Top economist warns that the AI math doesn’t make sense: 'Profits are currently being funded by investors rather than earned from customers'

Soon™

The bubble gonna poof?

Either that, or the rich and corps will go all in. The point was always to replace human labor, just depends how far from that they think we are. People still see a report an AI did something stupid and laugh at the idea of it replacing us, but just 5 years ago we wouldn’t have believed the things it can do today.

We can’t believe what it does not, or six agos what it does then. The shit is moving faster then literally anyone really seems to understand.

The avg person that makes fun of ai, seems to still think they have the same problems they had 2-3 years. Because the cheap free models they have access to are extremely out of date, or very limited.

The actual real deal big boy models are so far beyond what your avg even extremely technical user understands. Unless you are actively watching following and using the models you just flat out have no clue just how fast this shit is sprinting.

It’s got plenty of problems and the growth is not across every aspect of it equally. So it’s really easy to point and laugh at a particular point it’s struggling with while it skips ahead in other regards.

It’s wild. The progress is just as unsustainable as the profits are bad. As long as that progress keeps sprinting the bad profits flat out don’t matter.

IT WILL hit critical mass to replace your avg idiot long before the profit problem really hits at this rate.

The bigger problem is that even if it replaces people that profit problem doesn’t go away. So it will just hit AFTER people are replaced. And that’s a even worse outcome then replacing people.

We NEED the bubble to pop before that point and the industry recalibrates to a sustainable model.

Else we will have mass job loss promptly followed by a massive bubble popping and economy collapse AND companies flopping and job opportunity losses as places closing shop so there won’t even be jobs to back to.

It could get REALLY fucking bad.

Disagree.

It is true there were massive strides in the last 8 years. But fundamentally, the tech is still the same large language model it was before, just bigger and better optimized.

It’s like going from an ancient, slow, Ford Model T that topped out at 45mph to a Bugatti that can do 260mph in 8 short years. It’s impressive, it boosts productivity, it is a marvel of modern technology, but that’s not my point of contention.

The issue is that AI companies have been funded on the promise that with enough advancements and upgrades this tech will achieve AGI. Which is an absurd statement to anyone in the field actually developing these things. That is the equivalent of promising that this 260mph Bugatti, with a few years of upgrades and advancements, will become a Harrier Jump Jet!

It’s just not happening, a fundamental shift in model architecture or technology used is needed. And from what we’ve seen so far, no one has discovered any.

The issue is that AI companies have been funded on the promise that with enough advancements and upgrades this tech will achieve AGI. Which is an absurd statement to anyone in the field actually developing these things. That is the equivalent of promising that this 260mph Bugatti, with a few years of upgrades and advancements, will become a Harrier Jump Jet!

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 analogy makes the assumption that LLMs are dormant baby AGIs. There is no evidence that AGI is just a bigger and better curated LLM.

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.

I think that is where the hopes and hype point. The evidence, that which is gathered through controlled studies, points to an upper limit to this technology that does have emergent properties, but not ones that amount to cognition. That evidence also points to other hurdles, such as cognitive damage to the user and context windows nowhere near that of even a simpleminded creature, let alone a sapient one like a human.

The core problem is that these models are fixed; they are the same on day 1000 as they were on day 1. All their “learning”, as it were, happens in training before it is released. Everything it appears to learn after that date is contained in the rolling context window. Since they already have literally all the RAM they can get their hands on and are still at least an order of magnitude away from where they need to be on that, this technology either can’t do it or, at best, is so inefficient an approach that it can’t be done with all the planet’s resources.

Sometimes, especially in abstract constructions like software, you can start down the wrong path early and have to start over, because there is no path from where you are to where you need to get. In this case, they may have done that to the extreme, blinded by the lucrative prospects.

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 dont believe it requires sapience; that is what the marketers are saying. The AI boom (and many historic boom cycles) is predicated on marketing and sentiment, not facts and data. That is why they always pop; the facts dont back up the hype.

The fact that your search results turn up results that align with the marketing is just the marketing working.

Look–neither of us are data scientists, but we do have eyes. If this is working, where’s the company with runaway success creating unimagined leaps in productivity and technology? We’re pouring a whole planet’s with of resources in and nothing much is coming out. If we spent this much on world hunger, everyone would be obese by now.