There are lots of good reasons to think this.
The first is that LLMs used to be bad at each of these nerd things, then toppled them like dominos. There's a pattern over time.
Another reason to think this is g. Across every known measurement, human (and animal) intelligence is convergent. Being better at one thing correlates with being better at another thing. Although the reason is not perfectly clear, the pattern is well established, and seems to apply to LLMs too - GPT-6 is smarter than GPT-3 at everything, not just math. There's no reason to expect different for GPT-9.
If anything, in some domains frontier models have become worse - claudisms and chatgpt idioms are making them notoriously bad at prose without a considerable amount of prompting and tweaking.
However there are many other domains which have verifiable rewards in the process of learning them, despite their overall impact not being verifiable. For example, the life sciences, an LLM could be given access to data about an organism, and then make predictions about how a drug or gene therapy will affect that organism. In economics, LLM's could create models of behavior, evaluate predictions over time and see how well those predictions match reality.
>claudisms and chatgpt idioms are making them notoriously bad at prose without a considerable amount of prompting and tweaking.
I think this is because a lot of people genuinely like the claudisms, even though a small minority of technical people don't.
Anecdotally, creative writing communities do report drastic differences in quality between models (it is my understanding that Gemma 4 is especially praised) so there is something to it being verifiable but it's a far cry from being something that can be distilled into objective math/code-like evals. One could even say it comes down to vibes.
>For example, the life sciences, an LLM could be given access to data about an organism, and then make predictions about how a drug or gene therapy will affect that organism.
Yes, biotech companies are doing this right now but ultimately they are just predictions and you still need cold hard biological data to ground them and iterate on. That's slow and expensive, especially for the juicy fields (human biology, food and crops, clinical trials) and you can't just plug billions of VC capital into the pipeline and hope for RSI. Physical (gotta procure all those labs and their equipment), human (gotta hire and pay specialists to run experiments), biological (gotta wait for organisms to reproduce, drugs to take effect, crops to grow), regulatory (gotta convince agencies that your fancy new drugs are legit) bottlenecks get in the way.
And biology is one of the easier fields where a path to RSI (if not accelerating) is conceivable. Good luck iterating on macroeconomics data where there are no replicates and 'experiments' take literal years if not decades.
>I think this is because a lot of people genuinely like the claudisms, even though a small minority of technical people don't.
My uncharitable take is that the abtruse jargon makes people feel smart for understanding it and gives a sense of belonging (as a closed circle of initiates who understand LLM cant), just like rationalists love to repackage old or unsavoury ideas under new nerdy smart sounding names.
For example, they have become more and more unintelligible when you ask for explanations or descriptive text. They assume you see the same context as them and shortcut explanations.
I've had to craft a skill to get them to produce remotely understandable explanations of even mildly complex/non-mainstream subjects.
This may be Curse of Knowledge https://en.wikipedia.org/wiki/Curse_of_knowledge on their part, and it also impacts human experts but still. Becoming better at one thing does not mean you become better at everything else, although I do agree with you that the better they become the more things there are they become good at, but their ability is still quite jagged and maybe increasingly so.
Knowledge can occasionally get in the way of some tasks, such as a master illustrator trying to draw like a child. Or as you've mentioned, an expert trying to explain to a beginner.
Even so, I disagree with you about model progress on communication - maybe there's a little jaggedness between minor model versions, but Opus 5.5 is a much much better communicator than, say, Claude 3 Opus.
LOL. Human (and animal!) intelligence is notoriously jagged. We're all basically idiots except for very narrow areas where we focus.
Smarter just means smarter.
See for example Paul Framton https://www.theguardian.com/world/2013/mar/30/physicist-mode...
This is the classic error in mixing up knowledge / experience and intelligence.
Intelligence is the ability to quickly process, understand, learn, and use information. It does not imply having already been exposed to any given information.
A smart doctor may have terrible computer skills. But this is from lack of experience and interest, not lack of ability to learn how to use computers.
But take that smart doctor, and a relatively dumb person who is equally inexperienced with computers. Now give them a month and incentive to learn computers. The doctor will learn much more; that's intelligence.
Unlocking mastery of those domains would lead to incredible gains in all others. Whereas a fundamental advancement in many other domains doesn't spread as readily.
The most passionate, intelligent people are usually a decent indicator of where culture is going to go.
Many very smart people who I know are quite skeptical of LLMs and the hype around them. In my observation/echo chamber, the people who are very into LLMs are rather slick, career-minded people who love to present themselves as trendsetters for the "next fancy thing".
So you’re wrong pal, sorry.
>The most passionate, intelligent people are usually a decent indicator of where culture is going to go.
That's a naive view of cultural determinism. The software landscape would have been very different if a handful of lawsuits had gone one way or another. Hell, even Unix and Linux were hobby projects that accidentally got big.
Also crypto is a grift lol, one of those is definitely not like the others
I don't think this explanation is sufficient:
For example, many nerds care about 3D printing. On the other hand, my experiments (and the experiments of many nerds who I know) to let LLMs create files for 3D printing lead to horrendously lacking results.
Or many nerds care about linguistics or puns. Whenever a new LLM comes out to which I have easy access, I do the test, and let it explain some specific German jokes to me that are based on convoluted puns in the German grammar. Until now, no LLM that I had access to could give satisfying explanations of the puns on which these jokes are based.
Or even for coding (many nerds do care about elegant, sophisticated code): the LLMs that I could test were already overchallenged with the following task: I had written some code that is a very "artisanal", "clever" improvement of an algorithm over the version that one would find in a textbook. The task for the LLM was simply to write some code comments/documentation about the mathematical ideas upon which my improvement over the textbook version of the algorithm is based. It wasn't capable to do this. On the other hand, for a junior programmer, I would in such a situation expect that he goes through every single line and thinks through the algorithmic ideas so that he can learn from them.
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Thus: even for "nerdy" topics, it is very easy to find tasks where LLMs still suck. So, my hypothesis about the nerds that you mention in your post is that these nerds are rather people who want to believe (with religious fervor) that the current LLMs are exceptionally good instead of just looking into a slightly different direction than where the tech billionaires want the users of LLMs to look at.
Hippies live at the center of technology and humanities.
Nerds sit purely in technology.
Try again; astra has been driving fusion for me. I took a screenshot of a self watering cat grass bin on makerworld — not even the stl, a screen cap of one of the photos attached to the design — and it made me a parameterized version in one shot.
Its initial design was less than ideal for printability and it reworked the design correctly when asked to consider what it originally produced through the lens of printability.
Autodesk bundled an mcp server with fusion in the last few weeks and i expect things to get even better going forward.
I have no access to Astra, but with GPT 6.1 Sol, the results with respect to attempting to create STL, OBJ or even OpenSCAD files by using image references and a prompt were really bad.
It's worse than that. I am one of those nerds (a programmer), and I see that LLMs are shit at the things I care about. I have zero reason to believe that they will be good at other things either.