First, I don't think we will ever get to AGI. Not because we won't see huge advances still, but AGI is a moving ambiguous target that we won't get consensus on.

But why does this paper impact your thinking on it? It is about budget and recognizing that different LLMs have different cost structures. It's not really an attempt to improve LLM performance measured absolutely.

I can totally see "it's not really AGI because it doesn't consistently outperform those three top 0.000001% outlier human experts yet if they work together".

It'll be a while until the ability to move the goalposts of "actual intelligence" is exhausted entirely.

Well right now, my niece of 7 years outperforms all LLM contenders in drawing a Pelican on a bicycle

I know this was a joke, but LLMs are quite good at this now. If your niece draws better then she’s a good artist.

I tried it in Gemini just now, it seems to have done a decent job: https://g.co/gemini/share/b6fef8398c01

So you don't expect AGI to be possible ever? Or is your concern mainly with the wildly different definitions people use for it and that we'll continue moving goal posts rather than agree we got there?

Got it, and yeah I agree with you there. I've been frustrated by a different view of it though, many people seem to have a definition and they are often wildly different.

even AI does not have a concrete definition.

Doesn't mean there aren't practical definitions depending on the context.

In essence, teaching an AI using recources meant for humans, and nothing more, would be considered AGI. That could be a practical definition, without needing much more rigour.

There is indeed no evidence we'll get there. But there is also no evidence LLM's should work as well as they do

Well, if a human is GI, we just need to make it Artificial. Easy.

I like to say that it's not AI -- it's just A.

Given OpenAI definition I’d expect AGI to be around in a decade or two. I don’t expect skynet, though maybe it’s a more realistic vision outcome that just droids mixing with humans.

LLMs are not on the road to AGI, but there are plenty of dangers associated with them nonetheless.

Agreed, broadly. I never really thought they were, but seeing people work on stuff like this instead of even trying to improve the architecture really makes it obvious.

Just 2 days ago Gemini 2.5 Pro tried to recommend me tax evasion based on non-existing laws and court decisions. The model was so charming and convincing, that even after I brought all the logic flaws and said that this is plain wrong, I started to doubt myself, because it is so good at pleasing, arguing and using words.

And most would have accept the recommendation because the model sold it as less common tactic, while sounding very logical.

Or you could understand the tool you are using and be skeptical of any of its output.

So many people just want to believe, instead of the reality of LLMs being quite unreliable.

Personally it's usually fairly obvious to me when LLMs are bullshitting probably because I have lots of experience detecting it in humans.

LLM is only useful if it gives shortcut to information with reasonable accuracy. If I need to double check everything, it is just extra step.

In this case I just happened to be domain expert and knew it was wrong. It would have required significant effort to verify everything with some less experienced person.

> even after I brought all the logic flaws and said that this is plain wrong

Once you've started to argue with an LLM you're already barking up the wrong tree. Maybe you're right, maybe not, but there's no point in arguing it out with an LLM.

There are cases when they are actually correct, instead of the human.

Yes, and there's a substantial chance they'll apologize to you anyway even when they were right. There's no reason to expect them to be more likely to apologize when they're actually right vs actually wrong- their agreeableness is really orthogonal to their correctness.

Yes, they over-apologize. But my main reason for using LLMs is seeking out things that I missed myself or my own argumentation was not good. Sometimes they are really good at bringing new perspectives. Whether they are correct or incorrect is not the point - are they giving argument or perspective that is worth inspecting more with my own brains?

Is a random paper from Fujitsu Research claiming to be the frontier of anything?

Not just this paper, but model working shenanigans also seem to have been a big part of GPT-5, which certainly claims to be frontier work.

I'm not following this either. You'd think this would be frontier back in 2023

That and LLMs are seemingly plateauing. Earlier this year, it seemed like the big companies were releasing noticeable improvements every other week. People would joke a few weeks is “an eternity” in AI…so what time span are we looking at now?

That's just the thing. There don't seem to have been any breakthroughs in model performance or architecture, so it seems like we're back to picking up marginal reductions in cost to make any progress.

There have been very large improvements in code generation in the last 6 months. A few weeks without improvement are not necessarily a plateau.

Wait until it ramps up so much that people will say "it's a plateau, for real this time" when they go 3 days without a +10% capability jump.

I mean I wish there were a plateau, without one we're well onto our way into techno-feudalism. I just don't see it.

That's what it is: wishful thinking. A lot of people really, really want AI tech to fail - because they don't like the alternative.

Yeah, obviously nobody that actually though about the consequences wants a large part of the population to become unemployed. Even if your job is not threatened by automation, it will be threatened by a lot of people looking for new jobs.

And the kind of automation brought by LLMs is decidely different than automation in the past which almost always created new (usually better) jobs. LLMs won't do this (at least to extent where it would matter) I think. Most people in ten years will have worse jobs (more physically straining, longer hours, less pay) unless there will be a political intervention.

just because it’s on arxiv doesn’t mean anything

arxiv is essentially a blog under an academic format, popular amongst asian and south asian academic communities

currently you can launder reputation with it, just like “white papers” in the crypto world allowed for capital for some time

this ability will diminish as more people catch on

arxiv should really have a big red banner "NOT REVIEWED - DON'T USE AS A SOURCE" or something

Or we'll realize that human intelligence and machine intelligence is apple and oranges.