We 're joined next by Thomas Curian , 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 suma cum 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 Curian and Alexander Wang . This is a super exciting conversation . Thanks for being here , Thomas . Thank you for having me . You all just came off of your incredible Google Cloud next conference where you released a wide variety of functionality and features and new products across artisan television and also across the entire sort of cloud ecosystem . You want to just first by walking through , first start by walking through all the innovations that you sort of released and what you 're excited about when you come to Google Cloud ? Now our vision is super simple .  If you look at what smartphones did for a consumer , you know they took a computer and 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 , 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 cybersecurity , 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 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 were building for AI and how we make that available to everybody .  Second , what we 're doing with the software stacks and 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 , 30s we 've done a lot with AutoML and we announce 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 announce some new stuff .  So , it 's a busy conference , but 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 the enterprise . 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 your principles , or some of your thoughts and enabling these technological breakthroughs and actually enabling the enterprise with them ? And what are 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 who joined us at one point over 70 % of organization that joined your in COVID .  So they had n't met anybody .  They could n'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 a very expensive 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 . We help , we help renewal , for example , monitor all of the factories . The process roughly , a billion data sets every day . Obviously , humans can process that . But making it super simple to , and you guys have 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 give the data pipelines really good . And so a lot of technology work happens to make that possible for companies . Third is , if you shop at IKEA , for example , behind IKEA is systems , it 's our recommendation system . find 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 to 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 've sort of glossed over this , but you 've led since you took over at Google Cloud , just a meteoric growth of the platform . You know , I think the past few years , you 've tripled your sales force and ending last year , you obviously ca n't come in this , but end the last year at , I believe , 20 billion of annual revenue , which is incredible and this incredible growth journey . What do you attribute your success to ? And how do you think you 've been able to drive just to an incredible growth and success ? From our point of view , every industry , virtually in the world , is now becoming a software powered technology industry . If you talk to automobile companies , they 're increasingly vehicles are more about software than mechanical systems . If you talk to telecommunications companies , the 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 or data , and all of that becomes how do you differentiate in the value 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 hair , I really want , I need the technology to help me power my industry , 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 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 to things that are more packaged because different organizations have different levels at which they have expertise and want to adopt technology . Yeah .  I mean it 's been , mean it 's been obviously incredible .  You know going back to AI for a second , Google , Google obviously is an early mover in AI and Google Cloud has also been through , you know , starting with TensorFlow and Vertex AI and AutoML and so many incredibly innovative technologies . And AI has been obviously kind of a buzzword for some time now within the industry . And I think we see this in use as well . The adoption has maybe been a bit slower than we would expected until now . What do you think have been the barriers to greater levels of AI adoption , greater levels of enterprise that 's in value from AI ? And what do you think the future holds ? So we 've worked 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 . It 's often the barriers to adoption about very algorithm itself .  It 's often the various adoption about very different things . So when we work with customers in many , many industries , take retailers 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 challenges 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 to 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 upfront 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 results of the algorithm back into their core processes . So , you know , practical example , we work with OGE , OGE is a large , large electric producer , electricity and power producer in Europe . They are probably 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 grid .  Hey , if this went to wind is coming in , here 's all the other sources in each generation .  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 did n'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 bankers would do , right ? Just as a loan office as we 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 preaching the choir when you mention the data challenges that all these enterprises face and how critical that is to getting working in the early days . One of the things that I think is interesting about Google Cloud strategies that you really have products at different layers of the stack and different layers of closest to the bare metal all the way up to these package solutions . In what way do you think that the enterprise world and even the broader business world is going to adopt these AI technologies ? Do you think that the end state is that a lot of them are using your lower level , more infrastructure ? Products , or do you think that many of them are going to adopt solutions ? How do you think this plays out over 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 computational infrastructure , your large systems . We build something called tens of processing unit , which is our large scale systems .  We 're also working with Crossing Unit , which is our large-scale systems . We 're also working within video to build a really high-scale GPU Bay system . But many people , 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 , LG , the Korean company that makes appliances , their team is built 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 . So they built a model .  They said , I just need access to your infrastructures . That 's one way we offer a peak capability . A second level is people say look , I really do n'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 , image processing , video . And we 've said , we can offer an auto-email 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 if you look at the data sets that I used 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 your model in translation provide equivalent fidelity in sparse languages ? Because it 's always important to those people only understand that language that they get a high fidelity result . So we 've built something called translation hub and it 's being used in very mundane places but with extraordinary impact . For example , when people announce COVID guidelines or recently monkey parks , for example , which is another thing , they needed translate many , many languages . And normally the process would take a long time . We have movie studios , for example , in a different example , saying , hey , when we launch a movie , 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 . 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 , every medicine , 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 've got to be a software company to use AI is not fair . So for some industries , we actually build fully packet solutions . If you call many telephone companies , the context center , behind it , sits our 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 would n'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 do n'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 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 to the technology . I imagine one , and there 's so desperately , you know , everywhere from , you know , fraud detection to translation to translation of manuals , you know , there 's such a wide translation of manuals . There 's such a wide array of use cases . How do you all like Google Cloud think about helping businesses understand what is AI good for ? What can they use AI for ? There 's obviously such a wide diversity of different use cases , but what at a framework level do you tell them , 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 up on the view , some simple things , how many of you believe in a few years ' time there 's gon na be intelligence software and non-intelligence software , right ?  I mean , nobody would say in three , few years ' time , there 's going to be intelligence software and non-intelligence software . 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 , in 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 ? I used to do this in a specific way . I heard about image recognition . One example really fun or interesting one , US Navy , when you have corrosion on the base of ships , the old way was to lift it into dry dark and take a look at it . If you 've ever seen one of these ships , you can imagine lifting to dry dark is not an easy thing . So they said , can we fly a drone with your camera image recognition around it and detect corrosion ? And so what we 've seen is that as you lift up the capability where image , audio , text , et cetera , 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 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 . Yeah , I think it 's , I mean , all the use cases , the variety is incredibly exciting . It 's cool that these customers are coming to you directly with many of them . What is , again , kind of thinking bigger picture , what is machine learning an AI mean for Google Cloud on the whole over the next call 510 years ? So we feel that the boundary of what machine learning and what AI can do will change over time . When it started , it was about doing what we would call assistive things . Assistive 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 , your 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 primarily in that phase today .  The second phase is to do something where you could n'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 could n'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 , and to assist them in expressiveness . So I 'll give you a practical example . A lot of you probably use tools , slides , and things like that in your day to day job . 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 presented . Then the younger generation says , hey , I do n't want to draw things that 's really old-fashioned . I 'm going to go to the internet and copy images , right ?  Because when they do class projects , they 're copying images into the slides . And then , as people observe , you know , on the social media environment , people going from text , which may have been Facebook to short images , which is Instagram to short video TikTok , we would say , hey , why would n't we be able to record short video ? And be used 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 and a school saying saying I want to capture how the landscape and outside of Paris and France is right now . I think they need to be able to generate some of the ideas that they could 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 . Yeah , I mean , all of this is very obviously incredibly exciting and we 're all watching it happen in real time . There 's an artist who actually described the image generation models as , he sort of image generation models as he was , he sort of said like , you kind of think about like a camera . Like it 's a new tool that allows you to create fundamentally new 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 were a musician , and now these technologies allow you to blend all of it as a form of expressiveness . Yeah . You know , the last question I have for you is , you know , you obviously sit down with many of the sort of leading CEOs and business leaders of of the sort of largest organizations in the world .  And I 'm sure one thing that is on many of their minds is sort of as AI technology develops and it continues to progress is potential disruption that might come from art of film intelligence . What sort of , how do you approach that conversation ? What sort of your advice to these business leaders who are looking at this powerful new technology and thinking about what that might mean for the businesses and the business landscape . When we talk to CEOs , I mean the biggest things we talk to them about number one , productivity in the long term , productivity has always been the primary driver of improving both company productivity , meaning their own companies , as well as societal 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 economic textbook is productivity . Software and technology has probably been the biggest boomer 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 do in 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 .  There are always during the period of transition , anxiety about the consequences of that technology .  And it does n't mean the 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 a system 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 coastguard can rescue them . At the same time , being able to use a camera and say that 's Thomas Korean has , 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 regularly allowed by the legal framework , because law in many countries is not yet keeping up with how fast AI technology is moving . But to take the responsibility as a company CEO to say , here 's what I believe comfortable with , and here 's what I wo n't be comfortable with . Yeah . Well , Thomas , thank you so much for such incredible conversations . I think I 'm very heartened to hear all the incredible work that Google Cloud is doing to make artificial intelligence accessible to the entire business world and all of every enterprise around the globe . And I 'm so excited that you 're able to join us . Thank you so much . Thank you so much for having me .  Thank you . Thank you .