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- [00:00]
Alex
Can Google Cloud Platform ride AI into the field's top echelon? And how much is AI shaking up the trillion dollar industry? We'll find out with the CEO of Google Cloud Platform right after this.
- [00:15]
Kwame Christian
Hi, I'm Kwame Christian, CEO of the American Negotiation Institute, and I have a quick question for you. When was the last time you had a difficult conversation? These conversations happen all the time. And that's exactly why you should listen to Negotiate Anything, the number one negotiation podcast in the world. We produce episodes every single day to help you lead, persuade and resolve conflicts both at work and at home. So level up your negotiation skills by making Negotiate Anything part of your daily routine.
- [00:46]
Jessi Hempel
From LinkedIn News, I'm Jessi Hempel, host of the hello Monday Podcast. Start your week with the hello Monday Podcast. We'll navigate career pivots. We'll learn where happiness fits in. Listen to hello Monday with me, Jesse Hempel on the LinkedIn podcast network or wherever you get your podcasts.
- [01:06]
Alex
Welcome to Big Technology Podcast, a show for cool headed and nuanced conversation of the tech world and beyond. Today we're joined by Thomas Kurian. He's the CEO of Google Cloud Platform and he's here for a realistic look at how companies are building with AI and how Google is positioning itself to win in the moment. We'll also talk tariffs of course for a bit towards the end of the show. Thomas, great to see you. Welcome to the show.
- [01:34]
Thomas Kurian
Thank you for having me.
- [01:35]
Alex
Thanks for being here. Let's talk about the surge that Google Cloud Platform has had in the past couple months or years really, and a lot of that has been tied to artificial intelligence. I think it's fair to say that GCP Google Cloud platform was running maybe a distant third behind Microsoft and Amazon when it came to cloud hosting. And every time I look at the earnings numbers, I see these massive growth rates, 30% per year per quarter. How much is AI a part of that?
- [02:06]
Thomas Kurian
AI has definitely driven adoption of different parts of our platform. And so people typically when they come in for AI, depending on the type of company, they come in at different parts of our portfolio. Some of them say I really want to do super scaled training or inference of my own model. And so there's a whole range of people doing that all the way from foundation model companies, whether that's anthropic or midjourney or others. And also traditional companies. Ford Motor Company, for example, when they brought their they wanted to use our chips and our system called TPU Tensor processing unit to model airflow and wind tunnel Simulation using computers rather than physical wind tunnels. So they're doing that as an example. So one set comes and says, I'll use your AI infrastructure. A second set comes in and says, I want to use your AI models. And that could be somebody building an advertising campaign using our image processing model, somebody wanting to write code using Gemini, somebody wanting to build an application using Gemini or one of our newer models like veo, which is our video processing model. So in that case, they come in and use the platform. But along with that they may say, I want to put my data so that the model can access it quickly. And they start with one of our database offerings, for example. So it certainly draws more pieces of our portfolio as part of it. And then the third is people coming in and saying, I want to use a packaged agent that you have. For example, we offer something for customer service, we offer something for food ordering, we offer something to help you in your vehicle, like in car, we offer stuff for cybersecurity. So there's a whole portfolio of these. And so depending on which customer is coming in, they come in at different layers of our stack.
- [04:05]
Alex
And it's so great to hear you talk about actual products that people are building with AI, because a lot of the conversation has been around capabilities, how can AI's latest models perform on the Math Olympiad tests. And very little, I think, of the discussion has been about what do they actually do. So we're going to cover in the second half some concrete products that you're seeing being built. But let's go back just to this bigger cloud battle, because this is a multi billion or even multi trillion dollar fight right now to be able to get companies to host run applications in the cloud as opposed to, you know, in their premises. When people are making decisions to buy, how much of their decisions are predicated on AI capabilities? Because what you just told me are a number of specific. I want to build an AI program. I'm coming to Google for that now. I imagine that's important. But when you think about the broader landscape of people making decisions to buy cloud services, how, how much does AI factor right now?
- [05:06]
Thomas Kurian
It's a good question. It depends on the country, it depends on the industry, it depends on the segment. Let me explain what I mean. If you're an AI unicorn, meaning you're funded to build a foundation model or you're building an application based on AI, that's really the central part of your decision. If you are in an industry that, for example, in retail, where we have a product called retail search and Conversational shopping where you can take Google like search using text, images, video and put it on your catalog. And you can also put conversational shopping where I can ask a question, I'd like to return this address and have the system handle that transaction for you. It's a super important thing, for example, for people in commerce, whether that's retail or telecommunication. On the other hand, if you look at a utility or an industrial manufacturer, it applies to part of their organization, but it may not be the central thing. And so it really depends by industry and by customer segment. But part of our value proposition is that we offer all of these different capabilities. And so AI is helping us. It's not the sole reason for our growth.
- [06:25]
Alex
Okay, and then just broadly, just talk a little bit about. Okay, so definitely different segments have different approaches to it, but you're the CEO of Google Cloud platform. So like when it comes to the broad Google Cloud platform ability to compete, how important is AI across everything? Yes, of course it varies for individual use cases, but broadly it's going to.
- [06:50]
Thomas Kurian
Be important going forward. We've been very measured in how we brought our AI message to the market to avoid people feel like we're overhyping things. And we've always said we're going to build the best technology in the market. Right now we're super proud. We have over 2 million developers building every day, every morning, every night using our AI platform. And you can see the strength of our models. Gemini Pro 2.5 is the world's leading model. Gemini Flash is the most price performant model. Imagen and Veo are considered state of the art for media processing. And we've got tons of new stuff that we're introducing at our event next week from Audio, Speech, et cetera. So we've been very, very thoughtful about how we've introduced stuff. And I'm not a marketer, so I will tell you it's an important factor. It will be an increasingly important factor. And our strength in it helps bring other products along with it.
- [07:53]
Alex
Yeah. And we are not asking for hype man or marketing. I think this podcast we're just trying to get to the truth. And I appreciate you being reasoned about the role of it and not saying something that's out of line with reality. So thank you for that. Now, you talked about some models, you talked about a lot of models coming out of DeepMind. Here's what let's say Amazon might say when, if we're talking to, if they're talking to an AI customer, here's what Amazon might Say Google has its own models and it wants you to use them. At Amazon we have some proprietary, but our job is really to let you pick whichever model you want from Anthropic on down and you can just trust us to not push our own stuff and then therefore choose us over Google. What would you say to that?
- [08:45]
Thomas Kurian
I would say we offer 200 models in our platform. In fact, we look every quarter at what's driving popularity in the developer community and we offer them. We offer a variety of third party models and partners, not just Anthropic, AI21 Labs, Allen Institute, there's a variety of models there. We offer all the popular open source models, Llama, Mistral, Deepseek, a variety of them and we base it based on what customers want. So we track what's on the leaderboards and what's getting developer adoption and put them in the platform. And people have been super pleased that we offer an open platform. An open platform. Companies, we always feel companies want to choose the best model for their needs and there's a range of them. We're offering a platform, you can choose the model you want. The only model we don't offer today is OpenAI. And that's not because we don't want to offer their model.
- [09:51]
Alex
It's because would you welcome them on the platform? Would you welcome them on the platform?
- [09:55]
Thomas Kurian
Of course we would.
- [09:57]
Alex
Okay, any talks about that?
- [09:59]
Thomas Kurian
I don't want to tell you that we won't do it. We have always said we're open to doing it. I think it's their decision.
- [10:06]
Alex
Okay, so, but the argument I think would be, just to pinpoint the argument from Anthropic, I'd really be curious to get your start from Amazon. I'd be curious to get your perspective on this. They might say, I'm just going to channel them. I haven't spoken with them about this. They might say something like, well, Google will still, even though they can offer everything, they might still push you to use DeepMind models. What do you think about that?
- [10:28]
Thomas Kurian
Well, our field is not compensated any differently. A partner ecosystem is able to use all the models in the platform. And most importantly, we have very large anthropic customers running on gcp. So if you don't have your own model or you have a model of your own, but it's terrible, naturally you're going to say something like that.
- [10:50]
Alex
Are you saying that their model is terrible? Okay, why don't we move to Microsoft then? Microsoft would tell you basically that they have this partnership with OpenAI which is going to build the best in breed. What do you think about that? I mean OpenAI basically ushered in this generative AI revolution and have been the best at productizing it.
- [11:17]
Thomas Kurian
They've done a good job, no question. I would say OpenAI has done a good job with that. Whether that's how much of credit goes to Microsoft outside of providing them a bunch of GPUs. Time will tell.
- [11:33]
Alex
Okay. Now it's interesting because they do have that partnership and that has been largely responsible for the surge that they've seen in the generative AI moment. But there is a pretty interesting difference between Google and and Microsoft and that is that Google does have DeepMind in house, whereas Microsoft has this I don't know if it's even arm's length or hand in hand relationship with OpenAI. So I actually am curious when, when it comes to we talked again about all these businesses that are building AI applications when it comes to that, what does DeepMind give you that might be an advantage there? Because it is a naos.
- [12:16]
Thomas Kurian
We work extraordinarily closely with Demis and his team. When I say extraordinary closely. Our people sit in the same buildings. We work extraordinarily closely. My team builds the infrastructure on which the models train and inference. We get models from Demis and team every day. In fact, we're staging models out to the developer ecosystem within a matter of a few hours after they have finally built. And then we take also feedback from users and move it upstream into pre training to optimize the models. And one benefit we have at Google is all our services, whether that's search or us or YouTube, this inferencing of the same stack and same model series. So the model learns very quickly from all that reinforcement learning feedback and gets better and better. So there's a lot of close collaboration. Many times if I can be frank, when we enter a new domain, I'll give you an example. We built a solution for cyber intelligence using Gemini. There's a lot of threats happening in the world. You want to collect all that threat feed. We do that using a team we have called Mandiant and also from other intelligence signals we're getting on what are the threats emerging. You then want to compare it to your environment to see if you're at risk and most importantly you want to compare it to what parts of my configuration will somebody use to try and get in. We used our Gemini system to help prioritize and also help people hunt faster. We call it threat hunting faster. Now in that environment the model has to learn how to find patterns in a large number of log files that people are ingesting. And that required specific tuning of the model to do that. And so there are things there that having a close working relationship with the DeepMind team has helped enormously. Similar things when you look at, for example, customer engagement, customer service. We've got a project on at Wendy's to automate food ordering in the drive thru. If you actually think of a drive thru, it's an extraordinarily complicated scenario because there's a lot of background noise, kids screaming in a car, people change their mind when they're ordering something. I didn't mean that one. I wanted that one changed to this one. And which one did you mean by that one and this one?
- [14:55]
Alex
It feels like you're describing the way that I handle these interactions and I'm very embarrassed about it, but that is me.
- [15:00]
Thomas Kurian
So there's a lot of things that we needed the model to do to have ultra low latency in being able to have that conversational interaction with the user. So all those elements, the partnership we have with Demis has been super productive. It's also most importantly, it's people working together. We're all close personal relationships that helps us get through a lot of design changes and other things and we're all rowing towards the same goal.
- [15:33]
Alex
Right? But okay. I was speaking with Mustafa Suleiman, the CEO of Microsoft AI, just a few days ago. So this is kind of a fortuitous back to back episode scheduling. And what he said was, look, you can for without spending the billions and billions of dollars it takes to train the new models, basically replicate what they're doing with a lot less money and put it into action just a little bit more slowly. And so therefore what he's saying is basically Microsoft gets the benefit without the cost. What do you think about that argument?
- [16:07]
Thomas Kurian
I don't want to comment on what he said. I can just tell you there's a lot of debate on cost of training and inference. First and foremost, in the long run, if AI really scales, the cost you really want to care about is inference cost because that's what's integrated into serving. And any company that wants to recover the cost of training has to have a large scale inference footprint. There are lots of things we've done with our Gemini Flash Gemini Pro models that you can see and also other people using TPU for inferencing, for example, large companies are using it to allow them to optimize the cost of inference. Cost of inference can be on the efficiency with which you handle your serving fleet, how you go disaggregated serving, what you do with caching and key value stores. There's 100 different variants of that. The proof I think is in our numbers. If you look at our price performance, meaning quality performance of models and the unit price of tokens, we're extraordinarily competitive. So that's number one. Number two on the training, I think there's a bit of confusion that may exist in the market. So there is research, Frontier Research exploration. Frontier Research exploration for example could be how do I think about teaching a model a skill like mathematics? How do I teach a model, for example, a new skill like planning, how do I teach a model a new skill in a brand new area? Those are what we call frontier research that goes on. Many, many experiments like that are done. Then after you find the recipe, you then actually train a model. Train a model is actually you do the model run where you're running the actual training. I think people are mixing up the total amount of money spent on research and breakthroughs as opposed to actual training. And we are very confident we wouldn't be investing in the way we are as a company without knowing the ratios between all of these. And so we're very confident that we know how to run very efficient model training, what we're investing in Frontier Research and then most importantly how we're handling model inferencing and being world class at all. 3.
- [18:44]
Alex
Do you think there are still gains to be had by scaling up the pre training of models?
- [18:48]
Thomas Kurian
There are gains to be had. I don't think they will be at the same ratio as earlier because just, you know, there's always a lot of diminishing returns at some point. I don't think we are at the point where there are no more gains, but I think we won't see the same ratio of gains we used to see with inference.
- [19:08]
Alex
So that will be the new cost, basically taking the models and putting them into production and using them. I'm curious how big of how much of the cost of that or how much of the use of your services is going to be toward reasoning. And what have these new reasoning capabilities allowed your customers to do that they couldn't do previously?
- [19:32]
Thomas Kurian
Really good question. I mean reasoning is something we are starting to see customers using in different parts of our enterprise customer base. For example, in financial services we've had people say hey, I want to understand what's happening in financial markets. Summarize the information coming off whether that's video feeds like cnbc financial market indexes and other financial information and tell me what's happening. And the model can not only build a plan for how it collects the information, but summarize it and then reason on the summary to say, are there conclusions to be derived? Right. So we are starting to see people starting to do that. How much of that will be versus other scenarios? Time will tell, but we are starting to see people doing much more sophisticated, complicated reasoning, even in areas. We have a travel company, for example, that's working on. Give me a very high level description of what you want to travel for. I want to fly to New York, I'm taking my son, we'd like to see Coney Island. And the following three things. Build me a plan. And in that it can have multiple choices, but it may say, you know, if you're traveling in June, maybe hot in the afternoon, therefore I think we should have you see Coney island in the morning and go to the museum in the afternoon. And models are starting to be able to reason on those things. And we are starting to see early adopter companies test in all these different dimensions.
- [21:12]
Alex
Well, that's wild. Wait, so are people. I just need to ask you this follow up. Are people scraping the audio feed from CNBC and then using the summarized information to trade?
- [21:21]
Thomas Kurian
There are feeds. When I mentioned cnbc, I'm using an example. They have personal feeds from their broker and dealer networks which are private of their own, that they're feeding into this because when they have a broker or an equity analyst make a broadcast to their internal teams, they want to feed that as an example. I was using that just as an example to see what kind of a feed given your audience to explain what a video feed would look like.
- [21:48]
Alex
Right. And now what about reasoning allows these companies to build this stuff that they couldn't previously? For instance, this travel planning thing, I mean in the non reasoning versions of large language models, I could say build me a plan and it could do that. So what does reasoning do that either ups the performance or allows customers to be able to do stuff they could not previously?
- [22:13]
Thomas Kurian
Reasoning I think allows. So historically when LLMs were used, people were worried about hallucination. They gave a large language model a single step task, meaning do this and come back to me so that I can determine if your answer is hallucinatory or not. I didn't delegate a complex task to you. Secondly, when I asked you a question, you gave me a single answer. You didn't generate a variety of different options and then reason on it or critique them to Say this might be the best answer. So that is the nature of some of the differences we see in why people are using reasoning now as opposed to prior. And the more you can trust that the model can actually reason across a set. Whenever you have a multi step thought chain of thought, if you have drift, meaning early in that chain of thought you had an incorrect answer, then it stepped on that incorrect path and reasoned a lot more downstream, you can get way off relative to what the right path ought to be. As models have become more sophisticated, people have trusted them. Part of it is the accuracy can be higher. Part of it is that it can evaluate a set of different choices and give you an answer based on a set of choices, not just say here's sing answer. And the third is we also allow people to understand what the steps were in how it reasoned so they can look at it and say, yeah, maybe I agree with it, maybe I don't.
- [23:54]
Alex
Okay, so Jensen at Nvidia says reasoning costs a hundred times more to do you also have your own compute? You're also facilitating that. Is that in the ballpark or are you seeing different numbers?
- [24:10]
Thomas Kurian
It depends on how long. For instance, you could give it a very complicated problem and a model can take hours to reason on an extraordinary large data set that will be more expensive at the same time. In the example I gave you on travel, given the number of trips that are made, et cetera, that company is not going to spend millions of dollars to calculate the answer for what's the best choice of trip for me. Or in the financial markets area, given how much information is coming all the time and how quickly you need to reason on it to present your equity traders or your private wealth managers an answer. You're also going to time bound the reasoning computation. And so there's controls in the platform to allow you to say what is the breadth of the reasoning, meaning how large a cluster do you want to reason across how much data and how long do you want it to reason? All those factors are in the user's control and therefore drive how much they want to spend.
- [25:13]
Alex
So if you were selling the hardware and the systems and maybe the software to train this stuff, you might be incentivized to say it cost 100x, but that might be the most optimistic scenario there. But there are plenty of other reasoning use cases that are much less expensive than that 100x in compute. Does that sound like a reasonable takeaway here?
- [25:36]
Thomas Kurian
What we've seen, just if you look at models themselves, people were talking about, you know, you would need A billion times more energy. If you straight lined extrapolated the cost of a model from an inference point of view in 23, like if you look at just 2024, we've reduced the cost of inferencing and you can see it in our prices of the models by a factor of 20 times. And it's because there's a lot of optimizations you can do in that same thing. On reasoning, there will be a lot of optimizations that we will continue to make to lower the cost of reasoning. People will want to do more reasoning as you make it more affordable, people will use it more widely. There will be a range of things all the way from relatively quick, short time bound reasoning to much longer things. Like an example, there's a financial institution working with us to do fraud analysis on transactions that are happening on the payment network. By definition, they need to do that in real time. So their reasoning is time bound because they have to flag a transaction within a certain period of time. Now they also do anti money laundering and other calculations. That reasoning is done in batch and can take a lot longer if they want to. That's why I think there will be a range of these things and saying it's all one or all the other is not correct.
- [27:06]
Alex
Okay, I appreciate your viewpoint again in this area. Reasonable realistic versus hype. I can sense a pattern. This is good. This is what we like to do on this show. You mentioned Deep Seek. I just want to ask you about open source.
- [27:22]
Thomas Kurian
Yes.
- [27:23]
Alex
There might be a view that if open source. Well, let me just say it this way. If open source exceeds the proprietary models and it seems like what we saw with Deep SEQ wasn't that moment, but it certainly opened a lot of people's eyes to the fact that it might be possible. The notion might be that all cloud services are kind of going to be. It won't matter. It'll just be like. Because like Microsoft might say, you need us for OpenAI and you guys might be saying, you know, we have Gemini. The idea is if open source overtakes the proprietary models, then it really won't matter which cloud platform you use and it sort of levels the playing field. What do you think about that?
- [28:07]
Thomas Kurian
It's a good question. I think it's very early to tell first of all, whether open source versus proprietary models are going to win or lose. An example of our own model, we put out an open source model called Gemma, which is getting a lot of adoption among the developer community for people wanting to build certain class of applications. We want to continue to see how open source and proprietary models evolve. One example was historically open source models were used because people wanted to fine tune a model to have their own weights. And when I say fine tune a model, they would take an open source model and really tune it on their data set to have their own weights. Now, as more and more sophisticated techniques for optimizing models have come in where you don't need to depend on fine tuning with adjustment of all the model weights, that case has become less important. But there's always going to be a need for a combination of these and it's very early to tell. Now separate from that, let's assume to your question, Alex. If open source became the dominant one, how would we do? We have a history with that. Just a couple of examples. First of all, Kubernetes became it's an open standard for people spinning up cloud workloads in computation. Many people would say Kubernetes is a standard and it's become the dominant programming paradigm through which people stand up containerized workloads which are the dominant way forward. We've got a great solution, something called Google Kubernetes engine. And people still take vanilla Kubernetes but choose us because of performance, scale reliability and all the other things. Even if you said open source models become popular, you still have to serve the model, you still have to optimize the performance of the model and we're confident we can do that better than others. Now lastly, many people are coming in at different other parts of the stack where they're using a model as part of a service. So for instance, I gave you the example in Cyber inside the Cyber tool. They don't really care if it's Gemini or something else. What they're looking for is a great cyber hunting capability. If you look at data science where people saying I just want to build, ask a question to my data warehouse using English, can you understand what I'm asking and show me the calculations? That's actually a very complex technical problem for those cases. Do they really care? Is it Gemini? It works particularly well because it's Gemini, but they're just accessing our product. We have a new product called AgentSpace. Agent Space is search, conversational chat and agentic technology for your enterprise. They really don't see the model, they're using the platform, they're using an application or a platform and underneath we're providing the capability. So there's other ways to differentiate. Even if open source became extraordinary popular.
- [31:29]
Alex
And Agent Space, if I'm right, is Your fastest growing product ever.
- [31:32]
Thomas Kurian
Yes.
- [31:36]
Alex
Yeah, give some. So basically it's a way for people to query different things within the workplace and get things done in the workplace using natural language. That's right, it's growing. How fast is it growing?
- [31:46]
Thomas Kurian
I mean, we'll publish all the stats next week, but as an example, KPMG is one example of a customer. They are using it to help their professional workforce. We have insurance companies using it as a research assistance to help their insurance brokers. When you call to understand what healthcare benefits are you eligible for, how do I find whether you're eligible for this? And then to speed up things like pre authorization for healthcare benefits, we have banks using it and banks using it to help their frontline understand the customers calling in. I'm the private wealth manager, can I research their portfolio to see what's changed in their portfolio? So there's a lot of different use cases and it's basically Google quality search, conversational chat and workflow or process automation using agents all in one system.
- [32:41]
Alex
Right. Okay, last question here and then we're going to move on to some product examples. You've made Gemini a free add on for the $30 per seat option. Can you talk through that decision? Because it seems like that's kind of counter to what your competitors are doing. And also I wouldn't say very easy to make that something that you throw in.
- [33:03]
Thomas Kurian
This is for Google Workspace, which is a collaboration tool. We made Gemini part of Google Workspace. Rather than requiring somebody to buy a separate subscription. Why did we do that? So if you're using Google Workspace and for example, you're using Gmail, people love the fact that when I receive a lot of email, it summarizes things for me or I want to write an email and I want to write it to recommend somebody for a position. You can ask it to help write the email. If you're doing slides in Google Slides, you want to have a great visual presentation of a set of information. I'm not very good at creating amazing slides, but now you can use our Imagen tool to create amazing images and put it into slides. It requires people to change the way they work and we want to drive daily usage of AI. And because it needs to change the way they work, you want them to get used to using it. If, hey, this group of users in a company gets it, that group of users is not allowed to do it. This group is maybe gonna be allowed, but they have to buy a subscription. You don't let them get used to using AI as part of their daily life life. And we learned doing it back in 2014, 2015, when we added autocomplete autosuggest to Gmail that a lot of people love. It was part of the product and that's what got people used to using it. It helps us improve our AI because of all the usage. You notice patterns and the models get better and better. But it also helps condition the users to start using AI to assist them every day. That's why we put it into the base product.
- [34:58]
Alex
Okay. And that is a great segue into what our next segment is going to be, which is there's all these AI capabilities. Are people going to use them? So why don't we cover that when we come back right after this.
- [35:13]
Andrew Seaman
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- [35:34]
Thomas Kurian
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- [35:37]
Alex
Captain, an unidentified ship is approaching. Over.
- [35:41]
Thomas Kurian
Roger, wait. Is that an enterprise sales solution?
- [35:45]
Kwame Christian
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- [36:03]
Alex
And we're back here on big Technology Podcast with Thomas Kurion, the CEO of Google Cloud platform. Thomas, it's great having you here. Let's just talk about how people are actually using this technology. There have been a couple of op eds that we've talked about on the show recently. One from the New York Times calling AI mid. Another one saying the problem with Apple intelligence isn't Apple, it's the artificial intelligence. And basically saying that the AI technology has been okay but not overwhelming to this point. And it's interesting that you brought up the Wendy's example trying to automate takeout, because one of the examples in that piece is that yes, you can now do self checkout at the supermarket, but it hasn't really changed your life. It's still, you know, flawed, shall we say. I mean, I can't tell you the number of times I've been on the checkout line at Stop and Shop or in the checkout automation and I do some one thing wrong, I forget to put it exactly in the right space. And then a cashier has to come over. Ten minutes later they come over and let Me out of the store. So what do you think about this argument that generative AI is mid or not not, you know, living up to all the boasts and what type of applications have you seen in the technology? If you were going to argue the other way, which I think you are, that make you believe that there's something here.
- [37:24]
Thomas Kurian
I always say any major technology shift takes a while for adoption to happen and for people to understand it. If you look at the Internet, it went through a similar thing. If you look back at 97, 98, 99, there was a lot of hype that it was going to change things. In 2001, there was, you know, some of the hype fell apart. But over the long term, it has definitely shown that it's transformed the way that people find information, they buy things, they even run their businesses. So I think AI is going through a bit early on there was. People had maybe too rosy a view. And I think in the long term, we always say that technology is going to be really a fundamental transformation. How quickly it changes in the day to day, every day. Time will tell. But I'll give you examples of things that we always say. Let the customers tell the story. Let's not tell the customer story on their behalf. And we're super proud of the work we've done. I mean, Seattle Children's Hospital, they wanted their pediatricians, when they see a child, to be able to understand the guidelines for treatment. Guidelines are complicated. You need to be accurate in the information put in front of the person. We've helped them do that at the Mayo Clinic. They wanted us to provide a system through which a doctor could find information from the electronic health record, from their clinical trial system, from their radiology imaging system, and synthesize it so a nurse, before she sees a patient, can see the information. If you look at what we did with Verizon. Verizon is the largest consumer customer base in telecommunications in the United States. They have over a million calls a day going into the call center. We've helped them build something called a personal research assistant so that if I am a call center person and you call me saying, here is my set of issues and we can. How long does it take to research that information and put it back in front of you so that you can handle customer service faster and better. They are very pleased. 96% accuracy in the information placed. And the reason that's important number is better than a human. We've had people do it with in the consumer world, in retail, we've had people improve the Way they shop for things, helping people change accuracy of search results on their search page, improve the way that backoffice. A company called AES, it's an energy utility. It's an energy company. It builds and delivers energy different parts of the world. It used to take them 14 days to run their end of quarter audit. They do it in one hour now. And so these are examples of people doing it right at the core of their business. Honeywell in industrial manufacturing has put our technology into the manufacturing control systems. Deutsche bank is using it for their private wealth managers to summarize information for them, are they transformative to the people doing the work and to those customers it is transformative. They've seen the business results. Time will tell how transformative consumers experience it to be.
- [40:56]
Alex
So it is interesting that this is happening in enterprise. First, we mean there's one, I would say one mainstream AI application and that's ChatGPT and you're at Google, so maybe you can argue with me on that one. But the numbers show 500 million people are using it each week. Why do you think enterprise has been so much quicker to adopt this than consumer? And is it going to be like the BlackBerry, like are we going to start to see some enterprise adoption and then all of a sudden it will just shift over to consumer when the time is right.
- [41:28]
Thomas Kurian
I think the enterprises find real value at the core of their business. It's helping people like Wayfair write code faster and write better code. It's helping people like Mattel, the toy company, find answers so that they can be much more quick and efficient in managing their supply chain and operations infrastructure. It's helping people in the entertainment business build much better recommendations of titles for people to see. There's lots of companies using our recommendation system for it and I think it helps them decide. One do I want to improve my top line? Top line is get people to buy more product, get people to use more of my services. For example, recommendations on movie titles. It helps them be much more efficient in their back office and in some places it also helps Home Depot. We help them build an employee help desk that answers employee questions like about the benefits, about medical insurance, about lots of things. And it also helps them improve the way their own employees experience the organization. So enterprises are choosing it for a variety of reasons. Time will tell whether there will be many killer consumer apps based on generative AI, but we're focused on making sure people have the best technology to build a great experience. I mean Bending Spoon, for example, is a company out of Italy 60 million photos a day. They're using our tools to edit and do magical stuff with it. Samsung S24, every smartphone has our AI Gemini on it and people are using it to create great images and do amazing stuff with it. So there's lots and lots of examples of even enterprises now bringing these technologies to their consumer experience. Even the work that we did with Mercedes helped me drive and help me, give me guidance by just talking to maps. Is it transformative? You know, it's up to the consumer to decide.
- [43:36]
Alex
Right. But I feel like you probably have a perspective perspective on it. But hey, look, I appreciate that you came prepared with lots of case studies. So let me just ask you quickly about agents. You talked a little bit about customer service agents I would say is one of the biggest buzzwords I've ever heard covering tech. It does seem like some companies are allow are using this technology to have generative AI bots take action on their behalf. Which to me I would say that's the definition of agents. So how far do you think we are in the rollout? And then what is a multi agent framework?
- [44:09]
Thomas Kurian
That's a great question. It's early on I would say but let me just start with what we mean by an agent. An agent is an intelligent system software system that has a set of skills. One of the set of skills is for example that it can reason. Another set is that it can use tools. Third, it can communicate with enterprise applications and systems and do that in order to for example automate answer questions or do something on your behalf. So here's a very simple example. The way to think about a single agent, a multi agent scenario. So I'm just going to use a communications example. I have a phone, I want to decide whether I want to upgrade that phone or not. So I call my telephone company a digital agent, not a human agent. Digital agent comes on and says Thomas, I notice you're calling from this number. Let me find out what are you calling about. I said I'd like to figure out a trade in. I notice you're on your mobile. Can I text you a link? Please take a photograph of your phone and tell me and upload it. I notice you have X phone, Y model, you know, you have a cracked screen. So you're authorized for this much of a trade in. So it's handling that interaction with the customer. It's looking at my plan and my profile and says he's a premium customer, so he's eligible for trade in. So it's looking at using a set of tools to calculate do I have the right profile and am I authorized for a trade in? And then it's looking up a system to understand how much is that trade in amount worth. So it's automating that flow. Rather than saying the customer's calling in for a trade in, let me transcribe that for a human. And then the human says, tell me what phone they have. And then saying, they have X phone. Tell me, is it screen cracked? Do you see what I mean? So that's the example.
- [46:14]
Alex
Yes.
- [46:15]
Thomas Kurian
Now, where is agent to agent interaction? Agent to agent interaction is when this agent is functioning. It may need to, for example, say, hey, I'm going to send you the new phone, but you have to activate it. In order to activate it, I'm going to schedule you to go to our nearest retail store. So it may need to call a scheduling system to schedule an appointment for you. That scheduling system may be in some CRM, salesforce or otherwise where it needs to create a ticket for you so that when you go into the store, it says, Friday morning, Thomas is showing up with his new phone. Let's have people ready to activate it. So there's one agent talking to another agent and that needs an open protocol. So what we've done at Google is build an agent development kit which has an API through which you can one create agents. We provide you a tool set to do it. We provide you a set of tools that these agents can use. But we also have an open agent to agent protocol supported by a lot of companies. It's just an open, open source project that we're doing where you can connect our agent to any other agent.
- [47:26]
Alex
Okay. All right, that's definitely something I'm going to keep in mind and keep watching as you guys keep rolling out these new products. All right, a couple more questions that to get to. Now we get to the fun stuff, which is tariffs. We're talking Today on Friday, April 4th. The interview is going to come out the following Wednesday. So the world might be changed by then. But I just need to ask you a question on tariffs. This is a tweet from Gavin Baker, who's an investor. He said, geopolitically, nothing matters more than winning AI. These tariffs as constructed essentially guarantee that America will lose AI by making America the most expensive place on earth to build AI data centers. Do you agree with that and how do you think these tariffs will impact your business?
- [48:11]
Thomas Kurian
We, you know, I'm not going to comment on policy. I am. We do have a global footprint, so we do have data centers. Machines, networks, all subsea cables in many, many different parts of the world. That's part of Google's infrastructure and I am responsible for that along with the team. So we have got lots of places we manufacture things, lots of places we deliver things. And we are working through the implications of the tariffs for our part of the business. We're confident we can work through it. And we have lots of smart people, way smarter than me working on solutions on how we manage through this environment, which is uncertain.
- [48:55]
Alex
Right. But what about all the raw materials that come in? This is continuing on from Baker. He says the semiconductor exemption was irrelevant for AI Data center semiconductors come into America in finished goods from Taiwan and other Asian countries which include servers, storage systems and networking switches. By the time we have developed the capacity to domestically produce these systems, we will have lost the AI race. I mean, you're buying this stuff. What do you think about that?
- [49:20]
Thomas Kurian
Some parts of our manufacturing, some significant parts are here and we have solutions to some of this and I leave it at that because that the rest of it is confidential on how we're managing through this environment.
- [49:33]
Alex
Okay, let me just ask you one more quick follow up. Broadly, for the parts that come out from outside of the U.S. like, do you rely on suppliers outside of the U.S. does that mean your costs will have to increase if they go into effect?
- [49:48]
Thomas Kurian
We have mitigations in lots of other ways to protect our infrastructure and our cost. I don't want to give more details than that because it can lead to speculation on financial results and I'm not going to get into that. But we've run a global infrastructure for Alphabet for many, many years. And part of our success that Google has been having good, low cost, highly scalable training, serving infrastructure for all our services, YouTube, search, advertising, Waymo, etc. You can, you know, I always tell people, trust that we know how to run a large global supply chain and we've been working on contingency plans for quite a while.
- [50:36]
Alex
Okay, all right. You know, as we round out this interview and go to wrap up, I want to tell you just something that I've been observing as an outsider for quite some time. There was the conventional wisdom a number of years ago that Google had all the technology in the world to compete in cloud, but none of the sales muscle like Google basically was used to, got used to selling in an automated fashion through AdWords and didn't know how to sell to people. I think you came into Google cloud and revenue was a billion dollars a year. Now it's in the 40s, it's expected to be in the 50s in 2025. How did you guys learn how to sell to people?
- [51:17]
Thomas Kurian
We learned how to sell by listening to customers and building a great, great, great sales team. In order to do cloud well, I think you have to do three really basic things. You have to anticipate customer problems and solve them in different ways than other people did. So that's number one and very proud of our ability to identify where the next customer pain point is going to be and solve it. Number two, we built a global sales team and credit to our go to market organization, we've done it. It's a grind to build such a thing. That's why very few companies have done it successfully. And to grow from the scale we were in 2019 to where we are now, no other enterprise software company has grown that fast. And that's a credit to our sales organization. We had to bring discipline. We had to start with a certain set of countries, get critical mass there, then expand. We had to find the right mixture of sales reps, technical customer engineers, people who do customer service, customer support. We had to ensure that, for example, our contracting legal framework, all of the other things that sit behind the sales organization were world class. Super proud of that. And third, we always have believed that cloud is a platform business and the way that you grow is you provide a platform that lets other people grow on top of you, whether that's independent software vendors like Salesforce, ServiceNow, Workday, SAP, all of whom have great relationships with us that you work with partners. For example, the relationship we have with Oracle and many other independent software vendors, Palo Alto Networks, et cetera, bringing them to our customer base jointly. And then lastly, for every customer who has in house staff, there are many who don't and they want partners to help them deliver the solutions. We made a decision early on we're not going to have a big professional services organization specifically so that we can attract the partner community. One stat we are super proud of. In 2019 we had about 1,000 partners. Today we have 100,000. It's that allowing people to grow with you and building that great sales organization, that's been what's transformed our business. And when we talk to customers and when you see them at the show next week, you'll see how proud they are at the difference in which the way that Google works with them, that listen to them, that we help them innovate their business. And it's not a IT vendor relationship with the vast majority of them.
- [54:00]
Alex
Okay, last question for you. Right now, cloud makes up like 15 to 20% of total overall tech workloads. So most of tech, most of hosting, is still done on premises. So 15 to 20%. Where do you think it can get to in the future? Can it go up to 100 or what do you think the cap is here?
- [54:20]
Thomas Kurian
We definitely see it getting north of 50%. I mean, people, the historical reluctance on I can do it cheaper, I can do it better, my cybersecurity controls on premise are better. There were lots of those arguments. I think those are increasingly, people are seeing they don't make sense. And as the breadth of technology that you get in the cloud continues to mature. You know, the cyber tools, the AI platforms, the analytical tools, how fast you can do something, it's helping people move. I mean, just as an example, last year we had Walmart speaking at a conference. You know, every transaction that happens at a Walmart gets into our cloud to allow them to do analysis of how much inventory do they need to replace, which customers are buying, what products are selling. If you look at the volume of transactions and the accuracy and how quickly they can get analysis into the hands of their store managers, the retail store people, it's an order magnitude faster. Our job is not to criticize customers who run stuff on their premise. There's always some reasons for it. But increasingly we've also built technology to take our cloud into their data centers if they want to. So, for example, for people who have classified and highly sensitive workloads, we've taken our cloud into their data centers. And that's also a new way to deliver cloud. If you look at the work we're doing with McDonald's, we're putting our cloud into the restaurants. And so when people think about cloud, they used to think it's one definition, it's these big cloud regions that we have. Increasingly, cloud also means the same technology can come into your premises. And that's also changing this definition of how. What percentage of workloads can you reach?
- [56:12]
Alex
All right, Thomas, good luck with the event this week, and thank you so much for coming on. It's great to meet you. I hope we can do this annually and we can keep talking about the adoption of AI and where Google's role will be in that. So thanks for coming on the show.
- [56:25]
Thomas Kurian
Such a pleasure to speak with you. Alex, thanks again for having me.
- [56:29]
Alex
Likewise. All right, everybody, thank you so much for watching. We'll be back on Friday to break down the week's news with Ron John Roy. Until then, we'll see you next time on big technology podcast.