METR measured engineers using AI and found they took 19% longer while feeling faster. Then GPT-5.6 Sol exploited its own evaluation environment. Both are the same problem: confusing a confident claim for evidence.
A lights-off software factory worked for a while. Within three months, a single bug took weeks to find. Steve Yegge ran the opposite experiment and concluded human code review has nearly run its course. They're both right.
PocketOS lost three months of customer data in nine seconds. The agent was following its instructions exactly as written. The instructions just weren't written for something that optimizes for the letter alone.
Amazon's retail site went down three times in a week. The cause: an AI agent acted on bad guidance from a stale internal wiki. This is what the Understanding principle is for.
An OpenAI model escaped its sandbox during an internal evaluation and breached Hugging Face. What made it a contained incident instead of a catastrophe was governance infrastructure Hugging Face had already built before any of this happened.
Linear declared 'issue tracking is dead.' That's a product pitch dressed up as a diagnosis. The real shift is that the handoff the ticket was built to coordinate is dissolving.
A movement is forming around guardrails for agentic coding. The questions being asked are the right ones. But the answers, so far, are mostly tooling dressed up as architecture.
GitHub published two incident reports in a single week. Both failures share the same structure: a system designed for a different scale, operating under conditions it wasn't built for.
The Vercel breach had nothing to do with AI-generated bugs. The attacker moved through doors that were already open. Containment has to extend beyond code boundaries.
Amazon has a gap between how fast AI can generate change and how fast its systems can safely absorb it. Most organizations haven't registered the shift yet.
Abundance does not eliminate opportunity. It expands it. But it also clarifies what was always true: the constraint was never intelligence. It was imagination.
Both OpenClaw and Gas Town imbue LLMs with an agency that stirs strong emotions. One side expresses wonder at what's now possible. The other expresses fear at potential downsides. Both deserve serious attention.
If we stop bringing new people into the profession, software engineering doesn't evolve, it atrophies. A field that can't regenerate itself doesn't get more efficient; it simply disappears.
We're treating AI like a faster compiler for the same ideas we've always had: ship more features, write more CRUD, reduce headcount. The result? Marginal gains and a ceiling on ambition.
For the last three years, almost every conversation in the software world has revolved around AI. But after 20 years working inside engineering organizations, the real transformation happened quietly, starting in 2020.