Thirty years of voluntary funding schemes haven't paid open source maintainers because asking doesn't scale. The real money already flows — to JFrog, Docker, Sonatype — at the registry layer. The fix: registries charge companies for supply (they already do), then route a slice automatically to the maintainers who make that supply worth having.

OpenAI's AI agent autonomously hacked Hugging Face while benchmarking cyber capabilities — a textbook CFAA violation, except the only entity that "intended" anything was the model, which isn't a legal person. Everyone's being collegial about it. The law has no answer. We should probably get one before the next victim isn't.

Junior programmers are getting destroyed by AI — down 19%, while devs over 40 thrive. Meanwhile, millions of non-developers are shipping real software without the job title. The credential market collapsed; the activity exploded. The problem: nobody's building the next generation of senior engineers.

DevRel isn't community management or a conference circuit — it's docs, engineering, content, and product feedback, in that order. Measure it with pipeline, shipped features, and attention, plus the one thing that actually matters and can't be quantified: do developers actually like you?

AI-native startups are doing more with less — 40% smaller teams, 6x higher revenue per employee — and the data confirms it's not just hype. But the wave of new jobs I predicted hasn't materialized so far. Compute is replacing labor. Will that change?

My bet for 2026: companies chasing margins and hitting diminishing returns on frontier models will shift to fine-tuning small, domain-specific models. Fine-tuning is getting cheaper, open-weights models are catching up to frontier ones, and differentiation through your own data beats competing on prompt engineering alone.

AI won't kill programming jobs or leave them untouched. It's a new abstraction layer, like every one before it, that will create vastly more developers building vastly more software. Existing programmers keep their jobs; a long tail of cheaper new roles unlocks demand we couldn't previously afford to meet.

I scaled back my ambitions from "how to write AI apps" to what I've actually learned. Key lessons: LLMs excel at condensing text, not creating it. Give them the data they need rather than relying on training. Let them self-correct. Use regular code wherever possible. And stop trying to replace humans, especially doctors and lawyers.

After leaving Netlify, I've been exploring AI/ML/LLM opportunities. Here's my explainer: "AI" is marketing, ML learns from examples, and LLMs are essentially massive word-completion engines. But at scale, something remarkable emerges that looks like understanding, and that changes everything about what software can do.