Engineering Sacrifice: How Open Source Survives the Age of Free Code | Lum Ramabaja

Engineering Sacrifice: How Open Source Survives the Age of Free Code

Jul 9, 2026

1721 words

9 minute read

The Zig project now bans LLM contributions across the board: no AI generated pull requests, no AI generated issues, not even AI assisted comments on the bug tracker. Zig’s president Andrew Kelley calls AI assisted contributions “invariably garbage”, submissions with negative value because they consume the scarcest resource his team has, which is review time. Last week Godot followed: the engine’s new contribution policy requires all code to be human authored, demands disclosure of any AI assistance, and prohibits AI generated text in human to human communication. Even the Linux kernel now requires contributors to disclose AI tool usage, and a growing list of projects is adding restrictions of its own. But why? Are these maintainers simply the new Luddites of the 21st century? If AI is a labor productivity enhancing tool, and I spent the previous essay arguing that raising labor productivity is precisely what technology is for, why refuse it inside the movement that shares technology most freely?

Let me be clear about what this essay is not arguing. I am not interested in litigating whether the code quality of LLMs is good or bad. In capable hands the throughput of these models is enormous, and the quality of the output can be impressive. But quality was never the maintainers’ real complaint. Their complaint is about a broken signal, and about everything that breaks with it. This essay is about the new problem the open source movement is facing, and about an uncomfortable idea: that to remain robust, open source will be forced to adopt ideas from guilds and secret societies. In fact, as we will see, it has already started.

The problem, in a nutshell, is that projects are being flooded at a scale without precedent. Producing a thousand lines of plausible code now costs almost nothing. Reviewing those thousand lines still costs what it always did: the full attention of someone who understands the project deeply. Godot’s announcement states the imbalance plainly. The effort required to open a pull request has collapsed and submissions have multiplied, while the work of reviewing and the number of people able to review have stayed the same; the engine’s repository currently holds more than 5,000 unresolved pull requests. The pattern is older than LLMs. Brandolini’s law observed that refuting nonsense takes an order of magnitude more energy than producing it, and Herbert Simon named the deeper principle back in 1971: “a wealth of information creates a poverty of attention”. The scarce resource in open source was never code. It is the attention of the people who understand the codebase.

AI maximalists might answer that the fix is symmetrical: let AI do the reviewing too. But what is the point of building and maintaining a project in which no participant holds even a fragment of understanding of the whole? Before LLMs, contributors had skin in the game in practice, because making a pull request required effort, and that effort was not deadweight. You could not write the patch without acquiring some understanding of the project along the way; comprehension was a byproduct of the work. Strip that understanding out of contribution, and what remains is a repository without collective ownership, which raises the risk of hijack by hostile actors. In a project where AIs make the pull requests and other AIs merge them, who is really in control?

A computer can never be held accountable. Therefore, a computer must never make a management decision. - IBM internal training presentation, 1979

Forty seven years later, Godot’s maintainers reached for the same principle: “AI cannot take responsibility”, and someone must.

The spam problem reaches far beyond version control. Feeds, videos, and entire websites have filled with cheap generated content over the last years. Automated traffic now accounts for more than half of everything that moves on the web, according to measurements like Imperva’s annual Bad Bot Report, and I would argue the real numbers are worse. Many people now generate and share content in a drone-like fashion, without reading what they publish. By automating the production of code and text, they have outsourced understanding.

In Dissolving Markets I analyzed markets through their asymmetries. Asymmetries are a useful lens outside of markets as well; they show where a system is headed. LLMs have introduced a new asymmetry into open source and into the internet at large: the asymmetry of cost between generating and verifying. It sounds abstract, but its mechanism is simple. Biologists call it the handicap principle, after Amotz Zahavi: a signal carries information only when it is expensive to fake. The peacock’s tail means...