It depends on the levels. People with differing fitness levels and ages run at very different paces. Now, do cars make them more equal or less? On the bottom end, the tide lifts all boats. Most healthy people can learn to drive and will drive "fine", they get from A to B. Out there in the city streets the car flattens the differences, everyone roughly takes the same time to get from A to B in a car.

But at the top of top, the gap probably widens. A professional F1 driver will drive laps around some random guy. It amplifies reflexes etc, because at that speed little differences in timing make a big difference.

Now, AI coding isn't exactly analogous, but I think it also has these two regimes. It flattens things for simple tasks. If your task is to shovel data, do some trivial compiler wrangling staring at badly designed error messages, looking through GitHub issues hunting for the comment with many tadaa emojis to fix an issue etc, those things can now be done by anyone. Just as grandpa can also drive to the grocery store. But if you're pushing at things on a higher level, now only your above-AI ability matters. If all the things that AI can do well are subtracted out, how much other expertise do you have left? This will be proportionally a bigger and bigger difference between different people.

Exactly. The post read to me as another variation of the denial that people with expertise are reaching for right now. My sense is that we as programmers went through it over a year ago already (perhaps not all of us, but at least anyone paying attention), and so it's easy to overlook that it's still new to people who do other forms of "knowledge work," i.e. people whose identity is bound up with their expertise.

My working theory at the moment is that for programmers it was relatively "clean" and took the form of an inside-out transformation of the work, where AIs directly produced the central work product more or less adequately and relatively early on, but for other forms of work it will appear as some mixture of inside-out (in which case it will appear similarly first as a tool, then as something more than mere tool) and outside-in (the things surrounding their work and the supports their work processes rely on will be progressively automated). This is going to give rise to all sorts of pathologies in the white collar world, we'll get all kinds of variations on denial/negotiation, and so on, until it fully transforms the division of labor.

One interesting point of reference here: Yuval Harari gave a talk recently about the radical changes that will take place relatively quickly, in which he noted the AIs are not quite as good at writing as he is yet, although he expects they will be relatively soon. He then gave the timeline for what he considered "soon": 10 years! So we find the denial ("I still have time, they're not as good as me yet, maybe in 10 years...") even among the most vocal "prophets," among those supposedly most wised-up to what's going on and where the capability frontier lies.

This works better for math because math is self-verifiable. Once you have a proof it needs no outside evidence.

Expertise is needed to evaluate model outputs where it can't verify itself, or at the very least one's expertise can help steer the model in the right direction.

However this is irrelevant if models themselves are better at evaluating/leveraging expertise/information.

But the guy who writes the “just do it” prompt can neither formulate the conjecture in the first place, nor come up with any follow-up questions to build on the result.

So how many conjectures have you proved in your spare time?...

As the old joke goes, a mechanic charges you $5 for hitting it with a wrench and $495 for knowing what and where to hit.

It's not contradictory to say that expertise is a multiplier, and that models are systematically underconfident in themselves.

Hacker News is the biggest collection of idiots who think they are geniuses, so they will upvote anything that makes them feel smarter.

Who's end state took / is going to take more tokens / money, however?

"LLMs reward expertise" is the title, not that "LLMs only make things possible for those with expertise"

And what about problems that cannot be one-shotted but helped along?

LLMs are a collection of biases. Humans are also a collection of biases. So we project our biases as input through the biases of an LLM and get an output. Hence why I think getting optimal output requires being an optimal person. And in that sentence there are many points of expression.

Finally, we train our LLMs on who we are. Another reinforcement of biases.

> Like all of life, maybe the "winner" is the person who just does stuff.

Someone who just does stuff still has to be able to deal with errors and failures. That’s where an expert or a generalist may have an advantage.

> who these models reward/empower

The easy, straightforward answer is "the people who own the models". Who else benefits feels like a more complex question and we'll have to see...

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