Tal Zaks
speaker
203 appearances
1 recordings
1 series
first heard Jan 2025
last heard Jan 2025
Tal Zaks’s voice in public audio — every appearance, attributed to the second.
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Appearances
No, I think it is possible. I think it's been proven possible. I think those people who are successful in it are successful because they're smart in recognizing the multidisciplinary nature and knowing how to ask the questions. I've seen this upfront. In fact, some of my colleagues, even at Orbimed, are not people with operational experience at all.
And one of my realizations early on was, huh, I thought I needed all this experience to be good at it. Well, in fact, no. It turns out that people can be much smarter than me and can get there without the experience just by virtue of their wisdom. Now, that being said, I do believe that you can replace experience with wisdom up to a point. Beyond that, you better go and ask somebody.
And so I think those investors who can and have earned great returns without the deep domain expertise have done so because they know how to find the right expertise, how to understand the question. And again, given it's such a multidisciplinary challenge, look, even I don't have the expertise, I've got a narrow band of it.
All I've learned, if anything, is to ask the questions of the areas that I don't understand. And so if that's true, then of course, somebody doesn't even need my expertise to go ask those questions. They can come from wherever. And you've seen people do that.
It's that ability to ask the right questions and find the people whose answers you trust and the understanding of why you trust their answers that I think makes a great investor. It's also the traits that make a great general manager. I mean, again, I was fortunate to work with Stéphane Bancel and his executive teams were
His brilliance is being able to go function by function and just ask the five whys. Why is this? Why is this? Why is this? And the second thing that made him so effective is that when you gave him the answer, it had to be in plain English that he understood. If he didn't understand, he said, I'm sorry, I don't understand what you're telling me. Can you please? Say it again. Yeah.
Dumb it down for me. Why is it? And my PhD mentor taught me early on that if you can't explain what you're doing to a kindergarten student, then you don't understand it.
The bear case is that they make slow roads easier. one thin vertical domain at a time. That's already starting to happen, but it's slow. It gets encumbered by finding return on investment for each one of those thin verticals.
Well, it means that I've got a company that's figured out how to do better transcription for nurses, but not physicians. So they're going around and they're making a new AI tool just for nurses and nursing homes. Okay. And then I got somebody else who said, hey, I've got a great system that can read the charts and figure out who's a good patient for a clinical trial.
I'm going to go and deploy that in more institutions. Okay. That's all such thin piecemeal applications that's going to be super challenging to get integrated. The bullish case is that there's somehow an integration of these systems and a realignment of incentives that allows people to leverage productivity. I mean, I think it was one of your prior guests who made the point
when they were looking at the early days of EMRs, electronic medical records, now we call them EHRs, electronic health records, and realized that the implementation of those systems into hospital systems actually hurt productivity. Which is an abomination. I mean, the whole point of technology is to improve productivity. And I remember this as an intern.
I mean, people started to put PCs on nurses' desks and have the nurses start to spend time typing in. Okay, that just took time away from what they were doing. That didn't help anybody. If we figure out how to turn this on its head and actually find ways to put in systems that will improve productivity of the healthcare system, that's where I think the bull case is.
And once we figure out how to do that and align that with an economic return on that investment, then I think you'll see an acceleration of change and the pace of change can actually be very rapid.
That for me is still a little bit the realm of science fiction. You can get digital twins up to a point. They're going to be valuable, again, up to a point. The place where you need to be careful is we have to be able to balance this with the ethical obligations to patient autonomy and the other things that we hold dear and near to our heart. It can't be utilitarian because you
the utilitarian approach will be dictatorial, and that's not gonna work. And I think we're already seeing a public backlash against science trying to assert itself in the name of good. Science is only in the name of good in how we use it and deploy it. So you could probably get your best healthcare system
up and running in a place where patients had no autonomy if you measured outcomes in a certain way. But of course, that's not a way that's acceptable to us to measure outcomes. So I think the world's going to stay messy, purposefully so. I hope so. I think it's what makes us human beings human.
And so in that regard, I think I'm a little bit more circumspect for how rapid some of these technologies will actually be able to be deployed. And we're going to have to be careful to balance. And that's true of AI technology. in general, in every application sphere, no less healthcare.
I'll tell you, the kindest thing anybody's ever done for me was probably Steve Rosenberg when he took me into his lab back in the NCI in the late 90s. And the kindest thing he did was that as I probably wasn't one of his best postdocs, I kept proving why things didn't work as opposed to showing things that did work.
And as I was leaving, he said, look, Tal, if there's anything I can do to support you in your future, don't hesitate. You know, I didn't feel like I was that good. I wasn't sure did he really mean it, but it took me almost a quarter of a century to realize how deeply he meant it. Because not only did he come and support me over time, but I tried in his lab to make an mRNA vaccine for cancer.
We did it in mice. We published it back in 1998 or so. And when I was at Moderna, and now this is 2016, 2017, so 20 years later, and we've come up with this personalized cancer vaccine, I called him up and I said, hey, Steve, he's the best example of a public servant scientist that one can ever hope to meet. And he's one of these people who've actually not just moved the whole field of medicine,
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