Ethics

Loving this technology means being honest about what it breaks. Our writing on the uncomfortable questions.

  • Cheaper AI is more dangerous than smarter AI, and nobody is talking about it

    The AI safety conversation is obsessed with the ceiling: the smartest model, the frontier, the hypothetical superintelligence. I think we are watching the wrong number. The change that will actually reshape the world this decade is not that the best model got smarter. It is that a good-enough model got almost free.

  • Setting up a private, local AI stack for people who value their data

    If your work involves anything sensitive, client data, health records, unreleased products, legal material, the idea of piping it through someone else API should make you at least a little uncomfortable. The good news is that in 2026 you do not have to. A genuinely capable, fully private AI stack is now within reach for a normal team. Here is how to think about building one.

  • The EU AI Act is messy, late, and probably necessary

    I am instinctively allergic to tech regulation written by people who have never shipped anything. A lot of the EU AI Act fits that description. It is late, convoluted, and parts of it will age badly. And yet, reading through what actually takes effect in 2026, I keep landing somewhere uncomfortable: most of it is the kind of thing the industry should have done on its own and did not.

  • Who owns the words your AI trained on? The courts are about to decide.

    The most important AI story of the next year will not be a model release. It will be a court ruling. The lawsuits over what these models were trained on, the New York Times against OpenAI, Getty against Stability, are entering decisive phases, and the question they answer will quietly reshape the entire industry. I have a side, and I want to explain it honestly.

  • Why I still read the model cards nobody else reads

    Every model launches with a splashy chart and a breathless thread. Almost nobody reads the boring document that ships alongside it, the model card, with its dull sections on training data, limitations, and known failure modes. I read them, every time, and I think it is one of the highest-value habits you can build in this field. Here is why.

  • Why this blog exists, and why it stays skeptical

    There are enough AI blogs. Most of them read like a press release with the serial numbers filed off. This one is trying to be the thing I actually wanted to read: written by someone who uses these tools every day, likes them more than is probably healthy, and still reads the fine print.