If the former, no. If the latter, sure, approximately.

The business question is, what if AI works about as well as it does now for the next decade or so? No worse, maybe a little better in spots. What does the industry look like? NVidia and TSMC are telling us that price/performance isn't improving through at least 2030. Hardware is not going to save us in the near term. Major improvement has to come from better approaches.

Sutskever: "I think stalling out will look like…it will all look very similar among all the different companies. It could be something like this. I’m not sure because I think even with stalling out, I think these companies could make a stupendous revenue. Maybe not profits because they will need to work hard to differentiate each other from themselves, but revenue definitely."

Somebody didn't get the memo that the age of free money at zero interest rates is over.

The "age of research" thing reminds me too much of mid-1980s AI at Stanford, when everybody was stuck, but they weren't willing to admit it. They were hoping, against hope, that someone would come up with a breakthrough that would make it work before the house of cards fell apart.

Except this time everything costs many orders of magnitude more to research. It's not like Sutskever is proposing that everybody should go back to academia and quietly try to come up with a new idea to get things un-stuck. They want to spend SSI's market cap of $32 billion on some vague ideas involving "generalization". Timescale? "5 to 20 years".

This is a strange way to do corporate R&D when you're kind of stuck. Lots of little and medium sized projects seem more promising, along the lines of Google X. The discussion here seems to lean in the direction of one big bet.

You have to admire them for thinking big. And even if the whole thing goes bust, they probably get to keep the house and the really nice microphone holder.

I think the title is an interesting thing, because the scaling isn't about compute. At least as I understand it, what they're running out of is data, and one of the ways they deal with this, or may deal with this, is to have LLM's running concurrently and in competition. So you'll have thousands of models competing against eachother to solve challenges through different approaches. Which to me would suggest that the need for hardware scaling isn't about to stop.

Yes corporations need those numbers, but those few humans are way more valuable than any numbers out there.

Of course, only when others believe that they are in the frontier too.

Secrecy is also possible, and I'm sure there's a whole lot of that.

I just hope the people funding his company are aware that they gave some grant money to some researchers.

I agree these AI startups are extremely unlikely to achieve meaningful returns for their investors. However, based on recent valley history, it's likely high-profile 'hot startup' founders who are this well-known will do very well financially regardless - and that enables them to not lose sleep over whether their startup becomes a unicorn or not.

They are almost certainly already multi-millionaires (not counting ill-liquid startup equity) just from private placements, signing bonuses and banking very high salaries+bonus for several years. They may not emerge from the wreckage with hundreds of millions in personal net worth but the chances are very good they'll probably be well into the tens of millions.

Not necessarily. The problem is that we can't precisely define intelligence (or, at least, haven't so far), and we certainly can't (yet?) measure it directly. And so what we have are certain tests whose scores, we believe, are correlated with that vague thing we call intelligence in humans. Except these test scores can correlate with intelligence (whatever it is) in humans and at the same time correlate with something that's not intelligence in machines. So a high score may well imply high intellignce in humans but not in machines (e.g. perhaps because machine models may overfit more than a human brain does, and so an intelligence test designed for humans doesn't necessarily measure the same thing we think of when we say "intelligence" when applied to a machine).

This is like the following situation: Imagine we have some type of signal, and the only process we know produces that type of signal is process A. Process A always produces signals that contain a maximal frequency of X Hz. We devise a test for classifying signals of that type that is based on sampling them at a frequency of 2X Hz. Then we discover some process B that produces a similar type of signal, and we apply the same test to classify its signals in a similar way. Only, process B can produce signals containing a maximal frequency of 10X Hz and so our test is not suitable for classifying the signals produced by process B (we'll need a different test that samples at 20X Hz).

Models also struggle at not fabricating references or entire branches of science.

edit: "needing phd level research ability [to create]"?

Models aren't intelligent, the intelligence is latent in the text (etc) that the model ingests. There is no concrete definition of intelligence, only that humans have it (in varying degrees).

The best you can really state is that a model extracts/reveals/harnesses more intelligence from its training data.

> Maybe here’s another way to put it. Up until 2020, from 2012 to 2020, it was the age of research. Now, from 2020 to 2025, it was the age of scaling—maybe plus or minus, let’s add error bars to those years—because people say, “This is amazing. You’ve got to scale more. Keep scaling.” The one word: scaling.

> But now the scale is so big. Is the belief really, “Oh, it’s so big, but if you had 100x more, everything would be so different?” It would be different, for sure. But is the belief that if you just 100x the scale, everything would be transformed? I don’t think that’s true. So it’s back to the age of research again, just with big computers.

Would it?

We’d close one of the few remaining social elevators, displace higher educated people by the millions and accumulate even more wealth at the top of the chain.

If LLMs manage similar results to engineers and everyone gets free unlimited engineering, we’re in for the mother of all crashes.

On the other hand, if LLMs don’t succeed we’re in for a bubble bust.