Third Rock’s John Keilty on What Lies Ahead for Bioinvestment

January 27, 2026

As the new year settles in, the industry is anticipating new innovative biotech and learning from past failures. John Keilty, venture partner at Third Rock Ventures, discusses the current trends he sees in biopharma investment, separating the hype from reality of AI (and if the AI bubble will eventually pop), other ventures that should be considered, and how new companies are being built today compared to past years in this episode of Trends from the Trenches. With host Eleanor Howe, founder and CEO of Diamond Age Data Science, Keilty also reflects on the biotech failures of past years and shares his thoughts on the role of computational biologists these days, as well as where bioinvesting is working and not working now.

GUEST BIO

John Keilty, Venture Partner, Third Rock Ventures
John is a broadly recognized technology and informatics leader with extensive experience driving drug discovery and development across multiple therapeutic areas.  At Third Rock, he has been part of ideating, launching and operating tens of companies including Thrive, Decibel, and Inflection.  In addition, he works with the platform team to develop, implement, and refine technology roadmaps for portfolio companies, with a focus on identifying and integrating innovative drug discovery and development platforms. Before joining Third Rock, John helped start and run Infinity Pharmaceuticals, where he oversaw information systems, software development, computational science, biostatistics, clinical data management, and clinical informatics. He began his biotech career in the early days of Millennium Pharmaceuticals, working to build and scale the company’s core genomic capabilities.

TRANSCRIPT

Host And Guest Introductions

Welcome to BioIT World's Trends from the Trenches Podcast, your insider's look at the science, technology, and executive trends driving the life sciences. This month's episode is hosted by Eleanor Howe. Eleanor has been working at the cutting edge of bioinformatics for over 20 years. Trained as a computational biologist, Eleanor has deep expertise in transcriptional profiling as well as drug discovery and development. She earned her PhD in bioinformatics from Oxford University and spent years in biomedical research at the Institute for Genomics Research, Dana Farber Cancer Institute, and the Broad Institute. Now, as founder of Diamond Age Data Science, she has led the company's evolution from a small project-based service provider to a full-fledged consultancy that works closely with clients to tackle their most difficult research challenges. Let's listen in.

Hello, everyone. Welcome to Trends from the Trenches. Joining us today is an industry veteran who has been at the Center of Biotech New Company Formation for years. John Kelty is currently a venture partner at Third Rock Ventures, but his perspective is shaped by years of hands-on leadership in the build-out of complex RD organizations. He was an early contributor at Millennium Pharmaceuticals, where he helped establish the company's core genomic capabilities. Later, during his 13-year tenure at Infinity Pharmaceuticals, he led all of the data-focused functions like informatics, clinical biometrics, software development. He's been part of two drug approvals, including the proteasome inhibitor Velcade and the PI3 kinase inhibitor Copicra. At Third Rock, John builds companies. Specifically, he works to develop, implement, and refine technology roadmaps for the portfolio companies with a focus on identifying and integrating innovative drug discovery and development platforms. He's also been instrumental in the launch and direct operational scaling of firms like Thrive Earlier Detection, Decibel Therapeutics, and Inflection Medicine. John, it is great to have you here in the trenches.

Thanks, Eleanor. Great to be here.

JPM Week And Investment Sentiment

So why don't I jump right in? Right now it is JP Morgan Week. The conference is going on as we record on January 14th. The biopharma world is watching what comes from this conference after a pretty tough year in the industry. Can you tell us a little bit about the trends you're seeing in biopharma investment right now? What is and is not being invested in, and what's the atmosphere like?

Yeah, I mean, it's a kind of a complicated question. And what I will say is I think this year has been better than the previous year. So I think we are generally on a trajectory of, you know, getting back to where we were, you know, five or six years ago, which is great to see. A few things that are interesting. Obviously, most of what I'm going to talk about is much is very much from a third rock perspective. But, what what is an obvious statement is that now clinical data rules the day, right? And it used to be, you know, 10 years ago, even five years ago, you could build a platform company and show investors that you were, you know, creating value purely in a discovery mode. That is just not the case, right? And so there is a bigger and bigger push to be getting to clinical data as f quickly as possible. And so that's had a few sort of ramifications, and a lot of this will play out at JPM. The companies that we are talking about the most at JPM are the companies that are, you know, either ready to go in the clinic or, you know, already in the clinic and awaiting clinical data. And and you know, the pharma companies you talk to and whatnot, those are the things that they are interested in. And so trends that are related to that, at least in my mind, are more and more biologics, right? So small molecules, at least de novo small molecule development, takes a long time.

Clinical Data As The New Currency

And so here at Third Rock, you know, there has been less and less of an emphasis on, you know, de novo small molecules, where you know, you can go at things with an antibody and at least get to DC in a much quicker timeline, right? Is in that same sort of de novo mindset. On a small molecule side of things, a lot of what we're doing right now is once we get a target that we're excited about, can I go off and find existing chemical matter to lean into, right? And so a bunch of pieces with that. So, you know, we are constantly on the search. And even companies that we have started that have been successful, like Rapport, have started based on work that was done at JJ and the founders that started Rapport already had really strong ideas about what the chemical matter needed to be. And then maybe the other thing that I know is a hot topic right now in my world is is China. What's happening there? You know, it's certainly been on a trend of using them more and more for general CRO services. But now it's you know looking at China for assets to sort of trim that same sort of timeline. So I guarantee all those things are being talked about there. There will be a you know a real focus on, like I said, you know, how quickly and can you get to clinical data and you know and wait for that sort of flip of the card.

So speaking of that, the getting to clinical data and the different stages of the companies as they start. My understanding is that there are really very different interests in those different stages from the investors than there used to be. Can you talk a little bit about that?

Sure. So again, traditional investors, and I'll talk about Third Rock, but I think this generally applies to most of at least the Boston, Cambridge-based VCs, with maybe the exception of flagship. You know, the focus always has been on I want to get to a clinical drug. No, the Yagioses, you know, the foundations of the world are ones that had a very long time to finally get to commercial success, right? Have gotten there through an appetite by other investors in the progress they were making on the platforms, right? All of that has, I wouldn't say it's been flipped on its head, but those sort of timelines just don't work for us anymore. And so, you know, back to the previous comments. So now the focus is very much on you know anything you can do to get to DC. There are times, you know, I would highlight companies like Merida Biosciences where you can have the best of both worlds. It remains to be seen that running, they're in their first clinical trial right now. But a company that was based on leveraging some of these antibody type therapies, but doing in the context of a broader platform that now at least gives them an avenue into, you know, multiple DCs, right? In a relatively short period of time, in a cost-effective manner, and still having that clinical data. So we'll see how well they do, but like there are still opportunities like that to sort of balance the platform and products out of things.

So then speaking of seeing how everybody does, there have been a lot of biotech failures in the last few years. I don't, I think that's very much an understatement. Obviously some of them were bad luck, bad timing, bad environment. But what about the ones that were not? Is there a theme that you can see about where these small companies went on?

Yeah, I mean, back to your point, it's I wouldn't necessarily call it bad luck, but the whole environment changed, right? And so I'll give my two cents and does not necessarily represent Third Rock or whatever. During COVID, I was freaking shocked at how much money was pouring into biotech, right? Which, you know, we at Third Rock had talked about like maybe we need to slow things down, less investments as we figure out what's going on. And at that time, I was at Decibel. We took Decibel public during COVID, right? On without even having clinical data at that point. So my general commentary is like a lot of money flowed in, and for better or for worse, there were companies that were started that lacked, you know, the rack like the right fundamentals to even, you know, be there. And so you fast forward a few years after that, these companies are running out of money. They're, you know, largely pure platform plays. The environment has changed now to like to really give credit to clinical data. And suddenly you're in a whole lot of, you know, you have a whole lot of issues.

Platform Fatigue And Lessons From Failures

So part of that was of the company's own making, of like I said, not having the right fundamentals, not really thinking about value creation the way it needed to be thought about. So you can have cool science. That is not enough to build a company, right? And yet investors got excited about that. And then, you know, yeah, the the there were incredible headwinds. And so you expect that some companies at any time are gonna fail because of that. But I also feel like, you know, yeah, I , you know, not have to point a finger at at us in general, that I think we could have done a better job running these companies, right? Probably could have cut back on what we were spending a little earlier. You know, there are times where every company needs to pivot. Did we pivot quickly enough when we recognize these headwinds? And so, it was a you know a confluence of a whole bunch of different factors. And don't get me wrong, when you're in the middle of it, it is hard to know what exactly the right moves were. But, but I'll be honest, a lot of us just coming out of COVID, we're like, holy mackerel, I can't believe you know, some of these companies and you know, and the basis by which they're getting, you know, pretty sizable sums of Series A dollars.

And I remember during that time too, our heads were just spinning with the amount of activity that was going on in the field. I actually can't blame a lot of people for being a bit overwhelmed. That's right. So now here we are in this new climate and there are new companies being started. So what are you seeing and how they're being built these days? What is different?

Yeah, I'll continue to beat the theme of like needing to get to clinical data as quickly as possible. And maybe I'll refine that a little bit more. What we at Third Rock are trying to do is the series A dollars in particular should hopefully get you to some kind of clinical data that that if it works, investors will positively act to, right? And so what does that mean? It means that we incubate a lot longer now, right? And we spend more money during seed phase. So if you have an idea, you know, we're still not going to put lop down those series A dollars, right, until you know that A, you know, those dollars are enough to get you say 18 to 24 months, which in turn should get you past that IND and you know into the clinic and hopefully to some sort of readout that makes sense. So longer seed periods, we're spending more money during those seed phases, right? And that's meant primarily to sort of mature these ideas to the degree that I said. And then you're putting Series A dollars, sometimes a bit more than we had in the past. And and what we are really focused on is syndicating with other partners. And in previous license, we sort of said, we're gonna go it alone.

New Company Building: Longer Seeds, Leaner Teams

The upside of syndication is, you know, both having a strong group that brings complementary skills to the table, but also you can throw more dollars at that and give these companies a little bit more time for those sorts of readouts. And you know, the other thing that's been interesting is, you know, we have been thinking deeply about how can you start these companies with a smaller footprint, right? And try to be more efficient with dollars. And so part of that we touched on, which is more and more outsourcing, right? And not just a, hey, we're gonna throw some experiments over the wall to, you know, whoosh, right, or something like that. But doing this in a much more integrated way, right? And you know, I you know, as a whole, I find us leaning into just broadly data science is more than we ever have, and doing it in a way that is truly with a seat at the table. Like if you go back 10, 15 years, what we now call data science is would someone, you go hire someone, plug them in just because someone told you that you should do it.

That is that tide has turned in a very positive way in that those sorts of skills and being back to like, can we operate really efficiently? Well, there's so much data out there, right? How can we do as much as possible to de-risk these companies before we start them? And then once you're running, right, to lean into those more. And, you know, obviously there are some recent advancements in terms of large language models. You know, you look at Alpha Fold and stuff like that have gotten people excited. And we can touch on that more if you want, but, but all of that is coming together, but the theme is still the same, which is like, I need to get the bona fide clinical data as quickly as possible, right? And so anything we can do to make that happen and and reduce the, you know, the chance of failure, then we're gonna do that, right?

I'm really glad to hear you say the the computational biologists are having more of a seat at the table these days. I'm a little biased being a computational biologist, but I remember when Comp Bio was considered sort of sideline activity, something that was, you know, it was very difficult for a computational biology scientist to really become part of the development team and be respected as another scientist. And so are you saying that that actually my colleagues have more options to eventually grow into CSO positions and the like?

I do in the right companies, right? Whether CSO, I won't get bogged down in titles, but do I think that most companies now understand that they need the computational sophistication and expertise and it needs to have a strategic seat at the table? Hell yeah, right. And you know, I still believe that we're in a bit of a hype curve in terms of large language models and you know what we are collectively calling AI that I would still argue is largely machine learning, but let's not go into those nuances, Eleanor. Like what I will say is, you know, AlphaFold has done us all a huge favor, not just about protein prediction, but about helping, you know, folks that are not computationally native understand the importance of these types of tools and starting to think about like what else can we possibly do with this. And so, so with us, right, what we find is that we are, we're starting very small in terms of the computational teams we put in place. We have made mistakes, I think, in the past, of hiring one or two computational people to a an early stage biotech company of, say, 25 people, and thinking that they can bring all the expertise to the table. And, and that I wouldn't say it got us into trouble, but we could have been way more effective.

Data Science’s Seat At The Table

And so what you're seeing more and more of is, you know, yes, we still want someone that is a full-time member of the team that is sophisticated in their thinking, that can both speak the language of you know computational biology or chemistry, but can also understand value creation in the company context. And then we try to leverage teams and groups that bring, you know, broad skills to the table. And as much as we talk about computational biology as if it's one thing, it's not one thing, right? And anyone that goes into this, you know, a yes, in academia, you can go and write a bunch of R code and get real and do some great things with that. In a company, it's such a blend of, you know, really understanding biometrics, about understanding, you know, high performance compute, about understanding cloud infrastructure, you know, and then about understanding the disease itself, right? Such that you can lean in on those things. And so, so especially in those early days now, try to keep the whole company small, 20, 25 people. And so then you really need the right groups to lean into that bring these other things to the table.

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So then you you mentioned the dreaded AI word. And I think if we sat down and talked about what's AI and what should be called machine learning, you and I would be here all day. So I yeah, I agree we should pass on that topic. But I just want to say I agree with you there. But that is one of the things actually that this the industry is struggling with these days is separating the hype from the reality. So maybe just we could talk touch about it just briefly, but just to help people understand what is actually useful out there. What what is you mentioned AlphaFold, that that is one of the clear ones. But what is actually any good and what is not?

Yeah, maybe we can lump this into a few different buckets. Let's on the science side of the house, right? Absolutely now every one of our companies in seed phase is leaning into whatever the latest version of AlphaFold is, right? So protein prediction is huge. And in particular, I mentioned that we do more and more work on biologics and antibodies, right? That's a place where these AI machine learning techniques, right, actually are incredibly valuable, right? And speed things up incredibly. I mean, let's be honest, antibody design is becoming much more of an engineering challenge, right? You know, and that's exciting, right? That's part of why we lean into it the way we view. So that's a no-brainer. But more broadly, like I've, you know, right now I'm in the process of, you know, getting a a new genomics company out the door. And I've become fascinated by different ways that we can lean into AI and ML in that case. And you look at the evolution of things like the transformer models, right, within genomics.

Sorting AI Hype From Real Utility

Like, so these are areas that have tons and tons of data to train, right? That it's not surprising that these are areas where, you know, they're scientifically underpinnings make sense that you can train machine learning and AI models in a way that are going to have incredible impact. And they're still wrong and you still need the right expertise to sort of interpret them. But man, it is it's pretty impressive. And then the other thing that we're still trying to figure out at Third Rock, and you know, but it's very clear that you can be way more productive with these tools. So the science, that's exciting. There's tons of things we can do on that front. But everything from automating GNA functions, right, straight through to, you know, you know, I don't know about you, you're not nearly as old as I am, but you know, I'm actually leaning into some of these large language models just to help me synthesize all of the literature that's out there, right? And not that I think it's right, and I think it was Daphne Kohler told me, like, you should think about these large language models that the the you know, the broader Chat GBT types as just being like a second-year graduate student or something like that, which is a little bit pejorative, and but but her point being that like these are very powerful tools that you still have to go in, assuming that they're probably wrong, but they can synthesize.

So the other thing I'm excited about within Third Rock is can we use these tools? And we are already in the near term of like just getting to, you know, ideas faster, right? And refining those. And then in the long run, like, can we use those, you know, even if it comes to diligence and things like that, right? So so that's the whole other piece. And so just in general, no one is using these tools as effectively as they are because we're still trying to figure out what's right, what's wrong. And I think as we come down the other side of the hype curve, you're gonna find that like most of the time when this happens, you know, people are leaning into these now suddenly and in a way that is a lot more pragmatic, a lot more effective. And and then you look in the rear room, you go, Oh shit, this really did change the way I do things, right?

Yeah, absolutely. That's what we're seeing at Diamond Age also is some of these tools are really changing the way we work for the better. And learning how to use them all is a is a key skill, really. So now then what are you seeing about you know the reality of how using the tools has changed how people work? And I, of course, am always interested in the bioinformatics angle. And since you're right there in the mix with all of the bioinformaticians and informaticians and everyone, what are you seeing there?

Yeah. Let me give an example that is I'm in the middle of right now. So this genomics company I mentioned, I wouldn't necessarily call it generally, it's more about collision decision support company that's relying on genomics called Inflection Medicine. And it is a little bit more akin to Thrive in that, you know, huge data needs, right? And a big engineering build. And so, and so what we have now is a team of, well, let's call it six or seven hard. Core software engineers, right? And then some computational folks to be helping on the genomic side, you know, genomic interpretation. And my realization of inflection relative to Thrive is I need a fraction of the engineers that I had before. Now, I've stolen some of the same engineers from Thrive, and you even talk to them and they go, holy crap, like, you know, I can be 10 times as effective now, right? Now, these are sophisticated, like really well-trodden engineers that are still open-minded enough to lean into the latest and greatest. And so my takeaway right now is that you can take a great engineer and make them, you know, 10 times better, right, with these tools, leaning in in a pragmatic way, but understanding the broader design patterns, right? Understanding, you know, that this is wrong, but I'm using this as a starting point and got 80% of the way there.

And that's been really exciting to see. And it allows us to now build this company way more efficiently, maybe way more quickly. You see some of that on the computational biology side of things, but not to the same degree. And part of it's like biology is way more complicated, right? And, you know, design patterns and engineering, while they change over time, you know, are, you know, there's a lot of data to train off of, and you know, and these tools, I think, in my opinion, work a lot better there. So do I think that this is having an impact for our computational? Absolutely. They are still leveraging these tools, right? But they spend more time still thinking deeply about the biology, right? The algorithms that they develop lots of times are really, you know, tip of the spear, right? It's not like an engineering where you're trying to, I might want to refine something that someone else has done or I'm pointing out. In lots of times when you are working on new science, right, you're developing these tools on your own, right? And so that's a very, very different mindset. Now, all that said, you know, I've been thinking about this a lot as we discuss this podcast. What is what will be interesting to see is, and I'll just talk about engineering. Like I remember when I first started to write code in the early 90s, right? It was bash, it was C, it was assembly, right? You're managing memory, blah, blah, blah.

Productivity Gains In Engineering And Comp Bio

You fast forward, then Perl came out, and that's like it took away a lot of these issues you had to deal with that like were distracting me from answering a question. You fast forward again, and it's suddenly like Python and cloud, right? I don't have to worry about racking servers and stuff. Like, I am hopeful that when we hit steady state here with these, you know, AI tools, is that, you know, people will still have to understand the fundamentals, but we will be way more efficient in terms of actually creating the code. And then suddenly you're switching gears and like, I'm gonna worry more about the actual problem I'm trying to solve rather than, you know, like all this other bullshit that you're worried about, you know, debugging, you know, the nuances of some script that you wrote. And that that has the potential of being really liberating, right? And you just need to be smart about how you lean into that.

That makes perfect sense to me. I remember learning assembly language in college and thinking, what am I going to use this for? Right. This is this is and and and and I'm not that much younger than you. And I still never use assembly language professionally, right? I used Perl. I had memory management with Java. But, but that doesn't mean that it isn't useful to still understand assembly. You know, at the end of the day, you need to know what's going on over there.

Yeah. And so that's where, you know, I look at some of these folks that are leaving grad school now and and they are digital natives and now are more and more ML native, right? In terms of like they are in school and they're using these. And that doesn't mean they're not brilliant and using great thinking. They just are, you know, they think about things in a very different sort of way. Now, it does mean that some of the gray hairs, some of the us that have been doing this for a long time, it becomes essential for us to still be able to, you know, see the whole playing field and you know, help them understand the best way to take what now is a very focused view of the world, right? And in my case, a focused view of what this means for a company and expand that in such a way that we don't get ourselves into trouble, right? And so that that need you know remains there. And it's it's part of why Eleanor, like some of our companies work with groups like yours, right? To sort of make sure that we get that, you know, that level of sophistication, right? So thank you.

I really a ppreciate the support. It's very cool. Well then maybe we should get snarky. What about this AI bubble? First of all, do you think it's a bubble? And and assuming the answer is yes, when is it going to blow up?

Oh if I knew that, I'd be shorting everyone. I definitely think it's a bubble. The question is, how big of a bubble, right? And you know, even with the internet bubble, there was still so much utility that came out of that, right? So I'll put a bit of a positive spin on this, which is yes, I believe we're in a bubble, right? I think there's no question. And unfortunately for us, you know, well, we'll see. It could have broad economic impact, but you know, there will be some winners in the AI space, period, right? There's so much money going into hardware right now, right? And that is a little bit like the internet bubble, where the amount of money that went into fiber, into hardware, and even though like ultimately that first generation did not work out as well as we had hoped, right?

Beyond AI: Small Molecules And Genomics

That next generation was able to piggyback on all of the real, you know, physical investments. So our worst case scenario right now is we are gonna have incredible amount of resources to lean into in ways that, you know, maybe we're not anticipating right now. I doubt it's gonna be as drastic as Internet 1.0, right? But there let's be honest, there are a lot of companies dumping and sometimes with with large debt, you know, into building out these data centers and whatnot. I don't see how all of them can work out. I think for us being somewhat abstracted from all of that, then the trick is, you know, how can we be mature in our thinking about how to integrate these into our early biotech workflows, period, right? And you know, it's funny right now, we get to hear a lot of pitches here at Third Rock. And maybe this is why I'm a little bit jaded on the way people present AI, but there is not a single company now that comes in to pitch to us that doesn't talk about blah, blah, blah, AI, right. Where you know, for us in biotech, I think AI is important.

And we need to make sure that, you know, for us at Third Rock, we have an AI strategy, right? We need to make sure that that's the case. We need to make sure our companies are leaning into it, all with the mindset of like, I just need the companies to work, right? I just want the companies to make drugs, not AI for AI's sake, right? And a lot of these pitches right now are people desperately trying to figure out, you know, where to point large language models, for example, right? And I, you know, and this happens with every exciting new technology. You know, people are scrambling that are the sort of fast followers trying to figure out, oh shit, this is cool. Maybe I'll use this for clinical protocol, you know, design, right? And I'll start a company around that. So a lot of the shit will fall off. And then what we'll be left is the real concrete uses of the technology. So I don't I don't look at this as a bad thing. I look at this as just typical sort of, you know, the natural selection we see in our space. Part of the process. That's right.

So then while we're all thinking about AI, possibly entirely too much, what else should we be thinking about? What are we missing right now?

Yeah. So it's kind of cool. Part of my job here at Third Rock is to be scouting for new technologies, right? And so much as I talked about us not doing a lot with small molecules, I don't think small molecules aren't going away, right? And so one of the things that I'm challenging myself and Third Rock is are there ways that we can get way more efficient with early discovery, right? And novel chemical matter. So you know, I am particularly right now interested in leaning into new technologies for, you know, small molecule discovery, right? And we have a little bit of dollars we'll throw into these things just sort of test out are these technologies ready for prime time? But if they are, right, and you know, and part of why we don't go at small molecules from a discovery sample is it's just way too expensive and takes too long. Are there ways to compress that timeline, right? Because it wouldn't it be nice to have at least a couple companies in the portfolio that are truly, you know, leading edge rather than, you know, scrambling to sort of catch everybody else. So I'm really excited about that.

And then, you know, you and I have talked about Zillin Arm. I go back to my millennium days when we thought we knew everything about genomics. And I remember, you know, hearing from Eric Lander at some point about how close we were, and this is like mid-90s, how close we were to genomics just being part of the fabric of, you know, of healthcare, right? And we are just not there. But but I now feel personally like at some point Eric is gonna be right, right? And I think we are incredibly close right now. Sequencing has become cheaper, faster, better. So long lead technologies are just fascinating. And so that's an area where, you know, if we can finally nail that down, it's not just about, you know, leveraging clinical data for, you know, monogenic diseases, right? It's about what that can do for for us across the board in terms of new drug discoveries, right? Whole new, you know, areas that can spin off of that. So I am very bullish on that. Now, right now, we'll see if I'm right. Give me a few years. That's a big part of the, you know, the underpinnings for companies like inflection.

Cycles In Biotech And Optimistic Outlook

And then there are other cool technologies that continue to evolve around everything from you know, novel biosensors and whatnot that are going to give us more and more understanding about the underlying, you know, biology that you know we haven't had in the past. And each generation we say this turns out biology is really freaking complicated, right? But you know, each five or 10 years we get new technologies that allow us to go in and understand it with an additional level of nuance that then opens the door to new, novel ways of both developing drugs and treating patients.

One of the things about biology that I find terrifying and entertaining at the same time is how much of what I learned in school has since been demonstrated to be incorrect or utterly incomplete. And every every decade there's another like whole new layer of biology we find. Yeah. Yep. I can't wait for the next one. They're so fun. So then big picture. Where do you think that where do you think that biotech investing right now is working and where is it where specifically where is it not? Where is it going wrong these days? Yeah.

I actually don't think it's gone wrong, right? I sort of touched on this earlier. I think this is a natural cycle. You look at when Third Rock first started, what, 18 years ago now? There were there were no IPOs, pharma wasn't doing any kind of deals. And the idea was let's start creating companies that were doing novel science. We'll figure out ways to fund those and that is viable. And, you know, and then what was a headwind became a tailwind, right? Where suddenly the IPO market opened up, pharma sort of leaned into early biotech as a as a mechanism for you know getting access to the next generation of their drugs. So I feel like, yeah, God, I've been doing this now for about 30 years, right? And I feel like the pendulum swings back and forth, right? And we're in the middle of that. So I think this is totally natural. It's still painful, don't get me wrong. But what we are seeing is that this next crop of biotech companies are way more fundamentally sound, right? They are way more efficient.

And you know, ultimately, I think that is makes our space, you know, sustainable, remains to be exciting, right? And you know, and I think the the the bigger and better things are actually ahead of us, right? So as much as it may be doom and gloom right now, for again, for those of us that have been around us, this has happened before. It will happen again. And you know, some sort of pressure on us to operate better, right? With the right mindset around the blend of great science with great business sense, like that's that's what will still win, right? And, and that's why I do this. I think this is it's an exciting time, right? And you know, people shouldn't be, you shouldn't feel like it's end of days, right? And I feel like I get that sense from some folks right now. And I tell you, I've seen this before, right? And you know, people just need to focus on doing the right things, right? Keep with the right fundamentals. And we know what makes a good biotech company, right?

Closing And Listener Callouts

Thanks so much for that. It's good to have some uplifting talk these days. I think everybody needs it. So thank you for taking the time, John. Really appreciate it. And it was a pleasure having you on the on the call. And that's it for today. Thanks everybody for joining us on Trends from the Trenches.

Great. Thanks for having me.

Thank you for listening to BioIT World's Trends from the Trenches podcast.

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Eleanor Howe

Eleanor has been working at the cutting edge of bioinformatics for over 20 years. As founder of Diamond Age, she led the company’s evolution from a small, project-based service provider to a full-fledged consultancy that works closely with clients to tackle their most difficult research challenges. Trained as a computational biologist, Eleanor has deep expertise in transcriptional profiling as well as drug discovery and development. She earned her doctorate degree in bioinformatics from Oxford University and spent years in biomedical research at The Institute for Genomic Research, Dana-Farber Cancer Institute, and The Broad Institute.

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