cross-posted from: https://piefed.social/c/linux@programming.dev/p/2509642/linus-torvalds-posts-his-first-ai-assisted-patch-to-the-linux-kernel-synthetic-test-clo
cross-posted from: https://piefed.social/c/linux@programming.dev/p/2509642/linus-torvalds-posts-his-first-ai-assisted-patch-to-the-linux-kernel-synthetic-test-clo
Verifying 10 pounds of incoherent architectural choices and over engineered submodules is certainly harder than reviewing tee bits of coherent human work.
Sure, that’s why I said “not necessarily”. LLMs can be misused and made to do dumb shit too. But I have seen plenty of bullshit come from human developers too.
LLMs make dumb shit by default. Even Linus had a race condition caught in review when he used AI to bulldoze his way through a feature.
Agentic loops have major flaws which humans don’t, a tradeoff you seem to keep ignoring while you suggest that developers and agents are comparable in their output quality. The only comparison is, at surface value, they both produce code. Deeper than that, there are plenty of tradeoffs.
Yes tradeoffs in code quality, project quality, maintainability, technical debt, … you need to be a good programmer to get a good project out of an agent — and even that isn’t enough at times.
At the architectural level, there’s definitely a difference yes. But at the smaller scoped implementation level the latest models are pretty good compared to humans. Not perfect by any means, sure, but it’s not all drivel anymore. It was 2 years ago, but today’s definitely different.
Race conditions are generally harder to catch, even for human devs. And you’re saying it as if Linus wouldn’t have introduced it had he coded everything by hand, which I don’t think is remotely guaranteed (like he never makes mistakes). And the review system clearly works just fine still.
Good developers can accelerate their work with agents, and you’re right that you can generate tons of LLM-powered slop with it too. But that’s why dismissing anything an agent wrote immediately is also wrong. The review determines if it’s good enough, whether a human or an agent wrote it.
I’m not really discussing smaller scoped patches, though. An LLM can change a single line in a file, sure, but the prerequisite there is that the file adequately contains the necessary context to do so. With small patches, you’re advantaged in that way.
Larger patches suffer from a distinct kind of problem where the LLM can heavily over optimize submodules within the code, without proper consideration for the larger premise. This is a problem I’ve seen referred to as “decoherence.” In large projects, it can sort of take over the codebase after enough LLM engagement.
Decoherence makes your codebase unmaintainable. The cost of every change increases logarithmically and the situation gets worse. The heavily optimized submodules are often incompatible semantically, so the agent also optimizes channels between them. This is an explosion in unwarranted complexity. This isn’t a human problem.
Good developers can accelerate their work with agents. However, back to the topic on hand, reviewing agentic code changes is necessarily harder — because it’s prone to more issues that run far deeper. You have to review the code almost adversarially, with more cognitive load being used to unravel the gargantuan mess of barely sensible changes when viewed from a birds eye.
And verifying 10 pounds of incoherent architectural choices and over engineered submodules of human work is certainly harder than reviewing tee bits of coherent agent work.
You are? I can also make false dichotomy fallacies!
The difference is that AI is prone to this issue, even a single agentic process. Humans only really run into this issue with large, disorganized teams.
Humans burn out.
https://maevyn.codes/decoherence
edit: hold up. Presenting as though what I said works in reverse,
as though agentic and human contribution is equatable… wouldn’t that be false equivalency? Tisk tisk…
Human work is not swappable with agentic work without tradeoffs, which is kind of why I was making a point at all.