Uber earnings + using AI to revolutionize clinical trials w/ Unlearn.AI’s Charles Fisher | E1734

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1:13Sign up today at aka.ms slash thisweekinstartups. This is a great day for me. You can see I'm very enthused. My net worth went up, but also the bet I placed over a decade ago that defined my career as an investor. I always believe this company would start becoming a money printing machine. And thus that has happened to one revenue 8.8 billion. That's up 29 % year over year. And we're talking about cloud computing at 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, 100 million above analyst estimates, according to CNBC.

1:59The total trips in Q1, 2.1 billion. I remember when Uber had a total of three riders on the platform, and we'd done maybe a dozen rides, up 24 % year over year. That's incredible. This includes mobility and delivery, monthly active platform customers. Maps, maps, macubs. 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. I was 130 million. it. That is extraordinary.

2:45We're talking nine figures worth of 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 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 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.

3:20That'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 worth$31.4 billion, up 19 % year over year. So the revenue grew 29 % year over year, but gross bookings grew only 19%, which means Uber's more profitable and charging more for their services, which is great to see. Bottom line, they had a small net loss of$157 million, and that included a$320 million benefit from net unrealized gains related to Uber's equity investments.

4:07But 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 high free cash flow$549 million cash and cash equivalents and short-term investments 4.2 billion for the team over at uber and uh i think the uh really interesting part of all this is lyft's demise they are a shrinking uh 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 um you have people competing like public transportation and micromobility and people owning their own cars, rent-a-cars, and taxi and livery drivers, right?

4:59To 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 food but people order from other sources as well right they're still instacart you still have amazon delivering groceries and whole foods so it's not quite a monopoly but it's a strong position in those areas and the average mapsy did 14 rides or food orders in the quarter that's almost five per month which is very impressive because you take the 2.1 billion trips, you know, divided by 150 million, you know, mapsies, just taking a guess here, that maybe there are taking 14 rides or food orders in the quarter, which would be about five per month.

5:59That'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 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 uh because i just take it out and i'm just like you know what i want you to have a great childhood and enjoy some nice sushi and some boba so just to to dara and the team and dara's coming on the the show uh in uh over the summer.

6:40We'll have a great interview and catch up. But the stock is ripping. It's up almost 11 % 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. This year at Founder University, I'll make somewhere between 50 and 125K investments as a tribute to that 25K investment I made in Uber as one of the first investors, maybe the third or fourth i don't know uh so if you want twenty five thousand dollars 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 uh we're gonna have a we're gonna actually have our own space in san mateo soon and um just come hang out with jacal 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.

7:41I 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 hope to invest in maybe two or 300 names per year, which would put me at 2000 more investments in my second decade of investing to go with the 300 in my first or 250 in my first decade. I'm going to 10x that. And, you know, you never know, maybe I hit another Uber or two, and maybe that's you. So founder.university or launch.co slash apply to meet with our team.

8:21Great job, team Uber. All right, next up on the program, Charles Fisher, the CEO of Unlearn AI. time. Okay, everybody, you know, all of the complaints about building apps on the blockchain, it's slow, oh, it's less reliable. Oh, there's no support if things go wrong, right? The blockchain is this incredible innovation. And there's some good news here. Execution on the blockchain just got super easy. Quick note has solved all of these problems. They give blockchain developers unparalleled reliability and speed with access to unlimited endpoints across 18 chains, and 35 networks. Quicknode provides amazing response time and a dedicated 24-7 customer support team.

9:06They offer consistent performance at any scale, lightning-fast API responses that are 2.5x faster on average than competitors, and the most sophisticated and globally balanced cloud and bare metal Web3 architecture. Listen, everybody, this is the AWS or Azure of Web3. If you are building dApps, decentralized apps, you need to use QuickNode. It's that simple. Find out why companies like Twitter, Adobe, Coinbase, and OpenSea use QuickNode. Get one month free by using the code TWIST at go.quicknode.com slash TWIST. That's go.quicknode.com slash TWIST. And remember to use the code TWIST. All right, everybody.

9:50Welcome back to our special AI series. i've 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 all keep up with this crazy pace we found an interesting uh guest today his name is charles fisher he's the founder and ceo of unlearn.ai unlearn ai and um 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 3.5 for and some of the other tools bard from google and uh the quora ai tool they're frequently hallucinating and giving wrong data so maybe we could start with a little bit of uh how long you've been working on this and then getting right to how this is going to change clinical trials and the hallucination problem yeah definitely um yeah so thanks for having me um how long have i been working on 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 because 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, working on, yeah, developing generative AI to currently speed up problems in clinical trials.

11:45So, 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, turning all of medicine from something that's really today kind of an art form into a real predictive 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 you know some of my contemporaries do and they talk about how incredibly incredibly frustrating and humbling it is to beat the placebo as 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 actually having an impact so maybe the definition of in 2023 what is a clinical trial how does it work today and then how is your software going to change that a clinical trial is is simply a comparison so it's really it's the same thing as an ab test that you would run in any other area.

12:59So, 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 you would normally get for that particular disease plus a placebo. So, you're still receiving some treatment. And 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 drug.

13:35You 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 around a hundred people. And this is just an early signal of does this seem like 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 1000 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.

14:15And 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 criticism of that process. Objectively, what are the criticisms of that process? And then we'll get on to sort of what you're doing. There are a million criticisms of the process. Well, the major ones that are valid. Right. Right. So, I mean, the first thing that I think we tend to encounter is just the amount of time and cost that goes into running one of these trials. One of these big phase three clinical trials, just the individual trial itself can cost hundreds of millions of dollars to run.

14:59And they often take more than five years, right? So, you're talking about spending five plus years on a single experiment and hundreds of millions of dollars. and most of the time these trials fail so the majority of time actually only about 10 percent of drugs that enter clinical trials end up being successful so 90 percent failure rate uh in clinical trials so you're spending like a decade and hundreds of millions of dollars on a experiment with a 90 percent failure rate and would that mean if one out of ten actually work we're talking about billions of dollars to get a successful drug to market if you were to look at it as a portfolio of, say, 10 drugs.

15:40That's right. Yeah. Yeah. So incredibly expensive, incredibly time consuming. I think that there 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, you know, certainly issues like placebo control 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. 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 speed and cost of clinical trials, but also align them more closely with what patients want.

16:24Got it. so how are you using ai machine learning to test drugs because it would seem to me that the human body is complex uh in many ways in 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 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 run in a simulation that informs how you would then run these different trials? Explain to the audience how this works. Sure.

17:11So, 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 way to run that experiment 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 see what happens, right? And you do this comparison.

17:43person. We don't have a time machine, but we do have 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 is not really a machine learning problem, right?

18:23Like machine learning, we learn from data and we make new predictions, right? So, what we can do is we'll have data from like 100 ,000 patients receiving the current treatment. And then 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? That's a very, that's much harder. Yeah. So if I were to reflect this back to you in simple, plain old English. It wasn't simple enough. I'm going to even try to simplify it for me. Explain it to me like I'm a five-year-old kind of situation here.

19:01We have 100 ,000 people who have taken this current cholesterol-lowering drug. Right. You're in the new trial. you're going to get cholesterol lowering drug 2.0 it's completely new and then there's people who are going to 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 jcal 52 year old jcal 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 is state of the art these days and uh okay yeah look we have another out of those hundred thousand people we do have two thousand people who are just like jacal we're going to run a simulation to see what would happen with you on the 1.0 medicine you're going to take the 2.0 and of course we've got some other group of people who are taking the placebo is that is that about right what's happening yeah i mean well yes so we're taking this historic this data from the people that currently exist we're training this kind of machine learning model on that data yeah and then exactly so we would predict jcal 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 makes no mistakes at all that's not true but let's just imagine if it's 50 correct it's going to have an extraordinary impact.

20:37Right, 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, 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.

21:12It 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.

21:45It'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 placebos to guard against what you were calling earlier with hallucinations, right? So, we want to make sure that we can guarantee that the clinical trials that we work in produce 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 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 2 000 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 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 But we are very interested as well into, as drugs come out, incorporating.

23:24So, this is kind of the future world. So, the way I kind of view it, interestingly, is that clinical trials are a highly regulated area, super scientifically rigorous, right? But we think they're the easiest area, actually. They're easier than all of the other areas of medicine. And the reason for this is because treatments are randomly assigned to patients. So basically, 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.

24:01And basically, in the end, the mistakes end up canceling out. And so because of that, it's like that particular application is really robust to these mistakes that machine learning models make today. Listen, everybody, when it comes to the blocking and tackling of running your startup, you don't need to reinvent the wheel. CDNs, aka content delivery networks, are the place where startups can really overcomplicate things. You don't need custom authentications or custom codes. Nope. If you're a startup, you need to just check out Cashfly. It's a pure play CDN. And CDNs are literally all they do.

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25:17The faster you go with your delivery, the more people use your products. So go check out Cashfly. And Twist listeners are going to get 10 terabytes free forever when you sign up at twist.cashfly.com. That's twist.c-a-c-h-e-f-l-y.com. 10 terabytes are waiting for you for free. Stop what you're doing. Pause the podcast. Go to twist.cashfly.com and get your 10 terabytes for free forever talk about the the data well the the thing i'm curious about is the data you that is currently used for these trials uh my understanding is one of the problems is garbage in garbage out you get information from patients if they tell you information well have how many drinks do you have a week sure like ah like two and you know it's really 20 or you know they're so if they're reporting data it's obviously going to be 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 little it has it has more to do am i correct with billing than it does with reality is that right yeah yeah yeah yeah so like what data do we actually have that has some truth to it it feels like wearables you know are perhaps the holy ground blood tests you can't fake those 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 body scan and wearables if i gave you my fitbit data for 10 years and then my apple watch which i switched to 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 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?

27:27Like, and all of the information in between those dates is 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. It could be full genomic tests, like you might get a whole genome sequence potentially.

28:01So, 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 these diseases that is being captured in these clinical trials.

28:33So 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 just poof out of they're collected and they're not used again they're just like what so literally you have this incredible diamond mind yeah in a clinical study they collect all the diamonds and then they just throw them in the dumpster they don't exactly if they yeah they put them in a database somewhere it's like the end of indiana jones and raiders of the last that goes into some like warehouse 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 oh um and 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 so yeah there's a there's a rule that you have to make uh the data public two years after your clinical trial has been completed if it was funded by the government so that is sitting on a server somewhere or is it a public website 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 and then we aggregate the data yeah it's crazy like a 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 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 trial that's gone on then you can just set the ai on it a hundred percent i 100 agree yeah 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 they own the data from those trials so that's typically how that makes sense they pay for it yeah one could argue that maybe the patients should own their own data potentially but individually they should yeah they don't but uh but that's that's another point um they really don't they don't own a dual license to it 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 our government's not going to get this done because they're bought and paid for by pharma i said that not you but this is something where the eu could pass something where they just say listen your data you get a copy of it you you get to know that yeah i mean 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 um yeah there's a huge amount of opportunity there because there's data from right now every year about one million patients participate in clinical trials across the board so if you think about every year there's one 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 in a country where you have socialized medicine like canada let's say the canadian 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 or just give people a choice hey if you want to get free health care you have to give away some amount of data to the collective good anonymized uh or it could be opt-in but 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 if it gets complicated yeah it does get complicated 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 will discount your rate if you put it into this pool uh for future future research or like organ organ doning you could just do it out of the goodness of your heart uh 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 positive way is anything like that even being considered these days or no no why is it so obvious to us and not everybody else 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 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, like just from your medical records.

34:06All right, everybody, our friends from Microsoft are here. Tom Davis, a senior director at Microsoft for startups. How long has Microsoft been working on this cloud that you've now sort of uncovered 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. These large language models have really been the game changer. And that's really thanks to OpenAI and the work that they've done. And obviously our partnership there has helped us really get ahead of the game on this. And we're seeing great companies like Perplexity.ai.

34:41In 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 basically cash as well, which is always a premium when you're starting off your own company. The Founders Hub that Microsoft provides offers$150 ,000 in Azure Cloud Credits, all the development tools like GitHub and Teams, Office, all that great stuff. You get all that for free. Five minutes to sign up, six figures in benefits, aka.ms slash This Week in Startups.

35:22Thanks 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 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, months shorter, many months. So it depends a little bit, let's say six, two months to a year shorter. Um, and if you're a pharma company, you start your patent clock actually starts when you start your clinical trials.

36:02Um, 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 for, for the pharma company, if the drug's successful. And as we said, that's like one in 10. But so how do we do that? Well, the way we're going to do it is we're going to allow these pharma 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 your clinical trial. Well, there's a couple of things. One is that, you know, that again, it might take six months to find 100 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 yeah yeah yeah so that's where that 100 million dollar number comes from you the thousand people in a trial you're at 100 milli yep yeah exactly and the patents are i think it's pretty standard 20 years 20 years you're you're talking about a couple year trial what did you say five years five years so you're at 15 if you were to get them that 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 by the time you get it to market how many years you got left on the pen probably like 10 or 12 so you have 10 if you save them one year you get 10 more money yeah yeah that's right or 10 more time to exploit the drug.

37:37Exactly. Yeah. And not only, I mean, you know, that's the capitalist way. We can also frame it and say, well, 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 you wait another year, say you have, you know, groups of patients in a disease where people are dying, right? If that drug's not available, all of those people are dead. I know people with cystic fibrosis, and this is an area where it's particularly acute, and they've made incredible progress, but these drugs are extraordinarily expensive for a very small number of people.

38:13And there is some compassionate use of this, but it is really a challenging dynamic. Maybe you could talk about the long tail of diseases and how this should apply, because that does also seem to be something unique. we have the we have the me the big four horsemen uh you know of uh you know diabetes and cancer and whatnot um that that kill people alzheimer's i think is in that group um but uh this there's the long tail so so does this gonna have a dramatic effect on the long tail as well we think that this should be used in every single clinical trial period okay yeah um uh there's there There are challenges that I would call technical and data challenges to getting there.