This would be an interesting use case. But it seems API access is not included in the subscription? The value proposition would have been higher if it was (so one subscription for both human and agent use).
They could, for example:
1. Rely on users flagging suspected repos (with human moderators reviewing afterwards).
2. Deploying AI agents to evaluate repos against known AI-generated-code patterns (also with human moderators afterwards). Of course, the irony of the situation - using slop machines to fight slop.
Claude-written code is quite easy to spot - because it has this particular style of overly verbose "walls of text" comments, that 1. repeat verbatim what the code just below does, 2. include "list of things" that quickly go stale, 3. talk about "how things used to be before we changed it here" (useless to anyone reading the code now), 4. "talk to reviewer" in comments. And many other comment sins.
And even if you tell it to make it leaner, it'll maybe trim a little, but it'll still be a wall of text. Claude really likes to write its opuses and fables in comments.
And commit messages suffer from the same verbose prose.
And all of that happens even if you explicitly instruct in CLAUDE.md to keep things lean/concise. It's a disease.
Recent Kimi K3 release is supposedly frontier-grade, so would be interesting to see what it really costs to run inference for models of this class, when the weights get released and other independent providers pick it up (then we could reasonably expect competitive pricing with low-ish margins).
One significant advantage of Codex subscription over Claude is that you can use any agent harness with it. Meanwhile Anthropic insists that Claude subscription must only be used with Claude Code itself - and I have significant gripes with the quality of Claude Code in general.
Also, Fable access was now removed from small 20$ Claude plan, while Codex includes access to comparable model (gpt-5.6-sol) even on small subscription.
The article makes a point about agent harnesses being sticky (the supposed moat). I have been building my own agent harness for a while, and I can tell with confidence that the harness almost does not matter, the entirety of the AI magic is the model itself. The harness can be almost barebones (like, for example, mini-swe-agent used for benchmarks), and yet the model still does the task just fine.
So from my perspective, it's doubtful that this is the moat. Besides, for example, Claude Code in particular is so buggy (and always has been).
The article is rather light on "what to actually do about it". Even the basic "run it in isolated container without access to anything it does not need for the task" would have already improved the situation considerably (from the article it really seems like they didn't do that) - then the model would have to find local privilege exploits to actually escape (much cleaner misaligned behavior).
Also for typical normal use case for these smart models, you'd probably want an actual "max turns" limit to AVOID the pathological persistence (which in itself would be misaligned for "normal" tasks).
The code written by agents generally seems to reflect what you (the human driver) asked for. If you discuss the approach and architecture first, asking the right questions in the process, and then let it implement - the result is quite on point with the frontier models. Might need some minor touch-ups if agent missed some common conventions or guessed the expectations wrong (but again, salvageable with follow-up prompts). And when it comes to line-by-line logic within functions, I would argue that today's models are LESS likely to make a mistake in there than humans - especially if you cross-review with another model (e.g. "write with Claude, review with GPT").