I’m worried that you read “everything I love that would be reasonable to put in a sandwich.” But that’s not what I wrote. I wrote “everything I love.” So, for 8 year old me, that included Costco taquitos, and fruit by the foot, and bologna, and ice cream, and lunchables (which kind? Does it matter?)

And you know what? That shit tasted awful. It made no sense. Threw most of it out, never made one of those sandwiches again.

Anyway, let’s talk about Dune Imperium.

My buddy Azraf got me this game as a birthday present 4 months after my actual birthday, because he knows I don’t like birthday gifts but I’m ok with random “here is a gift because you’re my friend” gifts that land on a totally non specific Tuesday. In the time since he got it, we’ve played the game maybe a dozen times with the friend group. The box says that a game takes about 60 minutes to run through. This is a lie. We have yet to finish a game in under three hours.

Dune Imperium is what you get when you take your favorite mechanisms from every other board game you’ve played, and stick it into a single game. It’s got the worker placement mechanics of Agricola or Viticulture. It’s got the deck building mechanics of Star Realms or Dominion. It’s got the betting / positioning mechanics of poker. It even has the random bullshit cards that you get in Catan.

It is, in other words, an “everything-I-love” sandwich as a boardgame. Except unlike my childhood culinary monstrosity, Dune Imperium actually works. It works really well. Last I checked, Dune Imperium was sitting at number 6 on the boardgamegeek rankings.

How? What kind of alchemy makes a game this convoluted actually fun?

My current best guess: the game has a sort of circular rhythm to it, which makes the whole thing function.

The rough structure of Dune goes like this:

• The game has ten rounds, and each round involves players going in a circle taking a turn until no one can do anything anymore;

• On your turn you play a card. Based on the card, some things happen, and you can place an agent somewhere on the board that doesn’t already have an agent on it;

• Placing an agent may require some resources (water, spice, coin), and will often give you some resources (water, spice, coin, military units, persuasion, card draw);

• Once you run out of agents, you can pick up some new cards from a shared market using any permission you have, and add them to your deck;

• Once everyone has finished, you evaluate who invested the most in military units — that person wins the combat for that round and gets some award;

• The round resets and the next player in the circle goes first…unless someone has 10 victory points, in which case the game ends.

It sounds like there are a lot of options, but unless you do something really stupid you’re pretty much on rails for most of your turns. You get some water. The next turn, the water lets you get some spice. The next turn, you turn the spice into units. The next turn, you deploy all the units and win a combat. Now you’re out of units and out of resources, so you start the cycle again.

There’s something very civ-coded about these mechanics. At each step it feels like you’re making some complicated decision; in practice, you are highly limited by what resources you have, what cards you have, and where your opponents place their agents. You’re basically forced to do something useful with your agents each turn. Good players will negotiate with each other and try to block other players, and there is some skill required in figuring out how many troops to deploy for a given combat (any troops you deploy will be removed for the next turn, regardless of whether you won or not, so figuring out what to bet is all about reading your opponents). But in general this is not a tactics-heavy game. In half of our games, players would simply pre-move before the other players had finished their turns, because they knew that new information wouldn’t change their play.

Instead, all of the actual skill of the game is in the long term strategy. Again, the game is only ten rounds. Almost all of the victory points that you need to win are doled out in the last three rounds. And basically every game I’ve been in has been shockingly close, which I think is due to a combination of the relatively short amount of time to build an engine and the amount of ‘guidance’ even new players have in terms of basic tactics. As a result, good players are constantly scheming about their last few turns from the very beginning of the game. Mostly, that ends up being about the deck building — that is, understanding how to value different cards based on your existing deck, your particular leader abilities, and your personal road to 10 points.

O, and the random bullshit cards. Did you think I’d forget to mention the random bullshit cards?

Despite being a pretty ‘crunchy’ rules heavy game, there’s a surprising amount of variance built into Dune. Some of that is built into the market-place drafting concept. Maybe you’re unlucky and all of the good cards keep getting bought up before your turn, whatever, that’s the draw. But when someone pulls a god damn bullshit card right as you’re about to win a combat that you spent four turns preparing for, that’s when you see red and start looking around for actual daggers.

Bullshit cards (in the game they are called ‘Intrigues’ but we have only ever called them bullshit cards) are special cards that you can pick up through the game that have a random effect. These things have extremely high variance. One card gives you two coin, which is nearly worthless after turn three. Another lets you get the military equivalent of 4 units, which you can choose to spring at the last second when everyone has committed their bets for combat. I’ve been in at least two games where someone top-decked a bullshit card to steal the win.

In a lesser game, that variance would be enough for me to drop off entirely. Crunchy games like this one depend on making their players feel smart. If you lose to random bullshit, you don’t feel smart, you feel cheated. But Dune somehow manages to to transmute that frustration into a desire to play again. I think it’s because in most games, everyone feels like they are one turn away from winning. That “so close” energy makes the game stick in your head, constantly turning over what things you could have done differently to change the outcome.

Of course, I would say that, because I’ve actually won the last few games I played. Some of my other friends have lost 5 games in a row. I think they are less enthused. The biggest issue with the game is that there is no real come back mechanic. When you’re behind, you’re just eating glass for the next two hours. It’s still possible for the lead to screw up, but in a competitive game you want to have options for forcing errors (again, see ‘make the players feel smart’). Dune doesn’t really have that, in large part because of the ‘on rails’ tactical feel to the whole thing. If a player buys a fantastic card off the market in the first turn or two, the game might just straight up be over. Which sucks, because of course you still have another 3 hours of game ahead of you.

Still, it’s a miracle a game with this many moving parts works at all. There are literally 5 different currencies to keep track of! I cannot begin to fathom the amount of play testing they had to do to get this game to feel even remotely balanced. It’s a testament to the designer that you can pursue a pretty wide variety of strategy and still come close to winning.

At the meta level, I find Dune fascinating as a piece of game design. I’ve long felt that there are only like 15 different kinds of game mechanics,1 and most boardgames are just mixing and remixing those into different combinations and themes — for eg, Seven Wonders is just the card passing mechanic from hearts, Sushi Go is just seven wonders with different scoring, etc. Most games need to settle on one mechanic in order to have any hope of being good, anything more ends up just being too much.

Dune joins Spirit Island as a rare exception that proves the rule. Also it’s the only game where you can play as a scrawny pingpong player that got dropped in the dessert.

Note: this is our first Agentics newsletter case study! We want to use these as opportunities to talk with teams that are on their AI journey, to get a sense of what works for them in practice. If you or your team has an interesting AI case study, reach out to amol@noriagentic.com.

In a world where code is cheap, true differentiation comes from a simple heuristic: does your product actually serve your customers? Anyone can ship an AI slop app. You’ll only earn usage and growth if your code solves someone’s problems.

The team at Corvus knows this better than most. Corvus is bringing AI tools to chemical manufacturing, an old-school industry where relationships and reputation mean everything, and where the real conversations happen in person, on factory floors in places you’ve never heard of.

From Nikhil, CEO:

Most of the people we work with have been in the industry for 20-30+ years. For us to come in and say ‘we can build something better for you,’ that requires a lot of trust. We are constantly on the road, because that is the only way someone tells you what actually works and what doesn’t.

For a lean team, product velocity is the most important thing. Anything that slows velocity is deadly. But being on the road, in person, is a massive blocker for product. How can you keep your product responsive when you need to be out in the field?

That is why the Corvus team decided to roll out Nori Sessions as a background agent service. Background agents let you run coding agents in the cloud, and interact with them from anywhere. Background agents solve a lot of problems that are built into local coding agents like Claude Code. They work where you work — in Slack, Linear, GitHub. They have a single environment that can be shared across a team, so no more having your sales person fiddling with dependencies. And they are always on, so they can solve problems even when you’re busy.

Here are 3 key bottlenecks that the Corvus team has been solving with Nori.

Tax #1: The tracking tax

The default workflow is: hear a feature request, write a ticket, triage, prioritize, eventually build, ship, close the loop. When most conversations happen in person, getting feature requests and feedback can take days. Worse, the cognitive overhead of tracking and remembering compounds with every customer visit.

With background agents, the loop collapses. Hear it, spin up an agent from your phone, get a PR ready by the time you’re back at the hotel. Again from Nikhil:

We were on a customer onsite, and on Day 1 we got some feedback about our UX and some bugs. Instead of throwing those on the Linear backlog where it would sit, we just spun up 3 different agents while we were on the floor, and had the fixes ready to show on Day 2. Huge trust building moment for our users.

Tax #2: The context tax

Slack is where Corvus lives. Customer feedback gets pasted in, bug reports drop into #alerts, decisions get made in threads, services all pipe signals in. It’s the source of truth for what’s happening right now, before any of it gets formalized into Linear or GitHub or Drive.

Local coding agents can integrate with plenty of tools, but the workflow is backwards. They have to take the context that already exists in Slack (the system of record) and then re-stage it somewhere else that the agent can see. Or you have to have a human in the loop, mechanically pasting in context that the model should just have. The result is that every task starts with re-explaining what the Corvus team already knows.

The background agent lives in Slack directly. The agent reads the thread it’s tagged in, picks up the alert that just fired, sees the customer report someone pasted in an hour ago. No restaging, no rehashing. From Alex, CTO:

We discuss everything in Slack, and it’s very convenient to tag Nori directly there so it has all of our decision making and thinking. We’ve added Skills so it effectively takes this context, along with our broader docs (codebase, Notion, etc.), to build the feature and create a PR.

Tax #3: The toil tax

Every hour the CTO spends chasing a Grafana alert is an hour not spent on the hard product rocks. Context-switching is the real killer. This is why teams have massive backlogs that they never get to. Nori absorbs the investigations, the small bug squashing, the “look into this error” work, so humans stay on the complex features. Big features ship faster because no one is getting yanked off them five times a day.

From Alex:

I was at a client dinner Friday night, saw a bug in prod, had Nori look into it, deploy a fix, and re-run customer accounts all while still at the table. We surface all major bugs and errors from Grafana, Trigger, and Render into Slack, then tag an agent to dig in. The turnaround on small stuff is way faster, and it requires way less mind-space.

The compound effect

Remove all three taxes at once and a four-person team starts to feel like twelve. Corvus averages 20 Nori sessions per day, over 100 per week. The downstream effect is more time on the road, with customers, where the real product insight lives.

From Nikhil:

We get to spend so much more time with these manufacturers. It is so powerful to hear live feedback or requests, while onsite, like a dashboard, and then show them the feature a few hours later.

Alex:

This is just the cutting edge of how AI work is meant to be done. It’s made us leaner and more effective, and our customers have really benefited from the flexibility.

Learn more about Corvus (they’re hiring!) at corvusapp.com.

Learn more about Nori Sessions at norisessions.com.

You can find our previous Agentics post on background agents here, and Davy from Windborne gave a talk about background agents at our first meetup here.

Agentics is the study of how to use and reason about agents. If you are an expert in coding agents, or interested in learning more about agents, join our community slack. More articles here. Learn more about Nori at noriagentic.com.

It’s been over a year since I wrote this piece:

Back then, I wrote:

Assume that there are five senior engineers at a company. Each can produce 4k lines of code a month, or 20k total. And let’s say AI gets good enough that it can empower a single senior engineer to be 5x more productive.

There are two possible extremes here.

On one hand, the company could decide that it does not need the extra code. The primary company bottleneck is not feature dev or maintenance, so it just doesn’t need the extra coding power. In this world, the company holds the number of lines of code constant and gets rid of 4 of the 5 engineers.

On the other hand, the company may decide that it really needs all hands on deck. Maybe the company is a standard techco that needs to push out features as fast as possible. In this world, the company holds the number of engineers (that they can afford) constant, and increases the LoC output to 100k per month.

As with all things, the actual answer is likely in the middle somewhere. But notice that any setting of this knob that’s not the most extreme option — the one that sets the “lines of code” dial all the way to the max — results in someone being fired.

Well, AI has definitely gotten good enough that it can empower a single engineer to be 5x more productive. Naively, if you equate productivity multiples to the number of agents you have running at the same time, you may be able to get to 7-10x productivity (yes yes I know this is not a 1:1 measure of productivity improvement, it’s directionally correct). And companies have started setting the dial.

Meta:

Meta said on Thursday it plans to lay off roughly 10% of its workforce, or about 8,000 people, the latest in a string of tech industry layoffs fueled in part by artificial intelligence.

The company is also closing around 6,000 open roles, Janelle Gale, Meta’s chief people officer, wrote in a memo published by Bloomberg that Meta confirmed to CNN.

Oracle:

TD Cowen estimates the cuts hit between 20,000 and 30,000 positions, roughly 18% of Oracle’s global workforce of approximately 162,000 people. India was hit hardest, with approximately 12,000 employees terminated out of Oracle’s roughly 30,000 person Indian workforce. Affected workers included software engineers, account executives, program managers, and staff from Oracle Health, Sales, Cloud, Customer Success, and NetSuite.

Block:

today we’re making one of the hardest decisions in the history of our company: we’re reducing our organization by nearly half, from over 10,000 people to just under 6,000. that means over 4,000 of you are being asked to leave or entering into consultation.

Snap:

April 15 (Reuters) - Snap (SNAP.N), opens new tab will lay ‌off about 1,000 employees, including 16% of full-time staff, the company said on Wednesday, becoming the latest tech firm to shift toward leaner teams as it ramps up AI adoption to streamline operations.

The move, which also includes the closure of more than 300 open roles, comes weeks after Irenic Capital ​Management pushed the Snapchat parent to optimize its portfolio and improve performance. The activist investor has an economic interest of ​about 2.5% in the company.

Cloudflare:

Cloudflare, which provides internet security and performance services to millions of websites worldwide, announced it was cutting its workforce by approximately 20%, which equates to 1,100 people, it said as part of its first quarter 2026 earnings report on Thursday.

Microsoft:

Microsoft’s program announced on April 23, 2026 takes a different structural approach to the same underlying problem. Rather than involuntary terminations, Microsoft offered 8,750 U.S. employees — about 7% of its domestic workforce of approximately 125,000 — a voluntary separation package that includes 26 weeks of base pay, accelerated equity vesting and 12 months of healthcare. Acceptance window closes May 16, 2026. Internal estimates suggest Microsoft expects 60-70% of eligible employees to accept, implying actual departures of 5,250 to 6,125.

Coinbase:

On Tuesday, crypto exchange platform Coinbase Global Inc (Nasdaq: COIN) announced it was laying off about 14% of its staff, or roughly 700 employees. As Fast Company previously reported, Coinbase CEO Brian Armstrong cited two factors for the layoffs.

The first was the recent volatility in crypto markets in general, which Armstrong said necessitated cost-cutting measures.

And the second factor? AI.

Gitlab:

Four operational changes are part of the workforce reduction.

We’re reevaluating our operational footprint, and are planning to reduce the number of countries by up to 30% where we have small teams. We’ll continue serving customers in those markets through our partner network.

We’re planning to flatten the organization, removing up to three layers of management in some functions so leaders are closer to the work.

We’re re-organizing R&D to create roughly 60 smaller, more empowered teams with end-to-end ownership, nearly doubling the number of independent teams.

We’re rewiring internal processes with AI agents, automating the reviews, approvals, and handoffs to speed us up, and plan to right-size roles across the company to follow suit.

Most recently, Cisco:

Today we announced our Q3 FY26 earnings with record revenue of $15.8 billion, up 12 percent year over year, and double-digit top and bottom-line growth. The ELT and I could not be prouder of the growth you have all delivered for Cisco.

...

With this, we are making changes today that will result in the reduction of our overall workforce in Q4 by fewer than 4,000 jobs, representing less than 5 percent of our total employee base.

It feels like there is a new layoff announcement every week. The message is basically always the same: we’re making tons of money, AND AI is making people redundant. AI denialists like to point to COVID overhiring as the primary culprit behind these layoffs, but COVID was 6 years ago. At some point, you just need to catch up with the times.

From Claude:

As of May 10, 2026, there have been 179 layoff events impacting 113,863 workers in tech this year per the SkillSyncer tracker, while TrueUp counts 286 events and roughly 128,270 people impacted, averaging about 1,000 per day.

Yikes. AI specific roles (training models, optimizing GPUs, things like that) seem like they are still safe for now, but most everything else in tech is flat or down. Reskilling has hit the tech world; I know a bunch of SWEs who have started reading ML textbooks in their free time.

And I think it’s going to get worse from here.

AI tools are still new, and I wouldn’t say most of these companies are particularly good at using the AI tools they have at their disposal. As a particularly silly / egregious example, Meta has been evaluating AI usage based on number of tokens spent per employee.

Meta is taking token usage into account during perf reviews. A current engineering manager at the social media giant told me that the token usage of each engineer is now a data point — one of many! — for performance calibrations. By itself, it is not a positive or negative signal, but someone perceived as having low impact and with low token usage is now seen as a blatant low performer. For high performers with outstanding impact, very high token usage is seen as a good thing as it conveys to the manager group that they’re personally invested in AI and are improving their workflow – as proved by results.

Friends of mine at Meta talk about how they have agents that are literally just talking to each other during the day to ensure they can ‘tokenmaxx’, because that is obviously what is going to happen when you tie performance to total token usage. But I can’t really blame Meta. Their tokenmaxxing policy is a sledgehammer because the tech industry as a whole has not yet figured out how to use scalpels. It speaks volumes that, even with their inefficiencies, Meta is still laying off so many people.

[EDIT: While I was writing this draft, arstechnica released an article about Amazon doing the same thing:

Some employees said colleagues were using the software to automate additional, unnecessary AI activity to increase their consumption of tokens—units of data processed by models.

They said the move reflected pressure to adopt the technology after Amazon introduced targets for more than 80 percent of developers to use AI each week, and earlier this year began tracking AI token consumption on internal leader boards

These are not exactly subtle policies!]

The one silver lining is that it is easier than ever to start a company. Back to the Meditations piece:

So to recap, a lower barrier to entry in the video game industry meant:

• Massive increase in quantity and variety of output;

• A shift in viable business structures;

• An increase in the importance of taste and curation

…

Hopefully the parallels are obvious.

AI makes it easier for everyone to code. We should expect that this will dramatically increase the quantity and variety of software, enable new business and funding structures, and increase the importance of curation.

Anecdotally, more and more friends are going the startup route, many of them bootstrapping instead of trying for VC funds. Still, startups are not exactly known for being cushy cuddly jobs. The era of the noon-to-3pm tech worker is over.

Everyone’s Getting Hacked

Speaking of things that happen every week, have you noticed the uptick in major hacks, leaks, and outages?

Vercel:

We’ve identified a security incident that involved unauthorized access to certain internal Vercel systems. We are actively investigating, and we have engaged incident response experts to help investigate and remediate. We have notified law enforcement and will update this page as the investigation progresses.

…

The incident originated with a compromise of Context.ai, a third-party AI tool used by a Vercel employee. The attacker used that access to take over the employee’s individual Vercel Google Workspace account, which enabled them to gain access to that employee’s Vercel account. From there, they were able to pivot into a Vercel environment, and subsequently maneuvered through systems to enumerate and decrypt non-sensitive environment variables.

Canvas:

In early May 2026, Canvas LMS, a learning management system operated by private company Instructure, was affected by a data breach and outage.^[1] Instructure disclosed that it was investigating a cybersecurity incident involving certain user data, including names, email addresses, student ID numbers, and messages among users.

…

The incident came to wider public attention on May 7 at approximately 1:20 p.m. PDT (UTC-7), when students began posting screenshots of the defaced Canvas log-in page on Reddit. On May 11, Instructure issued an apology for their lack of transparency on their incident update page. In that statement, they claimed to have reached an agreement with "the unauthorized actor" and that the compromised data was destroyed. The terms of the agreement are not publicly known, but unconfirmed rumors suggest that US$10 million was paid.

Stryker:

Stryker is responding to a global network disruption to our Microsoft environment as a result of a cyber attack. We have no indication of ransomware or malware and believe the incident is contained.

Our teams are working to understand the full impact to our internal environment and while we continue to investigate, below are responses to some of your inquiries:

ShinyHunters in particular has been in the news a lot, hitting PaneraBread, Figure Tech, RockStar, Canvas (above), McGraw Hill, Aura, and the EU, among others.

And then there were a bunch of zerodays that were dropped, like CopyFail:

If your kernel was built between 2017 and the patch — which covers essentially every mainstream Linux distribution — you’re in scope.

Copy Fail requires only an unprivileged local user account — no network access, no kernel debugging features, no pre-installed primitives. The kernel crypto API (AF_ALG) ships enabled in essentially every mainstream distro’s default config, so the entire 2017 → patch window is in play out of the box.

Or BlueHammer:

On April 3rd, 2026, a security researcher operating under the alias “Chaotic Eclipse” dropped a fully functional Windows local privilege escalation exploit on GitHub - no coordinated disclosure, no CVE, no patch. Just working exploit code and a pointed message to Microsoft’s Security Response Center: “I was not bluffing Microsoft, and I’m doing it again.”

Coding agents are really good at blasting things at a wall and seeing what sticks. This is, for the most part, a pretty bad way of actually writing code that needs to work well. If you do this too much you’ll end up with a massive spaghetti codebase that is as hard to maintain as it is to secure. But the same strategy is a fantastic way to find unexpected security vulnerabilities in existing code bases. Coding agents can relentlessly attack systems at scale, trying to find every crack and crevice imaginable.

This has some upsides. Open source maintainers are reporting that the number of real bugs and vulns being reported is higher than its ever been. Here’s Greg Kroah-Hartman, the ‘Linux kernel czar’:

Months ago, we were getting what we called ‘AI slop,’ AI-generated security reports that were obviously wrong or low quality. It was kind of funny. It didn’t really worry us.

Something happened a month ago, and the world switched. Now we have real reports. All open source projects have real reports that are made with AI, but they’re good, and they’re real.

But it also has downsides, as state sponsored actors start throwing their token budgets at unsuspecting codebases. The result is something of a race: who can spend the most tokens? Drew Breunig calls this ‘proof of work’ (in reference to the bitcoin trustless consensus mechanism of the same name):

This chart suggests an interesting security economy: to harden a system we need to spend more tokens discovering exploits than attackers spend exploiting them.

…

If AI continues to find exploits so long as you keep throwing money at it, security is reduced to a brutally simple equation: to harden a system you need to spend more tokens discovering exploits than attackers will spend exploiting them.

You don’t get points for being clever. You win by paying more. It is a system that echoes cryptocurrency’s proof of work system, where success is tied to raw computational work. It’s a low temperature lottery: buy the tokens, maybe you find an exploit. Hopefully you keep trying longer than your attackers.

I more or less agree that this is where security is going, and in some sense all software. Increasingly differentiation comes from sheer effort and hours over ingenuity.

But I disagree that security ends up being just a simple tokens in vs tokens out calculation. There’s a massive asymmetry here, the attacker has a huge advantage.

Imagine you were building, like, a house. There’s basically only a few ways to actually get this right. You have to build a few walls and a roof and at least one door in order to claim success.

But there are infinite ways to get this totally wrong. You could forget the door. You could put the ceiling on upside down. You could build the whole thing with sand and have it blow away in the wind. You could accidentally build a water slide. There’s basically no limit to how creatively you can screw up.

Same with software. Secure software needs to cover all bases. Searching for security vulnerabilities, on the other hand, is a bit like finding a girlfriend or trying to get a job — you only need one. A world of cheap, easily accessible AI may not lead to bio risk, but it could certainly lead to ransomware hell.

Of course, I have to mention Claude Mythos. The latest model from Anthropic has been deemed so good at finding security vulnerabilities that the Anthropic team decided not to release it to the general public until a few select software maintainers had a chance to use it to debug their systems. Anthropic haters have roundly condemned this move as mere advertising and theatrics. OpenAI did the same “too dangerous to release” song and dance for the awesome, world ending AI that was GPT-2.

Due to concerns about large language models being used to generate deceptive, biased, or abusive language at scale, we are only releasing a much smaller version of GPT-2 along with sampling code. We are not releasing the dataset, training code, or GPT-2 model weights. Nearly a year ago we wrote in the OpenAI Charter: “we expect that safety and security concerns will reduce our traditional publishing in the future, while increasing the importance of sharing safety, policy, and standards research,” and we see this current work as potentially representing the early beginnings of such concerns, which we expect may grow over time.

I’m poking fun a bit here, but it’s worth noting that OpenAI’s concerns were actually pretty spot on. In the years since GPT-2 released, we’ve been absolutely flooded by AI slop that has pulled our collective ability to understand reality apart at the seams.

Seems like Mythos is much the same — it actually is pretty good at finding vulnerabilities. See XBOW:

About two months ago, Anthropic invited us to help them assess the capability of a new model they thought represented a significant shift in capability. So we put it through our security gauntlet. Benchmarks, workflows, interactive use, and integrations.

Today, we can finally share details on how we tested Mythos Preview, what we found, and what it means.

Spoilers: This model is a major advance. It is substantially better than prior models at finding vulnerability candidates, especially when source code is available. It communicates with unusual technical precision, reasons well about code, and shows strong promise in complex domains such as native-code analysis and reverse engineering.

or AISI:

The AI Security Institute (AISI) conducted evaluations of Anthropic’s Claude Mythos Preview (announced on 7th April) to assess its cybersecurity capabilities. Our results show that Mythos Preview represents a step up over previous frontier models in a landscape where cyber performance was already rapidly improving.

(Though Dan Stenberg, the maintainer of curl, is mostly unimpressed)

It’s worth noting that many of those same Mythos skeptics also claim that GPT 5.5 is as good at finding security vulnerabilities as Mythos and is publicly available. So…ha, I guess? Somehow this doesn’t really make me feel better about the state of software security!

Unfortunately I think our regulators are basically not taking any of this seriously. The only politician who seems at all AI pilled is Bernie (who keeps putting out videos where he directly quotes AI CEOs about how horrible AI will be, and then goes “see?!”). No one else has really picked up the ball.

I suspect we’re going to see a lot more hacks and discovered zero days for the next year or so, after which things will hopefully mostly stabilize. But it will take that long to adjust to the new normal, especially because state run hacker groups are probably better at AI enablement than, like, the First Bank of Idaho.

Other Things

General Catalyst Ad

The VC world is often fairly insulated from macro economic pressure. It operates in such a strange niche of the market, with such skewed returns, that most things that impact other asset classes just kinda slide right over. The one thing that matters to every VC I’ve ever spoken to, often more than anything else, is deal flow. VCs need to be able to see deals to do their job, which in turn means that they need to have a steady drumbeat of founders knocking on their door. You could basically view everything else about VC through this lens. Presentations, advertising, social media positioning — it’s all a way to get and maintain deal flow.

General Catalyst is one of the largest VCs in the world. They just posted an ad in which they explicitly criticize other VCs for investing in immoral companies, stating that they have a high bar for ethics in who they invest in (the ad is clearly taking the piss out of a16z. The guy on the left is meant to look like Marc Andreesen. Seems like it got under their skin, I’ve seen 45 quote tweets from Marc himself already 😬).

I think the right way to read this is that VCs are starting to realize just how much the vice signalling ‘we will invest in anything’ approach has hurt their reputations. I know a ton of founders who keep lists of VCs that they refuse to work with because of their past behavior and investments. Looks like that has finally hit mainstream.

Most founders care a lot about the world, their communities, their employees. They are conscientious and ethical people. But many worry about speaking out about things they find unsavory, because Silicon Valley is a small world with some, uh, famously thin skinned individuals wielding a lot of influence. Still, I don’t think most founders realize just how much power they have as a result of the need for deal flow. A few folks publicly voicing their discontent can move mountains.

Musk vs. Altman

Elon donated a bunch of money to OpenAI when it was a non-profit. Then OpenAI became a for-profit. Elon is mad about this. He claims to be mad about this because OpenAI “stole a charity,” i.e. misleading him about the nature of his donations. But it’s reasonable to be somewhat suspicious of this claim, since Elon is also running a direct competitor to OpenAI that stands to gain quite a bit if OpenAI is taken down a peg.

Anyway, Elon sued. I am so glad that I live in a country where court cases are public, because the trial has been hilarious so far.

On the Elon side, it’s true that OpenAI changed the company to a for profit, and it’s true that Elon is mad about it, but it seems like he’s mad mostly because he wanted to make the company a for profit first — specifically, by moving it under Tesla with himself as CEO. On the OpenAI side, both Altman and CTO Greg Brockman argued that they were not trying to steal the charity, until Brockman’s personal diary was entered into evidence where he was like “wow, can’t believe we’re going to steal this charity.” We also finally got some color on the whole Sam Altman ouster thing, thanks to the incredible Internal Tech Emails account:

Other silliness abounds. Excited to see what else turns up.

Gamestop and eBay

Gamestop, a company worth $10b, made a $55b acquisition offer for eBay, a company worth $50b. You may notice that this math does not math. From BBC:

The cash and stock offer values eBay at $125 a share, $20 more than the shares were valued at the close of New York trading on Friday, GameStop said in a statement.

In a letter to eBay, GameStop’s chief executive Ryan Cohen said he planned to make $2bn of cost savings at the firm within a year of the deal being completed.

…

GameStop, which currently has a stock market valuation of around $11.9bn, said it has a commitment letter from TD Securities to provide around $20bn in debt to help finance the deal.

…

Shares in eBay jumped by more than 13% in after-hours trading when news of the potential offer emerged on Friday.

Though many of GameStop has closed many of its stores in recent years, it still has around 1,600 outlets in the US.

Those shops would give eBay a national network for its “live commerce” and other business operations, Cohen said.

I…guess? like, if I squint, I can just about see the contours of a business that uses local gamestop brick and mortars as the equivalent of amazon fulfillment hubs, but for decentralized users?

But also, the entire economic engine of the last quarter century or so was ‘take thing that was offline and put it online’. Now we’re, what, taking things online and putting them back in person? Delivery logistics are certainly annoying but surely people can wait an extra few days to have their limited edition lemon juicer shipped last mile to their house vs getting in a car and going to the nearest gamestop (???) to pick it up.

Anyway, eBay said no in the most devastating way possible:

EBay Inc. rejected a $56 billion takeover offer from GameStop Corp. Chief Executive Officer Ryan Cohen, describing the unsolicited bid as “neither credible nor attractive.”

Ouch.

Apparently Cohen is unfazed.

In the letter to eBay Chairman Paul Pressler, Cohen emphasized that eBay shareholders deserved a say on his proposal and went on ​to criticize top management’s compensation.

“They should not dismiss a $125 per share proposal without engaging on its substance,” he wrote, adding: “The economics are clear and they are public. eBay’s own shareholders deserve the opportunity to evaluate them.”

But the whole point is that the eBay board said there is no substance. Anyway, given how everything else is going, I expect Gamestop to be running eBay by the end of the quarter.

Opus 4.7

At this point, I can pretty confidently say that Opus 4.7 is a miss. It seems worse than Opus 4.6 and much worse than Codex. The team has basically entirely switched to one of those instead of using Opus 4.7 for their main work. Hope Anthropic fixes the issues in their next release.

Last Wednesday, we had our first in person Agentics meetup. 1100 people signed up to come fill a space that could fit max 200 people. We had to turn folks away at the door. That’s sorta a surreal thing for me to say. I can’t quite fathom how quickly the community has grown, but then again I suppose that is the same story with all things AI.

Agentics started as a little slack group that we put together a few months ago, of maybe 20 people who were using ai tools and all had the same questions. “What’s a skill file? What’s an agent swarm? How should I use these things?” Instead of answering the same question over and over we thought it would be easier to just put everyone in a big soup. And now that little slack group has 300 people and we’re doing massively over subscribed meet ups.

At its core, Agentics has always been about learning about coding agents. We bring together people who know a lot about these things, who spend all day thinking about these things, and put them in the same room as people who want to learn more.

About a month and a half ago, we started thinking about bringing agentics out of slack and into the real world.

It started when I met Vas (CEO of Cognee) at a lunch in SF. We got to talking about AI dev tools. And at some point I said, “vas, you know, were here in SF which is the center of all this ai stuff. And I’m talking to folks from the big labs, from anthropic and OpenAI and Google. And I’m slowly realizing…no one on the planet really knows how to use these tools.” And I still kinda think that!

Everyone knows that coding agents are important. Everyone can see that they are important. But they completely change all the best practices. So everyone is scrambling to figure out what those new best practices are. And it’s not just the tech teams. Product, and sales, and ops, and hr. It feels like everything we know about how businesses work is being reinvented! It’s actually kinda exciting, in my mind. Because every single person who is using these tools gets to be part of the discovery process as we collectively figure out what the new best practices are.

So anyway, Vas and I got to scheming. How could we create gravity, intellectual density, that would let us share what we’ve learned about these tools?

He brought in Garrison from Vellum and I poked some friends at Modal, and the thing was on. This meetup, the first of many, dedicated explicitly to learning how to use and deploy AI agents across an organization.

When we set out on this little adventure, we expected to get like 150 sign ups at most. We had no idea that we would be so oversubscribed, we would have to last-minute book a new venue and still turn people away.

But I think that really speaks to just how much interest there is. We had all sorts of people sign up — students and artists, executives and professors, founders, scientists, researchers, and engineers. Each person with a completely different view on how to use agents, all sharing (and inventing!) best practices.

I think the event went great, and I’m very excited to start planning the next one. For folks who weren’t able to make it, we have all of the slides and recordings here: May 2026 Meetup

Please join the slack to continue the conversation! Join the Slack

Agentics is a series of posts about how to use and reason about coding agents. If you are an expert in coding agents, or interested in learning more join our community slack. More articles here.

Agentics is sponsored by Nori Agentic. We build customized AI software engineers that can talk on slack, use google drive and email, and write code better than out-of-the-box agents. Reach out to amol@noriagentic.com to learn more.

With OpenAI’s new raise at an $852B valuation, OpenAI Foundation’s stake is now worth $180B. Anthropic’s cofounders have pledged to donate 80% of their wealth. Nobody seems to have a concrete idea of how to deploy 100s of billions (soon trillions) of wealth productively to “make AI go well”. If you were in charge of the OpenAI Foundation right now, what exactly would you do? And when? It’s not enough to identify a cause you think is important, because that doesn’t answer the fundamental problem of how you convert money to impact. Identify the concrete strategy you recommend pursuing.

What should countries which are not currently in the AI production chain (semis, energy, frontier models, robotics) do in order to not get totally sidestepped by transformative AI? If you’re the leader of India or Nigeria, what do you do right now?

I say that this is an answer to prompt three, but it’s really about three and four at the same time.

AI is like oil. The combined economic output of America’s AI hyperscalers already contributes something like 75% of GDP growth. This is, we are told, just the handle on the hockey-stick: we await Final AI, the AI that can scalably replace humans on any arbitrary (non-fine-motor-skills-based) task. Let’s make some assumptions. If Final AI’s impact is more like the sewing machine, then all of this special pleading is unnecessary; let’s instead assume that Final AI won’t open up new jobs for humans. Competition between AI labs would be good for human opportunity, so as a worst-case scenario let’s say that Final AI is winner-take-all. Final AI doesn’t presuppose Final Robotics, so there may still be place in the supply chain for human manufacturers and primary resource-extractors.

If all of this is true, AI will be a kind of eternal oil fountain. It will endlessly generate money and drive output, and may well require some amount of labor in the margin, but it will also be a trap: it will funnel massive gains into one sector of the economy while atrophying everything else. The heads of AI labs may currently gesture towards their future generosity, but this is no guarantee that they won’t eventually decide to hoard their gains. This is how resource-rich nations fail, and how nightmares of a perpetual underclass are realized.

How do successful countries escape the resource trap? Oil-wealthy nations like Norway aren’t good places to live because everyone’s employed on an oil rig. They are able to fund their generous social services because they hold their oil wealth in a sovereign wealth fund. The US, too, now has a sovereign wealth fund, perhaps the one far-sighted act of Trump 2. The terms of the question presume a model where AI gains flow largely to private owners, and through them to philanthropy. (This is, admittedly, the most likely outcome.) But unless the future of private philanthropy is essentially private governance, it cannot replace the structural transformations enabled by strong state capacity to invest in infrastructure. Local knowledge matters. American philanthropy could not build Chinese high-speed rail. The field in general is waking up to these facts: OpenAI’s “Industrial Policy for the Intelligence Age” doc proposes a SWF, and even the taxes required to fund it. If Sam Altman really wants his UBI, this is how to get it.

The point here is that there is already a model for success and a model for failure for single-resource-based economies. The difficulties are not conceptual but political and practical: how quickly do we transition from private to public ownership of AI labs? Does the government begin acquiring small stakes now or do they await Final AI and deploy eminent domain? Should the state be part of the board? How much of a SWF should go to dividends and how much to improving state capacity? These are difficult questions to answer, but there’s at least a playbook one can consult. But this playbook only tells us what to do when the domain of single-resource dependence is one country. Final AI doesn’t just affect one country. So far only China has been able to provide a competitor model, and that too only by distilling American frontier output. There is no obvious future here where every country gets its own model, especially if Final AI really is winner-take-all.

Whatever political difficulties there may be in nationalizing or at least acquiring shares in the “winner,” these will be exponentially worse in sharing those gains internationally. If you imagine the future, picture Americans living off their UBI like the consoomers in Wall-E, while middle-income countries manufacture and low-income countries mine and/or starve. Work itself will be something only non-Americans have to experience, and that too only in its most malignant and back-breaking forms.

If I were in charge of India, then, I would begin trying to lay the political foundation for the belief that Final AI ought to belong to all humankind, not just the citizens of the country of the lab in which it is achieved. This will strike some as parasitic. But I think this is not just what’s needed for AI to “go well” for all mankind: it’s also philosophically true. If Final AI is achieved, Congolese miners will have contributed to its creation, however distally.

Practically, rather than some kind of massive global wealth fund, the most efficient approach here might be some kind of nation-based AI protectionism. In China, you use the Chinese model. In Nigeria, you use the Nigerian model. These models might, under the hood, be exactly the same, but when you use the Nigerian model, Nigerians get a payout. Poorer countries will still lag behind in terms of physical infrastructure, but they will at least have access to whatever information layer is needed to organize their resources most efficiently and hopefully build out quality-converging infrastructure long-term. (It is unclear what entrepreneurship will look like under Final AI: are there still human entrepreneurs who have to bring goods to market, or do AI-CEOs compete amongst themselves, hiring the occasional human where needed?) If these countries are able to implement local AI-backed UBIs, the global relationship to work also changes. Manufacturing and resource-extraction shift from being extremely low-wage jobs with a labor surplus of roughly every person in Asia and Africa to premium work that has to pay commensurate to its difficulty.

These bold predictions all depend, of course, on just how vertical the blade of the hockey stick turns out to be. A global UBI that pays 13 cents a month would be a farce. I remain somewhat skeptical that the Final AI described at the top of this post will really arrive so soon. That said, it’s still eminently valuable to consider the limit case and start laying the groundwork for adaptation now rather than when we’re all underwater.

PS: You may also like my answers, below.

Dwarkesh recently published an essay competition, asking participants to answer one of four big, open questions about AI. The rules of the competition only allow you to submit one essay, but for kicks and giggles I wrote up answers to all four. Now that the submission date has passed, here are my answers.

A couple years ago, there was this idea that AI progress might slow down as we make further progress into the RL regime. 1. Because as horizon lengths increase, the AI needs to do many days’ worth of work before we can even see if it did it right, so if we’re still in a naive policy gradient world, the reward signal / FLOP goes down, and 2. We’d crossed through many OOMs of RL compute from GPT 4 to o1 to o3, and it would not be feasible to replicate that many OOMs increase in compute immediately again. But AI progress seems to have been fast nonetheless - even potentially speeding up if rumors about Spud or Mythos are to be believed. What gives? What did that previous intuition pump that motivated longer timelines miss? Feel free to deny premise of question.

I don’t think there’s a single silver bullet thing that people missed. AI development is highly multimodal and increasingly depends on a full stack that spans from energy providers to UX designers. The hypothetical timelines above are limited by looking only at RL and RL compute, but many more things have gotten better outside of that narrow slice of the stack, including:

• More compute for pre training

• More and higher quality data for pre training

• A new understanding of how models function, which has led to more efficient training

• Better prompt engineering and context engineering

• Coding agent UX

And that’s before you look at RL specifically, which of course, has gotten better thanks to improved techniques and better sim environments.

I think of AI development like Moore’s law. I’ve said in the past:

A good metaphor for this kind of advancement is Moore’s law. Very roughly, Moore’s law states that the number of transistors on a chip should double roughly every two years.

Moore’s law does not make assumptions about where those gains are coming from. Moore didn’t say something like “chip capacity will double because we’re going to get really good at soldering” or whatever. He left it open. And in fact in the 60-odd years since Gordon Moore originally laid out his thesis, we’ve observed that the doubling of transistors came from all sorts of places — better materials science, better manufacturing, better understanding of physics, all in addition to the (obvious) better chip design.

You can imagine a kind of Moore’s law for intelligence, too. We might expect artificial intelligence to double along some axis every year. Naively we’d expect that improvement to be downstream of more data and more compute. But it could also come from better quality data collection, more efficient deep learning architectures, more time spent on inference, and, yes, better prompting.

This still applies. Note too that an increasingly large chunk of the world economy and the world’s smartest people are working in AI, which has compounding effects on all of the above. If I had to summarize this into one pithy response, it’s that the earlier timelines underestimate human ingenuity. Which is a bit of a cop-out, but is my honestly held opinion.

If I was forced to be more specific, I’d drill down on the implicit framing that the RL regime is the most important thing. In my opinion, improvements to pretraining have done far more for model progress than any amount of RL — in particular, more stable gradients, widespread sparse MoE, and multimodal data. The jump to Claude 3.7 or Opus 4.5 was on the back of new pretrained models. Same with Gemini 2.5 when that was the new hotness, and same with GPT-5.2 (but notably NOT GPT-5 and GPT 5.1) more recently. Anecdotally, over the last few years, Anthropic surged ahead of OpenAI because the latter lost all of their best pretraining experts due to OpenAI’s politics,1 which in turn resulted in OpenAI not having a successful pretrain for nearly a full year (!).

Bluntly, I’ve always been very bearish on RL, and I think that many people overfit to RL hype. At a low level, RL is about approximating gradients in discrete environments where you cannot get a continuous signal. This is valuable because there are many environments where you cannot get a continuous signal. But it is still an approximation. Models are highly sensitive to gradient error. As a result, it is always better to get real gradients from backprop, which in turn means that it is more fruitful to try and turn RL signal into supervised signal where possible.

In my mind, strong pretraining raises the ceiling of what these things can do, and RL is how you actually hit that ceiling. No one bothers trying to train models exclusively with RL, the rewards are too sparse for the model to do anything coherent. Training RL models on different pretraining mixes shows that RL ends up amplifying what’s in the existing mix, instead of learning brand new concepts or making new leaps. And distilling reasoning traces from larger models using supervised fine tuning gets better results than using RL on the same base model.

These results are reflected in RL scaling laws, which follow a sigmoid as a function of compute. Compare to pretraining scaling laws, which are famously power laws. RL training can mold clay, but can’t create more of it.

Stepping back from RL specifically, it’s worth asking the follow up: power laws ought to fall trap to the required OOM increases in compute and data as well, so what gives? But here I again fall back to the full scope of improvements that happen at every part of the AI stack. The scaling laws require more data, and we have more data. The scaling laws require more compute, and we have more compute. RL is great for shaping the output towards environments where we cannot get obvious gradients, but beyond that, everything else on the model side is commentary.

The last thing I’ll say is that I do think form matters as much as function. I’ve always been a believer that the right HCI can make someone 100x more productive with a given tool. I think we engineers underrate the importance of product design like Claude Code, both in terms of how it shapes the model behavior and in terms of how it shapes global perception. The question framing — that AI progress is speeding up — is on the back of an incredible run over the last few months. But that run in turn was more attributable to Claude Code being a good product than it was ‘Gemini posted top scores on ARC AGI.’ More generally, it seems very hard to untangle how much progress is actually because we just started fine-tuning tool calls to make these things actually useful in the real world, vs the models themselves getting ‘better’ under the hood.

What’s the most plausible story where foundation model companies actually start making money? If you consider each individual model as a company, then its profits may be able to pay back the training cost. But of course, if you don’t train a bigger, more expensive model immediately, then you stop making money after 3 months. So when does the profit start? Maybe at some point scaling will plateau, but if progress at the frontier has slowed down, then the combination of distillation and low switching costs (cloud margins result from high switching costs) makes it really easy for open source to catch up to the labs, eating into their margins. So how do the labs actually start making money?

I have three answers here, presented in increasing amounts of tongue-in-cheek.

First, the serious answer: token demand is outstripping total compute supply.

Empirically, I feel like I could just quote Dylan and SemiAnalysis here. They make a good case, and they do so more eloquently and in more technical detail than I could.

Demand per SOTA token just empirically seems highly inelastic. People want the best model, so much so that market correction comes more in the form of service degradation due to demand than it does competition.

But pointing at empirics isn’t really interesting for an essay; what’s the theoretical answer, the one that would satisfy an intro econ student, for why this is happening?

Massively simplifying, the token providers make money in the long term when:

• the demand for tokens > supply of token providers

• It is not easy for new token providers to come into the market and increase supply

It’s hard to say exactly what the demand for tokens looks like, but it is very very high. Regular users of coding agents spend thousands on tokens per month. Fully automated background agent teams are only just starting to come online. And huge parts of the economy have not yet woken up to the benefits of AI tools. I think demand for tokens is increasing on a timespan of days or weeks.

What about token supply? The primary input for token supply is compute. Compute build out timelines are measured on a timespan of months to years. And demand for compute is, in turn, putting pressure on energy and materials, the supply of which is measured on a timespan of years to decades (and this before the destructive wars in Ukraine and Iran, which further delays energy build out).

Even if every bit of spare compute was used to deploy tokens, it seems like this will not be anywhere close to meeting demand for the entire market. That means two things:

• First, new token suppliers who are attempting to play catch up with cheaper open-sourced models will simply be limited by the number of inference chips globally available, even if those open-sourced models are comparable.

• Second, even if a particular lab ends up with a new SOTA on a particular training run, they will not be able to support all of the demand for that model, and will either have to aggressively cap usage or degrade service — both of which will drive some demand to other non-SOTA models.

(As an aside, I suspect that those with access to compute — including the folks serving open source models on neo clouds — will not compete with each other on price too aggressively. I think they will act more like a landlord cartel. Maybe they won’t explicitly fix prices, but they will look at the prices posted by their “competition” and simply match. After all, all token suppliers have shared interest in making money on inference.)

But wait, why wouldn’t the money just all accrue to, like, NVIDIA? If the compute layer is the bottleneck, shouldn’t compute-layer companies be able to squeeze out the token providers?

For what it’s worth, Dylan et al ask the same question, and their conclusion is that NVIDIA (and TSMC) is reserving pricing power as a means of avoiding regulatory scrutiny while ensuring the broader market has gas during economic downturns. Having a healthy number of downstream token suppliers is a sort of long-termist ‘defense-in-depth’ strategy for ongoing demand. If there are 100 such suppliers right now and they are all able to save a bit by having good margins, it’s more likely that at least 1 will make it through the next recession — at least, compared to the counterfactual world where there are only 10 suppliers on extremely thin margins and they all die because they have no war chest.

I agree with this, and also want to add: I think the token providers are price makers more than takers, or at least more than we give them credit for. For the first two decades of my life, GPUs were sorta a joke. Their primary market value was letting gamers play video games at extremely high resolutions. As one of those gamers, I think this was a valuable service! But no matter how well you execute on that niche, you simply will not become the most valuable company in the world. OpenAI and Anthropic brought NVIDIA riches. If I were NVIDIA, I’d be very cautious about doing things that may kill the golden goose. That includes the framing that Dylan mentions, but it also includes things like “making it financially imprudent to develop competing full stack chip infrastructure.” There are many upstarts who want to eat the GPU lunch, and NVIDIA’s job right now is to make sure none of them are able to. The best way to do that is to just beat everyone on price, leveraging its massive economies of scale and procurement pipelines to price lower than their competition ever could. This in turn forces competitors to offer a meaningfully different product…which NVIDIA can then just acquire (see grok).

So, net net, I think as long as demand for tokens continues on its current trajectory, the AI labs will be fine. Note that long term, I tend to be bullish on Google just because of how much it is able to control its own destiny across the stack, from energy to data centers to chips to models.

That was all the serious answer.

Second, the halfway serious answer: government contracts. Turn the economic question into a political one. Sell to the buyer with infinite demand, zero price sensitivity, and a long history of inefficient purchasing by becoming friends with the right people. Anthropic may no longer be able to go this route, but OpenAI and Google are perfectly positioned to make boatloads of cash this way. It’s also totally possible for the big labs to just straight up be regulated like commodities or outright nationalized.

Third, the unserious (but maybe…) answer: just ask the AI to make you money. If the labs ever get to the ‘takeoff’ scenario, they should eventually just be able to go to the AI and ask the AI to make money for them, whether by investing in the market or telling them what to do to run their businesses. Matt Levine has a whole bit on this:

We have talked before about the business model that Sam Altman proposed for OpenAI in 2019, which was (1) build an artificial superintelligence, (2) ask it how to make money and (3) do that. “We will create God and then ask it for money,” as I put it.

What would the AI say? Well, when we talked about it in October, OpenAI was apparently getting into advertising, affiliate shopping links and porn, and I joked that that is what a large language model trained on the internet would come up with. But a more first-principles answer would be something like this: “I am a superintelligent AI, constructed to be a bit smarter than the smartest human in every domain of human knowledge. The way I can make money is: in all of the ways. I will start a biotech company and discover the best drugs. I will start an accounting firm and do the best audits. I will start a publishing house and publish the best books, which I will write myself. I will start a pest-control company and hire the best exterminators, schedule them in the most efficient way and do advertising and pricing with perfect efficiency, though I will need humans to kill the bugs. Certainly I will start an electronic proprietary trading firm. All the ways that humans make money, I will do, just better than them.”

Idk maybe it’s enough to just have the models trade stocks on the side. Or, like, steal everyone’s crypto:

I wrote yesterday about the generic artificial intelligence business model, which is (1) build an artificial superintelligence, (2) ask it how to make money and (3) do that. I suggested some ideas that the AI might come up with — internet advertising, pest-control rollups, etc. — but I think I missed the big one. Like, in a science-fiction novel about a superintelligent moneymaking AI, when the humans asked the AI “okay robot how do we make money,” you would hope that the answer it would come up with would be “steal everyone’s crypto.” That’s a great answer!

With OpenAI’s new raise at an $852B valuation, OpenAI Foundation’s stake is now worth $180B. Anthropic’s cofounders have pledged to donate 80% of their wealth. Nobody seems to have a concrete idea of how to deploy 100s of billions (soon trillions) of wealth productively to “make AI go well”. If you were in charge of the OpenAI Foundation right now, what exactly would you do? And when? It’s not enough to identify a cause you think is important, because that doesn’t answer the fundamental problem of how you convert money to impact. Identify the concrete strategy you recommend pursuing.

Solutions to x-risk demand different mitigations and different levels of urgency than solutions to biased depictions on minorities — and, frankly, require very different priors about what is feasible.

Up front, I’m going to focus on mitigating the negative impacts of mass unemployment caused by autonomous agents in white collar industries, and mass unemployment caused by improvements in robotics in blue collar industries. I’m also assuming that we can move the ‘we want to make AI go well’ people as a single political block — that is, I’m not going to spend words on the feasibility of getting the members of the OpenAI foundation / the founders of Anthropic to sign off on any of this, they sign off by fiat. Finally, any strategy to offset negative effects of AI should be seen primarily as a hedge against really rapid changes. If AI doesn’t end up growing super rapidly — if unemployment grows slowly over multiple decades — there are other avenues that existing institutions can pursue to offset instability. The real risk is if the unemployment is sudden and broad. I would consider any solution to AI unemployment a success if it caps downside risk.

The simplest answer is often the correct one. In this case, the simple answer is “give people money” through some kind of UBI stipend that is funded or tied to the revenues of the big labs. Welfare funding is a non-starter in the US. In the spirit of ‘you can just do things’, it is more efficient for the OAI foundation to set up a ‘sovereign wealth fund’-like entity that pools the wealth of ~anyone who wants to donate, and then gives dividends to all people living in a specific geographic area in a certain age range. Just run the UBI program yourself. The Alaska UBI fund is a reasonable model, as is the Norwegian oil fund.

Start with just San Francisco working age adults, a population of roughly 600k aged 18 - 65. Assume 180b from the OpenAI foundation and an additional 180b from the Anthropic founders and other EAs. Yoy growth for the broader market is something like 10%, and the various endowments and sovereign funds generally aim for 5% growth. We expect AI wealth to grow faster than this, but conservatively let’s say we have 18b each year to work with.

With this money, you should easily be able to fund checks of a few thousand dollars per person every year forever. So announce an “AI Day” with a lot of fanfare, where Dan Lurie comes out and gives a big speech, where Altman and Amodei and so on get medals for donating, and where the first checks are sent out. It is very important to be very visible with these checks. People should understand that these are coming from an AI wealth fund. Each check should come with a pamphlet explaining the purpose of the check and why each individual in SF gets a cut.

There should be three medium-term goals for this fund.

First, grow the amount disbursed, either by frequency or quantity. Second, grow the area covered — after SF, aim for all of California, then all of the US, etc. Third, use the political capital earned from giving people money to encourage politicians to explicitly pull the program into the government so that it can be further funded by corporate taxes.

All of the above goals require sustainably growing the fund. In the takeoff-causing-maximum-instability scenario, the growth of AI demand should power a lot of that, and reinvesting some of the per person amount should cover the rest (if you distribute, say, 5k per person per year, then based on the numbers above you should be growing the fund by 25k per person per year). Still, just to ensure continued growth, if the OAI foundation / Anthropic Founders are able to do so, they should fiat that some amount of revenue their companies earn each year simply goes into the fund. Publicly giving people money is a good way to win over some amount of good will, so this could be justified as marketing expense.

In terms of timing, set up the infrastructure as soon as possible while the companies in question are still private and the founders / foundation has some amount of additional control to require giving money to the fund. Then start disbursing the capital once the companies go public (which may be as soon as later this year) so that the funds themselves become liquid.

In my mind, the biggest issue in the first years of the fund is the inelasticity of housing supply in SF. A geo-fenced UBI in a low housing environment is basically the worst setting — creating a UBI of any kind will likely result in an inflow of residents, and landlords can simply raise rents to eat most of the benefit. Early on, we should just assume that there will not be meaningful improvements in the lives of SF recipients.

But again, the goal of this strategy is to hedge against the worse case outcome from rapid mass unemployment. If the AI companies are able to grow so rapidly that they do create mass unemployment within ten years, then the fund will be well positioned to move quickly and grow its coverage area before landlords can respond. And if the AI companies move relatively slowly, then we may be able to avoid mass unemployment doomsday scenarios entirely.

What should countries which are not currently in the AI production chain (semis, energy, frontier models, robotics) do in order to not get totally sidestepped by transformative AI? If you’re the leader of India or Nigeria, what do you do right now?

We actually have a playbook for countries playing catch-up on the tech tree: close the borders to foreign companies with protectionist policies like high tariffs or outright bans, develop internal industry through “stolen” technology with strong state capacity, and require domestic companies to compete internationally in order to survive so you don’t protect failing businesses forever. This worked for Japan, China, Korea, Taiwan, and Singapore, though the specifics vary — for eg, Singapore had less tariffs than Korea, but had near dictatorial control. This strategy mostly failed for India, parts of Latin America, and most of Africa. In the latter cases, it almost always failed because of a corrupt bureaucracy that was unable to jettison domestic companies that were not performing. In other words, state capacity wasn’t strong or rigorous enough.

Though the strategy laid out above was mostly used for rapid industrialization, I think you could implement this kind of strategy for AI. Arguably, China has already done this over the last few years to great effect. The basic contours: massively penalize using foreign token and chip providers, subsidize multiple home grown labs to develop other parts of the infra stack, and ruthlessly enforce corporate accountability against external standards.

Software does not seem like it will be the bottleneck. We know that it is possible to distill the big publicly available models, or just mine the APIs for data. That leaves real world needs, including raw materials, energy, and fabs. Unfortunately, these real world needs are perennial problems in developing countries. Any country that already has economic capacity to do complicated compute build out is already somewhere in the AI stack.

If we are able to control the entire governing apparatus by fiat, a country like Nigeria or India, or countries in LatAm, could actually be well positioned to implement a comprehensive AI-catch-up program. These countries have human talent and access to global markets (i.e. they aren’t sanctioned), with some amount of the raw materials at home. India already has a massive government sponsored chip fab program in the works. Nigeria is in a weaker position due to things like not having figured out consistent energy or not having fully functional ports, but with the entire government working in lockstep they could figure it out. And if we are fiating this level of government control, we can also maybe fiat some level of government cooperation — Nigerian and Indian companies have free trade and information sharing agreements, for example.

Conveniently, these countries do not have to develop an entire domestic stack. They only need to be able to offset demand at some point of the chain, whether that be memory or chips or even land and favorable government treatment of data centers.

If you can’t control the entire government by fiat — if you really are just a single individual leader with a lot of foresight — this whole thing becomes a lot harder. Leaders in these countries would need to centralize economic control, which is always risky because it requires going up against entrenched interests (both domestic and foreign).

None of the above addresses countries that are extremely small or countries that are profoundly dysfunctional. Countries that today struggle to provide basic infrastructure need to get their house in order before embarking on a complicated AI build out.

To be really specific for India:

• Continue investing in fabs. The Tata-PSMC fab in Dholera and the Micron packaging facility in Sanand are good starts. Subsidize two to three more now, so that you can get to the part of the ‘do things at scale’ part of the learning curve.

• Force domestic demand. Aggressive tariffs or outright procurement bans on foreign frontier model APIs for any government use (which is a big chunk of India’s economy). Route to domestic labs — Sarvam recently made waves for releasing a 30B and 105B model, Krutrim has been going the ‘Google’ route for full stack sovereignty, and BharatGen is a government/academic collab. Whatever labs that are picked should transparently be picked for technical merit rather than political connection, with hard sunset clauses if they miss benchmarks. (Note: using foreign models for training data collection is fine)

• Bring in diaspora talent. Lots of ethnically-Indian AI folks. Pay them a ton of money.

• Grow energy. India’s grid cannot currently support hyperscale AI buildout at the scale needed. Modi would need to burn some political capital to do the energy build out, but he has pulled this kind of stunt before for the rupee corruption crackdown, united payments (UPI), and digital ID (Aadhaar). India has uranium, so maybe nuclear?

Even though I have reservations about many of Modi’s nationalist policies, for this particular question India benefits a ton from having a leader who is extremely popular and competent, runs his party with a pretty firm hand, and has experience pulling off massive government projects without any obvious derailing corruption (not the same as no corruption!).

I know far less about Nigeria. My sense is that it is in a far worse position, but I don’t know the names of the major players. Still, here is my best shot at a policy slate:

• Implement heavy handed anti-corruption measures — everything else can only be done effectively downstream of a strong government that can evaluate companies on merit and move large amounts of funds without leakage.

• Fix the electricity and ports.

• Protect / formalize rare metals trading (prevent smuggling).

• Kill protected national companies that are not producing / meeting benchmarks.

First, thank you everyone who shared kind words and sent in home remedies for hiccups. None of them worked, but I appreciate you nonetheless. The hiccups DID eventually stop, still no idea what caused them or why, and my primary care provider and I are in wait-and-see mode. The H. Pylori test and the blood tests all turned up negative.

The essay about testing got shared around a bit, and sparked a lot of interesting conversation among friends. Notably, basically everyone who works in medicine (doctors, nurses, insurers, etc) had a strong reaction against the piece. Here are some responses, and my thoughts on those responses.

The medical system is overburdened.

This was the most common complaint to come up in conversation.

The basic idea is that we should discourage doctors from prescribing tests that are ‘unnecessary’ (and implicitly, we should have strong conceptions of what makes a test ‘necessary’) because there are not enough resources in the broader medical system. We want to ensure that the medical system has capacity for folks with more life threatening diseases — cancer, stroke, Alzheimer’s — and overprescribing tests for people who are approximately fine will end up reducing that capacity. Sometimes, this is framed in terms of class differences. Specifically, hospitals and practices in poorer areas do not have enough testing capacity, and overprescribing in richer areas will result in fewer available tests that end up being more expensive.

I understand the impulse here. During COVID, we really did need to ration tests because there was a limited supply of them. And we wanted to ensure that regular testing capacity was available for those who were most at risk. But I think many of the doctors who push back with this line of reasoning are missing some second order economic incentives.

The high demand for COVID testing led to higher supply of COVID test providers until, eventually, getting COVID tests was dirt cheap. More generally, there is not a finite pool of tests. We can create more reagents and produce more syringes and set up more clinics if there is demand for these things. If every doctor on the planet decided to make stool tests a part of their annual physical, the price of any given stool test should go down in the long term as suppliers flood in to meet demand. The same is true of more expensive tests like MRIs. They are expensive now because we do not mass produce these things. But we do not mass produce these things because there isn’t enough demand to justify figuring out how to do it. And if the reason there isn’t enough demand is because doctors are worried that MRIs are expensive, well, there’s a bit of circular logic embedded in the premise.

Interestingly, many of the folks who pushed back against the idea of prescribing more tests for non-critical cases were, simultaneously, very gung-ho about the idea of building more luxury housing as a method to alleviate the housing crisis. It seemed intuitive that building more luxury housing would end up easing the burden on lower income housing because supply is supply is supply. The same intuition did not transfer to tests. Just make more, and everything will become cheaper!

I think you could argue that supply is inelastic because healthcare is highly regulated or because certain tests are hard to mass produce, but I kind of don’t believe either of these things. Unless there is literal anti-competitive cartel behavior on behalf of the government, increasing demand should result in more shops trying to jump through healthcare hoops and more human ingenuity going towards producing cheaper tests. As an example of the latter, an MRI machine will run a hospital somewhere between $1-3m, because there are only like 5 companies that make them and they haven’t innovated on the core product in decades. Meanwhile, this startup has figured out how to make a decent MRI for 50k. If more doctors prescribed more MRIs, more hospitals and medical practices would start looking to procure an MRI machine, and then would probably pick up the $50k one!

Patient burden

Many folks felt that over testing would burden the patient.

Jacob

Surprised nobody mentioned what is probably the central issue with overtesting, which is iatrogenesis, either mild (making people constantly worried and running to the doctor for tests for reassurance) or significant (unnecessary biopsies, etc). Like it’s not that it’s wasteful to test everyone for everything, it is reasonably likely to cause net harm

From the DMs

telling millions of people “hey, you might have cancer, we see some lumps in your scans, but don’t worry about it, it’s probably nothing, don’t give it a second thought until your next scan next year” holds up well theoretically but causes a lot of harm/distress…the “lot of harm/distress” quantification -- there’s plenty of evidence that medical anxiety is a real thing, contributes to real physical harm and distress, and that’s ignoring all the other factors (cost on patients, cost on healthcare system, invasiveness, etc)

This was the second most common push back. We don’t want to give a lot of tests that will mostly result in a lot of false positives, because the patients falsely diagnosed will 1) suffer from medical anxiety and 2) potentially undergo treatment that they didn’t need, side effects and all.

I found this totally uncompelling.

The most common refrain was ‘false positives for cancer,’ so we can roll with that. Let’s say a patient gets a false positive for a cancer diagnosis. Obviously, up front, we know it’s a false positive, but the patient does not. Using some

. ݁₊ ⊹ . ݁˖ .Bayesian analysis . ݁₊ ⊹ . ݁˖ .

we can determine the likelihood the patient has cancer given the positive test. Because cancer incidence base rate is pretty low, the likelihood is probably like 5% even if the test is 99% accurate. That is, most people will still get negative results, and even the people who get positive results are pretty unlikely to have cancer.1

I was sent an article by a physician that was mixed about full body screening. In order to make the case against full body screening, this doctor writes:

Let’s do the math: For every 100 people scanned, 30 undergo follow-up investigations. Of those 30, roughly 1 has cancer. The other 29 endured anxiety, additional procedures, and potential complications for nothing.

This is the fundamental problem: whole-body MRI is so sensitive it finds almost everything. The challenge is distinguishing signal from noise—and that distinction often requires invasive testing to know for sure.

If I told you that there was a 3% absolute risk of you having cancer, you would rightfully want to get that shit checked out and take precautions! Things that can result in a 3% absolute risk of cancer include:

• Heavy alcohol use for over a decade

• High dose radiation exposure

• Long term immunosuppression

All of these people should regularly get tested. If you get a positive test, you’re about as likely to have cancer as someone who got a glancing blow from Chernobyl. At the very least, you would want to get another test done, which is basically what we already encourage patients to do when they get a hit by a potentially life changing diagnosis. Or, put another way, I think it is totally fine for 29 people to have some additional medical anxiety if it means catching a malignant cancer.

Moreover, it is incredibly paternalistic to assume that patients shouldn’t get checked out regularly because some of them may naturally follow up on the results of those tests. And the cost of that paternalism is measured in lives — people who could have spotted an actual cancer diagnosis early if they just tested more.

I know that the medical system needs to think about tradeoffs at the population level, but if you assume cost is not an issue, in my mind there would have to be an absolutely massive amount of ‘patient anxiety’ to justify even a single preventable death. If we fiated that MRIs were essentially free, would we really prevent people from taking them because of patient anxiety? If not, what are we even talking about?

Note too that there are certain tests that we do just do constantly. As I wrote in the original piece:

Every trip to the ER almost always results in a suite of tests, as does every physical. Hell, they’ll take your blood pressure and temp and oxygen levels before you even see a doctor. They’re thrilled, they’re practically jumping for joy to take your blood pressure / temp / O2. I don’t think these tests are all that different from a blood panel or a stool test. And I just refuse to believe that we’ve coincidentally settled on the exact optimal amount of testing for every person, which just so happens to be ‘once a year-ish.’

What makes blood pressure testing or O2 testing different from, like, a stool test, at least when it comes to patient anxiety? In my mind, the only differences are cultural acceptance among the medical establishment and cost. Whenever a test becomes cheap enough we magically stop caring about patient anxiety entirely and instead just order more tests.

I don’t mean to be dismissive of the folks who argue for patient anxiety. But it seems to me like this is a case of gigabraining — mistaking second order effects for first order effects in order to have a convenient just-so explanation for an unintuitive and contrarian take. I think the amount that we should build our medical system around minimizing patient anxiety is close to a rounding error, and when weighed against actual lives I think most people would agree.

The test results won’t matter anyway

Scott Alexander writes:

There’s a saying - never do a test unless some possible test outcome will change your plan.

Your doctor could order CRP - a nonspecific test that’s elevated whenever anything is wrong - but if they don’t have some plan for what they would do when CRP is high, what’s the point?

Level 50 Lapras adds:

The analogy in the software case would be where you have no ability to change the system anyway, so knowing the exact cause is pointless.

I’m a bit confused. Why are there tests that only show ‘general malaise’? I agree that that seems like a bad test.

Maybe I’m just too much of a software engineer, but to me, gathering data is upstream of making a diagnosis. In the same way that you may pepper a codebase with logs to find a bug, you do the testing so you can figure out what the diagnosis ought to be. In many situations you can take a reasonable first stab at a diagnosis based on how the patient describes their symptoms (which is just first line data gathering).

But if you can’t make a confident diagnosis because the symptoms are non-specific or because the previous diagnosis can be obviously ruled out, you should gather more data, which will then change your plan.

Just to be really specific about this, in the original essay, I mentioned that the doctor really didn’t want me to take an H. Pylori test. This is a test that, if it turned up positive, would immediately have given a clear diagnosis with a clear change to the treatment. There wasn’t a single person who could mount a good case for why I shouldn’t have had the H. Pylori test.

More generally, I got this fatalistic attitude from a lot of doctors and medical professionals. “I shouldn’t order the test because it’s not going to matter.” I don’t get it, what are they teaching in the med schools? Order the tests, it may matter!

Certificate of Need Laws

My cofounder, Cliff, previously used to work in healthcare, and he came out as a very strong supporter of the overall essay. His overall point was that our government tries way too hard to cap market forces when it comes to healthcare, often leading to really stupid and preventable shortages, and spent a while talking about Certificate of Need Laws and how stupid these things are.

I hadn’t heard of this before, but having read about it, I agree — they are stupid!

Certificate of Need Laws (literally CON laws, you can’t make this up) make it so that you cannot build more medical capacity in a region unless a federal or state agency affirms that that region needs more medical capacity. That is, a hospital needs to go to the state and say, “hey, we want to get a new MRI machine, we can afford to buy it and we have a willing seller” and then the state can just be like “no.” From wiki:

CONs have been criticized for granting monopoly privileges to existing hospitals and healthcare facilities, thereby driving down the number of hospitals and hospital beds in a community. One study found that CON laws resulted in 50% fewer hospitals per 100,000 persons, and 12% fewer beds at the typical hospital.

Believe it or not, it gets worse. A lot of state regulators aren’t experts in public health, obviously. So when they get a request to increase medical capacity at a hospital, they will seek out experts to help advise them on whether or not a particular proposal is reasonable. Who do they ask? Other hospitals. This is a massive conflict of interest! “Well, currently all the people who live in the boonies drive 4 hours to our hospital to get their MRI scans…so they don’t really need an MRI machine out there, in that other hospital branch with the other insurance network. They are perfectly well served by our hospital in our insurance network 😁😁😁”

CONs are sometimes sold in bankruptcy as an asset,[9] and the CON requirement is sometimes used by competitors to block the reopening of existing hospitals.[10] In June 2023, a Tennessee administrative law judge blocked the opening of a new hospital in Rutherford County by Vanderbilt University. The state had initially approved the hospital and granted it a certificate of need. But three existing providers intervened, claiming that there was not a need for another facility in the area.

What the fuck!

In 2018, the Department of Health and Human Services recommended that states repeal their CON laws because they are a significant reason for increasing medical costs, and they reduce patients’ healthcare choices

There are still 35 states that have these on the books, by the way. I’ve become markedly less libertarian over the last ~2 years or so, but wow does this make me want to reboot the entirety of our legislative code.

I guess maybe it does make sense to reduce testing when you have regulatory capture that prevents more supply from meeting demand.

Anecdotes

Basically every doctor I spoke to had some justification for the existing system. But there were a lot of people who weren’t doctors who commented, and their stories were all some variant of mine.

Fedaiken:

I feel your frustration here. I had a medical issue years ago where doctor after doctor just dismissed me and I came to the conclusion that you have to own your medical outcomes and direct the doctors just as you would any other service employee. Much harder as the domain is way more complex and the consequences very real. However, my experience demonstrated to me, that only I cared enough about my health to really guide the process. And that means demanding tests and not taking no for an answer. IMO

KSaucy:

This hits close to home for me because i once went to the ER for stomach pain. The ambulance ($3k bill) was like “probably the taco bell” and the triage team was... nonresponsive? To be fair it was 3am but i was screaming, begging for help, explaining that i was in the worst pain of my life and if they couldnt treat me immediately i at least needed painkillers. I was told to go outside and get some at a gas station- which i tried to do and failed

Anyway it was a burst appendix. I was literally 24 hours to death, in a hospital, and still could have died that day

The takeaway from all of this is that you CANNOT trust in the medical system on your own. You NEED an advocate, even as an adult. The system is overworked and being financially preyed upon by people outside of it.

Luke:

I’ll never forget the time I went to a gastroenterologist with inexplicable stomach pains and he was like yeah we don’t know what it is, probably IBS (which means we don’t know what it is, but your stomach hurts, I guess), though it could be IBD (stomach hurts and we DO know why and can help), but it’s probably not IBD based on random things you’ve said or something. And I asked if there was a test he was like yes, but I don’t think it’s IBD, so it would be a waste. And then he said, I swear to God, that I didn’t seem to be the type of person to waste the time and effort required to get a test done.

Genuinely, I think some doctors are just... Lazy? They know if you get tests done, they have more work to do.

But then I can’t really explain why no doctors ever bothered to test my iron levels despite my complaining about fatigue for years. Men don’t get iron deficiency is the consensus, I guess? Well, I paid out of pocket and found out I do.

Daniel:

I had a similar experience recently with some stomach pain. The doctor wasn’t really interested in why I had the stomach pain and just prescribed some anti reflux meds. Felt like it was just a guess a check approach.

The anti reflux meds didn’t work, but my stomach pain did eventually go away, so it does lend credibility to just do the “first line treatment”/do nothing approach, but definitely frustrating from the patient perspective.

And many more. I know that the plural of anecdote is not data, but it’s not not data.

Hiccups

After all of my complaining about tests and how my special snowflake hiccups needed to be analyzed more deeply by the best doctors in the world, the most embarrassing thing happened: the hiccups stopped. It’s been about a week since they stopped, so fingers crossed they are gone for good.

It seems to me that the most likely proximal reason they stopped was the baclofen. I took like two doses of the stuff, it knocked me out both times, and then on the third day I woke up and no hiccups.

It gets worse: the tests that I demanded both turned up negative. So not only did the hiccups go away, they went away without us ever figuring out why they started in the first place. And while I’m still curious about what exactly happened, a) it’s basically impossible to justify getting more testing done at this point, b) it’s not clear that anything would even show up, and c) I have very limited interest in spending a lot of time doing tests for things that aren’t actively causing problems.

It’s worth noting that I very purposely did not follow the doctor’s prescription — 10mg of baclofen every 8 hours is enough to tranquilize a horse, and they wanted me to take this stuff for a week. According to Claude and multiple patient studies, it has withdrawal effects, which is something that the doctor flatly denied was true. The doctors had also prescribed a bunch of gerd meds for like 4 weeks, and I also stopped those way early since they were not working and giving me diarrhea. And after the fact many other doctors were very confused by the baclofen prescription, arguing that I should’ve been prescribed something else.

But whining about dosages or whatever is cope. In the original post, my friend David said:

In your case, that discomfort requirement is clearly met. But that doesn’t mean jumping straight to a test is best for society as a whole -- the symptoms may resolve on their own / with medication before you’d even get the results back, the test may/may not find anything, etc.

“Do the thing cheapest/least resource intensive thing that’s likely to work first, then move on to the alternative.”

David was right, the hiccups went away with third line treatment. I could’ve gotten more tests and waited weeks to figure out exactly the cause before taking any meds; but that would’ve been worse for me and more expensive than just taking the baclofen that the doctor prescribed.

Does that mean that getting more tests is a waste?

No, I think it is still worthwhile to test. I don’t think any of the justifications for more testing depend on whether or not symptoms generally resolve on their own.

My ideal world is one where we shift medical care to being proactive instead of reactive. Testing more is a necessary part of being in a proactive regime. Everyone in the medical system is aware of and values preventative care, whether that’s an annual teeth cleaning or a regular physical. Expanding the depth of that care is a benefit measured in terms of current and future lives. Under this framing, the fact that symptoms may resolve has little to do with our ability to catch diseases before they become more serious, or our ability to leverage more data to figure out root causes for more diseases.

When a patient does show symptoms, I think following first line and second line treatments are generally fine. But the bout of hiccups underscored for me just how hesitant doctors are to order tests even in cases where they have literally nothing else to go off because first and second line treatments have failed.

In a previous life, I trained diagnostic neural networks for rare diseases. One common failure mode of such models is that they will simply say that every patient is healthy, regardless of the actual data, because 99.9% of the time that is the correct answer. If a disease is rare, it’s easier for the model to just cheat than to try and learn some complicated pattern matching algorithm that lets it properly handle the diagnoses that actually matter.

I think doctors are stuck in the same sort of failure mode.

It’s an engrained cultural norm to do nothing. If you start from the premise that the symptoms will always just go away eventually, you end up doing nothing. The Hippocratic Oath biases everyone to doing nothing. I think this actively harms our ability to understand disease and treat patients. We are living through an era where patient satisfaction with the medical system is at all time lows, and where more and more people have chronic diseases that don’t just go away. A medical establishment that lacks curiosity as part of its standard operating procedure is going to fail in this environment every time.

I still think doctors should feel comfortable ordering more tests. Data gathering is good and necessary.

It’s still early, but I think AI software devtools are increasingly coalescing around what I call SPACE Development. That is:

• Search

• Plan

• Assert

• Code

• Evaluate

Folks may use different terminology, and may drop the assert step (to their detriment), but this basic core has come up over and over again. First you do research to learn more about the problem and pull in context. Then you write up a plan that explains how you aim to tackle the problem. Then you define a set of success criteria. Then you write your code. And finally you evaluate if your code met the success criteria.

SPACE Development has become best practice for building software with agents. I’m not the first to notice. Here’s Shaiyan Rais, who owns the claude-code-best-practices repo and who basically already coalesced the relevant research that I was planning to do:

All major workflows converge on the same architectural pattern: Research → Plan → Execute → Review

Shaiyan drops ‘assert’, but it’s there in all of the frameworks that use TDD, including Superpowers, Everything Claude Code, Matt Pocock Skills, etc.2

But notice that this is not really a strategy for writing software specifically. It’s a guide for how to think about certain kinds of problems. You can take the same basic SPACE pattern and apply it to everything from building a web game to creating a language compiler. And if you are flexible with the ‘code’ step — replace it with something more generic, like Execute3 — SPACE development becomes a general purpose hammer for a huge range of tasks.

The only way to land on something like SPACE Development is through metacognition — essentially, thinking about thinking. Every time you look at a process and think ‘how can I do this better?’ or ‘how can I formalize this?’ you’re doing some kind of metacognition. For example, throwing a basketball is just basic cognition. You’re just doing a thing. Thinking about how to flick your wrist to get backspin on the ball every time you shoot? That’s metacognition. You’re abstracting out from a particular instance of a thing — in this case, shooting hoops — to instead reason about the underlying process.

If agents are bad at thinking, they are totally incapable of thinking about thinking. So you need to do the metacognition for them. That means thinking about how to think through different kinds of problems, and then giving the agents the steps you land on. To poorly paraphrase, teach an agent to solve a single problem, and you’ll get a lot of slop to review; teach an agent to think, and you’re set for life and/or you’ve automated your job away (the analogy got away from me).

Mechanically, what this ends up looking like is a very particular style of AGENTS.md and SKILL.md files. Something like this:

<required>
1. Add all of the following steps to your TODO list.
2. Research how to best solve my question WITHOUT making code changes.
  - Search for relevant skills using Glob/Grep in `{{skills_dir}}/`
  - Use the WebFetch tool to search in parallel
3. Read and follow the writing-plans skill. Present plan to me and ask for feedback.
4. Read and follow the tdd skill.
5. Update docs.
6. Push a PR with your changes.
</required>

The goal of these configs is not to provide context. Context is data, it lives on disk to be queried when needed. Rather, these configs describe processes that tell the agent how to make use of the context it has available. In my mind, there is a world of difference between the Superpowers brainstorming skill and the Anthropic PDF skill, even though they are both implemented as skill markdown files. The former is a thought process and the latter is just information.

Are there other kinds of thinking processes useful for software? Debugging comes to mind:

• Add logs.

• Replicate bug.

• Read logs.

• Change code.

• Repeat.

Which is basically just a version of the scientific method (form hypothesis, test hypothesis, adjust hypothesis based on results).

Sometimes you get processes inside processes. For example, if you’re trying to build something really big, you might have SPACE Development as a subroutine.

• Read large specification.

• Identify small, actionable piece.

• SPACE development on that small piece.

• Update specification.

• Repeat.

I could probably rattle off a dozen of these sorts of thinking patterns. I suspect the specific formulation doesn’t matter all that much. The larger point is that if you’re using an agent out of the box, it won’t have the awareness to do this sort of thing at all!

If you’re not the kind of person to invest a ton in configuration, pick up any of the open source LLM configs that encode SPACE development. We mentioned Superpowers from Jesse Vincent a few times here — it is really the OG LLM config set up in the space and is a great starting point. I’ve written about my own personal setup quite a bit, and have it hosted over on the nori skillsets registry.

What I wouldn’t do is try to build out a complicated mechanism for enforcing any explicit line of thinking. I have seen too many engineers fall into the tarpit of building complicated agent harnesses with complicated handoff scripts and, like, just don’t do it. See:

Even within the base providers, I’m very against “plan mode” as a concept. I’ve tried it and really don’t like it. I think that sort of forcing-the-model-into-a-box defeats the purpose of using a fuzzy general purpose machine.

Instead, teach the model how to think. It’ll take you ten minutes and a few dozen lines of markdown, and ends up being the equivalent of getting access to GPT-N+1 months before everyone else.

1

I didn’t realize when I was typing this out, but I was subconsciously channeling this meme from like 10 years ago

I’ve been hiccuping for nearly 70 hours. We are well past the stage of ‘huh this is funny’ and well into the stage of ‘what the FUCK is going on?!’ It’s reached the point where, when I call medical professionals, they do what I can only imagine is a spit take. “Wait, you’ve been hiccuping for how long?!” I can’t live like this, I have events I need to go to! I can’t like, show up to a speaker series or a client demo or a candidate interview and just be hiccupping the whole time! The only upshot is that my abs are getting the best workout they have had since I was on the highschool jv waterpolo team (go big blue).

Hiccupping is one of those weird things where we kinda sorta don’t really know what causes them. Like, mechanically, we get it. It’s a spasm of your diaphragm. Why does the spasm happen? Well there are these nerves that exist that get irritated sometimes and mumblemumble and then you have hiccups. And then when you point out the doctor just said ‘mumblemumble’ they get annoyed at you.

Hiccupping is also one of those weird things where we don’t really have any obvious targeted medications for it. I mean, there are approximately a million different home remedies. Swallow some air, sip water slowly, sip water quickly, sip cold water, sip warm water, eat a spoonful of a peanut butter (thanks Raymond!), each a spoonful of crushed ice, eat a spoonful of granulated sugar, hold your breath for 5 seconds, hold your breath for 10 seconds, hold your breath for 20 seconds and then sip some air (temperature unspecified), pull your legs into your chest, do the bridge yoga pose, do jumping jacks, and, of course, drink water upside down.

Perhaps surprisingly, none of these are respected by the medical establishment. If you show up at the doctor’s office hiccupping for 70 hours, they will not have any obvious treatments for you, and they may not have any great pointers for a diagnosis either.

II.

I’m not a doctor, but my Mom always wanted me to be one, so let’s just imagine I was. If I was a doctor, and I had a patient with some weird symptoms that I didn’t really understand, my first reaction would be ‘huh, interesting’ and my second reaction would be ‘we need to collect more data.’ And I would then go and order every test under the sun to try and figure out what was going wrong. Claude says that a really comprehensive blood panel costs somewhere between $25-50 to do — that’s the cost of the reagents and the labor and the blood draw supplies and so on. Let’s throw on a really comprehensive stool test too, which would add another $120-150. So the total is somewhere between $150-200 to get a ton of data about the patient.1 This seems obviously useful.

But I am clearly not a doctor, because no doctor seems to agree with me. Every medical professional that I have ever seen — including the two doctors and two nurses I saw at urgent care, and the doctor and nurse I saw at Mt. Sinai — has basically refused to do this kind of data gathering. Instead, they will take a stab at some cause based entirely on the medical history and a few words with the patient, propose some medication that treats that cause, and then refuse to budge at all from that initial guess.

For my hiccups thing, that initial guess was gerd. All three doctors heard that I had eaten Indian food and went “yup, you have acid reflux.” Nevermind that it has been three days since I last had Indian food, and that in that time I had only eaten bread and rice, and that I had already taken several doses of acid suppressants by the time the third day rolled around, and the fact that I am ethnically Indian and eat Indian food all the time.

The third time a doc told me that a) I had to keep taking the omeprazole and pepsid (acid suppressants) that I was already on and b) they weren’t going to give me anything else, I nearly lost it.2 I had to basically force the doc to put in an order for a blood panel and an H. Pylori test.3 And I forgot to also push for a stool test, which I’m still banging my head over.

When I first mentioned that I should get tested for H. Pylori, the doctor saw that I had taken a single dose of a proton pump inhibitor (PPI) and went “nope, sorry, you can’t take that test until you’ve completed a 4 week PPI course and then waited another 2 weeks for the PPI effects to wear off.” This is because taking a PPI can increase the likelihood of false negatives. I had to patiently explain that even if the medication did increase the false negative likelihood, we would still get a lot of useful information if the test came up positive, and that starting treatment 6 weeks earlier would be incredibly helpful. The doc was clearly unhappy with this line of reasoning, and pushed back at least three times before acquiescing.

I did eventually get the test order in, but then when I went downstairs to labcorp the guy there saw that I was on a PPI, saw that I was trying to get an hpy test, and decided that I wasn’t supposed to take it. He first tried calling the doctor, and then made me go back upstairs to get a signature from the same doctor that I’d already done three rounds with. Which I did. And then while I was waiting, a different doctor from the same practice called the labcorp guy back, both of them agreed that I shouldn’t take the hpy test, and it wasn’t until I got on the phone with the second doctor and explained the whole false positive thing that these people finally agreed to give me this test. Which, by the way, just involves blowing into two balloons and drinking some lemonade.

I got to the doctor’s at 9AM and left at noon. A quarter of my day, gone. I’ll get the results back in a few days, hopefully.

III.

If you read the title, this is obviously not a story about hiccups. This is an article about the ridiculousness of dealing with the US medical system while suffering from some random ailment that no one understands.

I’ve written about my dealings with the US healthcare system before. That post is one of my favorite articles, and my only regret is that I did not write it when I had a bigger audience — I had 54 subscribers when I first posted it. You should go read it:

In that post, I said "healthcare in the US is an exercise in indignities.”

It’s been ~two years since I wrote that post, and nothing has changed.

It’s easy to look at the story above and see a microcosm of everything wrong with how US healthcare works. There’s a fragmented healthcare system where most of the medical professionals did not see relevant medical history information like the previous H. Pylori diagnosis or the fact that I have EOE. There’s the difficulty in getting an appointment — I only ended up being seen by an actual GI because the lady on the phone took pity on me for not being able to get through two words without hiccupping.4 There’s a ridiculously expensive markup on everything. I paid ~$200 to see my providers and get the meds they prescribed, and I’m on pretty good health insurance.5

But in my opinion, the worst thing is that it is impossible to get the average doctor to act like a scientist instead of a bureaucrat.

Bluntly, I have a very hard time rationalizing why a doctor would hesitate to order tests.

IV.

But lets try anyway. A few possibilities, roughly ordered by most to least generous:

• The base rate for most diseases is very low, so the actual statistical likelihood of false information from any given test is quite high. Since these tests can lead to unnecessary treatment with real side effects (or at least, anxiety) it’s better to not do ‘data gathering’ tests.

• Insurers won’t cover it.

• Most patients get ~15 minutes with the doctor, which isn’t enough time to really go through and get a detailed analysis that would lead to a good idea of what tests to prescribe, and it’s much more efficient (and gets more $$$) to simply shoot from the hip.

• Doctors don’t want to admit they don’t know something.

In the interest of fairness, I talked to a few friends of mine who worked in healthcare and sent them a draft of this post.

First, my old college friend Omid, who is now a doctor (a statement that I am both incredibly proud of and mildly mildly terrified by).

Off the bat he was pretty surprised by the baclofen diagnosis:

Which, yea, based on my initial experience, that was weird choice to tell me to take that every 8 hours! He recommended gabapentin instead and spent a fair bit of time bashing urgent cares.6 Starting to understand why Mom always wanted a doctor in the family.

Omid pointed out that most doctors in the NYC area in particular are extremely triage focused. EDs just get so much random inflow from people who may have annoying symptoms but are just clearly not in any real medical emergency, coupled with way too many people who are at severe risk of dying right now. If you are not in the latter category, you are essentially told to go home and figure it out. That’s not to say that every doctor’s office is treated like an emergency room. But most doctors do end up doing some ED shifts, especially at the big research hospitals (Sinai, Langone, MSK, Rockefeller…). So the culture leaks through.

He also said that Occam’s razor is a meaningful part of med school training. Basic labs and an xray are cheap and easy to do. But why even bother doing all that? Yea, fine, testing may provide more data, but empirically the first line treatment generally works!7

My friend David, who works in health insurance, basically agreed on the latter point. I’m going to just quote him directly:

[From the insurance side] the tradeoffs here are individual versus macro scale optimization of health outcomes + patient comfort, while optimizing and operating under constrained resources ($, limited numbers of medical machinery, limited numbers of providers, limited numbers of providers with the appropriate specialty).

The idea is that we only dedicate resources at scale to the things with high signal to noise ratio, and the highest signal is patient discomfort.

In your case, that discomfort requirement is clearly met. But that doesn’t mean jumping straight to a test is best for society as a whole -- the symptoms may resolve on their own / with medication before you’d even get the results back, the test may/may not find anything, etc.

“Do the thing cheapest/least resource intensive thing that’s likely to work first, then move on to the alternative.”

He even made this cool graph:

My friends are (generally) reasonable people, and they (generally) say reasonable things, and all of this is, shockingly, pretty reasonable. Like, yea, that makes sense, doctors aren’t twirling mustaches and even if I (a patient) want more love and attention, I (a member of society) recognize that in the grand scheme of things my hiccups are simply not life threatening even if they are supremely annoying.

It would be convenient if there was just a stupid reason for doctors to hate tests, because then I could at least put my incades-hic-ent rage at these hiccups somewhere. But no one sets out to be a villain, and everyone is responding to incentives.

V.

But I still think the incentives of this system are fucking stupid.

At a societal level the goal ought to be quality-adjusted-life-years (QALY), not fastest-and-cheapest-treatment-of-symptoms-that-make-the-patient-go-away.8 And though I am not an expert, I assume that the best way to improve QALY is by getting at the root cause of a thing and actually fixing it. Most of the time, the first-line treatment for a bunch of symptoms works because we have an accurate read of the underlying disease being treated.

I think you could maybe mount an argument that testing actually reduces QALY, because most tests are very likely to false positive (thanks, Bayesian statistics). David had a great line:

If you go looking for an abnormality in a human body, you will always find one.

But even here I am unconvinced.

First, doctors are supposed to be the ones interpreting the information that they get from tests.9 I think it is probably true that the average lay person won’t understand what a false negative is or why a PPI may result in one for an H. Pylori test. But why does that even matter? The lay person shouldn’t be responsible for interpreting anything. The doctor should just prescribe the test and then interpret the data the way they were trained to.

Second, this clearly doesn’t apply in a situation where the first guess is obviously wrong and waiting for six weeks won’t do anything. When all bets are off, why are we still mechanically clinging to the script?

And third, there obviously are doctors who are totally willing to get in the weeds with a patient and order a battery of tests and figure out what is actually going on. These are some of the best doctors in the world!

I think the real kicker, the thing that really ticks me off, is that tests are by all accounts cheap to produce and cheap to administer. Far cheaper than prescribing the wrong thing and wasting the patient’s and the doctor’s time with a follow up. Every trip to the ER almost always results in a suite of tests, as does every physical. Hell, they’ll take your blood pressure and temp and oxygen levels before you even see a doctor. They’re thrilled, they’re practically jumping for joy to take your blood pressure / temp / O2. I don’t think these tests are all that different from a blood panel or a stool test. And I just refuse to believe that we’ve coincidentally settled on the exact optimal amount of testing for every person, which just so happens to be ‘once a year-ish.’

The growing ‘preventative optimization’ and ‘longevity’ industries seem to agree with me — these are all about extremely comprehensive full body testing, catered to folks who are willing and able to pay. Because these medical practices prescribe tests more often, they are able to evaluate deltas off the patient’s baseline instead of what we do today where we try to fit everyone into the same standard box. I know that the jury is still out on the efficacy of these things, I know that the longitudinal studies have not yet come back. But also, I have way too many friends with some story like “I was lethargic and sick all the time and then I took one of these tests and discovered I have celiac, and now I just avoid bread and I feel better than ever” (and then they proceed to do a triple backflip or whatever). Whatever amount of money we are spending on making tests cheaper and more accessible is simply not enough.10

At the end of the day I am an empiricist and a scientist. Data is in my blood.11 I know there are a lot of big systemic problems with healthcare in the US. But this tiny thing just frustrates me to no end. Make it ok to order tests for patients.

(Also, if you have any remedies for long lasting hiccups, ideally ones that do not prevent me from operating heavy machinery, let me know)

The actual install was pretty straightforward.1 Took about 5 minutes. Explaining how to configure the thing took another 55. “Most of your configuration should be in SKILL files, which need to live in the skills directory. Also you need a CLAUDE.md file, which is basically like a SKILL file but it gets added to every prompt…what do I mean by that? No skills don’t actually get added to the prompt, the agent has to choose to read those, the skill descriptions get added to the system prompt. No they don’t exactly get added to the CLAUDE.md, but they kinda do, they are both part of the system prompt…ok yes subagents are a different thing than skills, but slash commands are the same thing as skills. But it’s all markdown. No, subagents also get access to your CLAUDE.md and your skills. But that also gets added to the CLAUDE.md. Also you have different sets of these possibly from every folder. Some of these will live in your git repo. No the agent won’t pick up the ones in the git repo automatically unless you copy the files in the right place. What’s a git repo? uh…”

This shit is way too hard and way too unintuitive.

The problem is that there isn’t any stability. The macro environment is constantly changing, with Anthropic et al shipping new foot guns basically daily. Here’s a great example. Claude Code uses CLAUDE.md. Codex CLI uses AGENTS.md. Gemini uses both AGENTS.md and GEMINI.md. Most people have, at this point, switched to AGENTS.md support for standardization. But Claude Code, the industry leader for this sort of thing and the one that every CEO seems to insist on using, forces it’s own standard.2

And it is just way too easy for a given user to totally screw their environment up. “Claude, add a skill to automatically load my AWS credentials” and now you have a security leak that in two months will take out all the data centers in Wisconsin, whoops.

I can’t imagine being a TL in this setting. Every TL I know eventually reaches for some kind of stable cloud dev box environment, because debugging someone’s python env by spending 4 hours over their shoulder is a great way to want to throw your computer into a lake and take up a career in goose farming.

AI has pushed everyone back to their local boxes. So now you have all the usual dependency problems, but also you have a new batch of AI weirdness. Raise your hand if you have had someone complain about Claude “getting way worse”, only to discover that they have 50000 tokens in their CLAUDE.md and another 20000 tokens used up by random mcp server tools.

I think the ecosystem makes this unnecessarily harder too. It is too easy to download random skills from external repos. There is no curation, and every agent is capable enough to make everyone dangerous. We saw this in action a few months ago with all the OpenClaw leaks. As I said before:

Meanwhile, internal teams are still sending around config files through slack, which is only marginally more efficient than walking over to your buddy’s place with a USB drive. The lack of team-wide organization has made it virtually impossible to effectively distribute something to a team larger than 8.

Taking a step back, many people have written about how AI has been consumer first. Quoting Karpathy:

Transformative technologies usually follow a top-down diffusion path: originating in government or military contexts, passing through corporations, and eventually reaching individuals - think electricity, cryptography, computers, flight, the internet, or GPS. This progression feels intuitive, new and powerful technologies are usually scarce, capital-intensive, and their use requires specialized technical expertise in the early stages.

So it strikes me as quite unique and remarkable that LLMs display a dramatic reversal of this pattern - they generate disproportionate benefit for regular people, while their impact is a lot more muted and lagging in corporations and governments

But that means the enterprise ecosystem is uniquely underdeveloped despite the massive demand. Everything that’s been released has really been targeted towards consumers, hobbyists, and tinkerers. I think it’s great that tinkerers can configure things to their taste, but that is zero help for someone like my mom who just wants some kind of admin controlled environment that has everything set up already. Her area of expertise is not dev tools! Having her set up a ton of dev tools and integrations and whatever else wastes her time and wastes the company time. If your sales people are doing the equivalent of managing the company VPN, something has gone horribly wrong.

I think there’s a big gap in the market for managed agent runtimes (also known as ‘background agents’ because they can run in the background on the cloud). Everything else — org level skills, for eg — is just a band-aid. I’ve said in the past that for coding agents, the filesystem is the config. That means that you have to control the entire filesystem to get a consistent (and secure!) experience. It is simply not enough to assume that everyone in the org will learn not just how to use AI, but also how to use a CLI and how to use Bash and how to use git and how to use the million other tools that are required to get a coding agent off the ground. And even that may not be enough. One of our customers, a CTO of a highly technical eng team, was saying earlier today that any time he makes a change to an agent system prompt:

I need to take ten calls just to make sure everyone is on the same page, and for the more junior engineers those will be video calls. Just a ton of work for what is potentially a single line change to adapt to a new agent like Opus 4.7 or whatever.

So far, the labs have totally dropped the ball on this. I kinda understand why — OpenAI and Anthropic make money off tokens, so they do not want to provide configurable environments that would allow someone to switch to their competitor’s models. But that just leaves the ecosystem wanting for a better solution. Large corps recognize the demand and have started building their own in-house solutions, including Ramp, Stripe, Spotify, Uber, Shopify, Block, and Jane Street (which, as usual, built theirs in ocaml). Each of these companies has teams of 10+ senior engineers developing this infrastructure while also leveraging years of pre-existing bespoke infrastructure and standardization. This is totally non-tenable for most series C-and-lower tech companies, not to mention all of the valuable companies that do not have this kind of software expertise in house.

More solutions are starting to pop up.

For folks who want to roll their own software, Vercel and LangChain recently open sourced background agent repos (along with a few others), though there are surprisingly few good tutorials out there for setting this sort of thing up internally. I have a conflict of interest here (see below), but in my experience the lack of easy walk-throughs is precisely because this shit is actually really hard to roll out. To get real adoption you need a real product with real polish, and that’s before you get into infra management and security and integration hell. All of that in turn requires a fair bit of maintenance to keep operating smoothly, especially if you’re mostly looking for AI enablement among non technical or semi technical teams. The product surface area is huge.

I think most teams should go for a fully managed service. Devin has been in the space for a long time. I think Twill is a relative newcomer that also does something like this? But there aren’t a ton of other services that actually follow the Ramp-Inspect / Stripe-Minions model. So when we ran into this problem ourselves, we weren’t happy with most of the other options out there and set out to build our own, which we now sell as an off the shelf option with a lot of customization flexibility (if you’re a ~Series A to ~Series D exec with a lot of AI FOMO, and you’re thinking about building your own background agents, reach out).

I have to say, having a tool like this is pretty incredible, and I’m really glad we built it. More than 30% of our PRs are shipped entirely through slack, and we have all sorts of really cool automations for things like bug triage to newsletter writing. But all of that takes a lot of maintenance, and we spend literally all day just thinking about how to make this one thing awesome, and it would be impossible to do that if we were building in, like, healthcare or manufacturing instead.

Of course, the difficulty of building and maintaining the product doesn’t absolve the need for something like it to exist. I think if you are an exec who is thinking about how to get your team to use AI effectively, and you care about AI enablement, you need to pave the way. And that means removing all the setup, removing the need to learn a ton of jargon, and removing the implicit requirement to stay plugged into Twitter. Once anyone can use AI, you start getting real creativity from people who can use these tools to superpower what they are good at. But that won’t happen if your team is still trying to wrap their head around when to use a SKILL and when to use an MCP, or even how to set up a PowerShell CLI. Just hand it off to someone else and get back to shipping for your customers.

Agentics is the study of how to use and reason about agents. If you are an expert in coding agents, or interested in learning more about agents, join our community slack. More articles here. Learn more about Nori at https://noriagentic.com/

The way open source licensing works is pretty straightforward. By default, any code you write is yours, you have a copyright on it. No one is allowed to use it. This applies to million line codebases, and it applies to the smallest of code snippets on Stack Overflow.1 In order for someone to use code without running afoul of the law, the author of the code has to give explicit permission detailing what can be used and how. These permissions are often laid out in a LICENSE document.

Behind our current, relatively straightforward understanding of open source licensing lies ~45 years of court battles and lawfare, because trillions of dollars are at stake. That’s not a typo. Take Linux as an example. Your phone runs Linux. The web server your phone talks to runs Linux. All of the machines that pass messages between your phone and that server run Linux. Your blender used to run Java, but now it runs Linux. Linux is the most valuable individual piece of software in the world. It’s worth $0, you can download a copy right now, for free. So the question of who owns Linux and how it can be used are really really important.

One of the most important license innovations was the invention of ‘copyleft’. The basic idea: anything that is built with a copyleft license has to also use a copyleft license. Even though Linux is open source, and even though you can download a copy of it, you cannot package it up in a closed source box and try to sell it. You are legally required to release the source code for anything that derives from Linux.

It is shocking how much of the modern tech stack depends on copyleft licenses. Most compilers are copyleft. The GNU Binutils are copyleft. Git is copyleft. So is Bash. MySQL, VLC, coreutils, glibc, ffmpeg. More recently, wordpress, mongodb, elasticsearch. These are foundational pillars of the global tech stack. Thousands of companies have spent tens of millions of dollars of employee time on these open source projects, because the companies depend on them somewhere in their stack. Many of those companies would have preferred their employee time going to closed source software that they could then redistribute at a profit. Copyleft licensing prevents that. And many projects — like MySQL, which uses a copyleft license for general use but offers a paid option for companies who want to get rid of the copyleft requirements — could only get funded through the existence of copyleft licensing.

As you might imagine, many people have spent a lot of time thinking about how to get around these licenses. Code is and always has been in a weird gray area when it comes to intellectual property. You can’t copyright math. You can’t copyright an idea. But you can copyright code. So what, exactly, are you protecting? The courts say that you are protecting a specific expression of an idea. If someone copied the Linux kernel, or if someone wrote their own kernel while looking at Linux, all of that new code is based on the previous implementation. So it’s all protected by the Linux license. But if someone just, like, read about the Linux kernel, and got a really good understanding of how it behaves, and then made their own version of the kernel, that would be a new implementation and would be totally fair game.

The technical jargon for this is a “clean room implementation.” Team A spends time pouring over the code and writing an extremely detailed specification without explicitly writing code. And then Team B, which has never looked at the original source, writes new code to meet the specification. Team A and Team B don’t interact at all otherwise, to ensure the final output is ‘clean’.

Traditionally this kind of license circumvention is extremely costly. It requires a lot of time and at least two (teams of) people.

AI makes this trivial.

You have a session of Claude looking at the original code base and writing a spec. And then a different session of Claude looks at the spec and writes new code. The (untested) legal theory is that this is sufficient to remove the license, because the new code is “clean”.

People are already using this strategy to remove licenses.

Chardet, (HN Link)

In the world of open source, relicensing is notoriously difficult. It usually requires the unanimous consent of every person who has ever contributed a line of code, a feat nearly impossible for legacy projects. chardet, a Python character encoding detector used by requests and many others, has sat in that tension for years: as a port of Mozilla’s C++ code it was bound to the LGPL, making it a gray area for corporate users and a headache for its most famous consumer.

Recently the maintainers used Claude Code to rewrite the whole codebase and release v7.0.0 , relicensing from LGPL to MIT in the process. The original author, a2mark , saw this as a potential GPL violation…

Claude Code Leak

Context: The viral GitHub fork of the leaked Claude Code was at immediate risk of a DMCA takedown (Anthropic had killed prior mirrors in minutes), so its maintainer — worried about getting sued — used OpenAI’s Codex to rewrite the entire ~512k-line TypeScript codebase from scratch into Python overnight as a “clean-room” reimplementation.

This preserved the full agent harness, tools, and behavior without copying a single original line, instantly turning a copyright landmine into the safe, exploding open-source version everyone is now starring.

malus.sh, (HN Link)

Finally, liberation from open source license obligations.

Our proprietary AI robots independently recreate any open source project from scratch. The result? Legally distinct code with corporate-friendly licensing. No attribution. No copyleft. No problems.

…

Our proprietary AI systems have never seen the original source code. They independently analyze documentation, API specifications, and public interfaces to recreate functionally equivalent software from scratch.

The result is legally distinct code that you own outright. No derivative works. No license inheritance. No obligations.

I’m not sure if the last one is satirical — it is literally named ‘evil corp’ — but according to Reddit:

Clearly meant to be satire, with the name of the company basically being “EvilCorp” and the fake user quotes from names like “Chad Stockholder”, but it does actually accept payment and seemingly does what it describes, so it’s certainly a bit beyond just a joke at this point. A livestreamer recently tried it with some simple Javascript libraries and it worked as described.

Vercel reimplements Bash

A virtual bash environment with an in-memory filesystem, written in TypeScript and designed for AI agents.

Broad support for standard unix commands and bash syntax with optional curl, Python, JS/TS, and sqlite support.

after which Cloudflare rebuilt Next.js

Last week, one engineer and an AI model rebuilt the most popular front-end framework from scratch. The result, vinext (pronounced “vee-next”), is a drop-in replacement for Next.js, built on Vite, that deploys to Cloudflare Workers with a single command. In early benchmarks, it builds production apps up to 4x faster and produces client bundles up to 57% smaller. And we already have customers running it in production.

which Vercel then got mad about!

Open core here means having an OSS project with licenses like AGPL or BSL that allow anyone to derive from the software but only the original author to provide it as a multi-tenant platform.

This is very different from Cloudflare slop forking next.js. They made a choice to slop fork, but they could have just pressed the trad-fork button in Github since next.js is MIT licensed.

The licenses "protecting" open core software assume that making soften is hard, but they don't protect from a slop fork which reproduces the behavior without directly deriving from the license-encumbered implementation.

What I'm not sure is whether this means less open source or more liberal licenses as folks realize that they might as well put it out there now that everybody can copy it anyway.

I cannot stress enough how much this is a fully untested legal theory. In the background of all of the above, there has been an ongoing legal fight over whether AI generated content can be copyrighted at all. And so far, the answer is no!

From CNBC:

The U.S. Supreme Court declined on Monday ⁠to take up the issue of whether art generated by artificial intelligence can be copyrighted under U.S. law, turning away ​a case involving a computer ​scientist from Missouri who was ​denied a copyright for a piece of visual art made by his AI system.

Plaintiff Stephen Thaler had appealed to the justices after lower courts upheld a U.S. Copyright Office decision that the AI-crafted visual ⁠art ‌at issue in the case was ineligible for copyright protection ⁠because it did not have a human creator.

Thaler, of St. Charles, Missouri, applied for a federal copyright registration in 2018 covering “A Recent Entrance to Paradise,” visual art he said his AI technology “DABUS” created. The image shows train tracks entering ‌a portal, surrounded by what appears to be green and purple plant imagery.

The Copyright Office rejected his application in 2022, finding that creative works must have human authors ​to be eligible to receive a copyright. U.S. President Donald Trump’s administration had urged the Supreme Court not to hear Thaler’s appeal.

The Copyright Office has separately rejected bids by artists for copyrights on images generated by the AI system Midjourney. Those artists argued that ⁠they were entitled to copyrights for images they created with AI assistance - unlike Thaler, who said his system created “A ‌Recent Entrance to Paradise” independently.

A federal judge in Washington upheld the ‌office’s decision in Thaler’s case in 2023, writing that human authorship is a “bedrock requirement of copyright.”

If the most extreme version of that line of reasoning applies to code,2 then all of the code written by AI may very well be uncopyrightable, i.e. acts as if it was in public domain.3 But even in less extreme interpretations, the actual litigation will depend on whether or not the AI did substantive expressive work. And for a straightforward cleanroom overnight implementation, where the AI is doing literally all of the analysis while the user is sleeping, then it is totally possible that the output of the AI itself is a derivative work and carries the corresponding obligations of the input license.

Most open source maintainers are not about to go to court to litigate these license infringement cases — this is, in part, why all of this is still a grey area to begin with.4 But clarification of intent goes a long way. It is much easier to eventually defend copyright in a court of law if you are clear from the beginning about how that code ought to be use. And law is not code. Social weight matters. There is a huge difference between a lawyer going “this is a grey area but it’s probably fine” and “this is a grey area so I wouldn’t risk it.”

With all that in mind, we introduced the Ship of Theseus license to all of our open source codebases. This license aims to plug the AI clean room hole. It is a very simple license, with only two lines:

SHIP OF THESEUS LICENSE v0.1

* Using any AI tool to produce functionally equivalent software — by
  referencing this code, its documentation, its behavior, or any
  specification, description, or abstraction derived from the
  foregoing — creates a derivative work subject to the full terms of
  the primary license, regardless of whether the output shares any
  literal code with this project.

* Any derivative work must include this license alongside the
  primary license.

On its own, the Ship of Theseus license does not grant any claims or enforce any limitations. Rather, it makes clear that any AI derived work is exactly that: derived work.

We still haven’t fully nailed down whether AI tools themselves, which have almost certainly been trained on all open source material already, can even count as being ‘clean’ in any sense. But I’m not taking any chances. I’d rather have some explicit indication of my legal intent than to throw up my hands and assume any open source licensing is dead.

In order to get clarity, this sort of approach requires wider adoption, so if any of this resonates I strongly encourage other open source maintainers adopt this license or a similar one.

Note: after sharing the original Ship of Theseus license around, a friend linked me to Armin Ronacher’s blog from ~1mo prior where he independently came up with a similar analogy. The Ship of Theseus license wasn’t inspired by Ronacher, but the convergent evolution of the name hopefully means that it’s intent is intuitive to understand from the name alone.

Scott Alexander writes an excellent series of posts about Bay Area house parties. He’s written more than a half-dozen at this point. They all involve the straight-man audience/scott-insert (and the only sane man left alive, apparently) entering some strange lovecraftian event that, if you squint, could be called a house party. The weirdness of the event is, of course, the satire. Everyone else seems totally oblivious that they are the joke. These parties are completely and totally ridiculous, and any reasonable reader would assume that Scott is simply embellishing or straight up making up details and that these parties are not really real.

Which is why I am concerned that people do not believe these parties are really real.

Two weekends ago I was doing my quarterly trip to the Bay. I was invited to a spring gay peptide party.

O, sorry, you don’t know what peptides are? Wow, really behind on the times. AI is pretty lame in the Bay these days, because everyone is just swimming in it. People ask each other ‘what do you do? (for work)’ and if you say ‘O I work in AI’ they’ll look at you like you just said you’re best friends with Curtis Yarvin — who is now, by the way, deeply uncool (more on this later). Of course you work in AI, everyone works in AI. Saying you work in AI is like saying you work in tech, it’s already priced in. Which of course makes the phrase ‘I work in AI’ the least useful / interesting thing ever. I had at least three people give me a pitying glance before someone kindly informed me that since AI was obviously going to take over everything, it just wasn’t interesting to talk about.

Peptides. Now peptides are cool. And not just any peptides, but “cheap Chinese peptides.” I heard the phrase “cheap Chinese peptides” at least a half dozen times from as many people during my trip to the Bay, and I was only there for 2 weeks.

I have a bit of a molecular bio background so everyone being really into ‘peptides’ was a bit of a ‘wtf is going on’ moment for me. For those who don’t know, a peptide is a completely unspecific term. Saying that you are really into ‘peptides’ is about as specific as saying you’re really into ‘proteins’ or ‘molecules’. “Ah yea I’m really into those Chinese molecules these days” just doesn’t hit the same does it? A Chinese friend of mine quipped that he was also very into Chinese peptides, all things considered. From what I could gather, peptides-as-used-in-the-SF-party-scene are injectables like semiglutide (i.e. Ozempic). Most of them are for weight loss, but some folks swore that they had peptides for everything from skin rejuvenation to better sleep health.

So what is a spring gay peptide party? Well, the party was peptide themed in that

1. Everyone seemed to be on them

2. They had extremely strong jello shots being served out of big syringes

3. At least one person but possibly multiple people were injecting each other with peptides at the party

And also most of the guests were gay (obviously) and it was also spring.

At one point I was in a conversation with no less than 4 other founders who were all building peptide companies. I could fill a notebook of quotes from this conversation. “They change your personality, it’s literally made me less shallow knowing that we can just looksmax you.” “Ugliness is just a choice now.” “I shot up a twink with ozempic who did not need to lose any weight.”

My favorite exchange by far was between a guy in a sailor cap and another guy in a long black leather trench coat (it was pretty warm out).

• Goth: “What peptides are you guys on?”

• (crowd mumbles some answers)

• Goth: “Wait are you using that one, reta?”

• Sailor: “Everyone is doing street reta.”

• Goth: “Wait so you actually do it?”

• Sailor: “I’m on tirz too.”

• Goth: “You do both?”

• Sailor: …

• Sailor: “I’m on a lot.”

The pause gets me every time.

Fun fact, Scott actually first mentioned peptides back in January, also in the context of a bay area house party!

Sam types in spaghetti bolognese, delicious, scrumptious, meaty, trending on DoorDash, --dangerously-skip-parmesan and hands it back to Tran, who clicks ORDER.

“Nothing for you, Tran?”

“Nah,” says Tran. “I’m on Chinese peptides. Retatrutide, GLP-1 receptor agonist plus a bunch of other downstream effects.”

“Oh,” you say, “interesting. I’m still on tirzepatide, but I’d love to learn more. Where did you learn about suppliers and doses and stuff? Was it the locked Cremieux post?”

“Cremieux’s post is okay, but there’s a lot of tacit knowledge that didn’t make it in there. I’m actually working on a guide to all the GLP-1s. I’m calling it If Anyone Builds It, Everyone Diets.”

So he is way ahead of the game.

I suppose this is one of the downsides (upsides?) of living in NYC instead of the Bay. Although maybe that didn’t matter as much as I thought. Almost everyone I met was from NYC. “O yea, I’m just in town visiting” starts to feel a bit surreal after the 4th time. Even the host was visiting! It wasn’t even his house!

In college, my freshman year floor had a little motto: “always double down.” It’s a bit like the improv ‘yes and’. No matter what was happening, no matter how ridiculous the conversation was, you always double down. So normal chit chat would rapidly spiral into insanity that everyone would play with a straight face, which would often form the basis of recurring bits.

This was, of course, all a bit edgy and a bit cringe, but it was also a lot of fun if you were able to maintain a bit of ironic distance from the whole thing. It worked because everyone knew you weren’t serious.

I feel like SF is what you get when you take that motto and apply it really really seriously. You can’t just be taking ozempic, you have to be on reta, no, street reta, no cheap Chinese street reta, and also on tirz too! Or take the AI thing. Even though I’m generally concerned about AI, I’m not about to start a death cult that murders AI researchers.

What’s hard is that there clearly are some people in SF who are in on the joke, and who are purposely exaggerating precisely to get a rise out of the people who are taking everything seriously. (Maybe this is all the visiting New Yorkers?)

Someone once said that SF is a town of extremely high sincerity, and all of its modern and historical weirdness — the AI doomerism, the cults, the hippies, the drug use, the polycules — is downstream of people saying things and other people taking them extremely seriously. So you have people who are on way too many peptides. But that same high sincerity is also what makes SF so great. In my last post, I wrote

San Francisco is a city of dreamers. It's core enterprise is creating magic. And as a result, the town values the individual. It exalts archetypes — the founder, the builder, the prodigy. The question that people ask is "who are YOU? What are you interested in? How are you going to change the world?" Social life in SF is grounded in these questions…

You can never get the crazy valuations and capital necessary to build OpenAI in New York. Partially, that's because the appetite isn't there, it's too speculative. And partially, it's because the default mood is one of pessimism. "You think you can change the world? Who do you think you are?" People aren't on a mission in NYC, the way they can be in SF. They want to make money, sure. But maybe not change the world.

In some sense, a startup can only happen in an extremely high sincerity environment. If a 14 year old says that they are going to change the world, they are being very sincere even if an ‘adult’ knows that the likelihood is low. It takes an equally sincere kind of person to double down on that energy. But that’s SF in a nutshell. So, yes, you get crazy peptide gatekeeping, but you also get amazing things like self driving cars and the LGBT movement and YIMBYism and so on.

By contrast, I think NYC thrives on irony. Which sometimes leads to some funny / awkward moments, where I’d make a joke and someone else would take me at face value. For example, I recently posted an April fools joke on LinkedIn about firing my team and switching from building off the shelf background agent runtimes to “AI powered peptides”, and at least three people messaged me congratulating me on the pivot.

One last thought on house parties: citrus.

When Mia and I were in SF we got invited to a fruit themed party. Specifically, citrus. Everyone had to wear citrus clothes, you’d be turned away by a bouncer if you weren’t. There were oranges everywhere. Apparently the big theme was that you had to find a kumquat that was hidden somewhere in the house? Very unclear what you would win if you got the kumquat. I think just respect.

We seriously considered going — we coincidentally knew a lot of folks on the guest list — but decided to blow it off in favor of walking around the city. We walked a lot. On Sunday we walked from the ferry building to Baker’s Beach, all along the coast; on Monday we walked from Chinatown to the end of Golden Gate Park (which, by the way, is confusingly not where the golden gate bridge is).1

But maybe we should’ve gone to the party. Little did we know, this citrus party would go soft viral. Over the course of the next week, I met two dozen independent and unconnected people who had all heard about this citrus party. Some thought it hilarious, some thought it stupid, but all of them were talking about it. I’m sure the host was quite happy with himself (I know you’re reading this, proud of you king!)

Am I cool for having been invited to the citrus party? Am I cooler for not having gone?

AI billboards remain inscrutable.2

I saw a few from graphite in particular that I really didn’t get, all playing with this same “art” theme. I think the idea is that if you just put the graphite logo next to a bunch of arty-sounding platitudes, people will associate graphite with feeling artistic? But it feels like a tenuous connection at best.

A year ago, when I last wrote about the Bay, I was surprised and dismayed to find that edgy right wing black pilled nonsense was considered ‘cool’. I met several people with really just rancid politics, people who were unabashedly pushing insane far right ethnonationalist conspiracies. The general sentiment was that Kamala and the libs were stodgy and old and uncool, and Joe Rogan and the other podcasters (SF loves podcasters) were cool and increasingly not libs. This is why Curtis Yarvin, jester of the new right, had his own group house, his own house parties, complete with the acolytes and the entourage and so on.

I’m happy to report that most of that is gone.

Sometime in the last 6 months, everyone collectively decided that being super right wing is actually really cringe. A lot of people tempered their previously vocal opinions. Others, who stuck to their nihilism, were just increasingly not invited to parties. No one really talks to or about Yarvin anymore.

It’s hard to say what, exactly, led to the change. One friend said that Anthropic was ascendant and was single handedly making the left cool again. Another hypothesized that the Iran war was hurting San Francisco’s wealthier residents (you need helium to make chips, after all), and because those people host all the events, the increasing disdain trickled down into the water supply. A third said that it was all just becoming too hard to defend, that the incoherent whiplash made any kind of principled position impossible.

But regardless of the reason, everyone agreed: “wow, it’s kinda really embarrassing that we spent so much of last year partying with real life eugenicists.” No, really?! You think so?!

Something that surprised me: the new Tesla self driving model is actually very good. I had mostly been of the opinion that Tesla would always struggle to really provide a good self driving experience, because they didn’t have lidars or other sensors (something I wrote about here). Every time I had been in a self driving Tesla, I’d been very much afraid for my life — the thing was way too aggressive, took turns stupidly fast, and regularly had to be stopped before crashing into something.

But (according to friends with Teslas) about 6 months ago the model updated and now everything is gravy. My buddy drove his Tesla from SF to Santa Barbara and said he never touched the wheel.

It’s true that the Tesla’s still aren’t as good or as safe as the Waymos. But they are way better than human drivers and, more importantly, can drive anywhere. If I had to choose, I’d take a free roaming mostly autonomous vehicle over a location fenced fully autonomous one every time.

Very curious what led to the improvements. More data? Better sim? More compute? If you’re at Tesla and you have some insight here, drop me a line!

On our first day in SF, Mia and I did a brunch double date with an old friend from highschool. Normally this wouldn’t be relevant, but it’s worth mentioning that said friend is gay. This is worth mentioning because, while describing SF to Mia, he went on a long and surreal tangent about the “warehouse full of twinks down in Soma”, and I wanted to type the phrase “warehouse full of twinks” while making it as clear as possible that these were not my words.

On the one hand, this is an incredibly information dense phrase. What is it? A warehouse. What’s inside? Twinks. How many? A warehouse full.

On the other hand, what do you mean “warehouse full of twinks”???

“O, you know.”

No, Steve, I do not know!

Trying to get a handle of the situation, I asked some follow ups in the hopes that I would get something more legible.

Me: “What, exactly, do they do at this warehouse?”

Steve: “They chant to Claude about their desires.”

Me: “They…chant to Claude?”

Steve: “Sometimes ChatGPT”

Me: “Why?”

Steve: “They want to escape the permanent underclass by building b2b SaaS.”

Me: …

Steve: “they also fry their brains on Twitter. The twinks there aren’t getting enough sleep, it’s like rows and rows of tired twinks doom scrolling Twitter 40 hours a day while they chant to their machine god”

Me: “...are they happy? Like do they know that this isn’t the good life?”

Steve: “no one is making them stay in the warehouse. Also they have good parties”

My friend is a bit of an eccentric character, so I mostly assumed he was exaggerating. But maybe twenty minutes later, a girl pops by our outdoor table, and Steve just lights up. “Amol, Mia let me introduce you, this is my friend who runs the warehouse full of twinks I mentioned earlier.” Turning to our guest “We were literally just talking about you, how are the twinks?”

“O they’re good, working hard on escaping the underclass.”

???????

After a bit more conversation, I eventually piece together that this project started as a group house run by 19 year olds, one of the people in the group house had some kind of startup exit and decided to use the money to buy a warehouse, and it eventually became / doubled as a co-working space. “We have a digital sculpture that’s a pile of retro TVs in the center of our co-working space that we sacrifice goats to” I’m sure you do.

There are many forms of AI psychosis. Most people are familiar with the strain that breaks up relationships, where an overly sycophantic AI basically acts as the world’s worst relationship therapist by mimicking an AITAH comment thread (“ooo your husband of 7 years didn’t take out the trash on Tuesday? yea that’s a red flag, break up with him now.”) But I’m here to tell you that there is a unique strain that is prevalent in SF, that seems to only infect engineers.

It starts benignly enough. An engineer will set up Claude Code and try it out, making a little hobby project like a game or a web app. It will feel easy. In fact it will feel too easy. Here, the engineer makes a critical mistake: they begin to believe that everything that was previously hard must now be easy. They will build bigger and bigger applications, fed by the validation of creating and merging prs. They start building more things themselves. Who needs npm packages, who needs infrastructure, build your own containers and kubernetes and AWS. Things will spiral in complexity, bugs keep popping up but the AI always sounds optimistic, always on the verge of a break through, “Now I see the issue.” Soon the engineer is a husk, just clicking approve on everything while running ten agents at the same time. Yes, it’s true, code is being written, features may even be shipped. But the engineer has forgotten the number one rule: the best code is no code.

When someone comes to you and says that they’re going to make their own programming language because Claude said it would be faster than rust and easier to use than Python, they’re too far gone.

Mia says that SF feels like highschool.

SF has a population of about 850k. This is way smaller than I thought, especially given SFs cultural weight. NYC by comparison is a city of 8 million.

Of that 850k, 20% or so are between 18-35, and 20% or so work in tech. If you assume there’s no correlation between these things, you get ~34k people. That’s bigger than most highschools, but still roughly the size of a mediumish state school. I think you could cut it down a bit further — works or has worked in startups, is likely to go to a house party, has been in the Bay for more than a year. The point is that you pretty rapidly get to a group of people where it feels a bit like everyone knows everyone else, or is at least one hop connected to everyone else. Like, in my own network, I’m pretty sure I’m one hop connected to basically every famous tech billionaire, and that’s just because I happen to know a bunch of people who live in SF and go to parties.

This can sometimes make SF feel a bit like that scene in Mean Girls, where we get a rundown of the social graph through where people sit for lunch. There’s the crypto bros and the ai doomers, the big tech lifers and the creative coders, the health maxxers and the kinda scary powerhouse PMs. The group house culture and the social dynamics of the VC accelerators exacerbate the effect.

For a town that is ostensibly focused on tech, the only thing everyone talks about is the people. She’s dating who? They had a founder breakup? Wait he’s working where now? It’s a mix of “highschool social graph” and “celebrity gossip.” Extremely potent combination.

Anecdotally, I think this leads to some pretty aggressive stereotyping, as people try and fit you into one of the tables. I notice a lot of folks lean into the stereotypes, especially founders. It makes them legible. If you’re selling peptides, it helps your business to make your whole personality ‘peptides’. That’s how you get referrals! “O you’re looking for peptides? Let me hit up my peptide guy, he knows everything about peptides.” A good friend of mine moved to LA to work in show business. When he went down there, he changed his name to “Sven” because it was more memorable. In a town where ‘who you know’ determines whether or not you have a job next month, social currency has an exchange rate to USD. In this, SF isn’t all that different.

My SF based friends object to the highschool metaphor. It’s not tables at lunch, it’s techno-feudal houses.

There’s House Altman and House Amodei. There’s House Musk and House Zuckerberg. There’s House Brin and House Jensen (for some reason this one isn’t a last name). And underneath the Great Houses are their bannermen, like Ser Dwarkesh, a member of House Amodei; or Ser Roon, a longtime member of House Altman. Sometimes the bannermen can switch allegiances, as when Ser Alexandr Wang of the minor House Scale betrayed House Altman and joined House Zuckerberg.

On the one hand, I think this is mostly tongue in cheek. On the other, this is a very high sincerity group. And I think back to the warehouse, and the talks of “permanent underclass”, and wonder if maybe those peptides are responsible for more than just a few dropped pounds.

If I had a nickel for every time someone told me about “their friend Jim who was at HHS and recently moved to DHS,” I’d have two nickels. Which isn’t a lot, but it’s weird that it happened twice.

A few folks asked me what it was like running an AI company in NYC. I think they mostly expected me to say that it was a lot harder, that I was sacrificing access to clients and talent and marketing opportunities by stubbornly staying out in the East Coast. I can see why they’d think so — building with AI is so common in SF that it has become old news. There are few places in the world where people have thought as much about AI best practices.

But that exact trend is why I prefer building in NYC. Every single person that I met in SF was dangerously opinionated about AI best practices. It is impossible not to be! When everyone is constantly jumping from idea to idea, trying to stay on top of the Twitter firehose, you need some kind of opinion just to stay relevant and sane. So I met dozens of people building complicated multiagent harness systems with all sorts of handoffs and state tracking and pipelining, vibe coded scaffolding for vibe coding more scaffolding. Did it actually result in better products? Better code? Could you do more with less? These questions never came up.

I like building in NYC because I can talk about results instead of trends. Nothing, and I mean nothing, is set in stone anymore. Every part of every organization is reinventing best practices from the ground up. In that environment, the worst place to be is in a bubble. And SF is very much a bubble, a really really noisy one. It’s super hard to burst through with good foundations level thinking, even if you have good results, because it’s not ‘hype’ enough. In NY I can sell on what actually works instead of on blindly following whatever Karpathy posts about this week.

I think eventually there may be some need to focus more on the Bay, but for now I don’t feel any cost at all from building in NY.

Even though I got to catch up with friends — including some of you folks who read this blog! — and do my bi-annual data gathering trip, it’s nice to be back home. Sleeping on 7 couches in 13 days doesn’t quite work as well once you hit 30. At some point I suppose I’ll just start getting hotel rooms.

Which may be sooner rather than later. My rate of travel to the West Coast is picking up.

I want the record to show that I think this is dumb:

Shares of Allbirds (BIRD.O) surged more than five-fold on Wednesday after the footwear maker said it was raising capital and pivoting towards AI computing infrastructure.

The ​San Francisco, California-based company said that it would execute a $50 million convertible financing agreement ‌with an institutional investor and plans to use the proceeds to acquire graphics processing units (GPUs).

Allbirds also plans to rebrand itself as “NewBird AI” and, over time, shift focus to offering cloud computing ​capacity and AI services, though it did not provide additional details on ​its new strategy.

…

The stock was last up 435% at $13.33, valuing the company at $116 million, according to LSEG data. Allbirds was also among ​the most active ​orders on Fidelity's ⁠trading platform on Wednesday, signalling interest from retail traders.

As of writing, actually, its closer to 500%.

“SF based startup pivots to AI” is not, like, an uncommon headline in 2026. But normally those headlines are for

• companies that are small and have a lot of room to pivot because they do not have a ton of customers

• companies that are at least sorta kinda tangentially related to AI, or at least software

AllBirds is neither. It is a public company with over 500 employees that sells shoes. AllBirds spinning up a GPU arm is like SpaceX buying a social media company you know what nevermind.

If this whole thing reads like a Matt Levine bit, that’s because it is [EDIT: while I was writing this Matt beat me to it]. This is basically the same approach that MicroStrategy took with crypto / bitcoin back in 2020, that it has continued to double down on in the intervening years. According to wikipedia, MicroStrategy is a public company “that provides business intelligence (BI) and mobile software…to analyze internal and external data in order to make business decisions and to develop mobile apps.” According to anyone who has paid attention to the company since 2020 (including Wikipedia!), MicroStrategy is a pot of bitcoin that you can buy on the stockmarket.

In 2020, MicroStrategy went ‘this strategy isn’t working out, we need a new strategy’, and decided to go all in on crypto currencies, which, from 2020-2023, was a very hot thing.

Funny enough, the value of MicroStrategy shares is worth more than the equivalent value of the bitcoin its holding! From Matt:

MicroStrategy is primarily a pot of Bitcoins, though it also has a software business that is grudgingly acknowledged in its earnings release.[3] The pot of Bitcoins is worth about $18 billion, according to that earnings release, and is funded partly with debt; MicroStrategy reports about $4.2 billion of long-term debt.[4] This suggests a net asset value of, you know, $14 billion? Plus or minus 50%? Its market capitalization is about $50 billion.

I don’t really know what to make of this, except to say that this feels dumb and perhaps this is why I am not in finance.

This is also, by the way, the same thing that Kodak tried back in 2018. Do you guys remember KodakCoin?

Now, the 130-year-old company is trying an unlikely sort of comeback — one built by betting on cryptocurrency. It’s a bold gamble that has excited some investors, perplexed others and raised questions about how closely Kodak vetted its cryptocurrency business partners, who now include a paparazzi photo agency, a penny-stock promoter and a company offering what has been called a “magic money making machine.”

This month, Kodak lent its name to a digital currency called KodakCoin, which is billed as “a photo-centric cryptocurrency to empower photographers and agencies to take greater control in image rights management.” The basic idea behind KodakCoin is to use the blockchain to help photographers manage their collections by creating permanent, immutable records of ownership.

And it worked for them too! At least, until it didn’t.

Does the partial success of MicroStrategy / Kodak mean that this is a great idea for AllBirds? Again, I am not in finance. AllBirds is clearly struggling, and I don’t blame them for throwing a hail mary. But there is something silly about just being able to whisper the word ‘AI’ near a public stock and have the price go shooting up. The conventional wisdom in the startup world is that successful businesses are run by people who deeply understand the problem space they are operating in. You have to have a lot empathy for the user in order to build something useful. The AllBirds folks have spent years and years thinking about shoes. Do they really understand GPUs? Again from the Reuters article linked above:

“It looks like an attempt to capitalize on the AI movement. I don’t see how Allbirds brings anything to the table beyond name recognition,” said Bruce Winder, ​an independent retail consultant.

It’s not even clear the name recognition helps, because, again, people associate AllBirds with shoes and not AI.

The only kind of financial theory where any of this makes any kind of sense is if you see GPUs themselves as a store of value. You buy a pot of GPUs because you think that the future demand for them is going to be stable enough that it can act as a reserve for your organizaton/business/government.

Traditional stores of value include gold and actual currencies like USD. People have floated that bitcoin itself is a store of value — because it is fully decentralized and has a fixed supply, in theory it should retain value as long as bitcoin itself is deemed useful (and I do tend to think bitcoin will basically always be somewhat useful, I’ve written about some of that here). More recently, Sam Altman has been pushing the idea of compute as a store of value (cf ‘universal basic compute’) and others have spoken about compute credits as an asset class.

This of course raises the question: are GPUs a good store of value?

Hahahahahahahaha no. No of course not.

I was born in the mid 90s. A dollar back then is worth about half as much as it is today, a 50% decrease. Around when I was born, the top of the line best CPU was the Intel Pentium Pro. This thing would sell for $1168 back then, so lets say ~2k today. Do you know how much an Intel Pentium Pro goes for today? Well, they don’t make the things anymore, so you have to get them used, which costs like $80. This is, what, a ~99% depreciation? Despite the fact that global demand for compute has gone up a trillion-fold?

People like to talk about ‘compute’ and investing in ‘compute’ as if you can somehow get exposure to the abstract platonic concept of running electricity through silicon. Unfortunately, compute is tied to real world assets that depreciate very quickly. Just because everyone needs it does not make it a good store of value (see also: oil).

I can kinda sorta see how compute credits may be a reasonable store of value. You can get AWS credits and then be guaranteed some abstract floating amount of compute that will float on top of the depreciation of the underlying machinery. $2000 of compute in 1995 will buy you a single Intel Pentium, which is useless today. But $2000 of compute credits in 1995 will, in theory, get you $1000 of compute today, which is actually still pretty good!1

But this is basically just like saying gift cards are a good store of value, because that’s what a compute credit is. Like, yea, I guess gift cards do trade on a secondary market, but they are always discounted to their dollar value because they price in the likelihood of the company going under and the limitations on purchasing. You can buy a $100 Carnival Cruise giftcard for $93 — a 7% discount — because you basically never need to buy things from Carnival Cruise. Meanwhile, a $50 Amazon giftcard goes for $49.75 because it’s basically as good as money. At which point…you might as well just use money.

Anyway, I don’t expect the ‘buy a bunch of GPUs’ strategy to go well for AllBirds, but fingers crossed — they made comfy shoes, so I hope they succeed.

[EDIT: some friends mentioned that this is basically a spac, where the value of AllBirds is essentially that it is already a public company and so can skip the usual SEC filings and so on of taking something else public, and I also agree with that analysis]

Given how things are in 2026, I think it’s fair to say that that line of reasoning didn’t really go that well for them.1

The big issue is that it turns out there are lots of reasons why people will do things that are harmful to themselves. This is not exactly a brand new idea, at least in the common-sense understanding of things. Alcoholism is such a well known issue that supposedly an entire ethnic group developed a genetic aversion to the stuff, and history is littered with temperance movements and the like. But all previous attempts at regulating addiction were framed as ethical, pseudo-religious necessities. God created the world, but he also created alcohol, or something, anyway no you can’t have whiskey.

The regulations that sprang out of the mid-to-late 90s were grounded in science, which was a bit new, and was only really possible because science had caught up to our intuitions. We could point to causal mechanisms of addiction. Those mechanisms drove choices that these companies purposely made. Those choices made their products (cigarettes) more harmful to end users. And that harm was the basis for liability. The tobacco companies ended up having to pay massive fines ($200+ billion), while state and local legislation effectively banned smoking in most public places.

As a result of that work — both educational and legislative — we have a simplified but very well understood language for addiction. There are substances that can make someone behave as if they weren’t themselves, and these substances should be regulated.

It is massively helpful to have this baseline understanding of addiction in the general consciousness. Before, people would say silly things like “anyone can quit cigarettes, they’re just lying if they say they can’t” (while taking a drag of their fourth cigarette before breakfast). The language of addiction changed this. By making it clear that addictive substances changed a person’s brain chemistry, it became way more acceptable to shift the conversation from personal responsibility to corporate liability.

Of course, the language of addiction is very simple, and as a result there are a whole bunch of addictive things that don’t fit neatly into the language above. For example, gambling. I think basically everyone knows on some gut level that gambling can really screw someone up. And there’s lots of research to back this up. Just to pick one out of a hat, fMRI scans show that gambling really can change brain chemistry. But it’s not, like, a ‘substance’. There isn’t any obvious ‘thing’ that is changing brain chemistry. It’s the action of gambling itself that is a problem. And this really breaks some people’s brains, because how can an action, or a concept, or an idea change your brain chemistry as much as some good ol’ C2H5OH? As a result, gambling is way less regulated, and has crept into all sorts of weird places. Sports betting, prediction markets, loot boxes, gacha games. A lot of these target minors explicitly, because it’s not ‘addiction’.

In a past life, in my molecular bio days, I spent a fair bit of time doing addiction research.2 When it comes to addiction, there isn’t any special requirement for something to be a ‘substance’. You can get addicted to drugs. You can get addicted to fatty foods and to coffee. You can get addicted to visual stimuli (porn), or physical actions (a wide range of eating disorders), or even toxic relationships. Everything we do ends up being translated to protein pathways and electrical signals. Sometimes that warps our brains. And sometimes, people build things that warp our brains intentionally.

Last week, one of the most important cases about social media regulation wrapped up. From NPR:

A California jury on Wednesday found that Meta and Google were to blame for the depression and anxiety of a woman who compulsively used social media as a small child, awarding her $6 million in a rare verdict holding Silicon Valley accountable for its role in fueling a youth mental health crisis.

While the financial punishment is miniscule for companies each worth trillions of dollars, the decision is still consequential. It represents the first time a jury has found that social media apps should be treated as defective products for being engineered to exploit the developing brains of kids and teenagers.

Lawyers for KGM argued that Instagram and YouTube were deliberately designed to be addictive and the companies knew the platforms were harming young people, while the tech companies countered that their services cannot be blamed for complex mental health issues.

KGM’s legal team showed the jury internal documents from Meta in which CEO Mark Zuckerberg and other executives described the company’s efforts to attract and keep kids and teens on its platforms. One document said: “If we wanna win big with teens, we must bring them in as tweens.” Another internal memo showed that 11-year-olds were four times as likely to keep coming back to Instagram, compared with competing apps, despite the platform requiring users to be at least 13 years old.

Under questioning about those documents, Zuckerberg told the jury that keeping young users safe has always been a company priority. “If people feel like they’re not having a good experience, why would they keep using the product?” Zuckerberg said.

I think there are a lot of things you could say about who is to blame for the plaintiffs social media consumption habits. You could argue personal responsibility. You could say that the parents should have been more involved — the plaintiff started on the apps when she was 6 (!!!). You could even say that schools and teachers and peers all played a role. But all of these assume that social media itself is, inherently, a risky thing, something that needs to be managed and controlled. It would be insane to claim that, actually, the 6yo using social media every waking minute of the day is totally fine because they are actually having a good experience.

But of course, this is all new legal ground. The case wasn’t supposed to get this far to begin with.

Historically, the big social media companies would avoid liability in these kinds of cases because of Section 230, the law that exempts digital platforms from liability caused by user-generated content. The argument goes something like this: the harms created on these platforms are entirely driven by the content posted by other people; it’s really sad that people become depressed when they see content posted by other people, and the platforms are responsible for monitoring and removing content that is actually illegal to post; but the platforms cannot be held liable when someone becomes depressed because of legal content posted by other people.

This is an extremely compelling argument. There are lots of sad things that happen, and it would be ridiculous to penalize Meta or Google for, e.g., reporting on wars or famines or deaths or whatever. I’ve often said that Meta in particular catches a lot of flak for problems that are really just human problems. People are messy, and complicated, and when you put a billion of them on the same platform you get an exponential explosion of ways things can go wrong. So in general, I think Section 230 is an incredible law, a great example of smart and forward thinking legislators creating the scaffolding for decades of technological, economic, and social progress.3 Judges and juries tend to agree, and have consistently ruled in favor of the big Internet platforms.

Still. There’s clearly a blurry line between “unopinionated information pipe” and “highly editorialized opinion platform.” The gap lies in product design — the millions of choices made that makes Twitter different from Facebook, Facebook different from Tiktok, and Tiktok different from YouTube.

It’s not hard to construct a hypothetical example where the product design is itself clearly harmful. For example, imagine you had a social media company with the ticker MEAT (no relation to any other company).

And imagine that 0.001% of posts on MEAT are really terrible — ai slop racist incitement to violence, that sorta thing. You might rightfully raise an eyebrow if MEAT exclusively showed those 0.001% of posts to everyone browsing their ‘For You’ page. You might raise two eyebrows if the company simply washed their hands of the matter, arguing that because all the content is technically user generated, they have no liability — even though documents show that the code literally has a line that says

if (post == unsettling) visibility = high

It always seemed silly to me to argue that the algorithms that power personal feeds and addictive dark patterns fall under “user generated content”. Legally it’s obviously a grey area, as are many things in the world of software. But just intuitively, obviously the design of a product is the output of a company! What else are all the patents for?

This is, essentially, what the defense argued too. They avoided content. They avoided user behavior. They avoided moderation. Instead, they stayed laser focused on product decisions and the internal discussions and documents that drove those product decisions. And they were able to show liability. Again, from NPR:

The verdict validated the plaintiff’s lawyers’ approach of shifting the legal target; instead of focusing on the content people see on social media, the case put the spotlight on how social media services were designed. Meta’s apps, including Instagram, and Google’s YouTube, the jury concluded, were deliberately built to be addictive and the companies’ executives knew this and failed to protect their youngest users.

Jury trials are famously swingy, so it’s hard to say whether the legal reasoning will stick. But jury trials are great barometers for how the public feels about a topic, and right now, I think the public vibes are rancid. It is conventional wisdom that many companies do try to hack our brains. Everyone in the valley is aware of engagement metrics. Everyone knows that optimizing for engagement works, even when doing so is harmful. Personally I’ve often felt like my phone is channeling some kind of demonic entity. When I have kids they are going to stay as far from phones as possible for as long as possible.4 Most people I know feel the same way. Just about everyone I know is taking steps to try and mitigate their screentime, and failing miserably.

So in the wake of this ruling, many people are cheering that this is finally a way to discourage the addictive patterns that have become endemic to digital life. Including, as it turns out, the jury itself!

Another juror, who gave her name only as Victoria, acknowledged that the jury wanted to send a message to the companies. “We wanted them to feel it,” she said. “We wanted them to realize this was unacceptable.”

But I think it is too easy to blame the big social media giants. Big fines might feel good,5 but they are a hammer when what we need is a scalpel — literally and figuratively the wrong tool for the job.

Imo, all of these companies are reacting to incentives. If Meta puts down its guns and makes their algorithms less effective, they lose users and revenue to Tiktok. If Tiktok puts down its guns and makes their algorithms less effective, they lose users and revenue to Twitter. And so on. This is the kind of coordination problem that government regulation is designed to solve. I think the real indictment lies with lawmakers who have been extremely slow and unwilling to take meaningful steps on these issues.

I have long advocated for a ‘disabled by default’ policy — algorithmic personalization should be disabled unless a user manually enables it.6 I like this approach because it increases user choice, giving more control back to the consumer to set guardrails on their own usage without outright paternalistic bans. I think only the most callous lobbyist would be against this proposal.

But such a solution still requires some kind of law, because again, no one is going to do this unilaterally. I worry that if we keep trying to use the judiciary as a solution here, we will inevitably break Section 230 — either by completely gutting it or expanding it too far.

Right now, Meta and Google have stated that they intend to appeal the ruling. I think this is the obvious thing to do. But I also think this is a missed opportunity stemming from a lack of creativity. In an alternate world, Meta and Google could pledge to do better and then work with lawmakers directly to craft laws that deescalate the attention war. That is a win for everyone — the companies don’t have to spend countless billions on a zero sum attention game, and consumers get their lives back. It also is likely a better outcome for the big tech platforms. Like I said above, the public is angry and frustrated. If the tech companies continue fighting tooth and nail, the end result is not that they will avoid regulation. Rather, like the tobacco companies before them, they will simply not be given a seat at the table.

Other things:

• Delve was removed from the YC website. Delve was the main story of last week’s Tech Things, where we discussed the scandal and the fall out. I mentioned previously that the company was defacto dead regardless of whether it did or didn’t commit the fraud it is accused of committing. YC’s distancing all but confirms that.

• OpenAI acquired TBPN. TBPN is a hilarious concept, a sports podcast for the tech world. It works because they take themselves super seriously, but also because it was obviously a joke. This was an especially interesting acquisition because TBPN is basically only big on Twitter. But that’s also where the entire tech world (unfortunately) seems to be. Generally these kinds of acquisitions don’t have great results for the acquired brand — it’s hard to keep up the same kind of edgy irreverant humor when big money gets involved. Curious to see what happens.

• The Claude Code leak has been all over the news and I don’t have much interesting to say, except that I am very concerned about what this all means for open source. People took the CC leak and had an AI automatically clean-room the code. The argument is that this effectively removes the license. Sure, this feels fine when we’re looking at code from a multi-billion dollar company. But the same logic applies to any other open source library out there. If you can effectively delicense anything with AI, open source licenses as a regime will basically cease to exist. May write a more full post on this soon.

• Everyone who founded xAI left. Probably does not bode well for grok? Setting aside my personal opinions of Elon, I really can’t tell whether he is still making good business decisions. I mean, sure, 10 years ago it was uncontroversial. Now? He has so much money that he’s about as insulated from the market as an engineer working deep in some middle management layer at a FAANG for 15 years. That said, even though I have been very bearish on Tesla self driving, their new models are actually good. It seems throwing enough compute after the problem is enough to overcome (some of) the limitations of not having lidar. So maybe this bet will also pay off? But then I’ve also been bearish on Twitter, and that clearly was correct? For better or worse, the man has created a little universe entirely to himself. A bet on any of those companies is really just a bet on the one guy.

• We’re going to the moon!

I like giving presentations. I hate making slide decks. I spend way too much time making slide decks. Most of that time isn't actually that useful in terms of conveying information. It's bs tasks like fiddling with positioning, aligning text, creating figures. Or it's researching and pulling quotes or summarizing things that I already know but don't have exactly ready for a deck. These things are necessary to have a good presentation -- I've seen many a research PhD slide deck to know otherwise -- but man all these little details bloat out to like 10 hours per deck.

We eventually figured out a hack to use coding agents to bring our average slide deck creation time to ~25 minutes of active work. The finished output is generally more aesthetic, more informative, and more accurate than anything we could do ourselves by hand. Certainly not in the same timeframe.

If you want to skip to just trying this yourself, download the admin skillset (or the creating-slides and record-transcribe skills). In a single command:

npx nori-skillsets install admin  # then just ask claude to make you a slide deck

A few key insights:

• A slide deck as a finished product is separable from the software used to create it. For decades, the only way to make a slide deck is to use a tool like PowerPoint, so it was easy to just mistakenly conflate the two.

• Slides have two modes — a ‘editing’ mode that depends on an internal structured representation to manipulate visual elements; and a ‘presenting’ mode that rasterizes the structure to essentially produce a set of images. There is no strict requirement that the ‘editing’ mode uses a particular data structure.

• Coding agents are terrible at parsing visual media but are fantastic with HTML, which essentially constructs arbitrary visual media using structure.

• Edits to slide decks can generally be done in batch.

With that in mind, here is how we constructed the deck for our board meeting last week.

Give your agent access to relevant context. We maintain an instance of Nori (our coding agent harness) that has access to emails, Slack MCP, design docs, code, and of course, previous board decks.

Tell the agent to construct an HTML page that mimics a slide deck. Emphasize that there should not be complex dependencies, it should really be a single page bare html/css. It helps to provide an example.

Review the deck with audio recording on. My team got in a room and turned on a CLI voice recorder (sox, in our case). We went through the deck and just chatted about what we liked, what we didn’t. Google Meets voice recording will also serve fine here.

Have the agent edit based on the transcript. We used whisper, Google Meets will just provide a transcript, etc. Fed the transcript back to Nori, which went and updated the HTML.

Rasterize. Ask the agent to turn the HTML page to a pdf. It will generally do this without having to download anything else, using chrome in headless mode and the --print-to-pdf command.

The majority of the time was spent reviewing the deck, which we would have done anyway. The agent created figures, positioned everything correctly, used our branding, and even pulled out specific quotes and customer names.

I obviously can’t share the board deck, but I did want to share an example. I created a deck for our open source agent TUI. I did not go through any editing stage for this. These images are basically just slides from the raw deck. It took about 5 minutes to create, and virtually 0 thinking time actually spent.

The full PDF of the deck is here.

Misc other thoughts:

• I’m basically convinced that HTML will be / ought to be the lingua franca for all things visual with coding agents. We’ve started using static HTML sites for everything because it is so easy to just ask an agent to, for e.g., throw up a dashboard of our ec2 instances with a static HTML site. Faster than opening chrome, logging into aws, clicking through the EC2 panel, and trying to parse it. And because the static dashboard that the agent makes is fully customizable, we can often get a better user experience too.

• I’m not sure I would have come up with this flow for generating slide decks myself. My (non-technical) strategic advisor came up with this flow. More generally, I think the folks who are non-technical-technical people are going to innovate a lot, empowered by the agents. This is different than the usual thing that happens, where the non-technical people are years behind the techies.

• Gamma is a startup aimed specifically at taking down powerpoint. They are doing $100m ARR and have a $2.1b valuation. Is any of that sustainable, given the above? How soon before the admins and corporate strategy folks realize that the agents can just do this automatically, and at way higher quality?

Agentics is the study of how to use and reason about agents. If you are an expert in coding agents, or interested in learning more about agents, join our community slack. More articles here. Learn more about Nori at https://noriagentic.com/

It seems like the tech world decided to take a break from AI for a month and instead have all the big stories be about fraud. Interesting pivot, if you ask me. Let’s delve into it.

Delve

Traditionally, companies do not like regulation. There are obvious capitalist reasons for this. Regulation generally means that you have to spend money on things like paperwork and auditors and compliance teams and so on, which in turn means either the customer is getting charged more or the capitalist is making less money. But there are also less obvious but equally salient human reasons for this. Regulation is boring. If you’re a founder and you want to go out and change the world, you probably don’t want to sit around doing paperwork proving that your cancer-curing ai-powered glp-1 peptide minimizes harm to the Alaskan red-billed beaver population of Sonoma, or whatever.

Sometimes someone will propose that we get rid of all regulation. And then a river will catch fire a dozen times or someone will start a company called Enron or [everything with sports gambling] and then we go ‘ok maybe we do need regulation after all.’

Still. Even when we recognize some regulation is good, there is an inherent tension here. Regulation is a bit adversarial, because companies don’t really want to do it and because it is inefficient and costly to enforce.

The beauty of capitalism is that any time someone does not want to do something, someone else can make a bunch of money doing that thing.

A founder might go, ‘hey, wow, there are a lot of other companies that hate dealing with regulation. What if I came in and made that easier?’ And then that founder would start a company and would rake in tons of cash.

And a different founder might go, ‘hey, wow, all these audits are really expensive and could be streamlined. What if I came in and made that easier?’ And then that founder would start a company and would rake in tons of cash.

And then a third founder might go, ‘hey wow, there are a lot of people that hate dealing with regulation generally, what if I just helped companies do the audit thing and also did the audits myself?’ And then that founder would start a company, and rake in tons of cash, and then when people realized what was happening, that founder would go to jail. Obviously, obviously, you cannot be both the auditor and the auditee. It’s a massive conflict of interest!

Anyway, Delve. From TechCrunch:

An anonymous Substack post published this week accuses compliance startup Delve of “falsely” convincing “hundreds of customers they were compliant” with privacy and security regulations, potentially exposing those customers to “criminal liability under HIPAA and hefty fines under GDPR.”

Delve is a Y Combinator-backed startup that last year announced raising a $32 million Series A at a $300 million valuation.

The Substack post is credited to “DeepDelver,” who described themselves as working at a (now former) Delve client. In response to emailed questions from TechCrunch, DeepDelver said that they and their collaborators “chose to remain anonymous out of fear for retaliation by Delve.”

[DeepDelver claimed] Delve “achieves its claim of being the fastest platform by producing fake evidence, generating auditor conclusions on behalf of certification mills that rubber stamp reports, and skipping major framework requirements while telling clients they have achieved 100% compliance.”

DeepDelver also claimed that virtually all of Delve’s clients seem to have gone through two audit firms, Accorp and Gradient, which they described as “part of the same operation,” one that operates primarily in India, with only a nominal presence in the United States.

Delve responded to the accusations by saying it does not issue compliance reports at all. Instead, it’s an “automation platform” that ingests information about compliance, then provides auditors with access to that information.

“Final reports and opinions are issued solely by independent, licensed auditors, not Delve,” the company said.

In response to the accusation that it’s providing customers with “fake evidence,” Delve countered that it’s simply offering “templates to help teams document their processes in accordance with compliance requirements, as do other compliance platforms.”

“Draft templates are not the same as ‘pre-filled evidence,’” the company said.

Well, it kind of depends on what you mean by template, doesn’t it! If Delve is sending out, like, empty Google Forms, sure whatever. It’s a bit suspicious, Delve probably shouldn’t be even doing that because of the optics, but fine. But if Delve is sending out, like, finished Docusigns…that’s kind of a different thing altogether. There’s an ocean between ‘Google Form’ and ‘Docusign’, even if they are both ‘templates’ in a strict sense.

The claim is that Delve is doing the latter.

Delve’s model inverts this structure. By generating auditor conclusions, test procedures, and final reports before any independent review occurs, Delve places itself in the role of both implementer and examiner. This is not a technicality. It is a structural fraud that invalidates the entire attestation.

I’m not a compliance guy and I have no idea where this all falls on the axis of ‘totally fine and normal’ to ‘kinda shady’ to ‘jail time’. The Substack post is pretty in depth, feel free to take a look. But to be honest, it almost does not matter. Even if Delve didn’t commit fraud, the company is basically done. A compliance company in the center of a massive fraud scandal, caused by a leaked database due to poor compliance standards? Using Delve for compliance is practically inviting a malpractice lawsuit of some kind.

A few other misc thoughts.

30u30: The funniest thing to come out of the Delve saga is the realization that both Delve founders are Forbes 30 under 30 recipients. When I was growing up, 30u30 was a really impressive badge of honor. But the last ~10 years have not been kind to Forbes. 30u30 is a social club that now includes infamous luminaries like Sam Bankman-Fried (FTX) and Elizabeth Holmes (Theranos); Claude found fourteen prior examples of 30u30 winners who had then either been indicted or gone to jail for fraud. There aren’t that many 30u30 founders! The base rate of fraud has to be way higher than if you were to just random sample the population. At this point, the meme is that the 30u30 nominations are pay-for-play,1 that the only people who get the award are those who aggressively game the system, and that having 30u30 on a resume is a massive red flag possibly even signaling poor character. At least one friend of mine was offered a 30u30 spot and declined the award for that reason. Now there are two more data points.

YC: Delve hit the YC brand too. Delve is a YC company, part of the Winter ‘24 batch. YC is the most famous accelerator in the Bay, possibly the world. Historically it built its brand on:

• Selecting for fantastic talent

• Getting teams in shape to get more funding

• Having a track record of great exits.

Notice the one-two punch. YC appealed to future-round investors because it found great talent, and to talented founders because it could almost-guarantee future round investment. Not a bad place to be! But that brand value has steadily decreased as the company continued to expand its batch size while simultaneously (necessarily?) reducing selectivity. YC’s first batch, in 2005, was only 8 companies. Around 2012, it was ~40-80 per batch. Today, it’s over 150 per batch, and there were over 600 companies that graduated YC in 2025. That means less time per founder, more jockeying for attention at demo day, and, of course, a dramatically increased chance of fraud. Anecdotally, the common wisdom seems to be “join YC for distribution to companies in your batch, otherwise its mixed value,” and there seem to be more accelerators popping up that are trying to compete for the spot that YC previously occupied. All this to say, the Delve thing came at a really bad time for YC. The last thing YC needs is to fend off their previous (rather public) support for Delve. I suspect their PR department is unhappy right now.

AI: As with all things Bay Area ‘26, we must mention AI. One interesting note about the Delve story is how much their marketing leans on AI as their unique edge. Like, this is their home page:

“Delve AI agents eliminate compliance busywork,” they claim. If you squint, the pitch sorta makes sense. Compliance sucks because there’s a lot of manual-but-kinda-rote tasks that you have to do. AI agents are good at doing manual-but-kinda-rote tasks. The syllogism writes itself. The hard part though is, like, actually getting the AI agents to eliminate busywork. It’s a great pitch if the product was real, still gotta make the product real though.

There are elements of the Delve story that remind me of crypto mania a few years ago. With crypto, no one really understood how it worked. Arguably, that was the whole point of crypto. But that meant that you could more or less claim that it did anything, and it was hypey enough that people would fund it. Crypto solves centralization! Crypto will lead to new forms of corporate governance! Crypto will become the backbone of art, no wait, real estate, no wait, medical records, no wait, identity of a person. You could really just say anything! People who knew how crypto worked and who didn’t say insane things would get outfunded by the shameless folks who said whatever investors wanted to hear, regardless of feasibility.

Well, AI is hype now, and AI is technically complicated in a way that few people really understand, so AI is the new crypto. Just create a webpage that claims you can solve perpetual motion, slap some ‘powered by AI’ language on there, and watch the dollars roll in. Of course, AI has a much more obvious value-add than crypto ever really did. But that actually makes it harder to discern which companies are real and which ones are full of it. In some sense, the Delve story is just an old-fashioned memecoin rugpull.

Also, something ironic about naming the company after a word that is commonly used to identify low quality AI-generated slop.

DeepDelver: Speaking of funny names, DeepDelver is great. Really evokes, like, the Watergate scandal. 10/10, no notes.

Polymarket

Here’s an old story, tell me if you’ve heard it already. Back when the Brits were running Delhi, they had a small issue with cobras running amok. You’d step outside and BAM, cobra. It was hard. So to fix this, the colonial government decided to start a cobra bounty program. Folks would get paid for every cobra they killed and brought in. Within the system of the colonial government, this is an ideal solution. You create an essentially-infinite reward structure that effectively deputizes the entire country to act as animal control. The best cobra catchers get paid the most, and the cobra problem clears out pretty quick.

The problem, of course, is that optimizers don’t give a damn about your system, and humans are fantastic optimizers. After all of the low-hanging fruit cobras were picked, folks started breeding cobras for the express purpose of killing them and turning them in for cash. The amount of effort to break the system was less than the amount of effort to do things the ‘right’ way. Eventually the colonial government got wise to what was happening and shut down the program. All the breeders basically just let the cobras go, and overnight the cobra problem got even worse than when they started.

The broader lesson here is that any system subject to optimization pressure has an efficiency ceiling set by the difficulty of breaking out of the system itself. The walls and borders of the system act as a leash on the optimization process, and if they are too weak you won’t actually get much benefit out of the whole thing. Or, more broadly, “when a measure becomes a target, it ceases to be a good measure.”

Here’s a new story, tell me if you’ve heard it already.

On Tuesday, March 10, a massive explosion shook the city of Beit Shemesh, just outside Jerusalem, in yet another Iranian ballistic missile attack during the ongoing war.

Rescue services scrambled to the scene in search of possible casualties, though as it turned out, the projectile had struck a forested area just outside the city, around 500 meters from homes.

On The Times of Israel’s liveblog that day, I reported that the missile had hit an open area and no injuries were caused, citing the rescue services, as well as footage that emerged showing the massive explosion caused by the missile’s warhead.

Later Tuesday, I received an unusual email, in Hebrew, from someone named Aviv.

“Regarding your Times of Israel report that described today’s launch as an ‘impact’ — Beit Shemesh Municipality and MDA (Magen David Adom) later corrected their reports to clarify that what fell was an interceptor fragment, not a full missile,” he claimed…

The event that these people had bet on was “Iran strikes Israel on…?” More than 14 million dollars had been wagered on March 10.

The rules of the bet state: “This market will resolve to ‘Yes’ if Iran initiates a drone, missile, or air strike on Israel’s soil on the listed date in Israel Time (GMT+2). Otherwise, this market will resolve to ‘No’.”

However, there is a clause: “Missiles or drones that are intercepted… will not be sufficient for a ‘Yes’ resolution, regardless of whether they land on Israeli territory or cause damage.” …

Shortly after midnight between Saturday and Sunday, I started to receive threatening messages in Hebrew on WhatsApp from someone called Haim.

“You have exactly half an hour to correct your attempt at influence,” he wrote.

“Despite the fact that you received countless inquiries — you insist on leaving it that way.”

“If you do not correct this by 01:00 Israel time today, March 15, you are bringing upon yourself damage you have never imagined you would suffer,” he threatened, in a very lengthy message.,,

Haim also referred, with specific details, to my ostensible home neighborhood, my parents, and family.

“And as far as I know, there are also some people who don’t really care about the law, and you’re going to make them lose about 50 times what you’ll ever make.”

“86 minutes left. You are the only one responsible for your life.”

In regular markets, the price of an asset carries a lot of information about the future. For example, if oil futures are trading at $60 per barrel, you can infer that smart people think that the war in Iran may end soon, and if it is trading at $200 per barrel, you can infer that smart people think that the war in Iran won’t end soon. But this isn’t, like, the primary purpose of markets. When you buy oil at a certain price, you’re actually literally buying a claim on actual literal oil that is sitting somewhere. The price signalling thing is just a useful secondary byproduct of how markets are structured.

The core idea behind prediction markets is that you can use the decentralized brain of a public market to extract valuable signals about things. There is no asset, no underlying security — the whole point is to incentivize smart people to work on predicting the future (and only predicting the future) by getting paid for being right. Within the system, this works great. People who are right a lot get paid a lot and everyone is encouraged to be more right and eventually we can perfectly predict the future. The problem is that being right consistently is really hard. You know what isn’t hard? Hitting a journalist with a wrench.

Prediction markets have a lot of potential, but this is not the first time I’ve written about how they need more regulation. Right now, it is far too easy to break the system. I have many thoughts on laws that we can use to leash the markets (there’s at least some of this being proposed by lawmakers, see below), but the relevant one for this story is a no brainer: we need official resolution criteria that aren’t set by the random person who created the market! If you have a market that resolves only if “The New York Times reports that pigs can fly,” you may think you are evaluating whether swine are capable of taking to the air. But actually, any signal you uncover is something closer to “The New York Times takes bribes to report on ridiculous topics,” because it is easier to bribe the NYT than it is to have porcine aviation.2

Uncontrolled optimization is a theme that I want to keep returning to, because I think the Delve story also has roots in this kind of uncontrolled optimization for money. There, it turned out that lying about compliance is way easier than actually being compliant, so the optimizers (founders) decided to just lie. Same motivating principle as breeding cobras or harassing journalists.

Super Micro Computer

Supply and demand is econ 101. If there’s a lot of demand for something, and not a lot of supply, then the few suppliers get very rich. For the last few decades, we’ve mostly brought down barriers on who can supply and who can demand. You can be an Australian ordering an authentic New York bagel, and that’s just fine. You’re part of a global pool of demand for bagels and a global pool of supply of bagels, and the market does not care about the number of oceans and borders in between.

Except for a few small exceptions, of course.

If you’re an Iranian you can’t buy an authentic New York bagel, because the US government prohibits it. The US really cares about not giving the Iranians money or supplies — even before the current war — and as a result makes it really hard for anyone in Iran to buy anything at all, much less a bagel. The demand and supply are artificially constrained, which in turn means that if you are Iranian and you really really want a New York bagel, you need to pay up. Big time.

More generally, economic sanctions and export prohibitions don’t ever fully kill a market. Rather, they artificially increase the effective price of a good, making it so that the market clears way less frequently. Ideally, in the eyes of the one applying the sanctions, the market clears so infrequently as to basically be zero. No one would spend a billion dollars on a bagel, so it’s pretty easy to fully shut down the bagel black market with just economic pressure.

But people would spend a billion dollars on things like weapons, or rare metals, or, increasingly, computer chips.

Imagine you’re an executive of a computer chip company. Your chips cost $100 to make, and you normally sell them for $110, and you make a nice $10 profit. And then along comes a sanctioned buyer, and they say “hey, I’ll give you $100000 for one of your chips, wouldn’t you like $100000?” On the one hand, this is an illegal trade, and if you accept the $100000 you may go to jail. On the other hand, you make a nice $99900 profit, which is 9990x more than you would have made otherwise. And, like, how bad is jail, really? Apparently jails for rich people are kind of nice, and you’d get to pay off your kid’s college, and you’d get a nice car…the point is, you can see how this may be a tempting offer.

Too tempting for Super Micro Computer (SCMI):

The U.S. government has been trying to figure out how high-powered chips have reached China without authorization, as American artificial intelligence companies such as Anthropic and OpenAI face challenges from DeepSeek and other Chinese rivals.

In an indictment unsealed Thursday, the U.S. government alleged that Yih-Shyan “Wally” Liaw, Ruei-Tsan “Steven” Chang and Ting-Wei “Willy” Sun worked together to violate the Export Control Reform Act.

The server company’s products containing Nvidia chips “are subject to strict U.S. export controls barring their sale to China without a license,” the plaintiff said in the indictment. “Those controls are in place to protect U.S. national security and foreign policy interests, among other things.”

The efforts have yielded around $2.5 billion in sales for the server maker since 2024, with servers sold for $510 million between late April 2025 and mid-May 2025 going to the Southeast Asian company and on to China, the indictment said. The plaintiff said the server maker had no U.S. Commerce Department license to export servers featuring Nvidia GPUs to China.

“Roughly how many you can take by January? Feb? March? April?” Liaw wrote in a text message to an executive at the Southeast Asian company. “Just roughly forecast will be fine ... Then we can propose to [Nvidia] with the way they can accept ... This is the only way to have [Nvidia] to promise the B200 allocation so far as I know.”

When a broker who had bought Nvidia-powered servers from the Southeast Asian company sent Liaw a text message containing a link to an announcement about Chinese nationals being arrested for smuggling AI chips into China, Liaw allegedly responded with sobbing emojis.

Seems like Wally was breaking the Liaw.

We don’t know exactly how much the spread was between what the Chinese companies were paying and the normal server prices. The $2.5b is only what showed up on SCMI’s balance sheet. Claude thinks that the spread was about 50%, meaning that Wally and his conspirators captured ~$1.25b. That’s a lot!

I think that the most silly part of this story is that this isn’t even the first time SCMI has done this exact trade. Back in 2006, SCMI got in trouble for selling servers to Iran. And in 2016, SCMI got in trouble for selling servers to China. And then, in 2022, SCMI got in trouble for selling servers to Russia. And now, in 2026, SCMI is in trouble for selling servers to China, again. At this point, they basically specialize in selling to sanctioned countries! That’s their whole brand! Which, honestly, isn’t really the worst market niche, purely in business terms (not business advice). If you’re a sanctioned country, and you want chips, you know exactly who to go to. Extremely strong product market fit.

Other things

• Anthropic was granted a preliminary injunction on the supply chain risk designation. The judge in the case was really not happy with the US Government, stating bluntly that the “designation of Anthropic as a “supply chain risk” is likely both contrary to law and arbitrary and capricious…Nothing in the governing statute supports the Orwellian notion that an American company may be branded a potential adversary and saboteur of the U.S. for expressing disagreement with the government.” Generally if a judge uses the word ‘orwellian’ to refer to your side of the case, you should not be bullish on the odds of success.

• People are taking prediction market insider trading more seriously. “Reps. Nikki Budzinski (D-Ill.) and Adrian Smith (R-Neb.) will introduce the Preventing Real-time Exploitation and Deceptive Insider Congressional Trading Act, or PREDICT Act, Tuesday to ban members of Congress from participating in prediction markets related to political events or policy decisions. The ban would also extend to dependents and spouses of lawmakers, senior congressional staff, political appointees, the president, vice president and all senior executive branch employees, including special government employees.” In favor of this. Also, how much time do you think lawmakers spend just trying to come up with clever acronyms?

• NY proposes removing qualified small business tax exemption. Normally, if you start a small business and run it for a certain number of years, and then sell the business, you do not get taxed as much on the sale. This proposal would make it so that you would be taxed at the state level. Obviously I’m talking my own book here a bit, but I think this is bad. Startups are a center of innovation. Founders are actually job creators, they bring billions of dollars to the state, and in NYC in particular have been creating a economic (and political!) counterbalance to the bay. They are also highly mobile and very sensitive to risks to their exits, because startups are such a massively risky endeavor to begin with. Hoping they don’t pass this. If you know other founders in NYC, let them know.

This image is the TLDR.

Dev tools are, broadly, any piece of software that is used to make it easier to create software. IDEs are the canonical example. A good IDE exists to make the process of writing code easier.1 But there are others. Version control, code review tools, package managers, debuggers, testing, logging and observability, project management suites, etc. etc. If you expand your definition a bit, you could include cloud platforms, web containers, and more.

Our understanding of dev tools is grounded in ~60 years of code being expensive. Originally, code was expensive because the machines were expensive. Think: big super computer sitting in MIT’s basement, so pricey that you had to literally reserve your time on the thing like it was a NYC Resy top pick. Later, code was expensive because the people writing the code were expensive. There’s an old (probably apocryphal) story about how Larry and Sergei hated meetings so much, they toyed around with having a cost-of-meeting counter that would sum the per-minute salaries of all of the attendees and slowly tick up the cost as the meeting went on, but even a ten minute meeting had an obscene ticker cost and ended up discouraging all meetings.

But code is cheap now.

Increasingly people are just not writing code by hand at all. They use coding agents. The cost of a single line of code, previously denominated in tens of dollars, is now fractions of a cent. So, naturally, everything we know about software engineering is undergoing a seismic shift as the tools reorient from ‘person first’ to ‘agent first’. As evidence, see the CLI-ification of everything.

My team has been thinking about AI dev tooling since Jan 2025. With the rise of coding agents, we are seeing increasing consolidation around six areas of dev tools. I think that any team that is using coding agents seriously will need to either build their own solutions in each category, or buy products in each category. Let’s dive in.

Interaction

This is the UX layer of the coding agent, equivalent to the IDE of the previous generation. The thing the developer opens every day in order to interact with and guide a fleet of agents that write the actual code.

IDEs always had a fundamental limit: they had to actually show the code on screen. That meant that there were only so many ways to create an IDE. Individual IDEs varied in terms of integrations and interaction customisability, but the actual UX basically always involved a big text editor that takes up 80-90% of screen space. That form factor also means you need at least 10in of screen space. Even though there were experimental mobile IDEs, they never really took off because they never were ergonomic.

In a world where you do not actually manually write code, you do not have to see that code most of the time. That means an explosion of UX options, and a fragmentation of the market as individual people gravitate towards form factors that they like. We’re already seeing a few different UX patterns:

1. Terminal UI: Claude Code, Codex, Gemini CLI, Nori-CLI, Crush, OpenCode…

2. Multiplexers: Humanlayer, Antigravity,

3. IDE: Cursor, Windsurf, avante, Zed, various plugins to VSCode

4. Background / Slack: Devin, Claude Web, Stripe Minions (internal), Ramp Inspect (internal)

I don’t think anyone has cracked mobile coding agent UX yet, but it feels inevitable — you should be able to kick off jobs from your phone, the same way you can message a co-worker to tell them to work on something. OpenClaw feels like a step in this direction, though from a UX perspective it is relying on a standard chat interface.

It’s not clear that a good UX is, on its own, a sustainable business. VSCode has become dominant in the IDE world, but it is a free product sustained by Microsoft.

Still, developers will pay for tools that feel good to use, and business will pay for tools that are integrated to the larger corporate environment / process. You could imagine a coding agent UX for doctors (for eg) that just makes it really easy to hook into patient data systems like Epic or MyChart. The usual marketing playbook applies: if you are the obvious choice for <job category> then there’s money to be made.

Context

LLMs are zero shot learners. They learn a lot about the world from their training data, but the most important thing they learn is how to extrapolate from the patterns they are given. That means it is really important to manage the context your agent is running on. In my experience, many people who have bad experiences with coding agents are frustrated that the agent does not intuitively understand things that are not obvious. At Google, we had projects that were named all sorts of things. “We have to use red ant to get data from arachne so we can train starburst models” is entirely coherent at Google. But out of the box, the AI will never be able to properly parse that sentence.

I’m told that “Institutional memory” has become a big thing in the enterprise sales orgs of OpenAI and Anthropic. How do we get the right company information into an agent, especially for larger companies when even the search index for the total possible document space is way larger than what a single model can reasonably hold in context?

There is a lot of opportunity in this space because ‘context’ is so generic. We’ve seen a few strategies.

Give the model access to company data directly. MCP, Bash CLI integrations, and custom tool calling all fall into this category. Instead of changing the data or the data warehouse, simply give the model access to the underlying query system the same way a human may have access. MCP in particular is having a bit of a resurgence after mostly falling off the map a few months prior due to the popularity of SKILL files. Most of the companies operating in this space are aggregators — they create and maintain a library of integrations and create an easy drop in to access all of them. Credentials and authentication are still an issue though; more on that later. Aggregators include Composio, Manufact, Pica. If you expand the aperture a bit, you get products like fireflies (call transcripts to agent context) or sentience (consumer social to agent context).

Modify and compress the data to fit the model. SKILLs, AGENTS files, various forms of documentation all fit into this category. Certain kinds of organizational knowledge do not fit well into the traditional query format, because they represent processes instead of data. For example, think about the checklist of steps that needs to be done to do a security audit. Where would you store that? It doesn’t really make sense to put that in a postgres DB and tell the model “call this data when you feel like it.” If you’re doing a security audit, you need the checklist every time! The companies and projects in this space often look like package managers, or are literally just bundles of configs. See: Context7, Nori Skillsets, skills.sh, Enact, skillsmp. Increasingly, companies are also just releasing their own individual SKILLs bundles, e.g. this set of skills from astronomer.

I think building startups in the context space is very difficult, because it reaches asymptotic saturation within a company very quickly. Once a company has done the initial setup to get their integrations in place, why bother paying a subscription? The companies playing middle men may end up continuing to extract value, but I suspect that they will be squeezed by teams realizing that they can build their own integrations relatively easily.2 More generally, I’m somewhat skeptical of companies that follow the 5tran / zapier model. Those companies thrive in a world where code is expensive. Building an integration isn’t hard mechanically, it just takes time, and time is money. But as I keep saying, code is cheap now. So the marginal value of an additional integration point is way lower. My hunch: there will be a few aggregators that win3 and the rest will pivot to something else.

Memorization

The context layer is about getting information into the model. The memorization layer is the inverse — getting information out of the model. Recursive self improvement sounds like scifi, but there is a lot of low hanging fruit infrastructure that can make models better over time through some kind of ‘continuous learning’. This doesn’t take place in the model weight layer. Rather, it happens by constantly manipulating, updating, and compressing the context layer.

The name of the game is transcript analysis. Nearly every tool in this space creates some kind of watcher process that operates in or on transcripts between employees / users and their agents. Sometimes the transcripts are enough. The product is simply a search server over the transcripts, exposed as an MCP or CLI tool to the agent in the context layer. Other times, the product is some kind of additional analysis, some kind of aggregation step that uses graph / vector / metadata analysis to edit the context layer.

The core differentiation here ends up being ease of use and how the update loop is handled. Companies with clever and proprietary update loops will likely do better than those that are just ‘ingest and expose’. For example, at Nori, we do the usual transcript ingest, and then we use those transcripts to augment skills and skillsets instead of just making the transcripts blindly available.

Open Source: beads, Graphiti, A-Mem, SimpleMem, MemOS, OpenMemory, memU, Khoj

Companies: Nori, Entire, Mem0, Letta, Cognee, Zep, Supermemory, Pieces, Augment Code, Fireflies

Infrastructure

In some sense, all of the above is infrastructure, so I want to be a bit precise here. When I say infrastructure, I mean “where, exactly, is the agent running?” There are really only three answers.

1. On the model provider’s infrastructure. When you run Claude or ChatGPT in the web browser, you are running an agent on their inference machines. The agent maintains state, has access to tools, etc. but you have no ability to see or modify any of that. You cannot add tools, you cannot easily modify files. It looks, for all the world, like a chat bot. And that’s how the big model providers want it. This is the non-tech pure-consumer area, and is basically always going to be deeply tied to the model layer.4 If you have multiple conversations, they do not really have any ability to interact with each other (at least, not through the computer).
   

2. On a local machine. The agent runs on your computer like any other application. This is how virtually all of the coding agent cli tools run out of the box. Same with Claude Cowork or Cursor or Antigravity. Generally, the agent itself is pretty lightweight. It’s just a for-loop with some bash. But the agent can run very heavy processes, like builds or test suites. That in turn means you can get interference. Multiple agents running on the same machine can spike memory or block CPU. And of course, they can interact with the same files on the filesystem, leading to all sorts of weirdness. Also, a bit of a pet peeve: you cannot easily run the agent on the go. Since the agent is tied to the machine, you are stuck attached to the machine. Close the laptop, and the music stops. So why do anything this way? The main benefit is that it’s all local. You have full control of the context and all of the authentication. These are nonnegotiable for anyone who is trying to use agents for real developer work (and even more so in most enterprise settings that care about regulations and identity).
   

3. On a stateful remote box. This is generally a hybrid. You get some of the benefits of configuration and more control over the underlying machine, while still having remote sessions that can be accessed from anywhere that you don’t have to personally orchestrate. But there is a lot of variability here. Depending on how the infrastructure is set up (and for whom!) you could have anything from ephemeral isolated machines that can be seamlessly accessed from a bunch of different platforms (e.g. slack) to a big single box that is just treated as a remote dev machine to something that is a very thin wrapper over AWS with no additional features (Fly, Modal). Coding agents can do a lot, but they are not quite at the point where they can build a seamless multi-machine orchestration system. Ramp Inspect and Stripe Minions both follow this pattern for internal use. Nori Sessions follows this pattern, but as an off-the-shelf product. Other tools in this space: Oz, Coder, Ona

The main axis that matters is configurability — how easy is it to get into the machine and change what is on it? On face it seems like local usage should be squeezed out. But in practice, the literally-0 setup time and maximum configurability means there will always be demand for local infrastructure, especially among individuals and small teams. My team has a robust ephemeral cloud offering with nori sessions, and I’ll still pull up my local coding agent during the work day (I’ll just teleport those sessions to the remote when I want to leave my desk).

As with many things in this post-software world, I suspect a lot of value will accrue to compute providers. Building in the infra space is just a classic pickaxe business, and for highly autonomous coding agents they make so much sense. You want to be able to access coding agents from anywhere, so you need to build infra that supports that, and the infra usage is directly proportional to the agent usage.

Testing and Verification

I think historically, testing has been under-appreciated. Most people who write code don’t write tests. That is because they verify the program they write as they go, based on the outputs. The act of writing the code is itself an act of problem solving. And writing tests by hand is as time consuming as just building whatever you need to build; no one wants to double their investment for 0 up front additional gain.

LLMs fundamentally change the calculus around testing. Testing and verification has become one of the most important areas of development for modern LLM-enabled teams. Two reasons for this.

First, LLMs are stateless. They lose all memory from call to call. An LLM could write some code at 2pm and have no memory of it by 3pm. They are constantly seeing a codebase with fresh eyes, which means they are constantly at risk of misunderstanding something and unintentionally breaking some feature.5 Tests act as a backstop for coding agent error. Comprehensive testing is a memory mechanism.

Second, it increasingly seems that many software problems are search problems. If you have enough compute you can run enough LLMs to figure out a solution to just about any test case, or drive down some optimization criteria. For software products, this inverts the usual thinking: instead of building the product, you build test criteria that the product needs to meet, and then let the LLM finish the rest. So the upshot is that you can (must?) spend far more time thinking about product instead of implementation. LLMs have driven the cost of writing tests down in tandem, so building product has essentially become an exercise in thinking about comprehensive specification more than anything else.

As teams begin to fully grasp the implications of coding agents, they will need to invest in emulators, good high quality mocks, and testing infrastructure.

This is both very hard and very expensive. If you have an app that runs on slack, for eg, you are going to want tests that check slack behavior. How do you do this? To be complete, you would have to rebuild a lot of slack infrastructure as a local mock. But that in turn is a pain. What if you got your implementation wrong? What if slack changes out from underneath you? Also, slack has a lot of features with all sorts of complex interactions. Are you going to mock all of them? Are you going to do this for every dependency you have?

For some teams, the answer is yes:

Which leads us to StrongDM’s concept of a Digital Twin Universe—the part of the demo I saw that made the strongest impression on me…

[The Digital Twin Universe is] behavioral clones of the third-party services our software depends on. We built twins of Okta, Jira, Slack, Google Docs, Google Drive, and Google Sheets, replicating their APIs, edge cases, and observable behaviors.

With the DTU, we can validate at volumes and rates far exceeding production limits. We can test failure modes that would be dangerous or impossible against live services. We can run thousands of scenarios per hour without hitting rate limits, triggering abuse detection, or accumulating API costs.

This screenshot of their Slack twin also helps illustrate how the testing process works, showing a stream of simulated Okta users who are about to need access to different simulated systems.

Tools like localstack6 or playwright are invaluable here, and companies like Mercor or Mechanize explicitly sell RL environments.

Of course, the other approach is to just do it live.

Instead of investing in mocks, you just run the real thing. Spin up your actual app and then just try to break it by doing a bunch of things that a user would do. Here, LLMs act as QA operators. You feed an LLM what is essentially a markdown file describing a particular feature spec, and the LLM just makes sure the spec is met. In the limit, you could imagine CI/CD platforms evolving into full suites of agents trying to break every critical feature path. End to end integration tests are hard because they are so varied, and this is very much not a solved problem. Web developers and terminal developers have big advantages here, because it is fairly easy to emulate both of those environments. Companies in this space: Momentic, Functionize, Morph.

I think there will be a lot of demand in this space, but it is also really unclear how to avoid being super fragmented. Maintaining even a single really-high-quality mock environment seems hard? In the StrongDM blog post above, they talk about spending over $1k in tokens per developer per day. Seems like a lot, and I think they are understating how much they are able to clone entire applications from just looking at client libraries. Also, separate from the above, I have a strong suspicion that much of the value in using LLMs as QA is captured by the infrastructure layer that those LLMs are running on. After all, the QA model is basically just ‘feed a markdown file to your LLM’. Does that become a full product? Very unclear.

Identity and Auth

And finally we get to the security layer. I mean, what can I say that hasn’t been said by two dozen posts about how scary OpenClaw is?

The fundamental contradiction of using AI agents for ‘real’ tasks is that the agent is only as good as the amount of context it has and the number of actions it can take. An agent that has 0 tools and no context is barely even an agent. But an agent that has access to everything can seriously screw things up. People rightfully get spooked by the idea of giving agents access to api keys, ssh credentials, or, like, a credit card number. Forget about context injection or malicious prompting, the AI itself is fundamentally untrustworthy! Every time you interact with it you get a totally new session, and who knows if this session is going to behave or going to threaten you?

If you can’t trust the agent, you need something that you can trust, something that will be responsible for keeping the agent in line. As far as I can tell, the only way to do this is to put some kind of gate-keeper in between the agent and the external world. The question is simply where — or, more specifically, how far from the agent.

One place you could put the gate is inside the agent harness tool calls. A tool call is just code, and it works like any other code. So you can have tools that are authenticated, have some kind of permissions, and may even have externally injected secrets. The MCP spec supports this outright as an OAuth2.0 client, though anecdotally it does not seem like many MCP implementations really futz around with the auth side of things. See: Descope, Stych, Scalekit, Credal.

Another place you could put the gate is in front of the critical resources you care about. For example, you could imagine a service that sits in between your db. It perfectly understands the underlying protocol, and you can write up policy that is enforced in code to ensure that agent queries are logged and (where necessary) blocked. For example: Empathic, Formal, ProxySQL

The middle ground is a proxy. The agent thinks it has access to everything. The databases and third party APIs don’t know or care about the caller. In between there is a little service with all the responsibility, that ingests every message from the agent and analyzes the transcript for things that could be going wrong. See: Portkey, Agent Gateway, MCP-Proxy.

I’m most bullish on the last approach from a technical lens because it is generally transparent to both the agent and the underlying sensitive resources. But I also know that the shape of any authentication layer will need to very closely match the industry. Consumer vs enterprise. PII vs PHI vs PCI. Regulatory requirements for paper trails and humans-in-the-loop. And I’ve mostly conflated identity and authorization in this section, but those are potentially two very different things. Agents can run autonomously without having a person behind them, so who do they ‘represent’ from a liability perspective? Products in this area are very much in the ‘figuring it out’ stage, but the ones that do will be able to tap the big capital reserves currently sitting in banks and healthcare.

The through-line here is efficiency. Before, it did not make (as much) sense to think about how computers could identify themselves. There was always a person behind the screen. You don’t need to think about “context”, that’s what the first three days of onboarding new interns is for. You don’t need to think about “memory”, humans do that automatically. And humans do things at human speed.

Now that agents can act autonomously — and faster and more consistently than humans — the burden has shifted from “make humans more efficient” to “make agents more efficient.” Thus the tooling. Every single product described above aims to pull people out of the loop while giving the agent more agency. Some of that is explicit, eg tools and skills. Some implicit, eg better auth means more trust means ability to run agents more freely. But the end goal is the same.

One thing that is currently unclear is whether the large labs will suck all the air out of the dev tooling space. They are clearly trying. Anthropic has Claude Code (interface), skills (context), automatic Claude memory (memory), Claude Web (infrastructure). I’m not aware of any testing or authentication solutions, but any team using agents effectively will run into these problems and concerns, so I wouldn’t be surprised if something in those categories was in the works. Though I did not touch on the model layer above,7 there is leverage in owning the model layer. The big players could price their products to extract all the value, or they could fine tune the models to work best on their own developer tooling.

But right now, it would be profoundly arrogant to assume that any company has a lock on any part of the dev tooling space, or even to say with confidence what the dev tool space will look like in a few months/years. Lower barriers to entry mean an increase in diversity. It is far more likely that the market will fragment based on taste and aesthetic and minor feature convenience, things that are important to small market segments that the big labs simply can’t justify tackling. Let a million flowers bloom, as the saying goes.

Does this mean that everyone will simply build their own tools? After all, that is the best way to get maximum configurability. Where are the software moats?

If you ask me, the build vs buy line has changed, but it hasn’t changed that much. The majority of programmers are still people working unsexy desk job 9-5s at your major bank chain of choice. These people are not going to invest a ton in building their own AI dev tools, any more than they were likely to build their own IDE or Kanban board. Right now, there is no clear idea of what AI dev tools will look like or who the big trusted players are. CTOs and eng teams will fill that gap, because the gap needs to be filled. But when good, convenient, easy to use alternatives come along, I strongly suspect those same teams will offload their maintenance burden to other companies. A health tech CTO (for eg) probably doesn’t want to think about maintaining dev tools. Especially if those dev tools have a bunch of rough product edges that need to be rethought from first principles. The software moat is now very much a product moat. If your product feels good and is easy to use, it will still win out. And as I said before:

Every line of code is a liability, and there is always going to be demand for “you deal with this so I don’t have to.”

Last thought. You may notice that many companies appear several times in this article. As I said, ‘any team using agents effectively will run into these problems and concerns’. Teams that are using AI to build AI dev tools will naturally go out and build other parts of the dev tool landscape! That is 100% what we are doing at Nori. Looking forward a year, I suspect many AI dev tool companies will have suites of solutions, all of which will be considered table stakes for the AI dev tool category.

Agentics is the study of how to use and reason about agents. If you are an expert in coding agents, or interested in learning more about agents, join our community slack. More articles here.

If you are using coding agents and are thinking about building your own remote sessions infrastructure, check out norisessions.com and reach out instead!

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I’ve been invited to give a lot of talks about coding agents over the last few months. Big companies, small startups, VC, PE. So far all of those talks have been behind closed doors. But this week I was invited to give a talk at the OLAB at NYU Langone! And since this one was relatively public, I was able to finally post the talk itself.

The recording starts like ten minutes into the talk (because, of course, we didn’t think to properly record it at the beginning, whoops). But there are a bunch of nuggets of wisdom in there that I think are key to how we use coding agents at Nori.

If any of this is relevant to you, feel free to reach out. Happy to give a talk to your team if it would be helpful and relevant.

If you’re a tech team feeling the FOMO and struggling to build your AI capabilities, reach out. My team builds remote agent runtimes and company context so you can do what the best engineers at Stripe and Ramp are already doing. Or join our public slack to keep up to date on what’s happening in AI in real time.

The ironic thing about the founder journey is that after all is said and done, most founders will eventually leave the companies they create. Sometimes this is because you had a great exit. You get out of the souped-up car that you’ve worked on, polished, slaved over, and hand the keys to an exec at a mega corp. Sometimes it’s because the company is dead in the water. You turn off the life support and return any cash you have left and lament what could’ve been. And sometimes, most painfully, it’s because you can’t stand your company and your cofounders any longer, and you need to get out (or risk jail time for assault).1

In the span of a month, two close friends left their companies. One had a buyout that couldn’t meet the company pref-stack. The other was pulling his hair out due to co-founder conflict and needed to quit. They’re both still going through it. It’s hard.

When I left my first company, I left on pretty good terms, and I still had a pretty rough time of it. With a year+ of hindsight and a lot of writing in between, I think there are a few things that make leaving a company really really hard for founders.

• Most founders that I have met are ambitious, hyper energetic people. We can’t sit still. I suspect half of us are borderline manic most of the time. That energy is what sustains a founder. But it also makes leaving hard, because leaving means a vacuum. What do you do with the time? How many times can you do the routes at your local climbing gym, how many runs of Ascension 20 Slay the Spire before all of that sitting around starts to get a bit boring? Related: many founders are not exactly in tune with their emotions. Huge competitive edge when slugging through hour 33 of writing code, less good when left alone with your thoughts.

• Leaving a company is like leaving a family. Founders have close personal relationships to the people they hire; the best founders love and live for their teams. Leaving a company feels like a betrayal. “I joined this company because I wanted to work with you, and I put my faith in you, and now you’re leaving?” “How could you not see how hard this has gotten, why did you not do more to help?” Some founders lose their entire social network when they leave their companies, especially in the Bay where social life and work are so closely tied together. If you have a big co-founder fight, are you supposed to just meet up at the next ML Paper Reading Group & Cuddle Pile and chat-slash-cuddle like nothing happened? Founder breakups are more toxic than most actual breakups.

• Founders can’t actually talk about their departure, at least not publicly. Certainly not to the people they are leaving behind. Founders often have non-disparagement clauses tied to their equity. So you can’t share why and how you left with anyone in the industry, unless you do so with a smile on your face. Which, again, is many of your closest friends! Also, many founders are pathological rule breakers and misfits who see their work as part of their identity and self expression (myself included). Restrictions on speech hit especially hard.
  

• Founders wrap so much of their personal identity and self-worth into the success of their company. If the company failed, it’s because I failed. I am a failure. I didn’t work hard enough, or smart enough. That same mentality keeps founders in terrible working conditions.

All of this lends itself to a really common failure mode. Founders instinctively try to control the ambiguity — the moment they leave, they are setting up their next thing. This time I’ll make it. Some founders go further and try to recreate their first company. I’ll do it better this time, I won’t make any of the same mistakes.

Don’t do this.

If you spend months-to-years working on whatevercorp.ai, your entire way of thinking is clouded. You will see everything through the prism of being the founder of whatevercorp.ai, and as a result you will make bad and probably incoherent decisions.

Instead, you have to do something that is basically impossible for a lot of founders: slow down, sit back, and reconnect with yourself.

To the extent that I have advice, here are some notes:

• You need to clear your thinking, let the oil and water separate. The best way to do that is to dramatically reduce the timeframe of your thinking. You’re used to thinking weeks or months or years ahead. Stop that. Bring your timeframe down to one month. Then one week. Then one day. Then five minutes. What do you want to do five minutes from right now? Answer that question honestly, and then go do that thing. For the first month after I left my first company, I had huge plans about starting all sorts of projects. But what I really wanted to do was eat Costco taquitos and play Pikmin. I am so grateful I did the latter, because that eventually led me to what I was naturally curious about (coding agents!).

• Related: avoid the impulse to answer questions about what’s next too early. It’s fine to not have an answer when someone thinks they are being helpful and “a good friend” when they inadvertently ask ‘what are you planning to do next?’ It’s fine to say ‘I don’t know’ and mean it. There are no hard and fast rules, but I think if you have an answer within a month you didn’t spend enough time thinking. You should seriously consider whether you even want to do the startup thing. It’s fine if you don’t!

• Write. Write a lot. Write your founder journey. Don’t use AI. All this serves two purposes: first, writing is thinking, and you need to make time and space to think and process; and second, future you will want that memoir as something to look back on. Writing has this beautiful way of cutting through emotion, because you can look back at what you wrote after a night’s sleep and see in your writing a different version of yourself, and recognize that that version isn’t you now.

• Don’t travel. Stay in your hometown and let yourself be bored. The brain is remarkably efficient. It builds routines — including emotional states — around physical memory. You spent months working on your company. Your apartment / home and your city and of course your office will trigger certain emotions, and you need to rewire. Travelling is great, and can be relaxing, but travel is about creating new routines in new places. You need to give yourself some amount of time going about your usual day so your brain will understand the new normal (alt: just move. This is obviously less immediately attainable for most people, but would have a similar effect!)

• Get advice from other founders who have previously left their companies. This is not always easy to do, because you may not know other founders who have left their companies, or you may not feel like you can approach them. One of the things I don’t like about Bay Area founder culture is that everyone is constantly showing off to everyone else, which means there is little space for vulnerability. Even when people are really going through it, they will grit their teeth and smile and give the pitch for the ten millionth time. So, yea, not easy to find folks who are good to talk about these things with. But if you do have someone in your wheelhouse who has gone through it before and who you can talk to, call them. They will have a lot of detailed things to say, and will likely appreciate the call.

These are the things that were most useful for me. There are also the usual canned-but-useful bits of advice — spend time with friends, pick up new hobbies, get fit, sleep, read, etc. etc. Those are all helpful, but I think far less so than the five things mentioned above.

The last thing I’ll add here is that if you are reading this because you just left your company and it resonates:

• first, I’m sorry that you had to leave under less than ideal conditions;

• second, congratulations, this is basically always the right decision, and I hope you feel a lot of relief;

• third, it gets better, and you will eventually become your previous company’s biggest ally (because, you know, you are likely heavily financially incentivized to do so);

And finally, if you need it, feel free to reach out, happy to chat.

I want to reflect briefly on the surge of nihilistic discourse that seems to increasingly plague the tech community. This particular disease goes by many names — ‘black pill’1, ‘stoicism’2, ‘pragmatism’. But don’t let that confuse you. These are all forms of the same thing.

The core of this mindset is a logical fallacy:

• The speaker has realized that ‘everything is made up’, that ‘there are no rules’, that ‘you can just do things’;

• therefore having values, beliefs, or morals at all is misguided, naive, cringe, and uncool.

A classic example that exemplifies the mindset is something like “but it gets clicks/views/engagement.” Another one: “this is just how the world works.” A third: “it’s not my problem.”

I first noticed an exaggerated version of this mindset when I spoke to a group of young 20yo founders in April of last year. I wrote:

Here was a group of people who were, by every account, living some of the most blessed lives on the planet. They were all healthy. They had decent relationships with their family and friends. They were surrounded by people who were eager to give them the resources to fulfill their wildest dreams. That’s not even an exaggeration. In between complaining about the decay of the city, one of the group mentioned off hand how a VC had bought him an 8sleep mattress and sensory deprivation spa treatments. I looked it up later — the 8sleep that he got was worth $5000 dollars. At the same age I was sleeping on a $100 foam/plastic twin bed.

In spite of their obvious good fortune, talking to them was a bit like talking to a 4chan greentext. They would joke about edgy bullshit — how it was good that 10 year olds were addicted to phones so that they could make money off them, that it was time for democracy to die and be replaced by techno feudalism, that we should renormalize homophobia as a society. It took me a while to realize they were serious, they were laying the irony on so thick. Why on earth were they so cynical? What did they have to complain about, that they were all so eager to “burn it all down”?

Why were they so eager to burn it all down? I never got a satisfying answer. But in the intervening year, I saw this thought pattern metastasize into the wider ecosystem, turbo-charged by the industry’s reliance on Twitter as its platform of choice. And now the nihilism just feels like it is everywhere, from the prevalence of startups getting funding for gambling apps to crypto scams to the naked use of AI to destroy the commons in pursuit of money.

I understand why the nihilism is baseline seductive. There is a lot of truth to the idea that many things in the world are artificial constructs. Founders and early stage employees understand this more deeply than most. It turns out that many company norms and rules exist because some guy just decided that that’s how things would go. I vividly remember when I had to pick my company’s insurance policy. Like, I read through the policies and did my best but at the end of the day, there I was, some guy that just decided that’s how things would go.

If you are in the startup world for long enough, shifting norms — or outright breaking them — becomes a learned muscle. And you start to see those norms everywhere. Sometimes this is helpful. “I know your policy says you can’t give me a refund, but also I know that’s just something you have to tell me, so let’s see if we can work together and give me something partial.” Works almost every time.

The problem, of course, is in the second part of the syllogism.

Bluntly, 2026 isn’t the first year where someone realized that there is no inherent morality to the world. This is a subject that philosophers have debated for literal centuries.

“Meaningless! Meaningless! Everything is meaningless. What do workers gain from their toil? I know that there is nothing better for people than to be happy and to do good while they live.” (Ecclesiastes) “Waste no more time arguing about what a good man should be. Be one.” (Marcus Aurelius)3 “I do not reproach nihilism’s arrival. I believe it is one of the greatest crises, a moment of the deepest self-reflection of humanity. Whether man recovers from it, whether he becomes master of this crisis, is a question of his strength.” (Nietzsche) “One must imagine Sisyphus happy.” (Camus)

The nihilist founder thinks abandoning ethics is the brave, clear-eyed position, and that anyone still following some sense of order is a naive sheep.

That’s cope, used to justify a ‘got mine, fuck yours’ mentality. Opting out is still a choice. Cynicism is still a value system — just a lazy and self-serving one. The only thing that distinguishes humanity as a species is our ability to define values and seek out individual ethics despite an unordered and chaotic world.

And this is hard to do! It is genuinely extremely difficult to build a coherent ethical system. It requires a lot of uncomfortable questions about what you actually believe, even when those beliefs cost you something. Especially when they cost you something.

That does not mean “never compromise.” Compromise is healthy and necessary, both as a founder and, like, as a person. You need to find a medium between ideological purity and complete rejection of ethics.

But as someone who has gone through one founder arc, at the end of every startup journey you are left with yourself and your decisions. And I have never, ever met a nihilist who was fulfilled and happy, even when they have good exits. The nihilism leaves a void that even success cannot fill.

(If they even have good exits! I can't rigorously prove it, but I strongly suspect founders that have real ethical beliefs are on average more likely to be successful at running a business. It's easier to hire, easier to get customers, and of course easier to have conviction about what you're building when it gets hard in the long haul.)

All this to say, if having beliefs makes me uncool, so be it. I’d rather be uncool than be a nihilist.

Final thought: I think there is a lot that more senior members of the tech community can do to stem this self-destructive (and society-destructive) mindset. We should act in accordance with our own beliefs and think more deeply about where we compromise. And we should guide the ever-younger members of the Valley towards healthy founder mindsets and ways of being in the world. If you fall in that latter category — if you’re a founder and are unsure how to be — feel free to reach out. DMs open.

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