41–50 of 276 posts
And also; the frontier LLMs blow older LLMs out of the water. There is continual progress and this study would have been structured substantially the same 2 years ago with much smaller N on the graphs because the regimes were much tinier then.
Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things
Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things
We will be treating LLMs “like a junior developer” forever.
Fusion has been 25 years away for all of my life.
In 2025 they got a 313% gain (4.13 output factor).
Fusion is actually here and working. It’s not cost effective yet but to pretend there has been no progress or achievements is fundamentally false.
I've never seen this question quantified in a really compelling way, and while interesting, I'm not sure this PDF succeeds, at least not well-enough to silence dissent. I think AI maximalists will continue to think that the models are in fact getting less dim-witted, while the AI skeptics will continue to think these apparent gains are in fact entirely a biproduct of "increasing" "omniscience." The razor will have to be a lot sharper before people start moving between these groups.
But, anyway, it's still an important question to ask, because omniscient-yet-dim-witted models terminate at "superhumanly assistive" rather than "Artificial Superintelligence", which in turn economically means "another bite at the SaaS apple" instead of "phase shift in the economy." So I hope the authors will eventually succeed.
I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. And the question they are collectively trying to ask is whether this will continue forever. I've never seen this question quantified in a really compelling way, and while interesting, I'm not sure this PDF succeeds, at least not well-enough to silence dissent. I think AI maximalists will contin…
I don't know that I would call it an "illusion of thinking", but LLMs do have limitations. Humans do too. No amount of human thinking has solved numerous open problems.
Fusion has been 25 years away for all of my life.
Fusion is net positive energy now; that happened in 2022 (+54%). In 2025 they got a 313% gain (4.13 output factor). Fusion is actually here and working. It’s not cost effective yet but to pretend there has been no progress or achievements is fundamentally false.
I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. And the question they are collectively trying to ask is whether this will continue forever. I've never seen this question quantified in a really compelling way, and while interesting, I'm not sure this PDF succeeds, at least not well-enough to silence dissent. I think AI maximalists will contin…
I'm bullish (and scared) about AI progress precisely because I think they've only gotten a little less dim-witted in the last few years, but their practical capabilities have improved a lot thanks to better knowledge, taste, context, tooling etc.
What scares me is that I think there's a reasoning/agency capabilities overhang. ie. we're only one or two breakthroughs away from something which is both kinda omniscient (where we are today), and able to out-think you very quickly (if only through dint of applying parallelism to actually competent outcome-modelling and strategic decision making).
That combination is terrifying. I don't think enough people have really imagined what it would mean for an AI to be able to out-strategise humans in the same way that they can now — say — out-poetry humans (by being both decent in terms of quality and super fast). It's like when you're speaking to someone way smarter than you and you realise that they're 6 steps ahead, and actively shaping your thought process to guide you where they want you to end up. At scale. For everything.
This exact thing (better reasoning + agency) is also the top priority for all of the frontier researchers right now (because it's super useful), so I think a breakthrough might not be far away.
Another way to phrase it: I think today's LLMs are about as good at snap judgements in most areas as the best humans (probably much better at everything that rhymes with inferring vibes from text), but they kinda suck at:
1. Reasoning/strategising step-by-step for very long periods
2. Snap judgements about reasoning or taking strategic actions (in the way that expert strategic humans don't actually need to think through their actions step-by-step very often - they've built intuition which gets them straight to the best answer 90% of the time)
Getting good at the long range thinking might require more substantial architectural changes (eg. some sort of separate 'system 2' reasoning architecture to complement the already pretty great 'system 1' transformer models we have). OTOH, it might just require better training data and algorithms so that the models develop good enough strategic taste and agentic intuitions to get to a near-optimal solution quickly before they fall off a long-range reasoning performance cliff.
Of course, maybe the problem is really hard and there's no easy breakthrough (or it requires 100,000x more computing power than we have access to right now). There's no certainty to be found, but a scary breakthrough definitely seems possible to me.