Irene:

Hi everyone. Thank you for joining today's Office Hours titled: In 2023 Can We Go Beyond the Macro to the Potential of the BioRevolution where you'll hear from WisdomTree's Chris Gannatti, Global Head of Research, and Jamie Metzl, founder and chair at OneShared. World.

Chris Gannatti:

As a brief introduction, I am the Global Head of Research at WisdomTree. It is my pleasure to introduce Jamie Metzl today. You may know him from Hacking Darwin, which is a great book on the topic that we are going to discuss. He certainly has made the podcast circuit and it's always great to have him on our airwaves today. Jamie, if you want to maybe give some career highlights and different things to the audience, I think that'd be great.

Jamie Metzl:

Sure. Good. All right. Thanks, Chris, and happy to be here with all of you. At Chris mentioned author of Hacking Darwin. I was also a member of the World Health Organization expert advisory committee on human genome editing, which was created in the aftermath of the birth of the first CRISPR babies in China to try to figure out how these very powerful tools can best be deployed for the greatest good. I'm the faculty member for NextMed Health and Singularity University. I've been doing, and I do a lot of thinking about the big-picture implications of the intersecting genetics, AI, and biotechnology revolutions and what they mean across the board, not just healthcare, but healthcare, plant, animal, agriculture, industrial materials, energy, DNA data storage, all of the topics that we explore in [WisdomTree BioRevolution Fund] WDNA. And that's why it's been my great pleasure and honor to partner with WisdomTree in the creation of WDNA.

And I'm thinking a lot about these topics both because as I was just telling Chris prior to this going live, we have all of these new innovations which are adding jet fuel to this incredibly powerful revolution. And genome sequencing is more jet fuel and genome editing is more jet fuel. And now we have the rapid increase in the power of generative AI tools like ChatGPT and ProGen and others that will have massive implications for the future of essentially engineered biology, which will transform pretty much every aspect of our lives, including healthcare and industry and just how we make things and the types of things that we make. And then finally, I'm just finishing the first draft of my new book, which will be published next year by Hachette around the world. And that book is called The Great Biohack: Recasting Life in an Age of Revolutionary Technology.

And the thesis of that book also the thesis of this fund is that we, and by we meaning humans are at yet another inflection point in just how we interact with the living world around us. And the advent of agriculture was one of those inflection points. The industrialization was one, the beginning of the internet age was another, and this is yet another. And we tend to think of these different revolutions, whether it's AI, genetics, biotechnology, nanotechnology as separate things, but really it's just one interwoven technological revolution because each one of these revolutions inspires and is inspired by the others and the pace of this change is going to accelerate. So what we're doing, and the reason why I'm happy to be partnering with WisdomTree is saying, well, if that is true, and I certainly believe it to be true, what is the way, the smartest way to ride this very powerful wave?

Chris Gannatti:

Now Jamie, you mentioned ChatGPT and I think we mentioned it as well. We've done other Office Hours segments where maybe AI is the focal topic, but I was looking as a part of the preparation at a recent conference put on by Jensen Huang, the co-founder of Nvidia, and he was talking about their Clara platform, which it seems that the end goal here is to be able to rank and score and predict where you might find interesting molecules that could react favorably in helping to provide therapies for interesting diseases. And we're talking something on the order of they want to be able to analyze not a couple hundred or a couple thousand, but a figure in the billions. So I'd love to hear your perspective, Jamie, on some of the sort of more specific elements that you might be seeing as you're doing your research for the new book, talking about some of these platforms.

Jamie Metzl:

Yeah. So maybe I'll answer your specific question and then I'll take a step back. What these generative AI systems are able to do is just look at massive fields of data and information and basically make statistical predictions about what follows what and when that is applied to the internet and to written language is what it has so far that creates the experience that everybody I hope is happening, having, I'm sorry, as you explore ChatGPT. Biology is also a language. I mean that's been the last a hundred years or more, maybe 150 years since Mendel. We've increasingly recognized that biology is a language. We don't know it as much about the language of biology as we know about say the English language, but we've learned a lot and we're learning more. And if biology is a language as it is, then these tools are allowing us to one better analyze mass more massive fields of data and uncover patterns that would've just been impossible for us to see or even perceive with our naked eyes or our unaided brains.

And that is going to massively increase our ability to not just understand life but to think about how to generate tools for manipulating life. When you talk about manipulating life, that sounds a little bit scary, but every time we have any kind of medical intervention to cure or prevent or treat some kind of terrible disease, that's what we're trying to do. So it's really exciting and if you just go on online and look at all the research papers in the various fields of biology, whether it's human health or oncology or plant and animal agriculture or any of it.

In every one of those fields, there are really revolutionary papers coming out at an extremely rapid clip exploring what generative AI systems mean. So just to go back in time and give a little bit of history about and to explain or to show how we're just, everything is accelerating in 2016, as some of you may know, that was the year that the deep mind algorithm called AlphaGo beat Lee Sedol at the time the world's Go Champion in a high profile five matches in Korea, and Alpha Go had trained on the digitized games of Go played by human Go Masters.

So it had really mined the knowledge of human Go Masters. The next year there was an updated algorithm, AlphaZero and AlphaZero didn't train on any Go games, but it was just had the simple rules of Go and asked to play Go against itself and given a reward structure so that every time it won, it got a token or a little reward. And based on that, it started to essentially teach itself how to play Go, how to maximize the rewards it was programmed to seek. So it was a terrible Go player in the first hour and the second hour. Not that good after the first day decent after the second day. On the third day, it trounced AlphaGo, which the year before had defeated the world's greatest Go champion so three days. The next year, 2018, DeepMind set its sights on trying to solve what's considered one of the greatest problems in biology, which is predicting the shapes of protein based on the letters of sequenced amino acids.

To understand how a protein functions, you need to know the sequence of essentially the letters and you need to know the shape because the form follows the function or the function follows the form I should say. So they entered their algorithm into the biennial genome protein folding prediction competition. There were these different teams, they came in 20th place the next year in 2020 or I'm sorry, two years later in 2020, they refined their algorithm, went back to the drawing board, entered the competition. They didn't just win first place, they won first place so significantly that Nature Magazine said that the problem of predicting folded proteins had essentially been solved. The next year, 2021, the DeepMind released the predictions of protein folds for 350,000 proteins, including all of the proteins in the human body. That was a really big deal. The next year they released the predictions for 215 million proteins, essentially all of the proteins known to science.

Given that in the old days, meaning just a few years ago, it took about three years ago to characterize a single protein. That meant that around 645 million years were being thrown back into the pot of human innovation where humans could do other things with those proteins. Now, these new tools the protein equivalents of ChatGPT, are using these exact same tools to make predictions about new kinds of molecules I mean the word is hallucinating, which you sometimes on your ChatGPT, when you get a wrong answer, people say, "Well, the ChatGPT is hallucinating, and sometimes that's bad." But here they're saying, "We're looking for a protein that will achieve this goal, and this goal may be to do X, Y, and Z to help absorb petroleum when there is a spill in the ocean." It really could just say anything.

And then these generative AI systems are coming up with hypotheses and then we're able to connect those hypothetical molecules to build them using printing essentially of synthetic genomes and use high throughput systems, what are called self-driving laboratories, to test many, many thousands or hundreds of thousands of different molecules to see which among them can best achieve the desired result. So when you think that our world, our living world is made up of proteins, the implications for this kind of transformation are just unbelievably profound, not just for healthcare, but really across the spectrum.

Chris Gannatti:

It really is remarkable. I feel like as you look at this topic, you find examples like the example of Insilico Medicine, which has used a lot of these methods to build a novel preclinical candidate to treat idiopathic pulmonary fibrosis. They were able to do this in less than 18 months and at a cost of under two million USD. And that's, you think of how much it costs to discover a new drug. You're talking something a billion plus and usually somewhere in the order of 10 years of the research and development. But Jamie I'd love to-

Jamie Metzl:

On Insilico Medicine... So I gave keynote talks at two recent conferences, one in Dubai and one in San Diego where the Insilico founders were there and so the interesting thing, and then I spent a decent amount of time with them, and the interesting thing is it's not just that this incredible work is happening it's that different types of teams are solving these problems. It's not just that you have people who are in the healthcare biotech industry that are innovating in healthcare, biotech. You have people who are coming out of computer science, data analytics, physics, all these different fields who are coming together to think differently about the future in this case of human medicines. But it's really the same across the board. I think that's also, it's an important point for what Insilico Medicine is, as a matter of fact, the founder was a computer scientist and then he recruited somebody who had come out of big pharma as his partner. But we're seeing new kinds of teams because there are just different ways of thinking about how to solve some of the most intractable problems in applied biology.

Chris Gannatti:

It's fascinating when you look into it and you see one of the software packages that apparently every company used is called Schrodinger after the famous physicist. So it's absolutely the case and whether it's in video or the other companies, it's absolutely clear. It feels like right now you have anecdotes of companies doing this or that five, 10 years from now, this is just going to be the way that it's done and that it'll be more notable if people are not using these tools than if they are.

Jamie Metzl:

Yes. You do what we're doing right now. I mean, we are on a video call. If somebody had... I remember the first time when I was a little kid and I went to Epcot Center and I made a lunch reservation with a video. It was the coolest thing I'd ever seen. Now our ancestors, this would've been like magic. They would've been looking behind the screen to see where the person was like they did with the first telephone call. So I think that you're exactly right. These things that seem like magic just become how we function, and that's why all of these revolutions in AI and engineered life, it seems like it's this is some weird sci-fi stuff, but it very, very quickly, it's going to be integrated into just how our economies function.

Chris Gannatti:

Now, one of the things that Jamie and I are doing is we are taking questions from the audience. We had a few come in, in the first just under 20 minutes, give or take. So one question and what I'll do for each question is I'll give Jamie the chance to react and then maybe fill in on the backend myself. This one is predominantly about the market. I mean, Jamie and I were talking in the sense that we started working together at the peak of the market November, December of 2021. And obviously, now we have higher interest rates. VC funding is totally different. We've just had some banking issues, Silicon Valley Bank being sort of the big name. So how have higher interest rates, impatient VCs, and issues say surrounding examples like Silicon Valley Bank impacted early-stage biotech? And like I said, I'll let Jamie react and then I'll add if needed.

Jamie Metzl:

So as I was telling you Chris before, I have no doubt in my mind that the story of by the BioRevolution is real and fundamental. But that doesn't mean that every valuation of every company is exactly right. So I definitely think that in the earlier stages of the pandemic, the world and the investing world discovered biotech, and there was all of this money getting... Interest rates were low, there was all this money getting thrown around, everything was going up and biotech went up stratospherically. And then there was an overreckoning. And when interest rates started to go up, that certainly put a lot of pressure on companies, especially the pre-revenue, smaller biotech companies, because when things were going up, there was this gold rush where people thought, wow, we better go public now because valuations are high to gain access to capital because this is the moment.

And then it went down and then the ... came a lot greater then. So there is a little bit of the kind of faddishness where something's hot and then it's less hot, and now there's all this money is racing then it was Metaverse a year ago, and now that money's coming out of Metaverse and now it's chasing generative AI. But for me, the real question is two essential questions for here, what's the underlying story? If we, as I do believe the underlying story is the real one, then it's just the question really of just patience and a little bit of timing. But I see this, it's like a directional bet on this thesis. So it's just a question of when to come in, but I see this as really a long-term hold. And then the second is even if somebody decided, well, they believed in this thesis and wanted to bet on it, the question is, and one of the reasons why we've structured WDNA as we have is it's not just a single bet on small pre-revenue company. Oops, small pre-revenue companies. We have some of those, but it's really a distributed bet on this whole thing. So this isn't a story about small companies, it's a story about companies of different sizes, small, medium, and large and early stage, medium stage, and later stage. I've done advising and speaking to companies like Merck and Honeywell and Bayer and others, and these are all kind of big companies who really understand that something fundamental is happening. So what we've tried to do is, and I'll let Chris talk about the product, is think, well, what's the big story? And the big story isn't just healthcare, it's all of these fields. It's not just companies of one size it's of all companies, not just at one stage of development to say, well, if the thesis is right, the big picture thesis is right. What's the smartest long-term bet to make on that being the case?

Chris Gannatti:

It's absolutely the case. There's sort of a varying degree of risk within the exposure. I think everyone knows today, if you're a private company seeking series B funding, that is going to be rather challenging. But at the same time, there are companies pursuing interesting areas of science. And one of the things we did as well was we didn't only diversify across degrees of risk where you might have an AstraZeneca and an Eli Lilly and a Pfizer on one end of the spectrum and then a company pursuing novel CRISPR mRNA vaccine type of thing on the other end. We also diversified the areas of the science that you're going into. It's not solely a biotech human health portfolio with nothing else. There's also agriculture and food and interesting companies in that space. And even in that space, I mean, Jamie mentioned Bayer that they do very interesting things, but you also have companies like your Beyond Meats, which are at a very different stage of the life cycle and the adoption and the profitability journey.

And then you have the materials, chemicals, and energy where some of the energy companies will perform quite differently than the healthcare or even the agriculture companies. Then I always like to ask Jamie on these because as of yet, it's been fascinating to see the sort of ability of DNA to store data and information, but I don't think we've been able to find a publicly available company. And I just wanted to get Jamie's view. Jamie, do you think that's going to change in the near future or are we still going to have to keep our patience for biological machines and interfaces?

Jamie Metzl:

I think that we're already seeing it, so Twist Biosciences, which is actually in our portfolio, is actually one of the leaders. We don't count it as DNA data storage because they're a lot of other things that they do and they are part of an alliance, the DNA Data Alliance with Western Digital and Microsoft, and others. So again, the reason why we don't have it now is that there are companies like Microsoft that are betting on these technologies. It's just still a relatively small part of what they do. Even for a smaller company like Western Digital, it's just a piece of what they do. But we all know that our humans, our data, we're generating more data obviously every year than we ever have in history, but really it's more a couple of years of data equals the entire history of data before then.

And we're bumping into clear limits on just the storage capacity of silicone. So we're going to have to think of other ways and there are lots of different approaches that are being explored. 3D computer chips, new materials like graphene, but for long-term data storage, I mean DNA is by far the most efficient data storage mechanism ever known, far beyond anything that we humans have come up with on our own. And we know that it can last somewhere between three and five million years in the right storage conditions. And given that our data, it's not just for making ChatGPT work, it's the cultural inheritance of humanity and our brains, I mean our decent amount better than chimpanzees individually, but it's our collective cultural inheritance, our ability to pass on information, which is really the superpower of our species.

So I do think that this will be something that will become increasingly important and we will, I'm guessing have either existing companies like Twist Biosciences that are already in our portfolio and this will become a bigger part of what they do. And then there are smaller private companies like CATALOG that already exist. I imagine that at some point, one or more of those will go public and get to a size where they're appropriate for us.

Chris Gannatti:

It's a fascinating space. One cubic inch of dried DNA contains or could contain 11.2 exabytes of data an exabyte being one million terabytes. So if anyone has a cell phone with a terabyte hard drive, just picture a million of those times 11, and there you go. And just think one tiny cubic inch. So I always love and am blown away by such statistics.

Jamie Metzl:

Yeah. And here's another way of visualizing it. So all of the data that we have now and for all of history could currently be contained in something the size of a refrigerator box. When you buy a refrigerator, it's in one of those big boxes, and really for the next hundreds of years, it could be stored in the liquid or however it was formed, equivalent of three shipping containers versus when you look at... So there are issues, I mean the storage is kind of easier than ongoing retrieval. So it won't be just a linear process from here. It's not going to be that DNA, data, and computing is going to replace silicon anytime soon. But for certain types of things, the logic of DNA, certainly for long-term storage is kind of inescapable in my view.

Chris Gannatti:

So going back to our audience questions, which are still coming in, we thank everyone for that and this is a good one because we didn't talk about it in Jamie's abridged bio, but he does have some experience in Washington, DC. So, Jamie, the question is there any chance that the Food and Drug Administration will reduce the high cost and long testing of new treatments?

Jamie Metzl:

Yeah, so it's absolutely inevitable that that will happen. We're already seeing in places like Japan where for multiple reasons, including competitive ones, they have reduced the time, particularly for gene therapies, but other novel treatments to get to patients. We've seen it just in the generations of COVID-19 vaccines, just how the process, it went from the traditional path, which took about 11 months for the COVID mRNA shots, and then the boosters got faster. And then when they were working on the booster based on the BA.4 and five variants, then the virus mutated. So then they decided to go forward with the next booster formula based only on computer simulations and not based on the same level of tests in human subjects.

So we're going to see that it's inevitable and I think it will be very positive, but bureaucracies tend to move slowly and we want them to move quickly enough, but not too quickly. I mean, gene therapies are a good example. This is kind of the second gene therapy spring. The first one, as people may know, ended very abruptly when a young man being treated at the University of Pennsylvania died a couple of decades ago. But things are actually moving quickly and there's an appreciation that things need to move faster and things will move faster.

Chris Gannatti:

In the coming months. Even in the portfolio, there are some interesting ones coming ups, Sarepta Therapeutics has muscular dystrophy, Duchenne muscular dystrophy therapy potentially going to be approved. Biogen has the Tofersen that may be approved treatment potentially for ALS. The thing that Lou Gehrig and Stephen Hawking suffered from. Pfizer is on the docket for an RSV vaccine. So, Jamie, there's a lot of action coming down the pipe.

Jamie Metzl:

Yep, there is. And it's going to accelerate because every time there's an approval, it creates momentum for more approvals. So you mentioned RSV, I mean, RSV is something that humans have been suffering with for a very, very long time, and now there are roughly at the same time, lots of different extremely positive treatments, preventives for RSV, including an mRNA vaccine. So we're really seeing that across the board in human health and in all of these other areas.

Chris Gannatti:

So going back to our audience Q&A, we've got a good one here. So the question reads, why have biotechnology companies remained flat for the past three years?

Jamie Metzl:

...just to correct. I think it's been two years and then it came down and that's where we are. But it's been about two years and we see a little less than two years. So the peak is late 2021. I think, as I mentioned before, it's connected to you see that upswing. It was a lot of excitement. It was a lot of money being very low-interest rates, a lot of money being thrown into the stock market. So everything was going up. And then biotech, just the investing world, both the retail end, the wholesale investing world kind of suddenly got biotech, and then people realize that this is actually hard. It's not easy stuff that there are scientific hurdles that need to be overcome. There are regulatory hurdles that need to be overcome.

So then everybody got, all of the companies got penalized. I think now we're seeing a thing where there are some pre-revenue companies that are far away from conceivable revenues and those companies are going to suffer if they survive until they can start delivering. But there are other companies like the ones that Chris mentioned, which are starting to generate products and ultimately generate products at scale and so we're going to see a bifurcation. And right now things are actually so low that there are companies, including companies in our portfolio that are trading at lower valuations than the amount of cash they have in their bank accounts, which doesn't really make much sense other than in my mind, it shows that there was a lot of over excitement and now there's a lot of under excitement.

But like riding, when you're riding a wave, the wave itself gets a vote. I'll keep going with this metaphor, but the ocean keeps going, and that's kind of how this is.

This wave went way up, and then it went way down and the ocean is still going, and there are more waves to be had both up and down. But in my mind, I personally don't have any question about the correctness of this thesis, this philosophical thesis, scientific thesis, human society thesis. But it will not just be a linear ride straight up, other than in my view, when we look at it from the perspective of was this story right. Like if you had had a portfolio of companies in the earlier days of the internet, you would've had the AOLs and the other things that ended up not doing well, but you also would've had the Amazons and the Googles and the things like that. And that's we're the kind of portfolio we're trying to put together.

Chris Gannatti:

Now, Jamie, I feel like we'd be remiss to only talk about AI, something that is well represented within the strategy, and you see companies making lots of activities, different types of cell therapies, whether it's things like the CAR-T, ways to fight cancer or other interesting therapies where you get the potent stem cells from skin and blood. What are some of the notable things you might be seeing on the cell side, given that obviously we've definitely touched on AI so far?

Jamie Metzl:

Yeah, yeah. So it's really in incredible. So right now the rate is about 15 approvals for these kinds of cell and gene therapies per year. And that rate is, it's ticking up every year. Chris, you mentioned these CAR-T immunotherapies, and that basically is you take the cells out of, let's say, somebody who has a blood cancer or other things, take the cells out, engineer them to basically enhance their immuno powers, give them cancer-fighting superpowers, and then put those cells back and that's been used very effectively. Just last month there was the Third International Summit on Human Genome Editing and the kind of superstar of that event and the last one before that was in Hong Kong and the dark cloud was from the announcement of the Chinese CRISPR babies. In this one, the superstar was a woman named Victoria Gray, an African American woman from Tennessee who had suffered her entire life from sickle cell disease.

And she talked about how she would go and get the transfusions and feel really like she had no hope, and her life was coming to an end. Then she had gene therapy, which essentially turned on her fetal hemoglobin. So our body have two different systems, one for creating the hemoglobin when we're kids, and actually when we're fetuses, and then it turns off pretty soon after birth, and then you have your adult hemoglobin. So what basically this gene therapy does is it turns back on the genes producing fetal hemoglobin. So Victoria Gray was the first, there are now dozens of other people who have received these treatments, and basically, it looks like, and it hasn't been that long, it looks like they're cured. I mean, you hear these patients say that my life was total misery, I couldn't do anything. I couldn't think about the future.

And now it's a new lease on life. It's pretty exciting and there are these across the board, the cancer ailments and liver ailments, and eye ailments are kind of the first round. And the basic reason is it's just easier to reach blood and liver and eyes. But there's two different kind of approaches for gene therapies. One is in vitro, like I mentioned before, you take the cells out, you edit the cells, and put them back, and you can have a lot of quality control when you do that. The other is in vivo inside of the body. So you basically inject the tools that find the cells and make the changes that you want. It's higher risk because it's inside of the body, but it makes it possible to reach just to be in some ways more aggressive and reach different parts of the body that would be hard to reach otherwise.

And so there are the first trials, successful trials of in vivo as well. So it's really it's a whole and then not to mention the mRNA delivery mechanism. This is the MR people think of the COVID-19 vaccines, but mRNA is basically almost all of our cells, the way that they work is the genomes inside of the nuclei of our nucleated cells send instructions through messenger RNA to the ribosomes, which exist in the cytoplasm, which is basically the egg white to the egg yolk of the nucleus. And that's how proteins are created. So the COVID-19 vaccines were hijacking that process by giving alternate instructions. So now that same approach is being used successfully in trials for cancer vaccines and treatments for, as I mentioned before, RSV treatments. So it's a whole new era and then as I was saying before, all of this supercharged by the new tools of generative AI, which are both for finding new treatments, but also streamlining all of the processes of record keeping, of just time management, of workflow in labs. So everything is going to speed up, and that's pretty exciting.

Chris Gannatti:

And I just got another question in from the audience, Jamie. I know it's a good one because we might not have covered it in the bio, but I know you're quite active on the ethical side. So the question is, do you think that extreme caution should be taken with gene editing and implementations due to the potential unexpected consequences?

Jamie Metzl:

Yes. So the answer is absolutely yes. I dedicate a big portion of my life to this. I hope you'll read my book Hacking Darwin, which is ultimately about this. It's about, well, the science brings us to the conversation, but the conversation itself is ultimately about ethics and values. I've founded an organization, OneShared. World dedicated to exploring these issues, and that's why I'm a member of the World Health Organization expert committee on human genome editing. And so the way I describe this is it's really exciting. This technology has the potential to save not just millions, even billions of lives when you factor in all of the agricultural issues and addressing the consequences of climate change and preventing mass starvation as well as the human treatments. But like every technology in all of human history, there is a very real possibility that if we're not careful, it could be abused.

So the last thing that we should do is just jam our foot to the gas pedal and race forward. The way I say is we need to have our foot comfortably on the gas pedal, another foot kind of resting on the brakes, and our collective hand on the steering wheel. And we need to constantly be evaluating, well, what's the intended benefit, and how real is that? And what is the risk? And what are the things that we can do to optimize the benefit and minimize the risk? That's why I'm a huge believer in governance, which is really what happens at every level of society and in regulation. I think governments have a very, very important and critically essential role to play in this process. I know there are some kind of Silicon Valley utopianists who have been saying, well, we don't need governments, we don't need regulation.

But I hope the total chaos... Yeah, I guess I shouldn't say this because WisdomTree also has a fund, but the total chaos in crypto, unregulated cryptocurrency has shown people that having effective governance and regulation is not an impediment. It's actually the best and smartest way to build a market and build a future that has a stable foundation because this stuff, these are very powerful technologies and we really need to treat them with a great deal of respect. And we need to recognize that when the systems that we're talking about recasting in some ways are complex biological systems that have evolved over billions of years. The history of our species, the modern history is to take that into account. But we've developed agriculture and healthcare and industrialization and all these other mechanisms in spite of that. But we need to really balance our desire to do things better, to improve our healthcare, to build a more sustainable future with a deep respect for the complexity of the natural systems around us.

Chris Gannatti:

So I do have my eye on the clock here as we enjoy our time together with the audience and ultimately have to recognize we've spent a lot of time on the human health side. And if you were to look at the portfolio, you'd see probably something on the order of 75% of the total weight given to human health-related companies and activities. So it definitely makes sense to put the majority there. But Jamie, I'd love to hear some sort of interesting anecdotes, things that you might have been seeing recently either on the agriculture and food or the materials, chemicals, and energy side just to properly round it out and recognize, look, it's not only about human health here.

Jamie Metzl:

Yeah, so I'll give an example for both. For agriculture, the Netherlands, tiny little country, half the size of South Carolina but it's an agricultural powerhouse, certainly for Europe and increasingly for the world. They've set a national goal of doubling national plant agriculture output with half of the inputs, so half of the water, half of the fertilizer, half of the land. And they're making huge progress toward that end using what's called marker-assisted breeding, which is part of one of the areas of focus of our portfolio. And that's essentially doing what Gregor Mendel did 150 years ago, but doing, using all of the tools of modern genetics and AI analytics and seeing enormous progress. So it really makes me very hopeful that we're going, and we have multiple companies in our portfolio that are working on thinking about, well, how do we develop the kinds of seeds and practices, alternative approaches to fertilizers, not just the Haber-Bosch fertilizers that have been around for a hundred years but that are extremely energy intensive, that run off of fertilizers is causing huge algae, basically dead zones in places like the Gulf of Mexico.

So the Netherlands and other countries and certainly companies around the world are really trying to say, well, how do we think differently about agriculture? And there are tons of really innovative private companies, smaller numbers of public companies, and just like we have seen in healthcare, we're going to see a number of these smaller private companies go public and then reach our size threshold. I'm certain that they will join our basket of companies in the future and Chris mentioned we're 75/25 healthcare and everything else. The everything else, as these other sectors mature, which I think will happen very quickly, that 25% is going to grow. Advanced materials just two days ago it was announced that Salvi, which is a big chemical companies, they have purchased a company called Zymergen, which was an early-stage synthetic biology company, and they've agreed to a partnership with Ginkgo Bioworks, which is a synthetic they call themselves a BioFoundry, but a synthetic biology company.

So this is a case of another big company following in the footsteps of Bayer, which has recognized this. I mentioned Merck. I was the keynote speaker for one of Merck's big corporate conference a couple of weeks ago talking to them about this because they're trying to figure out what all of this means. Right now, the traditional way that we get raw materials is we cut them down or dig them up, but there are new models for how we generate those resources that will include cell culture, like Chris mentioned, cell-cultured meat or precision fermentation where we grow materials like we ferment beer. And then I write about this in my book. Essentially what we need is, well, we have raw materials and then we say, well, how can we re-engineer those raw materials? But then the question is, well, how do you get the raw materials? Because if you're going to use, for example, we have corn that we use for ethanol, but we can also use that corn for eating.

And if we just try to do everything with corn, which is, it's actually relatively easy to turn corn into food, fuel, plastic, and a lot of other things, we don't want to have a world overtaken by corn. But as some of you may have seen, there is a 50-mile-long mass of seaweed floating around in the Gulf of Mexico. And the reason for that is the warmer waters as a result of climate change and some of the agricultural runoffs from fertilizers into the Gulf that have created this. So it's 50 miles long, it's just massive, and there's all this seaweed, it floats ashore and then it rots and it smells basically like rotten eggs. So what do we do with all of this stuff? Nobody wants it. It's actually really kind of dangerous because it blocks the sunlight and it's killing these corals.

But we can use something like that really as an unlimited seed stock of turning this is exactly the kind of material finding the right enzymes and the right bioengineering approaches. We can just turn all of that into building materials and there are companies that are looking to do that. We can turn it into energy, into bioplastic, and it's a whole new way of thinking just how do we make stuff? And that's pretty exciting. McKinsey estimates that 60% of the physical inputs to the global economy could be produced by biology-based approaches, which is, it's not going to happen overnight, but it's pretty exciting.

Chris Gannatti:

With that, I would want to thank everyone on behalf of Jamie and myself for taking roughly an hour of time today and learning a bit more about the BioRevolution. Everyone take care and hope you had a nice holiday weekend.

Jamie Metzl:

Thanks everybody.

Chris Gannatti:

See you later.

Irene:

Thank you.

Important Information:

Investors should carefully consider the investment objectives, risks, charges and expenses of the Funds before investing. To obtain a prospectus containing this and other important information, please call 866.909.9473 or visit WisdomTree.com/investments to view or download a prospectus. Investors should read the prospectus carefully before investing.

There is no guarantee that any strategies discussed will work under all market conditions. This material represents an assessment of the market environment at a specific time and is not intended to be a forecast of future event or a guarantee of future results.

WisdomTree BioRevolution Fund (WDNA): There are risks associated with investing, including possible loss of principal. The Fund invests in BioRevolution companies, which are companies significantly transformed by advancements in genetics and biotechnology. BioRevolution companies face intense competition and potentially rapid product obsolescence. These companies may be adversely affected by the loss or impairment of intellectual property rights and other proprietary information or changes in government regulations or policies. Additionally, BioRevolution companies may be subject to risks associated with genetic analysis. The Fund invests in the securities included in, or representative of, its Index regardless of their investment merit and the Fund does not attempt to outperform its Index or take defensive positions in declining markets. The composition of the Index is governed by an Index Committee and the Index may not perform as intended. Please read the Fund’s prospectus for specific details regarding the Fund’s risk profile.

This material contains the opinions of the speakers, which are subject to change, and should not be considered or interpreted as a recommendation to participate in any particular trading strategy, or deemed to be an offer or sale of any investment product, and it should not be relied on as such. There is no guarantee that any strategies discussed will work under all market conditions. This material represents an assessment of the market environment at a specific time and is not intended to be a forecast of future events or a guarantee of future results. This material should not be relied upon as research or investment advice regarding any security in particular. The user of this information assumes the entire risk of any use made of the information provided herein. Unless expressly stated otherwise, the opinions, interpretations or findings expressed herein do not necessarily represent the views of WisdomTree or any of its affiliates.

Christopher Gannatti is a registered representative of Foreside Fund Services, LLC.