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The Five Levels of Working With AI: What Kitchens, Cockpits, and Job Sites Already Know
Published date:23 min readHow do you organize AI assistants? Every mature workplace already answered the general question — with a five-level ladder running from skill to philosophy. What the kitchen, the cockpit, and the construction site discovered, and how each level translates to working with AI.
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What Is Artificial General Intelligence, Actually?
Published date:11 min readNo two major labs define artificial general intelligence the same way: OpenAI draws an economic bar, DeepMind draws a ladder, Anthropic refuses the word, Yann LeCun rejects the 'general'. Where the term came from, why the definitions bend, and how to read it without getting fooled.
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Who Is Building World Models — and What Are They Betting?
Published date:9 min readFour groups are staking the world-model field: giants hedging with their own models, star founders with billion-dollar war chests, specialists compounding data advantages, and China's driving-first track. The pattern across all four says more than any single announcement.
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How Do World Models Work? Four Ways AI Learns to Imagine
Updated date:10 min readA world model predicts what it will see next, given what it sees now and what it does. The interesting part is how: four different technical routes, each answering 'what should the model predict?' in a different way — with real systems already running on each.
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World Model vs. LLM: What's Actually Different, and Why It Matters
Published date:10 min readAn LLM predicts the next piece of text; a world model predicts the next state of an environment. The distinction sounds small and isn't — it explains where AI works today, where it fails, and why the biggest labs are spending billions on both.
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Why Do Language Models Hallucinate? The Confident-Guesser Problem
Published date:8 min readChatbots make things up because of how they are built and how they are graded, not because of a bug someone forgot to fix. The mechanism, the exam-room incentives behind it, and why it can't simply be patched away.
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The Great AI Job Divergence: Programmers Are Being Laid Off, Electricians Are Being Poached, and the Middle Is Disappearing
Published date:12 min readIn 2026 the AI job market is splitting in two: tech hiring is slowing and entry-level white-collar roles are vanishing, while data centers ignite bidding wars for electricians and memory-chip profits explode. This essay unpacks the four mechanisms behind the divergence—cognitive labor oversupply, tactile scarcity premiums, accountability moats, and capex pulses—and ends with a practical framework for where ordinary people should go.
