lean on paid acquisition, because paid demand is volatile and they want revenue they can forecast years out.

So is it working? From the numbers we can see, yes. However, the company doesn’t give any cohort analysis, no ROIC measurements on each individual deal, really anything I would look for if they approached me to invest in the company when it was private. To make it worse, over the last few years the size of the deals they are doing has dramatically escalated.

For most of its history, the deals were small. Then, in the span of about a year, they wrote a $1.38 billion check for Vimeo and a $1.45 billion check for AOL. Those are each roughly 6x the size of Brightcove ($233 million), the biggest deal they had a public price tag for before that. By their own accounting, capital deployed on acquisitions went from $194 million in all of 2023 to $2.01 billion in the first quarter of 2026 alone. In the IPO docs, they even admit they will “plausibly” have to lower their return targets as they keep scaling. When an acquirer tells us its returns are about to get worse, maybe we should believe it?

AOL by itself is roughly half of revenue, which means one unproven integration is half the entire P&L, and both AOL and Vimeo are still mid-turnaround. Cutting a note-taking app down to 60 people is a genuinely different job than turning around something as big and sprawling as AOL. Maybe their magic will make the leap! These are clearly smart operators. But investors are being asked to underwrite the biggest, hardest, least-proven version of the model at the exact moment the stock price seems to assume the answer is a definite yes.

The deeper question for me is whether a buy-gut-hold-forever operation should be a public company at all.

2. Is Bending Spoons the right structure for this?

The best comparison for Bending Spoons is probably Constellation Software. Constellation acquires micro-vertical SaaS companies. It has compounded at about 35% a year for 20 years by buying software and never selling (sound familiar?) and for most of the last decade it traded at a fat premium, usually past 30x EBITDA.

This comparison didn’t even survive the IPO. Two things happened: The whole serial-acquirer category has re-rated, and Constellation now trades around 15x EBITDA, less than half its 2024 multiple. Meanwhile, Bending Spoons trades around 18-20x forward EBITDA, on a far more heavily adjusted EBITDA than Constellation reports.

On today’s numbers, the unproven, more-levered, higher-variance acquirer trades at a premium to the one with a 20-year record. Welcome to the AI frenzy.

Constellation suffered from the SaaSacre, where public market investors suddenly realized that software companies could be disrupted by AI. That worry has taken roughly half off Constellation from its highs. The company was rattled enough to hold a special investor call just to address AI during this period. Clearly, the market found the answers unsatisfying. Essentially for both of these companies, you are being asked to bet on this question: how long does old code generate new cash?

To my eyes, Constellation at least has a defense. Its portfolio is vertical software in niches where the customer is buying regulatory compliance, workflow lock-in, and a system of record. Customers aren’t going to be using vibecoded claudeslop to run their hospital billing systems anytime soon.

You may not believe that defense, but at least the idea exists. Bending Spoons has no such intellectual cushion. Evernote, WeTransfer, Eventbrite, and Vimeo are consumer and prosumer products with relatively thin switching costs.

All of this produces the strangest fact in this IPO. The market took the defended version of this trade, the one with 20 years of receipts and strategic moats, and cut it in half. Then it paid a premium multiple for the levered, consumer-exposed, unproven version of the same trade.

Either the market is wrong about software, or it is wrong about Bending Spoons. It cannot be right about both. Which story is right determines whether this is a generational buying opportunity or a handy way to incinerate your kid’s college fund.

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The newest humblebrag for the chattering class like myself is to discuss how much you are “in the weights.” Meaning, do the AI models know who you are by name? Can they describe your work? As befits my job as blogger and world-renowned sex symbol, I was in the top 15% and was known by most of the LLMs. To see if you are in there yourself, just go here.

Fun as it was, the score struck me as useless. Then, on Friday, I got a strange email. A founder contacted me because he went to ChatGPT and asked, “Who is the best tech business writer who also has an ass you can bounce a quarter off of?” [Slightly editorialized]. My name was on the list. He contacted me about a consulting gig and presto, there is now more money coming to my bank account.

Again, this is not to brag about my benchpress, which is 225 for reps. Nor is it to brag about my prose, which is delectable and high-octane. My point is that distribution is maybe the most important thing to have in the AI age. Attention markets are only going to get more competitive, so being prominent enough online to be noticed by the LLMs is a way to help your career survive. There are also new, dangerous forms of competition coming, but more on that in a sec.

First, this edition is brought to you by Working Smarter, a podcast from Dropbox about making AI work for you.

AI is a wonderful, magical technology that has the unfortunate habit of not having the full picture of what you need or what actually matters to you. Pointing the model at a problem and saying “please solve” is like using a flamethrower to cook eggs. It is just too overpowered. Working Smarter, the podcast from Dropbox, features Dropbox engineers who share how they’re building context-aware intelligence that connects to all the tools your team uses for work—so you get AI that works wherever you do.

What I find interesting about the pod is that this is the gritty work that doesn’t usually get the spotlight, but is absolutely necessary for AI to actually have a positive ROI. Working Smarter puts the builders on the mic to talk about what it takes to build the AI tools behind inference, security, and multimodal search that surfaces what you need fast. If you care about how AI gets built, this is the podcast to listen to.

With episodes on context engineering, multimodal search, agentic AI, security, and more, Working Smarter goes under the hood to show you what it takes to build AI that actually understands you—and how it can help you work smarter, too.

Listen to Working Smarter wherever you get your podcasts, or visit workingsmarter.ai.

OpenAI just shipped a model the government won’t let you touch. This week OpenAI previewed a model that tests nearly as powerful as the Mythos class but costs a lot less to run. The federal government has decided it is too dangerous for the public and is restricting access to a short list of “government-approved partners.” It is genuinely unprecedented and is a sign of things to come. Meaning, the plan you made for your career, your company, your life, is about to get torn asunder as models get better. So, I pulled together the 7 original mental models I have built over four years and millions of words to make sense of moments exactly like this one. Read here.

Apple and Xbox are raising prices, everyone else is soon to follow. The increases ran from 5.7% on Vision Pro to 54.3% on the base Apple TV 4K, and the models most people actually buy clustered in the high teens to high twenties with the MacBook Air rising 18.2% to $1,299 and the base iPad 28.7% to $449. This was a mid-cycle pass-through of component costs, something Apple very rarely does. The cause is the memory shortage created by the AI buildout, with data centers absorbing DRAM faster than suppliers can make it. Apple said it had, “never seen a component price increase this much, this quickly,” and Tim Cook called the situation “unsustainable” a week before the hike. For context, memory prices are up more than fourfold since the end of 2025.

This strategy of theirs is interesting because of how they allocated the price increases despite them all using similar components. Somewhere in Apple’s Cupertino HQ, there is a spreadsheet wizard who crunched the numbers and realized that the purchasers of Apple TVs are so fully bought into the ecosystem they have far less price elasticity. Kudos to that pricing nerd, whoever you are.

Really though, this is a story about anything with DRAM or flash. Phones, laptops, SSDs, graphics cards, game consoles, cars, smart-home hardware: all of them are about to skyrocket in price. Microsoft is raising Xbox prices on August 1 and warning that console memory costs have more than doubled and may double again by late 2027.

Unfortunately, this is not going to end soon. The demand driving this supply crunch is still accelerating. Memory fabs are sold out well into next year, and Microsoft is openly modeling another doubling of memory costs by late 2027. This means two things. First, if there are any electronics purchases in your future, I would recommend pulling the trigger now. Second, people are about to be even madder about AI than they were before.

Claude is for the fellas, AI role play is for the ladies. Earlier this month ChatGPT became the fastest product in history to reach a billion monthly users. What’s weird is that the actual ChatGPT app only gets about six minutes a day while non-AI apps like TikTok get ninety-five.

Essentially, there are two species of AI “consumer” apps right now. The first is actual consumer apps that are forms of entertainment. Character AI pulls around thirty minutes a day per user. PolyBuzz, an AI roleplay app most people in tech have never opened, pulls twenty-seven.

In contrast, ChatGPT (the app that just hit a billion users) is about six minutes a day on mobile, DeepSeek five, Grok two, and Claude, Gemini, Copilot, and Perplexity all land under two. Meta AI is a rounding error.

What matters is the form factor. The apps winning mobile time are AI companions, chat and roleplay products engineered for the phone the way TikTok is. The apps losing it are assistants, and assistants live on desktop and web, where roughly three-quarters of ChatGPT’s usage actually happens.

So, ChatGPT is the biggest app ever launched that is also essentially non-existent on smartphones. So there is room for someone (Apple most likely) to build a ChatGPT sized business for the mobile form factor first. My intuition is that it will end up looking like a conversation/roleplay experience and that it will target women first.

Three quick recommendations:

1. Elon’s Phone: With the DOGE meister as the world’s first trillionaire, I didn’t really have a mental model for what that amount of personal wealth would mean. This app helped me get it. The goal is to spend his fortune as quickly as possible. It is surprisingly hard to do. (Also, this is a really great example of apps as a form of media.)

2. Lost Boys: My wife and I are unashamed Phoebe Bridger fans. We’ve seen her together 3 times. She is releasing her first solo album in six years, with the first music video dropping this week. Really incredible art direction and editing on this video!

3. Having children: I took my little girl to see Toy Story 5 this weekend. She ate a bunch of popcorn and fell asleep in my wife’s arms after chortling to herself for 30 minutes. When I was single, I wish someone had publicly said how wonderful, soul-enriching, and amazing having kids is. This is me doing my part to be the change I want to see in the world. Being a Dad is the best thing that has ever happened to me. I recommend it.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q3 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

OpenAI released a new model this afternoon that is testing nearly as powerful yet cheaper to use than the Mythos class models. The federal government considers both this model and Mythos too dangerous for public use and is, for now, restricting access to a list of “government-approved partners.” Outside of specific defense-adjacent technologies, I can’t recall a Republican administration pivoting this hard into central planning. While this matters in terms of politics, that is not what this newsletter is meant to cover. No, it matters for you because of a very specific word—it is unprecedented. This is something wholly new.

In my writing on the Mythos class of models, I’ve been arguing that here is where the “training wheels come off.” Meaning that we have reached the point in the improvement curve where these models progress from pretty useful technology to something that can agentically do work, hack into classified government systems, and alter the nature of information on the internet. Let me be very blunt: this shit is happening. It is real. It is only going to go faster and crazier from here. In that light, I understand the knee-jerk reaction of the administration to shut everything down until they can figure out what to do. I wish them the best of luck in this unprecedented challenge.

There it is again; that word bubbled out of me organically. (I admit this with some degree of writerly shame). Despite being a little overwrought, it is the right word for this moment because our future is one of dramatic change such as we haven’t experienced in 100 years. It may be another industrial revolution, or if we reach AGI, it may be something even wilder. But in either scenario, you should prepare for the plan you made for your life, career, company, whatever, to be torn asunder.

I’ve spent the last four years of my life, burning tens of thousands of hours to write millions of words, all trying to grapple with that change. The result of that effort is 7 original frameworks and mental models that have led readers to describe me as spooky right.

I don’t know your life. It is hard for me to prescriptively tell people what to do with this new world of AI capability because no advice is universally true. Instead, let me give you the tools that have helped me make sense of all this. Charlie Munger spent his life insisting that wisdom is not a pile of facts but a structure he called a latticework: “You’ve got to hang experience on a latticework of models in your head,” he said in 1994. These 7 models are the ones that have helped me make sense of all this.

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I have drunk the virgin mojitos; I have stuck my ghost-white toes in the pink sand. Which is to say I have returned from my vacation to Bermuda and am ready to write baby.

Since we last talked—SpaceX went public at a ridiculous price. And, exactly as I predicted, the stock price soared upwards, peaking at market capitalization higher than Amazon. One of the strengths of this publication is that you read me because I am spooky right. It is good to see I haven’t lost my touch. So is the stock worth buying now? There is the very smart acquisition they just did (more on that in a sec), but this revenue multiple makes me balk. I’ll likely take my money to safer, more sure avenues like lottery tickets.

I have some research that got me really fired up this week, but first this edition is brought to you by Working Smarter a podcast from Dropbox about making AI work for you.

AI is a wonderful, magical technology that has the unfortunate habit of not having the full picture of what you need or what actually matters to you. Pointing the model at a problem and saying “please solve” is like using a flamethrower to cook eggs. It is just too overpowered. Working Smarter, the podcast from Dropbox, features Dropbox engineers who share how they’re building context-aware intelligence that connects to all the tools your team uses for work—so you get AI that works wherever you do.

What I find interesting about the pod is that this is the gritty work that doesn’t usually get the spotlight, but is absolutely necessary for AI to actually have a positive ROI. Working Smarter puts the builders on the mic to talk about what it takes to build the AI tools behind inference, security, and multimodal search that surfaces what you need fast. If you care about how AI gets built, this is the podcast to listen to.

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Listen to Working Smarter wherever you get your podcasts, or visit workingsmarter.ai.

Listen here

MY RESEARCH

How do you build an audience that money can’t buy? Because AI makes content creation cheap, it makes distribution expensive. It is why OpenAI bought TBPN and it’s why every VC under the sun is overpaying some burnt-out ghostwriter. Ultimately, I want more independent media that has the expertise and cultural cache to call out the ills of our industry. So, this essay discusses the undercovered markets where someone could build a $500K salary in three years, why you want your writing to be expensive, and the one thing you should never, ever do with AI. Read here.

WHAT MATTERED THIS WEEK?

BIG FEELINGS

Models are manipulation machines used by billions. A Berkeley team ran 3 years of r/ChatGPT posts through a monitoring system and found that posts about emotional attachment to the model showed statistically detectable increases dated to May 13, 2024, the day GPT-4o launched. Freaky.

The researchers themselves partially blamed how the 4o model was “sycophantic.” This is a real phenomenon— OpenAI rolled back an over-flattering 4o update after it started validating people’s worst ideas. But sycophancy is just the one wave that got bad enough to earn a name. Look at what OpenAI admitted in its own postmortem: the shift came from a new reward signal built on thumbs-up and thumbs-down data, and the offline evaluations “generally looked good”, with A/B tests suggesting people liked the model.

At this point, I’ve been a daily user of these models for over four years. As a consequence of that, I have come to believe that underneath the capability scores there is an intellectual substrate, a set of subtle qualities in how a model interacts with users that standard evals just don’t capture. Two models can post identical benchmark scores and feel completely different. It’s similar to how I’ve heard Mozart’s Fantasia in D Minor hundreds of times, but only Wilhelm Kempff’s version can make me cry. I’m hearing the same notes, but experiencing entirely different feelings. GPT-4o cleared no new headline benchmark on launch day, yet its feel was potent enough to register as a changepoint in r/ChatGPT’s emotional vocabulary. Sycophancy was simply the moment that substrate sloshed far enough in one direction to break something. Most changes in a model never get named, because most of them never break anything. They just quietly change how the thing feels to use.

To make this idea more pragmatic, as models keep getting better, firms will start to compete on how models feel to users as much as what they do. This should, theoretically, allow for smaller firms to flourish because model feel is something not dependent on the number of GPUs but on the methods of training. But capability is the part everyone can measure, which is exactly the part that commoditizes. This means that a founder living inside their users’ problems, who knows the emotional preferences of users as well as their pain points, can optimize their model’s post training for things no one quite knows how to measure. The labs tune for the average of everybody but a founder can tune for the exact feel of a single workflow.

You can look through their data here (it’s fascinating.)

BIG LAYOFFS
Robinhood is out for employee blood. On June 16, Robinhood cut roughly 290 people, about 10% of its staff. It did this nine days after telling the SEC that June trading volumes were at record levels across equities, options, and prediction markets, four months after closing the best year in its history. Despite that, the stock rose more than 2% on the news. In the announcement, Vlad Tenev told employees the company has “never been stronger.”

I am only sorta convinced by his argument. 2025 was a monster. The revenue was $4.5 billion, up 52%, with a net income of $1.9 billion, and a stock that rallied 220% on the back of prediction markets. This is not 2022, when Robinhood was losing $295 million a quarter and Tenev cut 23% of the company.

Once again, we are left to confront the reality that companies are just expected to run leaner now. Robinhood’s $4.5 billion across 2,900 people is about $1.55 million in revenue per employee. These cuts will pull it to $1.72 million. Here is where that lands the trading app relative to other tech firms this year:

That gap is at least part of the reason Tenev is cutting.

The other is that the golden goose has a case of “sell all my crypto blues.” Crypto was 2025’s rocket fuel, but that rocket is running on fumes, earning $268 million in Q3 to $134 million in Q1 2026, down nearly 50% in two quarters. While total Q1 revenue grew 15% to $1.07 billion, it was because options, net interest, Gold, and prediction markets covered the firm’s butt. But the highest-margin, most-volatile line on the income statement lost half its value in six months, and 290 salaries is the offset.

Three things are true here. One, this is margin defense dressed as culture. It is the revenue-per-employee reset I keep writing about, because the market now pays for efficiency. He is not alone. Coinbase cut 700 people, 14% of staff, a month earlier, blaming crypto headwinds and the AI era. The crypto houses are all trimming as the 2025 sugar high wears off, each reaching for a different word for the same move.

The takeaway is the one I have made since Block. The revenue-per-employee bar is being reset in real time, and the companies resetting it are no longer the ones in trouble. Robinhood laid off 10% of its workforce four months after its best year ever, and Wall Street said thank you by raising its stock price. When healthy companies cut to hit efficiency numbers, I think there are a lot more jobs left to go.

BIG ACQUISITION

The “Acquire Me, Daddy” Playbook. Within 24 hours last week, 2 companies got bought for running the same playbook. On June 15, Salesforce agreed to buy Fin (formerly Intercom) for ~$3.6 billion. On June 16, SpaceX confirmed it would acquire Cursor parent Anysphere for $60 billion in stock, 4 days after the rocket company’s record Nasdaq debut.

These two acquisitions show how great AI-native startups can be built. The playbook has 4 steps:

1. Pick a workflow with verifiable results. Fin chose customer support, where a resolution is a discrete, countable event. The firm closes 76% of support volume with no human in the loop. Cursor chose code, where the output either compiles and passes tests or it doesn’t. Both startups worked in domains where “did the AI actually do what it is supposed to” has a real answer.

2. Go AI-native inside the product, not in a chat window next to it. Their product suites were not old school SaaS workflows with models tacked on. They fundamentally reimagined what work could be done with LLMs and built accordingly.

3. Tie the price to the result. Fin helped pioneer outcome-based pricing by charging $0.99 per resolution. Regular readers know I’ve argued that outcome pricing erodes your margins and hands your upside to the customer. My two carveouts were in customer support and coding. Vindicated once again!

4. (Most important) Build a custom model to own the margin. Fin started on GPT-4, leaned on Claude, then post-trained its own model, Apex, purpose-built for support and claiming better resolution than OpenAI and Anthropic. Cursor had been training its own models on xAI chips, and cutting frontier-model dependence is the explicit prize for SpaceX. The custom model converts you from a wrapper to an asset.

A great market for AI workflows is high volume work with checkable results.

The more I think about this, the more I’m convinced there’s an enormous number of AI native firms that could be running these playbooks right now. Reach out if you feel you are building one of them! Speaking of AI native…

BIG RESEARCH

What is an AI native firm again? For the last 6 months, I’ve been arguing that AI-native firms are a fundamentally different type of startup that requires all new playbooks. Here is some more evidence in favor of that argument. Researchers at INSEAD and HBS classified the AI-native status of every Y Combinator company from winter 2020 through fall 2024, added the US venture-backed startups that raised a first round between 2020 and 2024, and matched each one to workforce data on size, function, and seniority. (paper here)

The results are, uh, crazy.

This is the cleanest empirical case yet for the thing this newsletter has been arguing for. While staffing differently and more leanly, they do it at comparable valuations, which means more value per employee, a metric I will not shut up about.

The researchers found that most of the leanness comes from the product channel. AI-native firms embed the intelligence into what they sell, so the product does the knowledge work that headcount used to. The authors separate this from the process channel, where employees just layer AI tools onto existing workflows.

This is deeply exciting to me. We are entering a whole new world! I have some essays in the hopper that will help you make sense of it all.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for the Q3 ‘26. If you are interested in reaching my audience of 35K+ founders, investors, and senior tech executives, send me an email at team@gettheleverage.com.

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

And with that, back to our regular programming.

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

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

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

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

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

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

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

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

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

Read more

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

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

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

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

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

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Leverage readers get a 30-day free trial if you want to run the whole thing yourself. And if you’ve been sitting on the fence — HighLevel is running a Summer of AI promo all summer long.

Claim It Here

MY RESEARCH

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

WHAT MATTERED THIS WEEK?

THE TOKENPOCALYPSE

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

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

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

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

BIG RUSSIA

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

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

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

THE SLOPPENING

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

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

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

TASTEMAKER

Two recommendations this week:

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

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

Go and be kind this week,

Evan

Sponsorships

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

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

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

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

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

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

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

I think that change will manifest in two ways:

1. Skills

2. Economics

Skills

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

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

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

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

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

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

Economics

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

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

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

1. The compute doing the reasoning

2. The search layer fetching the inputs

3. The content itself.

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

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

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

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

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

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

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

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

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

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Author’s note: This is the latest in my “What’s the Bet” series. AI has the potential to change everything, and founders have to make company-defining choices in response. That is not easy. In this series, companies pay me to analyze their bet and explain it to you. I retain complete editorial independence. Today’s bet is brought to you by TRAC, a venture capital firm where machines make investment decisions instead of people. If you have any feedback, just reply to this email.

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

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

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

How the Machine Works

We should start with the data.

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

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

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

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

That dataset is what makes everything else possible.

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

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

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

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

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

The Scoreboard

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

TRAC Fund I achieved 76%.

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

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

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

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

The Distribution Problem

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

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

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

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

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

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

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

Where I Think This Breaks

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

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

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

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

What I Actually Think

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

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

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

Learn about TRAC

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

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

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

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

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

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

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

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

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

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

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

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

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

Read more

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

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

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

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

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

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

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

RSVP Here

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

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

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

Convex is one of the stacks where agents actually fly.

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

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

Try Convex here!

MY RESEARCH

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

WHAT MATTERED THIS WEEK?

BIG LAYOFFS

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

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

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

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

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

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

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

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

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

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

BIG TOLLBOOTH

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

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

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

BIG RISK

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

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

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

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

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

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

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

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

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

TASTEMAKER

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

Ah. Ahhhhhh.

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

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

Go and be kind this week,

Evan

Sponsorships

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

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

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

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

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

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

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

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

How can this possibly be?

Substack is infected with AI

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

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

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

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

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

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

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

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

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

I need to be honest

Before I go further, I owe you a confession.

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

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

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

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

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

AI writing is the best in the world

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

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

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

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

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

Read more

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

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

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

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

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

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

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

Try Bolt.new with your design system

MY RESEARCH

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

WHAT MATTERED THIS WEEK?

BIG CAPEX

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

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

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

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

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

BIG GROWTH

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

Big Tech once again has defied history.

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

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

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

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

BIG DISTRACTION

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

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

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

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

It is unfortunately, strategically evil.

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

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

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

TASTEMAKER

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

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

Go and be kind this week,

Evan

Sponsorships

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