It doesn't matter whether they are lying. People want to hear it. It's comforting. So the market fills the void, and people get views and money for saying it.
Don't use the fact that people are saying it, as evidence that it is true.
It's not the AGI sceptics who are getting $500bn valuations.
There are interesting and well thought out arguments for why the AGI is not coming with the current state of technology, dismissing those arguments as propaganda/clickbait is not warranted. Yannic is also an AI professional and expert, not one to be offhandedly dismissed because you don’t like the messaging.
Telling us all to remember that there's potential for bias isn't so bad. It's a hot button issue.
There was no grand announcement of passing the Turing test or not. Instead the whole idea has faded in importance.
As the models get better, it wouldn't be shocking to me we get to a point that no one cares if the models are considered "AGI" or not.
We will be chasing some new vaguely defined concept.
The default position that does not need any more justification is the one that is skeptic or even agnostic to the claim that is made until proof is shown.
So when talking about evidence as a way to prove a claim: AGI is coming is the team that needs to provide this evidence. Someone saying AGI is not coming can add as many arguments or opinions as they like but it does not usually invite to such a high scrutiny as saying they need to provide evidence.
"Modern" definitions that include non-intelligence related stuff like agency sound like goalpost moving, so it's unclear why would you want them.
Given that AGI does not exist, “AGI is not coming” is the status quo until someone disproves it.
The inverse can be true too: Just because people ARE saying that Agi is coming, isn’t evidence that it is true.
"AI is getting better rapidly" is the current state of affairs. Arguing "AI is about to stop getting better" is the argument that requires strong evidence.
On the other hand, if you're telling your investors that AGI is about two years away, then you can only do that for a few years. Rumor has it that such claims were made? Hopefully no big investors actually believed that.
The real question to be asking is, based on current applications of LLMs, can one pay for the hardware to sustain it? The comparison to smartphones is apt; by the time we got to the "Samsung Galaxy" phase, where only incremental improvements were coming, the industry was making a profit on each phone sold. Are any of the big LLMs actually profitable yet? And if they are, do they have any way to keep the DeepSeeks of the world from taking it away?
What happens if you built your business on a service that turns out to be hugely expensive to run and not profitable?
Musk has been doing this with autonomous driving since 2015. Machine learning has enough hype surrounding it that you have to embellish to keep up with every other company's ridiculous claims.
Whether there is hype or not, the laws of money remain the same. If you invest and don’t get expected returns, you will be eventually concerned and will do something about it.
LLMs are great at forming models of language from observations of language and extrapolating language constructs from them. But to get general intelligence we're going to have to let an AI build their models from direct measurements of reality.
They really aren't even great at forming models of language. They are a single model of language. They don't build models, much less use those models. See, for example, ARC-AGI 1 and 2. They only performed ARC 1 decently [0] with additional training, and are failing miserably on ARC 2. That's not even getting to ARC 3.
[0] https://arcprize.org/blog/oai-o3-pub-breakthrough
> Note on "tuned": OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details. We have not yet tested the ARC-untrained model to understand how much of the performance is due to ARC-AGI data.
... Clearly not able to reason about the problems without additional training. And no indication that the additional training didn't include some feature extraction, scaffolding, RLHF, etc created by human intelligence. Impressive that fine tuning can get >85%, but it's still additional human directed training and not self contained intelligence at the level of performance reported. The blog was very generous making the undefined "fine tuning" a footnote and praising the results as if they were directly from the model that would have cost > $65,000 to run.
Edit: to be clear, I understand LLMs are a huge leap forward in AI research and possibly the first models that can provide useful results across multiple domains without being retrained. But they're still not creating their own models, even of language.
Think about this story https://news.ycombinator.com/item?id=44845442
Med-Gemini is clearly intelligent, but equally clearly it is an inhuman intelligence with different failure modes from human intelligence.
If we say Med-Gemini is not intelligent, we will end up having to concede that actually it is intelligent. And the danger of this concession is that we will under-estimate how different it is from human intelligence and then get caught out by inhuman failures.
I guess when it comes to the definition of intelligence, just like porn, different people have different levels of tolerance.
I believe that’s Eliezer Yudkowsky’s definition.
Maybe for LLMs but they are not the only possible algorithm. Only this week we had Genie 3 as in:
>The Surprising Leap in AI: How Genie 3’s World Model Redefines Synthetic Reality https://www.msn.com/en-us/news/technology/the-surprising-lea...
and:
>DeepMind thinks its new Genie 3 world model presents a stepping stone toward AGI https://techcrunch.com/2025/08/05/deepmind-thinks-genie-3-wo...
But are they sufficiently different that stalling progress in one doesn't imply stalling progress in the other?
Depends if you’re asking about real world models or synthetic AI world models.
One of them only exists in species with a long evolutionary history of survivorship (and death) over generations living in the world being modeled.
There’s a sense of “what it’s like to be” a thing. That’s still a big question mark in my mind, whether AI will ever have any sense of what it’s like to be human, any more than humans know what it’s like to be a bat or a dolphin.
You know what it’s like for the cool breeze to blow across your face on a nice day. You could try explaining that to a dolphin, assuming we can communicate one day, but they won’t know what it’s like from any amount of words. That seems like something in the area of neuralink or similar.
>It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model.
my point is more people can try different models and algorithms rather than having to stick to LLMs.
For me an AGI would mean truly at least human level as in "this clearly has a consciousness paired with knowledge", a.k.a. a person. In that case, what do the investors expect? Some sort of slave market of virtual people to exploit?
How to find out if something has probably consciousness? Much less clearly? What is consciousness?
(Edit: plus a question mark, as we sometimes do with contentious titles.)
