OpenAI's Applied AI lead explains why even $100M+ ARR startups break agent behavior by continuously tacking on prompt rules, and how treating prompts like modular code improves retention and lowers costs.
An evidence-based view of where frontier-model progress may be coming from. At small pretraining scales, the study estimates that data improvements from 2019 to 2025 produced roughly 12x compute efficiency, compared with 3.
Benchmark data showing why telling coding agents to use TDD, formal methods, or common testing skills usually fails to improve code quality over standard prompts.
a16z general partner Anish Acharya explains how automated agent loops and emergent defensibility are reshaping startup development. Acharya argues that new startups are structured around automated loops that can take a bug report straight to a low-risk production deploy.
OpenAI Chief Scientist Jakub Pachocki warns that reasoning models are nearing recursive self-improvement at the same time our ability to monitor their internal chain-of-thought logic is declining.
A look at how product management works at Anthropic and OpenAI now that coding is no longer the primary bottleneck.
Twenty recurring lessons from 2,463 profiles, organized around choosing what matters, building, leading, deciding under uncertainty, and making work last. Each principle connects people across disciplines and includes a practical limit or tradeoff.
Architectural takeaways on why simple chat loops break down and how running a harness like a game engine makes rewinds, forks, and resumes work reliably.