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HarHarVeryFunny01/17/20261 replyview on HN

> Certainly there is space for designs a LLM can't come up with, but lets be real senior developers are not cranking out never seen before novel architectures routinely any more than physicists are coming up with never thought of theories that work weekly.

True, but I didn't mean to focus on creativity, just the nature of what can be learned when all you have to learn from is artifacts of reasoning (code), not the underlying reasoning traces themselves (reasoning process for why the code was designed that way). Without reasoning traces you get what we have today where AI programming in the large comes down to cargo cult code pattern copying, without understanding whether the (unknown) design process that lead to the patterns being copied reasonably apply to the requirements/situation at hand.

So, it's not about novelty, but rather about having the reasoning traces (for large structured projects) available to learn when to apply design patterns that are already present in the training data - to select design patterns based on a semblance of principled reasoning (RL for reasoning traces), rather than based on cargo cult code smell.

> This is going to be lord of the flies in job market long before human parity.

Perhaps, and that may already be starting, but I think that until we get much closer to AGI you'll still need a human in the loop (both to interact with the AI, and to interact with the team/boss), with AI as a tool not a human replacement. So, the number of jobs may not decrease much, if at all. It's also possible that Jevons paradox applies and that the number of developer jobs actually increases.

It's also possible that human-replacement AGI is harder to achieve than widely thought. For example, maybe things like emotional intelligence and theory of mind are difficult to get right, and without it AI never quite cuts it as an fully autonomous entity that people want to deal with.

> UBI or whatever...because we'll have to.

Soylent Green ?


Replies

Havoc01/17/2026

re reasoning traces - not sure frankly. I get what you're saying in that there is only so much advanced thinking you can learn from just scraping github code, and it certainly seems to be the latest craze in getting a couple extra % on benchmarks but I'm not entirely convinced it is necessary per se. Feels like an human-emulation crutch to me rather than a necessary ingredient to machines performing a task well.

For example I could see some sort of self-play style RL working. Which architecture? Try them all in a sandbox and see. Humans need to trial & error learning as you say. So why not here too? Seems to have worked for alphago which arguably also contains components of abstract high level strategy.

>Jevons paradox

I can see it for tokens and possibly software too, but rather skeptical of it in job market context. It doesn't seem to have happened for the knowledge work AI already killed (e.g. translation or say copy writing). More (slop) stuff is being produced but it didn't translate into a hiring frenzy of copy writers. Possible that SWE is somehow different via network effects or something but I've not heard a strong argument for it yet.

>It's also possible that human-replacement AGI is harder to achieve than widely thought.

Yeah I think the current paradigm isn't gonna get us there at all. Even if you 10x GPT5 it still seems to miss some sort of spark that a 5 year old has but GPT doesn't. It can do PHD level work but qualitatively there is something missing there about that "intelligence".

Interesting times ahead for better or worse