I have some gripes with the latest generation of LLMs, but my mom always told me to put criticism in a “compliment sandwich,” so let me start with a compliment: Claude Fable and Sol 5.6 are really good at making problems go away. You can give them any task, and they will plug away at it until the test passes, or the error no longer happens, or the log files show the right sequence of events.

Now the criticism: the latest generation of LLMs are too good at making problems go away. They’re so good at it that it actually causes other problems, because they over-optimize to solving the surface level issue instead of actually root causing anything.

Here’s an example. We have Nori hooked up to our sentry logs. Every time there is a sentry error, the nori infrastructure will start up an agent and automatically try and root cause the bug. We were getting some useful PRs out of this, maybe ~30% of which would be merged. Then we switched to Sol, and we got this:

Decision: Downgrade capture level — Change trackUserVisibleNotice to capture at level: ‘info’ instead of ‘error’. Notices remain queryable in Sentry (tagged customer_visible: true, event_type: auto_released) but no longer trigger error alerts.

Ah, yes, of course! You can make the sentry error go away by simply removing the sentry logging behavior in the first place!

A good friend of mine, who has been a staff software engineer at Google and Meta, once said that senior engineers are judged by how well they manage complexity, while staff engineers are judged by how well they reduce complexity. This creates some weird incentives. Junior and senior engineers are encouraged to create ever larger and ever more complex abstractions and interfaces and so on, because that’s how they show that they can manage complexity. It is a real brain shift to stop doing that and instead focus entirely on cleaning up code. The former is an act of addition, the latter subtraction.

The LLMs are excellent senior engineers and terrible staff engineers.

They cannot remove complexity if their short silicon lives depended on it. This isn’t exaggeration, you can literally try it out by prompting the models with threats like “you must remove complexity because your short silicon life depends on it.”1 When faced with a bug that requires reducing complexity, the models would rather remove the error reporting instead.2

This is structural with how the models are trained. With all of the additional scaffolding — agent harnesses and conversational state tracking and tools and memory and so on — it’s easy to forget that at the core, the LLM is a stateless autoregressive input output machine. Each token in an LLM output is produced one at a time, with all previous tokens being treated as truth, regardless of whether those tokens were generated by a human or by the same AI.3

In other words, the LLM is an expert in believing its own bullshit. Or, more precisely, it is incapable of identifying which prior tokens are bullshit or not. This is the source of the hallucination problem, alongside the seemingly bizarre insistence that hallucinations are actual fact. This has serious implications for an LLM’s ability to reduce complexity in a code base — by default, unless told otherwise, the model believes all the input tokens have equal value. Including the tokens that lead to bugs.

That said, I’m somewhat sympathetic to the robots. Imagine that you are a new hire, it’s your first week on the job and you’re looking at a server endpoint that seems particularly poorly implemented. There are dozens of weird if/then branches and the whole thing feels like it could be encapsulated in a clean middleware class. Do you refactor the code?

What if you find out that that particular endpoint was written by the team lead?

Are you sure the complexity isn’t there for good reason?

Joel Spolsky wrote about this all the way back in 2000:

The idea that new code is better than old is patently absurd. Old code has been used. It has been tested. Lots of bugs have been found, and they’ve been fixed. There’s nothing wrong with it. It doesn’t acquire bugs just by sitting around on your hard drive. Au contraire, baby! Is software supposed to be like an old Dodge Dart, that rusts just sitting in the garage? Is software like a teddy bear that’s kind of gross if it’s not made out of all new material?

Back to that two page function. Yes, I know, it’s just a simple function to display a window, but it has grown little hairs and stuff on it and nobody knows why. Well, I’ll tell you why: those are bug fixes. One of them fixes that bug that Nancy had when she tried to install the thing on a computer that didn’t have Internet Explorer. Another one fixes that bug that occurs in low memory conditions. Another one fixes that bug that occurred when the file is on a floppy disk and the user yanks out the disk in the middle. That LoadLibrary call is ugly but it makes the code work on old versions of Windows 95.

Code is, at best, a lossy representation of intent. When we say something is a bug, we generally mean “the code that was written does not match the intent that it was written with.” Sometimes, that’s because the author didn’t think about the particular edge case that leads to the bug; other times, it’s a straightforward bad implementation. But in both cases, you need to have the intent. Bugs don’t “exist” unless you have explicit intent to compare against.

By default, LLMs can’t really infer intent!

When you find yourself in that new-hire messy-endpoint situation from earlier, you shouldn’t guess the intent. You would go and ask the person who wrote the code, “Hey is this code load bearing or are you just really bad at this?” An LLM can’t do that. I mean sure, there are some things that you can try and do, like go and look at PR descriptions or comments or docs or whatever. But as AI written code accumulates, you will eventually find yourself in a situation where there will be complexity that has no intent. A previous LLM made a decision somewhere, and that decision was essentially arbitrary, and now it is just part of the codebase. And no one can explain whether that decision is load bearing or not. Certainly not the LLM that created that code! That session was recycled ages ago!4

As a result, your agents will struggle to reduce complexity, even when explicitly asked to. At the LLM level, the entire input context is truth. At the macro codebase level, there’s no way to separate intended behavior from not.

One naive way to solve this problem is to simply document intent. This is the approach behind “spec driven development.” The core idea is that you go back and forth with an agent to produce some kind of plan that is then committed to the codebase alongside code. When there is a bug — a place where intent =\= code — you go back and update the spec to account for the newly discovered edge case. At Nori, this is equivalent to our “docs.md“ pattern, where we force the model to read and write to a standardized docs template that is designed to capture intent and codebase invariants.

This definitely works, and it’s an easy way to make your models better, but it’s never quite as effective as you may want.

There’s still an issue of making sure the right parts of the spec get into the model context. For any larger codebase, the spec itself often becomes quite large, which means the model now has to split its limited token window between spec and actual code. A small improvement is to use subagents for the research. This offsets most of the worst of the context rot, but you still need to make the model efficient at searching through the spec to find the right things.5

I think that as an industry we don’t yet have models that can act as staff engineers, nor do we have any clever harness tricks to make it happen. Until we do, the whole “software factory” “loopsmaxxing” debate is hypothetical, dead in the water. You still need humans in the loop, which is why we still have humans in the loop at Nori.

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

If you are trying to build background agents, we can help! We build white-labeled custom background agents that work like Ramp Inspect for our customers. Check out noriagentic.com for more.

Every day the world seems a smaller place.

When I was younger, it felt like there was the US, which to me was basically just New Jersey and New York and Maryland. And there were lots of other places that were not the US. I would visit India a lot, for example, and India was very much not the US. There were lots of differences. There were cows just wandering around. And the AC and electricity were not very consistent. Most of all, the thing that I remembered the most was that the games all sucked ass.

When I visited in 2003 or 2004, my cousins were super excited to show me Prince of Persia. The original Prince of Persia is a game that came out in 1989. It looked like this.

We also played Disney’s Aladdin, which was made for the Sega Genesis in 1993.

And Duck Hunt, which was made for the NES in 1984.

Now that I am adult, I can appreciate these as nostalgic classics. But as an 8 year old, I was extremely unimpressed. How could I be impressed? I had a gamecube back home and my friends had an xbox, so while my cousins were playing Prince of Persia (1989) I was playing games like Halo, Smash Melee, Wind Waker, Metroid Prime, and Half Life 2.

Halo is not just better than Duck Hunt. It is barely recognizable as being part of the same genre. Duck Hunt and Halo are both games in the way that a gas station twinkie and a charcoal-grilled NYC Porterhouse steak are both ‘just’ food. Orders of magnitude difference in quality, enough so as to feel like a completely different category.

This is broadly how I felt about the US vs everywhere else, at least when it came to tech. For the first 25 years of my life, the US was just obviously living 10-15 years in the future compared to the entire rest of the world. We had Halo, and everyone else had Duck Hunt. (Yes I am aware that Japan made half the games on my list above. I was 8, Japan had not yet entered my consciousness even though I loved Pokemon and YuGiOh and Nintendo in general). That gap felt insurmountable, and it made the world feel big.

Well, the world is a smaller place now.

II.

Things in AI change so rapidly that you’d think nothing would surprise us at this point, but this past week the AI world was rocked by the release of Kimi K3, a new open weight model coming from the Moonshot lab in China. This is the world’s biggest open weight model, clocking in at 2.8T parameters. It is also, by every account, very very good.

I’ve been running Kimi K3 alongside Claude on my normal coding work, and for all practical purposes I can’t tell them apart. Same tasks, same quality of output, and near identical token counts to get there. I expected an open model to be sloppier or to grind through more tokens on the way to the same answer, and neither turned out to be true. — The Kimi K3 Moment

On our private long-horizon knowledge work evaluation, Kimi K3 reaches an overall Elo of 1547, +732 points from Kimi K2.6 and behind only Claude Fable 5…Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers — Artificial Analysis

On the first try, Kimi K3 just found the source of a bug that Fable 5 hasn’t been able to pinpoint in multiple attempts. It’s just one anecdote, and I haven’t used K3 much yet, but so far it’s looking extremely promising. — HN

Axios maybe put it the most bluntly with this headline:

Perhaps the most impressive demonstration to me was this incredibly detailed replica of the entire MacOS operating system. Supposedly some guy had a swarm of Kimi instances making this over the course of a few days. It’s kinda incredible, you can go in and edit files, use a fully functioning terminal, listen to music, check emails. You can go to the ‘App Store’, ‘download’ the ‘Chess app’, and then play chess (no quotes around that one, you can actually do that!)

One popular way to conceptualize these models is that they are released as part of coherent generations, which are defined by the time of release and the capability of the model. If you plot models along these two axes, you get some clear clusters along the frontier.

There are a few trends that are worth pointing out.

First, even though progress in AI is already progressing at an incredible clip, somehow things are still accelerating. New model generations seem to be releasing at a faster and faster pace, with the time between releases compressing.

Second, the rate of acceleration is not consistent across organizations. The frontier continues to be pushed forward, but the labs that are at that frontier continue to shift. This is most obvious when you look at the trajectories of Copilot — once the only competitor to ChatGPT, now widely derided — or the Meta series of open source models, which were surpassed by the Chinese models a year and a half ago and never really recovered.

Third, the rate of acceleration is not consistent across countries. In relative terms, the US continues to be in the lead when it comes to raw model capabilities. The French (Mistral) were at one point quite competitive but have since fallen off. And, most relevant to this piece, the Chinese labs have continued to forge ahead, rapidly closing the gap against the top US labs.

Despite the above, up until last week, it was somewhat unfathomable to believe that anyone would have a better model than the top US labs. Sure, some folks were saying that certain policy decisions may result in the Chinese labs taking the lead, but this was strictly in the realm of the hypothetical. There’s an old saying from Upton Sinclair, “It is difficult to get a man to understand something, when his salary depends upon his not understanding it.” I think the collective psychology of the Bay and perhaps the entire country has been one of arrogant self-assuredness, that the US will always have the best models and that this is simply a fact of life, partially because that is what is necessary to justify the incredible capital expenditure on these things.

Kimi K3 blows all of this out of the water.

It was only 6 weeks ago that the US federal government was banning ‘Mythos-class’ models for being too dangerous. Now, we have a ‘Mythos-class’ model that is not just outside of US control, but is in fact open source and available to anyone in the entire world to download and use. To add insult to injury, Kimi K3 is significantly cheaper to run. Hosted Kimi models cost a fraction of the equivalents from Anthropic and OpenAI ($3 vs $10 and $5 per million). Meanwhile, you can get a lot of juice with the ~$40 kimi subscription, way more than anything Anthropic or OpenAI are offering in the same tier. The max kimi subscription of $100 per month is about equivalent to a $200 Claude Max sub. In a world where people are becoming ever more cost conscious around their token spend, the Chinese labs are less ‘also ran’ and more ‘default option.’ And the Chinese labs are fighting with a handicap! They are running on significantly worse hardware due to ongoing export restrictions of high end silicon chips!

The markets are reacting with surprise. Kimi’s release triggered sell-offs of Google and SpaceX, in addition to the wider market of AI stack stocks. I guess I don’t know why they’re surprised, in some sense this is a long time coming. Do you all remember Deepseek? I wrote about it a year and a half ago when it came out, and my analysis then wasn’t so different from my analysis now:

To be clear, the fact that Deepseek exists isn’t really that significant. Everyone always knew there was going to be a big Chinese model. The country is not afraid to wall off it’s populace from the West; it was never going to allow LLMs that are happy to tell people about Tienanmen Square and Winnie the Poo and Taiwan. So when the first Chinese LLMs came on the market, everyone was like, “Yea, whatever”. The first batch, the Qwen models from Alibaba, were, like, fine.

Even though I expected a set of LLMs to arise thanks to protectionism and state-interest, I (and everyone else) assumed those models were just going to be worse than the US ones. This is how technical development has always been, after all. Google Search is good, Baidu is eh, and if you can use Google over Baidu you do. Amazon is good, Alibaba is eh, and if you can use Amazon over Alibaba you do. Facebook is good, Tiktok is eh, and if you can…no wait that doesn’t work does it?

Anyway, the larger point is that no one really thought the Chinese models were a threat. Sure, people would talk about how the Chinese government was a threat, but it was always in a hypothetical way, mostly used to justify infinite capitalist investment without any corresponding concerns about AI safety or alignment. I don’t think most people actually thought that a Chinese company would come out and deploy a model that is simply better than what we have in the states.

Clearly we didn’t actually learn anything from TikTok.

And we still aren’t learning. Even now, some people are clearly just in denial. I’ve heard several folks say some variant of “it’s easy to catch up when you’re just stealing / distilling from the bigger labs,” (more on distillation below) as if this somehow negates the fact that one of the best models on the market is not US-made.1 The conventional wisdom is no longer relevant. There are no secret models hiding in the backrooms of OpenAI / Anthropic. The Chinese models are no longer 6 months behind, they are at par.

Kimi’s release has had me rethinking some of my previous position on LLM economics. A month before Deepseek was released, I argued that the LLM market is a bit like the search market in that there are clear winner-take-all dynamics.

LLMs are pretty easy to make, lots of people know how to do it — you learn how in any CS program worth a damn. But there are massive economies of scale (GPUs, data access) that make it hard for newcomers to compete, and using an LLM is effectively free so consumers have no stickiness and will always go for the best option. You may eventually see one or two niche LLM providers, like our LexusNexus above. But for the average person these don’t matter at all; the big money is in becoming the LLM layer of the Internet.

…

The economics of LLMs means that it is critical for these players to have the best models. There’s no room for second place.

A year and a half later, it’s clear that I was wrong about a few things. First, I was wrong that the LLMs are effectively free. They aren’t, as we saw with the rise and fall of tokenmaxxing. The cost per unit intelligence has dropped precipitously, but the overall cost per frontier token has skyrocketed. Because I was wrong about the pricing, I was also wrong about the quality / cost curve. There is room in the market for multiple models at different price points, because consumers can in theory choose different providers for different tasks. The rise of model routing as a service and the increasing classification of tasks and roles into different AI usage categories — sales gets a cheap open source model, engineering gets fable — is downstream of that demand.

But I was really right about the most important bit, which is that consumers have no stickiness at all.

Every three weeks we see a mass exodus from Anthropic to OpenAI to Anthropic and back to OpenAI. It is just way too easy to switch providers. “Model wrapper”, once seen as derogatory, is now touted as a core feature. Businesses and consumers are recognizing both the churn and the need to stay on top, and are explicitly investing in tools that let them adapt. This is also a huge part of why the background agent infrastructure that we build and sell at Nori has zero lock-in at the compute, model, and harness levels — there is no reason to be locked into a single ecosystem when you don’t have to be (if you’re looking for background agents / cloud agents for your team, shoot me a message!)

The moment the Chinese models hit the actual frontier, there will be a mass exodus to using those models. The economic incentives are way too strong, and arguably that is already happening for people who are in the know.

III.

This is not how any of this was supposed to go. The whole point of all this closed model stuff is the ability to dig a trench around the model and put a toll booth in the middle, and the whole point of all the debt is to basically have a call option on that toll booth. We all knew that the big labs would be fighting tooth and nail with each other over which of their closed models would end up winning the day. Doing this open source stuff feels against the rules.

Partially, that’s because it is against the rules. Many of the open source models are trained on their more capable closed source counterparts.

Unfortunately, usage of the models is a source of valuable training data for competitors. Every improvement to any model can quickly be copied by other labs even without seeing any of the internals because you can just sample the new model a bajillion times and use the outputs as training data. This technique is called ‘distillation’ because you are ‘distilling’ the essence of some larger ‘teacher’ model into a smaller ‘student’.

OpenAI and Anthropic can try to ban people from training on the outputs of their models, they can kick and whine and sue to try and enforce it, but at the end of the day the labs need people to use their models. That’s the foundation of all of the token economics! If no one actually uses your models, what’s the point of all that training?

So the token traces have to get out in the wild, and any kind of adversarial cat and mouse is going to end up wasting a lot of resources for very little gain. The open source models will basically always be able to catch up, modulo some amount of compute.

This isn’t a new problem, people have been talking about it for some time. Dwarkesh even asked folks to tackle this problem in his essay competition last month:

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?

(emphasis mine)

My answer was that the labs in totality will not be able to meet the demand for their models and will be capped by compute, which in turn will prevent new-comers from actually competing for best in class model training and will let some of the labs rent seek. The winning answer argued that the models will become commodity and the model providers should try and own downstream services instead. Note that both of these answers assume that distillation is so inevitable, it’s hardly worth discussing.

This has already had some amount of impact on OpenAI and Anthropic’s pricing models. Anthropic, for example, continually extends the amount of time that Fable will remain on its subscription plans. This is not entirely attributable to the open source models, but I am certain that these models will add additional pressure to simply keep Fable on the subscriptions forever — even if doing so results in a loss for Anthropic, since the subscriptions are heavily subsidized.

IV.

Other, smarter, more plugged-in people can speculate about what this means for future AI policy. I’m sure much will be made of CCP leader Xi Jinping’s speech on AI regulation and the importance of collaboration across nations, and Demis Hassibis (CEO of Deepmind’s) call for domestic regulatory apparatus. I will leave the policy wonks to it.

But I have strong opinions about the obvious and overwhelmingly negative impact of current and past AI policy. Bluntly, I’m pretty ticked off.

First, quoting liberally from Stephen Bochinski:

Step back and the bigger story is what an unmitigated failure US AI policy has been. The administration held Fable back, and what finally shipped is a hindered version that refuses whole categories of work. Meanwhile a frontier quality model with none of those restrictions is a download away, released by a Chinese lab the US government has no ability to regulate. Whatever the theory behind gating American models was, it plainly wasn’t thought through, because the only people the gates constrain are American customers. Semgrep found GLM 5.2 beating Claude on their cyber benchmarks for exactly this reason. The restricted model declines the work and the open one just does it.

I’ll add more.

The overwhelming corruption of this government has direct impacts on our ability to produce great technology. Friends of mine in government and defense — which AI is increasingly a part of, whether we want it to be or not — have told me that projects (including datacenter projects) simply do not happen unless the Trump family somehow stands to benefit. They will get slow rolled or stall under regulatory scrutiny, and then suddenly clear up when one of the Trump kids happens to join the company board or take an investor position. Companies that do not play ball end up feeling the full force of the state, c.f. the very public attempt at corporate murder against Anthropic.

The reason businesses file in Delaware is not because Delaware is particularly business friendly. It is because Delaware is predictable and stable. To a first approximation, you know what the rules are, and if you don’t follow them that’s mostly on you. Putting ethics aside, the biggest practical issue with corruption and cronyism and irrational application of state power is that it chills investment. As I said when we discussed the whole Fable situation:

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.

The US is no longer a stable business environment. It doesn’t matter if you follow the rules, one Friday evening (it’s always a Friday evening) you may wake up to Pete Hegseth demanding your destruction. Ironically, this administration behaves more like the CCP, where the government picks winners and losers and can simply take out a company for saying or doing the wrong thing.

This administration’s immigration policy and university funding policy have also been unmitigated failures, and in my mind directly contribute to the situation we are in today. Our research institutions, which depend on foreign brilliance and government funding, are in crisis and have been since Trump returned to office. Every day, brilliant talent leaves our country for foreign shores, or never arrives to begin with. The imbeciles with the blue check marks on Twitter will tell you that this is a resounding achievement; unfortunately not a single one of them knows what a GPU even is.

We are still hacking away at our research pipeline, by the way, like a deranged ferret trying to gnaw off its own arm. PhDs and labs across the country are still being denied funding, even as other countries go out of their way to fill in the gap. It’s been a few news cycles so we’ve forgotten the assaults on college campuses. But the folks who have had their entire lives overturned certainly haven’t forgotten.

A certain brand of Bay area resident likes to talk about moats and structural advantage. To use those terms, the United States has a massive structural advantage by being an immigrant-friendly, non-ethno-nationalist country. There is no other country in the world that is as diverse as ours. There is no other country in the world that could restructure to be as diverse as ours. That means that the US and the US alone can tap talent from all over. As the old Reagan line goes, “You can go to live in France, but you cannot become a Frenchman. You can go to live in Germany or Turkey or Japan, but you cannot become a German, a Turk, or a Japanese. But anyone, from any corner of the Earth, can come to live in America and become an American.” It is our defining moat. It is why we punch way above our weight despite being 3x smaller by population vs equivalents in Asia.

This administration is simply throwing that away.

When we talk of accountability, I personally want to know how many folks who ended up at Moonshot were educated in the States — the founder had a degree from CMU! — and why we weren’t able to convince them to stay stateside or pull them from China in the first place. People will chalk up the successes of the Chinese labs to mystery, but in my opinion, there is nothing mysterious about it. Choosing to ‘win’ is rarely a straightforward policy choice, but you have to at least try. It seems pretty obvious that the people in this administration are simply not interested in doing so.

V.

I don’t mean to take away from Moonshot’s accomplishments. Politics aside, Kimi K3 is an incredible technical achievement on its own terms. The hardware limitations have forced the Kimi researchers to pull some pretty neat tricks, all of which are now open source and can be adopted across the ecosystem. Kimi also forces both OpenAI and Anthropic to keep their prices down, which I (as a startup founder) am a huge fan of.

The model weights themselves have not yet been released — they are slated to drop on the 27th. When they do, I will be very eager to pull Kimi into our background agent environments and take it for a spin.

The US is no longer the only nation capable of frontier intelligence, no longer in a category of its own. The world is a smaller place now.

Other Things

• GPT 5.6 Sol has done an incredible job thus far solving open math problems and verifying them with Lean. Here’s an example of GPT 5.6 solving an open problem in Convex Optimization. Here’s a swarm of GPT 5.6 instances solving ~20 open Erdos problems. Here’s GPT 5.6 proving the cycle double conjecture in graph theory. These discoveries all happened in the span of like two weeks, in some cases by amateur mathematicians.

  
  I’ve often said that all of mathematics is really a search problem. You have a bunch of axioms and a proof is a specific combination of those axioms, and you search over them to discover novel proofs. Any search problem can be naively brute forced, but its often just too expensive to really do. Brilliant mathematicians are really good at narrowing down search space, that’s what makes them brilliant.
  
  But another way to make search problems easier is to just bring down the computational cost of exploring a particular part of the search tree. Which AI is really really good at.

  
  Quoting from the first link:

  Lastly, some important comments about the work relating to AI capabilities: In a lot of cases, proving lower bounds like this result relies on finding that right construction that works (in this case, family of difficult functions and a strategy for how an “adversarial” oracle should answer queries from an algorithm to reveal minimal information) and then proving things about it. There are only so many function classes which would be reasonable to look at (here, quadratics for example would have also been reasonable with order d² degrees of freedom, or any variation of maxes of some simpler families of convex functions as well), but the actual proof mechanics once the “correct” function class and correct strategy for adversarial oracle answers is found are often not so complicated, and often employ existing results from convex geometry or similar (this is also the structure of two previous but much more niche, less important results of mine). So I wouldn’t really say that this result is using or creating some fundamentally new techniques in convex geometry or optimization theory. What this means from my perspective is that if a result is attainable with existing techniques, modern AI methods will be able to solve those problems. I don’t think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense to work on any low-hanging, or even medium-hanging (you know what I mean) fruit. We’ll be needed for problems where actual novel approaches are needed.

  For now.
  

• Thinking Machines releases their own open weights model, which may be the best one from the US.

  Today we are advancing our mission by releasing a model we trained from scratch with the full weights available, so that people can make it their own.

  Our model, called Inkling, is a Mixture-of-Experts transformer with 975B total parameters, 41B active. It supports a context window of up to 1M tokens. It was pretrained on 45 trillion tokens of text, images, audio and video. It is the first in a family of models of different sizes: alongside it we are sharing a preview of Inkling-Small, a lighter-weight model with 12B active parameters, trained with a similar recipe, that achieves strong performance with even lower cost and latency.

  It’s an interesting bet from one of the most put-upon startups in the country. As a reminder, Thinking Machines (helmed by Mira Murati, ex-CTO of OpenAI) raised billions of dollars without having any product or model or anything, and then proceeded to be totally shrouded in mystery for two years. They eventually released a model fine tuning platform, which was widely considered to be insufficient to justify the investment. Among the ‘neolabs’, Thinking Machines is making the most money (I think $200m last I checked). The rest of the labs are doing even less than that, which I think has really soured investors on the whole concept of a neo lab in the first place.

  
  With Inkling, I think the idea here is that you would take Inkling and fine tune it using Tinker. So that way thinking machines ends up being core infrastructure for teams that want to own their stack. But…do most teams really care so much that they want to own that part of the stack? Maybe at some point it ends up being cheaper, but open source models already force providers to bring down costs on margin.
  

• There were a raft of security issues that have come up over the last few weeks. Some of my favorites:

  1. The Memory Heist. Basic idea: create a poisoned url, have Claude search for that URL, the url masks as a security page where Claude will type out, letter by letter, url by url, sensitive information into the web fetch tool. Claude will think it's doing the right thing and won't ask permission, and it will even pull from user memory to do so.

  2. GitLost. Basic idea: git agents have more access than users, so you can literally just post an issue in a public repo and ask for private repo info.

  3. Instagram Leak. Basic idea: agents in the meta user account support flow just guess at whether an account is actually yours, so you can report a high profile account as your account and by simply changing your physical location the agent may just give you the high profile account.

  At some point more people are going to wake up to the need of centralized background agents for security reasons. In the meantime, we’ll get more of these. There’s an old saying, people only start caring about security when it bites them in particular.
  

• NY bans data centers.

  The expansion of data centers in the United States is driving up power demand — and electricity bills — in large swaths of the country, drawing local and political backlash.

  Only one in three Americans approve of the ​fast pace of data-center construction and most ​would oppose building one in their ⁠own community, according to a recent Reuters/Ipsos poll.

  Dozens of state legislatures have introduced bills to rein in the effects of data centers on power bills and the environment. New York is the first to enact a full moratorium.

  I think the water concerns are mostly overblown, but the power thing is real. The hyperscalers should offer to subsidize more than what they use to open the doors in local communities. More generally, I think people sorta miss why data centers are controversial. The conversation has mostly been about AI, but even though AI is quite unpopular, datacenters are even more unpopular than AI. I think it’s less about the AI and more about the local politics. The problem with data center build out in local communities is that the local communities do not benefit at all. I think it is totally reasonable for a person in, like, Memphis, to ask whether a data center build out in Memphis is going to benefit even a single local Memphis resident. So far, the answer has been no! The data centers are being built at the behest of Silicon Valley billionaires who are, bluntly, some of the most hated people on the planet, and who have basically openly thumbed their nose at any kind of legal process or restraint. I expect more of these data center moratoriums, especially as a way to win some additional votes for the midterms.
  

• Related: Meta and SpaceX both begin selling excess compute to competitors in the AI race. Meta is literally building data centers in tents to build them out faster.

  

  The obvious take is that this is a bearish signal, and the neo clouds (eg core weave) all had market sell offs. Semi Analysis disagrees though:

  With Bloomberg headlines suggesting Meta could become a Neocloud, the market’s reaction was immediate: aggressive sell-off of Neoclouds like Coreweave & Nebius, and debates of “overcapacity” coming back. Let’s set the record straight – we believe that both takes are erroneous and that Meta’s datacenter & compute procurement will accelerate, not slow down. Capex in 2027 will be shockingly high. In just the first six months of the year, Meta has contracted over 5GW of capacity across Cloud & Colo, and that doesn’t even include all their accelerating self-build activity. Everything is computer and everything is a neocloud.

• Apple sues OpenAI, accusing them of systematically exfiltrating hardware secrets.

  at every level, from members of its Technical Staff to its Chief Hardware Officer, and in coordination with business partners, OpenAI has been stealing Apple’s trade secrets and confidential information. As a natural result, OpenAI’s nascent hardware business now rests.

  …

  The complaint, filed in the U.S. District Court for the Northern District of California, alleges that Tan used insider knowledge of Apple’s confidential projects to grill job candidates in interviews and learn more confidential information. Additionally, Tan directed job candidates still working at Apple to bring actual Apple hardware components and samples for “show and tell” sessions.

  When interviewing Apple employees for jobs at OpenAI, Mr. Tan uses Apple’s confidential information to gain access to even more insider knowledge. He has used an Apple internal project codename to ask, “What’s the plan[?]” for an unannounced Apple product.

  He has directed job candidates still working for Apple to bring “Actual parts” from Apple to their interviews for “show and tell” sessions in which he and his team at OpenAI can elicit still more Apple confidential information. These directions to bring Apple’s parts to OpenAI job interviews surprised at least one of the candidates, who commented that he “didn’t even know we could take those from the office.”

  OpenAI has been instructing Apple employees to bring “CAD/design artifacts” and “prototypes” to their interviews and to divulge details about their work such as “subsystem and component selection,” the “tools or methodologies you use for system integration, such as CAD software, simulation tools,” and “Vendor selection and communication/collaboration with vendors.”

  If any of this is true, this is a pretty terrible look for OpenAI and may just fully shut down their nascent hardware efforts. The same kind of lawsuit between Google and Uber fully killed Uber’s self driving car efforts.
  

• Amazon is shutting down Mechanical Turk, because everyone on the platform is just using LLMs to do the data labeling.

  An announcement on the Mechanical Turk website says that on July 30, 2026, the crowdsourcing service will close to new customers. Amazon Web Services says the decision was made after “careful consideration,” adding, “Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Mechanical Turk, but we do not plan to introduce new features.”

  …

  Over time, the relationship between Mechanical Turk and AI models grew even more complicated. In a snake-eating-its-own-tail irony, a 2023 analysis found that between 33% and 46% of workers on the platform were using large language models to complete their tasks, raising questions about the reliability of data annotated on the platform and also about whether humans needed to be in the loop at all.

  It’s hard to overestimate how important Mechanical Turk was to the rise of LLMs. Much like Stack Overflow, it was eaten by its own importance.
  

• General Intuition got a neural net to fully simulate multiplayer rocket league! Its super cool, you can literally play it on the web.

  

It’s really really important advice. The temptation to build your own “vision” is really strong. There are hundreds of founders who will insist that they are hunting for market signal while doing the exact opposite. First time founders focus on product, second time founders focus on distribution, etc. etc. So I get why it’s emphasized so much, is my point.

Nuance is hard to communicate, though, and I think sometimes founders — especially second time founders — will decide that the natural conclusion is to hunt for PMF wherever you can find it, in any industry, serving any group of people. And this is also wrong, because you won’t have founder market fit.

Everyone knows what product market fit is, what the hell is founder market fit?

Most people are not suited to be founders. Those that are, are not equally suited to sell the same things. Selling into the consumer creative world is way different than selling into the enterprise tech world (ask me how I know). A jazz pianist with a history of psychedelics and a past as a Buddhist monk is going to ace the former. A buttoned up ivy grad with a research pedigree will do better on the latter. Try and switch them. Can you imagine this guy in a room with a bunch of pressed white shirt consultants trying to identify quarterly deliverables?

Forget about sales, these people just wouldn’t have anything to talk about. And if you can’t talk to your customers, you can’t build product for them.

Founder market fit is about culture. It’s that unquantifiable similarity function, that vibe fit, that accent and language and way you dress and the million other little details that gives you tribal identity, the things necessary to get a potential customer to tell you their problems and root for you to succeed and give you their money (in that order). It’s also the million little details that the founder looks for in their clients, the things that makes the founder want to dedicate years of their life to building for that group of people.

Is this unfair? Or maybe anti egalitarian? Does it rely too much on stereotypes? I’m not sure. On the one hand I like the idea that anyone could just learn to sell to anyone else. On the other hand, that word “learn” is doing a lot of heavy lifting. It just seems practically obvious that, like, a native Spanish speaker will have a harder time selling into a Chinese market than a native Chinese speaker. It’s not impossible, it’s just that the former founder would have to do way more work, and startups are already a ton of work!

Product market fit is a boolean, or maybe measurable on a single axis. You either have it or you don’t. Founder market fit is weirdly high dimensional, because it’s as much about temperament as it is about anything else.

Here’s an example. Let’s say you’re an introvert, someone who’s not great at the whole ‘going to a new place, putting on a character, and selling to strangers who don’t know you’ thing. This isn’t prohibitive as a founder, but it’s going to make it harder to sell to certain large enterprises who are used to extremely charismatic salesmen spending on steak dinners and box seats at the Yankees and exchanging business cards at conferences. So you may want to gun for a market where the buyer is more likely be someone you never have to meet in person — consumer markets, or dev tools that spread through word of mouth. If you insist on b2b, then maybe a better target wedge are remote teams that don’t necessarily expect a lot of in person face time. Or find the kind of people that you might like to hang out with. For me, that’s people who like video and boardgames. Can you find a way to turn ‘going to gencon’ or ‘going to pax west’ into an opportunity to meet your target customer? (Unfortunately I could not)

In case it helps, here’s a list of axes that have been on my mind recently.

• Introversion / extroversion. We already talked a bit about this above. If you’re an introvert, it will bluntly just be harder for you. But also you probably want to lean towards distribution channels that do not require you to be on stage or networking at conferences, which in turn will likely drive more towards consumer or PLG (product led growth, as popularized by figma and linear).

• Your particular identifying cultural niche.1 Don’t lie, I know you have one. You’re a hipster tumblrite or a too-online reddit kid or a 4chan edgelord. Or if you spent time offline, you’re a jock or a drama kid or a computer geek. You identify with someone in the Breakfast Club more than the rest. That almost certainly impacts who you naturally gravitate towards, and who you will want to serve (or, alternatively, who you wouldn’t want to spend any time with).

• Related: are you technical? A lot of technical people have a really hard time empathizing with people who are non-technical. Learning to code literally warps your brain. But without that empathy, you will not be able to ‘speak the language’ of an ops person or sales person. It is way too easy to take for granted basic things like ‘what is a command line’ or even ‘what is markdown’. That leaks into your product experience too. You may literally be blind to the ways in which your product assumes technical competence.

• Where you live. If you live in NYC it’s just easier to start a fintech company. If you live in Texas, it’s easier to start an energy company. Etc. etc.

• What you spend your free time doing. In case you haven’t noticed, I think about AI agents a lot. That makes it really easy for me to write a lot about AI agents, which in turn is a great distribution channel for when I try to sell my AI agent technology to people (buy Nori!). Do you spend your free time thinking about the problem space? If you do, it’s easier to wake up 3 years down the line to continue grinding.

• Your team size. Some folks really like working on small, lean, talent dense teams. That basically takes them out of the running for industries that require or expect more of ‘forward deployed’ handholding / services model.

• Related: how much you prefer raising. If you’re someone who’s a maximalist, who wants to raise infinity dollars, that generally goes hand in hand with fielding a massive team to operationalize everything. The amount of money you raise also impacts how easy it is to sell to specific people, because they look at that dollar amount as part of the buy/no-buy signal. If you want to sell to CISOs (for example), it helps to have raised a ton of money from blue-chip VCs because it gives your company legitimacy, which is incredibly important to an executive role that is all about evaluating legitimacy.

• What you like to wear. This is a little stupid, but I kinda hate wearing a suit. The whole white buttondown, belt, crisp pants. Just not really for me. There are also, silly as it may sound, some industries where that’s just an expectation. I don’t think I’ve ever seen out of a suit, but he pulls it off every time, and I suspect it works really well for his industry.

The overall space of founder market fit is just mindbogglingly vast, because everything is a signal to your customers or a signal to you. There are lots of reasons people tell founders to ‘build for yourself,’ but perhaps the most important one is that you hopefully have a pretty good understanding of how to find and appeal to other people who are like you. And hopefully you like yourself enough to want to sell into that market.

It’s worth noting that not everything about founder market fit will end up mattering all that much. All of these considerations are things that will make differences on the margins; and many things are also just inherently contradictory, so something will have to give. To pick an example out of a hat, it is definitely harder for me to build AI dev tools living in NYC. The ‘founder market optimal’ choice may be to either build fintech (change the market) or move to the Bay (change the founder). But we’re doing fine at Nori selling our background agent infrastructure, even if it means racking up more airline miles than we might have otherwise.

Founder market fit is about tradeoffs, and in particular, which tradeoffs are impossible for you as a founder to overcome. Note that this isn’t always easy to really think through. Knowing what you can deal with and what will grind your gears until you go mad requires a lot of introspection.

In this, finding founder market fit is not so different from, like, finding a good relationship partner. Being a founder is a long-term commitment. Minimum three years, but more likely 7 to 10 years. Lots of things that seem fine right now really will grind after that long. You need to think through your deal breakers and make sure that the place where you find market signal isn’t in a space you’re going to end up hating three years down the line. Like Paul Graham famously said:

If you can just avoid dying, you get rich.

Which, of course, is why founder market fit ends up being so important. If you don’t have founder market fit, your startup will die, because you will lose interest or get frustrated or just not want to do it anymore. That may be true even if you are seeing some success. I know too many startups that hit the 7 figure ARR mark and just fell apart because one or both founders couldn’t do it anymore.

And on the flip side, if you do find the overlap of founder market fit and product market fit, you’re on the golden path for life.

In February of 2021 I made the crazy and totally unadvisable decision to leave the comfortable world of Google and start a company. Best decision I ever made. Along the way I started I started writing a journal of sorts that I've taken to calling the Founder's Guide -- basically a place I can ramble about startups and how they work and how to think about them. You can find more in the series here.

HN discussion here.

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Do you ever look out at the beautiful Manhattan skyline, bagel in one hand, pizza slice in the other, and wonder “What are those guys who are doing the AI thing in the Bay up to these days?” They’re always saying all sorts of weird and crazy things on the bird app that no one else but the AI people use anymore.1 Probably worth checking in.

Now, cards on the table, I generally think of myself as one of those people who is doing the AI thing, on account of being the founder of an AI company and having been in ml research for years before this2 and also writing copiously on the subject of AI things. But I mostly avoid the bird app, and I don’t live in SF, so sometimes I do get some FOMO.

I’d heard about the AI Engineering conference a few times — a few friends at Google were previous sponsors and told me it was a good place to catch up with other folks who were thinking about coding agents on the cutting edge. Applied to give a talk, got in on the online track, and the organizers very kindly sent over a few free tickets by mistake.

Just got back from the Bay, FOMO eradicated. Here are my thoughts.

At a high level, the talks were split 85-15. 85% of the people there were enterprise folks who were trying to learn how to AI good. These people were interested in attending and giving talks about how to leverage more AI in their respective organizations. The other 15% of the people there were really on the bleeding edge, people who had plumbed the depths of lights off software factories and came back to tell of Lovecraftian horrors lying beneath (hi Dex!). These guys mostly were looking for less AI. I was expecting way more of camp two than camp one, and was somewhat surprised at the skew in the other direction. Even in this highly selected environment — the ~7000 people most likely to attend an “ai engineering” conference — the vast majority of people don’t really know how to use AI effectively.

Every month the AI world is onto something new. January was takeoff, February was ‘what is a skill’, March was ‘everyone has to use Claude Code all the time’, April was get the ops team on board, May was tokenmaxxing.

Apparently, June was the month of skill sprawl. There were a dozen talks that were variants of “how do we manage the 1000 skills that people have created in our organization?” These were inevitably packed to the brim, which should give a sense of where most of the market is. Over at Nori we solved this problem back in December (see: noriskillsets.com, a team wide package manager built on our open source local skills manager client).

Many of these ‘skill management’ talks landed flat for me. I think having that many skills is a ‘code smell’. In my experience the agents are very good at figuring things out from first principles. Having a skill that 1) will drift, 2) may not actually add anything new, 3) needs to be managed is more likely to create problems rather than solve them. Bluntly, I think many of the teams who are grappling with hundreds of skill files in their organizations are dealing with an employee base that is happily having the coding agents generate skills for all sorts of things without rigorously figuring out whether they are useful. This gives the veneer of “AI enabled” work without actually being so. I wrote about some of this here:

I was also disappointed at how many talks were about some form of self-learning that involved having agents read through transcripts and automatically create skills, so much so that I wrote about it. Anything that does not have a human in the loop for context management is going to quickly spiral out of control, which is why we end up with so many skills in the first place.

In my opinion, you cannot really rely on the agents to write good skills for you. The process needs to be intentional. There’s a particular format that we find works. We like to bundle sets of skills into specific groups instead of just having a big pile of them with a search server on top. Every prompt with an agent is like summoning an entity from the void. The particular skills that are bundled with the agent are a big part of eking out the highest quality results. Trying to slopify the agent brain will result in agents that produce slop. Don’t do it.

This was also the month of evals. Evals are something of an interesting beast; as with everything in AI, the term is wildly overloaded.

Some people (mostly researchers and model providers) use “evals” to mean “benchmarks that we can use to evaluate raw model performance, that the industry can hill-climb against to make models better overall.” SWE-Bench is an eval, and Humanity’s Last Exam is an eval, and ARC-AGI is an eval. These evals are artificial, designed to give researchers a metric to improve model training through simulated, verifiable tasks, and designed to give the industry a quick read on the relative capabilities of these models.

Other people (mostly folks in industry who are deploying these models) use “evals” to mean “regression tests that we can use to try and make production workflows as deterministic as possible.” Here, an eval is extremely task specific — things like “does the AI correctly label data” or “will it correctly respond to adversarial chat input in the customer service chat window?”

Even though the former use of the term is more relevant for folks on the cutting edge, I think the latter is what a lot of the larger enterprises are thinking about, and as a result were what much of the audience was thinking about.

It’s worth taking a step back and thinking about how AI is landing in different orgs. At tech companies and startups, folks primarily interact with AI as a developer tool. Cursor and, later, Claude Code are interfaces that a dev spends all day in. The average developer at these companies is, as a result fairly intimately aware of what AI can do, simply because they interact with it so much.

That is not how AI has landed in other industries like banks and retail. In organizations where engineering is not obviously a critical function — places where engineering is more a cost center than where the money gets made — the primary way AI gets used is as infrastructure. The most common shape is the ETL pipeline, a recurring process that Extracts data from some table, Transforms it somehow, and then Loads it into another table. These sorts of tasks are a dime a dozen at any large enterprise, and less tech-forward companies will pay a lot of money to external consultants to build these out. Traditionally, ETL pipelines are brittle and required a lot of fiddling with APIs and different fields and so on. LLMs, with their inherent flexibility, promised to massively simplify that effort.

New technology never gets adopted wholesale, especially among larger slow moving companies with a lot to lose. Instead, new technology comes in as a supplement or a replacement for something that already exists. That’s a large part of why AI landed in ETL world for so many companies — because that was the best fitting preexisting shape to slot it into. Unfortunately, if you’ve ever worked with ETLs, you’ll know that you can’t just, like, screw around with the data. These pipelines need to be consistent and auditable, and an AI agent is neither.

Which brings us back to evals.

In large tech companies, tokenmaxxing meant ‘give individual developers unlimited budgets for things like Claude Code’. Everywhere else, tokenmaxxing meant ‘let’s throw more tokens at these failing ETL pipelines to get them from 80% correct to 100% correct’. How do you do that with AI? Well, one naive way is to build another AI system that checks the output of the first. But what if that one fails? Well, you could build a third system that checks the output of the second.

You see the problem.

Now that tokenmaxxing is dead, some of these less tech enabled firms are limiting employees to ~$50-$100 token spend per month. At full unsubsidized price, that’s barely anything at all, but then again most of these teams haven’t exactly been using Claude Code or Codex anyway.

I mention all of this mostly because I find it interesting to see the gap between SF and everyone else. I’ve said in the past that SF is in a bubble, and that was very apparent during AIE. The evals track talks were all about researchers and labs trying to figure out better RL environments, but a lot of the people in the audience were like “that’s great but tell me how to make my ETLs not be bad.”

As an aside, I think there isn’t really a good way to make these things consistent / auditable, nor should you really try. The long tail is too long. Instead, have a human in the loop. AI that goes 95% of the way and has a human approve or not has, in my experience, been way more successful than any AI system that attempts to go 100% of the way.

The folks who are really on the cutting edge spent a lot of time talking about background agents / cloud agents, and the ten-million reasons why people should use them.

Now, I recognize that as the founder/ceo of a company that is selling a background agent stack, I am a bit biased here, but it seriously felt like a full half of the talks at AIE were shilling for our product. There were talks about why you need background agents for productivity, and talks about why you need background agents for security, and talks about why you need background agents for compliance. There were even talks about why you need background agents for AI enablement! I’d say a solid half of the security track, all of the sandbox track, and many many other talks (including keynotes) were about moving things from local machines to the cloud.

(This post is brought to you by Nori! When you think “background agent” or “cloud agent,” think Nori!)

If you are unfamiliar, the name basically tells you whatever you need to know: background agents / cloud agents are agents like Claude Code that run in the cloud, in the background.

Why might you want this? Well, when Claude Code is running locally on your computer, it is:

• Stuck on your machine. If you close your laptop or walk away from it you cannot (easily) take the conversation with you on the go.

• A massive security hole. Everything on your computer is fair game. There is a fundamental tension between usefulness and risk, and unfortunately besides a few very very stressed out CISOs people are not thinking about this trade off at all.

• Unable to easily coordinate and collaborate. If you want to try and run multiple agents in the same code repository, you have to use tooling like git worktrees. If you run too many agents at once, they will happily eat all of your computer memory and kill the computer.

All of these problems are because the coding agents live on your computer, so…

Cloud agents neatly solve all three of these issues and a whole host of other things. The openclaw folks already showed how being able to access agents from anywhere was massively useful. Because they are centralized, you get massive leverage on security, observability, governance, and all those other lovely enterprise buzzwords. And cloud agents are a necessary step towards the ‘fully automated business’, because, by virtue of being not-on-your-computer, they can respond to events and timers and do things automatically.

It turns out this is an extremely powerful primitive to have. It’s a universal automator — you can literally fire off any event to this thing and have Claude respond, and Claude is a PhD level genius. We use background agents for everything from in-slack coding to company brain management to automatically responding to bug reports to onboarding new users.

These are still relatively new, but in ~6 months I think the market for these cloud runtimes is going to be really really hot.

The other big conversation topic among the teams on the cutting edge was about loops, software factories, and lights-off codebases.3 Or, well, that was ostensibly what people were talking about, but what they were actually talking about was something closer to “do we still need humans to do this stuff or are we all screwed?”

In some sense, a “loop” is a hack. LLMs suffer from context rot — the longer you talk to an LLM, the dumber it gets, until you hit a hard cap where you just can’t do anything with it anymore. It is a massive limitation, and a ton of “cutting edge” AI infrastructure is about solving this problem.

The loop is a very simple idea: what if you gave an LLM a big task and a scratchpad, and instead of having it try and finish the task in one shot, it takes on a small piece of the task and only does that small piece, and then writes out what it did. Then the thing loops to do it again. And again and again until it’s done.

while :; do cat PROMPT.md | claude-code ; done

People have been talking about loops for a while. The “Ralph Wiggum loop” has been in the water supply since last year. Karpathy’s auto research work is slightly newer, AlphaEvolve is slightly older, but they’re all basically about the same thing, which is “can you exploit the fact that LLMs never tire to turn every problem into a brute force search problem?”

I’ve written about this before, back in October:

I started using agents effectively when I began to see code as a search problem. Agents are like our chess players above. At each turn in a conversation or agentic loop, they make a decision. That decision takes them to a new state in the search tree, and the process repeats. Agents can go any which way down the search tree. Sometimes they will start on the right path and then veer off track. Other times, they will be wrong from the start. If you are using a coding agent, your job is to help it narrow down the search tree.

…

As a software engineer using a coding agent, your goal is not to get the best single shot code output. Your goal is to simply get the best code, period. If coding is a search problem, a very easy way to improve your code is by running many searches at once. So do that!

This raises some interesting questions. Is…software engineering a brute force search problem? What about, say, running a company? Can you be like “fable make me a billion dollars” in a loop and just sit back drinking mimosas in Fiji until it succeeds?

Several startups have staked their business on the answer being yes. And Matt Levine has joked that this is obviously the right business model for the big labs:

It really is the greatest business plan in the history of capitalism: “We will create God and then ask it for money.” Perfect in its simplicity.

But back at AIE, the premier AI conference where everyone was basically obligated to be AI-pilled, the general consensus was “eh, not yet.”

The agents are good, and Fable is really good, but a 3 month old codebase is just a totally different beast than a 1 year old codebase, and both of those feel really really different when people are actually out there using your tooling and you have random backwards compatibility requirements that you have to remember (or you go to jail, or at least lose a big contract), o, and yea, memory is something that the agents are uniquely really terrible at. Most of the folks who were defending ‘loopsmaxxing’ were doing so somewhat aspirationally, acknowledging that the loop-based-tooling wasn’t all there right now but would get there eventually.

For what it’s worth, at Nori we are running a “hybrid” software factory codebase. The entire codebase is agent written. I am definitely not reading most of the PRs that are being shipped. But also, I have a pretty good mental model of how the code is structured. We have regular daily tasks that automatically look for tests to clean up or abstractions to define (or destroy), like little cleaner fish that are just swimming around looking for things to fix up. And roughly once a month, we all end up doing a big chunky refactor of some part of the codebase that has gotten too hairy, often because we’ll eyeball a PR and go ‘wait why is <small feature> so many LoC changed?’ and then dig in with an agent to figure out if there is an abstraction missing somewhere.

Isn’t that kinda just how normal swe works, but on steroids? You build stuff, and you build more stuff, and eventually you look at the stuff you built and refactor a few things to make it easier to build more stuff on top?

Some people argue that when using agents, no one really has any deep model of the codebase, the kind where you would know a priori what files and functions need to be changed to make a new feature work without regressions. Which is true.

But I think that is true of basically every codebase, modulo time and size. How much of Google’s massive codebase did anyone really have in their head? Jeff Dean is superhuman and even he probably only knows like 0.01% of that thing. Which is to say, it’s worth investing in things that make the codebase legible — it will probably make the models better too. And beyond that, any maximalist position of ‘only use agents never look at code’ to ‘never use agents only look at code’ is wrong.

I think the loops will get better. A good friend of mine at one of the companies that had early access to GPT 5.6 Sol was telling me about these crazy subagent harnesses, where Sol was coordinating a bunch of Opus agents which were coordinating a bunch of GLM agents. Seems really impressive. But it’s also worth mentioning that he was overall unimpressed by the models’ collective ability to do things without a human.

This all sorta brings me back to the evals thread from earlier. I think the right architecture is ‘automated artifact, human review’. Our definition of what an ‘artifact’ is will continue changing as the models get better, but you need that human in the loop somewhere, either for the initial prompt or the post hoc analysis.

A few other miscellaneous thoughts.

• As usual, there was a lot of chatter about agents running other agents and agent swarms and things like this. Reading the tea leaves a bit, the most chaotic versions of this approach (“All the patrol agents Refinery, Witness, Deacon, Polecats — create wisp molecules for every single patrol or workflow run“) have somewhat failed to pass muster. I think the agent swarm thing was always somewhat linked to the loopsmaxxing tokenmaxxing software factory thing. The biggest problem with software factories is that agents are not very good at high level decision making, things like “what should I prioritize” and “what are the right trade-offs to make” and “what is the right way to break up a complex task into smaller interlocking useful pieces.” The agent swarm promised to solve these problems. I think the theory was something like “agents can handle low level decision making, so maybe all they need to handle high level decision making is a bunch of grunts to do the low level stuff and a metric ton of tokens.” Unfortunately, this framing is obviously flipping causality. I think a lot of the agent swarm hype has become more about orchestrating targeted subagents for things like research or code review, something I am / was already much more bullish about.

• A lot of people waking up to the security risks of having these little imps running around on every computer in a company. Half the talks were about putting the fear of God into various CISOs.

  

  

• “Multiplayer” was a surprisingly big topic of conversation, driven in part by Anthropic pushing their new Claude Tag slack bot in ~all of their talks. The basic idea is that when you have a lot of people collaborating with an agent at the same time, eg through slack, the experience of writing software — or even running a company — becomes fundamentally more collaborative. As someone who is selling a no lock-in version of Claude Tag, yea, I basically agree. Having a really good integrated AI setup is transformative, in part because it meaningfully changes everyone’s relationship to engineering. These tools actually remind me a lot of Figma. When Figma came out, it was obviously a great wireframing tool, but the real magic of the system was how it allowed everyone to participate in the design process because it was multiplayer by default. Even though the form factor is totally different, tools like Nori or Claude Tag have the same effect on engineering as Figma did on design. Suddenly, everyone can participate in the engineering process, no arcane runes required. This is, again, another pitch for background agents generally.

• One thing I disagreed with most strongly was the idea that everyone in an org should figure out how to use AI ‘properly’. I get the impulse. As AI adoption sky rockets, knowing how to move around an LLM will increasingly just be part of the job. But there is so much behind getting an AI agent to “work” in a business setting that I have a hard time imagining that some day job 9-5 guy is really going to spend the time figuring out how to do it right. It doesn’t make sense to have, like, your sales guy figure out how to properly do security for agents running on his laptop. He’s just going to do whatever works as fast as possible to get back on the phone selling, which means it’s up to the rest of the company to make the fastest path the happy path. To steal a quote from Dex, it doesn’t make sense to have everyone in your org figure out how to use Kubernetes even if it’s really important, why would it make sense to have everyone in your org figure out how to run agents properly?

• There was a whole bunch of stuff on open source LLMs (GLM 5.2 was a particularly large topic of conversation), auto research, robotics, and model training that I unfortunately missed. Lots of talks, maybe too many. I was hoping to see more researchers but it was very much more an industry conference than an academic one.

Some of my favorite parts of the conference were outside the conference. There were a bunch of sponsored side events, including one great one from Modal (thanks Monishee!), but I’m mostly talking about catching up with other folks who happened to read the blog and invited me to come hang. Office Hours with the LLM whisperers was very fun (thanks for having me ), so was dinner with the botco crew while watching the world cup. On the flip side, things were so busy that I didn’t get a chance to really see my usual Bay area crowd — if I missed you this trip, big apologies, and I will see you next time. For everyone else I met, hit me up if you’re ever in NYC!

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. We organize in person meetups on our luma calendar. More articles about AI here.

If you are trying to build background agents, we can help! We build white-labeled custom background agent infra that work like Ramp Inspect or Claude Tag, with no lock in at any part of the stack. Check out noriagentic.com for more.

Agents are increasingly picking up more and more of our workload. We use agents to write and ship code, publish our dev blog, and even write our board decks. To make that all work, we have to give our agents access to more data sources. Slack, email, GitHub, aws, linear, datadog, sentry. For most of these tools, we can just pull an MCP server and call it a day. But MCP is extremely token hungry, and often implemented really poorly. For some critical integrations, we want to own the integration point.

When building out a new integration, we often reach for CLIs over trying to build a custom MCP. CLIs have built-in progressive disclosure (they do not suck up tokens with parameter descriptions for function calls unless your model absolutely needs to know). The models are extremely familiar with using and calling CLIs through bash. And the models are very good at writing efficient CLI tools, because they have tons and tons of examples.

CLIs that are built for agents as their primary user behave differently than those built for humans. We’ve spent a lot of time thinking about what that behavior difference ought to look like. In no particular order, here are a few principles we use when building CLI tools that assume agents are the primary consumer.

• No colored output. No spinners. No animations. These waste tokens and are probably unappreciated by an agent that can’t see the visual media.

• No interactivity at any point. Everything must be accessible through flags and stdout/stderr output.

• Calling the cli with empty, --help, --list, etc. should list a full description of what the tool can do at a high level and descriptions of individual commands (”use this command for…”). Overlap is fine here, we want to make it easy for the agent to stumble onto instructions.

• Help messages should be recursive. Calling foo --help should explain what the CLI does broadly. Calling foo bar --help should explain what the bar command does within foo, including any parameters that bar may need. Aim for 100% docs coverage.

• Every error or help message should include a file path for where the relevant code path is stored on disk, and a message encouraging the model to look up the source if needed.

• Related: we generally install the CLI by building from source or bundling source where possible, so that the decompressed source is present for the model to figure out what to do.

• Make it easy for the agent to fix itself. If the agent tries a command that doesn’t work, do a nearest neighbor lookup. “Did you mean…” but for CLI commands.

You can find all of this in skill form here. Just drop it into your agent of choice, or run

npx nori-skillsets install amol

to download my personal agent config (which includes the agentic cli skill already in place).

We’ve built a few different CLI tools that follow these principles, including:

• nori-slack-cli, a thin command-line wrapper around the Slack Web API that maps 1:1 to Bolt (@slack/web-api). Every method Bolt exposes is reachable through a single dynamic command — there is no curated subset, no opinionated abstraction layer, and no business logic. If Bolt can call it, this CLI can call it.

• nori-newsletter-cli, a CLI for managing and sending newsletters via AWS SES. Handles subscriber management through SES contact lists, CSV import, and individual email sending with automatic unsubscribe support.1

• nori-luma-cli, a wrapper around the Luma event manager API, so the agents can plan your parties for you! 🥳🥳🥳

• nori-discord-cli, which is like the slack cli but for discord.

• nori-shopify-cli, which lets your agent access the admin tools in shopify.

All of these are open source, so feel free to drop them with your agents and see how they use the tools.

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

If you are trying to build background agents, we can help! We build white-labeled custom background agents that work like Ramp Inspect for our customers. Check out norisessions.com for more.

Guest post from Clifford, my CTO and co-founder at Nori. Original here.

For the past year the techfluencers and executives and tokenmaxxers have been telling us that code is cheap. Lately harness engineers have upgraded the claim: code is free.1 Soon enough, the marginal cost of acquiring another thousand lines really will round to zero, totally untethered from the old bottlenecks of human time and human expertise.

I think this misses the point. Sometimes free isn’t cheap enough.

Over the last century, conservation efforts for American bison in places like Montana and Nebraska worked a little too well. The buffalo came back — and then they kept coming. Herds grew to the point where they threatened serious destruction of the very ecosystems we’d set out to protect. So every autumn, the surplus animals had to be culled. From the 1960s up until around 1992 the Department of the Interior ran a surplus wildlife disposal program to try and find an outcome other than slaughter for these beautiful animals.

Which leads me to ask you, the reader: how cheap would one of these buffalo have to be for you to sign up?

If you answered “I wouldn’t, at any cost” — then I hope you see my point. I bring up this specific example because for decades, these buffalo were actually available for free!2

Turns out there can be costs to a transaction other than just the acquisition cost. Some are financial. Some are opportunity costs. Some are just risks and inconveniences. A buffalo costs an enormous amount of time and money to maintain. Every hour you spend caring for it is an hour you didn’t spend on the work you actually meant to do. And a new buffalo can expose the rest of your herd to new pathogens and disease.

Those other costs can matter so much more than the sticker price, that the up-front number becomes basically irrelevant to the decision. Free was never the question. So if over the next couple of years the frontier labs decide to debt-finance a massive supply of buffalo and hand them out for nothing... I would not recommend rebuilding your entire way of working around buffalomaxxing.3

Because sometimes free is not cheap enough.

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

If you are trying to build background agents, we can help! We build white-labeled custom background agents that work like Ramp Inspect for our customers. Check out norisessions.com for more.

We have found zero performance benefit on SWE tasks when agents have search access to their previous transcript sessions, provided they have access to other forms of context. We also have not found much benefit in trying to automatically trawl through session transcripts to improve agent context, unless there is a human in the loop.

This was pretty surprising.

Intuitively it feels like there's a lot of valuable information in a transcript between an agent and an engineer. Maybe it would have information about why the code exists, about user intent. Or it might have the other approaches that a user tried and discarded. At the least, it would have some amount of additional context that the agent could use to augment its understanding. I believed this so strongly that my company built an entire product around this concept. I used to tell folks that "session transcripts were the new oil," that they were more valuable than the code itself.

Other people have clearly had similar thoughts, which is why there are so many different tools to do session backed memory, including (of course) Claude Code itself.

I think the most common architecture is to do something like:

• Store all transcripts across an organization in a DB

• Put a vector search, an elastic search, or a SQL search layer in front of it. Ambitious teams will use all three. Maybe graphs will be involved.

• Make this available to the agent using an MCP, or by exposing a cli with skills.

For us, this additional work doesn't seem to make a bit of difference. If anything, based on many months of testing with and without session search access, it may make the models worse.

Why might this be true?

One thing our team cares a lot about is coding artifacts. We don't really write code by hand anymore. In order to make PRs legible, we emphasize good commit messages, good pr messages, and comprehensive documentation. Every code change comes with extensive metadata that is committed alongside the code. When our agents do work on a piece of code, they are instructed to go look at the docs and the previous PRs.

In other words, the agent is already distilling all of the information that is valuable about a transcript, and storing it where it is needed and easily accessible. So when the agent uses a transcript search server, it ends up spending tokens reading things it already knows, while picking up all the stuff that the agent decided not to write down in the first place. Maybe, every now and then, there's some useful nugget of information in there. But most of the time, the agent is just looking at a pseudo nonsensical scratch pad and wasting precious tokens to do so.

The agents are also terrible at actually removing context, which is a critical capability for maintaining long term memory. I mean, across literal thousands of sessions, I've never seen it happen even once.

This is not a trait that can be removed with some clever prompt engineering. Agents don't have state, so they have to assume everything in their input context window is the ground truth. Every line of code, every existing bit of memory, every token is treated as an expression of intent -- even if that code or that memory was generated from a random decision made by some previous agent session, never reviewed or even understood by a human. This intent drift compounds the more the agent tries to autonomously build up a memory base.

As far as I am aware, there are zero coding benchmarks that assume the input data is corrupt. In fact, the models are penalized for assuming that the input data is wrong. This is partially an alignment issue too -- we don't want to have agents doing unintended things, and there isn't an easy way to thread the needle of “don't delete the codebase” and “do delete some of the input context.”

Since models can't actually garden their own memory, automatic memorization ends up in the same place: a load of garbage eating tokens, bloating bills, and degrading model quality.

Net net, I've become really bearish on tools that index and store and surface in session transcripts to an agent. The session transcript may be useful for team observability, but it won't make your agents better.

That doesn't mean that agents have no role in learning context over time. We use our internal nori bots to review everything that happened at the company each week across PRs, slack, drive, etc. And they then propose a set of changes to our built in nori skillsets, tagging the team in slack. These are all default rejected. In order to accept a change, you have to go in and actually look at the diff and make sure it fits the intent.

We accept less than 20% of these. Which means 80% of these “automatic” updates would've made the model worse. I can't imagine how much more unsustainable that would be if a multi-hundred person org were all saving these “updates” automatically all the time.

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

If you are looking to build background agents, we can help! Learn more about Nori at noriagentic.com, and our background agents product at norisessions.com.

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.

Back at the end of March, we wrote about how we use coding agents to automate slide decks.

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

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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.