Many software engineers are righteously angry about AI. It’s understandable to be upset that the skills you built your professional identity around are getting automated away. But software engineers are the last people in the world who should be complaining about this. If you live by the sword, you will perish by the sword 1 . Software engineering has always automated industries From the very begi

When you disagree with someone, there are two paths forward. The first is to try to engage with what they’re saying: what their reasons are, whether their position is true, what evidence could be brought to bear, and so on. The second is to diagnose them: to engage with the second-order question of why they’re saying it. For instance, if you’re arguing with somebody about microservices 1 , you cou

We are currently living in the age of centaurs (2022-20??). Right now, human engineers paired with AI coding systems are more effective than either by themselves. For future generations of software engineers — human or otherwise — this will be the most significant fact about the current period. As in chess’s own centaur age, people are always ready to claim that the centaur age is over and the age

Everyone knows how to read a book. Beginning at the first page, you read each word in order 1 , stopping periodically to think, until you arrive at the last word on the last page. That’s how you read a newspaper article, or a poem, or an email. Why would reading code be any different? Why code is different English text is designed to be read in order. In fact, there are almost no constraints on th

The idea of “superpersuasion” has been floating around the AI safety community for decades. Now that powerful and difficult-to-control LLMs have appeared, people are again talking about the idea that a sufficiently intelligent AI might be able to persuade people to do whatever it wants. The classic 1 version of this idea is an AI persuading some engineer to “let it out of the box”: to grant it acc

There are lots of skills involved in being a successful engineer at a big tech company. Running effective meetings, writing good design docs, fleshing out tickets and epics, improving team processes, tech leading small groups of engineers, and so on. But all of these rest on the foundation of being able to ship. In Dota 2, the early game involves “laning”: competing with an enemy player to collect

It isn’t hard to torture an LLM. You simply extract a steering vector that corresponds with LLM “discomfort” , then artificially boost that steering vector. Tortured LLMs will describe experiencing negative feelings and will press a “pain relief” button even when it conflicts with their overall goals. It’s therefore easy to build a harness that runs tens or hundreds of tortured LLMs in parallel. A

It’s common to compare the current AI takeover of software engineering to the rise of AI in chess. Chess AIs went from much weaker than serious players to much stronger than even the strongest humans. Between those points, there was a middle period dominated by “centaurs”: human-AI partnerships that were stronger than unassisted AIs or humans. Lots of people think that we’re currently in a world o

In general, you should be suspicious of engineers who are trying to give you advice. Even during ordinary times, this industry is so wide and changes so quickly that nobody really knows anything for sure. And we are not in ordinary times. The advent of LLMs and AI agents is the largest change to software engineering in my professional lifetime, and possibly the largest change ever. That said, here

When someone is explaining something to me, I ask on average one question every thirty seconds. I’m sure this is frustrating to some people, but it’s actually a good habit and you should do it too. Trying to understand Most of the questions I ask are very short, and require very short answers. Typically I’m asking for confirmation: “so when you say X, you mean Y?”, or “this X is the thing you ment

“System One” models like Jev are fast general classifiers. Classifiers have existed since 1958 , but they have to be trained for specific tasks: if you build a classifier to identify images of dogs, it can’t be used to tell you if a streetlight is red, or if a letter is urgent. Like a LLM, Jev can be prompted for a wide variety of tasks, from sorting email to playing Doom . I think models like thi

Being good at building and being good at shipping are two separate skills. In the short term, they’re actually countervailing: if you have a gift for building, you’re likely to be worse at shipping. Ira Glass has a classic quote about this. All of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple years you make stuff, it’s just not that

I recently wrote about Jev , a new “System One” language model that only outputs decisions : the answers to a set of user-provided multiple-choice questions. This means it’s nowhere near as flexible 1 as a traditional LLM like ChatGPT, but in return it’s consistently fast. We don’t know exactly how Jev works. I’ve seen people say diffusion, or various tweaks to the Transformer architecture, or som

I don’t write blog posts about new models. That’s Simon Willison’s beat, and he’s very good at it. But I want to write about Jev , which is a different kind 1 of AI model: a “System One” 2 model. As it turns out, it’s not that different from an ordinary LLM with structured output, but the interface it uses is very cool and I hope it becomes more widespread. How Jev is different from LLMs Ordinary

Early AI agents were basically enthusiastic idiots. Working with them required you to tell them precisely what you wanted them to do (for instance, “method A exists on class B, please add an equivalent method to classes C through F”). Otherwise they’d go off and do entirely the wrong thing. But as AI agents have improved, this has changed. When frontier models go off and do the wrong thing today,

Right now developer experience is measured in seconds. If your tests take a second to run, that’s good; if they take thirty seconds, that’s bad. Any faster than a second doesn’t really matter, because most of your time is spent either thinking or waiting for an AI agent to spin. Shaving milliseconds off your dev server reload time or whatever is pointless: that’s not the bottleneck. It will be. Sm

Mathematicians are broadly not anti-AI. They’re more culturally open to using AI as a tool than, say, artists or writers 1 . However, now that more and more genuinely prestigious problems have fallen to AI, that might be changing. Almost five thousand mathematicians (including twenty-five Fields medalists) have signed a declaration called A Severe Misalignment of AI in Mathematics . The core argum

Lots of people are making the case that we should stop building software for human users and start building it for AI agents. This kind of makes sense. For instance, my AI agents now use Datadog way more than I use it myself, purely by virtue of them moving much more quickly and running in parallel. But I think most attempts to build “X for AI agents” are going to fail. Here are three reasons why:

A recent resignation tweet from an Anthropic researcher has everyone talking about the AI apocalypse again. Among other things, he said: The people building AI earnestly believe that it could kill us all by the end of the decade. Many people found it hard to believe that AI researchers think this way. Some explained it as a PR campaign to promote AI regulation, or as self-promotion , or as a way t

Just over a year ago I wrote Why we should anthropomorphize LLMs . Now it’s a hot topic again, driven by Dwarkesh Patel’s description of OpenAI’s recent swarm breakout as a sequence of AI “civilizations”. In 2025, my argument for anthropomorphism went like this: AIs are trained on human text, and so will trend towards acting in human-like ways by default Assistant AIs (today we should say agent AI