What Google Cloud Can Teach Enterprises Developing & Rolling Out AI Tools, With Kawal Gandhi

As the Lead for Generative AI in the Office of the CTO for Google Cloud, Kawal Gandhi has a unique vantage point on enterprise AI rollout. Sarah Guo and Elad Gil sit down with Gandhi this week to discuss his insights on how enterprises can effectively invest in AI development, the importance of TPUs, and Google’s internal AI applications. Plus, when will email get more intelligent? Kawal Gandhi has worked at Google for nearly a decade in search and ad roles before focusing on the development and marketing of AI tools. Show Links: Kawal Gandhi | LinkedIn Google Cloud  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @geeztweets Show Notes: (00:00) - Generative AI in Google Cloud (09:05) - AI Adoption in the Enterprise (13:31) - Multi-Modal AI Models (16:19) - AI Adoption, return-on-investment, anti-patterns (24:43) - Google's TPU and NVIDIA GPU shortage (31:00) - Data Marketplace and Model Training

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  This week, Alat and I are joined by Kawal Gandhi. He works in the office of the CTO at Google Cloud, where he's the lead for generative AI. Gandhi comes from a long history of working on search and ads at Google before Cloud. Welcome, Gandhi.

• 0:17Speaker 01

  Thank you.

• 0:18Sarah Guo?

  How did you end up working on Cloud and then AI in particular from other projects at Google?

• 0:24Speaker 01

  Sure. I really worked deeply with a lot of our advertisers around search and ads for shopping and travel, especially commercial-based queries. And as working with them, they required a lot of storage, compute, infrastructure, constantly around making their ads perform better, which led us to Cloud and then Cloud solutions around using some of that to create smart analytics, machine learning pipelines, more around documentation AI, conversational AI. And here we are. We've been doing it for a while, but now it's generative AI, where how can you make that customer experience much better with the information that they have?

• 1:07Sarah Guo?

  And just in terms of beginning to broadly at Google incorporate AI into GCP, like what was the origin story of that? Is it TPUs, APIs, some customer need that you specifically saw?

• 1:20Speaker 01

  As we were getting into the Google Cloud, this goes back into how can we provide them with latency, high response, better experience with data is how our customers started kind of leaning towards Google, in my mind. From the beginning, it was more around machine learning and AI as a differentiator to work with Google. And how could they use that data better in on our platform was a constant ask. So as we were on the journey of Google Cloud, it was all about data, AI, storage, privacy, security.

• 1:57Speaker 01

  And kind of having that same deep technology that we used inside Google, how could we leverage that and offer that in market? So lots of learnings because what we built internally, we had tools, frameworks, et cetera, that took us time to kind of make our platform rich for our customers from regulated to non-regulated environment and how to leverage some of their current investments on our platform.

• 2:23Speaker 02

  What are some of the internal use cases that have really driven that behavior in terms of the stuff that you ended up building for your customers? I know that, you know, a lot of what Google does is sort of dog food its own APIs or products, and then it starts launching them externally as sort of a service that other people can use. What were some of those first applications of generative AI that occurred internally that then caused you to decide to do these things externally?

• 2:43Speaker 01

  Yeah, the only ones I think it's all public now was around Workspace. Just using a documentation or email, you'll sure use it. So it was like, can you summarize this better? Can you personalize this better? Can you offer me a suggestion? And all this kind of gradually, as we, thanks, as we, you know, tested it internally and dog food, we gradually launch it externally because we see a lot of progress that we make internally in terms of efficiency, productivity gains, folks can use it in their spreadsheet creation, et cetera. Was gradually launched and now it's Duet AI, part of Workspace. So these are constantly being dog food and tested. We call them experiments.

• 3:27Speaker 01

  And as a research team leans in and looks at some of these, we add it to the platform and bring it forward in our products.

• 3:34Sarah Guo?

  Is there a single feature or product you launched within the internal Google version of Duet, the Workspace AI products that have come the most uptake or that you're most proud of?

• 3:46Speaker 01

  Yeah, I think we're seeing it across the board, AHA moments. As we launch, we're seeing it in documents in terms of generation, summarization. We're seeing it now in slides with suggestions on images and new image creation, which is to take time for someone to go ask a studio or an agency. And you have a prompt that you can give and say, here's something I'm thinking about. So Sarah, we've seen kind of intake on all those features. Also, email generation has been super helpful from productivity perspective, not only for the consumers, but for enterprises as well. Uh, security, we don't talk about it a lot. I think it's super secure, how it's sent, how the links are used. Uh, those are something we really take it seriously.

• 4:32Sarah Guo?

  How far do you think we can take it with email generation? Cause I maybe spend four hours of my workday just trying to keep up with my inbox. So this is of great personal importance to me.

• 4:43Speaker 01

  I'm not gonna predict because you predict something and something always comes back and surprises you. From a technology perspective, from a modern perspective. So I'm looking forward um to see what surprises you. And I'm sure we'll speak again in six months and you'll be like, Gandhi, I saw this feature inside Gmail. It's really helpful. I like the translation feature for my mom because she speaks Hindi. She's really fluent in it. And when I write my emails now, I can just say, translate this when she reads it. Now it does it automatically because she's using that app. So which is like fantastic. So I think it just bridges the gaps for a lot of our users internally and externally.

• 5:23Speaker 02

  I know that there's a lot of different services that Google Cloud provides, particularly around generative AI. There's a series of models and there's lots of domain specific models that Google's really been forward thinking on, things like MedPalm two or SecPalm or other things like that. Which of those are currently available and how do you think about that roadmap for things to expose over time?

• 5:40Speaker 01

  Sure, and I think it's helpful a lot to know that we have invested on the AI infrastructure. So you've seen people training, not only using our first 14 models, but also training their models and bringing it on to our platform. The next level up is the the vertex, what we call is capabilities of models that you can use from our model garden. That's where we have domain specific models. So it's MedPalm, uh, SecPalm, these are early days domain specific. You can chat with it. You can kind of get validation of things, the notes that are written out from a nurse to a patient healthcare from a doctor. And it kind of puts it in the right form. That can be saved up. So it's early days. We're seeing it from just foundational model capabilities

• 6:25Speaker 01

  to models that are built by our users and deployed on our platform to also open source models. I'm really excited to see like Llama, Stable Diffusion, other variations coming on to the platform. But what's key in all of this is your data is secure. You wanna be like, you know, fine tuning it on the platform and then all the model operation should be easy because we've spent a lot of time really specializing around the model drift, operations, tooling, safety around it. Super early, but I think those are the elements that will differentiate on the platform from the from the next few quarters.

• 7:04Speaker 02

  That's very interesting. Yeah, there's this guy, Anker Goyle, who runs a company called Brain Trust. It's focused on eval and a couple of other things. And one of the points he makes, which I think is pretty interesting, is that there tends to be a very sequential adoption of L L M's by larger enterprises or people training models for the first time. So often they jump straight into fine tuning and then they suddenly realize, wait, wait, wait, let me just prove it out in G P T four or Bard or some other A P I and then let me iterate towards something that actually works. And then maybe I go and train my own model. Do you see something similar in terms of the pattern of behavior that a lot of your partners adopt? Or do they just jump to your A P I's? Or what's the most common sort of sequencing of people really adopting this technology?

• 7:43Speaker 01

  Yeah, I think it's always the case. If you look in history, it's like, hey, I got an A P I. I can build a prototype and it creates a lot of excitement. It's when you really start thinking about deploying it, managing it, monitoring it, using different models to chain them, have responses that really answer a capability. You start thinking deeply about it inside an organization. So I see this as early excitement. It's not hype. So I want to set a couple that it's real excitement because engineers, we love it. Like I've been one, you wanna grab something, you don't wanna have restrictions around it and you wanna show the art of the possible. And I think from those experiments, we're gonna see in the next year or so,

• 8:27Speaker 01

  capabilities that are, you know, available uh to the users and to inside the inside the enterprise, but they will see differentiated output and capabilities. Uh, that's how I see it. So I'm excited in all those, all those phases, by the way. Uh, because it's like the best creative time for engineers to solve problems that they've been thinking about solving for a while. Uh, and now they have the tools that they can download, whether they can grab an open source or on a closed one. And we can then kind of see how they're using it and evolving, evolving the platform along with it.

• 9:00Sarah Guo?

  Do you advise uh your, your customers and, you know, major Google Cloud customers? How to think about when they need to train their own models or when they should be using domain-specific models? Like what advice would you have for them?

• 9:12Speaker 01

  Yeah, sure, Sarah. So we, we deeply think about it because it's, it's, you have to be responsible. Once you're building and fine tuning a model, you know, what are the guardrails? What are the use cases? What is the cost you wanna put behind that? Because it's a continuous uh learning process. And it also depends on the maturity of the organization. Do they have a, a team that understands how models are built, tuned, and then brought forward? And this, it doesn't mean that you cannot invest in it. It's just that where you are in the cycle is very important. So we lean in, talk about that from a, you know, board level perspective, from a strategy perspective, and then thinking through cultural transformations as well. So it's not limited to just one dimension in my mind.

• 9:57Speaker 01

  And it really helps them to think through how they see this transformation also happening inside their organization.

• 10:03Speaker 02

  Do you track the common use cases that your customers end up focusing on? Because it seems like there's almost three different things that people tend to do. There's, you know, let's go experiment and just see if this is interesting. There's, I'm gonna use it for specific internal tools. I wanna make customer success better, or I wanna make, you know, some ops workflow better. And then there's the people who are actually doing it for external products. You know, I'm actually going to launch a feature that includes generative AI. Do you have a sense of sort of how that breaks out across your customer base and the proportion that's doing each of this point in the, in this early cycle?

• 10:35Speaker 01

  Yeah, it falls into like, for me, it's like efficiency, productivity gains and also creativity. And I did it in order because inside the enterprise, folks want to be creative, but every time they think about efficiency gains inside their workflows. So how can I make my workflow better so I can invest back into my group? And then how can I make productivity better, then I can make them creative? And I think we're seeing the flow inside the organizations. So what you touched on in our this, right? How can I make my customers successful? Like, let's start that use case efficiency. How can I make my support better? So, and not drop my CSAT. That's the KPI I'm going to look at.

• 11:16Speaker 01

  And if see, if that matches, then I go into productivity gains, offering promotions, next recommendations. Then the trust increases. So let's put another dimension there of trust. So now you have your efficiency, you're going into productivity, you trust more and more. And the world we look at is like, how are these things going to work in the system of intelligence or agents in the future? That the trust goes up and then you really free up your human capital to be creative. Right? So I think we're on that trust cycle of like, how do we trust these models? They do what we are thinking, they don't go off, they don't hallucinate. All those things are important. So that's how we, how we think about it and then gradually make progress towards it and not get super excited.

• 11:58Speaker 01

  And then find that the KPIs are not working, for example.

• 12:02Sarah Guo?

  Are there particular verticals outside of technology you see the strongest adoption in so far or the strongest interest?

• 12:08Speaker 01

  Yeah, I see a lot of interest is started from sales and marketing. Like as soon as this came out from a. Horizontal perspective, I think it has an intersection between horizontal and vertical for every department, whether regulated or unregulated. There's like bottlenecks in content creation, distribution. All that became like you could see the efficiency gains immediately and creative gains. And then making that process and workflow shorter. So that was one. Customer care, you all touched on it, is another one that goes vertical across horizontal. Even if you are a B two B, you're touching a supplier. And a supplier wants a good experience with their current, you know, provider. So how can that experience get better?

• 12:52Speaker 01

  And then now we're seeing them verticalize all the internal experiences. So how do I make my employees' experience better? So you're kind of seeing that same technology applied internally. It's like, oh, my God, I'm looking for my HR benefits and do this X, Y, Z. Why can't you make that easy, right? So the Seri La, we're seeing the internal process now, whether it's regulated or unregulated. And I think there are these huge areas that we can, you know, provide opportunity with AI. That's my belief.

• 13:21Sarah Guo?

  I'm assuming that the vast majority of this starts with text, right? Text analysis, generation, et cetera. Where are you seeing, if at all, multimodality, voice input, text to speech output? Any sort of other, even within sales and marketing, perhaps, other modalities in people's use?

• 13:39Speaker 01

  Yes, so I think the core right now is language and text. And conversation fits really well into it, like, you know, what we're doing. Can you convert it into a script? And you do translations around it and you have a distribution mechanism. Sounds easy now, but was really hard three, four years ago. Multimodal, I think, are early stages now. So you're going to see the early models which are audio based. So I think about it like text, then images, then images and text with.

• 14:08Speaker 01

  Audio and then you're kind of combining these medias together. So as I said before, the trust on these as it increases, you're going to see multimodal coming forward. There are things to think about like identification, creation, deep fakes that get created, voices that are used out of band. How can we provide that layer of safety around that, especially when it's used internally? And the data and the creation becomes an element of cost. How do we store that? How do we retrieve that? How do we scale that? So if I give you an example, I think gaming is a really good industry, which is kind of scaling out multimodality. And we have a lot of customers who are using that.

• 14:48Speaker 01

  Now think of that when you're going shopping and then the latency and the progress bar like, why can't I see the video coming? Right? So that's why you see it's centralized with some organizations who are experimenting, but we've got to bring it at scale out, which will be like fantastic to see across not only the web internally as well.

• 15:07Sarah Guo?

  Are there any particular patterns that you see either organizationally or the types of projects that people start with that make your customers more and less successful with their AI efforts?

• 15:18Speaker 01

  Yeah, I think it goes back to kind of the how much investment, how much belief, how much, you know, they want to be in the vision quadrant, first of a kind versus fast follower. All plays into it. It's less about the technology. It's more about like where they see themselves in the industry. And if they're seeing themselves as like, hey, we want to really build something and then scale out and then keep investing in it. I've spoken with a lot of customers, even governments and all, the interest in AI, I have not seen this. It's so high. We have just scratched the surface of this right now. So we can see a lot of these gains which then can get invested back in the, in the business.

• 16:01Speaker 01

  So I, I see a lot of positive signs around that.

• 16:04Sarah Guo?

  What's the most expensive part of the investment cycle, you think? I'm sure it varies dramatically from customer to customer, case to case. But when you say, you know, it depends on commitment level and investment, like what, what's expensive?

• 16:19Speaker 01

  The expensive part is now becoming cheap. It's the models, the availability, the usage of the platform. Those were the things that were really expensive. I think if you do a cost curve, you know, curve right now and the investments you all are making into the ecosystem. I think we're seeing that just phenomenally coming down, which will allow people to adopt more. So I think we are, we are really entering a phase of, say, a growth phase of just people adopting this more and more. We are seeing signs of just like, how fast can you do? How fast can we learn? Um, we've had training classes and we've had people certified. I've never seen this for a year.

• 17:00Speaker 01

  We have more than 10,000 people certified now, just more on G C P, uh, generative A I globally, right? Ready to, you know, write code, take advantage of code capabilities. Uh, engineers productivity going up, which is to take time. Migration of systems, can we make that easier? There's a ton of use cases that are helpful, but we're just bringing this forward on the platform.

• 17:25Sarah Guo?

  Are there any anti-patterns or big mistakes you guys have made internally? Or that you see customers make when they're trying to get these efforts into production or even choosing use cases?

• 17:36Speaker 01

  Not mistake, but we think deeply about the data that customer bring to our platform. And they're not mistakes, but we worry about any of this, like, you know, not being used in a way or something happens. So we take that very seriously. Uh, so we run a lot of drills, we make sure that data is kept secure. The models that are trained, even if they are early days, no leaks happen. The adapter model, the certification, everything stays in their tenant. So from the beginning, we just make sure that all of their data, all of their models, all of their weights, that's like their I P. And we want to safeguard that. So none of the mistakes, just deeply have to think about how fast we move.

• 18:20Speaker 01

  And, and what are the checkpoints we need to make in between to make sure we don't move too fast that we get into mistakes? Uh, those rollbacks are expensive, by the way. Then you have to again educate like the industry, make sure they understand. Rather not commit those is where I'm going.

• 18:37Speaker 02

  Yeah, Google's always been very good at sort of data security and ensuring that, you know, customer data is well secured. I guess related to data, there's a number of verticals that are very data intensive or alternatively where the data can be quite sensitive. You know, healthcare is almost a canonical example of that where, you know, bespoke data can really help build a more interesting AI service, sort of like what you've done with Medpom. But at the same time, you want to secure it properly. Are there specific verticals or use cases that you see adopting AI soonest? For example, do you see healthcare moving ahead of education? Or do you see fintech or financial services as sort of the early adopter wave?

• 19:13Speaker 01

  I think they're all kind of early and adopting in all those verticals that I was mentioning are horizontal, like sales and marketing, creative approaches. I'm seeing engineering adopted much faster internally now with the coding models, with the open source. It's a surprise, by the way, if I had to share my personal, I thought engineering was so productive and they're writing there, right? Just design elements of UX and the discussion you have around that have been there, takes a long time. But then having a bot in the room saying, here's how you can approach it, or here's how code could be written capabilities. It's fantastic

• 19:53Speaker 01

  to see that. So I see a lack of, you know, resources which was causing engineering projects to be bottlenecked. Now the transformation is going to be much faster. So bringing that across to verticals is like fantastic. That's the IP that's getting created now inside those organizations.

• 20:10Speaker 02

  Yeah, I'm seeing something very similar in the startup world where a lot of the early startups are basically technologists building stuff for themselves or mid-market tech companies as sort of the earliest adopters outside of a Google or a Microsoft or sort of the the really cutting edge, large tech companies. And so it seems like, you know, at least on the startup scene, there's a very similar pattern being mirrored relative to what's happening on your cloud in terms of, you know, developers are building for themselves first, mid-market companies are kind of next, simply because they're often driven by a developer or CEO. And then it kind of is starting to eke into the other parts of the world in terms of adoption patterns. So it's interesting to see that parallel.

• 20:49Speaker 01

  It's very interesting. And think about the investments you all make. Now I have a platform in GCP which gives me models. I have a coding model that helps me code. And now I just need Capital allocation to go experiment. I think that's great. And then now I need to make it secure and scale it up. That's where we come in with our infra and then make it cost effective. I think we're like early stage of new era of even startups and big companies coming out of this and different solutions getting built out.

• 21:20Sarah Guo?

  What you said earlier was really interesting about having a bot in the room, so to speak, helping us with UX discussion or specking a product. Where does that live for a Google product team? Does it live in a chat interface? Does it live in documents? Does it live in the IDE or in version control? I think a lot of people are trying to figure out how this should integrate into existing developer workflows.

• 21:43Speaker 01

  Yeah, I think today in my mind, it lives in docs. So you get a lot of like, you know, benefit in the documentation and that's shared across. I think you've seen it like any project is like, let's start a doc. Any hypothesis or experiment, let's start a doc. I really think there will be a revolution as the platforms mature where the docs can make suggestions. And those suggestions could be here, based on, you know, what you've specked out. Here is output. Now you can choose to ignore it. It could be a notebook as well. You said IDEs and UX. It could be just code written with put the API key here. It's great to kind of do that, but then when you're scaling up, that's where I get scared. How does it going to scale up?

• 22:26Speaker 01

  You don't want these things to become something that doesn't have the guardrails around them. Who is entitled inside the organization to do this? So we have to make sure logging is on. Simple things like logging, we will not think about, but has to be who accessed, when did they access, et cetera. But I think once we get over that, if you're writing a monitoring tool and it knows the schema, you don't need to go back and look at it and have an engineer work on it for like four weeks. Right? Why can't it be generated out of, I just need a monitoring tool for model drift? Those benefits are going to just accelerate adoption in my mind.

• 23:02Speaker 02

  One of the things that Google really pioneered on top of all the cloud services and APIs and everything else is actually the silicon layer. I think it was seven, eight years ago when TPUs were first really rolled forth as something internal to Google. They said, let's invent silicon that's really good at dealing with AI and ML systems. And so that was my understanding of the origin of the TPU. And it obviously was dramatically more performant than GPU for a long time. Could you talk a little bit about the TPU versus GPU trade offs and how Google Cloud approaches that?

• 23:31Speaker 01

  Yeah, I think how we think about is like, can the platform be capable enough and have features, as I call them, that takes away the developer or engineer or team thinking about a project from, you know, the infra piece. Early days, like, you know, we used to write Windows app, I've written like Java application, and it's always about abstracting it away. So you can manage it, scale it, and then roll it out faster. So I think we'll see a world gradually, other than training and where it's trained and deployed, is it abstracted out from that layer in a way that it gives the fungibility and the adoption and the scale out that helps

• 24:12Speaker 01

  you kind of do that. That'll, that'll speed up in my mind. And then, to your point, innovation on that layer is going to also continue.

• 24:20Speaker 02

  Are there specific trade offs you see in terms of when you should use TPU versus GPU? Or do you think they're reasonable, they're reasonably fungible?

• 24:27Speaker 01

  They're reasonable in my mind. It's just more around how many folks know how to do it directly is something, you know, top of mind for us. Because there is this that was, you know, externally available and a lot of people are trained up. As more people get trained up, we're seeing cost and benefits across the board, right? So having optionality is what we want to kind of bring forward constantly.

• 24:51Speaker 02

  The people that I know who know how to use TPU very well are very comfortable in that environment. And to your point, that's often people who are at Google and therefore they're trained to know how to actually use that underlying silicon versus the GPU in terms of optimizations or, you know, little tricks that make things more efficient.

• 25:05Speaker 01

  How can we kind of make that more prevalent and available is something we should kind of invest in in the future. I'm going to see that. More than that, I'm on the team as such as thinking what inference, because you get the train, you get the application ready and then the scale is where you don't know of character AI is like really thinking about scaling it out. We have other examples in Cora and Bo Adams thinking about scaling out the application. So now you're going to see the shift of like we have V5V etc. we are investing heavily and you know, others are doing is how do you scale that's and then keep the cost curve kind of going in the other direction. We're going to kind

• 25:50Speaker 01

  of invest and kind of put our effort around that.

• 25:53Speaker 02

  Yeah, and I guess since Google trains its own models and they have an advantage in terms of thinking through how that scalability could work for others.

• 26:00Speaker 01

  That's right.

• 26:00Speaker 02

  And that may be an interesting differentiator in terms of that, that understanding.

• 26:05Speaker 01

  Yeah, I think inference will be something we really need it. You can see the numbers, right? Like an application launches and it goes from like five, 10, 1500, right? And you're like, how do I scale this further out? And Sarah touched on it. What happens when we get into multimodal world, right? And if I'm an educator thinking about an education site, I'm not having that kind of luxury. So that's like really something we think about. We didn't talk about that vertical at all is, you know, education and people want to be learning, deploying, etc. How do we help those organizations come on board and take advantage of this as well?

• 26:43Sarah Guo?

  Are you seeing the current Nvidia GPU shortage change your customers' perspectives in training or inference processor choices today? Or how does that change your conversations at all?

• 26:53Speaker 01

  It definitely is mentioned as we think about it in the conversations. It's always the discussion that comes up. But as you look at the applications and the usage and what they're really trying to solve, you then get to the next step. I think it just, the conversation sifts around capabilities and platform. As we were discussing over here, how do you keep the data secure? How do you make sure the right teams have the access to it? How do you make sure the data is regionalized, for example? And those answers are not easy to answer for some of the other providers, for example. And then it comes to like, if models are going to be available, do we really see that shortage playing out? Right?

• 27:38Speaker 01

  And I think we're seeing the concentration on training side, but then the world focuses on a different problem, which is like, let's focus on the real, how do we deploy it inside your organization and make it successful? That's, I think, where the narration changes and then it doesn't even come back to talking about the chip shortage and availability, et cetera. But having said that, we are absolutely focused on like, making sure that the platform is available for customers and we see the demand coming as well towards us.

• 28:10Sarah Guo?

  Yeah, I mean, there's different ways to look at the demand for training. You know, one point of view in the world is that most of the demand will end up quite concentrated in people who provide model services, right? Versus at least quantity of compute used in fine tuning or training outside of a few large labs. Like, how do you think about this? And how should customers think about Google's model quality versus others?

• 28:36Speaker 01

  Absolutely. So we think about, you know, going back that the customer should have Availability and optionality. So if there's a model that's coming out, it's been trained up. Can we make it available like Llama inside our model garden? And then they can use it inside their application and not worry about the platform capabilities at all. And it kind of fits into currently and leverages that current investment. We think about that a lot. So just starting from the customer first and then making the technology available. Rather than thinking about here's another set of models that you need to kind of go take and do the scaffolding and the build out around that. So absolutely like as these come out, Sarah, and you know, they will be new ones.

• 29:20Speaker 01

  Uh, you know, launched with some capabilities, different ways of training and approaches, optimization. And if we see our customers in our vertical kind of leaning towards that, we want to make sure it's available on our platform. Um, and I think models in my mind are like fifty, sixty percent is the, is the workaround. And how do you leverage your current investment is thirty, forty percent uh work that goes in uh from the groups, from our customers as well. And then the upkeep and maintenance and all the operational elements are also important.

• 29:52Sarah Guo?

  One or two last questions for you. Um, what is the, the thing you're working on right now within Cloud AI you're most excited about, that we should be looking forward to or more customers should know about?

• 30:03Speaker 01

  I'm working across the board with multiple customers on just making these user experience, the next generation multimodal, and how should we think about that? Um, and what are the real first use cases that we should kind of deeply work and partner together with them? So that keeps me really excited around that. And you know, how can we uh bring these to our platform and make it capable? Uh, second is just the amount of shared data some of our customers want for their model training or available on our platform. That's the next thing we think about, like are there data sets that we can offer out? And we're also looking at synthetic data as well.

• 30:45Speaker 01

  Can we use in regulated industries, some of the synthetic stuff and like recreate those simulation modes that can give them a good insight into some data that's missing?

• 30:55Sarah Guo?

  Does Google Cloud have a data marketplace, a labeling offering, labeling tools, anything like that today?

• 31:02Speaker 01

  So we have the partner ecosystem. Through that, we provide a lot of partners. So exactly the marketplace. And if they wanna take advantage of some of those providers, they can absolutely do that. They're integrated into our platform. So you can kind of say, I want this service for RLHF. And you know, reach out to them and then use it for your model training, et cetera. The most important thing, Sarah, is to make sure that they're working in congestion. And whatever they are, you know, getting from the customer, it's secure. So it's not something used with another customer. So we make sure those pipelines and entitlements stay in the project.

• 31:39Sarah Guo?

  Gandhi, is there anything that you wanted to cover today that we didn't? It's a great conversation. I feel like we covered a lot of ground.

• 31:45Speaker 01

  Yeah, I think it was. Thank you. I think we covered all of it. I would love to come back in six months, take a look back and see, you know, how we move forward.

• 31:54Sarah Guo?

  I know making predictions in AI is very hard, but I heard from you that my Gmail suggestions are going to get a lot better quickly. So I'll take that.

• 32:04Speaker 01

  Yes. Hold us to that. Yeah.

• 32:07Speaker 02

  Yeah. Great. Thanks so much for the time today. I really appreciate the conversation.

• 32:11Speaker 01

  Thank you.

• 32:13Sarah Guo?

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