Super interesting study. One curious thing I've noticed is that coding agents tend to increase the code complexity of a project, but simultaneously massively reduce the cost of that code complexity.

If a module becomes unsustainably complex, I can ask Claude questions about it, have it write tests and scripts that empirically demonstrate the code's behavior, and worse comes to worst, rip out that code entirely and replace it with something better in a fraction of the time it used to take.

That's not to say complexity isn't bad anymore—the paper's findings on diminishing returns on velocity seem well-grounded and plausible. But while the newest (post-Nov. 2025) models often make inadvisable design decisions, they rarely do things that are outright wrong or hallucinated anymore. That makes them much more useful for cleaning up old messes.

Interesting from an historical perspective. But data from 4/2025? Might as well have been last century.

Yes, it's not surprising that warnings and complexity increased at a higher rate when paired with increased velocity. Increased velocity == increased lines of code.

Does the study normalize velocity between the groups by adjusting the timeframes so that we could tell if complexity and warnings increased at a greater rate per line of code added in the AI group?

I suspect it would, since I've had to simplify AI generated code on several occasions but right now the study just seems to say that the larger a code base grows the more complex it gets which is obvious.

> On average, Cursor adoption has a modestly significant positive impact on development velocity, particularly in terms of code production volume: Lines added increase by about 28.6% (Table 2). There is no statistically significant effect for the volume of commits.

This doesn't equate to a faster development speed in my eyes? We know that AI code is incredibly verbose.

More lines of code doesn't equate to faster development - even more so when you're comparing apples (human written) to oranges (AI written)

They're measuring development speed through lines of code. To show that's true they'd need to first show that AI and humans use the same number of lines to solve the same problem. That hasn't been my experience at all. AI is incredibly verbose.

Then there's the question of if LoC is a reliable proxy for velocity at all? The common belief amongst developers is that it's not.

Depends on the nature of the tool I would imagine - eg. Claude Code Terminal (say) would have higher entry requirements in terms of engineering experience (Cursor was sold as newbie-friendly) so I would predict higher quality code than Cursor in a similar survey.

ofc that doesn't take into account the useful high-level and other advantages of IDEs that might mitigate against slop during review, but overall Cursor was a more natural fit for vibe-coders.

This is said without judgement - I was a cheerleader for Cursor early on until it became uncompetitive in value.

> We find that the adoption of Cursor leads to a statistically significant, large, but transient increase in project-level development velocity, along with a substantial and persistent increase in static analysis warnings and code complexity. Further panel generalized-method-of-moments estimation reveals that increases in static analysis warnings and code complexity are major factors driving long-term velocity slowdown. Our study identifies quality assurance as a major bottleneck for early Cursor adopters and calls for it to be a first-class citizen in the design of agentic AI coding tools and AI-driven workflows.

So overall seems like the pros and cons of "AI vibe coding" just cancel themselves out.

Now someone do a research study where a summary of this research paper is in the AGENTS.md and let’s see if the overall outcomes are better

I think the issue is people AI assisted code, test then commit.

Traditional software dev would be build, test, refactor, commit.

Even the Clean Coder recommends starting with messy code then tidying it up.

We just need to apply traditional methods to AI assisted coding.

AI is not perfect sure, one has to know how to use it. But this study is already flawed since models improved a lot since the beginning of 2026.