[lukehoban]

(Disclaimer: I work on coding agents at GitHub)

This data is great, and it is exciting to see the rapid growth of autonomous coding agents across GitHub.

One thing to keep in mind regarding merge rates is that each of these products creates the PR at a different phase of the work. So just tracking PR create to PR merge tells a different story for each product.

In some cases, the work to iterate on the AI generated code (and potentially abandon it if not sufficiently good) is done in private, and only pushed to a GitHub PR once the user decides they are ready to share/merge. This is the case for Codex for example. The merge rates for product experiences like this will look good in the stats presented here, even if many AI generated code changes are being abandoned privately.

For other product experiences, the Draft PR is generated immediately when a task is assigned, and users can iterate on this “in the open” with the coding agent. This creates more transparency into both the success and failure cases (including logs of the agent sessions for both). This is the case for GitHub Copilot coding agent for example. We believe this “learning in the open” is valuable for individuals, teams, and the industry. But it does lead to the merge rates reported here appearing worse - even if logically they are the same as “task assignment to merged PR” success rates for other tools.

We’re looking forward to continuing to evolve the notion of Draft PR to be even more natural for these use cases. And to enabling all of these coding agents to benefit from open collaboration on GitHub.

[polskibus]

What is your team’s take on the copyright for commits generated by ai agent ? Would the copyright protect it?

Current US stance seems to be: https://www.copyright.gov/newsnet/2025/1060.html “It concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements”.

If entire commit is generated by AI then it is obvious what created it - it’s AI. Such commit might not be covered by the law. Is this something your team has already analysed?

[qznc]

This is a very fascinating aspect which is not discussed much. So far in human history every text was written by someone and thus there is some kind of copyright.

Now we have text which is legally not owned by anybody. Is it "public domain" though? It is not possible to verify it, so maybe it is but it still poses legal risks.

[IanCal]

>If entire commit is generated by AI then it is obvious what created it - it’s AI.

Whether it's committed or not is irrelevant to the conclusion there, the question is what was the input.

[chiph]

For something like a compiler where the output is mostly deterministic[0] I agree. For an AI that was trained on an unknown corpus, and that corpus changes over time, the output is much less deterministic and I would say you lose the human element needed of copyright claims.

If it can be shown that for the same prompt, run through the AI several times over perhaps a year, results in the same output - then I will change my mind. Or if the AI achieves personhood.

[0] Allowances for register & loop optimization, etc.

[rustc]

> “It concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements”

How would that work if it's a patch to a project with a copyleft license like GPL which requires all derivate work to be licensed the same?

[olq_plo]

IANAL, but it means the commit itself is public domain. When integrated into a code base with a more restrictive license, you can still use that isolated snippet in whatever way you want.

More interesting question is whether one could remove the GPL restrictions on public code by telling AI to rewrite the code from scratch, providing only the behavior of the code.

This could be accomplished by making AI generate a comprehensive test suite first, and then let it write the code of the app seeing only the test suite.

[taneq]

Hmm, so basically automated clean room reimplementation, using coding agents? Our concepts of authorship, copying, and equivalence are getting a real workout these days!

[tough]

you'd need a pretty good opsec and non-search capable agent and logs of all its actions/chain of thought/process to be able to truly claim cleanroom implementation tho

[taneq]

The logs and traceability are the secret sauce here. It's one thing to have an artifact that mysteriously replicates the functionality of a well known IP-protected product without just straight up copying it. It's another thing to be able to demonstrate that said artifact was generated solely from information in the public domain or otherwise legally valid to use.

[anticensor]

GPL is a copyright licence, not a ToS.

[rustc]

> GPL is a copyright licence, not a ToS.

How is ToS relevant to this thread?

[jegudiel]

AI Code and Copyright - Risky Business or Creative Power-Up(AI Generated Podcast)

https://open.spotify.com/episode/6o2Ik3w6c4x4DYILXwRSos?si=5...

[jegudiel]

An unconventional license for AI-generated code. Maybe public domain, maybe not. Use freely, vibe responsibly.

https://jilvin.github.io/vibe-license/

[blagie]

> If entire commit is generated by AI then it is obvious what created it - it’s AI.

This is not the case. The output of a compiler is 100% created by a compiler too. Copyright is based on where the creative aspect comes from.

I have had very little luck having 2025-era AIs manage the creative aspects of coding -- design, architecture, and similar -- and that's doubly true for what appears to be the relatively simplistic model in codex (as far as I can tell, codex trades off model complexity for model time; the model does a massive amount of work for a relatively small change).

However, it is much better than I am at the mechanical aspects. LLMs can fix mechanical bugs almost instantly (the sort of thing with a cut-and-paste fix in some build process from Stack Overflow), and generate massive amounts of code without typos or shallow bugs.

A good analogy is working with powertools versus handtools. I can do much more in one step, but I'm still in creative control.

The codebase I'm working on is pretty sophisticated, and I might imagine they could implement more cookiecutter things (e.g. a standard oauth workflow) more automatically.

However, even there -- or in discussions with larger models about my existing codebase -- what they do is in part based their creativity on human contributions to their training set. I'm not sure how to weigh that. An LLM oauth workflow might be considered the creative median of a lot of human-written code.

I write a lot of AGPL code, and at least in the 3.5 era, they were clearly trained on my code, and would happily print it out more-or-less verbatim. Indeed, it was to the point where I complained to OpenAI about it at the time, but never got a response. I suspect a lot of generated code will include some fractional contribution from me now (an infinitesimal fraction most of the time, but more substantial for niche code similar to my codebase).

So in generated code, we have a mixture of at least a few different pieces:

- User's contributions, in prompt, review, etc.

- Machine contributions

- Training set contributions

[soamv]

This is a great point! But there's an important tradeoff here about human engineering time versus the "learning in the open" benefits; a PR discarded privately consumes no human engineering time, a fact that the humans involved might appreciate. How do you balance that tradeoff? Is there such a thing as a diff that's "too bad" to iterate on with a human?

[lukehoban]

I do agree there is a balance here, and that the ideal point in the spectrum is likely in between the two product experiences that are currently being offered here. There are a lot of benefits to using PRs for the review and iteration - familiar diff UX, great comment/review feedback mechanisms, ability to run CI, visibility and auth tracked natively within GitHub, etc. But Draft PRs are also a little too visible by default in GitHub today, and there are times when you want a shareable PR link that isn't showing up by default on the Pull Requests list in GitHub for your repo. (I frankly want this even for human-authored Draft PRs, but its even more compelling for agent authored PRs).

We are looking into paths where we can support this more personal/private kind of PR, which would provide the foundation within GitHub to support the best of both worlds here.

[ambicapter]

Do people where you work spend time reviewing draft PRs? I wouldn’t do that unless asked to by the author.

[drawnwren]

It’s hard enough for me to get time to review actual PRs, who are these engineers trawling through the drafts?