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, while 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 - and 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 Intelligence is commoditising, so the model stops being the moat. What's left is distribution and a business that profits from giving intelligence away, and Google is the only company with both - fighting Nvidia, Apple, Amazon, Microsoft and Meta each on one front while it works all five.
- Mechanistic interpretability as generative art If a network has learned a concept, that concept is a location in its embedding space. Steer a generator toward that location and you don't get a diagram of what the model knows - you get a picture of it.
- AI is an interface The interface was always the tax you paid to use a system: its menus, its query language, its API. AI's most durable job is to take that tax off, by turning plain language into the task you meant and the systems that carry it out.
- Three gaps: coverage, synthesis, intent Most AI insights requests get treated as a synthesis problem. That's the wrong reframe. There are three stacked gaps - coverage, synthesis, intent - and you can't skip a layer without trust collapsing underneath you.
- Measuring the platonic representation The platonic representation hypothesis says capable models converge toward one shared picture of reality. A language-anchored multimodal encoder lets you test a sharp version of it: encode one concept through four senses and measure what they agree on.
- Metacognition is the unlock Model progress has moved through paradigms: reactive, then reasoning, then agentic. The next is metacognition - thinking about its own thinking - and it's what separates a model that repeats mistakes from one that compounds on them.
- Context engineering is the design surface Prompt engineering treats the words as the lever. Context engineering treats the whole context window as the design surface, with a stage and an artifact for each step. It's the better abstraction for production work.
- AI should be a dumb renderer The default pattern for AI insights dumps data into a model and asks it to count, compare, and conclude. That's the wrong order. Precompute the numbers deterministically; let the model render and narrate them.
- The four-mode product manager Strategy, market analysis, solution architecture, implementation. The old role split these across people and handoffs. The job now is to move between all four in a single conversation.
- Production AI is mostly workflow design The model-intelligence obsession misreads where production AI actually succeeds or fails. Across government, enterprise, and consumer, the wins came from orchestration, retrieval, evaluation, and fallback handling, not a smarter model.
- AGI won't be one big brain The monolithic-superintelligence story is the wrong mental model. Real general intelligence looks more like an orchestra of specialists than a single giant model.
- Stop micromanaging your AI Most people prompting models have quietly become managers - and they're managing badly. The fix is to build systems for context, not to craft better one-off prompts.
- The intelligence illusion AI keeps hitting milestones that used to sound terrifying, and they keep landing as boring. That reaction says something specific about what intelligence actually is.
- AI welfare: foresight or premature? Anthropic hired an AI-welfare researcher. The question is real, the uncertainty is genuine, and the honest position sits between dismissal and panic.
- Chain of thought, and where it breaks Asking a model to reason step by step reliably improves its answers. The catch most advice skips: in a production app, that visible reasoning is often the last thing you want.
- Prompt chaining: split the work, raise the floor Asking a model to do one complex thing in a single call invites failure. Breaking it into a chain of focused calls makes each step more reliable and easier to debug.
- Simple prompting: less magic, more method After thousands of prompts in production, the lesson is that prompt engineering isn't about magic words. It's clear thinking and structured communication, and it reduces to three principles.
- Prompting with frameworks the model already knows Structure your prompts the way a consultant structures a brief, then borrow a framework the model was trained on. You give it a running start instead of describing every step.