AI Field Notes

GPT 5.4 dominates every benchmark. But when I gave both models the same complex product strategy prompt, the gap between benchmark scores and real-world output was staggering. Here's what actually happened.

I switched to API billing on Claude Code and finally saw the real cost of my sessions. Thousands of dollars on my laptop in 14 days. Thousands more on my cloud devbox. The Max plan is a flat monthly fee. The math is absurd.

AI doesn't lack execution power. It lacks people who know what to execute. The future belongs to systematic thinkers who can decompose problems from first principles and orchestrate armies of AI agents. This is the era of the super individual.

The same GitHub project is a quiet dev tool in the US and a nationwide mania in China. This isn't a knowledge gap — it's the product of WeChat's trust networks, Douyin's algorithm, A-share retail investors, an anxiety monetization industry, and government subsidies. None of these amplifiers exist in the US.

A year ago, two essays predicted AI would create permanent wealth lock-in and make humans economically irrelevant. Twelve months later, the scorecard is in: entry-level jobs are collapsing, capital concentration exceeded predictions, but open source and solo founders are the counter-force nobody saw coming.

Two reports dropped the same week — one says 6.8M digital workers today scaling to 720 billion by 2035, the other says 'no systematic unemployment yet.' Both are right. People are panicking about the wrong timeline. Short-term fear is overblown, long-term impact is wildly underestimated, and the right move is to become the person who orchestrates AI teams.

A former Google TPU engineer who worked on V7 and V8 revealed how TPU actually competes with Nvidia. The answer isn't about chip specs — it's about system-level design, software co-optimization, and a fundamentally different philosophy. Apple, Anthropic, and Meta are all using TPU now. Here's what that means.

Three demand curves, one deflation trend, and why compute is the new oil

First principles on AI: every trend, traced to its root, is about whether there's enough compute — and whether it's good enough

Stanford HAI's 2025 and 2026 AI Index reports, side by side, show that the countries building AI are the ones most afraid of it — and the countries using AI most intensively at work are not the ones the Western tech press talks about. The sentiment map and the capability map don't overlap.

I built a few TypeScript web games but always felt the ceiling was too low. If I wanted to build a proper game and let AI write most of the code, should I pick Godot, Unity, or Unreal? After a lot of digging the answer surprised me: the engine AI is best at isn't the one with the most training data — it's the one AI can read entirely as text, run headless, watch, and fix on its own.

A friend pinged me at midnight asking whether we should build a bot to follow Serenity, the supply-chain researcher who blew up on X, in and out of his trades. My first reaction was that it was pointless: a strategy that actually compounds gets quietly levered up, not broadcast. But by the end of the night I'd changed my mind. There is something worth distilling here — it just isn't in his holdings, it's in the order he asks his questions. So we turned that process into an open-source skill.

A group chat landed on a heavy question: once AI and embodied robots do most of the work and productivity explodes, do ordinary people actually get freer? Or do the gains flow to a few platforms, capital pools, and states, while everyone else goes from worker to managed case? The more I sat with it, the less I believed in an automatic utopia. The likely future is three paths braided together — AI dividend society, platform feudalism, and high-automation governance — and which one dominates decides whether AI frees people, or just frees productivity while people stay stuck.

A group chat got into promotions, kicked off by a friend's essay: if you use AI to crank your output 10x and become the model workhorse, do you get the raise and the title? The reality is the opposite — the faster and more useful you are, the easier it is for the org to recast you as a high-throughput execution node. I break promotion into a multiplicative model: capability × narrative × sponsor motive × org slot × politics/timing, where any factor near zero collapses the whole case. AI mainly boosts capability and speed, but can quietly damage your narrative and your positioning. The danger isn't being too slow. It's being so fast nobody bothers to understand your judgment.