Comment by jedberg

The bet is that they perfect a new kind of neural network which is roughly as good at "training" as the human mind is as far as "amount learned/experience gained per bit of information input".

Current LLMs are absolutely stupidly inefficient on this front, requiring virtually all human knowledge to train on as a prerequisite to early-college-level understanding of any one subject (granted, almost all subjects at that point, but what it has in breadth it lacks in depth).

That way instead of training millions of TPUs on petabytes of data just to get a model that maintains an encyclopedia of knowledge with a twelve-year-old's capacity for judgment, that same training set and compute could (they hope) instead far exceed the depth of judgement, planning, and vision of any human who has ever lived (ideally while keeping the same depth, speed of inference, etc).

It's one of those situations where we have reason to believe that "exactly matching" human intelligence is basically impossible: the target range is too exponentially large. You either fall short (and it's honestly odd that LLMs were able to exceed animal intelligence/judgment while still falling short of average adult humans.. even that should have been too small of a target) or you blow past it completely into something that both humans and teams of humans could never compete directly against.

Chess and Go are fine examples here: algorithms spent very short periods of time "at a level where they could compete reasonably well against" human grand masters. It was decades falling short, followed by quite suddenly leaving humans completely in the dust with no delusions of ever catching up.

That is what the large players hope to get with AGI as well (and/or failing that, using AI as a smoke screen to bilk investors and the public, cover up their misdeeds, play cup and ball games with accountability, etc)

Finance professor Aswath Damodaran, and others, have made many useful insights as to how AI as an investment is likely to pay out.

One technique is, instead of trying to pick individual winners, look at the total addressable market. Then compare the market size with the capital being pumped in. If you look on this basis, Aswath concluded that collectively AI investment is likely to provide unsatisfactory returns.

Here's a recent headline: "Nvidia’s Jensen Huang thinks $1 trillion won’t be enough to meet AI demand—and he’s paying engineers in AI tokens worth half their salary to prove it"

There are two parts to this. 1. A staggering $1t is expected to be invested in AI. Someone worked out that this was more than the entire capital expenditure for companies like Apple. We're talking about its entire existence here. IOW, $1t is a lot of dough. A LOT.

Secondly, this whole notion that AI is such a sure thing that half the salary will be in tokens should ring alarm bells. '“I could totally imagine in the future every single engineer in our company will need an annual token budget,” he said. “They’re going to make a few 100,000 a year as their base pay. I’m going to give them probably half of that on top of it as tokens so that they could be amplified 10 times.”'

I recall from the dotcom fiasco that service companies like accountants and lawyers were providing services to the dotcom companies and being compensated in stock options rather than cold hard cash like you'd normally expect.

Very dangerous.

As another poster pointed out, this really boils down to FOMO by big tech. I'm expecting big trouble down the line. We await to see if I'm early or just plain wrong.

Neither. It's the most severe FOMO in history. The best case scenario is equivalent to attempting to pick future winners just prior to the industrial revolution really kicking off. Except this time around the technological timelines appear to be severely compressed and everyone is fully aware of what's at stake. And again, that's the best case scenario.

Its just market euphoria.