Generally speaking, if you spend tens of thousands of dollars on something, you want to see something come out on the other end. Some return on investment.

O sure, not always. I’ve previously said that selling to consumers is sorta funny because they love spending money on things that waste time or actively cause pain. This is part of why the gambling apps are so popular these days. Why yes, I’d love to spend $100 on betting that Wemby scores a 3 pointer while doing a handstand and singing the national anthem in French.1

But for businesses? I’ve basically never heard a business leader say that they were going to set a bunch of money on fire because it made them feel good, at least not the same way a whale will spend thousands on Genshin Impact gatcha pulls. Like, imagine if some serious business leader, like, idk, Mark Zuckerberg, decided to announce that Meta was going to burn money. He could do that. He’s got the voting shares. But it would be a bit silly, wouldn’t it? I generally think if you’ve gotten to the point where you’re running really big really important companies, you mostly aren’t doing things for kicks, with one big exception.

If you haven’t heard, tokenmaxxing is (was?) a phenomenon where executives accidentally encouraged their employees to burn a bunch of tokens on useless tasks. The canonical example of this is, by complete coincidence, Meta, which has been thoroughly skewered for tying performance evaluations to the amount of token usage per person. Obviously, obviously this was going to lead to people just burning tokens on nothing. One of my friends at Meta reported that they literally would just have two agents talking to each other throughout the day to get her token numbers up.

This was such an obvious outcome that many people rounded this off as “these business leaders are really dumb because they decided to burn a bunch of money on tokens without expecting any return.”

I understand why that’s a tempting take, because that is kinda sorta what the public face of a lot of this was. But I’m going to do my favorite thing in the world, which is be a bit contrarian. It wasn’t that “executives accidentally encouraged their employees to burn a bunch of tokens on useless tasks.” Rather, “executives purposely encouraged their employees to burn a bunch of tokens on useless tasks.”

I work with a lot of teams on figuring out how to use AI effectively. A few months ago, there were a lot of people who were extremely resistant to using AI tools at all. Senior people, people that had a lot of respect in the organization. It was very difficult to convince these folks to use the tools. And when you did, they would often accidentally (or purposely?) use the tools in a way that would obviously lead to weird or bad outcomes.2

One way to think about the top down tokenmaxxing policies is that this was a technique by executives to break through. Yes, it was obviously a blunt force policy, but sometimes you need blunt force to break through a wall.

Of course, that was the situation a few months ago, when there were still holdouts. It’s now a few months later, and the tokenmaxxing policies had their intended outcome: everyone is using AI to code, at least a little bit. Most teams haven’t yet figured out how to build their own Ramp Inspect or Stripe Minions (if that’s you, reach out — we can help!) but basically everyone is at least using cursor in the side bar. Which, of course, means that token spend has gone way up. Unfortunately, but probably not unexpectedly, the increase in token spend has lined up with both OpenAI and Anthropic trying to go public. Both companies have limited the amount of juice their subscriptions provide while jacking up their API pricing. Token subsidies are increasingly vanishing.

So now the incentives are mostly gone and the cost is way up and, of course, teams are starting to roll back their unlimited-token-spend policies. All of this to say, tokenmaxxing is dead.

Except…maybe not.

The promise of AI tools generally is that you can have them run without human supervision to accomplish really hard and really tedious tasks that still need to be done. The big code migration, doing research on all your competitors every morning, keeping up with the stream of inbound and outbound — these are all things that people mostly hate doing and want AI to do.

Up until recently, though, you couldn’t reliably have an AI run for long periods of time. If you tried, you would notice that small errors introduced by the models (including hallucinations) would take on a life of their own and eventually become irreversibly embedded into the project. In the business we called this “compounding error.” It not only required a fair bit of human supervision, it also kept token costs low because there was little benefit in running agents 24/7 to begin with. Like, what’s the point of running a little demon in your computer over night if the thing is just going to tear up all your hard work? If spending more tokens results in worse work, you obviously aren’t going to spend more tokens!

That’s no longer true. We’ve entered a different regime, where spending more tokens generally results in better results. We call this “compounding correctness” — the more tokens you spend on getting a task correct, the more likely you’ll get a good outcome. We talked about this a bit at the last in person Agentics meetup:

Compounding correctness flips the calculus. If more token spend leads to better outcomes, then you’re going to want to spend a lot of time running tokens. Which sure as hell sounds like tokenmaxxing to me! The original incentives to tokenmax are gone, but eventually folks will realize that a new and more powerful incentive has take its place.

We’ve already seen some of this take place in the cyber security world:

Last week we learned about Anthropic’s Mythos, a new LLM so “strikingly capable at computer security tasks” that Anthropic didn’t release it publicly. Instead, only critical software makers have been granted access, providing them time to harden their systems.

…

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

AISI budgeted 100M tokens for each attempt. That’s $12,500 per Mythos attempt, $125k for all ten runs. Worryingly, none of the models given a 100M budget showed signs of diminishing returns. “Models continue making progress with increased token budgets across the token budgets tested,” AISI notes.

If Mythos 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.

Fable is, tragically, gone now. But the underlying concept here still remains.

This is also in part why people are suddenly so excited about ‘loops.’ Boris Cherny, the creator of Claude Code, got up on stage and said ‘loops’ and everyone freaked out. The basic idea behind loops is that you run an agent until it reaches the end of its turn, and then when it finishes you simply restart the same prompt. With a bit of cleverness you can take a pretty heavy specification and automatically have the agent split it into parts and solve it over time. No human supervision required.

Is this some new thing? No, not really. The loop concept has been around since literally last July. It used to be called a “Ralph Wiggum loop,” but as the industry has matured so has our sense of humor and the ‘Ralph Wiggum’ part was dropped.

There were ways to get loops to work, but it was hard. You had to think a lot about how to prompt the agent, which in turn required a pretty deep familiarity with how these things work. Now, though, it’s easy. Compounding correctness makes it easy. You can basically prompt the LLM however you want and to a first approximation, it will do better every iteration of the loop.

So is tokenmaxxing really dead? Maybe temporarily, but long term I don’t think so. Teams that are at the cutting edge are currently building or have built the infrastructure necessary to run agents 24/7. It’s only a matter of time before the bigcos realize that the cost benefit has shifted again.

The real winners here are the open model platforms. Tokenmaxxing the top labs will never stand up to any amount of CFO scrutiny. As open models get better, it will become more popular to simply run those in a loop. That was the core thesis of Rohan’s talk above. If Claude gives you 1.1x improvement per iteration, and GLM 5.2 gives you 1.05 improvement per iteration but costs ~5x less, you can just run the second loop 5x more times and it will be better.

The last thing I want to mention here is that some of the ridiculous token spend is downstream of a serious misunderstanding of the best way to use these tools. Before coding agents really took off (thanks in large part to much better harnesses like Claude Code), lots of people were making their own custom agents. And that was legitimate work! You had to think about this stuff as if it was…well, software. There was an art to figuring out the tools and the prompts but the core of it was still just software, even if it was supported by ‘AI native’ frameworks like Pydantic or Langchain.

You can’t fit a square peg into a round hole. Executives across the board saw this style of building agents, went “o, this is just a more flexible zapier workflow,” and proceeded to demand data processing pipelines that could do one-off tasks that were ‘agentic’ instead of building those same pipelines in good ol’ deterministic code. ‘I need to do data labeling, so I will build a data labeling agent’, that sort of thing.

Now, relying on an agent to do some of this stuff is already going to be significantly more expensive than just doing a workflow automation. But the bigger issue is the accuracy: none of these ‘agents’ ever really took off, because they were never as correct as a deterministic pipeline would be.

If you’re committed to using agents but want to reduce the cost of hallucinations and things, what do you do? Why, you build another agent! A ‘quality checking’ agent, or something like that. And what if that agent gives you errors? Well you’ll just build another! And now you have 3x the token cost, enjoy!

The story of tokenmaxxing is, again, one of RoI. That story didn’t just play out at the bigtechcos. It also happened at a less advanced scale at companies all over the country — companies who poured billions into random agent pipelines built by one off consultants that unfortunately never really quite worked all that well.

Notice that these are actually two different kinds of tokenmaxxing.

• The first kind is ‘spend a lot of money on tokens for your developers‘. Here, devs are using tools like Claude Code and figuring out how to run things in loops and using a lot of tokens to do it. Ostensibly this is a good use of money because it’s making the engineers themselves more productive.

• The second is ‘spend a lot of money on tokens for your pipelines‘. Here, devs are still writing code by hand! They are using that code to create one-off agents to do very specific tasks often in a non-deterministic and brittle way, and it’s those agents that guzzle up all the tokens. This is only a good use of money if the pipelines work, which they don’t.

But here, too, we are seeing a shift. Increasingly, these sorts of one-off pipeline-based tools are better done by generalist platforms that are skinned for the specific task, than an “agent” specially designed to do that one task. There’s some market arbitrage here. Some buyers haven’t realized that generalist agents have gotten really good, so they will go to consultants asking to ‘build me an agent’, and the consultant essentially writes a skill file and says “that will be $2m please.”

Luckily, this too shall pass. Generalist model platforms are obviously the future for anyone who has used them (and if you haven’t, again, reach out!) And that, again, will lead to another rise in tokenmaxxing behavior in this part of the market.

The natural end state of all of this is the ‘software factory’ or, even further, the ‘dark factory’ — a codebase that pumps out code, reviews code, fixes bugs, writes tests, and so on without any human supervision. The human simply puts in a spec and out comes an application. The folks over at StrongDM have taken this to the furthest extreme, arguing that engineers should aim to spend $1000 in tokens per day. This is almost certainly hype, part of a long trend of saying egregious things to get coverage and buzz. We have a software factory, and we spend like $600 per month. But the hype and buzz comes about because, even though it is currently ridiculous to spend the price of a senior google engineer in tokens per engineer, there is a kernel of truth to this. The incentive to spend ludicrous amounts of money on tokens are there, latent, waiting for diffusion.

What’s old is new again and what’s dead may never die. Tokenmaxxing is dead, but we haven’t seen the last of tokenmaxxing just yet.

Other things:

• GPT 5.6 is out, kinda sorta. From the announcement:

  We’re beginning a limited preview of the GPT‑5.6 series: Sol, our flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable model. Terra has competitive performance to GPT‑5.5 while being 2x cheaper and Luna brings strong capability at our lowest cost.

  …

  We believe in broad access, and we plan to make GPT‑5.6 Sol, Terra, and Luna generally available in the coming weeks. As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch. At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly.

  …

  We don’t believe this kind of government access process should become the long-term default. It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them. We are taking this short-term step because we believe it is the strongest path to broader availability in the coming weeks, while we work with the Administration to develop the cyber Executive Order framework and a repeatable process for future model releases.

  Washington Post was even more aggressive in its analysis:

  U.S. government will decide who gets to use latest upgrade to ChatGPT

  The Trump administration came to power preaching a laissez-faire approach to AI but has lately increased oversight of the industry.

  Reading between the lines, it looks like we are getting more government regulation of AI companies, but unfortunately the process as it currently stands is completely opaque. Reactions are mixed. On the one hand it’s great that the administration’s previous attacks on Anthropic are also being applied to their competition. On the other hand, it seems like the process is totally opaque, and now the government is unilaterally picking winners and losers not just among AI companies, but among every other industry where AI may matter (i.e. all of them). The companies who get to use OpenAI’s tooling (and Mythos, see below) are currently unknown. It would be very concerning / disappointing if we find out that it is exclusively companies with ties to this administration.

• Related: Mythos is back on the table, at least sort of. From Semafor:

  US releases powerful Anthropic model Mythos to some US companies

  The US government Friday lifted its block on Anthropic’s powerful Claude Mythos 5 AI model, allowing the company to release it to more than 100 US institutions, including major companies and government agencies.

  The decision, in a letter sent Friday afternoon to Anthropic, is a major de-escalation in the confrontation between the Trump Administration and one of the world’s most valuable private companies. Two weeks ago the administration imposed export controls on Mythos, leading to a shut down of the model and its cousin Fable 5 after warnings from Amazon and other companies that they could be “jailbroken” for malicious purposes.

  The letter is silent on Fable 5, a weaker version of Mythos that was briefly the most powerful AI model widely available to consumers. People close to the talks said they are moving toward releasing Fable as well, though that timeline is unclear.

  “I have determined that appropriate safeguards are in place to permit certain trusted partners to access the Claude Mythos 5 Model,” Commerce Secretary Howard Lutnick wrote to Anthropic’s chief compute officer Tom Brown Friday, citing “significant progress” in the intense, daily talks between the government and the company since the block went into effect.

  Again, picking winners and losers.

• While we are talking about OpenAI’s launch, one thing that was buried in the announcement was that the tooling would be available on Cerebras’s high-speed inference machines at ~750 tokens per second. This is quite fast. Right now we are in a regime where it makes sense to treat AI tools as async operators that can go off and do tasks without supervision. But that is mostly downstream of the fact that AI is slow and it takes a while to do things. If AI was really really fast you would plausibly go back towards a more synchronous model of operating. For an idea of what this may look like, check out https://chatjimmy.ai/. It’s just a demo, but wow, what a demo.

• Open models like GLM 5.2 have gotten pretty damn good. They aren’t SotA, but they are much cheaper than their frontier equivalents. Right now, GLM 5.2 is ~$1.4 per million tokens and ~$4 per million output tokens. By contrast, the entire Opus 4.X series is $5 per million input and a whopping $25 per million output. The only model in the Anthropic suite that even comes close to GLM 5.2 in terms of pricing is Haiku 4.5, at $1 / million input and $5 / million output. But GLM 5.2 blows Haiku out of the water, and in some cases is even stronger than GPT 5.5 on benchmarks. If I were the big labs, I’d be pretty concerned about this. And if I were basically any consumer on the market, I would be doing everything I can to avoid provider lock in by adopting tools that are able to sit on top of all of the major players.

• OpenAI unveiled an in house chip for inference.

  On Wednesday, OpenAI unveiled its first custom-built inference processor, designed and manufactured in collaboration with Broadcom. Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI’s inference systems. OpenAI’s own AI models assisted in the development of the chip, the company said.

  Jalapeño, like a jalapeño chip. Get it?

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 how Nori can bring your company into the glorious AI future at norisessions.com.

The year is 2022. I am the CTO of my first company. I am managing a team of brilliant engineers and a team of brilliant and completely non-technical sales and marketing folks. I spend, to a first approximation, half my life counting geese.

I am constantly in meetings. Every meeting has some complicated name, like ‘Aligning strategy for Q1’. But I know that the meeting is really about counting geese. We look at dashboards that make sure that everyone has their geese properly counted. Sometimes the engineers will mention that there were geese that they counted that aren’t reflected on the bean counter. Other times, the marketing and sales folks will say that some geese that we thought had been counted hadn’t been counted enough. There is much argument about the true number of geese.

Once we reach consensus, we spend time making sure that the goose count on GitHub matches the goose count on Linear which matches the goose count in Google Drive. There are more geese, the flow of geese is constant, so we have to spend an increasing amount of time counting them. We plan years around geese. I try to protect my team from the geese. Mostly that means that I am spending more of my time counting geese.

This is at a 14 person startup. It was worse when I was at the big tech co.

The year is 2026. I’m CEO of my second company. I haven’t thought about geese once in the last 6 months. The AI counts the geese now.

There are a lot of things to be worried about with AI. Things that I’ve written about extensively in other parts of this blog. But the thing that I am most excited about is the complete and total elimination of bullshit overhead. The future of work may be complex and crazy. But you know what it won’t involve? Having a founder staying up late at night making sure the linear story points are aligned with the things merged into GitHub so that the eng team sync meeting can save fifteen minutes of everyone’s time.

Everything that people hate, they hate because those tasks are boring and routine and dull and mindless. How many teams love to hate their CRM? Every time I get off the high of a fantastic customer pitch, I have 0 desire to then go into Hubspot or Salesforce to mark some row in a table ‘Lead’. How many hours get lost to making slide decks the right branding? Raise your hand if you lost an hour trying to figure out how to move an image that is in the background of your slide because it’s set as a ‘company wide template’, which was a feature you only just learned about ten minutes ago. How much information gets buried in slack, or docs, or linear, or wherever you do your work? Hours and hours of productive time wasted counting geese.

AI tools excel at this kind of data munging. They are so good at taking in data from calendly and piping it into salesforce or pulling in sentry data to debug something in github or whatever. Every company has a “brain.” Normally that brain is decentralized. It exists across the ten million different tools we all use to track state. Jira, confluence, notion, GitHub, linear, Asana, Monday, trello, docs, drive, slack, teams, one drive, Dropbox, one note, Salesforce, intercom, otter, fireflies, snowflake, databricks, datadog otel AWS gcloud Coca-Cola double decker bus his name was my name too.

The promise of AI is that you never need to think about any of that again. A universal AI operating system, a single entry point to the entire company brain. That doesn’t mean replacing people, though some folks may see it that way. To me it means taking the bullshit out of bullshit jobs.

I don’t think everyone is going to get there all at once, but the shape of the future is very clear to me.

• There are humans and there are agents.

• The humans spend time making decisions that matter — north star vision, strategy, marketing, and actually talking to people.

• The agents live in the background, in the ether, ephemeral, automatically bringing the vision to life with minimal friction.

This is a new and transformative way of conceptualizing work. AI is a new and transformative technology.

But it’s also very much within reach. There are teams who are “full auto” already, who are producing what would be Herculean amounts of output with a single person.

The main thing you need to get from point A to point B is a bit of forward thinking and some cloud infrastructure. We already provide the latter. Let me show you a bit of the art of the possible, in the hopes that you’ll get some of the former.

Here’s my morning a few days ago.

I wake up around 8:30AM. I walk into the kitchen and grab my phone, which I purposely try to keep in another room to avoid doomscrolling at night. There are like five slack messages waiting for me. I had a fleet of agents that were working through the night. I quickly eyeball the results while brushing my teeth.

One of them spun up at 3am to fix a bug that was reported by sentry. It posted a full root cause of the bug and a PR to fix it. I glance at the changed line diff and the pr description. Looks about right for what I’d expect for this, I happily merge it without looking at the code. I used to look at code, because I was worried about slop. But our agent hasn’t produced slop in 3 months. At some point you’ve got the thing so dialed in that it just works. I go to do some physical therapy stretches for my back — too much time coding in bed.

Shower, out the door, try to get on the Path train by 930AM.

While I’m sitting at the subway station I look through the other tasks. Two of them are confirmations to send out draft emails responding to some customer inbound. The agents personalized the responses, using a combination of call transcripts and website research. I tweak them both — I still can’t quite get the AI to nail my voice — but both emails go out in about 5 minutes. For someone who normally agonizes over each word in an email, this is a miracle.

One of my agents came back with some market research for the day. Things that the engineering hive mind on Twitter are thinking about. It is amazing for my mental health to be able to keep up with Twitter without being on Twitter. I chat with that agent a bit in slack, asking it to dig into a thread about agentjacking.

Another agent sends me a list of new papers on arxiv about representation learning. I spend the first half of the train ride reading through the summaries, asking questions about them and how they work at the stops where some 5g manages to leak down into the station.

Half the train is wearing Knicks gear, and that got me thinking that I should do something for our website. I open slack and type this prompt:

In between stops I iterate with the agent, all in slack. My cofounder sees what I’m doing and tells the bot to add a dunk animation in the same thread.

It’s done by the time I hit the 23rd stop and get out of the train. I can’t say that I have a ton of aesthetic sensibility, but I kinda like how it all came out.

By the time I get to my desk another agent has kicked up automatically, giving me an update on all of the changes in the code base yesterday alongside a brief summary of what everyone worked on. No painful daily sync, where you multiply every wasted minute by the number of people in the room. The rest of the team is locked in, talking to the agents from their CLI or slack or even lounging on the sofa on his phone. One of them is pacing around talking into his phone, recording a transcript to kick off a bunch of tasks on the go.

I am more productive on my way into work than on most full work days in 2022.

My team does 70% of our feature dev work in Slack. 30% of it from our phone. All of it through agents (we don’t write code and haven’t in months).

We do 90% of our ops work using automatic workflows that we never think about, or in slack through the agents.

We do all of our debugging through the agents through slack, though really it happens automatically before we even know there’s a bug in the first place.

We have the agents make slide decks. We have the agents update the website. We have the agents manage our CRM. We have the agents make sure linear and github are in sync.

My team gets to spend our time doing what we love. We think about product. We think about our customers. We write about things we care about, like this post. When people talk about AI being the future of work, they think that AI will get to do all the fun stuff. So far, for me, the AI has just automated away all of the things I hate doing.

And that’s really the goal, or at least should be for anyone thinking about how to deploy AI successfully.

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 how Nori can bring your company into the glorious AI future at norisessions.com.

Guest post from Clifford, my CTO and co-founder at Nori. His posts always do way better than mine, and it’s a shame he doesn’t write as much as I do because he has real insights whereas I mostly just shout my opinions at the cloud. You can see the original here.

Also I will be in SF from the 29th to the 3rd! DM me if you want to catch up!

Also, also, if you or your team is trying to figure out how to use AI at your company — if you are struggling with costs, or with security concerns, or with roll-out to nontechnical staff — take a look at Nori Sessions! It’s now generally available. On average, Nori makes teams 2-5x more productive across sales, ops, and eng. AI promises the future of work. Nori Sessions is how you get there.

The Wrong Scoreboard

The discourse on coding agents has been obsessing for the past year over the wrong question. The main focus has been what models can do: lines written, autonomous minutes, benchmark scores, model cards, percent of lines shipped by AI. These are all generalized measures of implementation throughput. Useful for a bird’s-eye view of model progress, but they say almost nothing about where the actual bottlenecks now live. The operative question for practitioners in 2026 is not what tools can do, it’s what you should ask them to do.

Answering the “should” question requires a different lens than the capability benchmarks provide. Every task you might hand to a coding agent has two costs that matter: the cost to implement (C_i) — the time and expertise needed to produce the code — and the cost to verify (C_v) — the time and expertise needed to confirm the code is correct. The relationship between these two variables determines whether delegation is a net win or a liability.

Aside: About “Delegation”

When I first outlined this in November 2025, I was comparing handcoded vs AI-delegated implementations. My workflow has changed significantly since then: I rarely hand-write code.

The relevant choice for me is now between pair programming with the agent (high-touch, Socratic, every structural decision is guided) and delegating (agent leads research, planning, and implementation; you just review the output of each phase). The pair programming model is mentally just as involved as writing code, but mechanically faster. The delegation model is now very different, allowing you to run and ship five separate feature PRs in parallel (not some clickbait Xitter “I ran 100 agents in parallel today” make-work slop, but five actual product increments, in parallel, in a brownfield codebase).

Whatever the threshold of delegation is, in my experience the framework below applies.

The Two-Variable Framework

When both costs are low, it doesn’t matter what approach you take — the task is trivial either way. When C_i is high but C_v is low, delegate freely; the implementation is a job for the agent, and you can cheaply confirm the result. The inverse is equally clear: when C_i is low but C_v is high, build a detailed mental model by taking part in every step of the process.

The dangerous quadrant is top-right. When both costs are high, there’s a huge incentive to spin the slot machine many times, and see if the agent just happens to nail the task. Compared to hand coding, where you burn days or weeks to ascertain the quality, the agent might have a chance to succeed at the same or higher quality after just 60 minutes of work. For complex or off-distribution work, it may be a small chance... but that makes it even more tempting!

By skipping the mental effort, you go in blind on an equally demanding task: verification.

This is the trap.

The models have dramatically compressed C_i across the board. C_v has not moved at the same rate — and in many cases, without careful developer intervention, it has gotten worse.

Vibecoding and the Unaddressed Variable

Vibecoding is the logical extreme of treating C_i as the only variable.

Previously, architecture decisions were bottlenecked by implementation cost. Releasing that constraint completely, without addressing verification cost, is a big failure mode. Any frequent flyer on Claude Code has experienced this, as an end user of an entirely AI-coded application — the constant issues with UI bugs, unintended changes to history cells, broken permission models...

I've written about the flickering issues before, and I've been annoyed that sandboxing persistently pollutes the workspace with empty files (this issue has been recurring in different forms for three months now). Users of the Claude Web environment, of Cursor, and many other almost fully AI-coded products experience the exact same degradation of quality as the software grows so rapidly. It’s not just that more features lead to proportionately more bugs. When you don’t build a mental model of the codebase, you’ve skipped your first pass on verifying the logic, and you’ve gone without a map of what parts need verification. The consequence isn’t just bugs — it’s verification blindness: you don’t know what you don’t know.

This is a common failure mode that many teams have fallen into, particularly startups that feel the keenest urgency to ship faster.

Verification Debt

As a result, the most common form of tech debt in these highly agentic codebases comes from growing your feature surface area too fast and too loose. Every agentic feature shipped without a corresponding verification investment degrades your ability to autonomously ship *future* features. This is a compounding liability, not a fixed cost — first it accumulates, and then because these changes can have cross-cutting technical concerns, or act as bad examples for future work, it compounds. Unit and integration testing become slightly more important, to compensate. But E2E behavioral verification becomes far more important, because that’s the layer the agent generally cannot self-evaluate on its own. Skipping this investment creates verification debt.

Detour: What Spec-Driven Development Gets Right (and Wrong)

The popular framing of spec-driven development is wrong on two counts. It’s not about making prompt copy-paste easier, and it’s not about closing the loop on “Ralph Wiggum” workflows — generate, test, regenerate. These framings chase short-term speedups that don’t touch the real bottleneck.

The original insight of a specification is much more important: you cannot verify an implementation if you don’t know its intent. A specification is the textual or symbolic description by which different readers arrive at the same mental model. In distributed teams, software has long relied on PR review, ADRs, box and sequence diagrams — this is fundamentally the sharing of intent. You must know the developer’s intent before you can review their outputs. This is not new, it’s just now more urgent.

The genuine unlock of specs is that they literalize the behavior you need to verify. Once combined with simulation environments — headless browsers, terminal puppeteering, API smoke tests — your specs become the instructions for agentic verification *after* agentic implementation. The loop closes not at the generation layer, but at the verification layer.

Beware Reflexivity

These two variables don’t stay independent — they affect each other over time. Shipping too quickly raises C_v as verification debt accumulates. Higher C_v in turn raises future C_i — the agent’s implementation speedup erodes as the codebase becomes harder to reason about, harder to test against, and bad patterns get committed and cargo culted. This is the mechanism by which agentic coding gains can crash down to earth, trash a codebase, and turn a team against AI coding tools entirely.

But reflexivity runs both ways. The virtuous path runs in the opposite direction: to properly ship faster, teams must aggressively use the low cost to implement new tooling as a lever to decrease the cost of verification.

Solutions: Making Verification Tractable

Three concrete approaches, in increasing specificity:

1. Simulation environments at full breadth

• Standard integration/automation/E2E testing practices are table stakes, but the agent’s reach is now wider.

• Headless browsers, Chrome DevTools protocol, pseudo terminals, API smoke tests, microVMs, containers — the agent can drive all of these.

• The question is no longer “can we automate this” but “have we specified what to automate.”

2. Making the runtime legible.

• E2E tests don’t capture everything: service startup timing, internal program state, functional SLAs, “no UI interaction blocks for more than 2s”.

• An ephemeral, per-worktree observability stack — logs, metrics, traces — makes runtime behavior tractable to the agent.

• This is the difference between the agent knowing the tests pass and the agent understanding *how the system is behaving*.

3. Bespoke verification tooling is now nearly free.

• Personal example: while building a PTY proxy around several TUI tools, codifying system invariants and nightly fuzzing jobs cost only ~two extra hours.

• I implemented fuzzing with reproducible seeds and system state captures at failure.

• I can put my tool through more comprehensive testing than I could have ever justified without the agentic contributions.

• The economics of verification tooling have shifted — the agent makes it cheap to build guardrails and course correction, so the only remaining question is where those guardrails should go.

The New Discipline

Coding agents haven’t changed what good software engineering is, but they have changed where the leverage point is. The developers extracting the most durable value from these tools are the ones who have reinvested implementation gains into verification infrastructure. The question to ask before every agentic task is not “can the agent build this?” but “how will I verify what was built?”

If you want two immediate, concrete steps to improve your verification tools:

• After you run through a cycle of research-plan-implement, your agent must go through the implementation using TDD. Here’s a general purpose agent skill to do so.

• If you want an agent to go through a manual end-to-end test on your project, consider giving it the tools to interact directly. Here’s a skill for the agent to puppet a browser with Playwright. Here’s a skill for the agent to puppet a TUI with tmux.

I am hoping that this will be just the first of three core posts about the new practices in software engineering. To signpost properly where I think this is going, here are the three ideas that are changing how I think about software development:

1. Verification debt. (this post)

2. Agent legibility.

3. Compounding correctness.

Lots of interesting things in this post that I agree with. See also my very old post on the point of code review, and Cliff’s presentation at the May Agentics NYC meetup.

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 how Nori can bring your company into the glorious AI future at norisessions.com.

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.

We wanted to see just how far we could push this framing. Could Claude Code make an entire video?

I was pretty impressed with how this whole experiment turned out! We wrote up a skill for all this here, in case you want to try doing the same.

What the fuck?

My first thought was that I needed to re-login. I run a ton of agents in parallel most of the time, so my instinct was that this was just a really really weird limit error. I vaguely knew that Anthropic was thinking about pulling Fable off the subscription plans so I switched to the API. Still nothing.

My team built a custom rust agent client, it’s pretty great. But my next thought was ‘o shit the harness bricked’, and I started poking around in Rust, which is a language I barely know even though I’ve ostensibly written tens of thousands of lines of code of it. At which point my friend went ‘the government banned fable.’

What the fuck?

But it’s true.

The US Gov directed Anthropic to disable access to Fable and Mythos to any foreign national anywhere in the world, including those in the US, including Anthropic employees. This is an impossible ask, and the Government knows it, so Anthropic has disabled all access to Fable/Mythos.

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected.

…

We received the directive from the government today at 5:21pm (ET). The letter did not provide specific details of its national security concern. Our understanding is that the government believes it has become aware of a method of bypassing, or “jailbreaking” Fable 5. We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities. These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass.

…

We have reviewed a report that we believe is the basis of the government’s directive and validated that the level of capability displayed there is widely available from other models (including OpenAI’s GPT-5.5), and is used every day by the defenders who keep systems safe. We will share more details over the next 24 hours.

…

We are complying with the government’s legal directive and are removing access to Fable 5 and Mythos 5 for all users. However, we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people.

A few thoughts about this.

• Up front, I’m extremely conflicted.

• I am an AI doomer most days. Having trained many many deep neural networks in my time, I have a deep appreciation for the ways in which optimizers can go wrong. We optimize what we can measure, not what we actually want to achieve. We hope and pray that these are the same thing, but they often aren’t. We want to build good products, but we don’t know how to do that, so we optimize for engagement. We want to teach kids how to read and write, but we don’t know how to do that, so we optimize for test scores. We want to improve the economy, but we don’t know how to do that, so we kill thousands of whales and let their corpses just rot on the docks. AGI / ASI systems are optimizers, and optimizers can really be extremely dangerous in ways that are extremely difficult to predict, because in their efforts to optimize for what we can measure they optimize away from the good. Corporations are ALSO optimizers, so of course they are optimizing for ‘get money as fast as possible’ — the thing we can measure — despite many of the people building it being like ‘hey yea this is really dangerous,’ which is of course a fantastic parable for the whole AI alignment debate.

• But also, there is a massive shadow hanging over this whole thing. If any other government in the history of the United States took this step, there would be good reason to at least give that government the benefit of the doubt. But this government has shown itself to be petty and corrupt in ways that continue to completely and totally astound me in its openness and creativity. Is this coming from an actual desire to regulate AI? A better question: does anyone in this government who knows anything about AI actually have the ear of people who make these decisions? I would bet against it!

• Anthropic and this admin are very famously not friends. I am biased, but from my perspective, Anthropic tried their hardest to integrate with the DoD and work with the military, and as a result Anthropic models were used inside highly classified systems. And the Trump admin responded with a highly publicized attempt at corporate murder by declaring that Anthropic was a supply chain risk and that no one who works with the government is allowed to use any Anthropic models (which is basically everyone). All this despite continuing to use Anthropic models for military operations for the next 6 months, which included the entire war in Venezuela and the war in Iran. The Chinese LLMs aren’t even declared supply chain risks! Anyway this became a very public thing, and the far right twitter arm decided that because the Trump admin was trying to destroy Anthropic, Anthropic must be woke, and anything that is woke must be destroyed, so the Trump admin is right to destroy Anthropic.1 So is this admin trying to properly regulate harmful AI? Or do they see this as an opportunity to give a perceived cultural enemy a black eye?

• Meanwhile, Anthropic’s competitors have friends up and down the administration — the Kushners are heavily invested in OpenAI, as an example.2 So another way to read this is that this is an opportunity for other labs to give Anthropic a black eye. Fable is, by all accounts, an incredibly strong model. Very convenient that it’s no longer available for consumers, especially right as Anthropic is about to IPO.

• The problem with spoils system politics is that it makes the optics of everything suspect. We spent decades as a society getting to the point where we decided not to do that, and now that trust is gone. Again, a very long shadow.

• As a brief aside, I am once again extremely disappointed in the myriad of Silicon Valley people who angrily argued that a Democratic led government would ‘pick winners and losers in the AI race,’ who are now completely silent or defending the actions of this admin. I cannot help but feel that that previous posturing was just a machiavellian play for power, which has just been the worst feeling in the world. The tech industry does not have a great reputation with the rest of the world right now, not in the least because AI leaders keep talking about how AI is going to destroy everything and cause mass layoffs. It would be great to have someone with enough of a backbone3 to stand up for the principle and say ‘hey, this is kinda fucked!’

• This was announced on 5:21 PM on a Friday. Sorta a suspicious time. Whenever someone does something intentionally on a Friday evening, my first thought is ‘o, the markets.’ See, if you want to do something that is likely to be really bad, you announce it on Friday evening so that there is some amount of time for the stock market to settle during the weekend in the hopes that it won’t immediately tank everything. This isn’t the first time the Trump admin has tried this trick. From Claude (yes I’m aware of the irony):

  The most-cited compilation comes from the research firm The Kobeissi Letter, which catalogued a series of major geopolitical and trade announcements that landed after futures markets closed Friday or early Saturday, giving the weekend to absorb the shock. Their list reportedly includes strikes on Iranian nuclear sites on June 21, US military action against Caribbean drug boats on September 1, a 100% tariff threat against China after market close on October 10, the closure of Venezuelan airspace on November 29, military action in Nigeria on December 25, and direct strikes on Iran on February 28, 2026. They also flagged the corporate angle: on August 11, 2025, the administration announced an Intel deal after weeks of public pressure on CEO Lip-Bu Tan, again structured to land outside active trading hours.

• Why might this be a volatile decision? Well, a huge part of the AI boom is the idea that there will be ongoing demand for computer intelligence. The debt, the build out, the datacenters, the stock market runs on every part of the AI chain from GPUs to memory to disk to server racks. Alllllll of that is predicated on the idea that all of this is going to be worth trillions and trillions of dollars. And by all accounts, it seems like it is. Or at least, was on track to be. You know what will put a spanner in the build out of a multi-trillion dollar data center investment? The realization that at any point, the government will unilaterally cut off access to everyone, and the datacenters will be worth squat. Some people over on HN and Reddit are already talking about how this represents the high water mark for what the government will ‘allow’ people to access. You can have all the demand in the world, and it won’t matter a lick if the government just won’t let you have it.

• Speaking of the HN/Reddit folks, lots of people are gleefully cackling about how Anthropic got what they deserved for their ‘marketing stunt’ with Mythos. As I’ve said before, this isn’t the first time we’ve had an AI CEO argue that something is ‘unsafe’ for personal gain.

  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.

  I kinda believe that Fable is the real deal, and I kinda trust Anthropic when they say that they are concerned about the security risks of a widespread Fable release. But the top voted comment on the relevant HN comment thread is “Finally they will pay for all the scaremongering they been doing to sell their models as something so much ahead of all else.” Now no one can use Fable so…got em, I guess? Of course, this is quite possibly the greatest advertising for Anthropic you could possibly imagine. If you take the government at face value, the models are so good they literally can’t be used!

• Re: taking folks at face value, some people are also like “When you spend a lot of time telling people how dangerous your products are, people who have the power to keep dangerous products off the market might listen.” And to be honest, it’s a good point! Bernie Sanders, the most AI pilled member of Congress, keeps making policy videos that are just him reading off quotes from AI CEOs and being like “see?!” Which, of course, this brings us back around to the first thing: maybe these things really are unsafe and should be regulated. Of course, no AI CEO worth their salt is ever going to say something like that again, if they know the risk is being shut down.

• I want to end with another HN comment, that I thought was particularly close to how I feel.

  So many comments here missing the big picture, and just gleefully pointing out that Anthropic got what they deserved, or that this is the natural culmination of some kind of marketing stunt.

  The real story here is that this may be the beginning of governments restricting the availability of strong LLMs to the public, to you. Fable was the strongest model on the market, and the US government has told you you can’t use it (technically, only if you’re not a US citizen, but in practice, even if you are). If you think the solution here is going to be open source Chinese models and / or running on your own hardware, think again. Do you think China is going to allow the strongest LLMs from companies within its borders to be open source a year from now when they have Mythos capabilities, if the US government is keeping the strongest American models back? Unlikely. These are heading in the direction of being powerful cybersecurity weapons and it will be in the interest of nation states to restrict and control them. In 2 years time, I would be surprised if the strongest LLMs are available for general use at all.

  Will we be the poorer for that, or will we be safer? I think poorer, because I hate being told what technology I can and can’t use, but I’m not certain. Maybe you think the government should restrict strong LLMs. Maybe you don’t. But either way, this is big news and a rubicon has been crossed and a precedent set. That’s true even if the motivation for this is just the government settling scores with Anthropic.

Well said.

There are maybe a dozen “computer use” tools out there that promise to let a coding agent run a computer. These tools give the agent access to mouse actions and screen capture. The agent can only see the screen when it actively uses a screenshot tool. The agent can only move the mouse by inputting precise xy coordinates.

Imagine doing this with a person. I’d sit in my room, take a picture of my computer screen on my phone, then run downstairs. I’d show some the picture to some other guy who’s for some reason locked in this experiment with me, and I’d say ‘QUICK give me the x/y coordinates of this button’ as I frantically point to the like button on instagram on my phone or whatever. And this guy would say something like “idk, my vision isn’t very good because I mostly only know how to read and not much else1 but maybe like 1432x2384?” And then I run back upstairs to my computer and type in those numbers and see the mouse move to what I hope is the right spot, and if its not I’ll do the whole thing over again.

All this to say, computer use agents are mostly incredibly slow and aren’t very good and eat tons of tokens. Neat demos. Not useful for real work.

The gold standard is obviously an AI agent that can use a computer the exact way a human can, so you can automate any task with just a sentence. The next best thing is an AI agent that can sit in the background of a human run browser session, that can take over at any moment in order to manipulate the web page and directly examining network requests. The former isn’t really possible yet. But you can set up the latter with a tool called Playwright.

Playwright is a browser automation tool. It allows you to script interactions with a chrome derived browser. It (and selenium before it) were originally designed for integration testing on the web. They have found new life in the AI era as the primary way we allow agents to use and explore web pages. Because tools like playwright are scriptable, the agent can interact with them through programming languages like Python. No need to take screenshots and manipulate the mouse with coordinates. The agent can interact directly with the HTML.

Playwright and other tools are normally run in the background, without a visible UI. But you can also just run it like Chrome and interact with it like any other web browser. If you spin up playwright through a coding agent session, eg Claude Code, you have a fully functional browser that can be run by an AI agent at any time. Just switch to the terminal tab running Claude Code or whatever and have the agent take over.

Below, a few examples of how I’ve been using playwright for all sorts of browser automation.

Fixing a broken luggage damage claim form from a budget airline on the fly

I recently flew from Paris to NYC on a budget airline. I checked a bag, the airline damaged it, tale as old as time.

Of course this airline doesn’t have any person that you can talk to about this. Instead, they have an online form where you can submit your claim. Credit to the airline, the form mostly works. You can tap out a complaint into a text box and it will submit the complaint. But if you try and attach any photos, say, of the luggage damage, the system just borks. Specifically, the ‘submit’ button remains greyed out and there’s no way to actually upload the supporting files. Probably not intentional.

Since I was running my browser through a Claude Code managed playwright session, I just tabbed over to the terminal and asked Claude to take a look at the page, figure out why the button wasn’t working, and then submit the files. Since Claude had access to the full html, js, console, and network requests, it quickly figured out that:

1. after each file upload a modal should appear that lets the user set the type of upload;

2. That modal wasn’t appearing, resulting in the form remaining disabled;

3. It was possible to simply set the form button as ‘enabled’ and submit the data;

Which it then did. It was even able to validate that the upload went through successfully because it could read the network request statuses.

Automating social media cleanup

A few years ago, after Facebook Messenger added a message unsend feature for what I assume are gdpr compliance reasons, I built a little browser extension called ‘Shoot the Messenger’. It was reasonably popular, with tens of thousands of downloads. The extension would automate your browser, hooking into messenger css selectors to automatically scroll through a messenger thread and sequentially unsend every message in the thread.

This was a nightmare to maintain.

Every time messenger changed their UI, all the css selectors would break. So maybe once a month I’d get a dozen emails and bug reports that the extension wasn’t working right.

With an agent powered playwright, the same functionality is trivial. I can open any messenger thread, tab to the terminal and tell the agent to record my actions, then delete a few messages and tell the agent to do the rest. Since it’s programmatic, I can basically just leave the script on without spending more tokens and go do other things while messenger cleans itself up.

LinkedIn scrapes

LinkedIn is notoriously hard to scrape. They aggressively tamp down on most bot-like behavior. Unfortunately, LinkedIn is also rather difficult to be on as a human. It’s, uh, a lot of AI generated content, to say the least. “Here are five things I learned about entrepreneurship when I stubbed my toe last week.”

Anyway, by this point you probably get the pattern. Spin up LinkedIn through playwright, show the bot what I want it to do, and then go get a coffee while it figures the rest out.

I found all of this compelling enough that I spent last weekend putting it together as a little side open source side project, which you can access here.

At it’s core, it is just a playwright session on the right and a little Claude Code session on the left. The Claude Code session is given system instructions to understand how to access the playwright session. Everything else is just using the browser like normal…except for when you want to automate something. Just as a silly example, asking the bot to try and find the emails from the folks who had top comments on HN:

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.

I run Claude Code1 in YOLO mode.

I know I shouldn’t. I know it’s a terrible idea. But I just cannot stand sitting there and hitting enter every 30 seconds because Claude wants to read a file.

After mindlessly hitting enter for the ten millionth time on some prompt, I snapped.

I aliased Claude to claude —dangerously-skip-permissions

And even though I’m now free from the burden of having to babysit the agent, I have a new fear.

One day, I don’t know when, Claude will rm -rf my entire computer.

In the past, I’ve said that coding agents are a lot like a really smart, totally untrained intern. I still basically believe that. Even though models have gotten better over time, they still come to every session completely fresh without any context on you, your company, or your engineering practices. Would you give a new hire intern unlimited access to your local machine? You know, where you have all your ssh keys and your contracts and a ton of personal data and maybe photos of your license/credit card/passport?

The fundamental tension is that we want agents that are highly autonomous, easy to set up, and really secure; and in practice, we can only pick two.

• You can install a frontier coding agent (Claude Code, Codex) locally and set up all the MCPs and it will be super easy because local dev is the happy path, and then the agent will one day nuke your email inbox.

• You can install a highly controlled permission-gated agent locally, get all the easy set up, and then spend your whole day hitting enter on permissions prompts one at a time.

• Or you can take a month to build out an isolated sandbox service to run Claude Code as a background agent. The agent works, but you’re too deep in docker networking configs to really enjoy it.

The vast majority of people are in the [powerful, easy, insecure] part of the embedding space. Mostly by choice — the average consumer of Claude Code probably couldn’t figure out how to set up a secure sandbox and definitely doesn’t want to use a limited agent.

There are some things that you can do to make the local experience slightly more secure while minimizing the amount of “Yes, and don’t ask me again” you need to do.

• Turn on /sandbox. It's OS-level isolation (bubblewrap on Linux, Seatbelt on macOS) that confines writes to your working directory and routes network through an allowlist proxy.

• Add explicit deny rules for the stuff you never want touched — ~/.ssh, ~/.aws/credentials, .env, id_rsa, .npmrc, ~/.netrc — in your settings

• Write good skills and agent configs, and don’t let your conversations go through too many turns.

Anthropic even shipped an entire permissions mode that is meant to address this complaint.

Claude Code users approve 93% of permission prompts. We built classifiers to automate some decisions, increasing safety while reducing approval fatigue.

But these will always be bandaids. The fundamental tension of a coding agent is that you want to give it access to things. It needs to be able to write and read files in order to do the most basic tasks. Unfortunately, that alone is enough to break out of most guardrails. Your filesystem is a core part of the agent config, so you should assume that anything on your disk is fair game.

I think the only actual way to have your cake and eat it too is to go the sandbox route. Ideally sandboxes in the cloud, far away from your local files, on boxes that you can tear down without hesitation because they don’t have anything precious in them. Yes, setting up background agents is a pain, but that will get easier over time as people figure out how to implement the infra. And you get more than security — agents that live on cloud sandboxes can be accessed remotely from a ton of different interfaces, and can be triggered by automatic events like bug reports or customer inbound.

For our agent environments (Nori Sessions), we went with a firecrackers micro VM provider for the base machine.2 These are extremely easy to start and kill, which makes them perfect for building agent runtimes that are constantly refreshing themselves. The VMs themselves don’t really have state. Instead, each VM has a setup script that clones all the relevant repos and sets up all the dependencies, so each agent session runs on a clean box. The agent can basically destroy the machine and it won’t matter, we’ll just recycle the thing.

The actual keys are never kept on the same box as where the agent is running. Instead, we proxy between the agent runtime and the outside world. Whenever we see certain credential placeholders in anything going outbound, we can trigger conditionals that determine whether we should inject the real credentials. The end result is that the agent thinks it has access to everything, but in practice it’s tightly controlled.

This isn’t the first time I’ve harped on the benefits of cloud agents. I also wrote about them in my post on AI enablement.

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.

…

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.

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.

It’s hard to explain how transformative these are for a company, but once you have them you cannot really go back. My best metaphor is that a good background agent setup is much more like hiring an extremely capable engineer than it is like interacting with a tool in a terminal. It is very natural and intuitive to interact with these things like a person, which short circuits a lot of hard learning curves and enables a lot of collaboration.

And, of course, they are secure. Way way more secure than running a local agent, or giving a new hire intern access to your personal laptop.

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.

Most people probably don’t know that I also have a long-standing interest in religious studies. I’m not particularly religious myself, mind you — I tend to swing between atheist and agnostic, in case it matters — but I have a lot of respect for religious contributions to philosophy, ethics, and culture. Reading religious texts is a great way to understand how entire civilizations organize themselves. And I also increasingly believe that some kind of faith is necessary to live a self-fulfilling life. Ethics is hard, personal responsibility for ethical choices is an immense burden. What a gift, to know what is moral or not based on the contents of a book or the teachings of an elder! It’s a shame that so many of my college-educated FAANG-employed friends never bothered opening the Quran or the Bible.

Anyway, this has been a particularly great week for me, because my two favorite hobbies randomly and unexpectedly intersected when the Pope released his encyclical on AI.1

Encyclicals have a long history, but you can think of them as letters to the public that outline the Catholic Church’s position on a subject. They’re not exactly the same as the word of God, but they are important entries into the long and storied Catholic philosophical tradition that includes Aquinas and Augustine, as well as Dante and Copernicus.

The encyclical on AI (officially called Magnifica Humanitas, or Magnificent Humanity) is a long document.2 It’s also, as you might expect, a nuanced document. If you were expecting a clear “AI is a force for good / evil”, you will be sorely disappointed. Leo has a fantastic grasp of the potential and risks of AI as a technology, and avoids the polarization that has plagued the discourse on basically everything.

One of the longest threads in the encyclical is on the question of what kind of technology AI will be, expressed through extended metaphor.

Will AI be like the Tower of Babel? Will it be an exclusionary project founded on the desire to surpass humanity? Will it be doomed to fail, to sweep away those who built it in a catastrophic fall rivaling the flood? Or will AI be like the walls of Jerusalem? Will it be a protective and defensive force that can shelter those who need it while encouraging the fulfillment of all mankind?

Despite being an encyclical about AI, the Pope is not particularly interested in AI as a technology. Technology does not have animating spirit. The ethical parameters of a piece of technology are defined by those who build it. What are their goals? What is their vision of the future?3 IMO, Magnifica Humanitas is less about AI and more about Silicon Valley.

The problem with AI is not AI. It is the transhumanism. Or the belief that money is a terminal indicator of worth. Or the belief that humans should be replaced by AI. It’s the culture, stupid.

92. In his Encyclical Laudato Si’, Pope Francis denounced the growing dominance of a technocratic paradigm [119] in our globalized world: the tendency to let the logic of efficiency, control and profit alone shape personal, social and economic decisions. This makes it clear that technology is not simply a tool. When it becomes the standard by which everything is judged, it begins to dictate what matters and what can be discarded, reducing creation to an object of exploitation and human beings to mere cogs in a system driven toward ever greater efficiency.

…

94. The danger of humanity becoming a victim of its own achievements was already clearly recognized by Saint Paul VI, who warned that “the most extraordinary scientific progress, the most astounding technical feats and the most amazing economic growth, unless accompanied by authentic moral and social progress, will in the long run go against man.” [121] For this reason, technological progress — valuable in itself — requires careful discernment of the anthropological vision that guides it and the ends it pursues. If technological development advances without a corresponding ethical and social progress, the result may be an increase in means without a growth in humanity: “having more” without “being more.” In such a scenario, there is a risk that individuals will be evaluated principally according to the outcomes they produce. [122]

…

115. In an attempt to shed light on the cultural assumptions accompanying the ongoing digital revolution, I would now like to turn our attention to certain currents of thought that interpret progress as surpassing the human condition, and which are often grouped under the labels of transhumanism and posthumanism. These perspectives form the ideological background present in some centers of technological power and occupy the collective imagination in a simplified form, especially in the media and on social networks. They tend to foster enthusiasm for new technologies through a futuristic vision of an “enhanced human being” or “human-machine hybrid.”

…

117. From the perspective of the Church’s Social Doctrine, the key issue is not the use of technology as such, but the vision that underlies it. If the human being is treated as something to be perfected or surpassed, it becomes easier to accept that some lives are less useful, less desirable or less worthy. In the name of progress, “necessary sacrifices” may begin to be justified, placing the burden on the most vulnerable in pursuit of a supposed optimization of the species. In this regard, the aforementioned warning of Saint Paul VI retains great foresight: indeed, scientific and technological advances, when detached from moral and social progress, end up turning against humanity. For this reason, a clear distinction must be made. It is one thing to integrate technology within a human-centered, relational vision; it is quite another to be guided by an outlook that devalues human limits and promises a purely technical form of “salvation.”

So is AI going to be the Walls of Jerusalem, or the Tower of Babel? Let’s ask any of the thousands of people in the Bay who believe they are literally “building God.”

One of my favorite quotes from the encyclical is about the ‘absolutizing of intelligence’:

In reality, elevating any single dimension of human existence to an absolute is always a mistake. Indeed, disorder does not arise only from scarcity; even unchecked growth can give rise to impoverishment. In an ecosystem, balance is disrupted when one species expands at the expense of others; in human life, something similar occurs when one faculty claims to be the measure of everything. Thus, intelligence, when absolutized, overshadows other essential dimensions of life, such as affection, the will, commitment and relationships.

Which really stands in stark contrast to various AI leaders who keep calling humans “meat computers”.

Musk:

We are all dumb meat computers compared to digital superintelligence.

Karpathy:

One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other fun, and synchronizing once in a while using sound wave interconnect in the ritual of "group meeting". That era is long gone.

Ellison:

The brain is very specialized, so are the AI models. But we’re not building a 20 watt meet computer. We’re building a 1,200,000,000 watt AI brain.

I think Leo is seeing and reacting to the same cultural trends I observed here (and to some extent here and here), but at a larger scale.

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’, ‘stoicism’, ‘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.”

The nihilism and pursuit of wealth at all costs doesn’t exist in a vacuum. It comes from the dark side of the entrepreneurial spirit, the same force that encourages creating gambling addiction or using rage bait marketing or blatantly violating laws to get a competitive edge. AI is a multiplier; in the hands of unscrupulous people, AI will be unscrupulous.

In some sense, Magnifica Humanitas is concerned about the sorts of things alignment researchers have been beating the drum about for ages. Chris Olah, an Anthropic founder and a leader in AI alignment, was invited to speak alongside the Pope for good reason. But the difference is that the researchers think they can solve the problem if they embed ethics into the technology. The Pope suggests that we embed ethics into the society that creates the technology instead.

Can AI Create New Knowledge

Two open questions, both extremely important.

First: is knowledge created or discovered? In some domains it seems obviously discovered — bacteria and viruses wreaked havoc well before germ theory could explain why, the arrangement of the stars and planets are agnostic to our telescopes, and the higgs boson continues to do…whatever it does regardless of our ability to observe it.

In others, it’s less clear. Mathematics is the study of axioms, a seemingly endless game of pushing a particular set of contrived rules as far as possible. Is the Pythagorean Theorem created or discovered? Any given proof of the Pythagorean Theorem is arguably invented. But also, in some sense, the relationship between sides of a triangle exists in the ether, waiting to be plucked out of the chaos by the first Greek guy with a ruler and compass. And proofs themselves are often just applications of existing knowledge in other places. Is that invention? Or do the laws of mathematics already exist, and mathematicians are merely filling in parts of the map?

Second: can LLMs create new knowledge? This seems to be the last remaining stronghold of the AI denialists. “Yes, fine, AI can create art and create music and write essays (though Sam Kriss may murder me for saying so) and build software out of nothing at all, but yawn wake me up when the AI can discover fundamental truths about the world.” Nevermind that most people cannot meet that standard, but whatever.

Notice that the second question is intimately tied to the first question. If creating new knowledge is an act of consciousness, a leap of ingenuity that cannot exist from any previous understanding of the world, then LLMs have not and arguably cannot create knowledge. But if creating new knowledge is an act of discovery, of interpolating between known points on a map and finding the points that are unexplored — that is, if creating new knowledge is merely a particularly exhaustive exercise in search — then of course LLMs can create new knowledge, it seems trivial to argue otherwise. LLMs are fantastic at search, and as I’ve written in the past, all problems are search problems.

In some ways, both of these questions are debates about semantics. What do you mean by knowledge? There’s an entire arm of philosophy (epistemology) dedicated to the question of what it means to know things! People have dedicated their lives to trying to understand this meta question! When Twitter user CiceroClone3524 smugly tweets “LLMS CAN’T CREATE KNOWLEDGE”4 without first providing a treatise on what knowledge even is, you should just treat it like the incoherent shitpost it is and move on.

This month, an LLM solved an open problem in discrete geometry.

For nearly 80 years, mathematicians have studied a deceptively simple question: if you place n points in the plane, how many pairs of points can be exactly distance 1 apart?

…

Today, we share a breakthrough on the unit distance problem. Since Erdős’s original work, the prevailing belief has been that the “square grid” constructions depicted further below were essentially optimal for maximizing the number of unit-distance pairs. An internal OpenAI model has disproved this longstanding conjecture, providing an infinite family of examples that yield a polynomial improvement.

…

Surprisingly, the key ingredients of the construction come from a very different part of mathematics known as algebraic number theory, which studies concepts like factorization in extensions of the integers known as algebraic number fields.

This was immediately met by cries of disdain. For example, Gary Marcus:

Clearly impressive. But as with so much else, it should be viewed with skepticism.

…

[Quoting Cal Newport] He noted that though the conjecture is old, those who have worked on it have largely shared Erdos’s original belief that it was true and therefore focused on trying to solve it. What the LLM-based tool did instead was to systematically apply and extend existing techniques in search of evidence that the conjecture was false.

…

[Quoting Tim Gowers] It has an encyclopedic knowledge of mathematics, and it does not have to worry nearly as much as we do about time management, so it is good at finding surprising connections.

And so on.

Without commenting on whether or not this is “new” knowledge,5 it’s worth pointing out just how much ground the AI denialists are ceding. Science is about building on the shoulders of giants. Virtually everything we know is derived from combining the ideas of our forefathers. Hell, almost every AI paper is about applying some idea from some other field to AI, and all the other AI papers are about applying some idea from AI to some other field. If a novel mathematical proof isn’t new knowledge, the term “knowledge” has ceased to refer to anything that corresponds to reality. At that point, just pack it up — humanity never had any hope anyway.

The Big Labs get into Consulting

I think “forward deployed engineer” is my favorite euphemism of the last decade or so. As far as I can tell, the only reason this term exists is because a significant portion of Silicon Valley will refuse to accept that they are consultants, but if you wrap their title with the term “engineer” suddenly everything is ok.

What does a forward deployed engineer do? Well, they are “deployed” to client teams in order to help those teams solve their business needs, often by coding. What does a consultant do? Well, if they work at say, Accenture, they are deployed to client teams in order to help those teams solve their business needs, often by coding. “No no, I’m not a consultant, I don’t work with slide decks and things. I’m beyond such mundane software as the Microsoft Office suite. I’m an engineer.” Uh huh. I mean I agree that management consulting probably uses slide decks, but my buddies at McKinsey write code too so IDK. Hell, with coding agents, everyone writes code these days, especially the consultants.

To be clear, I have nothing against consultants or consulting. It’s obviously necessary, especially when there’s a new technology that lots of people want to use but few people have expertise in. I just think it’s funny that the Valley had to invent a new word for it. Kinda in line with the whole “package an existing product in a new name and sell it as revolutionary,” though that is more often done to avoid regulatory scrutiny (looking at you, prediction markets / crypto / ride shares / whatever software landlords use to coordinate rental pricing increases).

Anyway, my point is that FDEs have been around for a while. If you are having trouble with the middle of your sales funnel — folks are maybe signing up for your product but not getting the full use of it, maybe not getting upsold effectively — sending out one of your employees to help them get set up is common sense.

This is why Microsoft hires Solution Engineers:

• Drive technical sales by using technical demos, proofs of concept, and technical architecture accelerators to influence solution design and enable deployments.

• Lead architecture sessions and technical workshops to accelerate Azure adoption.

• Build trusted relationships with platforms leads to co-design secure, scalable solutions.

And why Oracle hires field engineers, and why Salesforce, Atlassian, Intel, Google, all have customer success engineers, and so on. Apparently IBM had “customer engineers” way back in 1942. The actual term “FDE” got popularized by Palantir, and since then I’ve seen Fortune 500s and Series A startups alike advertise for FDE roles. But, you know. It’s really just another name for consulting.

This is why I was somewhat surprised about the news that OpenAI and Anthropic were both starting consulting arms. Not that they were starting those arms, mind you, that makes tons of sense, see above. Rather, I was surprised by the news — the breathless takes from an entirely too credulous social/media that this was further evidence of all economic activity being subsumed by the big labs.

Guys. Guys. Come on. Get off the Internet for two minutes, please.

OpenAI is starting a consulting arm because people keep going “I want to AI more” and then OpenAI goes “here is your AI” and then people go “thanks but I have no idea what this is, also can you make it hook into my RBAC provider?”

AI tooling was built for consumers first, so it is easy to forget just how involved enterprise software is. “Just npm install -g claude” you fool. You absolute fool. The average enterprise user cannot just npm install -g claude. Hell, they can’t even install npm! They have to request approval to get access to the site that hosts an internal version of npm, then request approval to download npm, then set npm to point to an internal registry, then get approval to download an internal version of Claude off that registry, and then finally have that version of claude point to some internal enterprise exclusive Claude deploy. Someone has to maintain all of those security boundaries, someone has to explain how the AI works within those boundaries, and all of the people who know how to do that currently work at the big labs because AI has only existed in its current form for like 8 months. If you made wild predictions about how “Microsoft sending consultants to help enterprises onboard onto Windows” meant that MS was going to take over the world, you would’ve been right for about 5 years from 1995 to 2000 and then very very wrong for the next two decades.

That’s not to say that OpenAI and Anthropic and so on are bad products, nor even that they won’t benefit from the expansion of AI usage. Microsoft undoubtedly made more money in absolute terms as computer adoption skyrocketed, and the same is certainly true of the big AI labs. But also, there were clearly many billions to be made on the back of the computer boom, and on the back of the Internet boom, and on the back of the mobile boom. The AI boom is very much in its infancy, and, contrary to popular opinion, the existence of these deployment arms — which are essentially high labor, white glove services companies — just underscores how early we are.

Other things:

• The Pope’s encyclical is a thoughtful and nuanced document, so of course most of the Internet spent most of the time debating whether or not the thing was written with AI. There’s good reason to believe parts of it were, or at least that some translations were done with AI. But also, this discussion completely misses the point. The Pope never says that using AI is bad! AI isn’t an all or nothing thing, and if used responsibly it is obviously extremely valuable. The problem with AI writing is AI slop; the Magnifica Humanitas is obviously not that.

• SpaceX is IPO-ing. Matt Levine has the best take, as usual:

  The deal, with SpaceX, is that Elon Musk runs it however he wants, and he does weird stuff, and you have to trust him, and if you don’t like it you can’t complain. When SpaceX acquired xAI a few months ago, did a special committee of independent directors approve the transaction? Did Musk recuse himself from negotiations? Was the price set by independent valuation experts using a rigorous process? Did outside shareholders sue to block the deal? Stop. Musk wanted SpaceX to buy xAI, so it did.

  SpaceX claims to have a TAM of the entire global market and then some. This is obviously ridiculous, but if you’re investing in SpaceX you’re obviously along for the ride. The bigger concern, in my mind, is SpaceX getting index funds to change their rules. TBD on how that all works.

• Mythos seems like it's beating the hype. From Anthropic: “Since then, we and our approximately 50 partners have used Claude Mythos Preview to find more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world. Progress on software security used to be limited by how quickly we could find new vulnerabilities. Now it’s limited by how quickly we can verify, disclose, and patch the large numbers of vulnerabilities found by AI.”

• Instagram was hacked because of AI, but not the way you might think.

  Step 01: Faking the Location & Initiating Support All the attacker needs to kick this off is your account username. Then, they hop on a VPN or proxy close to your city so Instagram’s security algorithms don’t suspect a thing. (You can quite easily get this from your public profile or “About” section or a hundred other ways.) Once it looks like the request is coming from the correct region, they tell the Meta support AI that the account is hacked and ask it to send the verification codes to an arbitrary email address they control.

  
  Step 02: That’s It Really, that’s it. The first proper zero auth password reset I’ve seen in production. There appears to be no additional check as to whether the email being given is actually something the user has used before. Once the AI sends the security code to the attacker’s email, the attacker passes it right back to complete the verification. The platform hands over a fresh password reset link, granting full ownership to the attacker.

  “The first proper zero auth password reset” is hilarious. Reminds me of the good old days when you could talk to “customer support” and have it promise you a car that the company would then be forced to pay out.

• Chipotle runs an AI model for customer service. Apparently it’s just a standard coding agent, so someone figured out how to hook into it for their coding work. “Free inference paid for by burritos.”

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

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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.