The Minds of Modern AI: Jensen Huang, Yann LeCun, Fei-Fei Li & the AI Vision of the Future | FT Live

Hello everybody. Good afternoon, good

morning. And I am delighted to be the

one chosen to introduce to you this

really distinguished group of people

that we've got here sitting around the

table. Six I think of the most

brilliant, most consequential people on

the planet today. And I don't think

that's an overstatement.

So these are the winners of the 2025

Queen Elizabeth Prize for Engineering.

and it honors the laureates here that we

see today for their singular impact on

today's artificial intelligence

technology.

Given your pioneering achievements in

advanced machine learning and AI and how

the innovations that you've helped build

are shaping our lives today, I think

it's clear to everyone why this is a

really rare and exciting opportunity to

have you together around the table. For

me personally, I'm I'm really excited to

hear you reflect on this present moment

that we're in, the one that everybody's

trying to get ahead of and understand

and your journey, the journey that

brought you here today. Um, but also to

understand how your work and you as

individuals have influenced and impacted

one another and the companies and the

technologies that you've built. And

finally, I'd love to hear from you to

look ahead um and to help us all see a

bit more clearly what is to come, which

you are in the best position to do. So,

I'm so um pleased to have you all with

us today and and looking forward to this

to the discussion. So, I'm going to

start going from the zooming out to the

very personal. I want to hear from each

of you your a personal kind of aha

moment in your career that you've had

that you think has sort of impacted the

work that you've done or was a turning

point for you that brought you on this

path to why you're sitting here today

whether it was kind of early in your

career in your research or or much more

recently what you know what was your

personal moment of awakening um that

that has impacted the technology do we

should we start here with you Yeshua

>> thank you yes uh with pleasure. I would

um go to two moments. One when I was a

grad student and I was looking for

something interesting to research on and

I read some of Jeff Hinton's early

papers and I thought wow this is so

exciting. Maybe there are a few simple

principles like the laws of physics that

could help us understand human

intelligence and help us build

intelligent machines. And the second

moment I want to talk about is two and a

half years ago after chat GPT came out

and I realized uhoh what are we doing?

Uh what will happen if we build machines

that understand language uh have goals

and we don't control those goals? What

happens if they are smarter than us? Uh

what happens if people abuse that power?

So that's why I decided to completely

shift my research agenda and my career

to try to do whatever I could about it.

>> That's that that's two kind of very you

know diverging things very interesting

build what tell us about your moment of

like kind of building the infrastructure

that's that's fueling what we have.

>> I'll give you two moments as well. So

the first was you know in in the late

90s I was at Stanford trying to figure

out how to overcome what was at the time

called the memory wall. fact that

accessing data from memory is far more

costly in energy and time than doing

arithmetic on it. And it sort of you

know struck me to organize computations

into these kernels connected by streams.

So you could do a lot of arithmetic

without having to do very much memory

access. That basically led the way to

what became called stream processing and

ultimately GPU computing. Um and we we

originally built that thinking we could

apply GPUs not just for graphics but to

general scientific computations. So the

second moment was I was having breakfast

with my colleague Andrew Ing at Stanford

and at the time he was working at Google

finding cats on the internet you using

16,000 CPUs in this technology called

neural networks

>> which fay had something to do with those

>> and uh um he uh he he basically

convinced me this is a great technology

so I with Brian Kenzo repeated the

experiment on 48 GPUs in Nvidia and when

I saw the results of that I was

absolutely convinced that this is what

Nvidia should be doing. we should be

building our GPUs to do deep learning

because this has, you know, huge

applications in all sorts of fields

beyond finding cats. And that was kind

of an aha moment to really start working

very hard on specializing the GPUs for

deep learning and and to make them more

effective.

>> And when was that what year?

>> Um, the breakfast was in 2010 and I

think we repeated the experiment in

2011.

>> Okay.

>> Yeah.

>> Jeff, tell us tell us about your work.

One very important moment was when I in

about 1984 I tried using back

propagation to learn the next word in a

sequence of words. So it was a tiny

language model and discovered it would

learn interesting features for the

meanings of words. So just giving it a

string of symbols it just by trying to

predict the next word in a string of

symbols it could learn how to convert

words into sets of features that

captured the meaning of the word and

have interactions between those features

predict the features of the next word.

>> So that was actually a tiny language

model from 1980 late 1984

um that I think of as as a precursor for

these big language models. The basic

principles were the same. It was just

tiny. We had 100 training examples. It

took 40 years to get us here though.

>> And it took 40 years to get here. And

the reason it took 40 years was we

didn't have the compute and we didn't

have the data and we didn't know that at

the time. We couldn't understand why we

weren't just solving everything with

back propagation.

>> Which takes us cleanly to to Jensen. We

didn't have the compute for 40 years and

here now you are building it. Tell tell

us about your moments that of real kind

of clarity.

Well, for my career, um, I was the, uh,

first generation of chip designers that

was able to use higher level

representations

and design tools to design chips.

and and uh that that discovery

um uh was helpful when I learned about a

new way of developing software

uh around the 2010 time frame

simultaneously from three different labs

uh what was going on in uh uh University

of Toronto researchers uh reached out

reached out to us at the same time that

uh researchers at the NYU reached out to

um as well as uh in Stanford reached out

to us at the same time and I I I saw the

early indications of what turned out to

have been deep learning around the same

time uh using uh a framework uh and a

structured design to uh create software

and that software turned out to have

been incredibly effective.

Uh and that second that second

observation

uh is seen again using frameworks rep

higher level representations

structured types of uh structures like

the deep learning networks. I uh was

able to develop software uh w was very

similar to designing chips for me and

the patterns were very similar and I

realized at that time maybe we could

develop software uh and capabilities

that that scale very nicely as we've

scaled uh chip design over the years and

so that was that was a quite a quite a

moment for me

>> and when do you think was the moment

when the chips really started to help

scale up today's sort of the the LLMs

that we have today because you you said

2010 that's still a 15 year.

>> Yeah. The the thing about about Nvidia's

architecture is is once you're able to

get something to run well on a GPU

because it became parallel, you could

get it to run well on multiple GPUs.

that same sensibility of scaling uh the

algorithm to run on many processors on

one GPU. This is the same logic and the

same reasoning that you could do it on

multiple GPUs and then now multiple

systems and in fact you know multiple

data centers and so that once we

realized we could do that effectively

then then the rest of it is about about

uh imagining how far you could

extrapolate this capability. you know,

how much data do we have? How large can

the networks be? How much dimensionality

can it capture? What kind of problems

can it solve? Uh the all of all of that

is is really engineering at that point.

You know, the the observation that that

uh the deep learn deep learning models

are so effective uh is is really quite

the the the spark. The rest of it is

really engineering extrapolation.

Fei, tell us about your your moment.

>> Yeah, I also have two moments to share.

So around 2006

and 2007, I was transitioning from a

graduate student to an a young assistant

professor and I was among the first

generation of machine learning graduate

students um reading papers from young

Yoshua uh Jeff and I was really obsessed

in trying to solve the problem of ob uh

visual recognition which is the ability

for machines to see meaning in objects

in everyday pictures and uh we were

struggling with this problem in machine

learning called generalizability

which is um after learning from certain

number of examples can we recognize

something a a new example new sample and

I've tried every single algorithm under

the sun from baset support vector

machines to neuronet network and the

missing piece that my student and I

realized is that data is missing that uh

uh you know if you look at the evolution

or development of uh intelligent animals

like humans we were inundated with data

in the early years of development but

our machines were starved with data. So

we um decided to do something crazy at

that time to create a internet scale

data set uh over the course of three

years called imageet that uh uh in uh

included 15 million images handcurated

um by by people around the world across

22,000 categories. So, so for me the aha

moment at that point is big data drives

machine learning

>> and it's now it's it's the limiting

factor the building block of all of the

you know algorithms that we're seeing

with

>> yeah it's part of the scaling law of

today's AI and the second aha moment is

um 2018

I was the first chief scientist of uh AI

at Google cloud uh part of the the work

we do is serving all vertical industries

under the sun, right? From healthcare to

financial services, from entertainment

to uh manufacturing, from agriculture to

energy.

And that was a few years after the the

what we call the image that Alex moment,

a couple of years after Alph Go, and I

realized

>> Alph Go being the algorithm that was

able to beat humans at playing the

Chinese board game Go. Uh yes and as the

chief scientist at Google I realized

this is a civilizational technology

that's going to impact every single

human individual as well as sector of

business and uh if humanity is going to

go enter an AI era what is the guiding

framework so that we not only innovate

but we also bring benevolence

to uh through this powerful technology.

technology to everybody and that's when

I returned to Stanford as a professor to

uh co co-found the human center AI

institute and and uh propose the human-

center AI framework so that we can keep

humanity and human values in the center

of this uh technology.

>> So developing but also looking at the

impact and what's next which is where

the rest of us come in.

>> Um Yan do you want to round us out here?

What's what's been your highlight?

>> Yeah, probably go back a long time. Um,

I realized when I was in undergrad, I

was fascinated by the question of AI and

intelligence more generally and

discovered that people in the 50s and

60s that worked on

training machines instead of programming

them. I was really fascinated by this

idea probably because I thought I was

either too stupid or too lazy to

actually build an intelligent machine

from scratch, right? So it's better to

let itself be um like train itself or

self-organized and that's the way you

know intelligence in in in life uh

builds itself. It's uh it's

selforganized. So I I thought this

concept was really fascinating and I

couldn't find anybody when I graduated

from engineering. I was doing chip

design by the way um wanted to go to

grad school. I couldn't find anybody who

was uh working on this but connected

with some people who kind of were

interested in this and discovered Jeff's

papers for example uh and uh he was the

person in the world I wanted to meet

most in 1983 when I started grad school

and we eventually met two years later um

and

>> and today you're friends would you say?

>> Yes. Oh, we we we we had lunch together

in 1985 and we could finish each other's

sentences. Basically, he had uh

um I had a a paper written in French at

a conference where he was a keynote

speaker and and managed to actually kind

of decipher the the math. It was kind of

sort of like back propagation a little

bit to train multi-layer nets. It was

known from the 60s that the limitation

of machine learning was due to the fact

that we could not train machine with

multiple layers. So that was really my

obsession and it was his obsession too

and um and so I had a paper that kind of

proposed some some way of doing it and

he kind of managed to read the math. So

that's how we hooked up and

>> and that's what has set you on this

path.

>> Right. So and and then after that you

know once you can you can train complex

systems like this you ask yourself

questions. So how do I build them so

they do something useful like

recognizing images or things of that

type? And at at the time Jeff and I had

this debate when I was a postoc with him

in the late 80s. Um I I I thought um the

only machine learning paradigm that was

well formulated was supervised running.

You you show an image to the machine and

you tell it what the answer is, right?

And he said no no no like the only way

we're going to get to make progress is

through unsupervised running. And I was

kind of dismissing this at the time. Um,

and what happened in you know the mid

2000 when he Yosha and I sort of start

getting together and restart the

interest of the of the community in deep

learning. We actually kind of uh made

our bet on unsupervised learning or self

reinforcement loop. Right?

>> This is not reinforcement. So this is

basically discovering the structure in

data without training the machine to do

any particular task which is by the way

the way LLMs are trained. So an LLM is

trained to predict the next word but

it's not really a task. It's just a way

for the system to learn a good kind of

uh representation or capture the

>> is there no reward system there that

sorry to get geeky but is there no

nothing to say this is correct and

therefore keep doing it because

>> well this is correct if you predict the

next word correctly right

>> from the rewards in reinforcement

learning where you say that's good

>> yeah okay

>> um and so in fact uh I'm going to blame

it on you uh it turns out produced this

big data set called imageet and uh which

is which was labeled and so we could use

supervised learning to train the systems

on and that turned out to work actually

much better than we expected and so we

temporarily abandoned the whole program

of working on self-supervised

unsupervised learning because supervised

learning was working so well we figured

out a few tricks

>> Joshua stuck with it

>> I said I didn't

>> no you didn't I didn't either but uh but

it it kind of

refocus the entire industry and and the

research community if you want on sort

of deep deep learning supervised

learning etc. Mhm.

>> And it it it took another few years

maybe around 201617

to uh tell people like this is not going

to tell take us where we want. We need

to do self-s supervised learning now and

that's what LLM really are the best

example of this.

>> Okay.

>> But uh what we're working on now is

applying this to other types of data

like like video sensor data which LLM

are really not very good at at all. Um

and that's a new challenge for the next

few years. So that brings us actually to

the present moment and I think you know

you'll all have seen this crest of the

interest from people who had no idea

what AI was before who had no interest

in it and now everybody's flocking to

this and this has become more than a

technical innovation right that's a huge

business boom it's become a geopolitical

strategy issue um and you know

everybody's trying to get their hands

around what this is so or their heads

around it Jensen I'll come to you here

first to I want you all

to reflect on this moment now here

Nvidia in particular has it's basically

in the news every day hour week you know

and you have become the most valuable

company in the world so there's

something there that people want

>> you'll be to hear that

>> yeah you know tell us about do are you

worried that we are getting to the point

where people don't quite understand and

we're all getting ahead of ourselves and

there's going to be a reckoning that

there's a bubble that's going to burst

and then it will write itself self and

if not what is the kind of biggest

misconception about demand coming from

AI that is different to say the dotcom

era or that people don't understand you

know if if that's not the case

>> uh during the dotcom era during the the

bubble the vast majority of the fiber

deployed were dark

meaning the industry deployed a lot more

fiber than it needed Mhm.

>> Today almost every GPU you could find is

lit up and used.

And so uh the reason why I think it's

important to take a take a step back and

understand and understand what AI is,

you know, for a lot of people AI is Chad

GBT and it's image generation and and it

that's all true. That's one of the

applications of it. Um, and AI has

advanced tremendously in the last

several years. The ability to not just

memorize and generalize, but to reason

and effectively think and ground itself

through research. It's able to produce

answers and do things that are much more

valuable now. It's much more effective.

and the number of companies that are

able to build businesses that are that

are helpful to other businesses. For

example, a software programming company,

an AI software company that that we use

called Cursor, uh they're very

profitable and we use their software

tremendously and it's incredibly useful.

uh or a bridged or open evidence who are

uh serving the healthcare industry doing

very very well producing really good

results and and so so the AI capability

has grown so much and as a result we

were seeing these two exponentials that

are happening at the same time on the

one hand the amount of computation

necessary to produce an answer has grown

tremendously on the other hand the

amount of usage of these AI models are

growing also exponentially these two

exponentials

are causing a lot of demand on compute.

Now when you take a step back, you ask

yourself fundamentally what's different

between AI today and the software

industry of the past. Well, software in

the past was pre-ompiled

and the amount of computation necessary

for the software is not very high.

>> But in order for AI to be effective, it

has to be contextually aware. It has to

it can only produce the intelligence at

the moment. You can't produce it in

advance and retrieve it. That's you know

that's called content. AI intelligence

has to be produced and generated in real

time. And so as a result we now have an

industry where the computation necessary

to produce something that's really

valuable in high demand is quite

substantial. We have created an an

industry that requires factories. That's

why I I remind ourselves that AI needs

factories to produce these tokens to

produce the intelligence and this is

this is a a once you know once in a it's

never happened before where the computer

is actually part of a factory and and so

we need hundreds of billions of dollars

of these factories in order to serve the

trillions of dollars of industries that

sits on top of intelligence. You know,

you go come back and take a look at at

software in the past. Software in the

past is they're software tools. They're

used by people. For the first time, AI

is intelligence that augments people.

And so, it addresses labor. It addresses

work. It does work.

>> So, you're saying no, this is not a

bubble.

>> I think this we're we're well in the

beginning of the buildout of

intelligence. And and the fact of the

matter is most people still don't use AI

today. And someday in the near future,

almost everything we do, you know, every

moment of the day, you're going to be

engaging AI somehow. And so between

where we are today where the usage is

quite low to where we will be someday

where the usage is basically continuous,

that buildout is is you know what

>> and if even if the LLM runway runs out,

you think GPUs and the infrastructure

you're building can still be of use in a

different paradigm and then I want to

open up to others to talk. LLM is a is a

piece of the AI technology. You know,

AIS are systems of models, not just LLMs

and LLM are big part of it, but there

are systems of models and and uh the the

technology necessary for for AI to be

much more productive from where where it

is today irrespective of what we call

it. Um we still have a lot of technology

to develop yet.

>> Can who wants to jump in on on this?

>> Um I don't think

>> especially if you disagree. I don't

think we should call them LLMs anymore.

Um they're not language models anymore.

They they

>> right

>> start as language models at least that's

the pre-training but but more recently

there's been a lot of advances in making

them agents. In other words, uh go

through a sequence of steps in order to

achieve something interactively with an

environment with people right now

through a dialogue but more and more

with a computing infrastructure.

And the technology is changing. It's not

at all the same thing as what it was

three years ago. I don't think we can

predict where the technology will be in

two years, 5 years, 10 years. U but we

can see the trend. So one of the things

I'm doing is trying to uh bring together

a group of international experts to keep

track of what's happening with AI where

it is going um what are the risks how

are they being mitigated and and and and

the trends are very clear across so many

benchmarks now you know because we've

had so much success in improving the

technology

uh in the past it doesn't mean that's

going to be the same in the future. So

then then there would be financial uh

consequences uh if the expectations are

not met but in the long run I completely

agree. Um

>> but currently what about the rest of

you? Do you think that the valuations

are justified in terms of what you know

about the technology the applications?

>> So I think there are three trends that

sort of explain what's going on. The

first is the models are getting more

efficient. If you look just at attention

for example, going from straight

attention to GQA to MLA, you get the

same or better results with far less

computation. And so that then drives

demand in ways where things that may

have been too expensive before become

inexpensive of now. You can do more with

AI. At the same time, the models are

getting better and you know, maybe

they'll continue to get better with

transformers or maybe a new architecture

will come along, but we will we won't go

backwards. We're going to continue to

have better models that also

>> they still need GPUs even if

>> absolutely transformer based

>> um in fact it makes it makes them much

more valuable compared to more

specialized things because they're more

flexible and they can evolve with the

models better but then the final thing

is I think we've just begun to scratch

the surface on applications so almost

every aspect of human life can be made

better by having AI you know assist

somebody in their profession help them

in their daily lives and you know I

think we've you know started to reach

maybe 1% of the ultimate demand for

this. So as that expands, you know, the,

you know, number of uses of this are

going to go up. So I don't think there's

any bubble here. I think we're, like

Jensen said, we're riding a multiple

exponential and we're at the very

beginning of it and it's going to just

keep going.

>> And in some ways, Nvidia is in to that

because even if this paradigm changes

and there's other types of AI and other

architectures, you're still going to

need the the atoms underneath. So that

makes sense for you. Did you want to

jump in Fay? Uh yeah, I do think that um

of course from a market point of view,

it will have its own um dynamics and

sometimes it does adjust itself, but if

you look at the long-term trend, let's

not forget AI by and large is still a

very young field, right? We walk into

this room and on the wall there were

equations of physics. Physics has been a

more than 400 year old uh discipline.

Even if we look at uh modern physics and

AI is less than 70 years old if we go

back to Alan Turing you that's about 75

years so there is a lot more new

frontiers that is to come uh you know

Jensen and Yoshua talk about LLMs and

agents those are more languagebased but

even if you do uh self uh introspection

of human intelligence there's more

intelligent capabilities is beyond

language. I have been working on spatial

intelligence which is really the

combination or the lynchpin between

perception and action where um where uh

you know humans and animals have

incredible ability to perceive reason

interact with and uh and create uh

worlds that goes far beyond language.

And even today's most powerful

language-based uh or LLM based models uh

fail at rudimentary spatial intelligence

uh tests. So from that point of view as

a as a discipline as a science there's

far more frontiers to conquer and to uh

open up and that brings the applications

uh you know opens up more applications.

>> Yeah. and you work at a company and so

you have the kind of dual perspective of

being a researcher and working in a

commercial space. Do you agree? Do you

do you believe that this is all

justified and you can see where this is

all coming from or do you think we're

reaching an end here and we need to find

a new path?

>> So I think there are several point of

views for which uh we're not in a bubble

and at least one point of view

suggesting that we we are in a bubble

but there is but it's a different thing.

So we're not in a bubble in the sense

that um there are a lot of applications

to develop based on LLMs. LLM is the

current dominant paradigm and there's a

lot to uh milk there. This is you know

what Bill was was saying to kind of help

people in the daily lives with current

technology that technology needs to be

pushed and that justifies all the

investment that is done on the software

side and also on the infrastructure

side. uh once we have you know smart

wearable devices um in everybody's hands

assisting them in their daily lives the

amount of computation that would be

required as as Jensen was saying to uh

to serve all those all those people is

going to be enormous so in that sense

the investment is not is not wasted but

there is a sense in which there is a

bubble and it's the idea somehow that

the current paradigm of LLM would be

pushed to the point of having human

level intelligence which I personally

don't believe in and you don't either

And

we we need kind of a few breakthroughs

before we get to machines that really

have the kind of intelligence we observe

not just in humans but also animals. We

don't have robots that are nearly as

smart as a cat, right? Um and so we're

missing something big still. Which is

why AI progress is not just a question

of more infrastructure, more data, uh

more investment and more development of

the current paradigm. It's actually a

scientific question of how do we make

progress towards the next generation of

AI

>> which is why all of you are here right

because you actually sparked the entire

thing off and I feel like you know we're

moving much towards the engineering

application side but what you're saying

is we need to come back to what brought

you here originally um on that question

of human level intelligence we don't

have long left so I just want to do a

quick fire I'm curious can each of you

say how long you think it will take

until we do reach that point where you

believe we're you know equivalent

machine intelligence to a human or even

a clever animal like an octopus or

whatever. How far away are we just just

the years?

>> It's not going to be an event.

>> Okay.

>> Okay. Because the capabilities are going

to expand progressively in various

domains.

>> Over what time periods?

>> Over, you know, maybe we'll make some

significant progress over the next five

to 10 years to come up with a new

paradigm.

>> F and then maybe, you know, progress

will come. But it'll it'll take longer

than we think. Okay. Parts of machines

will supersede human intelligence and

part of the machine intelligence will

never be similar um or the same as human

intelligence. They are they are they're

built for different purposes and they

will

>> when do we get to superseding?

>> Part of it is already here. How many of

us can recognize 22,000 objects in the

world? So part of

>> do you not think an adult human can

recognize 22,000 objects?

>> Um the kind of granularity and fidelity.

No. How many adult humans can translate

a 100 languages?

>> That's harder. Yeah.

>> So yeah.

>> So I think we should be nuanced and

grounded in scientific facts that uh

just like airplanes fly but they don't

fly like birds. and u machine-based

intelligence will do a lot of powerful

things but there is a profound

um place for human intelligence to to

always be critical in our human society.

Jensen, do you have

>> we have enough general intelligence to

uh translate the technology to an

enormous amount of uh society useful

applications

uh in the next coming years and with

respect to

>> Yeah.

>> Yeah. Yeah. We're doing it today.

>> Yeah. And so I think I think uh one

we're already there

>> and two the the other part of the answer

is it doesn't matter

>> because at this point it's a bit of an

academic question. We're going to apply

the technology to and the technology is

going to keep on getting better and

we're going to apply the technology to

solve a lot of very important things

from this point forward. And so okay

>> I I think the answer is it doesn't

matter

>> and and it's now as well.

>> Yeah you decide. Right. If you refine

the question a bit to say how long

before if you have a debate with this

machine it'll always win.

>> I think that's definitely coming within

20 years. We're not there yet but I

think fairly definitely within 20 years

we'll have that. So if you define that

as

>> AGI it'll always win a debate with you.

>> We're going to get we're going to get

there in less than 20 years probably.

>> Okay. Bill, do you have

>> Yeah. Well, I'm sort of with Jensen that

it's the wrong question, right? Because

our goal is not to build AI to replace

humans or to be better than humans.

>> But it's a scientific question. It's not

that we'll replace humans. The question

is could we as as a society build

something?

>> But our goal is to build AI to augment

humans. And so what we want to do is

complement what what humans are good at.

Humans can't recognize 22,000 categories

or most of us can't solve these math

olympiad problems. Um so we build AI to

do that. So humans can do what is

uniquely human, which is be creative and

be empathetic and and understand how to

interact with other people in our world.

And I think that it's not clear to me

that AI will ever do that, but AI can be

huge assistance to humans.

>> So I'll beg to differ on this. Uh I

don't see any reason why at some point

we wouldn't be able to build machines

that can do pretty much everything we

can do. Um, of course, for now on the

spatial and you know, robotic side, it's

lagging, but there's no like uh

conceptual reason why we couldn't. So on

on the timeline, I think there's a lot

of uncertainty and that we should plan

accordingly. Um, but there is some data

that I find interesting where we see um

the capability of AI to plan over

different horizons to grow exponentially

fast in the last six years. And if we

continue that that trend, it would place

roughly the level that an employee has

in their job to uh AI being able to do

it within about five years. Now this is

only one category of engineering tasks

and there are many other things that

matter. For example, uh one thing that

could change the game that is that many

companies are aiming to just to focus on

the ability of AI to do AI research. In

other words, to do engineering, to do

computer science, and to design the next

generation of AI, including maybe

improving robotics and spatial

understanding. So, I'm not saying it

will happen, but the area of ability of

AI to do better and better programming

and understanding of algorithms that is

going very very fast and that could

unlock many other things. We don't know

and we should we should be really

agnostic and not make big claims because

there's a lot of possible futures there.

M so so our consensus is in some ways we

think that future is here today but

there's never going to be one moment and

the job of you all here today has helped

to guide us along this route um until we

get to a point where we're working

alongside these systems. Very excited

personally to see where we're going to

go with this. If we do this again in a

year it'll be a different world. But

thank you so much for joining us for

sharing your stories and for talking us

through this this huge kind of

revolutionary moment. Thank you. Thank

you.

>> Thank you.