Deletion has no advocate. AI assistants add code faster than any team could two years ago, removal stays near zero, and the gap becomes migration scope, security triage, and the months a new engineer spends reconstructing intent nobody wrote down. The tilt is measured. ETH Zurich and LogicStar.ai built
The fastest way to build a commodity is to let an agent build it well. The harness is the durable asset: model capability commoditizes, and the evaluation pipeline and organizational context around it do not. I have been building on that premise rather than writing about it. Then I measured
Execution is the typing: turning a decided approach into a working diff. It decays fast. Context fills, compaction runs, and a session that wrote clean code an hour ago starts producing sludge. The fix is cheap: throw it away and open a fresh one. Judgment is the why: the approaches
"When change is easy, the need for it cannot be foreseen; when the need for change is apparent, change has become expensive, difficult, and time-consuming." David Collingridge wrote that in 1980, in a book on controlling technology that leaned on cases like nuclear power and heavy industry.
The code passed every test. It worked in staging. It worked in the demo. It shipped clean. Six hours later it took down checkout, on an input nobody had thought to try, on a path nobody had thought to look at, because the person who wrote it had been moving
Your vibe-code velocity slowing down is not the warning sign everyone says it is. The dominant take treats the plateau as debt coming due: you deferred the architecture, the system can no longer absorb change safely, and every small edit now risks breaking something you no longer understand. Sometimes
Most knowledge tools remember what you read. Almost none of them keep a track record of whether you were right. You can retrieve any fact you have ever filed. You cannot retrieve whether you were right about it. That second gap is the expensive one, and most knowledge systems are
I am a sucker for good words, and one from Tamil literature keeps surfacing the deeper I go into AI: மதக் களிறு, the bull elephant in musth, the periodic testosterone surge that turns a beast of burden into a force of nature. It breaks through fortress gates, shrugs off
Last week Claude Code shipped dynamic workflows. The first thing I pointed one at was the design decision I was least sure of, and verifying it took a prompt instead of a harness. Dynamic workflows are easy to underrate. Claude Code can now write its own multi-agent harness on
Anthropic's CEO used part of his own developer conference to explain why writing the code is no longer the hard part. His framing was Amdahl's Law: when you speed code generation up several times over, the work you did not speed up becomes the constraint. Review,
When AI was a private tool, PMs wrote for two audiences: business stakeholders and developers. Deck and PRD. That world is closing. PMs now write for three — and the third audience fails in a way the first two never did, in a way that's invisible at the moment
AIStrategy
Our position for months has been that the harness around a model, its scaffold, prompts, and configuration, does more work than its weights on whether the product holds up in production. The AI market is still mispricing this. Procurement and investment decisions anchor on model capability: benchmarks, parameter counts, training