AI is Already Building AI — Google DeepMind’s Mostafa Dehghani

Most of the people don't realize that this is like already happening especially over the past few months. In

almost every lab, the new generation of the models are built heavily using the previous generation of the models. What

is missing right now is long horizon and full automation and we're moving to that direction super super fast. The moment

that we have this full automation, we can close the loop of self-improvement.

We just got rid of the human bottleneck for improving these models which I expect to see a huge jump again from such development. Hi, I'm Matt Turk.

such development. Hi, I'm Matt Turk.

Welcome to the Matt podcast. Today my

guest is Mustafa Dani, a top AI researcher at Google Deep Mind and a core contributor to some of the most influential architectural breakthroughs of the last decade, including universal transformers, the vision transformer,

and the natively multimodal Gemini family. In this episode, we unpack

family. In this episode, we unpack what's hot in Frontier AI right now, including what it actually means for AI to think in loops and the immediate timeline for recursive self-improvement,

where AI autonomously builds the next generation of AI. We also dive into the technical evolution of image generation with Nano Banana 2 and why continual learning could completely disrupt how

enterprise data pipelines and rag systems are built today. Please enjoy

this fantastic deep dive with Mustafa Dani.

One of the hottest concepts in AI research right now seems to be the concept of loops. So I thought it'd be a fun place to start. This idea that models are going to improve not by being

bigger but by thinking recursively. What

does that mean exactly?

Definitely one of the topest active areas for um almost every lab to invest in uh looping and and it has like um like operation at different levels. The

one that is on on the uh on the micro level is basically the the the the looping that we use like architecture or at inference time for test and compute and stuff like that. And then at a higher level is basically the loop the

the loop that we have over the development of these models which is basically we refer to to it as uh self-improvement. If I want to put it

self-improvement. If I want to put it like like very like like let's talk about self-improvement as as like this general concept, right? If I want to u put it like very simply, it is really

just the continuation of the trend that we've been like writing for decades, right? And uh and uh think about it in

right? And uh and uh think about it in classical machine learning. humans had

to sit down and manually engineer the features and uh you had to decide like what the model actually uh paid attention to and and deep learning and neural network came along and and they

said okay let's just remove that let the model figure out the representation itself and um that was actually a huge deal and and we somehow removed a

massive human bottleneck and inhuman bias and then like further uh and instead of just like designing architecture we started learning them too. uh instead of curating like you

too. uh instead of curating like you know every piece of training signal uh we scale to kind of basically datadriven approaches and let the data speak and uh the self-improvement and and this like

loop into development is just the next step in in in the same direction and the whole idea and the whole point of it is you're removing the human bottleneck and

bias from improving these models right and now uh like you say that okay not not just human doesn't have to handcraft features anymore for but also we don't want the human to sit in the loop every

time that the model has to to get better and and I think that's basically on on the um on the development side. So it's

not radically new it's the same story just a new chapter of the same story and I think every time that we removed human from uh like human judgment from this process we kind of got over a bottleneck

I would say like this self-improvement and looping over the development is kind of like doing that at the highest level which is basically improving these models. If you want to go to more

models. If you want to go to more detailed uh level of looping, we can we can talk about ways of increasing test and compute for these models and

how we let these models to loop over their process within a specific problem to to refine it to to think about it and uh like I think the the most familiar

form is just like chain of thought and and letting the model to think with extra tokens that's beyond that and and you can think about different ideas is that you let the model to increase the compute for for any specific problems

like what if I have uh like dummy tokens that that they can use as like read and rot tape to kind of like you know reverify what I've done and and go through the the the solutions or the

process that I'm like doing over over different steps uh and and understand you know like what what what has been done growing uh what has to be done next or even like you know negative sparity

which is basically re reusing part of the model multiple times And um this sort of new pink is also like been shown to be super super helpful uh mostly because you just let the model to to

throw more compute on on a a difficult problem.

So that's self-improvement at inference time. I think you alluded to earlier there is um also a bigger concept that's maybe u I guess more

science fiction except it seems to be becoming a reality very quickly which is this concept of um recursive self-improvement or or RSI that seems to be um what a lot of people are talking

about I think is coming up in a in a few weeks and there's a bunch of papers focused on that. So what what is that what is recursive self-improvement as a concept? It's actually interesting

concept? It's actually interesting because you you referred to that as something that like looked like a bit of a sci-fi situation where these models are actually improving themselves and and that's true because a few years ago

when you were you wanted to talk about this you could just write a perspective paper at a at a conference and and like you know talk about it at super high level but if we go uh and and check out

what is happening right now like to a really good extent happening like most of the the the like and and and it's somehow like Most of the people don't realize that this is like already uh

happening especially over the past few months in almost every lab u the new generation of the models are built heavily using the previous generation of the models. I think that's basically

the models. I think that's basically like like the case again everywhere. Uh

and uh it's not fully automatic yet. Uh

but the direction is like super clear and it's like easy to imagine that you know like we're going to get to a situation with full automation. These

models are going to improve themselves and keep learning from the word and again it has like relation with other concept like continual learning and other other concepts that we are um

still not yet to to the to the most advanced uh like point of it. But if

someone comes and say that oh you know uh like uh I I have an idea to get a model to calculate the gradient and updates it weights uh like on the fly um it it just feels like very normal you

know it's not something that wow this is like such an amazing idea. I think what is missing right now is uh like long horizon and full automation and and we're like moving to that direction like

super super fast. the moment that we have this full automation I would say uh we we can close the loop of self-improvement and then um it becomes the like you know the problems become like you know mostly providing compute

for these models to actually do what they want to do and as I said like earlier comment we just got rid of the the the human bottleneck for for

improving these models which I I expect to to see a huge jump again um from from such development so people may have seen or heard about

kapathies auto research project a few weeks ago. Is that an example presumably

weeks ago. Is that an example presumably reasonably narrow to make it work? Is

that an example of a self recursive loop?

That is definitely and uh I think that was one of the early examples of uh like seeing these models actually doing something super sensible on the research side. So we've been seeing them like

side. So we've been seeing them like doing a lot of good work on improving the engineering part of the development loop. uh but on the research side which

loop. uh but on the research side which like you know you think about okay know maybe some sort of you know gut feeling or intuition is needed and like a researcher with a like a long time of

like you know playing with these models and experience can do this but but not necessarily you know like a model uh I think we've seen the sign that okay you know maybe that basically that kind of

like golden um part of the recipe a successful recipe that mostly coming from um like um intuition of a good researcher is coming to to kind of these

development loops by by these models and it's a bit hard to think about okay you know does it mean that we can replace like every genius researcher with these

bottles like like very soon uh maybe and I don't know like how how soon but this this is definitely a sign of u something that we we kind of doubted like you know a few years ago you know we couldn't

believe that wow this is going to happen that early uh which is very exciting I want to play it back just to make sure that people listening to this understand

we're talking about AI building AI and I think a few months ago if you talk to researchers people would say oh yeah we already use AI to build AI but that really meant that we use AI tools and

and and reasoning models to come up with ideas and and thoughts about building models but here what we're talking about is is AI automatically updating itself

updating its weights in a recursive manner leading to potentially a dramatic acceleration in progress and But what you're saying is that uh this is largely

upon us and a question of longer horizon and basically more more compute. Is that

fair?

I think so. Like this is one and the other one is also I'm not going to say that oh you know um soon we're going to have these models like fully automated and there are actually many problems

that we have to solve. Uh but

directionally I can see how this can happen. You know like uh like it's not

happen. You know like uh like it's not something that I would look at it as like super hard. It's like hard but but very possible.

Okay. So what what are the roadblocks?

So so you talked about compute is evaluation one of them because presumably the model needs to understand what is right and what is wrong in terms of the quality of the of the answer. Is

that one of the issues?

100%. At the end of the day you can only improve what you can measure right and then u getting evaluation is uh like just hard and um and at the end of the day it becomes almost a philosophical

problem not just a technical one. Like

this is actually a very interesting observation. So if you have a a like a

observation. So if you have a a like a team of super competent people, most of the time they can do like massive

progress on a problem if there is some concrete eval to heal climate. But if

there's no evout, it's just like really hard to to make progress. and uh and the fact that we don't have uh evouts that like or or like even defining evouts

that that can maybe measure oh you know how close we are to the point that we can actually get get a self-improvement look. It's just like we don't we don't

look. It's just like we don't we don't have that and and it's just making it like much harder to uh to to measure the the progress in that direction. Well,

there are proxies and there are definitely some evals that you know like we're going from uh oh maybe we can evaluate like every a step of the model toward this direction and maybe maybe we can evaluate up to this many turns of

the model or maybe we can evaluate the model helping itself to improve in a specific framework and in this specific setup and and this part of the the like the machine learning that needs uh like iteration. It's also quite interesting

iteration. It's also quite interesting because the difficulty of building eval is um um like the infrastructure that you need to reliably runs that are super

complicated is also like super it's quite funny but sometimes um figuring out that okay how can I uh create an

environment for a model that operates safely in in like within Google right uh and and does all the jobs that NRE and

and and and and like RA like research engineer or or research scientist can do like in a safe setup you know where they can like because right now we definitely we don't we're not confident about you know them doing the right things all the

time and measuring like how much they can push and and how long they can push a task is very difficult and um like connecting all these points into an

environment that these models are operating and then get them run efficiently and and like bringing diversity to EVA is definitely one of the bottlenecks of of like making progress in this direction.

A couple of weeks ago, we had a fun conversation with Karina Hong of Axium Math and we talked about formal verification. Is that a promising area

verification. Is that a promising area from your perspective? Is something like formal verification what would enable you to make sure that the you know the improvement loop keeps continuing? In my

opinion, formal verification is uh one of the most powerful uh like keys to to to enable like self-improvement, but it's not B key. And if you think about

it like for mass code logics, um it's great. You can you can run a proof

great. You can you can run a proof either it checks out or not. If you go to other domains that are a little bit messier, uh like for example, you cannot write a formal proof that if the doctor's recommendation is good, right?

Like so so it's not hard. It's not easy to to have like to extend this formal verification to all the domains uh in real world. But one question that is

real world. But one question that is actually an interesting question which is uh like uh very relevant to formal verification is how can we uh look at

these methods and and like formal verification and build that kind of tight and honest feedback loop for the messy part of the world. I think that that's like very inspiring to to build

like like on top of these like formal verification methods to to extend to domains that like not easy to verify easily. Uh uh but uh but but you need

easily. Uh uh but uh but but you need some sort of clean and tight feedback loop to be able to make progress.

So the same problem as reinforcement learning right like the second you start veering away from math and code uh you start getting into like very messy territory. Is model collapse uh one of

territory. Is model collapse uh one of the issues to think about or is that orthogonal? model collapse is definitely

orthogonal? model collapse is definitely a risk right and um I would say model collapse mainly happens when you have a loop that is completely closed right and

um if you don't have any outside signal and just the model for example talking to itself or or operating in a in a very uh like a like a restricted environment u there's a good chance that your mobile

call access but uh but if you have a strong verifier or some sort of like a real reward signal that anchor this this kind of like um like signals that is coming from like AI generated data for

example it can be quite powerful. I

think like the key here is to stay in grounded uh uh to to something real and then you can most likely avoid like you know things like model collapse and um I but yeah I mean again it's it's a risk

but it's not definitely like a major rocket and perhaps to make this accessible to to everyone can you define what model collapse is in the first place? So

basically when you have some sort of like data and environment that these models are interacting with but those environments and and data are are designed for example by another model

like like this is just an example of that right and then you become really really good at this specific part and then suddenly you lose generalization to anything beyond that and this is like

one of the kind of like definition or one of the cases that that like like a model collapsing uh would result to. So

you mentioned losing generalization. Is

that is that particularly in the concept of RSI a uh worry that either you have those self-reinforcing loops but they need to be fairly narrow or you have more general models but then you kind of have the loops.

This is an interesting question again like you know generalization versus uh specialization. Let me go a few steps

specialization. Let me go a few steps back. We had this discussion like like

back. We had this discussion like like many many times. How should we do a trade-off between generalization and specialization when we are developing these models? I think long term you want

these models? I think long term you want a model that knows everything and knows when to go deep versus wide, right?

Imagine like you have an agentic actor, right? Like if if you an agentic coder,

right? Like if if you an agentic coder, if your agent is like super strong at every step of operation, like a really really good program. It's amazing, you

know, like it's like super specialized.

But for many of the problems like coding problems you need some sort of planning and understanding what's going on and collecting information and and like you know based on the context deciding what

you do and then after you define the steps then your like super strong joint specialization just kicks in and and before that like being a generalist is is like super useful. Definitely

generalization is is like one of the things that u like you need to get to the ultimate side of AGI. Uh but short term um I would say like building a specialist model is like probably the

fastest way to learn like what is actually possible and um in in many cases these like specialized model are becoming a stepping stone toward a generalist model which which is like

super valuable right so so you can imagine that oh you know if I'm actually thinking about self-improvement uh maybe I I need to make sure that you know in a very specific area I can I can

build that maybe you know like I I focus encoding and then if it works out then I I go through like you know how to widen that and how to bring more into into this specialized setup. One thing that I

always like say is that people don't care what category their problem falls into, right? And and if a human calls

into, right? And and if a human calls like something a problem, then AI should be able to solve it. And I think that's like fundamentally a generalist need, right? So at the end of the day you need

right? So at the end of the day you need generalization and um like uh like playing this like like going through this spectrum of you know like super generalized model and super specialized

model is more about you know like a long-term short term and how to take advantage of each side uh uh during this process.

What's a specialized model today? Is

that a separate model or is that a broad general model that's trained in a specific way including in particular through through RL?

Okay, so here's here's the point. We

used to have constraint like compute uh and then if you wanted to push a model um to be like so we would choose specific dimensions and then we say that

okay you know we we want to kind of like allocate the compute that we have to that and then make this model really good at this like you know something that is like extremely expert at this.

So that was like basically the trade-off that we were we were um like trying to to make given the compute budget that we have as we as we go through this like like the the phase of compute becoming

like you know more available like cheaper and then you know like maybe we're constraint with other stuff like data and stuff. One of the other trade-offs that pops up is especially in post training like you know this game of

evacuable that sometimes it's like really hard to get your model to be good uh across the boards. So you try to kind of like make it good at like something

like you know multimodality somehow you see some regression on you know the coding and and you make it like good at like coding and multimodality it becomes a little slightly worse than than a

model that you had like you know at like math and reason. So, so it's hard to to kind of find a balance and and part of it is because post- training does a little bit of a like a overfitting you

know like like at the end of the day when you post train the model you are trying to overfeit it to the best local optima you have when recipe becomes um

like how can I find the best local optima it becomes the problem of okay there's no local optima that is good for everything so I need to kind of choose right and then uh like seeing this you

end up with like making some decisions along the way and saying that okay you know maybe for me at this stage because of the meat that I have in in my organization like with respect to the competition that is going on u I need to

choose you know this specific access like you know for example some companies have like a very strong focus on coding which is okay you know I I make my job like super easy you know like or or not super easy but like much easier than

than the competitors that they want to basically shoot a model that is good across the board I think short term it's like very very effective because uh first of all during development you care

less about you know like all the dimensions so maybe it's just faster to be trade like you you kind of free up some space from the mind of your researchers and engineers that okay you know like forget about this just let's

push this to the max and then the other one is also like you don't hit the the trade-off like immediately and specialist model is that like okay you know I'm going to pick this specific axis and then make the model look really

really good at this sometimes again like this this is the decision based on um the place that you are at you know again like organizationally like competitors like and stuff like that.

Great. You said something um a few minutes ago that I thought was so intriguing which is this idea that the carpathies of the world and and and and you of the world could be automated.

What happens if like the brightest minds in the world get automated and the and the AI creates itself? Like at at some point is there just no one knows how the AI works. Is that is that an actual

AI works. Is that is that an actual possible future?

This patch is very philosophical. I

don't know. Let me let me give you one uh quick things that I I thought about it a few a few days ago. I have a daughter. She's like one and a half

daughter. She's like one and a half years old. I've been impressed over the

years old. I've been impressed over the past few years. Like very interestingly like I've been proven wrong multiple times about like the the the timeline that I had in mind. For example,

sometimes I say like oh this is going to happen in 6 months. Never happened.

sometimes like oh this is just like so hard like within the next 10 years there's absolutely no chance to solve it and then boom like in two months 3 months someone had a brilliant idea and they solved it so so it's like really

hard to predict the future and I was thinking like okay you know like so you're talking about like like kipathy and and like you know again like other researchers but I'm thinking about okay like what about the next generation you know if if like my daughter at some

point comes to me and asks like okay like what should I do you know like what do you recommend to study like what major and like you know what branch of the the science or or or research should I kind of like, you know, dig in and and

like be the expert on? I I really don't have a good answer, you know, like almost it doesn't exist and and it's just like really hard to predict the future. What I know is there are a few

future. What I know is there are a few skills that are probably key to be able to uh to make impact in this world and

also be relevant staying relevant. Like

one of them is like a strategic and and having all the parameters on your table when you're making a decision and and becoming absolute

expert about a a very specific subject most likely is not going to be useful in in in like like the near future. I think

like you know the brilliance of gap I think is not like you know he's a good programmer or he's a good definitely he's a good teacher you know but but I'm I'm saying like you know these are not like the most impressive part of it like

the most impressive part for me is that he has a really good overall view of like what is happening like by putting himself in in the in the in the um like

the the stream of information he can make a decision about okay what is the next most impactful thing to do and now like you know the things that he does to make impact is very different from like

you know the things that he used to do like 5 years ago and I think he can be able to do that like continue doing that you know like what is that the things that he's going to do he's going to be doing like in like in 5 years I don't

know what I know is like he's smart enough to figure it out and and still keep making impact on on the board so AI researchers are not researching their way out of a job just yet

uh hopefully we are smart enough to to do that uh all right Maybe that's more of a macro question as I think about um you know where where the value lands in this

ecosystem but if AI just keeps creating itself then does is is data still needed in that equation or is that all compute concept of data is a little bit like

broader than just like you know tokens right like and and if you think about data as um whatever that the model can get signal from either it is like

predicting the next token in rock hex which we kind of like you know use in print training or super complex environment that the model interacts with and then get signal. Um this is something that basically like we can we

can like refer to it as like data right and it's not like data or or uh like the value of like having good data or working on data is going to disappear

and compute is going to become like the only things at the end of the day. I

think like the work that we're doing on the data side most likely is going to shift uh toward building environments or or making sure that these models can interact with with physical boards and

then it becomes more of a problem of okay how can I like provide more grounding for these models they are good at like improving themselves but as long as like you know I I have I expose them

to real world data right like and and like you know real world environment so so providing data becomes more about okay how can I give uh access to this

specific um like model to something that you know we never had for example like again like something came to my mind which is like again like a little bit sci-fi but how can I make like smell accessible to these models you know like

like right now it doesn't like there's no good way but then data becomes like okay you know like information or or anything that is for us because of all the sensory that we have is like really

easy you know like right now I'm sitting here I know how hard is my chair what is the temperature of this room all this sensor information is something that is coming to me and then I'm like the next

board that I'm seeing is based on all this input right and then providing this or a model that does self-improvement is already a really hard problem so I would say that the the work on the data would

shift toward making these sensory information more available to these models in a way that that it enables them to really improve themselves given all this information in a more effective way

yeah interesting yeah there seems to be a big trend towards sensors as a service. We're seeing the startups

service. We're seeing the startups emerge in that field. Okay. Super super

interesting. Zooming out from self-improvement for for uh seconds. The

big theme of the last year has been uh the um acceleration of post training in addition to pre-training. So the the whole um you know reinforcement learning

aspect um of of things. Where do you expect gains to come from in the next few months or or year? Is that more post training? Is that more pretending? Is

training? Is that more pretending? Is

that both? Is that something else?

The answer to this question really depends on when you actually ask this question. And like it's obvious that

question. And like it's obvious that like you know we're going to be having a bit of a swing back and forth between pre-training and post- training. At the

end of the day, I I I want to say that, you know, pre-training is still the foundation and like you can never post- train your way out of a veb based model, but right now the current like the

return on post training is really strong and I started working on post- training myself like like a few months ago like Gemini post training uh like mostly coding and agentic. I can see how a

brilliant small idea can make a model like 10x better for example in terms of behavior at a fraction of the cost of the pre-training right this is again I can like you know we can we can see how

post- training is like like the place to make a lot of impact and improve these models but on the other hand um like I I know at different companies it's also the case but at GDM a lot of exciting

reset work is going into the pre-training side and like new new recipe new ideas and Um uh and and I would say like you know the work that we're doing on the pre-training is uh

going to unlart a lot of downstream possibilities. Post training is just

possibilities. Post training is just like a different mode of operation. It's

like also super interesting for me because I'm again like a little bit like new to to this to this side of the the the operation. Uh but but at the end of

the operation. Uh but but at the end of the day I always expect to see like like going on like you know between post training and pre-chain. your comments on on pre-training are uh sort of against

like that narrative that that appeared a few months ago that pre-training was dead. That's not your take at all.

dead. That's not your take at all.

Right. I think everyone has ideas on pre-training side at the end of the day like going for that idea is a function of complexity and the expected gain right and sometimes you feel that okay

you know there are low hanging fruits and and it like you know instead of bringing this complex like you know recipe to the pre-training the one that I have like which is simple elegant

super scalable I'm going to push this and then move the the effort to the poster and then at some point like the the base model becomes the bottle like and then you're happy to take the

complex recipe and bring it to the pre-chaining and then like hit pushing it. I think pre-training is dead. I I

it. I think pre-training is dead. I I

would say like maybe like you know the old it's also like a little bit like typical to talk about old and new because like the the time frame is like very depend so when I say old maybe I'm referring to

like you know two weeks ago or something but uh but but the way that we used to do pre-shing maybe like you know like two a year ago or two years ago uh maybe

like you know diminishing return is like obvious but I can see how new ideas are are bringing like you know fresh fresh um uh energy into the pre-training and suddenly just open a door toward like

like something exotic that might actually drastically change um the base model capability over time.

So exciting stuff for Gemini 4 when whenever it comes out. You mentioned

continual learning earlier and that's another one of those hot topics that people have been talking about. Can can

you define continual learning for us so that uh this conversation is educational to for broad group of people? Uh maybe

compare and contrast that with the self-improvement loop. Like those are

self-improvement loop. Like those are two different things but um help us understand the the difference.

Definitely they're related but like they're distinct, right? So

self-improvement is about a model getting smarter over time and improving its capability like the model itself doing it. Continual learning is mostly

doing it. Continual learning is mostly about a model staying current, right?

like and and and think about a doctor that like keeps reading new research and they like refresh their their knowledge about um uh stuff and and like you know they're trying to make sure that you know the knowledge doesn't go stale. The

shared enemy between like um self-improvement and and continual learning is a model with frozen weights uh over time while the the board is just like going right like you know if if you

have if you have a model that is just frozen and the board is moving then like you neither get like self-improvement nor continue learning but continue learning is mostly focused on um making

sure that you know if there's like fresh knowledge in the board uh uh like the my the model knowledge cutoff is not um like uh in the past. So it's constantly

you know like for example overnight all the news everything that is happening in the board everything is just like you know updated. So if today you asked a

know updated. So if today you asked a problem if you ask a question from the model those knowledge which is like super fresh is already in the weight of the model. So it's it doesn't have to

the model. So it's it doesn't have to kind of like you know depend on external source to to to bring it in. And it's

hard. It's like really really hard. And

the biggest problem um uh like not the biggest but like one of the big problem is like catastrophic forgetting where you get your model to to uh learn about

new information um after you're done training that model like you know and then and and suddenly you see regression in in in the knowledge that you learn already in in the in the main training

uh phase and it's a very active u area of of research right now and uh what's the reality of continual learning as of now is that is that's built into existing systems not at all

about to there there are two sides of it like one side is I think like the research is not like yet to a a very like uh to a to a

point that you you think that oh you know this is this is the recipe you know I just need to kind of like you know exploit it and push productionization right but basically you know like every time that you have uh a new problem that

is like key you have this phase of exploration where people like try to kind of like you know try different ideas and you know like uh go jump over this like idea to another idea which

could be like so different and then when you're confident about this kind of working to some extent you go to the exploitation mode and say that oh you know let me just make it as good as it can be and you know this is the way to

to kind of push it and and let's scale it let's just like you know develop infra for it make it like super fast like you know productionize it and see what happens I think like that that is

not yet there the other point is also again like as I said because we we never had like like super confident um uh recipe for continual learning like building infra and investing in in

something that is like fast is hard given that I like I've I've seen like very impressive progress on this of this within GDM it's it's kind of interesting because it is one of the things that you

know it can be heavily theoretical I' I've seen people who are like you know like uh doing a lot of theory work and they got into this like problem and they're having a lot of fun and they're

also like making a lot of impact and uh it's impressive how much progress be made on on this but I don't think that you know we have yet like uh like any any idea that like like everyone says

that oh you know this is it you know like let's just do it you know like let's push this great I'd love to talk about you and and your background um tell us your your story in a few minutes like how did you

come to do this work and what was your journey to AI and then your journey to to Google deep mind so I I did my PhD at University of Amsterdam on machine learning and and mostly on the on the like the language

model side and text and and search and retrieval. And then uh I think what

retrieval. And then uh I think what kind of like pushed me toward trying really to be on the uh like on the on the mainstream and be part of this group that are like you know hustling to to

make like really good progress. I did a few internship like back in 2016 and 2017 and and the funny story is um I did an internship in at Google brain in 20

like early 2017 and then it was amazing.

It was just like you know I went to to to this team they were working on like LSTMs for you know like summarization summarization was actually one of the most like interesting problems at that

time. I was like amazed. I was like,

time. I was like amazed. I was like, "So, this is so good. I I really I just want to keep doing this for the rest of my life. You know, this is it." And then

my life. You know, this is it." And then I got uh I got a return offer to go back and then do another internship at the end of the the the same year. The

recruiter told me that, oh, you know, there's this team that they just published a paper, maybe you've heard about it, like Transformer, and then they're looking for an intern. And I

have a had a chat with I remembered I had a I had a chat with Lucash Tit and then Lucash was talking to me and was saying like yeah like we have this idea of building like aogorph machine based

on transformer and he was so excited about this and then like you know we like we finished that the conversation and I started sending a message to recruiter. I was like I don't know if I

recruiter. I was like I don't know if I want to go with this team. It's just

like they're doing something random like who like like everyone's doing LSD. I'm

like why should I go and and work with a like a a group of people who were working on this like random architecture like transformer it's just like it's going to die you know I and then he tried and he couldn't find any other

team for me to to join so I joined this team as an intern and that changed my life you know like uh like being among these like super

brilliant like super smart people that they believed in in some vision and direction where almost everyone was excited about something else was like very inspiring and then we work on on

like again like this colograph machine like idea of like turned into like you know universal transformer prepare which you know like recursion in depths and reusing parameters was was coming out of it and still this is like making a lot

of impacts after almost like 10 years.

Tell us about that quickly. So that was in 2019 I believe and you you so you were a co-author of that paper and that was very much that idea that we started with right at the beginning of this

conversation of like loops and recursive stuff. So universal transformer like we

stuff. So universal transformer like we wrote that paper in 2018 and I think it was also rejected one time from one conference uh and it was accepted in 2019 um I don't remember exactly but

yeah I think it was accepted I clear but it was rejected from new rips or something the whole intuition was um there is something about reusing

parameters and a model going through um its output another time you know like and and so so basically you generate something and then you kind of like you know pass it into the model again and

then the model has the chance of doing this. So we started with like I I

this. So we started with like I I remember Lucash had this like algorithmic data set which I remember he he used to call it like algorithmic tasks uh and and it was part of this

codebase based on TensorFlow like tensor to tensor was the name of the code that is still there and and I remember I I can even find my put request into that for for pushing the universal transformer code and we saw that

basically there are some problems like copying an input to the output or you know like doing something algorithmic with like super long input uh on on the

output side which is super easy but the normal models like the normal transformer was like failing awfully at this and we saw that you know like looping is like do it perfectly and then

at that point I remember we had this baddy like data set from from meta um and uh it was like doing great on that and then the idea of test time compute

which basically you train with fixed amount of compute but at test time you unleash your model to do more computation throwing more flops on the input was coming to our mind like super

excited about this and then we ended up with actually kind of like introducing this adaptive computation mechanism into this which was again like like some sort of inspiration from Alex uh um like Alex's paper like you know from from

LSDM and then like a very interesting interesting ride because we we were pushing for something that like at that time uh it it sounded exciting but and I have a guess like maybe at that time

like the whole field was a bit too focused on using adaptive computation for decreasing the cost on simple problem but now we know that maybe we can actually use adaptive computation to

increasing to increase the cost for hard problem you know it's actually like the other side of the same coin right so because at that time we were like you know like maybe you know uh like resource constraint and everything so we were really thinking about why we were

spending so much fops like you know going through all the layers and like you know everything's for dot like at the end of the sentence if that that token is like do we really need like 24 layers. So how we can decrease that but

layers. So how we can decrease that but now we we had a different perspective to that which is like you know how can we increase this for a physics problem that we want to run the the the inprints for maybe like you know for two weeks. So

that was that would be really fun to work on that with with these like brilliant people and um and I think like you know this recursion in depth and reusing the parameters or I've seen later like some some people actually

framing it as negative sparity which is a great way of you know like connecting it to mixture of experts that you know in mixture of experts you have flops free parameters. So, so parameters that

free parameters. So, so parameters that you they're not actually bringing any flops and in in like looping you have um parameter free flops where you don't

have extra parameters for the extra flops that you are throwing on this. So,

it goes the other direction of the sparity and it's quite effective and and I think people are picking it up you know so the we're seeing a lot of you know like excitement in this direction.

Fascinating. Another fundamentally

important contribution to the to the field that uh you did was the visual transformer paper in 2022.

So the paper is called an an image is worse 16 by 16 words transformers for image recognition at scale. Do you want to walk us through what that was?

That's also a funny story for that. I

got into vision and multimodality with that paper. So I've I've never worked on

that paper. So I've I've never worked on on on like any vision problem. It was

mostly because I was sitting next to people who were working on vision. So

like my desk was like next to people who were working on vision and that was the reason that I got interested because I was just talking to I was like oh this is actually interesting and uh and then and then I remember that at that time I

was like working on um uh on uh like externally we call it palm paper with like you know acha and other folks and I

was like why we have uh 400 billion parameter language models but the biggest model that we have on the vision side is just

maybe 100 million like arrest that like why like like why there's no benefit of a scale started looking into this with with folks on like okay like maybe you know like there's something in in transformer that actually kind of like

you know make it a scalable and then maybe you know like we we can move away from convolution to try this and and at the end of the day I don't want to say that you know like that's the only way of a scaling maybe you know if a group actually spend like enough time on

convolution they can also make it as scalable and like you know like as good but there was also benefit of doing That's simply because the rest of the that the machine learning field which was working on on language they were

using this a like a like architecture.

So they were building infra for it making it faster and and you know like like the sometimes the hardware is kind of like designed based on this architecture at least for short term. So

we started pushing and then I remember that you know we had um we had a bunch of ideas that okay what if each pixel is a token and then um like the cost was going high the context was just like

getting super long and then we had a lot of back and forth and it's also quite funny because we started thinking about this problem like from like very complicated point of view so we were

trying to mimic convolutions to be able to get this working and it ended up like you know like I I had a bunch of colleagues also in Zurich and they started with trying the simple idea of

what if just like divide the image into patches of pixel you know 16 by 16 and then get each patch as a pixel and forget about you know like overlapping patches or you know like windows and

stuff like that's it you know like you know like chop the image and then fit it to a transformer and then scale you know like like go with a lot of data and then like let's start with like you know something discriminating to train this

model and it worked and it was also a little bit like of a surprise for us that oh you know like we were all thinking about something like you know fancy and and very very complicated maybe in the integration of having like

you know um like convolutions and stuff but something that worked was basically the simple idea of you know like um patchify fit it to transformer scale it up and then boom you had a really really

good model for representation learning.

Yeah. And to play it back at a at the highest level, that basically meant that you could apply transformer architecture to image when in the past you had two different families. You had the CNN

different families. You had the CNN world and the transformer world for text. And your breakthrough was to prove

text. And your breakthrough was to prove that transformer could scale equally well to images which basically paved the way to a Gemini 3 today which is like a natively multimodal model. Is that is

that fair? Okay.

that fair? Okay.

Yeah, that is true. Yeah. Like so

basically we we like with that we kind of took a step toward having also videos like adopting transformers and and audio like you know adopting transformers. So

basically we like again like even even if this is not like the only architecture that would be like you know multimodal but it made it really simple to train these models like natively because you have like a single

architecture and can have all the modalities in during training.

Great. So that's a perfect transition into your work into nano banana and the the future of image AI. So, so you were part of the Nano Banana team and which

must have been so much fun when this came out and uh went just completely viral and what an incredible product.

So, since then there's been a couple releases. So, there's been a Nano Banana

releases. So, there's been a Nano Banana Pro in November of 2025 and then just a few weeks ago Nanobanana 2 uh aka Gemini 3.1 flash image. Yeah. At the end end of

February. So a lot of people assume that

February. So a lot of people assume that um image generation works as a translator, meaning that the AI reads the the text of the prompt and then

translates it into picture instructions and then draws it. But as we were saying, Gemini is natively multimodal.

So how does how does that work? How does

a model actually uh process the text and the pixels at the same time to build the image? I think the reason that maybe I

image? I think the reason that maybe I got to the generation. Okay, by the way, there's also one one uh one thing that uh I'm not an expert in image generation like like when I started working on

this, I I remember I had like meetings with people and then they were talking about like you know computer graphic and all the like you know like old like ideas about or or like intuitions and I had like zero idea what's going on. I

was like I I know how to train and transform and scale it and and you know if it helps I I can I can basically um contribute to this. But again like you know it was fun because I I worked with

like a group of like super smart like brilliant people with like really really good intuition and I think like the reason that I was excited about this was like like this is maybe not super

relevant to like nonabanana itself but to just mention this I was excited about the idea of uh like positive transfer across modalities. So when you when you

across modalities. So when you when you think about multimodel uh like natively one part of it is that oh you know I'm adding capability to my model you know so my model can understand images and

understand videos and understand audio but also like gener like and text but also can generate all these modalities you know like so so I have a model that actually does all these together right this is for sure exciting like from the

product point of view you know like you have a model that is like you know great model for for generating all these different like outputs and and like users are like finding it like very

useful and interesting. But the the the most exciting part for me was can I see a a glimpse of transfer from these modalities like you know like for

example if I train a model to become good at image generating images does it become also good at like better at like generating text there are different like different intuition of that you know

like what why this should happen I think this is like something like again like very old in the literature on on the linguistic site that they call it reporting biases right so like you for for example you know like visit your 's

place, right? And then you go to their

place, right? And then you go to their place and then you see that they have a banana shaped like uh like sofa. And

when you go home, the chance of talking about that sofa compared to a a normal sofa is like much higher. So you can actually talk to your friends or partner later. Oh, you know, I went there and

later. Oh, you know, I went there and then their sofa was like in the shape of a banana, which was really fun. But if

it's like normal, like you almost like it's weird if you go somewhere. It's

like, oh, by the way, I went to my friend's place and they had a sofa, which was like super normal. So, so this is this is the language reporting bias.

So, so, so language doesn't talk about things that are like at the middle of the distribution, right? But if you have an image or or if you have like vision input from from anything in the board,

you have that information like there's no need for reporting it. It's just like there, right? So, so because of that,

there, right? So, so because of that, like picking up a lot of knowledge about the world through language is just not really efficient. I I don't want to say

really efficient. I I don't want to say that it's impossible, but it's not efficient, you know, like to learn about gravity. If you kind of like, you know,

gravity. If you kind of like, you know, have your model train on videos, it's much easier to to get the model to learn about gravity because it just happens in a video than training your model on on

all the textbook to kind of like learn about the concept of gravity, you know, or or what is actually gravity.

Is that a concept of world model that's built into the image representation?

Exactly. Exactly. So, so basically you you want a v model basically you know like these models to be also like a vote model. So you you want these models to

model. So you you want these models to know about the war. There's a good chance that that you can actually teach your model about the war just by presenting text to it. But it's just not efficient and and and a good shortcut

would be to bring multiodality into this. And the best way of learning about

this. And the best way of learning about uh a modality is learning how to generate that right like so we we got to this point that okay you know u uh we've been we've been having Gemini generating

like images uh from Gemini one. So we

like like basically like Gemini was multimodal from day one and and the reason that we kind of first like released the image generation at like 2.5 instead of you know like Gemini 1,

Gemini 1.5, Gemini 2 uh was that it was not great and then like it really needed a push and then we figured out that okay you know how to push this without like you know introducing any regression to

other capabilities that the model has and you know like bring all of these natively into the into like this this And that was like one site that was like

super interesting for me. Like not sad news, but but but it's really hard to see positive transfer. So it it turned out to be a really really good battle, but it was like really hard to see that

wow, you know, I train on images and then like text perplexity goes down.

That that was hard to see, you know, like the fact that you know, you you train in native model and it's good at at like across all the capabilities is already impressive. But my hope is that

already impressive. But my hope is that you know multimodality and what model is the way to really push multimodel training to to enable like positive transfer across modalities. I work with

people that they were like expert on this you know for example one of the one of the things that I remember that you know at the beginning they were talking about this like visual quality and then I remember that you know it's like oh this model is a great model I send them

them and there's like no this is not a good model. I was like, "What do you

good model. I was like, "What do you mean?" And they they started showing me

mean?" And they they started showing me two images that to my eyes they were like looking the same, but they were saying like, "No, this is like way better." I was like, "No, they're the

better." I was like, "No, they're the same." So, so they had they had a good

same." So, so they had they had a good taste on on grasping the the visual quality of images. So, working with them was like really interesting to kind of understand that, okay, there are dimensions. And by the way, like their

dimensions. And by the way, like their intuition was the was the the things that actually made like not a banana like a success in terms of, you know, being a good product. But I was like okay what if we push this towards

something beyond like traditional image generation. So instead of like a

generation. So instead of like a translator that as you as you said like a a text to image it becomes a a a thinking machine about images. You know

for example you know you you enable interled text image generation where the model can think in not only text token but also in pixel space. Right. So, so

it generates uh text and then generates an image and it generates another text another image and you can leverage that for different problems like one of them is that oh if you have you know like

some sort of a story right like you know like text of the story image related to that text of a story like you know like children's story book right another one which I I was actually really excited about was like this incremental

generation like let me just give you an example so if you take like dolly or imagine or or standalone an image model, right? If you ask these models to

right? If you ask these models to generate an image of like, you know, a scene with 50 details, they might fade, right? And then someone can say that,

right? And then someone can say that, oh, you know, okay, I I can like generate a better model that like does up to 55 details and then say, okay, what about 60? And then I say, okay, no, let me just go back and train it and

then come back to you to cover your test. But at the end of the day there's

test. But at the end of the day there's a threshold that these models can kind of like follow instruction to to like to some extent about you know how many details that they capture from from the

text. But if you have incremental

text. But if you have incremental generation so if you have text and then an image and text and image you can't get your model to generate these details

one by one. So you you never expect your model to to generate an image a perfect image in the first shot right? So, so

you expect model your model to plan about this generation. So, it says that oh, you know, let me start with big objects because, you know, later I'm going to have a hard time if I put like

small objects and the big objects don't fit, right? So, let me just do that. And

fit, right? So, let me just do that. And

then like in the next turn, I go with like medium objects and and smaller. And

this like super smart, you know, and you're never bottlenecked by the capability of a single shot image generation because you you did planning and then you tune every step difficulty

to to match the capability of your model to generate a one one shot. So that was also one of the things that like you know Nana a banana and and native generation interle generation uh kind of

brought like a completely new perspective to image generation uh work which is like a little bit like far from like you know just translating text into an image.

Fascinating. Does part of this contribute to efficiency? So especially

nanobana too you have the the flash aspect of this. So you're able to create amazing images very fast and apparently I mean seemingly very efficiently. So

what's what's behind the scenes that is that what you describe is that how are you able to do that?

First of all I was I was involved in the original Nano Banana Nano Banana Pro and then the last version I g like you know because I I jumped on the post training and coding and agent and I find it exciting this one like this the team

actually shipped it. But if I want to say like like super high level that you know what is exactly the things that makes the model like uh like faster and more efficient part of it is just the size of the model. So like non banana

was pro size and this one is just like flash. So definitely the the the

flash. So definitely the the the parameter size like you know configuration of the and stuff. The

other one was uh people actually spent quite a lot of time on figuring out like you know uh um like nailing down like distillation recipe uh both on the side of like knowledge and like you know

other like things that basically you you kind of like need to distill to something like a process that is like you know lighter than the full process.

Surprisingly a lot of infra work for serving. So we have like really really

serving. So we have like really really really brilliant people that they're like you know serving engineers and it's kind of impressive that like you sit on your desk and then

like they they come and they say oh by the way but casually I made the model 10x faster. I was like you know like

10x faster. I was like you know like it's just like and they just kind of saying it like you know in a very like you know casual like wow this is like impressive. We had also a lot of work on

impressive. We had also a lot of work on the on on optimizing the serving uh how to serve these models and and you know like because these models are operating differently from like just like normal

nbridge model like they're not necessarily you know like um the same as you know next to prediction this is definitely something that you know a good serving engineer can figure out that okay you know I can think about a

different way of doing that and we had also a lot of improvement on the efficiency side by by their work. All

right. So, as we get towards the uh end of this conversation, I thought it'd be fun to end with a few hot takes if you if you're ready for them.

Yeah, absolutely.

All right. What is one thing the AI field is getting wrong right now?

not easy to pinpoint like a specific things but again like you know this is just like my personal opinion and and maybe I have colleagues and and like other people like sharing this with me

but uh I think we're underestimating how hard uh uh like jagged intelligence is to fix we're missing how like like understand like we're underestimating

how much it matters and uh we talk about almost like people laugh and and and go you know like if if you have a model that does like a like a very difficult

like math proof but has difficult time like counting like letters in a in a war. Uh as I said just people just laugh

war. Uh as I said just people just laugh and and move on but but I think it actually like pointing at something like deep and unresolved about these system like the way that these systems kind of

like you know represent unprocess knowledge and it's not a bug that you can patch. So definitely you know like

can patch. So definitely you know like like we we like we we see that you know this is happening you know like people sometimes you know oh like or or we have this problems that you know something is like awfully sad and then you can oh you

know let me just like you know patch by adding something for the system instruction or the opener instruction a bit of a structural property of how these models actually learn. So I I

would say this is probably one of the things that we're we're not getting it like super right at this point.

Great. What is one idea in AI research right now that is underrated?

Something that is underrated like you mentioned continue learning. I I think this is this is definitely underrated.

As I said, you know, like sometimes the problem stays in the exploration mode uh until we are confident about something and then it goes to the exploitation mode. I I think we're past the time that

mode. I I think we're past the time that we really had to push this to the exploitation. So maybe like foundation

exploitation. So maybe like foundation models are are essentially right now like frozen in time and like when the like the training ends, right? And then

everything is like built on top of this frozen model like you know rag pipeline and and fine-tuning workflows and retrieval system and all these like elaborate like infrastructure is like um

all based on this assumption that these models are frozen and uh it's a bit of a like a too much of a strong problem there assumption to make and and and I

think like we are going to like get to the point that we need to change these assumptions and like maybe like we need to think about it a little bit more actively and have um pushing it toward

like you know something that we actually push it to productionization and maybe it's a little bit like underrated right now like the continual learning. So you

think rag goes away over time?

It's not going to look like as is today and it's going to be different but like saying that it's going to go away completely. I'm I'm not sure about that.

completely. I'm I'm not sure about that.

And and one of the reason that I say that is rag is not just about bringing like fresh information to the model when when it wants to kind of like solve a

problem about like the current state of things, but it also has this kind of like in context learning and and there is a difference between in context learning like the information that you

have in in the context of the model compared to the to the information that you have in the weight of the model.

like continuous learning and and rag are are doing different things uh for bringing this fresh information. Maybe

it changes in a way that you know like it doesn't need to trigger rag for like everything but I'm pretty sure that there going to be some tail of the distribution that we're going to do rag still for it you know like what's the time

all right last couple of hot takes what do you think people are too confident about so people think that you know the pushing the technical side is uh is sufficient that if we just like get a

model that is smarter uh everything is going to follow and and in my opinion like a version of AI that is like you really really brilliant at um like technical problems, but it has a like a

blind spot about you know everything else. And that version is not going to

else. And that version is not going to be able to actually create meaningful prog like progress in in the war. And

the fact that you know people kind of assume and and confident about like like they're confident about this that that kind of like you know everything is going to everything else is going to follow or uh uh or or just everything

else is just like a small list. Um I

think it's wrong like you know we have governance we have like you know regulation we have social trust we have like for example distribution of access and the benefit like in the world for

for this technology and even the like you know the institutional capacity to to kind of like absorb and adapt this technology is is just like this is something that maybe we don't have

enough um like attention to and uh and and these are not really solved problems if not harder than the technical part.

They're they're really hard and like the pace of technical progress is definitely like like um currently running ahead of the world's capacity to to develop this

kind of like you know mechanism uh and this gap is getting like you know bigger and bigger but what I'm saying is basically like you know the field needs to hold both things at once. So maybe

that's uh yeah that's one of the things.

All right, and last one and I don't know if that's a hot take or maybe just advice for anybody entering the field today. If you were going to start from

today. If you were going to start from scratch today, what would you work on?

I don't want to start from scratch. It's

hard to start. Um I can tell you like you know there there are two things that I I think like you know would be nice to spend more time on it and there's one thing that I'm like very excited about it. I start from the the things that I'm

it. I start from the the things that I'm like really excited about it and and I would say like you know short term it's like really exciting to push it. I am

actually trying to even like you know be able to contribute to to this direction and and that's like full automation of like super long horizon task things that you know like you have a machine working for maybe like two weeks one months like

the agents today are are very impressive right and and and the demos are like very like very marketable but like there's this compounding reliability problem that doesn't get talked about

like you know enough and um and for example like imagine if an agent has to take 100 sequential steps to complete complete a task and imagine if each step

has 95% of like success rate which is like great you know like I like given the the the the the models that we have today 95% is like really good the

probability of um completing the whole task without a single failure is like 0.95 to the power of 100 which which is

like less than 1%. and and and this math is like brutal, right? Like you know and and this like 95 per step 95% per step as I said is like very very optimistic.

long horizon automation is like u like definitely isn't impossible but it requires a level of like you know per step reliability and and like you know error recovery and and and the current

system maybe don't have it and uh if if we want like you know social trust and and you know basically having like people like really using it you know at the end of the day like like people

don't experience average performance of these models then they experience like the failures if you have your model doing a dumb mistake the the damage in the trust that it makes is like bigger than than like you know the benefit of

getting 100 things right right like 100 imperfect uh things right so this like reliability in this like long horizon task is something that we definitely need the kind of like side of as I said you know two two kind of like more

philosophical high level things I would definitely like work on grounding problem and how we can build AI system that are robust and connected to physical world as I said you know like

like soon like the the concept of data like you know how to kind of like you know enable these models to kind of like you know be very good at like self-improvement becomes how can I ground these models in in real war so

this is definitely like something that you know would be the bottleneck of self-improvement if we don't actively think about it we should definitely move away from like this statistical pattern in text and pixels and the other things

that is uh maybe kind of like you know related is um even thinking about you know a better definition of like intelligence itself right a little bit like philosophical but um but it's a

definitely a practical practical question. Uh and uh like the whole field

question. Uh and uh like the whole field and and us you're we're building uh like more and more like something that we haven't like really defined you know like like we're we're trying to kind of make these models smarter and more

intelligent but like the definition of intelligent is just so handy and fuzzy that it's hard to actually kind of like you know measure the meaningful progress like which is related to your question that about you know like how about like

evaluations. It's good, you know, we

evaluations. It's good, you know, we have proxies, benchmarks, scores, capabilities and and and even vides, you know, which is I I find like super useful, but at the end of the day, uh we

really need a a systematic way of uh maybe defining intelligence that that is hard. And uh again, like making progress

hard. And uh again, like making progress based on what we have right now is good, but at some point that becomes a little bit more important to really pinpoint that, you know, what is the target and

what is the goal and then uh push toward that like like uh with maximum speed.

All right, Mustafa, it's been a absolutely fantastic conversation. Thank

you so much for spending time with us.

Really enjoyed it. Really appreciate it.

Thank you.

Yeah, thank you so much for having me.

It was like fun to to chat and uh thanks for for the invite.

Hi, it's Matt Turk again. Thanks for

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