Welcome, Sally. Hey everybody, welcome to dead cat. This is Eric newcomer, very exciting podcast episode. I think I was the first I was just checking this I was the first to write about dimension capital, new $350 million fund and I have the entire team, right are the three founders at least here zamindar and Adam Goldberg, both from locks, and then not only from obvious ventures. Thanks, guys for coming on. I really appreciate it.
Thanks for having us, dude.
And say who you are at the beginning when you start talking for the first time as people start to learn your voices. And I'm just gonna throw questions out and you guys can fight about who gets to answer what. But why did you decide to start a new fund in the first place? I mean, you guys were cool. venture funds like, why go independent?
This is often by the way. Thanks for having us. In so many ways. We kind of grew up in industry, we were best friends, whether Adam and now I would say that about me TBD. Not and I taught at Stanford for four or five years, we first met at Innovation endeavors. We were roommates in Oakland for a number of years before Oakland was cool. And then Adam and I actually met at JPMorgan in San Francisco in 2014. And also very quickly became good friends. Within nine months of meeting Adam, he had recruited me over to lux. And then we honed in original Dark crafts together we built our biotech practices at obvious were non went and was one of the founding investors and ultimately a GP and, and a lux as well. And so it was a chance how many years ago,
did the idea of Oh, the three of us might sort of fun first,
by the way, I will say at the 2014 JP Morgan healthcare conference, this is out. And by the way, you will know the mishmash of Australian and New York and my watch from Hopkins door. So it's a blend. But that that same conference, I met my wife and I met Zav in the space of Wow.
He met his life port partner, and then less importantly, his work harder when within one hour, which Yeah, insane.
So when you make all your portfolio companies go public with JP, you know, you'll know, close to the bank, like does the bank have deal flow out of this or
no deal flow? But we're obviously close to the bank as we're close to.
They were invited to the wedding.
Yeah, when did you first think, Oh, we might do a fun together? Or what was this like, text thread for a long time or
this is not, by the way, I really think it was a 10 year long conversation, you know, it was sort of the slow build of growing up in the industry together, getting to know each other. Definitely being on text threads about this space, and what we're seeing, and you're catching up every time we were in the same city or at a conference. So it was this really gradual build, I think, especially through the sort of boom period of 2020 2021, some of the earlier companies that we worked with were scaled and some of them went public, and the space just really exploded and activity. So I think there was a forcing function of the realization that, wow, this seemingly small area that we used to cover when we were coming up in an industry together, is now reorganizing the entire industry. And we're seeing signs of that everywhere. So the three of us just kind of looked at each other and said, you know, our ability to cover this as one member of this broad firm is really limited and the space is so large, and there's so much opportunity that we just felt like we were doing it a disservice. The sector deserves its own fund. And we just felt if we didn't do it, you know, the three of us knew each other. Well, we've been doing work for a long time. If we don't do it, we'll really regret not making that move.
Right. It's more fun to think of it as three friends go off and Silicon Valley version of Raisa you know, instead of a band, you raise a fund and get $350 billion to chase your wildest dreams. But yeah, I mean, you guys are all sort of diehard believers in this sort of thesis. And that's really what I want, you know, the episode to be about because I think this is a cool fund where it's like, Okay, we are, I mean, you kept evoking in our conversation before this Ribbit capital, you know, which is a very successful focus on very different space that went all in on FinTech and crypto and a wildly successful fund. And so I like this idea that you guys see a space that sort of coming and want to define the firm around it. So you know, it's life sciences and technology. I sort of get it but explain to me like what is the organizing principle of the investment thesis of dimension capital?
Well, I think you're you're spot on when we left and when he had kind of the conversations about leaving, and then when we think about what we can become, it's what makes the multitude with Ribbit. It's what Sunil and Mike have done with amplify. It's what Kirsten green did with DTC and for runner, even map and for add a paradigm in crypto and about three, there's a chance before things are obvious to build a firm and not only capture the best entrepreneurs and support and steward them, but also be a little bit of a rallying call for the broader ecosystem. I think if we do it right, at dimension, that's ultimately what we want to do and what we want dimension to become a form kind of in that mode. And in that ilk, what we saw on and we'll keep it high level, but double click in any and all questions, Eric, is that the leading practitioners on both sides, whether they were kind of machine learning researchers and computer scientists at Deep Mind, or open AI, or meta AI, or Microsoft Research, or D saw research, they were one by one uniformly and independently building out their own wet bench capabilities. And then simultaneously attacking biology and chemistry is most vexing problems really over the last three or four decades with increasing traction. And it's not just Alpha fold, there's a multitude of other examples we can point to. But that was happening on the computer science side. And then conversely, on the molecular biology and the chemistry side, every leading research lab to go to a Harvard, Yale, a Stanford and MIT so on and so forth. All of them were increasingly fluent in software packages like TensorFlow, they were all coding on a regular daily basis, in a way that even five years ago would have been pretty surprising. And then 10 years ago would have been absolutely no. And so the realization for us was that at the two polls, the leading experts were increasingly speaking each other's languages, and capital markets, were still entirely dichotomous. Today, if you're an investor, you're either a software investor or you're a biotech investor, you're either investing in SAS and ARR sorts of companies, or you're investing in single molecules and assets. But as the disciplines become increasingly fluid between themselves, the sorts of businesses that can be built are changing in and of themselves. And we had a chance to kind of bridge that gap
in really basic terms. And I'm cribbing, from lines that you guys have said, but the part of the idea here is that, you know, there are companies that did like drug research or whatever, and they're, they're the scientists, and then there were companies that had software engineers, and they're the tech companies. And what you're seeing, and what's already starting to happen, you know, is those two sets of people sort of work together and build a company, right? Am I getting that right? And like, why would I guess, if we're sticking with the drug development company example, in particular, why would a drug development company need software engineers?
Look, if you just kind of go back to the core unit of progress in life science is the experiment is for running experiments. And that's what drug discovery companies do. And that's what scientific labs do, the rate of experimentation, the quality of experiments, and the costs have all sorts of compounded exponentially over a decade. And the result of that is that labs today are just churning out tons of datasets on a daily basis. And it's getting sort of unmanageable. So before experimentation was very manual, and in some ways artisanal where the scientist could read the output of an instrument or an assay, and just sort of infer what that meant, and then go about designing the next experiment. But now the experiments are getting very high throughput, they're very cheap to run. And labs are generating data streams that look kind of like internet platform companies, there are certain biotechs that we work with, that generate more data per day than Twitter does. And that's where sort of data science and software must come in, it's really out of necessity. You know, the way a modern Lab works today, looks nothing like 20 years ago, and it's really the result of a tremendous amount of progress in essentially every single type of molecular tool that scientists would want to run.
So if you think Eric, then about the tools, the technologies and the products that need to be built for the modern lab and the modern day biotech, it's very different than what it was, you know, 10 or 20 years ago. And for us, in forming dimension, it was crazy that there wasn't a firm that could invest in what would be what we view as the next, you know, product discovery, drug discovery engines that are out there, a biologist, a chemist, and a computer scientist, but also didn't help build and partner with companies and founders that were building the tools and technologies and software solutions, that were powering those discoveries upstream and downstream. And so there's this continual, you know, language that goes between, you know, biotechs, and the tools and technologies products that they use, that you need a firm and a set of investors and partners that are multilingual, across that entire spectrum.
Would you invest in a drug development company that says, Okay, we have a great like, data software approach, or do you want to stick to companies that are broadly helping a bunch of different drug discovery programs with software that's shared across Companies are like, Yeah, I guess, is the only platforms are you willing to go with sort of a more vertical company,
we've already done both. And so even in our portfolio that that we just kind of announced this week on one end, you'll get an Veta, which is exactly that drug discovery platform. It's leveraging software automation next gen mass back in their case on the biology side, but it's ultimately in the service of finding discovering and ultimately developing therapies for human disease. On the other end, the hat that I'm wearing right now, science tools is from a company Kaleidoscope bio. And that's a software company. That's essentially building. Think of it as the GitHub for the modern in the evolving lab that Nan kind of painted out where it's becoming increasingly interdisciplinary, increasingly spread across both space and time. How do you kind of tag the metadata associated with the history of experiments that you've run? How do you make it increasingly reproducible, so on and so forth. And that's kind of the problem that Kaleidoscope it's solving, but it's exactly ultimately a SaaS company. And so it will be valued as a SaaS software company,
this could all get sort of wonky, so I want each of you just in a decade, or like, in two decades, what sort of like the thing the public gets out of this, you know, I feel like with AI, which I've been talking a lot of on this podcast, I know touches your world or with the, you know, it's like, oh, we get cartoons of my I get a cartoon of myself and I get to cheat on my homework or whatever, you know, it's fun, because you can sort of see, right away, what me sort of the consumer is getting out of us sort of the future is, can you each give me sort of, I mean, because you touch a bunch of different types of ideas and fields, like, you know, what's something fun that you hope in a decade or two decades, our lives might be different because of this space and the investments you want to do?
I think a big one, and this has already been happening. So it's not that much of a prediction, but really, a sort of a forecast of what's already begun to occur is that because of all the new datasets that are coming online, our ability to define diseases will get sharper and sharper. And when we look back after a decade, we're going to realize that a lot of the diseases that are commonly referred to unknown today, we're too broad. And this has already happened in cancer, where no one has cancer, they have these specific genetically defined sub indications of cancer, and those are treated as unique diseases, and they're diagnosed that way, that same thing is going to happen across every major disease area, fibrosis, neuro inflammation, GI disease, essentially, no one has Crohn's, no one has Alzheimer's. Those are just shortcuts that we use, because we don't know better yet. But right now, the next generation of pharma and biotechs, are using all of these experiments and assays to sort of build a data portfolio of these disease areas. And the vocabulary will totally change in a decade.
When you say like, no one has Crohn's, no one has Alzheimer's. I mean, that's fascinating. Like, the actual causes of those diseases are very different, or just the types of medicine that you would use to treat them as very different one from the next.
Yeah, how we define them. Like if I was to sum up in a sentence a little bit, what Nan just said, it's like the march towards personalized medicine is on the technology train, right? Technology is infusing into everything that we're doing from drug discovery, to running clinical trials to diagnosing and then ultimately commercializing. And so as you think about, you know, a future where we get better at targeting and discovery, we get better at diagnosing and we get better at treating that is that march towards personalized medicine, right, as you define diseases better, as you think about them as spectrum of diseases. And then, you know, we end up in a world where, you know, hopefully we're a much healthier and treatable population. And a lot of what goes on then in the healthcare services is much more about being proactive rather than reactive.
Is it about the fact that I'm different from the next person and my genetics are different, or it's the actual disease is different from the disease, someone else's getting a disease is probably even the wrong word here, but But yeah, is it about my unique genetic code or the,
it's somewhat magical, it can be a little bit about. And so it's, it's kind of non mentioned that if you look at kind of the chronology of oncology cancer over the last two or three decades, it's essentially moved from a death sentence into increasingly so kind of chronic and managed illness. And a lot of that is because we've found the kind of subcategories that are consistent across the population. But we've become more precise with the actual diagnosis of what the actual genetic condition is for the disease. On the other hand, if you look back, even maybe the last month or two Maderna, and Merck kind of had a had a pretty meaningful approval on a personalized cancer vaccine or drug that was an n of one kind of therapy. So it would look at Eric, your particular mutation, God forbid, and then build at therapy based on your particular mutation as it relates to the rest of your kind of genetic code.
Fascinating. All right. That's one it's not a really They're not whizzed bags because it's like, oh man keep me alive when I'm older. It's
we spent the first six months cohabitating and squatting out of Chris from runway, his office. Yeah, so we listen to a, he's a good friend, we get back to him and been fortunate to kind of lead the seed while at our prior firm. He's a good friend. And we told him to stay there for 24 hours. And we ended up being there for half a year. But we listened to his podcast, and he had all these kind of amazing kind of anecdotes about, you know, what, what's hap, GPT look like for video or media, and so on, and so forth. And candidly not not to mention this, because the modern kind of wetlab today is the largest producer of data, maybe only on par or surpassed by the half hyperscalers today. And so it's very provocative to kind of think about what this chap CBT or GPT x look like, against population wide genetic data look like against metabolomic, or proteomic, or phenomic data. And these are the sorts of problems, I think that we're just on the precipice of starting to think about an answer. And again, you get these bread crumbs like open AI, or meta AI, opening up a wet bench facility in New York to attack these sorts of problems. We can postulate it, it may well be 2030 years down the line, or maybe even 10 years down the line, you Eric feels sick, you go and you get some sort of advanced next gen diagnosis of your illness. And then at the point of care, wherever you are a custom n of one molecule, whether that's a kind of a small molecule, or an antibody, so on and so forth, kit gets printed for you, for your disease at that state in the moment in time, and you take it and you're healed. I can't tell you if that's 100 years from now, or if that's 10 years from now, but scientifically and technologically, there's no reason why that's not possible.
So this is sort of our second bucket where it's like, okay, applying sort of generative AI tools to sort of genetic sequence. And I'm gonna somebody come up with a third, but I wonder as, like, the whole, like, folded home or like when there was a whole leg proteins, I remember I literally had it like I was part of this, you know, I want credit, I was, you know, streaming on some laptop back in the day like, like SETI and fold at home, it feels like this technology fits into that or like, and then certainly the you guys know, what's going on? What happened with the whole protein folding thing? What happened with that? Or do you know or sort of? Yeah, and how does sort of the current state of technology help it?
Well, I mean, for me, it was this
too weird of a
no, no, it's it's, it definitely is in the lineage of, of all the sort of protein folding and AI breakthroughs you hear about today, for that home was a really successful, essentially, federated computation project. And protein folding has always been one of the most computationally intensive exercises. So for computer scientists, there was always this gauntlet of, you know, can we drive artificial intelligence and drive computation into the point where we can calculate the sort of native protein structure given the sequence given given amino acid sequence. And, you know, that has always been the hardest challenge in, in these different tests of AI, you know, computer vision annotation of images was one test, you know, beating a human at chess was a test beating humans at Go was a test. But accurate protein structure predictions has always been the one that that was unachievable. Until very recently, with, with Deep Mind, the work they did with alpha fold, but your Eric, your contributions to that definitely was part of the same competition, it's essentially the same challenge, you know, turning
over this problem, or it's just they're much better at like crunching through it. Now,
I think it's the earliest innings off, right, there is real value in being able to go from sequence to structure. But one of the things we like to say internally, and we wrote a letter is, you know, biology is the most complex and beautiful machine ever created. Right. And so as you think about protein structure, you know, there's primary, tertiary, secondary, and tertiary and quaternary structures, right, there's modifications, there's complexes, so there are more levels to go here in terms of what technology can do and deliver in terms of really going from, you know, sequence to structure and then to, ultimately to function, and then well out into the future the ability to sort of simulate various aspects of biology. But I don't want to diminish from how powerful you know, this moment in time is, you know, similar to, you know, sequencing the genome, similar to, you know, CRISPR, which is now permeated through every modern lab, and every lab on the planet, right, these are specific moments in time. And it's not just those three, there are more and more that are coming that are here, and that's why we're really excited. Can you
tell which one of us is the biologist?
Yeah, yeah. There's a little deference to the PhD. Everyone's which I think is Adam, right. CRISPR. I mean, it fits into my sort of folding, you know, maybe I think, you know, as sort of a layperson is following this industry. There are moments where, sort of, I don't know the technology of biology sort of like cracks in we all get excited about it. And then sometimes I feel like we just don't get like the update, or it's like what happened? And so like, yeah, with CRISPR, I remember, in 2014, I was writing like very enthusiastically I think, like Illumina is an important company now in your space and sort of one of the markers of success, you know, there were these companies that were like, oh, what's, yeah, that we're gonna build on it. And then I feel like it sort of happens and falls out of the conversation chart out CRISPR and and what it means for sort of your thesis here,
we kind of sit at the intersection of two technologies, that very much a kind of follow up a little bit of a truism, or saying, which is that they're magic until they work. And then they become yesterday's news. And in so many ways, that's true of AI and ML, and in so many ways, that's true of biology and chemistry and the life sciences. what's possible today, because of CRISPR, should have our jaws on the floor all the time. It's absolutely magical in 2014, Eric, when you recovering it, that was the very kind of tip of the iceberg of our ability to really kind of be precise and have the equivalent of a copy and paste and or scissors with text on the kind of genetic. That's,
you know, George Church was what trying to store like the book type information,
type information, or edit woolly mammoths, and all sorts of things. And the reality is, today, you might not still hear about something like CRISPR cast nine in the news, it has, in so many ways, kind of become that proverbial yesterday's news. But every modern research lab, biotech and pharma is using CRISPR on a daily basis, if they're doing any sort of research at all. And CRISPR is exactly kind of equivalent, or analogous to kind of nons point earlier, where it really has followed Moore's Law curves and cost and scale and fidelity and reproducibility. And so it's becoming increasingly powerful. And it's giving scientists and researchers and drug hunters and developers increasing leverage in what they're doing, it's just no longer in the news,
Adam, actually that when he was a PhD in the lab, you know, hundreds of years ago, the alternative, the alternative, you know, way would be for the sort of mechanism of CRISPR was invented, to get the same kind of genetic changes and cells, it will take them months to order these cell lines that are yours. You know, we're using these these molecular tools, you know, every lab today has access to those types of edits, in a day or two day turnaround. So that kind of enabling technology you're not going to hear about because it's there's no sort of publicly traded company that represents that technology, it's more of a tool that is essentially used in every single lab. But it speeds up the process, it speeds up a lot of the sort of inputs into experimentation and allows those labs to run much, much faster.
Right now, today, there are companies running clinical trials, and using CRISPR to treat diseases and genetically modify human beings. Right. And so when you talk about this downturn in public, you know, momentum, or press or conversation, I mean, maybe I'm a geek, and I geek out on these things, but that like that, that's science fiction to science fact, right that like your gene or editing people, today, in order to cure diseases, that's mind blowing. And that's coming, you know, much more in the future.
I mean, I touched on it. So we started did sort of almost bespoke drugs, AI for discovery, I promise three. So I don't want to deny the very attentive listener that we've talked about tons of wonderful things, anyone else have another area where you're optimistic sort of over the next two decades,
it might be fun to talk about something like Maderna, and kind of COVID vaccine as, as an existence proof or assurance as a kind of indicator of what's possible, if we rally the right resources around something. And that was a, again, if you look at the history of our ability to develop vaccines, it's certainly taking, you know, years, if not decades, and hundreds, if not billions, if not 10s of billions of dollars of research and development costs to develop vaccines. And so when COVID first turned her out, there was a very smart and sophisticated group of people who said, we're kind of fucked, like boots, gonna take five to 10, maybe 15 years if we ever find a vaccine for this thing. And if you look at how Maderna actually developed a vaccine, within within 24 hours of sequencing, kind of the epitope, or the protein, they had actually developed what ultimately would become the first vaccine that they took to humans. And all of the kind of time after that was in and around synthesizing it in and around testing it in various kinds of in vitro and in vivo models, and then ultimately also testing it and increasing kind of scales of patient populations, but the ability to within 24 hours actually find and develop the vaccine or the molecule in this case. Again, there's this kind of he's he's persona non grata as a comedian in many ways, but Louie CK has has this bit from I remember him when I was in my early 20s. About every time we fly, we complain Oh, the Wi Fi is not fastened. I have like, like, the hostess isn't bringing me my soda. Like I feel squeezed in the in the middle row in the middle seat. But like, Holy fuck, you're in the air going 600 miles an hour across the planet. And that's that's how we should feel about what's happening in biology. And what's an Kinlin was also happening on software and ML and AI. And the interesting thing is like, there are compounding and rates that I think Nan, Adam and I all sometimes feel like, we're the only people who are seeing what's happening, and it's unreal, and it should yield like unwavering optimism and excitement. But it's oftentimes easy to get lost kind of in the minutiae of the details and or take it for granted.
The reason why this question is really important is that to the general public, who are not in these labs, or, you know, working hand in hand with these companies, you know, they're not seeing the results, and essentially, commercially approved drugs are the last indication of progress in the biotech industry. You know, we talked about an acceleration of experimentation, and acceleration of asset discovery, and, you know, crafting new compounds that are more targeted, more powerful, but those are still moving down the pipes in clinic. So, you know, I would say the larger prediction is essentially, there are only 4000 commercially approved drugs in the market globally. And that number is sort of steadily ticking up, but you're not seeing that exponential growth, yet, in 510 years, we're gonna see the output of all of these sort of generational leaps and tools, and we're gonna see this era of really unmatched productivity, and that that directly affects patients.
If you have many companies where it feels like what they're doing is gonna touch the sort of the masses, or is it always the case that healthcare is sort of helping the very sick? And so it's, it's only really going to come up in your life when you want some sort of disease treatment? What are the very well, things are like if you Yeah, I think all of us,
I mean, frankly, if you interact with healthcare at all, you probably rated as a very low NPS, you hate going in, you're always in lines, like you go from a doctor to a specialist back to a PCP and they all lose their data, there's no kind of information flow with them. And healthcare is the largest sub sector of the GDP. It is the only sub sector of the GDP that doesn't have increasing economies of scale and productivity, brought to it from digitization and technology. And importantly, 90% of it is on clinical services is in health care, kind of patient care settings. And so it's when we don't have the ability to treat or to diagnose a disease early, they end up becoming so bad, so catastrophic, that these patients end up in these kind of heinous horrible kind of circumstances. And that's where really where the costs come up, if we do our jobs, right. And as Don mentioned earlier, if we move from a world of intense kind of discovery and development, scarcity into a world of radical abundance, both in terms of biology and chemistry kind of discovery, but then also the downstream effects of therapies kind of coming down the pike. If that happens, it starts to change kind of the cost curves. We're no longer spending billions and Keneally globally, trillions of dollars on health care services, curing patients are attempting to at least manage illnesses once it's too late. But we're treating upfront as early as possible, those patients so that those diseases never manifest or never mature into those kind of end states.
And Eric, like, I don't want you to think about what we're talking about is just for those that have access or just for these rare diseases to companies that, you know, we started out prior firms that locks, you know, one was a company called KALLIOPI, where, you know, I was actually founding CEO of that was focused on the gut brain axis, this new kind of two way communication highway between the gut and the brain. And that was deploying all these cutting edge technologies like single cell sequencing organoid technology, yes, kind of machine learning on top of their datasets in their Atlas that they generated, but they were focused on, you know, metabolic diseases, obesity, diabetes, things about IBD and Crohn's gastrointestinal disorders, right. And then thinking about other mental disorders, because you can, there's a world where you treat mental disorders through through your gut because it's, it's connected. The second company that we founded was a company called Kahal neurosciences, we were part of the founding team there. It's an amazing company over in Seattle, and PyCon. And the founders there, Andrew dove, and their incredible, you know, technologists, scientists, they're focused on outside, right, and Parkinson's. And so, you know, deploying these cutting edge technologies like viral technologies, imaging technologies to automate scale and do throughput and drug discovery is not just targeting the very, very unique, you know, diseases. It's targeting, you know, it's permeating across the entire disease spectrum, whatever that disease is.
Yeah, I think you can think about, you know, the average patient and the way we think about health care is very reactive. You know, as I've mentioned, you know, most patients are not self aware around their healthcare and to until they experience symptoms and usually severe symptoms, and then they're managed through this highly complex and inefficient healthcare system. But the reason that is is really, because of a lack of specificity and in diagnostics and an awareness of what's happening and health progression. So the human body is just a highly complex system. It's a, it's an engine. And it's essentially this bag of chemical reactions, sort of happening at a rate of billions per microsecond, all over your body. And then once in a while, things go haywire and you experience a symptom. And I think for the average patient and the healthcare, the healthcare industry is what more precise medicine comes along with it and more precise diagnoses and preventative diagnostics. So we're moving into a space where I think it's entirely reasonable within the short term, you know, a 10 year window, where an annual checkup will come with a blood diagnostic that gets funneled into this sort of genomics and proteomics analysis, to sort of pinpoint issues that are happening. Before you express disease before you you're symptomatic, before you have to go see a specialist and medicine has given to you at that stage. So to a normal person, I think that really changes their experience with healthcare. One of
the amazing things about one of the things I love about like the iPhone is that it's like a technology that the best version of which is available to sort of the mass public, obviously, sort of still a wealthy set of people in America, you know, the Western world, but But you know, like, there isn't this like billionaire phone, that is so much better, right? Like the fat and awesome, love needs to be mass produced, for it to work. And so there isn't some like secret phone that you're like you really wish you had with medicine? Where are we on that? Or like, do you think if I'm a 50 year old, healthy billionaire versus a 50 year old, regular person? Like, what's the gap between the quality of my like health? Is there a lot that billionaires like, really can do? Are they just like poking and prodding themselves with little benefit? Or what's your sense of the gap? And do you think it's going to grow or shrink sort of over the next decade,
I have some young mice in my freezer that I inject blood from on a regular basis.
Thinking about just the blood, the blood transfusion, the cavity
drive is doing a transfusion as we speak. So
what do you think? Yeah, is there a big gap between what, uh, what Yeah,
I think the biggest gap is around the dependency of most people on insurance companies and reimbursements to access health care, both medicines and procedures, and also diagnostics. And you know, those in sort of the 1% and whatnot, can can take charge of their own health care. So there are a lot of different sort of preventative scans, or diagnostic tests that can be run, that insurance companies would never reimburse, because it doesn't really make sense across their population. A really good one is a company called per nouveau which does essentially a whole body CT scan. And I think it's on the order of two or $3,000. That's not reimbursed, CTs are only reimbursed when there are symptoms by the healthcare system, if you have sort of symptoms that indicate you might have a tumor or a growth in you, then it's beneficial for the insurance company and for the clinics to go, image you and try to locate the problem. But if you if you have access to that cash pay, you could go do a pre nouveau scan every year, a whole body CT, you would see the earliest indications of sort of, you know, phase one, stage one cancers, before you would experience any symptoms, and it's getting into that sort of preventative diagnostic. Do you guys problem?
Like? Do you do that? Because I have,
yeah, I just went to my primary care provider for the first time for five years. Because, I mean, I am asked to tell me how to
go to the doctor. I've asked like rich people before, and just like the risk of these skin who don't do these scans, and like, the issue with the scans is, you know, you can scare the shit out of yourself or like doctors will always tell you to do them because you find these false positives. And then you drive yourself crazy or in other. There's just like lots of downsides, still to over testing. So that's why I do it's a legitimate question whether there's this secret sort of medicine or biology world that people are missing
for something like the pre novice can until until we actually know what to do with that downstream data, it's harder to kind of get it kind of insured. It's not to say that it shouldn't be offered to everybody but but quite frankly, like the things that do pass the FDA process to your point earlier, it's the one area in technology or consumer goods were what the poorest of the poor can access today is what the richest of the rich could access 20 years ago, there is exactly kind of this things are forced to go generic. Every drug that was kind of discovered 20 years ago, is now off IP off kind of patent life and essentially kind of should be available at cost plus margins in terms of how expensive it is to manufacture that and there's no other technology out there. I really love your kind of example of the iPhone and the laptop. We use the same iPhone In the same laptop that Elon Musk Bezos OR gates are using right now. And like that, that is that is power kind of the democratization of technology. We also use the same drugs, by and large, there are, and there will continue to be billionaires, millionaires on and so forth to kind of experiment, but they're also taking on the risk of those experiments themselves too. And again, until those things are provably efficacious for a broad population of people, they shouldn't be kind of reinsured, nor should they be kind of approved through a regulatory process.
I was just talking to Scott Sandell, you know, the top guy at NEA, they raised a $6.2 billion fund. And you know, when they announced it, I mean, they put health right in the headline, like, I think health broadly is very popular in like the venture capital world. Right now. I mean, you guys have a much more specific, sort of lens at it. Are there parts of sort of the VC conventional wisdom on health investing? Not any particular but just VC health investing generally, that you disagree with? Or are there some of these health bets that you're going to try and steer away from? Or? Yeah, how do you see your approach here fitting into the excitement around sort of health, digital health would have you broadly in venture,
as we think about the digitization of Life Sciences, the entire industry that you know, pharma and biotech and life science companies like Danaher and thermo consume, that is an enormous opportunity and an enormous industry. Right. And so we feel like that's enough to digest on our plate, and not have to, you know, also focus on partnering with companies that are building that are doing really interesting things, but are, you know, selling care coordination tools and technologies are selling into hospitals are selling to doctors are building the next EMR or building the next health insurance. It is an enormous industry, but the one that we feel like we're focused on deserves its sector focus fun, and that's why we built dimension. And we concentration and focus, we feel like comes you know, you know, better outcomes and greater capture of value, as we spoke about some of those firms before that, you know, I've done it on being sector focus. So I'd say that for us, you know, rather than being like, incredibly broad threw out the entire healthcare ecosystem, you know, our focus is on the life science industry.
I like the dynamic and the life sciences industry, where you essentially have completely aligned incentives across all the key stakeholders. So medicines are valuable to develop. Pharmaceutical companies want to get access to research wants to get access to budding medicine in the making, and biotechs. And startups are rewarded for contributing to that progress. And essentially, all the wheels are turning quite well, I think universally across both founders. And also, you know, generalist VCs that start to do work in healthcare, there's an under appreciation of how much incentive change incentive alignment and behavior change is required, even if the technology or the software or the product is developed. Well, in our world, if you develop a medicine that shows efficacy, and is screening well, and testing Well, there's a clear cut path for it to go through the developmental process and get in patients and generate revenue and build value. That's often not the case in healthcare, where you'd have to convince, you know, regular people to change their behavior, or you have to convince hospitals to change their behavior or insurance companies. So I think venture over the last decade, especially in sort of a cheap cost of capital world, funded a bunch of companies that are taking real shots at changing the healthcare system, and that's good for everyone. But in their world, it's not a meritocracy, they have to do work in sort of politics and galvanizing the change in our world. Everyone wants to change even the incumbents, you know, want what we're talking about.
Eric, your question on like, kind of like NAA, in the broader venture ecosystem, one, having just kind of grown many, many, many, many, many gray hairs with what feels like increasing arthritis, having raised 354, dimension one, we are humbled shock and awe by Scottson, Dell, Nea 612. But like, it's hard to compute what that looks like, and kind of the trust and conviction and proof that they must have shown over many, many, many decades of really kind of intense investing with their LPs together that especially in today's environment, so kind of all Hats Off. On the other hand, we saw directly at our prior firms, and then also from the eyes of the entrepreneurs that we had backed that founders today who are building up this intersection of technology and biology in the life sciences. They are stuck between a rock and a hard place on either hand, they have a Faustian bargain. They can go to you know, an NDA, which might have you know, five or 10% of their GPS, spending time in the life sciences. They might have their med tech device, med device team in DC, they might have biopharma team on Sand Hill, so on and so forth. But there's not cross collaboration. Ultimately, when the founders go in there, there's not A firm wide top down conviction, it is one of the many strategies that that kind of firm deploys. We not Adam and I, we kind of started joking early on when we launched I mentioned, who's going to go into meetings and say two things we would quack quack quack like a Mighty Ducks reference, we, we fly has won. And on the on the other side, Eric, you do have not mentioned kind of the traditional biotech investors, whether they're in Seattle, or Cambridge, or San Francisco or San Diego, so on and so forth. The atlases, the pluses, the flagships the arches of the world, and again, loads of respect what they have done, we were just at dinner last night, but you're catching us in Utah right now for a GP for kind of our team off site. But we were sitting, by the way, and we sat at dinner last night, kind of, for a solid 30 minutes really talking about kind of what they are uniquely able to do and their historical success rates, which are impressive. But they have also, at the same time simultaneously been historically very reticent to accept technology, they've been Luddites with respect to technology, and they're starting to come around. But we believe it's not just a muscle or a capability that you can Frankenstein, on, both on an investment firm and on to the actual company needs to be kind of organic and built from scratch, realizing the new world that we set it
in terms of your fun, specifically, do you think there will be deals that you do or the logical follow on round comes from those sort of old guard? biotech firms?
Yeah, I mean, I think you think about this, I've mentioned one of them before, like what Bob and his team at Archer doing is incredibly impressive. And I think, you know, Bob starts to think about the world notch down to think about the world where they, they want to get involved in some of these broader discovery engine platforms that we invest in solely across the biotech space, they are intrigued about the software, the tools and the instruments that are kind of digitally native, you know, our cloud forward. So, you know, and I think there's a ton of room to collaborate, they won't be, you know, not all of them, I think, is probably the best answer to your question. You know, some are focused on kind of those building biotechs that are focused on a single asset or a dual asset, or, you know, low hanging fruit that still continues to exist in bio and drug discovery. And that's very lucrative to some of these firms and their playbook, and they've created a ton of value, and candidly created a ton of really impressive, you know, therapeutics, drugs that have real treatments out in the real world. So, but you know, some of them will, we'll see, you know, what we're doing is too far, you know, too far of a stretch for within their own strategy, but some will, you know, will co invest with and partner up with. And, you know, I was texting back and forth with Bob, you know, a couple of nights ago saying, let's find deals to do together.
There's an argument that sort of the low interest rate environment was like the best time for this sort of futuristic technology investment, that was sort of the window. And now with interest rates going up, people are much more oriented around profits, like SAS company, software companies, where there's no question that they'll be able to figure out how to monetize need to make profits sooner. If that's the mentality of the moment, Will there really be a lot of appetite outside of, you know, your fund to bet on companies where maybe the follow on round? You know, the company still doesn't have revenue? And it's still sort of a project?
Yeah, I mean, I think that what we're recognizing is, you know, new industries are always the best investment opportunities, because there's true alpha, there's inconsistency of understanding, there's inconsistency of underwriting and investors, by their nature should be seeking out those opportunities, especially in venture and growth equity. So yes, cash is more expensive today than it was two years ago, you're going to start to see a, and you're already seeing a sort of a return to pre 2020 levels in terms of fundraising activity, funding levels, round dynamics, but that doesn't really take away from the broader worldview, which was I've mentioned, this is the largest category of the DDP there's real dollars going into life science and biotech, up and downstream, you know, in preclinical development, in selling off assets in selling data access into these these discovery engines and what insights they find. So along the way, you know, we think our businesses do inflect value in a short amount of time, they're not these sort of science fiction, deep J curves where they need a decade of funding and sort of countless hundreds of millions of dollars, you know, they're showing platform progress between cDNA they're starting to develop medicines between A and B, they're getting human data between B and C. So along the way, those are real milestones. They're not SAS milestones, but to this broader industry, which is a huge category, the most acquisitive industry in the world. Those milestones accrue value, you'd a farmer buys phase one assets, they buy phase two assets. So you know, we're here to help guide them and we think there's a whole world of investors that understand those milestones, and it's certainly our job to help sort of galvanize this space and to to standardize the KPIs why we're having this
right now. You know, we
play a role there, we just had a dinner earlier this week with 20 growth investors talking about this exact space, the intersection of technology and life science and a span from growth equity to hedge funds to sovereign wealth funds. And they're all interested in the space, they know that innovation is coming. They see it sort of empirically just through, you know, where the practitioners are, and what's happening. So we're very confident in Downstream capital, it will be disciplined capital. And I actually think that's a good thing, zero interest rate environments with infinite cash. Don't build companies, I actually think that better companies are built during times like we're kind of where we're headed right now
with a real theorems fit in your investment thesis. And will we ever see one, we backed
at our old firm a company Thrive looks, the firm that
will not be mentioned, you guys don't say anywhere. Back that box.
Firm got away like sorted out of things like having to do that. We backed out locks, a company Thrive detect, which was doing single blood draws for multi cancer kind of diagnosis. And so that's not quite their nose, but it's kind of adjacent. And those technologies are increasingly on the comp, it will happen. At some point. Again, there's no scientific reason why that's not possible. Obviously, like finding entrepreneurs who aren't fraudsters who aren't professors who aren't the equivalent of you know, what Elizabeth Holmes was, and or SPF at FTX was to, and then stewarding them and really underwriting but the technology, the science and ultimately the business?
How, you know, raising people said it couldn't be done. You know, it's like, your start, every every old school venture firm right now is saying, Oh, the new funds are in trouble, you know, and you guys are pretty big, new first fun. So congratulations. What were the worst? Or is what's the mood among the limited partners? And how did that play out?
Yeah, the the first thing I you know, I think this is back to one of your original questions is how do we come to be right? We've known each other for a decade were friends of anon have lived together. You know, it's been a decade long. I want to say conversation, but sometimes, you know, robust intellectual debates as well chiseling away at the thesis. But when we started and when we left our phones, we left with nothing, right? We had our reputation, we had our network, but, you know, we had an idea of where we want to go, but we didn't have an anchor LP or, you know, we didn't even have a name. And Nan shifted his family over to from California to New York for a month. And, you know, we put ourselves in into Chris's space and a runway and just hunkered down and came up with you know, everything from you know, thesis and collateral to thinking about how we wanted to position ourselves what was the message and as someone that like, hasn't truly in the past, jumped into like a, you know, build it from scratch, entrepreneurial, everything on the line. situation, it was the most exciting time of my life. And I think allies I'd speak to, for the for the three of us as well. So a lot of
them are spending money on lawyers now that I have my own like newsletter I need to get other people to do you're like, oh, no, there were there were jobs.
They're actually listening to this and billing us right now. This is like what
like we, the amount that are kind of friends, peers, contemporaries and industry kind of really came as kind of supporters, advocates, advisors, mentors. For us, as we launched, it was something that we absolutely needed. We didn't know we needed. But in retrospect, we absolutely needed in retrospect, we had no idea what to even ask for it or that it would happen. And if there's ever a chapter written about dimension in some future kind of anthology, which I hope one day we build a firm that demands that sort of attention. An entire chapter will be on Cemal and Chris and Matt, that paradigm, and Weston and in so many of our friends who on day one, both gave us exceptionally strong stewardship and advice and mentorship and guidance. But then also, we're so generous with their most scarce kind of resources, which is their l PACs and their advisors and their investors. And so that gave us critical kind of fuel entering what was as non Adam and you have already discussed a bizarre of a market and then we would not be here without them. So that that was just like the coolest part.
And who what, or I don't know if you can are getting the first anchor, you know, what can you say about that or that must have been quite the experience to have sort of an anchor.
It was a very, we started using fundraise.
Go ahead. No, I was just talking about the email. It was fun.
It was one of the leading allocators on the planet, and they were progressive and understood kind of the exactly the kind of this confluence of technologies between the life sciences, software automation and hardware, they were paying attention to this space. And to their credit, they were amongst the first people who reached out to us and who we spoke to. And then they remained the largest kind of check in our fund. And so you know,
and they just send you an email, they're like, Alright, we're gonna put this much in or what
we attract us and known us for a long time, you know, pre about throughout our prior firms. luxon obvious. And they move quick. We had a number of conversations we met there, it was a little bit like the roles, obviously, the roles reversed, right? They saw a really interesting opportunity. They wanted to really partner and they moved with ultimate conviction.
So you play like a startup you're like, I don't know there are a lot of people.
Think GTS don't have that leverage. LPS know, it takes it takes so many. Then I've been telling some of my founder friends that raising a venture firm is kind of like raising for a startup, but you need 20 term sheets, because of how much most LPs are committing. So you get to sort of briefly high five and celebrate but then it's, it's kind of rolling into the next meeting. And we just did that for six months straight between May and November.
Well, this was so much fun. Thank you guys for coming on the podcast. I really enjoyed it. And good luck to you. Thank you for
having us. Big fan. Yeah, thank you for hosting us. Goodbye
