SPEAKER_01: It's public knowledge in cutile, which is the CIA is 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 is a very good use of taxpayer dollars. So your company Black Shark has an investment from In-Q-Tel 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, its governments. So they
SPEAKER_02: are the ones who should be the most knowledgeable about what's happening on the surface of the planet.
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SPEAKER_01: Hey, 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, Stewart Butterfield, twice, he had a one game and then he made flicker, 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 bong fish bong fish built the 3d mapping software for Microsoft flight simulator, which by the way, if you've ever seen the tik toks with Microsoft flight simulator in them, it's impossible to distinguish sometimes these flights from an actual video of a plane flying. bong fish then created Black Shark AI to commercialize that 3d mapping software. Michael Hootz 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. And now it's being sold to government city planners and more Michael, welcome to the show. Thank you, Jason. Thank
SPEAKER_02: you for having me. I'm great, great into intro on the impact of video games. Yes, it is interesting how there are so
SPEAKER_01: 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? Because you agree there's some sort of a trend here? Definitely. Biggest
SPEAKER_02: lesson for me is that coming really down to the basics of the current generation of ices computers, you can build anything out of zeros and ones. So you can create your own worlds, you can create your own interaction. So why not do digital twin of the entire planet. So now that you are
SPEAKER_01: 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 latter first, why do we need a digital twin of planet Earth? Who needs this? And what are they going to do with it? I think the best way to explain is one of our advisors,
SPEAKER_02: Brian, on McClendon, who built the original Google Maps for back then his startup was Kehoe, like market, Google turned into Google Maps. And 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 two o 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 from planes, and stitch them together on a gigantic sphere, which is our planet. And which is great for human inspection, because we can interpret those 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 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 color 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. The 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.
SPEAKER_01: 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, in its ability to create those 3d models, is it not the wrong angle is a street level angle. Or, as I noticed with Google being they kind of had what I think they call it bird's eye view. I think there's flying turboprop planes like Cessna is 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 do you need multiple data sets in order to make this virtual planet Earth? That's a great question. I'm going into full length, I think
SPEAKER_02: we takes a couple days to answer. But making a short short version of it. You already said, very, very rightful. 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. And also the patch they can capture is very limited versus being high up in space with a 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 collections very, very higher up with 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 not enough. And then also, they are not satellite everywhere. So you have a very limited number they are growing in size in terms of how many satellites are circling our planet. But the ones who are there, they are placed on on circular passes 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 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 than in like, 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, 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 eye to come back with an well educated guess what the height of the building might be. And when you program this model to build the virtual planet
SPEAKER_01: 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, this is a satellite image. 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 how do you program this model, I guess? And how does it learn?
SPEAKER_02: The second option, the learning model. So basically, if you, if you train an AI, it comes down to the annotation or the labeling process, we are 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 understanding why these people are at least people at 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 very more in like a downtown area versus somewhere in the middle of a desert. So let me ask another stupid question. If you in
SPEAKER_01: 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, or what's necessary 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 you're currently targeting? You take 100 buildings, 1000 buildings, how many buildings you have to do you have to train it with?
SPEAKER_02: This is now almost a philosophical question. If you assume that we as humans as builders as architects, having certain patterns how we do buildings, actually, 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 one one architect, I won't build a 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 plane, I would use multiple images. Yeah, but does it take 500 or 100 to get to the fidelity is
SPEAKER_01: sort of what I'm getting at like, is it a month of training? Is it a day of training? What's the state of the art right now?
SPEAKER_02: We for for back then for the Microsoft Flight Simulator project, we have identified more than 1.5 billion buildings all over the planet, I think mostly all of them, we labeled about 10,000 buildings.
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SPEAKER_02: it the traditional way of labeling of doing annotations. But back then at the Microsoft flight simulator, the team was about 30 people we had very limited budget. So we only had two labelers to label the entire planet. So you cannot do this with just like a 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 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 offshore place that he should label a building, there might be cultural differences, what the best perception of building. So it's not like something could be
SPEAKER_01: a mosque in one country. It could be a library in other people could take it, you know, 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. The government, the US 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
SPEAKER_02: is done in sweatshops on like offshore places, which is not cool using taxpayers money. And if you think further, if you want to annotate label sensitive imagery, you cannot give this to some outsourced company. So this is the new tool I'm going to show you in a second, we are solving all those those issues on labeling AV we bring down the number of people you need for. So be accelerating the labeling process by I don't know, 101,000. And also, as you just said, is this particular example of building 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 in inside Google inside any enterprise who deal with geospatial images, they have like interpreters, GIS experts, and we basically instead of taking away their job, we are doing a tool which makes them way better. You can do this is living the it's public knowledge in cutile,
SPEAKER_01: which is the CIA is 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 is a very good use of taxpayer dollars. So your company Black Shark has an investment from In-Q-Tel, 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.
SPEAKER_02: Yeah, thinking from depth away, like, who is managing our planet, its governments, so they are the ones who should be the most knowledgeable about what's happening on the surface of the planet.
SPEAKER_01: Can we see what you're working on? Can you do a little demo here? And of course, since the audience is listening, if the audience wants to switch over to YouTube, just do a search for this weekend 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
SPEAKER_02: 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
SPEAKER_02: 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 satellite. And I'm now showing you in this map, you see a lot of those smaller ponds or lakes. Yeah, some of them are sorry, man. Those look like little man made lakes. Yeah, we exactly. And I now want to show you how to train an AI to detect all of these them into in this map within a couple minutes, not sending them to some outsourced company, etc. So first, in order to do have, we call it a detection run on this on this map for looking for lakes, we start a new AI model. I'm not pressing a button create new, I call the model water.
SPEAKER_02: underscore, I'm the author, it can deal with multiple classes now just one class which we call bonds. So what upon yesterday, so now I confirm. And now I'm starting training
SPEAKER_02: process. And as a first step, we identifying a small area, we call it the training area, where we see this target object, which which is this water pond is 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. And 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, like is, so I'm now switching to the yellow color. This is think of a gray on 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.
SPEAKER_01: So the machine is trying to figure it out. And they're all of a sudden painted in yellow, perfectly the areas that it thinks are water. And so what this is, is an annotation tool, you're annotating an satellite image. And then AI is learning from the human how to find the lakes, or I'm 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
SPEAKER_02: basically, I'm reinforcing the AI, this is what I look for. This is a lake, 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. In terms of color patterns, I take small training area on the airfield telling these, this all is not a lake. Because that's an airfield. That's not a lake. Yeah.
SPEAKER_02: 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 sure Right, because there are some dark spots, there are patches on
SPEAKER_01: the airfield that look like they could be lakes, but it's 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.
SPEAKER_02: Exactly. 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 then 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'm stopping the training. And let's do a first detection run. So now you trained it. And now you're
SPEAKER_01: saying, hey, here's the whole map. Get to work. And now I
SPEAKER_02: choose to subsection to speed up the process here, pressing start. And now it takes a couple seconds. This is just done on one. I think we 100 in a cloud setup. Behind this, we have a very powerful back end, which can scale 1000s of those machines, which enabled us to do a detection run on the entire planet. You're saying these are Nvidia H 100s or something?
SPEAKER_01: Yeah, yeah. So when our backend, basically, we also had to build
SPEAKER_02: 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. And 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.
SPEAKER_01: Previously, if an agency, let's say, in the United States, or another, you know, advanced government with resources, they would do this manually, they would put a bunch of humans on this and try to have the humans 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 where 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, development, you could basically find all the changes, new ponds, ponds that are changing size, and have some indication of where maybe nuclear materials being processed, if in fact, ponds had something to do with that, for example, and for example, it's just coming up with random.
SPEAKER_02: Yeah, random example coming to us. Yes. Listen, public markets
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SPEAKER_02: Now you can see here. To training areas, we have pretty good results on all of crazy even the C, which is the upper part got captured. That's upon two. Yeah, it's like that's a large pond. And, and
SPEAKER_02: as usually now you do this, you need to task and labeling company in house or external and tell them and give them like literally 1000s or even 10s of 1000s of training images and and they mark all the ponds. 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 shortcut it this now in a couple of minutes, but just me marking.
SPEAKER_01: 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, Spanish homes, or Mediterranean homes, you could literally say, Hey, you know what California, Arizona, 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 I do have Spanish tiles and give me their addresses and that person could do that in an hour or less.
SPEAKER_02: Right example. Similar one we got which is a little bit more complex, but similar mindset and process. Some large energy utility company asking us if we can identify potential locations like locations counting for renewables, we had to build gigantic wind parks like with this huge wind turbines, they know they need a certain like x y size of day of the area which should not be built with some existing buildings. It should follow a certain topology mostly flat, no mountains or forests nearby which can shield off the wind and certain minimum distance to the next human settlement for like noise regulations. And also there should be an 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 that I can build such a wind park.
SPEAKER_01: Amazing. 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 business is 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. And boom, all of a sudden, you could tell me where to put my 10 president Jason cities as part of my initiative, correct? Perfect.
SPEAKER_02: Now think of all the hedge funds investing in shopping malls identifying that they're not enough shopping malls yet. Yeah, or where shopping malls exist. I guess it will be
SPEAKER_01: interesting. Also, the changes you talked about how often things are updated and satellites are updated. You tell me, how often could you update the imagery of Taiwan in that example, with the satellite company work with today update the entirety of that every month, every year every day? What's the state of the art today?
SPEAKER_02: Here in this particular case, the limit is not on our side, the limit is on the image acquisition. And if you satellite companies doing a daily update of the entire planet, this company's doing that. Wow. Which ones do that?
SPEAKER_01: Which companies? That's Planet Labs. Planet Labs. Sure. They've been on the program. Yeah, they're not that resolution
SPEAKER_02: yet. Like this, the most static satellites are but this is I think, matter of time. And also the cadence will increase. But you can use drones or aerials if you want to have that. And we made an experiment 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, like when new buildings had been built, and we solve this with like five GPUs in the cloud, it took five hours, and then the client itself scaled it up using our back end to 1000s of machines, and they brought it down to minutes. So that's almost real time.
SPEAKER_01: 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 and 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. You could, in fact, every morning, say to your building inspectors, hey, somebody is 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 for building and say, hey, you got to be permitted to do this and make it safe. Correct? Exactly. I think of like, not a type of, how is it called
SPEAKER_02: building violations. I recently went to the Middle East, to KSA, and you know yourself about the gigantic construction projects that are doing the 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. And it's very easy to use what we can detect and then conflate it with some city planning, cadastre 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 price.
SPEAKER_01: 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 deforestation or a forestation where people are planting things, so you get a really accurate pulse on the trees being planted and that kind of stuff. So this is an internal to you have, and it's called Orca Hunter. Why is it called Orca Hunter?
SPEAKER_02: Coming back to the founding story of Black Shark, when we built as a very first project, Microsoft Fights, we had to develop all of the backend all of the tooling ourselves, there 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 call digital twin, which we're still working on 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 is 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 down images and do this scribble create an approach.
SPEAKER_01: Oh, wow. So it's gonna 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 can upload it and pay you a fee just to use the tool. Yeah, even our team for every today LinkedIn posting for
SPEAKER_02: Thanksgiving that I uploaded the image of a pizza, indeed, pizza topping detection. The classifications. Yeah, you don't want to get into any of
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SPEAKER_02: It's a it's a private invite only offering using a proceed license. And 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 most costly factor there. And if every user needs it, it's on H 100. I think it's more expensive. But if people can share, this is something we still need to learn what actually you know, $1,000 a seat or something a month 12,000 a year,
SPEAKER_01: 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 over charges like Amazon or Azure or Google Cloud charges you. Yeah,
SPEAKER_02: we start as a b2b offering, taking our lessons from that. And ideally then make it like a real like b2c offering. 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.
SPEAKER_01: Fantastic. Well, this is amazing. Hey, I noticed you're in an office there. Those people 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?
SPEAKER_02: Doing video games a decade before it's very easy having this kind of project projection behind me with avatars running around. And so yeah,
SPEAKER_01: those autonomous agents are there? No, but in all seriousness, you're in Austria. And those are human beings in an office. Am I correct?
SPEAKER_02: You are very correct. Yeah. Is work from home not a thing in Europe now are people actually
SPEAKER_01: coming to the office and maybe it's
SPEAKER_02: it's the same like in the US. We we had to come up with a good reason for people to come back to the office. We one of them is, as we all know, people are more efficient if you do something new. Being together in a group. No one, no one, no one can beat that the chemistry and this magic happening when people coming together. But it's to be to be fair. There are also chop chop titles, which can be done perfectly from home. So it's all about finding the middle ground.
SPEAKER_01: Ah, so that's it. Yeah, you. You have the people working on the product who need to collaborate in the office and then people who are doing stuff that's wrote and unnecessarily 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. Yeah. And 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,
SPEAKER_02: but you brought up some very good points. Maybe we should reconsider that how we deal with with our sales team and bringing because why not? Why not having the same like, multiplier effect of efficiency if you have to say this team together,
SPEAKER_01: how much are using AI to make your team more efficient? I'm 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 to 100% of your developers embrace a co pilot, you have holdouts who don't want to use a co pilot,
SPEAKER_02: actually, at adoption rate is phenomenal, especially interesting to ones who deal the most with AI, like our AI core developers, they use it the most. I myself coming from video games, I see like a lot of application outside of coding, like 3d artists, or the libraries we do for our traditional dreams, like the texture libraries of certain geotubical facades, I think there's lots of lots of room to automate this as well. And myself, for me, like any 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 just pretty significant.
SPEAKER_01: 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. And then there's somebody who's literally going home to their families to eat dinner quickly, send somebody to intervene and keep them at the office for about one more hour. I'm joking.
SPEAKER_02: I'm glad I don't see I don't have eyes on the back of my head. Otherwise, no, we have we have pretty strict working hours in Australia, actually. And what is that? How does that work? What's the culture like
SPEAKER_01: for that?
SPEAKER_02: We have a 38.5 hour week. People can stay longer if they want to, but they can't be forced.
SPEAKER_01: 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.
SPEAKER_02: Yeah, I think it's easier than that. It's self regulating. When we started very early in our game studio, we burnt ourselves out. Literally, we worked like in games testers were crunch time. Yeah, sure. Like many of our fixed release marketing is waiting for it, etc. And back then the DVD presses were waiting for your gold master CD Roma DVD to ship a game, then you worked like 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 thing that I balanced out.
SPEAKER_01: 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 and 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 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 gonna be less profitable, and we want everybody to work for 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 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 and you know, hang out on the rooftop drinking pina coladas all day, and nobody will know the difference. So 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
SPEAKER_02: found out it's triggering for for youngsters coming from university and think it all needs to be remote, because maybe they they graduated during the pandemic. And it's a learning process. Yes, see, that's actually a very
SPEAKER_01: interesting thing. I think there's a large amount of unhappiness in the world right now, especially amongst 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. And 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 can 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 yes, you know, it's not the end of the world is 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. That's not even 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. And we
SPEAKER_02: are adults. If you go to a startup, you shouldn't expect like your work life balance as much if you go to a government or a more mature company, it's different. So it's all one can decide.
SPEAKER_01: Alright, 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 black shark.ai. Michael, thanks for being on the program. Everybody check out black shark dot AI and we'll see you next time on This Week in Startups.
