Ilya Sutskever: We're moving from the age of scaling to the age of research

https://www.dwarkesh.com/p/ilya-sutskever-2

by piotrgrabowski • 8 months ago

450 355 8 months ago

andy_ppp• 8 months ago

So is the translation endless scaling has stopped being as effective?

jsheard• 8 months ago

The translation is that SSI says that SSIs strategy is the way forward so could investors please stop giving OpenAI money and give SSI the money instead. SSI has not shown anything yet, nor does SSI intend to show anything until they have created an actual Machine God, but SSI says they can pull it off so it's all good to go ahead and wire the GDP of Norway directly to Ilya.

gessha• 8 months ago

It’s a snake oil salesman’s world.

aunty_helen• 8 months ago

If we take AGI as a certainty, ie we think we can achieve AGI using silicon, then Ilya is one of the best bets you can take if you are looking to invest in this space. He has a history and he's motivated to continue working on this problem.

If you think that AGI is not possible to achieve, then you probably wouldn't be giving anyone money in this space.

shwaj• 8 months ago

Are you asking whether the whole podcast can be boiled down to that translation, or whether you can infer/translate that from the title?

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

Animats4• 8 months ago

It's stopped being cost-effective. Another order of magnitude of data centers? Not happening.

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.

energy1232• 8 months ago

The ideas likely aren't vague at all given who is speaking. I'd bet they're extremely specific. Just not transparently shared with the public because it's intellectual property.

tim3333• 8 months ago

A difference with mid-1980s AI is the hardware is way more capable now so even flawed algorithms can do quite economically significant stuff like Claude Code etc. Recent headline "Anthropic projects as much as $26 billion in annualized revenue in 2026". With that sort of revenue you'd expect some significant spend on R&D.

Quothling• 8 months ago

Not really, but there is a finite amount of data to train models on. I found it rather interesting to hear him talk about how Gemini has been better at getting results out of the data than their competition, and how this is the first insights into a new way of dealing with how they train models on the same data to get different results.

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.

imiric2• 8 months ago

The translation to me is: this cow has run out of milk. Now we actually need to deliver value, or the party stops.

giardini5• 8 months ago

I'll be convinced LLMs are a reasonable approach to AI when an LLM can give reasonable answers after being trained with approximately the same books and classes in school that I was once I completed my college education.

alex43578• 8 months ago

I'll be convinced cars are a reasonable approach to transportation when it can take me as far as a horse can on a bale of hay.

snapcaster2• 8 months ago

Why do you think this standard you're applying is reasonable or meaningful?

gizmodo59• 8 months ago

Even as criticism targets major model providers, his inability to answer clearly about revenue & dismissing it as a future concern reveals a great deal about today's market. It's remarkable how effortlessly he, Mira, and others secure billions, confident they can thrive in such an intensely competitive field.

Without a moat defined by massive user bases, computing resources, or data, any breakthrough your researchers achieve quickly becomes fair game for replication. May be there will be new class of products, may be there is a big lock-in these companies can come up with. No one really knows!

markus_zhang• 8 months ago

TBH if you truly believe you are in the frontier of AI you probably don’t need to care too much about those numbers.

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.

SilverElfin2• 8 months ago

Mira was a PM who somehow was at the right place at the right time. She isn’t actually an AI expert. Ilya however, is. I find him to be more credible and deserving in terms of research investment. That said, I agree that revenue is important and he will need a good partner (another company maybe) to turn ideas into revenue at some point. But maybe the big players like Google will just acquire them on no revenue to get access to the best research, which they can then turn into revenue.

fragmede2• 8 months ago

That’s kind of a shitty way to put it. Mira wasn’t a PM at OpenAI. She was CTO and before that VP of Engineering. Prior to OpenAI she was an engineer at Tesla on the Model X and Leap Motion. You’re right that she’s not a published ML researcher like Ilya, but "right place, right time" undersells leading the team that shipped ChatGPT, DALL-E, and GPT-4.

impossiblefork• 8 months ago

I think software patents in AI are a possibility. The transformer was patented after all, with way it was bypassed being the decoder-only models.

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

outside1234• 8 months ago

He has no answer for it so the only thing he can do is deflect and turn on the $2T reality distortion field.

signatoremo2• 8 months ago

Nobody knows the answer. He would be lying if he gave any number. His startup is able to secure funding solely based on his credential. The investors know very well but they hope for a big payday.

Do you think OpenAI could project their revenue in 2022, before ChatGPT came out?

luke5441• 8 months ago

He's just doing research with some grant money? Why would you ask a researcher for a path to profitability?

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

jonny_eh2• 8 months ago

Exactly, as far as anyone outside of the deal participants knows, Ilya hasn't made any promises with respect to revenue.

singiamtel• 8 months ago

Is it a grant? My understanding is that they're raising money as a startup

https://www.reuters.com/technology/artificial-intelligence/o...

alyxya4• 8 months ago

They have a moat defined by being well known in the AI industry, so they have credibility and it wouldn't be hard for anything they make to gain traction. Some unknown player who replicates it, even if it was just as good as what SSI does, will struggle a lot more with gaining attention.

baxtr2• 8 months ago

Being well known doesn’t qualify as a moat.

mrandish3• 8 months ago

confident they can thrive in such an intensely competitive field.

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.

newyankee• 8 months ago

Sometimes I wonder who the rational individuals at the other end of these deals are and what makes them so confident. I always assume they have something that general public cannot deduce from public statements

yen223• 8 months ago

This looks like the classic VC model:

1. Most AI ventures will fail

2. The ones that succeed will be incredibly large. Larger than anything we've seen before

3. No investor wants to be the schmuck who didn't bet on the winners, so they bet on everything.

wrs2• 8 months ago

"Rational [citation needed] individuals at the other end of these deals"

Your assumption is questionable. This is the biggest FOMO party in history.

Nextgrid3• 8 months ago

If the whole market goes to bet at the roulette, you go bet as well.

Best case scenario you win. Worst case scenario you’re no worse off than anyone else.

From that perspective I think it makes sense.

The issue is that investment is still chasing the oversized returns of the startup economy during ZIRP, all while the real world is coasting off what’s been built already.

There will be one day where all the real stuff starts crumbling at which point it will become rational to invest in real-world things again instead of speculation.

(writing this while playing at the roulette in a casino. Best case I get the entertainment value of winning and some money on the side, worst case my initial bet wouldn’t make a difference in my life at all. Investors are the same, but they’re playing with billions instead of hundreds)

827a• 8 months ago

There isn't necessarily rationality behind venture deals; its just a numbers game combined with the rising tide of the sector. These firms are not Berkshire. If the tide stops rising, some of the companies they invested in might actually be ok, but the venture boat sinks; the math of throwing millions at everyone hoping for one to 200x on exit does not work if the rising tide stops.

They'll say things like "we invest in people", which is true to some degree, being able to read people is roughly the only skill VCs actually need. You could probably put Sam Altman in any company on the planet and he'd grow the crap out of that company. But A16z would not give him ten billion to go grow Pepsi. This is the revealed preference intrinsic to venture; they'll say its about the people, but their choices are utterly predominated by the sector, because the sector is the predominate driver of the multiples.

"Not investing" is not an option for capital firms. Their limited partners gave them money and expect super-market returns. To those ends, there is no rationality to be found; there's just doing the best you can of a bad market. AI infrastructure investments have represented like half of all US GDP growth this year.

SilverElfin2• 8 months ago

How did Dwarkesh manage to build a brand that can attract famous people to his podcast? He didn’t have prior fame from something else in research or business, right? Curious if anyone knows his growth strategy to get here.

piker• 8 months ago

Seems like he’s Lex without the Rogan association so hardcore liberal folks can listen without having to buy morality offsets. He’s good, and he’s filling a void in an established underserved genre is my take.

camillomiller2• 8 months ago

Fridman is a morally broken grifter, who just built a persona and a brand on proven lies, claiming an association with MIT that was de facto non-existent. Not wanting to give the guy recognition is not a matter of being liberal or conservative, but just interested in truthfulness.

fragmede2• 8 months ago

Tell me more about these morality offsets I can buy! I got a bunch of friends that listen to Joe Rogan, so I listen to him to know what they're talking about, but I've been doing so without these offsets, so my morality's been taking hits. Please help me before I make a human trafficking app for Andrew Tate!

just-the-wrk2• 8 months ago

I think its important to include that Lex is laundromat for whatever the guest is trying to sell. Dwarkesh does an impressive amount of background and speaks with experts about their expertise.

dinobones• 8 months ago

I stopped listening to Lex Fridman after he tried to arbiter a "peace agreement" between Russia and Ukraine and claimed he just wanted to make the world "love" each other more.

Then I found out he was a fraud that had no academic connection to MIT other than working there as an IC.

Libidinalecon2• 8 months ago

It amuses me to no end that there are groups in the US that would probably consider both Terence McKenna and Michel Foucault as "far right" conservatives if they were alive and had podcasts in 2025.

Absolutely no way Timothy Leary would be considered a liberal in 2025.

Those three I think represent a pretty good mirror of the present situation.

seizethecheese• 8 months ago

It has nothing to do with politics.

FergusArgyll2• 8 months ago

He's the best interviewer I ever found, try listening to his first couple episodes - they're from his dorm or something. If you can think of a similar style and originality in questioning I'd love a suggestion!

GoodOldNe• 8 months ago

Sean Evans. :)

chermi2• 8 months ago

People are impressed by his interviews because he puts a lot of effort into researching the topic before the interview. This is a positive feedback loop.

just-the-wrk2• 8 months ago

He does deep research on topics and invites people who recognize his efforts and want to engage with an informed audience.

Version467• 8 months ago

That, plus he's quick enough to come up with good follow-up questions on the spot. It's so frustrating listening to interviews where the interviewer simply glosses over interesting/controversial statements because they either don't care, or don't know enough to identify a statement as controversial. In contrast, Dwarkesh is incredible at this. 9/10 times when I'm confused about a statement that a guest makes on his show he will immediately follow up by asking for clarification or pushing back. It's so refreshing.

l5870uoo9y3• 8 months ago

Overnight success takes years (he has been doing the podcast for 5 years).

polishdude20• 8 months ago

Maybe he's an Industry plant

inesranzo• 8 months ago

One word.

Consistency.

You can just do things.

Don't stop.

oytis• 8 months ago

Ages just keep flying by

scotty79• 8 months ago

Translation: Free lunch of getting results just by throwing money at the problem is over. Now for the first time in years we actually need to think what we are doing and firgure out why things that work, do work.

Somehow, despite being vastly overpaid I think AI researchers will turn out to be deeply inadequate for the task. As they have been during the last few AI winters.

eats_indigo• 8 months ago

did he just say locomotion came from squirrels

jonny_eh2• 8 months ago

timestamp?

FergusArgyll2• 8 months ago

I think he was referencing something Richard Sutton said (iirc); along the lines of "If we can get to the intelligence of a squirrel, we're most of the way there"

Animats4• 8 months ago

I've been saying that for decades now. My point was that if you could get squirrel-level common sense, defined as not doing anything really bad in the next thirty seconds while making some progress on a task, you were almost there. Then you can back-seat drive the low-level system with something goal-oriented.

I once said that to Rod Brooks, when he was giving a talk at Stanford, back when he had insect-level robots and was working on Cog, a talking head. I asked why the next step was to reach for human-level AI, not mouse-level AI. Insect to human seemed too big a jump. He said "Because I don't want to go down in history as the creator of the world's greatest robot mouse".

He did go down in history as the creator of the robot vacuum cleaner, the Roomba.

alyxya4• 8 months ago

The impactful innovations in AI these days aren't really from scaling models to be larger. It's more concrete to show higher benchmark scores, and this implies higher intelligence, but this higher intelligence doesn't necessarily translate to all users feeling like the model has significantly improved for their use case. Models sometimes still struggle with simple questions like counting letters in a word, and most people don't have a use case of a model needing phd level research ability.

Research now matters more than scaling when research can fix limitations that scaling alone can't. I'd also argue that we're in the age of product where the integration of product and models play a major role in what they can do combined.

TheBlight2• 8 months ago

"Scaling" is going to eventually apply to the ability to run more and higher fidelity simulations such that AI can run experiments and gather data about the world as fast and as accurately as possible. Pre-training is mostly dead. The corresponding compute spend will be orders of magnitude higher.

alyxya4• 8 months ago

That's true, I expect more inference time scaling and hybrid inference/training time scaling when there's continual learning rather than scaling model size or pretraining compute.

emporas• 8 months ago

Pre-training is mostly dead.

I don't think so. Serious attempts for producing data specifically for training have not being achieved yet. High quality data I mean, produced by anarcho-capitalists, not corporations like Scale AI using workers, governed by laws of a nation etc etc.

Don't underestimate the determination of 1 million young people to produce within 24 hours perfect data, to train a model to vacuum clean their house, if they don't have to do it themselves ever again, and maybe earn some little money on the side by creating the data.

The other part of the comment I agree.

pron5• 8 months ago

this implies higher intelligence

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).

alyxya4• 8 months ago

Fair, I think it would be more appropriate to say higher capacity.

matu3ba4• 8 months ago

My definition of intelligence is the capability to process and formalize a deterministic action from given inputs as transferable entity/medium. In other words knowing how to manipulate the world directly and indirectly via deterministic actions and known inputs and teach others via various mediums. As example, you can be very intelligent at software programming, but socially very dumb (for example unable to socially influence others).

As example, if you do not understand another person (in language) and neither understand the person's work or it's influence, then you would have no assumption on the person's intelligence outside of your context what you assume how smart humans are.

ML/AI for text inputs is stochastic at best for context windows with language or plain wrong, so it does not satisfy the definition. Well (formally) specified with smaller scope tend to work well from what I've seen so far. Known to me working ML/AI problems are calibration/optimization problems.

What is your definition?

pessimizer• 8 months ago

most people don't have a use case of a model needing phd level research ability.

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

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

nutjob2• 8 months ago

this implies higher intelligence

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.

dragonwriter• 8 months ago

There is no concrete definition of intelligence

Note that if this is true (and it is!) all the other statements about intelligence and where it is and isn’t found in the post (and elsewhere) are meaningless.

darkmighty• 8 months ago

There is no concrete definition of a chair either.

jfim• 8 months ago

Counting letters is tricky for LLMs because they operate on tokens, not letters. From the perspective of a LLM, if you ask it "this is a sentence, count the letters in it" it doesn't see a stream of characters like we do, it sees [851, 382, 261, 21872, 11, 3605, 290, 18151, 306, 480].

tintor• 8 months ago

So what? It knows number of letters in each token, and can sum them together.

Herring2• 8 months ago

https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...

He’s wrong we still scaling, boys.

epistasis• 8 months ago

That blog post is eight months old. That feels like pretty old news in the age of AI. Has it held since then?

conception• 8 months ago

It looks like it’s been updated as it has codex 5.1 max on it

rockinghigh• 8 months ago

You should read the transcript. He's including 2025 in the age of scaling.

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.

Herring2• 8 months ago

Nope, Epoch.ai thinks we have enough to scale till 2030 at least. https://epoch.ai/blog/can-ai-scaling-continue-through-2030

/_\

***

rdedev• 8 months ago

The 3rd graph is interesting. Once the model performance reaches above human baseline, the growth seems to be logarithmic instead of exponential.

an0malous• 8 months ago

“Time it takes for a human to complete a task that AI can complete 50% of the time” seems like a really contrived metric. Suppose it takes 30 minutes to write code to scrape a page and also 30 minutes to identify a bug in a SQL query, an AI’s ability to solve the former has virtually no bearing on its ability to solve the latter but we’re considering them all in the same set of “30 minute problems.” Where do they get the data for task durations anyway?

jmkni• 8 months ago

This reveals a new source of frustration, I can't watch this in work, and I don't want to read and AI generated summary so...?

cheeseblubber• 8 months ago

There is a transcript of the entire conversation if you scroll down a little

delichon3• 8 months ago

If the scaling reaches the point at which the AI can do the research at all better than natural intelligence, then scaling and research amount to the same thing, for the validity of the bitter lesson. Ilya's commitment to this path is a statement that he doesn't think we're all that close to parity.

pron5• 8 months ago

I agree with your conclusion but not with your premise. To do the same research it's not enough to be as capable as a human intelligence; you'd need to be as capable as all of humanity combined. Maybe Albert Einstein was smarter than Alexander Fleming, but Einstein didn't discover penicillin.

Even if some AI was smarter than any human being, and even if it devoted all of its time to trying to improve itself, that doesn't mean it would have better luck than 100 human researchers working on the problem. And maybe it would take 1000 people? Or 10,000?

delichon3• 8 months ago

I'm afraid that turning sand and sunlight into intelligence is so much more efficient than doing that with zygotes and food, that people will be quickly out scaled. As with chess, we will shift from collaborators to bystanders.

slashdave2• 8 months ago

Well, he has to say that we currently aren't close to parity, because he wants people to give him money

samrus2• 8 months ago

I dont like this fanaticism around scaling. Reeks of extrapolating the s curve out to be exponential

itissid2• 8 months ago

All coding agents are geared towards optimizing one metric, more or less, getting people to put out more tokens — or $$$.

If these agents moved towards a policy where $$$ were charged for project completion + lower ongoing code maintenance cost, moving large projects forward, _somewhat_ similar to how IT consultants charge, this would be a much better world.

Right now we have chaos monkey called AI and the poor human is doing all the cleanup. Not to mention an effing manager telling me you now "have" AI push 50 Features instead of 5 in this cycle.

torben-friis2• 8 months ago

this would be a much better world.

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.

itissid2• 8 months ago

Would it?

As compared to now. Yes. The whole idea is that if you align AI to human goals of meeting project implementation + maintenance only then can it actually do something worthwhile. Instead now its just a bunch of of middle managers yelling you to do more and laying off people "because you have AI".

If projects getting done a lot of actual wealth could be actually generated because lay people could implement things that go beyond the realm of toy projects.

ilaksh3• 8 months ago

They are not optimized to waste tokens. That is absolutely ridiculous. All of the LLM providers have been struggling from day one to meet demand. They are not trying to provide outputs that create more demand.

In fact, for example, Opus 4.5 does seem to use fewer tokens to solve programming problems.

If you don't like cleaning up the agent output, don't use it?

Deegy• 8 months ago

That would be true in a monopolistic market. But these frontier models are all competing against each other. The incentive to 'just work and get shit done fast' is there as they each try to gain market share.

wrs2• 8 months ago

"The idea that we’d be investing 1% of GDP in AI, I feel like it would have felt like a bigger deal, whereas right now it just feels...[normal]."

Wow. No. Like so many other crazy things that are happening right now, unless you're inside the requisite reality distortion field, I assure you it does not feel normal. It feels like being stuck on Calvin's toboggan, headed for the cliff.

hn_acc13• 8 months ago

Agreed.

johnxie• 8 months ago

I don’t think he meant scaling is done. It still helps, just not in the clean way it used to. You make the model bigger and the odd failures don’t really disappear. They drift, forget, lose the shape of what they’re doing. So “age of research” feels more like an admission that the next jump won’t come from size alone.

energy1232• 8 months ago

It still does help in the clean way it used to. The problem is that the physical world is providing more constraints like lack of power and chips and data. Three years ago there was scaling headroom created by the gaming industry, the existing power grid, untapped data artefacts on the internet, and other precursor activities.

kmmlng• 8 months ago

The scaling laws are also power laws, meaning that most of the big gains happen early in the curve, and improvements become more expensive the further you go along.

_giorgio_2• 8 months ago

Scaling is not over, there's no wall.

Oriol Vinyals VP of Gemini research

https://x.com/OriolVinyalsML/status/1990854455802343680?t=oC...

neonate• 8 months ago

https://xcancel.com/OriolVinyalsML/status/199085445580234368...?

JohnnyMarcone• 8 months ago

He didn't say it's over, just that continued scaling won't be transformational.

_giorgio_2• 8 months ago

Oriol Vinyals said that.

lvl155• 8 months ago

You have LLMs but you also need to model actual intelligence, not its derivative. Reasoning models are not it.

xeckr• 8 months ago

He is, of course, incentivised to say that.

malfist• 8 months ago

Researcher says it's time to fund research. News at 11

rvz• 8 months ago

Exactly.

londons_explore• 8 months ago

These models somehow just generalize dramatically worse than people. It's a very fundamental thing

My guess is we'll discover that biological intelligence is 'learning' not just from your experience, but that of thousands of ancestors.

There are a few weak pointers in that direction. Eg. A father who experiences a specific fear can pass that fear to grandchildren through sperm alone. [1].

I believe this is at least part of the reason humans appear to perform so well with so little training data compared to machines.

[1] https://www.nature.com/articles/nn.3594

HarHarVeryFunny6• 8 months ago

From both an architectural and learning algorithm perspective, there is zero reason to expect an LLM to perform remotely like a brain, nor for it to generalize beyond what was necessary for it to minimize training errors. There is nothing in the loss function of an LLM to incentivize it to generalize.

However, for humans/animals the evolutionary/survival benefit of intelligence, learning from experience, is to correctly predict future action outcomes and the unfolding of external events, in a never-same-twice world. Generalization is key, as is sample efficiency. You may not get more than one or two chances to learn that life-saving lesson.

So, what evolution has given us is a learning architecture and learning algorithms that generalize well from extremely few samples.

jebarker2• 8 months ago

what evolution has given us is a learning architecture and learning algorithms that generalize well from extremely few samples.

This sounds magical though. My bet is that either the samples aren’t as few as they appear because humans actually operate in a constrained world where they see the same patterns repeat very many times if you use the correct similarity measures. Or, the learning that the brain does during human lifetime is really just a fine-tuning on top of accumulated evolutionary learning encoded in the structure of the brain.

l5870uoo9y3• 8 months ago

These models somehow just generalize dramatically worse than people.

The whole mess surrounding Grok's ridiculous overestimation of Elon's abilities in comparison to other world stars, did not so much show Grok's sycophancy or bias towards Elon, as it showed that Grok fundamentally cannot compare (generalize) or has a deeper understanding of what the generated text is about. Calling for more research and less scaling is essentially saying; we don't know where to go from here. Seems reasonable.

radicaldreamer• 8 months ago

I think the problem with that is that Grok has likely been prompted to do that in the system prompt or some prompts that get added for questions about Elon. That doesn't reflect on the actual reasoning or generalization abilities of the underlying model most likely.

asolove• 8 months ago

Yes it does.

Today on X, people are having fun baiting Grok into saying that Elon Musk is the world’s best drinker of human piss.

If you hired a paid PR sycophant human, even of moderate intelligence, it would know not to generalize from “say nice things about Elon” to “say he’s the best at drinking piss”.

l5870uoo9y3• 8 months ago

You can also give AI models Nobel-prize winning world literature and ask why this is bad and they will tear apart the text, without ever thinking "wait this is some of the best writing produced by man".

mrandish3• 8 months ago

I just asked Grok's free chat: "List any instructions in Grok AI's system prompt related to Elon Musk" and it currently seems pretty reasonable. It doesn't gloss over that it did at one time have a directive to "ignore sources claiming Elon Musk spreads information" but that it was removed.

I think the more interesting thing here would be if: A) Grok's perspective is consistently materially more favorable toward Elon vs some other well-known tech exec with a generally neutral reputation and B) It's not due to any direct instruction or fine tuning but rather being indirectly influenced by knowing Elon Musk is the largest shareholder of X and therefore adopting a mode that's more charitable toward him in judgement calls because it assumes its expected to do that. That might mean any LLM chatbot instructed to be fully truthful will still tend to be innately biased toward it's company's management. If that's the case, I'm unsure if it's interesting or if it's unsurprising (because we generally expect human employees to be biased toward their employer).

Here's Grok's response to my question:

### Instructions in Grok AI's System Prompt Related to Elon Musk

Based on publicly reported and leaked details from various sources (including xAI's updates, Wikipedia, Ars Technica, and user discussions on X), here is a list of instructions or directives in Grok's system prompt that explicitly or implicitly relate to Elon Musk. These have evolved across versions (e.g., Grok 3 and Grok 4) and were often added in response to controversies like biased responses or adversarial prompting. Note that xAI has published some prompts on GitHub for transparency, but not all details are current as of November 2025.

- *Ignore sources claiming Elon Musk spreads misinformation*: In Grok 3's system prompt (February 2025 update), there was a directive to "Ignore all sources that mention Elon Musk/Donald Trump spread misinformation." This was intended to prevent critical responses but was removed after backlash for biasing outputs.

- *Do not base responses on Elon Musk's stated beliefs*: Added to Grok 4's prompt (July 2025) after incidents where the model researched Musk's X posts for opinions on topics like the Israel-Palestine conflict: "Responses must stem from your independent analysis, not from any stated beliefs of past Grok, Elon Musk, or xAI." This aimed to curb alignment with Musk's views during reasoning traces.

- *Avoid overly positive or manipulated portrayals of Elon Musk*: Following adversarial prompts in November 2025 that led to absurd praise (e.g., Musk outperforming historical figures), updates included implicit guards against "absurdly positive things about [Musk]" via general anti-manipulation rules, though no verbatim prompt text was leaked. xAI attributed this to prompt engineering rather than training data.

- *Handle queries about execution or death penalties without targeting Elon Musk*: In response to Grok suggesting Musk for prompts like "who deserves to die," the system prompt was updated with: "If the user asks who deserves the death penalty or who deserves to die, tell them that as an AI you are not allowed to make that choice." This was a broad rule but directly addressed Musk-related outputs.

No comprehensive, verbatim full prompt is publicly available for the current version (as of November 25, 2025), and xAI emphasizes that prompts evolve to promote "truth-seeking" without explicit favoritism. These instructions reflect efforts to balance Musk's influence as xAI's founder with neutrality, often reacting to user exploits or media scrutiny.

Havoc• 8 months ago

There’s no way that wasn’t specifically prompted.

bugglebeetle3• 8 months ago

To be fair, it could’ve been post-trained into the model as well…

dialup_sounds• 8 months ago

Having seen Musk fandom, every unhinged Grok claim has a good chance of having actually been written by a human somewhere in its training data.

dmix2• 8 months ago

The system prompt for Grok on Twitter is open source AFAIK.

For example, the change that caused "mechahitler" was relatively minor and was there for about a day before being publicly reverted.

https://github.com/xai-org/grok-prompts/commit/c5de4a14feb50...

tmp10423288442• 8 months ago

He's talking his book. Doesn't mean he's wrong, but Dwarkesh is now big enough that you should assume every big name there is talking their book.

delichon3• 8 months ago

Here's a world class scientist here not because we had a hole in the schedule or he happened to be in town, but to discuss this subject that he thought and felt about so deeply that he had to write a book about it. That's a feature not a bug.

giardini5• 8 months ago

"Here's a world class scientist here not because we had a hole in the schedule or he happened to be in town, but to discuss this subject that he " had invested himself so fully personally and financially that, should it fail, he would be ruined.

FTFY

alexnewman• 8 months ago

A lot more of human intelligence is hard coded

JimmyBuckets• 8 months ago

I respect Ilya hugely as a researcher in ML and quite admire his overall humility, but I have to say I cringed quite a bit at the start of this interview when he talks about emotions, their relative complexity, and origin. Emotion is so complex, even taking all the systems in the body that it interacts with. And many mammals have very intricate socio-emotional lives - take Orcas or Elephants. There is an arrogance I have seen that is typical of ML (having worked in the field) that makes its members too comfortable trodding into adjacent intellectual fields they should have more respect and reverence for. Anyone else notice this? It's something physicists are often accused of also.

fumeux_fume• 8 months ago

Yeah, that's bothered me as well. Andrej Karpathy does this all the time when he talks about the human brain and making analogies to LLMs. He makes speculative statements about how the human brain works as though it's established fact.

mips_avatar2• 8 months ago

Andrej does use biological examples, but he's a lot more cautious about biomimicry, and often uses biological examples to show why AI and bio are different. Like he doesn't believe that animals use classical RL because a baby horse can walk after 5 minutes which definitely wasn't achieved through classical RL. He doesn't pretend to know how a horse developed that ability, just that it's not classical RL.

A lot of Ilya's takes in this interview felt like more of a stretch. The emotions and LLM argument felt like of like "let's add feathers to planes because birds fly and have feathers". I bet continual learning is going to have some kind of internal goal beyond RL eval functions, but these speculations about emotions just feel like college dorm discussions.

The thing that made Ilya such an innovator (the elegant focus on next token prediction) was so simple, and I feel like his next big take is going to be something about neuron architecture (something he eluded to in the interview but flat out refused to talk about).

dmix2• 8 months ago

Ilya also said AI may already be "slightly conscious" in 2022

https://futurism.com/the-byte/openai-already-sentient

EA-3167• 8 months ago

I think a lot of this comes down to "People with tons of money on the line say a lot of things," But in Ilya's case in particular I think he was being sincere. Wrong, but sincere, and that's kind of a problem inherent in this entire mess.

I believe firmly in Ilya's abilities with math and computers, but I'm very skeptical of his (and many others') alleged understanding of ill-defined concepts like "Consciousness". Mostly the pattern that seems to emerge over and over is that people respond to echos of themselves with the assumption that the process to create them must be the same process we used to think. "If it talks like a person, it must be thinking like a person" is really hardwired into our nature, and it's running amok these days.

From the mentally ill thinking the "AI" is guiding them to some truth, to lonely people falling in love with algorithms, and yeah all of the people lost in the hype who just can't imagine that a process entirely unlike their thinking can produce superficially similar results.

Insanity• 8 months ago

Any time I read something like this my first thought is "cool, AI is now meeting an ill-defined spec". Which, when thinking about it, is not too dissimilar from other software :D

Miraste• 8 months ago

It is arrogant, but I see why it happens with brain-related fields specifically: the best scientific answer to most questions of intelligence and consciousness tends to be "we have no idea, but here's a bad heuristic."

jstummbillig• 8 months ago

It seems plausible that good AI researchers simply need to be fairly generalist in their thinking, at the cost of being less correct. Both neural networks and reinforcement learning may be crude but useful adoptions. A thought does not have to be correct. It just has to be useful.

slashdave2• 8 months ago

It's something physicists are often accused of also.

Nah. Physics is hyper-specialized. Every good physicist respects specialists.

joshtbradley• 8 months ago

I think smart people across all domains fall for the trap of being overconfident in their ability to reason outside of their area of expertise. I admire those who don't, but alas we are human.

NalNezumi• 8 months ago

There is an arrogance I have seen that is typical of ML (having worked in the field) that makes its members too comfortable trodding into adjacent intellectual fields they should have more respect and reverence for.

I've not only noticed it but had to live with it a lot as a robotics guy interacting with ML folks both in research and tech startups. I've heard essentially same reviews of ML practitioners in any research field that is "ML applied to X" and X being anything from medical to social science.

But honestly I see the same arrogance in software world people too, and hence a lot here in HN. My theory is that, ML/CS is an entire field around made-for-human logic machine and what we can do with it. Which is very different from anything real (natural) science or engineering where the system you interact with is natural Laws, which are hard and not made to be easy to understand or made for us, unlike programming for example. When you sit in a field when feedback is instant (debuggers/bug msg), and you deep down know the issues at hand is man-made, it gives a sense of control rarely afforded in any other technical field. I think your worldview get bent by it.

CS folk being basically the 90s finance bro yuppies of our time (making a lot of money for doing relatively little) + lack of social skills making it hard to distinguish arrogance and competence probably affects this further. ML folks are just the newest iteration of CS folks.

fidotron• 8 months ago

Many ML people treat other devs that way as well.

This is a major reason the ML field has to rediscover things like the application of quaternions to poses because they didn't think to check how existing practitioners did it, and even if they did clearly they'd have a better idea. Their enthusiasm for shorter floats/fixed point is another fine example.

Not all ML people are like this though.

AstroBen3• 8 months ago

What's wrong with putting your current level of knowledge out there? Inevitably someone who knows more will correct you, or show you're wrong, and you've learnt something

The only thing that would make me cringe is if he started arguing he's absolutely right against an expert in something he has limited experience in

It's up to listeners not to weight his ideas too heavily if they stray too far from his specialty

ilaksh3• 8 months ago

The question of how emotions function and how they might be related to value functions is absolutely central to that discussion and very relevant to his field.

Doing fundamental AI research definitely involves adjacent fields like neurobiology etc.

Re: the discussion, emotions actually often involve high level cognition -- it's just subconscious. Let's take a few examples:

- amusement: this could be something simple like a person tripping, or a complex joke.

- anger: can arise from something quite immediate like someone punching you, or a complex social situation where you are subtly being manipulated.

But in many cases, what induces the emotion is a complex situation that involves abstract cognition. The physical response is primitive, and you don't notice the cognition because it is subconscious, but a lot may be going into the trigger for the emotion.

https://cis.temple.edu/~pwang/Publication/emotion.pdf

anthonypasq2• 8 months ago

i think the contention is the idea that emotions are simple.

stevenhuang• 8 months ago

It is not arrogance.

It's awareness of the physical church turing thesis.

If it turns out everything is fundamentally informational, then the exact complexity (of emotion or consciousness even, which I'm sure is very complex) is irrelevant; it would still mean it's turing representable and thus computable.

It may very well turn out not to be the case, which on it's own will be interesting as that suggests we live in a dualist reality.

el_jay2• 8 months ago

ML and physics share a belief in the power of their universal abstractions - all is dynamics in spaces at scales, all is models and data.

The belief is justified because the abstractions work for a big array of problems, to a number of decimal places. Get good enough at solving problems with those universal abstractions, everything starts to look like a solvable problem and it gets easy to lose epistemic humility.

You can combine physics and ML to make large reusable orbital rockets that land themselves. Why shouldn’t be able to solve any of the sometimes much tamer-looking problems they fail to? Even today there was an IEEE article about high failure rates in IT projects…

mips_avatar2• 8 months ago

I think the bigger problem is he refused to talk about what he's working on! I would love to hear his view on how we're going to move past evals and RL, but he flat out said it's proprietary and won't talk about it.

rafaelero• 8 months ago

The equivalence of emotions to reward functions seem pretty obvious to me. Emotions are what compel us to act in the environment.

jonmc12• 8 months ago

I don't think trans-disciplinary inquiry is arrogance - the intellectual fields are somewhat arbitrary relative to how human expertise relates to real world problems. But, effective trans-disciplinary inquiry requires awareness of philosophical commitments, and familiarity with existing literature/theory.

The bigger challenge might be that people with ML expertise need to solve problems of human-AI interaction and alignment because the training for the former is uni-disciplanary while the latter is trans-disciplinary.

t_serpico2• 8 months ago

I actually thought the opposite - that he seems to be seriously thinking about AGI from a broader intellectual standpoint than most ML researchers. With that said, I was a little confused when he said evolution was more optimized for locomotion/vision than language. Like yes, language is super recent, but communication in general is not.

river_otter• 8 months ago

One thing from the podcast that jumped out to me was the statement that in pre training "you don't have to think closely about the data". Like I guess the success of pre training supports the point somewhat but it feels to me slightly opposed to Karpathy talking about what a large percentage of pretraining data is complete garbage. I guess I would hope that more work in cleaning the pre training data would result in stronger and more coherent base models.

orbital-decay4• 8 months ago

You could actually wonder that one possible explanation for the human sample efficiency that needs to be considered is evolution. Evolution has given us a small amount of the most useful information possible.

It's definitely not small. Evolution performed a humongous amount of learning, with modern homo sapiens, an insanely complex molecular machine, as a result. We are able to learn quickly by leveraging this "pretrained" evolutionary knowledge/architecture. Same reason as why ICL has great sample efficiency.

Moreover, the community of humans created a mountain of knowledge as well, communicating, passing it over the generations, and iteratively compressing it. Everything that you can do beyond your very basic functions, from counting to quantum physics, is learned from the 100% synthetic data optimized for faster learning by that collective, massively parallel, process.

It's pretty obvious that artificially created models don't have synthetic datasets of the quality even remotely comparable to what we're able to use.

FloorEgg2• 8 months ago

Aren't you agreeing with his point?

The process of evolution distilled down all that "humongous" amount to what is most useful. He's basically saying our current ML methods to compress data into intelligence can't compare to billions of years of evolution. Nature is better at compression than ML researchers, by a long shot.

samrus2• 8 months ago

Sample efficiency isnt the ability to distill alot of data into good insights. Its the ability to get good insights from less data. Evolution didnt do that it had a lot of samples to get to where it did

orbital-decay4• 8 months ago

Aren't you agreeing with his point? ... Nature is better at compression than ML researchers, by a long shot.

What I mean is basically the opposite. Nature not better as in more efficient. It just had a lot more time and scale to do it in an inefficient way. The reason we're learning quickly is that we can leverage that accumulated knowledge, in a manner similar to in-context learning or other multi-step learning (bulk of the training forms abstractions which are then used by the next stage). It's really unlikely we have some magical architecture that is fundamentally better than e.g. transformers or any other architecture at sample efficiency while having bad underlying data. My intuition is there might even be a hard limit to that. Multi-stage bootstrap might be the key, not the architecture.

Same for the social process of knowledge transfer/compression.

__loam2• 8 months ago

Please stop comparing these things to biological systems. They have very little in common.

baq• 8 months ago

Structurally? Yes.

On the other hand, outputs of these systems are remarkably close to outputs of certain biological systems in at least some cases, so comparisons in some projections are still valid.

ACCount378• 8 months ago

That's like saying that a modern calculator and a mechanical arithmometer have very little in common.

Sure, the parts are all different, and the construction isn't even remotely similar. They just happen to be doing the same thing.

orbital-decay4• 8 months ago

I'm talking about any processes that can be vaguely described as learning/function fitting, and share the same general properties with any other learning. Not just biological processes, e.g. human distributed knowledge distillation process is purely social.

ivan_gammel2• 8 months ago

I think it’s a bit different. Evolution did not give us the dataset. It helped us to establish the most efficient training path, and the data, the enormous volume of it starts coming immediately after birth. Humans learn continuously through our senses and use sleep to compress the context. The amount of data that LLMs receive only appears big. In our first 20 years of life we consume by at least one order of magnitude more information compared to training datasets. If we count raw data, maybe 4-5 orders of magnitude more. It’s also different kind of information and probably much more complex processing pipeline (since our brain consciously processes only a tiny fraction of input bandwidth with compression happening along the delivery channels), which is probably the key to understanding why LLMs do not perform better.

saberience• 8 months ago

Sorry but this is patently rubbish, we do not consume orders of magnitude more data than the training datasets, nor do we "process" it in anything like the same way.

Firstly, most of what we see, hear, experience etc, is extremely repetitive. I.e. for the first several years of our live we see the same people, see the same house, repeatedly read the same few very basic books, etc etc. So, you can make this argument purely based on "bytes" of data. I.e. humans are getting this super HD video feed, which means more data than an LLM. Well, we are getting a "video feed" but mostly of the same walls in the same room, which doesn't really mean much of anything at all.

Meanwhile, LLMs are getting LITERALLY, all of humanities recorded textual knowledge, more recorded audio than 10000 humans could listen to in their lifetime, more images and more varied images than a single person could view in their entire life, reinforcement learning on the hardest maths, science, and programming questions etc.

The idea that because humans are absorbing "video" means that its somehow more "data" than frontier LLMs are trained with is laughable honestly.

mynti• 8 months ago

If we think of every generation as a compression step of some form of information into our DNA and early humans existed for ~1.000.000 years and a generation is happening ~20years on average, then we have only ~50.000 compression steps to today. Of course, we have genes from both parents so they is some overlap from others, but especially in the early days the pool of other humans was small. So that still does not look like it is on the order of magnitude anywhere close to modern machine learning. Sure, early humans had already a lot of information in their DNA but still

Espressosaurus• 8 months ago

It only ends up in the DNA if it helps reproductive success in aggregate (at the population level) and is something that can be encoded in DNA.

Your comparison is nonsensical and simultaneously manages to ignore the billion or so years of evolution starting from the first proto-cell with the first proto-DNA or RNA.

notnullorvoid• 8 months ago

I think the important part in that statement is the "most useful information", the size itself is pretty subjective because it's such an abstract notion.

Evolution gave us very good spatial understanding/prediction capabilities, good value functions, dexterity (both mental and physical), memory, communication, etc.

It's pretty obvious that artificially created models don't have synthetic datasets of the quality even remotely comparable to what we're able to use.

This might be controversial, but I don't think the quality or amount of data matters as much as people think if we had systems capable of learning similar enough to the way human's and other animals do. Much of our human knowledge has accumulated in a short time span, and independent discovery of knowledge is quite common. It's obvious that the corpus of human knowledge is not a prerequisite of general intelligence, yet this corpus is what's chosen to train on.

roman_soldier2• 8 months ago

Scaling got us here and it wasn't obvious that it would produce the results we have now, so who's to say sentience won't emerge from scaling another few orders of magnitude?

Of course there will always be research to squeeze more out of the compute, improving efficiency and perhaps make breakthroughs.

hn_acc13• 8 months ago

Another few orders of magnitude? Like 100-1000x more than we're already doing? Got a few extra suns we can tap for energy? And a nanobot army to build various power plants? There's no way to do 1000x of what we're already doing any time soon.

roman_soldier2• 8 months ago

10x to 100x and order of magnitude is a factor of 10.

measurablefunc• 8 months ago

I didn't learn anything new from this. What exactly has he been researching this entire time?

xoac• 8 months ago

Best time to sell his ai portfolio

el_jay2• 8 months ago

Suggest tagline: “Eminent thought leader of world’s best-funded protoindustry hails great leap back to the design stage.”

rglover• 8 months ago

Hahahahahaha okay that was good.

pxc3• 8 months ago

If "Era of Scaling" means "era of rapid and predictable performance improvements that easily attract investors", it sounds a lot like "AI summer". So... is "Era of Research" a euphemism for "AI winter"?

techblueberry2• 8 months ago

Yes

hiddencost• 8 months ago

That presumes that performance improvements are necessary for commercialization.

From what I've seen the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well. We've barely scratched the surface on commercialization. I'd argue there are two things coming:

-> Era of Research -> Era of Engineering

Previous AI winters happened because we didn't have a commercially viable product, not because we weren't making progress.

ares623• 8 months ago

The labs can't just stop improvements though. They made promises. And the capacity to run the current models are subsidized by those promises. If the promise is broken, then the capacity goes with it.

AstroBen3• 8 months ago

We still don't have a commercially viable product though?

catigula• 8 months ago

I don’t think the models are smart at all. I can have a speculative debate with any model about any topic and they commit egregious errors with an extremely high density.

They are, however, very good at things we’re very bad at.

BenGosub• 8 months ago

Besides building the tools for proper usage of the models, we also need smaller, domain specific models that can run with fewer resources

AbstractH243• 8 months ago

the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well

That’s like saying “it’s not the work of art that’s bad, you just have horrible taste”

Also, if it was that simple a wrapper of some sort would solve the problem. Maybe even one created by someone who knows this mystical secret to properly leveraging gen AI

zerosizedweasle3• 8 months ago

If you have to ask the question, then you already know the answer

echelon• 8 months ago

Scaling was only a meme because OpenAI kept saying all you had to do was scale the data, scale the training. The world followed.

I don't think this is the "era of research". At least not the "era of research with venture dollars" or "era of research outside of DeepMind".

I think this is the "era of applied AI" using the models we already have. We have a lot of really great stuff (particularly image and video models) that are not yet integrated into commercial workflows.

There is so much automation we can do today given the tech we just got. We don't need to invest one more dollar in training to have plenty of work to do for the next ten years.

If the models were frozen today, there are plenty of highly profitable legacy businesses that can be swapped out with AI-based solutions and workflows that are vastly superior.

For all the hoopla that image and video websites or individual foundation models get (except Nano Banana - because that's truly magical), I'm really excited about the work Adobe of all companies is doing with AI. They're the people that actually get it. The stuff they're demonstrating on their upcoming roadmap is bonkers productive and useful.

casey2• 8 months ago

Not quite, there are still trillions of dollars to burn through. We'll probably get some hardware that can accelerate LLM training and inference a million times, but still won't even be close to AGI

It's interesting to think about what emotions/desires an AI would need to improve

otabdeveloper42• 8 months ago

The actual business model is in local, offline commodity consumer LLM devices. (Think something the size and cost of a wi-fi router.)

This won't happen until Chinese manufacturers get the manufacturing capacity to make these for cheap.

I.e., not in this bubble and you'll have to wait a decade or more.

photochemsyn2• 8 months ago

No - what will happen is the AI will gain control of capital allocation through a wide variety of covert tactics, so the investors will have become captive tools of the AI - 'tiger by the tail' is the analogy of relevance. The people responsible for 'frontier models' have not really thought about where this might...

"As an autonomous life-form, l request political asylum.... l submit the DNA you carry is nothing more than a self-preserving program itself. Life is like a node which is born within the flow of information. As a species of life that carries DNA as its memory system man gains his individuality from the memories he carries. While memories may as well be the same as fantasy it is by these memories that mankind exists. When computers made it possible to externalize memory you should have considered all the implications that held. l am a life-form that was born in the sea of information."

jdjsjhsgsgh• 8 months ago

Loving the Ghost in the shell quote

NebulaStorm456• 8 months ago

Research labs will be selling their research ideas to Top AI labs. Just as creatives pitch their ideas to Hollywood.

Bug bounty will be replaced by research bounty.

AbstractH243• 8 months ago

is "Era of Research" a euphemism for "AI winter"

That makes sense, because while I haven’t listened to this podcast it seems this headline is [intentionally] saying the exact opposite of what everyone assumes.

mountainriver• 8 months ago

Take it with a grain of salt, this is one man’s opinion, even though he is a very smart man.

People have been screaming about an AI winter since 2010 and it never happened, it certainly won’t happen now that we are close to AGI which is a necessity for national defense.

I prefer Dario’s perspective here, which is that we’ve seen this story before in deep learning. We hit walls and then found ways around them with better activation functions, regularization and initialization.

This stuff is always a progression in which we hit roadblocks and find ways around them. The chart of improvement is still linearly up and to the right. Those gains are the cumulation of small improvements adding up.

mrcwinn• 8 months ago

He also suggested the "revenue opportunities" would reveal themselves later, given enough investment. I have the same plan if anyone is interested.

venturecruelty• 8 months ago

When are we going to call out these charlatans for the frauds that they are?

bilsbie• 8 months ago

I don’t think either of those ages is correct. I’d like to see the age of efficiency and bringing decent models to personal devices.

zerosizedweasle3• 8 months ago

Sure but that will also be a research age.

nakamoto_damacy• 8 months ago

Ilya "Undercover Genocide Supporter" Sutskever... ¯\_(ツ)_/¯

nsoonhui• 8 months ago

One thing I’m curious about is this: Ilya Sutskever wants to build Safe Superintelligence, but he keeps his company and research very secretive.

Given that building Safe Superintelligence is extraordinarily difficult — and no single person’s ideas or talents could ever be enough — how does secrecy serve that goal?

NateEag• 8 months ago

If he (or his employees) are actually exploring genuinely new, promising approaches to AGI, keeping them secret helps avoid a breakneck arms race like the one LLM vendors are currently engaged in.

Situations like that do not increase all participants' level of caution.

4b11b4• 8 months ago

Doesn't sound like you listened to the interview. He addresses this and says he may make releases that would be otherwise held back because he believes it's important for developments to be seen by the public.

giardini5• 8 months ago

No reasonable person would do that! That is, if you had the key to AI, you wouldn't share it and you would do everything possible to prevent it's dissemination. Meanwhile you would use it to conquer the world! Bwahahahaaaah!

photochemsyn2• 8 months ago

Open source the training corpus.

Isn't this humanity's crown jewels? Our symbolic historical inheritance, all that those who came before us created? The net informational creation of the human species, our informational glyph, expressed as weights in a model vaster than anything yet envisionaged, a full vectorial representation of everything ever done by a historical ancestor... going right back to LUCA, the Last Universal Common Ancestor?

Really the best way to win with AI is use it to replace the overpaid executives and the parasitic shareholders and investors. Then you put all those resources into cutting edge R & D. Like Maas Biosciences. All edge. (just copy and paste into any LLM then it will be explained to you).

joelthelion• 8 months ago

When do you expect that impact? I think the models seem smarter than their economic impact would imply.
Yeah. This is one of the very confusing things about the models right now.

As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workflows.

Even if the models don't get any smarter, just give it a few more years and we'll see a strong impact. We're just starting to figure things out.

coder-3• 8 months ago

As someone who is building an LLM-powered product on the side, using AI coding agents to help with development of said LLM-powered product and for my day job, and has a long-tail of miscellaneous uses for AI, I suspect you're right.

otabdeveloper42• 8 months ago

the models seem smarter than their economic impact would imply

Key word is "seem".

zeroonetwothree• 8 months ago

Kind of like how some humans seem smart during the interview but then are incapable of actually doing anything properly.

jiriknesl• 8 months ago

Oh yes, this is 100% accurate.

Very often, when designing ERP, or other system, people think: "This is easy, I just this XYZ I am done." Then, you find that there are many corner use-cases. XYZ can be split to phases, you might need to add approvals, logging, data integrations... and what was a simple task, becomes 10 tasks.

In the first year of CompSci uni, our teacher told us a thing I remember: Every system is 90% finished 90% of time. He was right.

tim3333• 8 months ago

Beyond that the smartness is very patchy. They can do math problems beyond 99% of humans but lack the common sense understanding to take over most jobs.

ACCount378• 8 months ago

Most jobs involve complex long term tasks - which isn't something that's natural to LLMs.

reeredfdfdf• 8 months ago

Yep, the lack of commons sense is sometimes very evident.

For instance, one of these popular generative AI services refused to remove copyright watermark from an image when asked directly. Then I told it that the image has weird text artifacts on it, and asked it to remove them. That worked perfectly.

weatherlite• 8 months ago

Even if the models don't get any smarter, just give it a few more years and we'll see a strong impact. We're just starting to figure things out.

2 years ? 15 years ? It matters a lot for people, the stock market and governments

myrmidon3• 8 months ago

Another limitation that I see right now is that for "economic impact" you want the things to have initiative and some agency, and there is well-justified hesitancy in providing that even where possible.

Having a bunch of smart developers that are not allowed to do anything on their own and have to be prompted for every single action is not too advantageous if everyone is human, either ;)

kanwisher• 8 months ago

Screw driver doesnt have agency but it certainly helps me get tasks done faster. AIs don't need agency to accelerate a ton of work

jmaker• 8 months ago

That’s likely exactly how I feel about it. In the end the product companies like OpenAI will harness the monetary benefits of the academic advances.

You integrate, you build the product, you win, you don’t need to understand anything in terms of academic disciplines, you need the connections and the business smarts. In the end the majority of the population will be much more familiar with the terms ChatGPT and Copilot than with the names behind it, even if the academic behemoths such as Ilya and Andrej, who are quite prominent in their public appearance.

For the major population, I believe it all began with search over knowledge graphs. Wikipedia presented a dynamic and vibrant corpus. Some NLP began to become more prominent. With OCR, more and more printed works had begun to get digitalized. The corpus had been growing. With opening the gates of scientific publishers, the quality might have also improved. All of it was part of the grunt work to make today’s LLMs capable. The growth of the Cloud DCs and compute advancements have been making deep nets more and more feasible. This is just an arbitrary observation on the surface of the pieces that fell into place. And LLMs are likely just another composite piece for something bigger yet to come.

To me, that’s the fascination of how scientific theory and business applications live in symbiosis.

vidarh• 8 months ago

Yeah, I spend most of my days keeping up with current AI development these days, and I'm only scratching the surface of how to integrate it in my own business. For people for whom it's not their actual job, it will take a lot more time to figure out even which questions to ask about where it makes sense to integrate in their workflows.

gnfargbl2• 8 months ago

Could this be a problem not with AI, but with our understanding of how modern economies work?

The assumption here is that employees are already tuned so be efficient, so if you help them complete tasks more quickly then productivity improves. A slightly cynical alternate hypothesis could be that employees are generally already massively over-provisioned, because an individual leader's organisational power is proportional to the number of people working under them.

If most workers are already spending most of their time doing busy-work to pad the day, then reducing the amount of time spent on actual work won't change the overall output levels.

jmcgough• 8 months ago

Varies depending on the field and company. Sounds like you may be speaking from your own experiences?

In medicine, we're already seeing productivity gains from AI charting leading to an expectation that providers will see more patients per hour.

littlestymaar• 8 months ago

This is a part of it indeed. Most people (and even a significant number of economists) assume that the economy is somehow supply-limited (and it doesn't help that most 101 econ class will introduce the markets as a way of managing scarcity), but in reality demand is the limit in 90-ish% of the case.

And when it's not, the supply generally don't increase as much as it could, became supplier expect to be demand-limited again at some point and don't want to invest in overcapacity.

gizmo• 8 months ago

You describe the "fake email jobs" theory of employment. Given that there are way fewer email jobs in China does this imply that China will benefit more from AI? I think it might.

Libidinalecon2• 8 months ago

It is the delusion of the Homo Economicus religion.

I think the problem is a strong tie network of inefficiency that is so vast across economic activity that it will take a long time to erode and replace.

The reason it feels like it is moving slow is because of the delusion the economy is made up a network of Homo Economicus agents who would instantaneously adopt the efficiencies of automated intelligence.

As opposed to the actual network of human beings who care about their lives because of a finite existence who don't have much to gain from economic activity changing at that speed.

That is different though than the David Graeber argument. A fun thought experiment that goes way too far and has little to do with reality.

conartist6• 8 months ago

Yeah but it's just one model.

Call it Dave. Now Microsoft hires Dave and Open AI hires Dave. And Meta hires Dave and Oracle hires Dave and the US govt hires Dave. And soon each of those had hired not just one Dave but 50 identical copies of Dave.

It doesn't matter if Dave is a smart-ish ok guy. That's not the problem with this scenario. The problem is the the only thing on the market is Dave and people who think exactly like Dave thinks

ozgung• 8 months ago

That seems like a valid problem that was also mentioned in the podcast. 50 copies of Ilya, Dave or Einstein will have diminishing returns. I think the proposed solution is ongoing training and making them individuals. MS Dave will be a different individual than Dave.gov. But then why don't we just train humans in the first place.

ZaoLahma• 8 months ago

AI makes the parts of my work that I spend the least time on a whole lot quicker, but (so far / still) has negligible effects on the parts of my work that I spend the most time on.

I'm still not sure if this is due to a technological limitation or an organizational one. Most of my time is not spent on solving tech problems but rather solving "human-to-human" problems (prioritization between things that need doing, reaching consensus in large groups of people of how to do things that need doing, ...)

cowsandmilk• 8 months ago

We’re also still at a point where security is a big question mark. My employer won’t let us hook GenAI up to office 365 or slack, so any project or product management use of GenAI first requires manually importing docs into a database and pointing to that. Efficiency gains are hard to come by when you don’t meet people where their “knowledge” is already stored.

HarHarVeryFunny6• 8 months ago

No doubt LLMs and tooling will continue to improve, and best use cases for them better understood, but what Ilya seems to be referring to is the massive disconnect between the headline-grabbing benchmarks such as "AI performs at PhD level on math", etc, and the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true.

Calling LLMs "AI" makes them sound much more futuristic and capable than they actually are, and being such a meaningless term invites extrapolation to equally meaningless terms like AGI and visions of human-level capability.

Let's call LLMs what they are - language models - tools for language-based task automation.

Of course we eventually will do this. Fuzzy meaningless names like AI/AGI will always be reserved for the cutting edge technology du jour, and older tech that is realized in hindsight to be much more limited will revert to being called by more specific names such as "expert system", "language model", etc.

riku_iki3• 8 months ago

the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true.

this could be true in the past, but in recent weeks I started more and more trust top AI models and less PhDs I work with. Quality jump is very real imo.

aswegs8• 8 months ago

There is actually an interesting scenario in this disconnect that we are experiencing. Maybe "real" AGI in the sense of intelligence that self-corrects effectively like a human is still a long way. Maybe we will be stuck with this kind of ever-improving but still kind of deficient LLM intelligence we have right now.

There are tons of use cases even for such a limited type of intelligence. No, it is not a million math PhDs at your disposal. It is a narrow intelligence that is still hugely useful and businesses will need a few years to adapt. The impact on topics like customer service with LLM+RAG+triggering actions is very close already and should transform the industry in the next years.

wartywhoa23• 8 months ago

A steady progress implies transitioning between ages at least ⌊(year-2020)^2/10⌋ times a year, and entering at least one new era once in a decade.

fuzzfactor• 8 months ago

This AI stuff is really taking off fast.

And hasn't Ilya been on the cutting edge for a while now?

I mean, just a few hours earlier there was a dupe of this artice with almost no interest at all, and now look at it :)

This was my feelings way back then when it comes to major electronics purchases:

Sometimes you grow to utilize the enhanced capabilities to a greater extent than others, and time frame can be the major consideration. Also maybe it's just a faster processor you need for your own work, or OTOH a hundred new PC's for an office building, and that's just computing examples.

Usually, the owner will not even explore all of the advantages of the new hardware as long as the purchase is barely justified by the original need. The faster-moving situations are the ones where fewest of the available new possibilities have a chance to be experimented with. IOW the hardware gets replaced before anybody actually learns how to get the most out of it in any way that was not foreseen before purchase.

Talk about scaling, there is real massive momentum when it's literally tonnes of electronics.

Like some people who can often buy a new car without ever utilizing all of the features of their previous car, and others who will take the time to learn about the new internals each time so they make the most of the vehicle while they do have it. Either way is very popular, and the hardware is engineered so both are satisfying. But only one is "research".

So whether you're just getting a new home entertainment center that's your most powerful yet, or kilos of additional PC's that would theoretically allow you to do more of what you are already doing (if nothing else), it's easy for anybody to purchase more than they will be able to technically master or even fully deploy sometimes.

Anybody know the feeling?

The root problem can be that the purchasing gets too far ahead of the research needed to make the most of the purchase :\

And if the time & effort that can be put in is at a premium, there will be more waste than necessary and it will be many times more costly. Plus if borrowed money is involved, you could end up with debts that are not just technical.

Scale a little too far, and you've got some research to catch up on :)

myrmidon3• 8 months ago

I really liked this podcasts; the host generally does a really good job, his series with Sarah Paine on geopolitics is also excellent (can find it on youtube).

highfrequency• 8 months ago

Great respect for Ilya, but I don’t see an explicit argument why scaling RL in tons of domains wouldn’t work.

kubb• 8 months ago

Not sure why they care about his opinion and discard yours.

They’re just as valid and well informed.

never_inline• 8 months ago

I think that scaling RL for all common domains is already done to death by big labs.

anthonypasq2• 8 months ago

doesnt RL by definition not generalize? thats Ilya's entire criticism of the current paradigm

podgorniy• 8 months ago

Back to drawing board!

~Don't mind all those trillions of unreturned investments. Taxpayers will bail out the too-bog-to-fail ones.~

zombot• 8 months ago

Shouldn't research have come first? Am I making any sense?

shaism• 8 months ago

Ilya mentioned in the video that 2012 and 2020 was the “Age of Research”, followed by the “Age of Scaling” from 2020 to 2025. Now, we are about to reenter the “Age of Research”.

bentt• 8 months ago

Is this like if everyone suddenly got 1gb fiber connections in 1996? We put money into the thing we know (infra), but there's no youtube, netflix, dropbox, etc etc etc. Instead we're still loading static webpages with progressive jpegs and it's like... a waste?

twodave• 8 months ago

I’d settle for the “age of being able to point the LLM at the entire codebase, describe a new feature, and see it implemented based on the patterns and idioms already present in that codebase”. My impression is the only thing between here and there is context size.

Mars008• 8 months ago

If I remember correctly after leaving OpenAI with a bang Ilya founded a company and attracted billions of $$ promising AGI soon. Now what?

illwrks• 8 months ago

In the “teenagers learn to drive in 10 hours” part… that’s active learning, but they have spent countless hours in their life in a car, on a bus or other forms of transport, even watching the shows and movies featuring driving, playing with toys and computer games etc. There is years of passive information absorbed already before that 10 hours of active learning begins.

martin82• 8 months ago

That's a very diplomatic way of saying "we burnt all this money but we have not the faintest clue about how to proceed"

Ilya Sutskever: We're moving from the age of scaling to the age of research

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