Latest / The Learning Corner by Precursor / Episode #23: Unique Revenue Channels For Vertical AI, Ghosting in Venture, Is Data Driven VC Over?

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Episode #23: Unique Revenue Channels For Vertical AI, Ghosting in Venture, Is Data Driven VC Over? · The Learning Corner by Precursor

• 0:00
  We're in a moment where just the budgets are big, and I think no CEO wants to go to the
• 0:03
  board and say, we're not heavily invested in figuring out how AI is gonna impact our
• 0:07
  business.
• 0:08
  At some point, they're gonna have the answer.
• 0:09
  They're gonna be like, AI right now impacts this part of the business, so we're gonna spend
• 0:12
  money here.
• 0:12
  We tried this thing.
• 0:13
  It didn't work.
• 0:14
  We're not gonna continue to spend on that, and it's gonna settle out.
• 0:17
  But right now, it does feel like a gold rush.
• 0:19
  It's the week of March 10.
• 0:21
  Welcome to
• 0:22
  the learning corner, a Precursor Ventures podcast.
• 0:29
  Welcome to the Learning Corner by Precursor.
• 0:32
  I'm Mia Farnham, and I'm joined by our GP and managing partner, Charles Hudson, where we will
• 0:37
  be talking through the articles and Goodreads that caught our team's attention over the past
• 0:41
  week.
• 0:42
  Our first topic is from Tech Rundown by Caroline Thompson double clicking into the
• 0:47
  recent $300,000,000 series d led by Sequoia into Harvey AI, the legal tech startup now at a
• 0:53
  valuation north of $3,000,000,000.
• 0:56
  What makes this deal unique isn't just the size of the check, it's the strategy.
• 1:01
  As a three year old startup, Harvey has nabbed partnerships with massive professional services
• 1:06
  companies to create white labeled tools to support your client work, all built on Harvey's
• 1:11
  AI.
• 1:11
  PwC has secured exclusive rights to Harvey's models, and law firms are offering their white
• 1:17
  labeled product as a premium service built to clients under Harvey AI's tech?
• 1:22
  Because these professional services companies are opting to resell Harvey's tech through
• 1:27
  their own revenue channels, Harvey both sidesteps longer enterprise sales cycles and
• 1:31
  secures reoccurring revenue without having to build out a massive direct sales force.
• 1:36
  So I was curious to know what you thought about that one.
• 1:38
  I'm always hesitant to comment too much about a company where I'm not on the inside.
• 1:42
  But a couple of things, I remember five or six years ago, everyone was like, don't sell to
• 1:46
  lawyers.
• 1:47
  They're like the worst customers ever.
• 1:49
  And the more I read that article, I was like, this sounds kinda like Palantir and more like a
• 1:53
  platform and a set of capabilities.
• 1:55
  Because I've always wondered, like, what exactly is Harvey, and I've asked my friends in
• 1:59
  law.
• 1:59
  And the few friends I have who've used it, when they describe their product, it's like they're
• 2:02
  not talking about the same thing.
• 2:04
  They're all using it differently.
• 2:06
  I kept reading, I was like, oh, this sounds like a really powerful set of capabilities that
• 2:10
  also to the benefit of the consultants need to be customized and integrated with your
• 2:14
  workflow.
• 2:14
  And it wasn't so long ago that people were like services equals bad, software revenue equals
• 2:19
  good.
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  And in some ways, you kinda get the best of both worlds if you're Harvey.
• 2:22
  You've got a technology platform.
• 2:24
  You've got a service delivery partner who's gonna help integrate it.
• 2:27
  But my big takeaway is that to me would suggest that Harvey will end up being pretty sticky in
• 2:32
  these law firms because every other thing I can think of, whether it's HR, information systems,
• 2:36
  CRM, anything where you bring in a big integrator usually ends up being highly
• 2:40
  customized, expensive to maintain, and hard to rip out.
• 2:43
  And so I'm very curious to see more about, like, what these Harvey implementations
• 2:47
  actually look like, but it sounds like they're moving in a direction that to me feels more
• 2:52
  like a Palantir or a SuccessFactors or one of these, like, system of record type things that
• 2:57
  requires a lot of customization.
• 2:58
  So I was like, it's like a thin wrapper.
• 2:59
  I'm like, it doesn't sound like a thin wrapper if you've got those folks involved.
• 3:02
  Especially when you think about the consulting side of it.
• 3:05
  So when PwC kind of steps in, it's no longer a wrapper.
• 3:07
  It's a full blown product that they're offering, but under the guise of their own
• 3:11
  marketing.
• 3:12
  Mhmm.
• 3:12
  And so to your point about stickiness, that's something that you would assume will never
• 3:17
  leave their offerings as long as it continues to produce some sort of ROI for them and their
• 3:21
  end clients.
• 3:22
  That was actually my next question.
• 3:23
  We always talk about how AI is interacting with different fields.
• 3:26
  And in this case, it's professional services.
• 3:29
  So we're seeing here that big players in this space are admitting they can't outpace the
• 3:33
  tech.
• 3:33
  So what they're doing now is looking for ways to leverage it.
• 3:36
  Does this speak more volumes about the advancements and benefits you gain through
• 3:40
  vertical AI or how perhaps there has always been this cap on value for professional
• 3:46
  services at large.
• 3:47
  One of our LPs is a management consultant who does a lot of these integrations.
• 3:51
  And last year, I was giving him a hard time with AI.
• 3:54
  He's like, this is the greatest thing that happened to my business in forever.
• 3:57
  He's like, we have this new technology that my customer doesn't understand but desperately
• 4:02
  needs to use.
• 4:03
  I go, when was the last time this happened?
• 4:05
  He's like, the Internet.
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  We had all these people who were like, we need a website or we need an e commerce capability,
• 4:10
  and the tools weren't sophisticated enough that you could do it at the scale that a Macy's or a
• 4:15
  McDonald's or whoever would need it.
• 4:17
  So you bring in the consultants to help you do the Gen one integration and build.
• 4:21
  And that also tells me a lot to your point about probably the state of the technology
• 4:25
  being so new.
• 4:26
  You probably do need someone to come in.
• 4:28
  While you could probably get three engineers over a weekend to build you an MVP, you
• 4:31
  couldn't build something that an entire so I think it's really interesting that the
• 4:35
  professional services folks have found what sounds like huge new channel for both
• 4:40
  consulting services and, you know, well, pilot projects and real sounds like sticky
• 4:44
  deployments using this AI technology, and we'll see if it goes the way of the Internet.
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  In five or seven years, some of these things that you had to build custom with a partner,
• 4:52
  you'll be able to buy a standalone product.
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  So I'm very curious to see how quickly that evolves.
• 4:57
  Yeah.
• 4:57
  And it's not entirely new, the business model of teaming up with another company to extend
• 5:01
  out services.
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  I think of, like, Workday offerings Yeah.
• 5:04
  And how that exists in every single consulting firm, SAP, Oracle, all of those.
• 5:08
  K.
• 5:09
  What's different or feels different is leveraging Harvey AI's tech and white labeling
• 5:14
  it as your own proprietary, trying to get exclusive rights so the other big four can't
• 5:19
  have their take at it.
• 5:20
  That's what feels a little bit different, and I wonder if it's because they know that it's
• 5:24
  difficult to build that sort of tooling in house.
• 5:27
  So why don't I just partner with the best of the best and try and edge out everyone else?
• 5:31
  Our second topic is a TechCrunch article by Mary Ann Azevedo digging into a long standing
• 5:37
  frustration for founders.
• 5:38
  Why do some VCs completely disappear after initial meeting or eventually reject a deal
• 5:43
  even when the conversation seemed promising?
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  While investors don't own founders capital, the lack of communication oftentimes leaves many
• 5:49
  feeling confused about what went wrong.
• 5:52
  We had a conversation around this Yeah.
• 5:53
  From a piece Chris Newman had written a few months ago, and the article echoes a lot of the
• 5:58
  same possible reasons ghosting happens.
• 6:01
  So lack of conviction, internal dynamics, timing, etcetera.
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  Ultimately, we all agree that these reasons don't make ghosting right, but at least we can
• 6:09
  shed more light onto what might be the reasons you never hear back.
• 6:13
  One thing that we didn't chat through in an earlier episode were common red flags that
• 6:18
  investors might see that perhaps even before taking a meeting could really dissuade them
• 6:22
  from taking a call or responding to an email at all.
• 6:25
  One that was mentioned here, it hit so close to home, but it's AI based outreach and note
• 6:30
  crafting.
• 6:31
  It's becoming so easy to blast off your deck and a blurb to as many people at once and hope
• 6:37
  by just pure volume that you'll hear a response.
• 6:39
  I will go on the record and say it's very obvious still when you are doing that.
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  And for me, not the best or most effective way to get an intro to an investor, so I wanted to
• 6:51
  hear your thoughts on this as well.
• 6:52
  Yeah.
• 6:52
  Mercedes at Lightspeed also had a very brave post that the I would encourage people to read
• 6:57
  the comments.
• 6:57
  I remember the in the arms race of VC attention, the first Volley was really things
• 7:02
  like sales loss and outreach, just sequences.
• 7:04
  People were running sequences.
• 7:05
  I'd get an email, and then I'd open it, and then immediately get a, hey.
• 7:08
  Like, notice she checked out my deck, or, hey.
• 7:10
  Like, just wanted to follow-up.
• 7:12
  So I got used to these programmatic sequences, but most or at least a good chunk of those
• 7:16
  emails were written by a human.
• 7:18
  At least most of it was written by a human.
• 7:20
  And then I feel like we now have this data pipeline where you can go on LinkedIn, you can
• 7:24
  get these air tables of, like, thousands of VCs with their contact info.
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  You can hook that up to ChatGPT or Cloud or your LLM of choice.
• 7:33
  You can get some delivery sequence thing, and now you can have personalized turbocharged.
• 7:38
  And I've looked at some of these GTM stacks.
• 7:41
  There's a bunch of tools that will do enrichment, so you can give it all the emails.
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  It'll enrich them.
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  It'll pull out some fun facts, and you could push go.
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  And what do I owe that person?
• 7:49
  Yeah.
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  I don't think I actually owe that person anything.
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  We haven't interacted.
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  I haven't committed to meeting with you.
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  I don't think I owe that person anything other than the time I spend opening the email and
• 8:00
  making a decision whether I wanna read it.
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  I think the bar does go up when you meet with somebody, either in Zoom or in person.
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  I do feel like I owe those people, and I keep tabs on it.
• 8:08
  I'm in, like, high nineties closing the loop.
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  I will even sometimes, I'm embarrassed to say, I this weekend, I wrote a bunch of emails to
• 8:15
  people that I just had dropped the ball on closing the loop with them, like, two months
• 8:19
  ago.
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  And I was like, I'm sure you closed around.
• 8:21
  I just wanna apologize.
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  It was rude of me to not get back to you.
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  I should have done better.
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  I hold myself to a higher standard.
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  But the volume of inbound stuff, it's actually over person.
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  The ones that second to me, I'm like, you don't know that much about me.
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  Yeah.
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  And there's no the only way you would possibly know that much about one line about, oh, you're
• 8:36
  a Chelsea football fan or, oh, you're that's cool.
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  Three paragraphs about that stuff.
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  I'm like, this is I mean, I don't know.
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  What's your experience?
• 8:42
  No.
• 8:42
  Yeah.
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  I mean, it's the same.
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  It's the same.
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  I mean, we were just talking about it.
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  I think the use of AI tools to make certain manual tasks a lot more efficient for yourself
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  is a wonderful use.
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  When I think it's crossed the line, I think is when it's just adding fluff to, I don't know,
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  mask the ultimate reason why you're sending an email.
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  And I don't look down at all upon people who inbound me with no connection whatsoever.
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  I I read every single email that comes through my inbox.
• 9:07
  Yeah.
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  But now I'm finding I'm actually paying attention a lot more to the emails where it's
• 9:11
  very quick, very straight to the point.
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  You have the information there and a very clear way to reach you If I wanna learn a little bit
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  more, it becomes very clear that it's not actually personalized when it's five paragraphs
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  deep with all these fun facts about myself.
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  So I would just say it's almost like a reallocation of your effort.
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  Mhmm.
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  When you're thinking about those emails, maybe that's just not the best use of your time.
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  I also think like in all sales situations, people get ghosted.
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  LPs ghost me.
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  Investors ghost me.
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  Investors?
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  I'm like, I'm
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  like, I'm like, I'm like, people
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  wanna try to be that happens.
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  And it's hard to tell people don't take it personal when it's your company and your vision
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  and you're putting yourself out there.
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  So I don't tell people don't take it personal.
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  Of course, you're gonna take it personal.
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  And, like, just as a different way of saying no.
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  I thought, like, hey.
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  If I haven't gotten back to you and you make an extra effort to reach out to me, that gets my
• 9:59
  attention.
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  If we met and I said I'm gonna get back to you in a week, ten days, and it's been two weeks
• 10:03
  and we haven't heard from you, you are 100% entitled to nudge me.
• 10:06
  And as the post said, sometimes I'm like, hey.
• 10:08
  It's the end of the quarter for audit season.
• 10:10
  The only thing I can think about is, like, audit, and your company could be super
• 10:13
  interesting.
• 10:14
  Or I have a portfolio company that's going through something dramatic.
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  Everything is kind of behind that.
• 10:19
  So it's not always, like, you.
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  But the thing I tell our portfolio companies is when you're one of the most important things
• 10:26
  happening at a firm, you will know.
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  Yep.
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  Responses are quick.
• 10:29
  Resources are, like, you you will know.
• 10:31
  And if you don't feel that energy, then you should read that as I am not high enough on the
• 10:35
  priority list of this firm.
• 10:37
  It could be something that I haven't done in getting them excited.
• 10:39
  It could also be something internal to that firm that's going.
• 10:41
  I had someone I was trying to chase down who was really responsive and who'd sort of ghosted
• 10:45
  me on three VC intros, and he's leaving his firm.
• 10:47
  And he's like, I didn't really wanna talk about the decision until it was done, but I also
• 10:51
  didn't wanna waste those founders' time taking intros.
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  And I was like, oh, I was beginning to worry.
• 10:55
  It was three in a row.
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  So I don't know.
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  I really do feel badly when people have made the time to walk me through what they wanna
• 11:01
  build.
• 11:02
  I mean, one other thing, Mia, is I feel like back in the day, a pass was just a pass.
• 11:06
  You're like, we're not moving forward.
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  There's no excuse for not saying that.
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  But now I feel like the standard is your decision plus some rationale, and sometimes
• 11:14
  it's hard to put into words.
• 11:16
  Like, why am I passing?
• 11:17
  It wasn't one thing.
• 11:18
  It was like the combination of these three or four things not aligning, and sometimes I get
• 11:22
  I'm staring at the draft and just saying what goes in the box.
• 11:25
  Tying this all together in the thread of AI that we've continuously talked about, another
• 11:30
  red flag worth noting from the article is exaggeration and, quote, unquote, dishonesty
• 11:35
  around the traction for the business that could signal someone to pass, even if you've had a
• 11:39
  wonderful set of meetings.
• 11:41
  I'm finding in AI, some of this quote unquote dishonesty isn't really a % the fault of the
• 11:46
  founder at all.
• 11:46
  I think that we're just going to have to get used to seeing a lot of more false positives
• 11:51
  specifically with an AI startup.
• 11:53
  So early on in their journeys, you shared a great post by Sameer Kaji who shared the term
• 11:58
  annual curiosity revenue or capital burn by companies.
• 12:01
  So potential customers as a mechanism to try out AI tools only to realize there is really
• 12:07
  not a clear ROI and then churns as a result.
• 12:09
  So we both laughed about this line.
• 12:11
  So I was curious to ask you about your opinions here as well.
• 12:15
  I had someone who told me they're on a $10,000,000 run rate.
• 12:18
  I was like, that's amazing.
• 12:19
  They said, yes.
• 12:20
  If you multiply our best week on the revenue run rate by 52, I was like, that's not I was
• 12:26
  thinking you were doing more like, I don't know, dollars 800,000, dollars 9 hundred
• 12:29
  thousand a month.
• 12:30
  Not like you had one amazing week.
• 12:32
  And I think AI has distorted people's view because you hear these stories about companies
• 12:38
  going to 10,000,000 in ARR in six weeks, a hundred million in ARR in two quarters.
• 12:44
  And I don't know.
• 12:44
  I've always been a believer that if one company has standout performance, it's probably a
• 12:48
  special company.
• 12:49
  If a dozen companies have the same kind of vertical growth like this in a compressed
• 12:54
  period of time, there's probably something macro happening.
• 12:56
  And my belief is that annual curiosity revenue is a pretty I wish I'd been at that dinner and
• 13:00
  no one had said it.
• 13:01
  Because I do think there's a lot of companies who are just like, we're gonna try as many AI
• 13:05
  tools as we can in search of the thing that works.
• 13:08
  And is it very possible the thing you bought last quarter is no longer the thing that works
• 13:12
  because some new capabilities come out?
• 13:13
  And so you try a new thing because nobody wants to get left behind.
• 13:17
  At some point, it's gonna settle.
• 13:18
  People are gonna be like, alright.
• 13:19
  I'm not gonna pay for three coding Copilots.
• 13:21
  We're either gonna use Cursor or we're gonna use GitHub Copilot or we're gonna use one of
• 13:24
  these things.
• 13:25
  And there's gonna be a bunch of these markets where people are gonna be like, we're not gonna
• 13:27
  pay for 10 of these things.
• 13:29
  We're gonna pick one.
• 13:30
  And so I think we're in a moment where just the budgets are big, and I think no CEO wants to go
• 13:34
  to the board and say, we're not heavily invested in figuring out how AI is gonna impact
• 13:38
  our business.
• 13:39
  At some point, they're gonna have the answer.
• 13:40
  They're gonna be like, AI right now impacts this part of the business, so we're gonna spend
• 13:43
  money here.
• 13:44
  We tried this thing.
• 13:45
  It didn't work, and we're not gonna continue to spend on that.
• 13:47
  We might revisit it in two years, and it's gonna settle up.
• 13:50
  But right now, it does feel like a gold rush.
• 13:52
  How would you suggest founders who are building an AI communicate their traction
• 13:58
  revenue?
• 13:58
  I'm very empathetic in that I talked to one of our founders who competes with one of these
• 14:02
  companies, and I told him, I go, what do you think about that hockey stick growth?
• 14:05
  He goes, they probably have massive churn.
• 14:06
  I go, I probably agree with you.
• 14:08
  He goes, but I also know that that's the yardstick that we're gonna get measured by when
• 14:11
  we go out to fundraise because that narrative's out there.
• 14:14
  So we too need to make sure that we're on a very aggressive growth ramp when we go out.
• 14:18
  I think it's hard for founders because on the one hand, you're like, what do those numbers
• 14:22
  mean?
• 14:22
  Are they real?
• 14:22
  Is that revenue gonna be retained?
• 14:24
  But it's an anchor point in the mind of the investor.
• 14:26
  And if you're at a million of ARR and someone else is at 10, they're gonna go, well, you have
• 14:30
  the same amount of time and resources.
• 14:32
  They're at 10.
• 14:32
  You're at one.
• 14:33
  Why wouldn't I pick the team at ten?
• 14:34
  It's almost asking investors actually to be a little bit more methodical about the way
• 14:39
  they're thinking about it because what they deem to be as the benchmark become the
• 14:43
  benchmark and leak out into founder land, and then that kind of creates the churn of trying
• 14:48
  to embellish out the traction that you have and see it so future looking when it's impossible
• 14:52
  to do so just given annual curiosity revenue.
• 14:55
  And half the time, part of the story that is leaked is the part of the story that is most
• 15:00
  favorable.
• 15:01
  When someone tells me they had 200% NRR, a net revenue retention, that's really impressive.
• 15:05
  That tells me much more about the stickiness and utility of your product than a top line
• 15:10
  revenue growth number where I don't know anything about usage.
• 15:13
  I was at an event.
• 15:13
  I talked to someone who has bought, I don't wanna name the product, who bought a a big AI
• 15:17
  product, and they sold it top down.
• 15:19
  And so the c suite signed off on it, and I think they paid $2,000,000 for it.
• 15:22
  And I'm like, what's the usage like?
• 15:24
  He's like, nobody uses it.
• 15:25
  I'm like, what do you mean?
• 15:25
  He goes, well, it's a really cool tool, but everybody in our org either has, like, a
• 15:29
  ChatGPT or cloud subscription or has found some other collection of tools.
• 15:33
  So when management bought this, they were, like, super excited, and everyone else was
• 15:36
  like, if you'd asked us, we would have told you we've already kinda mostly solved that problem.
• 15:40
  And so on the books, that company's got $2,000,000 in ARR, but the odds that that
• 15:44
  contract gets renewed very low.
• 15:46
  So fascinating.
• 15:47
  I know we talked about it a couple of podcasts ago, but you hear in this industry the highs
• 15:52
  and then the extreme lows, but nothing typically in the middle of just, like, the
• 15:57
  companies and the founders who are just showing up day to day and just grinding.
• 16:01
  I believe all founders are doing that Yeah.
• 16:02
  In good faith, but those stories don't typically get shared.
• 16:05
  So I understand why there's this feeling of wanting to over embellish when you can.
• 16:09
  It's not like it's fake revenue entirely.
• 16:12
  And there's all these other weird incentives.
• 16:13
  Like, I saw someone, oh, one of the things we're looking for now is ARR per full time
• 16:17
  employee on my goals.
• 16:18
  So companies are just gonna hire contractors.
• 16:20
  They're just gonna hire contractors, and they're gonna have fewer FTEs.
• 16:23
  This is the eternal battle.
• 16:25
  VCs pick a metric that has meaning, and then once the metric is understood, it is gamed,
• 16:31
  losing most of the meaning that it once had.
• 16:33
  Like, Like, as soon as NRR became a thing, I met companies that were intentionally selling
• 16:38
  in small contracts so that they could have account growth.
• 16:41
  Very rapid account expansion.
• 16:43
  Like, if you just look at the total size of the deal, twelve months in and don't worry about
• 16:47
  how much it grew over time, you would actually get the same answer.
• 16:49
  But I think founders are clever and the window to exploit and quote as a VC, these metric
• 16:54
  inefficiencies is is getting smaller.
• 16:56
  Yeah.
• 16:57
  And founders are smart and they're figuring out that's how I'm gonna be evaluated.
• 17:00
  We are going to make sure that our data, when presented, puts us in the best light.
• 17:04
  Moving on to our last topic, Lawrence Lundy Brian released a new post from his son's stack,
• 17:09
  State of the Future, called data driven VC is over.
• 17:12
  In the post, he makes a pretty bold claim.
• 17:14
  Venture capital's reliance on data driven research is becoming obsolete, and the next era
• 17:20
  of investing will be shaped by intuition, contrarian thinking, and human wisdom.
• 17:25
  The argument is that AI powered deep research has made market research, thesis development,
• 17:30
  and diligence instantly accessible, removing the traditional edge that larger firms had.
• 17:36
  When everyone has access to the same research and insights, investment strategies converge
• 17:40
  and firms all start chasing the same deals.
• 17:43
  Lawrence predicts that the best opportunities will come from obvious trends that AI can map
• 17:48
  out.
• 17:48
  They'll come from decisions that defy data driven consensus, companies that maybe didn't
• 17:52
  make so much sense on paper, but succeeded because of just a deeper intuition.
• 17:57
  The key takeaway was that this pattern recognition, intuition, and contrarian
• 18:01
  conviction will become the new differentiator.
• 18:03
  And so in this world, solo GPs, emerging managers with deep sector expertise, and maybe
• 18:08
  stronger founder relationships will have an edge over mega funds bogged down by
• 18:12
  institutional processes.
• 18:13
  I'm actually curious to know at the GP level what sort of chatter is occurring right now
• 18:18
  with more folks leveraging deep research across their funds.
• 18:22
  I'm sure that some are like, this is a game changer and others are, this is just as good as
• 18:26
  what I have my 70 person team pumping out.
• 18:28
  Do you agree that it's leveling the playing field for market analysis, making traditional
• 18:32
  information advantages irrelevant?
• 18:35
  So I use deep research a lot, not just for work.
• 18:37
  I found deep research allows me to get 70% of the way up the curve on an unknown topic if
• 18:44
  there's a fairly robust amount of public data available, which before, that would have
• 18:48
  probably kept you out of the category.
• 18:50
  You'd have been like, this new industry, who are the big players?
• 18:53
  How do I learn about it?
• 18:54
  You'd have to go off and do research, or you'd have to have a team of associates doing these
• 18:59
  relentless market maps, finding every company.
• 19:00
  And I do think there was a period of time in which simply seeing more companies and seeing
• 19:05
  more data was an edge.
• 19:06
  But now I assume that every big multistage firm has well, I know they all have teams of
• 19:12
  engineers that have built data analytics capabilities that are sucking in all the data
• 19:16
  they're getting from both their own portfolio companies and from companies that they've met
• 19:20
  in diligence.
• 19:20
  And then they publish these charts.
• 19:22
  Best in class NRR for series b companies is 285%.
• 19:26
  And, like, where does that number come from?
• 19:27
  Well, that firm is a growth stage firm that met with a bunch of companies and says, we know
• 19:30
  what the distribution looks like.
• 19:31
  So I think the thing that deep research doesn't yet solve for is one of the benefits of
• 19:36
  investing in private companies is that the information private and is not easily
• 19:40
  accessible by AI yet.
• 19:42
  So I still think there is an information advantage if you are seeing more companies at
• 19:46
  your stage and you have the ability.
• 19:48
  I think more it just means I'm much more willing to invest in categories where I didn't
• 19:52
  know as much because I can start a cup of coffee, fire off a deep research query, and get
• 19:56
  back clues on where to look if I wanna learn more.
• 19:59
  And I I do think it's gonna take away some of the sort of tedious market mapping and
• 20:04
  background work that analysts and associates used to have to do.
• 20:07
  But I also think that discovery process is a skill.
• 20:11
  And I think if people just say, I'm just gonna assume that whatever deep research tells me is
• 20:15
  true and complete, they're gonna miss out on stuff.
• 20:17
  And as someone who doesn't do a lot of data driven investing, I, of course, rest the most
• 20:20
  rest, any of it was me.
• 20:21
  It will be interesting to see also just because for a lot of funds, those materials are seen as
• 20:26
  collateral that can push them into new circles or hopefully win founders over.
• 20:30
  So it'll be interesting if there's almost a democratization of access to this, what
• 20:35
  advancements you end up getting, and is there just a clear diminishing point of returns for
• 20:40
  that effort?
• 20:41
  Is there signaling if you go into deep research and there's so much information on this market
• 20:45
  you're looking into, then maybe it's just not quite the area that you wanna be spending a lot
• 20:49
  of time in as an early early stage investor.
• 20:51
  The article suggests that the best investments will come from decisions that feel irrational
• 20:56
  based on available data.
• 20:58
  The kinds of bets that bigger funds might struggle to make just given the deeper
• 21:02
  processes that exist there.
• 21:04
  I do wonder that bigger funds, because they simply have more money that they need to move,
• 21:09
  is this an argument that they maybe might have the luxury to make such bets in a haphazard
• 21:14
  sort of motion?
• 21:15
  Well, there's multiple dimensions of data driven investing.
• 21:18
  One is data about the company, KPIs.
• 21:21
  The other one is data about the people.
• 21:23
  And I do think the big multistage firms have gotten very good, I don't mean this in a bad
• 21:28
  way, of algorithmic investing around people based on where they worked, how many companies
• 21:32
  they started before.
• 21:34
  If you said, hey.
• 21:35
  Who are the first hundred engineers at OpenAI?
• 21:37
  I bet you every big stand up firm has their names, probably their birthdays, probably have
• 21:42
  them in harmonic, maybe put an air tag in their back.
• 21:44
  There is a lot of algorithmic investing I see around people.
• 21:47
  The impulse is like, hey.
• 21:48
  This is a person in a very important role in a very important company at a very important time
• 21:53
  in that company's life.
• 21:54
  On average, I think this person knows something.
• 21:57
  We've been doing that forever in venture, and the TLDR is like it doesn't always work.
• 22:01
  A lot of the most impressive interesting companies I've seen are people who are not
• 22:05
  wildly successful before and who are not super early.
• 22:07
  If you just said, hey.
• 22:08
  I I'm willing to invest in anybody who's one of the first hundred people at OpenAI.
• 22:12
  That's kind of all I need to know.
• 22:13
  You could run that play if you have access.
• 22:16
  And the reason I think the algorithmic stuff is interesting for the big funds is they have
• 22:19
  access to generally speaking, their brands give them access to the best people either because
• 22:23
  they're already on the cap table of those companies or because those founders will seek
• 22:27
  them out.
• 22:27
  I don't think that's the same for small funds.
• 22:30
  And, look, I would argue that in the last ten years, we hit peak data driven investing
• 22:34
  because our entire industry basically shifted from a healthy balance from b to b to b to c to
• 22:40
  almost exclusively investing in software as a service models in B2B, excluding a few brave
• 22:46
  people who did consumer or hard tech or deep tech.
• 22:48
  Most of venture capital became the business of funding business software companies and
• 22:53
  comparing them on a pretty strict set of metrics, churn, growth rate, MRR, revenue per
• 22:58
  activity.
• 22:59
  There was a set we'd gotten to a place where everybody was kind of running the same playbook
• 23:03
  and the only difference was how strict were you gonna hold those companies to the metrics.
• 23:07
  And the point about data driven investing, the thing I admire most about some of my friends at
• 23:11
  these big firms is the ability to spot a company that feels expensive in the moment, but
• 23:16
  understand that relative to the terminal endpoint, a billion dollar valuation or I just
• 23:20
  think about like David Z doing Facebook at $5.50 and everybody thinking that was crazy.
• 23:25
  And it was crazy.
• 23:26
  It was crazy low in the grand scheme of things.
• 23:28
  People got into Stripe at a billion.
• 23:29
  People got into OpenAI at a billion.
• 23:31
  Still TBD, but the data would suggest that you're up 300 x, not accounting for dilution.
• 23:36
  And I think that's a different kind of bravery is the ability to see a company that's already
• 23:40
  valuable and be able to envision a world in which it becomes significantly more valuable.
• 23:44
  And I think people like to knock the big funds that I'm like, I think it's very hard to see a
• 23:48
  company like Stripe and say, I know it's at a billion today.
• 23:50
  The ceiling is a hundred.
• 23:52
  And you'd be like, well, do you have to have a hundred x for a billion unless you do.
• 23:55
  I think that those are the outcomes that they need.
• 23:57
  So it makes sense for them to jump into those companies.
• 23:59
  Whereas I think of, I don't know, emerging managers who are on fund one and fund two.
• 24:03
  Those swings and misses are very painful because you come in at such a high price.
• 24:07
  We're seeing this divergent and this split out in types of deals that folks are looking at
• 24:11
  just given how big their firms are.
• 24:12
  There are deals that come in at prices that larger multi stage firms are best positioned to
• 24:17
  do
• 24:17
  and
• 24:18
  should.
• 24:18
  So it really will be all interesting how it ends up.
• 24:20
  We are all out of time for today.
• 24:22
  So thank you, Charles, and thank you for listeners for joining us here live in person
• 24:27
  for the Learning Corner.
• 24:28
  Please like and subscribe, and we will all talk to you next week.
• 24:31
  Thank you.
• 24:32
  See you.