Unlock the insights behind AI’s real impact on development.
AI coding is no longer judged by how much code it can generate —it is judged by how confident developers are with the code generated.
In this report:
- State of AI coding adoption
- AI Code Quality across the SDLC
- Grounding GenAICode Quality with Context
- Learn how a self-reinforcing loop of trust
and quality — the “Confidence Flywheel”
Whether you’re leading a dev team, shaping engineering strategy, or experimenting with AI tools yourself, this report will help you make more informed, confident decisions.
In a 2025 survey of 609 developers across company sizes and regions, three themes emerged.
This complimentary release represents the first chapter of a broader, in-depth report
Context is the foundation of trust
Implication
A learned, repo-wide context engine is not a nice-to-have—it is the foundation for accuracy, quality, and trust across the SDLC.
Confidence is the key to adoption
Implication
Any AI workflow that cannot prove its accuracy will stall adoption long before reaching scale.
Productivity and quality rise together — when review is automated
Implication
Continuous, opinionated review is the force-multiplier that converts raw speed into durable quality.
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