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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.

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?