Invest Like the Best with Patrick O'Shaughnessy - Michael Dempsey – Investing in Bleeding Edge Technology – [Invest Like the Best, EP.212]

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Starting point is 00:00:37 This week's episode is brought to you by the MIT Investment Management Company, also known as Matimco, the Endowment Office of MIT. New and small investment funds, listen up. Matimco is looking to find investors starting funds today. Matimco is partnership-driven, long-term focused, and has an extensive history of backing investors early in their careers. These partners are key to delivering the outstanding investment returns required to support MIT's pursuit of world-class education, cutting-edge research, and groundbreaking innovation. Matimco is focused on finding and partnering with the best investors across the globe, no matter the market environment. No firm is too small, too young, or too non-institutional. If you or someone you know is currently in the process of starting a fund or recently launched,

Starting point is 00:01:21 please email partner at matimco.org. Again, that's partner at MIT. IMCO.org or discover more on their website, www.mptomco.org. Some of MIT's best partnerships have been initiated during challenging market environments. Matimco looks forward to hearing from you. Hello and welcome, everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to at investorfield guide.com. Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management.

Starting point is 00:02:07 All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaunacy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaunice asset management may maintain positions and the securities discussed in this podcast. My guest today is Michael Dempsey, general partner at Compt. Pound Ventures. Michael invests in a broad range of areas but has a unique talent for combining

Starting point is 00:02:36 brand building and direct consumer relationships with technically demanding sectors. Our conversation covers the rise of virtual influencers, robotics, and how to best identify key inflection points in the evolution of new technologies. I hope you enjoy my conversation with Michael Dempsey. So Mike, I'd love to begin with a simple thumbnail sketch of your career to this point. Describe for the audience how you got into the business you're in now and what the major stages of your career have been to this point? I'd start with in college. I spent a lot of time thinking about finance and trading a little bit and really became obsessive with markets broadly. After school, went to work at a hedge fund doing everything from long short on the public side to

Starting point is 00:03:17 some derivatives trading to eventually some cross-border private equity as well from Asia to the US and eventually started to do a little bit of seed investing there, which was a little off for 2011 hedge funds doing seed stuff and weren't a ton of people doing it. At that point, really, like fell in love with the seed investing stuff. I think that was both intellectually stimulating to me and still had the level of paranoia that public markets brought me, which is there's always someone working harder to destroy you on the other end of a trade. I wanted to understand a startup ecosystem a little bit more. And so I joined a company called CB Insights pretty early on. The company's most well known for the newsletter, me and this other guy, Matt Wong, would spend all day looking at all the

Starting point is 00:03:54 data coming in across private market financings, fundraising, app store rankings, et cetera, and basically figured out what should we write about today. And we would write 12 to 15 blog posts each a week on data-driven venture capital, private market insights. I did that for a couple of years and really saw the team scale. And in that time, really two things informed my view a lot about my career. The first, as I got to sit in this really interesting seat, I was talking to a bunch of founders, talking to a bunch of investors, a lot of corporates, but we're never really,

Starting point is 00:04:24 really deeply digging into areas. And I think the first thing that I came to notice was obvious example of everyone. everyone's kind of standing around a pool of waiting to see who jumps in first in venture, and then once one person does, everyone does. And we saw that both on the data side, but also just on the topic side. Secondarily, I became really obsessed with a lot of these more kind of out there, deeply technical type areas. And those things were at the time like robotics, machine learning, computational biology,

Starting point is 00:04:49 AR, VR, space. And I just realized there weren't a lot of people focusing on that. And there weren't a lot of people really going deep on those areas. And the idea of people trying to pattern match lessons from the internet or mobile into those categories just felt really broken to me. From a lot of the hedge fund background, I kind of thought a lot about taking this very research first approach. I really started to write a ton about that and started to really focus on that and make that bet for my career of the things that I really cared about and the futures I believed in. And that eventually led me into venture where I am now at compound and relates a lot to how we think about investing. There's so many different areas we could dive into today.

Starting point is 00:05:25 I've went back and read your whole archive of stuff you've written. It's cool that you started your career with the 12 to 15 posts a week. It's pretty intense output. So obviously you developed that muscle early on. As I was thinking about how to structure this, I thought that beginning with your thinking on inflection points as a source of opportunity and therefore something that people should pay very close attention to would be the right groundwork for all the different other topics that we'll cover. Maybe you could begin by just introducing the concept, the who, where, where, and why of inflection points, why they matter so much. And then once you do that, I'll ask for some examples just to ground the audience and the sorts of things you mean. Inflection points are basically the moments

Starting point is 00:06:00 that things are changing. And that's something that I think a lot about and we think a lot about as a firm when it comes to investing. And I'd say specifically, when COVID started to happen in March and April, a lot of different people were kind of saying the same thing of great companies are built it every time and downturns don't matter for technology. And they talk about the Uber story of Uber being founded in 2008 and Airbnb and Dropbox and there's prior parables from the 2000s. And it's a really great thing to say when the world is melting down around you. But as someone who's a little bit obsessed about the fallacy of antic data, as we used to call it at CB Insights, I just started thinking about more of like, okay, what actually happened during these times

Starting point is 00:06:38 or actually the drivers that changed the trajectories for these businesses to be able to be built during this time. And I think that the economy and markets are one thing. What is actually happening in technology is another. And so really just went down a rabbit hole of reading kind of, both news articles from back then, a bunch of research papers on the different ways in which businesses have emerged over time, which industries have emerged over time, and started to dig into what actually happens when things change. And what I saw was across 20 years, like, yes,

Starting point is 00:07:05 there were companies that survived at each of these massive downturns, each of these scorched earth times for capital markets on the private side. But I think what really happened was they were pretty strong catalysts that drove a lot of the growth of these companies. And you could look at it from either the internet usage continuing to proliferate and continuing to mature within developed nations, to it becoming more global, as well as core infrastructure like AWS launching in 2006 or high-speed internet or launch of the app store. What it kind of looked at was the different types of inflection points over time from infrastructure level to distribution level and then eventually to technical level, as we call them. It kind of led me down a rabbit hole

Starting point is 00:07:41 as many things do of what are the takeaways that we should have when we want to look and understand what is a real inflection point versus not. There's a lot different avenues to I've been too there. Maybe we can talk about what is and what isn't the Fool's Gold type of inflection point that looks like one, but isn't something like crypto would be an interesting example of that. It looked like in 2017, we were going to have this unbelievable explosion of development and creativity because of a new technology. That may still happen, of course.

Starting point is 00:08:08 Maybe it is happening. But certainly a couple of years afterwards, it seemed like we'd gotten overexcited. I think about inflection points as like unlocks. and they make a lot of other things previously impossible possible. How do you think about the difference between a real and a fake inflection point or what looks like an inflection point, but it doesn't turn out to be one? Yeah, I think a lot of it is something of science project or is a venture scale business? Is there real scalability and production level processes that can be created from this change?

Starting point is 00:08:34 And sometimes there are companies that are creating their own inflection points. I'd say SpaceX is a great example of that. They spent a lot of money in R&D over time. They eventually are going to be able to be the company that unlocks the private space economy due to their ability to drastically lower the cost of launch. And I think that's going to pay off heavily for them. And they're specifically a really interesting company because all of the R&D they did along the way, they actually had a business use case for. And granted, there is a bunch of dynamics there on the regulatory side that's smarter people than I have litigated around

Starting point is 00:09:03 why that type of exact business within aerospace was able to be created. But I think SpaceX specifically, they were doing things where they were losing rockets, heavily expensive R&D, but they were still delivering a product during that time, which is a very rare point. There are other times where you see a false signal and you say, okay, this must be the moment in time, right? You could say that's the rise of crypto in 2017 with all these altcoins. Everyone was like, okay, this is it. But then if you really just stopped and litigated and said, okay, well, what can we actually do? It still takes a really long time to clear a transaction. I remember sending Bitcoin and took 30 something minutes. And I was like, this can't be the future at that time. And granted, you could pay more to push it

Starting point is 00:09:39 forward, but that doesn't necessarily make sense to me. And there are all these other alt-coin use cases as well. Taking a step back, the large tech companies have become so acquisitive and so heavily focused on R&D and so heavily focused on continual expansion and new categories and understanding the existential threats that they are faced by new entrants, that they're very good at creating these moments themselves. The examples I point to would be Oculus related to Facebook and Cruz related to GM, where Oculus gets taken out for north of billion dollars by Facebook, Zuckerberg later in his memo that he wrote about unity, it's very clear he has a view of how this plays out and he has a view of multi-decade. Everyone at the time, though, doesn't have that context.

Starting point is 00:10:16 And they do the demo and it's kind of mind-blowing. And they think this is a really interesting, really compelling new technology is going to change how computing works forever. I think they were too obsessed with the novelty of it and the idea that there was this massive outcome that they didn't really actually litigate, okay, what are like the processing and compute requirements? What are the cost requirements to have this be large scale? What is software engineering that goes into this to make the experiences good? If you were in VR early on, Hallmark was how many times you got sick each time you tried a new demo. That happened a lot, and it's because there's a hard engineering problem.

Starting point is 00:10:47 And the venture community, and I think the broader tech community, became really obsessive with VR for a few years, and then realized what Zuck had kind of been saying all along, which is this is a multi-decade bet. This is not, I'm taking a company out and we're scaling it right now. I would say that that specifically is something that is very unique to this set of incumbents, one, and also speaks a lot to how you have to really understand what some of the existential threats to the larger players are to understand what is actually happening in those markets. I'd say Cruz and GM would be the other one where that acquisition, again, north of billion dollars kind of out of nowhere, was also something that a lot of people thought about and said,

Starting point is 00:11:20 oh, autonomous vehicles must be ready. We have Waymo and what the 510 systems team had been doing there, and we got Cruz, they're going to solve this. And what you learn as you talk to people in that industry is, one, the amount of diligence that gets done on some of those types of deals is actually quite low because there's so much FOMO being driven by other players. And again, think about existential threats. You look at OEMs, you have to have a core belief when it comes to autonomy that there's going to be some amount of time between when the first person solves autonomy and when everyone else does or when it

Starting point is 00:11:46 commoditizes, if at all. How long you believe that amount of time is is how existential that is for literally the entire industry. The counterpoint of everyone who talks about Elon and Tesla being valued more than the entire automotive industry, you could say is, well, if he solves autonomy, me, then that's a pretty long existential threat that you can destroy a lot of enterprise value and have that value accrue to yourself very quickly. In that case, again, people kind of overestimated and overbought into this narrative that these technologies were ready without actually thinking for principles, okay, if we take this to a different city or a different path, or if we need to throw a ton of compute at it or a hundred, twenty million dollar a year engineer in some cases

Starting point is 00:12:24 at Google, are we actually building something that is scalable? And I think those examples show no. The trying to define these things in terms of what they unlock is another interesting way to approach this. And maybe this is an excuse to get into what you call computational creativity because I'm looking at that cool chart from one of your posts on inflection points where it shows the price history of the NASDAQ, the tech index. And it charts major inflection points along the way, such as internet penetration in the early 2000s or the launch of 4 or 5G, the launch of Bitcoin, AWS. Like these are all things CRISPR is another one that I want to talk to you about today and the secret. of the genome and how much that costs. You mentioned space. All of these things are sort of platform launches that enable creativity on the other side of it that didn't exist before. So maybe begin by talking to us a bit about this idea of creativity being a key component of why these inflection points matter and what that means today because I think we're faced with certainly as I watch an explosion, potential explosion of creativity as some of the tools that were only available to very expensive companies now might be democratized,

Starting point is 00:13:28 access to just about everybody. One of the really interesting examples is even something like Planet Labs, which is a nanosatellite company that images space multiple times a day. One of their most creative things was being able to look at all of the R&D that was going into the mobile phone wars and say, okay, what are the ways in which this technology is applicable to doing something else? And this is called kind of like a domain shift inflection points. What they basically did is they figured out they could build significantly more cost-effective

Starting point is 00:13:54 satellites thanks to the R&D that companies like Apple, Samsung, HTC, Nokia, etc. I'd been doing for years by throwing a bunch of these cell phone sensors onto a satellite and figuring out also that the ability to launch these things into lower orbit was going to continually fall down and cost. That's a really creative idea. You have to have, again, a different way of thinking of, okay, there's something that is being unlocked here and there's a moment in time that we need to understand what are the tertiary or secondary effects of what all these other people are doing in mobile phones to try and make consumers happy and how that then can relate to entirely different industries. I think with computational creativity,

Starting point is 00:14:27 broadly is something we think about these intersections of these industries. And luckily, machine learning is somewhat of a platform technology, we could call it, or more horizontal-focused technology, despite it being viewed often as a vertical. The path to getting there for us, and for me, really, was something that was really interesting where early on had started to hear a lot about how a lot of these teams post-crues were starting to spend more time using deep learning to understand perception. How do you see the world? How do you basically figure out, what is the car seeing?

Starting point is 00:14:53 One of the things I started to do is dive deeper and deeper into just various deep learning papers as well as the bleeding edge of that technology. And as you learn and spend more and more time, you start to see, okay, there's all these actually adjacent types of machine learning. And one of them was this guy, Ian Goodfellow, he was a researcher. He's now at Apple. Previously, he was at Google and Open AI, who wrote this paper called generative adversarial networks. What again is, is it's a dueling machine learning model where you have two different size, which in the most simple sense, one tries to fool the model into thinking something is real, the other tries to figure out what's real or fake. And they go back and forth in dual until they reach parity. What they're usually early on were used for was

Starting point is 00:15:29 generating all sorts of images. The most famous example now is these faces. Is this person real or not? It's improved immensely over the past few years just from resolution perspective, from all these different use cases, et cetera. One of the interesting things, though, is that in the University of Tokyo, a lot of researchers were doing it to do anime face generation. And they were kind of saying, we have this data set of all these different types of anime characters. They have their own facial properties that are actually different than humans, but we think this is kind of a fun, interesting research project, and there's a lot of different industries that could emerge from that. In the GAN rabbit hole, you just started seeing more and more people generating like these animation

Starting point is 00:16:07 centric tools or animation centric use cases. It kind of got me thinking a lot about if machine learning is ultimately used as a tool and if there is a tool that can create lots of scalability and also enhance creativity in some way, that's really, really compelling. So what I did was spend way too much time doing a deep dive into understanding the history of animation and understanding what are the bottlenecks of these different studios have. And we helped start a company in the space as kind of a machine learning first animation studio. And the company has since trades more money and is still very early, but has been a really interesting example of just seeing that end-to-end process of going from a Tokyo research paper to a fully formed venture-backed company and in the process,

Starting point is 00:16:45 really starting to understand the historical views of how technology has been wrapped into a single industry that is highly, highly creative. And what I learned there was that the film industry broadly, all the technical innovations have basically come from this idea of how do you build something that is servicing clients and how do you spin out that technology to create an independent technology company or fully vertically integrated approaches like a Pixar. They have Render Man. They also have some internal tooling. Pixar films, you can tell it look different. The things they do on the technology side on the research side is just leaps and bound above what other people do. And so that was kind of a close pocket into the animation thing.

Starting point is 00:17:19 what doing that then saw was, okay, there's a lot of other use cases that all sorts of creators are trying to solve for. And they can be fashion people, they can be music people, they can be visual artists, or they can be designers. And it's this idea of like, if we have this continual forward-looking idea of these individual creators, how do we give them scale, how do we drive the cost down, how do we build better tooling? A lot of the thinking that we had was that machine learning is going to continue to influence the creative process across these different types of arts essentially. Adobe is doing a lot of really interesting work, but it's likely that you're going to need to have some new primitives, new ideas of what UIUX look like and even new innovations on

Starting point is 00:17:59 like, how much do you build internally versus let the community decide? Because the thing that machine learning has proven is very good at is researchers continuing to break the state of the art every two to three months. In some cases, it doesn't make sense to try and build the state of the art yourself internally. Maybe we could just zoom in on animation as a special topic because everyone will be familiar with it. Everyone's seen a Pixar movie, everyone seen a Disney movie. I think fundamentally the technologies behind the evolution of animation are a great way of telling the story of technology itself. So maybe walk us through that history and the most interesting points from your perspective through to today. The first form that we saw of animation was this idea of this super laborious process.

Starting point is 00:18:38 You have to draw every single frame, you stitch it together, and it was incredibly expensive and no one had really figured out. The first big technological breakthrough there was in what's called cell animation, which is like, okay, we have this background. How do we now overlay a single moving image that we don't have to continually redraw and can much more scaleably kind of create multiple frames for longer periods of time? And that really was the big thing that broke through into creating Snow White among a bunch of other Disney-centric films. And again, if you look at what was happening there, a lot of these things began to happen within Disney. You had the multi-plane camera, which allowed depth of field in some ways and not having this very flat feel. We then

Starting point is 00:19:16 started to see skipping forward is moving this process into digital workflows. And that, again, was an internal project that Disney had built for computer animation. It drastically increased the scale, the pace, and also the ability to collaborate on different types of work. And that took over 60 years from beginning to end of that even happening. And I think where we are today is, again, this shift of now figuring out how do you make it so that there isn't a team that has to do 10, 20, 30 people that has to work on a given piece of content in order to, you know, to get something that feels very premium and feels very complete. And I think that's where things like Unreal Engine and Blender

Starting point is 00:19:51 and other pieces of software that have really figured out all of the processes that a single artist needs to push their view of creativity into their workflow, I think that is the future that we are now living in. And that's with things like real-time rendering. If you look at animation broadly, you see this arc of the scope of which people wanted to create beautiful art, to then the scope of people

Starting point is 00:20:12 wanting to scale that art, to now the scope of people wanting to do beautiful things that can scale. and giving them with fewer and fewer people. The key point of the posts that I wrote on animation was that animation is eating the world as that was the tongue-in-cheek title. And I think we've seen just like an insatiable demand for this type of content, especially as creative people, I figure out how to break it out of a kid's medium. It's been something that is now for adults more and more.

Starting point is 00:20:34 I think that expands the TAM materially. And all of the different buyers of this Netflix, Amazon, Hulu, etc. If you look at their slate or just pouring money into animation because of the retention dynamics within families because of, again, the ability to take that IP and spin it out into other adjacencies that are also digital. You can think of gaming as an example. And specifically with this year, the ability to create animation during COVID, you don't need to be onset around a bunch of humans, has proven a massive inflection point, if you'd say so, within that industry. So the lessons really are compelling as how creatives think about it. And I will say the last thing, though,

Starting point is 00:21:08 is one of the lessons of building a technology-centric animation studio early on was how do you just recruit the right talent that actually wants to do this. You're doing something that is augmenting both their workflow, but also their idea of what is perfection, what is the pursuit of greatness in their craft. And not a ton of people are super thrilled with you skipping their intricate steps of creation using technology. Some of the things we would see early on is we built a neural network that could do automated inking of art, basically taking a paper sketch and turning in into something that had harder lines. It was easy to then be colored and actually used in pure play animation. And a lot of the artists would say, you know, if this isn't 100% exactly how I would do it,

Starting point is 00:21:47 I'm not going to use this, despite it cutting off three to five hours of their time, perhaps, because it is such a romantic craft. So I do think one of the things that you do have to think about when you're working in these spaces that have a preexisting human component and you're trying to bring technology into them is how do you make sure that you understand all the stakeholders and like their emotions. And there's this saying you can't bring facts to a feelings fight. So I think that is the big lesson that I learned there at least. The title, obviously an homage to the software eating the world concept, which I don't think anyone would argue with now, probably people would say, wait a minute, that seems like an unfair analogy, like no way animation is as big as software. So I'd love to challenge that idea and describe what would have to happen for it to be something on the scale of how much software has impacted the world.

Starting point is 00:22:33 What sorts of things might animation, quote unquote, eat in the coming decade that might surprise. people that are thinking about this for the first time? The most interesting thing about animation is, like, you can build a relationship with an animated character and a significantly faster time frame. That allows a lot of really interesting things because you're suspending both judgment on work humans. We judge each other. And when we see each other and we interact with each other, we have a baseline of what

Starting point is 00:22:58 we like and what you don't like. With animated characters, often you don't have that same kind of baggage. And so even in World War II, Disney and Mickey Mouse was used as helping to sell bond stamps at the time. That's a very influential type of piece of IP. And so I think if you think about just the idea that IP is going to continually be incredibly valuable, this idea of this connections you can build with these different animated properties. I think that really you kind of can go two ways. You can say one, the technology is going to continually to increase where we can replace humans across everything that humans do from a artistic sense, acting, influencing, on the machine

Starting point is 00:23:35 learning side, voice acting, anything really. On the more pure play, animation side, you could say both the tool sets between gaming and animation have pretty much converged or beginning to converge much more materially in most people's workflows. You could make the argument that any type of entertainment, influence, or visual art will be ultimately done through these pipes. That speaks to a lot of why some of the value that is occurring within companies like Epic Games that own Unreal is really material. So I think it's, I would say, I'm more long-term bullish on the enterprise value of software combined than animation combined, but I do think people don't necessarily understand some of the dynamics that will relate to

Starting point is 00:24:13 both an insatiable desire just on the content side of people to buy these properties, as well as on all the adjacencies that exist within them. You mentioned this sort of what I'll call flat examples of the fake faces that have been generated, which are really stunning. It's creepy to go through like a carousal of them or something. It just looks so incredibly real and they don't exist. I love the Twitter account like, this person does not exist. That's a great one to follow if people haven't and want to see these. But more interesting is what I'll call the flat, but the alive version of these things. I'd love you to give an example to really hit the point home of a digital celebrity. You can pick whoever you want, maybe tell the story of how it was created, why it's interesting,

Starting point is 00:24:50 and maybe the scale of the impact that one of these examples has. Because my guess is people want to appreciate, it sounds silly to say, like, we're going to replace all the regular influencers and celebrities and actors and voices and stuff with digital variants. But I think there's some interesting existing evidence that this might be possible? There's two approaches and two examples. The first, the most famous one, is Hatsuni Miku, which is almost UGC-centric digital celebrity. And so she's a performer that has kind of an anime look.

Starting point is 00:25:20 It's very massive in Asia and is starting to actually become quite big here. The interesting component of how Hatsuni Miku was created was it was based on this idea of something called Vocaloid software, where they wanted to try and figure out how do you create music or other types of songs and sounds for this character and get people to use the software more and more. And what the approach that the creator took for her was, okay, we're going to basically make it so that in this celebrity, the audience sees a part of themselves in it.

Starting point is 00:25:47 It's almost taking the concept of crowdfunding where you invest in something because you feel like you're part of the story on steroids, like to 100x, where people will go on concert and they can see theoretically the clothes they designed for Tsudimuku, the songs that they made, the dances that they made. And it'll be a holographic performance. and she's sold over a billion dollars worth of merchandise and is massive. It's really this kind of like core like network effect of the more people that you get to care

Starting point is 00:26:11 about the celebrity, the more that will create for them, the more dynamic they will be. And also the scale that they can have theoretically, pretty incredible because nothing to stop Hotsunimiku from 7 p.m. performing in Tokyo and 8 p.m. performing in L.A. and 9 p.m. performing in Boston or all those places at once. And also the economics of that business are materially better than how I always say like humans are so beautifully volatile. And like, I think that is something that people start to realize. And I guess that leads to the second other core example that both a company that we helped start and we had an influencer called Astro. And then on a larger company, another company called Brad, has an influencer

Starting point is 00:26:46 named Michaela. And I think Michaela is a really interesting example where it's almost like the vertically integrated approach, we'll call it, where Trevor McFedries and Sarah both had a very core view of, again, working within the artist's management perspective. And humans are really volatile. And I wish we could cut out some of this volatility that makes it difficult to scale them. And it's difficult to scale them in two ways. One, if you spend any time in L.A. broadly, there's a lot of people who want to continue crossover into different industries, influencers who want to become YouTubers or the YouTubers want to become actors or different people trying to cross over. And what you really learn to realize and what people in talent management business will say is like, these people just don't have

Starting point is 00:27:22 all of those talents. They have one. And so what Michaela has done is it's a digital celebrity, in lack of a better word, synthetic media, 3D rendered. The process, at least last I knew about it, was they actually take photos of humans, and then they wrap them with a digital skin that makes her look somewhat fake, also anonymizes whoever the humans that are taking pictures of R. She's an influencer, and she can do anything. Again, she can be in New York, she can be in L.A. She has music.

Starting point is 00:27:48 She can be a runway model. She can change over time. They don't age as well. You can age with your demographic. You can continue to capture more demographics. And also what we were originally doing on some of the studio side stuff is you can create this flywheel where if you can build an audience with one given influencer, you can then spin out adjacent influencers for different niches and also different types of skill sets pretty quickly.

Starting point is 00:28:10 It all is being controlled by like your internal creative powerhouse. And what it enables you to do is build a portfolio of IP and also tell a story across that IP if you want. Brud has done that with the Michaela property. That idea of you're being able to fragment talent, being able to, age up and down, being able to capture all sorts of different types of markets. And then you either even go a layer further, which is you could use things like machine learning to scale the creative process for these types of IP means you're going to have just like

Starting point is 00:28:38 materially better economics. And also theoretically, if you do it right, materially better ability to connect with a larger audience. I would say the difficulty in that, though, is that you do have the uncanny valley effect where, you know, Michaela is, she looks like a human. And so it doesn't look exactly like a human though. And that kind of erodes a lot of trust. And you also have some social dynamics where early on that company kind of lied about whether

Starting point is 00:29:01 or not she was real. And when totally was buying into the story a lot and especially depressed, they've done a few different things publicly that has annoyed some people. And I think you do have a lot of interesting social dynamics as this becomes more widely accepted or known that you have to be very mindful of. As we know more than ever in 2020, like one misstep can really destroy your IP or your career. All of those things are really compelling, but do take a layer of thought in the creative process. Say a bit about what you think the most talented digital celebrity creators will share in

Starting point is 00:29:32 common based on what you've won so far. I think it's just like a understanding of what markets are drastically underserved by this like kind of aesthetic inflation we've had on all sorts of social networks. I think that that is one of the things that matters a lot, which is despite the internet being a place where anyone can get online at any time and create content, there's still both aesthetic inflation that happens towards a lowest common denominator and just entire parts of audiences that are just left out. One of the things that we noticed internally at Shadows, the company was there wasn't a lot of content for women between the ages of like 13 to 17, a pretty like untapped area because you had these 18 plus and you had these 12 and under pieces of IP, and there wasn't

Starting point is 00:30:17 anyone speaking directly to those people as much. It wasn't as high volume. And I think we've seen similar dynamics around different types of ethnic backgrounds as well. And so I think like that understanding is really interesting. And I would say to Brad's credit for Michaela, she's kind of a racially ambiguous character. And that can work in both their advantage and their disadvantage, right? You could say that they're playing it safe, but you could also say, well, they're not just trying to be someone that looks like a Kardashian or someone that looks like this prototypical view of what Hollywood thinks. Again, I think there's very fair criticisms on both sides. But I do think that core idea to then figure out, okay, how do I speak to this audience and how do I

Starting point is 00:30:53 realize that this audience is starved for content that is high velocity and big is a really valuable creative process that people should go through more when they're thinking about creating IP. I love how the internet has been in many ways all about the service of niches. It enables serving small audiences or market sizes. And that may not make sense for real humans, but if you can just spin up a celebrity, you can serve a niche more deeply. I love that concept. I'm sure we'll see it in all different ways. How do you think about consistency? At a point in time, over five years, Michaela's personality consistency is probably reliable. But since there's people behind her, how do you think about

Starting point is 00:31:33 the longevity of these potential celebrities or do you think it even matters? Because some of the best IP is IP that is valuable forever. And I guess Disney and Pixar are great examples of that, but there's others too. But real celebrities, they have to be consistent because they're a person. How do you think about the persistence of these things through time and how that will shake out? I think if you look at any of the like historical animation studios or I think a lot of IP houses in general, you do have this creative genius concept. And so I do think you bring in keyman risk a lot. I think the thing Pixar has at least been good at continually talking about is their brain trust and expanding that brain trust over time and trying to more recently bring more diverse thought to it. And obviously John Lasseter, he was kind of the core of that early on has since left the company for obvious reasons. But I think that idea is one of the core risks. But I think it's the risk and yeah, as you mentioned, like all creative pursuits is if you can build something that is this kind of unstoppable force where you go from, okay, we have established what the brand guidelines are, what the books are, what this person's profile is, which is ultimately what you are doing when you're creating a piece of IP is you're

Starting point is 00:32:36 creating someone that has core principles, core beliefs. One of things we think about is like the three goals, the goals that they have individually, the goals they have for the world and the goals they have for their future and like their loved ones. You kind of have these North Stars that you create when you create a piece of IP. That said, as with anything, like when people start to fall out of favor, if you start to slow growth or whatever, all hell can break loose and people can start breaking those rules pretty quickly, where in humans, like you said, they're humans, they are who they are. If people want to learn more, what would be one company that you would have them go investigate, read about, learn about? Honestly, maybe riot. What they're doing with League of Legends

Starting point is 00:33:08 and the idea of expanding IP outside of that, I think is just different. It's really interesting. And the other, I guess, would still be Epic Games and Unreal just because of that gut, I think is a very core piece of infrastructure. You give me my perfect transition. So, Lil Michaela and these other examples are broadcast examples, celebrities with a one-way relationship. Obviously, they don't personally know anybody. Gaming is sort of this other interesting application of rendering and animation and visual arts and visual technology where it's more one-to-one, right? A lot of interaction in games these days. How much do you think about gaming and the future of gaming as something both interesting for the world and something

Starting point is 00:33:48 that's investable at the stage that you invest? There's two views. The view we've taken is we think there's interesting infrastructure level of things. There's another view, which is the studio side, and there's larger funds who are deploying tons of money into similarly how people bet on pieces of IP, right? You're just betting on studios over and over again. As a small fund, it's not something that we're interested in. Where we've come across and where we've looked at game engine architecture and infrastructure broadly is there are two like massively entrenched players and unity and unreal. And both of them have pros and cons on the creation process. And they also

Starting point is 00:34:18 have really unique modes unreal specifically with the idea that Fortnite is such a cash cow that can then fund a lot of the other parts of the business over time. And they can also basically bring the cost of distribution down at the Epic Game Store to whatever they want to basically just continue to eat market share from other players. I think it's like a really interesting flywheel to have. But I do think that both of these companies still are like very stretched in terms of what they're trying to accomplish today without being on the focus too too heavily on what the future of gaming looks like. And so where we've been often looking, where we've been thinking more about is this fragmentation of these engines where right now they're like these very bundled things.

Starting point is 00:34:51 And basically you have this bundled rendering engine and a few other adjacent use cases that help people create games within Unreal or Unity. But then you have all of this custom engineering work that goes in over and over again for every single game studio. We've looked at a bunch of different companies that are thinking about, okay, if we could build the ultimate store essentially back end, the ultimate in-game economy infrastructure, all these different things, matchmaking, et cetera. does it start to look more like software development where you're piecing together elite parts of the stack in creating the best experience while still having the creative part of the game studio being what is at front at the front of the game.

Starting point is 00:35:28 So that's really where we've seen opportunities. And I think that there are other investors who like the game studio side and think this is a macro wave. And so you should continue to invest in games that can become platforms. And Roblox, we're seeing just the demand in public markets and how these things have risen both Roblox Unity and Epic on the private side even in the past six months is almost double in valuation. I think all of those things is still very good from an investment lens, but from us of we're thinking about futures we believe in in six to 10 years from now, not three to five. That's what we think about within gaming. I'd love to pivot hard away from bits towards Adams and talk about robotics. You mentioned earlier

Starting point is 00:36:04 your investigation of AVs. And I think robotics is an area that my guess is people don't think about all that much. I think there's been a lot of false starts in that space. And, progress maybe hasn't been as explosive as other software or internet-related stuff. So it's not on people's minds as much. What is exciting and interesting in 2020 in the world of robotics? So I think you're right. I think robotics companies generally, what it looked like was maybe in 2016, maybe even before then, what we saw was everyone saying, oh, robots are here, automation's coming, it's going to work. We're going to be able to do this across a bunch of different industries. these dull, dirty, dangerous jobs are going to be no longer great.

Starting point is 00:36:44 And again, back to the example of these components being subsidized by mobile phone wars, that was a key driver for a lot of these robotics companies, finally being able to build robots that are $100,000, not a million dollars, and different types of autonomy coming on that allowed them to truly operate in more dynamic environments and understand what they're doing. I think what we saw was a bunch of companies go out and say, we're going to build robots that do X. Robot as a service is the model essentially where you build a robot,

Starting point is 00:37:08 you lease it to someone, and they say, we'll pay you $2,000 a month to have this awesome Apple picking robot or this awesome robot that sorts packages or whatever. I think the reality, though, is kind of back to that point of false inflection points and understanding when something is production level versus research level engineering is a lot of these customers weren't really willing to shift and make a meaningful bet on robotics within their workflows. And that could be twofold. One, it could be massive KAPX expenditure on changing their factory or on changing their store or something like that. the other could be the uptime or the percentage automation that actually happens.

Starting point is 00:37:45 There's a saying within robotics, robots are great, but five-finger robots are often the cheapest option, which is humans. The thing that we saw was a bunch of companies go out, raise money with this promise, built pretty interesting technology that was maybe 90% reliable. Companies would pilot, and then when it came time to renew and expand these pilots, a lot of them, the companies would kind of say, yeah, I just don't really know if I want to spend seven figures a year and really start to meaningfully integrate this in my process and be reliant on all. automation, as you call it. I think that we saw a bunch of companies continue to just tread water and die because of that dynamic, if you have to convince these often entrenched players to adopt

Starting point is 00:38:18 this bleeding edge technology, how we then think about that space is we like doing the hard things. We'd prefer to have a full-stack robotics company because we are going to drive efficiency. We're going to have dominant economics relative to our competitors. And more importantly, back to the SpaceX example, we can perform R&D over time to get from 60 to 80 to 100 percent or 99.99% automation, but we're going to capture value as we continue to increase that. We invested in a company called Ono Food Co, which is in its most basic sense early on, was a robotic food truck. They built a highly fault tolerant, highly cost-effective robot, could shove it in a sprinter van, they could bounce around, it could be in a van,

Starting point is 00:38:56 and still to deliver automated food consistently over time. And they were both the brand and the robotics company. And there was a very unique set of founders, again, at this intersection of two creative and deeply technical areas. I think that core idea is, what we think is more and more compelling because you can short-circuit this candidly just annoyance of dealing with these go-to-markets. And I'd say secondarily, the other thing, too, is infrastructure things that need to happen to at least allow people to feel more comfortable to adopt those things. One is on the safety side.

Starting point is 00:39:24 How do we make sure robots can actually interact with humans or be around humans? How to make sure of it anytime human trips a laser, we don't have to stop everything. And that can cost companies hundreds of thousands of dollars per minute. We're invested in a company called Fort Robotics, which basically is safety-rated infrastructure for robots both on the telepresence side as well as managing different robots across a work site. And then we're also investors in a stealth company that tries to do the secondary thing, which is how do companies that really want automation or how to automated companies that are building automation-centric tools get to that 100% without spending $20, 30 million

Starting point is 00:39:55 over the lifespan of a company just to get to market. In that case, we think that human in the loop is a pretty interesting and compelling way in which you can do that to continue to drive efficiencies for your customers or expand into other adjacencies. And so I think the robotics industry right now has been at this tough moment where VCs have this belief, because we all have science fiction, we're nervous about all that stuff, but have been disappointed repeatedly and some of them have lost a lot of money. I'd say now we at least are starting to see business models that make sense or maybe are more ambitious, but also secondarily, I would say with COVID, we have seen a pretty massive reinterest in automation broadly

Starting point is 00:40:31 because we've seen just how fragile our companies are to their labor forces. And I think both from a the fragility component that we saw in 2020, as well as from a like PR component. Companies now are going to be able to make that shift and say, hey, we're going to introduce automation that might replace some humans. We're going to reskill those human workers without taking a ton of flack for it, either because they've already laid off a bunch of people and they've dealt with that blow or because people just realize this isn't scalable. It's necessarily in some of these more high variant situations have a ton of humans.

Starting point is 00:41:00 It's kind of an interesting parallel to the scalability of a celebrity, right? It's the same theme, right? Which is like, how do we rely less on humans? That begs all sorts of questions that we won't go into around UBI and employment and inequality that I think are really important, but probably beyond our pay grade. I'd love to talk about staying in the world of Adams space. You mentioned earlier Planet Labs, which is leveraging other technologies, blanketing, lower that orbit with the ability to map the Earth and obviously everyone knows SpaceX. What's the click or two deeper than those high level? It's become cheaper to launch stuff into space. That seems good, but why? Why is lowering the cost of launch and increasing the amount of technology off our surface of the earth? What might that lead to that matters to people over the next five, 10, 15 years? Jimmy Crawford, he was the CEO and founder of a company called Orbital Insight. He has this concept called the Macroscope, where we seem to clearly understand the value

Starting point is 00:41:57 of having a microscope and being able to zoom in more and more and more. But we don't really seem to appreciate as a society the value of being able to zoom out more and more and more and see what is happening across a broader scale and being able to do it in a repeatable sense. I think a lot of things related just like how do we observe the Earth. And the honest answer is a lot of these have been humanitarian and government related thus far. There's another type of technology called synthetic aperture radar, which basically solves the question of if satellites like Planet Labs have cameras, they can't see through clouds. Often a large portion of the world is covered by clouds at a given time.

Starting point is 00:42:29 Synthetic aperture radar can see through clouds, can see easily at night. and so you can actually see a lot more in this idea of having a revisit rate of the Earth. There's also the stuff on understanding weather patterns better, which is, for a bunch of reasons, is going to become more important to the world over time, as we've seen over the past few years. I'd say the thing that is most interesting to me as someone who thinks launches a race to the bottom, who thinks there's some really interesting comms infrastructure that SpaceX is clearly working on. And talked to this guy who's a space industry veteran. And he once told me about something he calls the Las Vegas principle,

Starting point is 00:43:00 which is once we get privatized space stations, we're going to have this concept of what happens in space stays in space. And it's this idea of that is the unlocking factor of truly understanding what is like the in-space economy. And so you see it on the manufacturing side. There's a bunch of things, whether it's pharmaceuticals, whether it's fiber optic cables, whether it's a bunch of different things you can test to understand that building them in a low gravity environment is materially more cost effective than dealing with gravity on Earth.

Starting point is 00:43:24 And then there's the farther out things of people who care about, things like asteroid mining or space civilizations. and all those types of dynamics. But I think the core thing with space has always been, how do you continue to finance it over time? And I think that idea of like sequencing has been what Elon is credible at as an operator across both of his companies. And I think that that will be the continual question is how do you figure out how do you get in this example of this space veteran told me with a privatized space station, you might need to subsidize it by six seats, sending four of them up with a couple millionaires who wanted to pay to go to space and two

Starting point is 00:43:56 scientists who will be able to be the subsidized version of that trip, getting people on the space station for a week, having them pay more money and getting those people to be able to do things in space over time. And I also think back to robotics, robotics will play a pretty large role in space as well until we reach the point where people want to spend material time up there, being able to remotely control or automate a lot of the processes that we have up there, I think will be pretty important. So from an investment lens, a lot of how I've been thinking about it is waiting for that moment and that inflection point to see. I think SpaceX has captured a lot of the value today, as well as some of these other companies we mentioned, of immediate

Starting point is 00:44:28 opportunities of Earth and space. I love it. I love the macroscope. I mean, that's just such an obvious idea once you say it out loud that it's going to be valuable to have a persistent, fine-grained view of what's going on on the Earth. It just seems like really valuable. I never thought about it that way, especially the cloud thing. So neat. The next area to explore is timely during 2020. Everyone's focused in thinking about health. We talked about machine learning a little bit earlier as it relates to some animation and creative tools. We haven't talked about it as it relates to health, biology, et cetera. One of the inflection points you point to on your interesting chart is the $1,000 genome sequencing, which happened several years ago. And there's sort of a race to the bottom on that,

Starting point is 00:45:07 I'm sure, too, that in who knows how long it'll be a dollar or something ridiculously cheap, the probability of that unlocking interesting technologies and research is high. What have you learned about computational biology? Is this something extremely early, meaning we have not really realized any of the benefits yet of this technology in terms of treatments or medicines or whatever it might be. What is your research taught you here? I think that is right. I think that if I had to make like a single bet on technology broadly is this idea of how little we understood about living organisms up until a decade ago is just mind-blowing and how quickly we are understanding them now. The bet I would make is that

Starting point is 00:45:47 is probably one of the biggest, is not the biggest opportunity over the next decade or two. It'll just be massive value created and I think it'll drastically improve quality of life. rallying cry I've had is the point of science to outpace the problems that we create, essentially. We creating is obviously a jaded view on it, but it is this continual thing that I think is very real, and I think we saw it most this year. And I'm not a scientist. So there's people who know this far better than I. How do we continually experiment and figure out with a bunch of experiments what the right path to go down is when we're trying to solve a given problem in healthcare biology? I think once you come to that realization, it kind of scares you a little bit because

Starting point is 00:46:21 you realize all those times you go to the doctor and they're asking all these questions. Like, they don't know the answer. They're just, again, probing towards this experimental questions of getting to a hopeful answer. And I think what we've been able to do recently has been able to automate a lot of that experimenting and scale a lot of that experimentation with things like high throughput screening, which are kind of enabled by things like lab automation, again, robotics and machine learning. So it allows us to run significantly more experiments over time. The next kind of frontier is actually truly understanding these biological components. So again, most recently, DeepMind solved a lot of the protein structure, understanding protein structures. And so using machine learning and the way they did

Starting point is 00:46:58 it was basically they had three different skunk works teams working on different approaches and eventually realized that what they needed to do was not use any traditional approach of machine learning, but actually one that took into account how scientists would think about this problem. So again, I think it's a good idea and a good framing of why a lot of these hard problems in these industries aren't just going to be a bunch of machine learning people sitting in a room, but are going to be an intersection of these core competencies that really matter. Yes, broadly, a lot of things are going to change. I think as an investor, how I think a lot about it is that idea of what are the areas that we can see breadcrumbs through our investing process in and what are the

Starting point is 00:47:33 areas in which we feel uniquely suited to invest in. And so sometimes it's, you're investors in a nanomedicine, robot company that you inject it minimally invasively point of care. You can steer it throughout the body with magnets and you can deploy drugs very, very precisely. In that case, we really understood the robotic component. There's a specific design. We did a lot of diligence there, and we partner with another firm that really understands like the full FTA process. For us, it's making sure that we have the depth to understand these areas that are very complex. And I think not trying to say what I think some VCs are doing now, which is these companies are essentially tech companies. And so thank you biotech investors for the past 30 plus years of innovation and funding and spinning these things out and understanding this business incredibly well.

Starting point is 00:48:13 We're going to take it from here. And we're going to take it from here and pay twice the valuation because we're tech investors. We have different ownership dynamics and different views on upside. I think that is a dangerous game to play. What else in terms of you think of 2020 as the genie out of the bottle year for a lot of different types of technology and behavior, like the way we work, the way we travel, the way we do lots of things, the way we conduct digital health, telehealth, all these sorts of things. What other things have radically changed this year, meaning 2020 and how it impacts your investing views, the opportunities that you see? What are other genie out of the bottle observations that you have this year? on the regulatory side, just pulling forward the futures of telemedicine, and that's obviously

Starting point is 00:48:54 incredible for a bunch of reasons, which we all kind of know about, but we think is very valuable and has drastically changed trajectories of a bunch of different companies. I'd say second is also a macro view, and I don't actually haven't parsed exactly how this manifests itself, but this idea that if you have an efficient and the incentives aligned enough around the capital component of a given scientific problem, turns out you can solve that problem quite quickly, and you can do it at a scale that we didn't think was possible before. You think about it in March, and everyone's saying, well, the fastest vaccine ever has been five years, and it's just really hard to get these things to market. And this doomsday

Starting point is 00:49:30 scenario where it's going to be at least until 2022, you better strap in, was quickly solved and it was like, no, we're just going to throw a ton of money at this problem. And the entire world is pretty closely aligned to figuring that out. And turns out we figured out pretty quickly. And so I think that is something that should be more top of mind where we now have this approach of how do we make sure that we're continuing to think about what are the problems that are maybe in the back of our mind today that we should be trying to knock off from a scientific sense over time. And I think that there are more and more people talking about that. And they're talking about it both because now they have a prior to base that on. We've done this vaccine thing. Fingers crossed, we're really putting the card for the horse here. But hopefully, secondarily, is we have enough. people that like, I think now this will be more of a calling for them because they've realized just how much our world can quickly come to a halt. And the third thing is digital living. And I think that it's really been a function of two things. I think one, it's been a function of TAM expansion for things like gaming and collaborative software and things we're doing here on Zoom,

Starting point is 00:50:30 where the age range of people that are now doing these types of things because they've been forced to move their entire workflow to digital whether they like it or not presents a pretty compelling opportunity from a TAM expansion perspective. I'd say the secondary thing, which kind of goes back to the inflection points. Mark Andreessen has said software is eating the world, but everyone just kind of looked at it and said, okay, this is a bubble. You guys are just plowing money into more and more software companies.

Starting point is 00:50:50 And what we very quickly saw was actually the opportunity that a lot of these people saw that were smarter than me was real. It was a future they believed in, but it was pulled forward in a matter of months instead of a matter of years. And the upside of these businesses, as you shift everything to digital workflows, is just materially more.

Starting point is 00:51:07 And we saw that very quickly become appreciated by public markets. And I think once public markets, appreciated that. What we then saw was a secondary flowback into private markets of March and April was a pretty scary time in venture. And then May everyone was kind of like, oh, wow, it turns out all of these tech companies that we've been talking about that are great are actually going to be way bigger than we anticipated because all of these futures that we believed in were pulled forward, maybe a decade, and maybe they were permanently changed. Maybe this was something that wouldn't have ever happened before. Those things is what we saw kind of this year. And there's a bunch of others in other

Starting point is 00:51:38 categories, but we're pretty narrow in our scope of how we think about things. What technology trends have we not talked about yet that you think are really important or interesting? The biggest thing I've been thinking a lot about over the past year and has been routinely repeated, has been more of a macro thesis. And I think that that macro thesis is something that I tongue in cheek call like, cyberpunk is now. What I say when I say that, and a lot of my friends are kind of like, every time something crazy in the world happens, I say that. And they say, all right, great, shut up. Is this idea of these themes that we've always had in a lot of lot of these scientific and dystopian-type novels are starting to, like, show little interesting

Starting point is 00:52:13 breadcrumbs and whether it's the scale of the largest companies in the world and watching the CEOs of four of the largest tech companies go in front of the government and kind of just say, we're here, we're dealing with this, but we're not really listening. It's like a really compelling, like, first wave. A week ago, we had a cyber attack from another government that has been in our country for months now. It's really compelling. The way in which Trump won the election in 2016 was very interesting, the way in which we're thinking about China as this superpower moving forward. All of these things relate to them like these narrow thesis areas. And so some of them are privacy preserving machine learning and how we think about continually guarding our data while

Starting point is 00:52:50 using all the benefits of machine learning. Some of them are adversarial attacks on machine learning models. If we continue to bring automation into the world, people are going to try and attack those systems in certain ways. And so how do we defend against those? So it's a bunch of different little breadcrumbs. And I'd say generally how I think about investing is either going into a category and saying, okay, what are the types of businesses that should exist and we'll go out and find those partner with founders, let them shatter our thesis as well, highly prescriptive, or saying, what are like the futures that we believe in? Like, what is happening in the world? And what does that then mean for venture investable businesses? And so I think that is one thing that has been on my mind. And I have a

Starting point is 00:53:27 very long piece written about it. But every month, something new happens that changes what that piece would look like. So it's kind of now just turned into this private log. I like the idea that using sci-fi novels is research for what might be going on in the digital world, right? Well, this has been so incredibly interesting. I highly recommend everybody that's found as fun or educational. Go check out your writing because I think I talked to Sam Hinky in a recent episode about this idea of putting breadcrumbs out in the world. And you've done a great job of putting breadcrumbs out for people to get to know you and your ideas. So highly recommend people go avail themselves of that. My traditional closing question is the same for everybody, which just

Starting point is 00:54:03 to ask you what the kindest thing that anyone's ever done for you is. One of the things I've learned is caring about these step function events is really important, but noticing these in the moment really individual type of things that are meaningful is equally as important. So I guess I'll say, you know, after a really hardest year of my life, the thing that I'm continually thinking about more, not to steal your thunder, is what is just the most recent kind thing someone did for me? That is something that we don't think enough about. We think three years later, wow, it was amazing that my boss gave me the chance to do this.

Starting point is 00:54:31 We don't think yesterday one of my friends woke up at 7 a.m. and had a call with me to talk through a bunch of stuff that I wanted to talk through. Over the past couple days, I've had a bunch of different calls with friends early in the morning. And in my life, I've had times where in the moment I've said, hey, I need to chat and you talk and have dropped everything and gone. And those are the kindest things people have done, things have done for me. And I would just say, think about it every day, not just in these reflection times where maybe it's too late to talk to someone or life is passed you by. and so it'll feel weird to reach out. I love that. What a wonderful challenge to the way of thinking about it. A great answer. Unique, too. Seems to be a theme with you. Michael, thanks so much for doing this with me today. I learned a lot, as I knew I would. I appreciate your time. Really appreciate it. If you enjoyed this episode, you can sign up for a new email newsletter sent out each week called Inside the episode. Each week, I condensed that week's episode to my favorite big ideas, quotations, and more.

Starting point is 00:55:24 I've been recommending books to members of this email list for years, and we'll keep doing so in this weekly email. You can sign up at investorfieldguide.com forward slash book club.

Invest Like the Best with Patrick O'Shaughnessy - Michael Dempsey – Investing in Bleeding Edge Technology – [Invest Like the Best, EP.212]

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