Awesome. Well, appreciate everyone being here. I'm Matthew Strom, Morgan Stanley Investment Banking. I lead the healthcare data and AI practice. Great to see you all and great to have Eric with Tempus here with us again this year. Maybe just before I start quickly, for important disclosures, please see the Morgan Stanley Research Disclosure website at morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative. With that out of the way, I think we wanted to jump right in, and today we're gonna, you know, really focus on a unique sort of healthcare data and AI story with Tempus and wanna dig right in with Eric.
Maybe, Eric, just off the, off the start, I think a lot of people think of Tempus in some ways as a genomics company. It's obviously still a large portion of your revenue. If you, if you zoom out, you've built a really large multimodal, longitudinal, clinically annotated dataset, and now you're sort of layering this AI on top of it. You know, maybe for the audience, just start with is Tempus a diagnostic company? Is it an AI company that happened to start in oncology? Maybe just start with that framing.
At our root we've always been a technology company. 10 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. 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 gonna build models, and that's our core business." We really come at it by saying, "We're gonna 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." 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 'cause that was the data that we needed in oncology to kinda generate these insights. That business over the last, you know, 10 years has...
We're now eight or nine years ago, has become the biggest part of our revenues, about three-quarters of our revenues, give or take. 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. We really have these two different businesses going, which makes it complicated because diagnostic investors are always kind of afraid of our data business 'cause they don't get it. They don't invest in AI or tech. AI investors are scared of the diagnostic business 'cause they're like, "I don't do diagnostics." We've had to straddle both of those worlds, as other companies like us, like Tesla and Amazon, other people have straddled both 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. Where does it come from and sort of what had to be true operationally as well as, you know, culturally to inject 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 'cause 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?" The big hurdle that is both cultural and logistical and administrative was saying to people like, "We'll sequence your patients, but you gotta give us all this data." We not only to 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 wanna generate insights that make drug companies better. This might sound like reasonable today, but 10 years ago, this was like heresy.
Like, you'd mention the word drug company, to most providers, they would be like, "I'm never gonna talk to you again." We would go into these meetings and say to people, "Unless we're missing something, you people don't make drugs, why would we not wanna make drug companies smarter?" I think culturally from our earliest inception, we weren't just sequencing patients, we were collecting clinical data for those patients longitudinally over time. Very quickly we ended up amassing a very large dataset of rich molecular data, DNA, RNA, other insights, connected to rich outcome and response data. It's the combination of that dataset that it is and was so valuable.
Once we began amassing huge amounts of data, you're talking hundreds of petabytes of data, we realized to go, "Okay, it's now time to start to license this data to biopharma." When we just handed them a bunch of data, they couldn't find real value in it. 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, you know, unpack the data, unpack insights.
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, you know, five or 10 times, you know, other people in our space. We've always invested a lot in making the data useful.
That's, you know, we announced, we may get to, if we look at our recent deal we announced with Merck, which is, you know, another very large strategic collaboration for us, you know, you just don't have people like AstraZeneca and GSK and BMS and Merck and others signing these, you know, $100 million plus deals unless the data is both incredibly useful and, you know, and they can generate real insights from it.
How does that differ from? Take the Merck example, you guys just announced a deepened 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. How does that sort of the relationships that you've got with your pharma customers differ on the data side from what they could go, theoretically go find in other parts of the market, whether it's real data, real world evidence, et cetera?
Yeah, I mean, I said that excuse me, I said this at JPMorgan, a few months ago. Sorry, I'm just choking for a second. We, you know, we saw a few years ago, we saw people entering the data market, especially our competitors, talking about how they were gonna launch data businesses. We had, you know, we had some of that noise, and today that noise has really, dampened. I mean, we just when we're working with big pharma, they're either, you know, licensing our data at scale or they're really just not licensing this kind of data.
At the present moment, we just have a unique product. We're never in a situation where at least, you know, if I think about the last year or two, we're never in a situation where someone says, "Hey, we wanna license your data, but we're also looking at somebody else, another big sequencing provider in the space," whether that's Caris or Guardant or whoever, and we're, you know, we're kind of, this is their price and this is your price. Like that's, that never happens. 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, all that.
They either believe the data is transformative or they don't. If they do, we're the partner of choice. 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, $1 million of data, they wanna solve, answer one or two questions, they wanna answer more questions, at some point they realize they wanna answer lots of questions. If you look at our data business, any biopharma can license 1 file for a few thousand bucks. Like, we don't mandate that you have to license lots of our data over multiple years. When a client signs up for, in the case of Merck, it's a five- year agreement, but four years are committed.
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. 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. I think it speaks to the fact that as people, they might start small, but pretty soon they realize, like, I'm gonna 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 y'all may be partially 'cause 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 gonna 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's gonna come from somewhere else, or you can vibe code your way into some sort of solution that's gonna 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, what?
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 moats that you feel like you're building up with your customer base, besides the uniqueness of the product itself?
Yeah, I mean, well, I think, look, I think if you, if you think about the existential threat these days more and more is that the large foundation models are gonna get so smart that they can do a lot of things other people can do. This is the whole like AI eating software. 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. There's varying estimates of when they run out of that data, but I think there's pretty good consensus in like 2027, 2028, they're hitting the ends of that.
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 gonna be the big frontier modelers trying to garner access to more and more proprietary data like the kind of data Tempus 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.
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, you know, have Epic or Cerner, these large systems, they have an enormous roadmap of work they have to get done. In order for us to get the data, we typically have to integrate at scale. It has to be longitudinal. You can't just get one time point. You got to get multiple time points and not just one kind of data. You need, you need structured data, you need unstructured data, physician progress notes. You typically need other forms of data. We built up this really large data set. You know, it's approaching 500 petabytes. It's connected to lots of hospitals.
It also, by the way, is connected to our own proprietary sequencing. 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. One of the flaws of other people that I think have tried to compete with us is you've had people who 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 it hasn't worked up until now. I think we're in a unique spot, and I would suspect that it's only a matter of time.
I'm writing a blog post on this, so I don't want to give that away. You know, we're in regular contact with the world's largest modelers and technology. I would say their interest in this kind of data on a scale of one to 10 was a one. I would say it's now like a five. 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 longitudinal patient histories at scale. If you think about it, the reason they want longitudinal 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?
Mm-hmm.
They become that good. And it's just because they've been trained to predict the next likely word. You know, see 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 next likely drug or the next likely therapy that would work for a patient. 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 5 mg of statin, should you be on 10? Or if you're on this antidepressant and this hypertension medicine, is it bad for you? Not for the whole world, but for you.
Individualized.
Individualized. I think at that point, you know, 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. If you're paying $20 a month to figure out, like, how you're 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 model is being tuned and trained with your data. You could see that use case, but, and so in that case, your data is very valuable. Conversely, if you're gonna 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.
I also think we've 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 neurosurgery. 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.
Right. Right.
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 would 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. I think at the end of the day, we have a moat. In terms of pharma, they're not just interested in like asking, at least at present, asking like superficial questions. They're interested in asking incredibly detailed questions that are influenced, and this is the key part, by their own data. In our case, we are a trusted provider both to 8,500 oncologists in the U.S. and most of big pharma.
We have their data and our, you know, data is moving back and forth. 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 OpenAI or Google or whoever. I just think it's too invaluable. 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 foundation 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?
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. 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 AI, 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. 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. Let's just take one of those insights. 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 ALK.
If I'm EGFR positive, I would go on an EGFR inhibitor. That would be like guideline therapy. 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 ranges. 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 'cause it's not working. Some percentage of the population, you're gonna be on that drug for five years and have an incredible response. There's a big part that's in the middle.
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 with a ton of heterogeneity. When you have a large model like we have, you can begin to train the models to look for those outliers and build predictions. 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.
That means do X? Is that
high would mean something like...
Right
... this patient is gonna... we predict this patient will be on an EGFR inhibitor. 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. And I think you will see that, like 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 5 mg of a statin." Like, really?
Should it be five, 10, 20, this? Like, what should I come in and have a calcium score? Should I have, you know, whatever, a stress test or an echo, like... You don't know because we're not good at stratifying risk, but these models can stratify risk. I would suspect that. That will, 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 gonna need to know where does their drug work and where does it not work, because physicians are gonna know that, and ultimately patients are gonna 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 'cause 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. 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. Like our ECG algorithms, we have to get FDA approval because GE got FDA approval for its electrocardiogram.
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.
Mm-hmm.
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. We have other competitors that have similar algorithms, I suspect more and more coming.
Just on the model side, maybe one last question. 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, you know, improve these models and improve what you guys can sort of deliver in the future?
I mean, at the present moment, no, but in the near term, I think yes. We in our most recent letter that Jim and I wrote, 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. We're growing at around 30% and the costs we need to run the business are much less. We're generating lots of leverage in the core business. 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 datasets, both outside of oncology in areas like immunology, but also in new data modalities in oncology, in particular, single-cell sequencing, spatial transcriptomics, epigenetic data at larger scale. I think there's other datasets that will become important, proteomics, beyond base-level proteomics. 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 other 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 Tempus. Maybe we have five and there's 15 to go.
I suspect over time, all those folks will also sign up.
If you think about.
At that level, they're all clients, just not that level.
Right. Expansion of the opportunity, right. If you think about healthcare AI, you know, where do you see the long-term value accruing? 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 accruing 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. We're at the part of the curve where those who have access to the data at scale likely have the proprietary asset.
Mm-hmm.
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. Our focus is on, you know, dealing with providers in biopharma. There's and that's just it. 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. That's a, you know, that's a healthy business in and of itself.
That provides all this data that has an even higher margin, 75%, and is growing even quicker. We think both of those businesses are kind of multi-billion dollar businesses over the next, whatever, several years. 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 gonna be generating all kinds of insights and how do we pay for those insights? I don't have, I don't have an answer for that. I think it, I think it's through like AI-enabled applications or some kind of algorithmic diagnostic that's paid for. I can't tell you that for sure because I don't know.
It, 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 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 are one of those two in terms of normal ECGs. 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 200 million ECGs a year in this country. That algorithm currently has partial reimbursement for a subset of that at about $128 an algorithm.
Like, at some point, you could imagine there being universal reimbursement at $50-$100 for that algorithm. If somebody runs $100 million, it's a big number. I think that is gonna be repeated over and over again, where we just have these algorithms that will predict mistakes that are made at scale. Which type two diabetes medication should you go on? You know, should you be on a statin, an ACE inhibitor, or some other cardiac... You know, pick an algorithm that where you've got people on the wrong drug, the wrong time, wrong dose. I think algos is a big business down the road.
Maybe just the, you know, two last questions to close. I think you've talked a lot about, you know, in the short term, AI in healthcare 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 healthcare are maybe sort of misunderstanding or could understand better about that paradigm?
Yeah. I think, by the way, I think it's interesting 'cause when I, when I said that, there was, and I think it was probably a year ago or, I don't know, seven or eight months ago, there was all this kind of euphoria around just AI more broadly. 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 certainly is still a bunch of euphoria around NVIDIA. Some of the, like, everything with the word AI in it a year ago trading high-.
Mm.
I think that has certainly gone away. One could argue, you know, I think I don't know if that has anything with Bitcoin, but, you know, you had some of these asset classes that felt like they were kind of risky and retail driven that were, that were trading high about a year ago that have all come way down. 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.
Mm.
It's way underappreciated in the long term, but it's probably also now underappreciated in the near term, the short term. I think it's because we're about to start to see, and I suspect in 2026, I believe in 2026, 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.
If you played out the next, you know, Tempus 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. 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 the next three years. If things go well, I would suspect or I would hope we beat that, you know, pretty materially, especially on the data side. It's harder to predict the diagnostic side only 'cause now I'm getting into, like, long-term trends of NGS. I think the data long-term prediction and the AI long prediction is much higher than 25%. I think if things go well, 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 $1.6 billion, just kinda you can do the math. 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, 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. That's what we wanna build.
Awesome. Well, thanks a lot for coming, Eric. Appreciate you being here, and we'll look forward to what you guys do in 2026.
Thank you. Thanks for having me.