Earth Reimagined: Crafting a planet-scale digital twin with Blackshark.AI's Michael Putz | E1859

0:00It's public knowledge In-Q-Tel, which is the CIA's venture capital arm, which is a very public thing by the way our CIA has been doing this for over 20 years. investing in startups that can help with military purposes. I think it's a very good use of taxpayer dollars. So your company, Black Shark, has an investment from Incutel, correct? Yes. Which means you're working with a three-letter agency like the CIA and understanding these buildings is very important for any government. They would want to have an accurate picture of the world. Who is managing our planet? It's governments. So they are the ones who should be the most knowledgeable about what's happening on the surface of the planet.

0:36This Week in Startups is brought to you by Arising Ventures is a holding company that acquires tech startups facing setbacks. Arising Ventures knows what founders care about because they aren't bankers, they are tech founders themselves. Go to arisingventures.com slash twist today to learn more and connect with the team. Masterworks is the first company allowing investors exposure into the blue chip artwork asset class. Twist listeners can skip the waitlist by going to masterworks.com slash twist and LinkedIn marketing. To redeem a$100 LinkedIn ad credit and launch your first campaign, go to linkedin.com slash this week in startups.

1:17Hey, everybody. Welcome back to this week in startups. You know, we've talked many times about the impact. In fact, an outsized impact that the video game industry has had on startups, right? Some of the best product founders got their start. in the video game space. Stuart Butterfeld twice. He had one game and then he made Flickr. Then he did another game. And then he did Slack. Discord founder Jason Citron. He was a game developer. My guy Raul from Superhuman. He started in mobile games. And today's founder has launched a startup as a subdivision of his last startup, which was a gaming studio called Bongfish.

1:55Bongfish built the 3D mapping software for Microsoft Flight Simulator, which by the way, If you've ever seen the TikToks with Microsoft Flight Simulator in them, it's impossible to distinguish sometimes these flights from an actual video of a plane flying. Bongfish then created Black Shark AI to commercialize that 3D mapping software. Michael Hoots is the co-founder and CEO of Black Shark AI. Their mapping software creates a full 3D digital twin of planet Earth, mirroring the planet's physical characteristics. This platform uses AI to extract detailed information from satellite imagery. What an amazing idea.

2:30And now it's being sold to government, city planners, and more. Michael, welcome to the show. Thank you, Jason. Thank you for having me. All right. Great intro on the impact of video games. Yes, it is interesting how there are so many lessons in video games. What are the lessons that you learn in video games about customers, product design, etc., that you think inform startup building so much? Could you agree there's some sort of a trend here? Definitely. um biggest lesson for me is that coming really down to the basics of the current generation of ic's computers you can build anything out of zeros and ones so you can create your own worlds you can create your own interactions so why not do a digital twin of the entire planet so now that you are building this digital twin of the entire planet let's talk about how that's done and then why you're doing it so let's start with the the latter first why do we need a digital twin of planet Earth?

3:24Who needs this? And what are they going to do with it? I think the best way to explain is one of our advisors, Brian McClinton, who built the original Google Maps for... Back then, his startup was Kehoe. Google turned into Google Maps. And when I, for the first time, presented what we do, he said, that's exactly what... If he could do it all over, this would be the 2.0 version of Google Maps. So what we do differently is Google Maps and other mapping like Bing Maps or Apple Maps. They mostly use satellite or aerial, like images from either high up in space or like a little bit lower from planes and stitch them together on a gigantic sphere, which is our planet.

4:07And which is great for human inspection because we can interpret those images. We know that this is a typical building or this is a typical patch of vegetation, but actually it's not machine readable. So you You need human interpreters to deal with it, to analyze it. And now the next step is basically to find a way like computer vision and AI to interpret those pixels, those colored pixels found inside those images and assign them to what we call semantics or contextualize them and say, this is a building of this certain size. And since it's placed in this part of the planet and in this part of the city, it should be or might be a school building or an warehouse or an office building.

4:49The same goes for every single object on the planet. It could be a piece of a railway track. It could be a piece of a road. It could be a bridge. It could be vegetation, single trees, you name it. Let me ask a stupid question on behalf of the audience and myself, which is when we do satellite imagery, it's obviously taken from a great long distance from space. The fidelity has gotten much better. It's much cheaper to do because there's many more satellites out there. So we all understand that general concept. But my stupid question is the angle in which the photos are taken, you know, and its ability to create those 3D models.

5:23Is it not the wrong angle is a street level angle? Or as I noticed with Google Bing, they kind of had what I think they call it bird's eye view. I think there's flying turboprop planes like Cessnas over cities to give you that. I don't know if it's a three quarter view or an angle view. so talk to me about the angle of the photos being taken from satellites versus the street versus airplanes and and do you need multiple data sets in order to make this virtual planet earth that's a great question um going into full length i think will take a couple days to answer but i'm making a short short version of it um you already said very very rightful um if you use planes you have more flexibility because you can fly directly over your target object city block whatever downside of planes is they are they are slow so this means they are expensive to capture the planet um and also the patch they can capture is very limited versus being high up in space with the satellite the cone of a satellite looking down is way more bigger than any plane can do also the time of collection is way more the cadence of collection is way way uh higher up with with satellites versus planes but with satellite the downside is you cannot really control the angle looking down you can in a limited way but it's it's not enough and then also there are not satellite everywhere so you have a very limited number they are growing in size uh in terms of how many satellites are circling our planet but the ones who are there um they they are placed on on circular paths to cover the most meaningful regions this is basically where you can monetize the most which is mostly our western hemisphere and where the most people to live so this means somewhere in in remote siberia you might have a way more angle it's called off nadir angle in satellite lingo you have a way more nadir angle uh than in like uh downtown manhattan now what this means for us sorry but what it means for us because there's something good and especially something really cool in the in this angle if it's there because it helps us to estimate the height of like a building because again with ai uh we we use this this this offset where you see a certain patch of the facade in combination with the shadow if there's a shadow and use all this to feed it into the ai to come back with a well-educated guess what the the height of the building might be and when you program this model to to build the virtual planet earth using ai do you have to give it explicit instructions or do you can you actually say to it this is a satellite image use the shadow use this angle and try to determine this like where are you at in terms of programming this machine learning ai interpreter because i know that in the early days of self driving they were giving it explicit instructions and then they went with a learning model later where it was just hey here's the input cameras of the world here's the instructions for the game stay within the two lines don't crash the car drive like a human etc and then the model kind of does the rest so so how do you program this model i guess and how does it learn we prefer the the second uh option the learning model so basically if you if you train an ai it comes down to the annotation or the labeling process where basically tell the ai this is this is a building by identifying the rooftop and then if you know you can tell the ai this building is 200 meters tall if you do this many times i'm going to show you a new product which solves this in a very clever way but that's for later but if you do this many times this labeling annotation the ai is uh understanding why these people are these people this building should be 200 meters tall and it starts to look in the surrounding of the building it looks into this maybe offset of the facade from the off nadia angle it might look into the shadow cast of this particular building It might even look where the building is because the probability of a high-rise building is way more in like a downtown area versus somewhere in the middle of a desert.

9:27So let me ask another stupid question. If you, in order to get to, let's say 99.9 % fidelity in terms of the height of a building within a couple of centimeters or whatever it is within a foot, I don't know what the, what the proper goal here is, um, or what's necessary to, to, to do what you're doing. how many buildings just ballpark do you have to train the ai in order to get a 99.9 fidelity or whatever fidelity uh you you're currently targeting you take a hundred buildings a thousand buildings how many buildings you have to do you have to train it with this is now almost a philosophical question um if if you assume that we as humans as builders as architects are having certain patterns how we do buildings.

10:13Actually, the AI might solve this by finding the regularity, the pattern that this particular building always has this amount of rooftop furniture, like AC units, whatever, on top of it. And it's placed in this part of the city. But if there's one architect or one builder doing a building which is not expected and not following this pattern, the AI will miss it. So actually, if you want to be really super precise on the heights of the building, I would not just use one image from space or from a planner, you would use multiple images. Yeah, but does it take 500 or 100 to get to the Fidelity is sort of what I'm getting at.

10:47Like, is it a month of training? Is it a day of training? What's the state of the art right now? We, for back then for the Microsoft Flight Simulator project where we identified more than 1.5 billion buildings all over the planet, I think mostly all of them, we labeled about 10 ,000 buildings. You've heard me talk about Rising Ventures a bunch recently. They're a holding company that acquires tech startups that are, you know, facing some headwinds, some setbacks. So it's hard right now out there in startup land. And they give these businesses a second chance, the second chance they deserve. So if you're going through tough times, you're trying to get back on solid ground, you know your startup's got potential, well, reach out to the team at Arising Ventures.

11:28Could be just what your startup needs to get back on track. They've helped companies like UpCouncil, which they took from burning a million a month and shrinking to profitable and growing. and jive where they relaunched a shutdown company and went from zero to 1 million in ARR in just five months. Listen, Arising Ventures knows what founders care about because they're not bankers, they're founders themselves. So go ahead and learn how Arising Ventures can help you give your company a new lifeline. Arisingventures.com slash twist to learn more and connect with their team. That's Arisingventures.com slash twist.

12:01That's really, in some ways, the value of your company. The asset is that you took the time to do that labeling. Who does that labeling? I understand there are outsourced groups in Africa, Manila, that do this, and they've got massive experience in labeling because Google at some point decided to take Google images and do a training data set. Is that how this all happened? Explain to the audience how training data is done in the modern era. This is, as you just laid out perfectly, this is, we call it the traditional way of labeling, of doing annotations. But back then at the Microsoft Flight Simulator, the team was about 30 people.

12:38We had very limited budget. So we only had two labelers to label the entire planet. So you cannot do this with just two. Those massive labeling companies have thousands of people. And so we came up with a total new and different approach, which we are now going to productize because it really solves this issue of labeling, which is not just um it's it's time consuming you need many people which means it's expensive it's not flexible because if you tell the labeler which sometimes is in an off offshore place that he should label a building there might be cultural differences what the perception of building so it's not like something could be a mosque in one country it could be a library in another people could take it you know in in italy these might be residences in another country they might be churches right just by design architecture and then you go to china and people take italian architecture and chinese architecture and they mix it together who knows what the building is it could be a school right and so these are cultural little touch points now the government this is interesting um the government the u.s government in fact is spending hundreds of millions a year having companies label and annotate data for them this is this is true actually it seems to be true seems to be true okay what also is interesting most of this work is done in sweatshops on like offshore places which is not cool using taxpayers money and uh if you think further if you want to annotate label uh sensitive imagery you cannot give this to some outsourced company so um this is with our new tool i'm going to show you in a second um we we are solving all those issues on labeling.

14:20A, we bring down the number of people you need for. So we're accelerating the labeling process by, I don't know, 100, 1000. Also, as you just said, this particular example of billing perception might be different in China than Italy. Our tool actually enables those people who already have this knowledge. It's not just in government, also inside Google, inside any enterprise who deal with geospatial images they have like interpreters gis uh experts and we basically instead of taking away their job we are doing a tool which makes them way better using their domain knowledge labeling the it's public knowledge in qtel which is the cia's venture capital arm which is a very public thing by the way our cia has been doing this for over 20 years investing in startups that can help with military purposes i think it's a very good use of taxpayer dollars so your company black shark has an investment from incutel correct yes which means you're working with a three-letter agency like the cia and understanding these buildings is very important for any government especially if there was you know there's many reasons i guess they they would want to have an accurate picture of the world yeah thinking from that way like uh who is managing our planet it's governments so they are the ones who should be the most knowledgeable about what's happening on the surface of the planet can we see what you're working on can you do a little demo here and and of course since the audience is listening um if the audience wants to switch over to youtube just do a search for this week in startups and black shark you do that on youtube you'll find this video real quick so i just started about my browser and i'm loading now a map of taiwan this is like a roughly 400 square kilometer map so it's pretty large and it's of taiwan this is literally some some part of taiwan yeah it's we took this from from from maxa from the leading satellite company providing this 50 centimeter which is state of the art high definition um satellite and i'm now showing you um in this map you see a lot of those um smaller ponds or lakes there are quite some of them oh sorry they look like little man-made lakes yeah exactly and i now want to show you how to train an ai to detect all of this them in this map within a couple minutes not sending them to some outsourced company etc so first um in order to to to have we call it a detection run on this on this map for looking for for lakes we start a new ai model i'm now pressing a button create new i call the model um water underscore I'm the author it can deal with multiple classes now just one class which we call ponds so the water pond it's the yellow color so now I confirm and now I'm starting training process and as a first step we are identifying a small area we call it the training area where we see this target object which is this water pond this man-made lake I press on it and now I'm getting a split screen view where on the left-hand side, I'm telling the machine this is a lake.

17:30And on the right-hand side, I'm getting the almost real-time output from the neural network. What's the interpretation of it? What I mean a lake is. So I'm now switching to the yellow color. This is, think of a crayon, scribble, kindergarten approach. And I scribble, this is water. And also the second one, this is water. And within a couple of seconds, actually we should see the interpretation of the machine so the machine still thinks this is not water yeah the machine's trying to figure it out and they're all of a sudden it painted in yellow perfectly the areas that it thinks are water and so what this is is an annotation tool you're annotating a satellite image and then ai is learning uh from the human how to find the lakes or i should say the ponds in taiwan and then you're using a second tool and you're just drawing with like a marker around the lake so you did a scribble and said hey in yellow is the lake and then you did a second scribble on areas that are not the lake so you're literally training the ai right now with the most simple human instructions you could possibly do exactly so basically i'm reinforcing the ai this is what i look for this is a lake or i use this negative color to tell no no this is not a lake so i'm doing this on this one area and then i already know because there's an airfield in this area and this has some very interesting formations uh in terms of color patterns i take small training area on the airfield telling this this all is not a lake because that's an airfield that's not a lake yeah exactly and waiting again a couple seconds until the machine understood that what i'm telling you that this is not a lake you see those those yellow areas are shrinking because there are some dark spots there are patches on the airfield that look like they could be lakes but it just happens to be a dark part of the runway so you're making sure it knows, hey, there's no ponds in the middle of this runway.

19:38Exactly. And then now you might question yourself, yeah, you can do this with computer vision. Yes, in a very controlled environment. But the more different input sources, the more different biomes you have, computer vision is hitting some capacity limits. So now this is where AI comes into play, because it not just learns what I'm telling, this is the water pond, it also learns the surroundings. And it learns that the water pond is not in the middle of an airfield, for example. And now I'm, let's say I just have these two training areas and now I feel confident. So I am stopping the training and let's do a first detection run.

20:14So now you've trained it and now you're saying, Hey, here's the whole map. Get to work. Exactly. And now I choose the subsection to speed up the process here, pressing start. And now it takes a couple of seconds. This is just run on one, I think, V100 in a cloud setup. Behind this, we have a very powerful backend which can scale thousands of those machines, which enabled us to do a detection run on the entire planet. You're saying these are NVIDIA H100s or something? Yeah. So when our backend, basically, we also had to build this backend for our own, for the Flight Simulator project back then, because there's nothing out there who can deal with this gigantic amount of geospatial data, like petabytes of data.

21:02And so our backend can process the entire planet in less than$70, which is less than three days. And back then to identify 1.5 billion buildings and more than 30 million square kilometers of vegetation. Previously, if an agency, let's say in the united states or another you know advanced uh government with resources they would do this manually they would put a bunch of humans on this and and try to have the humans uh annotate hey these are airfields these are the things we need to focus on now you could have a human do but one area and have the entire country or region depending on your positioning of taiwan mapped out and know all the all the airfield and all the ponds there and if the ponds in iran let's say you'd be able to say hey we know these ponds are used in some cases for nuclear you know uh development you could basically find all the changes new ponds ponds that are changing size and have some indication of where maybe nuclear material is being processed if in fact ponds had something to do with that for example and i'm just coming up with a random yeah some random example coming to your mind yes listen public markets can be volatile don't i know it and if you're looking for a unique asset class to diversify with let me tell you about blue chip art blue chip art has historically been uncorrelated with the stock market and bloomberg reported that as equities dipped in 2022 blue chip art had its best year on record last year the big three auction houses posted record high revenues of a combined$17.7 billion.

22:44But here's the problem. Blue chip art has always been an exclusive asset class until Masterworks. With Masterworks, anyone can invest in fine art without needing millions of dollars. This is because Masterworks securitizes blue chip pieces, then sells the shares to investors. And Masterworks provides liquidity. To date, Masterworks has sold over$45 million worth of art. And net proceeds have been paid out to everyday investors, not billionaires. Masterworks has more than 840 ,000 users and north of 800 million in assets under management, AUM. And Twist listeners get special access to skip the waitlist.

23:20Just go to masterworks.com slash twist. That's masterworks.com slash twist to skip the waitlist. Past performance doesn't guarantee future results. See important disclosures at masterworks.com slash CD. Now you can see here, just these two training areas we have pretty good results um on all those ponds crazy even the the sea which is the upper part um got captured that's a pond too yeah it's like that's a pond and um and and as you usually now you you to do this you you need to task and labeling company in-house or external and and tell them and give them like literally thousands or even tens of thousands of training images and and they mark all the pounds they give you back the vectorized annotation data and then you use this with your in-house machine learning engineers to train your ai and we all shortcutted this now in a couple of minutes by just me um marking it's wild it's very dynamic so if you think about it if you were say tesla building solar roofs and they have a tile for solar roofs that is like the spanish tiles the curved clay tiles that you see on you know uh spanish homes uh or mediterranean homes you could literally say hey you know what california arizona uh colorado are our main markets here's you know just some sales executive could go in here and say these are spanish tile roofs in you know the bay area los angeles san diego those are our highest end you know most likely to buy a solar roof and these are the ones that break down the most and are most likely to and get the most benefit from it tell me all the spanish tile homes that don't have solar but that do have spanish tiles and give me their addresses and that person could do that in an hour or less great example a similar one um we got which is a little bit more complex but similar mindset uh and process um some large energy utility company asking us if we can identify potential locations, like locations scouting for renewables, where to build gigantic wind parks, like with this huge wind turbines.

25:29They know they need a certain like XY size of the area, which should not be built with existing buildings. It should follow a certain topology, mostly flat, no mountains or forests nearby, which can shield off the wind and a certain minimum distance to the next human settlement for noise regulations. And also there should be a highway closer to whatever 500 meters so they can bring in the heavy construction machinery to build this wind park. And we all fed this into our basically AI. We use containers for that to run them in lawnmower style over large areas of the planet to identify potential sites for where they can build such a wind park.

26:10Amazing. Yeah, I mean, and then you're going to be able to do this with voice and just say to it eventually, hey, you know, you have enough training data in here. Show me all the places I could have. I could build a new city. I just did a tweet the other day that went viral where I talked about, hey, you know, when I'm president, my first order of businesses, I'm going to create 10 cities. And those 10 cities will have a million homes in each. I could actually use yours to say, hey, find me 10 locations that could have a city hub with 500 apartments, then 200, 300 townhomes in the next ring, and then 200, you know, 1000 single family homes and make it, you know, and that is near, you know, whatever, within 100 miles of another city so that it could be a satellite city to that one.

27:00And boom, all of a sudden, you could tell me where to put my 10 president Jason cities as part of my initiative, correct? Perfect. Now think of all the hedge funds investing in shopping malls, identifying where there are not enough shopping malls yet. Yeah, or where shopping malls exist. I guess it would be interesting also the changes you talked about how often things are updated and satellites are updated. you tell me how how often could you update the imagery of taiwan in that example uh with the satellite company you work with do they update the entirety of that every month every year every day what's the state of the art today here in this particular case the limit is not on our side the limit is on the image acquisition and if you satellite um their company is doing a daily update of the entire planet um there are companies doing that wow which ones do that which companies that's planet labs yeah they're not at the resolution yet like this the the more static uh cell lights are but this is i think matter of time and also the the cadence will will increase um but you can use drones or aerials if you want to have that and we we made an experiment with with a client who wants to hit there from a very small country i think 12 000 square kilometers and they want to monitor building changes.

28:19Like when new buildings had been built and we solved this with like five GPUs in the cloud, it took five hours. And then the client itself scaled it up using our backend to thousands of machines and they brought it down to minutes. So that's almost real time. That's insane. Because if you think about it, if you were doing, and listen, there's all kinds of privacy and surveillance issues here, I know, but let's put those aside for a second to think about the positive aspects here. If you were living in a country where maybe some people were building buildings without going through the proper channels and making them safe, you think an emerging or frontier market, they might be doing that.

28:57You could, in fact, every morning, say to your building inspectors, hey, somebody's breaking ground here, there's a bulldozer in these seven different locations, they're building a foundation of the seven, we have permits for two. So these other five we need to make a site visit today before they build this building that's like literally and then they could just stop them from building and say hey you got to be permitted to do this and make it safe correct exactly i think of like um another type of how is it called building violations um i recently went to the to the middle east to do to the ksa and um you know yourself about the gigantic uh construction projects they're doing their giga projects And one of the other issues is people are building like crazy and some of them don't have the right permit for it or build bigger than they are allowed to.

29:50And it's very easy to use what we can detect and then conflate it with some city planning, cadastro or other existing data and find out and pick up the ones who should pay more taxes because they build more than they are allowed to. yeah i mean the square footage determines your your price so you could actually estimate hey how did this change over time what's the square footage did somebody put an adu or shed in is it properly done you could also do this for i know a lot of people are studying uh deforestation or forestation where people are planting things so you could get a really accurate pulse on the trees being planted that kind of stuff so this is an internal tool you have and it's called orca hunter why is it called orca hunter coming back to the founding story of black shark when we built as a very first project, the Microsoft Isolator, we had to develop all of the backend, all of the tooling ourselves.

30:39There was nothing, and I still think there's nothing out there like that. And for us, it was the end product was this 3D world, this what initially I called digital twin, which we're still working on at many great applications. But when doing outreaches and talking with many, many potential customers, we found out all the intermediate steps we built to come to this digital twin are actually products on its own. And Orca is our outtake of our geospatial software solution. And Orca Hunter is basically this first tool we are going to release December 2nd for anyone who wants to license it to upload their own images and do this scribble crayon approach.

31:18Oh, wow. So it's going to be a SAS tool. I could basically, if I have images that I acquired from wherever, I could get it from a public satellite images. I could take, you know, old images, you know, that might be in the public domain and I could upload it and pay you a fee just to use the tool. Yeah. Even our team for every day LinkedIn posting for Thanksgiving that I uploaded the image of a pizza and did a pizza topping detection. Ah, well, critically important. Yeah, you don't want to get into any of that pineapple pizza. business to business marketing is not an easy job. It's much different than business to consumer advertising.

31:55Why? Well, the enterprise buying cycles are very long and they're filled with decision makers. And those decision makers are going to kill your deal if you can't get to them. That's why you need to check out LinkedIn ads. LinkedIn has amazingly, but not unexpectedly, has a billion users. This includes 180 million senior executives. There's also 10 million C-suite executives, those are the CEOs, CFOs, CTOs, the chief strategy officer, chief finance officers. This means 18 % of those users are the ones who are the decision makers. How do you get to them? You get to them through LinkedIn in a respectful business environment.

32:30They're ready to accept a business message as opposed to another platform where they might be consuming cooking videos or podcasts or political discourse. No, LinkedIn is about business. You want to get people when they're in that cognitive mindset and they're willing to accept a business-to-business message. 79 % of B2B content marketers said LinkedIn ads produces the best results for paid media. This is obvious. I can tell you this is true. When you think about business, you think about LinkedIn. It's just exactly what comes to mind. So here's your call to action. Make business-to-business marketing everything it can be and get$100 credit towards your next campaign by going to linkedin.com slash this week in startups to claim your credit linkedin.com slash this week in startups no spaces no dashes linkedin.com slash this week in startups for a hundy a hundred dollars in credit terms and conditions do apply so you're commercializing this now anybody will have access to it absolutely fascinating uh and then what is it going to cost what does it cost to do this how do you charge for something like this you charge based on the number of maps uploaded the number of seats the amount of data how do you some kind of usage for the h100s how do you yeah it's a it's a private uh invite only offering uh using a per seat license and um we need to learn how much people actually use it to have a better estimate on the consumption of the gpu power in the cloud because this is the the most costly factor there and if every user needs its its own H100, I think it's more expensive.

34:01But if people can share, this is something we still need to learn. So it could be$1 ,000 a seat or something a month,$12 ,000 a year. For example, yeah. For example, I'm just making a number up here. And then that could have a certain amount of usage. If you go over it, that could be just overage charges like Amazon or Azure or Google Cloud charges you. Yeah. We start as a B2B offering, taking our lessons from that and ideally then make it like a real like a b2c offering uh maybe maybe it should be part of a future photoshop or any other tool where you need to teach an ai to detect any type of objects fantastic well this is amazing hey i noticed you're in an office there for those people who are listening michael has a group of people behind him and he's built an ai simulation this is what it used to be like in silicon valley people would come to an office they would interact with each other they would build products together they would order pizza play laser tag foosball and generally enjoy each other's company and not be weirdos working from home in their garages so yeah how did you build that simulation behind you doing video games a decade before it's rather easy having this kind of project projection behind me with avatars running around and um so yeah those autonomous agents are they no but in all seriousness you're in austria and those are human beings in an office am i correct you are very correct yeah Is work from home not a thing in Europe now?

35:24Are people actually coming to the office? It's the same like in the US. We had to come up with a good reason for people to come back to the office. One of them is, as we all know, people are more efficient. If you do something new, being together in a group, no one can beat that chemistry and this magic happening when people are coming together. but it's to be to be fair there are also job titles which can be done perfectly from home so it's all about finding the middle ground ah so that's it yeah you uh you have the people working on the product who need to collaborate in the office and then people who are doing stuff that's rote and unnecessarily uh that are single player mode solo kind of stuff they can work from home yeah nice analogy yeah i mean i i kind of wonder about sales executives like a sale seems like a solo pursuit, you could just do it on your own working from home.

36:17And then I think also, though, about sales culture and people being in a, you know, like a boiler room, you know, kind of all in the same room ringing the bell, kind of feeding off each other's energy, you got the gong, you got the sales contest, wonder if sales teams at home versus sales teams in an office, which one does better? Actually, in my socialization, I only know remote sales teams, but you brought up some very good points. Maybe we should reconsider that, how we deal with our sales team and bringing, because why not? Why not having the same like multiplier effect of efficiency if you have the sales team together?

36:53How much are you using AI to make your team more efficient? Obviously your developers are using, you know, co-pilots of some kind to write code, I assume. How much more efficient are they becoming with their co-pilots? Do 100 % of your developers embrace a co-pilot? You have holdouts who don't want to use a co-pilot. Actually, the adoption rate is phenomenal, especially interesting the ones who deal the most with AI, like our AI core developers, they use it the most. Myself, coming from video games, I see a lot of application outside of coding, like 3D artists, all the libraries we do for our 3D digital twins, like the texture libraries of certain geotipical facades.

37:36I think there's lots of room to automate this as well. And myself, for me, like any type of chat GPT is amazing for any type of presentation, board meeting, any text you need to write. So I think the adoption is pretty significant. Just by the way, Michael, there's a person right behind you, and they're going home. You need to stop them now and get one more hour of work with them. There's somebody who's literally going home to their families to eat dinner quickly, and somebody to intervene and keep them at the office for about one more hour. I'm joking. I'm glad I don't see, I don't have eyes on the back of my head.

38:08But otherwise, no, we have pretty strict working hours in Austria, actually. Oh, same one. How does that work? What's the culture like for that? We have a 38.5 hour week. People can stay longer if they want to, but they can't be forced. Got it. And people are, on a societal basis, bought into this concept of, hey, come to the office for 7.x hours per day and leave it at that. And that's totally culturally acceptable, even in a startup. yeah i think it's it's easier than that it's a it's self-regulating um when we started very early in our game studio we burnt ourselves out literally we worked like in in in games testers were or word crunch time yeah sure like when you have a fixed release marketing is waiting for it etc and back then the dvd presses were waited for your gold master uh cd roma dvd to ship a game then you worked like uh 22 hours but on the long long if you do this a couple times a year it's fine if you do it every day every week it will kill you so you just need to find out the the thin the right balance there yeah you know i think that's wise in some organizations folks are driven they want to be excellent they want to hit high notes in other organizations you you know want to be sustainable have a joyful life and you know you both things work so if you got a really crazy group of people who want to ship a game and beat every other game and have it be the greatest game ever and they want to sacrifice and be navy seals and be olympians and work every saturday and put in 60 hours a week instead of 38.5 okay that's fine or 37.5 whatever it is 38.5 i think you said and then if there's another group that says you know what we're just going to hire 20 more people we're going to be less profitable and we want everybody to work four day work weeks more power to you i mean both things can work and everybody's an adult i think this is one of the weird things that's happened in society is everybody looking for the government to mitigate these things you can just quit the job of a company that works too hard and is too intense and then find one that fits your style more or if you're at a place where people are not grinding and they all do like an average job and you don't find it engaging enough you want to do more go find the company with a more intense leader who wants to do more you can work for elon musk you can work for you know google and hang out on the rooftop drinking pina coladas all day and nobody will know the difference.

40:33So pick what you want to do. I don't know why this is so controversial for people. It's triggering for people, isn't it? It's triggering, especially we found out it's triggering for youngsters coming from university and think it all needs to be remote because maybe they graduated during the pandemic and it's a learning process. Yes, see, that's actually a very interesting thing. I think there's a large amount of unhappiness in the world right now, especially amongst elites, people who are living in developed worlds, the most developed portions of the world are having the highest rates of depression and sadness and anxiety.

41:08And I think it correlates with working from home. I think it creates a lack of socialization, a lack of mentorship, a lack of belonging that then has this downside. Now, hey, listen, you may get to spend more time with your kids or if you have kids, but it could also make people weird. And so it's not one size fits all, but there is a generation that I think is going to have to relearn what it's like to be mentored and coming to an office and uh yes you know it's not the end of the world is it i think these are first world problems literally by definition if you're in the first world you can deal with this because if you're in the emerging or frontier markets the concept of you working at home to go pick vegetables or work at a restaurant or work at a hotel or work in a factory like that's not even possible it's not even uh it's not even on the table you can't work at a factory from home it doesn't that doesn't compute and as you said it's all about it's a matter of choice we are all adults if you go to a startup you shouldn't expect like your your work-life balance as much if you go to a government or a more mature company it's different so it's all everyone can decide all right listen it's been great to get to know you congratulations on your company if people want to learn more or if they want to work at 38.5 strict hours a week at black shark ai or maybe a different amount who knows it's up to you you can go to your website, which is blackshark.ai.