Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Oddlots listeners, you are going to be listening to a
Speaker 2: special recording of the podcast, one that we recorded live
Speaker 2: in San Francisco.
Speaker 4: Yep, that's right.
Speaker 3: This was a conversation that we had at the San
Speaker 3: Francisco MoMA on November twentieth. It was an event sponsored
Speaker 3: by Principal Asset Management, and our guest is Ethan Kurzweild,
Speaker 3: the founder and managing partner of Chemistry VC.
Speaker 5: Yep.
Speaker 2: We talked about all things tech software investing, how investing
Speaker 2: today is different than it was, say in twenty fourteen
Speaker 2: when rates are at zero. We obviously talked about AI
Speaker 2: and how that changes the game of software investing.
Speaker 4: Take a listen.
Speaker 2: Thrilled to be here with the perfect guest, Ethan Kurzweil,
Speaker 2: as his new fund, Chemistry It's it basically launched like
Speaker 2: three weeks ago or something like that, and prior to
Speaker 2: that sixteen years at Bessemer. So literally the perfect guest
Speaker 2: to talk about, you know, VC's the landscape.
Speaker 4: Changing over time, or something like that.
Speaker 5: Well, thanks for having me.
Speaker 6: This is actually the first episode of anything we've done
Speaker 6: since we launched Chemistry, So that's amazingly or thrilled.
Speaker 4: There is obviously so much.
Speaker 2: There's so much we could talk about, talk about the
Speaker 2: macro environment, we talk about AI, we could talk about
Speaker 2: the political environment. Maybe we'll touch a little bit on
Speaker 2: all of it. So I'm just gonna ask like a
Speaker 2: really simple question to kick it off, which is in
Speaker 2: the twenty tens, you know, people talked about the Zerp era,
Speaker 2: and some people even look on that quite fundly right
Speaker 2: now with nostalgia, even though at the time zerp was
Speaker 2: sort of seen as like this negative thing didn't seem that.
Speaker 4: Bad out here.
Speaker 2: Though strictly from a macro standpoint, you've been in this game,
Speaker 2: so to speak, for a long time. What's the difference
Speaker 2: right now versus say we were having this conversation in
Speaker 2: twenty fourteen.
Speaker 6: Oh, the good old days of twenty fourteen. I missed
Speaker 6: those days too.
Speaker 5: I wish we could go back.
Speaker 6: So right now, there's lots of things happening in sort
Speaker 6: of the tech landscape broadly as well as like venture,
Speaker 6: and so maybe just taking a few around venture. You
Speaker 6: had this era of explosion of different things, lots of
Speaker 6: different funds and products, money being kind of invested in
Speaker 6: the asset class beyond what it could take, beyond the
Speaker 6: capacity of those companies to absorb the capital and do
Speaker 6: good things with it. I'm an optimist about tech and venture.
Speaker 6: I think more is generally better, but there's a limit
Speaker 6: to that. I think everyone would now agree kind of
Speaker 6: in hindsight, we went a little bit beyond that limit.
Speaker 6: Now we're in this kind of new era where that's happened.
Speaker 6: We're kind of digesting the impact of that, of all
Speaker 6: this capital coming into the space, and you have this
Speaker 6: kind of new technology phenomenon.
Speaker 5: And by the way, it's not really new. It's maybe
Speaker 5: new as it applied to startups.
Speaker 4: You've heard about it for a while.
Speaker 6: I've been hearing about AI for I don't know, a
Speaker 6: few decades like that.
Speaker 4: We'll talk about that.
Speaker 6: We'll get there, but that's now kind of the building
Speaker 6: blocks are now there the technology. Startups without a lot
Speaker 6: of capital can take advantage of it. And so that's
Speaker 6: getting people kind of very very excited again. Even as
Speaker 6: everyone knows it's still this fresh memory in everyone's head
Speaker 6: of how we kind of over capitalized everything, and so
Speaker 6: those two forces are sort of countervailing and it's having
Speaker 6: some interesting impacts that I think we'll probably get into.
Speaker 3: We definitely will the new fund. Why does the world
Speaker 3: need a new venture capital fund? Or if I was
Speaker 3: going to phrase it more diplomatically, like what is it
Speaker 3: that you can do at Chemistry that you couldn't do
Speaker 3: at Best Mark?
Speaker 6: The world does not need a new venture capital fund.
Speaker 6: That's the last thing the world needs launched. Yeah, that
Speaker 6: was the last one before Chemistry. Okay, we were over
Speaker 6: supplied on venture capitals. But the world does need the
Speaker 6: right venture capital fund. And I'll get to why we
Speaker 6: launched Chemistry in a second, But I do think that
Speaker 6: the effect of the capital that came into the asset
Speaker 6: class over the past kind of five to ten years
Speaker 6: has been to create a little bit of a misalignment,
Speaker 6: a misalignment between LPs that's who's invest in venture firms
Speaker 6: and the venture managers, and then a misalignment with founders ultimately,
Speaker 6: and that's what got us the sort of passionate idea
Speaker 6: to bring venture.
Speaker 5: Back to its roots.
Speaker 6: Chemistry is sort of a simple idea. It's a boutique
Speaker 6: venture firm. It's small as designed to scale very slowly,
Speaker 6: so we are not a hyper growth startup, even though
Speaker 6: we try to find those to invest in. We want
Speaker 6: to bring some sort of personal service back to venture capital.
Speaker 6: We don't have big teams of people that we are
Speaker 6: the portfolio services team that works kind of hand on,
Speaker 6: hands on with our startups, and that ethos we felt
Speaker 6: like was missing from a lot of the way that
Speaker 6: kind of as the asset clut's got institutionalized, you lost
Speaker 6: a little bit of the personality and the personal relationships,
Speaker 6: and we felt.
Speaker 5: Like it didn't have to be that way.
Speaker 6: There's nothing bad about the way venture used to be practiced,
Speaker 6: and that it really it just became so missing that
Speaker 6: we felt like, Okay, we'll go do this.
Speaker 2: Let's say I have a lot of money. I'm an
Speaker 2: endowment or whatever, and I'm thinking about allocating money to
Speaker 2: a VC fund or firm. That sounds really nice, personal
Speaker 2: relationships all that, but mostly I just care about getting returns.
Speaker 2: And let's say my assumption is okay, yeah, again, it
Speaker 2: all sounds very nice, but there are advantages to scale.
Speaker 2: There's deal flow that large firms see that you know,
Speaker 2: maybe you know, they're the first call in some round
Speaker 2: or something like that. Why would that be wrong? It's
Speaker 2: not wrong, okay, but it's not the only way to
Speaker 2: practice venture.
Speaker 5: Okay.
Speaker 6: There's definitely advantages to scale, but I think it comes
Speaker 6: at a cost of being able to focus uniquely on
Speaker 6: companies that are at this inflection point moment, this pre
Speaker 6: inflection point where they're about to take off.
Speaker 5: Because when you have a lot.
Speaker 6: Of capital to manage, you're going to make decisions that
Speaker 6: aren't necessarily about how do I find that company that
Speaker 6: needs a three to seven million dollar check at that moment,
Speaker 6: You're thinking about how do I move the move the merchandise,
Speaker 6: move the money that I have in the system. And
Speaker 6: so it may be appropriate to make that investment, but
Speaker 6: it may be appropriate to make a whole host of
Speaker 6: others that are at cross purposes with making finding the
Speaker 6: one defining company of that era. And I think for
Speaker 6: us that have been experienced at working adventure firms and
Speaker 6: identifying the patterns that lead to that, we felt like
Speaker 6: we could pick those out pretty well and that we
Speaker 6: would have a good sense of where to spend our time.
Speaker 6: Without the resources and without the brand magnets of other firms.
Speaker 5: And that it's a small community.
Speaker 6: We could get our brand out there pretty quickly to
Speaker 6: folks that are used to identifying those patterns and referring
Speaker 6: those deals on to us.
Speaker 3: So I think you just finished your first fundraising? Was
Speaker 3: it three hundred and fifty million?
Speaker 5: Three hundred fifty million was the first fund and that's right.
Speaker 3: What was the fundraising experience like now versus say, going
Speaker 3: back to the good old days of twenty fourteen.
Speaker 5: Yeah, oh yeah, good, good, good question.
Speaker 6: So it's different in two respects for us because we
Speaker 6: were a new entity too, and so we had a
Speaker 6: whole bunch of vetting around, Hey, what's our track record
Speaker 6: and experience? The founders want to work with us, because
Speaker 6: this whole premise was on we're going to bring the
Speaker 6: individual personal service back. We need to be able to
Speaker 6: say our personal service is good, like you want to
Speaker 6: work with the chemistry team because we're known for that.
Speaker 5: So that was a lot of vetting around that.
Speaker 6: There were LPs that felt like the Acid class had
Speaker 6: under delivered. Those were generally, you know, came into conversations
Speaker 6: very skeptical and our argument to them and some of
Speaker 6: them invested, some of them.
Speaker 5: Didn't but our argument to them was.
Speaker 6: Look, that's true writ Large, but by having exposure to
Speaker 6: just the earliest stages of the acid class going back
Speaker 6: forty years, that at that phase of the market has
Speaker 6: always performed. If you took a slice of the venture
Speaker 6: market and looked at just early stage investing, just the
Speaker 6: phase of scuity, your typical kind of series A and
Speaker 6: Series B investment, maybe the median fund hasn't performed, but
Speaker 6: there's always been outlier funds throughout that period. If you
Speaker 6: looked at all of the asset class writ Large, including
Speaker 6: all the growth checks, the leader stage investments, the sort
Speaker 6: of pre IPO rounds that came on, that asset class
Speaker 6: is really underperformed over the last five years.
Speaker 5: So we were sort of orienting around, we give you.
Speaker 6: Exposure to just the early stages and we don't want
Speaker 6: to do anything else.
Speaker 5: That's what that's We formed the firm just to do that.
Speaker 2: So there's no guarantees ever in venture, and we know
Speaker 2: there's outliers, but the basic idea here is that venture
Speaker 2: may be cyclical or the maybe structural, but early stage
Speaker 2: is not cyclical in the same way that these potential
Speaker 2: returns have been stable at this level if we.
Speaker 6: Make the right if we make the right number of investments.
Speaker 6: We have to make the right investments, and we have
Speaker 6: to get a few right. Maybe there's a little luck involved,
Speaker 6: but if we do that right, that will outperform.
Speaker 5: We won't water it down with bad investments later.
Speaker 2: Let's talk about how to make good investments then, because that's.
Speaker 4: Really what matters.
Speaker 2: Obviously, the twenty tens, the sort of cheap cloud computing
Speaker 2: and all the SaaS trends that made people of fortune.
Speaker 2: How does evaluating a company today? And this is where,
Speaker 2: like I guess the AI part comes in, whether the
Speaker 2: company is AI specific or in some level is going
Speaker 2: to be plugged into an AI model somewhere. How does
Speaker 2: that make the process of evaluation effiluating a company different?
Speaker 6: It makes it radically different and exactly the same all
Speaker 6: at the same time.
Speaker 5: All right, So what do I mean by that radically different?
Speaker 6: In that the entrepreneur can now promise pretty incredible things.
Speaker 6: You can talk to a system and get it to
Speaker 6: code for you. Is something that three or four years
Speaker 6: ago you would have said, sure, good luck with that. Like,
Speaker 6: you know, you need some you need some engineers on
Speaker 6: your team. You can now make promises like that. But ultimately,
Speaker 6: the way we're evaluating companies is thinking about the end
Speaker 6: market that they serve, the business user or the consumer,
Speaker 6: and how are their lives made better? How is this
Speaker 6: process improved? If you're you know, making a you know,
Speaker 6: consumer video editing app, how is that awesome for consumers
Speaker 6: to use? And so you start with like the question
Speaker 6: of can the technology deliver what the entrepreneur says? That's
Speaker 6: radically different, and then you step back to hey, is
Speaker 6: this a good business opportunity or not? Is this something
Speaker 6: that people will pay a lot of money for that
Speaker 6: will be able to monetize itself in some other way?
Speaker 5: That's very similar to how we've always done the job.
Speaker 3: Is there a difference in the sort of due diligence
Speaker 3: process for AI versus you know, old school SaaS.
Speaker 6: Not terribly Honestly, old school SaaS often had a data
Speaker 6: element to it that's somewhat similar to AI, you know,
Speaker 6: how they harness data in the application.
Speaker 5: What's different now is you can apply a frame of
Speaker 5: reference of what's possible. That's just much broader.
Speaker 6: That's just much more interesting, that's just much more potentially
Speaker 6: transformative to business users or consumers.
Speaker 5: That's a little different.
Speaker 6: You might not be as skeptical about founder's ability to deliver.
Speaker 6: There's this whole democratizing element to AI. Just riff on
Speaker 6: that for a second, in that you maybe don't have
Speaker 6: to have the most ultra specialized skill set of engineer
Speaker 6: to be able to deliver something pretty transformative. And so
Speaker 6: if you back up from that and think about a
Speaker 6: due diligence process, you don't necessarily need to spend as
Speaker 6: much time questioning the entrepreneur's ability to deliver. And there
Speaker 6: is this maxim that like most entrepreneurs will build what
Speaker 6: they want to build, just is that the right thing
Speaker 6: and is the timing right with the aier that's you
Speaker 6: get that on steroids. Most products can be built the
Speaker 6: way the entrepreneur says them. Now will they have the
Speaker 6: impact that the entrepreneur thinks they'll have. That's still a
Speaker 6: question that we have to answer when we make our judgments.
Speaker 3: What's the differentiator in that case if it's not necessarily
Speaker 3: about the skill set of the engineer, or what is
Speaker 3: it that makes you think an AI project is better
Speaker 3: than another AI project?
Speaker 6: Ultimately confact to like, what impact will it have in
Speaker 6: the market? I don't think about AI as a category
Speaker 6: so much. I think about AIS and enabling tech just
Speaker 6: like cloud computing or mobile or mainframes back in the day,
Speaker 6: or you know, data center technology. It's just a way
Speaker 6: of building tech that can potentially allow an entrepreneur more.
Speaker 5: Weapons to be able to deploy. But there's nothing inherently
Speaker 5: like the end user.
Speaker 6: That's using a finance application or that's using a communication app.
Speaker 6: At the end of the day, they're using that app
Speaker 6: because they want to do something with it.
Speaker 5: They want to communicate, they.
Speaker 6: Want to run their expense reconciliation process, they want to
Speaker 6: do X, Y or Z. There's nothing different about AI
Speaker 6: that makes that any bit of a different analysis than
Speaker 6: we had before around what is the impact that that
Speaker 6: particular product is going to have.
Speaker 2: In Today we got in Nvidia earnings, a company that
Speaker 2: people may have heard of, and they were really strong.
Speaker 4: I think this doc slipped a little bit, but in
Speaker 4: an important company.
Speaker 2: Yeah, and Jensen Wong saying, you know, AI is full
Speaker 2: steam ahead and they have all these scarcity when you're
Speaker 2: writing a check to a company today, you know, one
Speaker 2: of the things that characterized the twenty Times was just
Speaker 2: persistently falling cost of computing power. When you're writing a
Speaker 2: check with how much of that today is going to
Speaker 2: pay some sort of in Vidia tacks to have access today?
Speaker 2: And how does that make the sort of capital decisions
Speaker 2: of a company or the types of companies that you're
Speaker 2: invested in going to look different than they were.
Speaker 6: Well, there's this interesting kind of tow counter valing forces
Speaker 6: because the more of your check that goes to attacks
Speaker 6: like an Nvidia attacks or an open ai yea.
Speaker 5: Or whoever built on going some generally for us, it's
Speaker 5: being built on a model that they're not using.
Speaker 6: The sort of baar metal of the GPU that there's
Speaker 6: puts in takes there. But the more you invest in that,
Speaker 6: the more you've got your own technology that's more defensible.
Speaker 6: So a lot of times we're seeing open source tools.
Speaker 6: So a lot of times we're seeing not a lot
Speaker 6: of money go towards that, but they're deploying open source
Speaker 6: tools or they're built on models that are freely available
Speaker 6: to anybody. And so it's a question of can the
Speaker 6: founder or the entrepreneur make a process improvement or productize
Speaker 6: commercially available technology to everyone in a radically different, unique, ten.
Speaker 5: X better way. So much better of a user experience.
Speaker 6: The deep tech founders who are doing what you're saying,
Speaker 6: where a lot of the check goes to building the
Speaker 6: core technology, you have to believe then in the business
Speaker 6: outcome being so great.
Speaker 5: That it's so it's worth it. It's worth this huge
Speaker 5: R and.
Speaker 6: D investment, or this huge investment in training specialized models.
Speaker 2: But just to be clear, you say, okay, the non
Speaker 2: deep tech ones that are build it using some existing models,
Speaker 2: is the amount of money that's going to say an
Speaker 2: open AI or some entity that already built the model,
Speaker 2: is that fundamentally look different than say the expense sheet
Speaker 2: of another software company in twenty fourteen, when they think
Speaker 2: about how much outside tech they're paying for.
Speaker 6: It's not radically different for the ones that are the
Speaker 6: thin layer. Yeah, the thin layer ones are not that
Speaker 6: radically different. I think of it as a as a
Speaker 6: small incremental tax on top of their Amazon Web Services
Speaker 6: built it they could already be paying. And the technology
Speaker 6: is so good, it's so performing, it's so available to
Speaker 6: everyone that most of the companies we look at because
Speaker 6: we believe in the lean startup and most startups that
Speaker 6: can be built on that kind of technology will build
Speaker 6: on that kind of technology.
Speaker 5: It's not a huge tax and costs.
Speaker 6: The huge tax and cost comes when you try to
Speaker 6: sell it and you scale up the go to market operation,
Speaker 6: the sales and marketing, but.
Speaker 5: The tech itself.
Speaker 6: There's only a handful of companies where that's a real
Speaker 6: barrier to entry, and there are some.
Speaker 3: Just on this sort of big versus small point. I
Speaker 3: think one of the weirdest things about the you know,
Speaker 3: the sudden rise of AI over the past couple of
Speaker 3: years has been the fact that Microsoft has been really
Speaker 3: good at it, which I think, you know, three years
Speaker 3: or so, no one would have expected going forward. Do
Speaker 3: you think, like, who's going to be the best at this?
Speaker 3: Is it going to be the incumbents who now have
Speaker 3: a head start, who have the deep pockets, the access
Speaker 3: to data, or is it going to be, you know,
Speaker 3: the leaner startups who are maybe experimenting with new things
Speaker 3: and building on top of existing models.
Speaker 6: Our view would be at the foundational layer, like the
Speaker 6: model layers that a lot of people build on top of,
Speaker 6: or that we as consumers use for sort of our
Speaker 6: basic kind of chatbot style applications, is going to go
Speaker 6: to the big players plus maybe one or two new entrants,
Speaker 6: and that looks like that game is sort of established.
Speaker 6: I mean, I don't know if you count open ai
Speaker 6: as a separate company from Microsoft, but that they're clearly
Speaker 6: around to stay, and maybe there'll be one or two others,
Speaker 6: but that's not, in our view, a humongous startup opportunity
Speaker 6: because there's such amazing capital investments that need to be
Speaker 6: made there. On top of that, la how do we
Speaker 6: take that tech, take those abilities that the amazing researchers
Speaker 6: that open ai, aided by Microsoft and others, have built,
Speaker 6: and make it useful to the end consumer and to
Speaker 6: the end business user. I think that's where we're going
Speaker 6: to see kind of this new era, kind of like
Speaker 6: we saw cloud computing.
Speaker 5: Where there were a few early entrants in our tech.
Speaker 6: And financial applications and things like that, and then this
Speaker 6: explosion of cloud computing applications that disrupted the status quo.
Speaker 2: The platforms that emerged to dumins in twenty ten just
Speaker 2: like exerted to varying degrees, but just tremendous locking for
Speaker 2: their clients and some I'm not talking in the formal
Speaker 2: legal sense, although maybe we'll get to this, but like monopolies,
Speaker 2: but like de facto just like some without truly without competition,
Speaker 2: And there's like this debate about like when it comes
Speaker 2: to these foundational models, there seem to be you know,
Speaker 2: there's there's a lot of entities. There's not thousands, but
Speaker 2: there's quite a few that can make incredibly impressive performance models,
Speaker 2: some open source, some closed source. Do you see any
Speaker 2: of them emerging with the same sort of like true
Speaker 2: lock in kind of dominance, or when you look at
Speaker 2: the companies that you're funding, do they seem like issues like, yeah,
Speaker 2: we could use open eye, but also without too much trouble,
Speaker 2: we could switch to another provider fairly trivially.
Speaker 5: I think it's a really good question.
Speaker 6: I think the lock in is not too dissimilar from
Speaker 6: the cloud era, where you could switch off of one
Speaker 6: cloud computing vendor from one to the other. But there
Speaker 6: weren't that many of them now in this are there
Speaker 6: might be a few more, And I think open source
Speaker 6: there's no open source cloud computing provider. You know, someone's
Speaker 6: got to plug in the hardware, air condition the data center,
Speaker 6: make the networking work with large language models.
Speaker 5: There are open source models that are going to.
Speaker 6: Get to be pretty good, and so I think that's
Speaker 6: another element here that's a little different from the cloud era,
Speaker 6: where probably allows a little more fluidity than even you
Speaker 6: have among clouds. But there's not going to be dozens
Speaker 6: and dozens of models because to be performant, to be
Speaker 6: humanlike be able to provide people with responses that make sense,
Speaker 6: that have emotion, that really fulfill on the promise of
Speaker 6: what AI can do. There's only so much money that
Speaker 6: can there's so much money needed to that that there's
Speaker 6: not that many companies that can capitalize on it.
Speaker 3: Setting aside the big foundational models themselves, what's the coolest
Speaker 3: application of AI that you've seen so far that sort
Speaker 3: of built on the big guys.
Speaker 6: Well, the consumer applications, the companion apps are probably the
Speaker 6: coolest right now. They're not very realistic yet, although they're
Speaker 6: sort of getting getting up there in that, you know,
Speaker 6: they start to emulate real people in.
Speaker 5: The world and can do it.
Speaker 6: Podcasters, I think there should be a Tracy There should
Speaker 6: be a Tracy character app that's out there in the
Speaker 6: public and could in fact with Google's with a Google system,
Speaker 6: you can actually create a whole podcast from a notebook
Speaker 6: that you used.
Speaker 4: Some people have done.
Speaker 5: It's lacking a little color. But this is a little This.
Speaker 2: Is really important because I've listened to some of those
Speaker 2: Google Creator. I mentioned this on another episode. I've listened
Speaker 2: to some of those and they're not as good as
Speaker 2: me and Tracy are, but nothing could.
Speaker 4: Be completely unbiased, but they're not terrible.
Speaker 2: Like it sort of disturbed me because I listened to
Speaker 2: to the AI generated podcast about some document that the
Speaker 2: Department of Energy made and I was like, oh, shoot,
Speaker 2: this isn't that bad. Like, it's not totally boring, It's
Speaker 2: not a terrible way of consuming that content.
Speaker 6: So someone I know, well, how to read an entire
Speaker 6: book and generate a twelve minute podcast on that book.
Speaker 5: And it was not it was. I totally agree it was.
Speaker 4: It was sort of like the.
Speaker 5: Personality was lacking.
Speaker 6: It's right, it was, and they tried to make it
Speaker 6: personable and it just fell flat.
Speaker 5: And I think that's a little bit what's missing today.
Speaker 5: But I will better. Yeah, not as good as you guys.
Speaker 4: Shoot, no odd lack.
Speaker 2: Well, Okay, I'm just gonna ask this question. You know,
Speaker 2: most people in this room have probably been talking a
Speaker 2: lot about AI for two years now. Probably in this
Speaker 2: room it's three years in the rest of the country,
Speaker 2: it's probably about two years. You, as you alluded to,
Speaker 2: have been probably thinking about AI in some respect for
Speaker 2: thirty probably forty years.
Speaker 4: Tell us a little.
Speaker 2: Bit about your background having thought about this for at
Speaker 2: least three or four decades longer than the rest of us.
Speaker 2: And how does that inform when you make predictions now
Speaker 2: when you try to pick winners, How does forty years
Speaker 2: worth of experience inform your choice today?
Speaker 5: All right, so a confession.
Speaker 6: The book that the podcasts that I just mentioned is
Speaker 6: writing about is my dad's book. Okay, my father AI
Speaker 6: Technology Future as has been thinking about AI for about
Speaker 6: sixty five years. What he would say and large language
Speaker 6: models for forty because that's his field. To his pattern recognitions,
Speaker 6: being able to recognize patterns and apply them to language.
Speaker 5: That was one of his first companies was that.
Speaker 4: Is a good job of toing.
Speaker 5: So I was debating with him.
Speaker 6: He thought it was great, and I said, I think
Speaker 6: the podcasters aren't as good as Tracy and Joe. It's
Speaker 6: thank you now your names. That's sort of what I said.
Speaker 6: And that was the debate we had. But they got
Speaker 6: the it got the substance right. But yes to your
Speaker 6: question AI, and it's maybe made me both more excited
Speaker 6: about AI and slightly more cynical about this moment because
Speaker 6: AI has been around for a long time and now
Speaker 6: it's become cycles. Yeah, and we will be in a
Speaker 6: disappointment about what I brings hype cycle in about by
Speaker 6: my calculations, two point six months from now, then we
Speaker 6: will come back out. It's a very precise estimate. And
Speaker 6: then we will come back out from that, and then
Speaker 6: it will start after the four point three Okay, wait, what's.
Speaker 3: The catalyst for disappointment?
Speaker 6: What's that's some of the companies that have been hyped
Speaker 6: to fulfill on this promise of completely human like lifelike understanding, reasoning, abilities,
Speaker 6: and emotion won't quite fulfill on that promise right away,
Speaker 6: and we'll have to wait another four point nine months.
Speaker 5: For that, or longer a couple of years.
Speaker 4: I can't tell how much of a joke anyway.
Speaker 3: You know what else was coming in about two months
Speaker 3: is a new administration.
Speaker 5: And I've heard about that.
Speaker 3: Yeah, it's kind of been in the news. You talk
Speaker 3: to lots of people in the VC space and in
Speaker 3: you know, the founder space. What are people saying about
Speaker 3: the incoming administration, Like what are the hopes, dreams, fears
Speaker 3: that people are talking about.
Speaker 6: You know, there's people that are prominted out there that
Speaker 6: advocated for this or for or against it, that are
Speaker 6: you know, have their passionate points of view about why
Speaker 6: the new administration is good or bad. At the kind
Speaker 6: of surface level day to day this felt like, Okay,
Speaker 6: you know there's a change coming, and there's just not
Speaker 6: a lot of translation between that and the day to
Speaker 6: day of venture capital because you know, technology is this
Speaker 6: force that sort of plows through market cycle, plows through
Speaker 6: market cycles technology administrations, unless you're in a very highly
Speaker 6: regulated industry, you know, crypto for instance, or something like that.
Speaker 6: I feel like in my circles around you know, how
Speaker 6: is AI going to be commercialized for business and consumer?
Speaker 6: It's not an event that people are as focused on
Speaker 6: as perhaps the most prominent personalities of it.
Speaker 2: I'm very curious about like the sort of tech inflicted
Speaker 2: side of this administration, the influence of Elon Musk gd
Speaker 2: Evans having been to VC. But the really low hanging
Speaker 2: fruit policy question is on the merger side, and we
Speaker 2: don't know who's going to run the FDC. We don't
Speaker 2: know who's going to run the DOJ. It certainly seems plausible, however,
Speaker 2: that the new administration will have a much more liberal
Speaker 2: attitude towards letting mergers go through. How much is just
Speaker 2: from a notes and bold steadpoint, when you think about returns,
Speaker 2: when you think about investments, exits of various flavors, there's
Speaker 2: a big sort of ninety degree turn or maybe one
Speaker 2: eighty degree turn on merger policy change your thinking.
Speaker 6: Not a ton, but there's no question at the current administration,
Speaker 6: not just here but in the EU, in the UK
Speaker 6: where I remember any global merger now is to get
Speaker 6: through basically three anti trust bodies has been really a
Speaker 6: lot tougher than any administration we've seen, Democrat or Republican
Speaker 6: in the past. So do you when you're getting when
Speaker 6: you're sitting with an entrepreneur getting excited about some big
Speaker 6: dreams or attech, are you thinking about well, I wonder
Speaker 6: what Lena Kahan's thinking about, you know, the consolidation of
Speaker 6: the market for design tools.
Speaker 5: No, not at all, but is it probably a.
Speaker 6: Good thing for entrepreneurs options to be able to exit
Speaker 6: their business and our business which relies on that.
Speaker 2: Yeah, probably, in the reality of like the prospect of
Speaker 2: an exit into machine design tools didn't like, or the
Speaker 2: prospects of who would be a buyer. Those kind of
Speaker 2: conversations weren't coming up in the early stages of a
Speaker 2: conversation with Ann.
Speaker 6: Not with an early stage found now with in a
Speaker 6: growth context, it's tremendously important because if you don't have
Speaker 6: the option to exit for billions and billions of dollars,
Speaker 6: you can only go public. There's a pretty narrow set
Speaker 6: of criteria you have to meet to be able to
Speaker 6: go public. So then it's a pretty material thing. We're
Speaker 6: at chemistry focused on the earliest stages this technology going
Speaker 6: to get out and have an impact there. You just
Speaker 6: kind of take a flyer. You kind of assume that
Speaker 6: if it does, it'll be valuable in any kind of context,
Speaker 6: whether it's in an M and A one or some
Speaker 6: other exit.
Speaker 3: So another aspect of the incoming administration is they seem
Speaker 3: to be crypto friendly. And I'm going to co opt
Speaker 3: a question that's been submitted by the audience, but what
Speaker 3: do you think about crypto in general as an investment?
Speaker 3: Is it something you're interested in?
Speaker 6: That's one where the administration probably matters a lot. I
Speaker 6: have been pro crypto for certain use cases in the past,
Speaker 6: thinking about what's the infrastructure layer needed to make crypto
Speaker 6: a part of the financial system, And so that's the security,
Speaker 6: the protocols, the permissioning, the privacy, all that kind of
Speaker 6: stuff I think is really necessary because crypto is still
Speaker 6: such a wild west to where you have to be
Speaker 6: pretty deep in it to benefit from it. And so
Speaker 6: I still think there's that like bridge technology to make
Speaker 6: it useful for kind of everyone in their everyday lives,
Speaker 6: Like you know, the coinbase is kind of wallet type
Speaker 6: software for everything else. And it's probably true to the
Speaker 6: extent you can kind of read the tea leaves on
Speaker 6: these things that the current administration is a lot more
Speaker 6: friendly there at least that's messaging how will that manifest
Speaker 6: itself and policy no idea, but right now a lot
Speaker 6: of people are scared of the space because there's a
Speaker 6: lot of uncertainty around it.
Speaker 2: Then the other element is just sort of the people
Speaker 2: in the orbit, you know, tech accelerationism, exciting things getting
Speaker 2: to Mars.
Speaker 4: Sindeka said.
Speaker 2: The Mars question specifically, a lot of it seems very
Speaker 2: vibes based, and I don't know.
Speaker 4: What policy levers. Any of that means in your view, are.
Speaker 2: There other policy levers that could be pulled that would
Speaker 2: be good for the American tech infrastructure or sorry not
Speaker 2: the industry.
Speaker 5: Probably yes.
Speaker 6: It's really hard to start a company and have it
Speaker 6: be successful and have it There's so many things stacked
Speaker 6: against you.
Speaker 5: So what are the things you.
Speaker 6: Can do to remove all the unknown obstacles that might
Speaker 6: come up beyond the really hard ones of like will
Speaker 6: you deliver the product on time? Well, the product, can
Speaker 6: you deliver it for a reasonable cost, and we'll have
Speaker 6: an impact on the market. The regulation that kind of
Speaker 6: is another curveball that you might have to answer to.
Speaker 6: That's an impediment, that's a blockage that serves at the
Speaker 6: cost of innovation for sure. And so I think what
Speaker 6: probably has an impact, just maybe using crypto as an example,
Speaker 6: is clear regulation and lack of uncertainty, where you have
Speaker 6: a sense of what are the rules going to be,
Speaker 6: Not that you know, we'll apply these arcane tests and
Speaker 6: we don't know exactly how a court will interpret it,
Speaker 6: but like, exactly, do these six steps and you'll be fine.
Speaker 6: That's taking out any uncertainty that's beyond the sort of
Speaker 6: normal startup risks is a good thing for innovation.
Speaker 3: Another question from the audience. I mentioned that obviously we've
Speaker 3: been talking about AI a lot, and you talked about
Speaker 3: the disappointment and redemption cycle. Are there any other nascent
Speaker 3: tech areas or growth areas that you are excited about
Speaker 3: beyond AI. Yeah.
Speaker 4: Oh, I think that's hard one.
Speaker 5: Beyond the AI rubicon. Let me think about that.
Speaker 6: I mean, I think the democratization of tech broadly, this
Speaker 6: is aided by a but not principally, the fact that
Speaker 6: a normal business user or even a consumer can now
Speaker 6: create an intelligence system, doesn't have to be a coder
Speaker 6: necessarily to be able to write a complex kind of
Speaker 6: logic flow and be able to build a build an
Speaker 6: application or a messaging tool or something for a business context.
Speaker 6: I think that's pretty powerful. I mean, I've always been
Speaker 6: interested in the democratization of tech. Like even cloud computing
Speaker 6: had a democratizing impact because you could give lots of
Speaker 6: people logins to a system and let them have impact,
Speaker 6: even if they weren't technical.
Speaker 5: You could let people kind of edit.
Speaker 6: The flow on a website, personalize a page, be able
Speaker 6: to engage with their customers directly on the website without
Speaker 6: having to code anything. And so I think there's this
Speaker 6: democrat to that tizing aspect of the Internet. Of cloud
Speaker 6: computing ais a part of this that's going to give
Speaker 6: more people the ability to be more creative, and that's
Speaker 6: going to have a kind of second order impact on
Speaker 6: just the kinds of things we're going to be able
Speaker 6: to do, even for like little niche audiences.
Speaker 2: For do you see yourself writing checks to companies that
Speaker 2: are making apps for virtual reality goggles for virtuality what goggles?
Speaker 5: Possibly is that I've.
Speaker 6: Written one before, but virtuality the goggles like a goggles?
Speaker 4: Yeah, Like is that exciting to you?
Speaker 6: Yeah, virtuality gaming could be a thing, virtuality, messaging, communication,
Speaker 6: working in virtuality. Before I was a venture capolist, I
Speaker 6: worked at a company on Lindenlab, which is coming behind SETH.
Speaker 6: Of course, you remember, it's not a core theme for
Speaker 6: us at Chemistry, so odds are we won't but I'm
Speaker 6: open to it.
Speaker 3: Sorry, I just I had a flashback to the time
Speaker 3: when you thought electric scooters were the future of training.
Speaker 4: They are. They're so great. I've said I love the
Speaker 4: electric scooters.
Speaker 5: Yeah, it's a part of the future.
Speaker 4: So I'm just gonna this will be now.
Speaker 2: I forget when was that that we came out here
Speaker 2: and I was like, oh my god, Lime scooters are
Speaker 2: going to change the world. But for me, like I've
Speaker 2: taken it's wimo this time, and like I'm just so
Speaker 2: completely wamo pilled. I'm just gonna, I'm just it's just
Speaker 2: so amazing. I don't never ever, I never want to
Speaker 2: an Uber again.
Speaker 6: The wow of the Way experience is greater than the
Speaker 6: wow of the Lime scooter experience.
Speaker 4: Yeah, it is.
Speaker 5: There's a lot less risk of death.
Speaker 6: Well, I mean maybe if you think the way I'm
Speaker 6: might crash, but they don't know.
Speaker 2: It feels so safe and I felt so comfortable. And
Speaker 2: actually then I took an Uber today and it felt
Speaker 2: worse and like it was it was.
Speaker 4: It was a worse experience.
Speaker 2: And now when I go back to New York and
Speaker 2: take an Uber, it's like going back to the land
Speaker 2: of flip phone.
Speaker 5: You're going to have to move out to the less
Speaker 5: cust Yeah.
Speaker 6: Yeah, okay.
Speaker 3: So when it comes to AI, one of the one
Speaker 3: of the debates that's been going on is like, well,
Speaker 3: do you invest in the actual AI companies or maybe
Speaker 3: you invest in sort of picks and shovels and data
Speaker 3: centers and things like that. Is that like on your
Speaker 3: radar at all, or do you this is a question
Speaker 3: from the audience, at a minimum, do you look at,
Speaker 3: for instance, investing in new technology that could help AI
Speaker 3: manage energy usage or something like that.
Speaker 6: Yeah, I think there's a lot of like second order
Speaker 6: of fects of AI that we could make investments to
Speaker 6: make better energy usage being one of those, or helping
Speaker 6: create primitives to allow developers easier access to some of
Speaker 6: the more advanced functionalities of AI. There's a whole side
Speaker 6: theme that's maybe orthogonal to your question around the provident,
Speaker 6: like how you don't really know what data has been
Speaker 6: inputed into an AI system is relevant to your answer.
Speaker 6: So it could have stolen some content, or it could
Speaker 6: have like who knows what trained it to provide you
Speaker 6: with that particular thing that it said. And so there's
Speaker 6: a whole side theme of like, okay, how do you
Speaker 6: make that okay for the for the people that made
Speaker 6: the actual IP that that AI.
Speaker 5: Was trained on. So that's another kind of side theme of.
Speaker 4: AI that is so interesting.
Speaker 3: So managing the IP.
Speaker 6: Managing the IP, the rights of that the privacy that
Speaker 6: you know, you might for a base level want an
Speaker 6: AI trained on you know, a corpus of data that's
Speaker 6: pretty basic, but then you know the tailor swift of data,
Speaker 6: you know, the really advanced IP holders, you want to
Speaker 6: pay more, but then you want to get some of
Speaker 6: the money to the people that created that IP that
Speaker 6: made that I AI even better. Actually that's hard to
Speaker 6: do right now, but maybe not yet solved.
Speaker 2: I want to go back to what you were saying
Speaker 2: about how having thought about in your life AI for
Speaker 2: four decades that in some ways it makes you more
Speaker 2: optimistic because you see like this grand sweep, but also
Speaker 2: at least a temporary sort of cynicism because you know
Speaker 2: that AI winters exist and it's very plausible, and you
Speaker 2: know there's all kinds of stories about you know, running
Speaker 2: up against current limits of scaling and all. Can you
Speaker 2: tell us a story about what was the past AI
Speaker 2: winter that happened? What was something that at some point
Speaker 2: people are like, oh, we got this, this is moving
Speaker 2: and then they ran into a wall. And what's a
Speaker 2: lesson that can be drawn from us?
Speaker 6: Well, speech recognition was probably a wall where people thought
Speaker 6: AI he'd be able to talk to AI.
Speaker 5: Yeah, when what was the un in the nineties.
Speaker 6: That was one of the one of the eras of
Speaker 6: AI my father was involved with. In fact, he named
Speaker 6: one of his companies Kurzweil. AI and AI stand stood
Speaker 6: for applied intelligence, not artificial intelligence, because it was about
Speaker 6: word to say AI and haven't mean artificial intelligence because
Speaker 6: it felt like it was under delivering on an artificial intelligence.
Speaker 6: It was more applied than the AI that we think
Speaker 6: of today. And so that was an era where.
Speaker 2: And then what do you discreete what it's like the
Speaker 2: moment to like, oh, this is not growing or scaling
Speaker 2: or improving the way we People.
Speaker 6: Forget and then something that that counters that a counterfactual
Speaker 6: comes out and then everyone kind of hones in on
Speaker 6: that and that period where people are forgetting about it,
Speaker 6: that's the troph of disillusionment. Where at the beginning of
Speaker 6: that is the troph of disillusionment period where there's a
Speaker 6: lot of prognostication about how this technology didn't deliver. Then
Speaker 6: people forget something does deliver, and then move on to
Speaker 6: a new cycle and the expectations get high again that
Speaker 6: probably can't be met all right.
Speaker 3: Another question from the audience also sort of a Trump
Speaker 3: related question. Poly market and other prediction markets? Do you
Speaker 3: think those are like in for well, where are they going?
Speaker 5: That's a really good question. I don't know.
Speaker 6: I mean I think that probably I believe that system
Speaker 6: had the best way of taking stock of kind of
Speaker 6: all the known universe of information that we're out there
Speaker 6: and distilling it down into a Okay, what does it
Speaker 6: mean for a particular event like the election?
Speaker 5: So that's kind of cool. Now is it legal? I
Speaker 5: don't know.
Speaker 6: It sounds like maybe not because somebody's going to jail,
Speaker 6: but somebody might face criminal prosecution.
Speaker 2: It easy for an American to use it, which does
Speaker 2: not seem like it's supposed to write.
Speaker 6: You know, people having real money on the line does
Speaker 6: create a more purest system that it's sort of hard
Speaker 6: to replicate with any other any other approach, similar to
Speaker 6: how the stock market sort of works and in theory
Speaker 6: gives you kind of the right price of every particular
Speaker 6: asset that's listed. So I think for prediction markets, that's
Speaker 6: that incentive is hard to replicate it any other way.
Speaker 6: Should there be prediction systems? And there's a policy question
Speaker 6: that I don't know, Speaking.
Speaker 2: Of prediction markets and sort of a broader philosophical question.
Speaker 2: I was wondering, like, we live in an era of
Speaker 2: people betting on everything. In one of the one aspect
Speaker 2: I think of people sort of betting and speculating on
Speaker 2: all kinds of things is that it seems to me
Speaker 2: that the sort of VC mindset of you want to
Speaker 2: just have a couple of gigantic winners and get that
Speaker 2: big score is spread to the non VC world right
Speaker 2: and people really look for those right tail opportunities, both
Speaker 2: in investing in stocks their careers. There seems to be
Speaker 2: a sort like is do you perceive that the sort
Speaker 2: of VC world view has seeped out of the VC
Speaker 2: realm and sort of I don't want to say infected
Speaker 2: because that's like a bad word, but as.
Speaker 4: A transfer, are we all VC? That's exactly.
Speaker 6: One hundred percent what you're pointing out is true, And
Speaker 6: it'd be interesting to do root cause analysis, like are
Speaker 6: we to blame for that?
Speaker 4: Yeah?
Speaker 6: But first of all, it's a bad thing or not,
Speaker 6: it's certainly happening, and I think the cause of it,
Speaker 6: I would say, is well, there's probably a number of
Speaker 6: kind of psychological causes, but there's been this democratization of
Speaker 6: access to private assets that's happened over the last ten
Speaker 6: years too.
Speaker 5: We haven't talked about.
Speaker 6: Either where rather than trading, you know, used to be
Speaker 6: you traded stocks. Then it was sort of a you
Speaker 6: could trade IPOs, and now there's some access for mainStreet
Speaker 6: consumer to private company assets as well, more typically venture vetted,
Speaker 6: and there's investor protection laws, but not it's the move
Speaker 6: has been towards more and more and more democratization, and
Speaker 6: so the VC way of thinking is just seeping into
Speaker 6: more things.
Speaker 5: Is that good? Is that bad? There's probably pros and cons.
Speaker 3: A lot of people here probably want to know how
Speaker 3: to spot winners in the market, but part of this
Speaker 3: is about avoiding losers as well. What's your best tip
Speaker 3: for spotting, I guess or finding identifying froth in tech?
Speaker 6: Well, I'm the wrong person to ask, because we back
Speaker 6: as a good even as a good venture capitals, we
Speaker 6: back so many losers, like it's just an occupational hazard
Speaker 6: of the job.
Speaker 5: Now you asked about froth though, and.
Speaker 6: So that's the sort of like perception disconnect of like
Speaker 6: what what's the reality of a particular tech And I
Speaker 6: think you have to go.
Speaker 5: To the source.
Speaker 6: You have to see like, what what impact is this having?
Speaker 6: Not are what are other people saying about it? What
Speaker 6: are these kind of second order effects? What is does
Speaker 6: the entrepreneur, you know, look the part in some way
Speaker 6: or are they kind of playing a role that makes
Speaker 6: their impact seem like what's the impact of the technology
Speaker 6: And kind of like have blinders on for the noise
Speaker 6: that's out there in the ecosystem, because that's another impact
Speaker 6: of everyone. You know, more and more VC like thinking
Speaker 6: is there's more and more hype around really exciting tech.
Speaker 5: Some of it's real, some of it's froth.
Speaker 2: We talked about this earlier, the fact that there are
Speaker 2: not terrible podcasts that are produced by AI and it
Speaker 2: does cause me as a professional podcaster, like, yeah, it
Speaker 2: causes me anxiety. But like this is the other sort
Speaker 2: of big question, the sort of future of labor question
Speaker 2: in a world where AI gets better and better, and
Speaker 2: like what are we as humans good at? I mean,
Speaker 2: I'll start with that, what are we And I'm talking
Speaker 2: now just like in the next year or five years,
Speaker 2: but like in twenty years or fifty years. And I
Speaker 2: know your dad made predictions that were fifty and sixty
Speaker 2: years out, so you probably think I wouldn't. I imagine
Speaker 2: you also have in your mind predictions that are fifty
Speaker 2: and sixty years out. And so when you think about,
Speaker 2: like what are humans good at? What are we going
Speaker 2: to be good at? People say the same thing might
Speaker 2: be seen by the way it happened for many years.
Speaker 6: Yea, like assist you'll be able to put this right
Speaker 6: data into a system.
Speaker 5: And it'll be better and many And.
Speaker 4: You can't really do it for stocks yet, right.
Speaker 6: I probably at some level there will be systems that
Speaker 6: aid people. But I still think what tends to happen.
Speaker 6: I'm not the fifties, sixty year out your anchor. I'm
Speaker 6: the sort of five year out, okay kind of thing,
Speaker 6: but let's go with that time horizon.
Speaker 4: Yeah, it's we.
Speaker 6: Tend to as humans kind of move up the stack,
Speaker 6: we still have to do the creative work. We still
Speaker 6: have to guide the AI systems. We still have to
Speaker 6: sort of harness the tech that's coming out of them.
Speaker 2: We can'tigure out how to apply it, so we can't
Speaker 2: just plug in more energy into our systems, and like,
Speaker 2: are we just going to like fall further?
Speaker 4: Like sorry, I think we'll still.
Speaker 5: Stay on top of the systems.
Speaker 6: We're going to tell the systems what to do, Like
Speaker 6: what challenges do we want them to solve, what's the
Speaker 6: problem space that we want them interested in? What success
Speaker 6: look like for these systems? I said, there's like there's
Speaker 6: real work that as we move higher and higher up
Speaker 6: and we do less of the grant work. That's the
Speaker 6: sort of pattern as it exists, has it been existing today.
Speaker 3: So we've been focused on software for obvious reasons. But
Speaker 3: when do we get the good robots that can do
Speaker 3: the terrible jobs? Like I don't like the future where
Speaker 3: AI can do a podcast and write songs and poetry
Speaker 3: and all the fun stuff, but I still have to
Speaker 3: vacuum and fold my launch flow.
Speaker 2: There are robot vacuums, yeah are, but they're doing they can't.
Speaker 4: Fold the laundry there, Yeah, talk about that.
Speaker 5: Yeah, I don't know.
Speaker 6: I mean there I have seen some systems now that
Speaker 6: have sort of like some robots and have like kind
Speaker 6: of human like characteristics and walk upstairs and carry things
Speaker 6: and you know, pick things in a warehouse. And so
Speaker 6: because it's hardware, there's less of an exponential to that.
Speaker 6: So it's more of blocking and tackling around the sensors
Speaker 6: and what's the cost of the particular parts that go
Speaker 6: into that. But cars now drive themselves, so that's a
Speaker 6: big step change what we used to have drive themselves
Speaker 6: in like difficult environments and rain, and so it's coming
Speaker 6: now how soon and which industries isn't going to hit?
Speaker 5: That's a hard one.
Speaker 2: The first question I asked you is like, how is
Speaker 2: a venture different today in twenty twenty four verse twenty fourteen,
Speaker 2: And you gave a good answer to the question I asked,
Speaker 2: But actually I meant to just ask you about what
Speaker 2: the impact of five percent interest rates work specifically versus
Speaker 2: zero And then I asked a very vague question, but
Speaker 2: I am curious about the sort of the strictly macro elements.
Speaker 2: We're also in a weird moment because rates have gone up,
Speaker 2: but the Nasdaq is at all time highs, and it
Speaker 2: seems intuitive that private company valuations have some tether to
Speaker 2: public company valuations, either by DENTTI being acquired or by IPOs.
Speaker 2: Is there a difference though, just from the sort of
Speaker 2: macro environment rates side that affects your thinking today versus
Speaker 2: zero percent rates in twenty four Well.
Speaker 5: In theory it should be a lot harder.
Speaker 6: Yeah, because there's so many more internatives for where to
Speaker 6: put your capital these days.
Speaker 5: That are appealing.
Speaker 6: There's macro big tech that's taking advantage of a lot
Speaker 6: of the trends that we're talking about, not just startups.
Speaker 5: And there's you know, risk free treasury bonds.
Speaker 6: Yeah, now countervail that with all this excitement around AI
Speaker 6: and you kind of have the current moment where it
Speaker 6: should just feel like a sort of post two thousand
Speaker 6: era bubble.
Speaker 5: That's what it should feel like.
Speaker 6: If you also feel like financial flows, that's where we
Speaker 6: should be and we're not because there's just excitement about
Speaker 6: what technology can do in the cycles are getting faster
Speaker 6: and faster. It's like people don't feel like it's ten
Speaker 6: years away now, it's it's like almost here.
Speaker 2: Ethan Cursewil, thank you so much. That was fantastic and
Speaker 2: I really appreciate you doing.
Speaker 5: This live odd lots of those Thanks for having me.
Speaker 5: This is fun.
Speaker 2: And that was our conversation with Ethan Curseweil, founder and
Speaker 2: managing partner of Chemistry VC.
Speaker 3: And a big thank you to everyone who came to
Speaker 3: this live recording. It was a very rainy evening in
Speaker 3: San Francisco, so appreciate so many people coming out, and
Speaker 3: a big thank you as well to our sponsor, Principal
Speaker 3: Asset Management for making this possible. Joe, should I leave
Speaker 3: it there?
Speaker 4: Let's leave it there?
Speaker 3: All right? This has been another episode of the Authoughts podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Jill Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Ethan Kurzweil at Ethan Kurz. Follow our
Speaker 2: producers Carmen Rodriguez at Carmen Erman dash Ol Bennett at
Speaker 2: Dashbot and kill Brooks at Kilbrooks. Thank you to our
Speaker 2: producer Moses Ondem. For more Oddlogs content, go to Bloomberg
Speaker 2: dot com slash odd Lots, where you have transcripts, a
Speaker 2: blog and a daily newsletter and you can chat about
Speaker 2: all of these topics twenty four to seven with fellow
Speaker 2: listeners in our discord discord dot gg slash.
Speaker 3: Od lots And if you enjoy a thoughts, if you
Speaker 3: like it when we do these live recordings, then please
Speaker 3: leave us a positive review on your favorite podcast platform,
Speaker 3: and remember if you are a Bloomberg subscriber. In addition
Speaker 3: to getting the first heads up about these types of events.
Speaker 3: You can also listen to all of the Authoughts episodes
Speaker 3: absolutely ad free. All you need to do is connect
Speaker 3: your Bloomberg account with Apple Podcasts. In order to do that,
Speaker 3: just find the Bloomberg channel on Apple Podcasts and follow
Speaker 3: the instructions there. Thanks for listening.