This podcast chose its listeners and kept it real. Thanks to everyone who makes it possible. Hope they get a fitting replacement for Melvyn and keep it going!

[1] https://news.ycombinator.com/item?id=1781013

Congratulations to the team at Ai2!

Wow. What a picture! Here's an optimistic take, fwiw: Whenever we have had a paradigm shift in our ability to process information, we have grappled with it by shifting to higher-level tasks.

We tend to "invent" new work as we grapple with the technology. The job of a UX designer did not exist in 1970s (at least not as a separate category employing 1000s of people; now I want to be careful this is HN, so there might be someone on here who was doing that in the 70s!).

And there is capitalism -- if everyone has access to the best-in-class model, then no one has true edge in a competition. That is not a state that capitalism likes. The economics _will_ ultimately kick in. We just need this recent S-curve to settle for a bit.

Aren't layoffs a version of that? Are we seeing any evidence that folks who have been let go from Intel have resulted in spin-offs and startups?

I know at least one person who went to work at Nvidia from Intel but that is neither of those things.

It _is_ a hype bubble but it is also an S-curve. Intel has missed the AI boat so far, if they are trying to catch up, I would encourage them to try. Building marginally better x86 chips might not cut it anymore.

I liked my jobs and bosses!

This is a great hypothesis for you to prove one way or the other.

> Doing this effectively will produce RL models with strong few-shot, task-agnostic abilities capable of quickly adapting to entirely new tasks.

I am not sure if I buy that, frankly. Even if you were to develop radically efficient means to create "effective and comprehensive" test suites that power replication training, it is not at all a given that it will translate to entirely new tasks. Yes, there is the bitter lesson and all that but we don't know if this is _the_ right hill to climb. Again, at best, this is a hypothesis.

> But achieving this will require training environments at a scale and diversity that dwarf anything currently available.

Yes. You should try it. Let us know if it works. All the best!

However, it is _fun_ to go over the barrier if it is chatting with a model to get a quick tutorial and produce working code for a prototype (for your specific needs) where the understanding that you just developed is applied. The alternative (without LLMs) is to first do the ground work of learning via tutorials in text/video form and then do the cognitive mapping of applying the learning to one's prototype. I would make a lot of mistakes that expert/intermediate React developers don't make on this path.

One could argue that it shortcuts some learning and perhaps the old way results in better retention. But, our field changes so fast... and when it remains static for too long, projects die. I think of all this as accelerant for progress in adoption of new ways of thinking about software and diffusing that more quickly across the developer population globally. Code is always fungible, anyway. The job is about all the other things that one needs to do besides coding.

Ultimately, individual habits do add up. But with climate, one would be hard pressed to find evidence that conservation is the path forward. It does not work, unfortunately.

"On one of these toy problems, the Tower of Hanoi, the models structurally cannot output enough tokens to solve the problem — the authors still took this as a claim that “these models cannot reason” or “they cannot generalize.” This is a small scientific error."

"it appears that a majority of critiques of AI reasoning are based in a fear of no longer being special rather than a fact-based analysis of behaviors."

[1]: https://www.arxiv.org/pdf/2506.06941

Comments URL: https://news.ycombinator.com/item?id=44260779

Points: 2

# Comments: 1

Yes, the more recent generation of GPUs optimize for attention math. But they are still fairly "general-purpose" accelerators as well. So when I see papers like this (interesting idea, btw!), my mental model for costs suggests that the CapEx to buy up the GPUs and build out the data centers would get re-used for this and 100s of other ideas and experiments.

And then the hope is that the best ideas will occupy more of the available capacity...

But, for models, this is an interesting finding because a lot of LRMs are LLMs with a _bunch_ of post-training done on top. We know this about DeepSeek R1 (one of the models evaluated in the Apple paper) for sure. They write extensively about how they took DeepSeek-V3-Base and made R1 with it. [1]

If the post-training is resulting in lower performance on simpler tasks then it ought to inspire more research on how to make it so that it doesn't -- i.e., with more training (of any kind), we should be gaining more capabilities. This has been a problem with DNNs historically, btw. We had these issues when fine-tuning text/image classifiers as well. Some weight changes can be destructive. So, it has to be done with a _lot_ of care. And, I am sure folks are working on it, to be honest. Maybe some of them will say something here. :-)

[1] https://github.com/deepseek-ai/DeepSeek-R1

Very clever, I must say. Kudos to folks who made this particular choice.

> we identify three performance regimes: (1) low complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse.

This is fascinating! We need more "mapping" of regimes like this!

What I would love to see (not sure if someone on here has seen anything to this effect) is how these complexity regimes might map to economic value of the task.

For that, the eval needs to go beyond puzzles but the complexity of the tasks still need to be controllable.

The question is do they still need 10? Or 2 would suffice? How about 5?

This does not need to be a debate about the absolutes.

I am curious -- what would have made it more effective at conveying information to you? Different people learn differently but I wonder how people get beyond the hurdles of jargon.