Interviewer (2:42)

How did Google recruit you, by the way?

Jeff Dean (2:45)

I kind of reached out to them, actually.

Interviewer (2:48)

And Noam, how did you get recruited?

Noam Shazeer (2:50)

Was it that you did that? I actually saw Google at a job fair in 1999 and I assumed that it was already this huge company that no point in joining because everyone I knew used Google. I guess that was because I was a grad student at Berkeley at the time. I guess I've dropped out of grad programs a few times. But it turns out that actually it wasn't really that large. So it turns out I did not apply in 1999, but just kind of sent them a resume on a whim in 2000 because I figured it was my favorite search engine and figured I should apply to multiple places for a job. But then, yeah, it turned out to be really fun. Looked like a bunch of smart people doing good stuff and they had this really nice crayon chart on the wall of the daily number of search queries that somebody had just been maintaining. And yeah, it look very exponential. These guys are going to be very successful and it looks like they have a lot of good problems to work on. So it's like, okay, maybe I'll go work there for a little while and then have enough money to just go work on AI for as long as I want after that.

Interviewer (4:06)

Yeah, in a way you did that, right?

Noam Shazeer (4:08)

Yeah, it totally worked out exactly. According to.

Interviewer (4:12)

Sorry, you were thinking about AI in 1999.

Noam Shazeer (4:15)

Yeah, this was like 2000. Yeah, I remember in. Yeah, in grad school, a friend of mine at the time had told me that his New year's resolution for 2000 was to live to see the year 3000 and that he was going to achieve this by inventing AI. So I was like, oh, that sounds like a good idea. But then I didn't get the idea at the time that, oh, you could go do it at a big company. But I figured, hey, a bunch of people seem to be making a ton of money at startups. Maybe I'll just make some money and then I'll have enough to live on, just work on AI research for a long time. But yeah, it actually turned out that Google was a terrific place to work on AI.

Jeff Dean (5:04)

I mean, one of the things I like about Google is our ambition has always been sort of something that would kind of require pretty advanced AI organizing the world's information and making it universally accessible and useful. Actually there's a really broad mandate in there. So it's not like the company was going to do this one little thing and stay doing that. And also you could see that what we were doing initially was in that direction, but you could do so much more in that direction.

Interviewer (5:32)

How has Moore's Law over the last two, three decades changed the kinds of considerations you have to take on board when you design new systems? When you figure out what projects are feasible, what has stayed? What are still the limitations? What are things you can now do that you obviously couldn't do before?

Jeff Dean (5:47)

I mean, I think of it as actually changing quite a bit in the last couple decades. So the two decades ago to one decade ago it was awesome because you just wait and 18 months later you get much faster hardware and you don't have to do anything. And then more recently I feel like the general purpose CPU based machines scaling has not been as good. The fabrication processes improvements are now taking three years instead of every two years. The architectural improvements in multi core processors and so on are not giving you the same boost that we were getting 20 to 10 years ago. But I think at the same time we're seeing much more specialized computational devices like machine learning accelerators. TPU's very ML focused GPUs more recently are making it so that we can actually get really high performance and good efficiency out of the more modern kinds of computations we want to run that are different than a twisty pile of C code trying to run Microsoft Office or something.

Noam Shazeer (6:59)

It feels like the algorithms are following the hardware. Basically what's happened is that at this point arithmetic is very, very cheap and moving data around is comparatively much more expensive. So pretty much all of deep learning has taken off roughly because of that. Because you can build it out of matrix multiplications that are N cubed operations and N squared bytes of data communication basically.

Jeff Dean (7:35)

Well, I would say that the pivot to hardware oriented around that was an important transition because before that we had CPUs and GPUs that were not especially well suited for deep learning. And then we started to build say TPUs at Google that were really just reduced precision linear algebra machines. And then once you have that, then you want to.

Noam Shazeer (7:58)

Right. You have to see the insight that seems like it's all about kind of identifying opportunity cost. Like okay, this is something like Larry Page, I think, used to always say, like, our second biggest cost is taxes and our biggest cost is opportunity costs. And if he didn't say that, then I've been misquoting him for years. But basically it's like, what is the opportunity that you have that you're missing out on? And like in this case, I guess it was that, okay, you've got all of this chip area and you're putting a very small number of arithmetic units on it. Like fill the thing up with arithmetic units. You could have orders of magnitude more arithmetic getting done. Now what else has to change? Okay, the algorithms and the data flow and everything else.

Jeff Dean (8:48)

And oh, by the way, the arithmetic can be really low precision. So then you can squeeze even more multiplier units in.

Interviewer (8:54)

No, I want to follow up on what you said, that the algorithms have been following the hardware. If you imagine a counterfactual world where suppose that the cost of memory had declined more than arithmetic or just invert the dynamic you saw.

Noam Shazeer (9:08)

Yeah, yeah, okay. Data flow is extremely cheap and arithmetic is not cheap.

Interviewer (9:14)

What would AI look like today?

Jeff Dean (9:16)

You'd have a lot more lookups into very large memories.

Noam Shazeer (9:19)

Yes, I think, yeah, I mean, I think it might look more like AI looked like 20 years ago ago, but in the opposite direction. I'm not sure. I guess I joined Google Brain in 2012. I'd left Google for a few years, happened to go back for lunch to visit my wife and we happened to sit down next to Jeff and the early Google Brain team. And I thought, wow, that's a smart group of people.

Jeff Dean (9:50)

As I said, you should think about B Brill Nuts. We're making some pretty good progress here.

Noam Shazeer (9:55)

That sounds fun. Okay. So I jumped back in.

Jeff Dean (9:58)

I will come back.

Noam Shazeer (9:59)

It was great to join Jeff. That was like 2012. I seem to join Google every 12 years. Rejoined Google 2000, 2012 and 2024.

Interviewer (10:10)

What's going to happen in 2036?

Noam Shazeer (10:12)

I don't know. I guess we shall see.

Interviewer (10:17)

What are the trade offs that you're considering changing for future versions of TPU to integrate? How are you thinking about algorithms differently?

Jeff Dean (10:26)

I mean, I think one thing, one general trend is we're getting better at quantizing or having much more reduced precision models. We started with TPUV1. We weren't even quite sure we could quantize a model for serving with eight bit integers, but we sort of had some early evidence that seemed like it might be possible. So we're like, great, let's build the whole chip around that. And then over time, I think you've seen people able to use much lower precision for training as well. But also the inference precision has gone. People are now using INT4 or FP4, which sounded like if you said to someone, we're going to use FP4 like a supercomputing floating point person 20 years ago, they'd be like, what? That's crazy. We like 64 bits in our floats or even below that. Some people are quantizing models to two bits or one bit. And I think that's a trend to definitely pay attention to.

Interviewer (11:22)

Just like a 0 or 1?

Jeff Dean (11:23)

Yeah, just a 0 or 1. And then you have like a sign bit for a group of bits or something.

Noam Shazeer (11:29)

It really has to be a co design thing because if the algorithm designer doesn't realize that you can get greatly improved performance throughput with the lower precision, of course the algorithm designer is going to say, of course I don't want low precision. That introduces risk. And then it adds irritation. And then if you ask the chip designer, okay, what do you want to build? And then they'll ask the person who's writing the algorithms today who's going to say, no, I don't like quantization. It's irritating. So you actually need to build, basically see the whole picture and figure out, oh, wait a minute, we can increase our throughput to cost ratio by a lot by quantizing.

Jeff Dean (12:24)

Then you're like, yes, quantization is irritating, but your model is going to be three times faster. So you're going to have to deal.

Interviewer (12:31)

Through your careers at various times. You've had sort of an uncanny. You worked on things that have an uncanny resemblance to what we're actually using now for generative AI. In 1990, Jeff, your senior thesis was about backpropagation and in 2007. So this is the thing I didn't realize until I was prepping for this episode. In 2007, you guys trained a 2 trillion token N gram model for language modeling. Just walk me through. When you were developing that model, was this kind of thing in your head. What did you think you guys were doing at the time?

Jeff Dean (13:09)

Yeah, so I mean, let me start with the undergrad thesis. So I kind of got introduced to neural nets in one section of one class on parallel computing that I was taking in my senior year and I needed to do a thesis to graduate, like an honors thesis. And so I approached the professor and I said, oh, it'd be really fun to do something around neural nets. So he and I decided I would sort of implement A couple of different ways of parallelizing backpropagation training for neural nets in 1990. And I called them something funny in my thesis, like pattern partitioning or something. But really I implemented a model parallelism and data parallelism on a 32 processor hypercube machine. In one you split all the examples into different batches and every CPU has a copy of the model. And in the other one you kind of pipeline a bunch of examples along to processors that have different parts of the model. And I compared and contrasted them and it was interesting. I was really excited about the abstraction because it felt like neural nets were the right abstraction. They could solve tiny toy problems that no other approach could solve at the time. And I thought, oh, naive me. Oh, 32 types of processors we'll be able to train really awesome neural nets. But it turned out we needed about a million times more compute before they really started to work for real problems. But then starting in the late 2008, 2009, 2010 timeframe, we started to have enough computer, thanks to Moore's law, to actually make neural nets work for real things. And that was kind of when I sort of re entered looking at neural nets, but prior to that in 2007.

Interviewer (14:51)

So actually, can I ask about this?

Jeff Dean (14:52)

Any questions? Sure.

Interviewer (14:54)

First of all, unlike other artifacts of academia, it's actually like a really like it's like four pages and you can.

Jeff Dean (15:01)

Just read was four pages and then like 30 pages of C code.

Interviewer (15:06)

But it's just like a well produced sort of artifact. And then. Yeah, tell me about how the 2007 paper came together.

Jeff Dean (15:11)

Oh yeah, so that we had a machine translation research team at Google led by Franz Ock, who had joined Google maybe a year before, and a bunch of other people. And every year they competed in a, I guess it's a DARPA contest on translating a couple of different languages to English, I think, Chinese to English and Arabic to English, I think. And the Google team had submitted an entry. And the way this works is you get, I don't know, 500 sentences on Monday and you have to submit the answer on Friday. I saw the results of this and we'd won the contest by a pretty substantial margin measured in bleu score, which is like a measure of translation quality. I reached out to Franz the head, the head of this winning team. I'm like, this is great. When are we going to launch it? And he's like, oh, well, we can't launch this. It's not really very practical because it takes 12 hours to translate a sentence. I'M like, well, that seems like a long time. How could we fix that? So it turned out they'd not really designed it for high throughput, obviously. And so it was doing like 100,000 disk seeks in a large language model that they sort of computed statistics over. I wouldn't say trained, really. And for each word that it wanted to translate. So obviously doing 100,000 disseqs is not super speedy. But I said, okay, well, let's dive into this. And so I spent about two or three months with them designing an in memory compressed representation of n grammar data. And we were using an n gram is basically statistics for how often every n word sequence occurs in a large corpus. So you basically have, in this case, we had like 2 trillion words. And most N gram models of the day were like using 2 grams or maybe 3 grams, but we decided we would use 5 grams. So how often every 5 word sequence occurs in basically as much of the web as we could process that in that day. And then you have a data structure that says, okay, I really like this restaurant, occurs 17 times in the web or something. I built a data structure that would let you store all those in memory on 200 machines and then have sort of a batched API where you could say, Here are the 100,000 things I need to look up in this round for this word, and we give you them all back in parallel. And that enabled us to go from taking a night to translate a sentence to basically doing something in 100 milliseconds or something.

Interviewer (18:02)

There's this list of Jeff Dean facts like Chuck Norris facts, like, for example, that for Jeff Dean, NP equals no problemo. And one of them, it's funny because now that I hear you say, it's like, actually it's kind of true. One of them is the speed of light was 35 miles an hour until Jeff Dean decided to optimize it over a weekend. Just going from 12 hours to 100 milliseconds or whatever. It's like I got to do the orders of magnitude there.

Jeff Dean (18:32)

But all of these are very flattering. They're pretty funny. They're like an April Fool's joke gone awry by my colleagues.

Interviewer (18:43)

Okay, so obviously in retrospect, this idea that you can develop a latent representation of the entire Internet through just considering relationships between words is like, yeah, this is large language models. This is Gemini. At the time, was it just a translation idea, or did you see that as being the beginning of a different kind of paradigm?

Jeff Dean (19:07)

I think once we built that for translation the serving of large language models started to be used for other things, like completion of you start to type and it suggests what completions make sense. So it was definitely the start of a lot of uses of language models in Google. And Noam has worked on a number of other things at Google, like spelling correction systems that use language models.

Noam Shazeer (19:33)

That was 2000, 2001, and there I think it was just all in memory on one machine.

Jeff Dean (19:40)

Yeah, I think it was one machine. But his spelling correction system he built in 2001 was amazing. Like, he sent out this demo link to the whole company and like I just tried every butchered spelling of every few word query I could get. I like scrambled Uggs Bundict.

Noam Shazeer (19:55)

I remember that one. Yeah, yeah.

Jeff Dean (19:57)

Instead of scrambled eggs Benedict and like it just nailed it every time.

Noam Shazeer (20:01)

Yeah. And I guess that was language modeling.

Jeff Dean (20:03)

Yes.

Noam Shazeer (20:03)

Yeah.

Interviewer (20:03)

But at the time when you were developing these systems, did you have this sense of look, you make these things more and more sophisticated. You don't consider five words, but if you consider 100 words, a thousand words, then the latent representation is intelligence. Or was that like, basically when did that insight hit?

Noam Shazeer (20:21)

Not really. I mean, I don't think I ever felt like, okay, N gram models are going to sweep the world. Yeah, the artificial intelligence. I think at the time a lot of people were excited about the Bayesian networks. That was. That seemed exciting. Definitely seeing those early neural language models, both the magic in that. Okay, this is doing something extremely cool. And also it just struck me as the best problem in the world in that for one, it is very, very simple to state, give me a probability distribution over the next word. Also, there's roughly infinite training data out there. There's like the text of the web. You have trillions of training examples of unsupervised data, self supervised.

Jeff Dean (21:19)

Yeah, it's nice because you then have the right answer and then you can train on all but the current word and try to predict the current word. And it's this kind of amazing ability to just learn from observations of the.

Noam Shazeer (21:32)

World and then it's AI complete. If you can do a great job of that, then you can pretty much do any.

Interviewer (21:38)

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Noam Shazeer (23:19)

It does feel like it's in the air. There were definitely some. There was like this neural Turing machine. So yeah, a bunch of ideas around this attention. There's having these key value stores that could be useful in neural networks to kind of focus on things. So yeah, I think in some sense in the air and in some sense you need some group to go do it.

Jeff Dean (23:50)

I mean, I like to think of a lot of ideas as they're kind of partially in the air where there's like a few different, maybe separate research ideas that one is kind of squinting at when you're trying to solve a new problem. And you kind of draw on those for some inspiration. And then there's like some aspect that is not solved and you sort of need to figure out how to solve that. And then the combination of some morphing of the things that already exist and some new things lead to some new breakthrough or new research result that didn't exist before.

Interviewer (24:22)

Are there key moments that stand out to you where you looking at a research area and you come up with this idea and you have this feeling of like, holy shit, I can't believe that worked.

Jeff Dean (24:35)

One thing I remember was in the early days of the Brain team, we were focused on let's see if we can build some infrastructure that lets us train really, really big neural nets. And at that time we didn't have GPUs in our data centers we just had CPUs but we know how to make lots of CPUs work together. So we built a system that enabled us to train pretty large neural nets through both model and data parallelism. So we had a system for unsupervised learning on actually 10 million randomly selected YouTube frames. And it was kind of a spatially local representation. So it would build up unsupervised representations based on trying to reconstruct the thing from the high level representations. So we got that working and training on 2,000 computers using 16,000 cores. And after a little while that model was actually able to build a representation at the highest level where one neuron would get excited by images of cats that it had never been told what a cat was, but it had seen enough examples of them in the training data of head on facial views of cats that that neuron would turn on for that and not for much else. And similarly you'd have other ones for human faces and backs of pedestrians and this kind of thing. And so that was kind of cool because it's sort of from unsupervised learning principles building up these really high level representations. And then we were able to get very good results on the supervised ImageNet 20,000 category challenge that advanced the state of the art by 60% relative improvement, which was quite good at the time. And that neural net was probably 50x bigger than one that had been trained previously. And it got good results. That said to me, hey, actually scaling up neural net seems like I thought it would be a good idea and it seems to be, so we should keep pushing on that.

Interviewer (26:41)

So these examples illustrate how these AI systems fit into what you were just mentioning, that Google is sort of a company that organizes information fundamentally. And then basically what AI is doing in this context is finding relationships between information, between concepts to help get ideas to you faster information you want to you faster. Now we're moving with current AI models like obviously you can use Bert in Google Search and you can ask these things questions and they obviously are still good at information retrieval, but more fundamentally they can write your entire code base for you and do all seems more like an actual worker, which is going beyond the just like information retrieval. So how are you thinking about? Is Google still an information retrieval company? If you're building an AGI, AGI can do information retrieval, but do many other things as well. Right.

Jeff Dean (27:40)

I think we're an organized, the world's information company and that's broader than information retrieval. Right. That's maybe organizing and creating new information from some guidance you give it. Can you help me write a letter to my veterinarian about my dog? It's got these symptoms and it'll draft that. Or can you feed in this video and can you produce a summary of what's happening in the video every few minutes? And I think our sort of multimodal capabilities are showing that it's more than just text. It's about understanding the world in all the different kind of modalities that information exists in both kind of human ones, but also kind of non human oriented ones, like weird lidar sensors on autonomous vehicles or genomic information or health information. And then how do you extract and transform that into useful insights for people and make use of that in helping them do all kinds of things they want to do? And that's, you know, sometimes it's I want to be entertained by chatting with a chatbot. Sometimes it's, I want answers to this really complicated question. There is no single source to retrieve from. It's, you need to pull information from like 100 web pages and like figure out what's going on and make a organized synthesized version of that data and then dealing with, you know, multimodal things or coding related problems. I think it's super exciting what these models are capable of and they're improving fast. So I'm excited to see where we go. I don't know what.

Noam Shazeer (29:13)

I am also excited to see where we go. And yeah, I think definitely organizing information is clearly like a trillion dollar opportunity. But a trillion dollars is not cool anymore. What's cool is a quadrillion dollars. And obviously the idea is not to just pile up some giant pile of money, but it's create value in the world. And so much more value can be created when these systems can actually go and do something for you or write your code or figure out problems that you wouldn't have been able to figure out yourself. And to do that at scale. So I mean, we're going to have to be very, very flexible and dynamic as we improve the capabilities of these models.

Jeff Dean (30:10)

I guess I'm pretty excited about a lot of fundamental research questions that sort of come about because you see something that we're doing could be substantially improved if we tried this approach or things in this rough direction. And maybe that'll work, maybe it won't. But I also think there's, there's value in seeing what we could achieve for end users and then how can we work backwards from that to actually build systems that are able to do that. So as one example, organizing Information that should mean any information in the world should be usable by anyone, regardless of what language they speak. And that I think we've done some amount of. But it's not nearly the full vision of no matter what language you speak out of thousands of languages, we can make any piece of content available to you and make it usable by you. Any video could be watched in any language. I think that would be pretty awesome. We're not quite there yet, but that's definitely things I see on the horizon that should be possible.

Interviewer (31:13)

Speaking of different architectures, you might try. I know one thing you're working on right now is longer context. If you think of Google Search as like it's got the entire index of the Internet in its context, but it's sort of very shallow search. And then obviously language models have limited context right now, but they can really think it's like dark magic in context learning. You can really think about what it's seeing. How do you think about what it would be like to merge something like Google Search and something like in context learning?

Jeff Dean (31:44)

Yeah, maybe I'll take a first stab at it because I've thought about this for a bit. I think one of the things you see with these models is they're quite good, but they do hallucinate and have factuality issues sometimes. And part of that is you've trained on, say, tens of trillions of tokens and you've stirred all that together in your tens or hundreds of billions of parameters. But it's all a bit squishy because you've churned all these tokens together. And so the model has a reasonably clear view of that data, but it sometimes gets confused and will give the wrong date for something. Whereas information in the context window in the input of the model is really sharp and clear because we have this really nice attention mechanism in Transformers that the model can pay attention to things and it knows the exact text or the exact frames of the video or audio or whatever that it's processing. And so right now we have models that can deal with kind of millions of tokens of context, which is quite a lot. It's like hundreds of pages of PDF or 50 research papers or hours of video or tens of hours of audio or some combination of those things, which is pretty cool. But it would be really nice if the model could attend to trillions of tokens. Right. Could it attend to the entire Internet and find the right stuff for you? Could it attend to all your personal information for you? Right. I would love a model that has access to all my emails and all my documents and all my photos. And when I ask it to do something, it can sort of make use of that with my permission, to sort of help solve what it is I'm wanting it to do. But that's going to be a big computational challenge because the naive attention algorithm is quadratic and you can kind of barely make it work on a fair bit of hardware for millions of tokens. But there's no hope of making that just naively go to trillions of tokens. So we need a whole bunch of interesting algorithmic approximations to what you would really want to make. A way for the model to attend, kind of conceptually to lots and lots of more tokens to trillions of tokens and attend to your tokens. Maybe we can put all of the Google code base in context for every Google developer. All the world's source code in context for any open source developer. That would be amazing.

Noam Shazeer (34:14)

It would be incredible. Yeah, I mean, right? The beautiful thing about model parameters is they are quite memory efficient at sort of memorizing facts. Maybe you can probably memorize order of one fact or something per model parameter. Whereas if you have some token in context, there are lots of keys and values at every layer. It could be a kilobyte, a megabyte of memory per token.

Jeff Dean (34:49)

You take a word and you blow it up to 10 kilobytes or something.

Noam Shazeer (34:51)

Yes, yes. There's actually a lot of innovation going on around okay, a how do you minimize that and the okay, what words do you need to have there? Are there better ways of accessing bits of that information? And Jeff seems like the right person to figure this out. Like, okay, what does our memory hierarchy look like from the SRAM all the way up to data center worldwide level?

Interviewer (35:25)

I want to talk more about the thing you mentioned about. Look, Google is a company with lots of code and lots of examples. If you just think about that one use case and what that implies. So you've got the Google monorepo and maybe you figure out the long context thing. You can put the whole thing in context or you fine tune on it. Yeah, basically. Why hasn't this been already done? Because you can imagine the amount of code that Google has proprietary access to, even if you're just using it internally for it to make your developers more efficient and productive.

Jeff Dean (36:02)

Oh, to be clear, we have actually already done further training on a Gemini model on our internal code base for our internal developers. But that's different than attending to all of it because it sort of stirs Together the code base into a bunch of parameters. And I think having it in context makes things clearer. But even the sort of further trained model internally is incredibly useful. Like Sundar, I think has said that 25% of the characters that we're checking into our code base these days are generated by our AI based coding models with kind of human kind of.

Interviewer (36:41)

How do you imagine in a year or two, based on the capabilities you see on the horizon, your own personal work, what will it be like to be a researcher at Google? You have a new idea or something with the way in which you're interacting with these models in a year, what does that look like?

Noam Shazeer (36:57)

Well, I mean, I assume we will have these models a lot better and hopefully be able to be much, much more productive.

Jeff Dean (37:07)

Yeah, I mean, I think one of the, in addition to kind of researchy context, anytime you're seeing these models used, I think they're able to make software developers more productive because they can kind of take sort of a high level spec or sentence description of what you want done and give a pretty approximate, pretty reasonable first cut at that. And so from a research perspective, maybe you can say, I'd really like you to explore this kind of idea similar to the one in this paper, but maybe let's try making it convolutional or something. If you could do that and have the system automatically sort of generate a bunch of experimental code and maybe you look at it and you're like, yeah, that looks good. Run that. That seems like a nice dream direction to go in and seems plausible in the next year or two years that you might make a lot of progress on that.

Interviewer (38:01)

It seems underhyped because you could have literally millions of extra employees and you can immediately check their output. Employees can check each other's output, they immediately stream tokens.

Jeff Dean (38:14)

I didn't mean to underhype it. I think it's super exciting. I just don't like to hype things that aren't done yet.

Interviewer (38:27)

I do want to play with this idea more because it seems like a big deal if you have something kind of like an autonomous software engineer, Especially from the perspective of a researcher who's like, I want to spec build the system again. Okay, so legislatively with this idea. As somebody who has worked on developing transformative systems through your careers, the idea that instead of having to code something like whatever Today's equivalent of MapReduce is or TensorFlow is, just like, here's how I would want distributed AI library to look like. Write it up for me. Do you imagine you could be like 10x more productive, 100x more productive.

Jeff Dean (39:06)

I was pretty impressed. I think it was on Reddit that I saw we have a new experimental coding model that's much better at coding and math and so on. And someone external tried it and they basically prompted it and said, I'd like you to implement a SQL processing database system with no external dependencies and please do that in C. From what the person said, it actually did a quite good job. Like it generated a SQL parser and a tokenizer and a query planning system and some storage format for the data on disk and actually was able to handle simple queries. So from that prompt, which is like a paragraph of text or something, to get even an initial cut at that seems like a big boost in productivity for software developers. And I think you might end up with other kinds of systems that maybe don't try to do that in a single semi interactive, respond in 40 second kind of thing, but might go off for 10 minutes and might interrupt you after five minutes saying, I've done a lot of this, but now I need to get some input. Do you care about handling video or just images or something? That seems like you'll need ways of managing the workflow if you have a lot of these kind of background activities happening.

Interviewer (40:36)

Yeah, Ajay, can you talk more about that? So what interface do you imagine we might need if we have. If you could literally have millions of employees, you could spin up hundreds of thousands of employees you could spin up on command who are able to type incredibly fast. It's almost like you go from 1930s trading of tickets or something to now modern chainsuit or something. You need some interface to keep track of all this that's going on for the AIs to integrate into this big monorepo and leverage their own strengths for humans to keep track of what's happening. What is it like to be Jeff or Noam in 3 years working day to day?

Noam Shazeer (41:18)

It might be kind of similar to what we have now because we already have sort of parallelization as a major issue because we have lots and lots of really, really brilliant machine learning researchers and we want them to all work together and build AI. Actually, the parallelization among people might be similar to parallelization among machines, but I think definitely it should be good for things that require a lot of exploration, like come up with the next breakthrough. Because if you have a brilliant idea that it's just certain to work in the ML domain, then it has a 2% chance of working if you're brilliant. And mostly these Things fail. But if you try 100 things or 1,000 things or a million things, then you might hit on something amazing. And we have plenty of compute. Like modern top labs these days have probably a million times as much compute as it took to train transformer.

Interviewer (42:33)

Yeah, actually, so that's a really interesting idea. If you have like, suppose in the world today there's like on the order of 10,000 AI researchers and this community coming up with a breakthrough.

Jeff Dean (42:44)

Probably more than that. There were 15,000 in Europe.

Noam Shazeer (42:47)

Wow.

Interviewer (42:49)

100,000. I don't know.

Jeff Dean (42:50)

Yeah, maybe. Sorry.

Interviewer (42:52)

No, no, it's good to have the correct order of magnitude. And the odds that this community every year comes up with a breakthrough on the scale of a transformer is, let's say 10%. Now suppose this community is a thousand times bigger and it is in some sense like this sort of parallel search of better architectures, better techniques. Do we just get like breakthroughs every year or every day?

Noam Shazeer (43:17)

Maybe. Sounds potentially good, but I feel like.

Interviewer (43:23)

What ML research is like is just if you are able to try all these experiments.

Noam Shazeer (43:29)

It's a good question because I don't know that folks haven't been doing that as much. I mean, we definitely have lots of great ideas coming along. Everyone seems to want to run their experiment at maximum scale, but I think that's a human problem.

Jeff Dean (43:49)

It's very helpful to have a 1/1,000th scale problem and then vet 100,000 ideas on that and then scale up the ones that seem promising.

Interviewer (43:59)

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Jeff Dean (45:39)

I mean, I think one thing people should be aware of is the improvements from generation to generation of these models often are partially driven by hardware and larger scale, but equally and perhaps even more so, driven by major algorithmic improvements and major changes in the model architecture and the training data mix and so on that really make the model better per flop that is applied to the model. So I think that's a good realization. And then I think if we have automated exploration of ideas, we'll be able to vet a lot more ideas and bring them into kind of the actual production training for next generations of these models. And that's going to be really helpful because that's sort of what we're currently doing with a lot of brilliant machine learning researchers is looking at lots of ideas, winnowing ones that seem to work well at small scale, seeing if they work well at medium scale, bringing them into larger scale experiments and then settling on adding a whole bunch of of new and interesting things to the final model recipe. And then I think if we can do that 100 times faster through those machine learning researchers, just gently steering a more automated search process rather than sort of hand babysitting lots of experiments themselves, that's going to be really, really good.

Noam Shazeer (46:59)

Yeah. The one thing it doesn't speed up is experiments at the largest scale because you still end up doing these N equals 1 experiments in there. Really just, just try to put a bunch of really brilliant people in the room and have them stare at the thing. Figure out why this is working, why this is not working.

Jeff Dean (47:18)

More hardware is a good solution. And better hardware.

Noam Shazeer (47:21)

Yes. We're counting on you.

Interviewer (47:26)

Okay, naively. So there's this software, there's this algorithmic side improvement that future AIs can make. There's also the stuff you're working on on awfulchip, I'll let you describe it. But if you get into a situation where just from a software level you can be making better and better chips in A matter of weeks and months. And better AIs can presumably do that better. Basically, I'm wondering how does this feedback loop not just end up in Gemini 3 takes two years, then Gemini 4 is like. Or the equivalent level jump is now six months, then the level five is three months, then one month, and you get to superhuman intelligence much more rapidly than you might naively think because of this software, both on the hardware side and from the algorithmic side. Improvements.

Jeff Dean (48:22)

Yeah, I mean, I've been pretty excited lately about how could we dramatically speed up the chip design process. Because as we were talking earlier, the current way in which you design a chip takes you roughly 18 months to go from we should build a chip to something that you then hand over to tsmc and then TSMC takes four months to fab it, and then you get it back and you put it in your data centers. So that's a pretty lengthy cycle. And the fab time in there is a pretty small portion of it today. But if you could make that the dominant portion so that Instead of taking 12 to 18 months to design the chip, and with 150 people, you could shrink that to a few people, with a much more automated search process, exploring the whole design space of chips and getting feedback from all aspects of the chip design process for the kind of choices that the system is trying to explore at the high level, then I think you could get perhaps much more exploration and more rapid design of something that you actually want to give to a fab. And that would be great because you can shrink that time. You can shrink the deployment time by designing the hardware in the right way so that you just get the chips back and you just plug them in to some system. And that will then, I think, enable a lot more specialization. It will enable a shorter timeframe for the hardware design so that you don't have to look out quite as far into what kind of ML algorithms would be interesting. Instead, it's like you're looking at six to nine months from now. What should it be rather than two, two and a half years. And that would be pretty cool. I do think that fabrication time is, if that's in your inner loop of improvement, you're going to like, how long is it? The leading edge nodes, unfortunately, are taking longer and longer because they have more metal layers than previous older nodes. So that tends to make it take anywhere from three to five months.

Interviewer (50:28)

But that's how long trading runs take anyways. Right. So you could potentially do both at the same time.

Jeff Dean (50:34)

Yeah, potentially.

Interviewer (50:35)

Okay, so I guess you can't get sooner than three to five months. But the idea that you could get. But also, yeah, you're rapidly developing new algorithmic ideas that can move fast, that.

Jeff Dean (50:45)

Can move fast, that can run on like existing chips and explore lots of cool ideas.

Interviewer (50:50)

So isn't that a situation in which you're, I think people sort of expect like, ah, there's going to be a sigmoid. Again, this is not a sure thing. But just like, is this a possibility? The idea that you have sort of an explosion of capabilities very rapidly towards the tail end of human intelligence that gets smarter and smarter at a more and more rapid rate?

Noam Shazeer (51:13)

Quite possibly.

Jeff Dean (51:14)

I mean, I like to think of it like this. Right now we have models that can take a pretty complicated problem and can break it down internally in the model into a bunch of steps, can sort of puzzle together the solutions for those steps, and can often give you a solution to the entire problem that you're asking it. But it isn't super reliable and it's good at breaking things down into five to 10 steps, not 100 to 1,000 steps. So if you could go from, yeah, 80% of the time it can give you a perfect answer to something that's 10 steps long to something that 90% of the time can give you a perfect answer to something that's 100 to 1,000 steps of sub problem long, that would be an amazing improvement in capability of these models. And we're not there yet, but I think that's what we're aspirationally trying to.

Noam Shazeer (52:09)

Get to is, yeah, we don't need new hardware for that, but I mean, we'll take it. Yeah, exactly.

Jeff Dean (52:16)

Never look new hardware in the mouth.

Noam Shazeer (52:20)

One of the big areas of improvement I think in the near future is this inference time compute, like applying more computer, you know, at inference time. And I guess the way I've liked to describe it is that, you know, a, like even some giant language model, you know, even if you're doing say a trillion operations per token, which is, you know, more than most people are doing these days, you know, operations cost something like 10 to the negative $18. And so you're getting like a million tokens to the dollar, right? So I mean, compare that to like relatively cheap pastime. You go out and you buy a paper book and read it, you're paying 10,000 tokens to the dollar. Talking to a language model is like 100 times cheaper than reading a paperback. So there is a huge amount of headroom there to say, okay, if we can make this thing more Expensive but smarter because we're 100x cheaper than reading a paperback. We're 10,000 times cheaper than talking to a customer support agent or a million times or more cheaper than hiring a software engineer or talking to your doctor or lawyer. Can we add computation and make it smarter? So I think a lot of the takeoff that we're going to see in the very near future is of this form. We've been exploiting and improving pre training a lot in the past and post training and those things will continue to improve. But taking advantage of think harder at inference time is going to just be an explosion.

Jeff Dean (54:17)

Yeah. And an aspect of inference time is I think you want the system to be actively exploring a bunch of different potential solutions. Maybe it does some searches on its own and gets some information back and consumes that information and figures out, oh, now I would really like to know more about this thing. So now it kind of iteratively kind of explores how to best solve the high level problem you pose to this system. And I think having a dial where you can make the model give you better answers with more inference time compute seems like we have a bunch of techniques now that seem like they can kind of do that. And the more you crank up the dial, the more it costs you in terms of computer, but the better the answers get. That seems like a nice trade off to have because sometimes you want to think really hard because it's a super important problem. Sometimes you probably don't want to spend enormous amounts of compute to compute. What's the answer to one plus one? Maybe the system should decide, you take.

Interviewer (55:19)

That to 100 and it comes up with new axioms of set theory.

Jeff Dean (55:21)

So you decide to use a calculator tool or something instead of a very large language model.

Interviewer (55:27)

Are there any impediments to taking inference time, like having some way in which you can just linearly scale up inference time compute? Or is this basically a problem that's sort of solved and we know how to throw like 100x compute a 1000x compute and get correspondingly better results.

Noam Shazeer (55:45)

Well, we're working out the algorithms as we speak, so I believe we'll see better and better solutions to this as these Many more than 10,000 researchers are hacking at it, many of them at Google.

Jeff Dean (56:02)

I think we do see some examples in our own sort of experimental work of things where if you apply more inference time compute, the answers are better than if you just apply X. If you apply 10x you can get better answers than x amount of computed inference time. And that seems Useful and important. But I think what we would like is when you apply 10x to get even a bigger improvement in the quality of the answers than we're getting today. And so that's about designing new algorithms, trying new approaches, figuring out how best to spend that 10x instead of x to improve things.

Interviewer (56:40)

Does it look more like search or does it look more like just keep going in the linear direction for a longer time?

Jeff Dean (56:45)

I mean, I think search is, I really like Rich Sutton's paper that he wrote about the bitter lesson. And the bitter lesson effectively is this nice one page paper. But the essence of it is you can try lots of approaches, but the two techniques that are incredibly effective are learning and search and you can apply and scale those algorithmic computationally and you often will then get better results than any other kind of approach. You can apply to pretty broad variety of problems. And so I think search has got to be part of the solution to spending more inference time is you want to maybe explore a few different ways of solving this problem and like, oh, that one didn't work, but this one worked better. So now I'm going to explore that a bit more.

Interviewer (57:31)

How does this change your plans for future data center planning and so forth. Where if you know, can this kind of search be done asynchronously, does it have to be online, offline? How does that change how big of a campus you need and those kinds of considerations?

Jeff Dean (57:51)

I mean, I think one general trend is it's clear that inference time compute, you have a model that's pretty much already trained and you want to do inference on. It is going to be a growing and important class of computation that maybe you want to specialize hardware more around that. Actually the first TPU was specialized for inference and wasn't really designed for training. And then subsequent TPUs were really designed more around training and also for inference. But it may be that when you have something where you really want to crank up the amount of compute you use at inference time, that even more specialized solutions will make a lot of sense.

Interviewer (58:33)

Does that mean you can accommodate more asynchronous training? Training or inference or just you can have the different data centers don't need to talk to each other. You can just have them do a bunch of oh yeah, I mean, I.

Jeff Dean (58:45)

Think I like to think of it as is the inference that you're trying to do latency sensitive, like the user's actively waiting for it or is it kind of a background thing? And maybe that's. I have some inference tasks that I'm trying to run over a whole batch of data, but it's not for a particular user. It's just I want to run inference on it and extract some information. And then there's probably a bunch of things that we don't really have very much of right now. But you're seeing inklings of it in our deep research tool that we just released. I forget exactly when a week ago, where you can give it a pretty complicated high level task like, hey, can you go off and research the history of renewable energy and all the trends in costs for wind and solar and other kinds of techniques and put it in a table and give me a full eight page report and it will come back with an eight page report with like 50 entries in the bibliography. It's pretty remarkable. But you're not actively waiting for that for one second. It takes like a minute or two to go do that. And I think there's going to be a fair bit of that kind of compute. And that's the kind of thing where you have some UI questions around. Okay. If you're going to have a user with 20 of these kind of asynchronous tasks in the background happening and maybe each one of them needs to get fermari more information from the user. Like I found your flights to Berlin, but there's no nonstop ones. Are you okay with a nonstop one? How does that flow work when you kind of need a bit more information and then you want to put it back in the background for it to continue doing finding the hotels in Berlin or whatever. I think it's going to be pretty interesting. And inference will be useful.

Noam Shazeer (60:28)

Inference will be useful. I mean, there's also a compute efficiency thing in inference that you don't have in training and that in general, transformers can use the sequence length as a batch during training, but they can't really in inference because when you're generating one token at a time. So there may be different hardware and inference algorithms that we design for the purposes of being efficient at inference.

Jeff Dean (60:56)

Yeah. As a good example of an algorithmic improvement is like the use of drafter models. So you have a really small language model that you do one token at a time. When you're decoding and predict four tokens and then you give that to the big model and you say, okay, here's the four tokens the little model came up with. Check which ones you agree with, and if you agree with the first three, then you just advance and then you've basically been able to do a 4 token with parallel computation instead of a 1 token width thing in the big model. And so those are the kinds of things that people are looking at to improve inference efficiency. So you don't have this single token decode bottleneck.

Noam Shazeer (61:40)

Right. Basically the big model's being used as a verifier as opposed to verify the generator and verification you can do.

Jeff Dean (61:47)

Hello, how are you? That sounds great to me. I'm going to advance past that.

Interviewer (61:51)

So a big discussion has been about we're already tapping out nuclear power plants in terms of delivering power into one single campus. And so do we have to have just 2 gigawatts in one place, 5 gigawatts in one place, or can it be more distributed and still be able to train a model? Does this new regime of inference scaling make different considerations there plausible? Or how are you thinking about multi data center training now?

Jeff Dean (62:25)

I mean, we're already doing it, so we're pro multi data center training. I think in the Gemini 1.5 tech report we said we used multiple metro areas and trained with some of the compute in each place and then a pretty long latency but high bandwidth connection between those data centers and that works fine. Actually training is kind of interesting because each step in a training process is usually for a large model is a few seconds or something at least. So the latency of it being 50 milliseconds away doesn't matter that much.

Noam Shazeer (63:00)

Just the bandwidth.

Jeff Dean (63:01)

Yeah, just bandwidth.

Noam Shazeer (63:02)

As long as you can sync all of the parameters of the model across the different data centers and then accumulate all the gradients. So it's. So in the time it takes to do one step, you're pretty good.

Jeff Dean (63:14)

And then we have a bunch of work on even early brain days when we were using CPU machines and they were really slow. So we needed to do asynchronous training to help scale where each copy of the model would kind of do some local computation and then send gradient updates to a centralized system and then apply them asynchronously and another copy of the model would be doing the same thing. It makes your model parameters kind of wiggle around a bit and it makes people uncomfortable with the theoretical guarantees, but it actually seems to work in practice.

Noam Shazeer (63:49)

It was so pleasant to go from async to SIG because your experiments are now replicable rather than your results depend on whether there was a web crawler running on the same machine. It's like one of your guys computer. So I am so much happier running on like CPU pods.

Jeff Dean (64:12)

I love Async.

Noam Shazeer (64:13)

It just lets you two iPhones and an Xbox or whatever it was.

Jeff Dean (64:17)

But like, yeah, what if we could give you asynchronous but replicatable results?

Noam Shazeer (64:22)

Ooh.

Jeff Dean (64:23)

So one way to do that is you effectively record the sequence of operations. So like which gradient update happened and when and on which batch of data. You don't necessarily record the actual gradient update in a log or something, but you could replay that log of operations so that you get repeatability. Then I think you'd be happy.

Noam Shazeer (64:47)

At least you could debug what happened. Yeah, but you wouldn't be able to compare necessarily two training runs because, okay, I made one change in the hyper parameter. But also I had like a web crawler machine messing up and there were like a lot of people screaming the super bowl at the same time. I mean, the thing that let us super bowl, right?

Jeff Dean (65:13)

The thing that let us go from asynchronous training on CPUs to fully synchronous training is the fact that we have these super fast TPU hardware chips and then pods which have incredible amounts of bandwidth between the chips in a podcast. And then scaling beyond that, we have really good data center networks and even cross metro area networks that enable us to scale to many, many pods in multiple metro areas for our largest training runs. And we can do that fully synchronously, as Noam said, as long as the gradient accumulation and communication of the parameters across metro areas happens fast enough relative to the step time, you're golden. You don't really care. But I think as you scale up, there may be push to have a bit more asynchrony in our systems than we have now because we can make it work. Our ML researchers have been really happy how far we've been able to push synchronous training because it is easier mental model to understand. You just have your algorithm sort of fighting you rather than the asynchrony and the algorithm kind of battling you.

Noam Shazeer (66:21)

As you scale up, there are more things fighting you. You know, like there's. Yeah, I mean, right. That's the problem with scaling that you don't actually always know what it is that's fighting you. Is it, you know, the fact that you've pushed like quantization a little too far in some place or another. Or is it your data or is.

Jeff Dean (66:41)

It maybe it's your adversarial machine MUQQ17 that is like setting the seventh bit of your exponent and all your radians or something.

Noam Shazeer (66:50)

Right. And all of these things just make the model slightly worse. So you don't even know that the thing is going on that's actually a.

Jeff Dean (67:00)

Bit of a problem with neural nets is they're so tolerant of noise. You can have things set up kind of wrong in a lot of ways, and they just kind of figure out ways to work around that or learn.

Noam Shazeer (67:10)

Despite that, you could have bugs in your code. Most of the time that does nothing. Some of the time it makes your model worse. Some of the time it makes your model better. And then you discover. Discover something new. Because you never tried this bug at scale before because you didn't have the budget for it.

Interviewer (67:28)

What practically does it look like actually to debug or decode what the. You've got these things, some of them, which are making model better, some of which are making it worse. You, when you go into work tomorrow, you're like, all right, what's going on here? How do you figure out what the most salient inputs are?

Noam Shazeer (67:45)

Right? I mean, well, at small scale, you do lots of experiments. There's, I think, one part of. Of the research that involves, okay, I want to invent these improvements or breakthroughs kind of in isolation, in which case you want a nice, simple code base that you can fork and hack and some baselines. And my dream is, I wake up in the morning, come up with an idea, hack it up in a day, run some experiments, get some initial results in the day, like, okay, this looks promising. These things work. Worked. These things worked and didn't work. And I think that is very achievable because at small scale, at small scale, as long as you keep a nice experimental code base, maybe an experiment takes.

Jeff Dean (68:36)

An hour to run or two hours or something, not two weeks.

Noam Shazeer (68:39)

It's great. It's great. So there's that part of the research, and then there's some amount of scaling up. And then you have the part which is integrating where you want to stack all the improvements on top of each other and see if they work at large scale and see if they work all in conjunction with each other. How do they interact?

Jeff Dean (68:58)

Right. You think maybe they're independent, but actually maybe there's some funny interaction between improving the way in which we handle video data input and the way in which we update the model parameters. And that interacts more for video data than some other thing. There's all kinds of interactions that can happen that you maybe don't anticipate. And so you want to run these experiments where you're then putting a bunch of things together and then periodically making sure that all the things you think are good are good together and if not understanding why they're not playing nicely.

Interviewer (69:35)

Two questions. One, how often does it end up being the case that things don't stack up well together? Is it like a rare thing or does it happen all the time?

Noam Shazeer (69:46)

It happens all the time, yeah.

Jeff Dean (69:49)

I mean, I think most things you don't even try to stack because the initial experiment didn't work that well or it showed results that aren't that promising relative to the baseline. And then you sort of take those things and you try to scale them up individually and then you're like, oh, yeah, these ones seem really promising. So I'm going to now include them in something that I'm going to now bundle together and try to advance and combine with other things that seem promising. And then you run the experiments and then you're like, oh, well, they didn't really work that well. Let's try to debug why.

Noam Shazeer (70:22)

And then there are trade offs. Because you want to keep your integrated system as clean as you can. Because complexity. Yeah, Code base and algorithmically. Complexity. Complexity hurts, complexity makes things slower, introduces more risk. And then at the same time, you want it to be as good as possible. And of course, every individual researcher wants his inventions to go into it. So there are definitely challenges there. But we've been working together quite well.

Interviewer (70:59)

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Noam Shazeer (72:55)

I've stopped cleaning my garage because I'm waiting for the robots. So probably I'm more in the second camp of what we're going to see a lot of acceleration.

Jeff Dean (73:05)

Yeah, I mean, I think it's super important to understand what's going on and what the trends are. And I think right now the trends are the models are getting substantially better generation over generation. And I don't see that slowing down in the next few generations probably. So that means the models say two to three generations from now are going to be capable of. Let's go back to the example of breaking down a simple task into 10 sub pieces and doing it 80% of the time to something that can break down a task, a very high level task, into 100 or 1,000 pieces and get that right 90% of the time. That's a major, major step up in what the models are capable of. So I think it's important for people to understand what is happening in the progress in the field and then those models are going to be applied in a bunch of different domains. And I think it's really good to make sure that we as society get the maximal benefits from what these models can do to improve things in. I'm super excited about areas like education and healthcare, making information accessible to all people. But we also realize that they could be used for misinformation, they could be used for automated hacking of computer systems. And we want to sort of put as many safeguards and mitigations and understand the capabilities of the models in place as we can. And that's kind of, you know, I think Google as a whole has a really good view to how we should approach this. Our responsible AI principles actually are a pretty nice framework for how to think about trade offs of making better and better AI systems available in different contexts and settings, while also sort of making sure that we're doing the right thing in terms of making sure they're safe and not saying toxic things and things like that.

Interviewer (75:02)

I guess the thing that stands out to me, if you were zooming out and looking at this period of human history, if we're in the world where, look, maybe if you do Post training on Gemini 3 badly, it can do some misinformation, but then you fix the post training and it's gonna stop doing that. It's a bad mistake, but it's a fixable mistake. Right?

Noam Shazeer (75:21)

Right.

Interviewer (75:21)

Whereas if you have this feedback loop dynamic, which is a possibility, then the sort of mistake of the thing that catapults this intelligence explosion is misaligned, is, is not trying to write the code you think it's trying to write and optimizing for some other objective. And on the other end of this very rapid process that lasts a couple of years, maybe less, you have things that are approaching Jeff Dean or beyond level or Noam Shazir or beyond level and then you have millions of copies of Jeff Dean level programmers. And anyways, that seems like a harder to recover mistake and that seems like a much more salient. You really got to make sure we're going into the intelligence explosion.

Noam Shazeer (76:11)

As these systems do get more powerful, you've got to be more and more careful.

Jeff Dean (76:18)

I mean, one thing I would say is there's the extreme views on either end. There's like, oh my goodness, these systems are going to be so much better than humans at all things and we're going to be kind of overwhelmed. And then there's the like, these systems are going to be amazing and we don't have to worry about them at all. I think I'm somewhere in the middle and I'm a co author on a paper called Shaping AI, which is those two extreme views often kind of view our role as kind of laissez faire. Like we're just going to have the AI develop in the path that it takes. And I think there's actually a really good argument to be made that what we're going to do is try to shape and steer the way in which AI is deployed in the world so that it is maximally beneficial in the areas that we want to capture and benefit from. In education, some of the areas I mentioned, healthcare, and steer it as much as we can away, maybe with policy related things, maybe with technical measures and safeguards away from the computer will take over and have unlimited control of what it can do. So I think that's an engineering problem is how do you engineer safe systems. I think it's kind of the modern equivalent of what we've done in kind of older style software development. Like if you look at airplane software development, that has a pretty good record of how do you rigorously develop safe and secure systems for, for doing a pretty risky task.

Interviewer (77:58)

The difficulty there is that there's not some feedback loop where the 737, you put it in a box with a bunch of compute for a couple of years and it comes out with the version 1000.

Noam Shazeer (78:08)

I think the good news is that analyzing text seems to be easier than generating text. I believe that the ability of language models to actually analyze language model output and figure out what is problematic or dangerous will actually be the solution to a lot of these control issues. We are definitely, definitely working on this stuff. We've got a bunch of brilliant folks at Google working on this now. And I think it's just going to be more and more important both from do something good for people standpoint, but also from a business standpoint that you are a lot of the time limited in what you can deploy based on keeping things safe. So it becomes very important to be really, really good at that.

Interviewer (79:28)

Yeah, obviously I know you guys take the potential benefits and costs here seriously and you guys get credit for it, but not enough. I think there's so many different applications that you have put out for using these models to make the different areas you talked about better. But I do think that there again, if you have a situation where plausibly there's some feedback loop process on the other end you have a model that is as good as Noam Shazir, as good as Jeff Dean. If there's an evil version of you running around and suppose there's a million of them, I think that's really, really bad. That could be much, much worse than any other risk. Maybe shorter, like nuclear war or something. Just think about it like a million evil Jeff Deans or something.

Jeff Dean (80:13)

Where do we get the training data?

Interviewer (80:17)

But to the extent that you think that's a plausible output of some quick feedback loop process, what is your plan of like, okay, we've got Gemini 3 or Gemini 4 and we think it's helping us do better job of training future versions. It's writing a bunch of the training code for us. From this point forward we just kind of look over it, verify it. Even the verifiers you talked about of looking at the output of These models will eventually be trained by or a lot of the code will be written by the AIs you make. What do you want to know for sure? Before we have the Gemini 4 help us with AI research, we really want to make sure we want to run this test on it before we let it write our AI code for us.

Jeff Dean (81:00)

I think having the system explore algorithmic research ideas seems like something where there's still a human in charge of that, that's exploring the space and then it's going to get a bunch of results and we're going to make a decision are we going to incorporate this particular learning algorithm or change to the system into kind of the core code base. And so I think you can put in safeguards like that that enable to enable us to get the benefits of the system that can sort of improve or kind of self improve with human oversight without necessarily letting the system go full on self improving without any notion of a person looking at what it's doing. Right. That's the kind of engineering safeguards I'm talking about. Where you want to be kind of looking at the characteristics of the systems you're deploying, not deploy ones that are harmful by some measures and some ways. And you have in understanding what its capabilities are and what it's likely to do in certain scenarios. So I think it's not an easy problem by any means, but I do think it is possible to make these systems safe.

Noam Shazeer (82:15)

Yeah, I mean, I think we are also going to use these systems a lot to check themselves, check other systems. I mean even as a human it is easier to recognize something than to generate it.

Jeff Dean (82:33)

One thing I would say is if you expose the model's capabilities through an API or through a user interface that people interact with, I think then you have a level of control to understand how is it being used and but put some boundaries on what it can do. And that I think is one of the tools in the arsenal of how do you make sure that what it's going to do is acceptable by some set of standards you've set out in your mind?

Noam Shazeer (83:02)

Yeah, I mean, I think the goal is to empower people. So for the most part we should be mostly letting people do things with these systems that make sense and closing off as few parts of the space as we can. But yeah, if you let somebody take your thing and create a million evil software engineers, then that doesn't empower people because they're going to hurt others with a million evil software engineers. So I'm against that.

Jeff Dean (83:33)

Me too, me too. I'll go on.

Interviewer (83:35)

All right, let's talk about a few more fun topics over the last 25 years. What was the most fun time? What period of time do you have the most nostalgia over?

Jeff Dean (83:47)

I mean, I think the early sort of four or five years at Google, when I was sort of one of a handful of people working on search and crawling in search and indexing systems. And our traffic was growing tremendously fast and we were trying to expand our index size and make it so we updated it every minute instead of every month or two months if something went wrong. And seeing kind of the growth and usage of our systems was really just personally satisfying. Building something that is used by today, 2 billion people a day, I think is pretty incredible. But I would also say equally exciting is sort of working with people on the Gemini team today. I think the progress we've been making in what these models can do over the last year and a half or whatever is really fun. People are really dedicated, really excited about what we're doing. I think the models are getting better and better at pretty complex tasks. Like, if you showed someone using a computer 20 years ago what these models are capable of, they wouldn't believe it. And even five years ago they might not believe it. And that's pretty satisfying. And I think we'll see a similar growth and usage of these models and impact in the world.

Noam Shazeer (85:02)

Yeah, I'm with you. I'm with you. Early days were super fun. I mean, part of that is just knowing everybody and the social aspect and the fact that you're just building something that millions and millions of people are using and same thing today we've got that whole nice micro kitchen area where you get lots of people hanging out. I love being in person, work with a bunch of great people and build something that's helping millions to billions of people. What could be better?

Interviewer (85:36)

What's this? A micro kitchen.

Jeff Dean (85:38)

We have a micro kitchen area in the building we both sit in. It's the new greedy, so named gradient canopy. It used to be named Charleston east. And we decided we needed a more exciting name because it's a lot of machine learning researchers and AI research happening in there. And there's a micro kitchen area that we've set up with. Normally it's just like an espresso machine and a bunch of snacks, but this particular one has a bunch of space in it. So we've set up maybe 50 desks in there. And so people are just hanging out in there. It's a little noisy because people are always grinding beans and Brewing espresso. But you also get a lot of face to face ideas of connections like oh, I've tried that. Did you think about trying this in your idea? Or oh, we're going to launch this thing next week. How's the load test looking? There's just lots of feedback that happens. And then we have our Gemini chat rooms for people who are not not in that micro kitchen. We have a team all over the world and there's probably 120 chat rooms I'm in related to Gemini things and this particular very focused topic. We have seven people working on this and there's exciting results being shared by the London colleagues. And when you wake up you see what's happening in there or it's a big group of people focused on data and there's all kinds of issues happening in there. It's just fun.

Interviewer (87:04)

What I find remarkable about some of the calls you guys have made is you're anticipating a level of demand for compute which at the time wasn't obvious or evident. TPUs being a famous example of this, or the first TPU being an example of this. That thinking you had in I guess 2013 or earlier. If you think about it that way today and you do an estimate of look, we're going to have these models that are going to be a backbone of our services and we're going to be doing constantly inference for them. We're going to be training future versions and you think about the amount of compute we'll need by 2030 to accommodate all these use cases. Where does the Fermi estimate get you?

Jeff Dean (87:44)

Yeah, I mean I think you're going to want a lot of inference. Compute is the rough, highest level view of these capable models. Because if one of the techniques for improving their quality is scaling up the amount of inference compute you use, then all of a sudden what's currently like one request to generate some tokens now becomes 50 or 100 or 1000 times as computationally intensive even though it's producing the same amount of output. And you're also going to then see tremendous scaling up of the uses of these services as not everyone in the world has discovered these chat based conversational interfaces where you can get them to do all kinds of amazing things. Probably 10% of the computer users in the world have discovered that today, or 20% as that pushes towards 100% and people make heavier use of it, that's going to be another order of magnitude or two of scaling. And so you're now going to have two orders of magnitude from that Two orders of magnitude from that, the models are probably going to be bigger. You'll get another order of magnitude or two from that. And there's a lot of inference compute you want. So you want extremely efficient hardware for inference for models you care about in.

Interviewer (89:03)

Flops, total global inference in 2030.

Noam Shazeer (89:09)

I think just more is always going to be better. If you just kind of think about, okay, like, what fraction of world GDP will be, you know, will people decide to spend on AI at that point? And then like, okay, what do the AI systems look like? Well, maybe it's some sort of personal assistant like thing that is in your glasses and can see everything around you and has access to all your digital information and the world's digital information. And maybe it's like you're Joe Biden and you have the earpiece in the cabinet that can advise you about anything in real time and solve problems for you and give you helpful pointers, or you could talk to it and it wants to analyze anything that it sees around you for any potential useful impact that it has on you. So I mean, I can imagine, okay, and then say it's like you're your personal assistant or your personal cabinet or something and that every time you spend 2x as much money on compute, the thing gets 5, 10 IQ points smarter or something like that. And okay, would you rather spend $10 a day and have an assistant or $20 a day and have a smarter assistant? And not only is it an assistant in life, but an assistant in getting your job done better, because now it makes makes you from a 10x engineer to a 100x or 10 millionx engineer. Okay, so let's see, from first principles, right? So people are going to want to spend some fraction of world GDP on this thing. The world GDP is almost certainly going to go way, way up to orders of magnitude higher than it is today due to the fact that we have all of these artificial engineers working on improving things. Probably we'll have solved unlimited energy and carbon issues by that point. So we should be able to have lots of energy. We should be able to have millions to billions of robots building US data centers. Let's see, the sun is what, 10 to the 26 watts or something like that? I mean, I'm guessing that the amount of compute being used for AI to help each person will be astronomical.

Jeff Dean (91:59)

I mean, I would add on to that. I'm not sure I agree completely, but it's a pretty interesting thought experiment to go in that direction. And even if you get partway there, it's definitely going to be a lot of compute. And this is why it's super important to have as cheap and hardware platform for using these models and applying them to problems that Noam described so that you can then make it accessible to everyone in some form and have as low a cost for access to these capabilities as you possibly can. And I think that's achievable. By focusing on hardware and model co design kinds of things, we should be able to make these things much, much more efficient than they are today.

Interviewer (92:47)

Is Google's data center build out plan over the next few years aggressive enough given this increase in demand you're expecting?

Jeff Dean (92:56)

I'm not going to comment on our future capital spending because our CEO and CFO would prefer our development probably. But I will say you can look at our past capital expenditures over the last few years and see that we're, we're definitely investing in this area because we think it's important and that we are continuing to build new and interesting innovative hardware that we think really helps us have an edge in deploying these systems to more and more people, both training them and also how do we make them usable by people for inference?

Interviewer (93:32)

One thing I've heard you talk a lot about is continual learning. The idea that you could just have a model which improves over time rather than having to start from scratch. Is there any fundamental impediment to that? Because theoretically you should just be able to keep fine tuning a model or what does that future look like to you?

Jeff Dean (93:50)

Yeah, I've been thinking about this more and more and I've been a big fan of models that are sparse because I think you want different parts of the model to be good at different things. And we have our Gemini 1.5 Pro model and other models are mixture of expert style models where you now have parts of the model that are activated for some token and parts that are not activated at all because you decided this is a math oriented thing and this part's good at math and this part's good at understanding cat images. So that gives you this ability to have a much more capable model that's still quite efficient at inference time because it has very large capacity but you activate a small part of it. But I think the current problem, well one limitation of what we're doing today is it's still a very regular structure where each of the experts is kind of the same size. The paths kind of merge back together very fast. They don't sort of go off and have lots of different branches for mathy things that don't merge back together with the, the kind of cat image thing. And I think we should probably have a more organic structure in these things. I also would like it if the pieces of the model could be developed a little bit independently. Like right now, I think we have this issue where we're going to train a model. So we do a bunch of preparation work on deciding the most awesome algorithms we can come up with and the most awesome data mix we can come up with. But there's always trade offs there. Like, we'd love to include more multilingual data, but that might come at the expense of including less coding data. And so the model's less good at coding, but better multilingual or vice versa. And I think it would be really great if we could have a small set of people who care about a particular subset of languages go off and create really good training data, train a modular piece of a model that we can then hook up to a larger model that improves its capability in say, Southeast Asian languages or in reasoning about Haskell code or something. And then you then also have a nice software engineering benefit where you've decomposed the problem a bit compared to what we do today, which is we have this kind of a whole bunch of people working, but then we have this kind of monolithic process of starting to do pre training on this model. And if we could do that, you could have 100 teams around Google, you could have people all around the world working to improve languages they care about or particular problems they care about, and all collectively work on improving the model. And that's kind of a form of continual learning.

Noam Shazeer (96:37)

That would be so nice. You could just glue models together or rip out pieces of models and shove them into other like Dr. Frankenstein kind of thing. Or you just attach a fire hose and you suck all the information out of this model, shove it into another model. The countervailing interest there is sort of science in terms of like, okay, we're still in the period of rapid progress. So if you want to do sort of controlled experiments and okay, I want to compare this thing to that thing, because that is helping us figure out, okay, what do you want to build in that interest? It's often best to just start from scratch so you can compare one complete training run to another complete training run, sort of at the practical level because it kind of helps us figure out what to build in the future and it's less exciting, but. But does lead to rapid progress.

Jeff Dean (97:42)

Yeah, I think there may be ways to get a lot of the benefits of that with kind of a version system of modularity. Like I have a frozen version of my model and then I include a different variant of some particular module and I want to compare its performance or train it a bit more. And then I compare it to the baseline of this thing with now version N prime and of this particular module that does Haskell interpretation.

Noam Shazeer (98:08)

Actually that could lead to faster research progress. Right. You've got some system and you do something to improve it. And if that thing you're doing to improve it is relatively cheap compared to training the system from scratch, then that could actually make research much, much cheaper and faster.

Jeff Dean (98:27)

Okay, Maybe it would be more parallelizable, I think.

Noam Shazeer (98:31)

Yeah.

Jeff Dean (98:32)

Okay. Across people.

Noam Shazeer (98:34)

Okay, let's figure it out and do that next.

Jeff Dean (98:36)

Yeah, good luck.

Interviewer (98:39)

So this is this idea that this sort of casually laid out there is actually would be a big regime shift, you think the way things are headed. This is a sort of like very interesting prediction about. You just have this blob where things are getting pipelined back and forth. And if you want to make something better, you can do like a sort of surgical incision almost. Right.

Jeff Dean (99:01)

Or grow the model. Add another little bit of it here. Yeah, I've been sort of sketching out this vision for a while in the sort of Pathways. Under the Pathways name.

Noam Shazeer (99:10)

Yeah, you've been building the.

Jeff Dean (99:11)

And we've been building the infrastructure for it. So a lot of what pathways the system can support is this kind of twisty, weird model with like asynchronous updates to different pieces.

Noam Shazeer (99:22)

Yeah, we should come back and figure out.

Jeff Dean (99:24)

We're using Pathways to train our Gemini models, but we're not making use of some of its capabilities yet. But maybe we should.

Noam Shazeer (99:31)

Maybe there have been times, the way the TPU pods were set up, I don't know who did that, but they did a pretty brilliant job. The low level software stack and the hardware stack that, okay, you've got your nice regular high performance hardware, you've got these great torus shaped interconnects, and then you've got the right low level collectives, the allreduces, et cetera, which I guess came from supercomputing, but it turned out to be kind of just the right thing to build distributed deep learning on top of.

Interviewer (100:13)

Okay, so a couple of questions. One, suppose Gnome makes another breakthrough and now we've got a better architecture. Would you just take each compartment and distill it into this better architecture and that's how it keeps improving over time.

Jeff Dean (100:30)

Yeah, I mean, I do think distillation is a really useful tool because it enables you to kind of transform a model in its current model architecture form into a different form. You know, often you use it to take a really capable but kind of large and unwieldy model and distill it into a smaller one that maybe you want to serve with really good, good fast latency inference characteristics. But I think you can also view this as something that's happening at the module level. Like maybe there'd be a continual process where you have each module and it has a few different representations of itself. It has a really big one, it's got a much smaller one that is continually distilling into the small version and then the small version. Once that's finished, then you sort of delete the big one and you add a bunch more parameter capacity and now start to learn all the things that the distilled small one doesn't know by training it on more data. And then you kind of repeat that process. And if you have that kind of running a thousand different places in your modular model in the background, that seems like it would work reasonably well.

Interviewer (101:38)

This could be a way of doing inference scaling. Like the router decides how much do you want the big.

Jeff Dean (101:43)

Yeah, you can have multiple versions and like, oh, this is an easy math problem. So I'm going to route it to the really tiny math distilled thing and oh, this one's really hard.

Interviewer (101:52)

At least from public research it seems like it's often hard to decode what each expert is doing in mixture of expert type models. If you have something like this, how would you enforce the kind of modularity that would be visible and understandable to us?

Noam Shazeer (102:07)

Actually in the past I found experts to be relatively easy to understand. I mean, I don't know the first mixture of experts paper. You could just look at the expert.

Interviewer (102:18)

I'm only the inventor. Mixture of experts.

Noam Shazeer (102:20)

Yeah, like, yeah, you could just see, okay, like this expert, like we did, you know, a thousand, two thousand experts. Okay. And this expert, like all of the was getting words referring to cylindrical objects.

Jeff Dean (102:32)

You know, like this one's super good at dates.

Noam Shazeer (102:35)

Yeah, yeah. Talking about time was actually interesting. Yeah, Pretty easy to do. But I mean like, not that you would need that human understanding to like figure out how to work the thing at runtime because you just have some sort of learned router that's looking at the example.

Jeff Dean (102:55)

I mean one thing I would say is there is a bunch of work on interpretability of models and what are they doing inside and sort of expert level interpretability is a sub problem of that broader area. I really like some of the work that my former intern Chris Ola and others did at Anthropic, where they could. They trained a very sparse autoencoder and were able to deduce what characteristics does some particular neuron in a large language. So they found a Golden Gate Bridge neuron that's activated when you're talking about the Golden Gate Bridge. And I think you could do that at the expert level. You could do that at a variety of different levels and get pretty interpretable results. And it's a little unclear if you necessarily need that if the model's just really good at stuff. We don't necessarily care what every neuron in the Gemini model is doing as long as the collective output and characteristics of the overall system are good. That's one of the beauties of deep learning, is you don't need to understand or hand engineer every last feature.

Interviewer (104:00)

Man, there's so many interesting implications of this that I could just keep asking you about this. One implication is currently, if you have have a model that has some tens or hundreds of billions of parameters, you can serve it on a handful of GPUs in this system, where any one query might only make its way through a small fraction of the total parameters. But you need the whole thing sort of loaded into memory. The specific kind of infrastructure that Google has invested in with these, these TPUs that exist in pods of hundreds or thousands would be immensely valuable, right?

Noam Shazeer (104:41)

I mean, for any sort of even existing mixtures of experts, you want the whole thing in memory. I mean, basically, if you are, I guess there's kind of this misconception running around with mixture of experts that, okay, the benefit is that you don't even have to go through those weights in the model. If some expert is unused, it doesn't mean that you don't have to retrieve that memory because really, in order to be efficient, you're serving at very large batch sizes of independent requests. Independent requests. So it's not really the case that, okay, at this step, you're either looking at this expert or you're not looking at this expert. Expert. Because if that were the case, then when you did look at the expert, you would be running it at batch size one, which is massively inefficient. Like you've got like modern hardware, the operational intensities are whatever, hundreds. So that's not what's happening, it's that you are looking at all the experts, but you only have to send a small fraction of the batch through each One.

Jeff Dean (105:56)

Right. But you still have a smaller batch at each expert. Expert that then goes through. And in order to get kind of reasonable balance, one of the things that the current models typically do is they have all the experts be roughly the same compute cost and then you run roughly the same size batches through them in order to sort of propagate the very large batch you're doing at inference time and have good efficiency. But I think you often in the future might want experts that vary in computational cost by factors of 100 or 1000 or maybe paths that go for many layers on one case and a single layer or even a skip connection in the other case. And there I think you're going to want very large batches still, but you're going to want to kind of push things through the model a little bit asynchronously at inference time, which is a little easier than training time. And that's part of kind of one of the things that pathwave was designed to support is you have these components and the components can be variable cost. And you kind of can say, for this particular example, I want to go through this subset of the model. And for this example, I want to go through this subset of the model and have the system kind of orchestrate that.

Interviewer (107:18)

It also would mean that it would take companies of a certain size and sophistication to be able to. Like right now, anybody can train a sufficiently small enough model. But if it ends up being the case that this is the best way to train future models, then you would need a company that can basically have a data center sized, a data center serving a single quote unquote blob or model. So it would be, it would be interesting change in paradigms in that way as well.

Noam Shazeer (107:49)

You definitely want to have at least enough HBM to put your whole model. So depending on the size of your model, most likely that's how much, that's how much HBM you'd want to have at a minimum.

Jeff Dean (108:07)

But it also means, I think you don't necessarily need to grow your entire model footprint to be the size of a data center. You might want it to be a bit below that and then have potentially many replicated copies of one particular expert that is being used a lot so that you get better load balancing. So, like this one's being used a lot because we get a lot of math questions. And this one on, maybe it's an expert on Tahitian dance and it is called on really rarely that one, maybe you even page out to DRAM rather than putting it in an hbm. But you want the system to kind of figure all this stuff out based on load characteristics.

Interviewer (108:48)

Right now language models, obviously you put in language, you get language out. Obviously it's multimodal. But you could imagine the Pathways blog post talks about sort of like so many different use cases that are not obviously of this kind of autoregressive nature going through the same model. So could you imagine basically Google as a company, the product is like Google Search goes through this, Google Images goes through this, Gmail goes through. It's just like the server, the entire server is just this huge mixture of experts, specialized.

Jeff Dean (109:24)

I mean you're starting to see some of this by having a lot of uses of Gemini models across Google that are not necessarily fine tuned. They're just sort of given instructions for this particular use case and this feature in this product setting. So I definitely see a lot more sharing of what the underlying models are capable of across more and more services. I do think that's a pretty interesting direction to go for Sure.

Interviewer (109:53)

I feel like people listening might not register how interesting a prediction this is about where AI. It's sort of like getting GNOME on a podcast in 2018 and being like, yeah, so I think language models will be a thing. It's like this is where things go. This is actually. That's incredibly interesting.

Jeff Dean (110:14)

Yeah. And I think you might see that might be a big base model and then you might want customized versions of that model with different modules that are added onto it for different settings that maybe have access restrictions. Like maybe we have an internal one for Google use for Google employees that we've trained some modules on internal data and we don't allow anyone else to use those modules, but we can make use of it. And maybe other companies you add on other modules that are useful for that company setting and serve it in our cloud APIs.

Interviewer (110:46)

What is the bottleneck to making this sort of system viable? Is it like systems engineering? Is it ML, is it?

Jeff Dean (110:54)

I mean it's a pretty different way of operating than our current Gemini development. So I think think we will explore these kinds of areas and I think make some progress on them. But we need to sort of really see evidence that it's the right way, that it has a lot of benefits. Some of those benefits may be improved quality, some may be sort of less concretely measurable like this ability to have lots of parallel development of different modules. And I think that would. But that's still a pretty exciting improvement because I think then that would enable us to make faster Progress on improving the model's capabilities for lots of different distinct areas.

Noam Shazeer (111:36)

I mean, even the data control modularity stuff seems really cool because then you could have the piece of the model that's just trained. For me, it knows all my private data.

Jeff Dean (111:45)

Like a personal module for you would be useful. Another thing might be you can use certain data in some settings, but not in other settings settings. And maybe we have some YouTube data that's only usable in a YouTube product surface, but not in other settings. So we could have a module that is trained on that data for that particular purpose.

Noam Shazeer (112:06)

We're going to need like a million automated researchers to invent all of this stuff.

Jeff Dean (112:11)

Yeah, it's got to be great.

Interviewer (112:14)

Well, the thing itself, it's like you build the blob and it tells you how to make the blob better.

Jeff Dean (112:19)

And Blob 2.0, or maybe they're not even version. It's just like an incrementally growing blob.

Interviewer (112:28)

Yeah. Okay, Jeff, motivate for me. Big picture. Why is this a good idea? Why is this the next direction?

Jeff Dean (112:36)

Yeah, I mean, I guess this kind of notion of an organic, kind of not quite so carefully mathematically constructed machine learning model is one that's been with you for a little while. And I feel like in the development of neural nets, the artificial neurons, inspiration from biological neurons, is a good one and has served us well in the deep learning field. And we've been able to make a lot of progress with that. But I feel like we're not necessarily looking at other things that real brains do as much as we perhaps could. And that's not to say we should exactly mimic that, because silicon and wetware have very different characteristics and strengths. But I do think one thing we could draw more inspiration from is this notion of having different specialized portions, part sort of areas of a model of a brain that are good at different things. So we have a little bit of that in a mixture of experts models, but it's still very structured. And I feel like this kind of more organic growth of expertise and when you want more expertise of that, you kind of add some more capacity to the model there and let it learn a bit more on that kind of thing. And also this notion of adapting the connectivity of the model to the connectivity of the hardware is a good one. So I think you want incredibly dense connections between artificial neurons in sort of the same chip and the same hbm, because that doesn't cost you that much. But then you want a smaller number of connections to nearby neurons. So, like A chip away, you should have some amount of connections, and then like many, many chips away, you should have a smaller number of connections where you send over a very limited kind of bottlenecky thing. The most important, important things that this part of the model is learning for other parts of the model to make use of. And even across multiple TPU pods, you'd like to send even less information, but the most salient kind of representations. And then across metro areas, you'd like to send even less.

Interviewer (114:52)

Yeah, and then that emerges organically.

Jeff Dean (114:55)

Yeah, I'd like that to emerge organically. Like, you could hand specify these characteristics, but I think you don't know exactly what the right proportions of these kinds of connections. And so you should just let the hardware dictate things a little bit. Like, if you're communicating over here and this data always shows up really early, you should add some more connections, then it'll make it take longer and show up at just the right time.

Interviewer (115:18)

Oh, here's another interesting implication. Potentially right now, we think about the growth in AI use as a sort of horizontal. So suppose you're like, how many AI engineers will Google have working for it? You think about how many instances of Gemini 3 will be working at one time. If you have this, whatever you want to call this blob, and it can sort of organically decide how much of itself to activate, then it's more of like if you want 10 engineers worth of output, but it just activates a different pattern or a larger pattern. If you want 100 engineers of output, it's not like calling more agents or more instances, it's just calling different subsets.

Jeff Dean (116:04)

I think there's a notion of how much compute do you want to spend on this particular inference. And that should vary by factors of 10,000 for really easy things and really hard things, maybe even a million. And it might be iterative. You might make a pass through the model, get some stuff, and then decide you now need to call on some other parts of the model as another aspect of it. The other thing I would say is this sounds super complicated to deploy because it's like this weird constantly evolving thing with maybe not super optimized ways of communicating between pieces, but you can always distill from that. So if you say, this is the kind of task I really care about, let me distill from this giant, like organic Y thing into something that I know can be served really efficiently. And you could do that distillation process, you know, whenever you want. Once a day, once an hour. That seems like it'd be kind of good.

Noam Shazeer (117:02)

Yeah. We need better distillation.

Jeff Dean (117:03)

Yeah.

Noam Shazeer (117:04)

Anyone out there invents amazing distillation techniques that instantly distill from a giant blob onto your phone. That would be wonderful.

Interviewer (117:12)

How would you characterize what's missing from current distillation techniques?

Noam Shazeer (117:15)

Well, I just want it to work faster. Sure.

Jeff Dean (117:17)

Yeah. A related thing is I feel like we need interesting learning techniques during pre training. Like, I'm not sure we're extracting the maximal value from every token we look at with the current training objective. Maybe we should think a lot harder about some tokens. When you get to. The answer is maybe the model should, at training time, do a lot more work than when it gets to the right.

Noam Shazeer (117:46)

Yeah. Right. There's got to be some way to get more from the same data. Make it learn it forwards and backwards.

Jeff Dean (117:54)

Which way? Hide some stuff this way, hide some stuff that way. Make it infer from partial information, these kinds of things. I think people have been doing this in vision models for a while. You distort the model or you hide parts of it and try to make it guess the bird from half like that. It's a bird from this upper corner of the image or the lower left corner of the image. And that makes the task harder. And I feel like there's an analog for kind of more textual or coding related data where you want to force the model to work harder and you'll get more interesting observations from it.

Noam Shazeer (118:32)

Yeah. The image people didn't have enough labeled data, so they had to invent all this stuff.

Jeff Dean (118:37)

And like they invented. I mean, dropout was invented images, but we're not really using it for text mostly. That's one way you could get a lot more learning in a more large scale model without overfitting is just make like 100 epochs over the world's text data and use dropout. But that's pretty computationally expensive. But it does mean we won't run it. Like, even though people are saying, oh no, we're almost out of like textual data. I don't really believe that because I think we can get a lot more capable models out of the text data that does exist.

Noam Shazeer (119:13)

I mean like a person has seen like a billion tokens.

Jeff Dean (119:16)

Yeah. And they're pretty good at a lot of stuff.

Interviewer (119:19)

Obviously, human data efficiency sets a lower bound on how or I guess upper bound one of them on maybe not.

Jeff Dean (119:28)

It's an interesting data point.

Interviewer (119:29)

Yes. So there's a sort of like modus ponens, modus tollens thing here of One way to look at it is, look, LLMs have so much further to go, therefore we project orders of magnitude improvement in sample efficiency. Just if they could match humans. Another is maybe they're doing something clearly different given the orders of magnitude difference. What's your intuition of what it would take to make these models as sample efficient as humans are?

Jeff Dean (119:57)

Yeah, I mean, I think we should consider changing the training objective a little bit. Like just predicting the next token from the previous ones you've seen. Seems like not how people learn. It's a little bit related to how people learn, I think, but not entirely. Like, a person might read a whole chapter of a book and then try to answer questions at the back. And that's a kind of different kind of thing. I also think we're not not learning from visual data very much. We're training a little bit on video data, but we're definitely not anywhere close to thinking about training on all the visual inputs you could get. So you have visual data that we haven't really begun to train on. And then I think we could extract a lot more information from every bit of data we do. See, I think one of the ways people are so sample efficient is they explore the world and take actions in the world and observe what happens. Right. Like you see it with very small infants, like picking things up and dropping them. They learn about gravity from that. And that's a much harder thing to learn when you're not initiating the action. And I think having a model that can take actions as part of its learning process would be just a lot better than just sort of passively observing a giant data set.

Interviewer (121:12)

Is GATO the future, then something where.

Jeff Dean (121:15)

The model can observe and take actions and observe the corresponding results Seems pretty useful.

Noam Shazeer (121:24)

I mean, people can learn a lot from thought experiments that don't even involve extra input. And Einstein learned a lot of stuff from thought experiments. Or Newton went into quarantine and got an apple dropped on his head or something and invented gravity. And mathematicians like Map didn't have any extra input. Chess, like, okay, you have the thing play chess against itself and it gets good at chess. That was DeepMind, but also all it needs is the rules of chess. So there's actually probably somehow a lot of learning that you can do even without external data, and then you can make it in exactly the the fields that you care about. Of course, there is learning that will require external data, but probably maybe we can just have this thing talk to itself and make itself smarter.

Interviewer (122:23)

So here's the question. I have what you've just laid out over the last hour is potentially just like the big next paradigm shift in AI. That's a tremendously valuable insight, potentially. How do you know? In 2017 you released the Transformer paper on which tens, if not hundreds of billions of dollars of market value is based in other companies. Not to mention all this other research that Google has released over time, which you've been relatively generous with. In retrospect, when you think about divulging this information that has been helpful to your competitors, in retrospect, is it like, yeah, we'd still do it, or would we be like, oh, we didn't realize how big a deal a transformer was, we should have kept it indoors. How do you think about that?

Noam Shazeer (123:12)

It's a good question because I think probably we did need to see the size of the opportunity often reflected in what other companies are doing. And also it's not a fixed policy PI. This is like the current state of the world is pretty much as far from fixed PI as you can get. I think we're going to see orders of magnitude of improvements in GDP help well than anything else you can think of. So I think it's definitely been nice that Transformer has got around and transformative. Thank God Google's doing well as well. So these days we do publish a little less of what we're doing. But yeah, I mean, I think there's.

Jeff Dean (124:14)

Always this trade off of should we publish exactly what we're doing right away, should we put it in the next stages of research and then roll it out into production Gemini models and not publish it at all? Or is there some intermediate point? And for example, in our computational photography work in pixel cameras, we've often taken the decision to develop interesting new techniques like the ability to do super good night sight vision for low light situations or whatever, put that into the product and then published a real research paper about the system that does that after the product is released. And I think different techniques and developments have different treatments. So some things we think are super critical we might not publish. Some things we think are really interesting but important for improving our products. We'll get them out into our products and then make a decision, did we publish this or do we give kind of a lightweight discussion of it, but maybe not every last detail? And then other things I think we publish openly and try to advance the field and the community because that's how we all kind of benefit from participating. I think it's great to go to conferences like Neurips last week with 15,000 people all sharing lots and lots of great ideas and we publish a lot of papers there as we have in the past and see the field advance is super exciting.

Interviewer (125:48)

How would you account for. So obviously Google had all these insights internally rather early on, including the top researchers. And now as of 2024, Gemini 2 is out. We didn't get a chance much to talk about but people will know it's a really great model. That's what I used to research for.

Jeff Dean (126:12)

This as we say around the micro kitchen. Such a good model. Such a good model.

Interviewer (126:16)

So it's top in lmsis chatbot arena and so now Google's on top. But how would you account for basically coming up with all the great insights for a couple of years? Other competitors had models that were more that were better for a while despite that.

Jeff Dean (126:33)

You want me to take a stab?

Noam Shazeer (126:34)

Sure.

Jeff Dean (126:35)

I mean I think, think yeah. We've been working on language models for a long time. GNOME's early work on spelling correction in 2001, the work on translation very large scale language models in 2007 and seeks to seek and word two vec and more recently Transformers and then Bert and things like the internal Mina system. That was actually a chatbot based system designed to kind of of engage people in interesting conversations. We actually had an internal chatbot system that Googlers could play with even before ChatGPT came out. And actually during the pandemic a lot of Googlers would enjoy everyone's lockdown at home and so they'd enjoy spending time chatting with Mina during lunch because it was a nice engaging lunch partner. And, and I think one of the things we were a little our view of things from a search perspective was like these models elucidate a lot and they don't get things right correctly a lot of the time or some of the time. And that means that they aren't as useful as they could be. And so we'd like to make that better. And from a search perspective you want to get the right answer 100% of the time, ideally going be very high on factuality. And these models were not near that bar. But I think what we were a little unsure about is that they were incredibly useful. Oh. And they also had all kinds of safety issues like they might say offensive things and you had to work on that aspect and get that to a point where we were comfortable releasing the model. But I think what we kind of didn't quite appreciate was how useful they could be for things you wouldn't ask a search engine. Right. Like help me write A note to my veterinarian or like, oh, can you take this text and get a quick summary of it? Or whatever. And I think that's the kind of thing we've seen people really flock to in terms of using chatbots as amazing new capabilities rather than as a pure search engine. And so I think we took our time and got to the point where we actually released quite capable chatbots and have been improving them through Gemini models quite a bit. And I think that's actually not a bad path to have taken. Would we like to have released the chatbot earlier?

Noam Shazeer (129:04)

Maybe.

Jeff Dean (129:04)

But I think we have a pretty awesome chatbot with awesome Gemini models that are getting better all the time, and that's pretty cool.

Interviewer (129:11)

Yeah. So we've discussed some of the things you guys have worked on over the last 25 years, and there's so many different fields. Right. You start off with search and indexing, to distributed systems, to hardware, to AI algorithms, and genuinely, there's like a thousand more just go on either of their Google Scholar pages or something. What is the trick to having this level of not only career longevity where you have many decades of making breakthroughs, but also. So the breadth of different fields, both of you in either order, with strict career longevity and breadth?

Jeff Dean (129:54)

Yeah, I mean, I think one thing that I have that I like to do is to find out about a new and interesting area. And one of the best ways to do that is to pay attention to what's going on, talk to colleagues, pay attention to research papers that are being published, look at the kind of research landscape as it's evolving, be willing to say, oh, chip design. I wonder if we could use reinforcement learning for some aspect of that and be able to dive into a new area, work with people who know a lot about a different domain or AI for healthcare is something I've done a bit of all kinds, working with clinicians about. What are the real problems? How could AI help? It wouldn't be that useful for this thing, but it would be super useful for this. Getting those insights and often working with a set of five or six colleagues who have different expertise than you do enables you to collectively do something that none of you could do individually. And then some of their expertise rubs off on you, and some of your expertise rubs off on them. And now you have this bigger set of tools in your tool belt as an engineer and researcher to go tackle the next thing. And I think that's one of the beauties of. Of continuing to learn on the job. It's something I treasure and I really, like enjoy diving into new things and seeing what we can do.

Noam Shazeer (131:15)

I'd say probably a big thing is humility and say I'm the most accountable. But seriously, there's to say, hey, you know, what I just did is nothing compared to what I can do or what can be done. And to be able to drop an idea as soon as you see something, as soon as you see something better, like you hear somebody with some better idea and you see how maybe what you're thinking about, what they're thinking about or something totally different can conceivably work better. Because I think there's a, there is a drive in some sense to say, hey, the thing I just invented is awesome, give me more chips. Particularly if there's a lot of top down resource assignment. But I think we also need to incentivize people to say, hey, this thing I am doing is not working at all. Let me just drop it completely and try something else. Which I think Google Brain did quite well. We had a very kind of bottoms up UBI kind of chip allocation where you had ubi. Yeah. Basically everyone had one credit and you could pool them. It was a good idea. Yeah. And then Gemini, I mean it has been mostly top down, which has been very good in some sense because it has led to a lot more collaboration and people working together. You less often have five groups of people all building the same thing. You're building interchangeable things. But on the other hand, it does lead to some incentive to, to say, hey, what I'm doing is working great. And then as a lead you hear hundreds of groups and everything is. So you should give them more chance. There's less of an incentive to say, hey, what I'm doing is not actually working that well. Let me try something different. So I think going forward we're going to have some amount of top down, some amount of bottom out up so as to incentivize sort of both of these behaviors, collaboration and flexibility. Because I think both those things lead to a lot of innovation.

Jeff Dean (133:55)

Yeah. I think it's also good to kind of articulate interesting directions. You think we should go. And I have an internal slide deck called go. Jeff Wacky ideas that I think is like, those are a little bit more like producty oriented things of like, hey, I think now that we have these capabilities, we could do these 17 things. And I think that's a good thing because sometimes people get excited about that and want to start working with you on one or more of them. I think that's a good way to kind of bootstrap where we should go without necessarily ordering people. We must go here.

Interviewer (134:37)

Hey, this is great.

Jeff Dean (134:38)

Thank you, guys. I appreciate you taking the time, and it was great.