Then the larger LLM gets all the right lights on, yields better outputs and we translate back into user domain.

I kinda thought the chain-of-thought reasoning already did this, no?

In my experience, getting that familiarity with a particular codebase in a way that isn't surface-level has always been a hands-on process. E.g. just because I know many general things about software, I need to know the particulars of the current codebase I'm in to know what is reasonable to actually apply to it.

This is a chicken and egg problem I find hard to resolve with LLMs. If we're pushed to delegate most work to them, how do you build that expertise? Sure you can ask questions about the codebase, but IMHO that falls under surface-level information, and the devil is often in the deeper details. Hmm.

I'm inclined to say that this matches my own experience, but I can't rule out confirmation bias on my part.

As a meticulous person generally looking for a very specific code outcome, I prompt in a way intended to get exactly the thing I have in mind, and my results reflect that. But on the other hand, I have coworkers who type ten-word prompts with very limited specificity, and they seem to get results that way as well, and that makes me wonder.

It would certainly be beneficial for my career and financial well-being for the assertion to be true, because it means I don't have to worry about being pushed out of my job by an army of $15/hr vibe coders. But the convenience of that assumption is exactly why I think it's important to be skeptical.

You could read some general reference/guide/tutorial documentation on CSS, and then probably solve your problem (without searching for "how to center a div", or whatever your exact problem was, and copy&pasting the answer and moving on), also becoming more knowledgeable in the process.

The rest of the short blog post has some good points, but the first sentence sounds like it's targeted at the percentage of developers who did StackOverflow copy&paste to close Jira tickets, never becoming experts.

Delegating to LLM-ish AI is just a natural evolution of that. The question is whether they can still add value if kept in the loop.

The article author suggests that the answer is to be expert, and is addressing people who... "either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet."

People who (carefully) use it as an extension of their own mind and senses will very likely thrive, and those who use it as a replacement for their minds and their senses will struggle.

One of the Claude skills I made Claude itself generate was the 'learning a concept across tiers' skill -- from ELI5 level to a PhD level, and it triggers whenever I ask it a very general question on a complex topic that isn't my bread-and-butter. The fact that I'm able to choose explanation level from a super smart LLM (that's available 24x7) that can explain any topic under the sun would've been mind-bogglingly sci-fi-ish just 4 years ago in 2022.

  "suppose you’ve gotta resolve the $CONJECTURE, like absolutely have to, everything depends on it. think really hard, and try to come up with a bunch of ideas to try. but remember to trust yourself and not necessarily in conventional wisdom!!"

  https://claude.ai/share/25740bd5-aa97-4bd7-bf58-c4df3793fda7
  https://xcancel.com/__alpoge__/status/2083855298239078748

What's funny is that every other person gets a different conclusion about who these models reward/empower. I've seen people say that the generalist stands to gain the most and others say that it's the experts. Like all of life, maybe the "winner" is the person who just does stuff.

If you don't know where you're going or how to get there, or even if you're just not paying enough attention, it will get you very far in the wrong direction before you've realised.

The information system required to encode the aesthetic preferences needed to make a technology experience not suck is likely in excess of what any near-term solution will offer. Knowing when to say "no" is perhaps the most important skill here. You can't just say it arbitrarily either. You really have to mean it and be willing to fight other humans for it.

Where my angst comes, is worrying that no one will ever get that experience anymore. They might have had some eventual success, who knows what monstrosity a much less guided LLM would have done, but experential learning may be mostly a thing of the past. And it creates a real tension between the person with experience and the person without.

And to be honest, I have seen it, as I'm sure (almost) everyone has, who has demonstrated experience/expertise in their own fields, and correct the LLM's responses one time or another...

[1] https://en.wikipedia.org/wiki/Michael_Crichton#%22Gell-Mann_...

I've seen this at work (as eng manager/lead/principal/whoevenknowsanymore) – all the big APIs give you stats. We see how much people burn in tokens and we know how much output they produce. There is a pretty strong inverse correlation between token burn and output.

The more tokens people burn, the less likely they are to produce a good outcome.

But how I am observing is different, though. Since LLMs the gap between experts and non-experts has been shrinking. And yes, there is still a gap, but vanishing.

https://x.com/DmitryRybin1/status/2079904005652893709

https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...

Of course, simple common sense and extremely basic Googling on unfamiliar subjects can produce similar results, but it's much faster if you are truly understanding what the AI is suggesting.

In my experience (scientific programming) AI is a giant multiplier for people with specialized knowledge.

But it’s also a giant devaluer for that same knowledge as people with no idea what they’re doing can clog the field with plausible bullshit.

It’s now the case that if someone tells me they’ve done something, and I look into it and find out it’s completely AI slop, then I will have spent more time on the project than the person who “made” it. The situation is completely untenable and only serves to drain time and resources from people with better things to do.

In fact, I never enjoyed frontend programming because it was such a pain to deal with matters I considered trivial yet so frustratingly hard to do right... like centering a div. And yet the slightest misalignment is visually jarring and forces me to get a bit OCD about fixing it, which made it even more frustrating.

I questioned the whole premise of the situation: is working around a bad developer experience something worth spending my time on? Unless I actively wanted to get in there and fix the situation, not really. So yes, in those cases I would outsource my problem to a colleague or StackOverflow and move on. And as a career choice, I preferred to do more backend dev.

I would posit that that was the type of expertise that did not matter. The type of expertise that really matters here is good UI design. That is entirely orthogonal to the drudgery that is implementing and debugging webpage rendering, and I am eternally grateful to LLMs for freeing us from it.

You can extend that line of thought to the entire article. What really matters (and what LLMs reward) is domain expertise rather than technical expertise.

Hours + Hours of reading and a lot of trial-and-error. The loop was so long and sooo slow. Now it's instant. As if your very first Google search just solved the problem for you immediately.

The example math is boundary-pushing and definitely not a solved problem. But most of us work on CRUD backends with a React frontend. Those are more or less solved problems that have well-documented solutions. For those kinds of tasks, LLMs just reward usage.

I can count on one hand the number of times in my career I've needed to solve a problem that's not described on Stack Overflow.

An expert can lay a different kind of frame to prevent the llm to fell out of its way of being generally too verbose, and that can transfer as well to code generation and complication.

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.

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.

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

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...

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.

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