0:00

I remember getting off the call and Josh was

0:02

also there and we looked at each other like, wow, like

0:04

the way companies are going to get changed

0:06

is going to be incredible. Software is going to

0:09

look so much different. That kind of transformation

0:11

is just so rare in our job, company after

0:13

company. I think OpenAI is quickly figuring out

0:15

the cost curve. They have done a lot in the open

0:18

source community on regulation,

0:20

security and safety. I think they're being pretty

0:22

proactive. This is 20VC, The

0:24

Memo with me, Harry Stebbings, and today we're focusing

0:26

on the most discussed company on earth right now,

0:29

OpenAI.

0:29

We're joined by the investing partner who led the

0:32

financing for Thrive Capital in OpenAI's

0:34

latest round, Vince Hanks. Vince

0:36

is a partner at Thrive where he's led the firm's

0:39

investments in OpenAI, Melio and

0:41

Airplane.dev. And he also sits on the

0:43

board of Airtable, Benchling, Lattice

0:45

and Melio. And prior to joining Thrive, Vince

0:47

was an investor at Tiger Global where he learned

0:49

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2:55

Vince, I am so excited for this. I've heard

2:57

so many good things from Kareem, from Josh,

3:00

from Jack Altman, from Brad at OpenAI.

3:03

I've clearly got far too much free time, but

3:05

thank you so much for joining me today. Thank

3:07

you so much for having me Harry. I'm really excited to be here.

3:09

I'm a big fan of the show. That is very kind

3:11

of you, but I want to start with a little bit of context.

3:14

How did you make your way to Thrive and become

3:16

a partner at Thrive where you are today? Started

3:19

my career like many folks. I studied business

3:21

and accounting and undergrad. I went to

3:23

a big bank out of school, which was Goldman.

3:25

In

3:25

a lot of ways, going to these big

3:27

banks is like doing an MBA. It's a two-year

3:29

program for the most part. If you go there,

3:32

you want to work on these really complicated

3:34

big companies. Most of what I worked on was

3:36

that. It was things like AT&T

3:38

and Verizon or actually Dell

3:40

announced it was going to merge with EMC at the time.

3:42

They shipped us down to Austin. We

3:44

worked on carving out a bunch of software companies

3:46

to go finance that big transaction.

3:49

After doing that for about 18 months, I ended

3:51

up wanting to do something different. I got staffed

3:53

on Flipkart, which is an e-commerce business

3:56

in India. Lo and behold, the largest

3:58

shareholder was Tiger. I worked

4:00

pretty closely with the folks at Tiger on

4:02

Flipkart for about three or four months and

4:04

as I was going to leave Goldman, the stars

4:06

kind of aligned and I ended up joining Tiger

4:09

to go work really on private company investing

4:11

for the Skyly Fixall. As luck would have it,

4:13

the first company he handed to me was a software

4:15

company. And so I spent the first three years

4:17

of my career with him looking at lots

4:19

of software companies and trying to really find what were

4:22

the next generational big companies there.

4:24

Lee ended up leaving Tiger and I was

4:26

also chasing Airtable at the time

4:29

and the folks at Thrive,

4:29

Josh and Miles, had led a round of the

4:32

company and what I've learned from Thrive

4:34

is always be recruiting. One coffee chat

4:36

led to another coffee chat and about four years

4:38

ago I ended up joining the team at Thrive. There's

4:40

so many things for me to unpack that. I think Thrive's recruitment

4:43

machine is just incredible. I'm always trying to unpack

4:45

from Josh how he does it. He never quite tells

4:47

me. I do want to ask Lee is one of the most special

4:49

people in this business in my eyes. I love

4:52

him as a person. He's gifted in many

4:54

ways. What did you learn from working with Lee

4:56

and from your time at Tiger? First of all, I feel

4:58

really lucky to have started my career working

4:59

with Lee's. I credit a lot of where I got started

5:02

to working closely with him. Tiger also

5:04

is in the news a lot more recently but the firm's

5:06

been around for more than 20 years and if

5:09

you look at how it got off the ground,

5:11

it is very much in these kind of hedge fund

5:14

roots. Chase Coleman who started the firm, he was in

5:16

his mid-20s, he was really young. He had his hedge

5:18

fund mentality, mindset coming out of Tiger management,

5:21

post the dot-com bubble. The

5:23

way we thought about investing in companies

5:25

was very financial. Look at the P&L

5:28

and understand deeply how the numbers

5:30

tell the story of what the business does and

5:32

how does that ultimately make a good business, a great investment.

5:35

I learned a lot just from the financial rigor of

5:37

doing that. What's interesting is when you contrast

5:39

that to what we do at Thrive and how Josh has

5:41

built the firm, we started from the roots

5:44

of where Josh was, which was a founder.

5:46

He was the same age as Chase when he started Thrive

5:48

but ultimately he was trying to build a startup.

5:51

The mindset was very much how do you empathize

5:53

with the entrepreneur and we focus much

5:55

more on the product and the customer

5:58

and ultimately how does that manifest itself. into

6:00

a business. At the end of the day, we're looking for the

6:02

same thing. I was looking for a Tiger, which is iconic

6:04

market-leading companies that are going to generate great returns.

6:07

I think every investor wants that stuff, but to

6:09

make it tangible, at Tiger, I would have asked a

6:11

question if you were the founder and I'm trying to figure

6:13

out who your customer is. I would say, who's your

6:15

ICP? Or like, who's your core customer?

6:17

That's a very investor lens. At Thrive,

6:19

the way we'd ask that same question is, if I'm a SDR

6:22

on your sales team and I'm trying

6:24

to qualify a prospect, what am I looking

6:26

for? At the end of the day, it's really the same question. You

6:29

could even argue that the way

6:29

we do it at Thrive is less efficient, but I think

6:32

it shows a much deeper level of empathy with

6:34

the founder and it's a very different

6:36

mindset. I think the words we use are very important.

6:38

I often kind of change my tone and say, we,

6:41

what do we need to do to achieve the

6:43

next core milestone? And these little things like

6:46

changing from you to we and how

6:48

you address that question, which I think actually make

6:50

a big difference in how tones go

6:52

in terms of founder relations. So I totally

6:54

get you there. Vince, I heard you had a fascinating

6:56

background and so before we dive into many

6:59

more technical things,

6:59

I have to ask, I always believe that we're

7:02

all a function of our past, which means

7:04

we're all running from something. What are you running

7:06

from, Vince? Everyone's motivators

7:08

are to actually unpack and understand. We

7:11

talk about this a lot in founder assessment and

7:13

I just like you're saying, I believe that we're all a byproduct

7:15

of these accumulated experiences and

7:18

I'm sure have had a lot. I grew up in Michigan, I've

7:20

got three brothers, I've got two parents, they're

7:22

divorced and so there's lots of things in that I'm

7:24

sure manifest into who I am today,

7:26

but I'm sure like a lot of people that are listening

7:28

or listening to your show, I'm trying to figure out

7:30

who I am and I'm trying to figure out how that

7:33

impacts the decisions I make and how I react

7:36

to what I'm seeing as I go through the world. And

7:38

I think for me, personally, the motivator

7:40

is just maintaining this mindset. How

7:42

do you maintain the steepest slope possible

7:44

that you're learning on? One of the things I've

7:47

grown on a lot over the last eight years

7:49

of my career is I think when you start,

7:51

you take a very solo mentality to

7:53

doing things because the ship on your shoulder, you want

7:55

to accomplish things and so you do that really

7:58

on your own, but over time, I've realized

8:01

your friends, your spouse, your coworkers,

8:03

the people you're around and so forth, all

8:05

of those folks, you can compound your learning

8:07

curves together. And so for me, I think

8:09

a big part of where I've come from and where

8:11

I'm going is trying to make sure I utilize

8:14

all of these people are ecosystem to really compound

8:17

how I learn in that slope. And ultimately

8:19

that's been, I think the most powerful thing for me over the last

8:21

10 years of my short career and really my entire

8:24

life. How do you approach trust, Vince?

8:26

Trust is a tough one. It's very difficult.

8:29

It's like hard to gain, easy to lose. How

8:31

do you approach that? We talk about this as a team

8:33

from our culture. A lot of when you join the Thrive

8:35

team, the focus is how do you build trust

8:38

with the organization? And trust

8:40

is one of these things where in greater organizations,

8:42

it can be given by default in many organizations.

8:45

You have to earn trust. I think we have a culture

8:47

in Thrive that definitely people get a lot

8:49

of trust by default because of how small our team

8:52

is and how autonomous our model is. For me personally,

8:54

I actually had really great investors that you guys would know told

8:56

me something that was interesting, which is trust with

8:58

founders is actually just being very predictable.

9:01

People want to not feel like they're

9:03

getting surprised by how you're thinking. They

9:05

want to understand how you think, how

9:07

you're going to react and feel like they

9:09

understand you. And that ultimately kind of breaks

9:11

down these boundaries between people and

9:13

allow you to have some mutual trust and empathy

9:16

with each other. And so I think with founders in

9:18

particular, when we talk about building trust, it's

9:20

like any partnership. You've got to increase

9:22

the reps, get in the water in the trenches with them.

9:24

And they have to understand how you think. And ultimately,

9:27

I think you've got to telegraph how you're going to

9:29

make decisions

9:29

with them and make sure that they get there

9:32

alongside of you. They don't feel like you're

9:34

super imposing things top down on them. And

9:36

they don't feel like they're getting surprised at a left field because

9:39

if they feel that mutual level of partnership

9:41

with you, I think it's really actually pretty easy

9:43

to have trust with founders. I spoke to many of

9:45

the founders who you have that trusting relationship

9:47

with. And I promise we're going to bring it back to schedule, but

9:49

I'm enjoying this too much. And they said bluntly,

9:51

your ability to keep a level head

9:53

is actually one of your strongest points as

9:56

an investor. How do you think about

9:58

maintaining an even keel?

9:59

in terms of mindset. I think part

10:02

of it is you're a byproduct of your environment. I think

10:04

for me, I've gone through a bunch of different waves in

10:06

my career and that's helped me understand

10:08

what volatility looks like, feels like. And so I don't

10:10

think you just become level-headed as a person. I think you

10:12

kind of build into that psyche over time.

10:15

Someone said to me once, which is the line that I've been saying

10:17

to the team a bunch internally is things

10:20

are never as good as they seemed and they're never

10:22

as bad as they appeared. And I think just

10:24

keeping in mind that the rest of the

10:26

environment around you does react to these

10:28

peaks and troughs of your emotion in the market

10:31

and the volatility. And ultimately, in

10:33

good times, people over extrapolate and in bad times,

10:35

people under extrapolate. If you maintain

10:38

this kind of more balanced approach, I

10:40

do think it helps you hold more clearly

10:42

what are we ultimately looking for and solving

10:44

around for a given investment or

10:47

person or situation. I've

10:49

just found that it's not productive to necessarily get

10:51

caught up in the emotions. You got to try to think clearly.

10:54

And if you can remove that noise from the volatility,

10:56

the emotion, it allows you to focus on the

10:58

core a lot more easily.

10:59

But that said, I think you do need to trust your gut

11:02

and you do need to be emotional and react that

11:04

way. And so I wouldn't say it's all about being just

11:06

this kind of robotic level-headed person. You

11:08

need to figure out one of the times to do that and one

11:10

of the times to lean into your instinct. Speaking

11:12

of kind of leaning into instinct, I think everyone's

11:15

leaning into the instinct around AI

11:17

being the fundamental next platform

11:19

that changes all of human history, which it

11:21

very much could be. But I just want to ask on the hype

11:23

cycle there and the ups and downs that we mentioned.

11:26

I think for AI has never been greater. Is

11:28

AI the next big thing

11:29

or is it the new hype cycle

11:32

that will fade? This is the question I think every

11:34

investor is probably asking themselves

11:37

right now or certainly the ones I talked to. And

11:39

it's the quintessential question of like, do you sit

11:41

on the sideline and be patient and stay disciplined?

11:44

Or do you jump in on the gold rush? Because

11:46

if we don't get in now, we're going to miss all the seed in

11:48

Series A companies that create tens of billions of

11:51

dollars of value, which understandable. I

11:53

think there's a reason why we're in the business of being optimist

11:55

in venture capital. And I think for us and

11:58

really thrive in general, we try to get. into

12:00

the psyche of why are people so

12:02

excited and less about chasing

12:04

the next deal and more about what's

12:07

the core value to the customer? What's the product?

12:09

What's happening? Crypto obviously had an

12:11

amazing run and I think has kind of pulled back

12:14

a decent amount. We've been comparing and contrasting

12:16

how much is AI potentially the thing people are latching on

12:18

to like crypto maybe for the marginal investor

12:21

a couple of years ago versus how much is

12:23

it real. I think just use crypto as an analogy.

12:26

It was very audially driven. Bitcoin

12:28

came out after a financial crisis and

12:30

it was all about take the centralized financial system

12:33

and make it decentralized. A lot of people take

12:35

their money anywhere, don't let the government insert

12:37

themselves in the financial ecosystem. And really

12:39

like you had to believe in that as

12:41

a concept to take all of the trade offs of

12:44

using crypto as a consumer is a pretty bad

12:46

user experience. And so when we think about

12:49

other hype cycles, I mean, if you go back to even

12:51

the dot com bubble when that ran

12:54

up, Microsoft, Intel, Cisco

12:56

were the top three technology companies in the peak

12:58

of the dot com bubble. They were all infrastructure

13:01

related in some way and they were 50 percent

13:04

of three trillion a market cap in the hype cycle. And

13:06

so people really latch on to these companies

13:08

and they can run for a long time when people believe

13:10

and speculation feels more speculation. And so

13:13

at the end of the day, you know, if you invested in Microsoft 2000,

13:15

it would have taken 15 or 20 years to

13:17

break even on your investment. And so it's hard to

13:19

really time and predict hype cycles.

13:22

And so we ask ourselves, what's the core premise of AI?

13:24

We don't know them all today, but we can try to figure

13:26

out what some of them are and we can really try to

13:28

understand products. And there's obvious benefits

13:31

to those things today. Obviously all the search that's

13:33

coming with chat, LGBT and the LLMs,

13:36

but also just like companies that allow you to trigger

13:38

actions without having to do 20 clicks or

13:40

I mean, for your show, I'm sure you're using AI

13:42

in some way to edit or create content or

13:45

something like that. The marginal cost of content

13:47

production has come down a lot with these tools.

13:49

I think when these are the foundational questions you're asking

13:51

because of technology shift, it forces

13:54

every company to think about what could

13:56

happen to their business over the next five years. That

13:58

would be really disruptive or.

13:59

how should we be thinking about re-disrupting ourselves

14:02

to ultimately take advantage of the platform? And

14:04

all the boardrooms I'm in and many of the founders I work with

14:07

are thinking about that question. The question which

14:09

really came to me was someone posted on Facebook,

14:11

this picture, put a picture of themselves below

14:13

and said, is it AI or is it real?

14:16

And I genuinely was like, I don't

14:18

know, that's not sure. And that's

14:20

a real realization moment of like where

14:22

we are today. I do want to focus specifically

14:25

on open AI, obviously thrive very prominent

14:27

in the list round. Tell me how did the

14:29

deal go down? How did it come to

14:31

be, Vince? Yeah, we really

14:34

first started focusing on the company maybe 18 months

14:36

ago, because there were a number of

14:38

startups that were really rising with

14:40

software products with these kind of tools that

14:43

you could use to do marketing copy, or you could create

14:45

blog posts with them. I think many of these companies,

14:47

we spent a bunch

14:48

of time with them. And we kept coming back

14:50

to the fact that they were thin user

14:52

experiences on top of this cool thing, which

14:54

was this model, which really no one talked about,

14:57

at least not mainstream 18 months ago.

14:59

And so that triggered us to go spend time with the company.

15:02

And ultimately, maybe in the classic Sam

15:04

kind of way, the way we kicked off the round

15:07

was he did almost like a closed demo with lots

15:09

of investors on a couple of calls of

15:11

the technology they're working on and ultimately GPT

15:13

for and we were on that call

15:15

like many other investors. And I think I remember

15:18

getting off the call and Josh was also there. And

15:20

we looked at each other like, wow, like the way

15:23

companies are going to get change is going to be incredible.

15:25

Software is going to look so much different. And

15:28

we were reflecting on it. And that

15:30

kind of transformation is just so rare in

15:32

our job company of our company. And so many

15:34

things seem marginal. But when you see these things that

15:37

are discontinuous, or seem so different, we

15:39

trigger this inner instinct to pause focus

15:41

reflect. And ultimately, we have to spend more time

15:43

learning about the financials and the products they were releasing

15:45

in the customers. But that's what encouraged us

15:47

to really lean

15:48

in. You said you're one of several firms

15:50

who saw this. Why do you think they chose

15:52

you? Because this was one of the most hotly competitive

15:55

rounds to finance. Why do you think they chose

15:57

you? I think there's a lot of people that said no, as much as

15:59

I

15:59

I like to think we want a really competitive deal. It

16:02

was not obvious to everybody. And even now I don't

16:04

think it's still obvious to everybody, even folks

16:06

that use the product. Why do you think it's not obvious?

16:08

Cause you have to assume that essentially we'll move

16:11

from a search interface to a chat

16:13

interface as the primary UI of engagement.

16:15

Is that why, or are there other reasons why it's not obvious?

16:18

I think a lot of investors get tripped up on

16:20

trying to be so precise on TAM

16:22

and market

16:23

and defensibility and the moats

16:25

around businesses and trying to map

16:27

that all to price. And those are so

16:30

important in the investment decision-making process. But

16:32

when it's so early in a technology

16:34

cycle like this, it almost is a little bit more

16:36

of a venture mindset where there are going to be 50 reasons

16:39

you can say no, and we're not going to have answers to every

16:42

question, but we need to really think about

16:44

the things that can go right. We're not talking

16:46

about building the next unicorn or decacorn.

16:49

We're talking about disrupting search

16:51

or Google. I mean, that's a trillion dollar opportunity.

16:53

Sure. I can't put a TAM around

16:55

chat GBT, but I can tell you like we're

16:57

not talking about a small price at the end of the day. And

17:00

so I think when we say it's not obvious to people are thinking

17:02

about it in this lens, they're thinking about it in the box

17:05

of what does this chat interface do? And oh,

17:07

those use cases aren't that valuable. And I think

17:09

it does take a higher level of creativity

17:11

or imagination to ultimately think about the world

17:14

that way. I totally agree with you in terms of applying

17:16

that kind of different lens and mindset. But

17:18

then there are also core that you have to do,

17:21

which we all do when we make an investment. When

17:23

you thought

17:23

top down on market analysis, how

17:25

did you approach top down market analysis

17:28

when you were sitting with Josh on this one? Yeah, as

17:31

these new technologies scale, it's so

17:33

hard to be precise about a TAM. And

17:35

so more of what we got into the psyche

17:37

of thinking about is if you looked

17:40

at other big technology movements,

17:42

how did they scale? The iPhone went from

17:44

a million phones a year kind of post-launch

17:47

to a hundred million in five years. AWS

17:49

took six or seven years to get to $100 million of

17:51

run rate, but that went from 100 million to 10 billion

17:54

in six or seven years. Google went from

17:56

nothing to 10 billion in the first six

17:58

years of monetizing. Obviously this is

18:01

rarefied error we're talking about. These are three of the most

18:03

transformational companies on the planet, but the

18:05

technology, if you really think about

18:07

it, and the zeitgeist it's captured, it is of

18:09

that elk of the transformation it can have

18:11

of the world. And so for us, it's less

18:14

about thinking about the exact TAM, and

18:16

it's more about if we think about what

18:18

the world's gonna look like in five or 10 years from

18:20

now, how are we gonna look back and say, wow,

18:22

I can't imagine the world without this kind of thing. When

18:25

you have experiences like that, that's what gets us

18:27

out of bed. These are the kinds of things that create new categories,

18:29

that was what a lot of the discussion was,

18:32

and maybe that sounds less precise to people, and

18:34

they think it's a little bit too finger in the air, but I

18:37

think for us, a lot of this is instinct mapped

18:39

with why it's a really great business. I think

18:41

something that dictates whether

18:43

it achieves that scale and

18:46

enterprise value is whether it is actually kind of

18:48

the one defining model to rule them

18:50

all, or whether there's an alternative mechanism

18:53

that will kind of bifurcate the market. Now,

18:55

when we look at open AI, it fundamentally

18:57

says that there will be one model that rules them all,

18:59

versus the world of many models

19:02

with hugging face and the like being others.

19:04

How do the two views differ,

19:07

and why do you think there will be one

19:09

model to rule them all? I mean, obviously, these are my opinions,

19:11

not the company's, just to make that clear

19:14

to everybody. I don't think I would characterize

19:16

open AI as one model to rule them all, and

19:18

obviously, there's all kinds of talk right now about it being

19:21

closed source, and the model should be open

19:23

source for the value of the community and all of those factors.

19:26

But I'd say, actually, if you give the company credit

19:28

for this stuff, which they've released, they have done

19:30

a lot in the open source and back to the

19:32

community. Clip was a model that they

19:34

put out there that helped a lot of the image generation

19:36

open source models. Whisper was for

19:38

audio. They open sourced the inference

19:41

framework called Triton, on top of

19:43

GPUs and CUDA, to ultimately drive more efficacy

19:46

and scalability of inference. And so

19:48

they have done a bunch of that side. They obviously have

19:50

kept the core GPT model behind

19:52

an API and paywall. I think thinking

19:55

about it as this entirely closed ecosystem, in

19:57

my mind, doesn't really give credit to what the company

19:59

has done in the open source. side, founders we

20:01

at least talked to are choosing their model has

20:03

quickly changed from who's got the biggest

20:05

model and who has the lowest cost

20:07

model to these new dimensions, which

20:10

is if you're going to build a company today, Harry,

20:12

you don't want to have to think about the scalability

20:15

of it or the reliability of the infrastructure

20:17

or supporting all of that stuff. If

20:19

you use an open source model, you've got to go figure out all of these infrastructure

20:22

problems. And so now if you talk to OpenAI and

20:24

you think about what they're spending time on, it's

20:26

how do you support this onslaught

20:28

of scale that has come into their

20:31

system? And that's infrastructure engineering

20:33

problems. It's the same thing we talked about in cloud 10 years

20:36

ago. I'm sure there were banks out there saying,

20:38

you know, we're not going to move to public cloud because

20:40

we can run all our infrastructure ourselves and do it really

20:42

well. You would never have told a startup 10

20:44

years ago to go run your own infrastructure if Amazon

20:47

was there doing it for you. I think we're kind of

20:49

getting into this world now where sure, there are lots

20:51

of open source models and they can use for great things.

20:54

And some companies will choose that path. But

20:56

by and large, for the vast majority of companies, particularly

20:58

ones that have scarce resources, which are many of

21:00

the ones I work with, I don't want you spending time

21:02

building infrastructure and scaling open source models.

21:05

I want you just getting your product out there with lots of value

21:07

to your customers. And I think OpenAI is

21:09

quickly figuring out the cost curve, which is probably the

21:11

biggest competing interest against that. They

21:14

put out 3.5 turbo, which is I think at least one,

21:16

if not two orders of magnitude less expensive than

21:19

before. And in my opinion, I'd bet on these ecosystems

21:21

to scale the infrastructure and ultimately that's

21:23

becoming more important, the decision factors. You

21:25

mentioned that kind of the model still being kind of behind

21:28

lock and key. Can I ask, people

21:29

often suggest like the commoditization of the model

21:32

as a challenge. When you thought about that

21:34

when making the investment, how did you get

21:36

comfortable in terms of model commoditization

21:39

and long term edge for OpenAI?

21:41

This is a question we talked about a lot. I

21:43

think part of it, we're already seeing the evidence of,

21:45

in my opinion, where commoditization of

21:48

the core model output is not really

21:50

what people will make decisions out over time. I'm

21:52

sure there are lots of companies that give similar search results to

21:54

Google, but the reason these companies

21:57

get built up to scale is because the ecosystem

21:59

around.

21:59

them grows. And you're even seeing this, they launched

22:02

plugins about a month ago and chat GBT

22:04

has taken off and taken the world by storm. I think it's the

22:06

fastest company in 100 million users ever. And

22:09

so even if the raw model

22:11

does get competed against, there are maybe

22:13

companies like Google or Facebook or

22:16

Microsoft or some of these startups that can

22:18

compete on that. I do think, again,

22:20

the dimension by which people are going to think about this

22:22

in five years from now looking back isn't going to be

22:24

about the commoditization of the model

22:26

and the raw output. It's going to be, oh, the ecosystem

22:29

around the model has become much more

22:31

robust such that you can do a lot more with

22:34

the model than just get text outputs or

22:37

the convenience of putting these things together

22:39

such that it's not just text. It's now text in

22:41

image or video and audio all through

22:43

one interface. That's complicated.

22:46

And again, it's not to be dismissive that the model

22:48

doesn't matter in terms of accuracy or size,

22:51

but it's to say that people are going to optimize

22:53

for companies, builders are going to optimize

22:55

for the inputs that allow it to

22:57

be much easier from the build and much

22:59

easier

22:59

for them to capture this new channel of

23:02

hundreds of millions of users that are flooding here. And

23:04

that is not about commoditization model. That's

23:06

about ecosystem and the kind of classic things that

23:08

make great businesses. In terms of the ecosystem

23:11

around it, you always highlight competitors or

23:13

competitive threats when making until

23:15

you've literally going down investment number here. It's

23:18

great training for any one of the investors. But like

23:20

going down the competitive threats, who did you

23:22

identify as the competitive threats that

23:24

you saw as having potential to

23:26

be noticeable? Elephant in the room

23:29

is big tech companies.

23:29

This is not something where they're

23:32

sitting flat footed Facebook, Google,

23:34

even Microsoft. I know they're great partners. I

23:37

have Tom Tungus on the show recently, and he said that

23:39

Google have been the most disappointing of all. And

23:42

they were his former employer. And he said, terribly

23:44

disappointing. We've seen AWS partner

23:47

with Hugging Face. Who do you think

23:49

is doing well? I think it's hard to be

23:51

dismissive of these companies. If you think about

23:53

where the best talent in AI is right now, I

23:55

think it's open AI. And then I think everyone

23:58

would tell you it is Google and

23:59

Facebook and Microsoft and Amazon. And

24:02

maybe there are folks that have kind of dripped their way into

24:04

the startup ecosystem, but by and large,

24:06

the talent is so clustered in these

24:08

big tech companies. And so I get it that they're

24:10

tripping over themselves, trying to figure out how to navigate

24:13

these giant organizations they've created.

24:15

But let's not also kid ourselves. We've talked

24:17

about this, maybe outside of the context of opening

24:20

up level it to AI, we work with lots

24:22

of startups that compete on things like

24:24

presentations or content creation.

24:27

What's scary to me right now is if you're

24:29

a startup, large companies are

24:31

shipping product. The kind of canonical

24:34

examples were that, oh, the big incumbent

24:36

can't move and they're slow footed and

24:38

you got years to execute before they do something.

24:40

Microsoft's 200,000 person company, they've

24:43

shipped AI and Bing, AI and PowerPoint,

24:45

they're rolling out of the other products. Adobe's got

24:48

the content creation in their product already.

24:50

Even the big startups like Notion, those

24:52

folks I've been so impressed with how much they've shipped

24:55

in their product so quickly. And

24:57

so if you're a startup, I get that you

24:59

have the advantage

24:59

of speed, but you need a multi-year execution

25:02

window to be able to take advantage of all that product

25:04

you can ship quickly. If these big companies are

25:06

able to ship this quickly, they've got so much

25:09

distribution, so much talent, I just

25:11

think it's gonna be very hard to compete with them. So

25:13

the reason I'm also so bullish on open AI is

25:15

like Alice Rampel says very wisely, the

25:17

big question when investing in startups is

25:20

will the incumbent acquire innovation

25:22

before the startup acquires distribution?

25:25

And I think the challenge for everyone with open AI

25:27

is, it's still really a startup in terms

25:29

of processes

25:29

and a lot of its structure,

25:32

but it's called the distribution of a large incumbent.

25:35

So it can move as fast with the

25:37

distribution. That is a very

25:39

real and competitive threat that I would

25:41

not want to come up against. My question to

25:43

your point there though is, actually in

25:45

this next wave of AI, is the

25:48

value captured predominantly by

25:50

incumbents or is it captured by startups?

25:53

I think it's too early to call, maybe that's

25:55

a cop out answer, but to put it in context,

25:58

if you go look back in time,

25:59

at these big technology cycles, they

26:02

are great moments to create

26:04

new categories. And so maybe

26:06

that's a better lens to look at it through. Where

26:08

will new categories get created where being an incumbent

26:11

or being a startup won't matter? And if you look back

26:13

at the dot com, Google, PayPal came

26:15

out of it. Google about finding information, PayPal

26:17

about it, Bank Online. Social came

26:20

after. Facebook was connecting all these people

26:22

online and Twitter was the town square for

26:24

broadcasting information. Mobile,

26:26

the answer wasn't Salesforce was gonna build mobile

26:28

CRM and that's where all the value was. The answer

26:31

was you put a computer in everyone's pocket. DoorDash

26:33

and Uber came out of that because there's geolocation,

26:35

the ability to have connectivity. In my view, what we're

26:37

looking for, if I always answer that question is

26:40

yes. If you're going toe to toe right now with

26:42

an incumbent on their home turf and

26:44

their shipping, I think it's a hard bet to

26:46

take the opposite side of, you know, in our business.

26:48

But if you're creating something that's totally a different user

26:51

experience that no incumbent has

26:53

today, I'd bet on a startup 10 times

26:55

out of 10 in that case. The reason the show

26:57

is successful, because I just get to interview smarter people

26:59

than me and

26:59

just ask questions that I'm thinking. Brilliant

27:02

model I've built. But my question is, if the value

27:04

accrues like incumbent versus startup,

27:06

the other question is value accrual at

27:09

the infrastructure layer versus the application

27:11

layer. How do you think about value accrual

27:14

there? Because you quite rightly mentioned earlier, three

27:16

companies, two trillion, you know, and I think

27:18

in the prior application layer, there's about

27:20

a similar market cap, two trillion, but 50

27:22

companies, so much more distributed kind of

27:25

enterprise value. Where does the value accrue

27:27

infrastructure or application layer? I have

27:29

to believe

27:29

it's gonna accrue mostly to the application

27:32

layer. I think if you think about infrastructure

27:34

companies as platforms, you know, if you think about

27:36

the software market as the relatives, the

27:38

value of AWS and Microsoft

27:41

and Google Cloud versus the software

27:43

companies built on top or all of the internal

27:45

software tools built on top, I think it's probably

27:47

an order of magnitude to one, you know, so I just

27:49

have to believe where value gets created to end

27:52

customers is where it will get captured. And

27:54

the nice part about the infrastructure business is there are

27:56

effectively toll roads on that

27:58

whole ecosystem.

27:59

applications might compete more aggressively with each

28:02

other and open AI or Amazon

28:04

or the infrastructure provider might be able to clip a coupon

28:07

on it That's really valuable We might capitalize it

28:09

at a high multiple But I have to believe that

28:11

there's gonna be a lot more value in the new experiences

28:13

and applications than the infrastructure How

28:15

do you think about investing in the space

28:18

when it moves so fast for your

28:20

investor and benchling air table? Atlantis

28:23

these markets are not changing on a daily

28:25

basis So how do you get

28:27

comfortable with the rapidly changing

28:29

market

28:29

like we haven't seen before it's hard

28:32

I think at the early stage, you know,

28:34

we think about investing in founders first

28:36

and foremost you want extraordinary

28:39

people in really great

28:41

sandboxes and those folks will

28:43

be able to figure out how to pivot and adjust

28:45

and iterate to Take advantage opportunity in

28:47

this environment If you told me would I rather be in

28:49

the sandbox with the great founder? Experimenting

28:52

and iterating or sitting on the sidelines I'd

28:54

rather be iterating even if things changed a bunch

28:57

or underneath you at the early stage, but that's

28:59

how I think you can't make every Investment without

29:02

knowing what's gonna happen. So some things there

29:04

is an obvious Evolution that makes

29:06

your business less interesting than it was six months

29:08

ago If you think about where we were on infrastructure companies

29:10

nine months ago There were all kinds of companies

29:13

branding themselves as ML ops or these

29:15

end-to-end solutions for AI

29:17

now in this environment We're in now there's specialized

29:20

companies that are doing individual components

29:22

of the infrastructure stack Whether it's laying chain

29:24

on connecting all these tools and stitching them together

29:27

or these vector databases that are allowing you to

29:29

store information And so things specialize,

29:31

you know our reaction It's not that hard

29:34

to say I have a friend and mentor

29:36

of mine that I spent a lot of time with who says

29:38

there's only two questions that matter for a company who

29:41

is the customer and What is the product

29:43

and you'd be shocked at how many folks can't

29:46

really answer that question with clarity? And if

29:48

you can answer that question with clarity Everything

29:50

else about the business stems from there And so if you're

29:52

really answering that question you're struggling because

29:54

you think in two weeks from now There's a chance

29:56

that the

29:57

customer doesn't care or the product won't

29:59

have any

29:59

any value, that's hard to build a company around.

30:02

And so in any of those environments, I think he'd rather

30:04

just wait for that. There'd be more clarity than

30:06

try to keep firing investments into

30:08

that market. I made an investment and I lost money

30:10

in it. And I lost money because of an externality

30:13

that was outside of our control, which

30:15

is a very frustrating reason

30:17

to lose. How do you think about

30:19

the impact in coming year

30:21

or so of regulation? Elon has been

30:23

very clear in saying we can't wait until

30:26

it's in the hands of everyone because then it is

30:28

too late. How do

30:29

you think about regulation in the next six

30:32

to 18 months? I don't want to speak for the company

30:34

on anything, because I know this is a topic that's out

30:36

there in the mainstream. I think regulation

30:39

will come to AI and it will be necessary.

30:41

No doubt that at all. In my mind, I think it

30:44

has to be done in partnership with the companies that are building

30:46

because your regulation for the sake of regulation

30:49

is not going to be what solves the problems people are

30:51

concerned about. You've got to go understand the nuances.

30:53

You have to understand the technology. And so I

30:55

would hope that other companies

30:58

building in AI are also working with regulators

31:00

to understand what is the technology and help educate

31:03

them to get to the right decision. I

31:05

do think there's kind of some negative stigma

31:07

going around open AI on regulation,

31:10

security and safety. And in my view, I

31:12

think they're being pretty proactive. As an example,

31:14

on GPT-4, we saw the demo

31:16

in the fall. They didn't just release the product then. They

31:18

took, I think, four or five months to test

31:21

and learn about safety and the edges of the model

31:23

and then ultimately released it to the world.

31:25

And so they're not going to get it perfect every

31:28

time. But I do think trying to let

31:29

people build with and understand it is important. And

31:32

poorly designed regulation on a technology

31:35

this early is not going to be effective

31:37

for anybody. And we've got to find the middle ground. My

31:40

concern is the chasm of knowledge

31:42

between private and regulator side

31:44

has never been greater. How

31:46

do we set effective regulation

31:49

when the regulators do not understand

31:52

so much of the infrastructure and

31:54

the opportunity? This is a solvable

31:56

problem. There's information out there. There's experts

31:58

out there regulators.

31:59

with how up to speed they've gotten on crypto

32:02

and they've really taken time to learn about it. And sure,

32:04

they're gonna make mistakes, but ultimately that

32:07

gap is solvable and it's gotta start

32:09

the dialogue between the people

32:11

working closest on AI and these regulators.

32:13

And it might take years to get to a solution so people gotta

32:15

be patient. But this isn't an unknown,

32:17

this is something that you guys can work together on. Final

32:20

one on open AI and then we are gonna move to kind of your investing

32:22

style before a quick fire. I have to ask,

32:25

I would get in trouble if I didn't and some

32:27

very charming British guy otherwise. The

32:29

price

32:29

was high, I reported $29 billion.

32:33

What was the discussion internally around

32:35

price? Cause you still have to see real upside.

32:38

What was the discussion and how did you

32:40

project upside scenario planning? We

32:42

certainly had a discussion, a heated discussion around

32:44

it. We have a very team oriented

32:47

firm. And so we disagree and commit

32:49

once we make decisions and we make decisions to this team,

32:51

we live by that. No one person makes a decision.

32:54

And the price here was high. It's on an

32:56

absolute basis. It doesn't matter what company you're investing

32:58

in at the prices of this round

32:59

was done. You're talking about very

33:02

upper echelon type outcomes to justify

33:05

good returns. But I kind of come back to

33:07

the intangible. It's really hard

33:09

to understand the pace of adoption

33:12

of these major technologies. And any

33:14

numbers I would put on paper for you would look

33:16

insane. You would look at them in a spreadsheet and tell

33:19

me there's just no evidence to support

33:21

this. But then you look at these iconic technologies

33:23

and the great ones all follow that insane

33:26

curve. And so the balance we try to hold,

33:29

I think what makes Thrive a really special firm is

33:31

we're able to kind of separate ourselves

33:33

from the kind of quantitative

33:35

rigor that we rely on

33:38

for a lot of investments we do and hold the tension

33:40

of what could go right, think creatively,

33:42

understand how this could look like the most transformative

33:45

things. And if that happens, again,

33:47

we're talking about search potentially getting

33:49

disrupted or something that enables

33:52

workflow automation for hundreds of billions

33:54

of dollars spent on different jobs

33:57

and categories out there. And even now, we

33:59

didn't know this at the time of the... investment, but with chat

34:01

GPT pro and the traction that seeing maybe

34:03

that won't be the durable revenue engine for the

34:05

company over time, but the velocity

34:08

with which that's ramping. You know, Kareem, my partner

34:10

Kareem has always saying it's the scalability

34:12

properties of what they have built are

34:15

so good that we'd rather bet

34:17

on the compounding upside of that scalability

34:20

than sit on the sidelines because once it's clear,

34:22

it'll get priced up very quickly. And

34:24

so we ultimately decided to lean into that. How

34:27

do you determine when to throw the financial

34:29

rigor that you do have in the team

34:32

out of the window versus when it's needed

34:34

to make a sound and wise investment?

34:37

It's less about throwing it out the window and

34:39

it's more about putting it in context. A

34:41

financial model is a tool to help

34:43

you make a decision. You need many tools

34:46

to make a decision. It's less about

34:48

is that the only thing we use and we

34:50

throw it out the window and it's more about what's

34:52

the weight by which we put on the financial

34:54

rigor and model as the tool to make the decision.

34:57

I think for us, there aren't that many

34:59

iconic companies that could create it. And so

35:01

no iconic company, at least that

35:04

we've been a part of, was it clear

35:06

and obvious in the early days of that technology,

35:08

they always get priced well ahead and

35:10

they look cheap in hindsight because

35:12

they defy the gravity of the model. And so it's

35:14

kind of the quintessential humans think linear and

35:17

the best things happen exponential. We have

35:19

to understand it going back to being level

35:21

headed. When do our psyche or

35:23

heuristics break down in an investment

35:25

decision making process? And when they do, we

35:27

need to compensate them with a different

35:29

tool

35:29

in our toolkit. Can I ask you a bit of a weird

35:32

personal one, Vince, but this is a big check

35:34

for you. It's a really big check and

35:37

it's kind of you leading. Were you nervous

35:39

about that? I think we had the same

35:42

level of excitement that we

35:44

have in most investments. We don't

35:46

have a culture where there's this

35:48

kind of sharp elbow mentality

35:50

or people feel on edge for making investments

35:53

because prices are high or checks

35:55

are really large. We have a culture

35:57

where we support each other and we have a growth mindset.

35:59

learn. And so if this ends up not working out,

36:02

I don't think we're going to look back on this and put all the onus

36:04

on one person or two people that made a decision.

36:06

I think we're going to look back on this and say we got there as a team.

36:09

It was the right thing at the time. But here's

36:11

the learnings from it. And here's how we're going to adapt our lens

36:13

and course correct in the future. It is intimidating.

36:16

It's nerve wracking sometimes to write large

36:18

checks and investments. But in the right team

36:21

and culture, this is how we enable ourselves

36:23

to make these kinds of transformational investments. And

36:25

frankly, I think there are a lot of firms that could

36:27

not have done this because their organizations

36:29

are not set up to make these

36:32

kinds of big decisions. Your ability

36:34

to make these big decisions is the one

36:36

single fund. It's the one single team.

36:38

So help me understand specifically with Thrive

36:41

about the structure that enables you to make a decision

36:43

that others maybe couldn't. I think the thing

36:46

that enables us to make this decision

36:48

is we have this single small

36:50

team that thinks extremely creative

36:53

about how the world works. And we

36:55

are ultimately trying to concentrate our

36:57

investments in the best products

36:59

that are out there in the world. And so when you

37:02

simplify it down to that and

37:04

we allow this autonomy and

37:06

focus to happen on a single team

37:08

across the entire investment cycle from

37:10

early stage all the way to growth, we are

37:13

unable to go find these kind of iconic companies.

37:15

And we're young in our career, obviously, but

37:17

we also we can appreciate when these kind

37:20

of transformational technologies come around. And

37:22

certainly this was the most transformational thing I've seen in

37:24

my career and being able to lean into that

37:26

and not be afraid that if it goes

37:28

wrong, we're going to

37:29

get fired or if it goes wrong,

37:32

we're going to be pushed down in the firm.

37:34

I think having that psychological safety

37:36

that enables a firm to do it. And I think we have

37:39

that at Thrive. I think we change

37:41

so much as investors over time in terms

37:43

of what we value in the companies we invest

37:46

in and the founders we invest in. When it comes

37:48

to what's changed in what you appreciate

37:51

in an investment,

37:52

what has changed about that mindset? It's

37:54

actually very clear. The thing I've developed the

37:56

most on in my investing mindset is

37:59

this deep. empathy for the customer.

38:01

Trying to really think deeply about not

38:04

just what's the product and trying

38:06

to write that down on paper, but really to understand

38:08

how the business is going to get built and mapping

38:11

those nuances to who the

38:13

person is, what the product does, how

38:15

does that manifest itself in the business?

38:18

Do you have a sales intensive product

38:20

that's going to require people to be kind of constantly

38:23

out there and on edge and with their customers?

38:25

Do you have a product that's more middleware and so you need

38:27

somebody who's going to be willing to grind

38:29

it out and not be in the limelight?

38:31

Do you have a product that requires a lot of creativity?

38:34

That means you need to set up your org to be creative.

38:36

There are certain org structures that promote that more. And

38:38

when you think about where you can develop as an investor,

38:41

I think where I have developed the most is

38:43

continuing to understand the connectivity

38:45

between this rigorous financial

38:47

lens, very much of where I started at Tiger

38:50

to how do you build a company? And

38:52

at that intersection, it's really hard to get

38:54

to clarity on, but when you do, it is clear.

38:56

And I think the best companies have very

38:58

simple explanations. It trickles down

39:01

the company. The CEO can articulate in one

39:03

sentence, but the manager for

39:05

rung is down also can articulate it. And

39:07

that means that manager knows how to

39:09

go left when they should go right or

39:11

they know how to make that decision and communicate it through

39:14

a team. And so having appreciation for that, I think is where

39:16

I've grown the most on these investment decisions. And again,

39:18

it's hard to quantify, but I think it results

39:21

in just like a deeper empathy for

39:23

what a great company looks like.

39:24

Final two questions. I think we learn a lot

39:27

from wins and losses, and we

39:29

don't often analyze the wins as well

39:31

in the same detail. If we start on the loss, then we'll

39:34

move to the win. What's been the biggest investing

39:36

mistake for you and how

39:38

has your mindset changed as a result? I

39:40

made lots of mistakes. The one that I think stands

39:42

out the most to me, or at least comes back

39:45

to me a bunch in my psyche is when

39:47

I was at Tiger, I flew out to Sydney

39:49

and spent a bunch of time with the Canva team in

39:51

person. And obviously now

39:54

people know

39:54

about it. It's a remarkable company. At the time,

39:57

it was much smaller than it is now. And

39:59

at the same same time we were so focused

40:01

on investing in enterprise software as

40:03

an emerging category at Tiger. And

40:05

so, as we were spending time with Canva, I very

40:08

much, and this is early in my career, but I very much

40:11

let the pattern matching and the

40:13

DNA of what I was thinking about of a great enterprise

40:15

software company creep into us

40:18

looking at Canva. And the learning,

40:20

I think, just to distill it down to something, is every

40:22

company is unique, even within the bounds of enterprise

40:24

software companies, they're unique. And we

40:26

took this lens of what a great enterprise software company

40:29

was, we retrofitted to Canva, and

40:31

we said, okay, well, the churn

40:33

looks a lot higher than what grade looks

40:36

like. And the engagement looks very

40:38

whimsical relative to deep workflows

40:40

and integrations. And so, we shouldn't

40:43

value this highly recurring software

40:45

business, we should value a consumer subscription business.

40:48

And there's merit to that. But ultimately, because

40:50

we were so much in this one-dimensional

40:52

mindset of enterprise software, we missed

40:55

what was so special about the company. The

40:57

learning for me is just you can't walk in

40:59

biased about what you're looking for out of the

41:01

metrics of a company or what the

41:03

product should result in metrics. You

41:06

should walk in and try to have a very open

41:08

mind and say, do these metrics explain

41:11

the qualitative of the business that I'm

41:13

so excited about and reinforce why

41:15

it's special. And if you think those things are true,

41:18

I think then you should lean into it a lot more

41:20

and try to really get to the guts of it. It could be a great investment.

41:23

And I see this a lot with investors. When you get too pattern-matchy

41:26

on why companies should be the way they are, I

41:28

think it leads to a lot of mistakes. Flip

41:29

side, because that could lead into another

41:32

fucking hour. You can see why I do

41:34

this for a living. But

41:36

on the flip side, when you think about biggest

41:38

investing win or success, I

41:41

get it say earlier and DPI

41:43

takes time. When you think about biggest investing

41:45

win or success, what was that? And

41:47

what did you learn from that process? You're

41:49

only as good as your next investment, Harry. And

41:52

I've still got a lot to prove. So it's hard to

41:54

say we won. But I do think an important

41:56

part of winning and just understanding

41:59

how to embrace.

41:59

kind of this culture

42:02

of making great investments is you've

42:04

got to figure out how to be authentic to yourself

42:07

and map that to how you go win. You

42:09

know, what a founder might be attracted to for

42:12

me might be different than you, Harry,

42:14

it might be different than the next investor that's

42:16

listening to this podcast. And so you

42:18

can't just apply someone else's style

42:21

and say it's going to work for you and you're going to win. You

42:23

got to try to figure out how to map what's

42:25

really authentic to you and evoke that

42:27

emotion in the entrepreneur and get

42:29

them bought into why that could be a really

42:32

fruitful partnership for them. And it's hard

42:34

to do that. You can't do a lot of those things at once. You

42:36

can't treat it transactionally. You've got to invest the time.

42:39

There's no substitute for the time. The things

42:41

I look back on that were the most exciting

42:43

and rewarding for us to be a part of, we've

42:45

been able to establish that kind of ground

42:48

with whoever is most important in that process or

42:50

the team. And when you do that, I think those things

42:52

become obvious. And the decision is less about

42:54

all these other variables in the process. And it's much more emotional

42:57

because they want to work with you. Vince, that was bizarre.

42:59

Like, one, most people

43:02

are like, oh, I'm not going to give

43:04

an answer to the mistake, but I'll give

43:06

you the answer to the success. You did

43:08

the opposite. And then two, when you were

43:10

like being political at the beginning, I was like,

43:12

oh, come on, man. But that was a really

43:15

good answer, which was fantastic.

43:18

I really like that. I want

43:20

to move into a quick fire. So I'm going to say

43:22

a short statement and you give me your immediate thoughts. Does

43:24

that sound OK? Sounds great. Karim

43:26

told me that you are basically the encyclopedia

43:29

of business.

43:29

What does your content consumption

43:32

look like? What are the favorites? The

43:34

short answer is just reading anything. I think

43:37

for me, I like to try to subscribe to lots

43:39

of different angles of reading. So sometimes

43:42

it's podcasts on topics. Sometimes

43:44

it's newsletters. Sometimes you go find the

43:46

blog focused on the developer to go learn

43:48

about the thing. Most importantly, like learning

43:50

about the history of things is really important. All this

43:53

stuff is happening right now. And so where I've

43:55

been reading is trying to trace the key figures

43:57

from all the way back to like 1980s in a.

43:59

AI to today. And so for me, it's

44:02

really just about variety. Simulate

44:04

the personas, simulate different people

44:06

that are in the ecosystem and go learn about

44:08

them. And wherever you can find content on that, I'm willing

44:11

to read it. Vince, you can invest in one

44:13

multi-stage firm other than Thrive.

44:16

Which ones you invest in and why then? Harry,

44:18

I'm only investing in Thrive. There's no other in that.

44:21

Like, you could do founders fund, and now it's upside.

44:23

Or you could do sequoia. Are we going for a sequoia?

44:26

Give me one. We're concentrated. We only

44:28

invest in ourselves. I can't give you

44:29

one, Harry. If you were to invest in one seed firm,

44:32

can you give me a seed firm? I can. I don't

44:35

know him personally, but Nat Friedman, who's

44:37

now focused a lot on AI and I think has

44:39

his own fund, I really respect some of the stuff that

44:41

I've seen him go do. And I would be certainly

44:44

interested in putting money in his fund. What have you

44:46

changed your mind on in the last 12 months,

44:48

Vince? This is not for the rapid fire, but one

44:50

thing that's become clear as we've kind

44:52

of gone into this more difficult

44:55

environment to operate in is in the

44:57

good times, we probably

44:59

over-attribute to teams and products

45:02

how good they are. And in the bad times,

45:04

you can't just blame everything on macro, but

45:06

I think it makes you reflect on the over-attribution

45:09

you probably did in the good times. And

45:11

one thing I've changed my mind on is you got to be

45:13

more balanced about how much credit you give

45:15

to the momentum of a company

45:18

from the market environment versus the

45:20

actual execution they're doing and know that

45:22

there's the balance there. And good execution

45:25

doesn't always mean that it leads to great momentum.

45:28

Sometimes great momentum is also influenced

45:30

by these market environments or

45:32

variables that are harder to quantify.

45:35

What do you think was the craziest thing we saw

45:37

happen in the low interest rate

45:39

environment of 2020, 2022? Crypto

45:43

stands out. We talked about peakmarketcap and

45:45

.com. I think crypto's peak market cap for

45:48

tokens was like 3 trillion, somewhat similar.

45:50

The most iconic company that people talked

45:52

about was a fraud. I think we're going

45:54

to look back on that, particularly from an investor lens and

45:57

say there's these hype cycles that lead to massive

45:59

speculation.

45:59

And sometimes even the things people think are

46:02

most real are just not. Okay, you're on incredible

46:04

boards. You can choose one board member

46:07

for your company. Who would you choose? The

46:09

person that stands out that I've learned

46:11

so much from is Eric Fichria. He's on

46:13

the benching board. He's on the board of this company I work with called

46:16

Airplane. Eric is, I think,

46:18

just an incredible blend of has the

46:20

operational instinct rigger, but also

46:23

is fun to be around. And he's able to land

46:25

his messages in a really effective way with entrepreneurs.

46:27

And he also just has a really great balance perspective

46:30

on being commercial and understanding how

46:33

all of that works and interplays with the strategy of the

46:35

company. And I found that perspective

46:37

to be something that I'm continually learning from

46:39

as I'm listening to him. And when you find those kinds of

46:41

people, I think you just want to surround yourself with them.

46:44

What was your biggest lesson from working with Josh,

46:46

then? There's so many lessons this hard. Josh is an amazing

46:48

person. This is not about investing,

46:51

but I think with Josh, biggest lesson is

46:53

really that, like, to be successful,

46:55

you don't have to compromise on all

46:57

of the things that are important that are not your career,

47:00

your family, your friends. Josh,

47:02

one of the most amazing parts of being around

47:04

him is his warmth and empathy

47:07

and his priority of his family

47:09

over everything else is so

47:12

obvious when you spend time with him. I've respected

47:14

that so much about him. And I think it's

47:17

even changed the way that I prioritize how I run

47:19

my life. Tell me, Vince, final one, what

47:21

are the next five years hold for you? When we sit down in 2028,

47:23

where do you want Vince to be then? Hopefully

47:27

we'll be talking about some amazing AI companies

47:29

we invested in that created lots of value

47:32

for us both. But I think we have a lot of

47:34

ambition at Thrive as a firm, and

47:36

I hope to be a big part of us building it

47:38

and ultimately going and backing some of the

47:40

next transformational companies, but also building

47:43

our team and maintaining this amazing

47:45

culture that I think we have and attracting some of

47:47

the most talented people that want to go

47:49

invest in these kinds of companies to come work with us.

47:51

And so I hope if we talk again in five years, we're

47:53

talking about those companies, we're talking about the people on our team.

47:56

And ultimately, we're really excited about all that stuff.

47:58

Vince, thank you so much for putting that on.

47:59

putting up with my prying questions

48:02

and kind of not letting you get off on some of them.

48:04

I really appreciate it, but this has been fantastic,

48:06

man. I've wanted to do it for a while because I've heard so many good things.

48:08

So thank you so much, man. Thank you,

48:10

Harry. It's really fun to chat.

48:13

I just love that discussion with Vince. And if you want

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