by joelthelion
13 subcomments
- > 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.
- 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"?
by orbital-decay
5 subcomments
- >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.
- Suggest tagline: “Eminent thought leader of world’s best-funded protoindustry hails great leap back to the design stage.”
- 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.
by londons_explore
2 subcomments
- > 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
- 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.
- That's a very diplomatic way of saying "we burnt all this money but we have not the faintest clue about how to proceed"
- 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.
- So is the translation endless scaling has stopped being as effective?
- 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).
by tmp10423288442
1 subcomments
- 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.
- 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.
- Ages just keep flying by
- Back to drawing board!
--
~Don't mind all those trillions of unreturned investments. Taxpayers will bail out the too-bog-to-fail ones.~
by l5870uoo9y
3 subcomments
- > 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.
- 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.
- https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
He’s wrong we still scaling, boys.
by JimmyBuckets
19 subcomments
- 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.
- "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.
by river_otter
0 subcomment
- 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.
- 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?
by highfrequency
3 subcomments
- Great respect for Ilya, but I don’t see an explicit argument why scaling RL in tons of domains wouldn’t work.
- 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.
by measurablefunc
1 subcomments
- I didn't learn anything new from this. What exactly has he been researching this entire time?
by SilverElfin
8 subcomments
- 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.
by wartywhoa23
0 subcomment
- 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.
- 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.
by eats_indigo
2 subcomments
- did he just say locomotion came from squirrels
- You have LLMs but you also need to model actual intelligence, not its derivative. Reasoning models are not it.
by gizmodo59
8 subcomments
- 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!
by roman_soldier
1 subcomments
- 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.
- He also suggested the "revenue opportunities" would reveal themselves later, given enough investment. I have the same plan if anyone is interested.
by _giorgio_
2 subcomments
- Scaling is not over, there's no wall.
Oriol Vinyals VP of Gemini research
https://x.com/OriolVinyalsML/status/1990854455802343680?t=oC...
- Shouldn't research have come first? Am I making any sense?
by photochemsyn
0 subcomment
- 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).
- He is, of course, incentivised to say that.
by alexnewman
0 subcomment
- A lot more of human intelligence is hard coded
- 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.
- If I remember correctly after leaving OpenAI with a bang Ilya founded a company and attracted billions of $$ promising AGI soon. Now what?
by fuzzfactor
0 subcomment
- 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 :)
by a_state_full
0 subcomment
- [dead]
by nakamoto_damacy
0 subcomment
- Ilya "Undercover Genocide Supporter" Sutskever... ¯\_(ツ)_/¯
- 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...?
by venturecruelty
0 subcomment
- When are we going to call out these charlatans for the frauds that they are?
- 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?
Hacker News