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Knowledge Was Never the Commodity
What AI actually disrupts — and what it never will. Twenty-four years of evidence on which learning decays and which compounds. Plus the full signal shelf: the books, the people, and the benchmark sources I actually use to separate honest signal from sponsored noise.
You Already Know AI — You Just Called It Search
Everything in modern AI traces back to concepts you already know from search engines. Tokens, attention, vector retrieval, graph traversal — the vocabulary changed in 2017. The problems did not. Here is the lineage, and here is what agents actually need to work.
AI Is Not Free — And That Was Always the Plan
The trillion-dollar infrastructure bet and the bill it produces at every level: nuclear contracts and HBM economics above you, my €1,000/month personal stack, the $500K–$1M/year enterprise, and the startup paying $500K/month for 5–10 autonomous agents. The complete cost conversation, with receipts.
Where This Is Going: An Honest Look at AI Forecasts
What the most credible forecasting work — the AI Futures Project, AI-2027, METR's time-horizon data — actually says about the next three years. Plus the moment Fable 5 made me feel, for the first time in my own domain, that something was more intelligent than me. No hype, no doom. Forecasts, error bars, and what to do about them.
The Six Fields AI Reaches Next
Coding, hacking, robotics, politics, bioweapons, forecasting — one honest section per field, each sorted into what currently exists, what is emerging, and what is still science fiction. The capability map that connects the forecasts to the ground.
AI Adoption Is Not a Tooling Problem
The teams struggling with AI are not struggling because they have bad tools. They adopted AI into processes that were never designed for it. 9 SDLC phases. One concrete failure per phase. No productivity statistics. No tool roundups. Just where things actually go wrong.
Before the First Line of Code: Forces 01–02
Requirements now have three readers: the user, the AI agent, and the reviewer. Domain models are the language of agent delegation. How to write for all three, and why DDD became a hard prerequisite for AI-augmented development.
The Build and the Store: Forces 03–04
Full-stack in 2026 is six parallel tracks — most teams run two. The database decision is now four decisions — most teams make one. What both forces actually require when AI agents are doing the building and the data has to support semantic retrieval.
The Pipeline and the Cache: Forces 05–06
One feature, five artifact types, one coordinated release — and the genuinely unsolved cross-track dependency problem. Plus the middleware layer where semantic caching quietly removes 40–70% of your AI inference bill if you design it right.
The Eval and the Runbook: Forces 07–08
Nothing in your current monitoring tells you if your AI feature is confidently wrong. The fourth test layer nobody has built. New SLOs for hallucination rate and retrieval precision. And why autonomous agent rollback is not a recovery strategy — it is a post-hoc audit.
AI Across the Full SDLC: A Practitioner's Map
Where AI creates real leverage — and where it creates real risk — across each phase of software delivery. Eight forces × nine SDLC phases, with tools, impact levels, and honest annotations at every intersection. Keep it open in another tab.
8 Forces Reshaping How Software Gets Built
The companion overview covering all eight structural forces end-to-end — from requirements to reliability. Each force has its own card: what is driving it, honest trade-offs, concrete tools, and what it means in practice. Use as a reference alongside the series. Articles 07–10 go deep into each force pair.
Coming with the In Practice arcWe Learned to Code So That Others Could Type a Prompt
What it actually means that the next generation may never write a line of code — and why that is both true and more complicated than the headline.
Read →Baba, What Should I Study?
My son asked me what to study in a world reshaped by AI. This is what I told him — not as a shortcut, but as a genuine answer about foundations.
Read →My Father's Gamble
In 2002, I built an AI-powered trip planner for my master's project. The technology did not exist to make it work. Twenty years later, it does. What that gap taught me.
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