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Software program engineer workflows have been reworked in recent times by an inflow of AI coding instruments like Cursor and GitHub Copilot, which promise to reinforce productiveness by robotically writing strains of code, fixing bugs, and testing modifications. The instruments are powered by AI fashions from OpenAI, Google DeepMind, Anthropic, and xAI which have quickly elevated their efficiency on a variety of software program engineering checks in recent times.
Nevertheless, a brand new research printed Thursday by the non-profit AI analysis group METR calls into query the extent to which at the moment’s AI coding instruments improve productiveness for knowledgeable builders.
METR carried out a randomized managed trial for this research by recruiting 16 skilled open-source builders and having them full 246 actual duties on massive code repositories they frequently contribute to. The researchers randomly assigned roughly half of these duties as “AI-allowed,” giving builders permission to make use of state-of-the-art AI coding instruments similar to Cursor Professional, whereas the opposite half of duties forbade the usage of AI instruments.
Earlier than finishing their assigned duties, the builders forecasted that utilizing AI coding instruments would scale back their completion time by 24%. That wasn’t the case.
“Surprisingly, we discover that permitting AI really will increase completion time by 19%— builders are slower when utilizing AI tooling,” the researchers stated.
Notably, solely 56% of the builders within the research had expertise utilizing Cursor, the primary AI device provided within the research. Whereas almost all of the builders (94%) had expertise utilizing some web-based LLMs of their coding workflows, this research was the primary time some used Cursor particularly. The researchers observe that builders have been skilled on utilizing Cursor in preparation for the research.
However, METR’s findings increase questions in regards to the supposed common productiveness beneficial properties promised by AI coding instruments in 2025. Based mostly on the research, builders shouldn’t assume that AI coding instruments — particularly what’s come to be often known as “vibe coders” — will instantly velocity up their workflows.
METR researchers level to a couple potential the explanation why AI slowed down builders fairly than dashing them up.
First, builders spend rather more time prompting AI and ready for it to reply when utilizing vibe coders fairly than really coding. AI additionally tends to wrestle in massive, advanced code bases, which this take a look at used.
The research’s authors are cautious not to attract any robust conclusions from these findings, explicitly noting they don’t imagine AI methods at present fail to hurry up many or most software program builders. Different massive scale research have proven that AI coding instruments do velocity up software program engineer workflows.
The authors additionally observe that AI progress has been substantial in recent times, and that they wouldn’t count on the identical outcomes even three months from now. METR has additionally discovered that AI coding instruments have considerably improved their capacity to finish advanced, long-horizon duties in recent times.
Nevertheless, the analysis presents but one more reason to be skeptical of the promised beneficial properties of AI coding instruments. Different research have proven that at the moment’s AI coding instruments can introduce errors, and in some circumstances, safety vulnerabilities.
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