AI

I wrote a book about working with AI, with AI, while practicing what it argues.

I’ve wanted a 3D printer for years. There is something about the idea of taking an object that exists only in my imagination, designing it on a computer, pressing a button, and watching it become a physical thing that I find absolutely fascinating. It feels like something out of science fiction. The kind of technology I used to imagine having when I was a kid. But for years, two things kept me away: the price and the amount of fiddling involved. ...

I loved Star Trek as a child. The ships and the strange worlds were the obvious draw, along with the sense that the universe was enormous and mostly unexplored. What stayed with me, though, was something I couldn’t have put into words back then. Star Trek assumed that humanity would grow up. In its future we hadn’t only invented warp drive. We had stopped organizing our lives around fighting each other and pointed the ambition of an entire civilization outward. ...

I had the pleasure of corresponding via email with Andy Nian, cofounder and head of growth at PracHub, a site that catalogs technical interview questions reported by candidates. He wrote to me in August asking whether I would cite his AI-assisted question set in a piece I had written about why interviews should measure judgment rather than output. I came back asking for his data instead, and he graciously answered a few questions on the record: figures, clear caveats about what those figures could not support, and three public examples. Almost no founder does that. One of his answers has stayed with me since, and most of what follows is an argument with it, in the good sense. ...

I recently came across a brilliant comedy clip by Deepak Chandran about how we use AI to write emails. Someone has a simple message. They give it to AI, which turns it into a beautifully structured, professionally worded, eighteen-page document. The recipient can’t be bothered to read it, so they ask AI to summarize it. We’ve built a remarkably sophisticated machine for turning one sentence into eighteen pages and back into one sentence. ...

Reading the “Man and Machine” chapter of Zero to One, I was struck by how confidently Peter Thiel and Blake Masters argued that computers would complement humans rather than replace them. Their reasoning was straightforward: computers excel at processing data; humans bring judgment. Build businesses that combine those strengths. Then AI advanced. In a December 2025 interview with The Spectator, Thiel described AI as “probably more of a substitute than a complement” to human labour. ...

During a train ride, I built a website for our family vacation. I used ChatGPT on my phone. After about an hour, I had a site with a route for each day, photos, maps, riddles for the kids, and ideas for things to do. The users were six people: my family. I had no business plan. I did not want to start a travel company or sell subscriptions. I did not want to make it useful for thousands of other families. I wanted something that fit our trip. ...

Dario Amodei, CEO of Anthropic, has published an unusually direct argument about the future of AI development: the companies building the most capable models need to slow down. He is not calling for them to stop, or to give up the benefits AI may bring. He is asking them to leave enough time to understand, evaluate, and control the systems they are making more powerful. What makes this worth reading is who is saying it. Amodei runs one of the companies competing to build those models. He faces the same pressure to move faster as everyone else, and he is arguing that the pace itself has become part of the problem. ...

Anthropic published a new piece last week, The AI-Native SDLC Playbook. It is long, detailed, and worth the time. It also lines up closely with what I have been writing here for the better part of a year. The argument at its center takes one sentence: code is no longer the bottleneck. Everything in the document follows from it. Once agents write most of the implementation, the constraint moves to the stages on either side, the ones still running at human speed. Approval queues build. Controls sized for human output stop matching what arrives at them. Governance that meets monthly cannot govern work that ships hourly. ...

A recent episode of Latent Space with Joon Sung Park, co-founder of Simile AI, caused a strange flashback to my student days in industrial engineering. Long before anyone talked about generative AI, we learned to use a piece of software called Arena to build probabilistic simulations of airports, supermarkets, factories, and other complex systems. The basic idea was simple and surprisingly powerful. Instead of changing the real system, you built a simplified version of it on a computer. Customers arrived according to some probability distribution. A cashier took a variable amount of time to serve them. Queues formed, resources became occupied and available again. You could add another checkout lane, change the staffing level, or rearrange a production line, run the simulation thousands of times, and see what happened before spending money in the real world. ...