Tagged ai
21 essays
- Cheap intelligence makes the incumbents richer Cheap intelligence does not destroy value; it relocates it. Follow the money down the stack as the model commoditises and it lands in the layers a falling token price can't reach: proprietary data, workflow lock-in, distribution. The SaaS apocalypse is real and aimed at the wrong layer. It comes for the thin wrappers and the pure-model labs; the incumbent with a real moat just got a cheaper engine bolted into it.
- The bet against itself Intelligence is deflating about fifty times a year, and the companies that make it are filing to go public at the largest valuations in history. The contradiction resolves once you see that none of them is priced as a model company: Anthropic is an enterprise bet, OpenAI a distribution bet, xAI a compute bet. Each is worth a trillion dollars only to the degree it can stop being the thing it is famous for.
- The internet goes headless If intelligence is ambient and free, the scarce layer becomes the interface: the agent that represents you and filters the world before you see it. That is the best filter we will ever have on slop, and the most valuable real estate on the internet, which is why everyone who owns your attention will try to be it. The headless internet doesn't free the individual; it moves the choke point from the platform to the agent, and the only question that matters is who that agent works for.
- Google wins consumer AI on distribution Models are getting cheap and similar to each other. Once that finishes, the winner is whoever already has the users and can afford to give the thing away. Google has both.
- Mechanistic interpretability as generative art Concepts inside a neural network have addresses. Point an image generator at the address for 'ocean' and let it run, and what comes out is the model's own idea of ocean, rendered by the model rather than charted.
- AI is an interface Every system makes you learn its menus and its query language before it will help you. The useful thing AI does is remove that step: you say what you want, and it works out which buttons to press.
- Three gaps: coverage, synthesis, intent When someone asks for AI insights, teams hear 'summarise this for me'. There are three separate problems hiding in that request and summarising is only the middle one. Solve it on its own and nobody believes the output.
- Measuring the platonic representation There's an idea that as models get better they all end up learning the same picture of the world, whatever they were trained on. You can test it: describe one thing as text, an image, a sound and a video, then see how close those four land.
- Metacognition is the unlock Models got better at answering, then at reasoning, then at working through many steps. What they're still bad at is noticing that their own approach isn't working and changing it.
- Context engineering is the design surface Prompt engineering is about picking the right words. The bigger question in production is what goes into the context window at all, and what each step is allowed to see. That's the part that decides whether the thing works.
- AI should be a dumb renderer Most AI insights features hand a pile of data to a model and ask it to do the maths. Models are bad at maths and very good at writing. Do the sums in code first, then let the model explain what they say.
- The four-mode product manager Product work used to be four jobs held by four people who met on Thursdays. Doing all four myself costs me some depth in each, and it's still faster, because the handoffs cost more than the expertise did.
- Production AI is mostly workflow design Everyone argues about which model is smartest. In the systems I've shipped, into government, enterprise and consumer, almost none of the hard problems were the model. They were what you feed it and what you do when it fails.
- AGI won't be one big brain We keep picturing AGI as one enormous model that finally got big enough. The systems that are actually getting good are collections of narrower parts that have learned to call each other.
- Stop micromanaging your AI Re-explaining your situation at the start of every chat is the same as briefing a new contractor every morning. The fix is boring: write the context down once, somewhere the model can always see it.
- The intelligence illusion Every time AI does something we said would be impossible, we shrug and decide that task didn't need intelligence after all. We've been doing this since chess, and it's worth asking what will be left when we run out of things to move.
- AI welfare: foresight or premature? Anthropic hired someone to work on whether AI systems can suffer. It sounds like a joke until you try to write down the test that would settle it, and find that you can't.
- Chain of thought, and where it breaks Telling a model to work through a problem step by step does make it more accurate. Nobody mentions the second half, which is that you usually don't want your users to see any of that working.
- Prompt chaining: split the work, raise the floor One big prompt that does five things will fail somewhere in the middle and you won't know where. Five prompts that do one thing each cost more to run and are far easier to fix.
- Simple prompting: less magic, more method There are no magic words. After a few thousand prompts in production, everything that reliably helped came down to three things, and all three are just being clear about what you want.
- Prompting with frameworks the model already knows Name a framework the model has already read a thousand times, like a SWOT or a user journey map, and you don't have to explain the steps. It fills in the shape and you spend your words on the actual problem.