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

I am now ready to disclose what happened.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Catching religion

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

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

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

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

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

The head start is manifesting itself in two places:

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

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

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

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

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

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

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

Read more

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

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

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

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

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

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

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

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

Read the Report

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Go and be kind this week,

Evan

Sponsorships

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

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

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

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

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

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

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

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

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

The Lord of the Apps

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

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

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

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

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

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

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

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

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

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

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

Read more

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

To regular, non-fluid related business.

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

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

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

Try Town

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Go and be kind this week,

Evan

Sponsorships

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Go and be kind this week,

Evan

Sponsorships

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

At its peak, Airtable was known as a SaaS company that was doing “everything right.” I had multiple conversations with founders, talking about how the business was making an incredible product. In 2021, it raised capital at a post-money valuation of $11.735 billion.

This morning the firm announced it had agreed to be acquired by Bending Spoons for an enterprise value of $1.285 billion.

The company’s metrics aren’t even all that terrible:

• Current ARR: approximately $480 million

• ARR growth: more than 20%

• Implied EV/ARR multiple: approximately 2.7×

Those metrics merited an 80%+ decline from its 2021 valuation. So today I want to talk about what the hell happened here (and why there is a fascinating spinout from Airtable that I’m not sure people have fully noticed yet.)

What Airtable actually sells

Airtable is a relational database with a top hat. It makes that database as friendly and accessible as a spreadsheet. (This sounds simple, but is actually quite a hard product to pull off.) To upsell it beyond hobbyists, the company would bundle in the usual stuff like permissions, integrations, and internal application building.

Think of it like this. You know that clunky spreadsheet that your team built, that has gotten so big and unwieldy that opening it makes your MacBook beg for mercy? Airtable fixes that by giving you a more performant database and then doing a no-code app builder on top of that.

If you think back to 2021, building an internal application required either:

1. Hiring engineers who would rather be making something customer-facing

2. Buying a specialized SaaS product that is overpriced and sucks

Airtable represented an appealing third option of just building and maintaining it yourself. Unfortunately, AI created a fourth option of just describing the application in plain English and letting an agent build it. Tools like Lovable, Replit, Claude Code, and ChatGPT can increasingly generate everything you need besides the database itself. For a small dataset, a company can use Notion or something inexpensive. If the application gets too big, your AI agent can connect you to Snowflake, Databricks, or a host of other options.

In this new world, what is the point of Airtable? The company’s original interface once eliminated the need to understand databases. Coding agents now eliminate the need to understand Airtable.

To be fair to Airtable, there is a reason why this business is still worth something. Coding agents do not magically eliminate permissions, security, backups, etc. So there is still some wiggle room for Airtable to thrive.

And to be even more fair to Airtable, it actually saw this future coming.

It missed the AI wave anyway.

Starting in 2021, right around the time it raised its last mega-round, it began to position itself as an app builder. Then in 2023, as an AI-powered one.

• November 2021: Interface Designer, marketed as a complete app builder for enterprises.

• October 2022: Repositioned as the Connected Apps Platform.

• May 2023: Announced a next-generation application platform with AI-powered workflows.

• July 2024: Launched Cobuilder, which generated applications from prompts.

• June 2025: Declared an “AI-native Airtable” and a “refounding moment”.

• January 2026: Launched Superagent, a standalone AI research agent that produces polished reports and presentations.

• February 2026: Announced Hyperagent, a platform for building and managing autonomous AI workers across business tools.

The failure of Airtable was one of execution. For years now, the company has been attempting to integrate LLMs, but it hasn’t made a lick of difference. It guessed correctly about the rough shape of the future and yet still failed to reach it before faster AI-native competitors collapsed the value of basic no-code app building.

The company’s ideal destination was probably a governed Snowflake–Lovable hybrid, where businesses could store trusted operational data underneath, and then generate applications and agents on top. They just couldn’t get there in time.

Thus enters Bending Spoons. It will deploy their usual playbook of firing everyone, raising prices on existing customers who have too much data to easily port over, and milking this thing until it is dry. (For more on the Italian conglomerate read my analysis here.)

However, the SEC filing for the transaction had a weird detail:

“Prior to entering into the Purchase Agreement, Seller and its affiliates implemented a reorganization pursuant to which Seller became the sole holder of the Shares, and assets and liabilities relating to the “Hyperagent” business line were transferred by the Company to Hyperagent Inc.”

So Bending Spoons is buying Airtable’s database, customers, workflows, and recurring revenue but someone else is keeping the agents. The company still had $965M in cash on hand, so it didn’t have to do this. The team could’ve done the hard work of transforming the company themselves. Most likely the Airtable founder wants to pursue Hyperagent as his next project and doesn’t want to do the required thing of laying off the majority of his company. So, bring on the Italians!

Airtable will, in one form or another, survive. The more important question is whether the rest of SaaS can survive the transition Airtable failed to complete.

Read more

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 Town, an AI personal assistant. If you have any feedback, just reply to this email.

Town’s bet is that the personal assistant requires a deeply friendly context layer.

Despite tens of billions of dollars and hundreds of startups, nobody has nailed the personal AI assistant. The idea was always obvious: use LLMs to make work admin not suck. But as someone who has tried nearly all of them and then promptly churned off each and every one, I can confidently say that nobody had built one a non-early adopter would tolerate. Until now. Town is the personal AI assistant I have wanted for years, finally made manifest.

The fastest way to explain Town, at least in SF group chats, is that it is OpenClaw without the management overhead. You get a Townie (a named, customizable assistant), give it permission to read your inbox, Slack, and other data sources, and it goes to work. Mine is a fat, magic gerbil who loves to read. His name is Luther. Lately I have been texting him more than my wife.

Out of the box, 3 workflows will provide some wow factor for users:

• Morning briefing. Every day at 5am, precisely timed to my daughter’s current wake window (please, someone help me), Luther emails me my schedule, the items that need addressing, and the news I care about. I listen at 2x speed while my dog does her best to kill my neighbor’s bush via potty break.

• Auto-inbox. This is where it felt like Town had a real breakthrough. Luther labels, sorts, and drafts roughly 45 emails a day. The drafts are close to send-ready and adjusted for who is on the other end. A buddy catching up gets one casual line. A PR person pitching me something stupid gets ignored. Hard to describe how magical this is.

• Meeting briefing. Before a call, it preps a note on who is in the room and why they matter relative to my work. For sourcing calls it tells me which piece the call is for and what I should be asking.

From there you can build automations specific to your own circumstances. (More on mine in a second.) As I mentioned in my disclosure, Town paid me to analyze their bet, but sponsors get no say in my analysis or verdict. So when what follows reads like worshipful praise, it’s because Town is genuinely that good. Many assistants offer products similar to what I’ve listed, but only Town’s were simultaneously easy to use and delightful. It’s worth understanding what bets they made to make that possible.

The expensive bet Town makes

Jean-Denis Greze spent 7 years as CTO of Plaid and before that led engineering at Dropbox. He founded Town with Tony Vincent, who led product and AI at Google. On June 3 they came out of beta with a $55M Series A led by Alex Rampell at a16z, with Forerunner, First Round, Alt Capital, and Conviction participating. Counting the seed, that is roughly $73M raised in about 15 months. When I asked Jean-Denis how they are using that capital to make the product feel so good, he gave me three answers.

1. Pre-processing as a capital moat. When you sign up, Town builds a personal wiki about you with sections on your communication style, your key contacts, your active projects, and how you like to work. From there it progressively constructs a mini CRM of your top relationships, building profiles for up to 100 people. This is expensive to do per user. Fully built out, Jean-Denis told me it runs about $100 a user. But by doing the research up front, the model receives pre-synthesized context instead of burning tokens on agentic search. To translate the technobabel, this upfront spend meaningfully improves output quality. It also creates brutal switching costs. Every week of use makes your Townie smarter about you and makes leaving harder. This is the type of bet you make when you have either 1) extreme confidence in your long-term retention or 2) $55M from a16z. My guess is it is a little of both.

2. Network effects in a category that has none. A personal AI assistant is inherently single-player software. Jean-Denis is betting he can bolt network effects onto it through 2 features. The first is a team layer, where Townies can benefit from routines, skills, and integrations built by other teammates’ personal assistants. If John and his Townie build a killer routine for drafting follow-up emails after sales conversations, Emma and her Townie can use (and contribute to) that same skill without starting from scratch.

It’s similar to a GitHub repo, where teammates can collaborate on resources and constantly bake in improvements and best practices. The biggest difference is that it’s easily self-servable for users of any level of technical proficiency and Townies create skills and routines proactively.

The second is what the team calls agent-to-agent. “My agent can work with your agent on things. I could ask your agent, how’s the partnership doing with Evan? Your agent looks in your Gmail and your Google Drive and figures out the answer, but it crucially doesn’t automatically reply to me. It asks you: ‘can I reply to Jean-Denis with the answer?’ You look at the answer, remove a part if you want, and then your agent replies to my agent.” No data leaves without your approval, and you do none of the work. This means your agent can dramatically cut down on annoying slack pings.

Jean-Denis uses it with his wife for personal tasks too. She will ask her agent whether he booked the kids’ dentist appointment, and when he has not, the reminder lands in his morning briefing. Adoption sits at roughly 10% of users today because the feature is new. But if it works, it means Town gets multiplayer lock-in on a single-player experience. As someone who gets too many emails about doctor appointments and school events, I am currently trying to convince my wife to get a Townie so we can coordinate better.

3. Feel as the differentiator. By my assessment there is relatively little net-new technical science happening here. These are variations and improvements on existing ideas about personal assistants. The uncomfortable reality of the category is that everyone has access to the same models, so the building blocks are commonplace. Memory, connectors, tool use, and scheduled agents are offered by every serious lab and startup. When the supply chain is commodity, firms compete on user experience and brand. The UX half is the pre-processing and personal CRM described above. The other half is a whimsical brand. Their website is chock-full of fun little guys that make the product feel friendly.

This sounds like fluff if you are a mega AI nerd, but it is the kind of bet that decides mainstream adoption. As much of an AI guy as I am, I still find it deeply funny that an obese gerbil is tasked with updating my CRM.

Still, none of that alone would have me this excited about the product. Town is the first AI assistant to clear a benchmark of mine.

What actually happened when I used it

My white whale has been a to-do list that maintains itself. This sounds like an easy idea! Just have an agent scan your emails, meeting notes, and work deliverables, then update a database a few times a day. However, there are two tricky gotchas that every previous version of this I’ve built has failed. First, the agent could not prioritize, because it had no context on my relationships, meaning that the to-do list couldn’t get what to-do actually mattered right. Town’s CRM and pre-processing solve this. Alternatively, a connection would fail within a few days, and fixing it would go on the to-do list that no longer worked, which meant it never happened. Town manages my connections so this problem is solved too.

Now I have a Notion database that sweeps my email, Slack, calendar, texts, and meeting transcripts 3 times a day and sorts tasks into my actual work buckets. Since I started, the back catalogue of small tasks has chipped down to near zero. It means I no longer worry about dropped balls, Town just tells me what needs to be done. My job is now managing the most difficult employee of all (myself) to make sure I just do what Luther says.

Town also watches how you work and proposes automations. In my case it suggested a centralized travel doc routine for some upcoming trips. Over time, this will build an army of custom routines that make my work higher quality and my personal life less stressful, and many of them were Luther’s idea rather than mine.

Jean-Denis told me these auto-recommendations already drive roughly 10 to 15% of in-app interactions. So more than a tenth of usage is self-generated, which is a meaningful number for a product with usage-based pricing. It also solves the blank-page problem, which is what kills most AI tools for normal people.

Where I think this could break

Despite my effervescence for Luther and the automations he has enabled, Town is competing in one of the most cutthroat markets in tech right now.

The first type of danger is the host of startups with similar pitches to Town. There are literally hundreds of funded startups chasing similar positioning. Shoot, just Y Combinator has backed 142 “AI Assistant” startups. Many of these are focused on verticals or product wedges different than Town, but that makes the category even nosier. The second is open source projects like OpenClaw. Every month it gets better, raising the floor on what a free alternative can do. It is possible that Town’s whole “we make OpenClaw accessible for normies” becomes increasingly unnecessary. The third bucket of threats is Native SaaS bundling. Every existing productivity app is adding agents and trying to become what Town is offering, and they already have your data. There are only so many times that people will ignore the CoPilot popup on Outlook before they give it a try. The fourth is the labs. Anthropic and OpenAI are the silverback gorillas that keep vaguely gesturing at this personal assistant idea, and they train the models everyone else rents, so in theory they can offer pricing and capability nobody can match. For now the two of them are chasing ASI and coding automation rather than the personal-assistant focus of startups like Town.

Luther’s best chance of still being alive in 12 months is the bundle of bets described above. By the time the labs get serious about consumer delight and per-user pre-processing, Town intends to hold years of accumulated personal context and team-level lock-in that no model release replicates. That is their bet. So far, it is the first one in this category I have been willing to keep paying for.

If you want to try it out, readers of The Leverage get their first two weeks completely free, and as a special bonus, you’ll immediately receive an additional 2500 credits applied to your account for an extended trial. I can’t recommend it more strongly. Simply click here to try this breakthrough out.

Try Town Out

We will have free dinner, free drinks, free tokens, the first-ever merch for The Leverage, Dandelion Chocolate bars — because why the hell not, I’m using this sponsor money to ball out, not wimp out — and a luxurious leather-bound prize for the winner of the evening. Attendees already include a New York Times bestselling author, senior operators at some of the best AI companies in the valley, and investors with copious amounts of drip. Really, though: this is about bringing together people who want to make the internet overflow with swag and dopeness.

Please join us! Attendance is free because I love you.

Register

When Ilya Sutvesker, the former co-founder and Chief Scientist of OpenAI wanted to rally the troops, he would have them chant out, “Feel the AGI! Feel the AGI!”

This week I felt a tickling along the back of my elbows, my neck hairs standing at attention, a robotic whisper in my ear. I didn’t fully feel the AGI, but I felt/heard/saw its coming promise. We’ll get to why in a second. First we need to talk about the stupidest website I’ve ever made.

I asked Grok if I was full of shit. Musk’s chatbot scored me somewhere in the “low-to-mid range for a tech newsletter guy,” which as an egomaniac I found deeply offensive. As an analyst I was annoyed because it wasn’t very precise. So I spent a day using ChatGPT and Claude Code building isevanfullofshit.com, a machine that ingests everything I’ve published and grades my forecasts. This is clearly dumb. But my point here is that my exercise is one that everyone everywhere is about to do with LLMs. Point it an archive of any data type and it’ll tell you, with sometimes undeserved confidence, what the data means. This will happen to Slack, text messages to your ex, everything you’ve ever said at work. I call this “semantic surveillance” and I dug into who’s already selling it. Read here.

OpenAI’s next model solved open questions in mathematics using $2000 of API tokens. Late Friday evening, my phone started blowing up with texts of “did you see this?” OpenAI used their new model, code name Astra, to solve 10 different outstanding math problems in areas like “arithmetic circuit complexity.” Now I have absolutely no idea what that means, but math professors on X are calling it “a big deal.” Capitalist pig that I am, I immediately thought about the costs. They did these 10 problems with $2,000 of tokens.

I would caution against saying that this will soon happen for everything. Math has verifiable proofs, which means there are victory-conditions for these models that are verifiable. Still, for problems with definite answers, there is a decent chance they may all end up being solved problems over the next few years. (Isn’t that a crazy sentence to read?) Lots could happen, but still, these models keep getting better, faster.

Nvidia’s revenue polycule keeps getting messier. After two years of monastic silence, Ilya Sutskever’s Safe Superintelligence announced a partnership with Nvidia that includes a reported $5 billion investment. The company says its research finally reached the point where it deserves scale, and Ilya posted “Time to scale that SSI” on X. This is the same man who has declared multiple times that “the scaling era is over” because the previous research paradigms wouldn’t get us to AGI. So, him taking this money now likely means he believes he has a research direction worth scaling once more. SSI still has no product, no revenue, and a few dozen employees, which puts it at roughly $200 million of capital per head. Not bad!

Nvidia keeps doing this very weird circular revenue game where they invest into their customers, who are all competing with each other. I tried to map them all, but there are so many of these deals: a reported $250 AI, $10 billion into Anthropic, checks into xAI, Mistral, Reflection, and Thinking Machines on one side, and stakes in CoreWeave, Nebius, and (soon) IREN on the other.

Keep in mind that neoclouds like CoreWeave are buying Nvidia hardware to rent to the labs Nvidia also funds. When the same firm is the financier, the supplier, and the appraiser, my inner security and exchange commission starts squirming.

LinkedIn attempts to deslopify. LinkedIn shipped a “Seems like AI slop” button this week. The company is simultaneously killing its own “enhance your post” AI writing feature, a rare case of a platform having to publicly say “whoopsie.” The platform is riddled with it. AI detector Pangram (disclosure, former sponsor) found that 41% of long-form LinkedIn posts are entirely AI-generated. This is by far the highest amount of the major internet platforms.

This actually places the company in an incredibly nasty bind. If you enforce this you vaporize two-fifths of your content supply overnight. Don’t forget that people LOVE AI slop. Their revealed preferences, over and over again, is that they are willing to read and pay for it. If you try to keep your platform AI free, that means your feed shrinks, sessions shorten, ad inventory evaporates, and then your ass and the curb are soon to meet. But if you do nothing and the ratio keeps climbing, you’ll likely rot the platform from within. It is a variation on one of my theories I’m best known for, “The Content Double Bind.” Platforms have no choice but to allow the most socially corrosive material because it is the most engaging type of content. If they don’t allow it, their competitors will. I do not envy the LinkedIn product team trying to solve this.

The only good interview of The Odyssey press tour. A humanities Phd who actually understands Greek psychology and mythology pushes Nolan on his Christian modernization of the classic tale. Watch.

Mr Rogers was one-of-a-kind. I have started to watch the classic children’s program with my daughter. It is peaceful, meandering viewing, something that stands in stark contrast with modern editing. I was reading about his life and found this speech where Rogers convinced Congress to give $20M to public television. His tone and passion gave me goosebumps. Watch.

The Matic Robot is the greatest electronics device in my life. I thought the hype would be overdone, but this little robot vacuum is INCREDIBLE. It is somehow making a dent into the mess that the grime tornado that is my toddler makes on daily basis. Really great technology paired with a delightful consumer experience. Seriously, go get one. Buy.

Go and be kind this week,

Evan

Sponsorships

We are now accepting sponsors for Q1 2027 (crazy!) Please reach out if you want to reach my audience next year.

I recently asked Grok an important question. “Is Evan Armstrong full of shit?” As an analyst read by tens of thousands of people, I have a moral obligation to be accurate and accountable. You (hopefully) demand this of the content in your life. In an age of AI slop, we have to deliberately seek out sources of beauty and truth.

Until about three years ago, this was a surprisingly challenging thing to know about writers. For bloggers in particular, the topics we cover are so esoteric, and the volume so prodigious, that unless you have a week or two to burn reading past posts, you couldn’t really know if the person you were reading was actually good at their job.

Now, though, we have magic 8-balls powered by GPUs and nerdy math, which can read all that for us. Thus, my question to Grok. Its response?

“He’s a standard opinionated tech analyst who puts more effort into tracking his record than most. Not a pure bullshit artist on the available evidence. Somewhere in the low-to-mid range for a tech newsletter guy who makes predictions and analysis for a living—call it ~15-30%.”

Egomaniac that I am, I found this offensive. I am much better than your standard tech analyst! 30 percent is way too high. So to prove it wrong, I combined the might of ChatGPT and Claude to build the stupidest website in the history of the internet. Behold, my masterpiece:

www.isevanfullofshit.com

It ingests everything I have published through April of this year, evaluates the forecasts that I made in this newsletter, and then scores me accordingly. I am very far from a programmer. Still, this type of analysis and presentation were remarkably easy to do. I used ChatGPT’s new Sites feature to build it in about two hours, but most of that was fiddling with the design. The algorithm design and scrape took maybe an hour and I did that with Claude Code.

Obviously, this was a silly exercise. But the point I am trying to make here is a larger one. If we give a language model an archive and a question, it will answer in a way that sounds convincing.

Everything this tool did to me, it can do to anyone with a paper trail. We have lived under mass data collection for years, but much of that data remained practically unreadable. Nobody could read every email, meeting transcript, Slack message, customer-service call, essay, post, or podcast. But now with LLMs anyone can just drop in the data and ask the machine what it thinks.

IsEvanFullOfShit.com is a joke website. But this new capability is something larger, a trend I’m calling “semantic surveillance.”

The business of surveillance

I define semantic surveillance as the automated interpretation of human communication at scale. It is easy to see why this is valuable to organizations.

Worried about nation-state enemies doing influence campaigns against you? Good news: there are “narrative intelligence” companies like PeakMetrics ($16.3 million) and Blackbird.AI ($58 million) that can track which narratives are spreading and who is advancing them.

How about hiring? Since job interviews are just two people talking, they are perfect candidates for this tech. If we semantically surveil the hiring process, we could make more data-driven decisions. Enter Metaview ($50 million, Series B led by GV) which sells interview note-taking and analysis.

Existing platforms are already launching these capabilities within the tools you use every day. Microsoft Purview Communication Compliance ships with Microsoft 365 and runs classifiers across Teams and Outlook for “discrimination, threats, harassment, corporate sabotage, regulatory collusion, and stock manipulation.” It even watches what employees type into Copilot! Zoom records sales calls and scores reps on sentiment, talk ratio, and objection handling. Anytime there is a database with conversations in it, the platforms are trying to monetize them.

In previous eras of software the monetization meant keyword counts and flagging “problem phrases.” (Remember a decade ago when everyone was doing word clouds?) But now with LLMs we can answer questions like, “Using Evan’s Slack messages, help me understand the underlying feelings he has about his manager.” The answer might be complete nonsense. But it’ll be cheap and fast. That is perhaps more important than accuracy. I’ve written in the past how computer vision models make it cheap and easy enough to discipline people’s bodies. LLMs make it cheap and easy enough to discipline people’s communication.

Which raises the only questions that matter. Who ends up wielding this? And what does it mean for everyone who’s ever left a paper trail? (That’s you.)

Read more

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.

REGISTER FOR LEVERAGE LAUNCH

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.

Read more

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.

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

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.