> Salvatore is right about the fact that we have not seen the full story yet, LLMs are stalling/plateauing but active research is already ongoing to find different architectures and models.

They will need to be so different that any talk implying current LLMs eventually replaced humans will be like saying trees eventually replaced horses because the first cars were wooden.

> And I think the effort here can be compared in scale to the Manhattan or Apollo projects, but there is also the potential for a huge backlash to the hype that was built up and created what is arguably a bubble, so this is a race against the clock.

It's not useful to blindly compare scale. We're not approaching AI like the Manhattan or Apollo projects, we're approaching this like we did crypto, and ads, and other tech.

That's not to say nothing useful will come out of it, I think very amazing things will come out of it and already have... but none of them will resemble mass replacement of skilled workers.

We're already so focused on productization and typical tech distractions that this is nothing like those efforts.

(In fact thinking a bit more, I'd say this is like the Space Shuttle. We didn't try to make the best spacecraft for scientific exploration and hope later on it'd be profitable in other ways... instead we immediately saddled it with serving what the Air Force/DoD wanted and ended up doing everything worse.)

> I also think he is wrong about the markets reaction, markets are inherently good integrators and bad predictors, we should not expect to learn anything about the future by looking at stocks movements.

I agree, so it's wrong about the over half of punchline too.

Current GenAI is nothing but a proof of concept. The seed is there. What AI can do at the moment is irrelevant. This is like the discovery of DNA. It changed absolutely everything in biology.

The fact that something simple like the Transformer architecture can do so much will spark so many ideas (and investment!) that it's hard to imagine that AGI will not happen eventually.

Manhattan and Apollo were both massive engineering efforts; but fundamentally we understood the science behind them. As long as we would be able to solve some fairly clearly stated engineering problems and spend enough money to actual build the solutions, those projects would work.

A priori, it was not obvious that those clearly stated problems had solutions within our grasp (see fusion) but at least we knew what the big picture looks like.

With AI, we don't have that, and never really had that. We've just been gradually making incremental improvements to AI itself, and exponential improvements in the amount of raw compute we can through at it. We know that we are reaching fundamental limits on transistor density so compute power will plateau unless we find a different paradigm for improvement; and those are all currently in the same position as fusion in terms of engineering.

LLMs are just the latest in a very long line of disparate attempts at making AI, and is arguably the most successful.

That doesn't mean the approach isn't an evolutionary dead end, like every other so far, in the search for AGI. In fact, I suspect that is the most likely case.

Or they are experts in one field and think that they have valuable insight into other fields they are not experts on.

I kind of want to put up a wall of fame/shame of these people to be honest.

Whether they turn out right or wrong, they undoubtedly cheered on the prospect of millions of people suffering just so they can sound good at the family dinner.

I wouldn’t want to work for or with these people.

do you know how undefined and difficult to measure it is to load silverware into a dishwasher?

What? Robotics will have far more ambiguity and nuance to deal with than language models, and they'll have to analyze realtime audio and video to do so. Jobs are not so clearly defined as you imagine in the real world. For example, explain to me what a plumber does, precisely and how you would train a robot to do so? How do you train it to navigate ANY type of buildings internal plumbing structure and safely repair or install for?

Innovation in terms of helping devs do cool things has been insane.

There've been next to no advancements relative to what's needed to redefine our economic systems by replacing the majority of skilled workers.

-

Productionizing test-time compute covers 80% of what we've gotten in the last 6-8 months. Advancements in distillation and quantization cover the 20% of the rest... neither unlocks some path to mass unemployment.

What we're doing is like 10x'ing your vertical leap when your goal is to land on the moon: 10x is very impressive and you're going to dominate some stuff in ways no one ever thought possible.

But you can 100x it and it's still not getting you to the moon.

Not sure the last time you went on reddit, but I wouldn't be surprised if around 20% of posts and comments there are LLM generated.