Latest / The Learning Corner by Precursor / Episode #19: Venture Capital 3.0, Deciphering Early Stage Traction, 90's Tech Bubble vs. Current AI Valuations

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Episode #19: Venture Capital 3.0, Deciphering Early Stage Traction, 90's Tech Bubble vs. Current AI Valuations · The Learning Corner by Precursor

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  The functional difference between 500 k in ARR and a million is nothing relative to the end
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  goal.
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  But I feel bad because founders read all of these blog posts that say you need a million in
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  ARR, you need 2 and a half million in ARR, you need to double every those things will get you
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  attention, but they won't necessarily get you money.
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  It's the week of February 10.
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  Welcome to the learning corner, a Precursor Ventures podcast.
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  Welcome to the learning corner by precursor.
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  I'm Mia Farnham and I'm joined by our GP and managing partner, Charles Hudson, where we will
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  be talking through the articles and Goodreads that caught our teen's attention over the past
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  week.
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  Topic number one was written by James from NFX outlining how venture capital three point o
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  impacts founders in the AI age.
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  The essay argued that venture is undergoing a significant transformation driven by AI,
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  software automation, and changing power dynamics between founders and investors.
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  Amongst a variety of insights, the essay suggests that the version of venture we live in
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  today, version three point o, or the version of Ubiquiti, where venture has explicitly expanded
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  internationally and across a multitude of sectors, which has created a whole host of new
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  dynamics for investors and founders.
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  James called out that VCs face more pressure than ever to compete with other VCs.
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  VCs.
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  This creates a system where investors will need to fund more outlier ideas and non consensus
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  deals in order to perform.
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  I agree with this, but I also know that a massive percentage of deals that we see getting
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  done are in AI, which feels, at the moment, like a consensus deal archetype.
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  So I wanted to hear your perspective on the article.
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  I enjoyed reading the article.
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  It's always fun to read James' writing.
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  He's always so positive and optimistic.
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  I just thought there were so many things in there that I hadn't thought through the
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  implications of.
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  You know?
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  For example, he talks a lot about how there'll be many more venture capitalists, and probably
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  my favorite line in the article is there are low barriers to entry in this business.
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  Anybody with money can become a venture capitalist, and that is true.
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  But I think, you know, this idea that we're gonna have many more VCs is kind of at odds
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  with a lot of the articles about firm contraction, particularly among emerging
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  managers and many of those firms going away.
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  But I think we have so many one and two person venture firms being started and spin outs from
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  big platforms.
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  Will there be tens of thousands of more VCs in the future?
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  I don't know.
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  But for me, the big question is, does AI make the venture business easier or harder?
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  We've certainly had companies where their data has been analyzed by some AI part of the
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  process and spit out a number.
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  And in some case, that number or conclusion's been illuminating.
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  And in some cases, it's been gobbledygook, and it's been contradictory.
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  The AI has said, like, the company is both great and flawed at the same time.
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  I have so many questions about where we're headed to James' article.
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  It does feel like we're on the cusp of a new air and venture, whether that's super big mega
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  funds, whether that's AI's impact on venture, whether that's just more dollars coming into
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  the asset class in general.
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  But for me, the other question I had is he talks about a bunch of new LPs trying to get
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  into the asset class.
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  And I think there are things that are probably true for venture that might not be true for
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  firms of all size.
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  And I haven't met quite as many LPs who are both clamoring to get into venture and whose
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  principal entry point is going to be small to medium sized firms.
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  I've certainly met people who are like, hey.
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  I haven't been in venture.
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  I'm gonna start with the big megafones.
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  I'm gonna go get Sequoia, Andreessen, founders, one of those, as the bedrock of my portfolio,
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  and maybe then I'll start to experiment with the stuff that feels more exotic.
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  But you know, I tend to think of this through the lens of not just what does it mean for our
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  industry, but what does it mean for firms of different size?
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  And going back to piece of we're not sure where AI tools actually end up affecting how people
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  do the day to day job in venture.
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  I do think that there is great opportunities there for founders, maybe, if we apply AI.
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  It might cut down on some low hanging for questions.
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  But then the tough side about exactly what you said, maybe you have biases baked into the
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  models, misinterpreting basic information.
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  I do think as big AI tool uses ourself on the team, we found different ways to tinker around
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  to feel comfortable.
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  From your perspective, what are some things that you feel like you really can't optimize
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  with AI in our roles?
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  I think so much of this job, and fairly or unfairly, is your subjective view of the
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  individual running the company.
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  Especially at precede and seed, the people are such a big component.
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  I was talking to someone this weekend who's like, well, what about late stage?
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  I'm like, the late stage people are already quite analytical.
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  It's unclear to me what the delta will be in terms of the output that those folks can create
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  today and what AI would enable them to do.
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  And I worry that if we get too algorithmic without the people piece, we'll miss out on
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  special people.
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  And, you know, as I've told a lot of our portfolio company founders, even things like
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  traction.
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  It's very rare that for a seed stage company, the amount of traction you have is the reason
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  that somebody invests.
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  That you go, wow.
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  This traction is so amazing.
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  I don't need to know anything else.
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  No.
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  You need to know about the market and the people.
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  And my hope is that AI makes the computational traction and analytical piece simple so that
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  people can spend more time on the people piece.
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  So that's my hope, is that you can get all that stuff out of the way.
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  And if I know anything about founders, if you give them a set of rules, somebody will figure
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  out a way to hack or optimize it.
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  And so I'm also just very curious in this arms race, VCs start using more AI to analyze
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  companies.
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  That will provoke some response, which I have not thought through what it will be, from
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  founders who will modify what they send us.
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  I don't mean in a fraudulent way.
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  They will optimize whatever it is that they send us to get a good answer out of the AI.
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  Yeah.
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  So it'll be interesting to see how that all plays out.
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  At PreSeed, also kind of embedding AI tools is difficult because there's typically no traction
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  whatsoever.
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  So ingesting any type of metrics or traction into a model that you've customized is I don't
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  know I don't know what the output I mean, I played around with it, it, but ultimately it
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  lands out in stuff that's probably like 25% of the way that I could get it if I just chatted
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  with the founder directly.
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  It does open the door for opportunities to support founders within our portfolio.
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  And I think specifically within our own team, we have 400 plus companies.
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  And I know we've talked about like, what are some ways that maybe we can bubble up insights
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  about, you know, perception of investors when our founders wanna go out to raise?
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  Are there ways that we can do this through a model to let them know what people are
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  thinking?
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  And that's a lot what the article calls out is that AI tools will be used more and more to
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  either find you, engage with you, evaluate you, or support you, which I believe is true.
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  I've seen a few people who've built these pitch bots on their websites where you can submit
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  your deck, and it will tell you this pitch bot is a proxy for us.
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  It's not a decision.
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  And the cool thing about that, it's you know, I get people who send me pitch decks sometimes
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  and, like, what do you think?
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  I'm like, I think I'm too busy to give you feedback on this because I'm working on either
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  our existing portfolio, but the idea that someone could get feedback before they submit,
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  go through a couple of revisions, that to me is interesting.
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  Also, I have a bunch of checklists and documents that I've put together for our
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  founders that are kind of pre fundraising things.
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  And the idea of folding those into AI is interesting to me because instead of relying on
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  them to actually manually go through and say, yes, I can just say, hey, give me the stuff,
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  we'll put it in there.
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  The bot knows what's on the checklist, and the bot will determine whether you've done a good
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  enough job addressing those issues.
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  Yeah.
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  That's a great summary.
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  I think there's a lot of low hanging fruit for us to make our founders' lives easier with
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  Partfolio instead of us kind of reaching out and asking them to fill out a specific set of
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  forms.
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  It's a great segue actually into our second topic.
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  It's a medium essay written by Jared at Rebel Fund who took the time and effort to try and
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  extract data driven insight on what early stage startup traction should mean to investors while
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  they're diligencing companies.
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  With a massive data lake built from investing in hundreds of YC startups, Jared wanted to see
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  if there was any correlation between early traction that you see leading up to those
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  coveted demo days and their latest respective valuation.
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  So it turns out after doing the analysis, Jared determined that there is weak to negligible
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  correlation between any of the traction metrics that he used and the latest company valuation,
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  or in other words, the hype around the up into the right revenue graph traction that you see
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  at demo days, or just in pitch decks early on, wasn't all that important.
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  And perhaps it was always the softer stuff.
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  So your team, your market, etcetera, things we just talked about five minutes ago, that should
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  have taken priority.
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  It's hard to ignore traction though, especially when there are such few metrics to point to as
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  early stage investors.
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  So how do we strike this balance of what to actually place weight on?
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  I've been going to YC Demo Day since almost the beginning, and I feel like to their credit, YC
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  does an excellent job of getting companies ready to fundraise and does an excellent job of
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  helping them distill, you know, what could otherwise be a complex narrative into something
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  you can explain to somebody in a minute and get them excited.
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  That should, like, not be trivialized.
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  But we also have a lot of YC alums in our portfolio.
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  Yeah.
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  And if I look at the ones that have been incredibly successful, many of them were not
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  the hottest companies on demo day.
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  And many of them, YC aside across all accelerators, many of them didn't hit their
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  stride until after.
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  And I always ask myself, like, what can you really get done in twelve weeks of focus work
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  if you're really going from zero to one?
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  Now YC has gotten more complicated because not every company that gets into YC.
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  It used to be they were mostly cold start companies.
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  Like, they were really starting from scratch.
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  That's not really the case anymore.
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  Also, the batches are bigger, and the YC network has gotten a lot bigger.
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  And I know for a lot of our b two b companies, the ability to sell to other YC alum companies
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  is a big benefit.
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  I don't know.
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  I feel like whenever someone tells me this is what we've done in three weeks, we were here on
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  day one.
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  I'm like, but your laser focus has been on driving revenue.
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  What would it look like if if you weren't in demo day?
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  What's not shown on this chart is did you ramp up marketing spend?
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  Where did all that come from?
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  And I always appreciate people who have traction, but to your point, I care a lot about
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  who the people are, the market they're in.
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  And after we walk away from Demo Day, I'm gonna wake up the next day and be like, what did I
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  just do?
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  What did I just put money into?
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  And so I go into this assuming that everybody's going to show me a chart that's up into the
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  right because almost everybody will show you a chart.
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  Why
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  would you not?
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  Why would you not?
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  You can take the labels off the act.
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  Like, there are a lot of things you can do.
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  And then every now and then, you do see someone who has such extraordinary attraction at demo
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  day that you do pay attention.
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  Go, wow.
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  That feels, like, more meaningful.
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  And I get it.
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  It's a it's another lens through which to view the companies in the batch.
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  And I enjoyed this article because I've always felt like Demo Day heat and revenue traction is
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  an input but shouldn't be the thing that guides your decision.
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  Yeah.
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  It's a beautiful thing about pre seed and seed venture funds and that, at least in the way
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  that I feel like we think about it, we're very unapologetic and not disillusioned by the fact
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  that there is probably little to no traction at the earliest stages.
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  And so to your earlier point, to place a lot of weight on it could almost just signal the wrong
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  things.
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  Once you're almost at a seed series A's fund, when there's a little bit of traction, you just
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  wanna generate as many insights as possible on it because you have something to base those
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  judgments off of.
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  And I find that oftentimes it just makes you swirl and maybe create conclusions that have
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  nothing really to do with where the company is.
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  Because even from pre seed to series a, like you still have so much left to go.
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  And we've seen within our own portfolio, people pivot post series a because they have to.
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  So it's funny.
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  I do find investors getting hung up and wanting to kind of push traction and metrics with the
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  things that got them over the line or when they're trying to sell me on the deal.
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  I always tell our portfolio companies two things.
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  One is traction in my experience is mostly confirmation bias.
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  Like, once you've decided that you like the company, if they have no traction, then you're
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  like, well, traction doesn't matter.
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  They just got started.
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  If you don't like the company, you're like, I wish they had more traction.
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  It's just confirmation bias.
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  And I think the best VCs I know understand what their partnership needs to see, and we'll make
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  sure that the traction level the company's achieved will satisfy whatever their internal
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  process is.
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  But people are like, well, if you get to 1,000,000 in ARR, I'm like, well, if you get to
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  1,000,000 ARR, it doesn't mean you're gonna raise money.
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  It means that a lack of traction is probably not the reason someone will say no.
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  Those are two really different things.
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  And the second thing I tell people is most VCs are underwriting companies that will eventually
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  get to, let's call it, one hundred, two hundred fifty million in revenue.
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  The functional difference between 500 k in ARR and a million is nothing.
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  It still rounds to basically zero relative to the end goal.
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  But I feel bad because founders read all of these blog posts that that say you need a
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  million in ARR, you need 2 and a half million in ARR, you need to double every those things
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  will get you attention, but they won't necessarily get you money.
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  And so what I tell a hundred all the time is you're in the zone where it's unlikely that
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  traction will be the immediate disqualifier for your business.
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  It doesn't mean people are gonna think you're a good founder.
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  It doesn't mean they're gonna like your market.
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  It doesn't mean they're gonna think your company is the winner.
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  It just eliminates that as the easy pass reason.
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  It's almost what I think about when folks at the earliest stages ask or work so hard on
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  their deck, and you're like, it's definitely never gonna win you the investment, but it will
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  for sure maybe make that conversation or solidify a call.
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  But by no means are they gonna use that as a way of, like, look how good this deck was.
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  Let's push it all the way through IC.
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  Not to make this, like, a YC fanboy session.
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  I really do think they have solved the problem that I have struggled with, which is oftentimes
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  I get first drafts of pitch decks from founders, and I'm like, I understand what you
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  want to talk about, but you don't understand what I wanna hear.
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  And I spend a lot of time with our portfolio companies trying to get them to translate the
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  story that they're trying to tell or they wanna tell into a story that will land with
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  investors.
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  And it's very rare that I get a deck from a YC alum or a current YC company where you have to
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  do significant work on that.
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  Like, they they seem to be able to teach people whether it's cynical or not.
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  This is what investors wanna hear.
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  This is how they wanna see that information presented.
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  And I generally find it works.
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  At least it works for me.
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  It's half the battle.
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  It's basically just the storytelling exercise.
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  The one last thing I will say is that it's great to tell founders all of this in advance
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  to just brace themselves for the investor perception at the earliest stages.
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  But, also, I don't wanna discount the excitement at the earliest stages when they
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  have seen some traction.
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  I don't wanna tell people traction doesn't matter.
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  I don't wanna tell them you shouldn't try to get it.
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  I just think it's important to contextualize the traction you have.
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  Like, at this point, if you're a series a investor, a million in ARR is not impressive.
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  It doesn't mean it's not a huge accomplishment for you as a founder.
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  It just means relative to the things that hit that person's desk, a million in ARR does not
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  help you stand out in the ARR quartile.
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  It just doesn't.
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  And those two things can be true at the same time.
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  And so what I'll tell people is, like, it's a it's really impressive what you've done, but
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  relative to other people, it's not maybe as impressive as you think.
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  Well, our last article for the day was an essay by Paul Krugman where he compared the price
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  bubbles days of tech stocks in the late nineteen nineties to that of the AI frenzy we
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  are currently living in today.
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  It's titled how we've been partying like it's 1999.
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  He published in the New York times in February, actually a piece titled the Ponzi paradigm,
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  which argued that bubbles in which asset prices lose touch with reality are common and deeply
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  rooted in human psychology.
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  The rise in tech stocks and in our current day scenario, AI company valuation create bubbles
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  that almost mirror Ponzi scheme behavior where, quote, those who get in early make money, not
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  from underlying asset return, but from rising prices driven by later entrants when folks
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  basically pile in haphazardly to join the party.
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  For listeners, you definitely have to read the article because it's just it's a really great
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  read, but Paul obviously doesn't wanna just dismiss the economic advancements that AI will
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  provide altogether.
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  Similar to the late nineties with the internet, he thinks that there will certainly be a boost
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  in productivity, but perhaps not in this enduring growth rate that folks are
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  underwriting to.
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  So this raises this interesting question.
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  If AI is more about efficiency gains rather than unlocking entirely new markets, shouldn't
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  that change how we should be thinking about its impact on startups and venture returns?
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  Yeah.
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  Well, I've like, I read this, and I was like, I've had some of these conversations with
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  people.
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  And I don't know.
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  I part of the reason I don't make big predictions is I think predictions are scary
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  and the world changes a lot.
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  And, you know, there was a period of time where he thought that the Internet wouldn't have much
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  more impact than the fax machine.
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  He since has admitted, hey.
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  I was wrong about that.
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  I admire anybody who can make public predictions and admit when they've gotten it
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  wrong.
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  I literally told somebody, Mia, that I was like, it felt like 1999, and that person's
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  like, it's 1996.
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  And I was like, okay.
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  We're arguing over windows of time.
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  I was like and we were both in we were both around the valley then.
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  And I said, look.
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  Almost everything we thought the Internet would do, it does.
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  It just didn't do it in '99 through 02/2003 because people weren't ready for it.
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  The technology wasn't up to for a bunch of reasons.
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  It just didn't happen on the schedule that people wanted.
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  And I look at a lot of the AI stuff, and it feels the same way to me that, like, a lot of
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  this stuff is eventually going to work the way people think it will.
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  It might not take twenty years.
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  It might take five, seven, ten years for this stuff to, like, fully play out.
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  But most startups are not set up to work that way.
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  They they need to be in a two to three year technology window.
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  But I think the thing that's interesting about this piece is what's different is in '99 and
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  February, if you were a venture capitalist who bought shares in a company, there was no way to
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  get out of that trade for the most part short of IPO or M and A.
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  And part of the reason that cycle was so disastrous is you it was kind of a long only
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  trade.
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  Like, there was no way to hey.
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  You couldn't go short, obviously.
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  But, also, you there wasn't a robust secondary market that, for the most part, would allow you
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  to offload some of the companies that weren't working where maybe you realized the valuations
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  were stretched.
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  And this environment's really different.
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  You could get in on a buzzy AI company at seed, ride it to the sea, and sell the whole thing.
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  And so I do think the dynamic that's different here you could be maybe a person who thinks
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  this stuff is overdone and there's too much enthusiasm, but you could still actually, like,
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  essentially trade it.
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  Easier said than done.
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  But I think it changes the dynamic when you can get out because you're you're not playing the
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  same game as the people who have to hold it forever.
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  What does that mean then for folks who would come in later stage who are looking into AI?
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  I think those folks had better believed that they're right.
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  The two funny things to me I told somebody at a dinner, we were talking about an AI company
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  that's had, like, a really strong revenue ramp.
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  I'm like, guys, you know, revenue can go down.
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  And everyone's like, what do you mean to go?
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  No.
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  No.
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  No.
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  Like, it it could go down.
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  Like, the thing they're doing is very important today.
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  It's not forever we don't know that it's forever important.
• 17:38
  And we've seen this.
• 17:38
  Like, you you look at Jasper.
• 17:40
  Like, Jasper's reinvented itself, but that company had quite the run up, and they had to
• 17:43
  reinvent themselves.
• 17:44
  And we have quite a few companies.
• 17:45
  Like, this company's at a hundred million ARR.
• 17:47
  I'm like, well, let's look at what are people paying them to do, and do we think those are
• 17:50
  things people will get paid to do three or four years from now?
• 17:53
  And are those real budgets or experimental budgets?
• 17:55
  And I say this not to pick on any AI company in particular, but I've certainly seen some use
• 17:59
  cases.
• 18:00
  You know, we had HTML website builders in web1.0 because there was a period of time where
• 18:04
  building an HTML website was hard for people, and then it just wasn't hard anymore.
• 18:07
  But we still have website builders.
• 18:09
  We have Wix and Squarespace.
• 18:10
  It's just they perform a different function.
• 18:12
  And, also, it's a different set of companies that are winning that business today than we're
• 18:16
  winning that business in the beginning.
• 18:18
  And I don't know.
• 18:19
  I told somebody at a different dinner, I've only seen in my entire venture career two
• 18:23
  consensus trades where there was almost no dissent.
• 18:26
  The Internet one point o had almost no dissent.
• 18:29
  Everybody was pretty much in on that trade.
• 18:31
  And that eventually worked out, but there was a lot of pain along the way.
• 18:34
  And AI, crypto had dissent, mobile had dissent.
• 18:37
  Even cloud SaaS had to send, like, people are like, no one's gonna rent software people, but
• 18:41
  this is probably the only the second time in my venture career where the resistance to the
• 18:45
  consensus trade is minimal to nonexistent.
• 18:47
  I do like that this article highlighted in a very data driven way why do we need to build?
• 18:52
  Like, what's the need?
• 18:53
  Yesterday night, I caught up with Lauren from MGV, and she just said this great quote when we
• 18:57
  were talking about the same thing.
• 18:58
  And she said, roller blades have been around forever, but you don't see me skating around
• 19:03
  New York City.
• 19:03
  Like, every day.
• 19:04
  Right now.
• 19:05
  And And you're like, it's kind of the same thing.
• 19:07
  Like, why why today?
• 19:08
  You know me.
• 19:08
  I I think AI is gonna be amazing for people who use and buy software and will make the business
• 19:13
  of selling software different.
• 19:14
  I don't know if it'll be better or worse, but definitely different.
• 19:16
  Well, we are all out of time today.
• 19:18
  Thank you, Charles, for stopping by, and thank you to our listeners for listening.
• 19:21
  Please like and subscribe, and we will talk to you all next week.
• 19:23
  Thank you.
• 19:24
  Bye.