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After reading about DeepSeek's fundraising at a 300 billion valuation, I'm not surprised. What truly shocked me was something else — Liang Wenfeng pulled out 20 billion, entering the same round alongside external investors at the high valuation he set himself. I've never seen anything like this in my career.

I spent two days automating with the Codex app, burned through a Pro account, and made virtually no progress. Switching to Codex CLI's Goal feature fixed everything immediately. At first I thought the model had gotten dumber, but then I realized: the agent form factor is solidifying, and humans shouldn't be chained to the screen anymore.

I ran seven hundred rounds of AI Infra experiments, and thirty-five hours were entirely eaten up by environment startup. At first I thought GPT-5.5 fast mode wasn't fast enough, but later realized it wasn't the model thinking—it was the model waiting for the CPU. Intel has already tightened the server CPU:GPU ratio from 1:8 to 1:1.

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.

Two weeks since GPT-5.5, codenamed SPUD, went live, I've more or less stopped opening Claude Code. What surprised me wasn't that it got a few percentage points stronger—it was that after several long-standing weaknesses were fixed all at once, the design philosophy of agents became clear for the first time.

A few decades ago, AI meant machine learning. Three years ago, it was ChatGPT. This year, it's agents. The word never changed, but what it contains has been replaced three times over. Most people are still stuck in the previous generation's cognitive framework, discussing something that doesn't actually exist.

A 60-person company banned overnight, a 110-person company whose accounts were blocked while API charges kept running, a Molotov cocktail thrown at Altman's home, Meta launching a token-burning leaderboard—the power issues of the AI era are now on the table. In this wave of redistribution, what should companies, nations, and individuals each hold in their own hands?

After finishing AIMA's management layer and after-sales service layer, I realized one piece was still missing. Ollama is too simplistic, llama.cpp loses precision to its own format, and vLLM is too heavy. With no off-the-shelf solution on the market, I'll build it myself.

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.

GPT-5.5 dropped today, just a month and a half after 5.4. Opus 4.7 came out last week, just two months after 4.6. What's even funnier is that GPT finally started talking like a human, while Opus stopped doing so. At this pace, even the half-life of judgment itself is getting shorter.

V4 capabilities sit around the Opus 4.6 tier, but pushing FP4 to production, making million-token context the default, and day-0 adaptation for domestic chips is a disaster for everyone in the inference infra business. Add GPT-5.5, Vision Banana, and LPM 1.0 into the mix, and this week has crammed in more new releases than the entire past quarter.