[thedevilslawyer]

They're incredibly more productive. LLMs are amplifiers, so where they'd have branched and tried out N things, they can easily try 5N pathways of RnD. LLMs are extending the frontiers of science fast -- math -> phy -> chem -> bio in that order.

[steve_adams_86]

In my own experience, the only path I truly gain intellectual benefits is the one where I work closely with the LLM, test very narrow hypotheses, and leverage it for learning over producing.

Trying 5N paths is useful and sometimes yields interesting insights I’ll retain, but it’s not the rich, challenging, deeply engaging kind of process I find I need in order to develop useful knowledge and skills.

So yes it’s an accelerant for people who want stuff from me, but that doesn’t map directly to learning and building skills. I think that mismatching is really important.

[chorsestudios]

To help learn I use LLMs to generate practice exams for whatever I'm trying to learn, then on the questions I struggle with have the LLMs explain the logic and point out my mistakes. I haven't been in college for over a decade, this is just for topics I'm curious about and want to learn. For any serious topic I recommend auditing the practice exams with a different LLM than the one used to generate to help reduce hallucinations. Seems to work well for me. I quite like reading the "thought" processes shown by DeepSeek.

[tstrimple]

I don’t see these at odds. Sometimes through working closely with an LLM, N paths emerge. Having it go off and test each with defined metrics to determine which is better is the natural follow up. Even better if you dive into the why it ended up being better which the LLM seems to be able to expose well in a lot of cases.

The part I find weird is all the claims that LLM usage leads to less thinking and exploring and just grabbing the first result. I constantly find myself going off on tangents and pulling on threads when I’m working with these tools. Is it really that different than before when my “peers” weren’t able or willing to be curious about their craft? They didn’t explore other programming languages out of curiosity or for fun? That covers literally 95% of all software developers I’ve worked with in the last 24 years across many domains. To them it’s just a job. Their only goal is to deliver tickets assigned to them and go home. They rarely go out of their way to learn something new unless the company assigns them some mandatory courses. Largely the LLM is capable of producing better and more consistent results than they ever could in the first place.

I don’t know how to cultivate curiosity in the work force. Maybe it’s not possible and you have to filter aggressively at the hiring step. But then your pool of hireable candidates shrinks to a few thousand developers most who are probably not actively looking for work.

[steve_adams_86]

You’re right, they don’t have to be at odds. Before LLMs, there were jobs where I had to power through and make a sort of ‘minimum effort’ approximation without applying much analytical or investigative energy or skill. This isn’t a lot different from churning something out with an LLM. There’s not much to learn, the end product is mediocre, it’s more of a rote path.

The only distinction I wanted to make is that the learning doesn’t come by default. Yet that was largely true when people copied mystery solutions from stack overflow and used black box libraries for 90% of the complex work their programs facilitated.

Perhaps not much has changed but we’re now operating at a much larger scale and the opportunity to not be curious is actually more present than ever.

People who are curious are massively benefited by this tooling, in my opinion. Like you’re saying, if you want to investigate and learn, there has never really been a better time. If you’re sincerely applying yourself and pulling all of those threads, there has never been a better teacher.

I’ve wondered about the matter of finding and cultivating curiosity too. I’ve come to believe most humans, let alone programmers specifically, are not all that curious. A lot of us are path-followers and we’d rather not get into the weeds most of the time. Then some of us see weeds and dive in, even when it’s not pragmatic to do so. I don’t know how much it can be cultivated or even removed from a person who has more than enough.

[cootsnuck]

I'm hearing different from PhDs. The bottleneck with much research isn't "trying out ideas" so much as it's all the bureaucratic minutiae, grants, mentoring PhD candidates, collaboration with other researchers, etc.

I've heard LLMs can be helpful in limited targeted ways. But not as some kind of "game changing" accelerant.

[sandeepkd]

Understanding in what ways it can be useful and in what ways it can be counterproductive in long run requires a certain degree of experience itself.

[intended]

It’s creating a daemon and machine spirit filled world of Warhammer 40k. We already scarcely understand how the world works, but LLM use actively degrades cognitive ability that way it is used by a majority of people (The bringing a forklift to gym analogy).

[ElProlactin]

The AI is among us.

[xadhominemx]

To me it is crazy that you are being downvoted. My experience in academia was that an incredible amount of time was devoted to data cleansing analysis, coding, etc., which were completely non-core to the actual underlying academic pursuit.

[thedevilslawyer]

There's an unnecessary feeling of fear that permeates any factual conversation on LLM's impact on science and engineering. You can just view the practitioner over the shoulder and see all the things they're able to do in a minute that would have taken days.

The downvotes are just a sign of the times. It's also something to observe and think about..

[grey-area]

Data cleansing is a terrible use for LLMs if you want reliable data.

[xadhominemx]

This comment may have been true 6-9 months ago, but not today