Barry McCardel spent years at Palantir before co-founding Hex, the AI data platform he describes as “Cursor for data.” In this conversation with Eric Newcomer, he breaks down Palantir’s business model, the truth about forward deployed engineers, how AI agents are changing data analytics and business intelligence, and why Hex is taking a different path in enterprise software.They also get into AI agents, the future of data work, the reinvention of business intelligence, whether white-collar jobs are really at risk, and the fight over Anthropic, defense tech, surveillance, and government power.
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If there is not 10s of millions, hundreds of millions of dollars
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worth of seats for us to sell because everyone's been laid off
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the world. Like, like, like I need, I need
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to own a lot of guns. Like the least of my problems is
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my fucking SAS business. Like this is what it looks like
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to live in a Republic. It's messy.
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You, you, you. Like so it requires on the part
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of other actors to sort of interpret and what the will of
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democracy is beyond just like difference.
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Like every generation has felt broken in some way.
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I always think it's funny when people we've never been more
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divided. Bitch, we fought a civil war.
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Palantir is one of the most mysterious companies in tech.
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If you're an outsider, it's a company created by Peter Thiel
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that's gathering intelligence for the deep state.
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If you're an insider, it's an overvalued enterprise software
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company that's heavily reliant on human consultants.
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Today I talked to Barry McArdle, CEO of HECS, which he says
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aspires to be the cursor for data.
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He was a long time forward deployed engineer at Palantir
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and saw the company from the inside.
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We talk about Palantir subcontractor Anthropics, fight
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with the government, and argue about how much tech companies
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should defer to the President of the United States in a
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democracy. But first we also talk about
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what it was like working at Palantir, which he called
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mundane, while he simultaneously talked about projects in unnamed
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Middle Eastern countries. We dug into the forward engineer
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craze that Palantir pioneered, and these days every tech
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company wants to copy. Barry defends the model but
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doesn't actually use it at his own company.
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Welcome to the Newcomer Podcast hosted by me, Eric Newcomer.
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I am the author of the Newcomer Newsletter, which covers the
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business and people in the startups and venture capital
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industry. Check us out at newcomer.co.
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Thanks for listening. Here's my conversation with
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Barry McArdle. Well, literally I heard today
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that we didn't get invited to perplexity some perplexity event
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because they're mad that they were the most shorted company on
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your fault you're. Just asking the question.
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Yeah, you put me up to the stunt.
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Apparently, yeah. I thought, I think it's an
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interesting question. Who would you short?
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I think we're all, I think it's good that you're not able to
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short start-ups 'cause I think that would get into a very
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pernicious and shitty game. And imagine if venture funds
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were like hedge funds where they've got long positions and
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they've got short positions and they're like hedging, right?
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That'd be fascinating. The All right, I'm here with
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Barry McArdle. Welcome to the newcomer podcast,
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CEO of Hex. He's been a long time at
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Palantir. And as someone who's in my ear
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with fun ideas and on Twitter, Assassin and Spicy.
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So I expect you to live up to your Twitter persona on the
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podcast. Sparse.
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Sometimes I tweet a lot and then I'll go quiet for like months.
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You had a what your most viral one was on the subject we want
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to talk about. I don't want to butcher it.
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It's the IT was. It was, it's only, it's only for
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deployed if it's from the Palantir, a region of France.
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Otherwise it's just sparkling sales engineering I think was
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the. Exactly.
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So I, we want to start off with Palantir, I think from 2 levels,
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one sort of, I don't know the YouTube show level, which is
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like, I don't know, people hear Palantir and the headlines and
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like what to make of it. And then we'll do our nerdy tech
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show question, which is I feel like every startup in the world
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has learned that you can actually be a consulting company
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if you call it a forward deployed engineer.
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And I don't think will you. Be punished on multiples, you'll
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be rewarded. It's like, oh, all of a sudden
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it's good. Like, throw humans at it.
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Yeah. But let's start with like the
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spooky YouTube. And, you know, I mean, I, I just
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think a lot of people on the Internet hearing about Palantir
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just have a like, this is this is the deep state or like,
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having spent a lot of time, what, like 4 1/2 years, five
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years, five years. Yeah.
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Yeah. What Yeah.
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Is is Palantir the big bad? No, I, I and I'll just, I'll
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just claim by I haven't, I haven't been there in a few
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years now, but. You stay close to and you know,
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the whole network of people starting companies.
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Yeah, yeah. And I, I'm some, a lot of people
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there. I think the, the interesting way
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we understood ourselves then, and I suspect it's still very
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true now. And we, we said this in almost a
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slightly diminutive way. It was like we're a data
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integration company. Like it turns out that like all
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these companies in the world that these institutions or
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private companies and public institutions that you could, you
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know, wanted to be able to operate better.
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It really the the problems I'll come back to like some form of
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data integration. Like you actually have a lot of
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the data like available in some way.
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What you're saying is. We're, it's actually a really
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boring company with the CEO that says and sometimes we have to
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kill people. There is, I mean, I, I'll, I'll
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be honest, there is a kind of boring core to it.
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Like there were times where it was kind of like we're kind of
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just doing a lot of data plumbing and it, it obviously
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the brand is incredible. And I think the leadership there
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has done a great job developing and, and nurturing that brand.
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And look, look, I, I'm not saying that the results aren't
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there. It's a fabulous company.
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It's a company I'm very proud to have been at.
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I'm proud of almost feeling. Like like $300 billion.
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I can't look at the stock price now because I sold most of my
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shares at less this less than the stock price.
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I have a very discordant relationship with it.
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But yeah, like I I think at the core there's some really boring
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stuff that we just got really right.
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And the initial brilliance of Palantir in the early days was
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the ontology. It was this kind of insight of,
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you know, the, the, the slightly hagiographic version of it is
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like, you know, early team was going, the founders were going
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to these three letter agencies and sort of realizing that the
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problems they had. And you know, the mythos was a
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little bit like the, the reason we missed 911 or one of the
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reasons you kind of missed events like that is 'cause you
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actually had all the information somewhere, but you weren't able
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to get the complete picture. And so by being able to
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integrate it and build the ontology on top of it, OK, now
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you can see the whole picture and you can sort of make it
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something that humans can reason about and understand and engage
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with. And that shape of dynamic is
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true in the government, you know, intelligence community,
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military, also very true in a lot of private companies.
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And so a lot of what I did when I joined, I joined when we had
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like a very nascent commercial business.
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So I started my career in consulting.
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I'd been like on the really nerdy data tech side of my
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consulting projects. My friend who worked at Pouncer
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was like, you just come do this at like for real.
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And I started, we didn't really have a commercial product at
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all. And most of what we were doing
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was just sort of going around and doing effectively these
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consulting projects, integrating data, kind of stitching things
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together, giving people a finally, like a complete picture
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of their organization and the problems they had.
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And that by itself was really valuable.
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Because they would buy tech services but then not get the
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most out of them, stitching them up with everything.
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Yeah, you'd have, you'd have like the that, that era, you
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know, 10 years ago, you'd have a lot of people building these
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like data lakes. I was like, oh, we'll get all
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our data in the data lake. I was like, yeah, you land all
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the data in this S3 bucket basically or HDFS cluster.
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And it's like, now what we have the data, it's like, yeah, you
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have it. Are you use?
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Are you able to make better decisions because.
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Of it and and palander would help them like visualize it and.
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Yeah, it wasn't even viz. I, you know, it's funny 'cause
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people picture it like, oh, this analysis with the viz stuff, our
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viz was always like pretty mid. Is, is really the integration
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behind the scenes, which I know sounds kind of sexy and unsexy
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and vague, but like it's literally like, OK, you've got
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these five different data sets. They're all sort of talking
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about the same thing. You want to get a complete
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picture between them? How do you integrate them?
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What are the relationships between these?
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How do I see an event or an incident that's happening here
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and go understand what are the other times something like that
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has happened? What were the resolutions?
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And is there a shared link? Like that is a lot of the kind
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of thing. And it's it's not like the
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actual like algebra behind the scenes is like, you know, it's
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just sequel queries. It's like fairly straightforward
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joins and stuff. And like every once in a while
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we'd get wild and do some regressions, but it's like.
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Really the reasons for deployed is you have to understand the
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customer enough to really understand the.
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Logic of this model around it then and, and I think the four
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deployed thing, this, this kind of gets into the, the, the role
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and what people misunderstand. Like when I joined, you know, we
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had these 4 deployed engineering teams and, and they still have
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them. At the time it was really like
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we didn't have a product. We were hacking a lot of stuff
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up there was that was the whole. Critique, apology over years it
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was it's just secretly A consulting like internally we'd
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we'd like. Take issue with that.
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Like I remember these emails someone of the guys in
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leadership would sign and being like, people don't understand
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us. They think we're just a services
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company. Like we'd kind of look around
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and be like, I don't know, it feels like we're doing services.
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I'd read this e-mail from a windowless conference room and
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so town where I'm like, basically I'm like hacking
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together an Excel spreadsheet to dump into a Jupiter notebook so
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I can export the results into our front end app builder to
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make it look like we did software.
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It was like, I don't know, it feels pretty servicey.
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But what wound up really working was you go and effectively do
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services. And at the time we didn't have a
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core platform really to speak of in the commercial side that we
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were doing these services on top of.
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But over time we built that platform leverage.
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But you go and you basically do these services.
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You go in and say, hey, we're going to solve your hardest
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problem. You know, Karp, Dr. Karp would
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meet Aceo or some leader and say, let me send you a team.
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They'll come in, just tell them your hardest problem.
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They'll come in and help try to solve it.
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And you'd kind of have like 3 months, the canonical pilot
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length to go and work super hard and fly around a lot and try to
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solve it. And what would wind up happening
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is you'd, you'd have to build new things in the field.
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And then like most of those things wouldn't work out, Like
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the pilot wouldn't convert, it wouldn't be valuable enough,
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whatever. But some of those things would
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go on to be multibillion dollar business, you know, like just
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products and and foundational parts of the platform and the
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whole model of forward deployed engineering that really, really
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worked. And the thing that people kind
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of miss is it's a radical deference to the field on what
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to build. Like most organizations, they're
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like, OK, we've got a road map and all these slides.
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We know the customers essentially.
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And I think especially a lot of founders like I, I'm like this
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sometimes, like I know what to build.
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We gotta go build this thing. We just gotta go find the
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customers. That's.
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Part of what you're selling the investors, it's like I have the
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vision. And the reality is like what
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really worked, at least in my time there, the way it worked
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was you just hired a lot of really smart people, kept the
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bar really high, gave them freakish, irresponsible amounts
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of ownership and autonomy. We were a bunch of like mid 20
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somethings being put in charge of these like really high stakes
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deployments and budgets. And it was just like.
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In some ways, you know Mckenzie's go to that too, where
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it's like, you speak for the firm, right?
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Or you. Kind of.
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I mean the McKenzie model's much more pyramidal and like command
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and control. You're improved at this top, but
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you get to speak you you set up a way that sort of your youngest
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people can sort of say the firm says blah blah.
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Blah, I guess, but those people are are parroting the firm's
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talking points, right? No one's hiring a 25 year old.
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No one's hiring McKenzie to get 25 year old coming in and
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telling you what you should do about your business strategy.
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You're hiring McKinsey because they ostensibly have this
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centralized expertise and they've got a practice around
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your thing. You're a telco.
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You're hiring someone from their telco practice to come in and
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they've got best practices and there's a lot of different ways
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to interpret. What you're hiring because the
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partner has inside information then everybody else is slowing
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down dresses it up in sort of process and it's like I know the
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answer we need to get to you like explain why we.
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There's a lot of different mental models of what consult,
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what job to be done consulting actually does.
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There's like the the like one that they'll advertise, which is
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we're the strategic advisors. We're here to provide our
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insider business insight and business expertise to help our
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partners make better decisions. Like, OK, yeah.
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But like there's different mental, there's different like
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sort of different versions of this.
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There's one that's like they work with kind of everyone and
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yeah, they teams don't talk to each other, but they're all kind
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of gets distilled into best practices.
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And, you know, it's kind of like how you can go get make sure if
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you're an executive, you're doing the right things.
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The other version of it and the version I saw in my brief stint
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in management consulting before Palantir was we were hired one
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time onto a project where we came to learn there was another
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consulting team from another firm working for another one of
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the VPS of the company on effectively the same problem.
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And these two VPS both had, they were like fighting about what to
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do. And they both hired consulting
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firms. And we were basically told, this
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is my theory from the VP, like I need you guys to build a deck,
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basically build a deck like a slide deck to back that up.
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And then we came to learn there was another VP who had told
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their firm that. And it was like we were both
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like the proxy army for these warring warlords, like these
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factions. And it was very weird and it was
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kind of like, what are we doing here?
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And like it. Says something about like
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intelligence. It's in the world of models, you
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know? Yeah, it's just like you're just
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accumulating your argument when really the the argument is not
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about the reason. So, so you can kind of, I
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actually thought about that more subsequently and I'm like, there
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is something kind of pure about it, which is like it's a war,
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it's a battle of ideas and you know, but I don't know.
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Anyway, the at the whole point is that pound here we were going
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in and you know, we would get attached to these problems and
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then try to work backwards to like, well, what's the product
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that could solve this? And sometimes it was kind of a
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hacky thing, like we just threw together this dashboard that
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lets you see this joint data set we have.
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So now you can correlate these two things.
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But but what actually wound up happening in mid twenty 10s is
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we had a lot of cases where we were coming into these
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commercial companies and they wanted some like higher order
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thing. They wanted like, well, we want
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to see an analysis about that, Sir.
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We want a machine learning model that shows us the correlation
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between that Sir, we want to see whatever and then we would have
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to go build and and the four deployed engineers would have to
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go build and kind of hack up a lot of infrastructure behind the
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scenes to be able to try to pull all that data together.
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And then you turn that into a product.
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Then we turn that into a product.
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So there's a team, the deployment in Europe that
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famously kind of like started productizing this like well
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we're writing these same shape of spark job orchestration
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things over and over again. We should build a thing to help
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do that. And then that sort of started
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snowballing and within that deployment and then spread.
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And one of the really cool things about the forward
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deployed model at Palantir that I saw was it was almost like a
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community driven adoption. Like you would see things go
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viral where you'd have one deployment go and invent
00:14:12
something and then another deployment would pick it up
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because it's on GitHub and it to the once we sort of had a core
00:14:18
platform and it got, we got better at that.
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Like, oh, like your deployment built that thing.
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Cool. Like can I, you could just
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install it and it would work on your platform too sick.
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And then you, you know, it's open for PRS so you can be
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contributing code back. And then these things would gain
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momentum. And someone who started a
00:14:35
project working as an FDE on a thing and they built this thing
00:14:39
just to solve this customer, maybe six months later, they're
00:14:42
basically on. They're basically a dev team now
00:14:44
that's kind of delaminated from the deployment.
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And now we're like the dev team. So you got too good at this all
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right now you're. Yeah, you'd see the cycle.
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So there's a product some very close friends of mine built,
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they were over in the Middle East working with a customer
00:15:01
there. And they built a product to be
00:15:04
able to do this sort of drill down data analysis to solve a
00:15:08
problem there and a very esoteric problem, signals
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intelligence problem. And then that product was sweet.
00:15:13
And a lot of people, including my deployment, were like, that
00:15:17
would be really useful. And so we kind of lobbied and
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got them to deploy it for us and it was great.
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And then it spread and spread and now it's like a super core
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part of the Foundry platform does.
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It have a name. Or yeah, it's called Contour,
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like every, like everything there.
00:15:30
It went through multiple names. It's been named like 4, it was
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named 4 things. And there's, there's so many
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examples like that. And, and there's so many
00:15:37
wonderful things about this, like contour is sick.
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And it was never on anyone's road map.
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It's just like 3 guys went and invented it because they needed
00:15:45
it and it caught on and spread and you know, it was never on
00:15:48
anyone. It was never on some central
00:15:50
road map like in Q32016. We're going to go build this
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thing Now. The downside, the sort of
00:15:56
invisible downside that's easy to ignore is there was also a
00:15:59
ton of shit we built that went nowhere that was a complete
00:16:02
waste of time. We had deployments where we were
00:16:04
flying super expensive people, first class every week
00:16:08
somewhere, you know, just just tons of time and opportunity
00:16:12
cost and just pyres of money. Basically like customers were
00:16:15
paying for it, right? Customers were paying for it,
00:16:17
yeah. And they were kind of paying for
00:16:18
the sort of service version of it in the short term.
00:16:20
And there were customers. I actually met someone recently
00:16:23
who was a customer of ours in that era.
00:16:25
And she was, she kind of was like, yeah, we're.
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We felt pretty burned by you guys and I, I just think it's
00:16:31
almost like this kind of necessary downside.
00:16:33
I hate to be so glib about it, but like of having this high
00:16:37
rate of innovation in the field, like, you know, people say fail
00:16:41
fast, fail fast, move fast and break things like these, these
00:16:44
ethos things in the valley. It's it's really representative
00:16:48
of what we what anyone who's really done this well really
00:16:50
knows, which is the reiteration and learning is everything.
00:16:54
And one of the big problems in enterprise software is a lot of
00:16:57
enterprise software is built by big lumbering teams who are
00:16:59
working from 10:00 to 3:45 in air conditioned offices in the
00:17:05
valley with ample ping pong breaks and what you know, blah,
00:17:09
blah, blah, blah, blah, blah. Working off of these long multi
00:17:12
quarter Rd. Maps and they're very sparsely
00:17:14
talking to customers and you know you're what you're going to
00:17:17
get from that is you're going to get consistency, you know you're
00:17:20
going to get predictability, you're going to get all these
00:17:22
things that that companies value as they get bigger.
00:17:25
You're not going to get emerged. But you're not going to get
00:17:27
that. Innovation.
00:17:28
Sometimes here it like we didn't really figure it out.
00:17:30
Other times it's like it's so valuable, we're just spread it
00:17:33
to the rest of government. Yeah, these structures wind up
00:17:35
being almost designed to squash like high variance innovation,
00:17:40
like what all process does. The whole point of a process is
00:17:43
to reduce variance. So you're you're an evangelist
00:17:47
and defender for the forward deployed engineer, but your own
00:17:51
company doesn't do that. Like what?
00:17:53
What is your heuristic for what company should use a forward
00:17:57
deployed engineer? And then why did you decide yes,
00:18:00
we have a lot of FD not to former?
00:18:01
FD ES at hex. So what you see is a lot of
00:18:03
start-ups now adopting the FD model where they're like we're,
00:18:07
we're, we have 4 deployed engineers.
00:18:09
Well, I and I, when I, when I critique this, it's not like to
00:18:13
gatekeep the title, whatever. Like it's really like, do you
00:18:17
understand what worked? What, why this works for
00:18:20
Palantir? Because I think a lot of people
00:18:23
are cargo colting. They're saying, oh, Palantir did
00:18:26
this and look at their revenue multiple and look at their
00:18:28
success. If we do forward deployed
00:18:30
engineering, we'll be as successful and they kind of go
00:18:33
through the motions and then they're not getting the same
00:18:36
results. And they're like, why?
00:18:38
Well, you don't under really understand the physics of it.
00:18:41
And from us when we were starting the company, you know,
00:18:44
we'd obviously lived that, but we were like, we want actually
00:18:46
want to kind of go the opposite way.
00:18:47
We want to build 1 consistent piece of software that we can
00:18:50
deliver in scale and. Super out of fashion right now.
00:18:54
Super out of. Fashion right now, I know.
00:18:56
But the raising rounds as an investor, but somehow, but not
00:18:59
on some of the narratives. But but, but no.
00:19:01
But it's, it's, there's a synthesis here, right?
00:19:04
Like what we do is we just talk to customers.
00:19:08
I'm in New York right now. I'm visiting customers.
00:19:10
I'm bouncing all over town the next few days just visiting
00:19:12
customers, getting up close. And I'm doing my best not to
00:19:15
just meet with them in a conference room, but like, I
00:19:18
want to go by someone's desk. I want to be like, great, what
00:19:20
are you up to? What are you working on?
00:19:21
Can you show me hex? Can you show me what you're
00:19:23
working on? Like go get up close.
00:19:25
I'm I'm bringing individual, I'm not bringing our sales, my sales
00:19:27
people on those visits. I'm bringing the engineers and
00:19:29
designers. I'm like, I want to go see this
00:19:31
stuff And I try to get our whole team doing that and go get in
00:19:35
front of customers, really understand what they're doing,
00:19:37
really understand what they're asking for, really understand
00:19:39
what they want and what they're using as an alternative.
00:19:41
Like go do a great job at that and and talk to customers.
00:19:45
And if we actually have a problem at Hex, it's like, I
00:19:47
think some days it's like, I worry our EPD team is our
00:19:49
engineer product and design teams are talking to customers
00:19:52
too much. Like it's like, guys, at some
00:19:54
point we got to go write the code.
00:19:54
You know, it's like we talk, we talk to customers a lot.
00:19:58
And some of it's just you're a smaller company and so you can't
00:20:00
just have everybody. Yeah, it's a.
00:20:02
Cultural, but it's a cultural thing.
00:20:04
It's like, I, I think it's really important to have people
00:20:08
really build empathy for customers.
00:20:10
I mean I I don't I think the worst engineering in the world
00:20:13
is done via ticket. Like if you wanted to have some
00:20:15
tickets to like manage your work.
00:20:17
Yeah, I mean it's a core Y combinator.
00:20:19
You know, principle of building companies is like what?
00:20:21
Build something people want know your customer.
00:20:23
You know that sort of sent. But a lot of most people don't
00:20:26
do that. They don't do that.
00:20:27
They said they have the road map but.
00:20:28
What what start-ups do you think or smaller companies, non
00:20:31
palantir companies are doing for deployed engineers in a smart
00:20:34
way? The only, I mean, the only one I
00:20:35
know who's doing at the Palantir version, which is basically, we
00:20:39
don't really know what to build. We're just going to go in bed
00:20:42
with customers, get them to pay us some services thing to cover
00:20:45
the time maybe and and have some exchange of value.
00:20:48
And we'll just build the platform depending on what we
00:20:50
learn. Is my friend Arjun at Distill
00:20:53
Distill with AY he he's. I think there may be other
00:20:59
companies now doing some version of this but he's the one who I
00:21:01
know is doing it the most pure. Is he super charismatic?
00:21:05
I think he's very charismatic. And not like what's just funny,
00:21:07
you refer to Alex Carp earlier as Doctor Carp.
00:21:10
He's he's like a philosophy PhD. Yeah, right.
00:21:12
Like, like not there uniquely has to sort of like, you know,
00:21:16
we we're like Wizard of Oz, like we're gonna do a magic thing.
00:21:19
I'm like, can anyone else really sell that besides that company,
00:21:24
you know, in the sort of he's just.
00:21:25
Called him Doctor Carp. He's just he's doctor.
00:21:27
It's like when you it's like when you grow up in your
00:21:28
neighbors like Mr. Mr. Smith, you know to you and like later
00:21:33
in life you run into them again. You still call him Mr. Smith,
00:21:35
this is. Funny you guys how you're like
00:21:37
I'm. Jim, I'm Jim now, son.
00:21:38
It's all right. He talks about it like, you
00:21:40
know, it's a culture of, you know, people will tell him like
00:21:42
he's totally wrong or whatever, but that's just a very
00:21:44
deferential way to refer to him. I don't know.
00:21:46
Yeah, but it was, it is a very decent, you know, they're very
00:21:49
open, very transparent, very best idea wins.
00:21:53
Not a lot of job titleature. I mean, I had the same job title
00:21:56
all five years I was there. I went, I had people reporting
00:21:59
to me. I was doing IC stuff, I was
00:22:00
doing big lead stuff. It didn't matter.
00:22:03
Like in when you're in that environment, you kind of forget
00:22:06
about job titles for a while. You forget about there's
00:22:08
different, you squeeze the balloon, the problems emerge
00:22:10
elsewhere. Like there's going to be shadow
00:22:11
hierarchies and stuff. Every cultural thing has
00:22:14
trade-offs, but I I thought the culture there really was
00:22:17
designed. It's, it's almost like it, it,
00:22:19
it, it was very sharply designed around getting a very specific
00:22:24
thing, which is maximum pace of iteration and learning.
00:22:30
And that meant there's all these negative trade-offs.
00:22:34
But culturally I think we always like acknowledge them and
00:22:36
accepted them. And there was a time, I remember
00:22:39
we had a period when I was there where a lot of people have left
00:22:41
because they felt like there wasn't a growth path for them
00:22:44
and because we didn't have like a job title ladder to climb like
00:22:47
a lot of people are used. To Was it hard to raise money
00:22:49
without a clear title where people knew where it's?
00:22:52
Hard for me to raise money. Or just I would imagine going
00:22:55
off and starting a company you want to be able to say I was
00:22:57
important here or somewhere, which is sort of a vague.
00:22:59
It was actually interesting because I did another thing in
00:23:02
between Palantir and Hex, I went to a relational biotech company
00:23:05
here. Yeah, I was just called Trial
00:23:06
Spark at the time, not confirmation.
00:23:07
I remember interviewing and I was like, what do I, what do I
00:23:10
say? I do like I'm a deployment
00:23:12
strategist. What's that mean?
00:23:13
Like, and I think people who are hiring from Palantir kind of
00:23:17
knew they were like, OK, we kind of knew that that was a thing
00:23:22
when I was raising money. It wasn't a big I don't that
00:23:24
didn't really come up. It was just the.
00:23:25
Product just sort of the. It was just that we clearly had
00:23:28
a sense of what we were doing and what we wanted.
00:23:29
To do but say what Hex does just we don't even think about the
00:23:32
company, but just so people know where.
00:23:33
You're coming. So we're building AI data
00:23:35
analytic software like sometimes people call us cursor for data
00:23:39
or things like that. It's basically you can use
00:23:43
natural language to go ask any. Other questions just list a
00:23:46
bunch of highly valued companies like Cursor for data, Anthropic
00:23:48
for. We were opening up for one data.
00:23:50
We were called. Bigma, Bigma for data, it's
00:23:52
like, yeah, because the first, the first arc of the company
00:23:55
really was like, there's all these local fragmented data
00:23:57
analysis things. We've spent all this time
00:23:59
bringing your data together and you're just putting dashboards
00:24:01
on top. You know, we wanted to build
00:24:03
this cloud based front end for all of your data work and it was
00:24:07
very similar to what Figma had done when we started the
00:24:09
company. Figma was, you know, on its way
00:24:12
north and was really taking off and sort of an inspiration of
00:24:15
like, oh, you can take these local fragmented workflows,
00:24:17
bring them to the cloud, bring make them collaborative and in
00:24:21
the process of doing that, bring more people in as participants.
00:24:24
Those are really the initial thesis of hex and then a couple
00:24:28
years end of the journey, like GPT 3 came out and then ChatGPT
00:24:31
came out and this was a thing we really got entranced by.
00:24:34
So the the second-half of the company or more than half now
00:24:38
the company has been figuring out how to really apply AI and
00:24:42
now agents to work for a data analysis, which is the Holy
00:24:45
Grail. Like the Holy Grail.
00:24:46
The reason people buy data tools, ostensibly invest
00:24:48
billions and billions of dollars over your data infrastructure is
00:24:51
because you want to know things and you want to make better
00:24:53
decisions. And we make that really easy and
00:24:55
we do a very different set of things.
00:24:57
Well, then what Palantir did, we don't do like data integration
00:25:00
and infrastructure stuff. We're very much like the app and
00:25:02
collaboration layer where. Where do you say relative to
00:25:04
business intelligence? Yeah, we are.
00:25:06
We think of ourselves as reinventing that like we are.
00:25:09
What? People don't like that space or
00:25:10
what's the like? You don't use that term.
00:25:12
I didn't, yeah. I didn't want to use that term
00:25:14
because it had so much baggage. Like when people think for a
00:25:17
long time, when people think BI, they're like, OK, well, I think
00:25:20
of all the features that Tableau has built for 20 years and I
00:25:24
think about dashboards and I want you to do that.
00:25:26
And we are actually originally were very insistent on like we
00:25:30
actually think that the old way of thinking about BI is like
00:25:32
super low value. We think dashboards is going to
00:25:35
raise more questions than answers is something I've said a
00:25:38
lot. And like the most of the
00:25:40
valuable data work is not happening happening in these
00:25:42
dashboards. And instead it's people are
00:25:44
trying to do it in this melange of other tools.
00:25:47
Jupiter notebooks and SQL queries and spreadsheets and
00:25:50
screenshots of a chart pasted into APDF of a deck attached to
00:25:56
an e-mail you're. More rooted in like the role
00:25:57
like you're hiring these smart data analysts and we want to.
00:26:01
Originally start, our initial sort of beachhead landing thing
00:26:05
was like we're going to come in and we're going to revolutionize
00:26:07
your way. Your data team works right and
00:26:09
very similar to Figma coming in and we want to revolutionize.
00:26:11
Now you're like you don't need to hire them or how do you?
00:26:14
Every AI embracing company has its trade off between supporting
00:26:19
the role versus saying, oh, we're helping.
00:26:21
I think we're. Helping to reinvent and evolve
00:26:22
it. And we have a great data team.
00:26:24
Hex. It's almost like over
00:26:25
provisioned in the sense of we just have really wonderfully and
00:26:30
talented people on it, but also hired people that have been
00:26:33
thought leaders and writers in our space for a while who are
00:26:37
amazing collaborators for us to build the product.
00:26:39
But also collaborators just thinking about like what is what
00:26:42
are these roles becoming? And it's interesting to see our
00:26:45
own employees kind of disrupting their own job a little bit.
00:26:47
And it's probably similar to what it feels like to be an
00:26:50
engineer or cursor or something like you're trying to reinvent
00:26:52
the way your job works. And so you know, where we see
00:26:55
ourselves as fellow travellers with that data audience of like
00:26:58
how we're helping reinvent the way their job works and what
00:27:01
that looks like and what it looks like to fulfill the
00:27:03
mission they've always wanted for their organizations, which
00:27:05
is you want data to be useful and help solve problems.
00:27:08
And now with with AI, you know, the the fastest growing part of
00:27:11
our user base, of course, is all these other people in these
00:27:14
companies that are now able to use data in a way they weren't
00:27:18
able to before. And in that way, we are
00:27:20
fulfilling what BI want, always wanted and purported to be and
00:27:24
what people thought they were buying with their billions of
00:27:26
dollars of BI spend, which was we want to make everyone
00:27:29
data-driven. Well, you got a lot of
00:27:31
dashboards. Are you feeling data-driven?
00:27:32
Are you able, you know, is everyone able to ask their
00:27:35
questions? Because if you can't interrogate
00:27:36
the dashboard, you don't know it's limited.
00:27:37
They're really good. Like dashboards are fine people.
00:27:39
There's this big discourse now. Dash dashboards are dead.
00:27:42
Everything has to be dead or broken or, you know, whatever.
00:27:46
Dashboards are dead. I'm like, dashboards are fine.
00:27:49
They're fine, but they've always been fine.
00:27:52
Like the? Extent they're dead, I see
00:27:53
people want to ask a question directly of data in dashboards,
00:27:57
which is. The same as when we started the
00:27:58
company. It's like dashboards are great
00:28:00
for seeing what happened, this KPI, blah, blah, blah, whatever.
00:28:03
It's a collection of these charts.
00:28:05
Great, fine. But often what you look at is
00:28:09
you look at that chart and you go, OK, well I want to ask a
00:28:11
next question. Why, why did that happen?
00:28:13
I want to drill down on this and there are other times something
00:28:15
like that has happened. That's what we built Hex to
00:28:18
really excel at. And now it like the architecture
00:28:22
and a lot of the things we built, we kind of feel like we
00:28:23
had right place, right time because the way we even
00:28:27
structured the product is really conducive to these agent work
00:28:31
flows, which is which are about depth.
00:28:34
A lot of times, like the magic of these things is you can ask a
00:28:36
first question, get an answer, and then you can ask follow-ups
00:28:38
and it can go now and drill in and have multiple different tool
00:28:41
calls and paths is exploring. And all these things we built,
00:28:44
even going back to the very beginning that we didn't know
00:28:47
were going to be useful because we have magic robots that can
00:28:50
talk to us now. It's all very useful now.
00:28:53
Now useful for way more people, which is really really cool.
00:28:57
Are you using cloud or what is the intelligence sort of?
00:29:00
Behind this, we use cloud and the GPT models and it's very
00:29:03
interesting. We have good relationships with
00:29:05
both companies. We spend time with their
00:29:08
research teams and who's. Doing better right now.
00:29:12
They're both doing great. I think the GPT 5.4 models which
00:29:15
are the latest ones to come out are really fantastic.
00:29:17
And we have a quote, my Co founder Caitlin, who I think
00:29:20
you've met has a quote in their launch blog posts summering up
00:29:23
our findings. But they really, really good and
00:29:26
getting better. I think what we're seeing is
00:29:28
these models, the main focus of the labs right now appear to be
00:29:32
kind of hill climbing the models on coding tasks, which is you
00:29:37
can kind of squint your eyes and look at hacks and be like, it's
00:29:39
like coding. It's like you guys write SQL and
00:29:41
Python And Dataviz. And the improvements on those
00:29:45
models of coding tasks does make our performance for our domain
00:29:49
better incrementally. But but surprisingly, actually
00:29:52
we even saw with, I won't say which one, but one of the latest
00:29:55
new model releases from one of the labs, it was actually a
00:29:57
regression for the evals we have.
00:30:00
We have a really extensive set of really rigorous data
00:30:03
analytics evals that are not like the naive ones, like how
00:30:06
many widgets did we saw last quarter, but like really brutal,
00:30:09
like the real world shit that people ask of data.
00:30:13
And it's interesting to see the models sometimes even regress
00:30:17
cuz clearly they've been trying to be like hill climbed on
00:30:18
something else. And one of the big problems is
00:30:20
in data analysis, it's non verifiable and there's actually
00:30:23
different ways to answer a question and it's it's.
00:30:26
Right coding, has this sort of code worked it built this thing
00:30:29
you can sort of right whereas the sort of analysis.
00:30:34
Yeah, it's a little squishier. And so this is something we
00:30:36
work, we try to This is why we spend time at the labs is like
00:30:39
trying to like help figure out like how, how can we make the
00:30:42
bottles better for this? Should we be RL in our own
00:30:44
models? I know there's a lot of like
00:30:45
even conflicting opinions internally.
00:30:47
Are you spending money on your own models?
00:30:49
We do we run some of our own inference for some specialized
00:30:52
tasks. The main we are using the like
00:30:54
GPT and clawed sort of state-of-the-art models for like
00:30:57
our main agent backbone. But we, we see some interesting
00:31:00
opportunities and it's not impossible that the lab, those
00:31:03
anthropic and open AI even allow they, they used to sort of in
00:31:08
the older era had a lot more fine tuning tools.
00:31:10
And I think they may come back in some places too, where they
00:31:13
sort of allow you to do these RL regimes on top of their base.
00:31:16
And where are you on sort of agent mania, you know, are you
00:31:19
building? Are are you going to be like
00:31:21
sell, you know, are agents as representative as humans in your
00:31:24
product? You're going to be selling seats
00:31:26
to agents or you think that's sort of out of control?
00:31:28
Or I, I think that's, I think it's probably overshot a little
00:31:31
bit. I mean, we've talked I, you
00:31:34
know, with the pricing thing is interesting.
00:31:37
You're selling seats. We sell seats.
00:31:40
Another not cruel thing. We sell seats usage.
00:31:43
Pricing right now, what's going on?
00:31:45
We are, we're adding, you know, usage on top because one thing
00:31:47
that's crazy is our weekly actives chart and our messages
00:31:50
per week chart is like straight vertical now.
00:31:53
It's like gone fully parabolic. Like the product is really
00:31:55
working. The agents are really good.
00:31:57
People are loving them and using them.
00:31:59
Yeah. They're costing you more.
00:32:00
Fortunate part is our cost chart looks the same and if you're if
00:32:04
you're a if you're a company today that has working agents,
00:32:06
these things are expensive to run and you need to charge for
00:32:08
them. So we're we're going to do it
00:32:09
with our seats have like a base amount of tokens and then or
00:32:13
credits and then you can get above.
00:32:15
It's pretty standard now for a lot of tools.
00:32:17
It's like lovable replay, etcetera, but.
00:32:19
Why seats at all? It's a good question.
00:32:22
I think in some ways it allows a base of predictability for
00:32:25
customers. We were, we're a product that
00:32:26
wants to get really wide in your organization and if we can say,
00:32:29
Yep, it's, you know, however many $50 or whatever for a seat
00:32:32
for a month, you, you know, and that comes with this number of
00:32:34
credits and the average user is going to their usage is going to
00:32:37
be covered by that. That's really nice for
00:32:39
predictability. I mean, that is a big thing.
00:32:40
People who have never sold or bought at scale enterprise
00:32:44
software know like having it be fully variable is like that is
00:32:48
tough As for a lot of companies. And, and generally, like I, I do
00:32:52
have these funny conversations sometimes about seats where even
00:32:55
with smart V ZS, which was funny, that are like, well, you
00:32:58
know, there's going to be pressure on the number of seats
00:33:01
you're going to be able to sell because customers are hiring
00:33:03
less people or whatever. And I'm like, we've grown a lot
00:33:08
and we're doing really well, but we're still, the amount of
00:33:10
revenue we make today is still very, very small to the
00:33:13
incumbents and where we want to go and all the time we think we
00:33:16
can capture. And I'm like, if there is not
00:33:20
10s of millions, hundreds of millions of dollars worth of
00:33:22
seats for us to sell because everyone's been laid off.
00:33:25
The. World's like, like I need, I
00:33:27
need to own a lot of guns. The least of my problems is my
00:33:31
fucking SAS business. Like I'm there's gonna be riots
00:33:34
in the street. And so I think it's kind of
00:33:37
funny this conversation about this like, yes, on the margin,
00:33:40
is it possible that some of our customers will just be like
00:33:42
small or in absolute terms, the number of people?
00:33:45
Yes. Are there still a lot of seats
00:33:47
to sell for our products like ours?
00:33:49
Yes. Do a lot of customers still want
00:33:51
to buy seats cuz it makes sense for what they're doing?
00:33:54
Yes. Can I layer a consumption model
00:33:56
on top that helps me both cover costs and also capture upside
00:33:59
from real power users who are getting excess value?
00:34:02
Yes. And over time we may tweak those
00:34:04
dials. I don't know, a year from now,
00:34:05
maybe we turn the seats down to 0.
00:34:07
Maybe we maybe it goes back. Elena Verna at Lovable has this
00:34:10
blog post that we talked about internally where she's like, I
00:34:13
think the token cost is gonna come down significantly and
00:34:16
we're all gonna be back to selling seats, Who knows?
00:34:19
So we like having the options and we wanna, ultimately our job
00:34:23
is to actually meet our customers where they're at.
00:34:25
And people always joke. Customers don't wanna pay
00:34:27
anything. Well, I actually disagree.
00:34:29
I think customers understand the value and want to pay for
00:34:32
something that's going to provide good value for them.
00:34:35
They want to support you. They want to be supported.
00:34:37
I'm a buyer of a lot of software at Hex now and I'm, I'm very
00:34:40
happy to pay for stuff that works well.
00:34:41
And if I feel like there's price to value and if I see the bill
00:34:43
for what we're paying for something and I'm like, yeah,
00:34:45
but we love it. I'm like great, cool.
00:34:47
Where are you on this? Yeah, White collar works about
00:34:50
to get decimated and our country's going to be, I don't
00:34:52
know, going for the guns. Like, I, I literally was talking
00:34:55
to a, you know, a very respected person, you know, who was like,
00:35:01
oh man, we're going to enter like an insane political moment.
00:35:03
Like every, you know, democratic cities.
00:35:06
You know, if you know, all the Goldman analysts or something
00:35:08
are about to get fired, you know, then you're going to have
00:35:11
this sort of weird class, you know, just like you're going to
00:35:14
have pockets of people who are very resentful about the new
00:35:16
economy. Like I don't know how I.
00:35:18
Already see it? I mean downward mobility,
00:35:20
downwardly mobile elites. That is right.
00:35:23
The most dangerous force in America is the leading.
00:35:25
Indicator well, I mean, forget about America, just study
00:35:27
history, right? Like that is a bad thing like
00:35:31
that, that is that will lead to problems and I, I don't know for
00:35:35
sure what the future looks like. I would say I applaud Dr. Karp.
00:35:39
I'll still say Doctor Karp, who I think has been a very bold
00:35:41
voice in saying like we should all be thinking about how we're
00:35:44
using AI to like increase employment and productivity.
00:35:47
And I, I agree with that. Now I'm building a tool that you
00:35:52
can kind of look at like, do I need as many data scientists or
00:35:55
data analysis I have? Sure.
00:35:58
I also see a lot of the data scientists and data analysis we
00:36:00
have using the shit out of it, doing a ton, being way more
00:36:03
productive, being more valuable for their companies and often
00:36:06
now starting to work at these higher levels of strategic
00:36:08
insight and maybe their roles are starting to evolve.
00:36:10
So it's not at all clear to me. We build an AI product.
00:36:14
We use a lot of AI tools internally.
00:36:15
I'm trying to hire as fast as I can.
00:36:17
So we'll see. The other thing I think about is
00:36:20
like where is the productivity? I mean, I, I can look within our
00:36:22
own customers and see them doing more and all of this.
00:36:24
I think a really interesting question is we're a year and a
00:36:28
half now into coding agents being mainstream and we're
00:36:33
billions of dollars of revenue into tokens paid to Claude and
00:36:38
Codex and Cursor and other things that start with C and
00:36:44
where where's the where's the explosion in software?
00:36:47
We're getting a lot of vibe coded stuff.
00:36:49
We're getting a lot of sort of slop wear.
00:36:50
But if you look at the the the software companies you buy from,
00:36:54
it's like, where's the where's the like cornucopia?
00:36:56
Like why am I not getting new features every day?
00:36:58
Right. It's so we.
00:37:00
We've built a maybe. The bottleneck looks elsewhere.
00:37:02
I don't know. My my my business lead has built
00:37:04
us, you know, internal like ticketing tool that he vibe
00:37:07
coded. We're we're about to see, you
00:37:09
know, when the metal meets the pavement or whatever the whether
00:37:13
you know, we can accept payments and everything safely through
00:37:16
it, but. Like why like you that?
00:37:19
But I get. That's right, you can.
00:37:20
Doesn't need to be perfect, is. That Riley, yeah, yeah, that
00:37:23
Riley can do that is cool. It's huge.
00:37:25
I mean, it's pretty. It's pretty, but.
00:37:27
But like, should he be doing that?
00:37:29
Yeah, there's a lot of things to do, but the I think it'll be
00:37:32
good, you know, we'll keep track of everyone.
00:37:35
But to your point, I mean, do you feel pressure as the at at a
00:37:38
start up, your investor was want to hear this story of like,
00:37:41
yeah, we're moving so much faster because of, you know,
00:37:45
cursor or whatever. And and you're saying no, but
00:37:48
it's not like I do. Feel like we are.
00:37:49
It is also interesting. We are living, you know, I've
00:37:52
got, I've just came from our office.
00:37:54
I've walked by any engineer's desk and they've got cursor or
00:37:57
cloud code or codecs. Now they've gotten very popular
00:38:01
up on their screen. They're using it.
00:38:02
I think we're still figuring out how to use these things, like
00:38:05
we're setting up cloud dev environments to make it easier
00:38:07
to have multiple different agents that can work in their
00:38:09
own dev boxes. And it's like, OK, so now our
00:38:11
engineers are just bouncing between 5 different agents
00:38:13
working on different tasks at a time.
00:38:15
Who's reviewing all this code? Who's initiating the thought on
00:38:19
what we should build? I mean, I'm almost like really
00:38:21
excited about the idea of just getting this torrent of PRS from
00:38:24
all of our engineers. I'm also slightly worried
00:38:26
because we're a product that's prided itself on sign and
00:38:29
consistency and fit and finish and feel.
00:38:31
And our designers are busy now like trying to do Polish commits
00:38:35
on to all these PRS. And it's how do we even get all
00:38:40
this out into the product in a way that our customers
00:38:42
understand? It's gonna be changing fast and
00:38:45
that's exciting, but also stressful.
00:38:46
It's like, I think we're all still figuring out how to
00:38:48
metabolize all of this something.
00:38:49
Tells me there's just like a pace of that humans can absorb
00:38:53
the products. Change like I, I think it's
00:38:56
literally like if you eat too much food, like your body can
00:38:58
only metabolize so much of it. And like, I think we're still
00:39:05
just figuring this out. And so I, I don't know that
00:39:08
these things are going to happen overnight.
00:39:10
And like, if your model of a Goldman analyst is that they're
00:39:12
just doing this really rote, repeatable work that an agent
00:39:16
can replace. Sure.
00:39:18
Is that all that a Goldman analyst does?
00:39:20
It's the same, I think about for data people, if you're like,
00:39:24
what, how do we understand those jobs?
00:39:26
What are actually the bottlenecks and companies doing
00:39:28
certain things we're supposed to early in figuring this out?
00:39:33
Particularly, it's interesting to me, I've observed almost like
00:39:36
coding agents have disrupted product management faster than
00:39:38
engineering because the engineers are almost becoming
00:39:41
PMS in a funny way where it's like, it's kind of unintuitive,
00:39:44
but like the, the a lot of our engineers, you know, are now
00:39:49
their, their, their job is less like, OK, I take the, the, the
00:39:54
product spec that was written in English and I translate it to
00:39:57
code syntax. Like that used to be the thing
00:39:59
it's like. Your engineering candidate.
00:40:01
Oh, you know how to take English and turn it into TypeScript.
00:40:04
Great. Welcome.
00:40:05
Right. That was kind of like at least
00:40:06
the first order heuristic of what we're like interviewing you
00:40:08
for with like coding interviews. Well, now there's magic robots
00:40:11
that can do that. And so and so instead, so we're
00:40:14
gonna keep our smartest people, the engineers, and shift them to
00:40:18
sort of the strategy rather than.
00:40:20
Yes, it's not like, it's not like.
00:40:22
A product manager, I would never be an engineer, but I mean, that
00:40:25
is sort of, it's like, OK, if they're not writing the code,
00:40:27
they're still very smart, understand sort of the core
00:40:30
product, and so they're gonna become the product.
00:40:32
Going back, by the way, the type of engineers we try to hire are
00:40:35
those that like talking to customers and like thinking
00:40:37
about what to build and enjoy our product space.
00:40:40
It's a cool product space. So yeah, those people have
00:40:43
always liked that, and now they just have kind of more time and
00:40:45
brain space for that, and they can be thinking about what to
00:40:47
build and how should this work and the architectures and all
00:40:49
this, and it's really cool to see.
00:40:51
So we'll see how these roles change what designers do.
00:40:56
Our designers have coded for a long time, but now they're
00:40:58
coding a ton. They're mostly coding.
00:41:01
It's like, well, are you, what, what do you do?
00:41:04
Are you just an engineer that has like really good taste and
00:41:07
more tattoos? Like what's that?
00:41:09
It's very interesting to see happen.
00:41:11
And internally I'm just trying to lead everyone through it
00:41:15
saying we're all learning this together and if you don't
00:41:19
disrupt your job, someone else. Well, and don't worry.
00:41:22
How many people do we need to get to a huge exit hex?
00:41:25
I don't know. More than we have today, so, and
00:41:28
more than our. Competitors so we wanna get good
00:41:30
ones you guys are all safe let's all.
00:41:32
Figure out how we can all use our time really well, and then
00:41:34
we'll go build this thing and have it be really valuable.
00:41:36
So I wanted to go back to Palantir and some of the, I
00:41:40
mean, obviously we just had this Anthropic took the headline
00:41:44
because they were the ones sort of fighting with the government
00:41:46
over, yes, fighting with the Department of War or Defence,
00:41:49
depending where you sit on. Yeah, sit on things.
00:41:52
Not going to take a position. I think we I've gone back to
00:41:55
defence. I mean defence was established
00:41:57
by Congress. So it is sort of a branding
00:41:59
exercise. You don't need to weigh in on
00:42:00
this, but I think core to the conflict the, you know,
00:42:06
Anthropic had two concerns, right?
00:42:08
One was this autonomous targeting and the other was this
00:42:11
piece that we are gonna do domestic surveillance.
00:42:14
And I think embedded in the domestic surveillance is that
00:42:17
fundamentally because of how much better their technology and
00:42:21
technology generally has gotten, the government can just acquire
00:42:24
publicly available data and then come up with this NSA dragnet.
00:42:28
That wouldn't have been possible, you know, when
00:42:30
Congress was writing laws about domestic surveillance.
00:42:34
And obviously pound tier like fits fits into this.
00:42:36
So what is your view of? Like what's possible in terms
00:42:39
of? Exhibit A in a tech company that
00:42:42
lives in that Gray area. I was there 10 years ago after
00:42:47
the 2016 election, and it had been not so long before that
00:42:51
that we had like an, it was like an all hands or a, some sync
00:42:54
version we had of this. That was like doing a like a
00:42:58
win. Like the deployment team lead
00:43:00
came up on stage to like talk about our work with ICE, which
00:43:04
we're really proud of because we were working with this team
00:43:06
called HSI. It's like the really good guy,
00:43:08
you know, the, the, whatever else you feel about other parts
00:43:10
of the government, he's stopping human trafficking and like drug
00:43:13
smuggling, Like go, go HSI. Yeah.
00:43:18
And then like, you know, that election, the vibe shifted on
00:43:21
all of tech shifted 10 years ago is kind of a long time.
00:43:23
Remember back. But like and all of a sudden
00:43:25
this became really tough. And we had internal concerns.
00:43:28
We had protests outside the office.
00:43:31
We had applicants not wanting to come people not people not
00:43:34
applying or pulling out of interview processes because we
00:43:36
were evil. And there was a big clamor
00:43:38
internally to get Doctor Carr, Alex Carp to, to say something
00:43:42
publicly say something engage on this.
00:43:45
And I, I was, I was among the frustrated on that.
00:43:48
I'll, I'll confess, like, you know, I'm interviewing
00:43:51
candidates who are like half the interview.
00:43:53
They're just asking me questions about how I like, I can sleep at
00:43:55
night or something. I'm like.
00:43:57
It's also so secretive that it's hard to even necessarily not
00:44:00
even inside the company. And inside we knew and and I
00:44:03
think we had an appreciation and I still have a deep appreciation
00:44:06
for like this was a company that recognized that to serve the
00:44:10
government, to serve the government in these spaces, you
00:44:15
have to be comfortable in the Gray areas.
00:44:17
And that doesn't mean moral compromise.
00:44:18
It actually means moral clarity and having principles, even when
00:44:22
they're hard. And, you know, in retrospect, he
00:44:26
was totally right. And they're, they're much more
00:44:27
public engaging in the forum now.
00:44:29
But I think at the time, the right thing for us was to just
00:44:31
stay quiet, not engage, not not inflamed us and say let's focus
00:44:35
on our work and let's focus on doing the best we can and also
00:44:41
embrace the fact that we live in a democracy.
00:44:44
But you have a view on this anthropic blow up specifically?
00:44:47
Well, yeah, that's my yeah. The kind of segue it's.
00:44:50
I love Anthropic. We're partners with them.
00:44:52
They're customers of ours. I don't, I say this with love,
00:44:56
but I do think it's like the heart and the head.
00:44:59
Like from a heart. You understand some of these
00:45:02
concerns. Like, would I, like, would most
00:45:05
Americans vote for doing mass surveillance?
00:45:09
You know, you can go gather up all this information and figure
00:45:11
out where everyone's loyalties lie or something like, no, I
00:45:13
think most Americans would not want that.
00:45:17
But that's why we have a democracy and you can vote.
00:45:20
People can vote for that. And I think what we came to
00:45:22
appreciate a Palantir, what sort of respect is like, well, what,
00:45:25
OK, we're gonna have red lines and what we're willing to do,
00:45:27
does the government have to call us every time they want to use
00:45:29
Gotham was our product every time there was any palantir to
00:45:32
use this thing like. Isn't this an extreme deference
00:45:35
to the executive branch specifically?
00:45:37
I mean, it's not like Congress has passed the law, said, oh, we
00:45:40
love Palantir. Like you.
00:45:42
You could have a belief that like our product is so
00:45:45
significant. They have, I mean they they sort
00:45:47
of every year. Did power, but they haven't.
00:45:50
Like expressly said, this is great I.
00:45:52
Mean I'm, I'm not like a constitutional scholar.
00:45:55
I'm just saying. But I do think, and I don't want
00:45:57
to, you know, you don't, you're not, you're just the one here
00:45:59
talking about it now. But like they're, they're, I do
00:46:02
think there's this argument that it's like, oh, we're deferring
00:46:05
to democracy. It's like you're deferring to
00:46:06
like 1 branch that's taking on a lot of power and saying we
00:46:11
represent democracy. This is what it looks like to
00:46:13
live in a Republic. It's messy.
00:46:15
You, you, you. Elect right.
00:46:16
So it requires on the part of other actors to sort of
00:46:19
interpret and what the will of democracy is beyond just like
00:46:23
difference to the executive. You described a Republican, but.
00:46:26
The argument that Palantir is making and trying to suggest
00:46:29
anthropic embrace is sort of like yield to just the
00:46:32
executive's interpretation of what the will of.
00:46:35
Democracy is yes, yes. And that will of that executive
00:46:42
is bound that that's Article 2 is bound by Article 1, the
00:46:45
legislative branch and the laws they pass.
00:46:47
And look, I, I, I'm not going to get into like debates on like
00:46:50
current Supreme Court issues or all that, but like, at least if
00:46:54
you read the Constitution, it's pretty damn clear, like the, the
00:46:57
legislator pass, the legislature passes laws, including the power
00:47:01
of the purse. They control what money is spent
00:47:05
and the executive executes on that.
00:47:06
And there's all these, you know, unitary executive and all these
00:47:08
things. I'm not going to get into all
00:47:09
that, but I will just tell you, even you see this play out now,
00:47:12
there is a funding bill issue in Congress right now for
00:47:18
Department of Homeland Security where there appears to be a
00:47:22
legislative majority or controlling legislative majority
00:47:25
that is not comfortable with funding Homeland Security at the
00:47:29
way that they have been. And that is the democratic
00:47:32
process working out. And you can look at the last
00:47:34
this current Congress and say, well, they've been overly
00:47:35
deferential to the executive and whatever and that may well be.
00:47:39
But you see this happening. And now people, we're looking
00:47:41
forward to the midterms and people are worried about that
00:47:42
and they're going to react to the public sentiment.
00:47:45
And if public doesn't want something great, what a
00:47:48
wonderful opportunity for a political entrepreneur in
00:47:51
Congress to get up and propose a bill banning this type of mass
00:47:54
surveillance. They're just outlawed.
00:47:56
You can pass a law to do that. That is a thing.
00:47:59
That's your job. Actually, that's literally their
00:48:01
job. If this is a thing we don't want
00:48:02
to have, we both want. Congress to take a more active
00:48:05
role in saying what they support and what they don't.
00:48:07
And I agree that I mean, ISIS and Homeland Security is a good
00:48:11
example where Congress is stalling on it.
00:48:14
I mean, we also live in this liberal democracy where unless
00:48:18
it is a I understand in a war, people get drafted and summoned
00:48:21
into government service. But the, the in normal times, I
00:48:25
think the belief in America is that you don't have to do
00:48:29
something for the government if you don't want, you know, you're
00:48:31
not like. Off the pay taxes, right?
00:48:33
Right. Besides sort of making the whole
00:48:35
thing work with taxes, you don't have to like go work for the
00:48:37
government. You don't have to, and Anthropic
00:48:40
doesn't have to. No, no one does but the.
00:48:42
Criticism of anthropic is they sort of need to be summoned into
00:48:44
it. Maybe they could just an option
00:48:50
they have is say we actually don't want to work with you
00:48:52
guys, we're going to withdraw from the contract.
00:48:54
Best of luck using other AI providers like that that's
00:48:57
allowed. They're not.
00:48:59
There's not the. Trump There's a war.
00:49:00
Powers Act, There's a War Powers Act where the government can
00:49:03
mandate like I think it's called war powers that I think it's
00:49:06
just like we saw some of this during COVID.
00:49:07
Like that's one of the ones they floated they but they ended up
00:49:10
saying, oh man, it's a supply chain risk.
00:49:13
It's more dangerous, apparently. But they're but that's them
00:49:16
that's almost doing the opposite.
00:49:17
They're almost cutting them out. I mean, I'm just saying, I'm
00:49:19
just saying that like an option they have is just to not do it.
00:49:21
Now going back at Palantir, one of the things we talked about
00:49:25
that I do believe to be true is if we're not engaging on this
00:49:27
issue, other companies with less scruples, with less principle
00:49:34
point of view and by the way less effective software are
00:49:37
going to. And we have a privacy and civil
00:49:40
liberties team account here that I loved.
00:49:42
If there's a lot of them are still there, they're incredible
00:49:44
people, very thoughtful that we worked with very closely on a
00:49:47
lot of these things that and, you know, you kind of look down
00:49:50
the street at the other defense contractors and you go, they
00:49:53
don't have that shit or they do. There are a lot of lawyers who
00:49:56
but it's, it's, it's like, would we rather be the ones engaging
00:50:00
on this and thinking critically about it and trying to find the
00:50:02
synthesis of how you have robust defense with civil liberties, or
00:50:06
would you rather see the field? And for us, the calculus was we
00:50:10
would rather be here, even if it's a little uncomfortable,
00:50:13
because that's our role in this pluralistic, messy democracy we
00:50:17
have. I mean, I agree.
00:50:18
That there was sort of a misguided time where the tech
00:50:20
industry said we shouldn't do, you know, Google didn't want to
00:50:23
work with the government or, you know, there was like, we're not
00:50:25
work with the Department of Defense.
00:50:26
We've moved to a point where it's patriotic to support the
00:50:30
government. Yeah, but I but I think there's.
00:50:32
Nothing like nothing like multiple wars breaking out to
00:50:35
kind of get Americans to like wake up and be like, oh, maybe
00:50:37
we need maybe we need good capabilities and weapons.
00:50:40
Like Anderol was like super unsexy off SPAC, not a sexy VC
00:50:45
thing. And then all of a sudden a war
00:50:46
breaks out in Ukraine that's becoming like a drone war.
00:50:48
And everyone's like Anderol's amazing.
00:50:50
But I've always thought Anderol's amazing.
00:50:52
And I know all the founders and, you know, but it was funny to
00:50:55
kind of see them going from these like black sheep to now
00:50:57
these darlings on the other side of a war that kind of reminds
00:51:00
you it's not the end of history. Like the Ukraine war is a
00:51:03
reminder that like, no, this is that we live in a dangerous
00:51:06
world. But the argument that you're
00:51:08
making that Palantir, you know, we're so it's so thoughtful
00:51:11
about, you know, sort of civil rights or whatever, or like
00:51:16
understanding the effect of its technology.
00:51:18
The argument right now is that tech companies shouldn't be the
00:51:21
ones deciding, and the government should have total,
00:51:24
total control. Like that's the line from what
00:51:29
heck, said. The Palantir seems to to sort of
00:51:31
say, whoa, we're not creating contracts that limit you.
00:51:35
But they can just choose not to engage in a contract.
00:51:38
There were, there were, there was work.
00:51:40
I can't talk about it. There were things at Palantir
00:51:41
that we just decided not to do, countries we decided not to work
00:51:44
with lucrative, lucrative deals where we sent teams out to go
00:51:50
diligent something and came back and said this is not an area we
00:51:54
are not comfortable here. We don't think we can actually
00:51:57
have our software to be deployed in a way that we think is gonna
00:51:59
preserve the things we want. We're just not gonna engage.
00:52:03
I mean, that's an option. But Andrew Abbott had a contract
00:52:05
they liked and then the Defense Department.
00:52:08
Yeah, I mean, I've read their stuff.
00:52:09
I've read Emil Michael's interview with Empire Wires,
00:52:12
Empire Wires, and I don't wanna. The last thing I want to do is
00:52:18
get in any of that. I'll just say this.
00:52:20
Do you want to be spicy? It's just of the moment.
00:52:23
I'm not trying to get. Are you neutral on it or are you
00:52:26
you? What is your you know you're
00:52:28
supportive of? The department I, I, I was
00:52:29
raised at pound tier and I, I part of the reason I was there
00:52:32
and part of the reason I still am supportive of is I think our,
00:52:34
we, we, the West needs the best capabilities.
00:52:39
And one of the things that defines the Western Western
00:52:41
society and the Western alliance is democracies and these
00:52:45
republics we have and in the United States, this wonderful
00:52:48
Republic we have that in every generation has felt broken in
00:52:52
some way. I always think it's funny when
00:52:54
people we've never been more divided.
00:52:55
Bitch, we fought a civil war like, like, like we this, this
00:52:59
just what this looks like and, and, and no one's ever going to
00:53:02
be totally happy with the current state of it.
00:53:04
And that is kind of just how these things work.
00:53:06
And I, I don't say that to be like fatalistic or even like, Oh
00:53:11
well, just whatever the government use the software
00:53:13
however you want. I just think it's like the, the,
00:53:16
the voters take the there's a saying I liked, which is people
00:53:22
deserve to get what they voted for good and hard.
00:53:25
And you know what? They're getting a good and hard
00:53:27
right now. I.
00:53:28
Just I just think it we're still a liberal society where people
00:53:31
participate or don't and like we are anthropic is being bullied
00:53:35
into participating. Like the government is using the
00:53:37
tools and you control whether you're supportive of them or
00:53:40
not. You know, it's it my my issue is
00:53:43
not yes, the Department of War is in charge of making war and
00:53:47
anthropics in charge of using their technology how they want,
00:53:50
and my issue is them using every lever they can to coerce
00:53:53
anthropic to acting against this their moral.
00:53:56
Beliefs, I mean, this administration has has been not
00:54:00
been shy about employing coercive techniques.
00:54:03
Look at law firms. I mean, there's a lot of.
00:54:05
And a lot of people want to extract away the current
00:54:06
administration, like whenever we're having this argument,
00:54:08
they're like, oh, the the executive, whoever they may be.
00:54:12
And it's like it's executive with the black record.
00:54:13
People had critiques of the last administration in the way that
00:54:16
they were acted coercively toward private enterprises and
00:54:19
some sectors as well. This.
00:54:20
Is by not inviting Tesla to Electro.
00:54:23
These other examples people have stuff on crypto.
00:54:25
I mean, I, I'm just sure I'm I'm.
00:54:26
I'm, I'm just saying I'm just, yeah, I'm just.
00:54:28
Saying like you can point these, this is not, this is not a novel
00:54:32
new like criticism of government.
00:54:35
I, you were right, we live in a liberal democracy and I and I
00:54:41
one way you could look at this as anthropic is getting bullied.
00:54:44
Another and something I will say I admire is anthropic is saying
00:54:47
we're patriotic Americans. We want our government to have a
00:54:50
great capability. We are proud of the product we
00:54:52
make. We want it to be employed and we
00:54:54
just have some concerns and limitations on where it can be
00:54:56
employed. That is a perfectly valid place
00:54:59
to be. And the government, if you take
00:55:01
them at face value, is saying we're we want to work with
00:55:04
private enterprise, but we need to have the flexibility to go
00:55:08
engage in the things we have to engage in to do the things we
00:55:10
want to do. Emil Michael on that post was
00:55:12
like, you know, we do a lot of stuff with guns, but if you're
00:55:16
not comfortable with, I'm paraphrasing, but if you're not
00:55:18
comfortable with that, maybe you can't work with us.
00:55:20
And like, I do think that's one of the things you kind of have
00:55:22
to stare into like, again, going back to the time of Palantir,
00:55:26
like there's things we did and worked on that you kind of look
00:55:28
at and you're like, not because it's like morally repugnant, but
00:55:32
it's like, does any you know, no, I would certainly never
00:55:36
stand up and like celebrate like people dying.
00:55:39
Like even if there are adversaries, there's something
00:55:41
there's just, you know, people kind of look at that and it's
00:55:43
like you can the the reluctant warrior, right?
00:55:45
It's like the and to engage in these areas is to have to engage
00:55:51
on the the reality of this, the Gray areas, the the grizzly on
00:55:56
the ground way that the sausage is kind of made.
00:56:00
And I think you can try to abstract that away, but it's
00:56:03
it's. I just think Ethereopia picked
00:56:04
this fight at a very savvy time. I mean, we just went to war with
00:56:08
Iran. We bombed a school, you know
00:56:11
like no all in they have no plan like.
00:56:13
It was all in Claude's plan. They were about to be wrapped
00:56:16
into this whole thing like, I don't know, I I just think that
00:56:19
some of us as if Claude. Planned it because.
00:56:21
People were deferring to our anyway.
00:56:24
I don't know. I, I, I'm not trying to get
00:56:28
overly involved. All I'll say is I have a lot of
00:56:31
optimism and hope this gets worked out.
00:56:33
And I have a lot of optimism and hope for how AI can just improve
00:56:36
the way our government works and improve the way our military
00:56:38
works when we need to unsheath it.
00:56:41
And I think that it is our job as citizens to be engaged, to
00:56:48
vote, to lobby our representatives, to make sure
00:56:52
that if there are limitations, we want to add around that, that
00:56:55
those are done. And I think we're going to see
00:56:57
that play out in the Democratic cycles ahead, as we always have.
00:57:00
That's how this process works, and we will watch it work.
00:57:03
Yeah, that's my take. The we could.
00:57:06
I want to move on to another topic.
00:57:08
I mean, our last topic before I let you go, just raising money
00:57:11
from venture capitalists. I feel like I have a lot of VCs
00:57:13
on on the show and, you know, they tell their story about how
00:57:16
things work. As a founder, I'm curious like
00:57:19
what you really look for in VCs or who you think's doing it well
00:57:24
right now. One thing I try to do is I when
00:57:28
I talked. When I do like rounds of VC
00:57:29
catch UPS, either current investors or perspective ones, I
00:57:33
try to have the same conversation each time, like
00:57:35
pick a topic. It's very interesting kind of
00:57:38
doing this side by side and not like it's not one-dimensional
00:57:40
like 1 is better or worse, just like a lot of different
00:57:43
dimensions. And one of the things I feel
00:57:45
like I've I've come to really look for is like first
00:57:47
principles understanding of things.
00:57:48
So like we were talking about pricing and so about pricing
00:57:51
like the, the kind of like thin VC version is like kind of
00:57:56
regurgitating A blog post and sort of while these other people
00:57:59
are doing this and these guys are doing this and here's what
00:58:02
margin they have in all this. I'm always trying to figure out
00:58:04
like the first principles version of it of like, if we
00:58:07
really strip it back and understand the technology and
00:58:10
understand our customers, what do they want?
00:58:13
And you can kind of tell when AVC has been like really
00:58:17
involved and very thoughtful with one of their portfolio
00:58:19
companies where they can say it. They're like, oh, well, at this
00:58:22
company, the way we looked at it is this.
00:58:24
And what we found is this, and we had this one customer told us
00:58:26
this and that made us think about this.
00:58:28
And you know, like, like they can really like unpack it and
00:58:32
it's very different from like sort of the blog post version
00:58:35
synthesis. So that's something I feel like
00:58:37
I, I really, really look for and someone who's like thinking
00:58:40
about things clearly and with fresh eyes and people, you know,
00:58:44
there's like a sort of criticism of VCs like the herd animals or
00:58:49
whatever. And there is a lot of that look.
00:58:51
But I'm always interested when I when I talked to someone who's
00:58:54
got like a really novel insider opinion or a contrarian take cuz
00:58:58
I at least I know I'm getting like fresh thinking it is.
00:59:02
I feel like I used to roll my eyes at first principles
00:59:05
thinking, I think now that I have a business, I mean, you
00:59:07
know, media is its own sort of animal.
00:59:10
But there are things where it's like if you apply generic
00:59:13
principles of like well run media companies, some I are
00:59:17
lessons I should definitely learn and some you'd say, oh,
00:59:20
you really don't understand some of the core things that make my
00:59:24
business work. And you really do need somebody
00:59:26
who has like empathy for like what is working about your
00:59:30
business and what is sort of like the core sort of non
00:59:32
negotiable. And if you have someone just
00:59:34
sort of sloppily applying like just like heuristics without
00:59:39
understanding like what's really working it can, it could send
00:59:42
you on a total. Totally.
00:59:44
I do a lot of Angel investing. I love it.
00:59:46
It's like a great, I'm busy with my day job, but it's actually
00:59:49
like a great kind of like mental stimulus that makes me think
00:59:51
more about hex and I'm I try to be really careful.
00:59:53
Like if you're a founder coming to me asking for advice, you
00:59:56
have to sit through my 5 minute like disclaimer this.
00:59:58
Is why you shouldn't trust anyone else you are in.
01:00:00
It in a one. This is like, I'm probably wrong
01:00:03
taking the greatest stop because I, I just, I like I and what I
01:00:06
try to do as best I can is tell people, I will tell you what we
01:00:10
did and why I think it worked or didn't work.
01:00:13
I will try to unpack from first principles the facts in the
01:00:15
ground, what we've seen, the data to back it up.
01:00:18
And then you can draw a conclusion on whether those like
01:00:21
ground facts, how well those map to you 'cause that I think is
01:00:25
the thing you're trying to do. And I find this experience
01:00:27
myself. I'll go talk to founders and get
01:00:28
advice, talk to three really smart founders and get 5
01:00:31
different opinions. And it's, it's like what?
01:00:34
They're all really smart. Like Dylan at Figma said this
01:00:38
and Kazar, an applied intuition said this and you know, I'm
01:00:41
like, Oh God, that there are opposite things.
01:00:43
What do you do? And it's like, well, you did you
01:00:45
really understand the like grounding thing and, and the
01:00:50
path dependency. And it's the other part of this
01:00:53
job is like, it's a lot of luck and it's a lot of circumstance.
01:00:56
And you know, you ask, how did you find that great executive?
01:00:59
Oh, well, I did the search like this, but like, also like
01:01:01
someone happened to be available on the market and you know,
01:01:06
it's, it's, it's, it's always like the survivorship bias thing
01:01:09
too of like, well, this worked for this person, therefore it'll
01:01:13
work for me. Or the failureship bias thing
01:01:14
that you also hear from a lot of VCs, which is, well, this other
01:01:17
company tried to do that and it didn't work, Therefore it won't
01:01:20
work for you. And So what was the first
01:01:21
principle of why it did or didn't work?
01:01:24
And can you really understand that?
01:01:26
That's like a a thing I'm always trying to grasp for as best I
01:01:30
can and usually failing, but sometimes get it ready.
01:01:33
Gary, thank you for coming on the show.
01:01:34
Thank you, this is fun. Great.
01:01:36
That's our episode and thanks for sticking around to the end.
01:01:38
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