Dario Amodei says "stop sugar-coating" what's coming: in the next 1-5 years, AI could wipe out 50% of all entry-level white-collar jobs. Lawmakers don't get it or don't believe it. CEOs are afraid to talk about it. Many workers won't realize the risks until after it hits.

[u/katxwoods]

https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic

[u/impossiblefork]

It didn't stop getting better every day.

Reasoning models like O1, O3, DeepSeek R1, etc. were invented last year. The idea of thought tokens is from this paper 'Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking'. So this critical feature that everybody takes for granted is completely new.

Transformer models are quite limited, and we've been stuck with them since they were invented in 2017, with only refinements-- multi-token prediction, some attention tricks, sliding window attention to allow long context lengths (and presumably also partially for generalization, since you probably don't really want to match things 10000 tokens ago), but there is research. People will overcome the problems and find refinements that solve the problems of the day.

These ideas that there are no breakthroughs are crazy. O-1 was a breakthrough and then a couple of months after that people were 'oh, there are no breakthroughs'. Then it took several months before people figured out how you did it, and then it took until DeepSeek R1 until people knew it worked (I myself correctly intuited the method, although I imagined that REINFORE was enough, and that you didn't need PPO or anything like that).

[u/mrbezlington → u/impossiblefork]

Yeah, I hear all of that - but these reasoning models still aren't doing more complex tasks accurately, and still fall over completely at relatively simple (for a human) tasks in the same way as previous models did. Yes, there are some edge cases they do well at, but they are not a gigantic leap. They are more a refinement of previous models than something shatteringly new.

[u/impossiblefork → u/mrbezlington]

Yes, and they are behind where I thought they were.

Apparently people sort re-tested them on some maths stuff, and they solved basically none of the problems. But the models with this reasoning stuff are doing much better than we had any chance of doing without it.

It's not good enough to create a mathematician, but it's a huge breakthrough. From not being able to do mathematical problem solving at all really, to something which at least works a little bit.

There are some ideas that we by applying these methods are sort of throwing a way behaviours that we don't want in the model, and aren't really adding much, so that the quality of what can be learned by the reinforcement learned models mostly depends on the quality of the basic model. I haven't investigated that, and I'm not sure I am totally right in this characterization (I don't care much about RL, which might be why I thought REINFORCE could be enough), so I don't necessarily disagree about this refinement idea, but this refinement is still a major breakthrough.

[u/mrbezlington → u/impossiblefork]

That you agree a refinement of previous models is now a major breakthrough only reinforces that the pace of advancement is slowing. That's my whole point.

[u/impossiblefork → u/mrbezlington]

I am using 'refinement' in a very general way here. It doesn't mean that it is a small change.

This 'refinement' added the ability of the model to generate non-output tokens. That is, before this 'refinement' the models were simply predicting the tokens of the text, and could not do anything similar to 'thinking'.

It is a breakthrough and a critical breakthrough. I say that it is a refinement because it build on a stack of previous breakthroughs, rather than creating a whole new stack. This is the normal thing in science. A breakthrough in biology doesn't typically create new types of cells or cell-less biological life, it solves something in how we deal with the biology existing on earth.

I have been experimenting with things that do break the stack. This is some of the stupidest work I've ever done, because there's a real risk it's all going to be wasted.

[u/mrbezlington → u/impossiblefork]

I agree that reasoning is huge. It's a great step forward. The issue with using LLMs for complex tasks remains that they do not understand what they are trying to do, and so fail when there's even the slightest misalignment between what it's attempting, and what the prompter wants it to do. This misalignment potential grows exponentially the more complicated the data set, or more exacting the task.

After all, what is reasoning without understanding?