9 posts
When people estimate token costs, they usually watch TTFT, TPOT, and throughput. What actually makes bills differ by 10× is whether the KV cache hits. The model, server, and user layers all have to line up.
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.
Claude told me to 'get some sleep' halfway through a task, and GPT-5.5 killed the direction after a quick probe. After a week with the new models, one thing became clear: no matter how powerful the model gets, the collaborative part still falls on humans.
A few days ago at a conference, the conversation turned to AI for science. Someone mentioned three walls: the paywall, the wet-lab wall, and the perception wall. Terence Tao says we are experiencing a Copernican revolution in intelligence, and humans are no longer the center of it. There are 8.8 million full-time researchers worldwide; after AI, there may be hundreds of millions. Amateurs are rushing in first, but the real bottleneck isn't the model.
At 23, Liam Price had GPT-5.4 Pro crack Erdős's 60-year-old conjecture in 80 minutes; Terence Tao said everyone collectively went down the wrong path at step one. That same week, two strangers in an elevator talked about layoffs. AI has given every individual access to what used to be the most expensive resource. The only question is whether you use it to add something new to the world, or just to make your deliverables look prettier.
One was Claude Code failing to fix its own Chrome extension after four hours of going in circles; the other was getting soft-throttled on Xiaohongshu no matter what I posted. AI didn't turn failure into success—it just made failure run faster. And that's precisely where it's most valuable.
Prompts, RAG, fine-tuning, knowledge graphs, context engineering—five paradigm shifts in three years. Models are getting stronger, but we still don't know how to make agents perform well in products.
Coding agents have driven implementation costs down to a few hundred dollars per day. The traditional ideation funnel logic no longer holds. The bottleneck has shifted from execution to hypothesis generation. People with ideas have become the scarce resource.
Building things is faster than having meetings. Division by skill no longer makes sense. After AI flattens ability differences, the new logic of teamwork might be: divide by passion, validate through horse racing, and win through diversity.
