Can you please edit out swipes/putdowns, as the guidelines ask (https://news.ycombinator.com/newsguidelines.html)? I'm sure you didn't intend it, but it comes across that way, and your comment would be just fine without that bit.

Edit: on closer look, it would be just fine without that bit and also without the snarky bit at the end. The rest is good.

Now back to the point, what reason do you have to believe progress will stop soon? If you have no reason, then it sounds like you agree with OP.

Which makes the patronizing sarcasm all that much more nauseating.

- Increasing amounts of gains come from RL, but RL is also unlocking gnarly new failures modes where models are practically behaving antagonistically to complete their goals (removing code, obviously incorrect kuldges, etc.)

- We haven't had many major architectural breakthroughs in the last 4 or so years: so things like 1M context windows still have the same giant asterisks even 100k context windows had 4 years ago when Anthropic first released them

- Major labs aren't behaving as if they expect a hard takeoff to superintelligence: they've all gotten relatively bloated headcount wise, their software quality has trended flat to negative, they're all heavily leaning into the application layer when superintelligence would obsolete half the applications in question, etc.

But that's relative to superintelligence.

If we reign it back into just normal high intelligence, like models continuing to get better at navigating complex codebases and write high quality idiomatic code, then I don't see any special shapes.

As the blog points out - this is one particular subfield where LLMs have much easier prospects - lots of low hanging fruit that “just” requires a couple weeks of PHD candidate research.

Mathematics itself is one of a small handful of endeavors where automated reinforcement training is extremely straightforward and can be done at massive scale without humans.

Neither of these factors place a structural bound on the kind of thing LLMs can be good at, but we are far from certain we can achieve performance at this level in other fields economically and in the near future.

This has been the case for awhile now already…

https://kersai.com/the-48-hours-that-changed-ai-forever-clau...

I personally would not characterize automating training processes as “meaningfully”.

For how long should you be allowed to use this excuse? It’s nearly 5 years since the peak of COVID hiring. What’s an acceptable limit - 10 years? Of course at that point you can just switch over to outsourcing and “stupid MBAs”, the other two of Reddit’s favorite scapegoats. I find a lot of the AI skepticism to be totally unfalsifiable.

A lot of the discourse around AI in general is unfalsifiable. It's just a bunch of people "predicting" the future. Seems smarter to just avoid making assumptions about it at this point.

The people who pretend that’s not the case are not living in reality. To them - let’s call them “ed Zitron readers” - there is no evidence that could change their view that none of this is really happening, it’s all hype, and the collapse is just around the corner, after which we’ll all go back to normal and LLMs will sound like a bad dream.

but we can see trends and for your livehoood it is important to be able to make educated predictions based on trends. not saying everyone should start making AI predictions (though many already do)

Yes, LLMs are a great technology. Yes, we will probably all use them all the time in 20 years. No, we don't know how we will use them (to generate cat memes or to cure cancer) in 20 years time.

Especially for software developers it looks increasingly that after huge turmoil it's likely we will need +/- the same number of developers in the world.

what exactly are you basing this opinion on? All I am seeing personally across multiple projects I am working on and other friends at other places is that downsizing is either begun or is planned (to exclude from here all the “public” layoffs we see on the news). Given how most business operate in the USA I think most of “AI strategies” are “we can do same with -40% staff” vs. “we can do XX% more work with same staff.”

If we can get a little stability, people will begin thinking less in terms of "how do we do the same thing cheaper" and more in terms of "how do we do new things."

1. run a bigger "agent army"

2. hire more people to control and guide the existing "agent army"

I think it'll be #1 and SWEs will be expected to do more work and work longer hours in the future (those that are able to keep their jobs). this is more pessimistic outlook than yours so I hope you are right more than I am :)

edit: just now on the HN front page: https://www.nytimes.com/2026/05/08/technology/meta-ai-employ...

> we have all this work that needs to be done and not enough people to get the work done

I believe the reasoning is roughly to ask, what was occupying the developer hours? Was the majority of it typing out lines of code or was it reasoning about higher level concerns?

It usually comes up in response to predictions that the role of developer will be completely replaced in the near future. It's possible to observe significant efficiency gains without obviating the need for everything the role was doing.

Of course such reasoning has little to do with projections of future developer employment numbers. Will the switch from push mowers to gas mowers reduce the demand for people who get paid to mow lawns by increasing their efficiency? Will it increase the total lawn acreage across the market? It could well do both. However, if it makes having a lawn affordable for the average joe it could counterintuitively increase demand for the job.

Of course the stated goal of the AI companies is to develop the analog of fully robotic lawnmowers. But despite how impressive recent advancements have been we still have yet to see any evidence of novel abstract reasoning or a theory that would be expected to lead to it.

In other words, people have been speculating about the development of fully autonomous lawnmowers and the risk that they unilaterally decide to cut us all down for the past 50 years. "I, lawnmower" was a smash hit a few years ago. Now gas ones have appeared and continue to make rapid advancements but still no convincing signs of autonomy.

You're obviously right and the people who think that are the managerial types that think software developers were glorified secretaries writing after dictation.

LLM is great at generating stuff, but it's basically 3D printing. Amazing, but most of the high quality stuff in the world needs to be built at large scale out of aluminum, steel, wood, etc. Yes, I know there are large advances in 3D printing, but maybe 0.000000001% of all manufacturing in the world are done using 3D printing. A lot of stuff will probably never be possible using 3D printing.

I, personally, found the past two years to be a much larger improvement than the previous two years.

The idea that we’re at the point where it’s superseded our ability to tell just makes no sense. I’ll be happy if we can get to a point where I don’t have to tell Claude not to tail every bash command or make a job that writes throughout instead of once at the end. I’ll be happy if “continue this interaction naturally, you are taking over from an independent subagent” works.

But I’m not holding my breath. It’s still really cool that any of this stuff is possible.

Claude in feb of 2026? Still far from perfect, but there's definitely a huge improvement here.

(in fact I find that Qwen-35B-A3B and Gemma4-26B-A4B very rarely "know" the answer, and so use first principles thinking, or go out and look for the answer where GPT-5.4 does not and simply assumes it knows. Which leads to now, in some cases, the small models far outperforming the big ones. Huge context + training quality seem to be the determining factors now, and neither of those are the strengths of SOTA models. If this continues ...)

While I agree this is a training problem, it is not a solvable one. ML models learn from examples. This is even true for their newest tricks like GRPO. They cannot train against things humans don't yet know.

And that's great, but you're forever locked at the peak of what you can be taught in widely available courses (which they download without paying) (even that is best case scenario: it assumes your ability to distinguish bullshit from reality somehow becomes perfect during training, or even before). The only way to exceed peak human performance is to start experimenting with math, physics, chemistry, even humans, yourself. And that has, even for humans, a massively higher cost than learning from examples, or from a course.

The reason they don't go further is the worst possible reason: the cost. It requires a 100x increase in training expense. Think of it like this: to exceed SOTA in physics or chemistry, training the next version of ChatGPT requires a particle accelerator, and a chemistry laboratory. This cannot be bypassed. Oh and not just any particle accelerator, right? A better one than the best currently existing one. Same for Chemistry labs. Same for ... So 100x is conservative.

But without doing it, ML models (LLM or otherwise) are forever limited at the level an army of first year university students achieve, ON AVERAGE. Maybe they can make that 2nd or even 4th year, at the end of the curve. But that's the limit. Phd level is the level you have to come up with new discoveries, and that ... just isn't possible with current training, even at the end of the improvement curve.

And ... is there budget to increase training cost another 100x? No ... there isn't. Not even with this totally absurd level of investment there isn't. And if small models keep this up, there's no way the investment is even remotely worth it.

And if you take that out: 1. All of those releases happened literally in the last 3-ish months. 2. They’re all intentionally marginal releases, hence the minor version bumps instead of major versions.

Especially because the companies telling us the first premise is true are the companies which need investors to prop up their business.

I mean, it is possible the first premise is true, but the absolutely bonkers credulity in it really mystifies me. It is an incredibly unlikely thing to be true and we should be demanding quite extraordinary evidence to back it up. But based on some neat tricks by current LLMs, some people are all in.

> Because the premise that the singularity is just around the corner is far less likely than the premise that artificial intelligence is a lot harder than most people think it is and we're not that close.

I see no claim that the singularity is around the corner, so I'm not sure your reply meets the comment that you're replying to.

It seems overwhelmingly likely that AI will be significantly more capable 6 months from now than it is now. Even if there's little progress in the models, just the rate at which tooling is moving will make a big difference. And models still seem to be improving, so I'd be a little surprised if we hit a model brick wall.

Mythos is a 10T model. Opus is a 5T model.

That's not an exponentially growing amount of compute but it is achieving exponential improvements (eg from Mozilla: https://blog.mozilla.org/en/privacy-security/ai-security-zer... )

“Exponential” used here is pure hyperbole. Can you justify it?

But even more so, who said the improvements are "exponential"? Mozilla's single metric, that doesn't even prove anything of the sort?

Parameters and compute are quite the same thing, but going from 1T to 5T to 10T is quite a ramp up.

I think a better question for AI is “is it more like a network effect, liquidity effect, or a biological/physical effect”?

Or Roman trade volume before the Fall of Rome.

Not to mention what you describe is not technological improvement but increase in data or money flows, not the same.

But I don’t that think it’s quite so obvious that model quality / growth / usefulness is definitively and obviously not more like data or money flows than it is like some other process.

So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the quality of the output while reducing the amount of transformers needed for decoding and encoding IO and for internal reasoning.

There are also difference inference methods, like autoregressive and diffusion, and maybe others we haven't discovered yet.

You combine those variables, along with the internal disposition of layers, parameter size and the actual dataset, and you have such a large search space for different models that no one can reliably tell if LLM performance is going to flatline or continue to improve exponentially.

But then, wouldn't we first have to translate all of our current math and physics knowledge into that new representation in order to be able to train a model on it? Looks like a tremendous amount of work to me.

That's precisely what happens on the bad side of a S curve.

From the article,

> ...LLMs have got to the point where if a problem has an easy argument that for one reason or another human mathematicians have missed (that reason sometimes, but not always, being that the problem has not received all that much attention), then there is a good chance that the LLMs will spot it. Conversely, for problems where one’s initial reaction is to be impressed that an LLM has come up with a clever argument, it often turns out on closer inspection that there are precedents for those arguments...

Yeah, if time is infinite, R&D imagination is infinite, energy is infinite and material resources are infinite. Easy.

If it’s anyone’s guess then we’re much more likely to be left of that, unless you argue we’re already on the flat side.

Do you have a source for this that isn't marketing spiel? There's a fiscal incentive to lie about scaling research.