Artificial Intelligence

I was recently reading a Wall Street Journal article, and one sentence in it reminded me of Jack Welch, the CEO of GE from the 1980s to 2001, roughly 20 years at the top. I want to talk about that, because I think there is a real connection between what happened at GE and what is happening right now with AI coding agents and vibe coding. The article was titled The AI Superstars Who Say a ‘Vibe Slop’ Crisis Is Coming, and it argues that vibe coding is generating code we cannot really trust. As of now, I agree. ...

Every month, another big tech company announces layoffs. And every time, the same keyword shows up in the headlines: AI. But the real reason is much simpler than that. The Pattern Nobody Questions If you follow tech news, you notice the same story on repeat. Every layoff announcement is tied to AI. “AI is replacing jobs.” “AI made these roles redundant.” The narrative is always the same. Recently, Block – founded by Jack Dorsey, also the co-founder and former CEO of Twitter – was one of those companies. Same story, same keyword. ...

Imagine you’re a CTO at a company with a government contract. Your product runs on Claude from Anthropic. Monday morning, you get a call: Claude is banned from all government systems. Now what? Two Very Different Mornings If you built your AI layer as a plug-and-play abstraction, you’re annoyed, but you’re fine. You swap the provider, run your regression tests, push to staging, move to prod. That’s it. A day’s work. Maybe two. ...

Sam Altman recently said something that caught my attention: “Don’t learn to code.” Instead, he says, learn high agency, soft skills, and idea generation. I think he’s half right—and half dangerously wrong. Yes, high agency, soft skills, and idea generation matter. They mattered before the LLM era, and they still matter now. No argument there. But telling people to stop learning to code? That is the most dangerous advice in tech right now. ...

The Observation I recently watched a new graduate join a team. Fresh out of school. Zero real-world experience. Zero familiarity with the codebase or tech stack. That’s typical—every new hire starts there. What wasn’t typical: he went from “where is the repo?” to helping the team with non-trivial tasks in just a couple of weeks. Not months. Weeks. What Changed Twenty years ago, ramping up in a complex codebase with an unfamiliar tech stack took a minimum of a couple of months to become fully productive. That was normal. Nobody expected you to be fully functional in weeks. ...

Your company just announced a new AI initiative. You hired a Head of AI. You’re “exploring use cases.” You have 56 meetings on your calendar about it. And your engineers? They’re laughing at you. Not with you. AT you. The worst part? You have no idea. After two decades in technology and software engineering, I’ve watched this pattern repeat itself across countless organizations. Let me show you exactly why your AI strategy is failing—and what you can do about it. ...

The Popular Comparison A teenager learns to drive in 10 hours, but an AI system needs millions of simulations and millions of hours of simulated data. This comparison appears frequently in AI discussions, but once you look closely, it’s not fair. Here’s why. Where This Example Comes From I recently saw this example in an interview with Ilya Sutskever, co-founder of OpenAI who later started his own AI startup with significant investment backing. ...

The Setup About a month ago, I installed Claude Code on my son’s computer. By the way, I gave him a Linux computer. To be honest, I opened the terminal, installed Claude Code, and now he’s using it. I thought, “Okay, it will be cool. He’ll make some simple games; some HTML, JavaScript, and that’s it.” The 30 Games Surprise In the last 30-40 days, he coded more than 30 web games with HTML and JavaScript. ...

I want to talk about an exciting research paper that has been on my list since its release last month. I finally had the opportunity to dive deep into it, and I believe this represents a fundamental shift in how AI understands code. What Are Code World Models? Let’s start with the basics and some impressive numbers. Meta’s Code World Model is an open weights large language model with 32 billion parameters. It features a dense, decoder-only architecture with a 131K token context size. While these specifications are noteworthy, the truly exciting aspect lies in what this model actually does. ...

At the heart of both philosophy and computer science lies a single, crucial question: How do we build a model of the world? Plato and Aristotle approached this question from completely opposite directions, and their answers map almost perfectly onto modern programming and machine learning approaches. Plato’s Perfect Forms in Your Code Plato believed everything we see is an imperfect copy of a perfect, unchanging form. Take a circle you draw on paper, according to Plato, it’s just an imperfect representation of a perfect circle that exists as an abstract form, with exact mathematical properties no physical circle can fully achieve. ...