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Demand for GPT-5.5 and Opus 4.7 is nearly infinite, the mid-tier has vanished, and low-to-mid-range compute sits idle. The token economy sounds like selling electricity, but it's more like a gas station: 98-octane is sold out, diesel tanks are full yet self-service only, and 95-octane sits empty.

Last month I wrote that ignorance is an advantage. This month agents burned me four times: performance inflated by orders of magnitude, a lab machine bricked, optimizations going in circles, and goals too high to ship. Ignorance gets you started; common sense keeps you from getting played.

The Gemini 3.5 Flash launch barely talked about intelligence; Zhipu’s GLM-5.1 High-Speed edition hit 400 token/s. Both tell the same story—once inference speed crosses that 5× threshold, the model unlocks an entirely different product category.

Today I went to the AMD Developer Conference in Shanghai. Over 2,000 people packed the venue, and AMD gave one of its two developer awards to a non-coder who used an AI agent to rewrite a project in Rust. A year ago everyone worried about demand; six months later, all anyone asks is: 'Got more supply?'

I've been using Codex's /goal for weeks. When Claude Code recently added it too, I moved over a few tasks Codex couldn't crack. The contrast was stark enough to be worth writing down.

When I show friends what my AI agents are doing, they always ask, 'Can you explain this to my boss?' But real change has never come from a single lesson.

Trump brought Musk, Jensen Huang, and Tim Cook to China. Red carpet, state dinner, talks between equals. Seventy-seven years ago this country couldn't feed itself — two generations was all it took.

I recently worked on a few AI content batching projects and realized something I'd miscalculated: going from a working demo to a fully automated pipeline takes at least 3× the time, not 50%. The extra two hours go entirely into QA and retries. Here's what I learned about where mid-tier models and local compute actually fit.

The day South Korea's presidential office proposed turning AI windfalls into a universal dividend, Samsung's union was threatening to strike. That sat in the middle of three questions AI poses to society: unemployment first, then a deeper one—when work no longer defines value, what keeps people going?

Five years from GitHub Copilot to Claude Code. The more the direction proved itself, the harder life got for the earliest believers. Small models, workflows, standalone IDEs—foundation models swallowed them layer by layer. What remains sellable is clear; it just isn't software anymore.

A friend asked whether I should take an offer as an AI transformation expert. After a three-hour conversation, I wrote down my verdict: don't skimp during the vanguard phase, don't touch IDEs, and don't let your agent become a black box. GPT-5.5 launched two weeks ago; in the remaining 10 weeks, the world will be stunned by a wave of unexpected breakthroughs.

A friend asked where to start with Vibe Coding. Two paths: start from work, or start from your digital identity. The prerequisite is that what you put out must be the real you—the internet has memory, and the old tweets dug up on Kevin Hart were from ten years ago.