Latest / The Learning Corner by Precursor / Episode #45: Hire the experimenters, Lessons from "failed" enterprise AI pilots, Valuations soar past the 2021 bubble

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Episode #45: Hire the experimenters, Lessons from "failed" enterprise AI pilots, Valuations soar past the 2021 bubble · The Learning Corner by Precursor

• 0:00
  Right now, we're in the let a thousand flowers bloom, spend a lot of money, throw a lot of
• 0:05
  spaghetti against the wall and see what sticks.
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  And that's normal when you get a new technology, but eventually, people have to find
• 0:10
  value.
• 0:11
  It's the week of August 25.
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  Welcome to the Learning Corner of Precursor Ventures podcast.
• 0:20
  Welcome to the Learning Corner by Precursor.
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  I'm Mia Farnam, 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 team's attention over the past
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  week.
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  Our first topic is a piece written by Rebecca Caden from USB, making the case for founders
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  and team leaders to hire the experimenters.
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  This strategy varies greatly from the tried and true model of startup hiring when taking a bet
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  on someone with a proven track record of success in a similar environment would nearly
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  always win out as an option.
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  Rebecca says that locating these experts lately has become incredibly difficult with the
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  reality that expertise in something in today's AI world might not have existed as a concept
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  six months earlier.
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  She said, quote, underlying this change is a deeper shift in where knowledge lives.
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  Increasingly, it's moving from our heads into large models and the tools they power.
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  Access is no longer the constraint.
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  Execution, speed, and creativity are the new moats.
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  I think Rebecca did a great job summarizing the hiring conundrum facing founders today.
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  Last week, we discussed how difficult it is to hire the obviously good founding engineer.
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  This week, it feels like Rebecca is encouraging founders to broaden their definition of what,
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  quote, unquote, good looks like in today's market.
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  Was there anything else in particular that stood out to you from this piece?
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  I also thought it was good advice for VCs too, not just for founders.
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  If I think about the evolution of what I've seen happen in AI investing, I think in the
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  beginning, everyone's like, oh, I know the answer.
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  Let's go find people who have expertise in a vertical and then have them apply like, those
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  people are gonna win.
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  The, you know, the insurance AI company started by the person with tons of insurance
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  experience.
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  And then I think what people realized was like, well, maybe in this AI world, the context and
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  experience in a category is maybe less relevant than it was in the SaaS world.
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  And I think pre AI, that expertise was helpful because building things took a long time.
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  And someone with previous experience would know what to build and what not to build and would
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  have those relationships.
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  And, you know, getting in the door at a lot of those enterprise places took relationships.
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  And now with AI, lots of companies have big budgets.
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  And so getting in the door even as a relative newcomer is easier than ever.
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  I wouldn't say easy, but easier than ever.
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  And I think the fact that most of the incumbent products didn't have AI capabilities as quickly
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  as their customers wanted them also created an opportunity for newcomers to show up and just
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  say, hey.
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  I'm I'm new to dental.
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  I'm new to pet care.
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  Yep.
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  But
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  I've got this AI powered thing that can meet your needs.
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  And so I think there's something comforting in VCs believing that, like, oh, the answer is
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  just fund the people who already have the relationships and context in this industry, and
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  they'll be the winners.
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  And I think that hasn't worked.
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  And then we've gone through various iterations.
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  I think we've we've landed in this place where I think the answer is, like, nobody knows.
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  Nobody knows what actually matters in terms of founder pedigree and attributes.
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  And I think we've ended up in this world where I think most people believe the only investable
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  moat whether it's at an individual level for keeping your job and getting promoted or as an
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  investor investing companies, it seems to be that it's, like, execution and speed.
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  And I'm just like, but how do you measure those things upfront?
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  Right.
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  Obviously, we have a point of view about how you can try to get your arms on it, but it's
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  really, really tough.
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  And that's a very different moat than we're used to.
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  I think if you abstracted away AI and asked most investors, would you wanna invest in a
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  category where there's very little technical differentiation, there's a lot of competition,
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  and the only potential moat is speed of execution and ability to figure things out?
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  Think most people would say that sounds like a hard way to make money.
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  Well, was, like, my next question.
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  Being able to take those quick turns and move with speed is just obviously critical for
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  founders.
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  How defensible can your company actually be if your current success is truly driven just by
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  speed, which I know we talked about this internally.
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  I don't think money equals speed equals success.
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  Obviously, that equation breaks all the time, but in more obvious areas, specifically within
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  AI.
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  It feels like large amounts of capital allow you to try a bunch of things at once, and maybe
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  you can make more mistakes.
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  But it's still unclear what works, what doesn't, because we don't even know who the
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  winners are.
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  For the first time, I checked one of our portfolio companies who told me that they were
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  making a pivot because they're in a regulated industry, and some of their customers were
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  like, hey.
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  Your competitor has raised a lot of money.
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  At a minimum, we know they're gonna be around for the next two to three years.
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  So if we work with them on this product, we don't know if it's gonna be any good, but we
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  know that they'll be around.
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  And I was like, that's like an actual tangible benefit of having a bigger but the the truth is
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  it doesn't mean that that company's necessarily gonna build a great product.
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  The money gives them a longer lease on life.
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  Yeah.
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  It means they'll be and it's interesting.
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  In SaaS, in 2021, we ended up with a bunch of companies that had raised too much money
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  relative to their ability to scale.
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  And I wonder if the same thing's gonna happen in AI.
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  We're gonna have a bunch of really well funded companies that don't have any idea what to
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  build, or the premise that they started building against turns out to not be that
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  interesting.
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  I mean, I think about some of these Aqua license companies that we've seen That, you
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  know, the the founding team members and early employees walked out the door, and those
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  companies, I think, are searching for a second act or some amount of relevance.
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  And are we gonna have a repeat of this sort of unicorn hangover where there's a bunch of well
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  funded companies that are casting about for something to do but with a lot of cash on the
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  balance sheet?
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  And I assume because they have capital, they'll continue to make changes, keep pivoting,
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  etcetera, because they have the room to do so, and so we'll continue to burn.
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  I don't know if these companies that have tons of money will just reach profitability one day
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  and be like, this is good enough.
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  I think that they don't fall into that bucket that might find that route attractive.
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  It's funny, Mia.
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  I talked to one of our portfolio company founders yesterday, and he said, hey.
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  I've raised this money.
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  I'm trying to figure out what the next milestone is.
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  And we went over this thing.
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  I said, look.
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  Nobody really knows in this environment.
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  You need to grow really quickly.
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  And I said, we're in this world where there are two binary paths for startups.
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  One is try to hit the high growth venture playbook milestones.
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  Hope it's enough to catch the eye of somebody and keep going, or be a rule of 40 company
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  that's profitable and grows at a good rate and hope that you have something that feels more
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  like a PE exit.
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  That in between zone feels like a horrible place to be right now.
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  And I told the founders that based on the money that you've already raised, you've already
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  chosen a path whether you realize it or not based on the way you've capitalized the
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  company.
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  And so I don't think most of the investors in these really well funded companies want the
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  money back either.
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  So I think we're gonna end up in situations where there'll be companies that have raised a
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  lot of money, don't have a clear idea of what to do.
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  And I think the the VCs are gonna say, well, find something to do.
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  That's your job.
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  Fine.
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  You raised the money.
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  You have it.
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  I would take it back in a pinch, but I'd rather not.
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  I'd rather you find something worthy of the capital that you raised.
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  Right.
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  Because those companies are typically the ones that have raised at this larger valuation
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  bubble, probably had the big announcement about it.
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  It's a loss for a loss in quotation marks.
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  For everyone involved from just a media standpoint if they decide, oh, we're actually
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  gonna cool down and not go after this massive extreme exit that we promised everyone.
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  I think people are also anchoring themselves in those roadmaps or those outcomes so much
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  earlier.
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  Gonna talk about more in one of our topics.
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  But if you come in and your seed valuation is $70,000,000, then you've already decided, and
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  you probably haven't even built traction yet, which is crazy.
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  And I don't know whose fault like, I could point fingers at, like, so many different
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  players in that dynamic.
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  One thing I liked about Rebecca's piece is that I think she is calling founders to just make
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  sure, like, your head is on a swivel always.
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  If you find yourself reaching a dead end, be able to change quickly.
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  And, actually, I find that that's been something that we've seen in our best founders
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  throughout times, being able to kiss a bunch of frogs and move on quickly.
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  That's been what separates people from success versus reaching these massive plateaus.
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  It's also been nice.
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  I feel like, especially in the SaaS era, we had gotten into startup creation by playbook.
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  These are the metrics.
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  This is the spreadsheet.
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  This is the only way to do it.
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  This is the only technique.
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  These are the only marketing channels.
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  And it was just getting really boring.
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  Yeah.
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  And I think it's terrifying but interesting that now there aren't actually that many
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  playbooks for this era.
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  And Saturn's always asking me, what's the playbook?
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  I'm like, I don't know.
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  You gotta go create it.
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  Yeah.
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  It doesn't really exist.
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  People are, like, figuring this stuff out as they go.
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  Pricing models for these companies that have raised tons of money haven't been figured out,
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  and they continue to change every ten months, which is crazy and would have never happened in
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  the SaaS world because the entire basis of that successful industry was based on the one
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  pricing model that had worked.
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  Well, for our next topic, this past week, MIT's Nanda initiative published a report analyzing
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  around 300 generative AI deployments across enterprise customers.
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  The study found that ninety five percent of pilots were failing to deliver on promises of
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  ROI and revenue growth.
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  Interestingly, the high failure rate wasn't primarily due to the quality of the AI models
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  themselves.
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  Instead, interviews with company leaders pointed to flawed enterprise integration,
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  especially in bridging the learning gap for both tools and organizations.
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  The data also highlighted a resource allocation problem.
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  Over half of generative AI budgets were being directed towards sales and marketing rather
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  than toward lower hanging fruit like back office automation.
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  One standout finding, companies that purchased AI tools from vendors had a success rate of
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  around 67%, while those that tried to build internally succeeded only about a third as
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  often.
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  So while the report highlights some successful deployments that make up the remaining five
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  percent, it also notes that many surveyed companies were hesitant to share their failure
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  rates.
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  My question for you is how long do you think it will take for companies to realize that their
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  investments aren't yielding returns, especially when so many are focused on building in house
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  solutions?
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  I read this, and I think I had a different take on this article than a lot of people.
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  A lot of people, at least on social media that I saw, were like, I knew this was all a scam.
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  I'm like, I don't think that's what I take away from this.
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  What I take away from this is those failures are the seeds of future success.
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  I build a lot of things internally using Cloud Desktop.
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  Most of them end up not being that useful, but every not useful thing I've built has informed
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  the next thing I've built.
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  And so I think this is the creation process.
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  And if you think about other tech waves, you've seen similar things.
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  The technology comes out and people say, well, we've gotta own this capability.
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  We're gonna do it ourselves.
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  And frankly, in the beginning, there also weren't a ton of great vendors for some of
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  these products.
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  So I think the combination of some companies wanting to build and own the tech themselves,
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  kind of relative immaturity from vendors, and probably overblown promises for what the tech
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  could do.
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  And, look, I'm sure some of these general AI pilots were just like, hey.
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  Let's just have Chet GPT write all of our marketing copy.
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  Unsupervised, untrained.
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  Yeah.
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  It's gonna it's gonna stink.
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  This isn't surprising to me that people who were not skilled in using these tools, who were
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  discovering them and applying them for the first time were largely unsuccessful.
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  That is, like, not very surprising.
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  The thing that I think is interesting is I'm sure all of those people who have failed
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  projects, whether they admit to them as failures or not, learn something in the
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  process.
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  Either, hey.
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  We shouldn't build this internally.
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  We should go find someone who can provide this tool.
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  Or, hey.
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  We to your point, Mia, we went after sales and marketing, but there's a ton of customer
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  support and other low hanging fruit on the cost side that we could tackle, and maybe that's the
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  right place to start before we go here.
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  Or gee, maybe we didn't succeed because we don't have the internal capabilities, but we
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  want to have them.
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  And we need to go find people to work at our company who have more familiarity with these
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  tools.
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  And I think the average employee in these companies has more familiarity today with these
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  tools than they did whenever the survey was taken.
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  So to me, this is a good sign.
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  I think it's gonna make people take these AI deployments more seriously.
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  It means they're gonna be more, I would say, circumspect around expectations.
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  I think it means they will throw out things that aren't working, and they will invest in
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  things that will.
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  And to the extent that AI delivers on its promise, it's only gonna it's only gonna work
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  when companies are getting real value from deploying the technology.
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  And right now, we're in the let a thousand flowers bloom, spend a lot of money, throw a
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  lot of spaghetti against the wall and see what sticks.
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  And that's normal when you get a new technology, but eventually, people have to find
• 12:23
  value.
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  How many CEOs of large enterprise companies understand that?
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  And so at the end of the year, they might see these quote unquote failures, but then have the
• 12:33
  same perspective that you will and be like, continue to keep trying, continue to keep
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  spending.
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  I think the pressure from boards is going to continue because, you know I mean, this is
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  silly.
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  That means 5% of projects were successful.
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  So if a 100% of projects had failed, the pressure to invest and squeeze value out of AI
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  would maybe get turned down.
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  I don't think that's gonna happen.
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  I think the pressure's gonna stay on for CEOs and management teams to figure out where in
• 13:01
  their org they can be successful.
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  And it might be that, like, hey.
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  We swung for the fences and tried the revenue side.
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  Let's build back some credibility by maybe working on the cost side.
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  That ought to be lower hanging fruit, lower probability of failure.
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  Let's get some wins on the board with AI before we try the more esoteric experimental stuff.
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  And it seems like on the vendor side, that's been more of a successful route so far.
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  If you were a startup founder, now taking a look at this data and knowing where people's
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  pain points are, what could possibly be the pockets of opportunity that you might try to
• 13:30
  pitch to an enterprise customer that might be attractive to them?
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  If I were a software vendor, I would really take this survey to heart because it means
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  you're gonna walk into a lot of buildings where either an internal or external team
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  overpromised and under delivered an AI related solution.
• 13:47
  Yeah.
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  And so whereas maybe twelve to eighteen months, you would have walked into a very optimistic
• 13:54
  wide eyed audience.
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  Now you're walking into an audience that might have some scar tissue, either from a vendor who
• 14:00
  overpromised and under delivered or from an internal initiative that was a failure.
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  And so I think understanding the context of the company that you're walking into as a vendor is
• 14:10
  gonna become extremely important.
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  If buying AI tools from vendors leads to higher success rates, with the market flooded with
• 14:16
  wrappers and surface level integrations, what signals should the buyers, so the other side of
• 14:20
  this, look to truly separate valuable products from ones that don't move the needle at all.
• 14:24
  I actually think it's okay if you're a vendor to say, I'm buying this product because I think
• 14:29
  for the next year, it's the best solution in my problem.
• 14:32
  And a year from now, I'm open to the idea that this problem will be solved in different way by
• 14:37
  a different vendor.
• 14:38
  And that's how I even at Precursor, that's how I approach all of the AI tooling.
• 14:42
  I look at them, like, for the next six to twelve months, this feels like a reasonable
• 14:45
  solution.
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  But I'm prepared to throw things away in twelve months if there's a better way to do it Yeah.
• 14:51
  Or a different to do it.
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  And take bits and pieces of things that we like from one and take that to the next vendor and
• 14:55
  say, like, I didn't like this, but I love this.
• 14:57
  Can you incorporate that?
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  I remember once upon a time, I paid $10 a month for a tool that helped me with text generation
• 15:04
  using ChatGPT, probably ChatGPT three five or something.
• 15:08
  Mhmm.
• 15:08
  And now I'm like, well, I don't need the wrapper in between me and ChatGPT or Claude.
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  I actually can just
• 15:13
  Right.
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  Prompt it myself and do it.
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  But there was a period of time where that was a helpful thing, but that opportunity was kind of
• 15:19
  ephemeral.
• 15:19
  And I think we're gonna continue to see these ephemeral things, which doesn't mean you
• 15:23
  shouldn't take advantage of them during the moment of time when they're valuable.
• 15:27
  Just don't get you attached to them.
• 15:28
  Well, our last topic is another insightful set of data from Peter Walker at Carta titled
• 15:32
  Startup Valuations on Steroids, where valuations across stages are categorized in
• 15:37
  buckets from low end, so, like, the twenty fifth percentile, to wild, which is the ninety
• 15:43
  fifth percentile.
• 15:44
  Yep.
• 15:44
  And then wow, which is the ninety ninth percentile.
• 15:47
  The wild column, so the ninety fifth percentile, looks quite similar to 2021, where
• 15:53
  the median valuation for seed stage companies hung around $72,000,000.
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  However, the new wow column increased a 123% with median prices landing around a 161,000,000
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  for seed stage companies at this ninety ninth percentile.
• 16:10
  Looking at the chart, the question that remains here is where will the strongest company
• 16:13
  service?
• 16:14
  Any additional thoughts on this data?
• 16:15
  Maybe more specifically also on the seed specific numbers too.
• 16:18
  I think Peter might be if we had a leaderboard, Peter's definitely number one or number two.
• 16:22
  None.
• 16:22
  Because it got me the Peter or honor.
• 16:25
  And, you know, Peter knows I'm a big fan of all his work.
• 16:27
  I just published our quarterly LP letter two days ago, and the opening paragraph was, this
• 16:33
  is the busiest summer I've had since 2020 or 2021.
• 16:38
  Take from that what you will.
• 16:39
  And I was like, I could replace not going on vacation because you're locked inside for COVID
• 16:45
  with not leaving your phone at home because you're worried you'll miss out on some super
• 16:49
  hot, expensive AI deal that's closing in six hours or some triple layered SPV into Anthropic
• 16:57
  that just came through on a WhatsApp group.
• 16:59
  This is a frenzy.
• 17:01
  It's funny, Mia.
• 17:02
  I feel like everybody I know at the GP level is like, this is insane.
• 17:05
  I'm like, it's insane, but we're all participating in the insanity.
• 17:09
  And we just went through this three or four years ago.
• 17:12
  I think we kinda know how this tends to end, but it all feels like a great idea until it
• 17:17
  ends.
• 17:18
  And I think there's a lot of people who are like, I'm going to be able to get out before
• 17:22
  the music stops, and I really hope they are right.
• 17:24
  Yep.
• 17:24
  And those valuations to me, they just don't leave a lot of room for companies.
• 17:29
  You know, there was an old saying when I got into venture.
• 17:31
  Was like, oh, that round is priced for perfection.
• 17:33
  If everything goes perfectly right, that price probably makes sense.
• 17:37
  These new rounds are priced beyond perfection in my mind.
• 17:40
  Like, it's not like, if everything goes right, these are reasonable valuations.
• 17:46
  My last question to close out is, from your perspective, does this kind of aggressive
• 17:50
  pricing reflect genuine progress and innovation at the company level, or does it speak more to
• 17:54
  investors' obsession with winning across the most competitive deals through increasingly
• 17:58
  aggressive term sheets?
• 18:00
  I think the biggest change I've seen in my entire career in venture over twenty years is I
• 18:05
  think founders understand how to manipulate VCs and VC attention far better than they did
• 18:12
  twenty years ago.
• 18:12
  And I think VCs have the capacity to pay higher prices for rounds.
• 18:17
  Richard Reason's one.
• 18:18
  The scale of outcomes has grown over time.
• 18:20
  Second, the funds are bigger.
• 18:22
  So at the margin, if you want your ownership, paying up to get it works.
• 18:26
  But I think founders are just more skilled at extracting higher prices from VCs because
• 18:31
  they've had more reps, and they better understand the model.
• 18:34
  And I think if you can convince VCs that your company is really hot and it's a deal that they
• 18:38
  will deeply regret missing on, you can have a lot of influence on the entry price, which is
• 18:43
  very different than the exit price.
• 18:45
  And I it's funny.
• 18:45
  I talked to one of our LPs this week, and he said, you know, like, I don't know that I feel
• 18:49
  that differently about you losing a million dollars on a $10,000,000 valuation versus
• 18:54
  losing a million dollars on a $100,000,000 valuation.
• 18:56
  You still lost a million dollars.
• 18:58
  And I was like, well, yeah.
• 18:59
  Well, when it doesn't work out, the price you paid doesn't matter.
• 19:02
  But when it does work out, the price you paid, just go look at the Figma waterfall.
• 19:07
  Look at the difference between what index got by being in at the seed and what everybody else
• 19:11
  got by coming in later.
• 19:13
  It's it is not trivial.
• 19:14
  So I think entry prices matter, but I also think being in really important companies
• 19:20
  matters.
• 19:20
  And I think not every important company starts off with a high valuation, and not every
• 19:25
  company with a high valuation is ends up being an important company.
• 19:28
  But we only usually talk about the things that are at the intersection of turned out to be in
• 19:31
  it, like, when Square raised its seed round and OpenAI.
• 19:35
  The companies that turned out to be important and raised at a high valuation end up being the
• 19:39
  thing that it's like the buoy that everybody hangs onto in the ocean.
• 19:42
  Of course.
• 19:42
  Yeah.
• 19:43
  Very few articles are gonna be written about the ones that raised in high valuations that
• 19:46
  kind of like went off into nowhere that we don't hear about.
• 19:48
  We're pretty good at bearing those is that copy?
• 19:51
  I don't remember.
• 19:51
  No.
• 19:51
  Yeah.
• 19:52
  No.
• 19:52
  Everyone just has a wonderful memory issue then.
• 19:54
  Well, thank you so much for joining.
• 19:56
  We are all out of time today.
• 19:57
  Thank you to our listeners for hopping in.
• 19:59
  Please like and subscribe, and we will talk with you all next week.
• 20:01
  Thank you.
• 20:02
  Bye.
• 20:03
  Bye.