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Speaker 1: Hello, and welcome to another episode of the Odd Lots podcast.

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Speaker 1: I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, you know,

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Speaker 1: we've had a number of episodes about tech and some

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Speaker 1: of the excesses of the VC boom, et cetera, maybe

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Speaker 1: the softening labor market. But it's one of these topics

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Speaker 1: that we can't talk about enough because it's so critical

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Speaker 1: I think, to the markets, to the economy, and so

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Speaker 1: I think ambiguous about like where the industry is going next.

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Speaker 2: Absolutely, So there's two big things that seem to be

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Speaker 2: going on right now. Are two big questions, and the

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Speaker 2: first one has to be we've seen a lot of

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Speaker 2: layoffs in the tech sector, and I guess the question

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Speaker 2: is is that reflective of the broader economy or is

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Speaker 2: that something tech specific as a more interest rate sensitive

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Speaker 2: part of the economy. And then I guess the second

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Speaker 2: thing is, you know, tech heck was one of the

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Speaker 2: big winners of the pandemic era, and how much of

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Speaker 2: that is getting rolled back now? How much of you

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Speaker 2: mentioned excesses, how much of the excesses are being taken

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Speaker 2: out of the system.

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Speaker 1: Well, And then I would say the third thing, which

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Speaker 1: is that and it just really leaped to the public

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Speaker 1: consciousness in the last several months. Is people being very

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Speaker 1: aware of AI and these questions about like, Okay, we

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Speaker 1: had this sort of like era of what we thought

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Speaker 1: of as like a software company over the last decade,

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Speaker 1: the software is a service company's VC funded, et cetera.

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Speaker 1: So in addition to the macro questions about hiring and

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Speaker 1: a different in addition to the question about financing, there's

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Speaker 1: this question like, well, what's going to happen with like

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Speaker 1: tech business models and how like upended will be and

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Speaker 1: what that's going to mean for existing talent and where

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Speaker 1: the money is going to flow from all that, And

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Speaker 1: I think, like, you know, everyone has posted threads on that,

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Speaker 1: but I've never like found something that's like, oh, that's

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Speaker 1: that compelling.

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Speaker 3: No, You're right, there's a lot to talk about.

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Speaker 1: So I'm very excited because today we're going to be

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Speaker 1: speaking with a guest that I would say has really

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Speaker 1: been I was going to say like the four front

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Speaker 1: of two sort of like major apocal tech trends, but

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Speaker 1: I kind of actually think it's two and a half

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Speaker 1: because so there was the web two point zero like

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Speaker 1: sort of like social kind of like user generated Internet,

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Speaker 1: and then there was the sort of like the SaaS boom,

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Speaker 1: software as a service, et cetera. And then in the

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Speaker 1: last few years with work from home like tech that

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Speaker 1: enabled work from home, which is almost like its own

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Speaker 1: distinct kind of story in my view.

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Speaker 2: Yeah, someone who brings those mega trends together and maybe

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Speaker 2: maybe might be on the verge of another mega trend

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Speaker 2: that we should.

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Speaker 1: Ask, well, well, the perfect person to see if there's another

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Speaker 1: mega trend. So we have the perfect guest. We're gonna

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Speaker 1: be speaking with one and only Stuart Butterfield, the co

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Speaker 1: founder of Flicker, the photo sharing site, the co founder

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Speaker 1: of Slack, the workplace messaging app that everyone used that

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Speaker 1: eventually sold the salesforce. Recently stepped down as the CEO

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Speaker 1: of Slack. So Stewart, thank you so much for coming on.

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Speaker 1: So I gotta say I meant to say. What's also

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Speaker 1: interesting here is both Flicker and Slack, as far as

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Speaker 1: I understood, it originally started because you wanted to create

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Speaker 1: an online game company, and both of them had to

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Speaker 1: pivot into these like mego world changing hits. So I

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Speaker 1: guess my first question is, are you working on a

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Speaker 1: new online game?

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Speaker 4: I am not. I get asked that a lot, and maybe.

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Speaker 1: It is a cliche questions like a good warm up. Okay,

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Speaker 1: but you're not right now, No.

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Speaker 4: I'm not okay.

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Speaker 2: So I mean, maybe we should start with the pandemic boom,

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Speaker 2: because I mean, Slack really became the sort of poster

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Speaker 2: child for the success of SAS.

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Speaker 3: In twenty twenty and twenty twenty one.

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Speaker 2: And you also had a kind of famous Twitter thread

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Speaker 2: where you explained what it was like being on the

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Speaker 2: front lines at that moment in time. But talk to

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Speaker 2: us about how would you characterize that experience, because looking

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Speaker 2: back on it now, there is a lot of talk

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Speaker 2: about maybe there was over hiring, maybe there was access

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Speaker 2: in various ways, but how did you experience it?

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Speaker 4: Yeah, that was a big moment, like the March of

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Speaker 4: twenty twenty, but I feel like it really started twenty

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Speaker 4: fifteen or something like that. I was looking back through

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Speaker 4: some old interviews and I think it was twenty and fifteen.

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Speaker 4: It might have the years a little bit off. Slack

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Speaker 4: raised money in a two point eight billion dollar valuation

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Speaker 4: and people were like, well, that's crazy. And even back

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Speaker 4: then it was a parent and we would say interviews,

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Speaker 4: this is just zero interest rate, like this is what happens,

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Speaker 4: and so I think that kind of boost that that

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Speaker 4: I don't know to say rocket fuel, but rocket makes

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Speaker 4: it sound like. I think this kind of like rocket

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Speaker 4: fuel propelled us all the way through and even you know,

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Speaker 4: before going back, I guess twenty fifteen, tenty sixteen, we

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Speaker 4: started to be launched. In twenty fourteen, we had a

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Speaker 4: million dollars of ARR the you know, the following week.

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Speaker 4: I think it took us about eighteen months to get

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Speaker 4: to one hundred million dollars of ARR, and that growth

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Speaker 4: was crazy through that whole period and then accelerated in

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Speaker 4: March of twenty twenty. I think the excesses are maybe

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Speaker 4: not a different story. But I guess there's there's two

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Speaker 4: parallel tracks, and one is like the Google Facebook track,

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Speaker 4: which is r they make a lot of money and

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Speaker 4: so there's no real constraint on hiring. And then there's

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Speaker 4: the VC back company, which is they raised a lot

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Speaker 4: of money and so there's no real constraint on hiring.

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Speaker 4: But absent those constraints, you hire someone, and the first

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Speaker 4: thing that person wants to do is hire other people,

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Speaker 4: right because they it's a very obvious signal. And it's

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Speaker 4: very true that the more people who report to you,

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Speaker 4: the higher your prestige, the more your power.

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Speaker 3: In the organization than the hire bility.

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Speaker 4: Yeah, exactly, and even you know, even if it's like

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Speaker 4: a two person empire, it's something like, you know, if

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Speaker 4: you're a we become a manager, you want to become

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Speaker 4: a senior manager. If you're a senior manager, you want

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Speaker 4: to become a director. And there's a very powerful intentive.

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Speaker 4: So if you hire a thousand people, you have you know,

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Speaker 4: nine hundred and ninety six people or you know, roughly

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Speaker 4: one hundred percent, we're like, I need to hire. So

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Speaker 4: every every budgeting process is I really want to hire.

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Speaker 4: And that to me is the root of all the

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Speaker 4: excess because if you don't have the constraint of we

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Speaker 4: just don't have the money. You know, if you're manufacturing

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Speaker 4: lighting or something like that, like yeah, you know, a

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Speaker 4: seventy year old industry where there's a lot of competitors

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Speaker 4: and all the margins been taken out of it, you

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Speaker 4: can't do that. And if you have infinite money, either

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Speaker 4: from being monopoly on search engines or having vcs give

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Speaker 4: you lots of money, you can get rid of that

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Speaker 4: constraint altogether.

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Speaker 1: You know. You mentioned like this sort of like this

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Speaker 1: flood of VC money and maybe like the sort of

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Speaker 1: zero interest rates or cheap money was like a sort

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Speaker 1: of jet fuel or rocket fuel for it. But on

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Speaker 1: the other hand, like there was something real going on

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Speaker 1: because the entire like the entire way the world consumed

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Speaker 1: software massively changed, probably between the Great Financial Crisis between

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Speaker 1: then and the pandemic. So like how would you wait

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Speaker 1: those two different factors, Like, sure, there's the cheap money

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Speaker 1: in the vcs, but also like there was this like

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Speaker 1: real shift in corporate spend on technology.

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Speaker 4: Oh absolutely, yeah, Sorry, I don't mean that, no.

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Speaker 1: No, no, no, And I'm always like but I but

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Speaker 1: I do think like I do think like you know,

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Speaker 1: for listeners, like it's easy to characterize the last decade

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Speaker 1: like sort of dismissively right as like well, cheap money VC,

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Speaker 1: but like something real, like really did change about how

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Speaker 1: we usually how the companies use it. For sure, technology.

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Speaker 4: Slack launched in the beginning of twenty fourteen, so not

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Speaker 4: quite ten years ago. Still, you know, it's like nine

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Speaker 4: and a half years in and it's at a two

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Speaker 4: billion dollar revenue run rate. You know, and we went

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Speaker 4: public five and a quarter of years after we launched,

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Speaker 4: and you know, we're acquired by Salesforce a couple years

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Speaker 4: after that. So that's that's very real, Like it's not

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Speaker 4: not many companies in the world ever get to a

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Speaker 4: billion dollars in revenue, and that's a real change in

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Speaker 4: people's behavior. That's you know, tens of millions of people

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Speaker 4: who spend hours and hours a day in a fundamentally

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Speaker 4: different way than they did before.

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Speaker 2: So Slack launched in twenty fourteen, as you just mentioned,

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Speaker 2: and since then, I think there's been a lot of

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Speaker 2: competition from the big incumbents. Microsoft would be the obvious one.

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Speaker 2: But I feel like there's always sort of mixed feelings

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Speaker 2: in Silicon Valley about startups versus incumbents, because on the

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Speaker 2: one hand, you think of a startup, maybe they're more innovative,

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Speaker 2: maybe they're more flexible in what they can do. You know,

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Speaker 2: they can zig when others are zagging. But then you

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Speaker 2: do have these giants that just have lots of money,

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Speaker 2: lots of capital, lots of talented engineers that they can

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Speaker 2: throw at these problems and either acquire you or maybe

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Speaker 2: do a similar thing with their own resources.

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Speaker 4: Yeah, it's funny. I used to go back to Wikipedia's

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Speaker 4: kind of registry of what the components of the DOW were,

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Speaker 4: and you can look, like one hundred years ago today

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Speaker 4: it was American Leather Corporation and I don't know US Steel,

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Speaker 4: maybe it wasn't quite But like the first tech companies

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Speaker 4: were Westinghouse in General Electric and a couple of Yeah,

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Speaker 4: and everything has a has a life cycle. Roughly, you know,

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Speaker 4: there's a couple of companies that survive for one hundred

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Speaker 4: plus years. But all of the and I know we

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Speaker 4: should all say the word H, E, G, E, M,

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Speaker 4: O and Y at the same time so you can

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Speaker 4: see what the real pronunciation is.

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Speaker 1: But one, okay, geminy.

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Speaker 2: We actually did a whole episode about the US dollar

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Speaker 2: roll where we said this quite a lot.

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Speaker 4: That eventually disappears. And I don't know how real that

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Speaker 4: AI threat to Google is, for example, but Google has

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Speaker 4: seemed absolutely invincible for a decade plus. Like it's just

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Speaker 4: it's inconceivable to me that anyone would compete with them

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Speaker 4: on their ground. To build that technology and to build

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Speaker 4: the infrastructure and to build the data centers and stuff

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Speaker 4: like that seemed impossible. But you know, maybe there's a

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Speaker 4: way around. And obviously, if you're a venture capitalist, you

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Speaker 4: know your job isn't to get a fifteen percent return

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Speaker 4: by investing in a basket of public tech companies. So

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Speaker 4: you're very much incentive to see the startups succeed, but

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Speaker 4: they're also you look back over the last twenty years

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Speaker 4: or even just make it from the Great Financial crist

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Speaker 4: the last fifteen years, it's been a lot of value created.

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Speaker 4: I mean not just in in you know, funny money,

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Speaker 4: but like a lot of revenue, a lot of shift

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Speaker 4: in how business is done, and a lot of huge successes.

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Speaker 1: Well, you know, I'm sure we're going to talk more

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Speaker 1: about like AI and how that's going to change the

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Speaker 1: whole world. But it is interesting to me that like

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Speaker 1: the one big publicly traded company that like a lot

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Speaker 1: people are giving a lot of credit to for AI

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Speaker 1: is Microsoft, which was like the dominant tech company thirty

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Speaker 1: years ago, thirty five years ago. Probably, like does that

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Speaker 1: surprise you that like the biggest, like powerful juggernaut, And

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Speaker 1: then of course obviously with the teams versus slack competition,

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Speaker 1: just like that there's this in this industry that's supposedly

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Speaker 1: so disruptive and like so competitive that there's like this

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Speaker 1: company and a couple of companies that are just so

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Speaker 1: like seem rock solid.

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Speaker 4: Yeah. I think that that one. It's interesting, very effectively done.

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Speaker 4: But the AI tech is all open AI. Sure, it's a.

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Speaker 1: Part like ahead of it, and they're like god, you

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Speaker 1: know they they did make that investment and people are excited,

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Speaker 1: like oh, they're going to create a word and all

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Speaker 1: that stuff. So it's like tech aside, Like people are

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Speaker 1: giving the company a lot of credit for how they're

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Speaker 1: positioned in this.

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Speaker 4: Yeah, it's using their enormous resources and a huge profitability

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Speaker 4: as leverage to leapfrog into the next next thing. I

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Speaker 4: think it is very impressive.

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Speaker 2: This was sort of why I asked that question about

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Speaker 2: you know, Microsoft versus startups, but like what are the

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Speaker 2: pros and cons of a smaller tech company versus a

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Speaker 2: bigger tech company, And you know, you can shade it

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Speaker 2: towards AI or talk about tech in general.

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Speaker 4: I mean, I think it's just classic innovator celebrity people

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Speaker 4: become when you have nothing to lose, then you can

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Speaker 4: do take any risks you want, and if you have

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Speaker 4: a lot to lose, then become much much more conservative.

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Speaker 4: And I think Google and AI is a great example

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Speaker 4: because obviously, if you asked anyone two years ago who's

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Speaker 4: the greatest company when it comes to AI, everyone would

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Speaker 4: have said Google, you know right, zero percent of people

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Speaker 4: would have picked Microsoft at that time. And why aren't

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Speaker 4: they in Microsoft's position today? I think a lot of

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Speaker 4: it has to do with that conservatism, because you know,

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Speaker 4: they see, we can release this thing and then it

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Speaker 4: says dumb racist stuff, and then we get in trouble,

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Speaker 4: or we release this thing and someone relies on it

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Speaker 4: and then someone gets injured or killed in an accident.

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Speaker 4: You can come up with a million yeires. Like you know,

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Speaker 4: I was up talking to the CEO of another company

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Speaker 4: once and we're just talking about you know, we're just complaining.

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Speaker 4: We're like, oh yeah, our life is hard. But he

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Speaker 4: was saying, if they're at a board meeting and the

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Speaker 4: board said, you really need to hire someone doing risk

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Speaker 4: and compliance, and he's like, okay, So it hires that person,

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Speaker 4: and then the first thing that person does, like it

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Speaker 4: started earlier, was they need to hire two more people

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Speaker 4: because they need you know, there's a lot of work too.

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Speaker 4: We haven't you haven't had anyone at risk. We've got

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Speaker 4: to catch up, we've got to make sure we have

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Speaker 4: the right policies and stuff like that. Fast forward eighteen

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Speaker 4: months and they're doing the agenda for this board meeting

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Speaker 4: and someone tells him that they're going to have eight

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Speaker 4: people from the risk team show up, and he's like

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Speaker 4: eight people? How many? How many people work in that group?

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Speaker 4: Twenty three? So this is like, you know, over over

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Speaker 4: the course of eighteen months, and every single one of

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Speaker 4: you know, it's just like the classic incentives. They get

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Speaker 4: zero upside if they say yes to something, and they

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Speaker 4: have this powerful incentive to say no.

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Speaker 3: Downside basically if they mess something up.

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Speaker 1: Well beyond just risk. However, like thinking about Google and AI,

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Speaker 1: like it is like, you know, an AI inference, I

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Speaker 1: guess is like costly, right in a way that a

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Speaker 1: search is not, like people seem to like you kind

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Speaker 1: of have to pay for it, chad GBT is a

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Speaker 1: service that people pay for, like Google has been free, this,

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Speaker 1: you know, has always been free. Seems like some of

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Speaker 1: the AI tools may not be as conducive to an

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Speaker 1: AD sales model since the answer is right there rather

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Speaker 1: than taking you somewhere else, and so I'm curious like

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Speaker 1: this tension that exists in Google where that maybe like

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Speaker 1: AI is like kind of like a I hate you

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Speaker 1: at the word paradigm because because I don't know what

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Speaker 1: it means. But a different business, like the business model

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Speaker 1: of AI is different than say the business model of search.

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Speaker 1: All these companies, like well many companies, whether large or small,

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Speaker 1: sort of find that in a more AI driven world,

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Speaker 1: like there's a real business model shift even sitting aside,

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Speaker 1: like some of like the risks associated with it right now.

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Speaker 4: One hundred percent. And I don't know that either. I'm

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Speaker 4: smart enough where I've thought about it long enough to

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Speaker 4: be able to predict all the way out obviously, but

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Speaker 4: something like you know, image licensing for stock photos, that

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Speaker 4: seems like a business that I wouldn't invest in today.

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Speaker 4: When you can just go to mid journey or whatever,

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Speaker 4: then they'll get better and better.

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Speaker 1: I think some big, some big stock photos. I just

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Speaker 1: like had a biout side for a biotofer for a

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Speaker 1: few billion. I don't know what's going anyway.

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Speaker 4: Oh yeah, yeah, there's a funny story said, if someone

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Speaker 4: gives me four billion dollars, then I will buy Getty Images.

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Speaker 1: I will do that too, if someone gives me four billion.

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Speaker 4: Okay, but yeah, so there's there's like a pretty trivial example.

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Speaker 4: But I think the longer term it's a little bit

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Speaker 4: harder to know how the knock on effects because you

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Speaker 4: think about previous ways of technological innovation. And I don't

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Speaker 4: mean software computers. I mean like just you know, going

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Speaker 4: back to steam power and stuff like that. It would

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Speaker 4: have been tough in eighteen forty to kind of predict

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Speaker 4: the impact of railroads on the world and then the automobile,

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Speaker 4: all those technologies, but they really become part of us

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Speaker 4: because when you think about I used to use this

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Speaker 4: example of slack all the time. If your job is

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Speaker 4: to dig ditches, you can only dig so many ditches

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Speaker 4: a day, and if you are given tobacco, you can

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Speaker 4: dig one hundred times more, five hundred times more, whatever

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Speaker 4: the multiple is. But those technologies come with I think

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Speaker 4: there's a better word than risks, but they come with

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Speaker 4: additional risks. You can like accidentally knock down a building

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Speaker 4: with tobacco, and you can't accidentally knock down to building

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Speaker 4: with the shovel. We get to the point now where

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Speaker 4: there's this incredible augmentation to our memory to our ability

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Speaker 4: to think. You know, I remember going to the library

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Speaker 4: when I was a kid and having to talk to someone.

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Speaker 4: Then they can maybe find the book, maybe they can't

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Speaker 4: find the book. And now it's like anything like a

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Speaker 4: little card. Yeah exactly, I can just like type it

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Speaker 4: a couple of words. I don't even have to spell

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Speaker 4: the words, right, Like, I just have to kind of

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Speaker 4: gesture at the words that I want and Google return

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Speaker 4: and everything I can want. What does that do to us?

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Speaker 4: Long run? And I think, you know, we're living with

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Speaker 4: the effects now, and this is pretty well recognized of

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Speaker 4: what social media does and the doom scrolling and the

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Speaker 4: changes to our physical posture because we're looking down on

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Speaker 4: our phone all the time. And I would really compare

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Speaker 4: this to give a couple hundred thousand years of evolutionary

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Speaker 4: pressure to seek calories and then not today we live

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Speaker 4: in a world of effectively infinite free calories for everyone,

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Speaker 4: and so a lot of people get diabetes, and we

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Speaker 4: have a couple hundred thousand years of evolutionary pressure towards

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Speaker 4: acknowledgment and recognition and all these social signals that suddenly

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Speaker 4: you can get a thousand x what you used to

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Speaker 4: be able to get and you end up with the

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Speaker 4: kind of cognitive diabetes. So when I say the long

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Speaker 4: term effects are hard to recognize, both in AI and

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Speaker 4: just in what technology does to us as human beings

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Speaker 4: in general, it can take a while for us to

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Speaker 4: catch up. Like the Thames and the Charles River used

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Speaker 4: to catch on fire routinely in the end of the

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Speaker 4: nineteenth century, and we kind of figured out how to

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Speaker 4: have benefits of the industrial evolution without those and content weathers.

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Speaker 4: I think we'll do that with all kinds of tech,

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Speaker 4: but it might take a generation or two.

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Speaker 2: Just on the business model idea, can you talk to

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Speaker 2: us about how tech companies actually make money off of AI?

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Speaker 2: And and here is where I, you know, very embarrassingly

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Speaker 2: confess that I still don't really understand what salesforce does

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Speaker 2: and how they make.

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Speaker 1: We're gonna ask this is the big question. We're gonna

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Speaker 1: have to let's segment this for like TikTok, because a

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Speaker 1: bunch of people have this question, and ever no one

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Speaker 1: has ever had the chance to get an answered. So

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Speaker 1: we're gonna like have to do a little like online

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Speaker 1: segment of Stewart's answer to that.

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Speaker 2: Okay, so I guess my question is one, what does

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Speaker 2: Salesforce actually do?

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Speaker 3: How do they make money?

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Speaker 2: And then secondly, like you know, if a company like

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Speaker 2: Salesforce were to build something like open ai, how would

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Speaker 2: they actually derive money from that product.

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Speaker 4: Yeah, that's it's a good question. I think that Salesforce

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Speaker 4: is actually really well positioned to take advantage because here's

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Speaker 4: what it does. I mean, obviously, it makes money by

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Speaker 4: selling software to people. And it was the first to say,

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Speaker 4: rather than just pay us one hundred dollars and you

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Speaker 4: can have the software and run it on your computer

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Speaker 4: like we used to do with floppy disks and stuff,

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Speaker 4: We're going to sell it to you for much cheaper,

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Speaker 4: but you got to pay us every month or every year.

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Speaker 4: But what the software does, I think is interesting because

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Speaker 4: the original Salesforce product was CRM, or Customer Relationship Management,

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Speaker 4: and it's just it's a database, right. It has like

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Speaker 4: here's all my customers, here's depending what's important to me

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Speaker 4: their phone number or if I'm a dentist the last

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Speaker 4: time they had to check up r or if I'm

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Speaker 4: a retailer, of the total of all their purchases. And

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Speaker 4: you can kind of extrapolate from there to more and

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Speaker 4: more sophisticated products. So being able to do marketing segmentation

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Speaker 4: and send the right promotional email to just the right people,

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Speaker 4: and that's incredibly valuable people. It's hard to build, it's expensive,

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Speaker 4: and so Salesforce has a lot of customers that kind

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Speaker 4: of rely on it for this core set of services

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Speaker 4: that the branch onto a lot of things, but are

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Speaker 4: roughly around customer relationship management and marketing. So what do

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Speaker 4: people do with that software, Well, they have you know,

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Speaker 4: they sit in conference rooms and they show each other

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Speaker 4: slide decks saying this would be a great promotional idea.

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Speaker 3: Which is very relatable.

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Speaker 4: That's the most Yeah if you who said this, But

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Speaker 4: someone's description of like, here's what a lot of people's

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Speaker 4: job are and this is this is actually a great

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Speaker 4: opportunity for slack. Their job is to get some data,

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Speaker 4: put it into Excel, make a chart, take a screenshot

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Speaker 4: of the chart, past into a PowerPoint, and then email

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Speaker 4: the PowerPoint to people. And then it really is like

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Speaker 4: that's that's what's seventy percent of people with desk jobs

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Speaker 4: do is But so sorry, people are trying to decide

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Speaker 4: how can we use this database of customer activity or

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Speaker 4: marketing to be to make more money, to be more

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Speaker 4: effective on our business. And that's almost certainly something that

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Speaker 4: AI will do better or at least, you know, having

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Speaker 4: an AI copilot alongside here.

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Speaker 2: So AI could, for instance, like look at your proprietary database.

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Speaker 2: I mean the software itself is not proprietary, but the

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Speaker 2: data within it is unique to your company and maybe

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Speaker 2: spot like opportunities within it that you, as a human

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Speaker 2: wouldn't have thought of.

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Speaker 4: Yeah, absolutely, I mean so both Like people who are

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Speaker 4: professional investers, I love the stories of you know, they

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Speaker 4: drive outside some parking lot in the mall and see

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Speaker 4: who's right, which retailer has the most cars parked?

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Speaker 2: And this is my favorite genre of sell side research.

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Speaker 2: It's when they send the analysts to like the shopping

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Speaker 2: mall to look at foot traffic and stuff like that.

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Speaker 4: But people do that inside their company too, right. They're

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Speaker 4: looking for opportunities. Should we sell more of access to

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Speaker 4: sell more of? Why should we sell more online? Should

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Speaker 4: we you know, open more smaller retail outlets so that

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Speaker 4: customers can come in and see the product, or should

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Speaker 4: we concentrate on whatever? I think most of that stuff is.

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Speaker 4: You're going to do a better job with with some companion,

436
00:20:56,240 --> 00:20:59,520
Speaker 4: let's say that finds those patterns more easily because they

437
00:20:59,520 --> 00:21:02,919
Speaker 4: can just day the AI.

438
00:21:03,920 --> 00:21:06,679
Speaker 2: I have this image of Clippy coming back and being like,

439
00:21:06,760 --> 00:21:08,320
Speaker 2: have you considered well, I.

440
00:21:08,240 --> 00:21:10,080
Speaker 4: Think you think about things much faster to you?

441
00:21:10,280 --> 00:21:13,280
Speaker 1: Well, the I mean the other area. And just as

442
00:21:13,320 --> 00:21:16,680
Speaker 1: you said customer relationship management, it seems like a I

443
00:21:16,680 --> 00:21:20,399
Speaker 1: would be really great at like remembering birthdays, and for

444
00:21:20,480 --> 00:21:24,920
Speaker 1: a salesperson specifically, it's like, hey, how did you know, oh,

445
00:21:24,960 --> 00:21:29,400
Speaker 1: your daughter turned sixteen and you know she need driver's

446
00:21:29,440 --> 00:21:31,800
Speaker 1: insurance or something like that. And then it's like, well,

447
00:21:31,840 --> 00:21:34,720
Speaker 1: does the salesperson even need to like pay attention or

448
00:21:34,720 --> 00:21:36,760
Speaker 1: can they just put in a rule that says, every

449
00:21:36,760 --> 00:21:40,680
Speaker 1: time I have a client that you know, has someone

450
00:21:40,720 --> 00:21:42,840
Speaker 1: in their family that has a major life event, and

451
00:21:43,080 --> 00:21:45,080
Speaker 1: just send them a message and something that seems like

452
00:21:45,119 --> 00:21:47,840
Speaker 1: it's coming from me, and so like, ay, like, well

453
00:21:47,840 --> 00:21:50,520
Speaker 1: it be good enough so that like sales salespeople can

454
00:21:50,600 --> 00:21:53,040
Speaker 1: like talk to one hundred clients in a day versus ten,

455
00:21:53,720 --> 00:21:56,919
Speaker 1: and not maybe like two out of those one hundred

456
00:21:56,920 --> 00:21:59,679
Speaker 1: things times say something so embarrassing that it destroys their

457
00:21:59,800 --> 00:22:00,520
Speaker 1: entire reputation.

458
00:22:01,240 --> 00:22:04,080
Speaker 4: Well, the I don't know if it's good news or whatever.

459
00:22:04,520 --> 00:22:07,199
Speaker 4: The fact is that people who have done that for

460
00:22:07,240 --> 00:22:10,520
Speaker 4: a while you don't even to kind of like automatically

461
00:22:10,560 --> 00:22:12,480
Speaker 4: get a prompt you send at your desk and pops

462
00:22:12,520 --> 00:22:13,800
Speaker 4: up and says you should email I got.

463
00:22:13,720 --> 00:22:15,760
Speaker 1: A happy birthday from the dentist, and it's like, yeah,

464
00:22:15,800 --> 00:22:18,159
Speaker 1: I'm not. I don't. I'm not. I'm not under the

465
00:22:18,200 --> 00:22:20,560
Speaker 1: illusion that the dentist is like paying attention to.

466
00:22:20,800 --> 00:22:22,359
Speaker 4: But also I get a lot of it. And like,

467
00:22:22,440 --> 00:22:26,880
Speaker 4: the dumb version of this is SDR sales development representatives

468
00:22:26,960 --> 00:22:29,400
Speaker 4: or like kind of people who are trying to drum

469
00:22:29,440 --> 00:22:31,600
Speaker 4: up some business for a company send a lot of email.

470
00:22:31,760 --> 00:22:33,720
Speaker 4: I think I probably get a disproportionate amount this because

471
00:22:33,720 --> 00:22:35,280
Speaker 4: I've made a bunch of databases as a CEO of

472
00:22:35,320 --> 00:22:37,480
Speaker 4: a company who will buy software from you. So I

473
00:22:37,520 --> 00:22:40,560
Speaker 4: just get all of these ridiculous pitches like Hello Stewart,

474
00:22:40,720 --> 00:22:42,760
Speaker 4: I couldn't help but notice that you are the CEO

475
00:22:42,800 --> 00:22:46,080
Speaker 4: of Slack Technologies in Corporated, and you're like, I mean them.

476
00:22:46,840 --> 00:22:48,840
Speaker 1: We get that people think that we can make purchasing

477
00:22:48,880 --> 00:22:51,119
Speaker 1: decisions on behalf of Bloomberg all the time.

478
00:22:51,280 --> 00:22:53,520
Speaker 4: So I think better versions of that. But I think

479
00:22:53,920 --> 00:22:57,240
Speaker 4: even more, remember Google demo that that voice assistant that

480
00:22:57,280 --> 00:22:59,879
Speaker 4: would call and make an appointment at the hairdresser for you.

481
00:23:00,359 --> 00:23:02,880
Speaker 4: It was like it even like it paused and said

482
00:23:03,320 --> 00:23:07,639
Speaker 4: and stuff like this very convincing. This image of like

483
00:23:08,119 --> 00:23:10,479
Speaker 4: I'm a salesperson and you know you're a buyer at

484
00:23:10,520 --> 00:23:14,040
Speaker 4: some company, and my AI automatically generates this long email

485
00:23:14,080 --> 00:23:16,440
Speaker 4: to you, and then your AI reads the email and

486
00:23:16,480 --> 00:23:19,280
Speaker 4: summarizes it and just says like Stewart has widgets sell

487
00:23:19,600 --> 00:23:22,520
Speaker 4: And then you know, your generates this long response to me,

488
00:23:22,720 --> 00:23:25,280
Speaker 4: and then my AI reads it. I can imagine a

489
00:23:25,320 --> 00:23:28,359
Speaker 4: lot of activity just becoming like AI.

490
00:23:29,359 --> 00:23:32,320
Speaker 2: So this was going to lead into my next question,

491
00:23:32,480 --> 00:23:34,159
Speaker 2: and I'm trying to think about how to phrase this,

492
00:23:34,240 --> 00:23:38,800
Speaker 2: but you can imagine how AI would be very very

493
00:23:38,840 --> 00:23:43,040
Speaker 2: relevant to an application like Slack. But I guess the

494
00:23:43,119 --> 00:23:48,199
Speaker 2: question is how extreme do you think companies will go here?

495
00:23:48,280 --> 00:23:52,520
Speaker 2: Because you know, there's a big difference between having a companion,

496
00:23:52,600 --> 00:23:55,359
Speaker 2: as you put it, who is helpfully pointing stuff out

497
00:23:55,400 --> 00:23:58,840
Speaker 2: and maybe you know, going over your data and spotting

498
00:23:58,880 --> 00:24:01,199
Speaker 2: things that you wouldn't have seen other wise. And then

499
00:24:01,240 --> 00:24:03,560
Speaker 2: at the other end of the spectrum, you can also

500
00:24:03,600 --> 00:24:06,960
Speaker 2: imagine where you have, you know, someone who actually uses

501
00:24:07,000 --> 00:24:09,720
Speaker 2: AI basically to do their whole job. You know, you

502
00:24:09,760 --> 00:24:15,160
Speaker 2: could just automate responses to Slack and retreat completely from

503
00:24:15,240 --> 00:24:18,920
Speaker 2: that communications platform. So how far do you think companies

504
00:24:18,960 --> 00:24:20,560
Speaker 2: will go with this tech?

505
00:24:20,760 --> 00:24:23,480
Speaker 4: Yeah, I think they'll go all the way and where well,

506
00:24:23,520 --> 00:24:25,040
Speaker 4: I don't know what all the way is, by the way,

507
00:24:25,040 --> 00:24:27,800
Speaker 4: but I see this example all the time. Ben Evans,

508
00:24:27,800 --> 00:24:31,040
Speaker 4: who is a former vc I Guess Andres and Horowitz,

509
00:24:31,280 --> 00:24:34,280
Speaker 4: wrote this great article called Office Messaging and Verbs, and

510
00:24:34,320 --> 00:24:37,160
Speaker 4: in the beginning he takes these stills from the movie

511
00:24:37,200 --> 00:24:40,000
Speaker 4: The Apartment Jack Lemon and Shirlie McLain. It's nineteen sixty

512
00:24:40,280 --> 00:24:43,040
Speaker 4: and Jack Lemon works at an insurance company and there's

513
00:24:43,119 --> 00:24:44,840
Speaker 4: like all these you know, like early in the days

514
00:24:44,840 --> 00:24:47,960
Speaker 4: of office buildings, all these shots of like long rows

515
00:24:47,960 --> 00:24:51,159
Speaker 4: and columns of desks, and on his desk there's an

516
00:24:51,200 --> 00:24:54,959
Speaker 4: adding machine, there's a typewriter, there's a telephone. And then

517
00:24:55,000 --> 00:24:56,639
Speaker 4: people come by with the push carts, you know, with

518
00:24:56,680 --> 00:24:58,440
Speaker 4: paper on it, and they like they'll put some paper

519
00:24:58,480 --> 00:25:01,520
Speaker 4: on his desk and then he'll perform some calculations and

520
00:25:01,520 --> 00:25:03,120
Speaker 4: then type up the results, and they put the paper

521
00:25:03,119 --> 00:25:04,680
Speaker 4: on the other side of his desk, at his outbox,

522
00:25:04,680 --> 00:25:06,679
Speaker 4: and someone will come along with a trolley and take that.

523
00:25:07,280 --> 00:25:10,640
Speaker 4: He's literally a sell on a spreadsheet. That is exactly

524
00:25:10,840 --> 00:25:15,160
Speaker 4: what he's doing. It's like take input, you know, execute formula,

525
00:25:15,280 --> 00:25:19,000
Speaker 4: produce output. And that's what each Ben talked about. Each

526
00:25:19,040 --> 00:25:22,200
Speaker 4: floor of this insurance company's office is like one worksheet

527
00:25:22,280 --> 00:25:24,600
Speaker 4: in a big fact sheet. The same number of people

528
00:25:24,600 --> 00:25:27,480
Speaker 4: work at insurance companies today. No one does that anymore.

529
00:25:27,600 --> 00:25:29,200
Speaker 4: So when you say that people do their whole jobs

530
00:25:29,200 --> 00:25:32,480
Speaker 4: with AI, that'll last for a little while. You know.

531
00:25:32,560 --> 00:25:35,480
Speaker 4: Let's say if you're content marketer, and honestly you couldn't

532
00:25:35,840 --> 00:25:38,040
Speaker 4: a I couldn't do worse than most of the garbage

533
00:25:38,080 --> 00:25:42,439
Speaker 4: content marketing. But eventually people say, look, that's not your

534
00:25:42,480 --> 00:25:44,920
Speaker 4: job anymore. Like no one's job is to perform a rhythm.

535
00:25:44,960 --> 00:25:46,119
Speaker 3: And that's a good way of looking at it.

536
00:25:46,160 --> 00:25:48,679
Speaker 2: I just remember my first job at Bloomberg when I

537
00:25:48,720 --> 00:25:51,840
Speaker 2: was an intern, was to monitor the fax machine and

538
00:25:51,920 --> 00:25:55,199
Speaker 2: to actually like bring the important faxes to someone. And

539
00:25:55,240 --> 00:25:57,639
Speaker 2: that has that job has thankfully gone away, and I

540
00:25:57,640 --> 00:25:58,880
Speaker 2: don't feel that bad about it.

541
00:26:00,160 --> 00:26:02,440
Speaker 4: I think hundred years the pace at which jobs become

542
00:26:02,480 --> 00:26:04,240
Speaker 4: obsolete has definitely increased.

543
00:26:04,520 --> 00:26:07,720
Speaker 1: I think this idea that you know, tech will not

544
00:26:08,240 --> 00:26:11,320
Speaker 1: end employment as we know it. It'll likely change and

545
00:26:11,480 --> 00:26:14,640
Speaker 1: certain specific careers or categories will go away and new

546
00:26:14,640 --> 00:26:18,000
Speaker 1: things will created. But as someone obviously in your career

547
00:26:18,000 --> 00:26:21,320
Speaker 1: who has hired many engineers and coders, and this is

548
00:26:21,320 --> 00:26:23,679
Speaker 1: a whole other aspect of AIS, like people are kind

549
00:26:23,680 --> 00:26:26,119
Speaker 1: of blown away frequently, like how do you see like

550
00:26:26,240 --> 00:26:28,119
Speaker 1: that specific.

551
00:26:28,800 --> 00:26:32,280
Speaker 3: Changing or specifically are freaked out right?

552
00:26:32,359 --> 00:26:34,879
Speaker 1: And is there going to be an infinite demand for

553
00:26:35,080 --> 00:26:39,000
Speaker 1: the skills that we call coding or engineering today, or

554
00:26:39,080 --> 00:26:42,240
Speaker 1: will like what we think of as a coder sort

555
00:26:42,280 --> 00:26:43,640
Speaker 1: of be a different skill set.

556
00:26:44,640 --> 00:26:47,720
Speaker 4: It's a little bit harder to extrapolate, but definitely it

557
00:26:47,760 --> 00:26:50,720
Speaker 4: will become different. But even people kind of tending to

558
00:26:51,200 --> 00:26:54,720
Speaker 4: these machines who are generating code for them, software is

559
00:26:54,720 --> 00:26:57,720
Speaker 4: really hard to make and it's like surprisingly hard, and

560
00:26:57,720 --> 00:26:59,680
Speaker 4: that's why there's so much crappy software. I don't know

561
00:26:59,720 --> 00:27:04,320
Speaker 4: exactly what their supervisory duties will be, but it is

562
00:27:04,359 --> 00:27:06,520
Speaker 4: going to be just like any wave of technology, it's

563
00:27:06,560 --> 00:27:09,119
Speaker 4: like this massive augmentation and that people end up moving

564
00:27:09,280 --> 00:27:12,720
Speaker 4: higher up the value chain, just like you know, I

565
00:27:12,800 --> 00:27:17,399
Speaker 4: sometimes think about banks in I don't know, nineteen tens

566
00:27:17,480 --> 00:27:19,800
Speaker 4: or something like that. I have no idea how they

567
00:27:19,840 --> 00:27:22,240
Speaker 4: knew how much money anyone had. I think this all

568
00:27:22,240 --> 00:27:23,680
Speaker 4: the time, talk about.

569
00:27:23,440 --> 00:27:26,720
Speaker 1: Card cattle all the time, like how did stocks work?

570
00:27:27,720 --> 00:27:30,200
Speaker 3: You opened the vaults, and I know, I'm glad you said.

571
00:27:30,400 --> 00:27:32,040
Speaker 1: It makes no sense to me that we had any

572
00:27:32,080 --> 00:27:34,679
Speaker 1: banking and finance before computers. I just cannot rep my

573
00:27:34,680 --> 00:27:35,080
Speaker 1: head around.

574
00:27:35,160 --> 00:27:36,960
Speaker 2: Well, you also had a lot of bank failures, which

575
00:27:37,000 --> 00:27:38,360
Speaker 2: suggests that they weren't.

576
00:27:38,040 --> 00:27:39,560
Speaker 1: Maybe actually very good in it.

577
00:27:40,280 --> 00:27:43,240
Speaker 4: But then imagine being like a stock trader in the

578
00:27:43,280 --> 00:27:46,320
Speaker 4: nineteen sixties than in the eighties, and then in the

579
00:27:46,320 --> 00:27:50,879
Speaker 4: two thousands, and now you know, like the thirteen milliseconds

580
00:27:50,920 --> 00:27:53,119
Speaker 4: you get by moving your servers closer to wherever in

581
00:27:53,160 --> 00:27:55,200
Speaker 4: New Jersey make a real difference. No one was doing

582
00:27:55,200 --> 00:27:58,119
Speaker 4: that before. But there again, there's no there's not fewer

583
00:27:58,240 --> 00:28:02,080
Speaker 4: jobs impact probably a lot more jobs that are trying

584
00:28:02,080 --> 00:28:04,479
Speaker 4: to make money off trading than there were the sixties

585
00:28:04,560 --> 00:28:05,960
Speaker 4: or the eighties. So I think the same thing is

586
00:28:06,000 --> 00:28:09,439
Speaker 4: true of coders, whatever that role evolves to be. You know,

587
00:28:09,560 --> 00:28:13,120
Speaker 4: like no one in the sixties was hiring physics PhDs

588
00:28:13,160 --> 00:28:16,000
Speaker 4: to be quants at some hedge fund. But now, look

589
00:28:16,040 --> 00:28:18,480
Speaker 4: there's another pathway for people who do PhDs in physics,

590
00:28:18,480 --> 00:28:20,160
Speaker 4: and so I think the same thing will be true

591
00:28:20,320 --> 00:28:21,160
Speaker 4: of coding as well.

592
00:28:21,720 --> 00:28:25,640
Speaker 2: Since this is now firmly an AI conversation, can we ask,

593
00:28:25,800 --> 00:28:28,480
Speaker 2: you know, is this something that you're interested in? And

594
00:28:28,680 --> 00:28:31,280
Speaker 2: is this maybe what your next project could be?

595
00:28:32,800 --> 00:28:35,800
Speaker 4: Weirdly, not really, I mean so I'm sorry. I'm interested

596
00:28:35,840 --> 00:28:39,000
Speaker 4: in it as like a human, as a citizen, I

597
00:28:39,040 --> 00:28:43,280
Speaker 4: haven't come up with anything where I'm uniquely able to contribute.

598
00:28:43,280 --> 00:28:46,080
Speaker 4: I don't think in AI. Maybe that'll change because you know,

599
00:28:46,200 --> 00:28:49,440
Speaker 4: it'll become part of the background of everyone's roles and

600
00:28:50,000 --> 00:28:52,239
Speaker 4: you come to rely on it more and more, just like

601
00:28:53,040 --> 00:28:56,120
Speaker 4: you know, this is probably a crappy example, but we're

602
00:28:56,240 --> 00:28:58,800
Speaker 4: processing or you know, I would never would have been

603
00:28:59,120 --> 00:29:02,520
Speaker 4: someone who is doing letter press layouts and moving bits

604
00:29:02,520 --> 00:29:04,960
Speaker 4: of lead around and something like that. But it definitely

605
00:29:05,080 --> 00:29:07,680
Speaker 4: changed the way we're prosably changed the way I wrote

606
00:29:07,680 --> 00:29:09,760
Speaker 4: because it's not longhand and I can infinitely edit it,

607
00:29:09,800 --> 00:29:13,240
Speaker 4: and I wrote a thesis for my grad school and

608
00:29:13,280 --> 00:29:15,640
Speaker 4: all that stuff. But maybe a better idea are better

609
00:29:15,720 --> 00:29:20,000
Speaker 4: comparison is EXCEL because I think about in my life,

610
00:29:20,360 --> 00:29:23,480
Speaker 4: I might have done as much financial modeling as all

611
00:29:23,520 --> 00:29:27,240
Speaker 4: of humanity did until nineteen sixty five something like that.

612
00:29:27,280 --> 00:29:29,160
Speaker 4: Because when you because you can just like make a

613
00:29:29,200 --> 00:29:31,560
Speaker 4: spreadsheet and then be like, oh, change those changes, changes

614
00:29:31,880 --> 00:29:35,560
Speaker 4: everything cascades through, whereas before that was you know a

615
00:29:35,640 --> 00:29:39,600
Speaker 4: lot of people with ledgers and calculators and pencils and paper,

616
00:29:39,880 --> 00:29:41,760
Speaker 4: and allows you to think at a much higher level.

617
00:29:41,800 --> 00:29:45,920
Speaker 4: So I think about AI again and that augmentation capacity

618
00:29:46,080 --> 00:30:01,480
Speaker 4: and what it will enable us to do.

619
00:30:04,280 --> 00:30:07,480
Speaker 1: Just actually going sort of back to the last decade

620
00:30:07,520 --> 00:30:10,640
Speaker 1: or two decades, I mean, you know, Slack was sort

621
00:30:10,680 --> 00:30:13,560
Speaker 1: of like the to my mind, one of like the

622
00:30:13,600 --> 00:30:17,479
Speaker 1: prototypical like software as a service companies, but there were

623
00:30:17,520 --> 00:30:19,280
Speaker 1: all these other ones, and many of which we've never

624
00:30:19,320 --> 00:30:21,200
Speaker 1: heard of, that probably made a bunch of money by

625
00:30:21,280 --> 00:30:24,720
Speaker 1: like we're going to improve you know, dentist billing or

626
00:30:24,880 --> 00:30:27,640
Speaker 1: ticketing at sports events. There's all all kinds of like

627
00:30:27,680 --> 00:30:30,280
Speaker 1: little like niche categories that someone found an opportunity for.

628
00:30:30,720 --> 00:30:33,080
Speaker 1: Is there still like I don't know if low hanging

629
00:30:33,160 --> 00:30:35,800
Speaker 1: fruit is the right word, but moderate hanging fruit in

630
00:30:35,880 --> 00:30:39,320
Speaker 1: that world that has not been exhausted of essentially still

631
00:30:39,320 --> 00:30:42,720
Speaker 1: taking like tech one point zero and really legacy tech.

632
00:30:42,760 --> 00:30:45,320
Speaker 1: We've talked about a little bit with like Patrick McKenzie

633
00:30:45,440 --> 00:30:47,160
Speaker 1: and you know, some of the old whether it's government

634
00:30:47,200 --> 00:30:49,400
Speaker 1: and just sort of like, is there still a lot

635
00:30:49,440 --> 00:30:52,160
Speaker 1: of the economy that hasn't really even got to like

636
00:30:52,160 --> 00:30:53,200
Speaker 1: twenty tens tech yet?

637
00:30:53,320 --> 00:30:55,360
Speaker 4: Yeah. I mean, I think anytime you see you're in

638
00:30:55,400 --> 00:30:59,200
Speaker 4: a financial transaction with someone and there's pen and paper involved,

639
00:30:59,200 --> 00:31:00,720
Speaker 4: that there's an opportunit there. And I think a lot

640
00:31:00,760 --> 00:31:03,080
Speaker 4: of the blockers tend to be regulatory. So like when

641
00:31:03,120 --> 00:31:04,760
Speaker 4: you go to the doctor and you know you're give

642
00:31:04,800 --> 00:31:06,440
Speaker 4: them like seven pieces of paper and you have to

643
00:31:06,440 --> 00:31:08,640
Speaker 4: write your name on five piece. Sometimes you have to

644
00:31:08,640 --> 00:31:10,400
Speaker 4: write your name two times on the same sheet of

645
00:31:10,440 --> 00:31:13,600
Speaker 4: paper and the date over and over again. That's a

646
00:31:13,680 --> 00:31:15,880
Speaker 4: regulatory block It's.

647
00:31:15,320 --> 00:31:18,240
Speaker 2: Also my big gripe about fintech, right, which is you

648
00:31:18,240 --> 00:31:20,080
Speaker 2: got a bunch of people going like, oh, it's ridiculous

649
00:31:20,120 --> 00:31:22,520
Speaker 2: that we're still writing checks nowadays or doing this or

650
00:31:22,560 --> 00:31:26,000
Speaker 2: doing that. But often the reason is a regulatory constraint

651
00:31:26,040 --> 00:31:27,440
Speaker 2: versus a technological one.

652
00:31:27,560 --> 00:31:32,160
Speaker 4: Yeah, So I think where eventually those regulatory dams break

653
00:31:32,240 --> 00:31:38,400
Speaker 4: because well maybe I've mentioned with such confidence, but I

654
00:31:38,400 --> 00:31:41,120
Speaker 4: can't imagine that one hundred years from now we're still

655
00:31:41,240 --> 00:31:42,880
Speaker 4: getting seven pieces of paper when you go to the

656
00:31:42,920 --> 00:31:44,960
Speaker 4: doctor's office and have to write our name on five

657
00:31:45,000 --> 00:31:48,040
Speaker 4: of them. I think there's a lot of opportunities for

658
00:31:48,200 --> 00:31:51,040
Speaker 4: real improvement, and the low hanging fruit maybe never ends

659
00:31:51,040 --> 00:31:54,760
Speaker 4: because as soon as you automate some layer and people

660
00:31:54,800 --> 00:31:57,479
Speaker 4: start operating at a higher level, the people who are

661
00:31:57,520 --> 00:32:01,160
Speaker 4: doing financial modeling aren't doing arithmetic anymore. There thinking about

662
00:32:01,520 --> 00:32:04,840
Speaker 4: the business. Then there's new opportunities for automation that comme

663
00:32:04,840 --> 00:32:06,560
Speaker 4: at higher and higher levels. And that's what we really

664
00:32:06,600 --> 00:32:12,840
Speaker 4: thought about at slack, the ability to maybe a little

665
00:32:12,840 --> 00:32:15,000
Speaker 4: bit abstract, we thought about Slack is like a messaging

666
00:32:15,040 --> 00:32:18,160
Speaker 4: bus inside of a computer. It's the like interchange or

667
00:32:18,160 --> 00:32:20,800
Speaker 4: the traffic controller for all the thoughts that people are having.

668
00:32:20,840 --> 00:32:23,680
Speaker 4: And a lot of those are just like, yeah, want

669
00:32:23,720 --> 00:32:24,960
Speaker 4: to get lunch or something like that, and some of

670
00:32:25,000 --> 00:32:28,400
Speaker 4: them are here's my extensive proposal for next quarters blah

671
00:32:28,400 --> 00:32:33,400
Speaker 4: blah blah. When you're able to improve the efficacy of

672
00:32:33,680 --> 00:32:38,400
Speaker 4: communication versus the all set of paper memo that can

673
00:32:38,400 --> 00:32:40,400
Speaker 4: schedule a meeting and it's three weeks from now or

674
00:32:40,440 --> 00:32:42,800
Speaker 4: something like that, it opens up new possibilities, and the

675
00:32:42,840 --> 00:32:45,320
Speaker 4: same thing is true with every bit of automation that

676
00:32:45,360 --> 00:32:48,800
Speaker 4: you can do. There's a huge amount of business processes

677
00:32:48,840 --> 00:32:52,440
Speaker 4: that are essentially humans translating between one database and another

678
00:32:52,480 --> 00:32:56,560
Speaker 4: because the databases aren't connected effectively. And eventually those databases

679
00:32:56,600 --> 00:33:00,520
Speaker 4: become connected effectively and people move up the chain again

680
00:33:00,680 --> 00:33:04,440
Speaker 4: and get to work on harder stuff or I guess

681
00:33:04,840 --> 00:33:06,120
Speaker 4: stuff that produces more value.

682
00:33:06,280 --> 00:33:10,000
Speaker 2: AI to standardize databases would be amazing. Just on the

683
00:33:10,040 --> 00:33:12,920
Speaker 2: topic of constraints and going back to the first part

684
00:33:13,040 --> 00:33:15,400
Speaker 2: of this conversation where you were talking about how you

685
00:33:15,400 --> 00:33:18,520
Speaker 2: know a lot of the empire building or hiring boom

686
00:33:18,520 --> 00:33:21,600
Speaker 2: that we've seen in tech companies was potentially driven by

687
00:33:21,640 --> 00:33:23,920
Speaker 2: low interest rates and you know, you didn't have a

688
00:33:23,960 --> 00:33:27,240
Speaker 2: financial constraint on the amount of resources you could accumulate.

689
00:33:28,160 --> 00:33:31,760
Speaker 2: It does feel like in twenty twenty three, Silicon Valley,

690
00:33:31,880 --> 00:33:34,400
Speaker 2: the tech industry in general, is in a very different

691
00:33:34,560 --> 00:33:35,680
Speaker 2: financial environment.

692
00:33:35,760 --> 00:33:37,160
Speaker 3: We have higher interest rates.

693
00:33:37,320 --> 00:33:40,400
Speaker 2: We just saw Silicon Value Bank fail and that seems

694
00:33:40,440 --> 00:33:43,360
Speaker 2: to have taken a big chunk of potential liquidity out

695
00:33:43,400 --> 00:33:47,080
Speaker 2: of the market. How do you expect that to impact

696
00:33:47,320 --> 00:33:49,520
Speaker 2: the industry and what are you seeing now.

697
00:33:49,960 --> 00:33:52,240
Speaker 4: Well, obviously we've seen a lot of layoffs, and I

698
00:33:52,240 --> 00:33:56,200
Speaker 4: think probably you know, I never want to suggest that

699
00:33:56,520 --> 00:33:59,800
Speaker 4: being laid off is a good experience for anyone. For

700
00:34:00,080 --> 00:34:03,720
Speaker 4: the companies, it's almost certainly a healthy thing because a

701
00:34:03,720 --> 00:34:06,640
Speaker 4: lot of companies are you know, that have twenty thousand

702
00:34:06,640 --> 00:34:09,360
Speaker 4: people could probably do what they're doing with twelve thousand

703
00:34:09,400 --> 00:34:11,959
Speaker 4: people or something like that. The higher indust rates, I think,

704
00:34:12,800 --> 00:34:14,440
Speaker 4: I don't know, I don't want to say that they're good.

705
00:34:15,040 --> 00:34:17,800
Speaker 4: But go back to two thousand and nine, which is

706
00:34:17,800 --> 00:34:20,240
Speaker 4: when the company that ended up becoming Slack was founded.

707
00:34:20,520 --> 00:34:23,080
Speaker 4: This is, you know, like March of two thousand and nine.

708
00:34:23,360 --> 00:34:26,720
Speaker 4: So I said, we're still probably in the Great Financial

709
00:34:26,760 --> 00:34:29,120
Speaker 4: Crisis or like you know, maybe just just getting out

710
00:34:29,160 --> 00:34:31,840
Speaker 4: of it. And if you're a venture capitalist like Excel,

711
00:34:31,960 --> 00:34:34,120
Speaker 4: you could buy twenty percent of what became Slack for

712
00:34:34,200 --> 00:34:36,880
Speaker 4: five million dollars, you know, and then you end up

713
00:34:36,880 --> 00:34:40,000
Speaker 4: making four billion or something like that. So I think

714
00:34:40,080 --> 00:34:42,799
Speaker 4: from a if you're a VC, it's it's probably not

715
00:34:42,840 --> 00:34:45,680
Speaker 4: all bad. You know, like some of your existing investments

716
00:34:45,680 --> 00:34:47,920
Speaker 4: I think lost a lot of value and maybe we're

717
00:34:47,920 --> 00:34:50,880
Speaker 4: going to have to defer realizing a lot of gains

718
00:34:50,880 --> 00:34:52,920
Speaker 4: that you might have realized much more quickly a couple

719
00:34:52,960 --> 00:34:54,960
Speaker 4: of years ago. But over the long run, I don't

720
00:34:54,960 --> 00:34:58,000
Speaker 4: think it's a net bad for them for the companies.

721
00:34:58,440 --> 00:34:59,680
Speaker 4: It can be a debt bad can.

722
00:34:59,600 --> 00:35:03,680
Speaker 1: I as a sort of cultural slash economic question, which

723
00:35:03,760 --> 00:35:05,920
Speaker 1: is like I was, I think slack is like an

724
00:35:05,960 --> 00:35:09,280
Speaker 1: acronym for something, right, Yeah, it's kind.

725
00:35:09,080 --> 00:35:10,080
Speaker 4: Of It's unclear.

726
00:35:10,120 --> 00:35:12,279
Speaker 1: So we have a lot of the conversation you're the

727
00:35:12,280 --> 00:35:13,279
Speaker 1: one who created.

728
00:35:13,080 --> 00:35:15,680
Speaker 4: Well, there's we have a transcript of a conversation we

729
00:35:15,680 --> 00:35:18,120
Speaker 4: were having internally where I suggest it as searchable log

730
00:35:18,200 --> 00:35:20,600
Speaker 4: of all communication and knowledge. But I'm not sure if

731
00:35:20,640 --> 00:35:22,799
Speaker 4: I Yeah, I'm not sure if I if we came

732
00:35:22,880 --> 00:35:24,200
Speaker 4: up with the name slack.

733
00:35:24,080 --> 00:35:26,239
Speaker 2: First, because I think slack is catch Here I got

734
00:35:26,320 --> 00:35:27,600
Speaker 2: to say, but the reason.

735
00:35:27,320 --> 00:35:31,760
Speaker 1: I actually asked the question is because the last decade

736
00:35:31,760 --> 00:35:34,560
Speaker 1: that edition everything else was characterized by labor market slack,

737
00:35:34,920 --> 00:35:36,640
Speaker 1: and so I wish think it's sort of interesting that

738
00:35:36,680 --> 00:35:39,480
Speaker 1: this big company slack was like came of the decade

739
00:35:39,480 --> 00:35:43,319
Speaker 1: of like loose labor markets. And it also seems like

740
00:35:43,680 --> 00:35:47,160
Speaker 1: that your technology changed in many ways, the relationship between

741
00:35:47,560 --> 00:35:51,600
Speaker 1: management and employees, and employees and places can unionize more

742
00:35:51,600 --> 00:35:53,960
Speaker 1: easily because they have slack channels and they can communicate

743
00:35:54,040 --> 00:35:57,760
Speaker 1: in ways that maybe the management doesn't love, or maybe

744
00:35:57,840 --> 00:36:01,279
Speaker 1: managers at companies want all their work, want them back

745
00:36:01,280 --> 00:36:02,840
Speaker 1: to the office, and it's like, well, yeah, but we

746
00:36:02,880 --> 00:36:05,560
Speaker 1: can communicate on slack et cetera. Can you, like, do

747
00:36:05,560 --> 00:36:07,680
Speaker 1: you have any like like sort of like broader reflections

748
00:36:07,680 --> 00:36:09,400
Speaker 1: on the way like, because this is not really like

749
00:36:09,440 --> 00:36:11,799
Speaker 1: a software question, it's a very slack specific question of

750
00:36:11,840 --> 00:36:16,600
Speaker 1: like how you saw firsthand your software like changing this

751
00:36:16,680 --> 00:36:19,640
Speaker 1: sort of employee management or just this sort of corporate environment.

752
00:36:19,920 --> 00:36:21,400
Speaker 4: Yeah. I think, going back to that idea of the

753
00:36:21,440 --> 00:36:24,719
Speaker 4: ditch digger given a back up, most technology kind of

754
00:36:24,760 --> 00:36:27,920
Speaker 4: amplifies our ability to accomplish something or augment us and

755
00:36:28,000 --> 00:36:32,680
Speaker 4: gives us extra power. So that's true everywhere. Obviously, where

756
00:36:32,719 --> 00:36:34,680
Speaker 4: you intend it to be true, that's great, or you

757
00:36:34,719 --> 00:36:38,320
Speaker 4: don't intend it to be true. Sometimes it's great, sometimes

758
00:36:38,320 --> 00:36:43,120
Speaker 4: it's terrible, sometimes it's somewhere in between. But the superpower

759
00:36:43,160 --> 00:36:45,960
Speaker 4: in people's ability to have conversations at work will include

760
00:36:46,239 --> 00:36:50,000
Speaker 4: conversations that sometimes managers where they didn't have. I do

761
00:36:50,080 --> 00:36:53,320
Speaker 4: think that's something that works itself out relatively quickly. And

762
00:36:53,360 --> 00:36:57,960
Speaker 4: I also think interestingly, internally we will call that slack divism,

763
00:36:59,120 --> 00:37:02,400
Speaker 4: andably someone else came up with that outside. But I

764
00:37:02,400 --> 00:37:06,000
Speaker 4: think that's probably moderated quite a bit over the last year,

765
00:37:06,400 --> 00:37:09,600
Speaker 4: just because the environment has changed. Because the other, you know,

766
00:37:10,200 --> 00:37:12,640
Speaker 4: fact that comes with the low interest rates is that

767
00:37:12,680 --> 00:37:14,680
Speaker 4: people pay a lot of money for engineers. The engineers

768
00:37:14,680 --> 00:37:17,480
Speaker 4: can always change jobs and get paid more and more money,

769
00:37:17,760 --> 00:37:20,040
Speaker 4: and that's less true today. People are more worried about

770
00:37:20,239 --> 00:37:22,799
Speaker 4: job security, so there's less And I'm not saying this

771
00:37:22,840 --> 00:37:24,680
Speaker 4: is a good thing necessarily, but there's certainly less like

772
00:37:24,760 --> 00:37:28,200
Speaker 4: labor organizing happening on people's slack insance that there would

773
00:37:28,200 --> 00:37:29,120
Speaker 4: have been two years ago.

774
00:37:30,239 --> 00:37:33,080
Speaker 2: This is a slightly weird question maybe, but I mean

775
00:37:33,160 --> 00:37:35,120
Speaker 2: one of the debates about work from home is are

776
00:37:35,719 --> 00:37:38,880
Speaker 2: people actually more productive in an office environment or at home?

777
00:37:39,239 --> 00:37:39,960
Speaker 3: Within slack?

778
00:37:40,080 --> 00:37:44,320
Speaker 2: Did you ever have conversations about maybe like measuring productivity

779
00:37:44,600 --> 00:37:47,880
Speaker 2: in some way using the communications channel?

780
00:37:48,160 --> 00:37:50,719
Speaker 4: We did, and they were all they all seemed kind

781
00:37:50,719 --> 00:37:53,680
Speaker 4: of doomed. You know that there's a had a I

782
00:37:53,680 --> 00:37:55,839
Speaker 4: don't know if she's still the CIO, but a woman

783
00:37:55,880 --> 00:37:58,600
Speaker 4: named Luri Beer was the CEO of JPY. Maybe still

784
00:37:58,640 --> 00:38:00,560
Speaker 4: is if so Hi, Lori and we have the great

785
00:38:00,600 --> 00:38:03,839
Speaker 4: conversation once they breakfast at a conference and we're talking

786
00:38:03,840 --> 00:38:06,240
Speaker 4: about like the history of trying to measure the productivity

787
00:38:06,239 --> 00:38:09,319
Speaker 4: of software engineers, which is like famously impossible. So you

788
00:38:09,480 --> 00:38:12,160
Speaker 4: would say, well, how many lines of code do they produce?

789
00:38:12,160 --> 00:38:13,920
Speaker 4: And then people would just change their coding style to

790
00:38:13,960 --> 00:38:18,160
Speaker 4: be you yeah, exactly. Or you would say how many

791
00:38:18,160 --> 00:38:20,759
Speaker 4: bugs get closed? And then instead of just fixing something

792
00:38:20,800 --> 00:38:22,640
Speaker 4: right away, people take the time to go file the

793
00:38:22,640 --> 00:38:24,640
Speaker 4: bug and then fix it and then close the bug.

794
00:38:24,600 --> 00:38:26,840
Speaker 2: Insert a bunch of bugs. Then you're the solution to

795
00:38:26,880 --> 00:38:27,520
Speaker 2: your own problems.

796
00:38:28,600 --> 00:38:32,960
Speaker 1: Like as soon as you start to measure something then yeah, yeah.

797
00:38:32,480 --> 00:38:35,520
Speaker 4: Yeah, and so you kind of I don't know what

798
00:38:35,560 --> 00:38:37,200
Speaker 4: we came up with, the way the conversation kind of

799
00:38:37,440 --> 00:38:42,680
Speaker 4: ended was looking at employee engagement surveys. If engineers are happy,

800
00:38:43,120 --> 00:38:46,239
Speaker 4: it doesn't necessarily mean that they're productive. But if they're unhappy,

801
00:38:48,239 --> 00:38:51,120
Speaker 4: I think, do I have the backwards If.

802
00:38:51,000 --> 00:38:52,960
Speaker 1: They're unhappy, it probably means they're unproductive.

803
00:38:53,040 --> 00:38:56,760
Speaker 4: Yeah, because no engineers are not productive and and happy

804
00:38:57,000 --> 00:39:00,680
Speaker 4: like they just it's it's frustrating to feel like you

805
00:39:00,880 --> 00:39:02,520
Speaker 4: come into work and you put all this effort in

806
00:39:02,600 --> 00:39:04,279
Speaker 4: and nothing happens, you know, like no one wants to

807
00:39:04,280 --> 00:39:06,719
Speaker 4: work on debt end projects that get canceled and all

808
00:39:06,760 --> 00:39:08,399
Speaker 4: of that. So the same thing I think is true,

809
00:39:08,440 --> 00:39:10,480
Speaker 4: Like if you think it's hard to measure the productivity

810
00:39:10,520 --> 00:39:15,960
Speaker 4: have a software engineer, like marketing, it's impossible to measure,

811
00:39:16,000 --> 00:39:20,480
Speaker 4: Like people just have no idea whether their marketing department

812
00:39:20,520 --> 00:39:22,640
Speaker 4: needs to be five times bigger or five times smaller,

813
00:39:22,800 --> 00:39:26,239
Speaker 4: or do they need one at all. I'm not sure

814
00:39:26,239 --> 00:39:29,200
Speaker 4: what you do about that, like the measurement, because there's

815
00:39:29,200 --> 00:39:32,560
Speaker 4: a lot of just kind of hand feel and in management.

816
00:39:32,800 --> 00:39:35,359
Speaker 4: It would be great if you could in some sense,

817
00:39:35,400 --> 00:39:38,880
Speaker 4: but I think you also run the risk of limiting

818
00:39:38,960 --> 00:39:43,720
Speaker 4: all the opportunities for serendipity and accidental discoveries and cool innovation.

819
00:39:44,719 --> 00:39:46,919
Speaker 1: I have I have one last question, and Tracy's gonna

820
00:39:46,920 --> 00:39:49,560
Speaker 1: hate me for asking it. I already asked the cliche question.

821
00:39:49,800 --> 00:39:52,000
Speaker 1: I already asked the cliche question about whether you're going

822
00:39:52,000 --> 00:39:55,280
Speaker 1: to start another games company. Do you still support minting

823
00:39:55,280 --> 00:39:57,560
Speaker 1: the coin to avoid a debt sealing disaster.

824
00:39:57,719 --> 00:39:58,680
Speaker 3: It's unavoidable.

825
00:39:58,719 --> 00:40:02,160
Speaker 2: We can anot have a single con we can talking about.

826
00:40:01,960 --> 00:40:04,239
Speaker 1: The coin, but when there's a when there's a prominent,

827
00:40:04,560 --> 00:40:07,440
Speaker 1: famous person who on Twitter has expressed support.

828
00:40:07,680 --> 00:40:09,880
Speaker 4: Yeah, I feel like that's that's you and me. We

829
00:40:10,000 --> 00:40:12,640
Speaker 4: bonded over over the coin, so yes, I think you

830
00:40:12,680 --> 00:40:16,040
Speaker 4: definitely could. I think it's really funny. The debate about

831
00:40:16,120 --> 00:40:19,240
Speaker 4: modern monetary theory seems to have come down to should

832
00:40:19,239 --> 00:40:21,360
Speaker 4: we spend lots of money? And if you think yes,

833
00:40:21,520 --> 00:40:24,040
Speaker 4: then MMT and if you think no, then you're against

834
00:40:24,080 --> 00:40:26,680
Speaker 4: empty which doesn't seem to capture it at all to me.

835
00:40:26,840 --> 00:40:29,839
Speaker 4: Like the the idea that the deficit is a myth

836
00:40:30,320 --> 00:40:33,680
Speaker 4: seems just obvious, and that the government's role in the

837
00:40:33,920 --> 00:40:36,920
Speaker 4: amount of money is like the adjudicator of a basketball

838
00:40:36,960 --> 00:40:38,839
Speaker 4: game deciding how many points you can have. Too many points,

839
00:40:38,840 --> 00:40:40,480
Speaker 4: I mean, you can mess up the game of basketball,

840
00:40:40,480 --> 00:40:42,600
Speaker 4: so it's not fun for anyone, Like you can cause

841
00:40:42,640 --> 00:40:45,839
Speaker 4: a lot of inflation. But that idea that you need

842
00:40:45,920 --> 00:40:48,080
Speaker 4: to have the money in order to be able to

843
00:40:48,120 --> 00:40:50,480
Speaker 4: spend it is obviously not true if you're the government.

844
00:40:50,520 --> 00:40:51,200
Speaker 3: For what it's worth.

845
00:40:51,239 --> 00:40:53,040
Speaker 2: I'm not a big fan of MMT, but I do

846
00:40:53,080 --> 00:40:54,480
Speaker 2: think the government should spend money.

847
00:40:54,560 --> 00:40:58,200
Speaker 1: So the Stuart Butterfield, this is such a treat to

848
00:40:58,239 --> 00:41:01,400
Speaker 1: have you on kind of funny that our first guest

849
00:41:01,560 --> 00:41:05,040
Speaker 1: in our new like physical video studio is the founder

850
00:41:05,080 --> 00:41:07,640
Speaker 1: of Slack, which, more than anyone else, contributed to the

851
00:41:07,680 --> 00:41:11,279
Speaker 1: ability to work on and operate and communicate remotely. But

852
00:41:11,480 --> 00:41:13,120
Speaker 1: such a treat to have you in and thank you

853
00:41:13,160 --> 00:41:13,840
Speaker 1: so much for joining me.

854
00:41:14,120 --> 00:41:14,480
Speaker 4: Thank you.

855
00:41:14,960 --> 00:41:17,560
Speaker 2: Next interview we can do via Slack. Just to just

856
00:41:17,600 --> 00:41:18,200
Speaker 2: to even.

857
00:41:18,000 --> 00:41:33,000
Speaker 1: It out, Tracy, I thought that was a lot of fun.

858
00:41:33,040 --> 00:41:35,400
Speaker 1: That was a real treat. That was just very I

859
00:41:35,400 --> 00:41:37,279
Speaker 1: don't know in my head, in my mind, I had

860
00:41:37,280 --> 00:41:39,080
Speaker 1: that hyped up and I felt like my mind it

861
00:41:39,080 --> 00:41:40,240
Speaker 1: lived up to my own hype.

862
00:41:40,320 --> 00:41:40,680
Speaker 3: It was.

863
00:41:40,880 --> 00:41:44,600
Speaker 2: It was a wide ranging conversation, and I love that

864
00:41:44,640 --> 00:41:46,799
Speaker 2: we went from like how did banks used to do

865
00:41:46,920 --> 00:41:48,600
Speaker 2: business in the early nineteen.

866
00:41:48,320 --> 00:41:51,800
Speaker 3: Hundreds to like the future of AI market.

867
00:41:51,920 --> 00:41:53,719
Speaker 1: We were going to have to do an episode on

868
00:41:53,880 --> 00:41:56,040
Speaker 1: just like how a bank work one hundred years ago,

869
00:41:56,080 --> 00:41:58,319
Speaker 1: because in my mind, I do not understand how they

870
00:41:58,400 --> 00:42:01,080
Speaker 1: kept track of anyone's how much money anyone had before

871
00:42:01,200 --> 00:42:03,680
Speaker 1: digital technology, Like they really just what had a piece

872
00:42:03,719 --> 00:42:04,480
Speaker 1: of paper for everyone?

873
00:42:04,600 --> 00:42:06,400
Speaker 3: They just opened the vault and see what's in there. No,

874
00:42:07,719 --> 00:42:08,799
Speaker 3: we're all good today.

875
00:42:08,560 --> 00:42:09,120
Speaker 1: We're all good.

876
00:42:09,440 --> 00:42:12,360
Speaker 2: No, there is a lot to pick through that conversation.

877
00:42:12,440 --> 00:42:16,200
Speaker 2: I mean, I do think like the idea of finances

878
00:42:16,239 --> 00:42:19,120
Speaker 2: being a constraint on resources in the workforce, I mean

879
00:42:19,160 --> 00:42:22,560
Speaker 2: it is kind of obvious, but also we are seeing

880
00:42:22,560 --> 00:42:24,520
Speaker 2: it play out in real time right now.

881
00:42:24,440 --> 00:42:25,440
Speaker 3: So it was good to hit that.

882
00:42:26,000 --> 00:42:26,080
Speaker 4: No.

883
00:42:26,280 --> 00:42:28,040
Speaker 1: And just like this idea is like there's it does

884
00:42:28,080 --> 00:42:30,600
Speaker 1: feel like there's multiple turning points at once, and I

885
00:42:30,640 --> 00:42:35,399
Speaker 1: don't feel like anyone knows it's like pretty clear right

886
00:42:35,880 --> 00:42:37,840
Speaker 1: like they're like, oh, well, this is who's this is

887
00:42:37,840 --> 00:42:39,840
Speaker 1: who's going to win, this is who's going to make money,

888
00:42:39,880 --> 00:42:41,719
Speaker 1: this is going to be the business model. Like it

889
00:42:41,760 --> 00:42:43,879
Speaker 1: does feel like even if we didn't have this sort

890
00:42:43,880 --> 00:42:47,520
Speaker 1: of like macro change, there would be a tremendous amount

891
00:42:47,560 --> 00:42:50,280
Speaker 1: of ambiguity about like how value is going to accrue

892
00:42:50,280 --> 00:42:52,560
Speaker 1: and how companies are going to work and all of that.

893
00:42:52,920 --> 00:42:56,160
Speaker 2: Well, also the conversation about Microsoft and how you know,

894
00:42:56,200 --> 00:42:58,920
Speaker 2: two years ago, no one would have expected Microsoft to

895
00:42:58,960 --> 00:43:01,399
Speaker 2: be the sort of front runner in the AI game

896
00:43:01,480 --> 00:43:02,680
Speaker 2: and yet here we are today.

897
00:43:02,719 --> 00:43:03,680
Speaker 3: That was really interesting.

898
00:43:03,960 --> 00:43:07,240
Speaker 2: Do you have any bets on what Stuart's next project

899
00:43:07,280 --> 00:43:07,960
Speaker 2: is actually.

900
00:43:07,719 --> 00:43:08,040
Speaker 3: Going to be?

901
00:43:08,480 --> 00:43:10,520
Speaker 1: Even though he said no, I bet it, I still

902
00:43:10,560 --> 00:43:12,239
Speaker 1: feels like he's probably gonna do a game.

903
00:43:12,840 --> 00:43:16,160
Speaker 3: Can I resist the lure? If you really try twice,

904
00:43:16,200 --> 00:43:18,080
Speaker 3: it's like what times.

905
00:43:19,160 --> 00:43:21,760
Speaker 1: And then it'll pivot that something can be another world

906
00:43:21,800 --> 00:43:23,080
Speaker 1: changing thing. That's my guest.

907
00:43:23,360 --> 00:43:24,080
Speaker 3: Shall we leave it there?

908
00:43:24,120 --> 00:43:24,799
Speaker 1: Let's leave it there?

909
00:43:24,840 --> 00:43:25,200
Speaker 3: All right?

910
00:43:25,239 --> 00:43:27,840
Speaker 2: This has been another episode of the Odd Loots podcast.

911
00:43:27,880 --> 00:43:30,239
Speaker 2: I'm Tracy Alloway. You can follow me on Twitter at

912
00:43:30,320 --> 00:43:31,160
Speaker 2: Tracy Alloway, and.

913
00:43:31,160 --> 00:43:33,919
Speaker 1: I'm Joe Wisenthal. You can follow me on Twitter at

914
00:43:33,960 --> 00:43:37,240
Speaker 1: the Stalwart. Follow our guest Stuart Butterfield under the handle

915
00:43:37,320 --> 00:43:41,240
Speaker 1: at Stewart, Follow our producers Carmen Rodriguez at Carmen Arman

916
00:43:41,360 --> 00:43:43,799
Speaker 1: and dash Oll Bennett at dashbod, and check out all

917
00:43:43,840 --> 00:43:47,480
Speaker 1: of the podcasts at Bloomberg under the handle at podcasts,

918
00:43:47,920 --> 00:43:50,399
Speaker 1: and for more odd Logs content, go to Bloomberg dot

919
00:43:50,440 --> 00:43:53,280
Speaker 1: com slash odd lots, where post transcripts. We have a blog,

920
00:43:53,680 --> 00:43:56,520
Speaker 1: Tracy and I have a weekly newsletter, and check out

921
00:43:56,560 --> 00:43:59,600
Speaker 1: our discord Discord dot gg, slash odd Logs, where we

922
00:43:59,680 --> 00:44:03,720
Speaker 1: chat four seven and stream Bloomberg originals on Apple TV, Roku,

923
00:44:03,840 --> 00:44:07,560
Speaker 1: Samsung TV, and more. Tune in at ten pm Eastern time,