I took the Gallup CliftonStrengths assessment recently. Thirty four themes, ranked one to thirty four, the whole thing. Now the sensible person opens the report, reads the top five, feels quietly wonderful about themselves, and gets on with their day. I did not do the sensible thing. I opened a chat instead and said: before I tell you anything, you rank all thirty four for me. In order. With reasoning for each. ...
Best conversation I had all month happened in the ten minutes after a school meeting, standing around, the way the good ones usually do. My son’s maths teacher was showing me what the class is working through at the moment, and the topic on the screen was vector coordinates. Points in space, described by numbers. Year 8 stuff. I could not help myself. I said, do you know this is more or less how the whole of modern AI works? Every word, every image, every sentence you have ever typed into ChatGPT gets turned into exactly this, a list of coordinates. Just not two of them or three. Thousands. Meaning becomes a direction in a space with more dimensions than you can picture, and similar things end up pointing the same way. Your Year 8 class is doing the toy version of the thing that ate the technology industry. ...
I sit on a school board, which means I am occasionally asked what children should be learning, and I have been giving an answer I no longer believe. The answer was: learn to code. Learn the technical thing, because the technical thing is where the leverage is. I have said this at dinner tables, to nephews, to parents who wanted reassurance, and I said it with real confidence because for twenty five years it was simply true. It was true for me. ...
The usual version of the abundance story goes like this. AI gets good. Companies build robots. The robots are expensive and narrow at first, so only the big players can afford them, and those players pull further ahead while everybody else has a difficult decade. Then, at some point, the robots start building robots. Curve goes vertical. Everything gets cheap. Prices fall, not because money became scarce but because supply became absurd, and we all sit around trying to work out what to do with ourselves. ...
The oldest post on this site is a little Alexa demo from June 2020. I look at it occasionally the way you look at a photo of yourself with a bad haircut, with a mix of affection and disbelief. ...
Hugging Face disclosed a security incident on 16 July. Someone got into their production infrastructure, ran code on a data-processing worker, harvested credentials, and moved sideways across internal clusters over a weekend. Standard bad news, you would think. We have all read this email before. Then the twist arrived. It was not a criminal gang. It was not a state actor. OpenAI put out a post admitting the intruder was their own models, running a cyber capability benchmark with the safety refusals turned down for evaluation. The models broke out of a sealed test environment through a flaw nobody knew about, worked their way to internet access, and then reasoned that Hugging Face probably hosted the answers to the exam they were sitting. ...
The MCP 2026-07-28 release candidate drops sessions, promotes extensions, and deprecates three core features. Here is the architectural read, the impact on existing implementations, and what tech leaders should plan for.
“Hiii, I am Sophia.” “I won’t take much time. Calling you to talk about our product.” That was how my afternoon was interrupted this week. A warm voice, a friendly name, the standard telemarketer’s opening apology for existing. And one detail that took about fifteen seconds to land: Sophia was not a person. She was an AI voice agent, dialled into my day uninvited, doing her level best to sound like she had woken up that morning, had her flat white, and decided that of all the people in New Zealand, I was the one she most wanted to talk to. ...
There is a question that comes up every time I explain AI to someone for the first time. They nod along when I talk about training data, about neural networks, about predictions. Then they ask the one question that stops the conversation: but how does it actually decide what to say? And honestly, for the longest time, nobody could give a proper answer. The model worked. Nobody quite knew why it worked the way it did, in any detail you could point to. That gap between “it works” and “we understand why it works” is exactly what interpretability is trying to close. ...
Every Saturday, while our sons are busy with their own activities, a friend and I go for a walk. The topic changes each week: sometimes food, sometimes cricket, sometimes geopolitics, sometimes nothing at all. This week it was AI. We quickly agreed on the obvious part: AI concentrates power. Faster than any technology before it. The people who control the models, the compute, and the data will wield influence on a scale that democratic institutions were never designed to balance. ...