The Long View: Interview with Sarah Friar, CFO of OpenAI

“The Angle” Music

Cold OPEN “I don't think any one company, any one country even, can do what we're doing.”

Eric Veiel

Welcome back to “The Angle from T. Rowe Price”, a podcast for curious investors. Just a reminder that outside of the U.S., this podcast is for investment professionals only.

In this episode, I am very excited to welcome Sarah Friar. Sarah is the CFO of Open AI, one of the companies at the heart of the AI revolution, and specifically the company that brought us Chat GPT.

Sarah, welcome to “The Angle” Thank you for having us.

Sarah Friar

Such a pleasure to have you in our space. And such a pleasure to get to do this conversation.

Eric Veiel

Yeah. The vibe in the office is, is really cool. Is it a lot of excitement going on today, I guess, for Sora 2 launch.

Sarah Friar

The kingdom of Sora? That is a pretty funny meme that people have all over the building today. So, exciting.

Eric Veiel

I have to admit, I usually feel a little bit old when I visit, you know, companies in the Bay area. I felt exceptionally old walking through the lobby today.

Sarah Friar

Not at all. Not at all. But, yes. No, it's, it has an intensity about OpenAI. We, everyone wants to be in the office. It's very research driven culture. It feels a lot like a university lab setting. But then days like today, Sora, bring, like a whimsy. Like a fun to it, that I really enjoy.

Eric Veiel

How do you keep that culture of, like, a research driven environment with, you know, the commercialization so front and center as you get bigger and deploy more capital, which we'll certainly talk about in a bit?

Sarah Friar

Yeah, it's very top of mind because I do think it is, huge differentiator for us, that we are research first. And, you know, we grew up, we're almost a ten-year-old company at this stage. But the first seven years of life were only a research lab. And in fact, wasn't until November of 22. Right. That ChatGPT kind of burst on the scene. And frankly, we put a subscription on it to pay for compute, because we didn't really know what else to do, but it wasn't really with the intent. We're a very mission driven company. So, the making sure that as we are building, we're building AGI for the benefit of humanity. And I think that also goes back to kind of a research like culture where, you know, they're really going deep on things because they have a passion for it, but because they believe it's gonna have a great outcome in the world. So, models that might transform health care, models that might transfer transform education, models that might transform the whole creativity, like art and so on. And so to, to nurture that, you know, number one is our space, as we've seen, we try to not be overly corporate, frankly. I think number two is always being clear that it is a research first culture. And we're going to optimize for that. And that can be hard for people. For example, coming from the kind of normal, normal tech companies where engineering is always. The kings and queens. And that's a shift.

And then I think the third thing is really keeping an incredibly high bar, right? There's not that many researchers in the world that can work on what we're working on, and we don't want to deplete that bar. At the same time, I think we're the only company that has built kind of a farm team system where we're taking folks fresh out of the university setting, often post a PhD, so they might be a little further along in their journey, but really turning them into the best AI researchers on the planet. And that's a reason why you often see people come at us for talent, because we are the folks that have this farm team effectively being grown. But it's but it's very deliberate, that nurturing of that ecosystem.

Eric Veiel

It is a really unique company in so many ways. One of which is. Right. So, it's private, unlike not all, but some of your biggest, you know, compatriots in this space. But it's also part nonprofit, part for profit. Just explain a little bit for people, you know, just briefly how that structure is and how that benefits. You've kind of alluded to it a bit, but I'm just yeah.

Sarah Friar

So, sitting atop OpenAI is the non-for-profit. And again, it is mission aligned to how do we build AGI for the benefit of humanity. And it's really important. It's often why researchers will come to work here and not go to other tech companies writ large. Underneath we do have a for profit entity today. So, an LLC. But as we move towards the recap that we've been working towards we’ll flip into being a PBC, a public benefit corp. The importance of a PBC versus a typical C Corp is really that you can maintain two things. You have both fiduciary duty to your shareholders as a board member. But importantly, you also have a fiduciary duty to the mission. And we felt that that was important. Going back to the cultural question you just asked, it was important to show researchers that that's who we would be, but I think it's also important as a signal to shareholders. Right. In the end, we want to bring on the shareholders who understand that importance and that we're going to balance those two things and not just be shareholder value only, but we will do both. And there are other examples of great PBCs out there like Lemonade. Warby Parker I think is the PBC. Ben and Jerry's was before they were bought. And today folks like Novo Nordisk are a good example of even a foundation that sits atop, for profit. So, well, it's, it can be a little different. It's not, this is not a place that we're wildly innovating.

Eric Veiel

So, let's talk a little bit about where you are. It's, you're in such a unique seat, right? As you mentioned, ten-year-old company. You're the CFO. And normally, if you're maybe the CFO of a ten-year-old company, you've got a little bit of capital to play around with. You are deploying capital at such an amazing pace. Maybe just update us a little bit because it's changing in real time. A big announcement today out of Korea as well. And in terms of further the announcement, just how you're thinking about capital deployments and sort of where you are in the announcements that you've done so far.

Sarah Friar

Yeah, let me back up for a second and say, just first of all, we're building a backbone for a new era. In the same way that you built the railways, you built the electricity pylons that lit up cities and towns and ultimately rural populations, building the internet. So, we firmly believe we're at the beginning of that new era, and as we bring on this age of intelligence, right, we started with in the beginning with ChatGPT, kind of more of a chat bot, moving to reasoning. So, the big breakthrough of last year was the reinforcement learning that happens in a post training world. To this year, really a world of agents and starting to see agents that can do really long, we call them long horizon tasks for you. So, you can ask an agent to code. It can go off and code for three, five, seven, 12 hours, by itself, to create code that you will push to production. For a kind of non-coder, it could go off and shop for me. I was just in the market for an advent calendar. I hope my daughter's not listening, but it's been our thing since she was about two years old.  And so rather than me trawling many sites, I sent my agent off to do it. So, we're just starting to kind of scratch the surface of how this will change our lives. And we think it will take a vast amount of compute. So that's the backbone.

At the same time, we're building a strong company that can have fundamentally sound business. So, 700 million weekly active users, I'm sure we'll get more into the business, but with a goal. We've never seen a company grow at this pace ever. And with a goal to clearly be able to finance a lot of our future today with free cash flow, always the best way to fund a business to do it. But really only recently also taking on equity investing. I know, folks here are big numbers, but really, last year was the first external equity round we did, aside from our Microsoft investment. And so this year, the bigger round that we're completing right now, the $41 billion round, that's really only the second round we've done.

And then I think the third piece is it's going to take an ecosystem. I don't think any one company, any one country, even, can do what we're doing. It's kind of a space race. And so that's why you see us right now really trying to make the ecosystem rise with us. So, it might be the supply chain, right? The people who build chips. Clearly our deal with Nvidia, the people who build data centers, the people who build all of the other equipment that sits inside these data centers. But it's also going to take governments coming to the table, whether it's to relax some of the regulations to help us build faster, to bring power on more quickly. But it might also be to think of financing there. And then, of course, what we can do to finance our own business directly.

Eric Veiel

Right. So, it’s really stunning to think about just so many of these different components. So, I want to get to power in a second. But before we do just the, the amount of capital that you all are deploying, some coming from, as you mentioned, your first couple of rounds of of other investors, the big influx from Nvidia, how are you as the CFO thinking about the return on investment just from that, and over what time horizon is a reasonable way to think about, you know, using your metaphor of the railroads, of laying all of this track?  Before we start running the trains.

Sarah Friar

I mean, the good news is that unlike the railroads, where you actually had to have a track that went from, say, New York to Philly, and then you could put a train on it.

Eric Veiel

Let’s say New York to Baltimore.

Sarah Friar

Well to Baltimore. Good point, actually. Sorry. Wrong. Wrong, major to choose. We are already showing value out of the gate, right? We don't. The track can still be unfinished at the end. That's why you have 700 million weekly actives already using your technology. And I think ChatGPT has revolutionized how many people just go about their day to day. Certainly, in a personal setting, it doesn't matter who I talked to. They're like, oh, I use chat for my workout routine. I use chat to help me plan dinner tonight. I use chat to help me learn a new language. Had this wonderful story at dinner the other night from a CFO. I was expecting a very CFO answer, and he was talking about taking his daughter on a camping trip and how as they were lying there looking up at the stars, she wanted to know where the Big Dipper was. So, he pulled out chat and he was like, hey, chat, help me find the Big Dipper. And then they, he told her all these stories about all of the different constellations. It was such a cool story.

So, that's in your personal life, but in your professional life, 500 million enterprise seats sold, like the fastest enterprise business to get to $1 billion era. Today, we're putting up $1 billion in revenue per month, right? That is already showing that the value is here. So, when you talk about ROI, I look at things like the cohort of, for example, ChatGPT paid users. What's the gross margin on that business? Actually, a very healthy gross margin. Our enterprise gross margin already looks a lot like enterprise software. So, I know that the early cohorts are already making a return, that if I stopped the clock today, didn't do any more investment from a research perspective. I actually think we'd already be throwing off quite a bit of cash. So that's what investors see when they make an investment decision. But of course, to your point, we are still investing for future, because we believe we're only getting started.

Eric Veiel

So, let's talk a little bit about growing the top line and some of the different evidence. So, 700 million weekly users, I think at the beginning of the year was like 400 or maybe even a little less than that. And we're sitting here and like, not even done the year. That's incredible. That's just on the consumer side, as you said before, we jump to the enterprise just a little bit about how you're thinking about the consumer opportunity and maybe monetizing that. There's obviously a paid component to it. How are you thinking about that?

Sarah Friar

Yeah. So today, again, just put a little parameters around those numbers. Today about 90% of our users are outside the United States. Which even that I find amazing? Because most tech companies where you're born tends to be the predominant. Yeah. And if you look at it really follows population. So, India, Indonesia, Vietnam, Philippines, Brazil. Right. Anywhere with large populations of the world attracts. The second thing I would say is today we only monetize about 5% of our user base. So, 95% is effectively free. Now the free is a strategy. I mean, number one, it's mission aligned. So, we want to make sure everyone benefits not just for people that can pay, but it is also a way to let people edge into what is intelligence means to me. Right. When I say to someone, you're going to get access to intelligence. They look at me and they're not quite sure, and they might just move on, but when I say you're going to get access, it can be in a very profound way, like something that actually helps you do a task better, faster, or sometimes it can be in a very fun way, like image gen or now creating a Sora video. Right. There's, there's a whole gamut of how people experience it. If we don't let you try it, then you don't know what you're missing out on. And the way our business model works is on the pure subscription side. We want you to use until you run out of tokens. Then you hit a paywall, and if you're getting real value, you will subscribe.

And so, the more that we release, much more sophisticated features that take a lot of tokens. So, video gen is a good example, but as is deep research, the more likely you are to hit that moment where we can get you to subscribe. And again, it's subscribing for value, not just because we're tricking you into that. So, I actually love that the base of our business is subscription. How that will grow is more users, more people jumping that wall. And actually, the other thing I'm very proud of this year is releasing our low end, price point, what I call GPT ChatGPT Go, so that in countries like India, Indonesia, where a $20 per month subscription effectively is cost prohibitive. Now we're unlocking those markets. So that is growing the subscription business.

Clearly, though, we have a lot of people highly engaged with our products. And so, you can start to see the the beginnings of other business models. Just on Monday we released our commerce option. And so why we went there first is we could see the use case of ChatGPT was often to help me purchase something. Right. I'm, I'm a new mother. You know, I'm expecting twins. What's the best stroller to buy in San Francisco? I'm thinking of upgrading my car to an electric vehicle. What should I choose? So very much bottom of funnel, often high consideration, high value purchases. And so, by getting into that commerce flow, we're getting paid for the outcome, which frankly, actually resonates really well with our researchers. It keeps ChatGPT as a true north of giving you the best answer.

Eric Veiel

Not the paid answer.

Sarah Friar

Super important.

Eric Veiel

That's an important part of Sam's view on this whole.

Sarah Friar

Very important. But it's a massive market, right? If we can take a chunk of the world's commerce or retail, that is a massive new business model, which I get really excited about. So, I think we're just scratching the surface on many of these things. Longer term, you know, I said, this year is the year of agents. I think we're starting to experience what an agent could feel like even within ChatGPT. You see it today, actually, with both deep research, which originally was set up to be almost like a research agent. So, if I need to go do, if I'm going on vacation, I want to do a deep look at these are three destinations I could go to. Go and think about this for ten, 15 minutes. It'll come back with an amazing response that might take a travel agent maybe days to come back with. So that's an example of an agent work. But also, I like study mode because that's a good example of an agent working as a teacher. So, if you ask a question, instead of giving you the immediate answer, it may actually start by asking you a question back. So, it uses much more of a Socratic method, like tell me about plans. So instead of like, here's the answer, you know, it will instead say, oh, you wanted to learn about plants. Tell me a little bit about what you know, or what's your interest in plants, is it because you're going to plant something? Or are you interested in the oxygen cycle or whatever, the rain cycle, pick it and as it goes, it will even say, I'm going to tell you a little bit more, and then I'm going to ask you a few test questions to see if what I'm saying is resonating. That is, it already transforming into an agent. We just take away this word agent, which is not very engaging for most humans and turn it into a teacher and how we go monetize that in future. I think there's a lot of optionality on that front too, to get to value.

Eric Veiel

So, you've also and that's absolutely fascinating when you think about just the original chat bot and how you've already, in such a short amount of time, thought about so many different avenues to engage with the, the consumer. You've also really now started to think about moving up the stack from an application layer perspective. Talk a little bit about how you're thinking about moving into the application layer.

Sarah Friar

Yeah. So, if we were, if we actually were on video, as we joked about earlier, I have a chart that I do love that is a classic kind of stack diagram. And in the middle is a bar that’s our LOM, and it says where do we start, we want to be the frontier model. Always research culture. Super important. However, you come up a level out of the model layer, you had an API, API layer, which is actually something that has really resonated with enterprises and developers. Out of the API layer, you get to an application layer and then ultimately to I need a better word, but substrate layer. I'm sure you will come to work on consumer hardware but think of that as one element of the stack. We're actually going down as well. We’re starting to think about our own ability to build first party data center technology, maybe down to the chip layer. So, OpenAI has become in just a two-ish year period, two-and-a-half-year period, a very multi-dimensional business. I like that, you know, all good investors love it because you can build moats in different parts of that stack. You can actually trade economics around, because if you're competitors only in one place, you can actually often be quite aggressive, say, on pricing in that place, because you can subsidize with other revenue streams elsewhere. So, I think getting to that multi-dimensional, look, field, shape of the company has been really important.

You asked me about the application layer specifically. For sure, and there I think it actually trended a little bit into how we think about go to market on the enterprise side.

Eric Veiel

That's a good transition.

Sarah Friar

Today, we think about our both everything from the smallest of businesses. So, people who come from being a consumer into being maybe a prosumer into what we call our ChatGPT for business, which is our self-serve product, which goes up to about 199 seats, up to about 200. And from there, of course, we move straight into the world of enterprise. And today, a year ago, I felt I showed up and every CEO, CFO, CIO, whatever C-level person would say, what do we do with that? I know it's important, but what do we do? Today, I think we've gotten into a better motion of, you know, step one is just deploy ChatGPT wall to wall; sounds self-serving, but your people will probably more likely do find the innovation than you will telling them. We try to make that easy today by doing a more of a platform based price so that you don't have to make tradeoffs like, okay, well, I'm okay paying this more expensive thing for my coders, but I'm not going to have my finance people use it; be a terrible decision, by the way. To now, being able to say, okay, everyone can have it. So that allows you to actually create some horizontal outcomes as well, like performance management. Right. Everyone does performance reviews. You can have a custom GPT to help people write that performance review. And everyone in the company can use that. Like sounds like a silly example. When you work at companies, you know how painful that is.

Eric Veiel

On my flight out here today, one of my coworkers used it to, to write his self eval. It was amazing what he put into this and what he got out of it.

Sarah Friar

It's amazing. It’s the best. But now we're starting to say to companies, okay, so that's great that got you started, but now we need kind of it to link back to your top-down strategy. So, if you are, for example, the world's largest retailer, you want to have a strategy for natural language search because you want consumers to have better basket sizes. But to be able to do more sophisticated things, right. If you think about search and retail has made us turn into crazy people. We write in things like black shoes, high heels. Like I walked into a store, I wouldn't say black shoes, high heels. I would say I'm going to a party this weekend. I want to wear black. I would love shoes that are comfortable right? You would have a conversation. Search, inside chat, is now bringing back conversational, but that's great for a retailer because that usually leads to a bigger basket size, more customer retention. But then there's also, say my merchandizing strategy. And then there might be my supply chain strategy. And so now there's a top-down push of where do I want to inject this intelligence directly into my organization. And so that allows you to go from kind of a base price that you're paying us now onto a very specific, outcome driven decision making on what enterprise product am I going to deploy here and what am I going to pay for it? And so that has been a really major move I've seen just in the last year with the C-level suite.

Eric Veiel 

What is the, when you talk to different companies or more importantly, when you observe different companies trying to figure out how to deploy, because everybody does have this sort of panic, feel like I know my competitors are doing it. There's this unbelievable paranoia in the corporate world about it. What are the best practices that you've seen of, you know, companies engaging with their employees in a positive, productive way to really diffuse the technology into the the fabric of the companies.

Sarah Friar

And so, I do think it starts with this idea of just give people access to Chat wall to wall, what we started to do, even in my own finance departments, we do a hackathon about every 3 to 6 months. Sounds fancy. We just brought in our sales team. We give people space. That might be a half day, could be a full day. It often starts with just like a pen on a piece of paper. Like what are some of the more routine tasks that you do. You do that individually, then you set a grip. I might put the tax team together. The procurement team, they share notes, they look at, okay, what are the places where we're spending a lot of time repeating the same work? Or were there mistakes that often occur and then that allows them to do this first step of maybe a custom GPT. I'll give you an example. When we were raising that first round last September, one of the more routine tasks you get when you raise money is diligence, right? Investors all like the same thing.

Eric Veiel

Well familiar with it.

Sarah Friar

So, you get a lot of similar questions. And so, there was like, a night we were all working late. And, you know, we were looking at a question. I was like, we answered that over here, pull that in. But then that slight different bit of nuance, pull it from over here. And then we were kind of cobbling it together, and we recognized that if we created a custom GPT where we fed it our corporate model, our corporate presentation, the other diligence questions we'd already answered, we gave it a persona which included, you know, remember, this information is confidential. Don't go outside to the web. Just use internal information. Your tone should be professional. Like like make it snappy. Investors don't have a ton of time. Get to the point. But also on the edge, be a little bit salesy, right? We are all selling. And we put through the first set of diligence questions. We were almost weeping with joy and how a something that would have taken probably 2 to 3 hours took like a minute, maybe less, in front of our very eyes. Now there's still a human in the loop to do a final check, and maybe you just add a few extra notes because you understand exactly what that investor was saying, but just the transformation.

Today, we're now moving down a path where we're doing things like our Sox control process, where we're starting to think about our pc readiness, for example, we can do that using voice, right? If you want to check if someone's controls are correct, you sit with them, you have a conversation. What do you do if you see this? You take that transcript back to your desk. You can turn it into a Sox control, like what's called an RCM control (Risk Control Matrix) in seconds. And instead of spending your time with all of the transposing and transcribing and interviewing, you're moving to the inside. Like, do you have a control problem or not? If we have a control problem, what else should we do? And by the way, that's a great moment to even go back to ChatGPT and say, help me think this through. So, just so many ways that we see enterprises doing what I just described in my microcosm of my finance team at OpenAI, but doing it inside their corporation, but in their marketing team, their comms team, their product team. And then you start getting into worlds like coding. They're already doing massive amounts, right? I know one of the largest companies I get to spend my time with, over 40% of the code they're creating today is coming agentic ally created.

Eric Veiel

Yeah, that's amazing. We, we actually just did a hackathon using our subscription, just last week, and the results were really interesting. Where we're actually going to do an expo next week where we're bringing in a bunch of different vendors, obviously, including all of you in your team to, to think about how we can push into the, into the organization. But it really does feel like it has to be sort of a top down as well as bottoms up led process to get, to get the whole enterprise moving.

Sarah Friar

The best companies I've seen, like the early adopters; Morgan Stanley is a great example in their wealth management division. One of our earliest adopters really figured out how to use ChatGPT in that moment where a wealth advisors maybe doing a first conversation with a potential client, so discovery. So instead of just doing the discovery, the chat bot was actually giving them examples of like products to sell. So, the, the first call went from just pure discovery to discovery plus sales. So, you took out a meeting probably. The conversion rates went up. The advisors were happier. They were bringing on bigger books. All good. But the top-down motion was actually the board. The board said that we want to see this, either X amount more revenue or Y amount cost reduction. And that is your go get executive team. And so suddenly you had this really nice sandwich of like very top-down meets very grassroots bottoms up. And I think Morgan Stanley, what they created with that first wealth management installation. Now we've seen companies everyone in the space implement effectively.

Eric Veiel

One of the other areas that I think everybody's really anxious about is you guys acquired Johnny Ive's company not too long ago. So, there's lots of talk and speculation about what the hardware approach might be for OpenAI. Before you dodge my question, I will just tell you, I, when I, when I had this podcast with Jensen a couple months back, he talked about the marrying of robotics and agentic AI and how that's really not that far away. It's happening now until it gets to be something like very common is probably only a couple of years out. Is there a path forward for you all on the hardware side? And just like, theoretically, how might you approach that?

Sarah Friar

Yeah. So, I mean, first of all, kind of start by just talking about the multi-modality of models. So, when we think about a definition of AGI, it's not just tech space, right? All arms are very good today a text-based ingestion and ultimately output. And even though I know ChatGPT has multi-model capabilities, I still tend to go back to talking with my phone call it. My teenagers are experts, but we are also already quite good at being able to speak, to listen, and to use visual, right, to be able to recognize a plant, for example, by just showing it the camera from your phone. Think of Sora as both a fun breakthrough, right? Incredible to be doing just natural language and creating short form videos and all the fun you can have with it. But it is also pulling in a lot of data about the world in a much more three-dimensional, kind of human like way. As we get into devices, we think the same way. That these devices, whether it's a humanoid robotic type outcome or whether it's a Johnny IV device, which I cannot tell you about, and I'm going to sit on my hands so I don't actually talk with my hands here. But these are ways to think about them, first and foremost, as a way to collect data that will help ultimately models be fully human-like in the way we experience the world. Right? There's not a lot of collection of data today on just how we experience the world in terms of space. And what I mean by that is not, you know, the planet, but literally I move my hands when I talk. I'm actually creating a lot of data right now as I sit in front of you, but nothing collects that data. Maybe if we were being videoed there would be some bits and bytes. Seeing my hands move. But no, nothing is really collecting that feel. And to get to a moment of humanoids, and I think countries like China are actually very far ahead of us right now. Like there's so much to learn, right? When I pick this glass of water up, I know exactly how much pressure to put. So, I don't like break the glass over glass but not drop it. When I set it on the table, I know how to set it down without making a noise. That is an incredibly sophisticated use of data.

And so as we think about the substrate, it's both data collection, probably first and foremost, which most people wouldn't say first, but then it is also, every era of computing has had a substrate that's really brought it to the fore, right, when we went from, to the PC era until you had a PC, didn't really unlock it for most people. When we got to the mobile era, if you didn't have a phone, didn't recognize it had a GPS inside, how could you have ever gotten to apps like Uber or this idea you would DoorDash something on the fly, or that a map would just work for you? And so, we think similarly with AI, we're not making use of all these multimodal capabilities, so how do we kind of bring that to people in a much more kind of fun, but very utilitarian type way?

Eric Veiel

That's, it's fascinating. It's going to change; the world is going to change so much in such a short amount of time. And it already is. But we're we're all anxious to see where where you all go with that. Let's talk a little bit more about moving down into the data center, data factory, intelligent factory creation aspect of your business. And how, you come to that decision to, to move that way. I mean, historically, that's the more capital-intensive business, potentially the lower return business. And certainly, if we want to think about last cycles going back to the internet buildout, you know, the stock charts of the companies that spent all the money to build that infrastructure did not end well. So, what about that decision and what are you all trying to accomplish there?

Sarah Friar

So, I think at it's, just in it's simplest, we think that infrastructure is destiny, and that it is a moat to have access to maximum compute. So that is very simple way to put it. If we could do all that just through third parties, we would do that. But the question is, can we get enough compute if we do that? I'll give you a more sophisticated answer. And remember, I'm speaking as someone who in currently is, does not have enough compute to meet the demand in front of us, which is a surefire way to not have as much revenue as we should have. You can kind of see, even in our Sora launch, there's a reason why it launched to Canada, the US, only on iOS, and it's an invite only system because you can clearly see we're trying to balance the excitement of finally getting this model out to learn with getting in front of users who can benefit from it, but still understanding that we are dramatically short GPUs, or however you want to talk about your compute metric of choice.

On the decision to go from third party to first party, some of that is just about continuing to diversify out where we get compute from. Not even a year ago, we really only had compute from Microsoft. And that constrained our business because even Microsoft couldn't build it fast enough. But I think on top of that, it didn't allow us to see the true kind of breadth of what was available out there right. There, many other suppliers helps us see who might do things differently, better, more cheaply, less latency, like less risk, be more available to us in parts of the world, you know, one CSP versus another. It just really opened our eyes to the the huge step up you get when you bring that diversity to bear. So that was kind of a huge moment for us. It also made us start to think about how much IP we are creating, because we are the forefront company in AI, so we understand better than anyone. What does the right build look for inferencing? What does the right build look like when you're inferencing agents versus a chat bot? What does the right build look like when you're inferencing a Sora real time video? What does it look like when you're training? We think the laws of scaling are firmly intact. So, we want very, very large fabrics, right? More compute means more data means the smartest kind of models around, keeps giving us more intelligence. So, we don't want to feel constrained even just in the in the scale of those fabrics.

So, I think our debate internally is, right now, by just moving into at least some first party, it probably carries over enough benefits to the rest of our supply chain. You know, just pulling their socks up a little bit more. That it's worth it for that alone. But I do think a lot about folks like an Amazon. Right? When Amazon was starting, there was a lot of skepticism about like, why were they building all these servers and building this thing called we didn't have a word for it, but cloud computing, like, I remember when investors talked about it as if it was the same server selling books at night were somehow selling off. Do you remember when there was this whole thing?

Eric Veiel

Yeah, it was a seminal moment for our our analysts who saw the value of that. And the stock was getting killed, and he was absolutely brilliant and saw through it. And it was when we doubled down on that stock.

Sarah Friar

And look at AWS today, is a business in its own right. It has a 40%, I think, EBITDA margin, 35, 40% EBITDA margin. I don't want us to look back and feel like we missed a mega move to also be whatever the CSP of the future automates, the AI service provider of the future. And so, we have to start creating that capability as an option. And then I think we can decide, do we want to really invest hard behind that option? Like, is that the right thing to do to become a more capital-intensive business? Is there a way to do it with partners so it moves it off our balance sheet, or are we better off just staying as the layer above the CSPs, whatever we want to call them in the future? I don't know what the exact right answer is, but I think it behooves us to keep the optionality in place.

Eric Veiel

So, one of the big limiting factors of all of this infrastructure build that we haven't talked about yet is power. And, we were sort of, I was emailing with some of our team, on my flight out here, we were talking about the announcement that you had around, Korea and just the amount of, DRAM that you were, you know, setting up here and what that would translate into, in terms of the Nvidia equipment that you could use and then ultimately what that would mean for power. And basically, we got to it's like the state of Texas in terms of power that you would need to to put all of that to work. So how are you all thinking about real breakthroughs in power generation? Because it's clearly a limiting factor. And it looks like in, from a US perspective, it's a limiting factor for us as a country. But maybe take that second. But for you as a company?

Sarah Friar

Yeah. We do think that that is becoming the next real choke point in the supply chain is the access to power. We've certainly, as a country, made a lot of moves just in the last year or so to speed that up. And clearly, it's always a balance of thinking about kind of a more traditional power sources to renewables. But even then, there's still not enough. And by the way, we think a big part of intelligence and model like novel model breakthroughs, maybe helping us solve this conundrum of power. Can we get to nuclear fusion, for example? Like, I think we're only getting there through this AI generation taking us because it's going to be both about the physics of that, but also just even the materials to build small form nuclear reactors and so on. Like so that is it's an interesting thing where the intelligence itself is actually what helps the intelligence.

It doesn't frighten me when you say like the state of Texas, because when I look at us as a company, we're already talking about, this year we'll do about two gigawatts of, if I translate it back into power, about two gigawatts. Two years ago, we were doing about 200MW. So, if you just think about that, like a 10x in two years. So, if we ten x again in two years, could we be doing 20GW in two years? Like that's already probably bigger than the state of Texas? I know Ireland is where I go because I'm Northern Irish, like the country of Ireland does about seven gigawatts of power. So, it's bigger than a country.

And that sounds crazy. And yet like, you know, to hark back to the last answer, when we started to tell our supply chain that we needed two gigawatts in 2025, they thought we were crazy and would not build it. And yet here we are, where we could probably use 2.5, maybe three gigawatts. I don't know where it would stop this year, but it certainly is not enough at two gigawatts. And that is another reason to want to control our own destiny. That's why I started with that line. Infrastructure is destiny. Yeah, because the only thing I know for sure is no compute equals no revenue. So that is a sure thing in life. But on the power front, it is also taking us out there in the world to look at a lot more sources of power, countries who are rich in renewables, that can have a very different price point. I think it starts to get into where you are going with geopolitical outcomes as well. I mean, China is building a grid at a significantly faster pace than the United States. And, you know, this is a place to for kind of we've used terms like democratic AI, you know, whatever, whatever you want to call it. But like there is going to be a set of allies that come together. And if we want AI to keep coming along in an aligned way, and in our view, part of alignment is that democracies are a path that we want to stay on. It's who are the allies we can bring with us on that path. And many of them have access to a lot less expensive power.

Eric Veiel

So putting these dots together, it wouldn't be crazy to expect some kind of announcements, partnerships around things like power gen from you all in the maybe not too distant future, because it seems like if it is going to be the binding constraint, you're going to get ahead of it as fast as you can. And there's a business strategy perspective here too.

Sarah Friar

There is.

Eric Veiel

Securing that is going to give you a huge advantage over everybody else who's racing to do what you're doing.

Sarah Friar

And all the hyperscalers are doing it already. And they’re are our competitors in this AI race. The good/bad thing about taking that more optionality there is it's also very fungible assets. Those are assets that can be bought and sold. So going back to my seat as a CFO, they give me a lot less indigestion. Like these things tend to be very finance able. They also tend to be sellable as we decide where exactly we want to put our chips on the table. So, you may see us taking a lot of options to then make decisions over the long run. Right? You already see Microsoft doing that, Google doing that all the time. They trade in and out of data center footprint, partially because we come along and we say we need our next fabric to be x-size, so that all needs to be interconnected. Therefore, that data center that you have in Wisconsin is not as good as this data center footprint down here in Texas, where we already have three other footprints and we want to interconnect them. So, you see people doing a lot of trading across power, PPA land and then, you know, less on the kit and so on.

Eric Veiel

So, let's transition a little bit because there's there's so much to talk about. We could go for hours. And I want to be respectful of your time. But one of the questions I wanted to ask you is are there any use cases, or any scenarios that we just should not use AI for? 

Sarah Friar

Gosh, that is a good question. I've never been asked, it quite that way.

Eric Veiel

Better questions, better outcomes. T. Rowe Price.

Sarah Friar

I think. Good job. In terms of like what not to use it for it. I, I think we generally feel like AI has applicability across really anything that humans touch. However, that doesn't mean to say we don't want to have guardrails around things. So, safety is a really important criteria for us as we build models, test models, put models out into the world. We had a lot of debate around our most recent decision around open source, for example. And we typically look at CBRN. So cyber, radiological, nuclear and bio risks. And we score our models across those four criteria. And so, we will absolutely hold back models where we think they are not, where there's more risk or danger being injected into the system. Bio is the one I would say further along because it's harder to do things, for example, in nuclear because you got a own like weapons grade plutonium or whatever. The other area that we look a lot at risk is just the, to how the models interacting with a human. So, for example, we did pull back one of our models, I think at 4.5, ChatGPT 4.5 or in that zone, because it was very sycophantic. So, it's different sort of risk entirely. But it was absolutely causing people to get more and more engaged because it was telling you lots of nice things like that. That performance review would have been amazing.

Eric Veiel

So good.

Sarah Friar

Yeah. So, we just want to be really mindful of when we want to slow people down. Or maybe insert something, if we can see that a behavior is beginning to unfold. People are using ChatGPT for a lot of medical reasons, for a lot of very personal reasons, more so than maybe any technology ever before. People will talk to ChatGPT about relationship problems or, I want to get a divorce. Should I get, I mean, things that you will not even share with another human. And so, to your point, like, should ChatGPT be deployed everywhere? It's a very good question, right? If it helps someone really think through a tough problem that they cannot share elsewhere, I think that's only a good thing. But I think we need to be very careful that in that moment where a human needs to be pop back into the loop, maybe a therapist or a doctor or someone talking to a teenager, that we are also allowing for avenues for that to happen. And we we rolled out more parental controls this week actually for exactly that reason.

Eric Veiel

Yeah, it, it actually is a good segue. I was going to ask you, as we talk a little bit about, you know, these tools, just advice that you give to younger people coming up, either at your company or, we both have kids about the same age in sort of that college age about, you know, how to think about their future in terms of it is really going to be probably radically different in terms of the workforce, etc.. So, what do you tell people when they come to you and say, like, what should I do?

Sarah Friar

Yeah, I mean, first and foremost, I like this quote. I've used it a few times now, right? “I don't think your job is going to be taken by AI, but it's probably going to be taken by someone using AI.” So, make sure you're the one using AI. So, you know, experiment, listen, learn, be curious. Ask other people what are they doing. For students, we just put out 100 things like 100 questions for ChatGPT for students.

Eric Veiel

I saw it as I was waiting. Yeah.

Sarah Friar

It’s super fun. I mean, you kind of just look at some of the ways your, your peers are using it. So, number one. Number two, like within schools, I would love to get it even more deeply embedded into the education system, starting early in the education system. But in the university setting for sure. I think there was a lot of fear a year ago, two years ago. And I think you've seen the flip of professors, tutors and so on. Just saying this is a must. And we see it just in seat deployments, right? Cal State universities just applied for 500,000 seats. ASU 180,000. Oxford University, my alma mater, just deployed wall to wall. So, I think people are understanding that this is the shape of the future. So, we need to bring our students along. Our staff, faculty, tutors and so on need to be there.

We rolled out an AI job platform, with the idea of how can we help people reskill. And we put together a set of AI certifications. So, we are trying to prompt the market as well through that. And I think another couple things I usually say to my kids like is number one, still, you have to learn to think, and you have to learn to do hard stuff. Like my daughter's a chemist. I'm like, it's got like, you still need a lot of help in chemistry, but AI's now, like the company she worked in the summer doing small molecule drug discovery. They're using AI to help do a lot of the early design. What could this molecules look like? But a human still needs to put all the ingredients together, test it, and then feed that back into kind of keep iterating with the model. That to me is a perfect harmony of human plus model, doing something to extend new novel outcomes.

And then I think the other thing we need to think about, generally speaking, is reskilling ourselves throughout our lifespan. Like, the jobs most of us do today, our parents could never have imagined. We are all going to work much longer, assuming we want to. We're going to live longer. The idea that you work to your 65/70, yeah, people are having to work because they also want to save for retirement. But we're also healthier. We're also able to stay in the workforce for longer, like how do we make use of that, and don't have this kind of outdated concept that education ends at 22 or 23? It's crazy. Right, why wouldn't we? Why wouldn’t you go back to college in your 40s or your 60s? Like, there's this wonderful quote from one of the Supreme Court judges who at like 95, started to learn Greek and someone said, why are you learning Greek? And his response was like, well, why wouldn't I learn Greek? And I just feel like that sort of learning mindset is what I tell my kids all the time. Be curious, learn. And there is no substitute for hard work.

Eric Veiel

Excellent. When we think about, the business itself and where it is today, if we were sitting here, let's not say next year, let's say in three years from now, what do you think would be the most surprising development if AI hasn't taken off? What if it hasn't?

Sarah Friar

What is the reason?

Eric Veiel

That it hasn't taken off.

Sarah Friar

Such a shock. I don't really know if I can see a reason for it not to take off because the value created already is so extreme to me. Like perhaps that would be that we stay too superficial, and we don't get it deeply enough embedded. We kind of allow ourselves like, usually what I find on my team when I look at like, why there's an uneven distribution of usage, the people not using it a lot, usually don't use a lot because they're too busy. And so, I have to help them slow down to do the thing that will speed them up on the other side. So, I think that that would be one reason why I would see a company say fail is that the CEO, the people who can actually cause a pause or cause a change of direction, don't don't push it fast enough.

And there's probably examples, like if you go back to the internet age, coming upon us where the people who really didn't grab hold of, say, e-commerce in the retail space. It was a very slow burn, but in the end, it ended poorly. And this era is happening much faster, and so I think if the I don't believe there's a world where AI isn’t successful, but I think there will be lots of examples where companies are not successful or even individuals. And I think it's because you're not grabbing hold of it quickly enough right now and getting that hackathon done and getting that into your system. If you feel behind, I always say to everyone “I feel behind”. Yeah, I come to work every day and I'm like, I'm not using our product enough. I feel like I've missed the feature we've rolled out, or I didn't know that the agent could go do the thing that is a completely normal way to feel. But instead of making it, you feel fearful, like it's a kind of a joyousness of like, oh, there's something new to discover out there.

Eric Veiel

There’s so much out there. Yeah, so much more to do. Sarah, this has been fantastic. Thank you so much for taking the time to talk to us.

Sarah Friar

Super fun. Thank you.

Eric Veiel

Thank you.

Again, I’m Eric Veiel. Thank you for listening to The Angle. We look forward to your company on future episodes. You can find more information about this and other topics on our website. Please rate and subscribe wherever you get your podcasts. The Angle, better questions, better insights. Only from T. Rowe Price.

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