this post was submitted on 11 Aug 2026
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A better analogy is promising that a toddler will, with enough knowledge and training, eventually become a heart surgeon.
15 years ago the assumption with AGI was that we needed some paradigm shift in technology to create it, but after transformer technology was invented (the T in GPT), and it was trained on a lot of information, we discovered an emergent property that it could take natural language queries and answer them with its knowledge base-- which was unexpected and unintended.
While science can always end up going down the wrong path, the current mainstream stance is that we were wrong about needing new technology for AGI; it seems that AGI may be a function of information and training, on hardware we already have. Hence all the data centers being built.
Anyone who says with certainty that it will result in AGI is just as wrong as someone who says with certainty that it won't.
That is where the evidence points. Now, I don't want to oversell it: "where the evidence points" is wildly different than "exactly how it works".
We have an emergent property that we don't understand, but we can reliably increase the functionally and complexity of that emergent property as a function of training data and available compute. Does that mean that there isn't some threshold where that stops working? No, there certainly could be a point where throwing more information and compute has no effect. We just don't know. However, so far, there is no evidence such a barrier exists, and everyone is racing to find out.
As far as I am aware, AGI does not imply sapience, consciousness, or whatever. You seem to believe it does. A high level Google search seems to suggest that I'm correct. Is there some reason you are lumping sapience with AGI?
I'm not sure if the rest of your comment presupposes this, so I'll wait to comment on where I think you're mistaken.
I think there is some confusion. I do not believe AGI implies sapience, nor does Google, and now it seems that you don't either. So who is discussing sapience?
Hypothetically speaking, what do you think this would look like?
What it would look like is layoffs from a company without a change in output/productivity. Notably, we're not at AGI yet, so I wouldn't expect sci-fi levels of change.
There would necessarily be a lag time between layoffs and unchanged productivity. And even then, if we're being fair, that's not conclusive evidence of AI being useful; it could just be that they had more employees than they needed.
My point is more that I'm not sure we'd see an obvious sign that AI was changing the market yet.
Elon Musk is an idiot, and you've already noted that the market doesn't track reality.
The paradigm shifting invention was the transformer (the T in GPT). If you'll forgive a shitty analogy, it's like someone invented a microscope and you're asking why we don't yet have insulin. Not having insulin doesn't make the microscope useless.
As far as these things go, AI is improving at a breakneck speed-- hence why so many people in the field are begging for a pause in development. We don't really understand why this technology works as well as if does, and that has a lot of people worried that we're going to do something dangerous without understanding what we did.
I assure you that 400 some odd years ago, most people would not have grasped the utility in the microscope. Just like many people today don't understand what the transformer has done for machine learning.
Again, you're using 400+ years of hindsight to inform your conclusion. "Looking at small things" doesn't immediately imply "unlocking the secrets of the universe", it's only after hundreds of years of building on that invention that you can easily point to that conclusion.
And admittedly the analogy was not great, I'm just trying to get across that you're looking at the output of the invention and asking "is that it"? But we've only had this technology for a decade or so, and it's only really been public facing for, what, 4 years?
These various technologies we refer to as "AI" are not yet at the point where they can replace any human at any task, but they can replace some humans at some tasks, and as far as the evidence shows, we can continue to improve it with more training and more compute. They are writing prose well enough to pass as humans, solving math problems we haven't been able to solve for decades, writing medium complexity code in minutes instead of days-- the technology isn't solving all the world's problems and ushering in a post-scarcity utopia, but no one is claiming it is, today. Will it? I doubt it, but that's more to do with capitalism corrupting everything it touches than anything else.
If you think "AI" is not useful, you're probably considering a very narrowly defined type of "AI" in a very narrowly defined scenario where AI does not perform well.
Edit: I want to take a second to say that I've enjoyed this conversation with you. I can't say that about many conversations on this topic. This isn't me ending the conversation.
When did anyone ever claim that? It's not going to break the laws of physics.
If you do it on a computer, current AI (an umbrella term) can probably make it easier for you to do, if not just do it for you.
I didn't realize you meant that. In my mind humans could drag stuff and catch fish but the inventions made it easier or more efficient.
In that view, pretty much anything on a computer can be done easier or more efficient with AI. Same as a net or a wheel. Just Google anything it is you might want to do on a computer and add "using AI" and you're likely to find a tool to do it. If you have the computer for it, you can download an open weight model to do locally (though probably not as well as using the crazy compute associated with using an API model.
Also, as agentic AI improves, we'll see the scarcity of attention decrease and maybe vanish entirely. It's not always obvious, but much of how our society is structured assuming that people have limited agency.
Do you mind sharing what your profession is?
It should definitely not take the same amount of time to check someone's work as it does to do the work.
"AI today" changes every few months, they aren't more error prone than humans at medium complexity code, and debugging a problem is different than checking someone's work. (And not for nothing, but Code Generation LLMs are really good at finding bugs in code, if passed the code.)
That's not to mention that prompting AI is a skill that needs to be learned, despite LLMs being able to accept natural language prompts. Not learning how to use a tool and then claiming the tool isn't useful doesn't really say much about the tool.
There's a saying about how its impossible to convince someone of something if they're financially incentivized to remain unconvinced that I think applies to most people when they discuss AI.
We have math PHDs with proofs current LLMs can't become AGI, because they'll always have a context issue
I would love it if you'd point me at these mathematical proofs.
This is the primary paper I reference.
https://arxiv.org/pdf/2507.07505
Vishal Sikka, advisory board member of BMW. Recommended to Stanford by Marvin Minsky, one of his professors were John McCarty. And I must stand corrected, he has a PHD of computer sciences, not Math as I remembered it as.
Varin Sikka is his son, co author of the paper and based on Stanford's site an undergraduate. https://profiles.stanford.edu/363374
Vishal has an AI based company himself, so there might be some personal reasons for why he'd advocate for using what AIs capable of rather than chasing an impossible (from his perspective) to hit milestone
That paper doesn't seem to rule out AGI, only an single LLM model that can answer every arbitrarily difficult question on demand.
AGI does not necessarily mean one model acting alone, or being able to answer any question on demand. Humans are the same way: we often need time or collaboration to arrive at conclusions, but that doesn't mean we don't have "general intelligence".
You're not going to context hack complexity, every time you summarise something and pass it onto the next agent. You're losing complexity.
We're going to get some massive models with incredible context, but AGI requires models to never hallucinate. Even when the complexity requires more context than it has available, which is not feasible
We've already had a month now of Psychiatrists writing articles backpedaling over all the times they'd insisted AI couldn't genuinely be thinking as we do and moving the goal posts to something else. Maybe there are researchers in the frontier labs who feel like they have a grip on what's happening, but the 'experts' on forums have no idea.