AI Sleepers: The winners hiding in plain sight

The Angle Music

Cold open: Shaun Currie

“The future is undefeated. Like this is coming. I would just say that it's really easy to look at, like the shiny object in front of you. But the idea is like if the shiny object is going to work, which I think we all think it is, it means there's going to be like a bunch more companies that we're not thinking about right now that are going to benefit and emerge from this.”

Jennifer Martin

Welcome to “The Angle from T. Rowe Price”, a podcast for curious investors. Just a reminder that outside of the U.S. and Australia, this podcast is for investment professionals only. I'm your host, Jennifer Martin, a global equity portfolio specialist at T. Rowe Price Associates here in Baltimore, Maryland. In the final episode of this season on artificial intelligence, we're exploring AI's hidden winners or what we like to call AI sleepers.

So much of today's narrative is dominated by a handful of market leaders, but the opportunity extends far beyond them. So where might these sleepers be and how do we identify them? Joining me today are Shaun Currie, a portfolio manager in our small-cap growth equities, and Jon Friar, a portfolio manager in our large-cap growth equities. Welcome to the podcast Shaun and Jon.

Shaun Currie

Thanks for having us.

Jennifer Martin

Great. Well, let's start with what, what, do we mean by AI sleepers? Shaun, why don't you start?

Shaun Currie

So, I guess how I think about it is there's a lot of talk about the AI cycle that we're in, and I don't really think about it as an AI cycle. I think about this as the data center cycle. Right. There's a lot of capital being spent on building out the infrastructure of artificial intelligence, and the AI cycle itself will probably last decades. And the question, like, I think we're all trying to figure out now is how long does the cycle of investing the majority of capex in data centers last? So, you know, I don't want to compare this too much to the internet cycle, but that was the last major hardware cycle that we saw. And we said we had, you know, a five/seven-year investment cycle in that.

But what happened for the next 20 years after that, as we had multiple other cycles, right. Building out fiber created the e-commerce cycle. It created the mobile phone cycle. It created the cloud cycle, which created the software-as-a-service cycle. So when we think of AI sleepers, I just think of like, what are the next cycles we're going to talk about after the investment we're making in data centers?

Jennifer Martin

Shaun, that's so, so that's so prescient because I don't think anyone would have anticipated coming out of the tech and telecom boom, all the cool technologies that we got to use, like the iPhone, the different apps, Netflix, there's a lot of really interesting things that kind of came because of that infrastructure build-out. So, Jon, maybe you want to add a little bit to that context of what an AI sleeper means for you in your asset class?

Jon Friar

Yeah, I mean, if I think about this kind of sleepers analogy and I take it a little broader, the market seems wide awake to the idea of the hardware cycle. Every day we talk about big themes around chips and memory and optical and all of these components that we know very well at this point. And the stocks are up and the revenue is being recognized, and we can project out where this trillion dollars of capex is going, who's spending it, what they're spending it on, who's receiving these revenues. And the sleepers are the people that are a little bit behind that. So, they’re may be building an application or a product, right? They really took advantage of the coding jump that came over the last six to nine months. And they're using all of these new tools to actually build something that may not yet be in market. They're in different parts of the ecosystem than just pure hardware.

They're not in a bottleneck story. And for most of them, we think of them as sleeping for a good reason. They haven't yet recognized revenue. They haven't shown the product. They're not accelerating their business because of the application of AI. But I do think we know a lot as a market and as a group of investors about that first category of sort of the wide awake stocks. And we know a lot less about the sleepers. And I think when we make a lot of money as investors, when we have real success, it's because we find something that the market doesn't quite know yet and we try and invest ahead of it. And that's what I think about when I think about a sleeper stock.

Jennifer Martin

Well, I think you're proving the point that I've heard many times to be good at this job, you have to have an imagination, which it sounds like you're thinking about in that sleeper category. I think what's notable is you both look at opposite ends of the market spectrum, Shaun in small-caps and Jon in large-caps, and I immediately go to David and Goliath in that analogy. So why are AI sleepers relevant now in your part of the market, and why do you think investors are really under appreciating them?

Shaun Currie

I mean, I think they’re relevant in every part of the market, right? If we think about how much money that we're spending on the AI hardware cycle, if there is not a productivity increase, a cost decrease, new companies that emerge out of this, the technology will not have worked. And I don't think that is going to be the case. So, I mean, if you think about it, there's a lot of studies out there—we're going to spend, you know, trillions of dollars (USD) on this. And people think that companies are going to spend US$2 trillion a year on the services that these AI products provide. If they're going to spend US$2 trillion, they're going to have to get a benefit from it. So that's like some number that's bigger than that annually. And if you look at small caps, they tend to have lower margins, they're less efficient, they can move faster. If you look at the spend necessary to get productivity increases in AI, it's not a very large number. There's not a scale advantage to spending on this.

So, I just view as small-cap companies are going to probably get more than their fair share of the benefit of this. And on top of that, like every other technology cycle we've ever had we're going to make this investment and all these new companies are going to emerge out of it, right. And the job is to find that next generation of great companies, whether it's public markets, private markets, and you're starting to see these companies emerge. And I think, like Jon said, we're just spending a lot of time talking about hardware and justifiably so. But the next phase of this is like, if we're making all this investment, all these other companies are going to have to benefit, too, and that's the area we're not spending a lot of time on yet.

Jon Friar

Shaun and I have worked together either as analysts or worked next to each other as portfolio managers for over a decade. And one of the reasons I love working with Shaun and I was excited to do this podcast is I think we both really like looking at stocks, and if you really like looking at a whole bunch of different stocks and really getting into the details of them, sometimes the most obvious thing isn't actually the most interesting thing to look at. And so when I think about what we're really trying to sort of find inside of these different areas and pockets of the market, are these things that maybe we haven't quite gotten to as a full market yet. So, you take a number like a trillion dollars of capex spend or someday a trillion dollars of revenue for some of these key frontier models.

And you think about how many dollars that is for some part of the ecosystem to spend. And you realize, well, you know what? Maybe there's actually a pocket of the market that will work on the efficiency of how that trillion dollars is spent. And that pocket is actually not yet fully priced. So, I can go find maybe a small stock that's been overlooked, maybe it's in the software space, maybe it's in the hardware space, maybe it's something else. But they just work on taking that trillion dollars of spend down to US$900 billion. That's actually a massive outcome. That's $100 billion of savings. And that might be an incredible small, mid- or even large-cap stock. So we're trying to find those little things that kind of pop up inside of this giant wave that aren't yet fully priced.

Shaun Currie

Yeah, think about it. I mean, we spend a lot of time on the application layer of what will come in AI. So one of them is robotics. And like, you know, what hopefully happens is that AI enables these robots. That means we use more of them. The price of the robots goes down. It's a self-fulfilling flywheel. But when you really like dive through it, programming a robot today costs more than building a robot. And if you program a robot, it's usually good at one task and one discrete task, so it's not super useful at this point. AI is the unlock, right? So, once it makes programming cheaper, it makes the ability for these robots to do multiple tasks. It makes the return on buying a robot better, which like then creates a self-enforcing flywheel. And we're trying to just think through in the small cap space. What what could we, what do we always want to do that we couldn't do? An AI is going to enable us to do it. Like those are where like the big outcomes are going to be in the application layer. So, like robotics is a good one we're spending a lot of time on. I think about stuff in biology, right.

Like all the frontier models are making significant bets on biology. So, who has to benefit from that, right? I think what you're seeing right now is that we're making material gains in models through reinforcement learning. It's probably like a little different than we would have talked about 12 months ago. Reinforcement learning takes a ton of data, right?

So, we spent a lot of time on basically data providers inside, like the biology complex. And if you think about like that, there's almost like an asymmetric risk/reward to this. Right, if you buy an AI hardware company, you are making a bet basically today that is sustainable for, there's duration to that bet. You're probably making the same bet with an AI biology data company, but you haven't had this big, massive run or these big earnings estimate revisions.

So, like your downside is less. So, you're basically making the same bet with both of them because Anthropic and OpenAI are making that bet. But you probably have less downside in that. And you probably have more upside if it actually comes through. I just think it's it's just worth more of our time to spend on the application layer today. So we still spend time on that hardware cycle too.

Jon Friar

For every bet there's conviction and there's magnitude. I can have a lot of conviction that we're going to use a whole lot of logic chips, right? A lot of semiconductors, no matter what, into the future. The magnitude of me being right is an interesting debate, but we're certainly embedding a lot of magnitude already. We have a lot of expectation in those stocks. I'm not exactly certain where we're going to go. I have a lot of hope for it. We've done a tremendous amount of work to figure out where. But the magnitude, if we're right, is completely unpriced today. And that's really exciting.

Jennifer Martin

And that's where and that's where alpha is. Alpha is in that change. And I think you're both kind of highlighting your your seek, you're like, a heat seeking missile looking for that change in some of these opportunities.

Shaun Currie

I also think, I mean, AI is the most interesting thing that I've ever seen in my career. It's awesome to use. It's amazing what we could get out of this, but if we don't end up doing all this cool stuff, it wasn't worth it. So, it's like very easy to bet on that these things are going to happen because the technology is improving, and if it's not, then we're just not going to talk about AI. And I just don't think that's going to be the ultimate outcome. I think we're going to have like real breakthroughs. So then just finding out where those breakthroughs are.

Jennifer Martin

And that's probably a really good segue to what is the one common misconception about AI disruption?

Jon Friar

I mean, I would always start by saying the first thing is that it's true, it's right, and it is very large. I've done this for about 16 years now. I am as bullish as I can possibly be about the future of AI and how it's going to be used. It has changed my personal experience of being an investor, more than any tool that I've been given over the last 16 years, and I expect that it's only going to get better.

I think sometimes when people give you this misconception question, you want to kind of quickly be contrarian. And I think the baseline is actually this is going to be a wonderful technology for a whole lot of people. I would say the piece that I am most skeptical about is that industry structure often is the most important thing to long-term returns, and I often find that when we get a whole lot more capital flowing very, very quickly into a given industry, returns go down. And right now, for the most part, what we are doing is saying that for some very traditional hardware industries, returns are going to go up and remain up for a very long period of time. And that hasn't actually been, if you believe in sort of this long-term returns on invested capital framework, big inflows of capital don't typically create higher returns over time.

So that's like the one space where I would say I feel this very strong kind of push pull versus my historical experience and really a historical experience that goes way before me and kind of what we're actually discounting in the market today.

Shaun Currie

I would say. I mean, I think there's going to be a lot of disruption, right? This is a profound technology, and it's much different and how you use it is much different. The skill sets you need to have to succeed with it are much, it's a very design-oriented technology which it lends itself to that there is going to be broad-based disruption as a result of it. There are going to be companies who you don't think about now who embrace this technology, and it just puts them ahead of all their peers, even if they aren't today.

You know, I would say I feel like the market in these areas, say like, software where disruption risk is high, right? All these companies have become bucketed, you have certain attributes. So, if you, I don't know, if you have vertical expertise or you're embedded, or you have some regulatory moat, that you're going to be okay relative to somebody else. And valuations like clearly show that there's a bucket of extremely cheap stocks. And there's a bucket of like okay stocks. And probably if you just have thought about it like a basket that's probably right. But the reality is like what's really going to matter is like do you have a management team and a culture that is willing to disrupt your own business model to embrace AI and become tech enabled?

Right, there's a lot of companies, like look at the software cycle, right. A lot of the companies that I covered, they were, you know, I covered payroll software. They were regional service bureaus. Some of them decided to embrace software-as-a-service and make it their business model. Some didn't. The ones that went public were a handful of hundreds of these things. That's going to happen here, too. So not necessarily because you don't have like the right attributes; doesn't mean you're not going to succeed. And just because you have the right attributes; doesn't mean you are going to succeed. It's the ones who are going to actually embrace this and be willing to disrupt their own business model to do it. So I think, I don't think it's like as straightforward as the market is trying to delineate it.

Jennifer Martin

And I want to go back to something that you said, and I think you've said this in prior conversations with clients about how AI maybe doesn't, you said AI doesn't have a scale advantage. You know, like it benefits smaller companies. So maybe you could expand on that because I think it's a really interesting insight, because I think we always think of large and scaled businesses as the ones that deserve to win.

Shaun Currie

Yeah, I just think I mean, if even if you looked at like all the companies that are public and how much money they're spending, there are handfuls that are spending materially more money than everybody else. But besides that, we're talking about if you're spending US$20 million (USD) this year on AI or like that's your token budget across an organization, you're probably in the top two deciles of spend. Any public company can spend that if they so choose to. Token costs are going to go down over time. So, I don't see like a real cost advantage to scale. Smaller companies have less bureaucracy. They're able to move faster. A lot of them, you know, they kind of know they're not the winner. So, it's actually easier to move on this, because they're like, this is the only chance to become the winner.

So I just I don't see a lot of like big advantages. Like Jon would probably have good answers in large-cap. But, so I don't see that. And you see that almost over every technology cycle that companies use this moment as an opportunity to change the outcome of their business. That happens every single time. It's going to happen this time. It's our job to find those companies. I do think that this cycle might lend itself a little bit more to those kind of companies than maybe the previous ones.

Jon Friar

I mean, I love that. I fully agree on the scale piece, and you think of all the sort of Munger type quotes on how valuable scale is, and this feels different. I'd add sort of two observations. The first one is when we think about sleepers in the large-cap space, one thing that we are constantly trying to sort of guard against are some of these really large scaled companies that did something which I think is somewhat cynical, but the world sort of rewarded them for.

So, let's say they were a 6% grower or an 8% grower, and they would do a deal and do a space that gave them slightly more accretive growth. But the quality of that business, like the truly differentiating moats were actually lower, and they ended up with a number of things that I actually feel much worse about in the AI world. And they have piled on enough of these sort of small deals, and those are actually the businesses that I am the most worried about. So, you actually have scale potentially, is this sort of real disadvantage because what scale did was it gave you a capital allocation benefit, and you used it in a way that maybe wasn't in your core business or isn't going to help you in sort of this next generation of technology. So, we're very careful about what I call sort of these maybe a generation of cynical capital allocation that happened in kind of a low-interest rate world.

The other thing I would observe, maybe in the different direction about scale, is that if you look at the history of the frontier model companies, the models were getting better the whole time. And one place where we really see scale matter. I think a lot; everyone's talked about how much better the big training run is or a big push training run, and what I would say is the whole time the models got better, that was somewhat predictable. What was not predictable was when or who exactly hit product market fit. And so, when you actually had a chat bot sort of interface and that created this really tight product market fit with the consumer, you had just shockingly explosive growth and tremendous scale emerge.

I would say the same thing happened with coding. It was not intuitive in the fall of last year that coding was going to have this level of product market fit once we put a proper harness around it. So, what I would say is I agree on all parts of scale, but I also want to be humble enough that if we see product market fit emerge in the AI world, I think you can get to shocking levels of scale very, very quickly.

Shaun Currie

Just one thing that you said on that every single technology cycle you've had these companies where scale has been a disadvantage, right? Okay. So, we have the internet cycle, then we go to e-commerce. It was not an advantage to be Macy's and JC Penney and have a lot of malls, right. You could go faster if you didn't have that legacy infrastructure you were going to have to disrupt. If you went to the software cycle, right, all of a sudden you had this legacy tech debt where a company could have five people in a garage and build a company out of it.

Jennifer Martin

Don't forget the dog.

Shaun Currie

Yeah, but I had companies like that was actually like an advantage that they, they weren't on COBOL (Common Business-Orientated Language). Right. That's going to happen here too.

Jon Friar

100%.

Jennifer Martin

One final thing on scale, Jon, for you is, you know, some some of your peers internally have said, you know, these large-cap companies have a scale advantage because of their their data. They can train their own data. They can monetize it better. They have more looks. How do you balance that with kind of your product market fit discussion?

Jon Friar

So, I think it's a wonderful point. What I have often found, and I covered a lot of the data names when I ran a sector team, it was and. Often a company has, let's just imagine 100 units of data. And the data that's really special is maybe ten units or five units. And this gets back to sort of that earlier point of, was there something cynical where you added a whole lot of data that's actually not that high quality? And what I have seen is in a world with more and more synthetic data, the really, really special data is more rare than people think. And the most commoditized data is extremely available. The price of it is going down, and it's actually doing a lot of the work you need to get to where you would like to end up.

Now, there are companies actively out there every single day producing more and more of this data. It's going to get very, very specific, very, very verticalized. But if that is your primary moat, I am curious about how it's going to work over a three to five-year period, but confident that it will work over a one-year period.

Jennifer Martin

That is a great insight. It looks like you might want to add something.

Shaun Currie

No, I mean, I think that's like completely right. I think Jon's all over that. I don't know. So what we do is like what companies do we think are going to benefit from this. Like where is data actually going to be important? I think if your company in a vertical, and you have distribution that the model companies don't have and that you're able to like get data from that distribution, if you have data that the model companies don't have as well.

I mean, you understand your workflows, right? We're all trying to build data because we're trying to get the models to do things. If you understand your workflows, you can do that and that you have some cost savings initiative inside your company, where if anything happens with AI or spending, you can spend through it because you're getting a return on it, I think you're going to be in an okay spot.

Jennifer Martin

So maybe the next question we should ask is when you're evaluating a potential AI sleeper, what are the key characteristics you are looking for?

Jon Friar

So for me I really want to see evidence of a great technical team, ideally all the way up through management, not just at the level of sort of the engineering team.

Jennifer Martin

So the CEO has to be AI enabled now.

Jon Friar

Well, AI enabled and ideally, right, not just a salesperson. I like people that really have the product-level experience. Maybe they are actually someone who grew up coding. This is someone in the organization who fully understands and is actually personally excited about this technology, and in it close enough to sort of see where it's going and what it can do. So, I actually care probably more, and I wouldn't have said this three to five years ago about that management team’s like real true capability inside of tech and deep tech. After that, it's a lot more diverse.

So this may be hey, things that we've always liked right, network effects. You know, network effects, maybe something very close to collaboration. You see this in a bunch of different products, and you can even see this pretty clearly in the roadmap of the frontier model companies, where they want to create more and more internal collaboration as it holds you more tightly. I also, I don't think it's all just sort of optimizing for a monetization or a business outcome. I think it's actually, you know, from from the frontier model perspective, it's actually really good for us as enterprises to be able to collaborate. I want to be able to share my models and my concepts with Shaun. I want him to be able to share them back and do it in the same sort of model environment.

The other one I would consider is anything that has some sort of like physical component, and I think this matters most just because it slows down the rate at which someone can disrupt you. If you have physical sensors, if you have a real logistics network, if you have something that sort of binds you to the physical world and will be difficult to get scale in and to replace, that is a place where I actually do believe scale still matters, and where you're really going to have a long time to get your business oriented around whatever is coming with AI. I’d highlight probably those. Yeah.

Shaun Currie

I think those are perfect. Those are how we think about it too, right?

Jennifer Martin

That's a really good clarification on, you know, some of the characteristics that you're looking for and some of the durability. And I think it's a really good distinction that the physical world still does create some barriers to those AI-enabled companies. I guess as we're thinking about AI, I know our audience is always really interested in how our own internal investment staff is including AI into your process. And so, I know both of you are I don't know, do you guys have wizard hats? You know, there's the AI Wizards group. I mean, where do you feel like you fall in that spectrum? But I think maybe the audience would love to hear about your AI process, what you're doing. I know, Shaun, you've been doing a lot of cool stuff in Small Cap.

Shaun Currie

I never got a hat.

Jennifer Martin

Have to find out the guy who hands them out.

Shaun Currie

I think, I mean, what we're doing is the same thing that Jon just talked about with management teams, right? Just think about what AI does at a high level. We are amplifying the design layer, and we are commoditizing the productivity layer. That's basically what we're doing. So how do we think about that, like, in our job. Right, like where we really drive alpha as an organization is that we have these really great analysts who truly are domain experts who understand their businesses. It's not like, honestly, it's not like the exact earnings estimates. It's not what they think about an earnings call. It's that they, like, truly understand what matters. Right, so if you have people like that, you can use AI to amplify that. So that's really what we spent a lot of time on. Yeah. There's a lot of productivity that we've done. Right. I can get an initiation about any company in the Russell 2500 in five minutes. Right. I can go through my own framework on a company. Those are really just like screening tools effectively at scale. But when an analyst like knows its industry, it knows like two to three things that matter. We can actually design like almost any kind of analysis now around those things.

And that's how we get a true insight over a multiyear period. So, we've been just trying to use it to, when I was an analyst and Jon was an analyst, like you would try to do these analyses like, I don't know, like, an industry model, right. And get some insights out of it. It would take, I don't know, the one I built it took me probably like three months to do it. I mean, you can do it in like six hours now, maybe probably faster, but like, maybe if you really want it, like exactly how you want it. So you're just able to do a lot more of like the cool stuff faster. You have more time to do.

Jon Friar

I mean, I feel like I, I've been sort of following that same journey as probably most people. So, you know, the beginning was a lot of chat, and the chat would look like deep research and it would be some sort of question about historical or something analytical. You know, in the last six to nine months, all of a sudden the ability to, like quickly build out an Excel template or use my existing Excel templates and, you know, generate kind of incremental updates to them using the the frontier models has been so much better. My favorite thing over the last six months has been these industry models, which they were labor intensive and you were pulling data from all over and it was, you know, very analyst, and now you ask it to sort of run overnight. And what you wake up with in the morning is just like exquisite.

Jennifer Martin

Magical.

Jon Friar

But for all of that, like I always think of trying to describe the strategy, like, what are we trying to do? Like we are trying to find insights from this incredible analyst platform that we have at T. Rowe Price, something where some industry expert who's an analyst here has found out some unique piece of data insight analysis that's not in the rest of the world. And it's often very hard for me to tell what our analysts know that the rest of the world doesn't. And we have built out a very large platform internally using AI so that we can examine the sell side, the internet broadly, expert networks, compare all of that content to what our own analysts are writing and help me find and really tell what are the most unique insights that are coming from this platform, so that I can be actively invested behind those specific ideas. And it has made that job much, much easier. And that's something we've really kind of rolled out over like the last three to six months.

Shaun Currie

Yeah, this is like the funnest time I've ever had doing this job. Right. If you I mean, you do this job because you're intellectually curious that you want to learn about things, that you want to uncover something that people aren't thinking about. And now you can do it at scale. It's I mean, it's awesome. It's it's the most fun I've ever had doing this job.

Jennifer Martin

Every PM (portfolio manager) that we've had on this podcast has basically said their job is so much more fun, it's much more joyful. And I think what both of you highlighted is the importance of an investment framework. It allows you to be repeatable. You can get through a lot of tasks in a very, I would say, standardized manner, and it makes you, with those insights, difficult to imitate. That's where your alpha is. I think that's really, really cool. And you made a lot of Directors of Research happy by talking about our analysts. So thank you.

Jon Friar

So yeah, they deserve that, they’re fantastic.

Jennifer Martin

We do have some really good subject matter experts. So I think the final question is thinking about, you know, if our listeners remember just one thing from today's discussion, what would you want it to be?

Shaun Currie

I feel like we just had a discussion and it almost made it sound like what we're in right now in this AI investment is maybe not as important. I don't want it to be like that. Right? Like, this is the most profound thing that has happened in my entire career. Right. And you asked the question on disruption. I mean, there's going to be a ton of disruption, right?

The future is undefeated. Like this is coming. I would just say that it's really easy to look at, like the shiny object in front of you. But the idea is like if the shiny object is going to work, which I think we all think it is, it means there's going to be like a bunch more companies that we're not thinking about right now that are going to benefit and emerge from this. And I think where you're going to really make money over the next five to 10 years is finding those companies.

Jon Friar

I couldn't agree more. And I would start with this core principle. There's going to be so much more AI in the world. And then I always want to match that with just as much humility about how hard it is to predict the exact magnitude and the exact timing via lens of risk/reward for that specific trend. And one of the reasons why Shaun and I are looking for those sleepers is because we just want to twist the odds a little bit in our favor, where we think we can be a little more certain about that risk/reward, since we're not already paying for the magnitude and the timing. And I just that's what I like doing. I was excited to talk about it, and I think there's actually a lot of opportunity for that at this specific moment.

Jennifer Martin

That’s perfect. I mean, I think you both did a very good job today explaining how transformational this technology is, how you both have to manage the existing cycle responsibly. There's a lot of excitement still in chips and the physical enablers of AI. But your job also is to be really curious and kind of think about the undetermined future and maybe what those next-gen winners will be, as we call them, sleepers. So let's stop there. Shaun and Jon, this has been really a great discussion, and we really appreciate your time. On behalf of the audience, thank you.

Shaun Currie

Oh, thanks a lot.

Jon Friar

Thank you for the opportunity.

Jennifer Martin

Again, I'm Jennifer Martin. 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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