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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.

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Speaker 2: Hello and welcome to another episode of The Odd Laws podcast.

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Speaker 2: I'm Joe Wisenthal.

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Speaker 1: And I'm Tracy Alloway.

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Speaker 2: Tracy, I love being out on the West Coast. I

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Speaker 2: love being in San Francisco. We did a live episode

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Speaker 2: last night. I love coming out here.

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Speaker 1: It's been fun. We also had some really good Chinese

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Speaker 1: food afterwards.

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Speaker 2: We had really good Chinese food.

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Speaker 1: Oh and I woke up to the dulcet tones of

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Speaker 1: the San Francisco parakeets this morning. That was fun.

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Speaker 2: There's wee protests happening. I don't know what, so I've

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Speaker 2: been waking up to the sound of protests. It seems fitting.

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Speaker 2: I think I've mentioned it in a million tweets and

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Speaker 2: another episode. I've taken a few way Moo rides, which

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Speaker 2: is amazing. I love that. But you know, we talked

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Speaker 2: to a lot of I guess traditional normy investors most

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Speaker 2: of the time, and when we're out on the West Coast,

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Speaker 2: we have to talk to vcs because that's a it's

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Speaker 2: certainly in San Francisco.

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Speaker 1: Our VC is not normis well, humans.

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Speaker 2: Different, you know, their offices feel different. They invest in

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Speaker 2: sort of wildly uncertain markets in the way that say,

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Speaker 2: someone investing in the treasury market is not. It's fun,

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Speaker 2: it's different. It's always eye opening from my perspective.

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Speaker 1: The interesting thing is, I think like ten or twenty

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Speaker 1: years ago, I think a lot of people would have

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Speaker 1: expected the VC world to move more towards the traditional

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Speaker 1: investment world, and instead it feels like the traditional investment

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Speaker 1: world has moved more towards VC.

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Speaker 2: Yeah, I think that's completely right. Actually, I mean, I

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Speaker 2: think like all of culture has kind of become VC

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Speaker 2: of five because everyone is just looking for, you know,

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Speaker 2: the lottery ticket. The lottery ticket in various ways, big

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Speaker 2: high conviction bets, and you sort of hope that one

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Speaker 2: of them, whether it's a bet on a presidential election,

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Speaker 2: in a sports game, a cryptocurrency, one's career, et cetera.

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Speaker 2: You want to find that right tail outlier, which has

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Speaker 2: been the VC mentality since the beginning of the industry.

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Speaker 1: Right, everyone is a VC now, so we should talk

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Speaker 1: more about the industry.

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Speaker 2: We should you if everyone is a VC, we should

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Speaker 2: talk about what a VC actually actually does in doing

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Speaker 2: now in your twenty twenty four I'm very excited. We

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Speaker 2: do have the perfect guest. We are here in the

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Speaker 2: offices of Index Ventures and we're going to be speaking

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Speaker 2: to Index Ventures partner Nina A Shadyn. Nina, thank you

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Speaker 2: so much for coming on the podcast.

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Speaker 3: Thank you so much for having me. I'm excited to

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Speaker 3: chat with you guys today.

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Speaker 2: What does a partner at Index Ventures do?

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Speaker 3: Well, our job is to find exceptional entrepreneurs, really build

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Speaker 3: trust with them and be part of their journey in

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Speaker 3: building a generational company.

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Speaker 2: Setting aside what a partner does or investor, you know

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Speaker 2: there's a lot of VC firms. Does Index Ventures have

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Speaker 2: like a specific sort of differentiator or mission or raison

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Speaker 2: debt lived different than anyone else?

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Speaker 3: Yeah, well, I think to answer that, it's helpful to

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Speaker 3: know a little bit about the history of Index. So

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Speaker 3: Index started in Europe almost thirty years ago and was

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Speaker 3: one of the first VC funds to really believe that

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Speaker 3: entrepreneurship could come from anywhere, not just Stanford's Silicon Valley.

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Speaker 3: And we've really had a global mindset, and so today

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Speaker 3: we have offices in New York, San Francisco and in London.

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Speaker 3: And the way that we think about investing is first

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Speaker 3: and foremost, it is truly all about the people. So

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Speaker 3: when we hear a pitch, we actually spend a ton

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Speaker 3: of time diligencing the entrepreneur and trying to build a

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Speaker 3: foundation of trust to understand what motivates this person, what

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Speaker 3: is truly this person's spike, And we try to see

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Speaker 3: them in many different settings in our office, in their office,

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Speaker 3: in a comfortable situation, in an uncomfortable situation, in a

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Speaker 3: professional setting, or you know, having dinner at a restaurant,

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Speaker 3: And so we spend a lot of time really getting

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Speaker 3: to know the individual, because at the end of the day,

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Speaker 3: when you're investing in capital, you're really taking a bet

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Speaker 3: on the trajectory of someone, which oftentimes is really hard

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Speaker 3: to see when you're talking to a very young, first

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Speaker 3: time founder.

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Speaker 1: This gets into something that I've always wanted to ask

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Speaker 1: a VC person, but what are you doing for founders

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Speaker 1: besides writing checks? Because I hear a lot about the relationship,

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Speaker 1: and the pitch is always, well, we have great relationships

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Speaker 1: with our founders and we really build them up as

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Speaker 1: both people and businesses. What does that actually mean?

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Speaker 3: Yeah, well, I can tell you. At Index we don't

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Speaker 3: pride ourselves on being the cheerleader for a founder. Many

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Speaker 3: of v school of many vcs, they show up to

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Speaker 3: board meetings and they're the cheerleaders, which is an important role,

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Speaker 3: right you want to have that positive energy in the

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Speaker 3: board meeting. For us, we really want to be business

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Speaker 3: builders along with the founder. So what does that mean Exactly?

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Speaker 3: Number one, we try to be a mirror to the entrepreneur.

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Speaker 3: So that could mean in a board meeting if they

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Speaker 3: have a blind spot on let's say, like an executive

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Speaker 3: that they've had around that has been incredibly loyal to

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Speaker 3: them for years. It's often hard to see, you know,

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Speaker 3: because we are going to all of these board meetings

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Speaker 3: in the quarter, we can kind of calibrate, hey, this

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Speaker 3: cro is excellent, or hey maybe you've outgrown this particular

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Speaker 3: person and you need to think about, you know, augmenting

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Speaker 3: them or putting them into a different role. So being

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Speaker 3: a mirror is difficult because oftentimes you have to share

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Speaker 3: with the entrepreneurs something that they don't want to hear

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Speaker 3: or maybe they don't see yet. And so that's one

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Speaker 3: thing that we definitely think about. Second, you know, I

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Speaker 3: think being able to know exactly what is going on

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Speaker 3: at a company and not feeling like a lot of founders,

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Speaker 3: I didn't appreciate this for a long time. They would

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Speaker 3: have so much anxiety preparing for a board meeting. You know,

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Speaker 3: they would tell me, like the night before they wouldn't sleep,

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Speaker 3: They'd be all worried about what's going to come up

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Speaker 3: in a board meeting. A lot of founders have a

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Speaker 3: fear of getting fired by their board that I never,

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Speaker 3: you know, really thought about or appreciated, because for us,

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Speaker 3: we're like, it's all about the founder. We're here to

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Speaker 3: back you and your vision. And so I think being

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Speaker 3: able to have a conversation that kind of transcends the

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Speaker 3: board meeting, where for example, on the first company that

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Speaker 3: I took a board seat of, I wou'd actually fly

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Speaker 3: down the day before the board meeting and spend a

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Speaker 3: ton of time with everyone who is going to speak

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Speaker 3: in the board meeting. Plus they're like minus ones to

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Speaker 3: really get a good sense of what's going on in

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Speaker 3: the company, what are they worried about, what are some topics,

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Speaker 3: And then before the board meeting, I talk to the

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Speaker 3: CEO and say like, hey, these are some of the

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Speaker 3: things that are on my mind. Just so they didn't

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Speaker 3: feel like they were blindsided. So taking that extra effort

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Speaker 3: to feel like an extension of the company, not just

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Speaker 3: like I show up, I grill you at the board meeting,

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Speaker 3: and then I go away.

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Speaker 1: Like a sort of neutral Greek chorus almost right, that

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Speaker 1: can spot the blind spots that the founders might not

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Speaker 1: necessarily see and be like, hey guys, this is happening.

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Speaker 3: Yeah, you really want to be that mirror.

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Speaker 2: So it's interesting because in my mind when I think

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Speaker 2: about public company boards, I always think of them as well,

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Speaker 2: there's a cliche, there's a stereotype that many of them

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Speaker 2: are rubber stamps, Yeah, for the chairman, the rubber stampds

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Speaker 2: for the CEO, particularly on questions like compensation. And there's

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Speaker 2: a lot of criticism of governance of public company boards.

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Speaker 1: My dream job is to have sixteen board an enormous

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Speaker 1: amount of money.

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Speaker 2: Yeah, well, you don't even need enormous If you have sixteen,

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Speaker 2: you just rack them up. What is it actually, I've

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Speaker 2: never asked this. What happens in a private company board meeting? Typically?

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Speaker 2: What's the agenda?

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Speaker 3: Yeah? So typically, if it's a well run board meeting,

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Speaker 3: the materials will get out, be sent out twenty four

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Speaker 3: hours in advance, and usually it's in a memo format.

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Speaker 3: Actually a lot of our founders write a CEO letter

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Speaker 3: where they say here's what's on my mind, and it's

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Speaker 3: like stream of consciousness, like what went well, what didn't

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Speaker 3: go well, What they're struggling with, things that they want

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Speaker 3: the board's view on. And then the ideal board meeting.

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Speaker 3: As you come in, everybody has looked at what happens

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Speaker 3: in the past quarter. What are the open questions? We

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Speaker 3: collect some data to help the entrepreneur. If they're like, hey,

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Speaker 3: I'm thinking about changing my usage from seat based or

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Speaker 3: my pricing from seat based to more like usage base,

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Speaker 3: how has that gone? What other companies have done this?

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Speaker 3: You know, what should I be thinking about? And it's

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Speaker 3: more of a discussion versus like a report. Those are

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Speaker 3: the best meetings in boards.

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Speaker 1: So one other thing I always wanted to ask a

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Speaker 1: VC person is how much technical expertise do you actually

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Speaker 1: need in this job? Does it get as granular as

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Speaker 1: you're looking at someone's like source code and saying this

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Speaker 1: looks really elegant or is it more about the business

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Speaker 1: case and sort of refining the business model itself.

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Speaker 3: Well, it probably depends on what you're investing in. Generally,

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Speaker 3: we would do a lot of that technical diligence at

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Speaker 3: the time of investing, to make sure that you know

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Speaker 3: what the founder is saying is built the way that

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Speaker 3: they're presenting it, and that there's no loopholes, and that

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Speaker 3: we feel really good about the engineering team. So we

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Speaker 3: will often bring in also deeply technical experts to help

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Speaker 3: with that diligence, But in general, i'd say going forward,

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Speaker 3: it's much more on the business building and also giving

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Speaker 3: feedback on the team. Like you know, at the end

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Speaker 3: of the day, because these companies start so small, the

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Speaker 3: people that they are bringing around the table and what

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Speaker 3: the bar of excellence is for the entrepreneur is really

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Speaker 3: important because that's the foundation of who's going to build

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Speaker 3: that next source code or that next product or that

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Speaker 3: next go to market motion, And so that's where we

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Speaker 3: spend the majority of the time, the business and the talent.

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Speaker 2: How much of your success would be attributable to perhaps

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Speaker 2: going out into the world and identifying promising founders or

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Speaker 2: promising companies that are going to be the next big thing,

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Speaker 2: And how much would it be there is a promising

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Speaker 2: founder and a promising company that's the next big thing,

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Speaker 2: and they want to call you. You are one of

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Speaker 2: the first calls that they placed. This actually came up

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Speaker 2: in another conversation we did recently about wanting to be

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Speaker 2: a first call, which strikes me as slightly different than

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Speaker 2: you bang the one to.

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Speaker 1: Call that that, by the way, that was what the

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Speaker 1: credit got. Yeah, but Nino also has a background in credit.

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Speaker 1: Amazing to be a bond trader.

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Speaker 3: Right, amazing.

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Speaker 2: But I'm curious about like that dynamic, the importance of

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Speaker 2: being a first call or a second call as opposed

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Speaker 2: to an eighth call for certain types of deals, and

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Speaker 2: like thinking about that sort of like that ranking for

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Speaker 2: potential opportunities.

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Speaker 3: Yeah, so maybe it's taking a step back. So the

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Speaker 3: job of a VC is number one sourcing, which means

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Speaker 3: being able to find interesting opportunities or having those entrepreneurs

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Speaker 3: come to you.

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Speaker 2: Yeah.

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Speaker 3: Number two is of course doing the diligence, where you're

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Speaker 3: making sure that you know the market thesis is right,

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Speaker 3: the product is great, the team is excellent. Number three

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Speaker 3: is winning, which I don't think a lot of people

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Speaker 3: actually talk about. So people think that you know, you

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Speaker 3: sit in this room in San Francisco, in this nice

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Speaker 3: office and people just come to you, and you know,

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Speaker 3: you say, oh yeah, this is a great idea, where

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Speaker 3: do I sign the check? But that's not actually the case.

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Speaker 3: Usually when you identify a great entrepreneur, chances are somebody

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Speaker 3: else has also recognized this person is spectacular, and so

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Speaker 3: actually being able to convince the entrepreneur that you are

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Speaker 3: the person in the firm that is going to work

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Speaker 3: the hardest, be there for the founder, and also that

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Speaker 3: you have a really specific investment vision and thesis that

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Speaker 3: aligns with their vision, I think is really important. So

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Speaker 3: the process of you know, once you know you want

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Speaker 3: to invest in this company, actually getting that founder to

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Speaker 3: choose you is actually quite quite difficult and takes a

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Speaker 3: lot of time. And then there's of course being a

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Speaker 3: board member and then finally exiting the company IPO or acquisition.

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Speaker 2: When it comes to winning, can you talk about the

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Speaker 2: difference in twenty twenty four versus twenty twenty one, when

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Speaker 2: the perception was that there were a handful of really

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Speaker 2: large firms that would write a check within an hour

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Speaker 2: that or maybe two hours or maybe a day. But

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Speaker 2: I think in many cases like yep, this looks good,

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Speaker 2: We're going to write your big check. I've never never

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Speaker 2: going to talk to you again, but the check's there,

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Speaker 2: et cetera. How real was that for you as a competitor,

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Speaker 2: and what is that like? In twenty twenty four that competition.

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Speaker 1: Also, Sorry just to tack onto it, but if you

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Speaker 1: can't write the big check, what do you do instead?

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Speaker 1: Do you send like red baskets and nicely written letters?

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Speaker 1: What's your edge in that case?

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Speaker 3: Okay, happy to give you an example there. So in

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Speaker 3: twenty twenty one, yes, absolutely, that's accurate. It was crazy.

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Speaker 3: We'd meet a company at nine am and by four

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Speaker 3: pm they would send us a text or an email saying, hey,

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Speaker 3: we have four term sheets, what's your decision? And you know,

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Speaker 3: we sat a lot of those out for a couple

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Speaker 3: of reasons. One, we didn't feel like we did our

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Speaker 3: fiduciary duty by actually doing the diligence. And number two,

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Speaker 3: it felt extremely transactional, you know, And I think that

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Speaker 3: there were some players that had very large funds that

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Speaker 3: could afford to do that kind of investing that have

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Speaker 3: really exited the VC game today because they realized that again,

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Speaker 3: unless you are getting the real, unfiltered view of what's

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Speaker 3: going on in the company, it becomes very difficult to

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Speaker 3: actually know. Like in these private companies, you know, what

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Speaker 3: are the challenges, what's going well? Being able to have

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Speaker 3: that authentic conversation with the entrepreneur. Now, you know a

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Speaker 3: lot of those people have eggs did A lot of

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Speaker 3: the smaller funds have not been able to raise a

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Speaker 3: second time fund. I think that the players have consolidated

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Speaker 3: back to a lot of these enduring platforms and institutions

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Speaker 3: that have kind of gone through a lot of these cycles.

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Speaker 3: And by the way, I think founders realize that those

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Speaker 3: who took the money from those firms that were more

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Speaker 3: like I think someone else through this phrase out like ATM,

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Speaker 3: like VCATM. When times got tough, those players were not

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Speaker 3: around because they weren't on the board. They had totally

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Speaker 3: set expectation of that and the founders realized they really

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Speaker 3: needed someone to help them walk through how to do

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Speaker 3: their first riff, or how to restructure the company, or

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Speaker 3: what to do take a down round or take debt.

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Speaker 3: So I think less players now and then also founders

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Speaker 3: have really re centered on the original value proposition of

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Speaker 3: venture capital, which is having someone along your side who

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Speaker 3: can really help you build this business.

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Speaker 1: And then how do you compete? Do you have any

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Speaker 1: interesting instances of I guess being really creative when pitching

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Speaker 1: to a potential founder.

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Speaker 3: Yeah, I want to share all my secrets, but okay, okay,

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Speaker 3: So look, I think winning is this incredible art where

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Speaker 3: you have to really try to get as many data

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Speaker 3: porns about the entrepreneur as you can. So, for example,

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Speaker 3: even before I meet a founder, let's say that I'm

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Speaker 3: really excited about and I have a thesis about I

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Speaker 3: will watch every single podcast video Twitter LinkedIn post to

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Speaker 3: try to gauge, like, what is this person like? Are

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Speaker 3: they going to respond to a super aggressive Hey, I'm leaning,

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Speaker 3: let's do this, let's go Which some founders love that?

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Speaker 3: Or are they more like, I want to get to

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Speaker 3: know you. Maybe I invite them over to my house,

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Speaker 3: they get to meet my husband and my kids. Maybe

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Speaker 3: we bond over our love for Formula one, or maybe

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Speaker 3: they want to hear about my values as a board member.

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Speaker 3: So I try to custom tailor the interaction with the

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Speaker 3: founder based on the data that I can get outside

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Speaker 3: in and of course that means talking to other people

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Speaker 3: that know them and try to understand what they care about.

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Speaker 3: And then in terms of the winning, you know, I

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Speaker 3: think the heart is part that a lot of people

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Speaker 3: don't share is once you've issued a term sheet to

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Speaker 3: someone and you know that they have term sheets from

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Speaker 3: other VC firms, like, what happens in that gap of

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Speaker 3: time until they sign? And so I try to map

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Speaker 3: out the entire process of Okay, if they're going to

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Speaker 3: sign by X date and most likely we're going to

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Speaker 3: give them a term sheet on this date, what needs

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Speaker 3: to happen in that gap of time And it could

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Speaker 3: be like this touch point, this thoughtful note, this special

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Speaker 3: you know event that we're going to invite them to.

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Speaker 3: And so I map that all out ahead of time,

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Speaker 3: and as the conversation goes, I'm continuously like tweaking what

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Speaker 3: I do in which order.

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Speaker 1: Joe, you kind of did that to me when you

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Speaker 1: were trying to get me to go to Bloomberg.

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Speaker 3: I remember that, and it was successful.

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Speaker 1: Yes, it was successful, which.

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Speaker 2: Actually dovetails into a question, which is that we have

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Speaker 2: listeners who many of them you know, some of them

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Speaker 2: in college, and maybe one of the things they think

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Speaker 2: about is venture capital and it looks pretty cool, like

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Speaker 2: you really do seem to meet a lot of very

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Speaker 2: interesting people, if nothing else. In this industry, some people

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Speaker 2: make a lot of money. Your offices are a lot

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Speaker 2: nicer than say other offices where people might go into

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Speaker 2: the investing realm. But like, you don't know if you're

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Speaker 2: good at So if I were in college and I

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Speaker 2: was like, I want to be VC, but I don't

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Speaker 2: know if I'm good at it, what would like you

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Speaker 2: have to know about yourself to know if like, oh,

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Speaker 2: this is a path I want to pursue.

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Speaker 3: Yeah, so first, you know, it goes without saying for

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Speaker 3: me at least that I think this is one of

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Speaker 3: the most incredible jobs in the entire world because you

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Speaker 3: are getting to be on the front row seat of

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Speaker 3: technology and innovation and the people you interact with. I mean,

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Speaker 3: every day I have to pinch myself that I'm getting

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Speaker 3: to meet.

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Speaker 2: These concentrations that, by the way, about our jobs.

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Speaker 3: Yeah, it's incredible, right, Like you get to meet these

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Speaker 3: incredible people who are putting their life on the line

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Speaker 3: to pursue something because they think, you know, this business

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Speaker 3: or this product or this broken system needs to be fixed.

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Speaker 3: And so you know, if even and that sentence inspires you,

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Speaker 3: you know, I would certainly encourage you to think about

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Speaker 3: venture capital. The beauty is right now, there's so much

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Speaker 3: information about VC. When I tried to get into venture

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Speaker 3: capital back in twenty fifteen twenty sixteen, there was nothing

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Speaker 3: like VC. Firms didn't have websites, there was no YouTube, videos, podcasts.

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Speaker 3: So the first thing I would say is try to

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Speaker 3: determine if you get excited listening to invest like the

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Speaker 3: best or some of these you know a lot of

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Speaker 3: these vcs that share their journey and how they think

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Speaker 3: about their day to day and what the trade offs

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Speaker 3: are of investing, you know at this stage. Second, I

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Speaker 3: would say, in order to be good at this job,

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Speaker 3: you kind of have to have some raw ingredients. One

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Speaker 3: of them is definitely thriving in ambiguity. So when you

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Speaker 3: show up at a VC fund, usually you're given a

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Speaker 3: laptop with a blank calendar and a lot of type

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Speaker 3: A people try to get into VC myself included, right

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Speaker 3: like where it's like give me the milestones, tell me

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Speaker 3: what the goalposts, and I'm just going to like crush them. Well,

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Speaker 3: when you get to VC, you don't really have that.

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Speaker 3: It's like make magic, make money, find great entrepreneurs, and

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Speaker 3: you're like where do I start? So you really need

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Speaker 3: to love thriving in ambiguity and kind of breaking.

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Speaker 2: Down your job to fill up that calendar. It's your job,

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Speaker 2: and no one else is going to tell you like

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Speaker 2: how to No one is.

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Speaker 3: Going to tell you how to spend your time. Of course,

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Speaker 3: like you get some guidance on like hey, find a market,

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Speaker 3: go deep, find the founders. So that's one thrive in ambiguity.

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Speaker 3: The second thing is being able to take the ball.

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Speaker 3: So oftentimes VC firms are pretty lean. That means a

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Speaker 3: lot of responsibility for a young person. So that means, hey,

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Speaker 3: you met a great founder at a conference, Like it's

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Speaker 3: on you to figure out, like how we get a

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Speaker 3: second meeting? You know, do you write them a really

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Speaker 3: thoughtful email? Do you invite them to this dinner? Follow up?

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Speaker 3: Follow up?

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Speaker 1: Fall up?

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Speaker 3: And then the third thing that I don't think a

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Speaker 3: lot of people really appreciate is these very long feedback cycles. Right,

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Speaker 3: So again type A people are usually like very you know,

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Speaker 3: driven by positive affirmation because you know, you do something,

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Speaker 3: you do well, great, next thing, next.

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Speaker 1: Thing, you get the sort of immediate feedback.

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Speaker 3: Yeah, exactly, the dopamine hit of like I'm good at

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Speaker 3: this at VC. Like in VC, you really don't get

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Speaker 3: that because it's like years before you know, and you know,

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Speaker 3: anyone that tells you like any company has only been

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Speaker 3: like good news and up into the right is lying

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Speaker 3: to you because it is such an emotional rollercoaster. Things

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Speaker 3: go wrong, you know, things go right. You think you're

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Speaker 3: a genius one day, and then the next day it's

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Speaker 3: it's very humbling. So I think, like those are core

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Speaker 3: attributes that you should have. And then finally, of course,

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Speaker 3: you need to be able to sell yourself. And that's

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Speaker 3: where a lot of people, I think feel very uncomfortable,

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Speaker 3: you know, because it's one thing if you can sell

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Speaker 3: a product. It's another thing if you can convince someone

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Speaker 3: to take a bet on you as a board member.

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Speaker 3: And you know, being able to articulate like your personal

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Speaker 3: pitch and why you I think is really important.

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Speaker 1: So you mentioned trying to get into VCS circa twenty fifteen.

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Speaker 1: Can you talk a little bit more about your previous

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Speaker 1: job history, because I was saying earlier before we started

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Speaker 1: the podcast, but honestly, one of the most eclectic bios

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Speaker 1: I have ever read. So in addition to being a

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Speaker 1: high old bond trader at City, you also worked at

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Speaker 1: a bakery in Istanbul for unknown reasons.

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Speaker 3: Yeah, yes, sure, so I grew up in the Bay Area.

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Speaker 3: I'm Armenian. My parents moved to US from Iraq. Actually

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Speaker 3: we're part of the Armenians that originally lived in the

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Speaker 3: southern part of Turkey before the Armenian Genocide. And basically

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Speaker 3: my parents raised me with this philosophy that I was

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Speaker 3: so lucky to have the right to work in the US.

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Speaker 3: My dad, who had the equivalent of a PhD in

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Speaker 3: electrical engineering, when he moved here, he had to wait

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Speaker 3: seven years for a green card, and so, you know,

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Speaker 3: I grew up with this perspective that, wow, I'm so

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Speaker 3: lucky to be able to even work in the United

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Speaker 3: States and of course speak English, and so that really

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Speaker 3: always drove me, and of course my Armenian heritage. And

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Speaker 3: so when I got to college, as many immigrant kids do,

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Speaker 3: it was either like become a lawyer or a doctor,

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Speaker 3: and I thought, you know, I'd love to be a lawyer.

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Speaker 3: So I originally decided to study pre law, and then

438
00:21:27,560 --> 00:21:29,520
Speaker 3: I interned at a law firm one summer and I

439
00:21:29,560 --> 00:21:30,159
Speaker 3: was like, this.

440
00:21:30,080 --> 00:21:31,680
Speaker 1: Is just terrible. It was.

441
00:21:31,880 --> 00:21:33,360
Speaker 3: It was just like I knew that it would take

442
00:21:33,480 --> 00:21:36,000
Speaker 3: so long for me to like actually do what I

443
00:21:36,040 --> 00:21:38,320
Speaker 3: thought a lawyer did based on what I saw on TV.

444
00:21:38,960 --> 00:21:41,120
Speaker 3: So I asked a couple of my friends who had

445
00:21:41,119 --> 00:21:44,320
Speaker 3: done some finance internships what their experience was like, and

446
00:21:44,400 --> 00:21:46,919
Speaker 3: most of them were investment banking, which honestly sounded very

447
00:21:47,000 --> 00:21:50,119
Speaker 3: similar to doing the you know, lawyer path of just

448
00:21:50,200 --> 00:21:53,640
Speaker 3: sitting and doing spreadsheets and power points. But a few

449
00:21:53,640 --> 00:21:56,359
Speaker 3: of my friends had done trading, and I just it

450
00:21:56,560 --> 00:21:59,520
Speaker 3: just struck me as wow, like here's something where you

451
00:21:59,560 --> 00:22:03,720
Speaker 3: can like form conviction very quickly. You have an opportunity

452
00:22:03,720 --> 00:22:05,879
Speaker 3: to put your money where your mouth is. And I

453
00:22:05,920 --> 00:22:08,199
Speaker 3: was like, this seems like the most intense job I

454
00:22:08,200 --> 00:22:11,320
Speaker 3: could possibly do after college. So if I do this

455
00:22:11,400 --> 00:22:13,920
Speaker 3: as like a training ground worst case scenario, any other

456
00:22:14,000 --> 00:22:16,520
Speaker 3: job after this would be like a cake walk. And

457
00:22:16,600 --> 00:22:19,520
Speaker 3: so I was really fortunate to get this offer at City,

458
00:22:19,640 --> 00:22:21,280
Speaker 3: and I really wanted to be on the high old

459
00:22:21,280 --> 00:22:24,560
Speaker 3: bond desk because the number one trader, this guy Scott Goodwin,

460
00:22:24,600 --> 00:22:28,480
Speaker 3: who now runs an incredible fund himself called Diameter, was

461
00:22:28,560 --> 00:22:32,280
Speaker 3: running that desk, and I really wanted to learn from him. However,

462
00:22:32,520 --> 00:22:35,280
Speaker 3: I had this terrible timing of graduating college in two

463
00:22:35,280 --> 00:22:38,320
Speaker 3: thousand and eight cool and so a lot of the

464
00:22:38,640 --> 00:22:41,359
Speaker 3: funds called individuals and were like, hey, would you defer

465
00:22:41,400 --> 00:22:43,080
Speaker 3: your offer for a year? And I had like my

466
00:22:43,200 --> 00:22:46,399
Speaker 3: apartment in New York, setup, roommate, the whole thing. So

467
00:22:46,560 --> 00:22:50,320
Speaker 3: long story short, I applied for a Rockefeller Fellowship, which

468
00:22:50,359 --> 00:22:52,000
Speaker 3: asks you, if you could go anywhere in the world,

469
00:22:52,040 --> 00:22:54,080
Speaker 3: where would you go and what would you do? And

470
00:22:54,320 --> 00:22:56,960
Speaker 3: I decided to write a proposal to work at a

471
00:22:56,960 --> 00:23:00,719
Speaker 3: baklava bakery because food is the perfect intersection between Armenian

472
00:23:00,760 --> 00:23:03,359
Speaker 3: and Turkish culture. And so yeah, when I got it,

473
00:23:03,480 --> 00:23:07,280
Speaker 3: call my parents they were like, are you serious? And

474
00:23:07,520 --> 00:23:09,679
Speaker 3: I worked at a baklava bakery per year in Turkey.

475
00:23:09,760 --> 00:23:12,800
Speaker 1: That's amazing. Okay, So here's my other question. What is

476
00:23:12,840 --> 00:23:17,680
Speaker 1: the ven diagram overlap between baklava baking and venture capital?

477
00:23:18,240 --> 00:23:20,040
Speaker 3: Okay, I thought you were gonna ask me about trading

478
00:23:20,080 --> 00:23:21,800
Speaker 3: and venture capital the.

479
00:23:21,800 --> 00:23:23,480
Speaker 1: Harder one, which I think is baking.

480
00:23:24,960 --> 00:23:29,000
Speaker 3: Yeah, I mean, I think the analogy is probably number one,

481
00:23:29,200 --> 00:23:31,119
Speaker 3: like you're serving customers at the end of the day,

482
00:23:31,200 --> 00:23:34,880
Speaker 3: and for us, we're serving our LPs and entrepreneurs. Number two,

483
00:23:35,160 --> 00:23:37,560
Speaker 3: you actually have to be like quite collaborative because one

484
00:23:37,600 --> 00:23:39,879
Speaker 3: person is like making the layers and the dough, like

485
00:23:39,920 --> 00:23:43,280
Speaker 3: super thin. Someone else is perfecting like the pistachio like

486
00:23:43,400 --> 00:23:46,119
Speaker 3: mixture that's like perfect, somebody else is doing like the

487
00:23:46,160 --> 00:23:49,000
Speaker 3: sugar syrup, and you know, to bring it all together,

488
00:23:49,080 --> 00:23:50,800
Speaker 3: it really does take a village. And I think the

489
00:23:50,840 --> 00:23:54,480
Speaker 3: analogy is to build an amazing company, it takes a village.

490
00:23:54,880 --> 00:23:56,800
Speaker 3: And then third, you just got to like roll with

491
00:23:56,840 --> 00:23:59,679
Speaker 3: the punches, like you know, the oven goes down, or

492
00:23:59,720 --> 00:24:01,800
Speaker 3: somebody it doesn't you know, show up to work or

493
00:24:01,800 --> 00:24:03,439
Speaker 3: whatever it is. You just got to roll up your

494
00:24:03,480 --> 00:24:06,360
Speaker 3: sleeves and help. Those things I think have been good

495
00:24:06,359 --> 00:24:07,840
Speaker 3: training ground for being a VC.

496
00:24:08,640 --> 00:24:12,719
Speaker 2: To use a line that we may say over and

497
00:24:12,760 --> 00:24:16,000
Speaker 2: over on the podcast, I imagine that baking back lava

498
00:24:16,200 --> 00:24:18,720
Speaker 2: is kind of like investing in bonds in the sense

499
00:24:18,720 --> 00:24:20,960
Speaker 2: that you want to repeat the exact same process over

500
00:24:21,000 --> 00:24:25,000
Speaker 2: and over again and avoid the screw ups more than

501
00:24:25,080 --> 00:24:28,119
Speaker 2: you're trying to at any given moment, make you know,

502
00:24:28,240 --> 00:24:31,600
Speaker 2: the one out of ten batches that's transcendent, right, Like,

503
00:24:31,640 --> 00:24:34,360
Speaker 2: you just want to keep that consistent process.

504
00:24:34,960 --> 00:24:38,800
Speaker 1: That was a stretch, Joe, I think, I think I think.

505
00:24:38,680 --> 00:24:40,400
Speaker 2: I got there in the end. Let's talk a little

506
00:24:40,440 --> 00:24:44,520
Speaker 2: bit about investing right now. Ai, Yeah, no, it's probably

507
00:24:44,520 --> 00:24:47,399
Speaker 2: going to be a big deal. I'm curious though, Within

508
00:24:47,720 --> 00:24:50,520
Speaker 2: AI there are various views on what it's going to

509
00:24:50,640 --> 00:24:53,159
Speaker 2: do and how it's going to change the world, and

510
00:24:53,200 --> 00:24:55,159
Speaker 2: who will be the winners, and who will accrue and

511
00:24:55,440 --> 00:24:58,200
Speaker 2: let a crew to legacy companies or startups or the

512
00:24:58,280 --> 00:25:01,760
Speaker 2: models of the chip company whatever to be successful right now?

513
00:25:02,400 --> 00:25:04,520
Speaker 2: Do you have to have a view on some of

514
00:25:04,640 --> 00:25:07,879
Speaker 2: these questions, because there's obviously the point about, yeah, you

515
00:25:07,960 --> 00:25:10,960
Speaker 2: want to find people who have for some reason likely

516
00:25:11,000 --> 00:25:13,680
Speaker 2: to be good founders, good operators, et cetera. Do you

517
00:25:13,840 --> 00:25:17,639
Speaker 2: also have to have a view on tech debates that

518
00:25:17,760 --> 00:25:18,639
Speaker 2: are happening right now?

519
00:25:19,880 --> 00:25:22,159
Speaker 3: I mean yes and no. For example, I think you

520
00:25:22,240 --> 00:25:24,639
Speaker 3: do need to have a view on foundation models right

521
00:25:24,720 --> 00:25:28,080
Speaker 3: These are the models that power chat Ept and Claude

522
00:25:28,200 --> 00:25:31,840
Speaker 3: and Gemini and some of these others, because there's still

523
00:25:31,960 --> 00:25:34,480
Speaker 3: many many entrepreneurs that are coming out and saying, hey,

524
00:25:34,560 --> 00:25:37,040
Speaker 3: I want to build like a better, faster model and

525
00:25:37,080 --> 00:25:38,920
Speaker 3: I'm going to raise hundreds of millions of dollars, And

526
00:25:39,040 --> 00:25:42,400
Speaker 3: I think having a view on okay, has it already consolidated?

527
00:25:42,560 --> 00:25:46,119
Speaker 3: Have we kind of already reached like the horizontal model

528
00:25:46,320 --> 00:25:49,720
Speaker 3: penetration with these four or five players, Like, that's definitely

529
00:25:49,760 --> 00:25:51,600
Speaker 3: a market view that you need to take.

530
00:25:51,800 --> 00:25:53,399
Speaker 2: What is your view on this question?

531
00:25:53,640 --> 00:25:56,119
Speaker 3: Yeah? My view is kind of does this settling on

532
00:25:56,440 --> 00:25:59,120
Speaker 3: and consolidating with these players, and I think it's really

533
00:25:59,240 --> 00:26:02,359
Speaker 3: hard to catch up. I think what's been really interesting, however,

534
00:26:02,760 --> 00:26:06,760
Speaker 3: is these smaller models that are very domain specific, so

535
00:26:06,880 --> 00:26:10,800
Speaker 3: they're really good at one particular industry or one problem

536
00:26:10,920 --> 00:26:14,640
Speaker 3: they solve, and their value proposition is not we're going

537
00:26:14,680 --> 00:26:17,119
Speaker 3: to be better than the underlying models. It's actually, hey,

538
00:26:17,200 --> 00:26:20,560
Speaker 3: as the underlying models get better and AI generally gets better,

539
00:26:21,119 --> 00:26:25,479
Speaker 3: our technology also improves. It's like rising tide lifts all boats,

540
00:26:25,920 --> 00:26:28,440
Speaker 3: and they've figured out how to incorporate AI not to

541
00:26:28,600 --> 00:26:31,960
Speaker 3: just be this feature but actually something that solves a

542
00:26:32,080 --> 00:26:35,600
Speaker 3: real workflow problem for their end customers and has a

543
00:26:35,680 --> 00:26:38,080
Speaker 3: real ROI. And so I think that's the areas that

544
00:26:38,119 --> 00:26:39,040
Speaker 3: we're really excited about.

545
00:26:39,119 --> 00:26:40,760
Speaker 1: And this is actually something I wanted to ask you

546
00:26:40,800 --> 00:26:44,240
Speaker 1: about because in all our conversations with VC people in

547
00:26:44,320 --> 00:26:47,560
Speaker 1: San Francisco or VC adjacent people, this kind of comes up.

548
00:26:47,560 --> 00:26:52,800
Speaker 1: There's enthusiasm about building more specific applications on top of

549
00:26:52,920 --> 00:26:57,040
Speaker 1: those large language foundational models and I'm kind of curious,

550
00:26:57,160 --> 00:27:00,560
Speaker 1: like how much of a moat is there for those businesses,

551
00:27:00,640 --> 00:27:04,040
Speaker 1: because it feels like, you know, if the essential technology

552
00:27:04,320 --> 00:27:06,960
Speaker 1: is the thing that you're borrowing from Microsoft or Google

553
00:27:07,080 --> 00:27:09,960
Speaker 1: or whatever, it feels like the customization is almost the

554
00:27:10,119 --> 00:27:11,400
Speaker 1: easy part there.

555
00:27:11,600 --> 00:27:13,000
Speaker 3: So I guess, like, how.

556
00:27:12,920 --> 00:27:16,240
Speaker 1: Easy is it to replicate a specific use case for

557
00:27:16,359 --> 00:27:20,360
Speaker 1: AI if you're not you know, developing the foundational model yourself.

558
00:27:21,040 --> 00:27:24,199
Speaker 3: Yeah, so I think actually history teaches us a lot

559
00:27:24,240 --> 00:27:27,240
Speaker 3: about this. If you think about sas software as a service,

560
00:27:27,600 --> 00:27:32,320
Speaker 3: there's no technical mode to SaaS. If somebody wanted to rebuild,

561
00:27:32,520 --> 00:27:34,199
Speaker 3: you know, there was always this question that a lot

562
00:27:34,240 --> 00:27:36,120
Speaker 3: of vcs would ask. It's like if Google through ten

563
00:27:36,200 --> 00:27:38,679
Speaker 3: engineers at this, would they build it? And like an

564
00:27:38,800 --> 00:27:41,399
Speaker 3: honest founder would be like, yeah, you know, if they

565
00:27:41,480 --> 00:27:43,399
Speaker 3: threw one hundred you know, engineers, they'd build it in

566
00:27:43,440 --> 00:27:46,840
Speaker 3: this amount of time. But I think finding one white

567
00:27:46,880 --> 00:27:49,639
Speaker 3: spaces that have been ignored, which I like to invest

568
00:27:49,680 --> 00:27:51,400
Speaker 3: in a lot of what I call like the forgotten

569
00:27:51,480 --> 00:27:54,240
Speaker 3: functions and forgotten industries which we can talk about. Yeah,

570
00:27:54,320 --> 00:27:56,960
Speaker 3: and then second, you really have to take a view

571
00:27:57,080 --> 00:27:59,800
Speaker 3: of where's your user spending time. You have to really

572
00:28:00,080 --> 00:28:03,320
Speaker 3: understand their day to day and just throwing an AI

573
00:28:03,480 --> 00:28:07,000
Speaker 3: feature that reads, for example, like legal contracts is not enough.

574
00:28:07,320 --> 00:28:09,600
Speaker 3: You either got to be like, okay, this is the

575
00:28:09,760 --> 00:28:11,720
Speaker 3: version control, or you're going to get like so much

576
00:28:11,760 --> 00:28:14,760
Speaker 3: more information, or it's just such a more delightful tool

577
00:28:15,200 --> 00:28:17,720
Speaker 3: that you, as a lawyer spend your entire day in.

578
00:28:18,119 --> 00:28:21,040
Speaker 3: That's the companies that we're excited about, because otherwise it's

579
00:28:21,080 --> 00:28:23,040
Speaker 3: really a race to the bottom of how much you

580
00:28:23,080 --> 00:28:25,320
Speaker 3: can charge on top of these foundation models, what.

581
00:28:25,359 --> 00:28:27,720
Speaker 2: Are some of those white spaces? I mean, law is

582
00:28:28,480 --> 00:28:32,000
Speaker 2: intuitively a big area because we know how much time

583
00:28:32,080 --> 00:28:34,280
Speaker 2: lawyer spend reading and yeah, documents of tech. But what

584
00:28:34,960 --> 00:28:36,320
Speaker 2: some white spaces that exciting?

585
00:28:36,600 --> 00:28:40,800
Speaker 3: Okay, so let's think about the physical world. So this table,

586
00:28:41,000 --> 00:28:44,280
Speaker 3: these chairs we're sitting on, the sneakers you're wearing, all

587
00:28:44,400 --> 00:28:46,760
Speaker 3: of the items that you see in the physical world

588
00:28:47,200 --> 00:28:50,760
Speaker 3: have been designed by an industrial designer. So an industrial

589
00:28:50,840 --> 00:28:54,000
Speaker 3: designer usually starts with a sketch like zero to one.

590
00:28:54,200 --> 00:28:57,680
Speaker 3: They draw the next air Jordan's Ferrari, whatever it is,

591
00:28:58,200 --> 00:29:00,760
Speaker 3: and then they have to do this painful of taking

592
00:29:00,840 --> 00:29:03,640
Speaker 3: this sketch and turning it into something that can actually

593
00:29:03,720 --> 00:29:08,160
Speaker 3: be manufactured. The textures, the color library, the specific way

594
00:29:08,320 --> 00:29:11,239
Speaker 3: that the shoe shows up, or the materials that they

595
00:29:11,280 --> 00:29:14,200
Speaker 3: can use, and that is a very manual process. So

596
00:29:14,560 --> 00:29:17,360
Speaker 3: almost a year ago I invested in this company called Viscom.

597
00:29:17,880 --> 00:29:21,880
Speaker 3: The founder, Jordan, worked as a car designer for Honda

598
00:29:22,160 --> 00:29:24,120
Speaker 3: and then he worked at Nvidia for a couple of years,

599
00:29:24,200 --> 00:29:26,200
Speaker 3: and he saw the power of AI, and instead of

600
00:29:26,280 --> 00:29:31,360
Speaker 3: replacing the human creativity of drawing that next incredible ikea

601
00:29:31,520 --> 00:29:34,480
Speaker 3: chair or H and M dress or whatever it was,

602
00:29:34,600 --> 00:29:37,400
Speaker 3: he was like, let's preserve the human creativity and then

603
00:29:37,520 --> 00:29:40,560
Speaker 3: just use AI to automate all the painful tasks of

604
00:29:40,680 --> 00:29:44,240
Speaker 3: taking that sketch into something that can be totally manufactured.

605
00:30:00,480 --> 00:30:03,080
Speaker 1: The other big topic in San Francisco seems to be

606
00:30:03,200 --> 00:30:07,560
Speaker 1: valuations and the inevitable Gartman style hype cycle that we

607
00:30:07,640 --> 00:30:11,480
Speaker 1: will probably encounter in this space. Where are we in

608
00:30:11,600 --> 00:30:15,000
Speaker 1: terms of enthusiasm and inevitable disappointment with AI?

609
00:30:15,800 --> 00:30:19,040
Speaker 3: Yeah? So could not be a bigger difference between AI

610
00:30:19,200 --> 00:30:23,560
Speaker 3: company valuations and non AI company evaluations. Like, for example,

611
00:30:23,680 --> 00:30:26,920
Speaker 3: if you are just a regular SaaS company and you're

612
00:30:26,960 --> 00:30:30,160
Speaker 3: making a million dollars of revenue, it's really hard to

613
00:30:30,280 --> 00:30:32,760
Speaker 3: raise over one hundred million dollar valuation, which would be

614
00:30:32,800 --> 00:30:33,960
Speaker 3: one hundred x multiple.

615
00:30:34,280 --> 00:30:36,120
Speaker 1: But even if I put out a press release saying

616
00:30:36,160 --> 00:30:38,840
Speaker 1: that I am now using AI in my SaaS business,

617
00:30:38,920 --> 00:30:40,920
Speaker 1: which seems to be the new strategy.

618
00:30:40,560 --> 00:30:42,239
Speaker 3: That does seem to be a strategy. But I mean,

619
00:30:42,520 --> 00:30:44,880
Speaker 3: if your core function is not you know, AI native,

620
00:30:45,240 --> 00:30:48,520
Speaker 3: and then within AI, valuations range from you know, even

621
00:30:49,160 --> 00:30:52,560
Speaker 3: half a billion dollars for some new seed companies because

622
00:30:52,600 --> 00:30:55,800
Speaker 3: they're run by incredible technologists. So I think there's a

623
00:30:55,920 --> 00:30:59,680
Speaker 3: broad sense that this is real. The market is huge,

624
00:31:00,160 --> 00:31:02,480
Speaker 3: and people want to have the right ships on the table.

625
00:31:02,880 --> 00:31:06,520
Speaker 3: There's also a lot of demand for these limited amount

626
00:31:06,600 --> 00:31:12,240
Speaker 3: of individuals that have very specific AI talents and experience.

627
00:31:12,320 --> 00:31:14,120
Speaker 3: There's not that many of them in the world, and

628
00:31:14,240 --> 00:31:16,520
Speaker 3: most of them are in San Francisco, where all the

629
00:31:16,640 --> 00:31:19,520
Speaker 3: vcs are. So basically, I think it's a combination of

630
00:31:19,640 --> 00:31:22,680
Speaker 3: limited talent, supply for people going out to solve really

631
00:31:22,760 --> 00:31:26,000
Speaker 3: really hard problems within AI, and then also capital that's

632
00:31:26,080 --> 00:31:29,280
Speaker 3: kind of flocking to those types of individuals, and supply

633
00:31:29,520 --> 00:31:32,840
Speaker 3: drives demand, and supplies is what drives valuations, and so

634
00:31:32,880 --> 00:31:33,840
Speaker 3: that's why we're seeing.

635
00:31:33,640 --> 00:31:37,720
Speaker 2: That Tracey mentioned the whole phenomenon of we're an AI company.

636
00:31:37,760 --> 00:31:40,960
Speaker 2: Now we're doing something with AI. Are there good companies

637
00:31:41,080 --> 00:31:44,720
Speaker 2: out there that are not AI adjacent, but good SaaS

638
00:31:44,840 --> 00:31:49,600
Speaker 2: businesses whose valuations are too low just because they're not

639
00:31:49,720 --> 00:31:50,440
Speaker 2: sexy right now?

640
00:31:50,720 --> 00:31:52,600
Speaker 3: Oh yeah, there's so many of them. I mean, I've

641
00:31:52,640 --> 00:31:56,000
Speaker 3: focused a lot on vertical software, which is software for

642
00:31:56,120 --> 00:31:59,880
Speaker 3: one particular industry, so software for architects, software for car

643
00:32:00,120 --> 00:32:04,200
Speaker 3: mechanic shops to run their business, software for HVAC, electrical

644
00:32:04,240 --> 00:32:08,200
Speaker 3: and plumbing to run their business. And these are incredibly important,

645
00:32:08,360 --> 00:32:11,440
Speaker 3: mission critical industries that you know, I think AI will

646
00:32:11,480 --> 00:32:14,840
Speaker 3: certainly be a feature for. But fundamentally there are things

647
00:32:14,880 --> 00:32:17,800
Speaker 3: that people need software to run their everyday business, like

648
00:32:17,960 --> 00:32:22,200
Speaker 3: dispatch or customer CRM, or figuring out procurement and supply

649
00:32:22,680 --> 00:32:25,360
Speaker 3: you know, supply chain type of problems, and so I

650
00:32:25,440 --> 00:32:28,040
Speaker 3: think that there is a huge opportunity. The challenge is,

651
00:32:28,120 --> 00:32:30,920
Speaker 3: though a lot of those companies raise at very high prices,

652
00:32:31,840 --> 00:32:34,080
Speaker 3: then they cut burn They laid off a bunch of

653
00:32:34,080 --> 00:32:36,520
Speaker 3: people in twenty twenty two to twenty twenty three, and

654
00:32:36,680 --> 00:32:39,480
Speaker 3: as a result, their growth rate has stalled. And so

655
00:32:39,680 --> 00:32:43,120
Speaker 3: I think there's like fourteen hundred SaaS companies that were

656
00:32:43,200 --> 00:32:45,960
Speaker 3: unicorns in twenty twenty one and twenty twenty that haven't

657
00:32:46,040 --> 00:32:48,520
Speaker 3: fundraised in the last like eighteen to twenty four months,

658
00:32:48,560 --> 00:32:51,360
Speaker 3: and that's because they have extended their cash runway. I

659
00:32:51,440 --> 00:32:53,760
Speaker 3: expect those to come to market. I also expect a

660
00:32:53,800 --> 00:32:56,720
Speaker 3: lot of consolidation to happen amongst startups. There are so

661
00:32:56,880 --> 00:32:59,800
Speaker 3: many companies going after the same space and just kind

662
00:32:59,800 --> 00:33:02,040
Speaker 3: of like race to the bottom in terms of unit

663
00:33:02,120 --> 00:33:05,360
Speaker 3: economics to like undercut one another. I think if they're

664
00:33:05,400 --> 00:33:07,240
Speaker 3: not success, if one of them is not successful in

665
00:33:07,320 --> 00:33:09,880
Speaker 3: breaking out as the clear winner, I could see startups

666
00:33:09,880 --> 00:33:12,440
Speaker 3: starting to join forces and go after the space together.

667
00:33:13,200 --> 00:33:15,840
Speaker 1: So at the beginning of this conversation, you mentioned that

668
00:33:16,120 --> 00:33:18,960
Speaker 1: Index had roots in Europe, or at least it looked

669
00:33:18,960 --> 00:33:21,360
Speaker 1: in Europe for potential opportunities, and I think there is

670
00:33:21,440 --> 00:33:25,360
Speaker 1: a sense nowadays that Europe is lagging behind when it comes.

671
00:33:25,200 --> 00:33:26,840
Speaker 3: To stuff like AI.

672
00:33:27,600 --> 00:33:29,640
Speaker 1: I guess I have multiple questions on this topic, but

673
00:33:29,800 --> 00:33:33,240
Speaker 1: a why is that or why does that sense exist?

674
00:33:33,440 --> 00:33:37,480
Speaker 1: And then secondly, have you seen good opportunities in Europe lately?

675
00:33:37,560 --> 00:33:41,040
Speaker 1: Are people doing interesting things there potentially beginning to catch up?

676
00:33:41,920 --> 00:33:44,200
Speaker 3: Yeah? Well, I think again it all comes back to

677
00:33:44,280 --> 00:33:46,800
Speaker 3: the density of talent, which I know everybody was talking

678
00:33:46,800 --> 00:33:49,400
Speaker 3: about like the doom loop in San Francisco and all

679
00:33:49,480 --> 00:33:50,920
Speaker 3: of that. But I have to tell you I was

680
00:33:50,960 --> 00:33:53,640
Speaker 3: at a conference a block away from here yesterday, Eric

681
00:33:53,720 --> 00:33:57,640
Speaker 3: Newcomers Cerebral Value Conference, and literally it was like probably

682
00:33:57,680 --> 00:34:00,360
Speaker 3: the top fifteen minds in AI that all o their

683
00:34:00,400 --> 00:34:03,080
Speaker 3: offices are within a five to ten minute you know,

684
00:34:03,360 --> 00:34:06,800
Speaker 3: drive of where this conference was. Everyone you know, from

685
00:34:07,280 --> 00:34:11,040
Speaker 3: Dario at Anthropic to you know, Ali from Data Bricks,

686
00:34:11,080 --> 00:34:13,000
Speaker 3: and the list goes on and on and on like fantastic.

687
00:34:13,400 --> 00:34:15,640
Speaker 3: So I think the density of talent still in San Francisco.

688
00:34:16,080 --> 00:34:18,560
Speaker 3: But you know, Google had a huge deep Mind presence

689
00:34:18,600 --> 00:34:20,600
Speaker 3: in Europe, for example, and we've been seeing a lot

690
00:34:20,680 --> 00:34:23,000
Speaker 3: of companies spin out of deep Mind and meta there.

691
00:34:23,280 --> 00:34:25,560
Speaker 3: I think Mistral is a great example. They're one of

692
00:34:25,600 --> 00:34:28,200
Speaker 3: the fastest grown you know, open source models. So there's

693
00:34:28,239 --> 00:34:30,560
Speaker 3: definitely talent there because a lot of these big tech

694
00:34:30,719 --> 00:34:33,880
Speaker 3: labs have had presence, you know, in Europe for a

695
00:34:33,960 --> 00:34:34,680
Speaker 3: while in AI.

696
00:34:35,400 --> 00:34:37,120
Speaker 2: You know, when you mentioned the types of industries that

697
00:34:37,160 --> 00:34:40,640
Speaker 2: you're interested in, like Europe may not have a booming

698
00:34:41,480 --> 00:34:43,520
Speaker 2: AI economy the same way San France, it's got it.

699
00:34:43,880 --> 00:34:47,480
Speaker 2: It probably has a lot of companies that would benefit

700
00:34:47,640 --> 00:34:51,120
Speaker 2: from them. So there's sort of like productivity problems. Whether

701
00:34:51,120 --> 00:34:54,960
Speaker 2: we're talking about like industrial chemicals, pharmaceutical areas, areas that

702
00:34:55,040 --> 00:34:57,600
Speaker 2: people are hopeful that AI can be a big solution.

703
00:34:57,760 --> 00:35:01,120
Speaker 2: Do you look at those companies as potential big customers

704
00:35:01,600 --> 00:35:04,120
Speaker 2: if nothing else for the types of technologies that you're

705
00:35:04,160 --> 00:35:04,600
Speaker 2: investing in.

706
00:35:04,920 --> 00:35:07,880
Speaker 3: Absolutely, I'd say like time to Europe as like a

707
00:35:08,000 --> 00:35:10,920
Speaker 3: market for US based companies is getting shorter and shorter.

708
00:35:11,120 --> 00:35:13,720
Speaker 3: And that's also one of the reasons why people Index,

709
00:35:13,800 --> 00:35:16,480
Speaker 3: you know, pick Index, because they want a VC fund

710
00:35:16,520 --> 00:35:18,320
Speaker 3: that kind of has one team across Europe and the

711
00:35:18,440 --> 00:35:22,080
Speaker 3: US that can help these entrepreneurs expand from being only

712
00:35:22,200 --> 00:35:25,080
Speaker 3: US customers to then of course a huge market in Europe.

713
00:35:25,560 --> 00:35:27,960
Speaker 1: Yeah. I've been asking this question a lot while we're

714
00:35:27,960 --> 00:35:31,200
Speaker 1: in San Francisco, But what's the coolest application of AI

715
00:35:31,360 --> 00:35:34,160
Speaker 1: that you've seen? I know you mentioned the product design aspect,

716
00:35:34,280 --> 00:35:35,319
Speaker 1: but is there anything else?

717
00:35:36,760 --> 00:35:40,000
Speaker 3: Well, we have an unannounced seed investment that I'll talk about.

718
00:35:40,160 --> 00:35:44,000
Speaker 3: It's in the agent space. So everyone is talking about agents,

719
00:35:44,120 --> 00:35:47,000
Speaker 3: right like agent for this, agent for that, you know,

720
00:35:47,440 --> 00:35:48,640
Speaker 3: want agent to rule them all.

721
00:35:48,920 --> 00:35:51,000
Speaker 2: This is an agent, is an AI that can actually

722
00:35:51,000 --> 00:35:52,960
Speaker 2: go and do things exactly exactly.

723
00:35:53,040 --> 00:35:55,680
Speaker 3: It's not like a sales AI agent. Let's put that aside.

724
00:35:55,719 --> 00:35:58,280
Speaker 3: It's basically like a command box where you say, okay,

725
00:35:58,600 --> 00:36:00,719
Speaker 3: please book my trip to Italy, and here are some

726
00:36:00,840 --> 00:36:03,080
Speaker 3: broad parameters, and here's my credit card. Go. So a

727
00:36:03,120 --> 00:36:05,560
Speaker 3: lot of companies are building agents, but not a lot

728
00:36:05,600 --> 00:36:09,840
Speaker 3: of people are thinking about the infrastructure or operating system

729
00:36:09,920 --> 00:36:12,520
Speaker 3: for these agents to interact with one another. You know,

730
00:36:12,600 --> 00:36:14,960
Speaker 3: if you think about it, probably an agent will have

731
00:36:15,040 --> 00:36:17,080
Speaker 3: to take your credit card and then interact with another

732
00:36:17,160 --> 00:36:20,600
Speaker 3: agent that is doing the hotel or flight booking, et cetera.

733
00:36:20,920 --> 00:36:24,080
Speaker 3: And being able to share you know, personal data or

734
00:36:24,200 --> 00:36:28,799
Speaker 3: financial transactions is really a complex problem. And so one

735
00:36:28,800 --> 00:36:31,279
Speaker 3: of the investments that we're excited about is building the

736
00:36:31,360 --> 00:36:33,800
Speaker 3: operating system for these agents to be able to interact

737
00:36:33,920 --> 00:36:34,480
Speaker 3: with one another.

738
00:36:34,960 --> 00:36:37,480
Speaker 2: I just have one last question. I think, you know,

739
00:36:37,600 --> 00:36:40,000
Speaker 2: as the time, at the time we're recording this, the

740
00:36:40,120 --> 00:36:43,440
Speaker 2: Nasdaq is a roughly all time highs I think, and

741
00:36:43,560 --> 00:36:45,400
Speaker 2: you know, I more or less think that when the

742
00:36:45,440 --> 00:36:49,960
Speaker 2: stock market is up, that's probably good for exit opportunities.

743
00:36:50,440 --> 00:36:53,719
Speaker 2: Either via IPO or the valuation of companies that would

744
00:36:53,719 --> 00:36:57,759
Speaker 2: buy your portfolio companies eventually, et cetera. I'm not going

745
00:36:57,840 --> 00:37:00,560
Speaker 2: to ask the Lena Khan if you see a question,

746
00:37:00,600 --> 00:37:02,520
Speaker 2: because I think the answer is kind of obvious that

747
00:37:02,560 --> 00:37:05,840
Speaker 2: there will be a more liberal environment for mergers and

748
00:37:05,960 --> 00:37:08,080
Speaker 2: deals in the next administration. That we could be wrong,

749
00:37:08,480 --> 00:37:12,520
Speaker 2: but from a IPO standpoint, stocks are up, but also

750
00:37:12,680 --> 00:37:15,799
Speaker 2: very heavily concentrated in a few gigantic companies. What does

751
00:37:15,880 --> 00:37:17,560
Speaker 2: that window look like to you these days?

752
00:37:18,160 --> 00:37:20,800
Speaker 3: Yeah, well, I think everyone's been holding their breath to

753
00:37:21,120 --> 00:37:23,759
Speaker 3: see what happened with the election, as well as how

754
00:37:23,920 --> 00:37:27,399
Speaker 3: the stock market performs given the question around interest rates

755
00:37:27,440 --> 00:37:30,600
Speaker 3: and inflation and what's the real strength of the underlying economy.

756
00:37:31,080 --> 00:37:32,440
Speaker 3: You know, I think that there are a ton of

757
00:37:32,520 --> 00:37:35,600
Speaker 3: companies that are having these conversations in boardrooms saying, okay,

758
00:37:35,880 --> 00:37:38,439
Speaker 3: you know, when the IPO market opens, are we ready?

759
00:37:38,520 --> 00:37:41,560
Speaker 3: And they're getting their systems, their CFO, their IR person

760
00:37:42,080 --> 00:37:42,759
Speaker 3: all ramped up.

761
00:37:42,960 --> 00:37:44,960
Speaker 2: Why isn't it open with stocks of all time high?

762
00:37:45,480 --> 00:37:47,719
Speaker 3: Well, again, I think that there were some big things

763
00:37:47,760 --> 00:37:50,600
Speaker 3: that folks were waiting for, potentially, you know, the election,

764
00:37:51,040 --> 00:37:53,200
Speaker 3: and again like what's going on with rates. I think

765
00:37:53,239 --> 00:37:55,880
Speaker 3: those are really important and so hopefully we'll see the

766
00:37:56,000 --> 00:37:57,960
Speaker 3: IPO market open up very soon.

767
00:37:58,600 --> 00:38:00,880
Speaker 2: Nina Sha Yam, thank you so much for coming on

768
00:38:00,960 --> 00:38:02,000
Speaker 2: to our lives. It was fantastic.

769
00:38:02,080 --> 00:38:02,759
Speaker 3: Thank you so much.

770
00:38:02,840 --> 00:38:04,280
Speaker 1: That was really fun, really.

771
00:38:04,160 --> 00:38:18,920
Speaker 2: Fun, Tracy, I really enjoyed that. First of all, I

772
00:38:19,000 --> 00:38:21,520
Speaker 2: really enjoyed how much of the conversation we got to

773
00:38:21,520 --> 00:38:24,480
Speaker 2: spend about what the job of VC actually is, because

774
00:38:24,800 --> 00:38:27,440
Speaker 2: I mean, I understand the broad perimeters. You talk to

775
00:38:27,640 --> 00:38:29,520
Speaker 2: a lot of entrepreneurs and most of them are going

776
00:38:29,560 --> 00:38:31,640
Speaker 2: to fail, and then hopefully you get a one hundred

777
00:38:31,800 --> 00:38:36,000
Speaker 2: x return in one of them. But hearing some of

778
00:38:36,040 --> 00:38:38,600
Speaker 2: the things like, Okay, it's not enough to meet them,

779
00:38:38,680 --> 00:38:40,560
Speaker 2: you also have to win, and what it takes to win,

780
00:38:40,640 --> 00:38:42,080
Speaker 2: I thought that was really interesting.

781
00:38:42,239 --> 00:38:44,440
Speaker 1: We probably should have asked, now that I'm thinking about it,

782
00:38:44,600 --> 00:38:47,040
Speaker 1: we should ask what happens to the failed founders because

783
00:38:47,080 --> 00:38:50,680
Speaker 1: there is there's a huge survivorship bias same VC, right,

784
00:38:50,760 --> 00:38:52,920
Speaker 1: and we're talking about identifying the winners and what it's

785
00:38:53,040 --> 00:38:55,160
Speaker 1: like when you actually find one. But I wonder what

786
00:38:55,320 --> 00:38:59,319
Speaker 1: happens to people who don't succeed on their first try.

787
00:38:59,560 --> 00:39:03,880
Speaker 2: I've always wondered about that too, and you know the

788
00:39:04,040 --> 00:39:08,840
Speaker 2: degree to which a good VC can I'm totally speculating

789
00:39:08,840 --> 00:39:11,480
Speaker 2: here find a good home for that entrepreneur. Yeah, is

790
00:39:11,560 --> 00:39:13,680
Speaker 2: that one of the services that's like, okay, we have

791
00:39:13,800 --> 00:39:16,319
Speaker 2: a network. I don't know. Actually, I'm just totally spitballing here,

792
00:39:16,360 --> 00:39:19,040
Speaker 2: but I've always wondered if one of the pitches from

793
00:39:19,160 --> 00:39:21,839
Speaker 2: VC is like, you're probably gonna fail because most of our.

794
00:39:21,800 --> 00:39:24,160
Speaker 1: Investments we have a place to put here.

795
00:39:24,320 --> 00:39:26,160
Speaker 2: Yeah, you know, residents.

796
00:39:27,000 --> 00:39:29,080
Speaker 1: The other thing I was thinking about, just in the

797
00:39:29,160 --> 00:39:33,439
Speaker 1: context of Europe US competition and AI. To your point,

798
00:39:33,600 --> 00:39:35,920
Speaker 1: it is kind of interesting to think of Europe like

799
00:39:36,000 --> 00:39:39,480
Speaker 1: not necessarily as a hotbed of AI activity, although as

800
00:39:39,560 --> 00:39:43,120
Speaker 1: Nina pointed out, there are some companies, but maybe as

801
00:39:43,320 --> 00:39:46,040
Speaker 1: a prime beneficiary of some of the product boosts.

802
00:39:46,200 --> 00:39:49,440
Speaker 2: Right, this is a story of our time, productivity boost

803
00:39:49,600 --> 00:39:51,600
Speaker 2: This is a story of our time that there is

804
00:39:51,800 --> 00:39:56,760
Speaker 2: the still important industrial giants of Europe and the fear

805
00:39:57,080 --> 00:40:00,480
Speaker 2: is that they're unproductive. Yeah, and there's huge pharmaceutical and

806
00:40:00,520 --> 00:40:04,480
Speaker 2: there's huge chemical companies in Germany and so forth. And

807
00:40:04,640 --> 00:40:07,279
Speaker 2: so if you actually think AI is going to lead

808
00:40:07,320 --> 00:40:10,720
Speaker 2: to some breakthrough, perhaps these are the big winners. Because

809
00:40:10,760 --> 00:40:12,840
Speaker 2: of the buyers of the technology, not the sellers of

810
00:40:12,880 --> 00:40:16,080
Speaker 2: the technologyrarian.

811
00:40:14,640 --> 00:40:16,840
Speaker 1: Approach to Europe's future.

812
00:40:16,920 --> 00:40:19,120
Speaker 2: I think I'm going to invest in Basf as an

813
00:40:19,160 --> 00:40:22,000
Speaker 2: AI player whatever. I'm not actually suggesting that, but that

814
00:40:22,040 --> 00:40:22,360
Speaker 2: would be.

815
00:40:22,360 --> 00:40:24,920
Speaker 1: A good I think this is actually an interesting investment.

816
00:40:24,960 --> 00:40:26,440
Speaker 2: Theaters good enough take for Twitter.

817
00:40:26,600 --> 00:40:27,920
Speaker 1: Yeah, okay, shall.

818
00:40:27,760 --> 00:40:28,200
Speaker 3: We leave it there.

819
00:40:28,280 --> 00:40:28,880
Speaker 2: Let's leave it there.

820
00:40:29,040 --> 00:40:31,600
Speaker 1: This has been another episode of the All Thoughts podcast.

821
00:40:31,719 --> 00:40:34,560
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway.

822
00:40:34,719 --> 00:40:37,440
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.

823
00:40:37,680 --> 00:40:40,920
Speaker 2: Follow our guest Nina Asha John She's at Nina A Shadyan.

824
00:40:41,080 --> 00:40:44,840
Speaker 2: Follow our producers Kerman Rodriguez at Kerman Arman, dash Ol

825
00:40:44,840 --> 00:40:48,200
Speaker 2: Bennett at Dashbout and kel Brooks at kel Brooks. Thank

826
00:40:48,239 --> 00:40:51,200
Speaker 2: you to our producer Moses Ondam. From our odd Laws content.

827
00:40:51,320 --> 00:40:53,560
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828
00:40:53,640 --> 00:40:56,640
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829
00:40:56,719 --> 00:40:59,480
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830
00:40:59,600 --> 00:41:02,600
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831
00:41:02,640 --> 00:41:06,200
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832
00:41:06,160 --> 00:41:08,160
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835
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