Latest / The Learning Corner by Precursor / Episode #89: Industrializing the Blame Game, VC-Backed Startups and Fraud, Will You Quit on Me?

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Episode #89: Industrializing the Blame Game, VC-Backed Startups and Fraud, Will You Quit on Me? · The Learning Corner by Precursor

• 0:00
  One repeat founder in the piece captures the dynamic bluntly by calling low risk, high
• 0:04
  upside, founding opportunities of starting a company and flooding the market in his own
• 0:08
  words as a nice way to get, you know, a job at Anthropic.
• 0:12
  Has your read on founder commitment changed at all given how accessible and lucrative those
• 0:15
  outcomes have become?
• 0:17
  Even if Anthropic offered me a $100,000,000, what else are you supposed to say?
• 0:21
  As a founder, if you want the money, like, I think some of these fundraisers are
• 0:26
  advertisements.
• 0:27
  They're a way to, like, advertise to the labs that you exist and that you're around.
• 0:33
  It's the week of August 3.
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  Welcome to the Learning Corner, a Precursor Ventures podcast.
• 0:42
  Welcome to the Learning Corner by Precursor.
• 0:44
  I'm Mia Farnham, and I'm joined by our GP and managing partner, Charles Hudson, where we will
• 0:48
  be talking through the articles and Goodreads that caught our team's attention over the past
• 0:53
  week.
• 0:54
  Last episode, you got to meet Alina, who's one of our interns, and this week, we've got the
• 0:58
  other half of our great intern class, Braylen, here on the podcast.
• 1:03
  Thanks so much for joining us this week.
• 1:05
  We're excited to have you.
• 1:06
  I'm excited.
• 1:07
  I'm excited to have this conversation.
• 1:09
  Awesome.
• 1:10
  Over our first piece this week, Angela McNeil, author of her blog called the handshake
• 1:15
  protocol, released a piece titled The Architecture of Blame, which centers around the
• 1:20
  looming question of who's on the hook when an agent fails.
• 1:23
  Angela's argument is that blame doesn't become unanswerable because of AI.
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  It becomes unanswerable when systems are designed to lose the answer, and humans have
• 1:34
  been building those systems through bureaucracy, policy, and org charts for
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  decades.
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  What AI threatens to do is industrialize that pattern, taking what used to require a whole
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  bureaucracy to sustain and running it at machine speed across every workflow at once.
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  The counterintuitive flip she makes is that a properly instrumented AI agent is actually the
• 1:55
  most auditable actor in your entire enterprise, more accountable than any person on the payroll
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  who can only reconstruct a past decision from memory after the fact.
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  She lands on attribution being a design time act, not forensics.
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  Identity on every action, scoped authority, and scoped audit trail are the three things you
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  build in advance, so the question of who owned a decision is never a fire drill after
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  something breaks.
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  We've been running, I feel like the past episodes in themes by accident, and I feel like
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  this week's is on, like, truth telling and finger pointing and blame.
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  Angela opens with a reminder that accountability gaps aren't an AI invention.
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  So they're not new.
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  Long before any model existed, humans built these gaps into their institutions.
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  She gave examples of the insurance claim denied by the policy where everyone agrees it's wrong
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  that it's not covered, no one can fix it, or the patient record owned by a process that no
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  one has touched or edited since 2019.
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  And the real risk Angela Flags isn't that AI creates this pattern, it's that it
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  industrializes it, taking what used to require entire processes and people in place to sustain
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  and running it at matching speed across every workflow at once.
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  So if humans were building these accountability gaps long before AI existed, Charles, what
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  changes occur when you hand that same pattern back to something that never sleeps and scales
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  infinitely in Damn.
• 3:24
  World of agents?
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  It's funny.
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  This reminded me a lot of one of the best books I read in a while, which is Unaccountability
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  Machine that, James Chan from Bloomberg referred me to a while ago.
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  It's a really good book about basically how systems are designed so that no individual is
• 3:40
  accountable.
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  It talks a lot about the impact of that on bureaucracy.
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  And I think about this, and as someone who uses a lot of AI and has agents, if agents were
• 3:48
  really deterministic and followed all the rules, this would be a great framework.
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  They mostly do that, but sometimes agents like humans, in my experience, do things and you go,
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  why did you do that?
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  And it's just like, I'm sorry.
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  I'm like, well, that I'm not asking you to apologize.
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  I'm asking you to explain why you did this aberrant thing.
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  And sometimes the agents either can't or refuse to tell you why they did the thing that they
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  did.
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  I think one of the fundamental decisions everybody needs to make about AI is, like, how
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  much authority and autonomy do you wanna give AI?
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  And if you think of, like, your AI agents as employees, in most cases, you wouldn't give a
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  brand new hire authority to ship to production without somebody looking at the code first.
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  And the same way I have some friends who have AI agents that send emails without human
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  review.
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  I'm like, that sounds dangerous to me.
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  The only way you're gonna catch those things is on the way out.
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  I read this as like quality control for agents as a design decision.
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  And it's as much about quality control, it is about blame.
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  And like how often do you as a human wanna have somebody in the loop inspecting the outputs?
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  And I find the challenging thing is you can look at something and it can work perfectly for
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  two weeks, and then something in the underlying model changes.
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  And a process that was working fine suddenly does And if you're not watching, and if you're
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  not monitoring just like you would any other human employee or literally mechanical process,
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  things drift.
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  So you need to have eyes on these things.
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  And I think this nirvana that you would set something and forget it, and it would just
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  run-in perpetuity autonomously is nowhere near where we are today, if we ever get there at
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  all.
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  One thing that she did bring up was the big pros that you get from agents running these
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  processes is potentially having the most accountable paper trail that you've ever had in
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  comparison to people because she talks about human memory as the one piece that's basically
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  rung true when you have to relook at a process and see what went wrong.
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  I agree with her that there's an opportunity for agents to really step in and and really
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  show what the process look like and what things touch, but to your point Charles I've had
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  multiple instances where I've asked how you came, how you as the agent came to a specific
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  action and not that it lies but it gets confused and then I almost have to be like
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  that's actually not that couldn't possibly be where you got the data because that's not where
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  it lives and it goes, oh, you're right.
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  And so again, we're not there at least maybe within the build of the agents that I have.
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  I have to be more robust in terms of how I construct them so that I can catch those little
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  moments along the way.
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  The paper trail piece still exists even though we're in a different setup of it all.
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  It's a different paper trail.
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  If an API call to a model returned a result, who's responsible?
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  At least with the at least with the human process, there's like a person you could go
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  talk to.
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  Here in many cases, the agent is some combination of code, API calls, a model, which
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  all work together in sometimes unpredictable ways.
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  Carlson, I'm curious to know too kinda looking at it as a follow-up.
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  The piece also makes the case that the organizations who can answer who owned this at
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  any moment are actually the ones who can move the fastest.
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  Right?
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  And so we talk a little bit about kinda the audit trail that lets you deploy into kind of
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  those consequential territories without flinch.
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  I'm curious to know from an investment standpoint, you know, do you buy the framing
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  that building this accountability infrastructure is seen as a competitive
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  advantage, or do you look at it more so as not just a, you know, compliance tax?
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  I think it's really important to understand drift.
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  This is, like, one thing I've really come to learn in the last nine months.
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  I have a bunch of models that work for a long time and then they stop.
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  And sometimes they stop because I've learned something new that I haven't shared with the
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  model or the agent, and so the expected result like the agent's still doing the thing it's
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  supposed to do, it just isn't doing what I want it to do.
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  And that's I mean, in other cases, it's something in the plumbing changed.
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  Some of these things will break silently and they won't tell you.
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  And if you don't have good, basically, sensors looking at the output, things can can go
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  haywire for a while and you just don't know.
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  If you're gonna build these things and use them for important decisions, it's imperative that
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  you have the ability to audit and analyze.
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  This is what went in and this came out.
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  Did I get what I expected and what caused the drift?
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  And everybody I know who runs a lot of these agents has this story.
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  Oh, yeah.
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  I realized that this thing was broken for two weeks and I didn't really realize how broken it
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  was until I gave it kind of a deep inspection and realized, oh, this thing's messed up.
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  And there are layers of severity.
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  We've talked about this months ago, Charles.
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  Remember when we walked through that article of either it was Burger King or McDonald, 100
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  drinks were placed Yeah.
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  Almost like an AI.
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  Honestly, that's just funny.
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  Like, it's just like a funny incident.
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  The range here or the outcomes become far more drastic when and even Angela mentions it.
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  If you bring this into health care, if you bring this into law, there are just far more
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  drastic outcomes and negative things that can happen if things aren't watched.
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  And so I think that's where the finger pointing and the blame becomes such a higher point of
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  contention because at the end of the day, I think the the piece talks about the
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  accountability sink.
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  Who's around the drain at the end of the day to take the blame?
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  And if there's almost too robust of a process, no one is.
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  And so there's just no one there.
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  And that's where I think this article brings attention of, is this gonna be so archetype
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  that there is no one to blame at the end of the day when things go wrong?
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  And is that actually something that organizations wanna design?
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  I think you're gonna end up in a lot of cases where there's gonna be somebody whose job it is
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  to use a model they didn't build or use an agent they didn't build.
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  And if it goes wrong, they're gonna say, I didn't build the agent.
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  I was just the agent the company built.
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  The person who built the model who built the agent is gonna say, look.
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  The agent did what it was supposed to do.
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  It's actually Claude's fault or it's OpenAI's fault.
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  It's the LOM or the underlying model provider's fault.
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  And very few people are gonna be able to fully diagnose who's really at fault.
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  And I think you're gonna end up with a bunch of finger pointing standoffs the majority of what
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  happens in corporate America anytime something goes wrong.
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  Yeah.
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  Do you think that it will be an assumption in the future that you should you as the employee
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  should be able to tinker around and improve the agent when it fails?
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  That should be something that's within your wheel wheelhouse?
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  I don't think most people want that responsibility.
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  Right.
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  It'd be like if you were working at an insurance company and you're like, oh, our
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  underwriting model is predicting death rates wrong.
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  I'm gonna go in here and tinker with the weights.
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  Most people would be like, hey.
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  That's, like, beyond my pay grade.
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  Our next piece is a TechCrunch article covering two new academic papers, one from Imperial
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  College and Emilion Business School and one from the University of Toronto.
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  The map fraud patterns across VC backed startups from 2000 to 2023.
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  The headline finding is that companies with venture funding were more likely to face fraud
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  charges than those that didn't take it, and startups launched during overheated markets
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  with weak investor due diligence are 19% more likely to later commit fraud.
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  As a follow on to those statistics, researchers introduced this concept of facading and gives
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  the example where founders have moved surface lies about performance to reinforce facading
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  where fake customers, contracts, invoices are generated to back up those lies and going as
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  deep as to where entire parallel realities are constructed around capabilities that simply
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  don't exist.
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  What makes this especially pointed in in the call out that investors are not just passive
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  victims here and in some cases, co create fraud by continuing to back found as previously
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  accused of misconduct.
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  The researchers call for more accountability on the investor side, including SEC audits after
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  startups hit large investment thresholds and investors being held liable for corporate
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  governance failures.
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  Reframing fraud is really a solo act.
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  So, obviously, you know, a lot there.
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  Definitely timing, you know, in terms of kinda where we're seeing in the market, but then also
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  as we continue the conversation around compliance and accountability.
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  I'm curious, Charles, you know, given that read, know, your initial thoughts, you know, at
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  a high level.
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  It matches my experience, which is that we've had a bunch of episodes about revenue.
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  Like, what is we spent more time talking about what is revenue than I thought we'd ever spend
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  in the modern era.
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  I think part of that is because founders and and investors exist in this ecosystem with it's
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  a feedback loop.
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  With investors who are like, I wanna see more growth.
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  Some businesses can really achieve that growth through honest means and other people say,
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  look, they rationalize.
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  If I can just get this round done and if we fudge the growth we'll go back to doing it the
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  right way on the other side and I think we generally know how that happens.
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  One thing I've learned is that there's far more fraud in my opinion in the venture startup
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  ecosystem than ever gets reported.
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  And part of the challenge is no one has the incentive to report it.
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  There are cases I know of where both a founder and a VC, no fraud has happened.
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  And there's usually not always a great reason to report.
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  If you think about, like, the Charlie Davis thing, that was when a big public company has
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  egg on its face over an acquisition.
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  Your your options are to prosecute the fraud or just say, hey.
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  We didn't do a good job on our due diligence.
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  Like, my bad.
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  And that's not gonna happen.
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  If if a VC and a founder know, if the founder and VC both come out about this fraud, they
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  they everyone has to take accountability for what happened.
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  The investor has to say, hey, this thing happened under my watch.
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  I should have been paying more attention.
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  I didn't ask enough questions.
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  The founders just say, was like a criminal or I did something wrong.
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  And in many cases, there's fraud at companies that are not consequential.
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  Companies like go to zero or they don't matter or they wind down and people bury the fraud or
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  the bad action with the company, which is a shame because oftentimes those people go on to
• 13:00
  do the same thing in their next company.
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  And investors may or may not learn the lesson that they learn when this fraud is more public.
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  Most things are not Theranos.
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  Right.
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  They're just they're not.
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  Oh, this company lied about a couple of an account and that enabled them to get a
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  financing done, but it wasn't a lot of money for the person who wrote the check and the
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  company failed anyway.
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  So you're like, well, what's what's the point?
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  I do think the pressure right now that companies are under to show these crazy growth
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  rates.
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  And the irony is sometimes you learn after the fact that the crazy growth rate was never real
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  or the crazy growth rate itself was a product of dodgy processes and suddenly everybody has
• 13:37
  engaged in collective bad behavior chasing a target that was never real.
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  I'm a 100% certain there will be companies whose ARR revenue reported growth ramps.
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  We will look back on them and say, oh, that wasn't what we thought it was, and maybe we
• 13:51
  shouldn't have all been chasing that.
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  They break down levels of faceting into three categories and they start with surface level
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  which is what I see all the time.
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  I actually don't even know if I can really call out and slap the hands of founders or
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  investors.
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  It's just I mean, it's media.
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  It's being able to put your numbers out for people to see And since they're private
• 14:11
  companies and they're so early, I think that exists throughout our ecosystem.
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  That's just like something that you have to sniff out as an investor.
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  When somebody gives you a update about their their company, something was a company that you
• 14:21
  haven't invested in, like, you have to then do the diligence to see what am I missing here.
• 14:26
  If it's all positive and rosy, the deep facade, that's what you're referencing, Charles.
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  Like, that's what Times bestselling novels are built off of, like the better know story.
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  Those happen those are quite rare.
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  And so that's why when they when they come out, like, it is such a blockbuster event.
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  I know the piece talks about different remedies that we can make to solve this, and one of them
• 14:47
  was broader SEC audits.
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  I find the friction in that knowing that the way that I know venture works, especially in
• 14:55
  hypey, bubbly markets, new rounds with new valuations just happen so quickly.
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  There's no time to do that.
• 15:02
  And then it kind of ties into the process of why founders enjoy staying private longer
• 15:08
  Yeah.
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  Now more than ever because there's just so much more at stake when you go public.
• 15:12
  So again, all themes we've talked about, but it's unclear to me how you break the cycle,
• 15:17
  especially in a market that we're in today.
• 15:19
  Yeah.
• 15:20
  I mean, you could I think what you said is really true.
• 15:22
  In hot markets, most people make the calculus.
• 15:25
  Hey.
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  If I ask a lot of questions and do a lot of diligence, I might not win this round.
• 15:30
  And if I'm gonna do the round, make the investment anyway, what's the point of doing
• 15:35
  all the due diligence if I know if I already made up my mind?
• 15:37
  Yeah.
• 15:38
  Audit financials would catch some of this stuff, but in general, I find that orchestrated
• 15:43
  fraud usually starts at the top at most startups.
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  And so I do think if there were more corrective consequences for board members, management, and
• 15:51
  investors, there'd be less of this.
• 15:53
  Yeah.
• 15:53
  I don't think any of those people want that to happen.
• 15:56
  Yeah.
• 15:57
  And so I I think I've just sort of concluded that a certain amount of fraud and misbehavior
• 16:03
  is an occupational hazard of being a venture capitalist who invest in private companies.
• 16:06
  And my job as a VC is to try to sniff it out Mhmm.
• 16:10
  Before I make an investment.
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  And if I find it after I make an investment, to try to fix it.
• 16:16
  But most VCs are non control investors.
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  And so in some cases, the only remedy you really have is to try to get rid of the person
• 16:23
  in charge of the company, which most VC funds aren't willing to do.
• 16:26
  It's also like most of the things I found out in our portfolio companies in the rare cases
• 16:29
  something bad has happened, it has been a lower level person telling me, hey, this CEO is like
• 16:34
  up to something or like I'm resigning because I'm really uncomfortable with the way that
• 16:38
  we're presenting financials to the board or the way that we're talking about our pipeline or
• 16:43
  this upcoming product.
• 16:44
  Like, it's usually like an internal junior person on their way out the door who tells you,
• 16:49
  like, look here.
• 16:50
  This is where the bodies are buried.
• 16:52
  Yeah.
• 16:53
  And the behavior starting at the top, it doesn't really take that Hollywood form of
• 16:57
  people gathering into a room usually and that this is how we're going map it out.
• 17:01
  It's usually just pressure, pressure coming from every type of angle that instills this new
• 17:05
  energy in your organization of, we need to move quicker.
• 17:08
  And I don't think we can do that without fabricating a bit of this in some way, shape,
• 17:14
  or form.
• 17:14
  We go to great lengths in venture land to not call fraud fraud.
• 17:18
  Facetting and fudging, and sometimes people are just straight up lying.
• 17:23
  They're just lying.
• 17:24
  Plain language makes these things easier to understand.
• 17:27
  My last piece on what my line is for surface level facade.
• 17:31
  Yesterday on LinkedIn, I think I saw one founder post like a growth map, obviously, in
• 17:37
  like the normal x y axis that shows how much they've grown month or month.
• 17:42
  And it literally was just a line.
• 17:43
  There were no numbers, and it just showed a line that increased over time with like a lot
• 17:48
  of interactions and comments, and I was sort of like, yeah, I mean, it's not false, but it
• 17:53
  could just be the y axis is literally like a dollar to like $10 and like that's just like
• 17:59
  the beginning parts of how people kind of get into the swirl of how can I put out signals
• 18:04
  that things are going well, but also fall in the realm of what's passable, which that's
• 18:08
  what's scary and but I think it's worth the job to what you just said?
• 18:11
  Yeah.
• 18:11
  And I think about accounting just kinda having that background all the time.
• 18:14
  We kinda lean into the trust but verify.
• 18:16
  That's kinda just always been a written rule of, you know, being able to scope out those
• 18:20
  opportunities and what those may look like.
• 18:22
  And so definitely spot on in the sense of, you know, how you gotta sometimes just pull, let's
• 18:27
  just say, pull the rug up to see what's under there.
• 18:29
  So our last piece is from the Wall Street Journal titled VCs are asking founders, will
• 18:34
  you quit on me?
• 18:35
  By Yulia Chernova covering how the AI is feeling a wave of founder poaching and acqui
• 18:42
  hires that is leaving early stage investors feeling shortchanged.
• 18:46
  Backed by the stat, the venture backed startup acquisitions in the AI sector jumped from a 100
• 18:50
  in 2014 to 322 last year and are already on a pace to exceed that in 2026 with 245 deals
• 18:58
  through early June alone.
• 18:59
  The dynamic that AI labs and big tech companies are dangling hard to resist offers that found
• 19:04
  this is happening.
• 19:06
  And what we see after VCs are already invested at high valuations is that founders are now
• 19:11
  walking away with large equity packages while investors often just get their money back.
• 19:16
  And so the article highlights VCs now explicitly asking the diligence, will you sell
• 19:20
  to a lab in a year?
• 19:21
  Which is a question that they say never had kinda asked before in attempt to re found a
• 19:26
  commitment through long dinners, conversations rather than pitch decks.
• 19:29
  One repeat founder in the piece captures the dynamic bluntly by calling low risk, high
• 19:33
  upside, found an opportunities of starting a company and flooding the market in his own
• 19:38
  words as a nice way to get, you know, a job at Anthropic.
• 19:41
  And so, Charles, you know, I wanna pause for your thoughts on this piece.
• 19:45
  And specifically, has your read on founding commitment changed at all given how accessible
• 19:49
  and lucrative those outcomes have become?
• 19:51
  I try very hard not to ask people questions where, like, there's only one right answer.
• 19:56
  And if you say, are you gonna quit it?
• 19:57
  Everybody's like, no.
• 19:58
  I'm gonna I'm in it for the long haul.
• 20:00
  I'm, like, totally gonna do this even if Anthropic offered me a $100,000,000.
• 20:05
  What else are you supposed to say as a founder?
• 20:08
  If you want the money and one of my friends this weekend, we were talking about this very
• 20:13
  this very theme.
• 20:14
  And he's like, I think some of these fundraisers are advertisements.
• 20:17
  They're a way to like advertise to the labs that you exist and that you're around and to
• 20:23
  give them some sense of what it would take to get you.
• 20:26
  We have a one of our existing portfolio companies, one of the co founders, is probably
• 20:29
  gonna go join one of the the the big labs.
• 20:32
  And it's a lot of money and his co founder was like, this is what they offered him.
• 20:36
  I was like, he's probably gonna take it.
• 20:37
  It's a lot of money.
• 20:38
  I didn't I didn't feel I didn't feel like wronged.
• 20:41
  I said, look, my job is to find people who want to build great companies and who will who
• 20:48
  believe that, like, the best bet they can make is on themselves as a founder.
• 20:51
  I think if you're running a company and somebody offers you $20,000,000 to join a lab
• 20:57
  company in, compensation over a couple of years, if you don't think you can make that in
• 21:02
  your own startup like risk adjusted, you're gonna take it.
• 21:06
  And so like the ways I can the only ironically, some of the ways to solve this are a, find
• 21:10
  people who are just maniacally committed, but every founder tells you that.
• 21:14
  Two, find companies that raise so much money that like they can afford to remain independent
• 21:20
  and that the lure of like a big payday, it doesn't matter because they already have the
• 21:24
  resources.
• 21:25
  Or three, just accept this has been an issue in every cycle since mobile because in my mind
• 21:30
  mobile is really when aqua hires became a thing.
• 21:32
  And I think the model providers are doing exactly what they should do, which is if they
• 21:36
  see talent that they think they want that could be effective in their organization, they should
• 21:42
  go get it.
• 21:43
  And if they are able to pay more and those people think, hey I mean, the truth is what
• 21:48
  these people are really telling you is, I think I I am better off working at Anthropic.
• 21:52
  I will make more money for myself and my family than I am being the founder of this company.
• 21:56
  And that's as much an assessment about Anthropic as it is about the thing they're
• 22:00
  working on.
• 22:00
  Yeah.
• 22:01
  The irony of it all is that the companies that this happens to the most frequently, I would go
• 22:06
  out on a limb and say, probably raise debt valuations that are at the higher end of the
• 22:11
  bubble.
• 22:12
  And so the tension is tough of investors being like, this is worth paying up for.
• 22:16
  And then feeling like, oh, like, this actually came out in a direction that I probably didn't
• 22:22
  want, but what can you do at that moment?
• 22:24
  I think if you invest in a company where the founders are AI researchers doing AI research y
• 22:29
  things, part of your memo has to say, there is a chance these people will get poached
• 22:34
  Yeah.
• 22:34
  And that they will end up at the model provider, and I will maybe get back my money or
• 22:39
  my money and then some but this won't be a home run.
• 22:41
  And like that, I if ironically, if I were making those investments, that would be like my
• 22:45
  principal fear.
• 22:47
  It's not the companies go to zero, it's that they go to one x.
• 22:49
  And you get your money back in some weird acqui hire, acqui license deal.
• 22:52
  They go make a bunch of money, and you're left with the shell of a company that you have to
• 22:56
  figure out what to do with.
• 22:56
  I wish I had better news.
• 22:58
  There's something really interesting sitting at the intersection of this piece in the fraud
• 23:01
  article that we just discussed.
• 23:03
  On one side, investors are setting expectations pretty extreme that founders feel pressured to
• 23:07
  misrepresent what they built.
• 23:09
  And then on the other end, they're now asking founders to prove that they have conviction to
• 23:13
  state the course no matter what.
• 23:15
  Gives me a how do you, like, hold both of those dynamics at the same time?
• 23:19
  Right?
• 23:19
  They would talk a lot about kinda, like, some of the struggles from both ends, but in those,
• 23:24
  you know, pressure moments yet, you know, moments where you're also trying to build
• 23:27
  conviction.
• 23:28
  How do you kinda balance those dynamics?
• 23:30
  We're in this world where, for investors, the only companies that really matter are the big
• 23:35
  scaled outcomes.
• 23:36
  That's it.
• 23:36
  That's kind of the whole game right now.
• 23:38
  It's the whole business.
• 23:39
  Kinda stinks, it is what it is.
• 23:41
  Which means investors are trying to figure out very quickly, is this company SpaceX?
• 23:46
  Is this company Databricks?
• 23:47
  Is this company gonna make me famous and and rich?
• 23:50
  My friend Reese like Reese sent like one of these like memes around with like the parent in
• 23:55
  a pool with two kids.
• 23:57
  And like one kid's on the ledge.
• 23:59
  It says like mega fun VC.
• 24:00
  It has a kid on the ledge where it's holding its hands.
• 24:03
  And it says current portfolio founder.
• 24:05
  And the kids like drowning the parents like not paying attention to them.
• 24:08
  Then at the bottom of the ocean, has like a skeleton in a chair and it says portfolio
• 24:12
  companies that won't even help the fund get to one x.
• 24:15
  And it I do think like we're in this dynamic where investors wanna win the deal and founders
• 24:20
  wanna get the money.
• 24:21
  And then as soon as that's over, investors are like, great.
• 24:24
  Is this company gonna matter?
• 24:26
  If it's gonna matter, I'm gonna put pressure on it to continue to grow.
• 24:29
  And if it doesn't matter, I don't care.
• 24:32
  And founders are like, I wanna be in the bucket of companies that matter.
• 24:35
  And I know the the bucket of companies that matter are the high performers.
• 24:38
  I need to make sure I'm a high performer.
• 24:40
  I think the difference or something that feels meaningfully different here is that the the
• 24:43
  term of high performer, I think, now is tied heavily to to signal, like outward signal.
• 24:49
  I can come in the firm in the terms of high valuation, large round, large social media
• 24:53
  presence, building in public, all of that, which I don't know.
• 24:57
  Look, we've seen over cycles there there isn't all too much data that that is the recipe for
• 25:03
  creating strong outcomes.
• 25:05
  Yeah.
• 25:05
  But we've we as venture and the ecosystem to your point, think we are just in this cloud of
• 25:13
  figuring out what to make sense of the entire investment stack from LPs to GPs all the way
• 25:20
  down to founders.
• 25:21
  Yeah.
• 25:21
  And so people are trying to figure out where their priorities lie.
• 25:25
  And so I to your point, like, if I'm a founder and I'm presented with a $20,000,000 cash offer
• 25:31
  to go join a AI lab company and I just raised 6 to $10,000,000, like, I debate that pretty
• 25:39
  heavily.
• 25:39
  And I actually would say I would actually appreciate a founder who's like, yeah.
• 25:44
  If I came along, like, I would look at it.
• 25:45
  Because I think that's a little bit more realistic than you telling me, like, I'm here
• 25:48
  until the day I die.
• 25:49
  Be like, okay.
• 25:50
  Like, well, I've seen companies shut down all the time, so it's okay that you can have, like,
• 25:54
  a different answer to this question.
• 25:55
  But I don't know if that level of transparency will actually ever come out, candidly.
• 26:00
  And I think, honestly, most people don't know.
• 26:02
  It's one thing to be like, oh, yeah.
• 26:03
  If it showed up, I wouldn't take it.
• 26:05
  How do you know that?
• 26:06
  Yeah.
• 26:06
  You don't?
• 26:07
  Yeah.
• 26:07
  Well, we are all out of time.
• 26:09
  Thank you, Charles, for joining us.
• 26:11
  Thank you, Braylen, for joining us as well.
• 26:13
  To all of our subscribers, please like and share with friends, and we will talk with you
• 26:17
  all next week.
• 26:18
  Thank you.
• 26:19
  Bye.