Speaker 1: Welcome to tech stuff. I'm os Vloschen. Every year we
Speaker 1: spend billions of dollars asking real humans for their opinions
Speaker 1: to predict how groups of people behave On a larger scale,
Speaker 1: surveys are the foundation of political strategy, marketing, and produge innovation.
Speaker 1: So what happens when you stop asking humans what they
Speaker 1: think and start simulating their responses? Today I'm joined by
Speaker 1: cam Think and Ned Coo, the founders of Aru. At
Speaker 1: the ages of twenty and twenty one, they've built a
Speaker 1: billion dollar simulation engine that uses thousands of AI powered
Speaker 1: digital twins to deliver behavioral predictions for everyone from Ernst
Speaker 1: and Young to Spindriff, Cam Ned, Welcome to text stuff.
Speaker 2: Thank you for having us having us.
Speaker 1: Thanks you very very excited. Tell me about the name.
Speaker 1: How did you choose it?
Speaker 2: Yeah? Well, Uru is the place in Egyptian mythology where
Speaker 2: all the worlds meet, and given we simulate human behavior,
Speaker 2: we simulate all these worlds. It makes a lot of sense.
Speaker 2: But it's also the oldest trick in the book. So
Speaker 2: you know, Auru starts at two a's at the top
Speaker 2: the top of every alphabetical list.
Speaker 3: The Yellow Pages trick you know, triple A plumbing, all
Speaker 3: those sorts of things. You look at any of our investors'
Speaker 3: websites and you see us and then Airbnb, which we
Speaker 3: think is the proper order. I'm just kidding.
Speaker 1: That's very very good, Am I right in thinking, or
Speaker 1: at least is Wikipedia right in thinking that to get
Speaker 1: into Uru, that your heart has to be weighed? That
Speaker 1: is true, That is true. Are you in the business
Speaker 1: of weighing hots?
Speaker 2: I think by so, I think by technicality, really is
Speaker 2: Uru is the place that describes the field of reads, right.
Speaker 1: So we are just kind of a heaven, shall we say,
Speaker 1: an ancient Egypt?
Speaker 3: Yeah, exactlytopia almost.
Speaker 2: This is, by the way, this is why r dot
Speaker 2: com our domain cost us so much, because I only
Speaker 2: learned afterwards that anything that has anything to do with heaven,
Speaker 2: like any domain, like our fortune, because it's really easy
Speaker 2: to bring something that's being positive. But we are not
Speaker 2: in the business of waite arts. I don't know if
Speaker 2: that would be as important for the global economy.
Speaker 1: So, because I was thinking, in a sense, you are
Speaker 1: definitely not judging people, but in a sense you are.
Speaker 1: You're looking into the digital arts, or at least trying
Speaker 1: to distill how real people might behave from people you've simulated.
Speaker 3: No, I think it's a it's interesting, and we like
Speaker 3: things that are thematic and actually mean things. I think
Speaker 3: there's a lot of tech companies nowadays that have names
Speaker 3: that don't really don't really mean much.
Speaker 1: So all the ones around Tolkien were taking.
Speaker 3: Exactly and the ones from Dune and the ones from
Speaker 3: Lord of the Rings, and it's, ah, well, there we
Speaker 3: go to mythology. You know, you gotta pull one.
Speaker 1: I want to sort of get to how aru actually works.
Speaker 1: But I saw the article about you two in the
Speaker 1: Wall Street Journal or you three, actually your third co
Speaker 1: founder is not here, and you know, basically the teenage
Speaker 1: teenage founders of a billion dollar company. And I thought
Speaker 1: it was a very fascinating and cool story. And I'm
Speaker 1: a huge tennis and it's been kind of interesting for
Speaker 1: me to observe how Djokovic, Nadal and Federer cast this long,
Speaker 1: fifteen year shadow over the game of tennis where the
Speaker 1: kind of next gen couldn't emerge. And it wasn't until
Speaker 1: Nadal and Federer left the scene and Djokovic started to
Speaker 1: age that Ciner and.
Speaker 3: Al Karaz emerged, you know, a lot of people say,
Speaker 3: Cameron looks like by the way, Yeah, I like that.
Speaker 1: So, I mean, you know, it's somewhat of a parallel
Speaker 1: with the tech industry. Right before the twenty twenty three
Speaker 1: chat GPT moment and the diffusion of llms, basically, most
Speaker 1: people with strong technical skills would go and work in
Speaker 1: corporate jobs at like Facebook and Google and Amazon. Yeah,
Speaker 1: and then something changed, and you and the censor at
Speaker 1: the vanguard of that something that changed.
Speaker 2: I think what happened is, for like a really long time,
Speaker 2: you had people who were very technical getting paid a
Speaker 2: lot of money to work, largely like kushy jobs. Right
Speaker 2: right now, we have this perception of Silicon Valley nine ninety.
Speaker 1: Six, like all this stuff, correct engineers.
Speaker 2: Yeah, it wasn't that way for the longest time. I mean,
Speaker 2: if you go back, like even five years in history
Speaker 2: to twenty twenty one, Silicon Valley was where people drank
Speaker 2: beer during the work day and played ping pong, you know,
Speaker 2: at four o'clock in the afternoon. It isn't like that anymore,
Speaker 2: because I think what happened is people got actually attracted
Speaker 2: to the idea of hard work. Right It's like this
Speaker 2: backlash against what was once a work culture of like
Speaker 2: go work ten hours twenty hours a week of productive
Speaker 2: time and then not spend the rest of your time
Speaker 2: doing as many productive things. People want to work hard,
Speaker 2: and I think that changed.
Speaker 3: I mean, with a barrier entry to building a business
Speaker 3: being so so much lower, so many things has changed, right.
Speaker 3: I don't think it's just young people being able to
Speaker 3: do it. I think it's also the people who are
Speaker 3: at the top of the fields also have changed a lot.
Speaker 3: Like something Cameron and I discuss a substantial amount is
Speaker 3: you know, where is this generation's you know, Steve jobs
Speaker 3: or those sorts of areas. And it's actually part of
Speaker 3: our thesis around this space is that people have and
Speaker 3: had to rally around a singular lumin area as much
Speaker 3: because capital has been so much more accessible. Right, So
Speaker 3: it's not just as part of language models. I think
Speaker 3: it's a state of the you know, overall economy and
Speaker 3: things that have changed that have allowed it to happen.
Speaker 3: But it's very interesting. Nonetheless, Yeah, it is interesting.
Speaker 1: Do you think it's like a stylistic thing that your
Speaker 1: generation want to work hard or do you think it's
Speaker 1: an opportunity thing that like if you work hard, you
Speaker 1: can be net and Cam and get a big Unicorn
Speaker 1: founders before you're twenty one.
Speaker 2: I actually think it's I think it's a bifurcation, right,
Speaker 2: Like I think you have a population of people who
Speaker 2: work really hard, and that might be like the top
Speaker 2: death style. But what just happened is like the distribution
Speaker 2: of how hard people work has widened so much, so
Speaker 2: like just as much as in my high school class,
Speaker 2: there are a bunch of kids who, like maybe ten
Speaker 2: fifteen kids who worked extremely hard orderly. You know, I
Speaker 2: was not in the top performing in my high school class.
Speaker 2: Actually proud to announce that I was thirty third rank
Speaker 2: out of one hundred and eight.
Speaker 1: You were high school together, what were you?
Speaker 3: Probably much lower. It's very difficult for me to focus
Speaker 3: on things I don't care about, so you know, it's.
Speaker 2: But you had like ten to fifteen, maybe twenty kids
Speaker 2: who would like go study six hours and they would
Speaker 2: be totally happy to do that, and on the contra,
Speaker 2: like the next eighty kids just did not want to
Speaker 2: put all the work in. And I think you see
Speaker 2: that consistently where it's like there's this really high visibility
Speaker 2: group of people who work a lot, but remember, there
Speaker 2: are tons of software engineers at legacy businesses who don't
Speaker 2: work as much as like the high visibility group of people.
Speaker 2: That is like a very small small group of people
Speaker 2: in comparison to like broader software engineering is a category.
Speaker 1: How do you think about your paid group? I mean,
Speaker 1: do you know the other twenty eighth year old Unicorn founders?
Speaker 1: Do you associate with them?
Speaker 3: Or?
Speaker 1: I mean you're in New York and most are in
Speaker 1: San Francisco, which is a difference, right, Like, what's your
Speaker 1: reflection on the culture that's emerging around around that? I
Speaker 1: love the idea of Steve Jobs no longer being a
Speaker 1: cultural reference point because the structure of how capitalist deploy
Speaker 1: is different. That's really interesting. But how do you think
Speaker 1: about the group you know?
Speaker 2: I'm curious for Ninn's thoughts as well. I really really
Speaker 2: think the fact that more people know that is a
Speaker 2: pathway that is open to them is brilliant. I think
Speaker 2: that's incredible, right, like one hundred percent people should go
Speaker 2: do what they want. I do think some of the
Speaker 2: current environment has created people who are not in this
Speaker 2: because they're missionaries, but are instead in this industry because
Speaker 2: they're mercenaries and not to say that that is like
Speaker 2: the founders of other unicorns or other young people. But
Speaker 2: I just see a lot of people now who I
Speaker 2: think go to tech because not of a mission, not
Speaker 2: because they think it's like important philosophically important for the world,
Speaker 2: but instead because tech is like the next highest opportunity
Speaker 2: earning category, like finance was in the nineteen eighties. And
Speaker 2: I think shifting away from that is going to be
Speaker 2: interesting because the pendulum will always come back around.
Speaker 3: No, I agree entirely with that. I also think, to
Speaker 3: the same extent Cameron mentioned, you've also seen like the
Speaker 3: pliferation of so many different industries, not just tech, and
Speaker 3: people saying, oh, I can actually make a business. Ow's
Speaker 3: something I like, And it's become infinitely easier to monetize
Speaker 3: right as like a very small business across so many
Speaker 3: different channels, whether it's e commerce, whether that's you know,
Speaker 3: starting a tiny little brand and CpG or whatever may be.
Speaker 3: So I don't think it's a trend that's just in tech.
Speaker 3: I do think people are a lot more public about it,
Speaker 3: but it's been interesting to see. Nonetheless, I mean, we
Speaker 3: have friends that also dropped out of university to start
Speaker 3: schools and have had other companies that have been starting,
Speaker 3: you know, starting very very young, and I think there
Speaker 3: was this large sort of cultural shift. Cameron noted about,
Speaker 3: you know, silkon Valian, it's perception prior, it's now in
Speaker 3: some ways like sexy to be a tech founder. So
Speaker 3: I think you've a lot of people who whose hearts
Speaker 3: really aren't in it and these sorts of elements who
Speaker 3: are going after it. And you know, like anything in
Speaker 3: the world that you commit to not fully is things
Speaker 3: have repercussions.
Speaker 1: There's also an accessibility thing, right, Like your third co
Speaker 1: founder is not here. I think when you Will started
Speaker 1: you were nineteen, you were eighteen, and he was fifteen yep,
Speaker 1: which is kind of remarkable. But he reached out and
Speaker 1: basically said you were an accelerator program, can I get
Speaker 1: your advice? And then you left the call basically with
Speaker 1: him as a third co founders That might probably have
Speaker 1: a simplified but it.
Speaker 2: Was pretty close to that. He called them me on
Speaker 2: LinkedIn and he just said, I'm applying to this accelerator,
Speaker 2: can I have your advice on my application? I don't
Speaker 2: even check my LinkedIn dms that often. I certainly didn't
Speaker 2: when I was eighteen, I mean, you know what I've
Speaker 2: been doing on LinkedIn, And so we ended up scheduling
Speaker 2: a thirty minute zoom call. We spoke for two and
Speaker 2: a half hours, and I called Ned immediately afterwards and
Speaker 2: I said to Ned, I've just met the smartest person
Speaker 2: I've ever met in my life. And that was the moment.
Speaker 2: More or less immediately, I think the three of us
Speaker 2: hopped on a call later that night and we were like,
Speaker 2: screw it, We're starting a company together. I mean, Nut
Speaker 2: and I were about to sell our previous business. John
Speaker 2: literally dropped out of high school and we set everything
Speaker 2: down and said, is, while we're passionate about.
Speaker 1: What was the vision you laid out to him? And
Speaker 1: what was it that he said in response that made
Speaker 1: you think he was the most intelligent person you ever met.
Speaker 2: Well, at the time, he actually was writing a paper
Speaker 2: focused on RAG for healthcare agents, so this space I was,
Speaker 2: Nett and I were in digital health as well, and
Speaker 2: so I remember reading it and thinking to myself, Wow,
Speaker 2: like just reading these benchmarks, If these benchmarks are real,
Speaker 2: it was a material improvement in the quality of models,
Speaker 2: specifically for retrieving like medical evidence and clinical studies. At
Speaker 2: that time it was called heel actually hal and reading that,
Speaker 2: I was just like, wow, this is insane. I spoke
Speaker 2: to him about it, and I was kind of digging
Speaker 2: in and I was like, well, it seems really real.
Speaker 2: So like a fifteen year old living in a dorm
Speaker 2: at a boarding school in like rural Massachusetts who's from
Speaker 2: New Hampshire is able to best the labs and that
Speaker 2: was incredible, And so all of us randomly having a
Speaker 2: digital health background, but then we also all shared this
Speaker 2: vision of population research. John was like an original population
Speaker 2: researcher when he probably also called dm'd its way into
Speaker 2: a lab at Tufts and a lab at MIT doing
Speaker 2: city science and population demographic research.
Speaker 1: But did you already have the idea for.
Speaker 3: Something that was developed in its current manifestation together, right,
Speaker 3: But in the idea of understanding and simulating populations. It's
Speaker 3: something we thought about for a while, but our own
Speaker 3: it's manifestation was certainly something that we developed together, which
Speaker 3: I think is cool.
Speaker 2: I think it was October twenty twenty three we started
Speaker 2: talking about predicting behavior for the first time, and I
Speaker 2: think from there on at it was something that just
Speaker 2: completely consumed us.
Speaker 1: See you mentioned missionaries versus mercenaries. What is the mission
Speaker 1: for us?
Speaker 2: It's predict the future. I think it's really simple. If
Speaker 2: you think that humans are the most impactful species on
Speaker 2: the club, and we're able to predict human behavior, then
Speaker 2: predicting human behaviors tend amount to predicting the future. And
Speaker 2: if you can predict the future, you can shape it.
Speaker 2: It's not just the power to say this is how
Speaker 2: your product concept will perform, or this is who we
Speaker 2: expect to win in an election, but instead it's actually
Speaker 2: the power to find the product that wins and to
Speaker 2: help the people ideas that matter win elections more consistently.
Speaker 1: So predict littles of influence.
Speaker 3: Predict and shape and help people that need to shape
Speaker 3: behavior shape that right for the broader good. And that's
Speaker 3: something we care a lot about. There's a lot of
Speaker 3: predicted technology that exis for the aspect of simply measuring
Speaker 3: behavior right. And at the end of the day, what
Speaker 3: has to be done after that? When you get a
Speaker 3: spreadsheet that says this is what this person said, this
Speaker 3: is what this person said, et cetera, it's not very
Speaker 3: actionable to anybody, and so it ends up being this
Speaker 3: humongous waste of an exercise, because as much as might
Speaker 3: have taken you months to get to that point, no
Speaker 3: matter what measurement it gets to, it's very difficult for
Speaker 3: someone to have to go from there and make their
Speaker 3: own assumptions upon that data to then make a conclusion.
Speaker 3: And then frequently that data ends up just supporting what
Speaker 3: that initial person's decisioning has been or gets sworn that
Speaker 3: way or another. We have no like manual waiting on
Speaker 3: the output of our system. We run simulations of behavior
Speaker 3: our system as analysis upon it, and people take action
Speaker 3: as a result. And that's part of what makes it
Speaker 3: so powerful.
Speaker 1: When I think about prediction and influence, I guess there's
Speaker 1: like black hat predictors and influences like you know, Goebbels
Speaker 1: or Cambridge Analytica, and then there's like would be white
Speaker 1: hat predictors and influences like cast Sunstein and his like
Speaker 1: famous nudge theory. Right, do you think about this spectrum
Speaker 1: all the time?
Speaker 3: And if we didn't, I don't think we would believe
Speaker 3: in our technology. But we do, and we see how
Speaker 3: strong it is every day, and we do as much
Speaker 3: as we can to be on the white out side
Speaker 3: of that. Right, So, like I think about things and
Speaker 3: you know, maybe not to announce it here, you know,
Speaker 3: formulae leans or not. We're not without finance and all
Speaker 3: that yet. But later this year we'll be launching a
Speaker 3: research institute within our where we're dedicating a significant amount
Speaker 3: of compute and infratructure from our side who works exclusively
Speaker 3: on pro bono aspects. So certain these areas are you know,
Speaker 3: women's self for example, getting people to go to preventive
Speaker 3: screenings for you know, preventive diseases and other elements like that.
Speaker 3: That's a behavioral problem, right. Things like getting people to
Speaker 3: understand that nuclear energy isn't dangerous, that's a behavioral problem, right,
Speaker 3: All those sorts of elements. There's so much good you
Speaker 3: can do in the world if you understand what people's
Speaker 3: awareness of certain issues are, how to make them aware
Speaker 3: of certain elements and how to change their behavior on
Speaker 3: things that are genuinely better for society. And that's something
Speaker 3: we spend a lot of time thinking about.
Speaker 1: Do you have philosophical ethical debates the two of you
Speaker 1: with your co found it do board? What is the
Speaker 1: count sort of I was going to account some elders,
Speaker 1: but it's not appropriate in this conversation. But how do
Speaker 1: you debate these questions together?
Speaker 2: I mean, we were a company founded in like really
Speaker 2: deep philosophical principles. We do have a lot of thoughts,
Speaker 2: you know, about the moral and ethical use of our technology. Right, Like,
Speaker 2: to be clear, predicting behavior is really powerful. Predicting behavior
Speaker 2: incredibly accurately, with the capability to shape it is like
Speaker 2: even more powerful. Right, this is something that not only
Speaker 2: economically is important, but for the good of the world
Speaker 2: it's important. And so I think part of the reason
Speaker 2: we built these company is because we wanted to use
Speaker 2: this technology as a force for good.
Speaker 3: So how does it work well within what we can share?
Speaker 3: Of course? No, it's really interesting because you think about
Speaker 3: the architecture or system, and you think about the proliferation
Speaker 3: of language models and how much more accessible starting certain
Speaker 3: elements has been and all these things. And certainly these
Speaker 3: systems would not be capable without language models. But they're
Speaker 3: not the only thing that makes them what they are. Right,
Speaker 3: you think about what a language model is built for
Speaker 3: and its most basic principles. It is not to capture
Speaker 3: the irrationality of human behavior, right, It's not. It's in
Speaker 3: fact quite the opposite. To average results exactly. It is
Speaker 3: a beta generating machine in many ways, right, And so
Speaker 3: you think about the things that make humans who we are,
Speaker 3: that makes us act or buy a product from a
Speaker 3: brand and a new category that they've never made before.
Speaker 3: Of course, a language model doesn't work for that use case, right,
Speaker 3: And so there have been many evolutions of the architecture
Speaker 3: that we've come to today. At its core, what it
Speaker 3: is is what we call multi agent simulation. So effectively,
Speaker 3: we organize these large populations of AI agents, assign them
Speaker 3: different distributions of income, age, race, gender, sex, you know,
Speaker 3: credit card purchase history, all these sorts of elements that
Speaker 3: makes them who they are. The entire principle of how
Speaker 3: we generate those distributions of what we call audiences in
Speaker 3: the first place, or the demographics that make that simulate
Speaker 3: and population up or that we never actually survey people
Speaker 3: at all, and so we're just generate these populations off
Speaker 3: of the basic building blocks and what makes them who
Speaker 3: they are. So what we call ground truth data, right,
Speaker 3: we only look at data from areas where we don't
Speaker 3: actually have to ask people because people lie, they have biases.
Speaker 3: And you go to trybeca near where our office is
Speaker 3: and you ask the average man how much money makes
Speaker 3: He's gonna say four and a half million bucks, you know,
Speaker 3: like whatever it may be, right, So we generate these
Speaker 3: populations based off these ground truth distributions of traits and
Speaker 3: then be able to sign those distribution of traits to
Speaker 3: these thousands of models to become simulated populations, or will
Speaker 3: then ask them questions to measure their behavior. Right, so
Speaker 3: you might ask someone you know, who you vote for,
Speaker 3: what will you buy? How much will you buy? This
Speaker 3: for all those sorts of elements to be a measure
Speaker 3: everything from proprency to purchase to who's going to win
Speaker 3: an election to you know, churn risk and everything in between.
Speaker 3: And is it falsifiable like by a validation?
Speaker 1: How do you like? How do you be tested exclusively
Speaker 1: to outcomes?
Speaker 3: Right, So we never try to benchmarker models on surveys
Speaker 3: or any sort of traditional primary research, so surveys, polls,
Speaker 3: focus groups, et cetera, because again we believe this to
Speaker 3: be biased. So exclusive look at the actual actions people
Speaker 3: take and not what they say they do. And there's
Speaker 3: a lot of different examples of where we've been able
Speaker 3: to do this publicly that have been really cool. You know,
Speaker 3: this is a fantastic example with one of our partners,
Speaker 3: Ernst and Young, who you mentioned. They took a survey
Speaker 3: that traditionally took the months to run. Thirty six hundred
Speaker 3: people responded, thirty countries, fifty three questions. We said, don't
Speaker 3: trust their models. Right, This is what we say to
Speaker 3: all our customers. They gave us the questions they asked.
Speaker 3: They gave us the people they spoke to, or the
Speaker 3: quotas for the assets under management. It was an investor simulation.
Speaker 3: We ran the entire questions that within our model in
Speaker 3: a matter of minutes returned it to them. They did
Speaker 3: a correlation, had a ninety percent correlation, but in the
Speaker 3: ten percent where we were not accurate, we were significantly
Speaker 3: closer to the real human action. So you think about
Speaker 3: a question in that example, right. One of those in
Speaker 3: that study was what's your likelihood to retain a wealth
Speaker 3: manager when your parents pass away? What are the three
Speaker 3: things that go through your head when you get asked
Speaker 3: that question. The first one is I hope my parents
Speaker 3: don't pass away. Second one is that might have been
Speaker 3: in five years, ten years, thirty years, I don't know.
Speaker 3: I'm going to do my money then, and the third
Speaker 3: one is my wealth managers asked me this question. I'm
Speaker 3: not going to tell them a lot, right, So you
Speaker 3: have this whole scenario where at the end of the day,
Speaker 3: eighty two percent of the people in their traditional measurement
Speaker 3: gold standard survey said they were going to retain wealth manager.
Speaker 3: Real market data from actual wealth management firms said this
Speaker 3: between the tune of like twenty to forty percent, depending
Speaker 3: on geography. Our simulation came back a forty percent. So
Speaker 3: you look at a result like that, like customers do
Speaker 3: all the time when they see the difference between our
Speaker 3: simulations and the surveys. They run eighty two percent in
Speaker 3: their survey forty percent in ours, and it looks totally wrong.
Speaker 3: You look at real actions in the real world.
Speaker 1: We're right on the money, okay, But imagine doesn't know
Speaker 1: even as I am. How is it possible that fake
Speaker 1: people can get better on so those than real people.
Speaker 2: Because real people lie. I mean, it's that simple one.
Speaker 2: When I go and I phone you and I say,
Speaker 2: you know, hey, my name's Cam. I'm a researcher with
Speaker 2: Consumer Research Incorporated. How do you feel about the coffee
Speaker 2: you just drank from Starbucks this morning. What are you
Speaker 2: going to tell me you're going to hang up the phone.
Speaker 2: Are you going to say it was great and I
Speaker 2: got off the phone. And so when you're looking at
Speaker 2: that in traditional consumer research world, it just doesn't align
Speaker 2: incentives for consumers to actually provide a real opinion, right,
Speaker 2: a real reflection of how they care what they feel.
Speaker 2: And so when you're looking at our data, instead, we
Speaker 2: don't ask consumers how they care what they feel, right,
Speaker 2: because we know that they're not going to be honest
Speaker 2: with us. We know that nothing that is trained on
Speaker 2: or built on top of surveys or traditional consumer research
Speaker 2: is ever going to be able to reflect that accurately.
Speaker 2: And so instead we run another direction. We train on
Speaker 2: top of the outcomes. As Ned said, we train on
Speaker 2: things like actual coffee mug sales and actual coffee cup sales, right,
Speaker 2: And by looking at that data we can track far
Speaker 2: better how humans opinions and behavior is changing over time.
Speaker 1: What's the most niche thing that you're proud of having
Speaker 1: got right? What's the whole in one double egle?
Speaker 3: I mean, there's some really really specific ones. So there's
Speaker 3: certainly been cases where we've done things like price elasticity
Speaker 3: of like bundling of very specific automotive parts. That is
Speaker 3: crazy that we get those correct. But then there are
Speaker 3: other really interesting things, like you know, breaking new artists
Speaker 3: in music. Right, we're on iHeart Radio right now, right,
Speaker 3: Like looking at being able to simulate the star power
Speaker 3: of a very specific emerging artist and predict that they're
Speaker 3: going to blow up or that they have propensity to
Speaker 3: blow up in these specific areas. That's incredibly cool.
Speaker 1: So this was an advanity fair piece. It was an
Speaker 1: artist you found who had forty thousand streams when you
Speaker 1: found them and went on to release a song with
Speaker 1: one hundred and seventy two millions off you'd identified them.
Speaker 1: What more can you share about this?
Speaker 2: Not that much, But it was a really really cool
Speaker 2: use case. And I am proud to say that I
Speaker 2: listened to their music on our way up there.
Speaker 1: Yeah, back up, yes we did. You can't say what
Speaker 1: the artist.
Speaker 2: Is, No, I can't say who the artists?
Speaker 1: What can you share about the criteria? Yeah?
Speaker 3: So this is really interesting. We were testing a very
Speaker 3: specific population. It was people simulating people listening to music
Speaker 3: who are under the age of twenty five, right, So
Speaker 3: we're looking at like young sort of looking at emerging
Speaker 3: underground these sorts of areas. It was really interesting because
Speaker 3: we ended up feeding in the Instagram feeds, Twitter feeds
Speaker 3: some of the actual songs and prossing the audio on
Speaker 3: those elements and understanding is this a res rating compared
Speaker 3: to all these other artists, Like how often you'd listen
Speaker 3: to this artist? Would you go to a concert for
Speaker 3: this artist? How would you pay for a ticket to
Speaker 3: see this artist if your friends told you about them,
Speaker 3: like all these super specific elements that we worked with
Speaker 3: to quantify whether or not, out of a large ranking
Speaker 3: of these specific performers, which one would do best. And then,
Speaker 3: of course that was when they were much smaller. They
Speaker 3: ended up getting signed and sorts of things and then
Speaker 3: had a lot of popular songs come out. So you know,
Speaker 3: we can't take full responsibility for the fact they got
Speaker 3: that large, but it was certainly interesting to see that
Speaker 3: we were properly able to simulate the general populations prepensy
Speaker 3: to listen to music, which is really cool because that
Speaker 3: is something that's so difficult to quantify.
Speaker 1: Well, I actually have one that I would love to
Speaker 1: work with you guys on. So I am the host
Speaker 1: of this tech Stuff podcast and tex Stuff is a
Speaker 1: co production of iHeart Where we Are and Kaleidoscope, which
Speaker 1: is the media company I founded, and our mission is
Speaker 1: essentially to communicate the wonder and excitement of this new
Speaker 1: age of which you two are representative of. And that
Speaker 1: idea that you articulated, which is that like a small
Speaker 1: group of people run with everything they have towards being
Speaker 1: like participant builders and a large group of people don't
Speaker 1: is perhaps one of the greatest social problems we have.
Speaker 1: And so what we think about a Kalidascope is how
Speaker 1: can we tell stories and do media products podcast, YouTube,
Speaker 1: et cetera, which are not polyannoriatee or just you know,
Speaker 1: celebrations of the tech industry, but but rather that are
Speaker 1: kind of engaging and inspiring storytelling about what's possible in
Speaker 1: this new world because a lot of the narratives about it,
Speaker 1: yes they are available, but they're also like by the industry,
Speaker 1: for the industry.
Speaker 2: It's a closed and ecosystem in a lot of ways.
Speaker 1: I was thinking this morning on the way to talk
Speaker 1: to you guys, how do we successfully identify the next
Speaker 1: cohort of the best science and tech creators, influencers, et cetera.
Speaker 1: So how would we go about doing Like, let's say
Speaker 1: I was a paying client, which I think I might be.
Speaker 1: It has to help like what would be the sales
Speaker 1: motion as it were, if I was a real client,
Speaker 1: Like what would you what would you guys? Where would
Speaker 1: you go from like this problem to providing me with
Speaker 1: the answer.
Speaker 3: No, I think this is really interesting. And the first
Speaker 3: part that you're bringing up there is one of the
Speaker 3: biggest challenges for insights, research, marketing, strategy, product pricing, whatever
Speaker 3: you are team today because what makes research, say, it
Speaker 3: takes so lethargic of a process today, like weeks, if
Speaker 3: not months sometimes Yeah, part of it is like asking
Speaker 3: the questions and finding the people to talk to and
Speaker 3: those sorts of elements. Part of it is figuring out
Speaker 3: what the numbers mean afterwards. The biggest challenge and the
Speaker 3: place where we actually see people go the most wrong,
Speaker 3: is figuring out what to ask in the first place, right.
Speaker 3: And so one thing that we've built our system to
Speaker 3: be able to do because we serve as so many
Speaker 3: different types of operators is feed in general objectives, feed
Speaker 3: in semantic descriptions, and our system will take that in
Speaker 3: and figure out exactly what the simulations you have to
Speaker 3: run to answer that objective are And so maybe for
Speaker 3: that example, the first thing you want to find out is, Hey,
Speaker 3: before we go identify what talent works with people, let's
Speaker 3: go see who has the appetite for this in the
Speaker 3: first place. Segmentation this is huge business for us. We
Speaker 3: do a lot in CpG and marketing in these areas
Speaker 3: because we don't need to get in the weeds of
Speaker 3: your CRM to segment your audience. We can just simulate,
Speaker 3: you know, half million people in downtown New York City
Speaker 3: and see who wants to listen to your product, or
Speaker 3: see who wants to buy it for a certain price. Right,
Speaker 3: So it's much more first principles, like top down because
Speaker 3: we have that scale. So maybe the first time we'd
Speaker 3: run there would be understanding a certain population. You said
Speaker 3: you're interested in the youth mostly right, probably as being
Speaker 3: an audience maybe given age rage. I'm curious let's say
Speaker 3: twenty one to twenty nine careert, so twenty one to
Speaker 3: twenty nine, right, And we probably feed that in any
Speaker 3: other descriptors that you have about this population that you're
Speaker 3: interested in, maybe psychographically like open to sort of personal
Speaker 3: self development. Not already checked out, but wondering how to
Speaker 3: live a better life.
Speaker 2: So we would be able to take in that description,
Speaker 2: you know, twenty one to twenty nine people who are
Speaker 2: interested sort of open to non insiders, not insiders, people
Speaker 2: who maybe insider rejectors, right, people don't insiders, but people
Speaker 2: who are open to learning more about technology, and our
Speaker 2: models are able to pull out that description and actually
Speaker 2: say okay, based off of who even something that simple
Speaker 2: is like what is their urban world breakdown? What is
Speaker 2: their home cooking meal frequency? You know, where do they
Speaker 2: spend their time on social media? How many hours a
Speaker 2: day did they go to school? Yeah, what is their education?
Speaker 2: What is their income level? You know, three hundred plus
Speaker 2: different variables that we're storing. And then once we chart
Speaker 2: all of that out, we're able to then go say, now,
Speaker 2: what questions do you want to ask? And that's when
Speaker 2: ND said we do a lot of work as well
Speaker 2: in identifying kind of the right questions to ask using
Speaker 2: our models. Then once we have that question list, we're
Speaker 2: able to take that audience. We start with the distributions.
Speaker 2: We generate tens of thousands of agents who are logically consistent. Right,
Speaker 2: So in the twenty one to twenty nine year old group.
Speaker 2: There's not going to be any seven year olds, right,
Speaker 2: There aren't going to be any eighteen year olds. There
Speaker 2: won't be any thirty one year olds. We make sure
Speaker 2: that everything is logically consistent, and then we're able to
Speaker 2: simulate the behavior of each individual and predict that on
Speaker 2: an individual by individual level. Remember, these aren't real people,
Speaker 2: but hypothetically likely people to exist. We can collect results
Speaker 2: across all of them, and hopefully in doing that, we'll
Speaker 2: have cracked the code and told you exactly who we
Speaker 2: need to put on air.
Speaker 1: I'm sure you can't choose amongst your children, But what's
Speaker 1: been the most fascinating project intellectually for you both so far?
Speaker 2: So I tell you I run a lot of simulations
Speaker 2: all the time, just because I'm interested in it. Like
Speaker 2: it takes all the willpower in the world not.
Speaker 1: To just like it.
Speaker 2: Sit there all day long, logged into platform dot dot
Speaker 2: com running SIMS. So I just ran. I run a
Speaker 2: lot of really interesting ones. I just ran one I
Speaker 2: was really curious about. For the upcoming twenty twenty six
Speaker 2: primaries and the twenty twenty six midterm election season, we
Speaker 2: both kind of come from a politics background, So that's
Speaker 2: an area where we've been interested in for a long time.
Speaker 2: And then we always track glps as you may know,
Speaker 2: you know, which we like to talk about pretty openly.
Speaker 1: So I interested in jlps because they're in a sense
Speaker 1: of intervention in behavior at scale, and the implications of
Speaker 1: that intervention are thus far not fully known.
Speaker 2: I think I'm I've personally I don't know about you.
Speaker 2: I'm personally interested in GLPS because this is like the
Speaker 2: biggest mass movement and mass change we've had in public
Speaker 2: health care in a long long time, Like healthcare has
Speaker 2: never been more culturally relevant. I think by the way,
Speaker 2: a lot of things fed into that GLP moment. It
Speaker 2: wasn't just the existence of GLPES, but like health care
Speaker 2: and wellness has been something we talk about increasingly over
Speaker 2: the last five six years. And then it is going
Speaker 2: to create massive behavior change, and most of the largest
Speaker 2: businesses on the globe don't know how to navigate that
Speaker 2: behavior change with the clarity they need, and so that's
Speaker 2: actually why we do so much work in the GLP space.
Speaker 1: Now you've got a little bit held your feature The
Speaker 1: fire by Andrew Rossulkin on Squawkbox because you said, basically,
Speaker 1: our prediction is that there may be a short term
Speaker 1: decline alcohol consumption, but actually as people start to feel
Speaker 1: better and more confident because they're at a healthy weight
Speaker 1: and whatever else I feel good about themselves, they'll be
Speaker 1: out more and so they may have won drink, won drink,
Speaker 1: won drink five times a week versus having five or
Speaker 1: ten drinks, you know, in one day. The Squawkbox team
Speaker 1: didn't seem to quite buy it.
Speaker 2: Well, I tell you what, they don't have to buy
Speaker 2: it themselves as opinion, because it's actually now starting to
Speaker 2: become a matter of fact. I don't know if you've
Speaker 2: been watching earnings for alcohol businesses, but Q one earnings
Speaker 2: have been coming back, and for the first time in
Speaker 2: a long time, we're now seeing like case volumes four
Speaker 2: alcohol come up. That tells you a lot about the
Speaker 2: fact that already the alcohol industry is probably starting to
Speaker 2: see some of the tail ones that the market was
Speaker 2: a little bit too negative on GLPS. I've been reading
Speaker 2: the analyst notes. The analyst notes talk about GLPS maybe
Speaker 2: having some long tail benefit, and so I think it
Speaker 2: makes a lot of sense that sure has it played
Speaker 2: out completely, No, But do I feel like we're starting
Speaker 2: to see the early signs in an alcohol industry recovery,
Speaker 2: mostly driven by the fact that glps aren't as bad
Speaker 2: as people think one hundred percent.
Speaker 3: Yeah, I was just going to say, you ask again
Speaker 3: why it is so interesting to us and why this
Speaker 3: is so important. The most valuable people on the globe
Speaker 3: to understand the behavior of will always be the most
Speaker 3: difficult to access. People on GLP ones aren't even telling
Speaker 3: their family they're on these drugs. I'm not going to
Speaker 3: tell you to survey, right and so right now, if
Speaker 3: you're a I'll call producer. If you are a QSR,
Speaker 3: whatever it may be, you're in a really, really, really
Speaker 3: tough spot. Same thing goes with like ultrain it with
Speaker 3: individuals or any of those groups, Like any very difficult
Speaker 3: to access populations are always going to be common for
Speaker 3: us to simulate because we can offer people data that
Speaker 3: nobody else on the planet can.
Speaker 1: So could you do, for example, the younger crop of
Speaker 1: revolutionary gods in Iran who haven't been assassinated, and what
Speaker 1: they think the deal tends to make a piece deal
Speaker 1: would be? I mean, I'm sort of being slightly faceties,
Speaker 1: not like do you have enough data on Iranian revolutionary
Speaker 1: gods or ah, we do it? Do that need to
Speaker 1: be kind of like us based, would you be ablignate
Speaker 1: that population?
Speaker 2: So we're we're totally global, right. We cover over one
Speaker 2: hundred and sixty countries across the globe, all of Europe,
Speaker 2: almost all of Asia, spare a couple of countries in
Speaker 2: the Middle East, all of the Americas and most of
Speaker 2: africas well. I'd have to look into it, but I
Speaker 2: can tell you we probably don't have all the data
Speaker 2: we need to simulate that. We're very clear about where
Speaker 2: we do and don't work, and so for us it
Speaker 2: is important there needs to be data in order to
Speaker 2: simulate an audience accurately. But I would say there needs
Speaker 2: to be less data than you think. And so young
Speaker 2: IRGC commanders, is there enough data on that? Probably no, right,
Speaker 2: But is there enough data for us to understand you know,
Speaker 2: say Filipino household purchase decision makers for you know, cleaning
Speaker 2: products in the country, Yeah, one hundred percent. And we
Speaker 2: can understand regional variations between foss food purchases in Hochiman
Speaker 2: City versus in Hanoi and Vietnam. Right, And that's because
Speaker 2: of really two factors, the first being less data is
Speaker 2: required than I think most people think, and the second
Speaker 2: being that way way more data is being created than
Speaker 2: most people think everywhere we go nowadays.
Speaker 3: Yeah, there's a really big challenge when you go to
Speaker 3: foreign markets or you go well, not even for markets.
Speaker 3: Let's just talk about in general modeling very specific populations
Speaker 3: and niche geographies. Of those elements, one is you want
Speaker 3: to model this population accurately. Great, you need really up
Speaker 3: to date, you need really fairly granular demographic data, and
Speaker 3: all those sorts of elements. Yeah, some of that is
Speaker 3: very specific, like having credit card purchase history on a
Speaker 3: very granular level is incredibly impactful. But if you're modeling
Speaker 3: the population of even New York City, you still need
Speaker 3: to know like what percentage of people have running water,
Speaker 3: and me not having credit card history on a very
Speaker 3: specific population. With iron on understanding how certain refugee populations
Speaker 3: in the Middle East may react to a United Nations
Speaker 3: announcement about peacekeeping or delivery of food supplies, it's probably
Speaker 3: more relevant for me to have data on whether or
Speaker 3: not they have running water than it is for credit
Speaker 3: card things. Right, So there are certain things that are
Speaker 3: more or less relevant depending on geography. I also say
Speaker 3: the next thing that is always super important to keep
Speaker 3: in a mind when you're talking about specific populations is
Speaker 3: understanding of the environment more than it is just understanding
Speaker 3: of them. So it's great if you have a perfect
Speaker 3: demographic representation of Singapore. What actually matters a lot if
Speaker 3: you're trying to simulate the difference between how people feel
Speaker 3: today and in three weeks from now, is like, was
Speaker 3: there some crazy news that happened? Right? Is there some
Speaker 3: like big new trend on TikTok or whatever that's like
Speaker 3: totally altered the way people think about a certain thing.
Speaker 3: That's more important to capture than just knowing who people
Speaker 3: are alone.
Speaker 2: I would say even more when you look at why
Speaker 2: people use us for totally global businesses. Right, We've some
Speaker 2: customers were supporting in you know, dozens, if not over
Speaker 2: one hundred different countries. They're using us in part because
Speaker 2: no one has a consistent bar for measurement across all
Speaker 2: these different markets today. Right. So you know here in
Speaker 2: the United States, if a place has below four point
Speaker 2: five Google rating, it's probably not great. If it has
Speaker 2: below four, it probably is really not great. And then
Speaker 2: it has below three I probably wouldn't go there due
Speaker 2: to food safety issues in Japan. Most rating systems start
Speaker 2: with three and one would be terrible, two would be bad,
Speaker 2: three would be good, and four would be excellent and
Speaker 2: five would be incredible. And so if you're trying to
Speaker 2: take yeah, if you're trying to take consumer survey across
Speaker 2: the United States and Japan and you get you.
Speaker 1: Know, there's not there's apples and orange is basic point.
Speaker 1: One product that look the same different arts.
Speaker 2: One product has a top two box score of like
Speaker 2: sixty five percent in the United States, and one product
Speaker 2: has a top two box score of like thirty percent
Speaker 2: in Japan. Little do you know, thirty percent top two
Speaker 2: box score in Japan is actually like an incredible indicator.
Speaker 2: And in the States, sixty five percent top two box
Speaker 2: scores like not that impressive, right, So when you use us,
Speaker 2: A lot of the reason that people are bringing us
Speaker 2: in is because we offer global consistency. It's the same
Speaker 2: level of bar for every single market we support in
Speaker 2: every single audience because it's all based off of real,
Speaker 2: real behavioral outcome, so you can actually trust that something
Speaker 2: is going to be the same across different countries.
Speaker 1: You mentioned that the longer term mission is to predict
Speaker 1: the future. How far out does the future that you
Speaker 1: can predict go today, and what might lengthen the horizon.
Speaker 3: Yeah, so this is kind of interesting because it's not
Speaker 3: even a time frame or the amount of years, it's
Speaker 3: the amount of degrees behavior that leads you to that. Right,
Speaker 3: So you take something like an election, which of course
Speaker 3: there's going to be environmental changes that happen in between.
Speaker 3: There's you know, hundreds of millions of people that vote
Speaker 3: in the US. There are all those people making decisions,
Speaker 3: but they're making one singular decision. So it's not that
Speaker 3: difficult to predict. You look at a election, and then
Speaker 3: you look at how people react, and then you look
Speaker 3: at who protests, and then you look at how it
Speaker 3: affects trade, and then look at It gets harder and
Speaker 3: harder and harder to be able to model that because
Speaker 3: even if you're ninety nine point nine percent accurate, you
Speaker 3: multiply by itself enough times and it decreases.
Speaker 2: I think for us, what you see where we really
Speaker 2: truly excel today. We're incredible at population and group level
Speaker 2: and cohort level behavior predictions. Right, so we are the
Speaker 2: most accurate in the world at predicting something like an
Speaker 2: election by quite a distance, or the most accurate in
Speaker 2: the world of predicting consumer purchase intent by quite a distance,
Speaker 2: the most accurate in the world of predicting social media
Speaker 2: shareability by quite a distance. We aren't as good at
Speaker 2: organizational action protection. We aren't good at looking at an
Speaker 2: individual by individual level. That's not something we offer today.
Speaker 2: If you say, I want to take this from just
Speaker 2: how do we impact the population to like, how do
Speaker 2: we truly predict the future? We need to nail those
Speaker 2: as well. And so as you see where we're headed
Speaker 2: over the long run, we're going to take our fundamental
Speaker 2: model advantage, our tech advantage, and we're going to apply
Speaker 2: that to these new domains so that we get even
Speaker 2: better at predicting the future.
Speaker 1: So you're raising eighty million Series A at a valuation
Speaker 1: whatever billion dollars, what's the use of prosus? What are
Speaker 1: you investing the money in? And what support is the
Speaker 1: valuation of the investors?
Speaker 3: People on both sides actually in many ways, I would say,
Speaker 3: but more importantly, what we're spending the most amount of
Speaker 3: capital on today and really really focusing on our number
Speaker 3: one priority is getting the best people, the best people,
Speaker 3: the smartest people, the most driven people. The only reason
Speaker 3: we're able to get so far is as the nature
Speaker 3: of our team. We have aongous, humongous priority on diversity
Speaker 3: of thought, and that goes across research, engineering, product right.
Speaker 3: People who spend time looking at understanding awareness of social
Speaker 3: media when we see put it into our models in
Speaker 3: those elements used to do Alzheimer's and dementiary research. There
Speaker 3: are other people on our team who come from traditional
Speaker 3: social science, demography, applied demography backgrounds, not you know, just
Speaker 3: applied AI in those elements, because this problem can't be
Speaker 3: solved in one singular way.
Speaker 1: And I came to your office and it was notable
Speaker 1: for the no shoe optional, no shoe policy, which I'm
Speaker 1: a big fan of, and also for the cigarettes, which
Speaker 1: you guys living the gen z trend.
Speaker 2: We predicted it. We said cigarettes were coming back in
Speaker 2: twenty twenty four.
Speaker 3: Yes we did not.
Speaker 1: Everyone's in love with what you're doing. The New York
Speaker 1: Times racing right OpEd with the headline this is what
Speaker 1: we'll ruin public opinion polling for good quote, pure fictions
Speaker 1: are on the brink of being treated as scientific and
Speaker 1: political knowledge. If we do not pull back, our understanding
Speaker 1: of society might become artificial too. And this was a
Speaker 1: direct criticism of an Exios story that had used our
Speaker 1: research about trust levels in doctors and nurses, which didn't
Speaker 1: have a disclosure that it was based on synthetic respondents.
Speaker 1: And The New York Times, these two writers basically did
Speaker 1: a broadside against you.
Speaker 2: Why they wrong, I think when you actually read that story,
Speaker 2: and by the way, I read that story, I read
Speaker 2: many of the other ones that followed, because I think
Speaker 2: it's important to see how people feel about technology like this.
Speaker 2: I think a lot of people are misunderstanding what makes
Speaker 2: our technology fundamentally different. I actually agree with them. By
Speaker 2: the way, there are thousands of papers about this idea
Speaker 2: of you know as they call it, silicon sampling as
Speaker 2: we call it, you know, the anthology approach where you
Speaker 2: essentually take a profile that you have like generated, or
Speaker 2: a profile of some human being, and you give it
Speaker 2: to a language model and you say, be this person,
Speaker 2: that is not accurate. Right. We tried that approach, actually,
Speaker 2: trust me, like we when we started this company, that
Speaker 2: was our base approach, and that's when we learned that
Speaker 2: it actually doesn't reflect public opinion, and we very quickly
Speaker 2: discovered that we had to be training our own models,
Speaker 2: We had to build our own technology. We needed frontier
Speaker 2: research of our own in behavior simulation in order to
Speaker 2: actually have accuracy that delivers, right. And so I think
Speaker 2: when they talk about the issues of that technology, I agree, So.
Speaker 1: What do they misunderstand about what you were doing? In
Speaker 1: that case?
Speaker 2: The core thing to look at us is using real
Speaker 2: behavioral outcomes and a fundamentally different model architecture and a
Speaker 2: fundamentally different program. Right. We're training our own models. Our
Speaker 2: models blend LMS and traditional mL with dozens of other techniques, methods,
Speaker 2: things that we have built internally, right, And so I
Speaker 2: think if you peaked under the hood and actually saw
Speaker 2: how this worked, it's probably a lot more similar to
Speaker 2: the traditional models that you know, people like that have
Speaker 2: trusted for a long time than what people have read
Speaker 2: about this idea of silicon sampling in the papers. And
Speaker 2: so the real thing is when you're looking at real
Speaker 2: behavioral outcomes. We've shown time and time again that we
Speaker 2: can be more accurate than traditional public opinion research.
Speaker 3: And that we are. But to be very direct on that, right,
Speaker 3: it's like why was that piece written, right? I like
Speaker 3: think about it from why was that piece written? Was
Speaker 3: it written because someone has like a passion to like,
Speaker 3: you know, break apart at Teckning methodology? Absolutely not right.
Speaker 3: It's written because someone sees that as a flawed approach
Speaker 3: or something that's like, you know, bad, and that's important
Speaker 3: to recognize. And I think it's something that you know, Look,
Speaker 3: we're in New York for a reason because we're outside
Speaker 3: of the echo chamber and.
Speaker 2: We hear this.
Speaker 3: But I think, like from the perspective of how we
Speaker 3: look at this, we didn't build this company from a
Speaker 3: perspective of like exclusively AI efficiency. And you know, all
Speaker 3: these ads that we see in New York that we
Speaker 3: think are despicable around like not hiring humans in those
Speaker 3: sorts of areas, It's not the approach that I would
Speaker 3: ever approach that I would ever think about, right. We
Speaker 3: think about this in perspective of We'll go to someone
Speaker 3: who's been a marketer for the last two decades, last
Speaker 3: two decades, three decades. They might have thirty different areas
Speaker 3: and like ten different managers that tell them what they
Speaker 3: can and what they cannot put out, and people within
Speaker 3: organizations for the last twenty some years have been looking
Speaker 3: for an ability, for something else to give them the
Speaker 3: confidence to take swings. And that's the way we think
Speaker 3: about it. Right, you take something that nobody's ever launches
Speaker 3: an now before, how do you go to your CEO
Speaker 3: and say, let's put five million dollars on this instead
Speaker 3: of you know, paying some random athlete who you know.
Speaker 3: Ex Soap company just put on something and it did
Speaker 3: well for them, So why shouldn't we do the same thing.
Speaker 3: You're going to get turned down, you'll get fired, you
Speaker 3: won't have that right. We give people numbers that are
Speaker 3: more accurate than anything else on the globe, that we've
Speaker 3: proven to be more accurate than survey research, so that
Speaker 3: they can take those big swings, so that they can
Speaker 3: make those actions, so that they don't have paralysis inside
Speaker 3: of businesses waiting for like ten other people to agree
Speaker 3: with their decisioning. We give people quantifiable numbers and reasons
Speaker 3: why they're right if they are, and reasons why they're
Speaker 3: wrong if they're wrong. And that's what our goal is.
Speaker 1: Final question to you both. You met in high school,
Speaker 1: you became best friends, more or less love at first sight.
Speaker 1: Now you live together, you have a billion dollar startup together.
Speaker 3: Where's this gone?
Speaker 1: Well, do you have any fear that in enormous success,
Speaker 1: this room maybe could come between you?
Speaker 2: No, not announced.
Speaker 3: I think too much money and effort has gone to
Speaker 3: nurturing unremarkable talent, and we have a very heavy performance culture.
Speaker 3: I think being able to be our age and be
Speaker 3: able to work every single day with people that we
Speaker 3: love and we care about on the thing that we
Speaker 3: think is the most important technology in the world is
Speaker 3: something that is more unifying that you can ever imagine.
Speaker 3: And I could be more grateful to be able to
Speaker 3: work alongside.
Speaker 2: You, much more eloquent than me. I love you. He's
Speaker 2: a legend.
Speaker 1: Yeah.
Speaker 3: No, I mean look, I think people who say don't
Speaker 3: do business with friends need better friends. You know.
Speaker 1: That's what it is. There, You go, mat Cam, thank
Speaker 1: you so much.
Speaker 3: Thank you so much for having us. It's great chat.
Speaker 1: For tex staff iMOS Voloshin. This episode was produced by
Speaker 1: Eliza Dennis. It was executive produced by me and Julian
Speaker 1: Nutter for Kaleidoscope and Katria Norvel for iHeart Podcasts. Jack
Speaker 1: Insley mixed this episode and Kyle Murdoch wrote our theme song.