Episode 114: How AI, Supply Chains, and Smart Strategy are Redefining Biotech Investment and Venture Capital

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Serving the entire Gulf South, but recorded in the heart of New Orleans. Welcome back to Bio from the Bayou. I'm your host, Dr. Elaine Hamm, the executive in residence at Tulane School of Medicine. And today's guest is Dr. Kane McClary. He is the founding and managing partner of KDT Ventures, a seed-stage venture firm with a focus on the intersection between technology and science. And he's also a Tulane School of Medicine alum. Welcome to the show, Kane.

Thanks for having me. Little wave.

Yeah. And the last time I saw you, I feel like it was at a Bio from the Bayou conference. And There might've been some sticky floors at James's house that we were trying to navigate.

I think that's right.

Between some cocktails. That was a good time. But alas, no James. He's dead to us now that he's left. So he no longer exists. I love you, boo, but you're not a part of this anymore. But, but I actually, I'm planning on, on bringing him back.

Onwards and upwards.

That's right. Well, we're so excited to have you here. So, all right, doctor, scientist, you know, founder, you've done so many different things. I wanna know your origin story. Of how, I mean, you, you started out as a physician and now, now look at you all grown up. So what's your story?

Yeah, so I was lucky enough to train in medicine here at Tulane and lucky enough to be supported by individuals here at the school that allowed me to pursue activities outside of medicine. The short story is I've always been a supply chain junkie, whether it's supply chains of how—

I don't think I've ever heard anyone say that. That's amazing. Okay.

I mean, whether it's, How money moves through a system, or how new evidence is embedded into evidence-based medicine, or how companies are started and founded and scale. And I think it's just an intense curiosity. And so was given the latitude to explore that while here at Tulane, and then continue to follow that passion forward.

Yeah.

When I moved out west, I did a residency in pathology at Stanford. Luckily. Pathologists obviously don't see patients. There's an old adage where you have surgeons, they don't know anything, but they do everything.

Yeah.

Medicine knows everything but doesn't do anything. Pathologists know everything and do everything. It's just a week late. And so— That's good.

I like that.

What's nice is that I could spend a lot of time on Sand Hill Road and really trying to learn the supply chain of venture and what that looked like. And so focused on building a network of large technology venture firms that were starting to become interested beyond web-based or digital-based technologies, more in the physical layer of the world. So this is circa 2013, 2014.

Okay.

And that really opened my eyes. I literally leveraged my .edu address there to do anything and everything that I could.

Absolutely.

And had a very supportive department at Stanford to allow me again to explore these extracurricular activities. Now, at the same time, I was also putting what little money I had where my mouth was and totally lying on accredited investor forms.

Yes.

The SEC isn't listening. And trying to actually build a book or a track record of backing companies that I was involved in or looking at, at the time. Was lucky to have met some really amazing founders and companies and ideas during that period of time.

Yeah.

Left Stanford after joining the faculty there for a quick second. And part of that was driven by the fact that if you think about 2014, that's when AlexNet came out, which is computer vision competition. That's the first year where computer vision actually surpassed human accuracy.

Yeah.

Now I'm sitting there as a wallpaper matcher, looking at a microscope, telling people how blue things are.

Yeah, sure.

And computers all of a sudden have surpassed what we could ever hope to do in a pathology lab. And so to me, the writing was on the wall. And so my next step was to join a large healthcare technology firm and run a data analytics program focused on how to quantitatively define what quality means. So if you think of value-based care, value equals quality over cost. Everyone's focused on cost. Quality is very opaque. And are there ways in which we can use data science to look at quality?

Sure, yeah.

That was fun, and I got to experience what scale felt like. So went from around 50, I was around employee 50, to 400, 500 employees over 2.5 years. And it was a lot of fun, and got to learn a lot of paradigms and playbooks. Now, during that time, the book that I had built started bearing some fruit. And so I thought, maybe I have something here.

Lying on that form paid off.

And so— I love it. In 2017, decided to launch KDT Ventures.

Yeah.

Because it felt like there were, not enough technical underwriters at the earliest stages of what I saw as a convergence of technology and science. And we broadly define that across, again, physical layer supply chains of the world. Most folks, particularly I would say listening to this, focus on the biotechnology, therapeutics, healthcare delivery services supply chain.

Yeah.

We also focus on things like specialty and commodity chemicals, materials, agriculture. These are massive swaths of the economy through which you can use science to wedge in and arbitrage supply chains and build durable businesses. And so, you know, I think that training as a physician and a scientist, these experiences at the bench and the bedside really taught me that science is rarely linear, and it only becomes more complex when, like, financial incentives are layered on top of it.

Yeah.

That being said, What that showed me, and I think our firm, is we're looking for founders that are systems thinkers. And so importantly, you're not just focused on mechanism, but you're focused on the system in which that mechanism functions to build durable value. And when I say, you know, physical layer of the world, what I'm really focused on is science helps describe rule sets in my mind.

Yeah.

And, Engineering those rules around these systems is really what creates durable value. Now, I, the one thing I'll add is along the way I've learned there are no rules out there and everyone's scared. They're scared of different things. And so what's important is just getting started and moving forward. And I think that, you know, Keep going. A physician with no financial background starting a firm is a, is a good example of that. But it's just been the joy of my life to be able to build KDT Ventures and be able to interface with amazing founders in science every day.

Well, what are some of the biggest mistakes that you see biotech startups making now? And has that changed since the inception of KDT Ventures? Are we just sort of repeating history, you know, in terms of the mistakes that were—

There are different versions of the same game being played, I think. And so in this climate, I think that there are some strategies that one can use to try to play the game on the field that's given to us in this climate today. For example, we've actually become quite involved in legislation and lobbying recently.

Sure.

And if you can write the rules of the game, and in this current climate, it feels like—

It's important.

All of the rules are on the table and—

There's rules?

And can be rewritten. Yeah. And so, you know, that's not a mistake, but I think that there's different strategies. In terms of mistakes, the mistakes are generally the same. And when we meet, founders, oftentimes they're simply leading with data rather than a hypothesis or an understanding of the system in which they're playing.

Yeah.

And so what we really wanna see is, is an understanding of the entire system and the incentive structure that exists within that system and how that interfaces with the data or technology that you have. So, you know, Charlie Munger once said that, You show me an incentive and I'll show you an outcome. And so if you think about, let's say, healthcare services, for an example, oftentimes we see pitches that are cost-saving.

Yeah.

The incentive is growth of the top line.

Yeah.

In health systems.

Yeah.

Not necessarily bottom line. And so it's really important to understand, again, that playing field and the incentives that are on the playing field of the supply chain you're trying to go attack. Secondly, I think that oftentimes founders potentially underestimate the power of storytelling.

Yeah.

And it's as simple as like, why now? Why this? And why me? Or why us?

Mm-hmm.

However, oftentimes they haven't put enough emphasis, say, on the storytelling, and are expecting then the person on the other end of the line that may be on their 6th pitch call of the day.

Yeah.

Or have left a board meeting, or whatever else, To put the pieces together for them.

Yeah.

Why can't you see it the way that I see it? And I look at it every day. Yeah.

Exactly.

Yeah.

And so I think that it's really important to have that honed-in story. And then the last part is there's an overbuilding oftentimes prior to understanding what value you're creating. This is more common, I think, in the large venture creation shops that we see where things come out overbuilt, overcapitalized. I really believe if you're, I won't mention a lot of the peer firms, but you know—

Explain the overbuilt piece.

Yeah, so, you know, for me, the best founders we see understand exactly where the risk is in what they're building in this stage. And they're focusing every dollar and every effort on de-risking that one or two things that really matter.

Yeah.

Rather than, oh, I need to build out my entire C-suite, you know, have a head of people and everything, you know? And so there's, Oftentimes there isn't enough focus on what the actual de-risking milestones are within a timeframe of which that capital—

Yeah.

Can—

Which a CFO may not even help you with.

Exactly.

Yeah.

Exactly. Yeah.

And so that's what I mean by that.

Yeah.

Yeah.

Okay, supply chains geek here. All right, so how have supply chains changed since you first geeked out about them back in, what was it, 2000—

when?

Yeah, '17.

'17, yeah.

But started investing in 2010.

Yeah. How's that changed?

What you see, at least for the listeners here, are there weren't chief AI officers at, you know, all of these pharma companies. No, no, absolutely not. And an understanding of the power of data and data sources. And so, you know, something that we focused on early on at the firm was how do you garner and maintain proprietary-scale datasets? So for example, right, Google has a search and language dataset that, that outrivals anyone else in the world.

Sure.

And not only did they not have to pay for it, they were paid to generate that dataset.

Yeah, sure.

So you can apply the same thing within science as well. And examples of that are PathAI, right? A pathology company in our portfolio where you can partner with pharma, they can pay you for insights. Well, that's your Trojan horse to build the world's largest computational pathology dataset in the world to someday potentially first augment and then maybe replace the pathologist sitting in a lab at your AMC or hospital.

Right.

Similarly, you can think of large computational chemistry platforms like Terray in our portfolio that are paid to discover molecules for pharma. However, every run of their platform generates hundreds of millions of data points, that they can use to then infer for their own internal pipeline. And so that shift in the understanding of the value of data is new. The goal was to try to get ahead of that. It may have been like the only insight I ever had in the world.

Yeah, no, I was gonna say, like, you got in very, very early, and, but isn't it messy now with everyone? Like, oh, I feel like pharma sometimes are like, oh, we need to have an AI officer because everyone else is doing it, but not necessarily understanding what that means.

Exactly, I think we're going through a maturation right now, And folks will understand where I think the value accrues and where it doesn't become, say, a model race. It's more of a data aggregation race. But I think right now, the top line on all the articles is, my model's better than your model. And that, in our humble opinion, models commoditize, and they will commoditize. What won't is the underlying data layer that you've built there.

Yeah. Yeah. Well, in academia, what we see is a lot of scientists thinking very highly of their data, and it may not be worth what they think it is. How shall I say that?

Yeah, exactly.

So we have sort of the opposite problems. We're not— they're not thinking about the models. They're just like, oh my gosh, oh yeah, I have tons of all of these, you know, all of this data that I've been collecting for years, but it's probably not in the right format. It's dirty, it's messy, and it's challenging to make that connection between you have great models, You think you have great data, and then not much happens in between.

Exactly. And then the other way in which, you know, let's say on the therapeutic side of things, that we've seen things change is, obviously, we've had the largest drug launch ever in the GLP class.

Oh, yeah.

And understanding what that does to the consumerization of medicine, and the supply chain of going direct to consumers now, is really interesting.

Compounding pharmacies.

Exactly.

Trying to skirt around all the things.

And then if we imagine that, you know, everyone has sufficient weight loss and insulin sensitization, but obviously there are side effects, like what are the next things we need to skate towards from a puck perspective? If people are living longer, what are the things that haven't seen innovation in many years? Take osteoporosis, for example, or other indications. And if you imagine a future in which some large percentage of the population is on some therapeutic agent that allows them to live longer and better, What are then the problems we need to solve?

Yeah, I imagine sort of like the muscle wasting will be— now that we have GLP-1s everywhere, you know, okay, we're living longer, but let's not—

You want to be skinny and ripped.

Yeah, yeah.

So, yeah.

Exactly. So we're skinny and ripped. That's going to figure into our stinger somehow, I'm sure. So, but yeah, these— all these new problems are created. You know, it's funny, we had this conversation with Walter Isaacson last night about sort of the AI replacing jobs, and people are being terrified of that. But At the same time, there's this, a whole vacuum that we don't even see yet of like with the new jobs that will come out of the replacement of AI. You talk about the AI and the pathologist. Yes, that's probably, as you saw, that's the writing on the wall. But at the same time, we don't know what's next in that sense too. So are there things that you're seeing in your portfolio, for example, that— are solving that kind of problem of creating the new piece? I know that's a weird question. Not yet.

Not yet. I think that there's still a lot of value to be captured on—

We're not there yet.

Yeah. And I, to be honest, it's a struggle of mine. You know, I think that there's a part of me, if you think about the effective altruist movement and everything else, and there's people that are focused on P-doom, which is probability of doom, right? Is AI like either the best thing or the worst thing in the world?

Yes.

It's hard, and I probably have a higher percentage P-doom than maybe many folks in my field. At the same time, I left, say, a labor stack to move to a capital stack so that I could capture enough value so that my family and the people around me would be okay.

Yeah, absolutely.

If and when this large displacement of labor happens.

Sure.

You know, I can talk out of one side of my mouth and at the same time need to believe something else every day when I go to work. And so it's a really, really difficult problem here. I wish I had answers for you, but—

But, well, on the other side of what gets you excited these days?

Yeah. Oh, so much. We're running our first incubation right now at KDC.

Oh, really?

And we've in-licensed a clinical-stage asset and are pivoting indications, and that's been a lot of fun, and it's focused on metabolic rewriting. And so right now, what's super interesting is when you look at the asset class, say, of GLPs and others, you're seeing many other effects that were not clear when we first started to develop these assets, particularly at the intersection of, say, inflammation. Even we have a large novo readout coming at the end of this year on Alzheimer's within GLPs.

Yeah.

And so there's this intersection of energy, inflammation, and longevity that seems really interesting.

Very.

And so I think that that's That's an area of the firm that we're very excited about. We're also really excited about human-computer interfaces moving forward. Not as far as, say, like the Neuralinks of the world, because I just don't know how many people want their, you know, skulls opened. I don't want that. But are there other ways in which we can interface with technology using some type of neural interface? For example, we've recently invested in a silent speech company. Oh, that's cool. And so if you think about, there are medical applications to that as well. You can imagine subvocalized speech for patients that have strokes or ALS. And are there ways in which we can actually look at how the musculature and nervous system is moving in the head and neck to bring out speech? And it's almost like mind reading in a way. And then you could also, though, take that supply chain out to, well, if I'm gonna have smart glasses pretty soon here, am I gonna interface through some series of movements with my hand finger EMG, which is the CTRL-Labs approach, is what Meta has done?

Yeah.

Or should I just use what my brain knows best, which is speech?

Which is speech.

And knowing how to talk. And so, you know, I think that that interface is a really interesting area of the world that we're focused on. And then also, as I mentioned earlier, consumerization of of medicine. And so you're seeing large swaths of the payer system moving more cost center to patients, while you also see patients reaching for semi-unproven therapeutic approaches. If you even look at the peptide businesses right now. Oh my gosh. What does that look like in its final end state? And what does direct-to-consumer science translation actually look like if you had to imagine that world? And that's an area of the world we're spending a lot of time on right now.

That's really cool. That would get me excited to get out of bed today too. So always great to chat with you. Thank you so much for taking time out of your day to share what you're working on and your origin story. And hopefully we can get you back on the podcast again soon.

Thank you for having me, Elaine, and thank you for doing this podcast for everybody.

Yeah, well, great. As always, we'll have more information about our guest and KDT Ventures in our show notes, so be sure to check them out. Of course, like and subscribe to our podcast as well, and we'll see you next time on Bio from the Bayou.

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