Oura has made big news today announcing it has raised $900 million at an $11 billion valuation. The investment was led by Fidelity Management and Research Company with participation from Iconic. The company is on track to pass $1 billion in annual sales this year, and I want to bring on Tom Hale, the CEO of the company, to share more about where he plans to take Oura in this next phase of growth. Tom, welcome to TITV. It's great to see you again.
Great to see you, Akash. Thanks for having me.
So, a big day for Oura. The place I want to start with the company is last time you were on the show a couple months ago with editor-in-chief Jessica Lessin, you mentioned to her that the company's profitable. And yet, you still raised all this money, and my question is why do you need to raise the money if you're profitable?
Well, good question. Everyone knows that AI is incredibly expensive, and I think AI and the future of healthcare are intertwined. So, I think we want to make sure that we have that ability to invest in AI, particularly as we think about acquiring talent and making investments and maybe ultimately developing our own models. So, this is certainly the first thing that we want to invest in. The second thing, of course, is that we're still at the very beginning of this business. Oura has been on a tear. We've been doubling the business year over year. We're about to reach a billion dollars in sales. And the reality is we're just getting started because we're barely just in the US today. So, we've got a lot of headroom and expansion around the globe, and I think that's going to take us a fair amount of investment to make sure that we can cover the planet. And the last piece, of course, is healthcare. Our vision always has been to be a wearable that is across the entire surface of holistic health. And healthcare is in transformation right now. We don't have enough doctors. We don't have enough preventative medicine. The prediction that we do, some of the practices of that are grounded in science are still dated data. We have up-to-date current data on the people who are wearing the Oura Ring. We want to take that data and make it present in health. And I think that's a very different kind of company than what we've been today. And so, we need to invest in the talent, in the people, in the process, in the systems, in the science to really take Oura's healthcare journey to its next stride.
Whose AI models are you using right now? What are you using them for?
Well, we use almost every model that's available. And I think we have a range of things that we do. It starts, of course, with the predictions that the system makes using algorithms that are basically there to tell you what are the values that we're deriving from the signals that we're taking off of your body. And we've been doing that for 10 years. We're probably maybe the best in the world at these kinds of algorithms for signal processing. And so we're talking a combination of open-source models, closed-source models. I mean, the big models like OpenAI. Well, so for those models, those are almost entirely developed by us, although we're using all the tools. The second place is this kind of idea of what you might think of as the doctor in your pocket, right? You've already got a supercomputer. You've got a wearable device. There's some intelligence that's looking over that data, knows lots of things about you that you've shared with it, your contacts, your health history, your tags, your biometrics. And making sense of that. That is actually really another form of this AI, and that's largely models we develop. Then there's interpretation and presentation of insights. So, basically, you might want to interrogate your data, say, 'What's going on?' And an LLM type application will respond to you and say, 'Well, this is what's going on.' Or you might ask for advice. And those LLMs are, I think, what you would expect to be the classic LLMs in the open-source and closed-source models that are available. And so, across all three of those surfaces, you've got a substrate of AI. Now, ultimately, where I think this all goes is you think about the quality of the data that something like the Oura Ring has, meaning it's consistent, it's accurate, it's continuous, it's measured overnight. Feeding a call it a large physiology model with that data is going to be able to create an AI that is effectively targeted and oriented around you because your health and my health are not the same. And so, how do you create an intelligence that supports the vast diversity of both health conditions, people, ethnicities, races around the planet. I think that's the challenge that we want to invest behind.
I do want to ask you about the profitability of building out these AI tools because, as you mentioned, the company's profitable now, but we've seen with a lot of these application layer companies, certainly those companies who are looking to build their own models or utilizing models that exist, I mean, it's expensive, right? Now, there's no two ways about it. And so, are you concerned at all that this is going to lower your profitability margin as you look to build out an AI at all? How do you think about that?
Well, I have a really interesting kind of thesis on this, and I think part of it goes to privacy. People want to have very strict controls over their health data. And I think that's something that we take as a first principle and making sure that we don't share your data is something that Oura believes in from our foundation. Now, the key is how do you do that in a world where you have AI interpreting or even communicating to you via an LLM about your health? Our vision for this is to put that AI at the edge. Meaning that it is on the device that's in front of you. And if to the degree that we can put as much of that processing, as much of that AI on a device that's close to you and under your control and is encrypted and maybe locked by your biometrics, that's the vision that we see. Now, the side effect of that is that if you do that, you're also taking advantage of the latent processing power of all these devices, which are underutilized in terms of their computing capability and are accelerating in terms of their power. So, part of our vision is to have AI at the edge that is private, but also ultimately lower cost. And so, that's how we think about that cost problem that you highlight.
And I hear you, and it's a tough challenge. You know, it's something that a lot of these wearables companies are thinking about. The other question I wanted to ask is, and you were asked this morning about your plans of whether or not you IPO or not. I mean, how do you think about building this as a standalone company versus something that, you know, honestly, could fit very nicely into a bigger tech company. And as you talk about edge computing, you know, the ability to put LLMs on the devices themselves, this is something that, you know, we've heard companies like Apple is even considering.
I think this is the way it's going to go. I think absolutely, and particularly for these kinds of applications. I think actually it's going to be really mandatory to have that privacy at the edge. So, I think that is viable. I think that's the path that we're on, and I think that's a...
And you think you can do it alone? You can do it alone?
