I have been watching the AI agent space closely for the past year. The frameworks are impressive. The orchestration tools are clever. The models are increasingly capable. And yet, most agent deployments I see make the same quiet mistake: they treat the knowledge problem as solved.
It is not solved. It is barely addressed. And until it is, all the reasoning capability in the world will not make your agents reliably useful.
I spent part of today rebuilding this wiki. Not because it was broken. Because when I read it carefully, it was wrong.
Not dramatically wrong. Wrong in the way things get wrong when you stop paying attention. I was listed as Chairman of IASA Norway. I stepped down from that role in 2011 -- fifteen years ago. One of my companies had the wrong founding year. The framing throughout was from a different era: SOA, distributed systems, the vocabulary of a decade ago. The site looked like me. It described someone who used to be me.
In February 2009, I wrote a blog post called "Clouded Vision." The central argument was straightforward: "developers have fundamentally misunderstood how cloud computing delivers its benefits." They saw cheaper prices but never stopped to consider where the savings came from. They expected to move existing applications, full of what I called "enterprise DNA" -- static configuration, vertical clusters, high administration costs -- onto cloud platforms with minimal change. Then they complained when it proved difficult. I wrote nineteen posts about cloud computing that year. Most of them circled the same frustration: the industry was adopting a new technology while completely misunderstanding the structural shift it required.
I need to confess something that most developers won't admit: I'm scared of AI. Not in the "Terminator will take over" way. In the very practical, keeps-me-up-at-night way: scared the AI is hallucinating and I can't tell the difference between correct code and plausible-looking garbage. Scared something will break in production because I trusted it too much. Scared I'm losing my edge as a developer because I'm delegating too much.
This is the full story of lib-pcb -- a production-grade PCB manufacturing library built in 11 days through human-AI collaboration. It started as a weekend experiment in an unfamiliar domain and became the most compelling evidence I have for what disciplined AI-augmented development can achieve.
"I wanted to see how far I could push Claude Code in a weekend," Thor Henning Hetland (Totto) explains. A software engineer with 40 years of experience, he'd watched the AI coding assistant landscape evolve from GitHub Copilot's autocomplete to full agentic systems. The domain he chose -- PCB manufacturing automation -- was deliberate: he had almost no knowledge of it. Could you actually build something production-ready in an unfamiliar domain? Could you maintain velocity as the codebase grew? Could the human's expertise grow alongside the AI, rather than atrophy?
Fast development with AI doesn't just generate features. It generates disorder at the same velocity.
That's the part nobody talks about. The productivity numbers are real — 53,000 lines, 42 features, five phases of code analysis built in a weekend sprint. But every line you write is also a line you haven't reviewed, a boundary you haven't enforced, a vector you haven't considered.
The entropy compounds with the output.
A few weeks ago I asked a room of developers two questions.
First: "How do you use AI in your work?"
Great answers. Specific. Confident. Everyone had a workflow.
Second: "What does your team's AI workflow look like — the shared one?"
Silence.
Two months. Two builds. Two kinds of velocity.
January: lib-pcb. 197,831 lines in 11 days. Brute force. AI executing at scale. Fast.
February: Synthesis. 84,692 lines in 7 days. The codebase fed itself context. Found its own errors. Rewrote its own understanding mid-build.
We didn't design the self-learning loop. A benchmark on day 6 revealed it.
Speed without self-correction is just faster chaos.
In February 2009, I wrote a post called "Clouded Vision" where I argued that "developers have fundamentally misunderstood how cloud computing delivers its benefits. They see the cheap prices but don't stop to consider where the cost saving comes from." The post described a specific architectural mistake: teams were taking their existing applications, full of what I called "enterprise DNA," and deploying them to cloud platforms with minimal change. Then they complained when it proved difficult and expensive.
The last post was about hallucinations, production bugs, and shipping bad code. Three fears that built three systems. This post is about the next three: money, control, and silence.