SPEAKER_00: All right, everybody. It's a great day for your boy, J. Cal. I am still long Uber. I still have

SPEAKER_01: a large amount of holdings and the stock was up massively today because they had record high free cashflow and they beat on revenue. This Week in Startups is brought to you by

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SPEAKER_00: I'm very enthused. My net worth went up, but also the bet I placed over a decade ago that defined my

SPEAKER_09: career as an investor. I always believe this company would start becoming a money printing machine. And

David Friedberg: thus that has happened. Q1 revenue, 8.8 billion. That's up 29% year over year. And we're talking about cloud computing. I had Alex on last week and we're talking about, oh, it's falling to single digits. Well, you know, it's not falling to single digits. Uber up 29% year over year. Revenue came in

SPEAKER_11: 100 million above analyst estimates according to CNBC. The total trips in Q1, 2.1 billion. I remember

SPEAKER_14: when Uber had a total of three riders on the platform, and we'd done maybe a dozen rides

SPEAKER_11: up 24% year over year. That's incredible. And this includes mobility and delivery, monthly active platform customers. Max, Max, Max, there's no way to pronounce mapsies, mapsies, actually mapsies works monthly active platform customers. This is Uber's sort of mouse monthly active users. But what they want to say is these are customers who are active on the platform, not dormant accounts, ones that actually did something. That was 130 million. That is extraordinary. We're talking nine figures worth of

SPEAKER_09: customers monthly. And looking at the revenue, 72% revenue growth for mobility, 23% year over year for delivery and freight. The freight business dropped 23% year over year. I'm not sure what the details are

SPEAKER_08: there. We'll double click on it in a future episode. Can't win them all. Two out of three ain't bad, but that's a small number. So it's an emerging business for them that they're investing

SPEAKER_09: in. Total gross bookings, right? This is the top line before they give a large percentage of the money they make to the drivers. And the drivers are doing spectacular. That's why so many people are driving for Uber. Don't believe the fake news, which keeps saying like Uber drivers are making $8 or $9 an hour. That's all made up nonsense. The truth is they're making $25, $35 an hour. And the gross bookings were 31.4 billion of 19% year over year. So you know, the revenue grew 29% year over year, but gross bookings grew only 19%, which means Uber's, you know, more profitable and charging more for their

David Friedberg: services. Just great to see. Bottom line, they had a small net loss of 157 million. And that included

SPEAKER_09: $320 million benefit from net unrealized gains related to Uber's equity investments. But really, cash flow, that's what matters, right? How much cash makes it into the coffers, as we say, and this includes excludes capital expenditures, right? So you'll have some things on the books that are capital expenditures, but the actual amount of cash into the business. So this is accounting issues, record

SPEAKER_08: high free cash flow $549 million cash and cash equivalents and short term investments 4.2 billion

SPEAKER_09: for the team over at Uber. And I think the really interesting part of all this is lifts demise. They are a shrinking amount of this industry and Uber, I don't want to say as a monopoly, because it's not a monopoly, you have DoorDash out there, you have Lyft out there. You have people competing, like public transportation, and micro mobility, and people owning their own cars, rental cars, and taxi and

David Friedberg: livery drivers, right? So to say Uber has a monopoly, or they don't on mobility, nor do they have it on delivery. What they have is they have the majority now of app driven rides. And they have a strong presence. I think they're number two in the United States behind DoorDash in delivering groceries and

SPEAKER_30: food, but people order from other sources as well, right? They're still Instacart, you still have Amazon

SPEAKER_00: delivering groceries and Whole Foods. So it's not quite a monopoly, but it's a strong position in those

SPEAKER_09: areas. And the average map see did 14 rides or food orders in the quarter, that's almost five per month, which is

David Friedberg: very impressive, because you take the 2.1 billion trips, you know, divided by 150 million, you know, map sees, just

SPEAKER_09: taking a guess here, that maybe they're taking 14 rides or food orders in the quarter, which would be about five per month. That's the average. Now that means there's people who use Uber a lot. There's people who drop in. But if you're like me, I'm using Uber Eats, and taking Uber rides, at least 10 times 15 times a month. So I think I'm probably in the 40 or 50 a quarter 200 a year, kind of group as a family, because man, my daughters love to, they get me every time, oh, we did our homework, can we get boba? Or, you know, can we order sushi? And yeah, I'm a sucker. Because I just take it out. And I'm just like, you know what, I want you to have a great

SPEAKER_30: childhood and enjoy some nice sushi and some boba. So just to Dara and the team and Dara's coming on the

SPEAKER_09: show in over the summer, we'll have a great interview and catch up. But the stock is ripping, it's up almost 11%

David Friedberg: today, which puts me in a great mood. Not just because money, which is nice. But I got enough of that. It's just about being right and betting on a team, and really seeing the investment come to fruition. And this year at Founder University, I'll make somewhere between 50 and 125k investments as a tribute to

SPEAKER_14: that 25k investment I made in Uber as one of the first investors, maybe the third or fourth, I don't

David Friedberg: know. So if you want $25,000 from me, to start your company, get two or three founders, have one technical person, make an MVP, come to Founder University, hang out with me, we're going to have a we're going to actually have our own space in San Mateo soon. And just come hang out with J. Cal, let me give you that lucky 25k first check so you can incorporate, maybe come to our accelerator, the launch accelerator, we'll give you 100,000 and let us syndicate you on the syndicate.com, share it with other angel investors and get you a milli or two. I think the average is like 700k to the syndicate. So it's been an amazing journey with Uber as my best investment in history. And I'm trying to hit another one, or a bigger one. And so that's not going to be easy. But in the next 10 years, I

SPEAKER_09: hope to invest in maybe two or 300 names per year, which would put me at 2000 more investments in

David Friedberg: my second decade of investing to go with the 300 in my first or 250 in my first decade, I'm going to 10x

SPEAKER_30: that. And, you know, you never know, maybe I had another Uber or two, and maybe that's you. So

SPEAKER_43: I'm going to go to founder.university or launch.co slash apply to meet with our team. Great job,

SPEAKER_46: Team Uber. All right, next up on the program, Charles Fisher, the CEO of Unlearn AI.

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David Friedberg: All right, everybody, welcome back to our special AI series. Never seen anything move this fast in the 30 years I've been in the technology business. So we are having people on the program three, four times a week here at This Week in Startups to share what they're working on and why so that we can

SPEAKER_52: all keep up with this crazy pace. We found an interesting guest today. His name is Charles Fisher. He's the founder and CEO of unlearn.ai. And he is taking AI models to try to speed up clinical trials for pharma companies, which seems like a really interesting idea, Charles, and welcome to the program, but also one that has me a bit concerned because using chat GPT, uh, 3.54 and some of the other tools barred from Google and, uh, the core AI tool, they're frequent, uh, the hallucinating and giving wrong data. So maybe we could start with a little bit of, uh, how long you've been working on this

SPEAKER_53: and then getting right to how this is going to change clinical trials and the hallucination problem.

David Sacks: Yeah, definitely. Um, yeah. So thanks for having me. Um, how long have I been working on this? That's a really interesting question. I think, um, how long I've been, have I been working on like generative modeling as a, as an area? Cause I was an academic researcher before starting unlearn. So I don't know, like 15 years, probably since I've been working on sort of generative AI. Um, but we've been at this with unlearn now for about six years, not quite almost six years, um, working on yet developing generative AI to currently speed up problems in clinical trials. So like let's make clinical trials biggest bottleneck. We want to make that faster, but eventually we want to roll this out to think about how we can really sort of revolutionize the way we think about medicine,

SPEAKER_57: turning all of medicine from something that's really today kind of an art form into a real predictive

SPEAKER_39: science that's founded on computer science. So let's talk about what is a clinical trial? What is the, you know, state of the art architecture of that? Because I don't invest in this area, but

SPEAKER_52: you know, some of my contemporaries do, and they talk about how incredibly, incredibly frustrating and

David Friedberg: humbling it is to beat the placebo as, uh, I've been told, which is placebo seem to work 10% of the time, 15% of the time. They have some efficacy that is not zero. It's in some cases, pretty amazing what the placebo can do to people's minds in terms of having an impact versus, uh, actually having an impact. So maybe the definition of in 2023, what is a clinical trial? How does it work today? And then

SPEAKER_67: how is your software going to change that? Uh, a clinical trial is, is simply a comparison. So it's

David Sacks: really, it's the same thing as an AB test that you would run in any other area. So I, I have some new experimental treatment and I want to compare that to usually what is currently available. So placebo usually doesn't mean that you get no treatment at all. Usually given whatever would you would normally get for that particular disease plus a placebo. Um, so you're still receiving some treatment. Yeah. I just want to know which of these two things is better, which is safer, which works better, like so forth. Um, typically clinical trials are staged out. And so we have three different phases. Phase one is usually done in around 10 or so often healthy people. You give them your experimental

SPEAKER_67: drug. You just increase the dose until you see too high of dose. And then that lets you figure out how much is like a safe dosage. Then after that, you move on to a phase two trial. That's usually like

David Sacks: around a hundred people. And this is just an early signal of, does this seem like a, a drug that is worth continuing to pursue? And then the last thing would be a large phase three clinical trial. That's usually around a thousand people. And here you're going to randomly assign half of the people to receive your new experimental treatment. You're going to randomly assign the other half to receive the control. And then at the end of the trial, you're going to compare, see if it was better or worse. And then you can submit those data to the regulators, like FDA, to help them make a decision about whether or not your drug should be marketed. Got it. And so that process, I've heard a lot of

SPEAKER_52: criticism of that process. Objectively, what are the criticisms of that process? And then we'll get

SPEAKER_73: on to sort of what you're doing. There are a million criticisms of the process. Um,

SPEAKER_67: I think the major ones, you know, that are valid. Right. Um, so, I mean, the first thing that I think we, we, we tend to encounter is just the amount of time and cost that goes into running one of these

David Sacks: trials. One of these big phase three clinical trials, just the individual trial itself can cost

SPEAKER_67: hundreds of millions of dollars to run. And they take some, they often take more than five years, right? So you're talking about spending five plus years on a single experiment in hundreds of millions of dollars. And most of the time, these trials fail. So the majority of time, actually only about 10% of drugs that enter clinical trials end up being successful. So 90% failure rate, uh, in clinical

SPEAKER_57: trials. So you're spending like a decade and hundreds of millions of dollars on a experiment with a 90%

David Friedberg: failure rate. And would that mean if one out of 10 actually work, we're talking about billions of

SPEAKER_79: dollars to get a successful drug to market? If you were to look at it as a portfolio of say, 10 drugs,

SPEAKER_80: that's right. Yeah. Yeah. So incredibly expensive, incredibly time consuming. Um, I think that there

SPEAKER_67: are other things in terms of like, we need to get participants to be willing to join and take part in these clinical trials. And then you get into other issues of, um, you know, certainly issues like placebo control, um, are controversial amongst like patient advocates. Why is it that you're participating typically in a clinical trial? Usually that's because you want access to this new experimental therapy. Um, so I think that what people are thinking about and certainly what we're thinking about are ways that we can leverage new technologies to alleviate these problems of the

David Sacks: speed and cost of clinical trials, but also align them more closely with what patients want.

SPEAKER_70: Okay. Got it. So how are you using AI machine learning to test drugs? Because it would seem to

David Friedberg: me that the human body is complex, uh, in many ways and other ways, probably very simple and interactions are hard. So are you literally running a simulation of, Hey, here is this new drug, uh, for, I don't know,

SPEAKER_39: uh, lowering your cholesterol and you can model the human body and how it would interact. And does this occur in parallel to phase one, two, and three, or is this something that is just running a simulation

David Friedberg: that informs how you would then run these different trials? Explain to the audience how this works.

SPEAKER_67: Sure. So like I said, every clinical trial is a comparison. And what we really want to know is for an individual person, we wish we could tell like, what would happen to this person if I could take them and I gave them this new experimental drug and I observe how they respond. And I simultaneously don't give them the drug and I observe how they respond. Right. And so the, the way to run that experiment is, is to invent a time machine. So you give them the drug, you take your time machine back in time to the point you did it, and then you don't give it to them and you, and you see what happens. Right. And you do this comparison. Um, we don't have a time machine, but we do have, uh, computer models. And so the whole idea kind of behind what we do is that what we're going to do is for every individual person in a trial, we create a digital twin of that person. And it's a computer model that allows us to simulate what would happen to that individual person. And in our case, in the trials, we're always simulating what would happen if they got the control. So we don't simulate what would happen if they got this brand new experimental treatment, only what would happen if they got the existing treatment. And the reason is brand new experimental treatment. It's not really a machine learning problem, right? Like machine learning, we learn from data and we make new predictions, right? So what we can do is we'll, we'll have data from like a hundred thousand patients receiving the current treatment. And then what our task is, given a new patient, how will they respond to this current treatment? And that's kind of a standard machine learning problem, as opposed to here's a brand new molecule. What will it do to a person?

SPEAKER_88: That's a very, that's much harder. Yeah. So if I were to reflect this back to you in a simple,

SPEAKER_90: plain old English, I'm gonna even try to simplify it for me, explain it to me like I'm a five-year-old

SPEAKER_52: kind of situation here. We have a hundred thousand people who have taken this current cholesterol lowering drug, right? You're in the new trial. You're gonna get cholesterol, uh, lowering drug 2.0. It's completely new. Um, and then there's people who are gonna get the placebo, but hey, since we know these hundred thousand people's age, cholesterol level, uh, BMI heart rate, whatever battery of information we can say, Hey, J Cal 52 year old J Cal 174 pounds, uh, you know, this blood pressure, this cardio fitness level, whatever it happens to be. We take your watch data. I don't know what data

SPEAKER_39: is state of the art these days. And, uh, okay. Yeah. Look, we have another, out of those hundred thousand people. We do have 2000 people who are just like J Cal. We're gonna run a simulation to see what would happen with you on the 1.0 medicine. You're gonna take the 2.0. And of course we've got some

SPEAKER_01: other group of people who are taking the placebo. Is that, is that about right? What's happening?

SPEAKER_67: Yeah. I mean, well, yeah, so we're taking this historic, this data from the people that currently exists. We're training this kind of machine learning model on that data. Yeah. And then exactly. So for we would predict J Cal comes in, we'd say, what would happen to you if you got the placebo? And, and version 1.0 cholesterol medicine. Um, and so the interesting thing is if you take that to the extreme and let's imagine this case where that machine learning model we've built is perfect,

David Sacks: makes no mistakes at all. That's not true, but let's just imagine.

SPEAKER_70: If it's 50% correct, it's gonna have an extraordinary impact. So.

SPEAKER_67: Right. Yeah. But this interesting scenario, which you can kind of work backwards from is, well, if that were very true, then for every patient, I can just give them the experiment, the new 2.0 cholesterol medicine, and I can see how they respond to it. And I don't need any compare re I would not need any real patients receiving a placebo because for every patient, I'm just predicting exactly perfectly what would happen if they got a placebo. So if you could sort of get to that point where our machine learning models are sufficiently accurate, you get to a world in which you're running clinical trials that don't have placebo groups. It would be 100% of the patients receiving your new experimental treatment and zero patients receiving a placebo. So that would mean you'd have a clinical trial that's got half as many patients in it, which is way faster and cheaper to run. Also, all of your patients are getting access to this new experimental treatment, which is what they wanted, right? So that future is really great for our customers, the pharma companies because they get faster trials, actually great for all of medical research because you basically speed up medical research twice as fast. It's also great for patients. It's not perfectly achievable because our models aren't perfect today. And so then we get this question about how we still run randomized studies where some patients receive placebo is to guard against what you were calling earlier with hallucinations, right?

David Sacks: Yeah. So we want to make sure that we can guarantee that the clinical trials that we work in produce

SPEAKER_52: the right results, even if our models are not perfect. It seems to me that you when a new drug comes out, depending on the corpus of data you have about individuals, you could give it a shot and say to the AI, Hey, make your best predictions. And give me, you know, I mean, they're depending on compute power and they're available. Give me all the possible predictions you could come up with in some reasonable amount that a human could actually compare them and say predict what will happen, uh, with these 2000 people who are joining the trial as best you can, and then give them the trial and then see which sets of thinking the AI got correct. That's also a possibility and would also cost nothing. Because all you're doing is saying just make a simulation and be like running a simulation on who's going to win the NBA finals based on the data you have from the regular season. Is anybody doing that as well? Because it seems like it could

SPEAKER_67: be a worthy, uh, use of time. Yeah. I mean, right. So what, what we are basically doing again is we're simulating how every single patient in this trial is going to respond. If they got the new treatment, we are very interested as well into as drugs come out incorporating. So this is kind of the future world. So the way I kind of view it interestingly is that clinical trials are highly regulated area, uh, super scientifically rigorous, uh, right. Um, but we think that the easiest area actually, they're the easier than all of the other areas of medicine. Um, and the reason for this is because treatments are randomly assigned to patients. So basically wait, the way that this will work is that the model will make mistakes. It will make those mistakes on patients who are randomly assigned to receive the placebo. And it makes the same mistakes on the patients randomly assigned to receive the treatment. And basically in the end, the mistakes end up canceling out. Right. So because of that,

David Sacks: it's like that particular application is really robust to these mistakes that machine learning models make

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SPEAKER_125: Talk about the, the data. Well, the, the thing I'm curious about is the data you,

SPEAKER_52: that is currently used for these trials. Uh, my understanding is one of the problems is garbage

David Friedberg: in garbage out. You get information from patients. If they tell you information, well, have you, how many drinks do you have a week? Sure. They're like, ah, like two. And it, you know,

SPEAKER_129: it's really 20 or, you know, they're, so if they're reporting data, it's obviously going to be

SPEAKER_52: flawed. And then if you look at the data that's available in medical records, well, why do we even have medical records today? It's for billing, right? It has nothing to do with, right? It's a little,

SPEAKER_132: it has, it has more to do. Am I correct with billing than it does with reality? A hundred percent. Is that right? Yeah. Yeah. Yeah. Yeah. So like, what data do we actually

SPEAKER_39: have that has some truth to it? It feels like wearables, you know, are perhaps the holy grail,

David Friedberg: blood tests. You can't fake those. I, I don't believe, uh, you can tell me if I'm wrong, but blood tests, uh, that are historical, maybe body scans, which I just did the pro novo, uh, body scan and wearables. If I gave you my Fitbit data for 10 years and then my Apple watch, which I switched to

SPEAKER_39: all of those seem to be like, that's pretty rock solid. So are any of those type of things being currently used in these trials or is it still just like they go to people's medical records and they

SPEAKER_67: give them a survey to fill out? Well, clinical trials are a really unique space when it comes to data and medicine, because it's a research study. So one of the problems with, um, medical records, like, if you looked at my medical records, you would see that I've been to the doctor like four times in the last 20 years, right? Like, and all of the information in between those dates, just not even there. Like, cause I didn't go to the doctor, so it's gone. It doesn't exist. Right. But a clinical trial is really different. So in a trial, it's set up ahead of time and you define this giant battery of exams that you're going to give to patients. And that always includes things like blood tests. Now it includes other new things. There are times where people, it's going to be wearables. Sometimes it's going to be imaging, maybe people are getting MRIs. Um, it could be full genomic tests. Like you might get a whole genome sequence, potentially. So it could be a huge amount of information. It varies from trial to trial, but everyone is going to get this giant battery of tests. And then they're going to come in like once a month for the next year and a half and they're going to make the same battery of tests every month. So regardless of what happens to them, whether or not they're feeling good or they're feeling bad, it doesn't make any difference. You enroll in the study and you come in like once a month and you get this giant battery of tests. So there's actually a huge amount of information about, you know, these diseases that is being captured in these clinical trials. So this is an opportunity in two ways. First of all, we run tons of clinical trials every year. Like as a society, we run a ton of them. Actually, the government runs a ton of them. The NIH funds a ton of clinical trials and all of those data

SPEAKER_73: just poof out of they, they're collected and they're not used again. They're just like, what?

SPEAKER_00: So literally you have this incredible diamond mind in a clinical study, they collect all the diamonds and then they just throw them in the dumpster.

SPEAKER_144: Yeah, exactly. Yeah, they put them in a database somewhere.

SPEAKER_146: It's like the end of Indiana Jones and raise all our stocks that goes into some warehouse.

SPEAKER_148: That's right. Yeah. The top men are working on it. Exactly. You know, it's like it's in some warehouse and it's never used again.

SPEAKER_125: Oh, how do we, now, hold on a second. Let's pause there for a second. If this is paid for by the government in a lot of cases, the government owns it.

SPEAKER_67: So yeah, there's a, there's a rule that you have to make the data public two years after your clinical trial has been completed, if it was funded by the government.

SPEAKER_153: So that is sitting on a server somewhere or is it a public website?

SPEAKER_67: There's no like government server where like it exists, that's like all put together. So like we have a group of people. So we aggregate data from lots of sources to train from and we love clinical trial data because there's this high quality, amazing data sets. So like we have a group of people who like call up professors at universities and are like, Hey, you ran this clinical trial two years ago. So the data must be public now. Yeah. And then we aggregate the data. Yeah. It's crazy.

SPEAKER_52: It's like the freedom of information act. Like people will do in journalism, this freedom of information act to, Hey, listen, the government arrested this person, their documents available, JFK assassination, you know, give us the information. The government will release some percentage of it or whatever. You can actually start going and getting this

SPEAKER_39: information. Yes. And then putting it into AI models. This is something that we should have a Manhattan project on where some organization is paid like yours or another. It could be private sector, public sector collaboration to make a database of anonymized data of every clinical

SPEAKER_166: trial that's gone on. And then you can just set the AI on it a hundred percent. I 100% agree. Yeah.

SPEAKER_67: And right now the other part of it is the industry sponsor trials. Like, so there are pharma companies who are running all of these trials. Hmm. They own the data from those trials. So that's,

SPEAKER_172: typically how that makes sense. They paid for it. Yeah. Yeah. One could argue that maybe the patients should own their own data potentially, but individually they should. Yeah. They don't,

SPEAKER_73: but, uh, but that's, that's another point. Um, they really don't, they don't own a dual license to it.

SPEAKER_67: No, in many cases, they're not given it at all. Yeah. See, that's something that some patients never find out whether or not they got the placebo or the real drug. Okay. So this is somewhere where like,

SPEAKER_70: our government's not going to get this done because they're bought and paid for by pharma. I said that on you, but this is something where the EU could pass something where they just said,

SPEAKER_176: listen, your data, you get a copy of it. You, you get to know that. Yeah. I mean,

David Sacks: or the regulators, right? The FDA could say that, you know, if you want to submit your drug, you have to also submit anonymized data. It's going to go into a database, right?

SPEAKER_67: Hmm. Um, yeah, there's a huge amount of, of opportunity there because there's data from right now,

David Sacks: every year, about 1 million patients participate in clinical trials across the board. Hmm. So if you think about every year, there's 1 million people participating in that level of experiment. Um, and, uh, the data are not really being collected. So, but that's, that's where what we focus on is learning from those style of data.

SPEAKER_125: Yeah. In a country where you have socialized medicine, like Canada, let's say the Canadian

SPEAKER_52: government with a, with a pen stroke, Justin Trudeau. And it could just say, all this is put into an anonymized database, all the data, uh, or just give people a choice. Hey, if you want to get free healthcare, you have to give away some amount of data to the collective good anonymized, uh, or it could be opt in. But I, I think if you're giving socialized medicine, it's not too much to ask that your blood results, which the government paid for, get to be put anonymously. Your name, you know, your approximate region, you know, maybe ethnicity, DNA, whatever. Uh, if you, or some amount of it gets, I gotta think this through, because it could get a little dystopian. It gets complicated. Yeah. Does get complicated that the state owns your data. Yeah. But there is some trade of services here. So in a commercial country, like the United States, it could be, we'll discount your rate. If you put it into this pool, uh, for future, future research or like organ organ donning, you could just do it out of the goodness of your heart. Uh, in a, in a socialized medicine, they could say, listen, we just want everybody in the country to give their blood data to this research. I mean, there could be a way to do it in a very

SPEAKER_30: positive way. Is, is anything like that even being considered these days or no?

SPEAKER_190: No. Why is it so obvious to us and not everybody else?

SPEAKER_67: Yeah. I mean, it's, yeah, it's so obvious. Yeah. It's difficult. I think that there are a handful of, there are definitely a handful of

David Sacks: countries that have better medical records, um, uh, where, you know, if you have a national health system, it's easier to have a national medical record system, but it's still not the case that these are the, these are like a repository of people taking part in these kinds of research studies where you have a really rich, much more rich information about those people than you do a normal, uh, like just from your medical records.

SPEAKER_195: All right, everybody, our friends from Microsoft are here. Tom Davis, a senior director at Microsoft

SPEAKER_52: for startups. How long has Microsoft been working on this cloud that you've now sort of uncovered and

SPEAKER_198: and offered to founders? It's been years in the making, so to speak. The evolution of AI has taken many twists and turns in its journey, but these large language models have really been the game changer. And that's really thanks to open AI and the work that they've done. And it's obviously our partnership there has helped us really get ahead of the game on this. And we're seeing great companies like perplexity.ai in six months, they've built out an application that has now got millions of users that wouldn't have been possible in a more traditional way of working. So it's great to see the innovation that startups are able to bring to the table now and not have to make these huge investments in time, resources, and, and basically cash as well, which is always a premium when you're

SPEAKER_199: starting off your own company. The founders hub that Microsoft provides offers $150,000 in Azure cloud

SPEAKER_52: credits, all the development tools like GitHub and teams office, all that great stuff. You get all that

SPEAKER_195: for free five minutes to sign up six figures and benefits, AKA.ms slash this week in startups. Thanks

SPEAKER_52: so much, Tom. Tell me, uh, how does your company make money? Because you are a startup, you raised a series B I understand you've done pretty well for yourself here. PCs are placing a big bet on you.

SPEAKER_67: What's the business model. So we actually have a relatively simple to understand business model. Our value proposition for a, a pharma company is that by working with us, your trial can be months,

David Sacks: months, shorter, many months. So it depends a little bit, let's say six, two months to a year

SPEAKER_67: shorter. Um, and if your pharma company, you start your patent clock actually starts when you start your clinical trials. Um, so every six, two months shorter or a year shorter, that's an extra six months to a year of on patent sales. So it's billions of dollars in revenue

David Sacks: for, for the pharma company, if the drug is successful. And as we said, that's like one in 10, but, um, so how do we do that? Well, the way we're going to do it is we're going to allow these pharma

SPEAKER_67: companies to run clinical trials that have smaller control groups than normal trials. So you have fewer patients that you need for your trial. So let's say you need 100 fewer patients in, in your clinical trial. Well, there's a couple of things. One is that, you know, that again, it might take six months to find a hundred patients who are willing to participate in your clinical trial. So right there, you've saved a whole bunch of time, but pharma companies also pay about $100,000 per patient in their clinical trial. Yeah. Yeah. That's where that $100 million number comes from.

SPEAKER_206: You get a thousand people in a trial, you're at a hundred milli. Yep. Yeah, exactly. And the patents

SPEAKER_52: are, I think it's pretty standard, 20 years. 20 years. So you're, you're talking about a couple of year trial. What did you say? Five years. Five years. So you're at 15. If you were to get them that

SPEAKER_148: extra year. Oh, that's just one trial. That's one trial. You still have your phase one. You have your phase two. You've got your trial. So what is he, by the time you get it to market, how many

SPEAKER_55: years you got left on the pen? Probably like 10 or 12. So you have 10. If you save them one year,

SPEAKER_129: you get 10% more money. Yeah. Yeah, that's right. Or 10% more time to exploit the drug. Exactly.

SPEAKER_67: Yeah. And, and not only, I mean, you know, that's, that's the, that's the capitalist way. We can also frame it and say, well, there's, there's a whole group of patients during that year who needed a drug who now get access to it, right? Because otherwise, if it was, if it was, you wait another year, say you have, you know, groups of patients in a disease where people are dying, right? If that

SPEAKER_178: drug's not available, all of those people are dead. So I know people with cystic fibrosis and there,

SPEAKER_30: this is an area where it's particularly acute and they've made incredible progress, but these drugs

SPEAKER_52: are extraordinarily expensive for a very small number of people. And there is some compassionate use of this, but it is really a, uh, challenging dynamic. Maybe you could talk about the long tail of diseases and, and how they should apply because that does also seem to be something unique. We have

David Friedberg: the, we have the me, the big four horsemen, uh, uh, you know, of, uh, you know, diabetes and cancer

SPEAKER_30: and whatnot, um, uh, that, that kill people, Alzheimer's I think is in that group. Um, but, uh,

SPEAKER_50: this there's the long tail. So, so does, is this gonna have a dramatic effect on the long tail as well?

SPEAKER_67: We think that this should be used in every single clinical trial period. Okay. Um, uh, there's, there are challenges that I would call technical and data challenges to getting there, um, for all

David Sacks: of these small diseases. That also means there's a small amount of data to learn from, right? So if we're talking about Alzheimer's, so many people have Alzheimer's, we can build really, really big data sets. Um, but you start talking about, um, I don't know, something like, uh, cystic fibrosis is a lot smaller population. And so we still need to have enough data to train a machine learning algorithm, but we are working on that all the time, how we can do better with these, uh, small populations. And over the next few years, we want to be able to roll things out across everything. Uh, actually our, our ultimate goal right now, we basically, the way we do our machine learning is there will be one model per disease. So we have like a model for Alzheimer's. We have a model for ALS. We have a model for multiple sclerosis like that. We want to build one model for everything. One model for like all human health. Um, no, that's an extraordinary mission. I mean,

SPEAKER_52: this is very hard, but, uh, it's kind of like AGI versus vertical, right? Like you're, it is, yeah, verticalized cholesterol or heart disease. You know, you need a certain, uh, data set for that, but Alzheimer's might be overlap 50%, but not a hundred percent. Am I, am I ballpark correct here?

SPEAKER_96: Exactly. You know, that's right now. Yeah. We're so you get these specialized data sets. We build

David Sacks: specialized models, but the, the whole point of it is, I think that in order for us to get into these smaller disease areas, we want to have something that looks sort of like a foundation model for health. Um, so one of the things we talk about these, these large foundation models today doing

SPEAKER_67: is that they can do either zero shot or few shot learning. And what that means is that they can learn to predict things having seen one example. So instead of having to give it like, ah, we need a million examples for you to figure it out. We can show here's one example, what would happen in these next few examples. And so we want to probably be able to build something

David Sacks: similar where for patients, even with really rare diseases, you can still figure it out from just

SPEAKER_50: a couple of examples. That's extraordinary. Uh, in a way it's like the, the foundational models

SPEAKER_52: of chat GPT or stable diffusion and some dolly and all this stuff. My understanding is people think you're going to be able to fit this on your smartphone. And so the model will eventually be on a chip. And when that happens, that's going to be pretty wild that you're just like you have a wifi chip or, you know, a graphics chip on your phone or computer, the concept of having an AI chip on there that just yes is the next word in a sentence. And it's kind of starting you on third base every time you could do that for health. Then my watch, my Apple watch might have this built into it. And

SPEAKER_30: it'd be like, wow, we're seeing something with your heart. Uh, we know what you ate and we have your blood sugar level. Cause you have a continuous glucose monitor. It can be like doing stuff in real

SPEAKER_50: time. You can be doing real time interventions. Yeah. There's all kinds of stuff that are really

SPEAKER_67: interesting and again, hard problems, but I want to know not just what is happening with me today, but what will happen with me in the future. I want to have a, something that can predict the state of my health in the future, depending on what I do today. If I change my diet in this way, how will that actually really affect my state of health over time? If I take on this different workout plan, how will that affect my state of health over time? And, um, it's a super duper duper hard problem, right? You talked about how complex, like a human, human, the human body has 37 trillion cells in it. It's actually 100 times the number of stars there are in the galaxy. So it's like a really, really complicated system. It's actually so complicated. I think that AI is going to be the only way we can tackle it. Right. Um, it's too complicated for us to try to build up piece by piece. So I think that AI is really going to be fundamentally the new language of biology in the, in the end, like we are going to describe biology in 10 or 20 years entirely in terms of like AI algorithms that are, that are learned to understand and tame this complexity. And once you get to that point, yeah, the idea of you have your own digital twin that's on your computer that talks about your health and maybe your doctor also has that same thing. You don't even need to go to the doctor's office anymore. They just pull up your digital twin and they can see what hap is happening with

David Sacks: you today and what's going to happen with you in the future. And they can design treatment plants.

SPEAKER_52: Maybe it's even an AI doctor, but I mean, you already have this happening, uh, in, again, back to vertical AI versus general AI, which is analogous to what we're talking about here. You already have, uh, in, um, x-rays, people are starting to build technology to look at the x-rays or to look at like heart rate monitors over time and just highlight stuff that then goes to a doctor and that's augmentation. And so what I've been really thinking about in this AI future, because this is moving rapidly. You've been doing this for six years. How would you describe the pace we've seen of the past

SPEAKER_73: year compared to the decade before? It's definitely moving faster. Um, it's interesting in like, uh,

SPEAKER_67: I think what's happened to more is that we finally reached a threshold of utility. So things seem like they are moving really fast when you're near a threshold of utility, even if they're moving slow, because like if you just stay at a linear line, you just increase by x a little bit every year, but there's some threshold at which you need to pass before people care about it. You will seem like no progress has happened and all of a sudden you'll pass that threshold and everybody like, wow, amazing progress. Um, and I do kind of think that's where we're at, that the, the AI research has been pretty steady progress over the past 10 or 15 years to bring us to this point. Um, but it's all, all of a sudden got good enough that we're willing to use it. Right. I mean, if you think about like GPT three, the, uh, API to that was released three years ago. Right. So it's not like we're in some exponential speed up of like terminator world because that was a three years waiting period between three and four. Right. So that's actually not that fast. It's more that, that what's happened is people have figured out, oh, wow, these models are actually able to solve stuff now and we can build applications on them. And so now it feels like it's incredibly fast because it's all these applications because before they weren't useful and now they are. And so it's really more of a cross that threshold of utility than I think a real

SPEAKER_172: like speed up in the research. The research is kind of the same.

SPEAKER_52: Yeah. There is something perhaps to humans using it, finding the utility and then the reinforcement

SPEAKER_30: learning or these GPT starting different language models, different AI instances, learning from each

SPEAKER_52: other. That also is, once you get humans using it, it's like, well, GPS is really interesting for sending a missile or tracking a plane, but it's also pretty good at, uh, finding a bakery or getting an Uber, right? It's like the GP, the street finds it's used for it's used for technologies and what William Gibson said. And, you know, it's like, that really feels like what's happening. Once you put language models into a chat format, it's just, or you start building auto GTPs or plugins. It's like with streets, figuring out all kinds of interesting use cases for it. Hey, listen, thanks for doing this work. Um, and, uh, and on, on the revenue question, since you save them that extra year,

SPEAKER_39: you just want to take a percentage of that or take a percentage of how much less, uh, people can be in

SPEAKER_67: the study. Is that the ultimate goal? Yeah. So that's what I was saying earlier. Yeah. The business model is relatively simple. So if you're paying a hundred thousand dollars per patient and we remove one patient, you should pay us $100,000. If you remove two, $200,000. So we just get paid

David Sacks: based on how much smaller we can make your clinical trials. Um, that's our main, that's our main

SPEAKER_244: business model split at 50, 50. So they get a little savings. Yeah. We tried to take them,

SPEAKER_67: which again, they're getting, I see millions of dollars and say an additional sales. So they're getting a great deal, uh, by, by working with us. Um, it's, it's a, it's an example of our work in clinical trials, I think is a really unusual example of something that kind of everybody wins from because the pharma company, they definitely benefit, right? They can make a huge amount of additional sales. But the patients also very clearly benefit because you have a smaller control group and you have a

David Sacks: faster time to market for the drug. Um, even the regulators and people benefit from this because we, we, it's a use of AI where actually we can prove it's very, we can actually prove that the clinical trials produce the same rigorous, scientifically rigorous results. So kind of everybody benefits from this, this technology. Um, I think that there's going to be a lot more areas in health where this is the case where technology is just going to totally benefit everybody. Um, I think the difficult part of building in this space is that, you know, it's kind of this legacy conservative industry and trying to figure out how to get people to trust and adopt new technologies is hard.

SPEAKER_201: Yeah. You know, we have the Wikipedia as an example of a foundational dataset

SPEAKER_52: and the DBpedia that's been kind of built off of it. It's really helped train these models. Is there an equivalent in your world? And if not, would that not be something noble for the government to work on if way of saying, Hey, let's find 10,000 people in the United States and give them a battery of tests for their lives and really get that dataset and open source it to the, to the world to learn from.

David Sacks: I don't think that there's one, um, there have been attempts to kind of go in that direction. The UK biobank is an example of something that kind of starts to look in, look a little bit more like this, which is the, you know, NHS's version of this. So exactly that the NHS is like, Hey, we have a national health system. We could create a big open source dataset for everybody. And there is a big open source dataset. Um, barely also, uh, and Google had tried to run something they called project baseline. Uh, I don't know what its current status is, but the whole idea was enroll 10,000 people into a big observational study and follow them for a bunch of years and collect all this information. And then we'd have this dataset to learn from it.

SPEAKER_255: That was Verily right there. Verily. Yeah, yeah, yeah, exactly.

SPEAKER_256: That was Google's live forever healthcare thing.

SPEAKER_67: Well, I think you need something to learn from, right? Like kind of what you're saying. So baseline is like, Hey, let's collect this dataset. Let's build this thing that we could learn from. Um, I, again, I don't know what the status of that is, but so there've been a few different options,

David Sacks: but I, I think that, uh, I a hundred percent would support the U S government trying to build

SPEAKER_211: a similar type of dataset. Yeah. The ability to reduce suffering, uh, extend, um, health span.

SPEAKER_30: Maybe we don't add years to life, uh, as Peter, uh, and his new book has been talking about Tilla, I guess this is how you pronounce his last name. Uh, you know, he talks a little bit about health span versus lifespan. Hey, you live the same number of years, but you're, you're skiing in your seventies and eighties or riding bikes in your nineties. It feels like we're on the cusp of something very interesting here. And so just on behalf of humanity, thank you for choosing this for your entrepreneurial journey. Uh, and that you're doing God's work, or if you're an atheist, uh, you're doing humanity's work. So pick whichever you like, uh, no judgments either way. Thanks for coming on the program. And, uh, maybe we catch up in a year and, and here, how are you doing next year with this? Yeah, sounds great. Thanks for having me. All right. Cheers. Thanks for coming on the program. Thanks for coming on.