Canonical List of Data-Backed AI Developer Productivity and Code Quality Research from 2025-2026

GitClear's first-party research has been cited by numerous reputable sources, including MIT Technology Review, TechCrunch and The New Stack. But we don't have the monopoly on great, data-backed research. This page collects widely-cited third-party research on AI developer productivity and code quality between 2024 and 2026. If you have a link to AI research that has changed your thinking, let us know?

	Research Title & Source	Publication Date	Key Takeaways
	Coding Tools Attract Top Performers – But Do They Create Them? GitClear Research	January 2026	Heavy AI users are generating 4-10x more durable code than non-AI users Those same users also generated 9x more code churn than non-AI users The 4x jump in productivity reflects truths about who is using AI most, as of early 2026
	AI Copilot Code Quality: Evaluating 2025's Increased Defect Rate with Data GitClear Research	2025	Analyzed 211 million lines of code from 2021 to 2025 Finds that percent of moved/refactored code plummeted from 25% in 2021 to less than 10% in 2025 Over the same period, copy/pasted (duplicated) code rose from 8% of changes to 18%
	Google DORA AI Capabilities Model Report Google Research	2025	Most “lead time” is still waiting, not building: ~21% flow efficiency. In the value-stream example: ~24.5 hours active work vs ~92.5 hours waiting (total ~117 hours lead time) → ~21% flow efficiency. Estimated impact of AI on "Software Delivery Instability" is 0.1x, second only to the increase in "Individual Effectiveness" (at 0.17x) Using AI to resolve a microservice can take less than 5 minutes of process time, but still incur a 5 day cycle time due to review waits
	Stack Overflow 2025 Developer Survey on AI Stack Overflow	2025	Daily AI use is common: 47.1% use AI tools daily (plus 17.7% weekly and 13.7% monthly/infrequently) Professional developers are even more “daily”: 51% of professional developers use AI tools daily. Trust is the constraint: only 3.1% “highly trust” AI accuracy; overall 33% trust vs 46% distrust AI outputs.
	Data on AI Impact: Changes to Developer Productivity from 2022 to 2025 GitClear Research	2025	Analyzes 70,000 developer-years of data to quantify the impact of AI tools on developer productivity Median developer productivity has increased 9% from 2022 to 2025 Among developers averaging 500+ annual commits, the median productivity increase vs 2022 is 14.1%
	Jetbrains Developer Ecosystem 2025: AI Focus JetBrains	2025	68% of developer anticipate that AI proficiency will become a job requirement The most used AI tool in 2025 is still ChatGPT web/desktop/mobile apps (41%) - ahead of any IDE or plugins Most likely to delegate to AI (in descending order): writing boilerplate, searching the internet, converting code to other languages, writing documentation, summarizing recent changes

This page will be updated monthly throughout 2026 with new research as it becomes available. Bookmark and check back later for more?