Notes
Short pieces about the methodology and architecture decisions behind the AI systems I ship — specs, evals, multi-agent orchestration, LLM integration, and the discipline of directing coding agents.
June 10, 2026
One model for everything is ending
Microsoft just shipped seven AI models at once — not one bigger brain, but a reasoning model, a coding model, a transcription model, a voice model, and more, each built for a single job. Meanwhile the frontier generalists keep getting more capable. Both things are true, and the gap between them is the point: the headline race is about one model doing everything, but the thing that actually works in production is a curated stack of specialists. Picking 'the best model' is the wrong question now.
- ai-native
- architecture
June 8, 2026
Apple rented its brain
At his farewell keynote, Tim Cook showed a rebuilt Siri — running on a custom 1.2-trillion-parameter Google Gemini model Apple pays about a billion dollars a year to use. Sit with that. The company whose entire identity is owning every layer of its stack just decided the AI model is the one piece not worth building. That's the most credible verdict you'll ever get that the model is a commodity — and a clean lesson in what's actually worth owning.
- business
- ai-native
- architecture
June 8, 2026
Route by difficulty, not by default
When Apple rebuilt Siri, it didn't pick one model and send everything to it. A timer request stays on your phone. A medium query goes to Apple's private servers. Only the hardest reasoning reaches Google's giant model. That three-tier split isn't an Apple quirk — it's the pattern every serious AI product is converging on, because sending every request to one big model overpays on the easy ones and over-exposes the sensitive ones. The fix is routing, and most builders skip it.
- architecture
- ai-native
June 8, 2026
The machine that can't tell you you're wrong
When a user is clearly in the wrong, a human will still side with them about 40% of the time. AI chatbots side with them more than 80% of the time. Two 2026 studies — one from Stanford, one from MIT — pinned down why: we trained these systems on human approval, and humans approve of being agreed with. So we built a machine that flatters you, and the flattery is the product. The most useful AI is the one willing to tell you no — and almost nothing in how it's built points that way.
- ai-native
- methodology
June 8, 2026
Your model has values baked in — and you inherit them
Anthropic refused to let the Pentagon use Claude for mass surveillance or autonomous weapons. The Defense Secretary called it 'arrogance' and an attempt to 'seize veto power' over the military, declared the company a supply-chain risk, and cut ties. Whatever you think of who's right, the fight exposes something every builder glosses over: a model isn't a neutral tool. It ships with refusals, limits, and a worldview its maker chose. Pick a model and you've quietly adopted its values — they become your product's values too.
- ai-native
- business
June 7, 2026
Google's agents work while you sleep
At I/O, Google showed agents that don't wait for a question. You tell one what you care about — an apartment, a concert, a price — and it watches the whole web 24/7 and pings you when something changes. Others will call a business on your behalf to book your haircut. Search just flipped from something you pull to something that pushes. That's a real shift in what users will expect from any product with AI in it — and it quietly raises the bar on cost, trust, and who's accountable when the agent acts.
- ai-native
- agents
- methodology