Zavain Dar 101:
Zavain on Twitter: https://twitter.com/zavaindar
3:00 - The firm created a name for ourselves as one of the first funds specifically focused on deep tech, or emerging tech. Over the years, that’s really encompassed anything from nanotech to metamaterials, spaceships, crypto, satellites, biotech, AI, blockchain, nextgen manufacturing, autonomous cars. All sorts of weird and wonky ideas that are out there.
You’re not only an allocator of capital, but it does feel like you’re pulling the metaphysical strong forward that connects the future or science fiction to the present.
I focus on complex software systems that may or may not be coupled to the real world.
5:21 - How do we not fund the next Theranos? It’s a great question. I’m lucky to be part of a team that’s not scared of primary literature. All of us taken pride in having the ability to scour and read and understand from a first principles basis, a lot of the technologies and engineering systems we invest in.
I wouldn’t say we’re only bottoms up. A lot of what we talk about internally is ‘If this works, then what?’ If this technology is actually able to get off the ground, are there real, applicable strong market forces that this dictates that this captures value, that it’s great for the entrepreneurs, for investors, and for our investors as well. Candidly, that can be the harder part to asses.
12:16 [On advice / lessons from his first startup] Trust your instincts. Be intellectually disciplined to think through all of your decisions without relying on high level proxies, like what’s on TechCrunch or what else in the ecosystem is getting funded, what’s hot or what’s not. Those things are fads and often times its layered iterative processes of others peoples proxies for what other people are thinking over and over again, which ends up being decoupled from reality. If there’s one thing in my career I’ve looked back on, and wished my former or past self had done more of, it would be that I wish my younger Zavain had listened to his instincts with greater enthusiasm or confidence.
The other is to surround yourself with phenomenally intelligent people.
14:02 - That company was acquired by Twitter, and Given my own disposition against social media, or at least working at a social media company, I obviously left. That was really the catalyst towards my future in venture capital.
16:20 - Todd Davies at Stanford first gave me that quote, that capitalism is a phenomenal tool but not a great ideology, it’s not a dogma. I often think in the Valley and in the US at large, we confuse the two. That the laissez-faire capitalist outcome is the moral or ethical outcome. While it’s true you can point to capitalism and say wow, it’s phenomenal for its ability to drive distributed decentralized innovation across various groups - and I think inarguably is one of the most impressive systems to do exactly that, and we have empirical data for that - it doesn’t equate the end outcomes as necessarily the just outcomes.
17:30 - If you walk around San Francisco, there’s a very clear separation between the Haves and the Haves Not. Generally, the Haves are the folks in Tech and the Have Nots are everyone else. For a region with the ability to create so much value and capture such a large portion of that value, it’s frankly disappointing. I think it’s a failure that we have such a large number of people on the streets. That’s not necessarily something that capitalism points at as a problem to solve.
There’s more capital and more upside in optimizing e-commerce on Instagram. I don’t say that in a pejorative way I just say that that’s actually the case. So we need to be honest with ourselves about what capitalism is actually geared towards. If at all moments in time all firms are geared towards increasing profits or increasing revenue or margins, at what moment in time do we actually solve issues in society for classes that are most vulnerable?
21:52 - The advancement of technology - it’s an awesome tool and an awesome outcome. But we should sit there and really think about how it affects society at large.
29:09 - Some truths are simply out of the realm of complexity that potentially a human brain can actually access. Two examples here:
AlphaGo - We saw a computer Go player start to access strategy that not even the best of the best of the best of the best experts of Go in real life could understand. It might be the case that one day some genius Go player will look back at those games and understand exactly the strategies that AlphaGo was employing. But it also may not be the case. It might just be beyond the level of cognitive ability of humans.
I’m an investor in a company called RecursionPharma. They took pictures of human cells and they track how - based on various genetic changes to the human cell - how those genetic changes manifest morphologically or structurally in the pictures of the cells. Often times, what you get is images of 10,000 cells, all with 5,000 features in each cell, all with highly complex, highly non-linear relationships between the features and the cell. And there’s absolutely no way even the most expertly trained pathologist could look at these 10,000 cells and finds all the correlations. It’s not feasible. If you allow a computer to do that, it can find interesting, highly complex formulas that split apart perfectly the diseased cells from the non-diseased cells. It’s really interesting, and feels like we are in fact coming to something that is scientifically valid and scientifically true even if it’s maybe beyond the capacity of a human to understand. Candidly, I think most of biology fits in that realm.
32:07 - So for the majority of human history, that’s what we’ve had to rely on as true - the the metaphysical, the language, the epistemological. And what we’re starting to see now with advancement in AI, Machine Learning and Data Science is that you can one by one mix all three of those assumptions.
34:28 - [On investing time to learn about these changes in technology] My own suspicion is that technology is only increasing in its power to rapidly drive change and command attention. Such that if you have the time and the resources to invest in learning about it, it’s absolutely worth learning about it. That’s everything from learning about how networks emerge, what network effects are, to really thinking through and trying to understanding how emergence and connectivity of data will affect the types of problems we can solve. And also of course how that too gives rise to all sorts of social, political and anthropological effects.
37:41 - I look back on my training in philosophy and theoretical computer science as the most impactful for the ability to do my job day to day.
45:24 - Mehran Sahami’s inspirational speech on Computer Science
47:40 - [On his work with the Philadelphia 76ers] - The work there was around understanding this new modality of information coming into the league. If you think about the history of most sports, most sports data is recorded in what we refer to as box scores. If you read a newspaper the day after a game, you’ll get these box scores - who the players are, what their numbers are, maybe how many shots they took, how many shots they made, etc.
At this point now, we’re tracking players at the specificity of where every player is on the court at every moment in time. So you end up with a very big, unstructured data set, where at each moment in time - for basketball, you’re getting 11 geo-coordinates. Where are each of the 5 players on each team, and where’s the ball. And the question was - how do we actually manage this?
There are two problems we want to solve:
One is portfolio management. What players are undervalued, who are overvalued, who should we get off our team, who should we draft.
And the other is game ops. You are the Warriors and you’re playing LeBron James and - at this point - the Lakers tomorrow. What’s the best defensive matchup you can have based on how he’s trending over the last 10 games and how your defense has been playing in some prior window in the past.
So the question was - how do we move towards a radical empiricism in sports?