Stanford research across 120k developers shows median AI coding ROI of just 10%, despite millions in tool spending. The variance between teams is massive—and telling.

Coding evals jumped from single lines to full codebases in four years. Static benchmarks miss the production gap that destroys rollout timelines.

Lessons from autonomous driving reveal how to make AI agents robust: focus on infrastructure, feedback loops, and simulation over model selection.

AI coding productivity gains evaporate at enterprise scale. Bloomberg's deployment across 9,000+ engineers reveals why platform thinking matters more than tool quality.

Building AI agents for non-technical users requires unsupervised autonomy, not just longer runtimes or better orchestration.

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.

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.

AI coding speeds development but risks defects and review overload. See how context-rich quality workflows can sustain velocity.

Shifting from monolithic agents to portable skills makes AI more adaptable, composable, and faster to deploy across domains.

AI coding tools fail in complex codebases without systematic context engineering. Here's how teams break through the slop cycle.