Tal Zaks - Bridging Science, Medicine, and Returns - [Invest Like the Best, EP.406]｜Invest Like the Best with Patrick O'Shaughnessy

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Hello and welcome, everyone.

I'm Patrick O'Shaughnessy, and this is Invest Like the Best.

This show is an open -ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money.

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This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

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My guest today is Tal Zacks.

Tal is a physician scientist turned biotech executive and investor who served as Moderna's chief medical officer during their COVID -19 vaccine development, giving him an extraordinary perspective on one of modern medicine's pivotal moments.

His combination of medical expertise, platform innovation experience, and investing acumen allows us to explore the interconnected challenges of turning scientific breakthroughs into viable medicines while generating venture scale returns.

We dive deep into the lessons from Moderna's MRNA platform, examine how emerging technologies might reshape drug development, and the fundamental question of what it means to make a healthy person healthier.

For investors, entrepreneurs, and anyone interested in the future of medicine, this discussion provides a window into both the immense potential and profound challenges of advancing human health.

Please enjoy my conversation with Tal Zacks.

So Tal, maybe to begin, you could give us your one -minute summary of your career, just so people have context of where you're coming from in all the incredibly interesting topics that we'll get into today?

Well, in a nutshell, I'm a physician -scientist who spent all my life figuring out how to translate the wonderful innovations of science in our era into better medicines for patients.

I was fortunate to have been trained by some of the best.

I trained for more years than I would recommend anybody in the sense of having done an MD, a PhD, a postdoc, a residency, a fellowship, two internships.

I mean, you get the picture.

And then I finally went out to do something in the world and spent the first part of my career in drug development, mostly as an oncologist and oncology drug development.

And then the second good chunk of time at Moderna as the chief medical officer, developing that as a platform, as opposed to any one medicine.

And for the past several years, I've moved over to the investor side to help aligning the translation of science into medicine with actually a return on investment of realizing how important it is to get those two in alignment to do good in the world.

Well, your intersection there of investing and deep science and personal hands on work and as a physician is exactly the Venn diagram that I've been searching for to have this kind of conversation, which is really about the current state and potential future states of medicine, therapeutics, and the investing

returns that might be earned from paying some special attention to those areas, which I think is the key distinction between the therapeutics themselves and the potential for returns.

Maybe you could give us the equivalent of a state of the union on that topic of how do you see the world today of medicine versus maybe your career?

Like if you compare today's snapshot to everything you've seen across your working career, I think that'd be a great place to start just to give us context of where we are today?

I think we're overall in a good place.

I'm optimistic because the advances in science and technology have been so robust and amazing.

Now, that is tempered, I think, by two opposing forces, if you will.

The first is the translation of all that wonderful science into medicine is probably as challenging as it ever was in terms of the unpredictability of what makes a good medicine, and we'll come back to that, but also in terms of the public perception and the willingness to pay for innovation, which I

think are a challenge in the current political environment that is dampening, I think, some of the prospects, if you will, or optimism of what is possible.

I was invited to give a short talk at one of the panels of the National Academy of Science, Medicine and Engineering back in the middle of 2024.

And it's a panel that asks, how do we better align investment and innovation with the unmet need?

And so they asked me, OK, given your intersection, what can you tell us?

And one of the points I made to them is that investment in innovation requires a return on the investment.

And I stated this publicly.

I said, you guys use the term reimbursement.

Reimbursement is what I do when I put in my expenses to get paid for a meal or a flight.

We don't do this for reimbursement.

We do this, A, because those of us in the trenches really believe in the mission, but B, because there is a need to return capital to shareholders.

And in a funny way, I've been with this VC firm, Orbeamid, for a little over three years, and one of the executives from a large pharma who shall go unnamed, he was at a much more prestigious role than anybody would ever offer me.

He came in, knocked on our door.

He wanted a role to be a venture partner with us.

And on the interview, he tells me about his background.

And at a certain point, I asked him, I sit back and said, let's call him John.

John, what is it that you think we do here?

And John looks at me and says, well, you know, you do clinical trials, phase one, phase two, increased value, da, da, da.

And he looks at me and he sees my face and he says, well, clearly I didn't exactly answer your question.

So where did I go wrong?

And I said, look, on the tactics, what you're saying is right.

But the answer to what we do here is very simple.

People give us money.

And a number of years later, we need to give them more money back.

Otherwise, we're not going to be doing what we're doing.

And so framing the potential in terms of return on investment for me has become the focus of this phase of my career, because without it, all this wonderful progress will be for naught.

And it's not by chance, I think, that the modern armamentarium of medicine has come from the United States.

I think it's a combination of the infrastructure we set up on the public side, the National Institutes of Health basic research, as well as the infrastructure we set up on the commercial side and the ability to garner return on investment.

And the part that is often lost on the public and the people who debate this return is the very long tale of benefit to society that what we do brings.

I happen to be on the board of directors of Teva and I'm very proud of that because Teva is one of the largest and the highest quality generics manufacturer and people forget that once the brand price erodes and we're in generics land, that now is a benefit to society, all of society across the globe

at pennies. My father who passed away a few years ago, he was diagnosed when he was 50 with his first MI and he lived to his mid to late 70s.

And he did that because of a generation of drugs that even in his time, half were generic, but today all of them are generics.

And so the benefit that we bring in what we do is not just the short term during the patent period where all the angst is about pricing and it's legitimate and I get it.

But somehow society has to also take into account the tail effect of what it is we're doing that is leaving for the next generations.

What is your assessment of the potential for the world of therapeutics to explode in a similar way as what we're seeing with artificial intelligence and some other areas of technology?

And I'm curious to ask me this because you have exactly the right balanced perspective of an excited investor, I'm sure, but also a practical realist from your time designing and rolling out drugs at Traditional Pharma and at Moderna, which is a very neat platform that we'll talk about.

What do you think is that potential?

Should we be excited for a thousand flowers to bloom and an explosion of progress in therapeutics?

Or do the realities of incomplexity of biology mean that it will be slower than the most excited people think it might be?

It's a question I've been asking myself quite intensely, as you can imagine, for the past few years.

In fact, I joined the venture community because of the excitement around where that branch of technology has the potential to bring us.

But one of the things that I think is often lost is the difference in the predictability and the nature of investment in biotech versus some of the other technologies.

Tech, in its various manifestations.

The challenge is that it's still hard for us to predict what is going to work.

And so if you look at what makes a return on investment, it's basically three things.

It's how much money you are required versus what you can get at the end, how long it takes you to actually get there, and what is the probability that you will actually arrive at that destination.

And that's basically what determines your return.

Now, that's where biotech and tech are very different, and we can come back to that.

But in biotech, you have to account for two variables that are extremely hard to predict.

One of them is whether the biology will pan out, and the other is whether the pharmacology will pan out.

Now, biology, it means, is this protein that I think is involved in disease, is it actually involved in disease?

And the pharmacology is, okay, yeah, it is.

Now, I'm going to change that protein's function.

Am I actually able to do it with a drug I have at hand?

Does it get to the right place in the body?

Does it do its effect?

Is it tolerable from a safety perspective?

And so those risks of biology and pharmacology are very hard to predict.

And some of it has to do with the nature of the beast, if you will.

It's not a system we designed.

So we're constantly learning about it versus engineers.

But some of it, and this is one of my biggest lessons from Moderna, it's also a different mindset.

Engineers and physicians are almost diametrically opposite in how they think.

And I had the good fortune in my life to work with, I think, one of the most brilliant engineer leaders, Stefan Boncel, the CEO of Moderna.

And what it taught me is that engineers have two things that physicians struggle with.

One is vision, and the other is process.

So what do I mean by vision?

Well, if you look around our life, take a look out your window if you live in the city.

Everything you see is a function of vision of people who came before you.

It would not otherwise have been there.

That's why we love nature so much, because it's uncluttered by visions of other people.

But if you live in any other environment, you're constantly faced with a vision.

So you have to ask yourself, well, what's my vision for the thing I will leave for the future?

And then to enact on it, OK, it requires a process.

So what does it actually take to get there?

And here's an interesting thing.

If you ask an engineer an engineering problem in their domain, say, I want to get to the MOOC.

Is it possible? Well, anybody who knows the domain will tell you, yeah, it's possible.

Here's what it's going to take.

And they can even map out the resource and the likelihood of it getting there.

Well, you ask a physician, hey, I want to cure cancer.

Is it possible? And the answer is, I don't know.

I mean, I can think of some approaches, but we got to go try them.

One of the first things I did when I joined Moderna, and probably the scientific thing I'm most proud of in my time there, is actually not the COVID vaccine.

It's something called a personalized cancer vaccine.

So we've learned enough about cancer to know that everybody has a different cancer.

Everybody's immune system is different.

And we know that mRNA is a phenomenally good platform for making vaccines.

So can we immunize people against their cancer?

Now to do that, we'd have to figure out what each individual needs to get immunized against.

And so it would be personalized.

And that's a very challenging, complicated task.

It's expensive. So we set up to do that.

And as we were starting down the path, I remember we had a phase two study that was running.

So that's basically a study where you give half the people your vaccine and half the standard of care.

And the question was, are we actually going to be able to prevent some cancers from returning relative to the comparator?

And Stephane used to stop me in the hallway and said, Tal, is this thing going to work?

And I'm like, Stephane, I honestly don't know.

What I know is I feel I've been put on earth to do the experiment.

And we've been fortunate to align the technology and the resources, including the financial ones to run the experiment.

But I'm not going to predict whether the experiment's going to work.

People have been trying to do cancer vaccines.

I've been trying to do cancer vaccines since I was a postdoc, and so far we haven't.

I think this one has a chance of working, and here's why, but until we test it in the clinic, we won't know.

And we've been fortunate that that phase two actually read out with quite a successful readout last year.

In fact, the phase three now is enrolling, and hopefully we'll have a personalized cancer vaccine on the market before too soon.

Sorry. So just so I understand that one, which sounds incredibly exciting, I could get tested blood work or something else or genome or something, some combination of things that's unique to me.

And then a vaccine would be created to reduce the odds or eliminate the odds that I would get the cancers I'm most prone to get.

So that's still in the future.

The way this works is people with early stage cancer, specifically, we started with skin cancer.

So cancer has been diagnosed, but it's early.

Likely a surgeon will cut it out.

And then there's some probability that it will come back.

And what can we do to improve the odds that it won't come back?

I see. In that context, at least as far as a randomized phase two goes, when you give a personalized vaccine to those patients, it cuts the recurrence rate of cancer by about half, by about 50%, which is quite significant.

So going back to this, what sounds like a or the key bottleneck, which is that we just don't know how to predict whether or not something will work.

Can you break that down further?

Because I'm trying to wonder if there's a version of the future where it does become easier to simulate, model, predict, more like an engineering challenge versus a complex biological system challenge that we can't predict.

Practically or theoretically, what is in the way of us predicting things better and therefore drastically improving the efficiency of our efforts?

So I'd break it down to three phases, and I do think that the world is improving.

The first phase is, is this target for intervention actually relevant to the disease?

That's basic biology.

And I think we're making great strides there in understanding biological processes.

Some of it is big data, some of it is just old school grunt work.

But there's a lot of tools that have been developed that are being deployed that are making this much more accessible to us understanding disease better.

The second has to do with what's called drug discovery.

Okay, so I've got a protein whose function I want to alter.

What is the ability to actually discover a new chemical entity or new protein entity or nucleic acid entity that will actually interfere there, that will actually do the pharmacological effect?

And there, I think you're seeing a very significant deployment of these modern AI tools across the industry now.

And it's very quickly becoming to a certain degree commoditized with the advancement of alpha fold predicting protein structures and people applying the same kind of tools into chemical discovery space to come up with new chemistries.

I've seen people even apply these tools into figuring out these lipid nanoparticles that will shepherd mRNA into different tissues to come up with better formulations and ways of bringing that medicine into the right places in the body.

So I think drug discovery is getting a leg up in a significant one from the various applications of AI tools.

The part where we're still behind is in what's called development or clinical development, putting it in people.

There's no shortcut here.

We don't have a holistic model of a human being.

We have made progress in understanding what natural outcomes are for people with high quality, big data sets that you can apply machine learning tools.

So our ability to predict outcomes on the control arm is getting better.

And so people are leveraging those tools to make trials shorter and smaller.

But in the end, the gold standard is and will remain okay.

I have to put it in people and see how those people respond and make sure that the people who get this have a better outcome than the people who don't.

And I know I've got a new medicine on my hand and I don't see the tools that we have today in the near future, changing that.

Now, is there a version of the future where they will?

Yes, absolutely. But that will require some level of evolution of our healthcare system.

Could you just describe the basic investing process for you when you're looking at a company and a product?

Because I know it's quite distinct from, you know, me looking at an AI software application or something.

What are the key variables that in most investments that you're analyzing, you're looking at?

Is it typically just a team that's going after one specific target or one disease?

What is the nature of an atomic unit of an investment or an investment diligence process?

That's a good question.

So for us, again, I term us sort of as the collaborative spectrum of venture capital.

We will invest at any stage, starting from seed all the way through public pipes.

And so we've got a pretty flexible mandate.

For us, I think the key elements as we look at an opportunity, it is what is the thesis that this team is trying to do.

How much capital is required to get to the next value inflection point?

What is that next value inflection point?

What is the likelihood of achieving it?

If it's already a drug in the clinic, okay, what's the transition?

If it's an early bet on a target, what's the likelihood of that target panning out?

And if it's a different strategy, then we look at that.

So it will depend by the stage of investment, but ultimately it boils down to what is the thesis this team is trying to prove for making the world a better place.

How soon will we know that we've achieved some milestone that people will recognize the value inflection, the value created?

One of the most recent opportunities I had that I had to walk away with was a vaccine where there was an interesting idea and it was an early concept, but it became clear that the phase one data are probably not going to be de -risking.

You're going to have to take it all the way to phase three to know, okay, now you're asking me for a quantum of capital and a time horizon that is just not commensurate with the risk involved.

I have to see my way to building value incrementally over the life of the investment and the additional capital that will be required.

So it's got to be the content, the drug, the theme, the scientific content.

It's got to be the capital requirements, value, and it's got to be the people.

What is the talent around the table?

What are the missing pieces?

Am I going to be able to help you uniquely from my experience to do that because I'm here not just to deploy capital but actually to deploy my experience as well granted it's at the board level but nonetheless I'm not going to be making investments in fields that I know zero about or I'm not interested

in leveraging what I do know so I think these are the three elements now in terms of process and I don't think in that regard we're very different it's really interesting being a venture capitalist a partner at a firm, you've got two very different sides of the same coin.

So the first part of the job is deciding where to deploy capital.

So that's the diligence process.

You look at an opportunity, you've got a team of junior folks helping you out.

You bring in external experts for the stuff you don't know and it takes four to six to eight weeks and you come to a decision on whether that's worth the investment.

Now, if you decide to invest, the next part of it, the coin flips.

Because now the diligence team is not going to help you.

They may dive in and out once or twice, but actually you're there on a weekly, quarterly, monthly basis with the CEO, with the management team, with your other board members, which is why for me, looking at the syndicate of who my partners are for this investment is so critical, because this is a long

-term commitment. There's a lot of capital.

It's not over and it's likely not even over with this investment round.

They're going to need more capital down the road.

I got to make sure I got a reserve.

I got to make sure I got other colleagues around the table who are also going to be able to be in it for the longer run.

And so making sure that that syndicate is the right one, that I've got colleagues around the table on the board who bring in complementary experiences and expertise to what I bring in so that we can help the CEO and this young management team actually get there.

Those are the other elements that then become that second part of the job.

It sure makes me wonder the role of capital, which is not rational, whether that's government or otherwise, that's willing to fund things that sound like that vaccine story that you mentioned, where you're not going to get the appropriate readout until phase three.

And it just doesn't make sense to provide them with capital.

Sure makes me wish like someone in the world, whether it's philanthropies or otherwise, it was intentionally funding things that are irrational explorations.

That's an interesting takeaway from your summary.

When I look at investments, I will often tell the folks, look, there are three pockets of capital in my book.

There's government, which is what's there for the greater good.

And I hope those investments are rational.

There's philanthropy.

And then there's us.

I'm not in the business of philanthropy and I'm not a government.

So for me, the lens has got to be what it is.

But there are investments that are fit for philanthropy.

And there are investments that are right for governments to make because it's not an immediate return either the capital scale or more often the time scale and the risk is not commensurate with the money I need to return to my OPs.

If you were to think about the changes in general that would most accelerate our ability to identify a target, understand a disease and create a therapeutic, it sounds like it's some mix of, if we could somehow test these things, not in humans, but in simulation would be one big one.

And that just like the healthcare industry itself would need to change in key ways.

Do I have that roughly right?

Like those are the two things that most impede our ability to go faster?

You do. And I think your roundabouts are coming back to what I think is one of the central tenants that makes this endeavor so different, which is the number of different disciplines that need to come together to solve this is quite wide.

I've not found many or maybe any other industry where you need so many different types of expertise around the table to solve the problem.

I've spoke about physicians, you need biologists, you need engineers, you need, of course, the financial understanding of how this works.

The number of disciplines that actually need to come together is vast, and frankly, it's been one of the fascinating threads of my career, and to the degree that I've been successful, I think it's because I've always been curious not to approach it as the physician in the room, but to try to understand the language

of the other functions that need to come together.

So if you look at pharma, a project team that tries to make a drug will usually get kicked off around a target.

And if they're successful, 10 years later, you will have a drug.

But if you look at that project team, you will rarely find anybody around the table who is there at the start.

And yet the thing works.

So the great success of the pharmaceutical industry has been to build these multidisciplinary teams and enable them to progress this very complicated process.

Yes, you can cut it short in time if you have the right technology and we can come back to mRNA vaccines as the obvious example of that.

But ultimately, you do need these different disciplines.

And when I started sometime during med school, I chanced upon a really interesting book, and it's by a Canadian philosopher called John Ralston Salt.

It's called Voltaire's Bastards, and the subtitle is The Tyranny of Reason in Western Civilization.

And he made a point that ever since, quote -unquote, the Age of Reason, we have mistakenly assumed that science in itself and reason will be an ethical force for good, which of course it isn't.

Science and reason are science and reason, but it's the humanistic moral compass that needs to frame how we deploy them.

But the other point he makes is that in this age of reason, the way that experts have survived and grown is in silos.

And that silo is enshrined in language.

And so every discipline develops its own language over time.

And it becomes a barrier to common understanding.

And if you're trying to solve a multidisciplinary problem, you very quickly realize that one of your greatest barriers is language.

I mean, the finance world knows this very well.

We coined the term FedSpeak.

That's clearly an example of language as a barrier constructed, so on purpose.

But we all fall into the trap.

And what I realized early on is that physicians are probably as guilty or guiltier than anybody else in doing that.

And I'll give you an example.

So back in the day, if a patient came to me and his placelet counts, these are the blood clotting elements we have in our blood, were low for reasons that the physician didn't know.

But it would lead to a skin rash because when blood doesn't clot, it will accumulate under the skin.

So people would show up with this skin rash.

And you'd go, the patient would come to the physician and the physician would look at him and say, Hey, yeah, you have idiopathic thrombocytopenic purpura.

Oh God, that sounds scary.

Now, what did I just say?

Well, all I said is in Latin, you've got low platelets and a skin rash.

And I don't know why idiopathic, I don't know why purpura is a skin rash and thrombocytopenia is low platelets.

So just by using Latin, we made ourselves sound smart, but the physician had no clue actually what it comes.

Now today we learned a little bit about the biology.

So it's not as bad as I make it out to be, but you get the picture.

And so when I came into the industry, I was super curious always to understand what is the language of the other functions that have to come together to solve this problem.

And yes, I'm the physician in the room, but the guys doing the biology and the guys doing the chemistry and the manufacturing, they're just as important.

And by the way, one of the reasons I'm now in the investor seat is because I realized from my time in Moderna how critical it is to understand the language of investors and the expectations that they have in order to align across this mission to be successful.

So it is a multidisciplinary challenge, and that's not going to change.

We'll be able to make it more efficient.

Yes, these tools are already making an impact in drug discovery.

And as we integrate and have better and better ways of looking at human data, I think it will also make a dent in development.

But it's interesting to sit back and figure out, well, how can we actually radically change it?

And I don't know that I have a good answer, although I'm trying.

And this is especially, I think, germane given the experience we had at COVID.

One of my favorite books that I read last year was For Blood and Money about the development of two cancer therapeutics.

And one of the takeaways from reading that was a story in which the primary investors made incredible amounts of money, but the story was quite chaotic.

The things that had to go right were many.

And many of the bounces of the ball seemed just crazy and random and impossible to predict.

And sheer persistence and a lot of luck and a lot of factors had to come together for that group of people to make a lot of money.

And a couple of people were a few degrees away from making a lot of money and made very little.

I'm curious with stories like that seemingly all over the therapeutics world, what the best therapeutics investors do that bad investors in therapeutics don't do.

If there's stories like that happening everywhere, it's tempting to think you just have to get really lucky versus be really good.

So what is the combined role of skill and luck in making money as a therapeutics investor?

So I joined one of the best VC firms I could find, Orbimed, because they have a track record of actually being successful.

And I wanted to learn the answer to that question.

And luck certainly plays a role, but here's a few thoughts that I think are not as obvious.

We all recognize the importance of learning from failure.

And I listened to your podcast with Jared Kushner the other day, his version of what is God trying to teach me.

And I thought that was extremely well put.

For many, many years, my favorite quote of all times was Nelson Mandela's who said, in life, I've either succeeded or I've learned.

And coming out of the COVID success, it actually made me realize that's a misguided quote because it suggests that you haven't learned from your successes as much.

And I would argue that the best investors actually learn more from the successes than the failures.

And that's not trivial.

When you fail, there's a whole bunch of things you can point at as causal elements.

But when you succeed, as you say, what was it?

Was it luck? Was it talent?

Was it getting the right people around the table?

It's a combination of factors.

And I think the good investors develop this sense of pattern recognition of what works.

And as I've tried to uncover it for myself, frankly, out of my own curiosity, I think in the domain in which I function, which is venture capital, and it's very different from private equity or some other corners of investors, but for venture capital and specifically for what I call collaborative venture

capital, which is the type of venture capital we have, which is to say that, yeah, we will often seed companies, but we very quickly look to syndicate deals.

We look to work with other investors.

We look to broaden the investor base, but also broaden the competencies we have around the board.

For me, the answer has been really a combination of talent, of people, and the content that we believe has a leg to stand on.

And what it means, and when I call ourselves collaborative venture capitalists, it's because, at least for me, it's always critical to look at the talent that comes together as much as it is the content.

Yes, I get excited by the science of what they're trying to solve, but I have to get excited about the people who have the experience and the wisdom to navigate and understand what it takes.

because as that book points out, and you correctly point out, it's almost never a straight line.

And if it's not a straight line, it means that you need people around the table who have the experience and ability to look around corners.

And it means that you need enough of a capital and strategy structure to give you some degrees of freedom of movement.

I can tell you early on in my career as an investor, I made an investment in a small company.

It was a very rational thought.

It was for a certain idea of a drug that would have a certain effect against cancer.

And it was a small team and it was very linear and it didn't pan out.

And one of the lessons I took from that failure is that this was probably not well enough funded and not given enough opportunity either with the team structure or the financial structure or the scientific structure to have that degrees in freedom to adjust.

And if you look at investments that have been successful, I think you're giving them some degrees of freedom and you've got a management team that has the right functions able to do that.

For me, it's another learning from Moderna.

If you go back and look in the formative years of Moderna, the strategy was always to develop this technology of mRNA, understanding the engineering potential as much as the medical and biology potential.

In fact, in the early days, we were more certain of the engineering benefits than we were of the medical benefits in the sense that it wasn't clear, is this going to be optimally used for rare diseases, for oncology, for vaccines, or for something that we have yet to discover?

Because it was all an interplay of delivery and medicine and biology.

These were the big risk factors.

Once you could engineer the mRNA, you could do it again and again and again And again, you could do it reproducibly, cheaply.

That was clear. That was the engineering benefit.

But what kind of medicines could you make of it?

And so I think the brilliance of Stefan and that initial team before I joined was to set up enough degrees of freedom to go and explore those opportunities in parallel.

And so when it was clear early on that vaccine was the straightest shot to proving pharmacology of this technology, we went after vaccines.

we didn't drop the other elements, but we made sure to have the right capital allocation strategy.

I remember in the early days, the loudest conversations we had at the executive team around the executive table was around the relative capital deployment of these various different applications.

I was fortunate to work with, I think, some of the smartest people I've ever met.

I've already mentioned Stefan, but we were at the time three physicians on the executive team.

Lawrence Kim, who trained as a physician, but then became a finance executive at Goldman Sachs and joined us as our CFO.

Today, he's a very successful investor in his own right.

Stephen Hogue, who's the president of the company to this day, a physician who then spent years consulting with pharma and then joined as the president of the company.

And Stefan, it was a very robust dialogue of where do we invest the marginal dollar?

Where do we see this technology panning out.

And to take agility to the extreme, in December of 2019, when somebody started coughing in Wuhan and that first Wall Street Journal article came, the person who in the company first picked it up was the head of the vaccines.

The next person who actually took it and ran with it was Stefan himself.

And he saw right from the start the importance of chasing this.

It wasn't clear to the rest of us, frankly, whether this would peter out like prior vaccines.

If you remember January, February, even beginning of March of 2020, common wisdom would have had that, yeah, this is going to peter out.

There's not going to be a big thing.

Stefan was absolutely operating in a very different mindset.

He saw the opportunity and with his own will and management team took the company there.

And the rest, as I say, is history.

Obviously, you're part of one of the most central medical episodes, certainly in our lifetimes and also in recorded history, in the development of that vaccine and rolling it out.

What is your postmortem on that whole process?

I think parts of it are exactly what everyone wishes, which is something happens and we're able to, with technology and a prepared platform in mind, address it incredibly fast.

There's all the stories about how quickly the vaccines themselves were developed.

And then most of the time delay was just testing them.

That seems amazing.

Everyone wants more of that, then there's been maybe a hangover of side effects of the vaccines, which I personally don't understand any great detail.

I'm sure you do. And we all wanted these rush because we wanted to get back to our lives and to not have loved ones die.

And I would just be curious, this is such an interesting real -world experiment of moving very fast to address a huge problem.

What your post -mortem analysis is of the whole thing having been a dead center of it?

So, first of all, we were very well positioned in the beginning of 2020 to get there.

What most people don't realize is that by the beginning of 2020, Moderna had already tested the ability of mRNA to generate neutralizing antibodies in humans against eight different viruses.

COVID was to be our ninth, and our success rate was eight out of eight.

Okay. That is unprecedented in drug development.

It's a function of the platform nature of this.

And so if you get the antigen right, that thing you're trying to immunize against, you're going to hit.

And so we were very well prepared.

And the other thing that helped us along was that we had already been collaborating with the NIH before then.

So the government got to know us, BARDA and the NIH during Zika.

People forget that.

But in 2017, Zika was all the scare.

So we partnered with the government.

We started to develop the Zika vaccine.

By the time we got there, it was of no interest, okay, but at least the NIH took notice of the rapidity and potential of this platform.

And in fact, I've told this story before, but in September of 2019, Stefan and I went down to see Tony Fauci and the NIH, and we were talking about the latest vaccine.

It was something against cytomegalovirus.

And Dr. Fauci looked at me and says, so if what you're telling me, Tal, is you've got the best vaccine platform I've ever seen.

And with a bit of chutzpah, I said, yes, sir, and let me tell you why.

And so the outcome of that meeting was twofold.

Number one, the NIH wrote a paper.

They published it in 2019.

You can find it online with Dr.

Fauci and his team, citing mRNA technology as a leading platform in our readiness for pandemic.

This is November of 2019.

The second thing was that we had agreed with the NIH, we would run a demonstration project where the NIH team would pick a virus, something nobody ever heard of, we haven't sequenced before, they'd send us the sequence, we'd make a quick batch, they'd test it in a phase one, and we'll start the clock

and we'll see how fast we can go.

That was the outcome of that meeting, and then suddenly someone started coughing in Wuhan in December.

Two months later, the rest is history.

So we were well prepared as a company with a platform, and it's interesting, there was a recent Economist piece on personalized cancer vaccine and how it all benefited from COVID and mRNA vaccine.

What people don't realize is actually the opposite is true.

When we set out to do the personalized cancer vaccine in order to treat somebody with cancer at a personalized dose, you have to make a small batch and you have to have a very quick turnaround time because people with cancer, they can't wait.

And so we had set out years before to build a manufacturing process that would allow us to churn out a small batch in a rapid turnaround time.

Guess what? That same size batch is exactly what you need to run a phase one trial with just two doses because the cancer patient's going to get doses for six months every three weeks.

And so that served us well to be prepared to move so quickly.

So that's what enabled us to start quickly.

But how did we get to the endpoint quickly?

Well, that was two factors, really.

The first was the private partnership.

And this had to do with the NIH and the CDC and the FDA under Operation Warp Speed actually getting the government's act together and giving us guidance and working with us to be able to move quickly.

So, what usually take weeks and months of back and forth with the agency was literally a week.

Agreeing on a protocol, agreeing on endpoints, agreeing on trial design, etc.

That was all done very expediently, in fact, at a pace that it takes a global pandemic to get done.

And that also enabled the investment in manufacturing and the scale up in manufacturing that usually is done later in a much more circumspect environment.

By the time we had to scale up the investment in manufacturing, there was a period of several months that it was challenging and the company had to actually foot the bill, go raise money in the capital markets.

People think the government funded it.

No, it was private capital.

If you go back and look at the history that actually funded the commercial expansion, what the government did at a certain time point was backstop the investment and said, OK, we'll pay for these doses.

But the capital actually came from the private sector.

And then the final factor was actually, well, to test a vaccine or any medical intervention in a phase three trial, you need a certain amount of events to happen.

So you give half a population your vaccine and half don't.

And then you see, well, what are the event rates?

And this is where modern technology actually helped us.

So we were looking at the spread of the pandemic as we were rolling out the phase three trial.

And we could predict weeks ahead where hotspots would be.

And we use those prediction to go and open clinical trial sites in places where we knew that event rates would be high.

And if you look at the original design of the trial, the event rates that was expected to take up to a year actually occurred within three months.

So because we were able to target the trial to where the events were happening, we could get the answer quicker and be ready with the vaccine.

So all those elements, I think, are what enable that unusual success.

Now, I do have to mention one word about safety here, and we'll come back to the public perception question.

these mRNA vaccines have been the most well -studied medical intervention in the history of mankind.

Full stop. I can tell you as the one responsible for setting up the collection and analysis of the adverse events data, that we had thousands of people poring over tens of thousands of adverse event reports.

We had given this vaccine to probably, at the time, it was millions and counting as we were rolling it out.

It ended up being billions.

We collected the safety data with the rigor and breadth that has never been done before in human history using tools that have never been available before in human history.

The event rates in terms of side effects that people were reporting was about 10 to 20 -fold higher than had ever been reported before with even prior vaccines.

And the reason was, A, the public was fearful.

It was a pandemic. B, this was all quick.

Nobody had heard of mRNA.

And C, all the government entities were pushing out tools to report safety.

So this was a real concern, and we had to stand up systems to capture all of that.

Now, the proof is in the pudding.

If you remember some of the other vaccines that ended up getting on the market and then getting pulled and people don't use them anymore.

The adenovector vaccines, whether it was J &J vaccine or the AstraZeneca one, we discovered event rates that happened at one in 150 ,000 cases.

And they were discovered within weeks to months.

They were assessed as true and relevant, and they were quickly added to the label.

Even we discovered this rare myocarditis finding in young adults.

It was reported and appropriately put on the label.

Still, by the way, the risk of getting the virus is much higher, even just for that side effect of myocarditis, but it appeared to be related to vaccine and it went on the label.

And so I've never been as sure of anything in my life as I am of the safety profile of these vaccines because the data and the way it's been collected and analyzed.

And look, when you're the executive on the other side of it, A, this is your life's mission to get it right.

But B, if you don't, you go to jail.

I mean, this is as important as it gets in terms of getting it right.

And believe me, all of us sitting on that side of the table, we're super conscious of the responsibility and making sure we got that safety profile right now.

Where did we miss a beat?

it was probably in terms of the public backlash.

And as I stand back to look at that success, and again, both on the efficacy side, I mean, people have done the math.

These vaccines have saved millions of lives, full stop.

Anybody who believes in reason and science will agree that that's incontroversial.

But where did we fail?

Because obviously the sentiments I'm expressing here are not uniformly shared by the public.

I even have people in my own family who refuse to take the vaccine.

So I think it's a question of trust in institutions.

And I think it's also a question of probably scientists, myself included, overstepping our boundary in a sense.

And it goes back to the point I started with of the misguided philosophical framework that we operate under which assumes that because it is science it is good.

Science per se is not an ethical framework of a force for good.

People who deploy it for good have the obligation to explain why their moral framework is what it is and that abuts in other ethical and moral considerations having to do with people's autonomy, freedom, and other aspects that have also ethical and moral perspectives to them.

And our society is a balance of these different forces.

And as a democracy, and I'm very proud of our democracy, and I'm a proud immigrant into this country for both the opportunity and the democratic processes we have, I also have to recognize that that means that the public gets to say and vote on the things that have ethical and moral frameworks.

And forcing people to take vaccines is an ethical and moral consideration that the public has to buy into.

And it's legitimate that they don't.

And that's a hard and messy thing in a democracy.

In a dictatorship, it's easy.

And yes, on average, in a dictatorship, everybody gets a vaccine, no question, they'll be healthier.

That being said, you now have a balance of two opposing moral forces, and that's a reckoning that has to happen in the public square.

It is not the scientists or the physicians' purview to dictate that.

And I think that's where we probably overstepped.

And I think my biggest learning has been the importance of the open and honest dialogue with the public on what it is that we do.

It's an incredible recounting of a crazy episode in our history.

I like your balance take a lot.

Obviously, it's above my pay grade to have some long drawn out conversation on the pros and cons of these things.

But I do think it highlights how complicated and nuanced all of these issues are when you really start digging into them.

And it makes me wonder a little bit about the future of applying these platforms to other things.

Maybe we won't have something on the scale of billions again, maybe ever in our lifetimes, maybe we will.

But as you think about something like an mRNA platform, and I'm curious what other, I'll call them technologies or platforms like mRNA, you think are the most important to address disease in the future.

What do these things now enable for us?

You were eight for eight.

That's so cool. But eight is small.

What do you think the future is of let's start with mRNA specifically?

and what it will change about the way that we deal with our health in the future.

I will broaden it out to sort of nucleic acid medicines, if you will.

Which mRNA is an important part of.

There's other types of RNA, most notably siRNA.

And I think these medicines are enabling new types of pharmacology that we haven't had before.

They need a lot of investment in technology and delivery and getting them to the right tissue, but they all benefit from the same thing we benefited from in the vaccines as a platform, which is once you get the first one right, the marginal cost for the next one is a fraction.

Nucleic acids have that unique property of almost software -like, we used to call it in the early days of moderna a software -like platform because basically it's the same physical construct of a drug but the information in the nucleic acid you change the information you get out a different drug you get

out a different vaccine but actually under microscope it looks exactly the same in fact it's the same components it's the information they encode that makes the drug so these drugs as information that's a new concept and then there's a whole world of opportunity in what's called gene therapy, which is the ability.

I started by mentioning rare genetic diseases where somebody is born missing an enzyme or protein.

And if you can actually just put in the information that encodes for that protein, you fix the problem.

And so now physiology should reassert itself as normal physiology of any other person.

You can do that in mRNA with the traditional approach.

Moderna is doing that for rare diseases, but then that mRNA is transient and you need to redose every time.

But if you could actually use that mRNA as an intermediary to get in and fix the genome once and for all, then you would potentially have a long -term beneficial effect without having to redose for the entire lifetime.

And the company I've been spending the last year as the acting CEO, Exilio, is doing exactly that.

It's taking this mRNA in a lipid nanoparticle, which is the same as a Moderna vaccine, if you will, And conceptually, it's not the same chemistry, but then the information that's encoded in that is actually allowing that mRNA to insert itself into the nucleus and make a change forever.

Now, that is going to bring a whole slew of new challenges.

One of the criticisms and the public backlash against mRNA was the mistaken conception that it will somehow change the DNA.

And the truth is mRNA does not.

It can't based on first principle, and there's no empirical evidence that anybody's ever been able to show because it can't.

So mRNA doesn't do that.

But there are ways to encode different information with mRNA where it will do that.

And so now we're very intentionally changing somebody's DNA to have an effect that will last for the lifetime.

Well, there are some people who are going to find that challenging based on their ethical and moral framework.

that's legitimate. There's a concern for what this means for future generations and making sure that we're doing it for the individual and not with unintended consequences for their offsprings, or maybe sometimes with intended consequences.

And so that will raise a whole slew of new moral questions and ethical debates that we will have to resolve before this is widely acceptable.

But at the end of that, if you can have a medicine that you take once, twice, or three times, and then you're cured for the rest of your life as opposed to having to take an injection every two weeks.

I think that's huge progress for those patients.

I wonder if you could, this is meant to be a little bit fun and no one will hold you to these predictions, but if we think five, 10, and 20 years hence, to pick three time periods, what sorts of things you think may be happening or possible in terms of the way a single person manages their health.

And as an example, I've always been interested in this idea of fairly constant ongoing data collection for myself on blood work or scans or genetic information or whatever, input data that might help identify things early, suggest ways that I live a healthier, better lifestyle.

I think basically everyone wants to live longer and better.

And today that's incredibly crude.

Maybe the best in class people get their blood work once a year and pay attention to it and know what it means, but like almost nobody does.

How do you think that general state of how people approach this problem will change in that 5, 10, 20 -year timeframes?

There's an old saying in the Talmud, which is my ethical framework, given my religion, that since the time of the destruction of the temple, prophecy has been given to fools.

So to the degree that I'm going to give you any prophecy, that makes me a fool.

But to the degree you listen, well, that's on you.

So five years is probably the easiest.

I'm of a certain age and generation and fairly conservative in my views.

So up until now, I looked at all these data collections and thought to myself, well, you know, everybody knows what it means to eat healthy and exercise more.

So getting more data to tell you what you already know is actually not going to change much.

But I'm actually coming around because I do agree that I think there's a lot of differences between people in terms of what is the right intervention for them.

And I think my generation of medicine is guilty with peanut buttering the effect across a population without really understanding the chunkiness of it.

I actually told my wife the other day that this is the year for me and for us to actually start collecting data in a more thoughtful manner and a much deeper level than we've done in the past.

Because like most people, I go once every year or two and get the usual blood work and take my statin for a tad of high cholesterol and think I'm done.

But I think there's a whole lot more that we're gonna learn that we can do.

It is amazing to me, and one of the triggers is the recognition.

You go and get the blood work in a hospital.

And the thing that I find very depressing is the fact that if you go today into an emergency room and you get blood work, you're going to get the same panel that you did when I was a medical student 40 years ago, maybe with two additional analytes that have been added in the last 40 years.

I find that so depressing with all the progress we've had, and it's the same blood work.

Now, here's an interesting fact.

There's a brilliant AI scientist at the Weizmann Institute called Amos Tanai, and he spent most of his career designing better proteins to make drugs and things of that nature that you would associate with AI.

But he was curious to understand how much of that regular blood work variability can be trimmed if we understand you as a person better.

And so what he did is he teamed up with one of the Israeli healthcare networks to collect information on, I don't know, quarter million people over a decade.

And he took that same blood work.

And he asked himself, well, if I correct somebody's blood work for other parameters that the machine measures, but I don't even know what they are, can I reduce the variability?

How much of the variability in the normal is actually true variance versus things we could correct for that are based on you?

And it turns out that about half the variance of the distribution of what normal is, is actually false distribution.

If we could correct it relative to your factors, it would make that distribution much narrow, which means that we could now pick up things that otherwise look normal.

But based on all those other things that the machine can figure out, this is a perfect application of AI, because it can start to pick up abnormalities much sooner.

Now, the question is, does that lead to interventions that we know will be of benefit sooner.

Well, that's where a whole world of drug development has to come in.

But that's where I agree with the folks that have been saying that we've got a sick care system as opposed to a healthcare system.

And look, my greatest learning as a physician from the COVID vaccine, I started this journey as a medical oncologist.

Now, what's a medical oncologist?

Somebody who deals with somebody who's got cancer.

What's my vision for a patient that comes into my office?

We started talking about vision.

Well, my vision for that patient is their past.

Think of that. The best I can envision for somebody is to be as healthy as they were before they had cancer.

Now, think of a vaccine.

What does a vaccine do?

It actually takes a healthy person and makes them healthier in the future.

A serious medical doctor like myself in the previous era, if you told me that I would be interested in spending time making healthy people healthier, I would have looked that you strange and said, yeah, no, the people who do that are the yoga instructors, the quacks, and the nutritionists.

I mean, I'm a serious doctor.

I don't make healthy people healthier.

If you're healthy, get out of my office.

I got sick people lining up.

I don't do that. But actually that's what vaccines do.

And that's what this new age of technology is going to enable us to do.

Now, how do we deploy that information?

I think in the 10 -year horizon, you're going to see some of those insights now becoming institutionalized.

I think it's going to take a long time because it will bring a profound change in what it is to be a physician.

I think that change is already occurring.

If you look in the last 10 or 20 years in the US, we talk about physician burnout.

Well, physicians have gone from having agency making decisions, spending time with the patient and earning a return on their time to now being employees of large healthcare systems, whereby the treatment they meet out is a function of algorithms, the quality control is a function of algorithms, and the reimbursement,

what they can actually prescribe is a function of what the healthcare system will tell them.

And by the way, they're all employees of the healthcare system to begin with.

So all of the agency has been taken out.

The time that they're able to spend with people has been trimmed and trimmed and trimmed.

And so the role of what it is to be an internal medicine doctor has changed.

I think that with the system of knowledge that AI is gearing up to be, it will continue to evolve.

And the role of the physician will change into being the translator for people of some of this wisdom, as opposed to the person who is entrusted with having all that wisdom in their head.

My kids are 10 and eight.

So they're young and they're going to be the beneficiaries even more than you and I are of so much of this science and discovery and research and product building.

Do you think that their adult lives, that they'll look back on this time and think that it was almost barbaric how we discovered, identified, and dealt with disease?

Is that degree of radical change coming that we might look back on pre -vaccines or something and just think, can you believe that people would die from this stuff?

How crazy is that? Infectious disease and otherwise, which no one dies from anymore relative to the past.

Is that change coming, do you think?

I'm thinking the 20 -year, 30 -year horizon now.

I think in the 20 to 30 -year horizon, they will experience healthcare very different than we are experiencing healthcare.

I think what it means to go into the hospital, what it means to sit with a physician, what it means to get diagnosed and treated will be different.

I think it will have more interventions far earlier.

I hope that there will be an emphasis on maintaining health and promoting health as opposed to treating illness.

Illness is always going to be with us, and we will need to evolve our tools of treating with it.

But yes, I do believe it's going to be different.

Now, that being said, we are also living in a time where, and this is what makes my unique job so fascinating, technology today can do more than our ecosystem has figured out how to integrate.

And I say this both as an investor and a consumer.

There are programs that can scan a pathology slide and give you a more accurate reading than a pathologist, hands down.

They're FDA approved.

But the problem is that less than one slides of any tissue microsection ever gets read by a computer.

We still have microscopes and people looking down the microscope.

Who's going to pay for changing all those microscopes to be digital?

And who's going to get a return on that investment.

So you can see everywhere you look bottlenecks in the ecosystem of applying a technology that's already here today and continues to improve.

And it's also true of drug development.

And one of the examples I gave at that National Academy meeting, and I think is true and is a warning sign for me, is the failure to develop antibiotics.

So back in my dad's era, he benefited from drugs that are today generics.

But when he went into the hospital with an infection, doctors had an antibiotic off the shelf that worked against the bugs in the hospital.

We don't have those today.

This is a growing problem.

The likelihood of dying from significant infections continues to go up.

And it's not a lack of scientific tools to understand bacteria and how to make better drugs.

There's just a complete lack of commercial incentive to do so.

I've got colleagues, I'm working on some rare diseases and I've got colleagues who ask me, oh, you know, what do you think?

Because pharma's pulled back a little bit from rare diseases.

Is that going to be a challenge?

And I use this example to say that it's not a given that the economic incentives will always be there.

And we need to be super careful and super mindful of the policy implications that we have at the society level of what we think is important and how we reimburse these efforts, because it is the return on investment engine that ultimately drives the innovation that benefits not just us today, but our

kids and our grandkids in the future.

It reminds me to ask another investing question, which is what investors of your type most consistently get wrong when underwriting therapeutics investments specifically?

What risks do they consistently care too little about, too much about?

As we said before, it's a heart area to earn a return.

There's huge potential returns available because the market's all of humanity.

What do you think investors like you get wrong most consistently?

As I joined this field, as I became an investor, one of the more interesting books I read is Scott Kapoor's The Secret of Sandhill Road, where Andreessen Hurwitz talks about what it takes to be a VC investor in tech.

And they have a mindset where their returns can be 1000 to one, and so they're okay to win only one in 100.

the math still works.

In our space, that actually doesn't work like that.

Our returns are not gigantic.

To get a 10x return for us is wonderful, not something you see commonly.

And to get a larger than 10x return is rare.

So we shoot for three to fourx returns.

5x return is a great outcome for us, which means that we can't afford to get as much wrong as the tech folks can because the upside is just not as much.

It means that when you look at your investment portfolio, you have to take a very careful view of the balance of risk.

Now, each individual company, you got to, as Carl Gordon, my boss says, somebody has got to come and pound the table in believing that this thing's going to work.

There's nobody around upon the table.

We're not making the investment.

So there's got to be that sense of belief in the content and the people and the potential.

We try to take a very hard notes look at what the probability of success is across the myriad of dimensions.

And there are, as I said, many.

Now, what do we get wrong?

We probably get wrong each and every one of the dimensions.

Sometimes we get the team wrong.

Sometimes we get the science wrong.

Sometimes we get the clinical application wrong.

And sometimes we get the commercial opportunity wrong.

I don't know that there's anyone that stands out because we try to look at the portfolio, but I think the one that we get probably the most wrong is the one that is still the hardest to predict, which is what is the magnitude of clinical benefit that this will bring?

Because clinical benefit is not a black and white.

We typically invest in things where we understand the biology and we think we can de -risk it along the way, okay, that's going to work.

And then we have a belief in the drug because we understand the preclinical pharmacology and that's all good.

And so it should work to some extent in the clinic, but how good is it going to be?

And in a world which is so competitive, it can't just be something that somebody has already done before or even slightly worse, that's going to be dead.

So it's got to somehow differentiate.

It's got to somehow be better.

It's got to offer something.

And that's probably the part that's the most challenging to predict.

There's a wonderful obituary that Malcolm Gladwell wrote years ago of Albert Hirschberg, who was an economist in the prior century.

His whole economic thesis was that what drives economic progress is the naivete of thinking how easy it's going to be.

You get excited about an idea.

And the example he gives is back when they had to dig a tunnel through the Hoosack Mountains to connect the Boston area with the Hudson Valley.

And they said, this is critical for economic development.

So what's it going to take?

So they brought in a bunch of geologists and they started picking at the stone and they came to the conclusion that, look, it's a big mountain.

We got a tunnel through it, but here's the thing that the shell of the mountain is really hard, but the core is soft.

And so we'll get through the hard part and then it'll be soft.

It'll be easy digging.

Then we get to the other side, a little bit of hard and we're done.

So give us a couple million dollars in those terms and two years and we'll be through it and five years in and it's still hard there was never any soft part in the middle of that mountain but what are you going to do you're halfway in you're going to stop now so they mustered up and they dug their way

through it and the moral of the story is it's always going to be harder than what we anticipate when we make the investment as long as we're clear to hide about what it is and you got the right people around the table and you're in a position where you can take the portfolio approach of those.

Now, it is interesting to me, coming over to this side, the difference between being an investor and being an executive in one of these companies.

So I can take a very hard -nosed look at the risk.

And I know I owe my LPs a certain return over time, and it's going to be met by this portfolio.

An executive in the company?

Oh, no, they're going to live and die by that being successful.

And I remember when I joined Moderna, Now, my wife, who's a smart one in the family and has a PhD in biochemistry, looked at me and said, Tal, come on, this thing, I mean, mRNA is never going to work.

Really? And I looked at her and I said, honey, not only is it unlikely to work, we're also never going to make any money because the book value was yay high when I joined and who thought.

But I said, I'd rather fail on something big than succeed in something small.

I can afford to take the risk where we are in life.

And if this were to work, this would be big.

So she said, OK, fine, go have fun, do whatever.

And we agreed that I'd do this for three to four years, and then it would flame out.

I'd have an interesting experience, and I would still be employable probably, and it'd be OK.

When investors came to me at some point, and I remember when we turned public, and so investors started asking me, well, Paul, what do you think about the stock price?

And the only answer I could come up with, I'm investing in this company something far more valuable to me than capital.

It is my time on Earth.

So you do with your money what you see fit.

But I'm telling you, I'm putting my time on earth into this.

That's how I believe in this company.

And so it taught me the primacy of sweat equity, if you will.

And so I want to make sure that my capital equity follows the sweat equity of people I believe in, because at the end of the day, that is the driver.

Yes, the science and the technology and the medicine, they all have to work.

But it's the people who make it work.

And it's the people who believe, who are putting their time on earth and their talents to drive it that are going to make the difference and are going to make the returns.

Do you think it's possible for an investor who's incredibly talented in lots of traditional ways, but not formally trained like you are as a physician, as a scientist, as a medical professional to earn great returns investing in this space?

Or should it just be the domain of people like you that have deep domain expertise?

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 knowing how to ask the questions i've seen this up front in fact some of my colleagues even at orbimid 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.

Again, I was fortunate to work with Stefan Bansel and his executive teams.

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 so? 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.

My penultimate question for you, given that it's the one on everyone's mind, I'll try to think of a unique way of structuring it, is the impact that AI is going to have or might have on this field, just writ large.

Maybe the way to think about it would be your bear case, your base case, and your ball case for the ways in which AI models may affect the world of medicine from this point forward.

The bear case is that they make slow roads 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.

What does that mean?

What is inroads in a thin vertical?

Just bring that to life.

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.

But 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.

Do you think there's a world in the bulk case category where biology becomes engineering that we're able to run simulations in digital twin humans, not in real life clinical trials, and therefore we're able to iterate at a speed that's impossible in the real world and solve all the problems?

This is a utopian take on how AI will affect drug discovery and therapeutics and medicine and health.

Is that possible? It feels like if it's possible, we're going to get it on some time horizon because of how fast technology is evolving.

But I'm curious if you even think it's possible.

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 the utilitarian approach will be dictatorial, and that's not going to 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 and 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 in general, in every application sphere, no less healthcare.

Tal, this has been such a total blast to learn from you today.

I asked the same traditional closing question of everyone.

What's the kindest thing that anyone's ever done for you?

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, But 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.

I mean, the whole field of immune oncology owes a great debt of gratitude because not only has he uncovered himself some of the key themes, but he's actually trained an entire generation of us, and I'm proud to be one of his fellows.

But I called him up and said, hey, we've got this personalized cancer vaccine.

I think it's got a shot of working.

would you be interested in maybe collaborating from the National Cancer Institute?

Now, of course, I'm at this mRNA company back in 2016.

Nobody thought this would work, my wife included.

And yet he invited me to come down to Bethesda and give a seminar and talk to his team.

And they partnered with us and they ran a small clinical trial.

And he actually listened and he continued to support.

He bought into it. And I have to say that that belief in me, that true mentorship that has really lasted, I feel, a lifetime, that's certainly the kindest that I think anybody has ever been to me.

And in a small way, when people call me up, whenever somebody has ever worked with me, I always have that in the back of my mind.

I feel a debt of paying it forward, if you will.

If we could all do that for one other person, it feels like the world would be a lot better place.

Tal, great story. Thank you so much for your time.

Patrick, it's been a real pleasure.

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Next, stay tuned for my conversation with Katie Ellenberg, Head of Investment Operations and Portfolio Administration at Geneva Capital Management.

Katie gets into details about her experience with Ridgeline and how she benefits the most from their offering.

To learn more about Ridgeline, make sure to click the link in the show notes.

Katie, begin by just describing what it is that you are focused on at Geneva to make things work as well as they possibly can on the investment side.

I am the head of investment operations and portfolio administration here at Geneva Capital, and my focus is on providing the best support for the firm, for the investment team.

Can you just describe what Geneva does?

We are an independent investment advisor, currently about over $6 billion in assets under management.

We specialize in U .S.

small and mid -cap growth stocks.

So you've got some investors at the high end, they want to buy and sell stuff, and you've got all sorts of investors whose money you've collected in different ways, I'm sure.

Everything in between, I'm interested in.

What are the eras of how you solved this challenge of building the infrastructure for the investors?

We are using our previous provider for over 30 years.

They've done very well for us.

We had the entire suite of products from the portfolio accounting to trade order management, reporting, the reconciliation features.

With being on our current system for 30 years, I didn't think that we would ever be able to switch to anything else.

So it wasn't even in my mind.

Andy, our head trader, suggested that I meet with Ridgeline.

he got a call from Nick Shea, who works with Ridgeline.

And neither Andy or I heard of Ridgeline.

And I really did it more as a favor to Andy, not because I was really interested in meeting them.

We just moved into our office.

We didn't have any furniture, because we just moved locations.

And so I agreed to meet with them in the downstairs cafeteria.

And I thought, okay, this will be perfect for a short meeting.

Honestly, Patrick, I didn't even dress up.

I was in jeans. I had my hair thrown up.

I completely was doing this as a favor.

I go downstairs in the cafeteria and I think I'm meeting with Nick and in walks two other people with him, Jack and Allie.

And I'm like, now there's three of them.

What am I getting myself into?

Really, my intention was to make it quick.

And they started off right away by introducing their company, but who they were hiring.

And that caught my attention.

They were pretty much putting in place a dream team of technical experts to develop this whole software system, bringing in people from Charles River and Faxit, Bloomberg.

And I thought, how brilliant is that to bring in the best of the best?

So then they started talking about this single source of data.

And I was like, what in the world?

I couldn't even conceptualize that because I'm so used to all of these different systems and these different modules that sit on top of each other.

And so I wanted to hear more about that.

As I was meeting with a lot of the other vendors, they always gave me this very high -level sales pitch.

Oh, transition to our company, it's going to be so easy, etc.

Well, I knew 30 years of data was not going to be an easy transition.

And so I like to give them challenging questions right away, which oftentimes, in most cases, the other vendors couldn't even answer those details.

So I thought, okay, I'm going to try the same approach with Ridgeline.

And I asked them a question about our security master file.

And it was Allie right away who answered my question with such expertise.

And she knew right away that I was talking about these dot old securities and told me how they would solve for that.

So for the first time when I met Ridgeline, it was the first company that I walked back to my office and I made a note And I said, now this is a company to watch for.

So we did go ahead and we renewed our contract for a couple of years with our vendor.

When they had merged in with a larger company, we had noticed a decrease in our service.

I knew that we wanted better service.

At the same time, Nick was keeping in touch with me and telling me updates with Ridgeline.

So they invited me to Basecamp.

And I'll tell you that that is where I really made up my mind with which direction I wanted to go.

And it was then, after I left that conference, where I felt that comfort in knowing that, okay, I think that these guys really could solve for something for the future.

They were solving for all of the critical tasks that I needed, completely intrigued and impressed by everything that they had to offer.

My three favorite aspects, obviously, it is that single source data.

I would have to mention the AI capabilities yet to come.

Client Portal, that's something that we haven't had before.

that's going to just further make things efficient for our quarter -end processing.

But on the other side of it, it's the fact that we've built these relationships with the Ridgeline team.

I mean, they're experts.

We're no longer just a number.

When we call service, they know who we are.

They completely have our backs.

I knew that they were not going to let us fail in this transition.

We're able to now wish further than what we've ever been able to do before.

Now we can really start thinking out of the box with where can we take this?

Ridgeline is the entire package.

So when I was looking at other companies, they could only solve for part of what we had and part of what we needed.

Ridgeline is the entire package.

And it's more than that in that, again, it's built for the entire firm and not just operational.

The Ridgeline team has become family to us.

[투자, 의학의 미래, 그리고 혁신: 탈 잭스(Tal Zacks)와의 심층 탐구]-[Tal Zaks - Bridging Science, Medicine, and Returns - [Invest Like the Best, EP.406]]

📋 Summary

바이오테크와 투자의 교차점: 탈 잭스의 관점

1. 의학적 혁신과 투자의 현실적 조화

2. 모더나의 교훈: mRNA 플랫폼의 잠재력

3. 기술과 인간의 영역: AI와 의학의 미래

4. 결론: 더 나은 미래를 향한 제언

🎯Key Sentences

📝Key Phrases

📖 Transcript