Josh Woodward: Google Labs is Rapidly Building AI Products from 0-to-1

what I found too Building Products over

the years is it's very common everyone

talks about product Market fit you'll

know it when you see it and all that

which is true but at least for me I've

always felt in the first part of

Building Products you iterate a lot on

the product and sometimes you forget to

iterate on the market and finding the

right Market side is also just as

important as the right product and you

have to connect those two and so I think

that in these early stage things with

Mariner that's where we are it's like

does is it possible for a computer to

like an AI model to drive your computer

yes that's a huge new capability is it

accurate sometimes is it fast not at all

yet like that's kind of where we are um

in terms of the actual kind of use case

or the capabilities and then it's about

finding the right Market

[Music]

today we're excited to welcome Josh

Woodward from Google Labs the team

behind exciting Google AI launches like

notebook LM and the computer use agent

Mariner Google Labs is Google's

experimental arm that's in charge of

pioneering what's next and how we

interact with technology by thinking

about how the world might look like

decades from now Josh is helping to

reimagine human AI interaction from the

provocative claim that writing prompts

is already becoming archaic to the

emergence of multimodal AI as a default

user

experience he shares insights on the

rapid Innovation culture in Google Labs

offers a glimpse of what's next in

generative video and much

more Josh thank you so much for joining

me and Ry today we are excited to hear

everything that you're doing over at

Google Labs maybe first to start you

mentioned a provocative topic to me uh

on your way in here writing prompts is

old fashed what do you mean by that okay

so um thanks for having me uh I do think

it's old fion we'll look back at this

time from an enduser experience and say

I can't believe we tried to write

paragraph level prompts into these

little boxes um so I kind of see it

splitting a little bit right now on the

one hand as a developer an AI engineer

you should see some of the prompts that

we're writing in Labs right now are

these beautiful like multi-page prompts

but I think for IND users they don't

have time for that and you have to be

almost like some sort of Whisperer to be

able to unlock the models ability so

we're seeing way more pull and traction

and I kind of seeing this in other

products in the industry too right now

how can you bring your own assets maybe

as a prompt drag in a PDF or an image

sort of recombine things like that to

sort of shortcut this giant paragraph

writing so I think it's going to kind of

divide I think as Engineers AI Engineers

you'll keep writing long stuff but I

think most people in the world we're

probably in a phase that'll sort of fade

out here pretty soon so the form of of

the context will change right you know

so you still have to get give the model

something right but it might be that you

can communicate it via picture or

communicate it via like just look at

this set of documents yeah your voice a

video any of that these models love

context so the context is not going to

go away but we're making a lot of bets

right now that the type of context and

the way you deliver the context that's

changing really fast right now I love it

okay uh we're going to go deeper into

the future of prompts and multi mulle

models in this episode maybe before we

do all that say a word on what is Google

Labs you know what what's the mission

and uh tell us a little bit more about

how you sit where you sit with inside

Google yeah so Google Labs if anyone's

heard about it we had one a long time

ago that went dormant for a while and

this is kind of back about three years

ago it got started it's really a

collection of Builders we're trying to

build new AI products that people love

so they can be consumer products B2B

products developer products it's all

zero to one um it tends to attract an

interesting mix of people maybe people

who have been at Google a while but also

a bunch of startup Founders and exf

Founders and so we kind of mix these

people together and we basically say

what's the future of a certain area

going to look like say the future of

creativity or software development or

entertainment and they go off in small

little teams and they just start

building and shipping and so that's how

it operates and it sort of sits outside

the big traditional Google gole product

areas but we work a lot together but

there's kind of an interesting interplay

there and I think that's been part of

what's been fun about it is you can kind

of dip in maybe work with search or

Chrome or other parts of Google but you

also kind of have the space to explore

and experiment and try to disrupt too

and that's that's kind of what we're up

to how do you create the culture inside

of labs that you want right if you think

about there's got to be a lot more

failure presumably than there are in

other parts there's got to be a

different metric for Success than there

is just the sheer scale of Google so

what is the culture you're trying to

create and how do you create it so we

really pride ourselves in trying to be

really fast moving as a culture so we'll

go from an idea to end users hands 50 to

100 days um and that's something that we

do all kinds of things to try to make

that happen so speed matters a lot

especially in kind of an AI platform

shift moment the other thing is we think

a lot about sort of big things start

small and one of the things if you're in

a place like Google you're surrounded by

some products that have billions of

people using them and people forget that

all these things started with solving

usually for one user and one paino and

so for us we get really excited if we

get like 10,000 weekly active users it's

like you know we'll celebrate that

that's a big moment when we're starting

a new project and for a lot of our other

kind of groups inside Google their

dashboards don't count that low right I

mean it's like so there's kind of this

moment where you know the size of what

we're trying to do is very small um it

probably looks a lot like companies you

all work with honestly from that uh

perspective and I think the other thing

we're trying to do is because we sit

outside the big groups at Google we kind

of have one foot in the outside world we

do a lot of building and kind of

co-creating with startups and others but

also one foot inside Google Deep Mind

and so we've got kind of a view of where

the research Frontier is and more

importantly where it's going and so

we're often trying to take some of those

capabilities in so we take a lot of

Pride and sort of finding people who are

very creative people who are almost like

see themselves as underdogs um they have

kind of a hustle to them and so we have

this whole dock called labs in a

nutshell and my favorite section in the

dock is called who thrives in labs and

there's like 16 or 17 bullets that just

list them out um and that's kind of how

we try to build the culture but you do

have to normalize things like failure

you have to think about things

differently around promotion

compensation all these things that you

kind of would do in a company too

you mentioned the Deep Mind links I

think that is super cool what have you

found is the kind of Ideal kind of

product Builder Persona inside Labs is

it somebody with a research background

is it somebody with a who comes from a

successful consumer product background

is it you know is there the magical

unicorn that's great at both research

and products what type of person we take

as many unicorns as we can find and we

actually I found some uh which is great

you do look for that kind of deep model

expertise as well as kind of like a

consumer sensibility in terms of those

people exist they exist they're great

too um if you can find them uh and we

also kind of have found ways to kind of

train or develop people so that's

another thing we think a lot about is

like how do you bring in often people

that might not be the normal talent that

you look for so like we're always in the

interesting kind of zone of like who's

undervalued who's kind of like really

interesting but maybe not on paper but

when you interact with them look at

their GitHub history I mean there's all

these different signals you can look at

um but yeah that's kind of how we would

think about it really cool how do you

decide what projects to take on next is

it is it bottom up top down how does

that work yeah great question we kind of

do um a little bit of a blend actually

so at the top down side we're looking at

what are the areas that are kind of on

mission for Google that are strategic to

Google because we sit inside it so we're

thinking about ourselves in that broader

context so that may be for example like

what would the future of software

development look like there's tens of

thousands of software developers at

Google and obviously this is an area

that AI is clearly going to make a big

change in so we'll be thinking about

could we build things for other googlers

but also externally how do we build

things like that so we take that kind of

top down view think of it as almost I'm

from Oklahoma we like to fish a lot in

the summer but like you're trying to

figure out what's the right Pond to fish

in so we put a lot of thought into those

like ponds to fish in but then we let a

lot of these teams often their four or

five person teams come up with the right

user problems to go try to solve and

that's where we kind of meet in the

middle and I think for a lot of other

teams they might look at what we do it's

a little chaotic you know we don't have

like multi- quarter road maps like we're

trying to survive to the next whatever

10,000 user Milestone and then try to

grow it uh but I would say it's kind of

that sort of blend what's one of the

products that you guys have built that

you're excited about now oh yeah so I

guess if you've ever used um the Gemini

API or AI Studio notebook LM or any of

VO any of these things these are

products that we've kind of worked on

from Labs I mean maybe I'll talk about

one that's maybe well better known and

one that's coming up so the very excited

about where notebook lm's going I think

we've hit on something where you can

bring your own sources into it and

really AI just like grips into that

stuff um and then you're able to kind of

create things so a lot of people maybe

have heard the podcasts that came out

last year there's so much coming that

follows this pattern

um so watch this space uh they um

there's just a lot you can do with that

pattern and I think what's really

interesting is it gives people a lot of

control they feel like they're steering

the AI we have this term on the team at

actually one of the marketing people

came up was like an AI joystick that

you're kind of controlling it so that's

interesting um I would say there's a lot

of stuff coming right now we're very

excited about vo um Google's imagery

model and sort of video model and where

those kind of come together so we've got

really interesting products coming along

in this space I think maybe we can talk

about that some at some point but I

think generative video is kind of moved

from this moment of almost possible to

possible and I think let talk about it

now tell us yeah yeah well I think it's

it's interesting these models are still

huge to run like V2 takes hundreds of

computers right so the the cost is very

high but just like we've seen with the

text based models like Gemini and even

ones from open Ai and anthropic you know

the cost is reduced like 97 times in the

last year so if you kind of assume cost

curves like that what you're going to

see with these vo models what's kind of

brand new say with V2 is it's really

cracked really high quality and physics

um so the motion the scenes the if you

talk to a lot of these AI filmmakers

they talk about what's your cherry pick

rate which is a term for like how many

times do you have to run it to pick out

the things that's really good and what

we're seeing with something like VI is

the cherry pick rate is going down to

like one time got what I want and so the

instruction following the ability for

the model to kind of adhere to what you

want is is really cool so I think when

you put that in tools um you're now able

to convey ideas in a whole different way

what do you think are the solved

problems and the unsolved problems in AI

video generation cuz I remember you know

uh last year it was like you know even

last year there were all these you know

there was so much talk about you know

generative video is you know a physics

Simulator for example right right can

kind of emulate physics and it's like

that's amazing is the physics stuff

solved do you think like what else is

you know what's done and then what's to

be solved yeah I would say physics is a

hard thing to solve forever but it's

close I would say it's close enough yeah

but you're six months ago year ago few

years ago you had Will Smith eating you

know pasta was a disaster and then even

last year you had kind of these videos

of like knives cutting off fingers and

there were six fingers you know it was

like that's where we were um so I think

physics tons of problem ress the ability

to do photo realistic quality uh very

huge progress the ability to kind of do

jump scenes and jump cuts and different

sort of camera controls that's really

coming into almost solved there's paths

to solve all this stuff um still going

to solve the efficiency and serving cost

I would say and probably still have to

figure out a little bit more of like the

application layer of this cuz I think

this is another big opportunity as we've

seen like a lot of other modalities with

AI you get kind of the model layer you

get kind of the tool layer and then the

real value we think is in this

application layer and so I think that's

really interesting to rethink workflows

around video and I think that's pretty

wide open right now do you think the

models are capable of you know even

having video that is malleable at the

application layer so for example if I

want to have character consistency

between scenes are the models even

capable of that or I imagine you want

model steerability in order to be able

to kind of work with it at the

application Level like what what is

model Readiness um and what's required

in order to be able to do magic at at

the application yeah so I was talking to

a couple AI filmmakers this week and

what they're really interested in is

exactly what you're saying character

consistency scene consistency camera

control it's almost like we need to

build an AI camera you think of some of

the cameras that are kind of filming us

right now this is sort of like Decades

of Technology that's kind of been

perfected for a certain sort of input

output and I think were on the verge of

kind of needing to create a new AI

camera and when you do that you can

generate infinite number of scenes you

can generate like oh you're wearing a

red sweater now make it blue and not

just in that scene but in like a whole

2hour film so there's all kinds of ways

that we're starting to see these

prototypes that we're working on too

internally where this is this is here

like it's coming um we're kind of entire

I think things that used to either be

too expensive or too Timely or it

required a certain skill level um we

kind of talk internally on the team

about how do you kind of lower the bar

and raise the ceiling and what we think

about that when we're building products

is how do you make something more

accessible or how do you make like the

pros take it and just blow you know the

quality out of the water and making

incredible stuff um so that's what we're

seeing with video it's kind of right at

that point where both are happening

there was an interesting tweet from or

post from Paul Graham recently on this

idea I think of based on this pace of

progress he's like you sort sort of want

to be building things that kind of don't

quite work yes and are way too expensive

yes right because they're going to work

yeah and their cost is going to come way

down y right and so I would imagine that

has applicability for you guys too

particularly in video that's exactly how

we do it yeah I mean right now I don't

know off the top of my head but each

video 8sec clip generated is obscenely

expensive but we're basically building

for a world where this is going to be

like you're going to generate five at a

time not even think about it one of the

actual principles I've kind of learned

just over the last few years working all

this AI stuff is make sure your product

is aligned to the models getting smarter

cheaper faster and if your core product

value prop can benefit from those

Tailwinds you're in a good spot if any

of those are not right question Your

Existence like that would be my uh my

summary takeway on that yeah I like that

how far do you think we are from having

uh economics of video generation that

are you know right side up where where

you know it costs less to to generate

the thing than the economic value of of

generating it yeah oh wow this is tough

this is a prediction you're never really

sure about I don't know but I would say

one thing we're seeing just as we're

modeling out a lot of costs because

we're starting to put vo into some of

our own tools that are coming out is

we're probably going to need Innovation

on the business model side in addition

to just the product and the application

layer and what I mean by that is you

could our first thought was oh let's

just make a subscription and then just

charge per usage on top that might be a

way to to do it another way to do it is

when you talk to some of these creatives

whether they're in Hollywood um or even

these AI filmmakers that are popping up

they're kind of like okay I want this

output and I'm willing to pay this much

and it's kind of a pay per output kind

of which you've seen in other cases AI

companies are starting to do some of

this too but for sort of film and video

that's it's a little bit how you'd think

of doing a project if you were a

producer but now you're kind of

imagining it like the individual cre

creative level which is kind of

interesting so that's more like almost

like an auction type model potentially

so I think there's a lot to explore I

think we're probably though you know the

pace things are moving it's it's on the

it's on the scale of like quarters I

think where it starts to get interesting

as opposed to like many many years um so

that's yeah I think there's a path you

talked about the pace of progress a

couple times yeah do you think it's

accelerating you have the a unique view

in the Deep Mind and let's use that as a

I don't know Harbinger for some of the

others yeah yeah as a propy

yeah what what where are we at are we

accelerating are we you know on a crazy

uh trajectory and maintaining the same

one like yeah I'm interested yeah yeah

um I keep thinking it will slow down and

it's never slowed down in the last three

years um so you know you think oh

pre-training might be plateauing

inference time compute a whole another

Horizon opens up and I think there is so

much um there's an author on the team we

actually hired his named Steven Johnson

he Co found in Notebook LM when we first

brought him on and he talks about this

notion of like there's adjacent posses

he has this really interesting book on

the history of innovation and I feel

like right now it's like you walk into

this room and there's all these doors

that are opening up into these adjacent

posses and there's not just like one

room and one door it's like one room

with like I don't it feels like 30 doors

that that you can go explore so I think

that's what it feels like on the inside

I love that visual of the the rooms and

then the adjacent posses I'm going to

steal that and maybe take it and call it

my

own plasic VC over

here um what do you think the future of

video consumption looks like for us as

consumers like am I still looking at

Hollywood style feature films that are

created by Hollywood Studios just done a

lot more coste efficiently am I looking

at a piece of content that's dynamically

generated to what you know about me and

it's only for me to watch am I like what

what what do you think the future of is

as so this is one of those that could go

in spider in many different ways I would

say I'd say some of the things we're

excited about and what we see so I think

the future of entertainment is way more

steerable so right now you think about

you sit on your couch like this and you

maybe scroll through something or

whatever you cast it on you bring it up

on the TV so it's going to be way more

steerable where you can kind of

interject if you want and maybe take it

certain ways we think that's one area we

think another is personalization like

you said if you think today about

YouTube Tik Tok any of these algorithms

that can kind of figure out this is what

you're interested in imagine that I

think way more extreme uh that could be

kind of fine-tuned to sort of what you

want to share with the model um I think

the other bit is a lot of this I think

is going to be generated on the fly so

another theory we have is that just like

there was a rise of kind of a Creator

class couple whatever 10 15 years ago

that powered YouTube and the rest

there's going to be a shift or maybe

it's a different set of people that we

think of as like curators where you

curate stuff and you work with the model

to maybe create things and I think

another loop in that is how you can

remix all this and so that's another big

part of what we see in the future of

entertainment is that there'll be like

oh I kind of like that but they'll make

it more like this and if you think you

know at some level the cost the time the

skills required of this is literally

maybe just like tapping a button or just

describing it and you get kind of

different versions that's kind of where

we see some of this going it will be

really interesting to see if like some

of these same

percentages hold like we know today that

a lot of times certain percentage like

90 95% just consume from platforms and

you have very small Creator class so

like will that balance change um but I

see a totally different ways you could

think about content platforms that have

some of these native controls um like

for example will we expect uis that have

a join button where you know today our

uis maybe have a play pause whatever

save bookmark something star heart it

like will there be like new things where

you join and they're like oh hey Sonia

Ry what do you want to talk about you

know what I mean and I think like that's

totally possible we're building that in

the notebook LM today uh so that you can

imagine Play it Forward you've got

avatars or humanik characters or not

with lip animation voice cloning all

that can come together in sort of new

ways I think do you think movies and

games start to blur yeah I think that's

a real possibility yeah there's a whole

interesting intersection that's

happening right now between movies or

video content games and sort of World

building and 3D and it's really unclear

to us right now where that's going to go

but there's so many areas right now

where we're seeing learnings from each

and even down to some of the training

techniques we're finding things like

that yeah so actually that was going to

be one of my questions like if you look

at all the companies building generative

video models right now some people are

kind of going straight from the you know

the pixel stream so to speak and some

people are going from the 3D angle with

with the idea that you know to really do

video right you need to get 3D do you do

you have an opinion on that yeah we've

actually got bets on both sides right

now I don't know I don't

know yeah we're hedged we're hedged on

this one so on the 3D side we have this

project we got started where we

basically said like take six pictures of

a sneaker and create a 3D spin of it and

we put that on search it's been really

great and it's amazing how it fills in

the details but I think what's

interesting as we've been going down

that path something like V2 shows up now

you don't need six photos anymore you

need like two or three and you can

basically do like an entire product

catalog like every product that's ever

been indexed at Google just overnight

sort of can create it so now you've got

a 3D object basically of any object

bookshelf chair whatever from any angle

that you can pan tilt Zoom relight and

now that's like an object that you can

drop in anywhere so that's kind of the

3D angle from the video angle it's

interesting or kind of the World

building we had this little prototype we

built we're like wouldn't it be cool if

you could recreate landing on the moon

for like every classroom and like give

teachers a tool where they could put the

kids in the like you know lunar module

as it's coming down so we built this

thing it's kind of terrifying actually

because we also built a little side

panel where you can inject problems

where it's like oh no something's on

fire in the back like simulate things we

had a little fun with it but that was

interesting cuz the models you could say

like look right and it would actually

fill in the details um and so you start

to get this that's why it feels like

it's kind of blurring and I guess why

we're hedging on both sides right now

yeah we're not sure 2025 everyone's

talking about agents yes yeah computer

agents yeah you just said it three

times exactly proba being a VC again

exactly I've been called a VC twice

today um this is a very big insult uh

can you talk to us about Google Mariner

yeah yeah so Mariners one we put out in

December last year this is a fun one

actually because we started seeing this

capability developing in the model we're

trying to understand if you could let

these models control your computer or

your

browser what would happen um good and

bad um and so that was a good example of

a project where we went from hey this

capability is kind of showing up let's

put it into right now it's a Chrome

extension just because it was quick to

build idea in people's hands 84 days uh

very fast very a lot of memories made on

that but I think what's interesting is

you're seeing both across anthropic open

AI obviously Google and a bunch of other

startups in the space are all hitting on

kind of the same idea that models are

not just about maybe knowledge and

information and synthesis and writing

but they can do things and they can

scroll they can type they can click they

can not only do this in one browser in

one session but like an infinite number

in the background um so I think with

Mariner what we're really trying to

pursue is like of course there's the

near-term thing of like can it complete

tasks in your browser but the bigger

thing is what's the future of human

computer interaction look like when you

have something like this kind of not

just one of these things but basically

like an infinite number uh kind of at

your disposal and so that's what we're

chasing with that project what do you

think the ideal use cases are maybe even

in the near term for Mariner because I I

think all the demo videos I see not

necessarily from Mariner specifically

but with computer use more broadly or

you know here have this agent go book a

flight for me or go order a pizza on

door dash for me right like that's nice

but like I like doing those things yeah

yeah yeah you're pretty good on those on

your phone is one of my one of my uh

Delights in life and so um what do you

think are the the killer kind of

consumer consumer use cases yeah well

that's what's interesting it may not be

consumer it may be Enterprise and one of

the things we're seeing when we do all

the user research right now on Mariner

because we have an usted tester and

people are playing with it and giving a

lot of feedback is it's really these

high toil activities toil is kind of an

oldfashioned word that doesn't get used

a lot but this is when people talk about

it it's like this is what makes me

grumpy and this thing is helping me

solve it but what's interesting is a a

lot more of those are showing up on the

Enterprise side just to give you a

couple examples from yesterday we were

hearing from one of the teams and

they're basically they have this co-

browser use case so imagine you're in

like call center somewhere some customer

calls in they right now have this very

complicated way the agent in the call

center can like remotely take over your

machine that's not working browse

through things and do something for you

they were like we would love to have

Mariner do this um and that's like a way

another one we heard which was kind of

interesting was people they are like

part of a sales team or something they

have take a customer call then they've

got all these next steps they need to do

and they just want to fan that out and

it's often updating different systems

sys that are all probably I don't know

some SAS subscriptions they're paying

everywhere and they're just like the UI

is clunky it takes a long time I just

want to send Mariner do all this so

these are the kinds of things that are

kind of interesting that are just

naturally coming up on the consumer side

I don't know have you found one yet in

your mind that you like because I we're

we've got a few but I it's I'm curious

I'm think I'm trying to think what the

toil I have in my everyday life yeah

talking to Ry uh I'm kidding I'm kidding

talking to Ry the best part of my day

want to appreciate

that I think but I like the framework

even if we don't have the exact use the

framework of like what are the things

that are the heavy lifting that you

don't enjoy right throughout the day

that take up time away and I do think

that that was actually the same logic

that yielded things like door Dash or

instacart right um you see how I had to

get insta card in there I'm just making

sure that that was there um on the

Enterprise side when you think about it

yeah um how are you testing that are you

testing that with existing you know uh

customers are you testing that with

Google Cloud customers like who are the

Enterprises that you guys will actually

like test things with yeah so in that

case we kind of go across big and small

so there will be some Cloud customers we

have a lot of cloud customers who always

want the latest and greatest give us

that they have like Labs equivalents

inside their companies right so those

are awesome test beds we also work with

a lot of startups um and I mean if

there's others listening to this that

are interested let like DM me let me

know like cuz we're always trying to

learn kind of from different sides of

the market what I found too Building

Products over the years is it's very

common everyone talks about product

Market fit you'll know it when you see

it and all that which is true but at

least for me I've always felt in the

first part of Building Products you

iterate a lot on the product and

sometimes you forget to iterate on the

market and finding the right Market side

is also just as important as the right

product and you have to connect those

two and so I think that in these early

stage things with Mariner

that's where we are it's like does is it

possible for a computer to like an AI

model to drive your computer yes that's

a huge new capability is it accurate

sometimes is it fast not at all yet like

that's kind of where we are um in terms

of the actual kind of use case or the

capabilities and then it's about finding

the right Market but yeah to answer

short it's kind of in these early days

we do lots of stuff really quickly and

what I kind of Coach our product

managers on and other people on the team

because we have engineers and uxers they

all go to these sessions is like don't

look at the dashboards it's too small

numbers right now look at their eyes

like look at the customer's eye and when

you show them stuff do they light up or

not you know what I mean and like that's

kind of the signal you're following it's

way more art than science at this stage

can we go back for a second just to the

context point because I was thinking

about this V like you working at Google

right and you talked about bringing your

own you know um is there a world where

where someone can just opt in of like

Google knows a lot about me right

already you know my searches my Gmail my

calendar is there a world where you can

just sort of opt in be like I don't want

to bring it all now I just kind of want

you to use what you got and make magic

right is that something that could

happen because Google's uniquely suited

to be able to do something like that

probably more so than anybody U is that

something that you guys can play with in

Labs or have a possibility for or is

that not possible we do some more kind

of internally with some of our own like

data on the team right where like I've

opted into a lot of things it's just

like take it all like let's make good

stuff um but I think you'll see some of

that come through in the Gemini app too

where you can link different things but

I think it's actually an area that's

like actively kind of being explored too

of like what types of data is like the

most interesting and the most useful and

of course also the right controls where

people feel like okay I'm not just

giving it away yeah so I think that is

an area though that we do experiment on

um some but I'd say right now a lot of

the experiments are more on our own

stuff as we're trying to figure out

you're going to have to tell us

separately some of the things that you

could have done now now that they know

everything about you you know like what

is the magic that can be created for you

yeah I think certain things that

immediately come to mind that are pretty

powerful is you can you can see things

like in my own data I feel like I have a

second brain that is a true like there's

always been this vision of a second

brain and tools for thought and all this

stuff and I feel like you can get pretty

close to that and I think the Gemini

model specifically is really good at

long context the ability to have this

like impressive short-term memory and so

Gemini too that's an area we're really

trying to exploit right now like how to

use that on Mariner yeah similar

question to what I asked on on vo uh

when do you think we'll have computer

use that is accurate enough and is fast

enough to do some of these use cases you

talks about yeah that's another one it's

kind of hard to tell at the pace though

right now I mean not just inside Google

but what you're seeing from some of the

other labs too they're on like about an

every month or two rev so you can

imagine just this year we're going to

see four five six revs of each of these

things right um again that's just what

we know is happening um I think the

areas that are a little bit trickier or

harder right now is how the computer

like finely or precisely navigates like

the XY coordinates almost you almost

want like a lat long of your screen and

that's still kind of really interesting

Jagged edges on that I would say the

other big area I would say is like this

it's more of a human thing like when do

you want the human involved or not when

do they want to be involved or not and

kind of creating the right construct

almost was like Hey I'm about to buy

something oh no I want to know about

that or I'm okay for $5 but nothing more

than that do you know what I mean and so

there's a whole bunch of almost like

hardcore like

HCI like research and like really going

deep on the empathy of like how you set

those controls that I don't think any of

them including the Google Mariner one

right now we don't have I mean we do

certain very blunt things like don't buy

anything don't consent to any toss you

know like so there there's so like crude

uh things right now that you can do but

I think people are going to want a more

fine grain way so these are some of the

things that are I consider more unsolved

again that principle just banking on the

model is going to get smarter faster

cheaper um and you're going to get four

or five six seven revs this year um yeah

okay I have a meta question yeah how

come all of the research Labs converged

on computer use at like as far as I can

tell the same exact point in time was

that an accident was that just all the

technology happened to converge at the

same time like what happened there it's

a good question I mean this is I don't

know the specifics there of each of the

other labs but I would say you know when

you read about the history of innovation

and there's like all kinds of things on

this there it's not uncommon that

discoveries kind of

around the same time and I think there's

kind of a new paradigm now with these

models and I think lots of people are

seeing the potential in certain ways and

I'm sure there's also I don't know

people changing labs and other things

that are cross-pollinating all these

ideas too but it does feel like it's one

of those is kind of how I'm interpreting

it is like I think similar with coding

right you saw there's already even the

agent stuff right now there's lots of

this stuff kind of bubbling um which

makes it really fun but also keeps you

on your toes right cuz this is kind of

the underdog mindset here are you going

to hire any other authors the reason I

asked is I was thinking about I think

Matt Ridley is the one who's written

about some of these things about like

adjacent Innovations you know you have

Stephen Johnson maybe why did you hire

stevenh Johnson how did that happen and

are you going to think about other

people that don't have obvious

backgrounds that you would bring into

Labs yeah yeah so um the quick story on

Stephen was um the guy who kind of

restarted Google Labs was a guy named

clay bore who

mut friend exactly and um he and I big

fans we've basically read everything

Sten had written and Sten was a very

interesting guy because for like decades

he's been in search of the perfect tool

for thought and so clay clay cold

emailed him and we were both subscribers

to his substack we kind of messaged them

and we're like we love you will you come

work with us we can build the tool

you've been wanting to build that's

where it started actually and this was

like I mean it was like summer 22 so

like before any of the you know CH Chi

moment or anything and stepen picked up

the phone he was like yeah let's do it

so he came in he was a visiting scholar

the job ladder didn't exist uh I had to

go figure out with our HR person how to

create a role that he could take on so

was very kind of unconventional in that

way um and then the rest is kind of

History obviously um I've read a bunch

of Matt's books I don't know Matt he'd

be awesome so if he's listening like

he's listening come

that's right that's right uh I would say

we've done this quite a bit so we've

actually brought in musicians I'm

actually really we're trying to figure

out right now like a like a visiting

filmmaker that's cool um so it's kind of

a model Stephen kind of pioneered it he

was the first one that it's like how to

bring in it's a big value in Labs of how

do we co-create we don't want to just

make stuff and throw it out there we

actually want to co-create it with the

people that are in the industry and what

we find when we do that is you actually

get Way Beyond the like oh that's cool

toy AI feature you get into the workflow

and if you're working with someone like

stepen Johnson who's written you know

dozen plus books there's a certain way

he thinks about and almost like a

respect for like the sources and the

citations all that stuff comes through a

notebook LM and we're doing similar

stuff with music and video and IND and

other stuff yeah is the goal to create

ne new products that you can take from

one to 100 to to a billion Standalone or

is the goal to you know find product

Market fit with things like notebook LM

and then really fold them into the

Google Mothership so to speak yeah it's

interesting so when we first started I

would say it was all about build

something graduated so kind of a

traditional incubator sort of model it's

been interesting as it's gone along

we've done that some cases like AI

studio and the Gemini API we graduated

and that's now in Deep Mind and they're

kind of running with it um something

like notebook LM we're just going to

keep in Labs right now for the

foreseeable future cuz it's kind of a

different creature like it's only

possible with Ai and a lot of the stuff

we're working on now I mean we'll have

to see how many of these we can put

together that actually can kind of get

escape velocity but we're really

interested in turning them into

businesses and making them sustainable

and kind of you know that's been a lot

of the the focus actually is like take

big swings and that gets back to your

point a lot of these won't work um

because if you're just if they're all

working you're not swinging big enough

yeah so it's like trying to find that

balance but that's definitely we start

with kind of could we make this a

business work backwards from that and if

we end up graduating it that's still a

good good outcome for us another good

outcome is we stop it and was like cut

the losses we did our 100 day Sprint or

whatever move on to the next thing yeah

you mentioned at the top of the episode

that you try to do some top down

thinking of you know what are the most

interesting pools for us to be building

in yeah what are your predictions on the

most interesting pools to be building in

for 2025 like where are you hiring um

talents like where you where you

sniffing around where are you

co-creating with the the Deep Mind folks

yeah yeah there's a lot happening with

agents there's a lot happening with

video some of the things we've talked

about with computer use but I think

about those ponds a little bit different

I think about them we have this doc

called Labs is a collection of Futures

and it's 82 predictions about the future

um which is always dangerous to make one

prediction about the future let alone 82

but the thought experiment on the team

where we got to this was imagine you're

in a room like this the ceiling just

opens up and this little capsule comes

down we all jump in it and it slings us

into the future it's 2028 you can get

out you get five minutes look around

write down everything and you're brought

back to the present and then write what

you saw and that's what this dock is is

so what's the future of knowledge look

like what's the future even though

prompts are oldfashioned that's a pretty

good prompt that you gave to the team

tell you right now yeah yeah so that's

you know we think about we think about

it at that level at kind of a high level

so say something like what's the future

of knowledge going to look like we think

it's going to be one piece of that

prediction one of the 82 is that it's

infinitely

remixable and anything that comes in can

be transformed and become anything on

the way out if you believe that then you

take certain bets and you build products

kind of with that future in mind so that

might be one of them but I think like

going back to maybe some of the ones

that a lot of people might be listening

or building I do think we're kind of at

the moment for video we're at the moment

for very interesting agent stuff with

the thinking and reasoning models and I

think there's also maybe something kind

of under the radar right now a little

bit still think coding has major leaps

we're going to see this year um and so

those would be some of the ones that are

top of mind for us are you guys doing

work on coding out of labs too yeah we

are we are so right now at Google 25% of

all the codes written by AI yeah I saw

that Jeff te yeah that's right that's

right and that's up a lot in the sense

of just how fast the progress is um this

is an area that that I think there's

kind of two approaches you could think

about like how again think of lower the

bar raise the ceiling right how do you

make coding available for people who

could never write code before massive

opportunity you know like I've been

coding my whole life I mean some of that

well it's kind of interesting is some of

the most interesting stuff happening

here I don't know if any of you have

played with like repits agent stuff

really interesting right couple of

weekends ago I'm with my fourth grade

son we are struggling right now in our

household to implement chores we created

a chore tracking app 28 minutes 45 cents

done we're daily active

users and so it's a way to kind of get

into software and a world of kind of

software abundance that's really

interesting um so we've got some stuff

in that area uh we're also interested in

how do you take a professional trained s

programmer and make them like 10x to

100x and there's kind of I think

interesting bets on both sides of that

yeah what do you think is overhyped in

AI right now oh that's an interesting

question I wish we move beyond the

chatbot interface a bit like that's one

area that feels like we're kind of

reusing that in a lot of places Google

included um I'm also not

sure there's still a lot I think of like

people jamming AI into stuff like AI

itself is a bit overhyped I wish we were

a little more precise about how

disruptive or like where to apply it and

so I think again we're trying to think a

lot about like workflows not just taking

existing product in bolt on AI um so I

think that's maybe a little there's a a

race like you're seeing the first

generation of AI put it in and it

reminds me a lot actually when I first

started at Google it was like right as

the iPhone moment was kind of Just

Happening and taking taking hold you

when Steve walked on stage in 2007 said

this is the iPhone if you look at the

App Store three years later which is

roughly where we are in this AI

Revolution the App Store in 2009 is I

went back and

checked websites that have been shrunken

down to fit on your phone flashlight

apps and fart apps these were like the

highest top downloaded things that were

happening so I think we're kind of in

this stage where the real stuff is going

to start to come out kind of this year

next year the next year that's when you

start to see the Ubers the airbnbs the

instacart the things that really change

kind of how you do stuff and so that's

that's kind of my thought on it all

right then Sonia ask you the overhype

question I'll ask you the uh under the

radar underhyped question what some

areas that deserve more attention within

AI we talked about coding a little bit

maybe just one other thought on that is

I think if you can get code models uh

that can kind of write code and

self-correct and self-heal and migrate

and do all this stuff it just makes you

think the pace is fast now that totally

changes the curve so I think that's a

huge I still think it's underhyped like

it's hyped a lot by the way um but I

think as hyped as it is it could be

hyped more that's one um um I don't

think we fully internalized the notion

of like what does long context or like

infinite context mean it gets to some of

your personalization questions

potentially but it also gets to some of

the stuff we were talking about around

how can you make things like a mariner

literally just keep going like um and so

uh that whole notion of long context I

mean you'll you see a lot from Google

but we're investing a lot in that

because we think that's a strategic

lever um that's important uh especially

as you get more agentic chain together

kind of workflows um maybe another one I

think there's there's not enough talk

about

taste and like I think if you believe

the value is going to be in the

application layer if you believe there's

going to be some percentage of AI slop

if you can just see a few of these

Trends and I think there's going to be a

value in Good Taste and good design and

it doesn't mean it has to be human

created necessarily although I think

there's going to be high value on that

too as like human crafted content

becomes more Artisan um but I think

that's another one I would say I think

maybe related to that it's like veracity

and Truth um and sort of what is real

like these are things that I think are

going to become way more important than

they already are today I think the

context Point within there I like really

firmly agree with on like what can

happen with you um your infinite context

point because if you think about the

relationship in your life where you have

like the most context shared context

it's probably with your spouse right and

if you think about that what ends up

happening is you can communicate with

your spouse literally with just like

like the flick of an eye right and all

of a sudden they know exactly what you

mean they know it's time to leave the

party whatever it might be that's right

right and you think about that's the

aspiration for what can happen with

infinite shared context we know that's

the ceiling exactly right and so you

think about you're like think about how

far away that is from now where you're

like typing things in about what it is

in your point of like well hold on

there's all these different ways you can

communicate it and can get to know you

better if it has memory and so I I think

there's so much gold in there of it just

being able to keep going right but

giving it the right context and whatever

it needs you think of any company that

you all back or even Google like what's

one of the most painful things is when a

long-term employee leaves CU all that

context walks out the door so I think

it's exactly right whether it's a

personal relationship or a work

relationship yeah okay we're going to

wrap with a a rapid fire round you ready

yeah sounds good okay favorite new AI

app I mentioned it earlier I'm having a

lot of fun with repet love it the new

agent thing and on the phone I think

they're doing some really interesting

stuff there you know one of our partners

Andrew Reed is known for slinging like

creating these amazing memes and sending

around it's now so easy to create an app

he just creates these all the time and

sends them to me um they're they're

really good yeah we have this concept of

like disposable software you you use it

once and you kind of throw it out after

you're done with it so yeah okay what

application or application categor do

you think is going to really break out

this year

video okay uh recommended piece of

content or reading for for AI people oo

that's an interesting one um you know

this one's not a traditional AI pick

because I think probably a lot of the

listeners here I was going to say over

the break I I read a lot and one of the

books I picked up was actually it's the

Lego story and it's the history of Lego

and it's on its third generation of

family ownership um I'd recommend that

one it's a really interesting uh yeah

here's why though there's a pivotal

moment in the company's history where

they had 260 products and maybe for a

lot of Founders that are listening you

can imagine your company could go in

like all these different ways you're

trying to figure it out and the

grandfather the CEO at the time

basically identified like the little

building blocks this is it and he bet

the company on it and he bought these

incredibly expensive machines and so I

think it's like an incred I like to read

biographies a lot and this was one that

really stood out Josh has an Inc

incredible taste in books and he has

this wonderful reading list that he's

been kind enough to share with me oh no

way that's really wonderfully curated it

has this very good formatting as to when

it's something you really got to read

versus not and so uh you should to all

the listeners you should take Josh's

suggestions seriously I actually really

want a great AI reading app that's like

my wish list app what would in part

because I have terrible memory but out

of out of everything I've ever read or

listen to which I think is a different

set of things than all the books on the

planet like there's all these things

that are kind of on the tip of my tongue

and ideas that connect but you know

they're all kind of in an abyss and

they're all pretty inaccessible to me

and and so something that surfaces some

of those thoughts and ideas that I've

had things that I've read you know that

next layer of thought I have from

reflecting on two different things that

I've read and the connections probably

across them yeah it's a good idea I

think even within that like just the

hard copy version the Kindle version and

the audiobook version being like you

know seamlessly intertwined like you

just the most basic level you know so

that you can continuously pay attention

to something that you like and then we

can get to the version that you said

yeah request for startup okay uh

pre-training hitting a wall agree or

disagree

o maybe lean agree I think there's still

stuff to squeeze out there but I think a

lot of the the focus has shifted yeah

Nvidia long or short I don't give stock

advice Index Fund would do you ever uh

sit with Demis and be like look as

someone between us we won a Nobel Prize

do you ever start with that you know

because you know that feels like

something that's true you know between

the two of you there's one Nobel

Prize it's all one directional it's Den

John jumper those are the people that

won the Nobel Prize not Josh Woodward

yeah

uh okay any other contrarian takes an AI

any other contrarian takes I I guess

maybe I'll leave it with this I think we

are kind of one thing is like what a

time to be alive and building because I

feel like there's this window where

there's like so many adjacent possibles

opening up I think the second would just

be like I'd encourage people listening

to like really think about of course

there's the models and who's winning and

the back and forth but like what are the

values You're Building into your company

cuz I think this is one of those moments

where there's going to be like tools

created that shape like follow on

Generations I think it's really

important people think about that and

like are you trying to replace and

eliminate people or are you trying to

amplify human creativity I mean there's

like one that's like you know going

immediately comes to mind when I'm

thinking a video for example I'm on the

side of wanting to amplify human

creativity but I think there's like

there are these moments that happen in

our Valley here where like things change

and they change often for generations

and they can change for good or bad and

so I would just encourage people that

are in spots where you're building and

you have this incredible technology

that's only getting smarter and faster

and cheaper to put it to good use and

think about the consequences Downstream

thank you so much Josh for joining us we

love this conversation yeah thanks again

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