Tony Roth, Chief Investment Officer
Vik Bajaj, Co-founder, Foresite Labs
Roth, Tony
Hello, you're listening to Tony Roth and capital considerations. We've got a great episode today on a topic that's been around for many years, which is biotechnology, but there's a an advent to it, if you will, which is artificial intelligence, and we have a great guest today to help us understand this very fascinating and powerful subject.
Vick Bajaj, who is a principal over at Foresight Capital Partners, which is a private capital and venture shop, and I can spend a long time credentializing Vic. He teaches at Stanford he's he's a doctor.
He is an entrepreneur. He has done a lot of really interesting things. He was at Google previously where he was the chief scientific officer for Verily, which was a company that Google had acquired at one point.
Vik is not only managing director of Foresight Capital, but he's also the CEO and and co founder of Foresight Labs. Foresight Capital is the general partner of a series of private market funds, Foresight Labs is an affiliated company that is involved in the investments within a lot of those funds, but is also independently focused on its own agenda in a sense of of finding early stage, whether it be drug development or other types of medical delivery opportunities within the medical ecosystem that that Vik runs.
I want to remind everybody before we start the conversation today that we will talk about certain companies, but we are not endorsing or speaking negatively about any company as it relates to investment recommendations.
I thought the place to start, Vik is just to explain what foresight Labs is about and what you guys are trying to accomplish there, and then we can widen the aperture and and look a little bit more broadly at the the space right now and what's going on on this fascinating intersection between genetic medicine and AI all coming together under the heading of biotech.
Vik Bajaj
Yes. Well, thanks Tony. I've been looking forward to this discussion. Well, maybe to take a step back, and we could discuss AI in general
Roth, Tony
Sure.
Vik Bajaj
…before getting into Foresight Labs if, if that's ok.
Roth, Tony
Yeah, and we just had an episode on AI with another great guest and spent a lot of time talking about how the advent of some new algorithms and a lot more computing powers sort of coming together to create a lot of opportunities in a lot of spaces in the economy.
Vik Bajaj
Definitely. Well, most of the effort that we've seen in AI, at least what's captured the popular imagination in the last year or two, has been in the domain of language and images and other things for which there is a tremendous amount of content that you can harvest and harness from the internet. This is content where users who are really using the previous generation of consumer products generate the content, right? And there's companies that have emerged to really assemble that, make it available to another set of companies that are training some of the largest machine learning models ever built in the world. And that's what's responsible for a lot of the things that you see that are just remarkable progress across industries, but it's all industries that work in language, in text or images. And the thing that I've cared about a lot in my career is
the related part of the industry, which is how can you use machine learning and AI to affect the things that we actually touch every day? That is the world of science, of engineering, of complex systems, and ultimately the most complex systems of all biological systems.
How can we understand them? How can we produce interventions in those systems that keep people healthier or reverse the effects of disease? Those domains of AI are really different than consumer AI principally because the data don't exist. The data for the most part have to be created to train models. They have to be explicitly generated, sometimes at great expense, sometimes at the scale of an entire nation state, to actually answer basic questions, let alone build models that can learn from those data and predict things outside of what those data strictly contain.
Roth, Tony
Just to orient us, are you talking about clinical data or even more upstream, if you will, analytically? Are you thinking about molecular data or core science data?
Vik Bajaj
Actually all of the above it depends on the nature of the problem.
Roth, Tony
Okay.
Vik Bajaj
But, if you're talking about drug discovery and drug development in particular, you want to understand and need to have data which associates the molecular and genetic makeup of a human being with disease outcomes that you really care about. And that means you need large data sets that associate all those clinical factors, the clinical data that you were mentioning with molecular and genetic data in order to drive insights that teach you even about how to name and classify diseases.
And how to understand which targets, which part of biology is actually functioning to create a disease or malfunctioning, if you will, and how to change that. So those data sets have to be created, they are being created. And that's really different than in other domains of AI. So at Foresight Labs, what we focus on, are all those things outside of consumer technology where AI is still a mixture of getting or creating the right data sets and producing sometimes completely new categories of models to query those data, to harness them to answer real scientific questions or product development questions.
Roth, Tony
So when you started Foresight Labs, how many years ago?
Vik Bajaj
Yeah it's about five, six years ago now.
Roth, Tony
You guys have sort of these side by side approaches to investing or on the one hand you're going out into the marketplace and you're finding companies that are in flight, that are developing capabilities, drugs, et cetera, that maybe attractive to acquire at that stage of their life cycle and may have potential, but then separate from that and and and adjacent to that, you have this business that you run, which is actually nurturing, cultivating from much earlier stage drugs. So tell us about that and tell us about when we started at five or six years ago, was that even at that stage focused on the intersection between drugs in AI or is that a new, a newer development because it's new for most of us. We like to think about it as having started with the ChatGPT although of course we know it was around before that. So, give us a perspective on that if you could.
Vik Bajaj
That was the explicit purpose in starting Foresight Labs because, you know, it's a area that I've had been working on since I was at Google in the very early days of marrying AI with science and engineering and biology in particular.
But the capabilities and results of those tools were just growing quite a bit over the last ten years. And what I noted is that very few people were putting them together in the right combination, in the right way that could actually accelerate product development. So we tried and creating Foresight Labs to kind of catalyze that transformation by focusing on getting the data in the 1st place and then building the machine learning models or sometimes just simpler statistical models so that our companies could harness those data. They harness them in many ways. Sometimes the genesis of a company idea is an insight that comes from the platform, like an insight in to targets in a particular disease area. Other times, you know, we assemble very large companies that use the platform in a durable way to conduct their business. An example of that is a company called Zera, which was a Foresight Labs incubation that we incubated and launched with our partners at Arch ventures with Bob Nelson last year. That launched with a considerable amount of seed funding more than a billion dollars because it brought together all of these ideas in a vertically integrated way, you know, a toolkit that can accelerate all the steps of drug development and drug discovery.
Roth, Tony
So that business wasn't a single drug. You launched a business that you guys helped incubate.
Vik Bajaj
Yes, we do both. We assemble businesses and launch companies that are very focused around particular therapeutic ideas. Some of those are things that are created genobo, and so the drugs are built from scratch.
Others like candid, e.g., which we announced earlier this year, are companies where our colleagues on the investment side have found assets that are in-licensable, and so we're able to bring them into a company and get a head start.
But we do that kind of company, they are very product oriented, they're progress and the way that the market views them is going to be very conventional. It's based on the progress of their products towards clinical validation. And then on the other hand, we create companies that are ambitious but grounded enough in a legacy of validation that that combination galvanizes investor interests and allows us to recruit sometimes massive amounts of capital to really take the financing risk off the table so that these companies are essentially fully funded to have a product development cycle. They can develop the innovative machine learning models or other platforms and apply them in the product development process in a way that validates them, and so createsa great deal of value.
Roth, Tony
Since the lab was created five or six years ago, something, some kind of tangible example of a product, a drug, a, I mean, I know these things take years and years to develop. But even if it's in clinical stages, what's the, the by-product of these efforts?
Vik Bajaj
Yeah, I mean, example of that is a company early on called Alumis that we launched. It's a public company now, that was launched around a drug that we in licensed. We and others had insights into a target called tick two, which is one of these master regulatory nodes that are involved in many many autoimmune diseases. And so the question was how could we harness the data platform that we have to understand which diseases to go after first and how to run efficient clinical development programs against them. And that was the genesis of a in-licensing effort, which then found an asset and that asset is in clinical trials now, there will be a set of readouts over the next year or two, which really will determine the fate of that company.
Roth, Tony
And that clinical trial, what type of diseases is that going after again?
Vik Bajaj
Well, psoriasis is the main. But the target represents something that is pleiotropic, has very, very broad implications across autoimmune disease.
Roth, Tony
So, before we pivot the conversation a little bit to talk about some some aligned areas, I'd love to just get your assessment of the space right now and the velocity of the space. You started five or six years ago, I would have thought that you were pretty innovative at that point in terms of actually founding a, a vertical and an effort at that scale to, to find the overlaps between AI and drug development. Again, I know that you're constrained by data, but outside of this, this arena, AI has just taken off, is the data, is that not true then for your area because you're so restricted based on data or do you see the same kind of hockey stick curve, if you will, going on within your area where what you've accomplished in the last year or what companies have accomplished in the last period of time supersedes many prior years? How how is the the ecosystem evolving here?
Vik Bajaj
So, it's a it's an interesting question. In areas where there is data, take protein structure, the area for which the Nobel Prize was awarded this year for protein structure and then the generation of new proteins that have a desired function. Actually, one of our cofounders at Zera was awarded the Nobel Prize shared it for that work along with some former colleagues from Google this last year.
And you know that work rests on a data set called the protein data bank that scientists have been painstakingly curating since the mid-1950s. Generations of scientists have contributed to it. Even I in grad school solved protein structures which are, which are there, and that's very rare in science, especially biomedical science, if you have a data set that's that good and that representative and expansive. And the other data sets that we have relate to just sequencing many organisms, fungi, plants, human beings, less so, but just many organisms, bacteria, other things. So in those areas, you do see this accelerating progress. In other areas like the 1st step of drug discovery where we really want to understand the architecture of the disease and we want to understand what are causal drivers of disease because those are the best targets. That is very much data constrained and data limited and there's massive efforts, particularly in the UK, believe it or not, to increase the amount of data that's being generated there, and there's massive efforts within Zera to actually generate the kind of data that's needed to train foundation models of biology, but nobody's done that yet and it's because the data are limited. So there there's progress, but the progress is slow and gated by that factor.
Roth, Tony
I try to read it as much as I can in every area, and this is an area that's always been fascinating to me. And I know that I've read some accounts of the space around the idea that there's a sort of a hit ratio and you take a drug whether it be a compound or a bio engineered type of drug into clinical trials and whatnot, and I've read that because of artificial intelligence, that hit ratio has gone up considerably in a lot of different areas.
Does that trigger any truth to you or am I saying something that resonates with you? Because I've read about this idea that it's gonna help the efficiency of the development of medicine, and it really is intended to raise that broader question, which is we spent so much time and effort of money in years developing medicines. Is this AI helping to increase the efficiency of the development process?
Vik Bajaj
Well, those are the right questions. You know, and again, I would say in the consumer technology world, a AI has been increasing the efficiency of many consumer tech processes for a long time for more than a decade. Recently in the last two to three years, it's become clear that it's a fundamental business process innovation that will touch everything, right? From accounting systems to just everything that we do in business to even now research and production of knowledge. If you look at biotech, it's really different. 1st of all, right now we have a ten year product development cycle that's characterized by very expensive late stage failures. You can't judge things as quickly as you can in other domains of science, engineering, technology because in the end they have to be tested in human beings. So to understand if this is working and where it would work, you really have to dissect that product development process and break it down into its steps where we can analyze what's worked and what hasn't and tell you about the future potential. You know, the 1st step in drug discovery and drug development is figuring out facts about biology.
What is causing the disease or what is a target that you can actually touch, modulate in some way, the drug target to either alleviate the symptoms of the disease or ideally reverse the harm that the disease is causing, right? Modify the disease trajectory. So, in the past that was done almost completely empirically or through a cartoonish understanding of biology and biology being a very descriptive science, those predictions generally sometimes worked, sometimes didn't work, and so you have these very late stage failures. Today, if we use human genetics information, genetics being kind of natural experiments of nature that teach us which targets are associated with disease, targets that have that kind of genetic validation, they have a more than three-fold increased probability that they'll turn into products.
Which doesn't sound like a lot by consumer technology standards, but in the risky world of drug development, that's enormous. To be three times more likely to succeed is enormous. So that's the floor from where we're starting and the question that one should ask is, can machine learning do better than that? We think the answer is yes, but even if you increase that probability of success by two-fold more, it will be enormously transformational for the industry. So that's the 1st step.
Roth, Tony
Okay.
Vik Bajaj”
That's a kind of hit rate. The 2nd step is you have to actually make a drug which is a molecule that interacts with biology and has the desired effect, right? Making that drug in many cases is a completely empirical process even for things like antibodies, where you have to screen or you immunize an animal. And that leaves many targets undruggable also takes a long time. So now the frontier is to produce machine learning models that can replace that empirical process to a large extent with inference in silicon.
Roth, Tony
Right.
Vik Bajaj
And design and silicon. Rather than through laborious experiments. If we can do that, then those drugs will hit the right parts of the proteins that they target if they're proteins. They'll achieve the desired functions and they may be cleaner in terms of their unintended impacts on the rest of biology.
Roth, Tony
Right.
Vik Bajaj
That will improve the product development process incrementally more.
Roth, Tony
Was that happening now or it.
Vik Bajaj
That's happening now, all the things that I'm mentioning, for example are happening within Zera. But this one, producing the drugs in a computer, that is advanced enough that in our hands, we're actually using that in every program that Zera is prosecuting.
Which is a very different way of approaching the problem. Then there's the 3rd area of clinical trials where you actually test that the drug is safe and that it is also effective. And there Oncology teaches us that there are better ways of doing clinical trials. In oncology we understand all the disease drivers. We produce drugs that are targeted to those disease drivers, and then you only test those drugs in populations whose tumors, individual tumors that harbored those genetic rearrangements, the disease drivers. So the trials are smaller, they're faster, and the response rates are higher because you have a selection factor that tells you about the susceptibility to the drug. Now machine learning along with the huge amounts of data, still too small but large, you know, compared to five years ago, that are available, e.g., in Foresight Labs’ data corpus, they now allow us in a wide spectrum of non oncology common diseases, heart disease, autoimmune disease, to make similar predictions about which patients will respond in a clinical trial and not.
That will lead to faster, more efficient trials that are more likely to succeed. So if you put all of those things together, then you have a product development process that has several steps, each step is already today being impacted by larger and larger data sets and machine learning driven analyses of those data. We can only assume that that impact will increase over time.
Roth, Tony
Okay, so let's put it all together in terms of what the science, and when I say science I mean both the biology, the computer work that intersects with that and supports it and then ultimately the the empirical work, the clinical trials and the development of the, the medicines. I think of medicine Vik as having two impacts. It can either increase or improve the quality of life or it can extend life, the longevity. Whether it be preventing or eliminating disease or whether it just be extending healthfulness. When you think about the impact and how fast these things are coming for somebody that's our age, and I don't know, you're probably younger guy than I am, but I'm in my late fifties, but for someone that's, you know, say in their fifties versus someone that's maybe in their twenties versus someone that was born today, what impact is for those of us that are fortunate enough to access these kinds of things, how would you expect it to impact our lives?
Very little for you and I and for our children very much and for their children per family, they'll to 200, and I know that you can't promise this anything, but you know more about this than we'll ever know, so what do you think the possibility are.
Vik Bajaj
Well, maybe let's start with the most likely area of impact where the technology is ready today and it is merely a business model issue that that technology is not more broadly deployed. And, in that sense, the low hanging fruit is, the healthcare system in the US. is actually not designed to do what you are asking it to do. So let's start there because if we were to postulate the existence of all kinds of things that keep you healthy, you would need a healthcare system that's capable of delivering things that keep you healthy before you're sick. Now, you know, many of my colleagues, they go to these fancy concierge practices, and I used to make fun of them because this concierge doctors largely don't do anything that's medically validated, but they do provide a sense of convenience and psychological safety. That's changing. The amount that we know today about an individual's disease risk, as scientists is grossly more than anything that's put into clinical practice. So just to give you some examples, I can give you a simple genetic test. By the way we do this a lot in the UK. I'm on the board of Genomics England. And the UK for historic reasons is a leader in deploying genetic medicine in all contexts to the by genetic medicine, I mean genetic tests not gene therapies to the NHS population. But anyway, I can give you a simple blood test that would, for tens of dollars tell you your lifetime risk of 50 some diseases.
Roth, Tony
Wow
Vik Bajaj
Right now if you, if you were to get a state of the art clinical genetic tests, very few people get it because far fewer than 1% would receive something actionable, meaning that your health changes. This new family of tests, that's completely different. So I can tell you about your risk of heart disease. And if it's high, even though you cholesterol maybe fine, you may not have any family history or other health risks. If it's high, then perhaps you should be put on statins or other therapies much earlier or at least you know about it.
If you have high risk of one or other kinds of cancer, then you could be screened for cancer more frequently. So I would say now the state of the art, more than two thirds of people, maybe 80 % would receive something actionable from this kind of test. And everyone today in principle could be born with a lifetime fingerprint of what your health risks are. So that's one example.
Another example, and this is work that we started at Google, you know, more than a decade ago, is the amount of information we can get from seemingly simple medical investigations is enormous.
If you use machine learning to uncover the latent information that's buried within, work
e.g. to look at images of the retina, to diagnose diabetic retinopathy, which is a leading cause of preventable blindness, that work has shown that with machine learning not only can you diagnose diabetic retinopathy, better faster and cheaper than any human reader, but actually you can learn all kinds of things about cardiovascular fitness, glycemic control, general health, maybe even neurodegenerative disease.
Or with a simple chest x ray now, just a chest x ray, you can predict somebody's five year probability of dying with an accuracy that is rivals a lot of the results from these very big famous studies like Framingham, because there's just so much information invisible to the human eye that these tests uncover. And final example I'll give is just, we can also predict when a disease is operating in its earliest stages in the body. I was the chief scientific officer at Grail. We produced a test that can detect most cancers early, it will do about a hundred and 50 some million probably of out of pocket business people who can afford to pay for this, and it'll be a very long time before that kind of technology is deployed broadly in the health care system.
Vik Bajaj
So you take all that science, it's incredible, and it's not in practice yet.
Roth, Tony
A lot of our listeners have the, are fortunate enough to have the means to, if they have the knowledge to potentially take advantage of the kinds of things you're talking about, what would you recommend that these folks do to undertake this to learn more about their bodies so that they can undertake the right, the right steps to prevent themselves from getting sick or or improving their odds? How how do they go about doing it?
Vik Bajaj
Well, like many things in the American Healthcare system, the answer unfortunately is to know someone who knows how to do this and to spend money on it.
And that's not an acceptable answer. It's not the way to design a healthcare system that uses this new technology. So e.g., they could just order the test from Grail. But many of the other things that I'm talking about, they're not put into clinical practice yet or there are individual companies doing it, but nobody has stitched them together in a way that represents a fundamental advance. And that's something that we're working on within Foresight Labs, the idea that we could redesign healthcare delivery based around some of these principles and that new technology allows us to do that affordably and efficiently. It doesn't put on the burden on the physician to interpret all these data because keep in mind that even if you get all these tests and then you show up in your physician's office, the physician will not have guidelines, knowledge or experience in how to interpret them, what to do next. So we're working on solutions to that, but I don't have one to offer your listeners right now and that's part of the barrier that we see to translation of these technologies.
Roth, Tony
So Vik, just to square the circle on this this subtopic, you did mention England and one of the phenomena that occurs across all industries is that a lot of times there are discoveries that occur in the United States, whether it be in the university context or whether it be in the commercial context, we seem to be really effective at, ideas and discovery development. We tend to be less adept at application and scale and commercialization and so on. Are there other countries that we could go to today that would have some of these basic, science generated, AI biotech generated capabilities that are not available to Americans, but if you go to England or Canada or Japan or Germany, you would see a much wider application of these things. Or is that not really the case?
Vik Bajaj
I would say, you know, from my perspective and in the industries that I care about and the things that I've worked on in my career, which now spans everything from basic quantum information science to other branches of engineering to the kind of life sciences and health care that we're talking about, right? The real drivers of innovation. I would say the US globally is dominant in the basic research.
It's dominant in the efficient translation of that research and it's also dominant in how that research is applied in the private sector and often the public sector. So there is NO better alternative than the US model for these things. And as much as we hear criticism of all of the activity now that would seem to check that model, curtail it, dismantle a little bit, actually in the worst case, there will still be way more research funding available in the US than anywhere else. And the question we should be asking is how can we enhance that competitive advantage? It's one that draws all the world scientists and engineers to work on projects here especially in the basic sciences, and we should be doing everything possible to preserve that lead because it is responsible for our innovation, and it will be responsible for our innovation in the future.
Roth, Tony
So even though you’re certainly identifying that a lot of the developments that are available today have not yet been put in practice, you're making it clear that neither is anybody else for the most part. What about and you've I think you're alluding to it, what about the change in TAC, if you will, and the part of the current administration? It's it's one that I believe in the last couple of weeks we've learned that they've laid off tens of thousands of people in, in the NIH arena, the, you know, the broader, health federal health system and it would seem that the dollars are going to be really dry up from the governments. Is, is that ok the the private sector is gonna come right in and we're gonna be better off in the long term, is it going to be very disruptive. What are the risks? Cause who knows how it's gonna play out I suppose unless you have an idea.
Vik Bajaj
Your last question is one that we should deal with 1st. I have no idea how this will play out. And I have number of friends and colleagues who are very passionate opponents of what's going on, but I have a more neutral to optimistic view, not about the way in which things are being done today, that's chaotic and chaos doesn't seem to be beneficial, at least for what we do, but just that there are things that we could do more efficiently, there are things that we could do better in academic research. That said, there is no substitute for the kind of blue Sky academic research that US universities and national labs do so well. All of the things that we care about, really and touch every day, including in consumer technologies, the entire semiconductor industry, really everything derives from blue sky research. It's not just in the life sciences and health care.
But our whole economy depends on that. We do it very well. I would hate for the present chaos, to affect that. And my hope is that, you know, will result in some accommodations, some detente.
Some solutions that really improve transparency, efficiency in basic research, putting more things towards research rather than administrative overhead, that that's how we'll emerge from this, but that there'll be in fact a redoubling in what's after all the foundation of US global competitiveness in all these areas, right? So I'm optimistic that what is going on is temporary, and I find it very hard to believe since there's so much bipartisan support and recognition of this that we really would do anything to weaken our research advantage, because that's the only way that we can compete globally and grow our Economy.
Roth, Tony
And it's the dollars we're talking about. We all know Columbia University $400 million, that's across the whole university, not necessarily health per se, but these numbers are are astronomical.
Vik Bajaj
Astronomical, yeah.
Roth, Tony
They're so they're so big. And it's it's happening across the the entire academic economy, if you will.
Vik Bajaj
It's happening, chaotically.
Roth, Tony
Happening chaotically, yes.
Vik Bajaj
But let's say that you wanted to, in a more balanced way, change the way that indirect costs are handled? Well, it's not unreasonable to say that you want incentives to be aligned so that the individual faculty member who's making the decision about what kind of services do I want, should choose. Do I want this set of services? Do I want a bigger office or do I want more money to go to my research? That's, you know, possible to create aligned incentives so that more is financed through direct costs.
There's no reason that a faculty member, you know, can't be billed for different things so that it's not built into the indirect cost environment. Now that doesn't mean that it should be 10 % or 15% or something abnormally low, but also does it really have to be high 70% as some institutions have as their indirect costs.
Maybe, maybe not, but in the exploration of a new way of doing things, and we have to remember, you know, and it's speaking as a former academic, that the electorate has handed the government a broad mandate to do just that, explore new ways of doing things. That doesn't mean that it should be done chaotically as it's being done now. But I wouldn't say that that exploration is entirely unwarranted either.
Roth, Tony
We're running out of time, unfortunately, but this is a really amazing discussion.
Certainly we get the very strong sense that you, you do what you do because you find it to be intellectually engaging and you find it to be probably ethically very, very satisfying in terms of the difference that you can make, right? And it's all about making a difference. But from an investing standpoint, how do you size up the opportunity set today versus when you started five or six years ago? How do you see the space right now for the area that you're in, which I sort of loosely describing here is genomic drug development and also broader healthcare delivery?
Vik Bajaj
So I would describe the area that Foresight Labs is in as opposed to foresight capital. Foresight Capital being an investment organization. Foresight Labs, as we discussed, is a company incubation organization and is separate. What we're doing is a little broader than that. We believe that science and engineering in all branches, not just limited to life sciences and healthcare is going to be impacted greatly in the next few years by AI approaches. And ingredients of that impact of that transformation are, you know, very different than what is happening in the consumer technology world because it involves the world of everyday objects, whether they're engineered complex systems or living systems and people.
But that's what we specialize in, and we think across the board there's accelerating impact. When will you see that impact really depends on the length of product development cycles in those industries. In some branches of engineering, it's short.
In some branches of engineering, there are still ten year product development cycles like there are in the life sciences in healthcare. Ten years from now we should be judged on whether we have accelerated those product development cycles.
Whether we have reduced the risk so that capital can be deployed more efficiently, and to be particularly ambitious, can we use AI to escape the bounds of human imagination to extend human creativity into different domains so that these punctuated advances that we see every decade, maybe those leaps can begin to happen every year. So that's the potential that I see over the next ten years. I would define certainly what we do at labs as being very broad across the space of science and engineering and AI. And I think it's gonna all look very different ten years from now than it does today.
Roth, Tony
Which I think it inherently implies that there's a real opportunity set here to be taken advantage of because if it's changing that quickly in terms of the the methodologists approaches, it's gonna have to yield as that data becomes far more available, it's gonna yield advancements and those advancements are gonna be commercialized.
Vik Bajaj
Yes, definitely. And they will upend the current way in which we discover, develop, engineer, build things, and distribute them, whether that's in healthcare or outside of healthcare.
Roth, Tony
Well, Vik, we got to stop here, but what a cool conversation and I think there's one takeaway in my mind, it's what I just said a couple moments ago. There’s probably no area of our lives that people feel more passionately about than our health, and there's just so much going on here. So I want to thank you for having this conversation, more fundamentally. Thank you for doing what you do for a living because I know that it has made a difference and will continue to make a difference in people's lives for for many years going forward.
Vik Bajaj
Well, Tony I really enjoyed being on your podcast and good to see you again and thank you for the opportunity to have this great Conversation
Roth, Tony
So, thanks to all of our listeners. Beevt Trust dot com is where you can go to find all of our past podcasts and all of our other thought leadership within the investment and planning arena for wealth management. So thank you all and we'll talk soon.
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Past performance cannot guarantee future results. Investing involves a risk, and you may incur a profit or a loss. Any reference to company names mentioned in the podcast should not be constructed as investment advice or investment recommendations of those companies. Third party trademarks and brands are the property of their respective owners.
Third parties referenced herein are independent companies and are not affiliated with M& T Bank or Beevt Trust. Listing them does not suggest a recommendation or endorsement by Beevt Trust. Private market investments are only available to investors that meet the U. S. Securities and Exchange Commission's definition of qualified purchaser and accredited investor.
Facts and views presented in this report have not been reviewed by and may not reflect information known to professionals in other business areas of Beevt Trust or M& T Bank and may provide or seek to provide financial services to entities referred to in this report. M& T Bank and Beevt Trust have established information barriers between their various business groups.
As a result, M& T Bank and Beevt Trust do not disclose certain client relationships or compensation received from such entities in their reports. Investment products are not insured by the FDIC or any other governmental agency, are not deposits of or other obligations of or guaranteed by Beevt Trust, M& T Bank, or any other bank or entity, and are subject to risks including a possible loss of the principal amount invested.
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