Awesome.

Well, appreciate everyone being here. I'm Matt Strom, Morgan Stanley Investment Banking. I lead the healthcare data and AI practice. Great to see you all, and great to have Eric with Tempest here with us again this year. And maybe just before I start quickly, for important disclosures, please see the Morgan Stanley Research Disclosure website at morgansanley.com slash researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative. So with that out of the way, I think we wanted to jump right in. And today we're going to really focus on a unique sort of health care data and AI story with Tempest and want to dig right in with Eric. So maybe, Eric, just off the start, I think a lot of people think of Tempest in some ways as a genomics company. It's obviously still a large portion of your revenue. But if you zoom out, you've built a really large, multimodal, longitudinal, clinically annotated data set, and now you're sort of layering this AI on top of it. Maybe for the audience, just start with, is Tempest a diagnostic company? Is it an AI company that happened to start an oncology? Maybe just start with that framing.

I think it's – okay, at our root, we have always been a technology company. Ten years ago, you really couldn't be an AI company other than, you know, in theory. Today, if you're a technology company, you're probably an AI company, at least in some way, shape, or form. So today, we are absolutely an AI company. One of the challenges we've always had is that we don't come at this in terms of like, oh, we're going to build models. And that's our core business. We really come at it by saying we're going to garner access to proprietary data, use that data, whether it's enhanced by our own models or other people's models, to generate insights and deploy those insights back into the clinic. And so that's our – has always been our core business. In order to get the data, we had to basically open up a lab and start sequencing patients because that was the data that we needed in oncology to kind of generate these insights. And so that business over the last, you know, 10 years has, we're now eight, maybe eight or nine years ago, has become the biggest part of our revenues, about three quarters of our revenues, give or take. And so we live in both of these worlds where we are both a NGS company sequencing patients and generating proprietary data, and a data AI company that takes that data and generates insights, licenses the data, licenses the models, all that. So we really have these two different businesses going, which makes it complicated because diagnostic investors are always kind of afraid of our data business because they don't get it, they don't invest in AI or tech. AI investors are scared of the diagnostic business because they're like, I don't do diagnostics. And so we've had to straddle both of those worlds, as other companies like us, like Tesla and Amazon, other people have to straddle both of those worlds.

Maybe just double-click into where your data actually comes from. You mentioned the genomic side, but there's a lot of people that produce genomic data. But where does it come from, and sort of what had to be true operationally as well as culturally to ingest that data, get the trust of your customers, and be able to actually use that data?

Yeah, the first thing we had to do, which was unique, is when we began, because we're a tech company that began sequencing patients, from our earliest inception, we would go to people and say, hey, we'll sequence your patients, but you have to give us the clinical data for these patients, because we're not just interested in sequencing your patients, we're interested in understanding whether or not the insights we produce from sequencing is working. Like if we found a mutation and we recommended a drug, did your patient go on that drug and how did they respond? And so the big hurdle that is both cultural and logistical and administrative was saying to people, like, we'll sequence your patients, but you got to give us all this data. And not only does it give us this data, we have to be able to de-identify the data and use it for any lawful purpose we want. Because not only are we interested to generate insights that make our reports better, but we want to generate insights that make drug companies better. And this might sound like reasonable today, but 10 years ago, this was like heresy. Like you'd mentioned the word drug company, and to most providers, they would be like, I'm never going to talk to you again. But we would go into these meetings and say to people, unless we're missing something, you people don't make drugs. And so why would we not want to make drug companies smarter? So I think culturally, from our earliest inception, we weren't just sequencing patients. We were collecting clinical data for those patients longitudinally over time. And so very quickly, we ended up amassing a very large data set of rich molecular data, DNA, RNA, other insights, connected to rich outcome and response data. And it's the combination of that data set that is and was so valuable. However, once we began amassing huge amounts of data, and you're talking hundreds of petabytes the data, we realized, okay, it's now time to start to license this data to biopharma. But when we just handed them a bunch of data, they couldn't find real value in it. So we had to build a whole array of tools around that data that make it useful, not just harmonizing and structuring the data, but really allowing people to interrogate the data, build cohorts of interest, refine those cohorts, unpack the data, unpack insights. And so if you look at our financials relative to other people in our space, especially on the diagnostic side, the most glaring standout is we have a very – and have always had a very large technology team, things like 700 software engineers and product folks, people like that, and enormous investments in cloud, five or ten times other people in our space. So we've always invested a lot in making the data useful. And that's, you know, we announced, we may get to, but if we look at our recent deal we announced with Merck, which is, you know, another very large strategic collaboration for us, you just don't have people like AstraZeneca and GSK and BMS and Merck and others signing these $100 million-plus deals unless the data is both incredibly useful and they can generate real insights from it.

And so how does that differ from, take the Merck example, you guys just announced a deep end collaboration with them today. It strikes me that those are, again, veering more towards real deep collaboration relationships rather than, as you said, just access to data or something. But how does the relationship that you've got with your pharma customers differ on the data side from what they could theoretically go find in other parts of the market, whether it's real-world data, real-world evidence, et cetera?

Yeah, I mean, I said this at J.P. Morgan a few months ago. Sorry, I'm just choking for a second. But we, you know, we saw a few years ago, we saw people entering the data market, especially our competitors, talking about how they were going to launch data businesses. And so we had, you know, we had some of that noise. And today that noise is really dampened. I mean, we just, when we're working with big pharma, they're either, you know, licensing our data at scale where they're really just not licensing this kind of data. At the present moment, we just have a unique product. And so we're never in a situation where, or at least if I think about the last year or two, we're never in a situation where someone says, hey, we want to license your data, but we're also looking at somebody else, another big sequencing provider in the space, whether that's Keras or Gardens or whoever. and we're kind of, this is their price and this is your price. Like that never happens. They either want the kind of data, they either believe that the kind of data we have can be transformative to their oncology programs, what assets to pursue in early R&D, how to design a more intelligent phase two, how to manage site selection to ensure you're enrolling the right patients, They either believe that data is transformative or they don't. If they do, we're the partner of choice. And then what ends up happening is these deals all kind of start small. Merck's a great example. They all start relatively small. Somebody licenses, you know, whatever, a million dollars of data, and they want to solve, answer one or two questions, and then they want to answer more questions. And then at some point they realize they want to answer lots of questions. And if you look at our data business, any biopharma can license one file for a few thousand bucks. So we don't mandate that you have to license lots of our data over multiple years. So when a client signs up for, and in the case of Merck, it's a five-year agreement, but four years are committed. So when someone signs up for like four years of locking into lots of data, all they're getting is access and a discount, right? They're essentially getting access to our tools and a discount on the data. And so our pricing works very similar to AWS or GCP or Azure, where you can buy a little bit or a lot, and all that varies is really price. And so I think it speaks to the fact that as people, they might start small, but pretty soon they realize, like, I'm going to need a ton of this data. and it's integral to my programs and I want the best price I can get and so I'm happy to sign up for a multi-year commitment.

It strikes me that the data business for you all, maybe partially because of the more healthcare focused investor base has always been a debate. The debate when I first started working with you guys was, oh, you can't produce revenue out of this. No, you know, pharma's not going to pay for data. I think at least part of the debate in the market now is what's facing a lot of tech companies, which is, you know, the data is going to come from somewhere else, or you can vibe code your way into some sort of solution that's going to work, which seems ridiculous in the pharma context, but so be it. I'm just sort of curious, as you guys look at the data today, you know, is it the scale? Is it the density? Like, is it the size of the asset that makes the difference? Is it the tools you've built around it? Like, what are some of the motes that you feel like you're building up with your customer base, besides the uniqueness of the product itself?

Yeah, I mean, I think if you think about the existential threat these days more and more is that the large foundation models are going to get so smart that they can do a lot of things other people can do. This is the whole like AI eating software. The, you know, one of the challenges those models have by their own admission is that at some point they run out of kind of free public data to train the models on. And there's varying estimates of when they run out of that data, but I think there's pretty good consensus in, like, 27, 28, they're hitting the ends of that. So more and more of those companies are coming to people like us saying, what data do you have? And I think the next frontier, for lack of a pun, is going to be the big frontier modelers trying to garner access to more and more proprietary data like the kind of data Tempest has to train their models. In our case, the data we have is really hard to replicate. First, you have to go to, in our case, I think 5,500 of the roughly 8,000 hospitals in the United States and convince them they should give you their data, which is not quick. Then you have to get through legal, which is even slower, and then you have to get through IT, which is even slower, because these people have Epic or Cerner, these large systems. They have an enormous roadmap of work they have to get done, and in order for us to get the data, we typically have to integrate at scale. And it has to be longitudinal. You can't just get one time point. You've got to get multiple time points and not just one kind of data. You need structured data. You need unstructured data, physician progress nodes. You typically need other forms of data. So we built up this really large data set. You know, it's approaching 500 petabytes. It's connected to lots of hospitals. And so I think, and it also, by the way, is connected to our own proprietary sequencing. So even if somebody could get their hands on the clinical data, they can't get their hands on the VCFs and BAM files, all that rich molecular data that a company like ours has unless you partner with some company like ours and try to marry it all up. And one of the flaws of other people that I think have tried to compete with us is you've had people that have lots of molecular data trying to cobble together clinical data or people with clinical data trying to cobble together molecular data, and it just doesn't work or hasn't worked up until now. So I think we're in a unique spot, and I would suspect that it's only a matter of time. I'm running a blog post on this, so I want to give that away. But, you know, we're in regular contact with the world's largest modelers, and I would say, and technologies. And I would say their interest in this kind of data on a scale of 1 to 10 was a 1. One, I would say it's now like a five. And interestingly, every one of these companies that we're engaged with, again, this is coming out in like a week or two, is asking the exact same question. They want longitude, no patient histories at scale. And if you think about it, the reason they want longitude, no patient histories, not to digress is these models are very good at predicting the next likely word. They're so good at predicting it that you can ask almost any question and they give you incredible insights, right? They become that good. And it's just because they've been trained to predict the next likely word, you know, C spot and the next likely word is run. I think these folks believe as we do that with enough data like the kind of data we have, you can predict, instead of predicting the next likely word, you can predict the the next likely drug or the next likely therapy that would work for a patient. And my guess is that we're relatively close to being able to train these very large models that can be truly predictive that can start to say, like if you're on five milligrams of statin, should you be on 10 or if you're on this antidepressant and at this hypertension medicine, is it, is it, is it, is it bad for you? Not for the whole world, but for you individualized, individualized. And so I think at that point, that use case, I think, for these folks is very compelling because, you know, if you're paying $20 a month to, like, write an essay or to write an email or, like, whatever, and something else comes up that's nearly as good, you might stop paying $20 a month. But if you're paying $20 a month to figure out, like, what drugs you should be taking and how to protect your health, it's a pretty durable use case.

It strikes me in the example you just gave, though, that the models being tuned and trained with your data, you can see that use case. And so in that case, your data is very valuable. But conversely, if you're going to go back to trying to impact the patient at the point of care, your pipes in and out of the hospitals are very valuable, too, as sort of a go-to-market partner, essentially.

And I also think, yeah, I also think we very much view it as our data is going to be central, not just in oncology, but we've got large data sets in cardio and neuropsych. Our data is going to be invaluable to build models and generate insights. On the consumer side, I suspect those models will be delivered by the big consumer companies. Like we have no aspiration to be that company. So they'll be delivered by Apple and Google and OpenAI and Anthropic or whoever. On the provider side, on the pharma side and provider side, I suspect those insights will be delivered by companies like ours, both because in order to connect to the U.S. healthcare system, you have to be a covered entity. It's complicated. There's all kinds of logistical issues. So I think at the end of the day, we have a moat. And then in terms of pharma, they're not just interested in asking, at least at present, asking superficial questions. They're interested in asking incredibly detailed questions that are influenced, and this is the key part, by their own data. And in our case, we are a trusted provider, both to 8,500 oncologists in the United States and most of big pharma. And we have their data, and our data is moving back and forth. And I just don't see a world anywhere in the near term where the biggest pharmaceutical companies are uploading their critical clinical trial data to open AI or Google or whoever. I just think it's too invaluable. So I suspect we've got a pretty good moat on both sides.

Maybe we could move from the data level to the sort of intelligence or AI level for a second. You guys have had some announcements around foundational models in this space. What does that actually mean in healthcare? What are you referring to when you're talking about building those for, you know, in partnership with your customers?

Yeah, so I think I'll give you the most tangible example because I think it's relatively close to being at a point where this is public as well. So, like, if you think about it, the foundation model we're building, and we're building two. We're building one with AstraZeneca and Pathos. We're building a second. That's Pan Disease on our own, two different compute clusters that we've established. One's about 1,000 H200s, one's roughly that size, but GB200s. And what's happening is we're building these models so we can generate multimodal insights that you just can't see unless you have enormous amounts of data. So let's just take one of those insights. So if I'm a non-small cell lung cancer patient, the standard of care is that I would be profiled for two particular biomarkers, EGFR and ELK. And if I'm EGFR positive, I would go on an EGFR inhibitor. That would be like guideline therapy. And like most drugs in cancer, and like most drugs in many other disease areas like, you know, diabetes and cardiac conditions, these drugs tend to work in episodic, in different So some percentage of the population, the drug doesn't really work at all. You'd go on the drug, and within three months, you'd need to go off the drug because it's not working. Some percentage of the population, you're going to be on that drug for five years. You're going to have an incredible response. And then there's a big part that's in the middle. So it's very hard to take all the different clinical characteristics of patients and build models that are predictive because, as you can imagine, patients that get non-small cell lung cancer are quite varied, a ton of heterogeneity. So, but when you have a large model like we have, you can begin to train the models to look for those outliers and build predictions. And so I think we're not far away from, on our tests, unlike other tests, not just saying this patient's EGFR positive, but also providing context. This patient's EGFR positive high, EGFR positive mid, EGFR positive low.

And that means do X?

High would mean something like this patient is going to – we predict this patient will be on an EGFR – like this will do very well taking an EGFR inhibitor, whereas EGFR low would be we predict this patient will not do well. Like if you give the patient an EGFR inhibitor and tell them to come back a year later, don't be surprised they have metastatic disease. So – and I think you will see that. But we're about to open that Pandora's box, and I think it just is the beginning of an entirely new era of precision medicine, where you can collect vast amounts of data, train very big models, and be unbelievably predictive, so that you start to have this N of one contextualization of every drug, instead of the way we are today, which is, oh, your cholesterol is high, go on five milligrams of a statin. Like, really? Should it be five? 10, 20, this, what, should I come in and have a calcium score, should I have, you know, whatever, a stress test or an echo, and you don't know because we can't, we're not good at stratifying risk, but these models can stratify risk, and so I would suspect that that, and so that I would think will be highly catalytic to our diagnostic business because we're just, our tests are smarter and more personal than others, and also highly catalytic to our data business because every pharma company over time is going to need to know where does their drug work and where does it not work because physicians are going to know that and ultimately patients are going to know that.

Does that change the – is there a regulatory infrastructure that needs to change for you to deploy those specific insights, you know, the EGFR example and a reimbursement regime that needs to change, or does that fit into the current sort of world?

I think it fits into the current world of oncology because in the current world of oncology, we give oncologists a great deal of latitude to make decisions as to how to treat these patients because that's just the world of oncology. Other disease areas are far more rigid. And also because most of these tests are LDTs, they're not FDA-approved tests. We have an FDA-approved test, and a few others do, but the vast majority of tests in the market are just non-FDA approved, there's a different regulatory structure to modify those. If you want to append a medical device that's FDA approved, you have to go back through the FDA. So like our ECG algorithms, we have to get FDA approval because GE got FDA approval for its electrocardiogram. But for laboratory diagnostics, you can say all kinds of insightful things on top of that because these tests go through an alternative pathway. And they have to be reviewed by a physician in order to take action. So I think there's a pretty wide amount of latitude. I would suspect, though, over time, you know, our competitors on the diagnostic side will want or need similar tools that help them quantify their tests. We have a test out in the market now called immune profile score, which basically modifies another test called tumor mutational burden, which is wrong about 20% of the time on both ends, meaning it misses patients that should get an immunotherapy and it captures patients that shouldn't. And we have other competitors that have similar algorithms, and I suspect more and more are coming.

Just on the model side, maybe one last question. So I think you've talked today, and you've certainly talked a lot publicly in the past around the sort of integration of a lot of different modes, you know, types of data, genomics, pathology, clinical notes, et cetera. Are there sort of other large data sets or forms of data you need to either produce yourself or get your hands on to improve these models and improve what you guys can sort of deliver in the future?

Yeah, I mean, I think at the present moment, no, but in the near term, I think yes. And in our most recent letter that Jim and I wrote, But we called out the fact that we were fortunate that the business was generating more gross margin because we're running it, you know, whatever. I think our growth last year was like 33% or something. But we're growing it around 30% and the costs we need to run the business are much less. And so we're generating lots of leverage in the core business. And so we made a decision to not just hand all that EBITDA, incremental EBITDA gain to the bottom line, but to hold back some of it to invest in sustaining that growth. One of those buckets of investment is new data sets, both outside of oncology in areas like immunology, but also in new data modalities in oncology, in particular single cell sequencing, testing, spatial transcriptomics, epigenetic data at larger scale. So I think there's other data sets that will become important, proteomics, beyond base level proteomics. But right now in oncology, we have an enormous amount of data, and still even with people like Merck coming on board, which is amazing, you know, joining the ranks of some of our There are large strategic partnerships. There's still, you know, I don't know what the total number is, but we still have well more than 50% of the biggest oncology companies, top 20, that aren't strategic clients of Tempest. Maybe we have five and there's 15 to go. And so I suspect over time all those folks will also sign up. If you think about – At that level. They're all clients, just not at that level. Expansion of the opportunity, right.

If you think about healthcare AI, where do you see the long-term value occurring? There's all this debate right now. Is it the data layers, is it the model layers, is it the application workflow layer? Where do you sort of see it occurring in healthcare as you play out the next sort of phase here?

I think we're still at the part of the curve where the data is the scarcest asset to train the models that change both patient and physician behavior. So we're at the part of the curve where those who have access to the data at scale likely have the proprietary asset. Over time, we'll move to what you do with the data becomes more important. I think there's a fork in the road, as I mentioned. There will be consumer companies that dominate one side of it, And then there will be enterprise companies that dominate the other side. I tend to think they'll be different. And so our focus is on, you know, dealing with providers and biopharma. There's, and that's just, so we've always thought of our business in kind of three buckets. There's a data generation part of our business. We're very lucky that the data generation side of our business is both high growth and generates really high margins, like 65% margin. So that's a healthy business in and of itself. And then that provides all this data that has an even higher margin, 75%, and is growing even quicker. And we think both of those businesses are kind of multibillion-dollar businesses over the next whatever several years. And then I think the real interesting part of the story, which I think you're getting at is, look, when data is pervasive and there's all these models out there, whether Tempus is the leader in that or one of the leaders, you're going to be generating all kinds of insights. And how do we pay for those insights? And I don't have an answer for that. I think it's through like AI enabled applications or some kind of algorithmic diagnostic that's paid for, but I can't tell you that for sure because I don't know. But it feels to me like that business eventually is the really big business. Like if these are big businesses, that's the mega big business because, and I just use our ECG algorithm as one example. Like we have this ECG algorithm that predicts undiagnosed AFib and undiagnosed low EF, about 70% of heart attack and stroke or one of those two in terms of normal ECGs. So in theory, we can predict about 2% of the total error of ECGs in the United States just off our two FDA-approved algorithms. We run two, three hundred million ECGs a year in this country. That algorithm currently has partial reimbursement for a subset of that at about $128 an algorithm. But, like, at some point you could imagine there being universal reimbursement at $50 to $100 for that algorithm. If somebody runs $100 million, it's a big number. And I think that is going to be repeated over and over again, where we just have these algorithms that will predict mistakes that are made at scale. Which type 2 diabetes medication should you go on? Should you be on a statin, an ACE inhibitor, or some other cardiac? Pick an algorithm where you've got people on the wrong drug, the wrong time, wrong dose. And so I think algos is a big business down the road.

So maybe just two last questions to close. I think you've talked a lot about, you know, in the short term, AI in health care is probably overhyped. In the long term, it's probably underhyped. You've maybe played a little bit of that vision out today, but, you know, what is it that you think investors and maybe especially technology investors who don't spend as much time in health care are maybe sort of misunderstanding or could understand better about that paradigm?

Yeah, and I think, by the way, I think it's interesting because when I said that, there was, and I think it was probably a year ago or I don't know, seven, eight months ago, there was all this kind of euphoria around just AI more broadly. And that seems to have dissipated, at least in our cases, it seems to have dissipated quite a bit. I think there's still a bunch of that private euphoria as it relates to maybe Anthropic and OpenAI. We'll see how they trade in the public market. There's certainly still a bunch of euphoria around NVIDIA. But some of the, like everything with the word AI in it, a year ago trading high, I think that has certainly gone away. And one could argue, I think probably, I don't know if that has anything with Bitcoin, but you had some of these asset classes that felt like they were kind of risky and retail driven that were trading high about a year ago that have all come way down. So now if someone said to me, is AI overhyped in healthcare in the short term? I would say, no, if anything, I think we've actually crossed that chasm where the opportunity of AI in the near term is probably underappreciated. It's way underappreciated in the long term, but it's probably also now underappreciated in the near term, the short term. And I think it's because we're about to start to see, I suspect in 26, I believe in 26, you will start to see very tangible evidence that AI is going to impact healthcare at incredible scale, both from companies like ours on the provider and pharma side and companies like OpenAI or Anthropic or Google or whoever or Apple on the consumer side.

So if you played out the next, you know, Tempest is, I think, around 10 years old right now, a little over. If you play out the next, you know, five years from the company, it strikes me that there's sort of a big transformation ahead of you. So if you guys execute well on that next five-year journey, what does the company look like at that time? Where do you think, you know, the real drivers of the business sort of sit at that point?

Yeah, we've projected 25% growth for next three years, but if things go well I would suspect or I would hope we we beat that you know pretty materially, especially on the data side. It's a hard harder to predict the diagnostic side only because now I'm getting into like long-term trends of NGS, but I think the data long-term prediction and the AI long-term prediction is much higher than 25 percent. So I think if things go well while the business is just significantly larger. If you compound something at around 30% for five years, it's a much bigger number. You know, we're starting on a base of about a billion six, and so just kind of you can do the math. And so, you know, we're focused on that. We're focused on building a business that is growing rapidly, that generates lots of leverage, that allows us to reinvest in ways other people can't, to compound our leverage so that, you know, 20 years from now, not five or two, but 20, you know, that's how you, I think that's how you build a very big company, right? When you look at companies like Amazon or whatever, they've just been compounding for a long, long, long time. And that's what we want to build.

Well, thanks a lot for coming, Eric. Appreciate you being here, and we'll look forward to what you guys do in 26. Thanks for having me.