Author here: it's not even clear that agents can reliably permute their training data (I'm not saying that it's impossible or never happens but that it's not something we can take for granted as a reliable feature of agentic coding).

As I mentioned in one of the footnotes in the post:

> People often tell me "you would get better results if you generated code in a more mainstream language rather than Haskell" to which I reply: if the agent has difficulty generating Haskell code then that suggests agents aren't capable of reliably generalizing beyond their training data.

If an agent can't consistently apply concepts learned in one language to generate code in another language, then that calls into question how good they are at reliably permuting the training dataset in the way you just suggested.

But what's the point of re-building "standard software" if it is so standard that it already exists 100 times in the training data with slight variations?

I'd say that's pretty much the definition of standard, yeah. And it's why you can't make a profit selling a simple ToDo app. If you expect people to pay for what you build, you have to build something that doesn't have a thousand free clones on the app store.

That isn’t saying much. Every software is a permutation of zeros and ones. The novelty or ingenuity, or just quality and fitness for purpose, can lie in the permutation you come up with. And an LLM is limited by its training in the permutations it is likely to come up with, unless you give it heaps of specific guidance on what to do.

In my experience, the further you move away from the user and toward the hardware and fundamental theoretical algorithms, the less true this becomes.

This is very true for an email client, but very untrue for an innovative 3D rendering engine technology (just an example).