Ep 155: AI’s Edge in Pharma – Lowering Drug Failure Rates

 

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Overview

Recent advancements in artificial intelligence (AI) have paved the way for revolutionary transformations in the pharmaceutical industry. The integration of AI technologies in the drug development process has shown promising potential in reducing the high failure rates of drugs during clinical trials. Business owners, decision makers, and professionals in the pharmaceutical and healthcare sectors are increasingly recognizing the impact of AI in this domain, and the implications it holds for more efficient, cost-effective, and innovative drug development.

The Challenge of Drug Failure Rates

The pharmaceutical industry has long grappled with the challenge of high failure rates during the drug development process. With an average of 90% of drugs failing during clinical trials before reaching the market, the industry has been seeking innovative solutions to address this inefficiency. The complexity of biological systems, the enormous volume of data, and the limitations of traditional research methods have contributed to the persistently high failure rates.

The Potential of AI in Drug Development

Artificial intelligence has emerged as a transformative tool in the endeavor to mitigate drug failure rates. By leveraging AI-powered algorithms and machine learning, companies are capable of analyzing vast datasets containing genetic, protein, and molecular information. These AI models enable the identification of complex relationships and patterns within the data that traditional methodologies may overlook. As demonstrated by recent developments, AI has the potential to revolutionize the efficiency of the drug discovery process and significantly reduce failure rates in clinical trials.

Utilizing AI in Practice

The integration of AI technologies in drug development involves extensive data generation and analysis. Companies have employed advanced AI tools to conduct millions of experiments and extract invaluable information from biological systems. By utilizing AI for designing and screening potential drug candidates, researchers can expedite the process of identifying promising compounds and molecular targets with enhanced precision.

Impact on Healthcare and Beyond

The successful application of AI in lowering drug failure rates holds significant implications for the healthcare industry and society as a whole. Firstly, the reduced failure rates lead to more cost-effective drug development, potentially lowering the prices of medications for consumers. Furthermore, the accelerated discovery of effective medicines for various diseases could open avenues for addressing previously unmet medical needs and enhancing patient care.

Conclusion

The utilization of AI in drug development marks a significant milestone in the pharmaceutical industry, offering new avenues for innovation and efficiency. Through the harnessing of AI's analytical capabilities and data-driven insights, businesses in the pharmaceutical sector have the potential to confront the longstanding challenge of high drug failure rates. As the integration of AI becomes more prevalent, it is evident that the impact on drug development and the healthcare landscape could be profound, ushering in a new era of transformative advancements in medicinal research and patient care.


Topics Covered in This Episode

1. Why drugs fail at a high rate
2. Work being done in drug testing
3. AI's impact on drug research
4. How AI and drug testing work
5. Future of medicine with AI


Podcast Transcript

Jordan Wilson [00:00:19]:

Why do drugs fail at such a high rate, and what can artificial intelligence do about it? We're gonna be talking to an expert who's helping lead the field in helping to answer that question. So welcome. This is Everyday AI. My name is Jordan Wilson. I'm your host, and Everyday AI is for you. It's for us all. It's so we can all learn and leverage what's going on in the world of generative AI so we can use that To grow our companies and to grow our careers. If you're new here, thanks for joining.

Daily AI news


Jordan Wilson [00:00:53]:

If you're live, as always, get your questions in. What do you wanna know about and How AI can help lower drug failure rates in the field and hopefully make safer and better medicines for us all to use. If you're joining from the podcast, thanks for listening. As always, make sure to check your show notes. There's always more information in there, you know, other related episodes, all of that. So, before we answer that question and we start to dig into how is AI helping to reduce drug failure. Right? Let's first go over what's happening in the world of AI news. So, Amazon's reInvent 2023 conference is still ongoing.

Jordan Wilson [00:01:29]:

Some new announcements aside from, you know, earlier in the week with their nuke Amazon Q. So, Amazon Did just announce yesterday, their Titan AI image generator. So, right now, it is available in preview in Amazon's Bedrock counsel for AWS customers, and we'll see we'll see how this works. We'll see if it can hang with, MidJourney, DALL E, stable diffusion, and some of those others. So, some some exciting news, there from Amazon. Second, and kind of relevant to today's discussion, so DeepMind, just used AI to discover millions of new chemical materials. So Google's AI division deep mind used machine learning to discover two point 2,000,000 new crystals promising to revolutionize industries such as computer chips, solar panel, and just new discovery of of materials. Right? So this new tool that they're using called, NOME, I believe that's how it's pronounced, bypass centuries of experimentation and has the ability to significantly improve The efficiency of materials discovery.

Jordan Wilson [00:02:33]:

So researchers there at DeepMind said that this new discovery equated to 800 years worth of knowledge. My gosh. What a great day for DeepMind. I don't know if I accomplished 800 years worth of knowledge yesterday. It was just just kind of a Tuesday, a Wednesday for me. Last but not least, it is the 1 year anniversary of of ChatGPT. So, and some some new news, about They're bored. So, yes, it's been a full year, finally, or already.

Jordan Wilson [00:03:02]:

I don't know what it seems like to you, but ChatGPT has been around, and we've seen a lot. Everything from going from the free model, which was not very good, to the introduction to the paid chat GPT plus with GPT four and plug ins and the recent introduction of custom GPTs and all the Sam Altman firing drama. But on that and some new news in this. So Sam Altman announced last night, on the OpenAI website that Microsoft will now have a seat on the OpenAI board as there's new board members, but They will have a nonvoting seat. So let me know in the comments. What do you see happening in the next year for year 2 of chat GPT. Drop a comment. Maybe we'll feature it in the newsletter.

About Chris and Recursion


Jordan Wilson [00:03:43]:

So, alright. With that, let's let's talk. Hey. And if if you do tune in for the news because I've been told a lot of people do. There's always more, so just go to your everyday AI .com. Sign up for the free daily newsletter that comes out, you know, every every day around 11 AM Central and Standard Time. So, but I wanna talk about drug failure rates. Why are they so low, and what is AI doing about all of this? So, it's it's not just me today.

Jordan Wilson [00:04:10]:

Don't worry, guys. We have an an actual expert in in the AI field. So, please help me welcome To the show, Chris Gibson, the cofounder and CEO of Recursion. Chris, thank you so much for joining the show.

Chris Gibson [00:04:22]:

Thanks for having me.

Jordan Wilson [00:04:23]:

Yeah. Absolutely. Tell, can can you just tell our audience a little bit about what recursion is and what work that you do?

Chris Gibson [00:04:30]:

Absolutely. So recursion is bringing together the world biology and technology to try and discover new medicines faster and to bring them to patients at lower cost in the coming decades.

Jordan Wilson [00:04:41]:

So why do Drugs fail. I'm I'm curious. Like, why does that happen? Right? So, you know, all these, you know, great great minds are creating these these drugs, but It's it's high failure rates. Right?

Chris Gibson [00:04:53]:

It's extraordinarily high. So so the biopharma industry is a multitrillion dollar industry Where 90% of drugs that go into clinical trials fail before they ever make it to patients. And it's not because these people don't know what they're doing. There are extraordinary scientists who have been working for decades. Some of these scientists will work their entire career without ever having a medicine make it all the way to market. And the reason is because biology and chemistry are so complex. So you've got a trillion cells in your body. Every one of those cells has about 400,000 proteins encoded by about 20,000 genes, and there are trillions of interactions happening every second.

Chris Gibson [00:05:32]:

The fact that we can find any medicine works is kind of amazing. But but ultimately, in the face of this incredibly complex system, the tools that we've had at our disposal for the last 40 years in the industry have just been 2 reductionists. We can't study that system in its full complexity. We study it in little tiny pieces. And, ultimately, that's why we believe that AI has such a prominent role to play in the future because AI is so great at taking these extraordinarily large datasets that companies like Recursion are creating And turning them into, models that help us understand how different genes and proteins and and and molecules might be interacting. And, ultimately, we believe, and I think we're starting to show, that we have the potential to bring down those failure rates. And what's that what I tell the team, If you could go from 90% failure rates to 80% failure rates, where 8 of your drugs out of 10 fail in clinical trials, you could reduce drug prices by half.

Impact of AI and reducing drug failure


Jordan Wilson [00:06:29]:

Wow. And and, Chris, like, when I hear that 90%, you know, as someone that's, you know, not familiar with the pharmaceutical industry and and the and the science, and the Artificial intelligence that that goes on behind the scenes. Like, I'm shocked by that. Right? Like, 90% seems, jaw dropping, but then you just said, hey. Even getting it down to in 80%. That what that means for the or maybe talk about, like, what that means for the world. So lower lower drug prices, But what else would would just reducing that failure rate by 10% mean for for the rest of us?

Chris Gibson [00:07:05]:

Well, in In addition to lowering prices, it means that there would be thousands of diseases that today have no treatment that maybe we could actually make a difference in in combating. I mean, How many people on your show have lost a relative to cancer? Almost everybody or a relative to heart disease. And we're seeing extraordinary progress there. I mean, people have probably heard about things like Manjarra from Novo and Lilly, these medicines that are going after these targets that the world really wasn't working on until just a few years ago that seemed to be Creating all kinds of interesting shifts, not only in our industry of biopharma, but also you know, I I I don't know if you saw weight watchers talking about how they think the the world of dieting is over, thanks to these, GLP one agonists. And so we're seeing we're seeing the impact of 1 or 2 really compelling medicines across so many different fields. Imagine if we had really compelling medicines for every disease, and I think That is the future, and the question is how fast can we get there?

Jordan Wilson [00:08:01]:

So I'm curious. How have things changed Recently. Right? Because, I I mean, most people know this if if if they're in a related field, but I'd say the everyday person maybe doesn't know this. Right? Like, artificial intelligence intelligence has been used in the medical fields for decades. So, how specifically have have some of the newer, you know, advancements in artificial intelligence maybe, You know, large language models or, you know, kind of this resurgence or resurgence of generative AI. How is that changing, things specifically, when it comes to reducing drug failure rates.

Chris Gibson [00:08:38]:

So one of the challenges in our industry is getting the right training data. If I'm building a large language model like ChatGPT, I can go train that model across all the language on the Internet. And and in Our world, I liken it a bit to something like, autonomous transportation or self driving vehicles. If you wanna get a large training set To train a vehicle to drive itself, you need to put people out on roads and have them driving around and recording all of that data. For us, in our field, We need to do experiments. And so in the laboratory over my shoulder here, we actually have robots doing millions of experiments every week in real human cells and extracting thousands of different measurements from these cells as we break different genes and add different drugs, and we generated over 25 petabytes. This is like Netflix scale datasets. 25 petabytes of data about, like, what happens if you break this gene or this gene or this gene or add this Potential medicine or this potential medicine.

Chris Gibson [00:09:35]:

And it's so much data that no human could ever even scratch the surface of looking at all of it. And so we use AI tools to look across all of that complex data to find relationships and patterns that that we can drive forward. And then we use generative AI tools, and other companies do as well, To help design molecules, if we find a new target, design a molecule that can fit in and bind to one of these proteins in In a way that we think could be really compelling. What what's interesting though is this as you said, this has been happening for decades in our field. Really, the last half decade is where it's and the most impact, but our industry has been pretty slow to adopt a lot of these technologies until just the last year. Chat GPT, as strange as it sounds, seems to be the thing that finally got the biopharma industry excited about AI.

Jordan Wilson [00:10:23]:

Yeah. That is that is funny. Right? Because it's something you know, is something, you know, in theory, it is so simple that anyone can pick up and use it, but, you know, even giving, even given the amount of, I'm sure, You know, models and and deep learning, that that you all are doing. It's something like or that the industry is doing. It's something as simple As as chat GPT that opened up, kind of, big pharma's eyes to how AI can help. Why do you why do you think that is? Like, why why do you think it took something as, you know, quote, unquote, simple as chat GPT To kind of shift the the the attitude change toward artificial intelligence.

Chris Gibson [00:11:04]:

Well, you know, I think our industry is tricky because we do experiments in in people, and there's an extraordinary ethical and moral obligation if you're doing an experiment in a person. There's a lot of regulation, the FDA, the EMA in Europe, Like, this is a pretty conservative industry for those reasons. And on top of that, we fail 90% of the time in the clinic. And so I think People are a little bit resistant to trying new things because the consequences are so high if those things go off the rails. What Chatchee PT did was tap into Language, and language is so fundamental to everything all of us do that I think ultimately that's what you know, it's probably some pharma exec is sitting there and their 10 year old came home and said, hey. Check out Poem that I wrote in the form of doctor Seuss using Chat GPT, and it was that that kind of, that kind of just fundamental application of of language that I think maybe finally got their their imagination going. And and to be fair, some of the large farmers were doing a lot of work in this field before. It's just that it's finally moved like the middle of the distribution into being excited about AI.

Work being done in drug testing and AI


Jordan Wilson [00:12:06]:

Yeah. And maybe maybe, Chris, help help explain so we can, so we can all better understand the process from start to finish. So, you know, you're using artificial intelligence to help create, you know, safer, better drugs that we can all use in the long run, but how does that work from from after, you know, you kinda said, hey. Behind me, this is where all the the testing and is going on, but What happens after they are? Are you creating then your own drugs? Are you partnering, with other big pharmaceutical companies to, you know, for them to kind of use, you know, your your discoveries. How does it work, you know, from the testing process forward?

Chris Gibson [00:12:42]:

Yeah. We're doing all of that and more. So we actually have in Five drugs that are in human clinical trials now. So we are literally enrolling patients and testing these medicines with the FDA in In the clinic right now, that's our own internal pipeline. We then have partnered with some of the large pharma companies. So we're going after areas of oncology, cancer with Bayer, and then we're going after neuroscience with Roche and Genentech, one of the really, preeminent players in the space. And those are, you know, Diseases like Alzheimer's and ALS, they're so complicated and large that we really needed to partner with these big companies to have Some of the elements that we would need to go into clinical trials. Most of our internal programs are against pretty rare small diseases.

Chris Gibson [00:13:24]:

And then we also recently partnered with NVIDIA To actually make available some of the tools we're building to the rest of the industry and even to academic researchers, and we'll share more about which tools and how we're gonna How we're gonna, distribute those tools in the future, but some of the tools we've built for our our own internal scientists will be available on the place with NVIDIA in the coming months.

Jordan Wilson [00:13:46]:

You know, we we had a guest on the show a couple of months ago that talked about even Using AI in the clinical trial process because, correct me if I'm wrong, but even once you get it there so, right, even if you make, you know, safer and and better drugs and get that failure rate down from 90% to 80%. It can still take a very long time, right, to to get this, You know, new and improved drug to market, you know, sometimes, years. Is that, like how does even that process work? I know maybe You don't have as as as much a hand in that process, but maybe help ex help explain that, for the rest of us.

Chris Gibson [00:14:23]:

Well, we're starting to deploy AI in those kinds of ways as well. It's amazing. If you take the amount the industry spends and divide it by the number of new drugs approved every year, The industry spends about $2,500,000,000 of r and d for each drug that gets approved, and it takes on average about 12 to 15 years From starting a project to getting a drug to market. Remember, most of that work, most of that cost is in the failures of the drugs that never make it to market. And so We think AI is likely to be deployed or other technology tools. Maybe it doesn't have to always be AI. But technology tools are likely to Play a really prominent role in many of the hundreds of steps it takes, including how you run a clinical trial. How do you identify patients? We actually partner with a company called Tempus recently started by Eric Lefkofsky, who was the the the the founder of Groupon.

Chris Gibson [00:15:15]:

He started this this company to gather, cancer patient data after his wife went through a really traumatic, oncology experience, And he's built now one of the largest datasets in the world of cancer patient data so that we can find cancer patients to enroll our trials faster. So, like, I don't think there's a silver bullet, one software program that's gonna all of a sudden unleash the next magic medicine. It's gonna be hundreds of tools built in a full stack from discovering new fundamental truths about biology all the way to even how we market and medicines.

Jordan Wilson [00:15:51]:

Mhmm.

Chris Gibson [00:15:51]:

And and the companies that can put together that full stack and create compounding efficiencies, 2 1 tool, 2 tools, 3 tools that Make a little bit of a difference at every step. Ultimately, I think that's how we're gonna see AI tools and other technology tools Fundamentally shift this industry so that in 10 or 20 years, anyone who needs a medicine has it. It's inexpensive. It doesn't have a lot of side effects. And maybe even one day our industry can shift away from treating disease to preventing it, which ultimately would be way better, right, if we could actually shift one day to just preventing diseases in the 1st place.

LLMs being used in Pharma


Jordan Wilson [00:16:24]:

Yeah. That would be great. And, you know, I I do have to mention, you know, you brought up Tempest. Hey. I just I just always have to shout out, like, awesome people doing work in Chicago because I think Tempest is like, 3 blocks, you know, from where I am now. I've had, you know, family and friends work there, but, actually, a a great question that I I wanted to to bring up, hear from Douglas. So, Douglas, thanks for the question. So, asking out of curiosity, are you using public large language models like chat GPT or Bard? Because you kind of, Chris, Talked a a little bit about the different ways that you're using AI.

Jordan Wilson [00:16:57]:

So, you know, presumably, there's a lot going on kind of more proprietary, Kind of the things that are happening in the lab, but then what about from there? Yeah. Are you using, you know, some of the same systems that, the rest of us are using as well?

Chris Gibson [00:17:10]:

Absolutely. So great question, Douglas. So we built a lot of our own AI tools for a lot of these very niche problems, but we have generated next because we found 5 trillion relationships from the datasets we've generated. So we actually use GPT 4 on, Azure With a large number of tokens, like like an extra sort of enterprise level of tokens to take those trillions of relationships, find the very best ones, and Does somebody has somebody already figured this out? So we know that there are hallucinations with LLMs, and we don't wanna use them as a central portion of our research. But to give our scientists a prioritized list, we like to go after completely new stuff, and we use GPT 4 to essentially say, If this drug and this gene, have they ever been talked about before? Do we know about this? And if the answer is no, and we did a lot of prompt engineering to kinda whittle this down, A lot of benchmarking, we could get to the point where we could essentially 80/20. A GPT 4 can give us an 80/20 answer, 80% accurate about whether or not something we have found is novel. And if it's novel and the relationship is strong and there's a lot of unmet need in that disease, we use the LLMs to and answer all those questions, then we hand it to our scientists. And that helped us take those trillions of relationships and whittle it down to a couple 100000 that are gonna be the the the programs that we try to focus on at Recursion.

Chris Gibson [00:18:38]:

So, yes, we're using it there. And I should also say, like all the other businesses in the field, Our EA teams are using it. Right? Our finance teams are using these kinds of tools. We're using it across all the other kinds of normal business things that, all of us have to do in any kind of company we're

Challenges with data


Jordan Wilson [00:18:54]:

What are the what are some of the next largest hurdles, right, to get from that that 90% To that 80%. Because I'm sure when you said that and, you know, you said, hey. The the implications of that are potentially paying half as much for, you know, for the drugs that we all use, which I'm sure a lot of people would would be very interested in that. So what are those next hurdles because, yes, advancements in AI technology, it seems like, Chris, you know, from someone a much more nontechnical perspective that they're coming so fast, it's hard to keep up. So, what are the next hurdles, and just how much do some of these more recent, in innovations and, advancements in AI help in clearing those hurdles.

Chris Gibson [00:19:38]:

Well, it's not a very sexy answer, but I think it's a data problem. Right? And and and there are, You know, if you look at so many of these, tech companies in the world making all these incredible tools, data availability is is not the primary limiter. The Scaling laws apply, but it's mostly compute that we see being deployed against existing datasets to help take us to the next level. We have done a lot of work to demonstrate that these scaling laws apply. By scaling laws, I mean, more data and more compute give you better answers. Right? And that that's why we see more data, more compute together, they give you better answers. In our field, data is the limiter. The right data is the limiter, and that's why we have if if I were to take you on a tour right now, you'd see this factory full of robots Doing millions of experiments every week.

Chris Gibson [00:20:25]:

And so for us and so many other companies, it's about building the dataset because there doesn't exist Some large public data set that's nicely relatable, that's free and open source that we can use to train these models. And so it's about generating that data and and accessing that data. That's the hurdle that we're all facing in the field, and it's why there's a few companies like Recursion who have really, for the last decade, been working to build those datasets and And and find ways to accelerate our ability to to scale that that piece of the puzzle.

Jordan Wilson [00:20:56]:

You know, Chris, I like I like the, the, comparison that you made earlier with kind of what, you know, recursion and other in your fields are are are are trying to do, when it comes to better using AI to create, drugs that fail less kind of to the self driving or autonomous vehicles. Right? Because At that point, you had to create the data. Right? You couldn't tap into, a data set before. How challenging is that process. You you know, for for those of us that aren't, you know, in the field and and trying to understand that, how challenging is that to, you you know, create that data. And then what what happens, right, is is is there kind of that inflection point that that once you hit, right, it's like, oh, once The self driving car is, you know, better than the average driver, then things, you know, completely open up. Like, what what does that process look like? And is there that point that that you will get to or the industry will get to, that then all of a sudden, the floodgates will open.

Chris Gibson [00:21:58]:

Well, I think we're nearing that point, and that'll be the first Molecule that was discovered with the help of AI being approved by the FDA, and and and and being applicable for real real patients on the market. And we've got 5 drugs in clinical trials right now that are on their way there. There's a number of other companies who have medicines in clinical trials. And when the 1st medicine where AI helped make it possible is being used every day to help patients with some disease. I think that will really be the unlock point for our industry to more broadly accept this. Because today, as with any industry, there's tons of skeptics. Right? Even in the The self driving vehicle space. Some of those companies have already outperformed humans, but we still We see a lot of resistance in our in in the world to accepting self driving cars.

Chris Gibson [00:22:50]:

Right? When one of those cars gets into an accident, it still makes and The front page news even if human drivers get in, what, 30,000 accidents a day or whatever it is. I I think it just is gonna take time and real proof points. And As I said before, it takes 12 to 15 years in our industry to go from start to finish. Recursion's only 10 years old. So the fact that we're on the precipice of having medicines In the clinic and maybe one day in the near future approved. I think that'll be the point where it starts to be real for people. That'll be our our version of We're better than the, you know, the average human driver.

Future of medicine with AI



Jordan Wilson [00:23:24]:

Sure. I I I don't like doing this. I don't like asking guests To to to look into their crystal ball. But I can't help it in in in this case, Chris, because, you know, even going from that 90 to 80%, You know, hopefully, you know, cutting down, you you know, the the the price of drugs, which you know, and and treatments as well because that can be extremely, expensive. But what does that future look like? Maybe once we do hit that point or once there is that 1st successful drug that, You know, you know, it has an artificially, or has a molecule kind of created by artificial intelligence and, you know, kind of, oh, then the floodgates open. But what could that look like In the long term. Right? Is that, you know, going from that, you know, 10 to 15 year, drug approval process to to to months? I mean, what could, the future of a better medicine look like with the help of artificial intelligence.

Chris Gibson [00:24:18]:

Well, you know, we tend to overestimate what and we can achieve in 1 year and underestimate what we can achieve in a decade. I'll go out to a decade. I think a decade from now, if you look at the and Biopharma companies in our industry. There's gonna be some new names, maybe ours. There's gonna be some older companies. Some of these biopharma companies are 100 plus years old. I think some of them are gonna fall by the wayside just like we've seen in you know, like, look at the market caps of, like, a Tesla versus maybe a A more traditional, auto brand, so we're gonna see a shakeup in the industry. But for patients, what this is gonna mean is and Better medicines available more quickly.

Chris Gibson [00:24:54]:

I don't think months. I think there's still gonna be a necessary FDA approval process that will take years. But But if you can go from 12 or 15 years to 2 to 5 years, and you can go from 90% failure rate to, say, 50% failure rate in the next decade, It means medicines are no longer gonna be really, really expensive for folks 10 to 20 years from now. It means that That there's maybe even gonna be new models of care. Maybe you maybe you don't have to go, description for may maybe it's part of Amazon Prime. We're we're already seeing this. Actually, Amazon has this service now where you can get your medicines delivered via Amazon Prime. Maybe we get rid of PBMs, these middlemen in our industry that basically increase the price of drugs really substantially, and your your doctor, just Put your order in and the Amazon drone drops it off.

Chris Gibson [00:25:45]:

And and and I hope in 10 of 20 years, Jordan, that we're in a position where This means we can move from treating disease to preventing disease, and that'll be the next real frontier for our industry. How do we make sure you never get the disease in the 1st place?

Chris' final takeaway


Jordan Wilson [00:26:00]:

That would be great. You know? Hey. I'm I'm looking forward to this this this future that you just, kind of described, Chris. But, I mean, we've talked we've talked about a lot. We've talked about the problem with with collecting data and, you know, kind of drawing that comparison To, autonomous vehicles, and we talked about why drugs fail and and how artificial intelligence can can help improve that process. But maybe what's what's the one takeaway That you hope people, you know, maybe not even people in the field, but what's the one takeaway that that you hope people can have from hearing you speak today, specifically on on how it impacts the future of of their medical care. What is that takeaway?

Chris Gibson [00:26:39]:

Well, look, there's so much doom and gloom, even just the last, like, 10 days looking at this existential world of AGI. I think the work that we and so many others in our industry are doing is such a great example of the potential for AI for good. And so and I would just say to everybody out there who's working in an industry where maybe it's Come come to come to an industry like this where you have the potential to deploy these tools in the service of a mission Like alleviating suffering at scale. This is such an exciting place to to work. I'll I'm just I'm a nerd. I love being in the science. I love working in these complex problems Because I think it's gonna do so much good impact for humanity in the coming decades.

Jordan Wilson [00:27:21]:

Oh, I love that. I love that. Well, hey. Thank you so much, Chris, For joining the Everyday AI Show and helping us all better understand what's going on and how companies like Recursion are using AI To bring down that failure rate in drugs. Thank you so much for your time in coming on the show.

Chris Gibson [00:27:40]:

Thank you.

Jordan Wilson [00:27:41]:

Hey. And as a reminder, maybe you joined late. Maybe you're you're you're driving your your car and someone was beeping and you missed the important parts. Don't worry. Every single day, we go over this. So make sure, If you haven't already, go to your everyday AI .com. Sign up for that free daily newsletter. Yes.

Jordan Wilson [00:27:57]:

We always recap the news and trends and and new tools, but we also, Every single day, break down the conversation that we had with our expert guests and tell you even more about what it means for you growing your career, growing your company, Or, hey, maybe in this case, growing your longevity in life. So thank you for joining us, and, hey, you all voted. Hey. In in the newsletter yesterday, I said, What do you wanna hear on Friday? So tomorrow, because you you wanted it, we're gonna be talking about the Amazon q new model. So hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.

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