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SPEAKER_03: Hey, everybody. Welcome to another episode of this week in startups. I'm your host, Jason Calacanis. And today on the program, a founder I met when I was starting my angel investing career. His name is Andrew Farah, and he is the CEO and co-founder of a company called density. And you can go visit their website at density.io. Oh, somewhere around six years ago, while I was hosting the launch festival here in San Francisco, a festival where I made it free for 10,000 people to come and for 50 companies to come. Uh, pitch on stage, all free for everybody. The sponsors, I flipped the bill along with myself, uh, and Andrew pitched and he had a great idea using technology to know the number of people in a location. And here we are six years later in the age of the pandemic. And the idea of knowing where everybody is and how many people are in a location is critically important.

SPEAKER_01: And Andrew has been one of the, the great delights in my career as an angel investor, having found him when he was somewhere in upstate New York, where I always forget if it's Syracuse or Albany, somewhere upstate New York. It's all the same to us, Brooklyn boys. It's up, it's up there three hours north of us. And, uh, and, uh, I watched him come to the big leagues here in San Francisco, hire a team with alumni from places like Apple and watched him run the table, raising money from the greatest venture capitalists in the industry, uh, from up and comers like Mark Suster at upfront, who was just on the podcast, uh, to Connor Perkins, uh, and Cyan Bannister over at the founder's fund, uh, Peter Thiel's famous fund. So, uh, welcome to the program, Andrew Farah, six years later. How are you doing, brother? I'm doing well. Glad to be here.

SPEAKER_09: A long, strange trip it's been, uh, for you and I, uh, going back and we'll, we'll, we'll throw the video in here. So people who are watching on the YouTube or the video podcast, well, we'll put it a picture in a picture. I'll create extra work for the, for the team over here. But, uh, tell us about the original vision and then how it's evolved to today.

SPEAKER_12: Sure. Um, when we started, we wanted to know how busy our favorite coffee shop was. Um, it was really irritating. So you're mentioning upstate New York. Um, if you've ever been to upstate New York, which, you know, maybe you Brooklyn boys never get up, up north.

SPEAKER_14: No, no, we used to summer in the Catskills. Au contraire. Oh, okay.

SPEAKER_15: We're up there all the time. Skiing in Wyndham, going to Woodstock. Yes, we would make many trips a year.

SPEAKER_12: Well, should you, should you ever choose to winter in upstate New York, especially in Syracuse, it can get extremely cold. And, uh, we ran a design consultancy and, um, we would do like web, web application development and product development. And we did that out of school. And, um, about five blocks away was our favorite coffee shop, a place called Cafe Kubal.

SPEAKER_17: And we would always hit a line and, um, we would have walked five blocks through negative 20 with the windchill and two and a half feet of snow. And it just seemed kind of ridiculous that, uh, there was an API for the weather, but there wasn't an API for how busy a place was.

SPEAKER_12: And, um, I, I think we, we assumed it was going to be a weekend project. I mean, we were pretty sure it was going to be a weekend project, like 48 hours, a little bit of a hackathon. And then, you know, on Monday, we'd be able to ship something, build the software and figure out how many humans were in line. And, um, I can say six and a half years later, it was definitely not a weekend project.

SPEAKER_22: It could have been a long weekend, a six year long weekend. Uh, but that original product, uh, the, the original mission was there, people counting. Uh, but you've changed the hardware multiple times. When it started, it was collecting, uh, people's phones, pinging the wifi in the location. Uh, shortly thereafter, you went to, um, breaking, and there were some privacy concerns around that. Active, active, active, active infrared. Yep. Active infrared, where we, uh, set it up here in, uh, San Francisco at Phil's, I believe on Van Ness. And when you cross the threshold of the door, you would break the, the, the lidar radar. I guess it was a radar.

SPEAKER_12: Active infrared, active infrared looks, it looks a lot like a Roomba. So you're looking at essentially just doing distance sensing. Um, it turns out that like human behavior is extraordinarily hard to make sense of.

SPEAKER_17: And, um, the, the, the, the little active infrared sensors that we were using that were typically for, uh, distance, they just created all sorts of noise. So lines would form and people would linger and they'd bring bags and stuff. And you just very quickly realized how inaccurate the sensing systems, uh, of the present day were. And MAC address tracking was just as, it turns out just as inaccurate. It was maybe 65, 70% accurate. Uh, lots of issues with data, data consistency and quality.

SPEAKER_01: And the MAC address, of course, is the address on your phone. Uh, and there were security issues and also privacy issues. So where did you wind up with all that technology? How many years did that take and what's it like as a founder taking the previous technology and thousands of hours of work put into it collectively by your team, perhaps even upwards of 10,000 hours and throwing it all the way to move to a better platform?

SPEAKER_11: Well, I think, um, you may hear, um, you may hear my newborn, uh, in the background. Oh, it's great. And I apologize. No, not that you apologize for.

SPEAKER_12: Um, you, you know, I think, um, if you enjoy the people that you work with enough, uh, it doesn't really matter how hard the problem is. In fact, the, the, how hard the problem is, is actually the harder it is, is sort of a function of how, how long you're willing to work on it with those folks. Um, and we were really fortunate to have a bunch of people that were in it for like a, kind of the long haul. They were interested in figuring out how to work on hard problems over a long period of time. Um, I can tell you, um, I actually, remember when we first saw what, um, our entry sensor could do, like the technology of depth sensing above a point of entry, which is the primary product that we sold leading up until our more recent announcement this fall. Um, and I think it was maybe a 15 minute conversation. We decided to kill, it was called a one. We decided to kill the active infrared, uh, system, um, because it was so compelling. And, and you've, I think you've seen the depth data, but yeah, the depth data was

SPEAKER_17: it was so compelling. And it was so obvious that we'd be able to get accurate count from, from this type of a technology that we, we had to invest in it.

SPEAKER_01: So that is a little box. Looks like a Mac mini goes over a doorway. Uh, and when it uses computer vision to look down, you get the depth. You can see a human being silhouette. You could see two, if they were side by side, or if they happen to be really close to each other. Um, and you'd be able to count in and out of a room. And then have of course the net number, whether it's a cafe or a lunchroom at a, a big campus of a technology company or a conference room.

SPEAKER_03: And that basically started the company off on getting customers who were interested in knowing space utilization. That was really the tip of the spear. It wasn't cafe lines. It was people who had real estate portfolios. Wasn't it?

SPEAKER_12: Yeah. That that's definitely where sort of our customer base, um, began to, to kind of grow and evolve. But before we, I, you know, maybe to revisit the question of like, why do this for as long as we, we did. Um, when we were, I remember very early on, we're talking the first three, four months of this, like project with Mac address tracking. We were in upstate New York. Our apartment was some of the co-founders. I have, I have five co-founders. Oh, wow. All six of us still work at density six and a half, seven years later. Incredible. Um, uh, they very much are gluttons for punishment. Yeah. So our apartment was above a bar, uh, called the saltine warrior. Um, and I remember it was really late one night, it was about 11 o'clock or so. And, uh, Brian, who now runs engineering, um, was trying to get this Mac address tracking unit to work. And just for those who are unfamiliar with how Mac address tracking works, I just want to quickly explain how that system works. So each of your, your phone, your smartphone has what's called a Mac address or a wifi address, and it allows you to do, uh, when you get home and it automatically connects to your home network, it's, it's using this unique identifier to provide internet to your phone.

SPEAKER_17: Now, because it's unique, you can use that as a proxy for count, for, for counting people. So, um, count the number of phones and you back into the number of folks that are probably in the space. Anyway, uh, we don't use that today, but back then we were testing out a Mac address tracking unit with a little raspberry pie. So it's 11 o'clock at night. Brian's trying to figure out how to solve this problem. It's taken a while to figure out how to get Mac addresses into his computer onto his terminal. And, um, he finally gets the thing to work and his terminal, uh, starts spitting out. What we're expecting to see is the five or six co-founders phones sitting around. Yeah. Um, and, uh, what comes out instead is, is 115 unique Mac IDs. What? And Brian like throws his laptop down and he's like, this thing's broken. I don't know what's going on. Um, and he sits there quietly crosses his arms and doesn't really speak for a minute or two until all of a sudden this smile crawls across his face. Yeah. And he goes, oh my God, it's the bar. Yeah. We had accidentally counted, uh, uh, a hundred plus folks five floors down.

SPEAKER_12: And, uh, I remember the conversation that followed was probably one of the most consequential discussions I've ever had short of meeting my wife.

SPEAKER_17: Um, which was, um, essentially if you, if you could figure out, if you can remotely know how many people were in a bar without having to be there, could you do that for, uh, a cafe or a grocery store?

SPEAKER_30: Right. And we think the answer is objectively, yes, of course you could.

SPEAKER_17: Yeah. And if you could do it for a bar, a cafe and a grocery store, then why not for an entire city? Um, and if, if, if, if, if New York, if, if New York only knew how it was used in real time and historically with extraordinary accuracy, if you could just snap your fingers in New York, all of a sudden knew how it was used. Would it design itself differently? Of course.

SPEAKER_51: I mean, just think of transportation and store hours, everything.

SPEAKER_17: That's right. And, and it, we, we think that the answer is objectively. Yes. Like, of course there's value in understanding how a city is used. So, so then the question is, uh, well, if it's, if it's, if it's useful for New York, whatever's useful for New York is probably useful for San Francisco and probably used for, for Den, useful for Denver and Tokyo and Paris and Berlin and every major city in the world. And so the question really isn't, um, uh, one of, of technology. It's, it's, it's one of distribution. How do you get an intelligent device into every relevant room in the world? Because if you do, you earn the right to remake it. Yeah. And that's, that's an extremely exciting objective. It just, you know, it was like pretty clear. It was going to take 20 years or 15 to 20 years to do that.

SPEAKER_57: And corporate real estate happened to be this excellent mechanism of distributing, selling once and distributing thousands of times.

SPEAKER_51: Yeah. What a great, uh, beachhead market people who have a lot at stake.

SPEAKER_01: Uh, when we get back to this quick break, we'll talk a little bit about the fact that, uh, Kleiner Perkins put in $50 million or Kleiner Perkins led our $51 million series. And, uh, the latest product. We're going to show the latest product.

SPEAKER_58: So if you're watching on YouTube, you'll see it. If not, we'll sportscast it and explain it to you when we get back on this week in service.

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SPEAKER_58: Uh, and just amazing to see how far the companies come. It's a really great success story, uh, in New York, my hometown. Welcome back to this week in startups. Andrew Farah is here. He is the CEO of density.io. Uh, we were lucky enough to invest. I think we're, I guess we were some of the first investors. Yeah. That's right. Seed investors. When you were up in Syracuse, I guess we were the first major investors.

SPEAKER_11: Uh, so I believe you were the, the second, the, the third check in.

SPEAKER_17: It was Billy Draper. Oh, great. Uh, very early on. And then Ludlow Ventures, Jonathan trees. Yes. And then, um, and then that's how, uh, I think we, we got introduced to, to you.

SPEAKER_58: Ah, so I owe Ludlow, man. I owe Jonathan again. We've traded a lot of deals. I got us on Jonathan over at Ludlow, uh, a little bottle of something. Um, so we have, uh, and I'm on the board of the company.

SPEAKER_01: So I've been, it's been great to watch this upfront and watch you develop as an executive and build this team, um, without getting into too much detail. Let's just say corporations who have space have embraced the technology. I'm not sure which companies, uh, you've been public about or are on the website at the current moment, but, uh, to the extent you can talk about who's using it or how they're using it with this, what I would call the 3.0 product. I'm not sure what you call it internally, but the over door sensor. What's who, who's using that and what is the result of deploying that bin?

SPEAKER_11: Well, um, it's probably important to clarify pre pandemic or, uh, during pandemic.

SPEAKER_12: Sure. So pre pandemic, you know, we would work, we work with fortune 10, uh, uh, fortune 50, fortune 100. Um, and then we have a whole like long tail of folks that are, you know, four or 5,000 employees. So, um, uh, smaller corporations. We, we kind of, if you have physical space of any substantial size, um, uh, we tend to help solve the problems that you encounter.

SPEAKER_32: Hundreds of thousands of square feet, millions, tens of millions. This is who really gets benefit from it.

SPEAKER_12: That's correct. I mean, we even work with one, we, we have two customers that have over 400 million square feet of space individually. Um, I mean, Lord, at this point, you know, the people that we work with, with our managing over, I mean, billions of square feet of space. Um, amazing.

SPEAKER_17: So these, these organizations, you have to imagine, imagine you're a company and we'll talk about what happened after the pandemic in a second. But imagine you Jason run, uh, a multi region, um, 75 million square foot of, of, of, of space portfolio offices, uh, mixed use, uh, retail, but it's like one company, a brand name company across 75 million square feet of space is an enormous amount of space. Um, and you're operating offices across say a dozen countries. All of those buildings were filled, designed, leased and otherwise used without knowing, uh, performance. So, so there, there's no data. There's no hard data on how many times a particular room was used or how many times a particular open space was used or, um, how many people actually entered through one particular door as opposed to another, or how many people tailgated into a particular space, meaning they didn't badge in or authenticate in. Um, there, there just isn't hard data. And the reason there isn't hard data is because it's extraordinarily hard to figure out how many humans are inside of a space without invading privacy.

SPEAKER_01: Right. The only two ways I can think of to do it would be to have the receptionist or somebody who's an auditor walk around with a clipboard, which I understand, uh, from you, that's what some companies were doing to just audit, uh, at various points in time to figure out the utilization of a space. And then the other way is to have creepy cameras everywhere.

SPEAKER_17: That's right. So the state of the art actually, before we got into large corporate offices, um, was hiring a consultant from, uh, one of the large sort of brand agencies, um, who would come in

SPEAKER_12: and essentially do a space study. They walk around with a clipboard. You'd pay them $750,000. They'd show up four times a year for five days. They would observe behavior. They'd count the number of folks in different rooms and spaces, and then they give you a report. Um, we are an order of magnitude less expensive and an order of magnitude more valuable.

SPEAKER_17: And we end up essentially being able to scale automated sensing systems that are real time, extremely accurate.

SPEAKER_30: And without invading privacy to make sense of, uh, millions and tens of millions of square feet of space.

SPEAKER_32: And then the pandemic hits and what happens then?

SPEAKER_17: Well, so, uh, prior to the pandemic, we were being used to essentially identify where you could do lease avoidance. So, um, you know, we were working with one corporation, uh, who found across 2,300, uh, seats. So like neighborhoods of seats, they were using, um, on average, just 23% over a hundred, over a sample of say 180 days. Wow. Um, so average utilization was extremely low. Peak utilization was 43%.

SPEAKER_26: Um, they just weren't even using half of the space they had. Wow. So wasteful and, and space is typically what the second or third line item for a company.

SPEAKER_11: Yeah. So it's typically payroll than, uh, than, than your real estate portfolio. So it's extremely expensive. Facilities.

SPEAKER_17: So, uh, so you've got sort of like the space utilization problem. It's about a trillion dollars get spent every year in the U S alone on space that has nobody in it. So counting zero is just as useful as counting one. Right. Because you can figure out where there's empty space. Um, so that's pre pandemic, um, pandemic hit and, you know, we were right before COVID,

SPEAKER_12: we were on track to grow 350% quarter over quarter. Like we were, we were ramping pretty quickly. We're talking, um, uh, January, February timeframe. Like we, we were going to do rather well quarter over quarter. Tripling.

SPEAKER_17: Yeah. It's amazing. Yeah. Pandemic hit. And I mean, when, when you have a black swan, you have no idea what's going to happen. And anyone that says that they do is probably just making stuff up because, um, and so, uh, we weren't really sure what was going to occur, especially with shelter in place. And as people sort of returned home and we ended up growing instead of three, 350%, we grew 490, 92% quarter over quarter.

SPEAKER_12: And what, what had happened was, um, the industries that cared about knowing the number of folks in a space exploded. Right. So essentially anyone that was open manufacturing, distribution centers, logistics, meat processing

SPEAKER_17: plants, universities, you sort of name the physical space.

SPEAKER_23: And as people who were trying to stay open and operate, especially if you were in a central business, all of a sudden really cared about the capacity and the safety of their employees.

SPEAKER_93: So just knowing how many people are in a space in a pandemic is absolutely critical because

SPEAKER_01: you might've been told you could be at 25% capacity. In fact, a lot of the restaurants that are opening here in the Bay area, um, they're allowed to have, I think it was 25% or up to 50 people in a space or some number in San Mateo in the peninsula. And then the city had some other number. And then as it opens up, then it goes to 50% and so on and so forth. And so you just had all these new customers, uh, and the, the question I have is that, uh, does help in a pandemic to know the number of people in a space, but there's also this more pressing issue, which is the distance between people and how people are spaced out. Um, and, and how do you determine that?

SPEAKER_17: Well, so, so many companies came out and said, Hey, we're going to figure out how to, uh, tell you whether or not people are too close to one another. Yeah. There's a lot of marketing.

SPEAKER_12: There's a lot of marketing that came out. I should mention that our response to the pandemic was less. How do we figure out social distancing? And it was more, how do we help you with capacity management?

SPEAKER_17: Sure. Um, so, so we built a product called safe. Um, uh, it's really cool. It uses your existing, um, existing deployed sensors, your entry-based sensors, and it will keep track of how many people are inside the space relative to the overall capacity. When it shows on your televisions or digital displays, uh, it's safe to go in or wait.

SPEAKER_01: Um, wow. So if you had some sort of an iPad system or we work would have cameras in the lobby, your API allows them to say, Hey, we're at capacity here. Go to another floor, use another conference room, find another bathroom, find another coffee machine. Yeah.

SPEAKER_12: That's, that's exactly right. In fact, safe, um, is now, uh, deployed with, I believe a quarter of our customer base because

SPEAKER_17: it was just a software upgrade. If you had the existing density entry sensors, you could immediately turn on the ability to tell your occupants, your employees and your visitors and so forth, whether or not it was safe to go in and socially distance. Now that doesn't mean we were measuring the number of feet in between people, but we were

SPEAKER_12: able to tell you relative to the overall capacity.

SPEAKER_01: And people are pretty good at that. I mean, people know what their distance is supposed to be. We've all been trained on that. So, you know, it's, it really is up to them because it's not like the sensor is going to jump in between two people and say, you're too close.

SPEAKER_104: Like that's our job as humans.

SPEAKER_17: Yeah. I mean, self policing, I think this is also a really important argument. I think a lot of folks, you know, what would happen if a sensor could figure out the distance? Um, you know, do you have an alarm that goes off? Like, do you have a guard swoop in? Do you have like a net come out? Like I, you know, it seemed like there was a lot of marketing, um, and hadn't really thought through what you'd actually do.

SPEAKER_01: I think in the NBA bubble, they gave the journalists wristbands of some type, like Disney ones that would chirp if they were in proximity to each other. I'm not sure exactly how accurate they were, but they, they did have that, or maybe it

SPEAKER_34: was when they got close to players or something. They weren't allowed to get close to the players.

SPEAKER_12: Yeah. Yeah. They, I remember there was this really interesting interview with a reporter that said the chirping was just incessant.

SPEAKER_110: Like there was always chirping.

SPEAKER_112: Yeah.

SPEAKER_114: That was the New York times reporter who lived in the bubble and he reported on, he did like, uh, he did the daily, like the New York times daily podcast.

SPEAKER_116: It was really interesting. Yeah. He said that we got on the bus and we just chirped like mad because there's no way on the bus to social distance. Yeah, exactly. Yeah.

SPEAKER_17: And so I, I think, um, you know, I think we, um, people are either going to socially distance or they're not. Right. And the question is like, can you provide them data, better data to, um, help keep them,

SPEAKER_12: them safe and they can self police. And so an example of this was we got picked up by a, I can't mention who, but we, we got one of the customers that, uh, we expanded with is, uh, someone that does very large scale, very fast logistics and distribution. Got it. And we, we got put into their warehouses.

SPEAKER_17: And, um, the reason we got put into their warehouses was because inside their break rooms and other sort of shared spaces, they had, uh, an average of 10.5 or 10.4, uh, breaches

SPEAKER_12: of safety policy every day, meaning too many people relative to capacity inside of these

SPEAKER_17: rooms. They installed density and, um, 15 days later and a big display that would show it's safe to go in or it's, you know, wait, uh, the policy breaches dropped, um, overnight to, uh, 1.3% or, uh, 1.3 times per day. It was just, it was just this dramatic impact and all it was, was providing data back to the occupants as opposed to trying to police it yourself. Yeah.

SPEAKER_01: If you measure it, you can manage it. And there's always gonna be one jokester who's like, ah, it's filled up. I'm just getting a quick cup of coffee and they're gonna go rogue and just run in and sound the alarm off or make the screen flash red. When we get back from this quick break, we're gonna see the new version.

SPEAKER_22: Sure. What is the new version called? Open area. Open area. The, what I will call the 4.0. I think this is your fourth swing at bat.

SPEAKER_58: This is your fourth iteration. Am I correct? If I count them? Yeah. I think that's fair. I think that's fair.

SPEAKER_126: The light, not the lidar, the.

SPEAKER_125: Active infrared. Active infrared.

SPEAKER_126: And then we had depth and now we have this one.

SPEAKER_107: And now we have, now we have, yeah, that's right.

SPEAKER_01: And the code, and the code word for this or the product name is?

SPEAKER_107: Uh, so there's, there were two, there's a stupid one, um, which I, I, I loved named Panda.

SPEAKER_30: Panda. And then there was an actual code name internally, which I'll explain when we get back. Um, uh, which is Obi-Wan.

SPEAKER_131: Obi-Wan. Okay. Here we go.

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SPEAKER_10: Obi-Wan has taught you well. Here we go. We're going to see the Obi-Wan. Let's just get right to it. Enough teasing. Congratulations on the business, by the way. Congratulations on all the success. And this to me is the mind blowing moment for everybody.

SPEAKER_03: You know, as an angel investor, when you get to see a company really hit its stride, fundraising,

SPEAKER_09: customer delight, great team members, man, there is no greater joy professionally as an investor. And here is one of those great moments.

SPEAKER_137: So Obi-Wan us away. All right. So let me pull this up.

SPEAKER_10: Here we go.

SPEAKER_114: This is super impressive, by the way. I've seen it already, obviously. But for those of you not watching. Okay.

SPEAKER_141: We're introducing open area. Obi-Wan.

SPEAKER_57: So I'll explain the Obi-Wan reason.

SPEAKER_17: It actually is not a Star Wars reference, although it never hurts to have a Star Wars reference. Yes. So we introduced this open area sensor that's aerially deployed. It's a radar based system.

SPEAKER_12: And I thought that it'd be kind of cool to actually just show you a live demo where we can show actual utilization of space in real time without having to be able to do just entry count, but actual, actual open area measurement.

SPEAKER_32: And so what we're seeing looks like the Nest smoke detector, like the Nest Aware. It's just a little square box. Beautifully designed. Looks like an Apple product. That's right.

SPEAKER_113: Stuck on the ceiling.

SPEAKER_12: That's right. It's stuck on the ceiling. Yeah. And once it's mounted on the ceiling, you can start to understand sort of open space. So let me actually jump into a, I'll show you what it sees.

SPEAKER_17: So a lot of times people talk about like the output, but I think if you ever deploy in any type of system, it's like critically important that you actually ask what, what is this thing see?

SPEAKER_12: Because most systems are cameras. So I want to show you what, what this actually looks like. John, this is John up here.

SPEAKER_09: Um, this is a person sitting at a, on a stool at a, you know, high top desk with his laptop open in office space.

SPEAKER_12: That's right. And you can see beneath the, the ground truth camera, this, uh, sort of dark three dimensional, uh, view of, of dots. It looks like Tron.

SPEAKER_51: Basically.

SPEAKER_03: It looks like a grid from, uh, Tron or when you were fighting on the Millennium Falcon shooting TIE fighters. It looks like the grid in which you would try to align the TIE fighter into 3d space.

SPEAKER_12: So as much and much and much like, uh, the, the, the much like Tron, this is all interactive, right?

SPEAKER_26: So I'm, I'm actually clicking and, uh, dragging around in 3d space.

SPEAKER_93: And we see a person has walked up, uh, to the other person and they are each represented by dots, uh, that are flicking in and out of space.

SPEAKER_03: And it knows those are humans as opposed to plants or monitors of computers.

SPEAKER_153: How?

SPEAKER_12: So, um, the cool thing with radar is that it's not only, um, uh, three, it's not only anonymous.

SPEAKER_17: Um, and it's not only extremely accurate because of, uh, the, the, the sheer number of measurements that you get.

SPEAKER_12: You can actually see there are a lot of measurements of, uh, what, what are actually movement.

SPEAKER_17: So as John moves through the scene, you're able to picture, we're able to pick up on like very small movement and that very small movement actually creates the outline of his body. You see, you see this sort of seated motion. Yes. Or perspective. And then this separate color, there's two colors of dots, um, indicate that there are multiple people inside the scene. This is, it's just never been possible before. And it's never been possible because a lot of this technology was, uh, it just wasn't available at this type of scale. Usually you see this in self-driving. So, um, that's what the device sees. Let me show you what that means for an end customer.

SPEAKER_159: So here we have an overhead draft, like CAD drawing of the office space, which shows all the different offices, conference rooms.

SPEAKER_79: Like you would see somebody unravel in a blueprint. That's right.

SPEAKER_12: This is a floor plan. It is a blueprint.

SPEAKER_17: Um, and, uh, customers can upload a floor plan, um, to understand. So as, as people return to offices and as people start to think about how people are, um, using buildings and what portions of buildings they actually need. It's really hard to figure out whether or not people are too close together. You know, if they're seated too close together or whether or not the number of folks inside of a conference room is, is exceeding the capacity. And also just the overall flow and heat map of people as they move through. So we designed a real time floor plan. And what you're looking at is the, is a circle with a bunch of gray grids. This gray grid is represented representative of what the sensor is capable of measuring.

SPEAKER_160: The higher it's installed, the larger it's field of view, which I'm going to show you in just a second.

SPEAKER_51: Like the aperture of a camera. It's if it's low, it's got a narrow aperture.

SPEAKER_10: If it's high, it's got a wide aperture, which is how satellites work, right? The higher the satellite, the more I can cover, but the more time it takes in terms of distance from the earth to the satellite. Yeah, that's exactly right.

SPEAKER_113: Yeah. So what's cool is all of this is editable, meaning you can essentially place a device onto your floor plan.

SPEAKER_17: And those blue dots, there are these little blue flickering dots that you'll see. Yeah. That's me.

SPEAKER_12: So I'm, I'm actually showing you live and you can see me where I'm moving. I've installed the device above my head and what's really cool is I can create digital spaces.

SPEAKER_23: Got it.

SPEAKER_32: So you can pick little zones that if somebody were to go into this zone, you would know and it could trigger something.

SPEAKER_01: So if somebody goes into this area. Yes. This area is a sofa. This area is a cubby. You know, we have these open floor plans. Sometimes you got like a little bean bag area or video game area, whatever. So you might want to know that two people are on those bean bags specifically. That's right.

SPEAKER_171: And so like if I move backwards, I'm sitting on a couch right now. Yep.

SPEAKER_12: Kind of tethered to my laptop, but you can actually see that we fill up that particular digital asset. And if I decide to move to a chair, it should light up.

SPEAKER_173: I can't really see my screen. Yeah, it is.

Chamath Palihapitiya: We, we basically watch the dots walk over and sit in the chair. Yeah.

SPEAKER_57: No, this is, no, this may be, this may seem silly or straightforward, but the reality is this has just never been possible before.

SPEAKER_17: And the cool thing is that if we zoom out, you can actually see, you see the zebra and donkey here. These are just procedurally generated. That's the amount of time that each of these spaces were used.

SPEAKER_180: So if I go, if I go back to the couch, you'll actually see, um, the zebra count went up a couple of seconds.

SPEAKER_22: That's madness. So literally the, you now have gotten to the point where you've built a tool where anybody, the receptionist, somebody who works in facilities can map the space themselves. And then report back how many seconds this phone booth was used. That's exactly right. And you could obviously do that.

SPEAKER_01: You could say, what percentage of the time was this phone booth used during the day? Uh, and then you could also say how many unique people were in there.

SPEAKER_22: I'm assuming or no, would you know they're unique? I guess you don't have anybody tagged.

SPEAKER_26: Well, that that's where this starts to get a really interesting when you start to think about coverage.

SPEAKER_12: So if I add another sensor, um, we are going to go find some more folks. Got it. So you can see that there's, these are, um, this is actual, actual data, but we're loading it into the system. There's actually not like I'm in my apartment. So this is not a real floor plan from where I'm sitting, but, um, should be emblematic of, um, what happened. So I can just sort of populate as many, uh, sensors and this is actually, uh, this particular install, um, is a 12 foot as opposed to 10 foot.

SPEAKER_139: Right.

SPEAKER_93: And you see the circle, the radius of the sensor grows, uh, with the height of the ceiling.

SPEAKER_10: So you can really start to know, you know, in a open floor plan with, you know, hotel style seating or a conference room, which seats were being used in the conference room. You could actually figure that out.

SPEAKER_12: Yes. So, um, and you, you just hit the nail on the head. So let's say I want to understand this IDF closet. Um, you can see this, uh, green border. This, this is like now a room.

SPEAKER_17: Now, if I were to, to say, I, I want to actually want to measure the, the conference room over here, you can see how that green border changes to a filled space. Um, the, the magic of this is that this, because this is all software, um, I know that this room is occupied. And we can create spaces inside spaces.

SPEAKER_01: Oh, here we go. So here you go. You're saying how many people are at the table? How many people are at the couch in the back of the room? Yes. Yeah. Yeah. And I'm assuming you could set off alarm. So if somebody goes into that closet, if that's an, uh, a special industrial closet. Yes. You want to know if somebody went into that closet, if that's the elevator closet, you know, like there's always that elevator closet that you're not supposed to go into. Sure. Or if that's like the fire stairwell. Now, you know, somebody's in the stairwell. So this now is a security and an audit log of where people were and if they should have been in those rooms.

SPEAKER_12: That's, that's exactly right. In fact, the system natively supports alerting based on capacity. So if you wanted to say, I don't want anyone to show up here, or if you wanted to say, Hey, I'm not okay with more than two people in this particular room, we can push out notifications in real time to security or safety staff or whatever else it might be.

SPEAKER_17: But the reality is that most of this, when you're talking 75 million square feet, 150 million square feet of space, uh, you know, 415 million square feet of space, like you're not going to be looking at a live view.

SPEAKER_12: You're going to take all this and it's going to get pulled into analytics so that you can understand the relative safety across a really large portfolio. Amazing.

SPEAKER_93: Um, so, you know, if you had, if you were a large tech company with a campus or you were a library at a great university or dormitories, you could put this into dorm rooms where people are sleeping and having personal space getting changed, but it's not a camera.

SPEAKER_62: Yeah. That's right.

SPEAKER_03: So you could actually put this in every dorm room in a college and know there are 20 people in a dorm room when the capacity of that really should be no more than four.

SPEAKER_62: And so that is a, I mean, I know it's, I don't want to get, uh, too, uh, uh, explicit here, but you, uh, this is a situation that does occur where you literally have security guards and TAs or whatever they call those people who run in the, in the college dorms to make sure that there isn't some crazy party for safety reasons.

SPEAKER_17: That that's, that's right. Yeah. I don't know if stopping parties will necessarily be our core, uh, customer base, but you don't want to be a narc.

David Friedberg: Yes.

SPEAKER_17: But what you're, but what you're describing is the cool and interesting creativity of being able to understand how space is used once, once you have the baseline fundamental platform for understanding how space is used. You can do all sorts of cool stuff. So for instance, um, this system is designed unlike a camera, uh, to be able to do contiguous measurement. Meaning, um, if for those who can't see it, this grid actually becomes seamless between fields of view.

SPEAKER_26: Wow.

SPEAKER_01: So it overlaps a bit in these circles and then now you've just seen the whole thing lighting up. That's right. When we get back on this break, people have talked about doing this with cameras. Cameras are ubiquitous. They're, they're, they're cheap. I want to know the argument and what you hear from your customers about cameras in workspaces and that big controversy when we get back at this week in startups.

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SPEAKER_58: Andrew Farah from density.io is here. Not the company's density, but you can go visit at density.io and see these incredible, uh, Obi-Wan, uh, incredible Obi-Wan. Mm. Um. Tell that story.

SPEAKER_01: Wow. So you talk to, oh, you didn't tell the Obi-Wan story, but I also want to know about like, when you talk to your customers, it's gotta be somebody in every IT department of facilities. Like why don't we just throw up some drop cams and you know, we already have these like security cameras in the hallway. So why don't we just put them everywhere and then we'll count on there. And there's gotta be some computer vision software.

SPEAKER_03: What is the reality of that? Are people doing that now? You know, just counting people with computer vision on cameras?

SPEAKER_11: Yeah. Yeah. Um, yeah, so the way the cameras, uh, count people is by looking for changes in color.

SPEAKER_12: Um, it's a, it's a, depends on how sophisticated the camera is also depends on how sophisticated the software company is. But the way that most of these systems work is it's looking for, um, you know, the changes in pixel color as someone moves contiguous color changes.

SPEAKER_17: Um, and that allows someone to draw a box around or do computer vision to detect, uh, do object recognition as like this black shirt moves past a, um, a brown floor. Now, when you don't have high enough contrast between those two colors, you end up with, uh, data quality issues. Um, in fact, uh, there's a technology called stereoscopic vision, which is how our eyes work. So two cameras that are sort of set next to one another. And as someone walks beneath, you can detect, you can estimate depth. You don't get actual depth. You, you sort of see, um, uh, as a person moves from one field of view to the next. The, the problem is that if you stop moving, uh, you don't get a depth reading because they're just flat images. So all cameras are two dimensional. Most cameras are two dimensional. So you've got an X and a Y, which is why color matters so much. Um, so you have all these issues and then you deploy them and you get pushback from people who they're observing. And so my, my opinion on cameras is that they're not, I don't think cameras are inherently bad. I just think that there are places where there's a reasonable expectation of privacy. And when you're in a place where there's a reasonable expectation of privacy, um, there ought to be a system that, that is, uh, is not continuously surveilling me. Hmm. And, um, you know, that's why, why we care about anonymity. We, we also think that the market for, uh, anonymous products is, uh, substantially larger than there's essentially like blind spots to buildings that cameras can't go into. Yeah. Because as soon as you try to scale them, you run into privacy issues.

SPEAKER_22: All right. Here's an edge case. Bathrooms. Are people going to deploy this in a bathroom to know the capacity of a bathroom? That's creepy. Seems unnecessary, but I bet you there's a group of people in the facilities department who would really like to know how utilized the bathrooms are.

SPEAKER_113: So, so we actually have a lot of deployments of our entry sensor outside bathrooms. Um, so you know, the in and out counting.

SPEAKER_17: Yeah. And the reason for that is, um, so in the U S there's, there's 11 billion roughly, there's 10.9 billion square feet of leased or owned occupied office space, just office space. 41% of it is vacant, but paid for. And, and I mean like people show up to the floor, but don't actually use portions of the space kind of vacant, not vacant as in there isn't an occupant. That 41% gets cleaned. Sometimes multiple times a day, including bathrooms and other spaces. And so a lot of the industry is starting to think about usage based cleaning usage based service. That's a no brainer.

SPEAKER_22: If this bathroom, I mean, we've all had this experience, uh, in life where. Maybe the bathroom on floor nine, the legal department, which has so much huge offices and everybody's office got a conference and their bathrooms are never used. And then floor eight is like, whatever. That's the typing pool back in the day. And it's packed with people. And that bathroom is overused. So people go find the less used bathroom, but for the people who have to clean the bathrooms. Or clean the offices, it would be great to know if somebody has been in that office in the last five days, if they have it, why clean it?

SPEAKER_121: It's like a hotel room being turned over for no reason. It's complete waste.

SPEAKER_17: These examples that you're referring to, like these are the types of examples that sound almost like boring. Yeah. But the reality is like, we have built, all buildings have been built without knowing how they get used for millennia. Like we've been building buildings based on an architect's best guess of 250 square feet per head or 150 square feet per head or whatever sort of arbitrary thing that worked with a previous client. Um, since built, since the dawn of buildings. And as long as humans continue to build buildings, continue to use space in any material capacity, there is no future in which humans don't eventually figure out how all of the space is used. It's just a question of whether or not it's being done by a, uh, you know, a lot of different technologies or by a single platform. Hmm.

SPEAKER_01: So this would allow, uh, the cleaning crew, or just to use like the co-working space example in a, we work where you have, you know, whatever, a 20 floor or 12 floor. We work with 12 kitchens. You could just look at which kitchen has had the most number of people in it.

SPEAKER_22: And the least number of visits, the longest time since a visit from the cleaning crew or the restocking crew. Yep. And boom. Now they're going to be what? 30, 40% of efficient. I mean, that is just going to unlock and save so much money. Yeah.

SPEAKER_12: I mean, the, the efficiencies gained from just, just simply understanding what doesn't get used, um, so that you can redeploy assets is, um, uh, is really, I mean, you see it immediately. Um, yeah. I also, I also wanted to mention, like, there are really two really important user groups.

SPEAKER_17: There's the people that manage the space, which we've been talking a lot about, but then there's the occupants and the occupants want to know whether or not a room is available. They want to know whether or not, you know, a desk is available. They want to know whether or not the, the, the coffee shop has a line, which is literally the, the founding problem for us. I mean, most of this is really just an exercise in laziness. You know, we, we've just been trying to solve our own lazy problem for the last seven years. And I think we're finally circling back to it.

SPEAKER_106: Well, lazy is also efficient.

SPEAKER_01: So when you think about it, you know, what is the most efficient way to tell people when to go to lunch?

SPEAKER_03: And I won't say which company it is, but I know that you have a high profile companies, uh, cafeteria or cafeterias. Yeah.

SPEAKER_22: And in literally their first deployment, I believe, if I remember correctly, because it was a while ago, was just let's figure out when to tell people to go to and how long the line is going to be.

SPEAKER_62: Because we have so many employees that if a hundred employees wait 20 or 30 or 40 minutes, God forbid on a line, that's costing us real money.

SPEAKER_153: We'd rather they wait till 115 and wait in a five minute queue than a 30 minute queue. Correct?

SPEAKER_17: Yeah. Yeah. There's also this very interesting. So there's, there's always the efficiency question, like, how are we making this more efficient? How are we making people more productive? But there's actually a very, um, uh, surprising other side to this, which is, um, not just cost savings, but serendipity. So most, um, modern technology companies have designed their offices based on, um, uh, Collisions. Collisions. Exactly. It was, it was a concept that was actually, I think brought about, uh, during the Bell Labs days. And, um, Apple's, uh, headquarters does the same thing. They essentially position communal areas like cafeterias, bathrooms, and other things, um, far enough away from one another. So that you have to collide with folks that are not on your direct team. And so cafeterias are actually not just designed to feed people. They're designed to cross pollinate ideas. Wow. There's a great book called, uh, um, how innovation works. Um, and a, and a companion that's called, uh, where great, where good ideas come from by Stephen Johnson. Both of those books talk a lot about this sort of cross pollination, serendipitous idea generation. And so I would say when, at least as it pertains to measurement, it's not just about like how quickly can we get people fed and back to work. It's also like, are we actually like, are there dead zones that we can read back, like load balance, uh, to get more folks in so that there's, uh, higher levels of creativity.

SPEAKER_160: Now that's a very abstract concept, but, um, without measurement, you can't manage. You know what I mean? Yeah. Like just get a baseline at the very least.

SPEAKER_03: What, what does it cost for companies to deploy this like per sensor per square feet?

SPEAKER_01: And then how do they make that justification internally when the sort of rubber hits the road? Do people want to use this forever or are they using it to figure out, Hey, how do I get rid of half my space or optimize my space and then rip it all out?

SPEAKER_238: How are people thinking about this today in 2020? Yeah.

SPEAKER_17: So, um, our pricing is public. It's all on the site. Um, the, the newest sensor is, uh, it's $199 per device per year for access to the data. And there's a, a one-time fee for the device itself is $399. Um, certainly cheap. Yeah. The intent, I mean, the intent is, is to provide a system that can be used, uh, you know, ubiquitously for, for these, these customers. And, um, you know, I, I think that our, our intent is to make it financially irresponsible, not to deploy some type of measurement system. Even if that's not us, we want to encourage folks, you know, if you're a manufacturing line, if you're a university, I mean, we, we onboarded something like 20 universities over the summer. Largely as they were returning to students were returning to university, um, specifically around the safe product. And so whether you're deploying entry or you're deploying open area, um, we just think that the world is the pandemic has, has accelerated the timeline of, um, modern infrastructure.

SPEAKER_12: So, um, most of the sales that we saw after the pandemic hit were multi-year deals.

SPEAKER_17: Many of them were multi-year deals, five year terms. The pandemic will be over in five years, but the pandemic has accelerated sort of this, these questions about how much space am I actually using?

SPEAKER_12: And I, I think that, um, not only is that really exciting for, uh, it's great for us, but, but more importantly, it's, it's really interesting at a, um, uh, at sort of, uh, a global footprint level.

SPEAKER_17: So we had a customer who deployed a, it was $25,000 worth of sensors into their offices. Nothing. Nothing. They, they were about to, this is a very big company, their fortune, fortune 100, I think. And, um, really small sort of deployment. They, they were looking at a lease that they were going to open adjacent to their office. They had deployed these sensors in, and it was a million dollars a year on a three-year term. Something like a week or two before they signed the lease, this three year, uh, you know, $3 million lease, they noticed that their utilization was 37% in their existing office. So global head of real estate goes up to the COO and says, Hey, you know, we've got a ton of space. All these folks that we're about to hire, we can just put them in the office. We already have. Yeah. We should walk. So they walk from the deal and I get a phone call. It's Andrew.

SPEAKER_26: Andrew, you saved us $3 million on a 25,000. Save them 99%.

SPEAKER_12: And we want to do a national rollout and then we want to do a global rollout. Um, it's become abundantly clear how blind we are to how space is used.

SPEAKER_17: And the thing that I loved about that wasn't necessarily the account growth, which is awesome. Like I love that the account expanded. The thing that's remarkable about that is if you follow it to its logical conclusion, that customer didn't take that lease, which meant that lease remained on the market for somebody else to take. Yes. Which means eventually down the line, a building didn't get built. Yes. And if you can do that at, you know, a thousand X the scale, we're talking about actually having an impact on, uh, The environment. Yeah. Like concrete and carbon emissions and, uh, climate. So it was really cool.

SPEAKER_03: Well, you, you affect climate. And then also, if you just think about, uh, utilization in space, maybe we have too much corporate space.

SPEAKER_01: And in a city like San Francisco, maybe some of it will be redeployed for people to have apartments and residential.

SPEAKER_22: Or maybe people will get to have a lower price for residential, which is more efficiency, which means they can, you know, people can invest in themselves, a career, their families, et cetera, or more people can live closer to a city.

SPEAKER_01: Yeah. And this is also virtuous. The efficiency will have second and third order effects that we can't even imagine that, you know, the first order effect of, uh, let's not get this lease creates a second order effect that we don't need to build as many buildings. The third order effect might be, maybe we need to transition some of these buildings to residential. And then all of a sudden the $3,800 apartment in San Francisco goes down to 2000. Totally.

SPEAKER_12: Uh, when, when the pandemic hit, we watched, we work with a number of, um, uh, airlines and hospitality. Mm. And we watched foot traffic drop 87% week over week nationally.

SPEAKER_160: We're in something like 40 airports or something like that across the U S.

SPEAKER_09: And so like a lounge type situation might come to mind, knowing the utilization of the business class lounge or outside of a gate, you know, the, do, do people deploy this at gates?

SPEAKER_26: Uh, most of our deployments are inside like, um, airline lounges where they have more control.

SPEAKER_12: Yeah. Um, I, I think that the, the, the sort of salient point was that we, we got a call from the New York times and they were just like, Hey, we're looking for data on the impact of the pandemic across different industries.

SPEAKER_17: And so we kind of cobbled together a bunch of data and was, were able to provide them the relative impacts on foot traffic.

SPEAKER_12: And I think that the, the, the thing that's most exciting about that is it goes back to the founding principle of the business. Um, the night that we could count the number of people that were in a bar without having to be there and sort of this logical conclusion of what happens at a city scale or at a, uh, Eastern seaboard scale or a global scale. Like the surface of the earth is unmeasured. And if you could fit the built surface of the earth is unmeasured.

SPEAKER_17: And if you could figure out how to measure all relevant human space, so not just corporate office, but all relevant human space in a way that is non-invasive. It's real time. That's extremely accurate. Um, all of the things that you just suggested start to become possible. And that is, um, you know, that that's, that's a, that's a thing. That's a pursuit. That's worth going after. You know, it's a, it's a thing. 100%. Yeah. You know, really trying to strive for.

SPEAKER_03: Do these exist on buses and subway cars yet?

SPEAKER_01: Like, do they have, are there other companies that provide the utilization or a number of people on a bus or a subway? Or do they do that with clipboards today too, and cameras? Yeah.

SPEAKER_12: So the, um, uh, buses are a very interesting one. Um, there are, uh, depending on where you, you operate buses. So we've heard from a lot of bus companies, strangely enough.

SPEAKER_17: Um, and depending on where you operate the line, there are, uh, state laws, state and federal laws around how often you clean buses. Um, local ordinances that say after a certain number of uses, you have to clean the space. Well, the thing is much like, um, like the ticketing system, uh, shows how many people entered the bus. It doesn't show how long that bus was used because people leave and you don't like, you know, click a ticket on the way out. So this concept of like assets, um, the use of an asset is something that has been talked about a lot, but short of deploying a camera into every space that a human's in, which you see in China, um, uh, you know, like.

SPEAKER_138: To disastrous results on a privacy basis, I might add.

SPEAKER_26: Yeah.

SPEAKER_58: I mean, and, and you look at like the Uyghurs, what's happening with the Uyghurs.

SPEAKER_26: Yeah, it's crazy.

SPEAKER_01: I mean, they're able to identify anybody and they're going to be able to do things that are very similar to what we saw in, um, what's the famous sci-fi movie with Tom Cruise? Minority Report. Minority Report. I mean, you're like, this person is walking around a car. They look suspicious. Those are, you know, let's go arrest them. Yeah. You know, like that could be virtuous. If somebody was lingering around a door to mug somebody, but it could also false positives are pretty bad in that situation. Yeah.

SPEAKER_264: False positives are really bad. In fact, and it'll eventually.

SPEAKER_01: What were you saying with the Uyghurs? Are they identifying the Uyghurs or were they using this camera to find out who went into certain churches or something?

SPEAKER_12: Yeah. So they're essentially doing racial profiling using, um, uh, a national surveillance structure, a national, uh, surveillance infrastructure. And like that is the China or otherwise, like that is the natural consequence of technology that is overly invasive.

SPEAKER_17: Depending, depending on whether or not there's a distinction, a very important distinction between anonymized and anonymous at source. Yeah.

SPEAKER_12: And when you are anonymized, it means that if you're compromised, then someone gets access to something that might be of real value.

SPEAKER_17: And the only deterrent is whether or not you can stop them from compromising you. Anonymous at source preserves the ability to protect the individual that you're observing, even if you are compromised by a bad actor. And so if you snap your fingers and knew how every space was used in every major city across the US, but the system that was used to do that happened to be mass surveillance cameras in every, every relevant human space.

SPEAKER_23: Yeah. Eventually you will see abuse. Abuse. Of course.

SPEAKER_93: Any system that can be hacked or abused will be hacked and abused. So if the source material is anonym anonymized, as you're saying at source, there is no identifying material here.

SPEAKER_10: I mean, the most you could do is say, well, maybe we can track this person from their office to leaving the office. Cause they're the only person who has the key to the office. I mean, there might be some light edge cases, but yeah, you're right.

SPEAKER_09: I mean, in deploying this everywhere, you, you, you really do have to think in a much bigger way about how this data is going to be used. What about in the streets, uh, and in open spaces that don't have ceilings? How do you think about that? Can you put this on a lamppost?

SPEAKER_10: Could you put it on a bus shelter? Can you put it sideways against a wall? How does that work?

SPEAKER_83: Yeah. So I don't want to scoop myself.

SPEAKER_12: Oh, uh, so I can't speak to sort of like some of the, there's a couple of things that we, we like thinking about the cool thing about radar systems. Um, so the, the radar system with open areas that it's completely unaffected by light or any type of ambient light. Huh? And so you really don't, um, run into issues with like optics, like you would with an optical system, um, or even illumination. Like we use lasers in our entry sensor, but we don't have to use an optical or illumination package in our radar sensor for open area.

SPEAKER_17: Um, there is no reason why you couldn't put a radar system on a, on a lamp or on a, on a wall. It's just that this particular implementation is, you know, aerial. Yeah. Um, I think, I think outside is actually a rather good area for cameras, um, better lighting conditions. Um, you know, you've, there's more reason to have cameras, whether it's for security purposes or it's for, um, uh, public good or it's cameras for, uh, cars.

SPEAKER_260: But yeah, I kinda like cameras on city streets, like the, the expectation of privacy, at least in a democracy.

SPEAKER_01: And at least if there is some. Right.

SPEAKER_09: Uh, data retention, uh, and access that is mitigated by, let's say a judge, you know, in a subpoena. Yeah. Or maybe it's only saved for 30 days. We don't need to keep this stuff for 30 years. Uh, this would be quite, uh, I mean, and we have it in London.

SPEAKER_01: We have a, we have a pretty good, uh, test case of a democracy with it.

SPEAKER_65: And I think crime went down massively when London deployed their CCTV systems. Yeah.

SPEAKER_12: Yeah. I, I think like, I, I don't know that outside, I, I really like built structures as sort of a, a little bit of a confining concept, at least for now for like current product roadmap. Um, but I just think that there's so much space. I mean, we're talking, I think it might be the most valuable asset in the world whose performance we don't measure. Hmm.

SPEAKER_75: Yeah. It's pretty amazing.

SPEAKER_10: What, what is the secret to, uh, running a great company, raising money, getting product market fit, all the lessons you've learned as an entrepreneur. Now, if you could go back in time six years ago, when you were presenting on that stage at launch festival and just whisper into, you know, younger Andrew's ears and say, Hey, here's two or three things you need to keep in mind that are going to be important for you over this next six years. What would they be? Um, you take a minute there. Cause yeah.

SPEAKER_11: Yeah. First off, um, I think founders tend to get, uh, lionized a bit, bit more than the teams that, um, are at companies.

SPEAKER_12: You know, we, I, I work with a team whose voluntary retention at density is 93% since our founding.

SPEAKER_17: Um, you know, we, we have 62 people working on, on this particular problem and the average tenure is, you know, it's like over three years. Um, so I might be the person up on stage, but there are folks who have been working on this as long as I've been working on this and are still trying to solve the problems for customers today. So number one is like, uh, trying to create an environment that, that, that facilitates retention. Um, I think retention is incredibly important, at least for the right folks.

SPEAKER_160: Um, why is it so important?

SPEAKER_01: Unpack it for a second. Um, what happens when people stay for the third and fourth and fifth year?

SPEAKER_17: Well, you, you no longer are mandating culture, culture. So imagine, you know, a startup that has 50% voluntary retention. Well, that means every two years, uh, the culture has completely changed because cultural culture is additive. Culture is not fixed. Culture is not values. Um, you know, and, and, um, and so a lot of people talk about like, oh, we need culture fit for instance. And that, that's a, that's a misnomer. Like you want values fit. Um, and you want cultural addition. And, um, because every net new person has some type of impact on what you, um, what you do on a day to day basis and how you talk about the product. Um, how you think about customers and those things are cultural. Um, if you've got high churn, it's great that you're getting new people in and maybe that's what's important, but I would rather pair high retention with, um, uh, fast to fire when it's not working out.

SPEAKER_12: Then, um, uh, low retention and, uh, and just like, you know, you're just constantly hiring really great people because it's very hard to create a durable business in my opinion.

SPEAKER_57: Um, I agree.

SPEAKER_12: Um, you know, the, the other thing is, um, I remember, I think it was the founder of big screen VR. Um, he said, uh, he said, your, your job as a founder is to survive long enough for the market to need you. And, and so if I were to say, I guess if I were to counsel, uh, our early team, it would be don't, it would be don't die.

SPEAKER_17: Um, stay in the game. I mean, if you look at the average lifespan of like a lot of startups, it's a lot shorter. Um, 18 months, 30 months.

SPEAKER_290: Yeah.

SPEAKER_12: There's so much richness to the experiences that happen at year three and year four and year five. Um, and when it's hard, like even when it's hard and especially when it's hard.

SPEAKER_01: Um, I feel like that's when you learn the most, you get, you get past those first couple of years and now you got something.

SPEAKER_09: And now you've really got to figure out how to scale it and how to build that team and how to plug in each of those little, um, each department has to become super high functioning for the whole thing to work. And the first couple of years when you're stumbling around trying to figure out product market fit and what exactly is the product here, you're not really developing that high throughput organization yet.

SPEAKER_58: Right?

SPEAKER_11: Yeah.

SPEAKER_17: I feel like a total hack to just, um, like reading incessantly about previous, uh, technology companies, um, and other companies, but especially technology companies. You know, if you read like hard drive, which is by, um, which was recommended to me by Ilya Fushman over at Kleiner, which is about Bill Gates in the early years of Microsoft. Or you read, um, uh, you know, creativity, Inc about Pixar, um, which is a Dick Costolo special. Like you, he loves that book.

SPEAKER_131: Um, or you watch, uh, I had, uh, the author on the spot. Yeah. I forgot the episode numbers, but I'll get them in a second. Ed Catmull?

SPEAKER_10: Yeah. Catmull came out for a two part series. It was one of my, I read the book. I fell in love with it so much, uh, that I, it took Jackie, uh, the, the original, uh, one of the original producers here. I don't know, two years to get them on the program and we just kept trying and trying. And then finally he gave in and episode six, six, five and six, six, six, for those of you thinking about, uh, creativity, Inc. And I think it's right behind me right here. Either I'm pointing at it. No, right there.

SPEAKER_297: That's it.

SPEAKER_10: Right there.

SPEAKER_299: Yeah.

SPEAKER_10: That's creativity, Inc. Such a great book about the formative years of Pixar. And even before that, when he was trying to figure out just how to make a movie with computers.

SPEAKER_17: I think, I, you know, I mean, even if you're like, I remember episodes 20 of this week in startups. Like I, I remember, do you remember talking to a bunch of kids, uh, who were at university? It was a digital, you were dialing in, you're talking to a bunch of students. Oh, the samurai episode. Samurai episode.

SPEAKER_305: Samurai or rice pickers. Yes.

SPEAKER_17: Yes. And you, you said, uh, don't be a rice picker, be a samurai. And then, and then you, you got emotional talking about, uh, the sale of, um, of, uh, Weblogs, Inc. Yeah. And you were refreshing. You were just refreshing, um, bank account, bank account. Yeah. And, and, and it was like, uh, and after doing it, you know, you just sort of had this sense of like all those times you were told, no, um, that this was sort of a validation for having gotten through that. And look, I, I just, there's startups. I don't, I don't actually know how it's legal that, um, I don't know how startups are legal because you're essentially being given control over like the structure, communication methods, uh, uh, cultural imperatives, values, compensation, healthcare, downtime. Like you're given a lot of influence on people's lives, especially in the U S. Yeah. Um, and if done right, uh, I think the good times are things that you just don't even realize you're in until after they're over. Yeah. And they're often the hardest moments.

SPEAKER_30: Like they're often the things when you like solve the thing that was really, really different.

SPEAKER_22: It's, it's, uh, uh, on that same theme, when people cheat at the American entrepreneurial

SPEAKER_01: system, I'm always flummoxed because I'm like, the system is already so pro founder. It is so aligned for you to start something where it costs so little to start a company

SPEAKER_22: today. And even when it costs a lot of money, it's still, you know, in other countries, you have to get a business license and pay somebody off, you know? And maybe there's some union or some governor or prefect that you gotta give money to. And here in the United States, you could fold up shop and start again tomorrow, work on the same idea, try again, fail, try again the next time. And we've all gotten accustomed to, you know, it's employment at will. You can leave. I can leave. The whole thing could shut down. It could blow up. Doesn't matter. We all just keep trying. Why American exceptionalism has driven the world for so long. We short capitalism can have bad moments where people get laid off and it's done in a horrible way or people cheat and the fire festival and we work and other excruciatingly painful examples. There are no of, of outright fraud.

SPEAKER_03: But when it overall, it works overall, it works so wonderfully to allow a platform to allow people to innovate and to try amazing, important projects and missions like yours. There's this, um, sapiens in sapiens.

SPEAKER_12: There's this great, um, description about like, what are the three most important things that humans have ever invented? And the first was, um, language, I think I may be misremembering, but as language, um, shared myth. So the ability to tell stories and like organize religions, a good example of this, but essentially just organize. And I think startups are a good example of this organizing around a mission.

SPEAKER_42: Um, the story does matter. Yeah.

SPEAKER_17: Yeah. And then the third was the limited liability company. And it was because it was because, um, people who would make wheels, uh, for carriages were personally liable for any damages that happened if a family got hurt or if it broke. Yeah. And as soon as you disem, you, you disentangled the individual from the liability of the corporation or the liability of the product, all of a sudden GDP just went globally. Yes. It was because everyone took more risk.

SPEAKER_32: Yeah. And then do you want to live in a society that takes those risks? And those are the societies that may cure cancer.

SPEAKER_01: And now you do have to have an FDA. You do have to have some, you know, uh, rules of engagement so that people don't fly off the, uh, the rails and flip the car as it were. And we see that in China where, you know, they adopted capitalism, but, uh, maybe the rules aren't perfect and people can put their thumb on the scale or they put plaster into a baby formula to try to make it last longer and do all kinds of crazy stuff. By the way, that stuff happened here. Yeah. And we just built some infrastructure around.

SPEAKER_22: You know what? Let's have some food safety rules. Let's have some entrepreneurial rules. Let's have some rules around employment. Uh, you know, the, the, the game of capitalism doesn't mean no rules apply. They should be rules, right?

SPEAKER_58: And then people can break rules and you know, Theranos or Bernie Madoff, whoever breaks the rules

SPEAKER_03: can go to jail, right? Yeah. But you don't need, I mean, it's so ridiculous that people, I mean, I'm always just absolutely aghast when I watch people cheat or do something that's, you know, it's like, it's, everybody is such a winner in this game. Even if you fail, you've learned so much and the limited liability corporation to your point,

SPEAKER_22: it's not like you're going to be destitute for the rest of your life. There was, there's no risk of ruin here. You can just keep going. Try again.

SPEAKER_12: I'm a white man, um, who lives in San Francisco, uh, who's had the privilege to be able to raise, um, a lot of venture capital to work on something that I care about personally over the last six, seven years. The, the, the stuff I didn't have to go through or deal with the barriers that I didn't have to overcome. Um, I, I think the barriers that, you know, we're talking about here kind of pale in comparison to those that. 100%. Yes. Like by virtue of who they were, you know, the color of their skin or their, their, their sex, um, gender. Yeah.

SPEAKER_93: Yeah. And so I, I mean, just, there was a big bias against people from New York.

SPEAKER_09: The fact that you were from New York, I don't know what college you went to, but I, I don't remember it being Harvard or MIT.

SPEAKER_227: No, I went to, I went to Syracuse university, both undergrad and grad.

SPEAKER_09: The, the idea here is like, maybe you weren't, you know, in a previous area, you would have been considered like odd person out. Right. Even, even as a white male. Right. We were seeing this kind of change. I thought actually the dunking on Quibi was instructive yesterday where somebody came

SPEAKER_10: up with a very clever tweet. That was total funding for female founders. 1.7 billion total funding for Quibi, uh, total founding for female founders was like 2 billion total funding for Quibi 1.75. And I was like, um, by the way, pause for a second, two different pools of capital. That capital came, did not come from venture capital. Yeah.

SPEAKER_22: From all media companies. There's not one venture capitalists on it. Yeah. But even if that is true, the co-founder and CEO is Meg Whitman. So it would actually be the total would be 3.75 million of which one woman got 1.75 billion. So if you wanna be cynical about it, but we've seen a massive change.

SPEAKER_01: I mean, it's just watching the last 10 years as an angel investor myself, I guess I'm my 11th now. I mean, just the number of founders getting funded who are not white males is just extraordinary.

SPEAKER_22: I mean, I watch it every single accelerator class.

SPEAKER_52: You know, it went from being like the number of applicants. The pool has just blossomed in terms of diversity.

SPEAKER_12: There's an organization called the national center for women and, uh, information technology, or NC wit that does like basic research out of, uh, I think it's, uh, CU Boulder out of, out of Colorado, um, on, uh, like unconscious bias and like cultural structures and how to like deconstruct things, uh, with corporations. And there was a story that the founder who used to work at Bell Labs, her name is Lucy. She's amazing. Um, uh, uh, used to, used to talk about. And I, the only reason I know any of this is because of my amazing wife, uh, Dori, who, um, worked at NC wit and now, now works at, at, uh, Apple. But, um, one of the things that, uh, she used to say was, you know, when the, when the airbag

SPEAKER_17: was invented, um, they finally sort of got it through whatever regulatory body they were going to get it through back in the forties or fifties, started to enter into mainstream cars. And very shortly after its introduction, um, uh, women and children in passenger seats started and, um, it turns out that the, um, I have to find the source material material, but it turns out that the dummy that they used, the crash dummy that they used had the build and structure of the men who invented the airbag.

SPEAKER_327: Yeah.

SPEAKER_12: Complete blind spot. And it's not that these like scientists and engineers were like trying to hurt or not consider folks. Yeah.

SPEAKER_17: That they just didn't have the life experience and maybe couldn't make the logical leap that their stature wasn't the only one they had to protect against, which is, you know, kind of on them. But I just, this is the value of diverse teams. Like people aren't diverse teams are diverse, you know, groups are diverse. And so the question is like, if you have the ability to build a diverse team, that's sort of has some type of an inclusive environment, you are substantially more likely to build better products. And Lucy used to always say half, you know, the world's technology was built by half the population.

SPEAKER_227: How much better would our technology be if it were built by, by a larger percentage of the population? Yeah, absolutely.

SPEAKER_332: And yeah, I mean, if you, I think one of the statistics I'd like to see, I think one of

SPEAKER_22: the problems with the statistics is there's an overhang and we look at the total amount of venture capital deployed each year. Well, the majority of the dollars go to existing companies that are, you know, if they're in

SPEAKER_10: year five or 10, they're going to get $100 million checks, $200 million checks, $50 million checks. And then if you look at the early stage, they're going to get 500k checks or $1 million checks, $2 million checks.

SPEAKER_03: So we have to look at the, this year, how many people who are first time founders or first, you know, series A's, how are series A's and seed rounds deployed and the diversity of those, not the overhang of the series C or D's.

SPEAKER_93: Right. Which shows the diversity in investments 10 years ago. Right.

SPEAKER_227: Because you're going to start to understand what happens five years from now, as opposed to. Absolutely. As opposed to just the reinforcing issues today.

SPEAKER_62: Well, and you could do it with new funds.

SPEAKER_10: When you look at a fund that was formed today or in, you know, this year, the, the 2020 cohort of new funds, you know, whatever Sequoias fund this year, if you compare the diversity in that group versus the fund from three years ago or five years ago, that's still being invested out of for continuation funds and follow on rounds, et cetera. Well, they, they might be very different, right?

SPEAKER_62: As this culture change.

SPEAKER_160: Do you have a sense? Do you have a sense of what your, I mean, you, you invest regularly. Do you have a sense of what your numbers look like?

SPEAKER_62: Uh, yes, it's massively diverse in the last couple of years massively.

SPEAKER_09: Uh, but we specifically did something to change the number of inbound, which was, we started doing founder university just for women, which you frequently join me for. Thank you. We do founder university six times a year. And I think three times, I think four out of the six times it's either for underrepresented founders or female founders. Um, and then that gives us a pool of 250 people each time to meet, to explain to them what,

SPEAKER_01: you know, our goldilocks zone is for the accelerator. And then each accelerator class it's, you know, gone from one female founder and six male founders

SPEAKER_09: to two and five and three and four. And you know, you gotta look at the founding team of course.

SPEAKER_75: And yeah, it's really changed dramatically, dramatically.

SPEAKER_12: Um, and we can really interesting to see how returns, how returns change.

SPEAKER_58: Um, yeah, that's, that's, I've heard both sides of the story. Like sometimes people, uh, think, you know, the, and this is the critique that I never

SPEAKER_09: had, but this is what I would hear years ago is that the female founders would maybe pick, um, safer bets. And I even had a female founder say to me, like, I don't wanna go that crazy and outlandish

SPEAKER_58: because if I screw this up, I'm never gonna be able to raise money again.

SPEAKER_93: So already in her head was playing not to lose because this is the only chance I'm gonna get. If I fail now, I'll never be funded again. I'm a woman, which is a perverse, right?

SPEAKER_26: Yeah. It sucks. Well, what's, well, what you're hitting on is actually, um, a really interesting part

SPEAKER_12: of like how systemic bias like ends up like resulting. Um, I mean, without generalizing, you know, you have so many kids who are brought up, uh, who are, who are boys told that, you know, uh, go try, fail experiments, like it'll be okay.

SPEAKER_17: Um, and, and then, you know, you have these, these other circumstances, which don't provide nearly as much, uh, structural support. And I, I just, you know, it'll be really, I'm also curious. Like, do you think that the pandemic will, will accelerate kind of the democratization of investment, meaning like remove some of the in-person biases or do you think that you're gonna see? Yeah. Just like what, what's your sense? It's interesting one.

SPEAKER_01: I, a lot of people have told me privately. Uh, and again, I like to be always be honest with my audience of what I'm hearing privately. So I will anonymize it, but I have heard from more than one investor. And this is not my opinion, um, that they are just going to wait out 2020 and the pandemic and keep their dry powder for their companies. In other words, make new, no new investments, which would mean a steady state for a new investments, uh, for myself. And I, I know some other investors, they're looking at this and saying, well, we can take more meetings and we can diligence companies. And we recaptured two hours a day of people commuting and chit chatting in the office. And every meeting is half an hour instead of an hour.

SPEAKER_10: So we can do twice as many meetings is literally what's happened. You know, going from two meetings a day or three meetings a day to three or four meetings is pretty amazing, right? Uh, cause nobody wants to be in a zoom for now. They want to be on and off a zoom in what? 20, 30 minutes, 40 minutes max. So we're investing twice as much. I would say our velocity has gone up close to double.

SPEAKER_58: Uh, and then the number of people who want to get involved investing has increased back to the efficiency and second order, third order effects of a pandemic and you know, of space. So when you recapture time, you get to redeploy it. Mm hmm. And this is why I think coming out of the pandemic will be one of the greatest recoveries ever. And that we're massively underestimating the impact of this recovery because every single company now is a remote company, at least in technology and entrepreneurship. And so the gains of remote are you can hire twice as fast, three times as fast because you're not limiting yourself to a location.

SPEAKER_03: Mm hmm. You're also not paying the premium of a city of 30 or 40%. So once you figure out how to manage people remotely, you now hire remotely, which means you lower your prices, uh, and your cost of hiring people by 50%.

SPEAKER_09: So you could double the number of people you hire or, um, you can just hire quicker. Right. And everybody's balance sheet is getting corrected because people are not going out and spending companies are not spending on office space.

SPEAKER_10: They're not spending, maintaining these spaces. And then you reclaim what? Two hours a day. Every employee claims two, three hours a day of commuting, showering, getting dressed up, big, long lunches, all this bullshit. So you reclaim three hours a day, I think conservatively. And I think the social contract that's not spoken is the employee gets half and the employer gets half because you get to work from home. So people are, some people are complaining, Oh my God, there's no line between working and being at home.

SPEAKER_09: I think people are, I don't know if you're seeing this. I'd be curious what you see, but I think people are working an hour more a day, uh, at least, but I think they're saving another hour or two.

SPEAKER_172: So they feel like, okay, cool. Yeah.

SPEAKER_12: I mean, it's going to be interesting to see how many people actually, um, maintain this, like going forward. There's so many people that are eager to get back to the office and 45% of American built businesses. Um, uh, like 45% of American workers have to physically show up to their office or space to do the work that they do for their like job type. Yeah. Tech is like extremely lucky in that we could just like, Oh, you know what? Like let's deal with the pain of having to jump on zooms back to back and still get paid. Yeah.

SPEAKER_17: And move to some third or tertiary city and, you know, drop my San Francisco lease. Um, it's incredible. The balance sheet is incredible. Yeah. I think the thing that I'm most excited about even just like, just as an observer is the number of companies that are getting started.

SPEAKER_12: Yeah. Crazy. Folks that couldn't like who either got furloughed or laid off or whatever else. I think Stripe had some crazy number of companies that are using Atlas, um, like to get, to get started.

SPEAKER_17: And these aren't just venture back companies. I mean, these are, these are lifestyle businesses and venture back companies. These are sort of all types. Um, yeah, I think you might be, I think you might be right.

SPEAKER_160: I just hope it's not a, I hope it's not a K shaped recovery, you know, where people are left out.

SPEAKER_22: Um, I think there'll be a group that are left out. There always are.

SPEAKER_01: Um, and what we've seen is I think our economy is pretty resilient. Uh, and, you know, usually recessions last two quarters, sometimes three. I think this is going to be one where we inject so much money into it. And then there's all this other efficiency that occurs and people balancing their, you

SPEAKER_58: know, and moving around and all the money pumped into it. I think it's going to be a quick recovery. I'm hoping that's, that's my hope. Um, but yeah, I mean, it's right now, the amount of capital in the world looking for, you know, uh, some acceleration in returns is phenomenal. It's phenomenal. Right. Uh, so we'll see. Uh, nobody know it, right? You said it earlier yourself, like in a black Swan, who knows, right? Yeah. Um, I do think people will move to hybrid. I mean, you'll know better than anybody what this looks like, but most companies I'm talking to are just thinking hybrid, like we're going to be in the office three days a week, four days a week, two days a week, whatever it is. And you can go wherever you want for the long weekend. And so I think you're going to start seeing these people like come into the city for two or three days. Maybe they'll get a hotel room. Maybe they'll get an Airbnb. Maybe they'll have a rental share or something. Well, they'll have two long commute days and then three days at home. And they'll just deal with the long commutes those days.

SPEAKER_12: There's so many, so many exciting things with, uh, transportation infrastructure, because as soon as you can support this, like hybrid model, things like hyperloop, things like, um, uh, you know, fast rail hyper, you know, ultra fast rail, um, or even like, um, uh, being able to take a six month period of time where I just go work in some other part of the world.

SPEAKER_17: Um, you just dramatically changing the workforce, which is going to have all these second and third order effects on price of, uh, you know, rentals and leases and homes.

SPEAKER_12: Um, certainly an impact on broadband and how fast internet is just because there'll be larger demand for, um, cities that aren't just hubs.

SPEAKER_332: Yeah. It's going to, it, the, the, the small cities are going to be big winners. The big cities are going to go through a major change.

SPEAKER_01: San Francisco, I think is got a massive risk now because I don't know if you saw the statistics, $3,800 was our peak rent or 3,700. Then it went to 28 and then Oakland's down 20 or 30%. San Francisco is down 30%. And the number of homes in San Francisco for sale is at a 20 year high. And the mortgage rate is at, uh, the lowest in our lifetime. So like the last 50 years is lowest. So this is very weird that San Francisco has all this availability.

SPEAKER_09: And then the lowest mortgages, other places are seeing no inventory, LA, Tahoe, you know, Austin, like low record, low inventory and, you know, mortgages are freely available at 3% or something insane.

SPEAKER_01: Do you think that you'll move? That's a good question.

SPEAKER_03: It's come up a bunch. Um, I'm going to see what happens. You know, I don't, I don't think I'll do it this year. Um, but you know, and I don't think I'll do it in the next year.

SPEAKER_10: Put it that way. I want to see what happens. Yeah. Post pandemic. If nobody wants to come to San Francisco, uh, you know, I'm, I might consider living other places. I think I might consider a nomadic lifestyle.

SPEAKER_58: I know it sounds crazy, but maybe Austin and San Francisco or Austin and Salt Lake City. I don't know. I'm going to leave it open.

SPEAKER_10: Uh, you know, at this point in my career where I am, uh, people come to me. I've kind of, when you get your second decade, that's what happens. And then does it make sense to be here financially? I don't know. You know, I don't know that this is not going to be the center of the world.

SPEAKER_26: Right. So we'll see. I feel like this is fulfilling the, the, the long held promise of technology. You know what I mean?

SPEAKER_12: Absolutely. People who have lived in San Francisco have been talking about this for years, which is like once eventually technology gets its grips into every other industry where you can't

SPEAKER_17: not be a software company. Yeah. Eventually you'll have this great democratization of, and decentralization of technical power. I mean, if you, if you look at, um, uh, and the, and the positive economic consequences of that. I mean, if you look at the amount of dollars that flow in because of Uber's headquarters. Yeah. Crazy. It's crazy. You know what I mean? All these local economies in Menlo park and Atherton and all these other that are, that are expensive are expensive for a reason. And it's because we export scalable technology. We being the region. I don't know that I am necessarily we, but we, we export scalable technology. Yeah. Once you're manufacturing stuff in upstate New York, which is something that we do all of a sudden that becomes a place where you're exporting technology.

SPEAKER_01: Um, it, the playbook for Silicon Valley has gotten too big for the Bay area. That's the bottom line. I mean, if you just look at the top three or four companies, they're, they're so large Apple, Facebook, Google. I mean, these companies are so large and, and growing.

SPEAKER_58: They just can't possibly, uh, continue to grow here with the NIMBY ism and the lack of

SPEAKER_09: development here, but other places like New York allowed people to build tons of skyscrapers and buildings, Houston, other places have completely different land use than California, which is, you know, the most NIMBY of all NIMBY. You know, states in, in the highest taxes and the most regulation. So I do see a lot of my friends moving to places that are either less regulation, lower tax, more, uh, easier to build easier to operate. And so I do think that those states will be beneficiaries and that's going to have. Talk about second, third order effects. Like right now we're taping this at the time of the presidential election and, uh, Biden and Trump are exactly neck and neck in Texas. I mean, think about that. I mean, Texas is. Yeah.

SPEAKER_12: They're expecting it to. Yeah. They were expecting to flip 10, 15 years from now.

SPEAKER_26: Not. Not in 10 days. Yeah.

SPEAKER_09: Potentially, you know, and that's an incredible moment in time that, you know, people, are, are starting to move around.

SPEAKER_01: I do think, you know, to your point about collisions, we talked about earlier. You know, I think that this town will become the elite, you know, to live here is going to be hard and expensive. But if you're a young person coming, I, you know, I was talking to a young person. I'm like, do you think I should come here? And I was like, yes, absolutely. Do it. Um, because it's, and you experienced it when you came here. How many more meetings? Did you have? What access to talent? Did you have? I mean, you just jumped right into it and all of a sudden you're validated. Where have you had stayed in Syracuse as the CEO? Would that have happened? I don't think so. Well, I think. I think. Farmer's fund and Connor Perkins, we're not getting on flights to Syracuse. I'll do respect to upstate New York. Although that's changing.

SPEAKER_12: There's a very cool, uh, uh, company called, um, uh, other side, uh, AI that, um, is actually

SPEAKER_30: out of Syracuse. Extremely, uh, competitive round. Um. Interesting.

SPEAKER_75: And that, that new upstart in Albany, NXIVM. I heard that they're crushing it. Their statistics are just through the roof for their multi-level marketing.

SPEAKER_96: Yeah. Like, I think, I think that like, like Keith, Keith, something is really just a spectacular visionary visionary.

SPEAKER_125: He's, he's got Jordan Belson all over him. Didn't the Dalai Lama invest? I think the Dalai Lama. Dalai Lama is on the cap table. 100. The Bronfman's are in. Yeah.

SPEAKER_362: Yeah, exactly. Are you watching that series? Oh my God. I just finished it.

SPEAKER_114: Just finished it. Yeah. It's deranged. I am. I'm watching it going. Are people that stupid?

SPEAKER_10: And, and I, I was, um, listening to somebody who's a hypnotist who understands persuasion very well. And he was explaining it.

SPEAKER_01: And he was basically Scott Adams, the, the Dilbert creator, who's kind of like a Trumpian. And he does like this little daily thing. And he was talking about the vow. And he's like, well, when, you know, when you go to a stage hypnotism and they ask people who wants to be on stage? Well, now you've filtered out 80% of people who are not suggestive, right? So the 20% who want to come on stage are self-selecting. And then they do that first round of, okay, now you close your eyes and I'm going to lift your hand and they lift everybody's hand. And what they're doing is seeing who drops our hand, like, and okay, now they sort out the 10, the half of those that were resisting and doing it to maybe be a goof. Then they whittle down and whittle down. And what that thing did was they just whittle down to the people who are the most suggestive to being manipulated. Right. So if you're trying to understand it, if 50,000 people take an executive course on how to be better executives at work, and then you slowly go down, Scott's position was that's, and you don't want to say stupid people, or maybe gullible, gullible is the right word, or people who could be manipulated easier, or just people who want to believe.

SPEAKER_69: They want to believe in something. Maybe. I think also you're looking for permission, you know?

SPEAKER_17: You also seem to get a self-reinforcing thing.

SPEAKER_12: The reality is, this is very similar to, I mean, not very similar. It is in the same galaxy as the culture or cult of personality that happens inside companies or startups. Yes.

SPEAKER_17: It's just that it doesn't end in a really deranged sex cult. Yes. Typically.

SPEAKER_114: Typically not branding, typically tattoos, where people get apple tattoos.

SPEAKER_370: There's no density tattoos out there, are there?

SPEAKER_16: No. Although we've got a very cool mark, I don't know that we've got any tattoos yet. Can't show it here? Yeah. Please don't.

SPEAKER_370: Tattoo's okay.

SPEAKER_10: Tattoo's okay. All right. Listen, you got a baby waking up. Great to have you on the pod. Great job. Congratulations. Thanks for having me along for the ride. It's been a great friendship and a great partnership all these years. It's just great to know you, Andrew. Likewise. And, you know, if you get a chance to work there, you will have an amazing time. Andrew is a great cult leader. I mean, CEO.

SPEAKER_08: We are hiring. We are hiring. Oh, good. What do we got? What's the most pressing need?

SPEAKER_12: Sales. Sales is growing really rapidly. Excellent.

SPEAKER_302: Great place if you're looking to make big commissions. Product is growing.

SPEAKER_17: Customer success is growing and engineering is growing. Fantastic. If you are interested in any type of, like, we're a vertically integrated hardware software

SPEAKER_12: company. So if you're interested in pretty much anything that's full stack, we can pull you in.

SPEAKER_376: All right. There you go, Andrew. Andrew at density.io. I bet you that's your email. I could guess.

SPEAKER_377: It is. The company name. There you go. I'll see you all next time on this week in startups. You'll see you all next time on This Week in Startups.