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Lessons from autonomous driving reveal how to make AI agents robust: focus on infrastructure, feedback loops, and simulation over model selection.
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- Published on
AI coding productivity gains evaporate at enterprise scale. Bloomberg's deployment across 9,000+ engineers reveals why platform thinking matters more than tool quality.- Published on
Building AI agents for non-technical users requires unsupervised autonomy, not just longer runtimes or better orchestration.- Published on
Kitze draws a sharp line between vibe coding and vibe engineering. The difference isn't the tool. It's whether you can judge when code is good enough. That judgment is the new core skill.- Published on
McKinsey surveyed 300 enterprises and found most stuck at 5-15% AI productivity gains. The bottleneck isn't the tooling. It's the operating model: agile ceremonies, two-pizza teams, and roles designed for a world where humans wrote all the code.