Announcing Google's Secret New AI Model With The Person Who Built It | Logan Kilpatrick

In this episode, we speak with Logan Kilpatrick from Google DeepMind about the innovative Gemini AI model and its advanced image generation and editing capabilities. 

Logan discusses Gemini's processing power, efficiencies from Google’s TPUs, and how its AI Studio enhances creative productivity. We also explore future applications in robotics and scientific advancements, along with AI's impact on search technology.

------
🌌 LIMITLESS HQ: LISTEN & FOLLOW HERE ⬇️
https://limitless.bankless.com/
https://x.com/LimitlessFT

------
TIMESTAMPS

0:00 The Rise of AI in Image Generation
0:50 Announcing the New Model
1:14 Image Generation and Editing
4:44 Use Cases
5:42 Behind the Scenes
6:59 The Future of Video Production
8:23 AI Accessibility
10:16 Google AI Studio and API
11:13 Live Demo
15:31 Multimodal AI Models
16:36 AI Advancements in Science
17:35 A Revolutionary Tool
19:51 The Intersection of AI and Search
28:32 Expertise in AI
37:19 Simulations and Robotics
41:41 Scaling AI Infrastructure
44:25 Understanding Token Consumption
50:58 AI Applications in Drug Design
54:57 Accelerating Scientific Progress
1:03:33 Google's Journey in AI Development

------
RESOURCES

Logan: https://x.com/OfficialLoganK

Gemini Models: https://deepmind.google/models/gemini/

Josh: https://x.com/Josh_Kale

Ejaaz: https://x.com/cryptopunk7213

------
Not financial or tax advice. See our investment disclosures here:
https://www.bankless.com/disclosures⁠

Creators and Guests

Host

Josh Kale

What is Limitless Podcast?

Exploring the frontiers of Technology and AI

Logan:
The best image generation and editing model in the world.

Ejaaz:
It's scary how realistic this stuff is. VO3 has kind of like killed the VFX studio.

Logan:
And this is, I think, principally enabled by Vibe Coding. My hope is that it

Logan:
actually ends up creating more opportunity for the experts and the specialists.

Josh:
How much of the tools that you build do you find are built with Vibe Coding?

Logan:
I'm like almost 85% of everything that I do Vibe Coded.

Ejaaz:
I remember when I first booted up a PC and I just had access to all these different

Ejaaz:
wonderful applications all within one suite. This kind of feels like that moment for AI.

Josh:
Gemini is feeling faster, but it's also feeling better, and it's also getting cheaper.

Ejaaz:
What's happening behind the scenes?

Logan:
We crossed quadrillion tokens, which comes after a trillion if you're not.

Logan:
You haven't thought about numbers higher than a trillion before.

Logan:
It's what comes after a trillion, and there's no slowdown in sight.

Josh:
We have an incredibly exciting episode today because we are joined by Logan

Josh:
Kilpatrick. Logan is the product lead working on the Gemini platform at Google DeepMind.

Josh:
We have an exciting announcement to break right here today with Logan,

Josh:
which is the announcement of a model that we previously knew as Nano Banana.

Josh:
The reality is this is a brand new image generation model coming out of Google

Josh:
and you can access it today.

Josh:
So Logan, tell us about this brand new model and what we need to be excited about.

Logan:
Yeah, for people who are not chronically online and seeing all the tweets and everything like that,

Logan:
part of the excitement has been in over the last, I think, like six months,

Logan:
we've seen the emergence of like native image

Logan:
generation editing models historically um you

Logan:
would see models that could actually do a really good job of generating

Logan:
images um they were usually like tend to be like very like beautiful um aesthetic

Logan:
images the challenge was like how do you actually use these things in practice

Logan:
to do a lot of stuff that's where this editing capability is really helpful

Logan:
um and then so we started to see these models that can actually edit images

Logan:
if you could provide an image,

Logan:
it would, and then prompt it, it would, it would actually change that image.

Logan:
What's really interesting though, is this fusion of those two capabilities with

Logan:
the actual base intelligence of the Gemini model.

Logan:
And there's a lot of really cool ways in which this like manifests itself.

Logan:
And we'll look at some examples of this.

Logan:
But it's this benefit of the world knowledge. The model is like smart.

Logan:
So as you, as you ask it to do things, and as you ask it to make changes,

Logan:
it doesn't just like take what you're saying at face value.

Logan:
It takes what you're saying in the context of its understanding of the world

Logan:
and its understanding of physics, its understanding of light and all this other

Logan:
stuff, and it makes those changes.

Logan:
So it's not just blindly making edits or generations. They're actually grounded

Logan:
in reality and context in which that's useful.

Logan:
And we can look at some examples of this. My favorite thing is actually this editing capability.

Logan:
So this is an AI studio, and we'll have a link somewhere, hopefully in the show

Logan:
notes, that will let us do this. My friend Amar, who...

Logan:
Is on our team and drives all of our design stuff um build

Logan:
this and it's called past forward and what you can do is you can

Logan:
put in an image of yourself and it'll regenerate a

Logan:
version of yourself um in this

Logan:
sort of like polaroid-esque vibe um following all the different trends from

Logan:
the last 10 or 20 uh 30 years um so if you look at this example this is from

Logan:
me from the 1950s and And I'm sure I have a picture of my dad from the 1950s

Logan:
somewhere or my grandpa who looks somewhat similar to that.

Logan:
Here's me in the 1980s, which I love. Here's me.

Ejaaz:
Some of these facial expressions are also different. Like you're showing your

Ejaaz:
teeth more in some and then it's a smirk in others. That's super cool.

Logan:
I like this sweater. I actually have a sweater that almost looks exactly like

Logan:
this 1970s one, though I don't like my hair in this 1970s one.

Logan:
Same with the 2000s. um so one of the cool things

Logan:
about this uh this new model and one of the features i

Logan:
think folks are going to be most excited about is this character consistency which

Logan:
is as you took the original image um and you

Logan:
made the translation to this 1950s image it actually

Logan:
looks like me still um which is really cool so

Logan:
there's lots of these really interesting use cases i think we'll

Logan:
we'll go out with a um um like a

Logan:
sports card demo where you can sort of turn yourself into

Logan:
a you know a figurine sports card um

Logan:
which is really cool so lots of really interesting examples like

Logan:
this um and another thing you'll notice is actually the

Logan:
speed and this is where the underlying model is not

Logan:
uh the code name was nano nano banana um

Logan:
the actual model is built on gemini 2.5 flash which is our workhorse model it's

Logan:
super fast it's super efficient um it's relatively priced in the market which

Logan:
is awesome so you can actually use it at scale um and yeah so this model behind

Logan:
the scenes or for developers who people want to build with it is Gemini 2.5 flash image,

Logan:
which is awesome. So this is a use case that I love.

Logan:
And it's a ton of fun. You can do this in the Gemini app or in AI Studio.

Ejaaz:
I mean, as you said, the character consistency just from these examples is like astounding.

Josh:
I need to give a round of applause. This has been my biggest issue when I'm

Josh:
generating images of myself.

Ejaaz:
Genuinely. And Josh and I are early users of, you know, Mid Journey V1,

Ejaaz:
OpenAI's Image Generator as well.

Ejaaz:
And one of our pet peeves was it just couldn't do the most simplistic things,

Ejaaz:
right? We could just say, hey, keep this photo and portrait of me exactly the same.

Ejaaz:
But can you show me what I would look like in a different hairstyle or me holding

Ejaaz:
a bottle of Coca-Cola instead of this martini?

Ejaaz:
And it just could not do that, right? Just simple video, like photo editing.

Ejaaz:
Can you give us a bit of a background as to what Google did to be able to achieve this?

Ejaaz:
Because, you know, I've been racking my head around like, why other AI companies

Ejaaz:
couldn't do this? Like, what's happening behind the scenes? Can you give us a bit of insight?

Logan:
Yeah, that's a good question. I think this actually goes back to and I'll share

Logan:
another example in a second as well.

Logan:
But I think this goes back to this story of what happens when you build a model

Logan:
that has the fusion of all these capabilities together.

Logan:
And I was actually just This is a sort of parallel example to this,

Logan:
but it's another example of why building a unified model to do all this stuff

Logan:
and not having a separate model that doesn't have world knowledge and all these

Logan:
other capabilities is useful. The same thing is actually true on video.

Logan:
Like part of the story, and we haven't, we have a bunch of stuff coming that

Logan:
sort of tells this a little bit more elegantly than I will right now.

Logan:
But part of the story of like VO3 having this really state-of-the-art video

Logan:
generation capabilities, if folks have seen this, is that the Gemini models

Logan:
themselves have this state-of-the-art video understanding capabilities.

Logan:
And a very similar context, actually, on the image side, which is since the

Logan:
original Gemini model, we've like,

Logan:
with the exception of probably a couple of months in that like two and a half

Logan:
year time horizon, have had state-of-the-art image understanding capabilities.

Logan:
And I think there is this like capability transfer, which is really interesting

Logan:
as you go to do the generation step.

Logan:
And if you can fuse those two things together in the same model,

Logan:
you end up just being able to do things that other models aren't able to do.

Logan:
And this was part of the bet originally that like, why build Gemini to be the original Gemini?

Logan:
Gemini 1.0 model was built to be natively multimodal. It was built to be natively

Logan:
multimodal because the belief at the time,

Logan:
and I think this is turning out to be true is that like that's on the path to

Logan:
AGI is that you combine these capabilities together and like similar to what

Logan:
humans are able to do we have this fusion of all these capabilities in a single

Logan:
entity just like these models should be able to do.

Ejaaz:
Wow. So if I were to distill what you just said here, Logan,

Ejaaz:
the way you've trained Gemini 2.5 or all future Google Gemini models is it's

Ejaaz:
in a very multimodal fashion.

Ejaaz:
So you're basically, it gets smarter in one particular facet,

Ejaaz:
which trains itself or has transferable capabilities to other facets,

Ejaaz:
whether it's image generation, video generation, or even text LLMs to some extent.

Ejaaz:
I just think that's fascinating.

Ejaaz:
I'm curious, I have one question for you, which I want to hear your take on.

Ejaaz:
How are you going to surface this to the regular consumer, right?

Ejaaz:
Because right now, you provide all of these capabilities through an amazing

Ejaaz:
suite, you know, called Google AI Studio.

Ejaaz:
But if I wanted to use this in, say, an Instagram app, or my random photo imaging

Ejaaz:
editing app, is this something that could be easily proved to someone or sourced?

Ejaaz:
Or do we need to go via some other route right now.

Logan:
Let me just diverge really quickly, which is if any of the researchers who I

Logan:
work with are watching this, they will tell me, they'll make sure that I know

Logan:
that capability transfer that we just talked about, you oftentimes don't get that out of the box.

Logan:
So there is some emergence where you get a little bit of that.

Logan:
You do have to do, there's real true research and engineering work that has

Logan:
to happen to make sure that that capability fusion happens.

Logan:
It's not often that you just like make the model really

Logan:
good at one thing and then it translates oftentimes actually it's

Logan:
like uh it has a negative effect which is as you

Logan:
make the models really good at code for example you trade

Logan:
that off against some other you know creative writing as as a random example

Logan:
of this um so you have to do a lot of like active research and engineering work

Logan:
to make sure that you don't lose a capability as you make another one better

Logan:
but then ultimately they benefit as if you can make them on the same level they

Logan:
benefit from this interleaved capability together.

Logan:
To answer the question about like, where is this going to be available?

Logan:
The Gemini app is the place that like for by and large, most people should be going to.

Logan:
So if you go to Gemini.Google.com, there'll be sort of a landing page experience

Logan:
that showcases this new model and makes it really easy.

Logan:
And you can put in all your images and do tons of fun stuff like the example that I was showing.

Logan:
If you're a developer and you want to build something with this,

Logan:
in AI Studio, we have this build tab.

Logan:
And that's what we were just looking at as an example of one of the applets

Logan:
that's available in the build tab,

Logan:
the general essence is that all of these applets can be forked and remixed and

Logan:
edited and modified so that you can keep doing all the things that you want

Logan:
to do with the AI capability built in.

Logan:
So it'll continue to be powered by the same model. It'll do all that stuff, which is awesome.

Logan:
So there's lots of cool fusion capabilities that we have with this.

Logan:
Same thing with this other example that we're looking at. So if you want to

Logan:
go outside of this environment, we have an API.

Logan:
You could go and build whatever. saw if your website is, you know,

Logan:
AI photos.com or whatever, you could go and build with the Gemini API,

Logan:
use the new Gemini 2.5 flash image model to do a bunch of this stuff, which is awesome.

Josh:
Awesome. So while this is baking, I noticed you had another tab open,

Josh:
which means maybe there's another demo that you were prepared to share.

Logan:
There is another demo. This one I actually haven't tried yet.

Logan:
But it's this idea of like, how can you take a photo editing experience and

Logan:
make it super, super simple? So I'll grab an image.

Logan:
Actually, we'll take this picture, which is a picture of Demis and I.

Josh:
Legends.

Logan:
We'll put an anime filter on it and we'll see.

Logan:
And so this is a completely vibe coded UI experience and all the code behind

Logan:
the scenes is vibe coded as well.

Logan:
And we'll see how well this works with Demis and I.

Josh:
How much of the tools that you build do you find are built with vibe coding

Josh:
instead of just hard coding software?

Josh:
Are you writing a lot of this as vibe coded through the Gemini model?

Logan:
I think you sometimes you're able to do some of the stuff,

Logan:
completely vibe coded um it depends on like

Logan:
how specific that you want to do i do i'm like almost 85

Logan:
of everything that i do vibe coded somebody else on

Logan:
my team built this one so i don't want to misrepresent the work it could

Logan:
have it could have all been human programmed because we have an incredible set

Logan:
of engineers the general idea is how can you make this oh interesting how can

Logan:
you make this photoshop like experience let's go 90 or do you have suggestions

Logan:
what would a good filter for this be i don't know oh.

Josh:
Man i yeah like perhaps uh going back to

Josh:
the last example maybe like a a 90s film or an

Josh:
80s film grain all right and i guess while we wait for that to load is there

Josh:
a simple way that you would describe a nano banana or this new image model to

Josh:
just the average person on the street who's oh look there we go we have the

Josh:
film grain okay so what we're watching for the people who are listening um you're

Josh:
retouching you can retouch parts of the image you could crop adjust there are filters to be applied

Logan:
I'm just clicking through buttons to be honest, I've never done it before. So it's been fun.

Logan:
Live demo, day one. This is the exploration you're going to get to do as a user

Logan:
as you play around with this.

Ejaaz:
Logan is vibe editing and that's what's happening. Yeah. He's experimenting.

Logan:
Vibe editing, which is fun. I love it. That's a great way. And the cool thing,

Logan:
again, is like what I love about this experience is as you're going through,

Logan:
oh, interesting, this one's like giving me edited outline.

Josh:
Oh, yeah, a little outline. This is helpful for our thumbnail generation. We do a lot of this stuff.

Logan:
Let's see if I can remove the background as well.

Ejaaz:
Oh, yeah. Let's see. I should be.

Josh:
If this removes the background, this is going to be trouble because this is

Josh:
a big feature that we use for a lot of our imagery.

Logan:
Hopefully. Come on. Oh, nice.

Ejaaz:
Oh, done.

Josh:
Nicely done.

Ejaaz:
For those of you who are listening, he's typed in, put me in the Library of

Ejaaz:
Congress. So we're going to hopefully see Logan.

Logan:
Yeah, the context on that image was that Demis and I were in the Library of the DeepMind office.

Ejaaz:
Oh, nice.

Logan:
Yeah, so that was the Library of Congress reference in my mind.

Logan:
But yeah, so much that you can do.

Logan:
Again, what I love about this experience is that as you go around and play with

Logan:
this stuff, if you want to modify this experience, you can do so on the left-hand side.

Logan:
If you say, actually, here are these five editing features that I really care

Logan:
about, the model will go and rewrite the code, and then it'll still be attached

Logan:
to this new 2.5 flash image model.

Logan:
So you can do all these types of cool stuff. This experience is something that

Logan:
I'm really excited about that we've been pushing on.

Josh:
Yeah, this is amazing because I myself, I do photography a lot.

Josh:
I was a photographer in my past life and I rely very heavily on Photoshop and

Josh:
Lightroom for editing, which is a very manual process.

Josh:
And they have these smart tools, but they're not quite like this.

Josh:
I mean, this saves a tremendous amount of time if I could just say,

Josh:
hey, realign, restrain the image, remove the background, add a filter.

Josh:
I think the plain English version of this makes it really approachable, but also way faster.

Logan:
Yeah, it is. It is crazy fast. I think about this all the time.

Logan:
Like there's definitely cases where you want to go deep with whatever the pro tool is.

Logan:
I think there's, there's actually something interesting, like on the near horizon

Logan:
that our team has thought a lot about, which is how you can have this experience

Logan:
and how you can sort of in a, in a generative UI capacity,

Logan:
have the experience sort of subtly expose additional detail to users.

Logan:
And I think about this like if you're a new you know photoshop user

Logan:
as an example and you show up like the chance that you're

Logan:
going to use all of the bells and whistles is zero like you want

Logan:
like the three things i want to remove a background i want to crop something

Logan:
whatever it is like don't actually show this all of these bells and whistles

Logan:
i think the exciting thing about like the progress on coding models is that

Logan:
in the future the challenge with the challenge with doing this in the present

Logan:
rather is that software is deterministic.

Logan:
You have to build software to build the sort of like modified version of that

Logan:
software for all of these different like skill sets and use cases is extremely

Logan:
expensive. It's not feasible.

Logan:
It doesn't scale to production environments. But if you can have this generative

Logan:
UI capability where like the model sort of knows and as you talk to the model,

Logan:
it realizes, oh, you might actually benefit from these other things.

Logan:
It can create the code to do that on the fly and expose them to you,

Logan:
which is really interesting.

Logan:
So I think there's lots of stuff that is going to be possible as the models keep getting better.

Josh:
This is amazing. So the TLDR on this new announcement, how would I,

Josh:
if I were to go explain to my friend what this does, why this is special,

Josh:
how would you kind of sell it to me?

Logan:
The best image generation and editing model in the world, 2.5 flash image or

Logan:
nano banana, whichever you prefer, is the model that can do this.

Logan:
And I think there's so many creative use cases where you're actually bounded

Logan:
by the creative tool. And I feel like this is one of these examples to me where

Logan:
it's like, I feel like I'm 10x more.

Logan:
I was literally helping my friend yesterday doing a bunch of iterations on his

Logan:
LinkedIn picture because it was like, you know, the background was slightly

Logan:
weird or something like that.

Logan:
And we were just like, I did like 15 iterations and now he's got a great new

Logan:
LinkedIn background, which is awesome.

Logan:
So like, there's so many like actual practical use cases where you,

Logan:
and I literally just like built a custom tool on the fly vibe coding in order

Logan:
to solve that use case, which was a ton of fun.

Josh:
Yeah, this is so cool. Okay, so this model, Nano Banana Gemini 2.5 Flash Image Gen, it's out today.

Josh:
So we'll link that in the description for people who want to try it out.

Josh:
I think one of my complaints for the longest time, and I've mentioned this on

Josh:
the show a few times, is a lot of times when I'm engaging with this incredible

Josh:
form of intelligence, I just have a text box.

Josh:
And it's up to me to kind of pull the creativity out of my own mind.

Josh:
And I don't get a lot of help along the way. but one of the things that you

Josh:
spend your time in is this thing called Google AI Studio.

Josh:
And I've used AI Studio a lot because it solves a problem for me that was annoying,

Josh:
which is just the blank text box.

Josh:
It kind of has a lot of prompts. It has a lot of helpers. It has a lot of guidance

Josh:
into helping me extract value out of the model.

Josh:
So what I'd love for you to do for people who aren't familiar,

Josh:
Logan, is just kind of explain to everyone what Google AI Studio is and why

Josh:
it's so important and why it's so great.

Logan:
Yeah, I love this, Josh. I appreciate that you like using AI Studio.

Logan:
Go. It is a labor of love. Lots of

Logan:
people across Google have put in a ton of time to make progress on this.

Logan:
I really want to, so I'll make a caveat, which is we have this entirely redesigned

Logan:
AI Studio experience that's coming very soon.

Logan:
I won't spoil it in this episode because it's like half faked right now.

Logan:
And I wish I could show, and I think actually some of the features that you

Logan:
might see in this UI might be slightly different at launch time than what you see here.

Logan:
So take this with a grain of salt. We've got a bunch of new stuff coming.

Logan:
And I think actually it should help with this problem that you're describing,

Logan:
which is as you show up to a bunch of these tools today, the onus is really

Logan:
on you as a user to like try to figure out what's capable,

Logan:
what all the different models are capable of, what even are all the different

Logan:
models, like all of that stuff.

Logan:
So at a high level, like we built AI Studio for this like AI builder audience.

Logan:
If you want to take AI models and actually build something with them and not

Logan:
just, you know, chat to AI models, this is the product that was built for you.

Logan:
We have a way to, in this like chat UI experience, sort of play with the different

Logan:
capabilities of the model, feel what's possible. What is Gemini good at? What's it not good at?

Logan:
What are the different tools it has access to? But as you go into iStudio,

Logan:
you'll see something that looks like this.

Logan:
You know, we're highlighting a bunch of the new capabilities that we have right now.

Logan:
This URL context tool, which is really great for information retrieval,

Logan:
this native speech generation capability, which is really cool.

Logan:
Folks have used notebook lm and you want to build a

Logan:
notebook lm like experience um we have

Logan:
an api for for people who want to build something like that and

Logan:
we have this live audio to audio dialogue experience where

Logan:
you can share a screen with the model and talk to it and it can see the things

Logan:
that you see and engage with it of course we have our native image generation

Logan:
and editing model the old version 2.0 flash now the new version 2.5 flash um

Logan:
and lots of other stuff that's available as you sort of experience what these models are capable of.

Logan:
Um, so really this playground experience is one version. We have this chat prompt

Logan:
on the left-hand side. We have this stream.

Logan:
This is where you can talk to Gemini and sort of share your screen.

Logan:
And, um, actually you can like show it things on the webcam and be like, what's this?

Logan:
How do I use this thing? You can do this on mobile as well, which is really cool.

Logan:
We have this generative media experience where like, if you want to build things

Logan:
with, we have a music model, we have a VO, which is our video generation model.

Logan:
We have all the text to speech stuff, which is really cool.

Logan:
As I overwhelm people with so much stuff that you can do in AI Studio.

Logan:
The sort of key threat of all this is we built AI Studio to showcase a bunch of these capabilities.

Logan:
And everything you see in AI Studio has an underlying sort of API and developer experience.

Logan:
So if you want to build something like any of these experiences, all of this is possible.

Logan:
There's like no Google secret magic that's happening pretty much anywhere in AI Studio.

Logan:
It's all things that you could build as someone uh using a

Logan:
vibe coding product or you know by hand writing the

Logan:
code um you could build all these things and even more

Logan:
um and that is the perfect segue to this

Logan:
build tab where we're trying to help also you know actually help you get started

Logan:
building a bunch of stuff so you can use these templates that we have you can

Logan:
use a bunch of the suggestions you can look through our gallery of a different

Logan:
stuff um and we're really in this experience trying to help you build ai powered

Logan:
apps which we think is something that folks are really really excited about and,

Logan:
we'll have much more to share around all the ai app building stuff uh in the near future.

Josh:
Awesome thanks for the rundown so as i'm looking at this i'm wondering who do

Josh:
you think this is for what type of person should come to ai studio and tinker around here

Logan:
Yeah so i think you know historically and and so you'll see a little bit of

Logan:
this transition if you play around the product where there's some interesting

Logan:
edges we were originally focused on building for developers So it was built

Logan:
and there is like a part of the experience which like is tied to the Gemini

Logan:
API, which tends to be used mostly by developers.

Logan:
So if you go to dashboard, you can see all your API keys and check your usage

Logan:
and billing and things like that. By and large, though, I think the really cool

Logan:
opportunity of what's happening right now is this transition of like who is creating software.

Logan:
And this is, I think, principally enabled by Vibe Coding.

Logan:
And because of that, like we've recentered ourselves to be really focused on

Logan:
this AI builder persona, which is like people who want to build things using AI tools.

Logan:
Also, people who are trying to build AI experiences, we think is going to be

Logan:
the market that creates value for the world.

Logan:
So if you're excited about all the things that you're seeing,

Logan:
if you want to build things, AI Studio is very much like a builder first platform.

Logan:
If you're just looking for like a great everyday AI assistant product,

Logan:
you, you know, want to get help on coding questions or homework or life advice

Logan:
or all that type of stuff,

Logan:
the Gemini app is the right place for this. It's very much like a.

Logan:
DAU type of product where like you come back and it has memory and personalization

Logan:
and all this other stuff, um, which makes it really great as like an,

Logan:
uh, as a, as an assistant to help you in your life versus AI studio.

Logan:
The artifact is like, we help you create something and then you go put that

Logan:
thing into the world in some way.

Logan:
Um, and you don't necessarily need to come back and use it every day.

Logan:
You use it whenever you want to build something.

Ejaaz:
It's funny. Um, I'm dating myself a bit here,

Ejaaz:
but I remember when I first booted up a PC and I loaded up Microsoft Office

Ejaaz:
and I just had access to all these different wonderful applications that were

Ejaaz:
at the time super new or within one suite.

Ejaaz:
This kind of feels like that moment for AI.

Ejaaz:
And you might not take that as a compliment because it's a completely different

Ejaaz:
company, but it was what I built my childhood off of and my fascination with computers.

Ejaaz:
So I appreciate this and I love that it's this massively like cohesive experience.

Ejaaz:
But kind of zooming out, Logan, I was thinking a lot about Google AI and what

Ejaaz:
that means to me personally.

Ejaaz:
I have to say it's the only company that I think beyond an LLM.

Ejaaz:
And what I mean by that is when I think of Google AI, I don't just think of Gemini.

Ejaaz:
I think of the amazing image gen stuff that you have.

Ejaaz:
I think of the amazing video outputs that you guys have. I think of the text-to-voice

Ejaaz:
generation that you just demoed and all those kinds of things.

Ejaaz:
I remember seeing this advert that appeared on my timeline.

Ejaaz:
And I remember thinking, wow, this must be the new GTA. Then I was like,

Ejaaz:
no, no, that's Florida. That's Miami.

Ejaaz:
No, people are doing wild stuff. That's an alien. Hang on a second. This can't be real.

Ejaaz:
And then I learned that it was a Google VO3 generation of an advert for Kalshi,

Ejaaz:
which is like this, you know, prediction markets situation.

Ejaaz:
And I remember thinking, how on earth have we got to AI generated video that

Ejaaz:
is this high quality and this high fidelity?

Ejaaz:
I think in my mind, VO3 has kind of like killed the VFX studio.

Ejaaz:
It's kind of killed a lot of Hollywood production studios as well.

Ejaaz:
Give me a breakdown and insight into how you built or how you guys built VO3

Ejaaz:
and what that means for the future of movie video production and more

Logan:
Yeah that's a great question i think there's something really interesting along

Logan:
these threads and and not to not to push back on the notion that it's killing

Logan:
hollywood because i think i think there is like um,

Logan:
I think it's an interesting conversation. The way that I have seen this play

Logan:
out and the great example of this, that folks have seen Flow,

Logan:
which is our sort of like creative video tool.

Logan:
And if you're using VO and you want to sort of get the most out of VO,

Logan:
Flow is the tool to do that.

Logan:
If you see lots of like the creators who are building, you know,

Logan:
minute long videos using VO and it's like this really cohesive story and it

Logan:
has like a clear visual identity, similar to what you'd get from like a.

Logan:
Probably not the extent of a Hollywood

Logan:
production, but like somebody thoughtfully choreographing a film.

Logan:
Flow is the product to do that. And actually interesting, like Flow was built

Logan:
in conjunction with filmmakers.

Logan:
And I think that's actually like there is, and I feel this way about vibe coding as well.

Logan:
And it's this thought experiment that I'm always running through in my head,

Logan:
which is, yes, I think AI is like raising the bar forever or it's raising the

Logan:
floor for everyone. We're like, now everyone can create.

Logan:
What does that mean for people who have expertise?

Logan:
And I think in most cases, what it means is actually the value of your expertise

Logan:
continues to go up. And like, this is my personal bet.

Logan:
And I don't know how much this tracks to like everyone else's worldview.

Logan:
My personal bet is that expertise in the world where the floor is lifted for

Logan:
everyone across all these dimensions is actually more important because there was something about,

Logan:
and I think like video production is a great example for me because I would

Logan:
never have been able to make a video.

Logan:
Like it's not in the cards, like for my skillset, my creative ability,

Logan:
my financial ability, like I will never be able to make a video.

Logan:
I can make things with VO. Um, and now I'm like a little bit closer to imagining

Logan:
like, okay, if I'm serious about this, I need to go out and like actually engage with people.

Logan:
And I've like, sort of, it's like whetted my appetite in a way that I don't

Logan:
think I would. It was just like too far in a way.

Logan:
And I think software is another example where Vibe Coding, if you were to pull

Logan:
a random person off the street and you start talking to them about coding and

Logan:
seeing C++ and deploying stuff and all this, they're like, brain turns off, not interested.

Logan:
I don't want to learn to code. That's not cool. It's not fun. It sounds horrible.

Logan:
And then Vibe Coding rolls around and it's like, oh, wait, I can actually build

Logan:
stuff. And like, yeah, I don't really need to understand all of the details.

Logan:
But there's still a limit to what I can build and who is actually well positioned

Logan:
to help me take the next step.

Logan:
Like I, you know, vibe code something. I'm like, this is awesome.

Logan:
I share with my friends. They all love it.

Logan:
I want to, you know, go build a business around this thing that I vibe coded.

Logan:
There's still a software engineer that needs to help make that thing actually

Logan:
happen. So if anything, it's like it's increasing this.

Logan:
I mean, on the software side, there's this infinite demand for software,

Logan:
and it's increasing the total addressable market of like what software engineers

Logan:
need to help people build.

Logan:
I think there'll be something similar on the video side.

Logan:
You know, there will be downsides to AI technology in some ways.

Logan:
I think there is like as the technology shift happens, there is some amount

Logan:
of disruption that's taking place.

Logan:
And like someone's workflow is being disrupted. But I do think there's this

Logan:
really interesting thread to pull on, which is my hope is that it actually ends

Logan:
up creating more opportunity for the experts and the specialists.

Ejaaz:
So it sounds like you're not saying VFX Studio teams are going to be replaced by software engineers,

Ejaaz:
but rather that team in itself will become more adept at using these AI tools

Ejaaz:
and products to kind of enhance their own skill set beyond what it is today. Is that right?

Logan:
Yeah, yeah. And I think we've seen this already play out in some ways, which is interesting.

Logan:
I think code is a little bit wider distribution than perhaps the VFX.

Logan:
And it's VFX also in a space that I'm less familiar with personally.

Logan:
But yeah, I think this is likely what is going to play out if I had to guess and bet.

Ejaaz:
Can you help us understand how a product like VO3 gets used beyond just like

Ejaaz:
the major Hollywood productions?

Ejaaz:
Stuff, right? Because I've seen a bunch of these videos now.

Ejaaz:
And I'll be honest with you, Logan, it's scary how realistic this stuff is, right?

Ejaaz:
It's like from a high quality triple A game demo, all the way to something that

Ejaaz:
is shot like in an A24 film, you know, the scenes, the cuts, the changes.

Ejaaz:
I think it's awesome. I'm wondering whether that goes beyond entertainment in

Ejaaz:
any way. Do you have any thoughts or ideas there?

Logan:
Yeah, that is interesting. I think one of the ones that is like related to,

Logan:
it's sort of one skip away from video generation itself, which was Genie,

Logan:
which was our sort of world simulation work that was happening.

Logan:
I think if folks haven't seen this, go look up Genie 3 and you can see a video.

Logan:
It's mind blowing. It's like a fully playable game world simulation.

Logan:
You can like prompt on the go and this environment will change.

Logan:
You can control it on your keyboard similar to a game. I think that work translates

Logan:
actually really well to robotics, which is cool.

Logan:
So as you like one of the if folks aren't familiar with this,

Logan:
like one of the principal reasons we don't just have robots walking around everywhere.

Logan:
And the reason why we have LLMs that can actually do lots of useful stuff is

Logan:
it's this data problem, which is like there's lots of, you know,

Logan:
text data and other data that's like representative of the intelligence of humans

Logan:
and all this stuff that's available.

Logan:
There's actually not a lot of data that is useful for making robotics work.

Logan:
And I think VO could be part of, or like generally that sort of segment of video

Logan:
generation and this like physics understanding and all that other stuff,

Logan:
I think could be really helpful in actually making the long tail of robotics use cases work.

Logan:
Then I can finally have a robot that will fold my laundry so that I don't need

Logan:
to spend my time doing that.

Logan:
But that's my like outside of entertainment bet as far as like where that use

Logan:
case ends up creating value in the world.

Ejaaz:
With VO3, the goal is to enable humans to become a better version of themselves,

Ejaaz:
a 10x, 100x better version of themselves using these different tools.

Ejaaz:
So in the example of a VFX studio, you can now kind of like create much better movies.

Ejaaz:
How does that apply for Genie 3 exactly, right?

Ejaaz:
You gave the example of like being able to create simulated environments,

Ejaaz:
but that's to train these robots. That's to train these models.

Ejaaz:
What about us? What about the flesh humans that are out there?

Ejaaz:
Can you give us some examples about where this might be applied or used?

Logan:
Yeah, that's a good example. I mean, the robot answer is like the robots will

Logan:
be there to help us, which is nice.

Logan:
So hopefully there's a bunch of stuff that you don't want to do that you'll

Logan:
be able to get your robot to do.

Logan:
Or there's like industries that are like dangerous for humans to operate in

Logan:
where it's like if you can sort of do that simulation without needing to collect

Logan:
a bunch of human data to do those things, I could see that being super valuable.

Logan:
I think my initial reaction to the Genie use case, like I could see lots of,

Logan:
actually, the two that come to mind is like one entertainment I think will be cool.

Logan:
Humans want to be entertained. It's a story as all this time.

Logan:
I think there will be some entertainment value of a product experience like Genie.

Logan:
Um, I think the other one is actually back to a bunch of use cases where you'd

Logan:
actually want robotics to be able to do some of that work that don't yet, uh,

Logan:
the robot product experience, like isn't actually there.

Logan:
Um, this could be things like, you know, mining or like heavy industries,

Logan:
things like that, where like, there's actually like a safety aspect of like,

Logan:
how can you do these like realistic simulation training experiences, um,

Logan:
in order to make sure that like you're You don't have to like physically put

Logan:
yourself in harm's way in order to like understand the bounds or like the failure cases,

Logan:
like disaster recovery, things like that, where it would be you don't want to

Logan:
have to show up at a hurricane the first time to like really understand what

Logan:
the environment could be like.

Logan:
And like being able to do those types of simulations is interesting and building

Logan:
software deterministically to solve that problem would actually be really difficult

Logan:
and expensive and like probably isn't a large market that lots of companies are going to go after.

Logan:
But if you have this model that has really great world knowledge,

Logan:
you can throw all these random variables at it and like sort of do that type

Logan:
of like training and simulation.

Logan:
So yeah, it's perhaps an interesting use case.

Logan:
I don't know if there's actually a plan to use it for things like that,

Logan:
But those are things that come to mind.

Josh:
This is something I've been dying to ask you about because this is something

Josh:
that I've been fascinated by.

Josh:
When I watched the Genie 3 demo for the first time, it just kind of shattered

Josh:
my perception of where we were at because you see it work.

Josh:
And I saw this great demo where someone was painting the wall.

Josh:
We actually filmed an entire episode about this and it retained all of the information.

Josh:
And one theme, as I'm hearing you describe these things, as I'm hearing you

Josh:
describe VO3, Genie 3, you are building this deep understanding of the physical world.

Josh:
And I can't help but notice this trend like you are just starting

Josh:
to understand the world more and more and I could see this when

Josh:
it comes to making games as an example where like a lot

Josh:
of people were using genie 3 to just make these like not necessarily

Josh:
games but virtual worlds that you can walk around and interact with and I'm

Josh:
wondering if you could just kind of share the long-term reasoning why because

Josh:
clearly there's a reason there's a lot of value to it is it from being able

Josh:
to create maybe artificial data for robots if you can emulate the physical world

Josh:
you can create data to train these robots is it because it creates great experiences

Josh:
like perhaps we'll AAA design studios using Genie 5 to make AAA games like Grand Theft Auto.

Josh:
I'm curious the reasoning behind

Josh:
this like urge to understand the physical world and emulate it even.

Logan:
I had a conversation with Demis about this who's our CEO at DeepMind and someone

Logan:
who's been pushing on this for a long time.

Logan:
I think a lot of this goes back to like there's two dimensions.

Logan:
It goes back to like the original ethos of like why DeepMind was created and

Logan:
a bunch of the work the initial work that was happening in DeepMind around reinforcement learning.

Logan:
If folks haven't seen this, one of the challenges of, again,

Logan:
making AI work is that you need this flywheel of continuing to iterate and you

Logan:
need a reward function, which is what is the actual outcome that you're trying to achieve?

Logan:
And the thing that's interesting about these simulated environments is it's

Logan:
really easy to have a constrained environment.

Logan:
World, and it's really easy to also, or not maybe really easy,

Logan:
is overly ambitious. It's possible to define a simple reward function and then

Logan:
actually infinitely scale this up.

Logan:
And the opposite example of this, if folks have saw there was some work a very

Logan:
long time ago, and this is in the AI weeds, but there was this hand,

Logan:
this physical hand that could like robotic hand that could manipulate a Rubik's cube.

Logan:
And they were using AI to like help try to solve this Rubik's cube.

Logan:
And the, the, again, the analogy of why this, of why Genie and some of this

Logan:
work is so interesting is if you were to go and try to like,

Logan:
Hey, we need all the data to go and try to make this little hand,

Logan:
physical robotic hand, be able to do this.

Logan:
It's actually really challenging to scale that up. You need to go and build a bunch of hands.

Logan:
You need to like, what happens when the rubik's cube drops you need

Logan:
to have some system to like go and pick it back up and you just like go

Logan:
through the long tail of this stuff the hand probably can't run

Logan:
24 hours a day like there's all these challenges with getting um the the like

Logan:
data in that environment to scale up um and these virtual environments don't

Logan:
have this problem which is if you can emulate and like self-driving cars is

Logan:
another example of this like again for folks who aren't familiar lots of you You know,

Logan:
there's lots of real world data that's involved in self-driving cars.

Logan:
There's also lots of simulated environments where they've built simulations of the world.

Logan:
And this is how they can get like a thousand X scale up of this like data understanding

Logan:
is by having these simulated environments.

Logan:
Robotics will be exactly the same. If you want robotics to work,

Logan:
it's almost 100% true that you're going to have to have these simulated environments

Logan:
where the robot can fall down the stairs a thousand times.

Logan:
And that's okay because it's a simulated environment and it's not actually going

Logan:
to fall down your stairs.

Logan:
So I think Genie is, there is definitely like an entertainment aspect to it.

Logan:
I think it's more so going to be useful for this like simulated environment

Logan:
to help us not have to do things in the real world and, but still have like

Logan:
a really good proxy of what will happen in the real world when we do them.

Ejaaz:
That's pretty funny. I spent the weekend watching the World Robot Olympics and

Ejaaz:
there was some very real fails and crashes of these robots, which is pretty funny.

Ejaaz:
Okay, so when I think of Genie, I think that it blows my mind because I still

Ejaaz:
can't get my head around how it predicts what I'm going to look at.

Ejaaz:
I remember seeing this demo of someone just taking a simple video of them walking and,

Ejaaz:
you know, it was like a rainy day on a gravel path and they stuck that into

Ejaaz:
Genie 3 and they could look down and see their reflection in the puddle.

Ejaaz:
So the physics was astoundingly accurate and astute.

Ejaaz:
Can you give us a basic breakdown of how this works?

Ejaaz:
Is this like a real engine, game engine, like happening in the background?

Ejaaz:
Or is there something more deeper happening? Like help us understand.

Logan:
My intuition, and we can gut check this with folks on the research side to make

Logan:
sure that I'm not, I'm not fabricating my intuition.

Logan:
But if folks have an intuition as far as like how next token prediction works,

Logan:
which is at some given, like if you're looking through a sentence of text,

Logan:
for each word in that sentence, there's a

Logan:
distribution uh between like zero

Logan:
and one basically of like how likely that

Logan:
word was to be the next word in the sequence um and

Logan:
if you look the and if you like look through this is like the basic

Logan:
principle of llms um this is why you get like the you know if you're to ask

Logan:
the same question multiple times um the the llm will inherently perhaps give

Logan:
you a different answer And that's why like small changes in the inputs to LLMs

Logan:
actually change this because like, again, it's this distribution.

Logan:
So like if you make one letter difference, it perhaps like puts you on a like

Logan:
a branching trajectory that looks very different than what the original output

Logan:
that you got from the model.

Logan:
Similar similar like rough approximation of this just like much more computationally

Logan:
difficult and i think they use a bunch of um architectural differences that

Logan:
sort of it's not truly next token prediction that's happening for the sort.

Ejaaz:
Of like pixels colors bunch of other things yeah

Logan:
Exactly yeah so it's like you can like roughly map

Logan:
the mental model of like as the as a model

Logan:
looks down or as like the figure looks down

Logan:
in some in some environment like again it has

Logan:
all this like context of the state of the world but then it also knows like

Logan:
what are the pixels that are preceding it etc etc it like loosely is doing this

Logan:
like next next pixel prediction you could you could sort of approximate with

Logan:
um that's happening at the at the genie level which is which is an interesting way to think about it.

Josh:
So ijaz one of the things you were mentioning was that um it's happening much

Josh:
faster right and it's happening presumably much cheaper because now i heard this crazy stat.

Josh:
You're at like 500, hundreds of trillions of tokens per month that is being

Josh:
pushed out by Gemini. It's unbelievable.

Josh:
And I want to get into the kind of infrastructure that enables this because

Josh:
Gemini is feeling faster, but it's also feeling better. And it's also getting cheaper.

Josh:
And behind you earlier in the show, you mentioned you have a TPU.

Josh:
I understand TPUs are part of this solution. And I want you to kind of just

Josh:
walk us through how this is happening.

Josh:
How are we getting these quality and improvements across the board?

Josh:
And what type of hardware or software is enabling that to happen.

Logan:
I think like one, you have to give credit to like all of these infrastructure

Logan:
teams across Google that are making this happen.

Logan:
If you think, and I think about this a lot, like what is Google's differentiated advantage?

Logan:
What does our expertise lend us well to do in the ecosystem?

Logan:
What are the things we shouldn't do because of that? What are the things we

Logan:
should do because of that? It's something I think about as somebody who builds products.

Logan:
One of the things that I always come back to is our infrastructure.

Logan:
And like the thing Google has been able to do time and time again.

Logan:
Scale up multiple products to billions

Logan:
of users, have them work with high reliability, et cetera, et cetera.

Logan:
And that's like a uniquely difficult problem. It's a even more difficult problem

Logan:
to do in the age of AI where like the software is not deterministic.

Logan:
The sort of compute footprint required to do these things is really difficult.

Logan:
The models are a little bit tricky and finicky to work with sometimes.

Logan:
So again, like our infrastructure teams have done an incredible job making that scale up.

Logan:
I think the stat was IO 2024, we were doing roughly 50 trillion tokens a month.

Logan:
IO 2025, I think it was like 480 trillion tokens a month, if I remember correctly.

Logan:
And just a month or two later, and this was in the conversation I had with Demis,

Logan:
we crossed a quadrillion tokens, which comes after a trillion,

Logan:
if you're not haven't thought about numbers higher than a trillion before

Logan:
it's what comes after a trillion um and there's

Logan:
no slowdown in sight and like i think this is just a great

Logan:
reminder of like um so many

Logan:
of these ai like markets and

Logan:
product ecosystems is still so early and there's

Logan:
this massive expansion i think about in my own life like how

Logan:
much ai do i really have in my life helping me like not

Logan:
really that much on the margin it's like you know maybe

Logan:
tens of millions of tokens a

Logan:
month maximum and like you think

Logan:
about a future where there's like billions of tokens being spent on a monthly

Logan:
basis in order to help you in whatever you're doing in your professional life

Logan:
and your work and your personal life whatever it is there's we're still so early

Logan:
and tpus are a core part of that because it allows us to like um control every

Logan:
layer of the hardware and software.

Logan:
Delivery all the way to the actual like silicon that the model is running on

Logan:
and we can do a bunch of optimizations and customizations,

Logan:
other people can't do because they don't actually control the hardware itself

Logan:
and there's some good examples of the things that this enables

Logan:
one of them is um you know we've been at the Pareto

Logan:
frontier from a cost performance perspective for

Logan:
a very long time and again if folks aren't familiar the Pareto frontier is this

Logan:
like trade-off of cost and intelligence and you want to be on the highest intelligence

Logan:
lowest cost um and we've been sitting on that for for you know basically the

Logan:
entirety of the Gemini life cycle um so far which is really important so people

Logan:
get a ton of value from the Gemini models.

Logan:
Another example of this is long context. Again, if folks are familiar,

Logan:
there's a limit on like how many tokens you can pass to a model at a given time.

Logan:
Gemini's had a million or 2 million token context windows since the initial

Logan:
launch of Gemini, which has been awesome.

Logan:
And there's a bunch of research showing we could scale that all the way up to

Logan:
10 million if we wanted to.

Logan:
And that is like a core infrastructure enabled thing.

Logan:
Like research, There's a lot of like really important research to make that

Logan:
work and make that possible.

Logan:
But it's also really difficult on the infrastructure side. And you have to be

Logan:
willing to do that work and pay that price.

Logan:
And it's a beautiful outcome for us because we have the infrastructure teams

Logan:
that have the expertise to do this.

Josh:
Okay, Logan, one quadrillion tokens. That's a big number.

Josh:
We need to talk about this for a little bit because that is an outrageously,

Josh:
mind-bendingly big number.

Josh:
And when I hear you say that number, I think I'm reminded of Jevin's Paradox

Josh:
for people who don't know. it's increased technological efficiency in using

Josh:
a resource which can lead to higher total consumption of that resource.

Josh:
So clearly with these cool new TPUs, this vertically integrated stack you've

Josh:
built, you are able to generate tokens much more cheaply and produce a lot more

Josh:
of them. Hence the one quadrillion tokens.

Josh:
Do you see this trend continuing? Is there going to be a continued need to just

Josh:
produce more tokens or will it eventually be a battle to produce smarter tokens?

Josh:
I guess the question I'm asking is the quality of the token more important than

Josh:
the amount of the tokens?

Josh:
And do you see a limit in which the quantity of the tokens starts to like kind

Josh:
of go off of a cliff in terms of how valuable it is?

Logan:
Yeah, I could buy that story. And some of this is and it's something that's

Logan:
actually super top of mind for our teams on the like Gemini model side is around

Logan:
this whole idea of like thinking efficiency,

Logan:
which is like ideally you want

Logan:
to get to the best answer using the limited amount of thoughts possible.

Logan:
Same thing with humans like ideally like you're the example

Logan:
of like you're taking a test you want to as you know

Logan:
the shortest number of mental hops possible to get you to the answer.

Logan:
Of whatever the question was is ideally what you want you don't want

Logan:
to have to just like think for an hour to answer one question

Logan:
um and there's yeah there's a bunch

Logan:
of odd parallels in that world to like models and

Logan:
and humans doing this approach um so i

Logan:
do think thinking efficiency is top of mind you don't want to just

Logan:
like use tokens for the sake of tokens um i

Logan:
think even if we were to like 10x reduce the

Logan:
number of tokens required which would be like awesome and

Logan:
would be like a great innovation the models are like much more

Logan:
token efficient i do think there's like a um a

Logan:
pretty low ceiling to how far that will be able to go

Logan:
specifically because of this like next

Logan:
token prediction paradigm of like how the models

Logan:
actually approach solving problems using using

Logan:
like the token as a unit so it's

Logan:
not clear to me that you'll be able to just like you know a thousand x reduce

Logan:
the amount of tokens required to solve a problem i think it probably looks much

Logan:
more like 10x or something like that and then the there'll be a 10x reduction

Logan:
in the number of tokens required to solve a problem and there'll be a 10 000

Logan:
x increase in the total amount of ai and and sort of token consumption in the world.

Logan:
So I think you probably, even if we made that reduction happen,

Logan:
I think the graph still looks like it's going up and to the right for the most part.

Josh:
It still keeps going. There is no wall. We have virtual data to train models

Josh:
on. We have tons of new tokens coming into play.

Josh:
There's another question I wanted to ask, which is just a personal question

Josh:
for you, which is a feature that, because I find when a lot of people leave

Josh:
comments on the show and they talk about their experience with AI,

Josh:
a lot of them are just using like ChatGPT on their app or they have Grok on their phone.

Josh:
And I think Gemini kind of has some underrated features that don't quite get enough attention.

Josh:
So what I'd like for you to do is maybe just highlight one or two of the features

Josh:
you shipped recently that you think is criminally underrated.

Josh:
What should people try out that you think not enough people are using?

Logan:
I think the one that continues to surprise me the most is deep research.

Logan:
I think deep research is just like a, is the North Star for building an AI product experience.

Logan:
And if folks aren't familiar with this, so you can show up with,

Logan:
yeah, it's so, you can show up with like a pretty ill-defined question that's

Logan:
like very open and vague.

Logan:
And the model will traverse essentially across the internet hundreds or thousands

Logan:
of different web pages, try to accumulate enough context and then come back to you with initially,

Logan:
basically like a research report, could be like a 40 page report in some cases that I've seen.

Logan:
You might hear a 40 page report and say, that's not very useful to me because

Logan:
I'm not going to read 40 pages.

Logan:
And I'd say you and me are exactly the same because I'm not reading 40 pages either.

Logan:
There's a beautiful feature, again, if you've used Notebook LM,

Logan:
this audio overviews feature.

Logan:
The same thing actually exists inside of the Gemini app with deep research,

Logan:
which you can just press that button and then get like a, you know,

Logan:
10, 15 minute podcast that sort of goes through and explains all the different

Logan:
research that's happened.

Logan:
You can, you know, listen to that on your commute or something like that or

Logan:
on a walk and not need to read 40 pages, which is awesome.

Logan:
The part of this that makes it such an interesting experience to me is,

Logan:
I don't know if other people have felt this before, but most AI products back

Logan:
to that, Josh, that like blank slate problem or that like empty chat box problem.

Logan:
You, you as the user of the product have to put in so much work in order to get useful stuff.

Logan:
I talk to people all the time who are like, yeah, I use these models and like,

Logan:
they're just not useful for me. And like, actually what's happening behind the

Logan:
scenes is the models are super capable.

Logan:
They're really useful. It just requires that you give the models enough context.

Logan:
And I think deep research, there's this new emerging like prompt engineering

Logan:
2.0 is this context engineering problem where it's like, how do you get in the

Logan:
right information so that the model can make a decision on behalf of the user?

Logan:
And I think deep research is this really nice balance of going and doing this

Logan:
context engineering for you,

Logan:
bringing all that context into the window of the model, and then

Logan:
being able to answer what your original question was and principally showing

Logan:
you this like proof of work up front I think about this proof of work concept

Logan:
in AI all the time which is I have so much more trust in deep research because

Logan:
as soon as I kick off that query it's like boom it's already at like 50 web

Logan:
pages I'm like great because.

Logan:
I was never going to visit 50 webpages. Like there's pretty much nothing that I'm researching.

Logan:
I could be going and buying a car and I'm going to go and look at less than

Logan:
50 webpages for that thing or a house.

Logan:
I'm looking at less than 50 webpages. Like I'm just, it's not in the car.

Logan:
At least this is maybe personal to me and other people are doing more research. I don't know.

Logan:
But so automatically I'm like in awe with how much more work this thing is doing.

Logan:
And I think there's, this is, again, this is the North Star from an AI product

Logan:
experience standpoint. and there's so few products that have like made that

Logan:
experience work and just every time I go back to deep research I'm reminded

Logan:
of this and that team uh crushed it so.

Ejaaz:
And it's not just deep research from a LLM context that is so fascinating about

Ejaaz:
Google AI um you guys have created some of the most fascinating tools to advance

Ejaaz:
science and I don't think you guys get enough flowers for what you guys have built.

Ejaaz:
Some of my favorites, AlphaFold 3 is crazy.

Ejaaz:
So, you know, this is this model

Ejaaz:
that can predict what certain molecular structures are going to look like.

Ejaaz:
And this could be applied to so many different industries, the most obvious

Ejaaz:
being drug design, creating cheaper,

Ejaaz:
more effective, curable drugs for a variety of different diseases.

Ejaaz:
And then I was thinking about that random model that you guys launched,

Ejaaz:
where apparently we could translate what dolphins were saying to us and vice versa um

Ejaaz:
Kind of stepping back from all of these examples, can you help me understand

Ejaaz:
what is Google's obsession with AI and science and why you think it's such an

Ejaaz:
important area to focus on?

Ejaaz:
Are we at a point now where we can advance science to infinity or where are we right now?

Ejaaz:
Are we at our chat GPT moment or do we have more to go?

Logan:
I'll start with a couple of cheeky answers, which Demis, who is the only foundation

Logan:
model lab CEO to have a Nobel prize, uh, in the science domain,

Logan:
which is, uh, for him, for him, chemistry, um,

Logan:
had this comment, which is actually really true.

Logan:
There's lots of people talking about this, like impact of AI on science and humanity.

Logan:
Um, and there's very few, uh, if not only one, um, being deep mind research

Logan:
lab, that's like actually doing the science work.

Logan:
And I think it's this like great example of like deep mind and just being in

Logan:
the like culture and DNA, DNA of like Demis is a scientist,

Logan:
all of these folks around, um, around DeepMind are scientists and they like

Logan:
want to push the science and, and push what's possible in this,

Logan:
this future of discovery,

Logan:
um, using our models.

Logan:
And I was in London a couple of weeks ago meeting with Pushmid who leads our

Logan:
science team and hearing about sort of like the breadth of the science that's happening.

Logan:
Um, and how like Dolphin Gemma is like a great, like kind of like funny example,

Logan:
cause it's, it's not super applicable in a lot of cases, but it's interesting

Logan:
to think about, um, alpha fold, like if, if folks haven't, um.

Logan:
Watched the movie, the thinking game, it's about sort of the early days of,

Logan:
of, uh, Google deep mind.

Logan:
And, um, they're talking about like folding proteins and why this is such an interesting space.

Logan:
And I'm not a, not a scientist, um, but the, to like hit on the point really

Logan:
quickly of like why Alpha Fold is so interesting. The the

Logan:
historical like context is humans to fold

Logan:
a single protein would take many humans millions

Logan:
of dollars and it would take on the

Logan:
order of like five years in order to fold a single protein the original impetus

Logan:
and like why demas won the nobel piece the nobel prize for this in chemistry

Logan:
was because deep mind was able to figure out using uh reinforcement learning

Logan:
and other techniques They folded every protein in the known universe,

Logan:
millions of proteins, released them publicly,

Logan:
made them available to everyone.

Logan:
And it was like, you know, dramatically accelerated the advancement of like

Logan:
human medicine and a bunch of other domains and disciplines.

Logan:
And now actually with isomorphic labs, which is part of DeepMind,

Logan:
like actually pursuing some of the breakthroughs that they found and like actually

Logan:
doing drug discovery and things like that.

Logan:
So like overnight, you see that hundreds of thousands of human years and hundreds

Logan:
of millions of dollars of like research and development costs saved through a single innovation.

Logan:
And I think we're going to continue to see that like acceleration of new stuff happening.

Logan:
A recent example of this, Alpha Evolve,

Logan:
which was our sort of like geospatial model that came out and being able to

Logan:
like fuse together all of this, the Google Earth engine with AI and this understanding of the world.

Logan:
Like it's just so much cool science and so much is possible when you sort of

Logan:
layer on the AI capability in all these disciplines. So I think to answer the

Logan:
question, I think we're going to see this acceleration of science progress.

Logan:
I think DeepMind is going to continue to be at the forefront of this,

Logan:
which is really exciting.

Logan:
And the cool thing for even for people who aren't in science is all of that

Logan:
innovation and like the research breakthroughs that happen, it feeds back to

Logan:
the mainline Gemini model.

Logan:
Like we had a bunch of research work about doing proofs for math.

Logan:
And it's like, oh, that's not very interesting at the face value. But like that research.

Logan:
Fuels back into the mainline gemini model it makes

Logan:
it better at reasoning it makes it better able to like understand these like

Logan:
really long and difficult problems um which then benefits like every like agent

Logan:
use case that exists uh because the models are better at reasoning through all

Logan:
these like difficult problem domains so there is this like really cool research to reality,

Logan:
science to like practical impact flywheel that happens at deep mind as.

Ejaaz:
A former biologist this warms my heart. This is amazing to see this get applied at such scale.

Ejaaz:
Okay, we can't talk about Google AI without talking about search.

Ejaaz:
This is your bread and butter, right?

Ejaaz:
However, I've personally noticed a trend shift in my habits.

Ejaaz:
I've used a computer for decades now, and I've always used Google search to

Ejaaz:
find things, Google Chrome, whatever it might be.

Ejaaz:
But I've now started to cheat on this feature.

Ejaaz:
I have started using LLMs directly to do all my searching for me,

Ejaaz:
to get all my sources for me.

Ejaaz:
And you've got to be thinking about this slogan, right? Is this eating the search business?

Ejaaz:
Is this aiding the search business? Or are we creating a whole different form

Ejaaz:
factor here? What are your thoughts?

Logan:
There's an interesting form factor discussion. I think on one hand, the AI...

Logan:
Sort of answer market is definitely distinctly different, it feels like,

Logan:
than the search market to a certain degree.

Logan:
Like, I think we've seen lots of AI products reach hundreds of millions of users

Logan:
and, you know, search continues to be a great business and there's billions of people using it and.

Ejaaz:
All that stuff.

Logan:
There's also this interesting question, which is like, what's the obligation

Logan:
of Google in this moment of this platform shift and all this innovation that's happening?

Logan:
And I, You know, as somebody who doesn't work on search, but,

Logan:
you know, is a fan of all the work that's happening inside of Google and has

Logan:
empathy for folks building these products, it is really interesting.

Logan:
And like my perspective has always been that search actually has this,

Logan:
you know, as the front door to the internet has this stewardship position that

Logan:
makes it so that they actually can't disrupt themselves for the right reasons

Logan:
at the same pace that that sort of,

Logan:
you know, small players in the market are able to do.

Logan:
And my assertion has always been that like, actually, this is the best thing

Logan:
for the world, the best thing for the world and for the Internet and for this

Logan:
entire economy that that Google has enabled through the Internet and bringing

Logan:
people to websites and all this stuff doesn't benefit by like,

Logan:
you know, day one of the LLM revolution happening.

Logan:
All of a sudden it's like a fully llm

Logan:
powered search product and like feels and looks completely different

Logan:
not only i think whether that throw you know users who

Logan:
are still trying to figure out like how do i use this technology what is the

Logan:
way that i should be engaging with it um what are the things that it works well

Logan:
for and it doesn't work for not only to throw those people into a bad um perspective

Logan:
from like a user uh from a user journey but i think it also has impacts on like

Logan:
people who rely on Google from a business perspective.

Logan:
So I think you've seen this sort of like gradual transition and like lots of

Logan:
shots on goal and lots of experiments happening on the search side.

Logan:
And I think we're now getting to the place where like they have confidence that

Logan:
they could do this in a way that is going to be super positive for the ecosystem

Logan:
and is going to create lots of value for people who are going and using these products.

Logan:
Like the understanding of AI technology has increased the adoption

Logan:
and the models have gotten better and hallucinations have gone down and all

Logan:
this stuff and I think there'll be also some like uniquely search things that

Logan:
like only search can do and I've spent a bunch of time with folks on the search

Logan:
team like Robbie Stein as an example who leads all the AI stuff in search and,

Logan:
There's all of this infrastructure that search has built, which as you think

Logan:
about this age of AI, where the ability to generate content,

Logan:
which actually like looks somewhat plausible.

Logan:
Has basically gone to zero. Like it's very easy to do that.

Logan:
Great search is actually more, it's like this premium is like more important than ever.

Logan:
There's going to be a million X or a thousand X or whatever than X number of

Logan:
like growth and content on the internet.

Logan:
How do you actually get people to

Logan:
the most relevant content from people who have authority who

Logan:
have you know expertise and all this stuff it's a

Logan:
really difficult problem and it's like it is the problem of

Logan:
the decade that like search has been solving for the

Logan:
last 20 years and is now a more important problem than ever um so i'm i've never

Logan:
been more excited for for the search team and like i think they've never had

Logan:
a bigger challenge ahead of them as they try to like figure out how to make

Logan:
you know these internet scale systems that they build continue to scale up to

Logan:
solve this next generation of problems,

Logan:
while also becoming this frontier AI product experience where billions of people

Logan:
are experiencing AI for the first time in a different way than they've done.

Logan:
There's so many interesting use cases too,

Logan:
even around like image search is a great example of this like new sort of,

Logan:
it's like one of the fastest growing ways in which people are using search now

Logan:
is showing up with an image and asking questions about it.

Logan:
And just like the way people had just traditionally used search is already changed.

Logan:
It's like different than it was five years ago or even two years ago.

Logan:
And I think we're gonna continue to see that happen. I think search as the product

Logan:
you see today will evolve to have things like multi-line text input fields as

Logan:
sort of user questions change and all that stuff.

Logan:
So there's so much cool stuff on the horizon for search that I'm really excited.

Josh:
Yeah, as I'm hearing you describe all of these cool new things,

Josh:
particularly funneling into a single model, like the science breakthroughs are unbelievable.

Josh:
And I think that's what gets me personally really excited, like Ejaz,

Josh:
is this is actually going to help people.

Josh:
Like this is going to make a difference in people's lives. Right now,

Josh:
it's a productive thing. It's a fun thing. It's a creative thing.

Josh:
There's a lot of tools. But then there's also the science part and a lot of

Josh:
this all funneling down to one amazing model.

Josh:
I think it leaves us in a really exciting place to wrap up this conversation.

Josh:
So Logan, thank you so much for coming and sharing all of this,

Josh:
sharing the news about the new model, sharing all of the updates and progress

Josh:
that you're making everywhere else. I really enjoyed the conversation.

Josh:
For you, you also have a podcast called Around the Problems.

Josh:
Is there anything you want to leave listeners with to go check it out or to

Josh:
check out the new AI studio or the new AI model?

Josh:
Let us know what you have interesting going on in your life.

Logan:
I love seeing feedback about AI studio. So if you have things that don't work

Logan:
that you wish worked, even for both of you, please send them to me.

Logan:
Would love to make things better. for the new model as well.

Logan:
Like if there's, I think this is like still, this is still early days of what

Logan:
this model is going to be capable of.

Logan:
So if folks have feedback on like edge cases or use cases that don't work well,

Logan:
please reach out to our team.

Logan:
Send us examples on Axe or Twitter or the like.

Logan:
Would love to help make some of those use cases come to life.

Logan:
And I appreciate both of you for all the thoughtful questions and for the conversation.

Logan:
This was a ton of fun. We got to do it again sometime.

Josh:
Awesome. Yeah, we'd love to. Anytime. time please come and join us we really

Josh:
enjoyed the conversation so thank you so much for watching for the people who

Josh:
enjoyed please don't forget to uh to like share it with your friends and do

Josh:
all the good things and we'll be back again for another episode soon thank you so much

Josh:
i have a fun little bonus for those of you still listening all the way to the

Josh:
end the real fans when we were first going to record with logan we actually

Josh:
had no idea that he would break the exclusive news of nano banana on our show

Josh:
it was super cool so we wanted to kind of restructure the episode to prioritize that at the front,

Josh:
we did record a separate intro where I said, hey, Google makes some really good stuff.

Josh:
In fact, you guys have an 80 something percent chance of being the best model

Josh:
in the world by the end of this month.

Josh:
Can you explain to us why, why Google is so amazing at what they do?

Josh:
And this was the answer to the question. So here's a nice little nugget for

Josh:
the end to take you out of the episode.

Josh:
Thanks for listening. I really hope you enjoyed and we'll see you guys in the next.

Logan:
My general worldview of like why Google is in such a good place for AI right now.

Logan:
There's many layers of this, depending on sort of what vantage point you want to look at.

Logan:
I think on one hand, it's like, I think search is this like incredible part

Logan:
of this story, which I think people have historically looked at Google search

Logan:
as this legacy Google product.

Logan:
And I think search is going through this transition and is actually like today

Logan:
actually just announced as we're recording this earlier, that AI mode is rolling

Logan:
out to 180 plus countries.

Logan:
English supported right now, and hopefully other languages in the future and is a great example of.

Logan:
Ai overviews and ai overviews sort of double

Logan:
clicking into ai mode being this

Logan:
product that actually like for many

Logan:
people around for for billions of people around the

Logan:
world is the first ai product experience that they

Logan:
actually touch um and i think there's like something

Logan:
really interesting where like google has been on this

Logan:
mission of like deploying ai and like you know there's some

Logan:
you know the the some naysayers on

Logan:
twitter will be like you know google created the transformer and then

Logan:
did nothing with it and it's actually uh very far from

Logan:
the truth which is search has been this like transformer which

Logan:
is the architecture that powers language models and uh

Logan:
and gemini um has been powering that experience with this

Logan:
technology for the last like seven years the product experience maybe

Logan:
looks slightly different today than it did then um but google's been an ai first

Logan:
company uh for as long as i can remember um basically as long as ai has existed

Logan:
that's been the case and now we're seeing more and more of these product surfaces

Logan:
like become these frontier AI products

Logan:
as sort of Google builds the infrastructure to make that the case.

Logan:
I think people also forget like it's not easy logistically to deploy AI to billions

Logan:
of people around the world. And now as you look at like, I think Google has

Logan:
like five or six billion plus user products.

Logan:
So the challenges of like, even just making a small AI product work today,

Logan:
if anyone's played around with stuff or tried vibe coding something like it's not easy,

Logan:
doing that at the billion user scale is also very difficult um

Logan:
so i i continue to be more and more bullish and

Logan:
like part of the thing that allows us to do that billion user scale

Logan:
deployment is the whole infrastructure story like if you're watching on video

Logan:
i don't know if you can see but i have a couple of tpus sitting behind me um

Logan:
yeah and like that tpu advantage which is our sort of equivalent to gpus um

Logan:
is something that i think is going to continue to play out so there's there's

Logan:
so many things that I get excited about,

Logan:
and the future is looking very bright.