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Very interesting aside! However, it doesn't address the question of diminishing returns.
I've used AI for coding, which you mention further down as a crowning triumph. It is... not particularly good. It struggles at anything past a very general form of the problem. It was very useful for copy-pasting similar pieces of code! Not very useful for building new features. It had a distinct habit of waiting until the interesting or important part of the problem and leaving a comment saying "Implement a function to do X!" Hmm, very interesting, if I tried that I'd get fired. So no, I think this is a valid argument. AI can be taught to the test, and indeed appears to have been, but the actual world involves far more de novo work than the test includes. That's why school-trained pre-professionals tend to need a pretty hefty ramp-up to start being really useful - they've only been working on tests so far. Pokemon is interesting precisely because it has not been trained for. You should expect more, not less, untrained situations for AI to do anything meaningful in the job market - and you should weight untrained situations in your analysis several orders of magnitude higher than trained situations.
Do you use AI to augment your work? Is it going to take your job? On what kind of a timescale? Do you think you'd be able to substitute yourself for an unmonitored AI without issue on any tasks? If not, what errors do you think it would make, and why? Honestly interested in your answers here, if nothing else. I would greatly respect you for putting your money where your mouth is on this one and bringing receipts.
Hmm... you think getting stuck in what appears to be a permanent loop is not terrible? Is this the behavior you'd accept from anyone working for you?
The thing that keeps puzzling me about your comments is that you seem to simultaneously view ANY capacity in a task as an impressive accomplishment at the same time as you assert that AI has overwhelming general ability. Those two don't go hand in hand, except maybe by this little quote. Any capacity seems to be, for you, an indisputable sign of unlimited future capacity - as though the only question to be answered is total disability versus infinite ability. There's no clear reason that this has to be the limit of the answer space. Line go up... forever? Like with bitcoin? There's also the rather bizarre fixation on LLMs - even though something like, say, an octopus is very obviously not an LLM and still has meaningful if primitive intelligence. The sheer gnostic power of your position is hard to argue against, and unfortunately I don't find it very convincing based on my own experience. It takes rather a lot on faith.
Diminishing returns != No returns or negative returns.
The important question is whether the gains/$ invested are positive.
GPT 4.5 is extremely expensive, for the very limited increase in benchmark performance it represents.
And how expensive is it, that people are throwing a fit? Barely more expensive than the original GPT-4. That was absolutely worth paying the money for, when compared to GPT 3.5. GPT 4.5 has the disadvantage of peer competition.
That being said, the price of GPT-4 tokens and that of equivalent models dropped an OOM in price. DeepSeek R1/V3 and Gemini Flash 2.0 spank the OG GPT-4 with paddles and are practically free.
We've known that scaling laws are log-linear for a while now, at least since the Chinchilla days. Now that pure scaling of model size is getting super expensive, we've managed to discover a brand new opportunity to start scaling something else entirely, in the form of RL. Since we're starting off at the bottom of the curve, we've got several orders of magnitude of growth to spare there.
GPT 4.5 is not, however, a bust. The very capable and inexpensive reasoning models benefit immensely from having a strong and capable base model to RL further. You can then distill down, drastically cutting model size and inference costs, while keeping almost all the performance. It may or may not have been the progenitor of o3, which is very good.
There are probably a thousand people on my Twitter feed, some of them rather famous, who disagree. Of course, I concede that there are people who think they're slop. And it also depends on which model you're trying to use for coding. There was a period where GPT-4 was updated and became ridiculously lazy. That was fixed pronto. Claude 3.7 Sonnet is apparently overeager, if left unchecked, it'll turn a request for a basic app into a full SAAS business.
If you have had issues with a model being lazy, you can always ask for it to output complete and working code! Prompting has become less and less important, but it's not dead yet.
I'm a doctor. Yes, I use LLMs on the regular. Yes, I expect them to put me out of business eventually, probably in 3-7 years for a 50% CI, 2-10 for a 70%.
A current LLM would do an excellent job at medical diagnostics and formulating treatment plans. It could probably handle patient interviews, for less complex cases where voice or text suffice. You could also use video if you had to.
The main reasons they couldn't replace me today are regulatory and implementation concerns. Governments mandate people with medical credentials in the loop, because that was a sensible thing to do for most of recent history. Hospitals aren't set up for LLMs.
I'm a psychiatry resident, which is uniquely safe and also uniquely at risk in some regards. It'll get the radiologists first, surgeons last. I'll be somewhere in the middle.
I am capable of verifying the information that SOTA LLMs provide in terms of medical advice. Almost all of it is good. Clinical medicine, outside of procedural specialties, hinges far more on factual knowledge, including that of guidelines, over having to figure things out on the fly in entirely novel situations.
Hallucinations aren't a solved problem, so if I had to replace myself with an LLM, I would probably set up a sort of democracy, with multiple models arguing to build consensus, a best of N scheme for multiple instances of a single model, with multiple rounds of grounding through search. I expect this to work very well, and if you do need to keep a human around for physical tasks or procedures, they don't have to be a highly paid doctor.
In other words, I'd be happy to go to Dr. LLM for my medical care, presuming very cheap measures are taken to stop it hallucinating.
Given that we're testing Claude at a task it was neither designed nor trained to do, it's very much not terrible. For important tasks, it'll be trained to do them. An employer seeking to replace employees will, if they have any sense, test models for obvious flaws. For all practical use cases, LLMs don't really mode collapse these days, and in this particular case, it's more of an artifact of Claude's limited context window than an insurmountable difficulty.
Like I said in this thread, it can take decades for AI models to progress from as bad as random chance to better than random chance. It takes far less time to go from there to human or superhuman performance. We are nowhere near the physical limits, and as I've said before, diminishing returns in absolute terms do not mean diminishing returns in value.
Forever? Not likely in a constrained universe. Unfortunately, the point on that line that equals human performance, or even superhuman performance, is not uniquely privileged.
All we have to do is get past that, and in many aspects, we're there. Terence Tao is on record saying that o1 is equivalent to a competent grad student in research mathematics. Once again, that's Tao, considered one of the world's best mathematicians, for his high standard of "competent".
I'm not aware of companies spending hundreds of billions of dollars on scaling up Octopus Intelligence. LLMs are by far the most intelligent entities on this planet other than humans, and they're only getting better. I know which one I'd worry about, even if it is entirely possible that LLMs as we understand them today prove to be a dead end, and what really kicks things off is another discovery on pat with the original Transformer architecture.
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