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CEO of Google cloud and Alexander Wang

CEO and founder of scale AI Thomas

joined Google in November 2018 as the

CEO of Google Cloud prior to Google

Thomas spent 22 years at Oracle where

most recently he was president of

product development before that Thomas

worked at McKinsey as a business analyst

and engagement manager his nearly 30

years of experience have given him a

deep knowledge of engineering Enterprise

relationships and Leadership of large

organizations Thomas's degrees include

an MBA in administration and management

from Stanford University as an RJ Miller

scholar and a bsee in electrical

engineering and computer science from

Princeton University where he graduated

summa laude Thomas serves as a

member of the Stanford Graduate School

of Business advisory Council and

Princeton University School of

Engineering advisory Council please

welcome to the stage Thomas kurian and

Alexander Wang

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this is a super exciting conversation

thanks for uh thanks so much for being

here Thomas thank you for having me you

all just came off of uh your incredible

Google Cloud next conference Where You

released a wide variety of functionality

and features and sort of new products

across artificial intelligence but also

across the entire sort of cloud

ecosystem do you want to just first by

walking through uh first start by

walking through uh all the innovations

that that you sort of released and uh

and what you're excited about when it

comes to Google Cloud

you know our vision is super simple if

you look at

what smartphones did for a consumer you

know they took

a computer

an internet browser a communication

device and a camera and made it so that

it's in everybody's pocket so it really

brought computation to every person

we feel that you know our our what we're

trying to do is take all the

technological innovation that Google's

doing

but make it super simple so that

everyone can consume it and so that

includes our global data center

footprint

all the new types of hardware and

large-scale systems we work on

the software that we're making available

for people to do high-scale computation

tools for data processing tools for

cyber security

tools for machine learning but make it

so simple that everyone can use it

and every step that we do to simplify

things for people we think adoption can

grow and so that's a lot of what we've

done these last three four years and we

made a number of announcements that next

in in machine learning and AI in

particular you know we look at our work

as four elements

how we take our large-scale compute

systems that we're building for AI and

how we make that available to everybody

second what we're doing with the

software stacks on top of it things like

Jacks and other things and how we're

making those available to everybody

third is advances because different

people have different levels of

expertise some people say I need the

hardware to build my own large language

model or algorithm other people say look

I really need to use a building block

you guys give me so third is we've done

a lot with automl and we announced new

capability for image video and

translation to make it available to

everybody and then lastly we're also

building completely packaged solutions

for some areas and we announced some new

stuff so it was a busy conference but

you know lots of exciting stuff going on

yeah it's incredible I mean I want to

zoom out for a second to start with

which is that this is obviously not your

first time taking and packaging new

technology breakthroughs for for the

Enterprise you know both in your time at

Oracle and now CEO of Google Cloud this

is something that you've been doing for

quite some time now when you sort of

Zoom all the way out what do you think

are some of the things that have some of

of your principles or some of your

thoughts and enabling these

technological breakthroughs and actually

enabling the Enterprise with them and

what are sort of the key insights that

you have there thank you a lot of the

work so first of all we've really built

out the organization the last three

years we've seen a huge ramp up in our

business credit to all the people you

know who joined us

at one point over 70 percent of

organizations that joined during covid

so they hadn't met anybody they couldn't

meet their managers but they all did an

amazing job together

the adoption of Technology by companies

and I'll give you just some elements

particularly in the application of AI in

different domains that we've seen

we work with a large financial

institution in Hong Kong and Shanghai

bank which uses our machine learning to

detect fraud

you know fraud detection and banking

there's a lot of false positives which

makes it hard to really you know to it's

very expensive for people doing

something called anti-money laundering

and our AI algorithms are really able to

be super precise on detection

explainability is a critical thing there

right so people ask why did you why did

you approve why did you flag this one

and not that one because Regulators are

involved so explainability becomes a big

deal

um we helped we helped uh Renault for

example monitor all of the factories

they process roughly a billion data sets

every day obviously humans can process

that

but making it super simple to and you

guys had given all your expertise in

labeling and other things you would get

a sense Factory floor data is not clean

data and so you have to actually clean

imagine doing a billion data sets into

an environment every single day you have

to get the data pipelines really good

and so a lot of Technology work happens

to make that possible for companies

um third is if you shop at Ikea for

example behind Ikea is systems it's our

recommendation system

and the way that people shop for

furniture

and products is not the same in all

countries and so how are you able to one

deal with the benefits you get from a

global model

but also take contextually the specific

elements in each country because people

have different buying habits those are

all things that we've learned applying

our AI in different contexts in

different parts of the world yeah you

know you've you've you're uh you sort of

uh glossed over this but you've LED

since you took over at Google Cloud just

a meteoric growth of the of the platform

you know I think in the past few years

you've tripled your sales force and

ending last year you obviously can't

comment on this but ended last year at I

believe 20 billion uh of annual revenue

which is which is incredible and and

this incredible growth Journey what do

you attribute your success to and how do

you think you've been able to to drive

to such an incredible incredible growth

and success

you know from our point of view every

every industry virtually in the world is

now becoming a software powered you know

technology industry right if you talk to

automobile companies they're

increasingly their vehicles are more

about software than mechanical systems

if you talk to telecommunications

companies their networks are Commodities

unless they can make them platforms to

deliver applications so they need new

ways to slice manage the network

if you look at banks at the end of the

day they're about all the products of a

bank are data and all of that becomes

how do you differentiate in the value

you're delivering clients through a

digital medium because increasingly I'm

sure all of you look at yourselves and

go when was the last time I went to a

branch of a bank so a lot of our work

has been pushing the Technology

Innovation really far but bringing that

technology super easily to people in

different Industries and given the

demand that people have for a hey I

really want I need the technology to

help me power my industry that the

change I'm seeing in my industry the

more accessible we can make it the

easier and the faster we get adoption

and our approach has been to be

completely open and when I say

completely open we offer every part of

the stack that we have from the hardware

and network to the software abstractions

above

two things that are more packaged

because different organizations have

different levels at which they have

expertise and want to adopt technology

yeah yeah I mean it's been I mean it's

been obviously incredible you know going

back to AI for a second Google Google

obviously is is an early mover in Ai and

Google cloud has also been through you

know or starting with tensorflow and

vertex Ai and automl and so many

incredibly Innovative Technologies and

uh ai's been obviously kind of a a

buzzword for some time now within the

industry and and

um you know I think we see this and you

see as well the adoption has maybe been

a bit slower than we would have expected

until now what do you think have been

the barriers to Greater levels of AI

adoption greater levels of of

Enterprises seeing value from Ai and and

what do you think the future holds

so we work with a huge number of

companies doing work having them adopt

AI

a lot of the lessons we've seen and

observed from it

are the barriers to adoption are rarely

about the algorithm itself right it's

often the barriers to adoption about

very different things so when we work

with customers in many many Industries

take retail as an example

and you think of a very mundane example

like recommendations to make product

discovery on the web much easier for

their own products the biggest challenge

is standardizing the meaning of the

product and the catalog Because unless

you have a standardized definition of

the products and the data behind the

algorithm is clean it's super hard to

actually get a recommendation and so in

the work we did with h m for example or

at Macy's or at Ikea or Bloomingdale's a

huge number of these Brands the big part

of the program is actually how do you

label and clean the data up front and

standardize it before you get into the

algorithmic phase so that's one part of

things we see

second part is for large organizations

to adopt AI they have to need to

integrate the the the results of the

algorithm back into their core processes

so you know practical example we work

with Angie Angie is a large large

electric producer electricity and power

producer in Europe

they are probably the one of the largest

renewable energy producer in the world

they use wind farms

one of the things they really struggled

with was how do you predict how much

wind is going to be there three days

from now because the power grid requires

that prediction in order to capacity

plan how much power is going into the

grid so they work with us and they use

our AI to do that

but that needs to be tied into how

they're telling the rest of the power

sources that work on the grade hey if

this much wind is coming in here's all

the other sources need to generate so

tying it back in is not as simple as

people think and so a lot of time is

that

the third on the people side there's

change management you go through to get

people to trust the algorithm so one of

the things we've done work with many

banks particularly during the pandemic

when the government issued small

business loans

there was a giant bottleneck in being

able to get loans out to individual

consumers

and frankly because the banks didn't

want to bring a huge Army of loan

officers in

they had to use software and algorithms

to process it now the challenge people

had is they needed to trust the

algorithm was being fair in saying yes

to some and no to others

and that it would mirror for example the

recommendations that their best mortgage

you know Bankers would do right just as

a loan officers would do so it gave them

the benefit of scale because we

processed literally millions and

millions of mortgages through our

technology but it required them to get

comfortable that things like fairness

and other things were working so often

when people look at AI they think it's a

skills issue there's certainly a skill

issue involved there's not enough talent

in the ecosystem but things are getting

easier and easier as the models get more

and more sophisticated often people

forget about these other issues that are

important in getting adoption yeah I

mean you're uh you're preaching in the

choir when you mentioned the the data

challenges that all these Enterprises uh

face and uh and how critical that is to

getting AI working in the early days

um you know one of one of the things

that I think is interesting about Google

Cloud strategy is that you really have

products that different layers of sort

of the stack and different layers of of

um you know closest to the bare metal

all the way up to these package

Solutions you know I'm with in what way

do you think that the Enterprise world

and even the the sort of broader

business world is going to adopt these

AI Technologies do you think that the

end stated that a lot of them are using

your lower level more infrastructure uh

products or do you think that many of

them are going to adopt Solutions how do

you think this plays out over the the

next few years

so we offer four layers of technology

for people

there's a set of people who say look I

just need your

you know computational infrastructure

your large systems we build something

called tensor Processing Unit which is

our large scale systems we're also

working with Nvidia to build a really

high scale gpu-based system

but many people some some customers say

look I just need access to that and we

make that available because the tpus are

what we use within Google and we make

that available along with the

compilation software to optimize models

on the tpus

take as an example of LG you know the

the Korean company that makes appliances

their team has built a a large I mean

multi-hundred billion parameter model

because they wanted to make that a way

that people can interact with appliances

without having to press buttons on them

they so they built a model they said I

just need access to your infrastructure

so that's one way we offer capability

a second level is people say look I

really don't need access to the raw

infrastructure itself what I need is the

ability to build models using your

platform and so we offer a platform

called vertex and people build models

and push them using our machine learning

platform and there are many many

organizations in logistics and financial

services in retail and others who build

their own models on top of the platform

the third is to make things even easier

we've taken some of the core pieces

translation documents

uh image processing video

and we've said we can offer an automl

based solution which further simplifies

how you use our platforms

and so for example translation we have a

capability to handle translation in 135

languages

one of the important things that people

ask when they go to many languages is

the if you look at the data sets that

are used to

to train models

they are primarily there's a large set

in English because you have the whole

internet is primarily in a very small

number of languages but once you get to

more narrow languages for instance

Swahili or some of the African languages

or even in Asia there are many languages

even from where I grew up in India there

are languages that are not as widely

represented on the internet can you

model in Translation provide equivalent

Fidelity in sparse languages because

it's always important to those people

who only understand that language that

they get a high fidelity result

so we built something called translation

Hub and it's being used in very mundane

places but with extraordinary impact for

example when people announce covet

guidelines or recently monkey pox for

example which is another thing they need

to translate in many many languages and

normally the process would take a long

time

we have movie studios for example in a

in a different example saying hey when

we launch a movie

uh we have a high fidelity set of

languages we're actually going to hold

the movie up and show that people do it

but for the long tail we just need

captioning uh we're not necessarily

going to do voice dubbing we're going to

do captioning and they use our

translation solutions to go to that even

within companies Avery Dennison for

example uses it to translate all their

instruction manuals into many languages

for their technicians

and then lastly in some places there are

companies like retailers who tell us

look a handful of the largest retailers

May build their own software teams

but some of us who are small Merchants

we're not software companies and telling

us you got to be a software company to

use AI is not fair

so for some Industries we actually build

fully packaged Solutions if you if you

call many telephone companies their

contact center behind it sits a voice

agent

and the rationale behind that was super

simple when a new smartphone launches

like an iPhone or a pixel typically in

the morning of the launch some of these

contact centers get three four million

calls in an hour

and it's hard to hire that many agents

to handle the phones so we said why

wouldn't software be able to handle it

we then evolved it so that the natural

language interface can become actually

the workflow for these organizations but

that's a much more of a package solution

so that telephone companies don't have

to have armies of data scientists to do

it so our work spans all of these

because people have different needs and

we find that you know as you improve the

maturation of this and you make it more

easy for people to adopt it you will get

broader proliferation and Adoption of AI

as a whole

yeah you know you walk through so many

different use cases and so many

applications of the technology I imagine

one um and they're so desperate you know

everywhere from uh you know fraud

detection to translation to sort of

translation of manuals you know there's

such a wide array of use cases how do

you you all at Google Cloud think about

helping businesses understand what what

is AI good for you know what what can

they use AI for you know there's there's

obviously such a wide

um uh diversity of different use cases

but what at a framework level do you do

you tell them like how can I use AI

within my business

it's a really good question I mean a lot

of our work actually comes from clients

asking us now and that's actually

an encouraging thing because you know

see from our point of view some simple

things how many of you believe in a few

years time there's going to be

intelligent software and

non-intelligence software

right I mean nobody would say in three

four years time we're going to write

software that has not powered in some

form of fashion by AI so you know and

most companies actually it's really

encouraging to see that they look at

domain problems they're having and say

for instance I used to do it using a

rules engine which is an older model for

defining kind of workflow within

organizations can you apply AI to do it

in a new way

um I used to do this in a specific way I

heard about image recognition but you

know one example really fun or

interesting one U.S Navy

um when you have corrosion on the base

of ships the old way was to lift it into

Dry Dock and take a look at it if you've

ever seen one of these ships you can

imagine lifting into dry dock is not an

easy thing so they said can we fly a

drone with Geo camera image recognition

around it and detect corrosion and it's

so the what we've seen is that as you

lift up the capability where image audio

text Etc all these forms of input

can be processed extremely accurately

most customers start figuring it out and

so they call us with most of our work

has come from customers calling us

saying hey I have this need can I apply

AI to it and so we talk to them about

how and when it makes sense to use AI

but we also talk to them about the

consequences if the models are not you

know handling things like skew in the

data how do you ensure that for example

you're treating fairness properly how do

you ensure that the model is safe etc

etc

yeah you know I think uh it's it's

exciting I mean all the use cases the

variety is is incredibly exciting it's

cool that these customers are coming to

you

um directly with many of them what what

is you know again kind of uh thinking

bigger picture what is machine learning

and AI mean for Google Cloud on the

whole over the next call it five ten

years

so we feel that the boundary of what

machine learning and what AI can do will

change over time

uh when it's started it was about doing

what you know what we would call

assistive things

assist if things are where a human being

is able to do it but the computer

assists the human being in some ways to

do it better right so common examples

people talk about is hey you're a doctor

or radiologist

you used to look at x-ray images now a

computer is going to look at it and

detect tumors but it's assisting you to

find something that you may have done

another way

so that's the first phase and a lot of

the work we see is is primarily in that

phase today

the the second phase is to do something

where you couldn't do it with a human

because the quantity of data you need to

process or the amount of people you need

would be just far too significant and so

the machine is doing something that

humans couldn't do but it's still an

incremental element on top of what

humans could do themselves

the third phase I think is where we

think generative AI for example goes

because it's about enabling people to

express themselves in a different way

right and to assist them in

expressiveness so I'll give you a

practical example a lot of you probably

use tools uh like slides and things like

that in your day-to-day job right

PowerPoint was invented a long time ago

and was really just about drawing things

you know I've got a 14 year old and so

if you look at the younger generation

if you look at what slides were they

were really tools to help people draw

and then to take what was on the slide

projector and present it

then P you know the the younger

generation says hey I don't want to draw

things that's like really old-fashioned

I'm going to go to the internet and copy

images right because they when they do

class projects They're copying images

into the slides

and then you know as as people observe

you know on the social media environment

people going from text which may have

been Facebook to short to images which

is Instagram to short video Tick Tock

people say hey why wouldn't we able to

record short video and we use that as a

mechanism to share but recording short

video is still capturing the real world

through the lens of the camera

what people want is a more expressive

way of saying I have an idea can I

translate it and it may not be something

I can capture imagine a kid in

California in a school saying I want to

capture how

the landscape and outside of Paris and

Francis right now I think they need to

be able to generate some of the ideas

that they couldn't capture by physically

being there and so we're working on all

of this and we're bringing some of these

into our products to change what people

could possibly do through the

application of AI so they improve

expressiveness for people

and so every boundary as the technology

gets more sophisticated we think it

moves from just assistance to assistance

on things that human beings may not have

been able to just linearly do

to now things like expressiveness which

is a very different capability than

people could actually do themselves

uh yeah it's an it's I mean all this is

very is obviously incredibly exciting

and we're all watching it happen in real

time you know there's an artist uh who

actually described the these sort of

image generation models as he was he

sort of said like you kind of have to

think about like a like a camera like

it's a new tool that allows you to

create fundamentally new uh you know

forms of art that's right yeah and not

just one medium of art right because if

you look in the past people said you

were a painter you were a sculpture

you're a musician and now these

Technologies allow you to blend all of

it as a form of expressiveness yeah you

know the the last question I have for

you is you you know you obviously sit

down with many of the sort of leading

CEOs and Business Leaders of many of the

the sort of largest uh organizations in

the world and I'm sure one thing that is

on many of their minds is sort of um as

AI technology develops and it continues

to progress is potential disruption that

might come from from artificial

intelligence what sort of how do you

approach that conversation what's sort

of your advice to these these Business

Leaders who are looking at this powerful

new technology and thinking about what

that might mean for for the businesses

and and the business landscape

when we talk to CEOs I mean the biggest

things we talk to them about number one

you know uh productivity in the long

term

productivity has always been the primary

driver of improving you know both

company productivity meaning their own

companies as well as societal you know

benefit things like affluence of a

society Etc and the means and equality

of distribution of income to people

across all Spectrum Society eventually

the most important metric and you can

look at any economics textbook is

productivity

uh software and technology has probably

been the biggest Boon of productivity

over the last 30 40 years

this is the next evolution of that and

so we always say if you approach it the

right way for example labor shortages

are going on right now

the biggest potential benefit is the

application of some of these platforms

like AI to doing that

the second

with any technological generation

Revolution like artificial intelligence

but if you went back in time and looked

at the Industrial Revolution Etc they're

always During the period of transition

anxiety about the consequences of that

technology

and it doesn't mean that technology by

itself is good or bad it's the

application of the technology that's

good or bad

so it's incumbent upon both the

technology providers and the users of

the technology to ensure that the

negative consequences of it are managed

properly right

the obvious example is for instance if

you look at a very simple thing image

recognition

image recognition can help doctors find

tumors way better than having the best

radiographer

it's assistive in that context and it's

like helping people with a better

quality microscope than they had before

object recognition is helping people

find for example people who are in the

ocean much more accurately so the Coast

Guard can rescue them

at the same time being able to use a

camera and say that's Thomas kurian

has uh you know a lot of potential

negative consequences and so as a

provider of Technology we at Google have

chosen not to do that third part but we

also tell companies it's important not

just to say this is what's regulatory

Allowed by the legal framework because

law in many countries is not yet keeping

up with how fast AI Technologies is

moving but to take the responsibility as

a Company CEO to say here's what I'd be

comfortable with and here's what I won't

be comfortable with yeah well Thomas

thank you so much for uh such an

incredible conversations I think uh I

think I'm I'm very heartened to hear all

the incredible work that Google cloud is

doing to make artificial intelligence

accessible to you know the entire

business world and all of every

Enterprise around the globe and uh I'm

so excited that you're able to join us

thank you so much thank you so much for

having me

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