The Vertical Space

#104 Edward Barraclough, Drone-Hand: Why ranching will scale autonomy before defense

• Luka T • Episode 104

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Autonomy may scale in agriculture long before it does in defense or UAM, and today’s guest makes a compelling case why. We speak with Edward Barraclough, founder and CEO of Drone-Hand, about applying autonomous drones and on-device AI to the realities of livestock operations across Australia, New Zealand, North America, and beyond.

Edward explains why ranching is the perfect proving ground for autonomy: massive land areas, urgent labor shortages, permissive operating environments, and ROI that’s measured in days - not years. We explore how drones are already replacing helicopters on million-acre cattle stations, why biological data creates one of the deepest moats in autonomy, the role of trust and repeatability for producers, and how CASA’s regulatory evolution compares to FAA and EASA. It’s a rare look at autonomy where economics, biology, and geography collide.

Edward:

The reality is agriculture is one of the fastest adopters of technology out there. They just have to see the ROI. They just have to see that it is going to improve things for them in some manner, whether it's actual cash ROI or time or efficiency or long-term gains, whatever it may be. They need to see that immediately.

Luka:

Today we're joined by Edward Barraclough, founder and CEO of Drone Hand, a company using autonomous drones and edge compute AI to transform livestock operations. Edward brings a contrarian view that agriculture, not defense or urban air mobility, will be the first environment where autonomy reaches true commercial scale. We explore why ranching might be the perfect proving ground for autonomy, how drones are already replacing helicopters on million acre cattle stations and what real ROI and trust look like for producers who are just trying to survive the season, not really chase sci-fi. We also dig into the real adoption drivers in livestock management, why biological data creates one of the strongest moats in autonomy, how to build trust with producers and how CASA's evolving regulations compared to the FA, A and ia. It's a rare deep dive in autonomy where economics, biology, and geography collide. Thanks, Edward for a great conversation. Let's get into it. Edward, welcome to The Vertical Space. It's great to have you on the show.

Edward:

that's great to.

Luka:

So first question, is there anything that very few in the industry agree with you on?

Edward:

Yeah. I would have to say the thing is, that agriculture, not defense or urban air mobility will be the first true at scale autonomous UAV success story. I say this because I think agriculture has all the ingredients to make autonomy commercially viable right now you've got huge land areas, repetitive tasks, a major labor shortage, not just here in Australia, but as I gather in the US as well, and environments that actually benefit from autonomy rather than resist it. To me, the economic seems clearer than almost anywhere else. you're not flying over cities. The regulatory burden is lower, you know, sure defense and UAM will eventually scale, but they face far more regulatory safety, infrastructure and public acceptance barriers. Whereas agriculture has the demand and the economics and the operating environment, I.

Luka:

That's a very bold, contrarian view. so how about you elaborate and double click on it a little bit because on the one hand, autonomy is, broadly early when it comes to mission critical safety critical applications. But at the same time, the defense market is where, these technologies are being developed and matured currently as we speak. And there's a, favorable risk reward equation there and there's a lot of capital, behind it. And so how do you compare and contrast that to agriculture?

Edward:

Yeah, I mean those, those are very good points. I mean, you can't deny that funding behind the military. That's hundred percent. You are limited by where and how you can test that. I mean, really there are situations where, you have to have very specific environments, very specific situations where you can actually test a fully or semi-autonomous system in a defense capability. Whereas with, with agriculture, there are literally millions of square hectares of, land that can be tested on and be used on with. Little to no risk to humans, on the ground or in the air. in many ways, agriculture can, can be like the perfect proving ground, you know, for things like defense and other use cases, you know, tested out in agriculture first where it's not going to face regulatory barriers around, around, human welfare and, and people on the ground and, and, law, international laws, and then translate those systems into defense. You know, identifying animals is not that different to identifying enemy combatants. And if you can prove it on an animal first, then you can, then use it in a defense, standpoint.

Luka:

What are some of the other things that you can test in the agriculture environment that maps into defense?

Edward:

Now my background's not hugely in defense, but I would have to say that any of this autonomous systems around, mission planning and flight or organization, target control. Finding and locating, subjects, analyzing those subjects from a distance without disrupting them, without them hearing all of those elements are things that we are doing in agriculture that I would imagine have a crossover into the defense space. you know, I, I would hesitate to comment too much on that. I don't have a defense background, but I do, from what I can see in the industry, that there is, a lot of. requirements, that you must, must address to test anything in, in the defense space.

Peter:

I mean, I get that the point you're making is that agriculture has a big and valuable use case right now. I don't really think about comparing it against defense though, because defense is such a different animal. It has different economics driving it. the use cases in defense are an absolute moving target because the tactics are changing every few weeks. Whereas in the part of agriculture where you're working, you know, especially around ranching, I think the, the ultimate mission. That the systems would fly is out there, and it is a relatively stable set of requirements. It's just a question of when did the underlying technology become good enough to actually go serve that mission and give the practitioners in the space the chance to explore how to use these platforms in order to achieve the end goals of. You know, bringing more productivity into a ranching operation, saving on labor costs, improving animal welfare, all all of the things that are so germane to what you live every day. But it's just, it's a different animal than the craziness going on in defense right now

Edward:

And I agree entirely, and I think. that's where I'm sort of coming from here. And that stability and that difference, allows for it to become one of the first, truly at scale success stories in autonomous, UAVs.

Peter:

people have been talking about using these in agriculture for, about 10 years. And there have been reasons why it's, received sort of a limited level of adoption. up until now, but it feels like things have really changed now. Do you agree with that? And, and how do you, how do you see it? How do you explain that? How do you explain that people came into the agriculture space and, you know, they were looking at row crops. they were looking at all kinds of different ways of using drones. And for 10 years there was Hope and promise and a lot of talk, but, but when you actually talk to the end users, they weren't getting a hundred percent of what they needed, and so the adoption just wasn't really happening.

Edward:

Yeah, that's a really good point.

Peter:

You've seen that history and now you are coming into the space base. But the foundation is a little different now. The underlying technology is different. I think you're in a unique market, geographically that might be a good fit for this, but walk us through that.

Edward:

Yeah, that's some, some really good points. I mean, I think we've seen a few major changes that have. allowed for this. I mean, let's start with a simple one. A really common issue with any, technological solution provider, particularly in the agriculture space, is not addressing pain points directly. So coming out with a new technology and saying, this is this really cool new tech, let's use this for you. It'll make things better for you. Whereas the user is saying, yeah, I don't really have that problem. I don't particularly need it right now. What we are seeing the difference today is that far more innovators, far more, technology providers are listening to the users themselves and building or adjusting or refining their technologies to suit what is actually needed on the ground as opposed to what the technology is capable of. it's that chicken and egg thing where it's like, here's this cool thing. But if you don't have anyone who needs to use it, it's not gonna take off. On top of that, you've got the, development of the industry itself. You've got, you know, drones becoming cheaper and higher quality. the technologies are better, the cameras are better, All of those things have come into play to make it really, really accessible. and then again, on top of that, you've got the age of the demographic. People are becoming more and more exposed to drones, more exposed to robotics, more exposed to autonomy. You know, you can go to your, tractor dealership now And have tractors that are already set up for GPS. Not so much autonomy, but, but guided, plowing and guided spraying through GPS systems. The idea of, of autonomous systems are no longer science fiction in the eyes of the user. It's no longer a a, a strange or daunting thing to think about.

Peter:

So in a way, the, the technology not only just needed to mature in an absolute sense, but it needed to be around long enough for the practitioners in agriculture themselves to develop the technology into a way that they would. Really understand, in terms of how it's employed, rather than it being like Silicon Valley coming to the rescue and telling the agriculture industry how to change the way they work, which obviously is bullshit and, failed over the last 10 years. But what it really takes is the practitioners themselves harnessing the technology in the ways that they know best because they understand the complexity of the biological systems that are at hand here. And they understand truly what needs to get done, you know, in the different environments on in a ranching operation or any agriculture operation.

Edward:

A hundred percent. I mean, that, that's exactly right. It's, it's. a lot of this comes back to the ideas of collaboration, you. know of of, getting out of that space of we produce this technology, you are going to use it Instead, it's becoming this space of let's all work together to make or refine the technology to suit The industry's needs, and this is collaboration everywhere, not just from the tech providers and the farmers and producers, but actually the investors, the industry bodies themselves and the government. You know, the difference in just the short period that I've been in this space has been incredible. when I first, started exploring this, this route, there were a few autonomous robotic tractor companies and things that were starting to get off the ground here. But then just last week, Swan Farm Robotics, they've just announced they've done over 10 million hectares, from their robotics tractors. You know, this type of thing is being adopted so quickly and so much faster than it ever was before. And this also plays into the stereotypes of the agricultural industry and of farmers in general, is that, oh no, they're too conservative, too traditional. They're not gonna try anything new. The reality is agriculture is one of the fastest adopters of technology out there. They just have to see the ROI. They just have to see that it is going to improve things for them in some manner, whether it's, you know, actual cash, ROI, or time or efficiency or long-term gains, whatever it may be. They need to see that immediately.

Jim:

Ed, great responses about 10 years or so ago. There was a a study done that talked about agriculture. one of the, probably the best use cases for, we'll use the word automation, and I know we're talking about, aerial automation right now. First of all, regardless of the ability to address that need with technology over the last 10 years, why was agriculture considered to be such a great use case and give us specifics as to the before drones and after drones, what's the cost before and what's the cost after? Is there result of this aerial automation? Why is agriculture such a good use case?

Edward:

Yeah, I mean, it really comes down to efficiency and time savings. when you think about a country like Australia or the US. I mean, it's, it's, it's slightly different in Australia where we've got such a large land mass with So few people compared to a country like the us. if you look at the difference between farms in the south or southeastern parts of Australia where I'm from, through, to the massive cattle stations or ranches, in the northern parts of Australia. You know, the average farm where I grew up was around 10,000 acres. Not huge in Australians, eyes. In the Northern Territory, you are looking at properties that are more than a million hectares. Just for the one ranch, the one station. Now, how do you deal with that sort of landmass? How do you deal with that sort of space? Well, the large, majority of people would use helicopters, and these are usually Robinson, R 20 twos, they'll have light aircraft. and you know, this, this was great back, post Vietnam war when people were coming back, they had pilot skills. Those sort of helicopters were comparatively accessible and cheap. Yeah, but now labor shortages are increasing. Fewer and fewer people are actually going into the ag space. The cost of keeping those Robinsons running is going through the roof. I'm not sure if you've, you've heard, but you know, we've had some rather high profile legal cases recently about keeping those type of helicopters compliant. The focus has been very much on reducing the cowboy mentality of the helicopter users in the north. and so compliance has become a major cost with these types of things. So give you a real world example. there is a cattle station or cattle ranch in the Northern Territory that we've been working with. Which is not huge, but also by no means small, it's, it's probably about, 200,000 acres. and the owner of that property uses helicopters twice a year to go and check her cattle and to bring them in to either be sold or to be vaccinated and all those types of things. Each time she does this, she will hire a, team of heli musters. so helicopter herding, groups. She'll spend anywhere between 60 to a hundred thousand dollars each time. She'll do that twice a year, sometimes more You're already seeing a cost of, anywhere between 120 to 200 plus thousand dollars a year being, spent on just finding and moving some animals. The alternatives to that is sending teams out on the ground for a week or more. again, not cheap, not as expensive as helicopters, but certainly not cheap. Bring in A-A-A-U-A-V. Bring in a, a fixed wing VTAL drone that can fly for 3, 4, 8 hours. Even just to do the simple jobs of checking. Is there water in the, in the, dams and troughs for the animals? Where are the animals? And then to add on some autonomous factors to that, like autonomous herding or autonomous flight missions to check these types of things. And suddenly you're not just saving, you know, a few thousand dollars, you're, you're potentially saving hundreds of thousands of dollars for a user in a year. But these systems had to become cheap enough and accessible enough, and accepted enough to, to start to augment these operations. It's a slow process, but It's starting to happen more and more often, particularly as we see the effects of not doing these things right. a few months ago there was a very, big, story in the news where one of Australia's biggest beef agribusinesses lost, around 1500, cattle died because the water was not turned on for one of their troughs. You know, that's a simple check. If you're on a smaller property, you could just drive out and go and check it. You could send out your quadcopter and go and check it. But to check it, out there means driving for two days. Or flying in, in a helicopter or a light aircraft. Send the drone out. Do that once a week. You'll never have that issue. You'll know that there's water there, you know that they're under control. You're not losing, a hundred thousand dollars worth of Wagyu beef. I.

Jim:

Okay. That's meaningful. Now, that's terrific. What technologies over the last couple of years have enabled this return on investment that we didn't see before?

Edward:

Yeah, so I mean, that's a great question. I mean, the, the larger properties are not the only example. I mean, we can look at the smaller ones as well using just simple quadcopters and, and here in Australia the most common is the DJI ones, quad copters and drone docks. You know, those types of things have reduced in price so much, and the camera quality has increased so greatly that almost anyone can go and pick one up. Start using it within 10 minutes and start getting, returns from it. what had to change was that accessibility was, was the price coming down? Was the, the quality of cameras and things increasing and the battery life, of course, for those larger properties. You know, there have been some amazing Australian long range, fixed wing tto, drone manufacturers. the the cost of producing them has been far too high. It takes time? to build an economy of scale, to bring the cost down and make again, make them more accessible. And then, of course, regulation, which I assume is something we'll get into a bit later on.

Luka:

We'll, and since we already stepped pretty deep into use cases and products, how about you give a a quick overview of what is it that you're actually doing at Drone Head?

Edward:

Yeah. well, with Drone Hands we are. redefining livestock management through the use of AI driven, drones and fixed cameras, to increase efficiency and reduce labor shortages, reduce labor use, and reduce preventable livestock mortality. we've built this in a way that is scalable and flexible so that we can move beyond agriculture, but we're seeing that, a great opportunity here in agriculture. Far greater than I actually ever realized, that in using autonomous systems to, to create a more efficient, livestock production operations and really anything from, from high density feedlots right through to those massive cattle stations, I.

Luka:

And so what does the product look like, Edward? How does the customer use

Edward:

Yeah, well, we have a few different tiers. So, for example, a smaller property owner like, in the south here might use, an enterprise, quadcopter, A DJI type quadcopter, that would download our software as an app to the controller. From there, they can define their paddocks, very simple process of you know, defining the areas of your farm, the paddocks of your farm from then on, take the drone out, turn it on, put it on the ground, choose Paddock one, choose your subjects, whether it be cattle or sheep, water sources, pasture, whatever it may be, and press go. The drone will fly itself in a semi-autonomous manner and in real time, the machine learning algorithms that are working in device are able to tell the, the farmer in real time Where the livestock are, if any, are in trouble, any, have any issues, all of those types of things, and giving an accurate count at the same time in real time. Most importantly, offline. So it doesn't use cloud processing, it uses the actual remote controller to process. Its, its, machine learning or algorithms. At the end, you get a report. You can use this for, compliance. You can use this for animal husbandry planning. You can use this for, tax purposes, insurance, all those types of things. It stays as a, piece of data that you can keep, moving forward. Now that translate this across, all different size systems from Quad cos to fixed wing vetoes to fixed cameras. and we basically, we end up covering the entire sector of the livestock industry, here in Australia and hopefully beyond.

Jim:

Edward, what's novel about what you're doing? It sounds fascinating. Would love to know a little bit more about the machine learning AI component to it. So start with if you could, what's novel about what you're doing?

Edward:

Right.

Jim:

I, I'm no expert in agricultural drone use, so educate, Jim And the

Edward:

yeah, for sure. So, I mean, most, farmers, regardless of the size or type of property, will spend a number of hours every day or every week just checking their paddocks, checking their stock. this will be either. In a in a ute or a side by side, a TV, or in the north helicopter or a light aircraft, just checking the basic things. Where are my animals? How many are there, are any injured, sick, deceased, having birthing troubles? Those types of basic things. Is there water available? Is there food on the ground? Is the infrastructure sound? So are the gates closed and the fences whole? And that's a really basic task, but it is. A fundamental part of livestock production and it's, it remains for hundreds of years and it's, and it's still here today, but it takes a lot of time and it takes a lot of effort. The bigger the property, the more effort, the more time. If we can, reduce this by even a fraction of a percentage, then we are saving significant dollars. In reality, we are reducing it by 80 plus percent. across the case studies we've done so far, add to that is preventable livestock mortality. Now, this is something that not many people actually are aware of so much is that for the majority of livestock farmers. You have an acceptance level of a certain percentage of your stock dying, of, of not making it to market. for example, if we take sheep during lambing, you will expect to have on average, around 5% plus loss of the lambs that are born on the property. That's just the reality. It's not nice to look at. You know, you'll drive around, you'll see dead lambs around. It's, it's just a reality. But to reduce that just by a small percentage, suddenly you're translating that into hundreds of thousands of dollars millions of dollars once, at the market level. Take that into disease and, higher value

Jim:

How would you, how would re ed, how would you reduce that? re land mortality rate?

Edward:

Simply by having more, information, by knowing what's going on out there. Animals can be pretty dumb to be honest. You know, a, a cow will leave its calf and walk away from it, get separated. The calf dies of dehydration by knowing what's going on out there, by having, information daily or. Every couple of days, you know what's happening. You can get out there, you can address it before the animal passes, and the same translates to disease as well. If you can see animal behavior that is, is indicating disease, you can address it while it's still able to be treated. you know, the livestock industry in Australia loses over$4 billion a year to preventable livestock mortality. If we can reduce that by a fraction, you're saving a huge amount of money.

Jim:

And then the differences between what you are offering and what was otherwise available from other companies. You've talked about other techniques, or you know. There, methods of surveillance and the, like, how were, how was what you're doing different than other companies?

Edward:

Yeah, yeah, of course. Right. So, use of drones in agriculture largely previously was all something that was post-process. So, for example, you'd use some amazing multi-spectral technology to check, crops and to check pasture levels and things like this. But the drone itself would just be a data capture device. You go out, you take the images, you bring it back, put it through the computer, get the results. It's a slow process. That's fine in horticulture and in cropping and things like that. Not so good for livestock when animals move around. So what we are doing differently is being able to process that imagery, process that data in the actual controlling device. So in a quadcopter, it's as an app in the, the remote controller in the long range fixed wing vetoes, we've actually got onboard edge compute. So we've got some Jetson, Nvidia products in the drones themselves or on the ground station. So you can get very high level capability in machine learning processing without needing to go to the cloud. These types of systems take away that need to have internet access. which in most farming areas is pretty difficult. Yeah, sure. Starlink has improved things, but starlink is back in the house. It's not necessarily out in the paddocks, and it allows for real time actionable information. You can know right now what's happening as opposed to collecting the data, going back and finding out in an hour what the results were.

Jim:

That's very impressive. That's very cool. Could, could you just talk, so we talked a little bit about agriculture, we talked about what's unique, to Australia. Is there anything else about the agricultural market that you'd like to share with us before we get into kind of the regulatory landscape? I think you've painted a pretty good picture.

Edward:

yeah, I mean, I think, I think we've covered most of it except to say that I, you know. Agriculture producers are not trying to, try something exciting and new. What they're really trying to do is survive a season. They're looking for tools that can save them time and money today. and if it does that, they'll adopt it today. You know, that's, I think, one of the most fundamental differences between, the agricultural industry and, and looking at Any other, common uses is this is not an experiment. This is something to survive. This year, this season, make it through, make, food security, stable and all those types of things.

Luka:

Yeah, because the ROI is so near term and it's so tangible, it's very different than taking a bet that a particular technology will help increase yields, you know, X percentage points in 10 months from now.

Edward:

That's exactly right. I mean, you have to remember that most farmers and ranchers, they are operating on debt. You know, they'll take loan facilities. They'll take loans and debts. With the assumption that they'll get that back at harvest or at sale or whatever it may be. With the, increase in extreme climate events that are happening more and more often, those good years are happening, fewer and fewer, and it's getting harder and harder to make it through. So you expect us to do a lot more with a lot less. and if we can find tools to make that happen easier and more efficiently, Then they'll be adopted.

Jim:

where is the most sophisticated agricultural farming taking place in the world?

Edward:

Well, that's a good question. I would like to say Australia, but we're also seeing a lot in Europe as well. I mean, the some of the smaller European countries, like Denmark and, and other places are doing some amazing things in the ag space and trying out new technologies. But with agriculture being so broad. It's very difficult to, to say there's just one area. you know, the cropping space. Huge things are happening in Vertical farming, in the livestock space. A lot is happening here in Australia and New Zealand. It, it all really depends on where you are in terms of the, the, sector as a whole.

Jim:

So let's say we go to a large agricultural farm in the United States what technology are they using that's comparable to what you do and how are you different from what they're using today?

Edward:

Yeah, great question. If we went to say a cattle ranch in Texas or Montana, somewhere like that, you'd expect to see some level of technology adoption. You know, it will vary from, property to property depending on their, risk appetite and so on. But you'd probably see some water level sensors on their troughs and dams. You would probably see some use of satellite imagery if they're a bit more forward thinking. Considering, things about their biomass and their land management planning, You may see the use of smart tags, whether GPS or satellite enabled, tags or collars that are being used on, the cattle themselves. You know, all of these, technologies are amazing and and are proving to show some really good returns. However, they're all very siloed. They're all very specific in their use case and very, specific in their data that they're delivering. They don't tend to work together, and they tend to all require their own dashboard and their own app on your phone or whatever it may be. And look, to be honest, more and more farmers are getting pretty annoyed about this. They don't want to have to download 15 different apps just to check each of these data points. So we're seeing an a. A trend towards collaboration where more and more, of these technologies are interacting with each other and integrating with each other. And what we are attempting to do is to provide a more holistic solution where we're able to cover all of these issues from water source to your livestock behavior and health and condition to the pasture conditions and infrastructure and, staff safety and all these types of, things. In the one simple system, and then we are looking to integrate that into the existing systems. So for example, if we have those smart tags that I mentioned. If a smart tag happens to indicate that there's a, a cow in, in distress or showing some issues, that can trigger one of our drone in a box systems to release a drone, go and visually confirm whether or not that is the case or whether it's a false alarm, which can then send out the staff member to deal with it if necessary, and, and so on. So by interacting and integrating with all of these systems, we can hope to provide a more easily accessible, greater level of, of actionable data to the users as opposed to these separate, siloed individual systems that are, you know, great in their own way, but losing popularity simply because of their lack of integration.

Jim:

so let's say you're, in Australia, Canada, the United States, Norway, different places. You talked about, do you need to learn anything about those locations before you deploy? I mean, is there a, is there a data set here that you need to adapt to, to be able to optimize your capabilities?

Edward:

Yeah, that, that's a great question. I mean, yeah, over the last year we've had some amazing experiences, starting to do trials around the, the world. We've been here, in Australia and New Zealand, rolling out in Canada and the US as well. And yes, it's, it's all generally based off our core data sets, but every time we move into a new, a new, area, we have to be prepared to be flexible, to refine, to adapt, And in some cases even, build new, elements to those data sets just because there's a sheer amount of variance. You know, we've got anything from, you know, we did trials at the beginning of this year in New Zealand. I don't know if you've ever been to the south island of New Zealand, but they have the. Steepest most beautiful mountain rangers down there, but to have a farm trying to run their livestock where they're changing altitude from, you know, a thousand meters and below in just the one paddock, that's quite an an adjustment to make to your mission planning parameters in the the autonomous flight systems. Even down to things like soil color, you know, the difference in the, the color of the dirt, how that is throwing off the, the machine learning and the image recognition. I was chatting with the, guys from Montana, two days ago, and we're talking about is it gonna work with mountain sheep in the snow? My answer was, let's find out. No, I haven't done that yet. Let's go. Let's get it going and we'll work out what we need to adapt, what we need to refine to get it working in those conditions. You know, we can't expect what we've built in one place to be universal. All we can expect is that the foundation is there and that we will refine and adapt. And to each situation as we meet it. And we've managed to improve that, dramatically. And this is becoming one of our very, very, important parts of our business, is we've reduced this data collection to functioning, integrated model to weeks instead of months, sometimes, faster as well. And this has just come from, from scaling, from, experience and time within this, industry.

Luka:

Would you say that, this variability is the single hardest generalizable problem in deploying autonomy across these different, geographies?

Edward:

a hundred percent, you know, variability. It's, it's amazing, you know, having to cope with, you know, wildly different terrain, vegetation density, livestock behavior, connectivity issues. as I said, the, the color of the soil, the, the amount of snow, the amount of of grass cover. Even down to animal behavior, you know, how often does this animal interact with people? You know, cattle in, in the Northern Territory will see people maybe once or twice a year, that, can be very skittish, very reactive cattle that are on a dairy, in, in, in, other areas. They'll be meeting people every single day and they'll be like, your house pet. You know? So all of these things create huge, variances. There's no, way to sort of, you know, generalize one system to work on all. You have to be ready to adapt. You have to be ready to refine. I.

Luka:

And since we're talking about autonomy at scale, it's a good time to highlight another company building the infrastructure behind it. SkyGrid is your trusted partner for high assurance aerospace integration. SkyGrid develops third party services that connect and coordinate autonomous and piloted aircraft through data automation and secure digital infrastructure. Whether supporting Advanced Air Mobility UAS operations or next generation air traffic services, sky Grid provides the foundational technology that ensures safety, scalability, and compliance in a rapidly evolving sky. Learn more about how Sky Grid is enabling autonomous flight@scaleatskygrid.com. How much do you rely on synthetic data and is my intuition correct? That. Unlike in certain, other use cases in autonomy where, it is mostly based on physics and you can simulate physics with relative high fidelity in simulations. and you can also simulate different shapes and, different objects and, different weather conditions, et cetera. You can't easily simulate biological behavior and animal behavior. On the one hand, that really increases the moat, of a company that can accumulate that type of data in the field, in the real world. does it also slow down the development timelines because you cannot leverage that synthetic data.

Edward:

those are really good points. I mean, yes, synthetic data could work a lot faster. You could grab, gather a or create a lot more information, a lot faster. There's, there's no doubt about that. However, you are never going to get, as You said, those variances in behavior, those natural variances that are gonna occur between animal to animal property to property, and so on. It's really interesting actually, and this is something that. We are all finding out together at the moment. I mean, who will win out? Will real world data, stay ahead of generated data? I certainly believe so. The data seems to to suggest that it will, but only time will tell, but. I'm not gonna name names here because it hasn't been a hundred percent released to the public, but we've just got an approval last week for an upcoming development project we're doing where we're actually going to be competing with one of the biggest names in generated, AI and ourselves to both create a disease detecting machine learning model. This is gonna be done, Concurrently to see which model is going to be able to produce the most accurate data, the most accurate results, and, which will, which will be used by this agribusiness moving forward. This is funded partly by the Australian government and partly by this agribusiness, and it's gonna be really, really exciting. I expect, you know, I, I believe that we'll win out for the, for these very reasons that you can't replicate. A hundred percent of the variances you see in nature. But you know, time will tell. this will be interesting. I'm excited. I.

Jim:

so you just, you just triggered something. Edward and me. What's the greatest part of your differentiated offering? Is it the ability of the drone to be able to react? Is it the data gathering, is it the processing, of the data? I mean, if you're competing against a, I think a, you said an ai, a large AI company, in being able to determine the smartest way to be able to react to a agricultural condition. What's the real significant differentiated part of your company? Is that a question

Edward:

It's a very broad question. It's a good question. I like it. the differentiator I think, will come down to real world knowledge. It'll come down to the data sets we're able to build through interacting with actual producers. It's not just the imagery we collect, it's also the knowledge and experience that we are collecting from the people on the ground. You know, to be able to speak to people who've got generational knowledge about these things. Not just the scientific knowledge, which is fantastic, but the generational knowledge to say, look, actually, if you look, at it this way, you get a better result. Or if you, notice this minor point instead of that minor point, you'll get a, a better result.

Luka:

Can you give a. An example of that and how do you encode that into a model?

Edward:

that's a really good question. a, a specific example could be to do with behavior. For example, how the animal is, is holding its shoulders or the way it is standing to a lay person. You put two images side by side and you wouldn't be able to tell the difference to a person with a generational experience. They'd be able to say, oh, that one on the right. Is indicating this issue. How we then translate that into the models, into the machine learning elements is by focusing the data set creation on the specific conditions that the user has pointed out, that the producers have pointed out, and then having those relationships with those producers to have access. To those animals to take the imagery to, to build the models from, I don't know, what your experience in this might be, but if you went along to a cattle ranch and said, Hey, show us your, show me your worst animals, they're not going to necessarily respond in the best way. You have to build those relationships. You have to build that, and that's something that we've been doing throughout the industry. You can generalize, you can generate I imagery to try and do these things. You can look at science, you can look at broader reports that have been done, but they'll always be delayed. they'll always be from last year, five years ago, 10 years ago. They're not necessarily gonna be about what people are experiencing in that area today.

Luka:

And I think it's also how well do you build trust with the end user? How do you build a product that is easy to use, that is delightful, to the customer. So on that point. What does trust look like on a farm? both from an, autonomy angle, but also from a, customer relationship angle. How do you convince a producer that autonomy is reliable enough to replace, some of their routine?

Edward:

I mean, like in any industry, trust is completely built through repeatability. So it's consistency, it's consistent results, no surprises, predictable routines. So what farmers will care about is very much. Does it work every time? You know, can I trust? Can I believe that this thing, whatever it may be, whether it's an autonomous UAV or a robotic system, or even just a sensor, is going to be consistently returning accurate results. No one expects perfection a hundred percent of the time, but they do expect repeatability. They do expect that it is gonna give the same context of answers. Repeatedly, over and over again. So does it give the right answer? Does it actually create extra work for me or does it reduce work for me? Does it keep my staff and livestock safe? these are the things that build consistency and that's not something you can do with marketing. It's only something you can do in the, actually in the field, in in use. so after like three or four or 10 flights where the system has behaved predictably, where it's, produced reliable results in different conditions, you know, different times of the day, different weather, different variables involved, and produces those reliable, repeatable results, that's when we get the trust. That's when we achieve that level of, of confidence in the user, in ourselves.

Luka:

And is there a difference in how you're thinking about building trust from a, product and technology perspective. And, also a bit of a collaborative, relationship with the customer because these are early days for these technologies, and so many times you have to almost co-develop the technology of the product with the customer.

Edward:

Yeah, a hundred percent. That's so true. I mean, in one thing we've definitely noticed here in Australia, in the ag tech space is that as a community, we in many ways have kind of burnt ourselves. Where previous ag tech solution providers have have gone to farmers and producers and said, look, here's this amazing technology that's gonna change everything for you. Then being early stage, they haven't delivered a hundred percent of what they've promised. And that's put the customer against them. They've said, well, you know, you, you promised this. It didn't happen. So what we're doing today, what more and more innovators like ourselves are doing is bringing the farmer along on the journey to say, look, we're an early stage startup. Come with us. We can work together. We can build this thing together and make this thing, uh, successful. And that involves honesty. And honesty is one of those things that builds trust. You know, going to the farmer and saying, yes, I own that mistake. I own that problem, and we are going to fix it in a very short period of time and make it work better for you. But the only way I can know something needs fixing is if you share that feedback with me is if you tell me you wish it did this instead of that, or whatever it may be.

Luka:

What are some of the common misconceptions that farmers have with respect to autonomy and how have you observed these misconceptions change and shift after experiencing autonomy?

Edward:

So a lot of expectations around autonomous systems is one of overwhelming complexity. You know, when the average person of, of any industry hears about robotics and autonomous systems, they immediately think of, of science fiction and the future, and. Things that are gonna require a lot of technical knowledge to understand, implement, or use. The reality is, with most autonomous systems, they're built to be simpler, to be easier to use than current systems. Hence. The, uh, the name, autonomy and autonomous. So it just takes a few times of letting the farmer see or experience that this technology is a really simple to use, and b doesn't require them to be trained in mechatronics or, or some level of engineering that they're not prepared for. You know, for us for drone hand, we built it very much with the average user in mind and. For example, that would include my 80-year-old father. So it's, if we can make it simple enough that they can just pick it up, use it, uh, understand it within half an hour or less, then we've, we've hit the mark. Now, by no means are we saying that farmers are not technologically savvy? The vast majority are. I mean, they, they adopt amazing technologies in their systems. But the point is. Any new system being used in an industry needs to be accessible. You know, it needs to be able to be picked up and used straight away. No one wants to be told here, you can save, uh, all this money by using our system, but you're gonna have to retrain your staff and you're gonna have to learn new techniques that's gonna put people off. So you need to make sure that that bar is very low that. Ease of entry is there get them interested in using the product quickly.

Luka:

Yeah, that makes sense. What about, in your view some of the gating factors, for mass adoption of autonomous systems in agriculture? What, what are those take it however you want, both, from a technical perspective, or commercial or regulatory, which we'll talk about a little bit later, but which ones would you highlight?

Edward:

Yeah, I mean, regulatory issues in any country are always, there, they're always a problem. there's something that every country's, air services departments are finding their own way through, as we've discussed, before about the differences between, say, CASSA and EA and FAA. But as we've seen in Australia, the regulations for, for scalable BV loss operations are changing. You know, maybe there are many in the, in the industry who would like them to change a little bit faster, but they're definitely changing. You know, the changes we've seen over the last couple of years from the way we, have to. Get certified to be a BB loss operator. Previously, it was a full IE exam, instrument rating exam that's no longer necessary. It's now been simplified to recognize the, the differences between, manned aircraft and unmanned aircraft. And now the changes coming in with broad area approvals becoming easier and easier. These changes are slow, but very positive. And so we'll see, more and more BB loss operations happening in a much more streamlined fashion in the very near future, if not already. Aside from that, then you know, you've got, the regulations are often based around urban. Centric decisions as opposed to rural centric where, population densities and risks are very, very different. But, you know, regulatory stuff, that's a whole issue that can be discussed. The, the next, gating factor would be the technical side, and that can be anything from edge case handling. So how are you dealing with the. The large amount of variabilities that you're gonna encounter in these types of environments. Anything from climate to dust to erratic animal behavior, and so on. So these types of things you have to make sure are built into your data sets and built into your models. And then there's the technical side that, the hardware side of that, when you're dealing with such broad areas with such. Large variabilities, you're gonna deal with things from snow, to dust, to heat, to winds, all these different things that are not necessarily as much of an issue when you are thinking about, delivery. Drones in an urban environment. Not quite the same level of, of. I wouldn't say lesser or more just different types of variabilities that are gonna encounter the hardware, that is being used. So all these things are definitely there as gating factors, but they're all being, addressed and they're all evolving as the industry vol evolves.

Luka:

And, and obviously getting the price down to a level where, almost anybody can afford something like this, right?

Edward:

Oh, of course. Yeah, definitely. I mean, you know, we've seen that the price of commercially available, drone systems come down so much in recent years. I mean, gosh, you look at things like the DI Doc two and three. They're so much more accessible than when the first one came out, for example. You know, and this is just gonna continue, hopefully, particularly as more competition comes

Luka:

I, I remember, not that many years ago, some of these drone docks were priced at a couple hundred thousand dollars, and now, you know, some of the ones that you mentioned are, anywhere between 10 and$15,000 that comes with the drone as well.

Edward:

Yeah, that's right. And often with, with, various numbers of, of, software built into it.

Luka:

Okay, so Edward, did you say that previously in order to fly BV loss you had to take an instrument rating exam.

Edward:

Yeah, so previously to be B BV loss certified, you'd have to have an ire, which is an instrument rating exam. So that's the same exam you'd be taking in a manned aircraft for, for flying. you know, outside of visual flight rules.

Luka:

Wow. What, what was, what was the rationale for that?

Edward:

Well, I mean, I think it really goes back to the early days of, of unmanned systems where it literally would be an aircraft, with very manual systems in it, you know, fly by, actual control wires and stuff instead of computing power. where you had on your ground station. All the same number of dials and, instruments that you'd see, on an actual aircraft. And you'd need to have that understanding, you know, on top of the, scientific sides of meteorology and all those other things you're, you are taking into account. Obviously that's a little bit outdated, casa Real.

Luka:

that was almo. So that was, with the expectation that you might be flying, MQ nine Reaper and having to do a precision landing at an airport. and, and fly on, you know, published routes,

Edward:

Exactly. Yeah, that's exactly right. and so, I mean, obviously that's outdated and not necessary. So Casa brought in a change to the baby loss exams, made it much more, suitable for UAV use. much more relevant to today's UAV technologies. you know, I wouldn't say it's easier, but it's just more, aligned with what is available and what we use.

Luka:

And now we're introducing a new segment, a quick q and a with our sponsor, sky Grid on the digital infrastructure required to scale autonomous operations.

Jim:

What are some critical technical and regulatory gaps in digital infrastructure as advanced inter mobility and UAS scale?

Brenden:

So I would highlight I think, three main gaps, surveillance, standardization, and certification. Firstly with surveillance and data fusion at low altitude is really necessary to ensure reliable, low latency fusion of cooperative and non-cooperative tracks, which is incomplete in many regions, you know, across the, the country and globally today. Uh, secondly, uh, interoperability standards will allow for the kind of integration required to enable pre-flight and in-flight deconfliction intent exchange and constraint publication. But we. Really need globally harmonized, machine readable standards to achieve this. So finally, certification pathways, uh, you know, we need mature, predictable approval paths for ground-based digital services and not just for the aircraft and the operators. So again, coming back to, standards that are being explored introduced today through part 1 46, as well as those being developed by A STM and IKO as well for harmonization. We've certified airplanes, you know, for, for more than a century now. And moving forward, we need to start certifying the digital aerospace. They fly in in order to really, realize the next generation of aviation that we envision through a a m.

Jim:

What are the biggest technological enablers to safely scale advanced air mobility and drone operations in shared airspace?

Brenden:

You know, I think it ultimately comes down to connection, coordination, and certification. So connectivity, it really means resilient c two and data link exchange intent, constraints and advisories in real time. While coordination through automation means strategic deconfliction, schedule synchronization and cooperative separation that supports tactical conflict management through highly assured and certified services, and not just the ad hoc apps. So regarding technology specifically, we need low altitude surveillance fusion and micro weather nowcast feeds integrated into the planning and execution of operations if we were looking to achieve scale. So I think this requires assured software solutions with continuous compliance, cyber resilience, and performance monitoring with clear service level agreements. At the end of the day, autonomy in the aircraft only works when it is paired with autonomy in the airspace as well.

Luka:

So de describe this evolution of the Australian regulatory landscape, and perhaps compare that to what we are seeing in Europe or the us.

Edward:

Right. So the, CASA's regulatory landscape is very much focused around individual risk based analysis. So it's a, it's a sawa based, system where they are literally looking at case by case basis about. The risks involved and the operator's experience and demonstrated history with risk assessment. This is quite different to things like FAA and EA. I mean, it's fits somewhere in the middle. FAA is is quite different again. But with casa what we are saying is more of a, a shift towards an understanding that. Spending So much time analyzing each, proposed flight area and flight situation is not sustainable across an industry. you, know, the, the paperwork is not that complicated for an operator like myself. However, the bottleneck comes in at the actual. Department state, department level where they just don't have enough staff to deal with the hundreds of applications that are coming in, you know, so this mix between industry pressure saying, look, something needs to change. And within their government department realizing that. To, to suit the, the future of, of, online deliveries and, rural drone use and all these types of things. They either need to have more staff or make the process simpler, and so they've come to a bit of a compromise where they've increased staffing, but also allowed for broad area approvals. Those people who've already demonstrated good sawa, good risk assessment abilities, can then get, approvals much, much faster. This is in trial at the moment and is looking like it'll be put into some sort of permanent place, in the new year.

Luka:

I, I wonder what are the differences in the, SOA frameworks that IA adopted and CASA adopted? Because they all stems from the same jars, SOA framework, right? And so, I wonder, to what extent CASA is perhaps leveraging some of the standard scenarios or waste to streamline some of these approvals. given that the bottleneck seems to be cas itself and the bureaucratic, bandwidth.

Edward:

Yeah, definitely. I mean, Issa is definitely a much more, rigid than, than Casa. FAA is much more around waivers, but, but with Casa what they're doing is, is with this broad area, approvals is allowing for that process of, of being a bit more streamlined, of being more, based on what is the, the region or environment that you are working in. And if it is demonstrated that. You know, you're out in the middle of the Northern Territory where there's less than one person per square kilometer. There doesn't need to be that same level of, of, granularity put into the risk assessment as it would be if you're doing it in the middle of

Luka:

What's an example of a broad area approval, and how is that different to receiving approval for, you know, let's say a sale two operation or a sale three operation that comes with an inherit, level of err and ground risk.

Edward:

Well, it would come down to that, again, to that level of risk. So the, the understanding that that level of risk is much lower. That there is, very few people, that there are very few, infrastructure, whether it be buildings or telegraph poles, whatever it may be, in these areas compared to, areas such as in an urban environment. So it's, it's really just understanding the realities on the ground. You're not going to hit something, you're not going to come across a flight lane from a nearby aerodrome. Any other aircraft in the area are likely to be from the same, cattle ranch or cattle station. And so there's more, communication going on beyond the, systems involved to, avoid for aircraft

Luka:

And when you talk about broad area approvals, how broad are these areas and is that typically geographically limited or risk bounded?

Edward:

They have left that definition quite open at the moment. that's very much in name. but they leaning it towards, rural and agricultural areas. Um, and. During this trial process, they're refining what this will actually mean. Whether it will be delineated by geography to say, past this point is X or Y. You know, it's all Class Gs based, but I mean, it's, it's whether it's geographical in terms of this is an urban environment on the map, and this is a rural environment on the city map, they haven't defined it as that yet. I would expect that past this trial period. They will define things like that, which will make it simpler again.

Luka:

I see. And so can you briefly describe the process for, getting a BV loss operation approved in a rural environment?

Edward:

Right. So it's a, a series of paperwork. It's quite simple, but, generally you'll have to put in, Flight data of where you're gonna be flying and when. So, KML map of this is the, the area that we're gonna be flying in. We're gonna be flying at this altitude approximately these times with this equipment. The people involved. So who are the license holders, who is, who are on the team, whether it's one or more pilots and, and all those types of situations. and then, a little bit more about what the, mission is gonna involve. Is it a drone spraying operation? is it a surveillance operation? Is it, testing some kind of new equipment for, For lidar or whatever it may be. So those, details you'd pop into the, uh, applications, put them through to cassa that gets assigned to a case O officer. They then will review it and review your, uh, history with Cassa as well. So obviously the more times you've put in successful applications, the faster it moves and their approval comes through. In practice, it's a relatively short and painless thing to do in reality because of lack of staffing and a backlog of, applications going in, these things can drag out quite a while. so it's really a matter of making sure you are doting all your i's and crossing all your T's to make sure that nothing is going to cause a hiccup in this process, so you can get it through as quickly as possible. having these new broad area approvals will just streamline that further by saying. Look, I've proven to you already that we can do safe and responsible risk assessments and safe and responsible flight operations. You don't need to necessarily assign a case officer to be so granular with reviewing all the details and go straight into saying, yep. They're good. They know what they're doing. They're just doing it in a new place. Right now, it's not, near an air airport, not near a dense city center or anything like that, and you get the approval a lot quicker.

Luka:

Interesting. Now, again, with respect to rural use cases, what is CASA's way of mitigating air risk? Do they require certain detect and avoid solutions? What do those look like?

Edward:

That's right. So in Australia at the moment, they do not require specific, DAA systems. There is obviously a requirement for man aircraft to have a DSB systems. There are a lot of, Voices in the community talking about bringing in some forms of DAA systems in the near future? Currently it's not required. it is really dependent on the situation and the user in the rural state, when you're thinking about, say, Northern Australia, where there's these very large cattle ranches and operations where they, they have their own light aircraft and have their own helicopters being used. It'll all come down to communication. So Casa will have, a requirement. For the operators to show that there is something involved, some technique, some method, some method of, of operation that is going to be allowing for that, risk mitigation. So whether it's radio use, whether it is, delineation of boundaries, or whether it is a use of a DAA system, they will require it, but they don't require a specific single DA, a system at this time.

Luka:

And I was under the impression that there is no broad A DSB mandate in Australia for all VFR traffic. I thought that that was limited to IFR. Or certain

Edward:

It's,

Luka:

High-Fi. Okay. So so handling light aircraft traffic or helicopter traffic at a low level, that might interact with drone operations that are flying BV lost in that same area. CASA's, they just, from what you're saying, it seems like they're, they're open to suggestions on how you. coordinate with any traffic that might come up. And, and you're saying that many times in those rural areas, traffic is, well understood and predictable because it usually is the neighboring farms that are doing something, if anything, right.

Edward:

Yeah, that's right. So what, what you're talking about here is, is we're talking about cattle ranches or, or stations as we call them here. That could any, could be anything up to a million hectares. So massive operations, which we are talking about families and and farming operations. Where to get to the local grocery store, you may need to fly, you may need to take your, your Cessna to get there. otherwise you'd be driving all day. So when you do encounter other air traffic in those areas, it's highly likely that they're either originating from your own, property or from your neighbor's property and the operations that are involved. Everybody knows what's going on. You know, you know that, you are mustering today or you are running this flight today for this purpose. And everybody has radios. Everybody has, you know, down to the A TV that the person is riding on the ground, they'll all have a CB radio, they'll all have a radio to communicate, and if they've got flight operations, then they'll have a air flight radio with them as well. So there's a lot of communication going on, knowing where exactly. People are and what is going on to reduce this potential risk within the air. And then on top of that, you've got fantastic visual lines as well. You know, these are broad open farming range lands. and then, you know, I believe there's the, definitely the opportunity in the future for adding in other types of technologies to help with these things like, like those, audio, sensors to detect, where. Aircraft are coming from and, and so on.

Luka:

Yeah, there are some interesting, acoustic, based detect and avoid systems that have been, developed and matured in the battlefield over Ukraine that have proved to be, very effective and also low cost. And I, I suspect that in some of these rural areas with the acoustic signature being quite low, that some of these systems would be fairly effective.

Edward:

Yeah, I would think so. I mean, I think it'd be a perfect, use case for those types of things, being in such a open, natural environment where there is, you know, you're gonna have very little outside human noise.

Luka:

What's the applicability of, some of the. changes that Casa came out with with respect to their fit with urban or perhaps overfitting on the urban use case. And what's the unintended consequence of somebody trying to run BB loss operations in rural areas?

Edward:

Yeah, that's right. So being that so many regulations, not just in Australia but worldwide, are often built for those initial urban environment use cases, whether it be, early use of drones and surveying and so on to delivery drones. Now, a lot of these regulations were built around that thought process of there are people, there are buildings, there are uninvolved bystanders on the ground who haven't given permission to have a drone fly over them. So bringing in newer rules like the recent OONP, changes, the overall near people changes. These types of things are fantastic in that kind of urban environment, but it just doesn't really translate so well to rural environments. It adds another level of complexity and another level of regulatory requirements. You know, for a, for a user who is working on a livestock property or a person who is, a drone spraying operator, they're not gonna need to be flying over a crowd of people who haven't given permission. I mean, it's, it's just not gonna happen. They're out in a paddock. So to be required to have these types of, licenses or checks can be restrictive. But again, this is an evolving situation and I believe that most of these types of services like Cassa and the FAA and others will gradually evolve more and more understanding, and it's just literally a result of the, the how young this industry is. it all just takes a bit of time, I believe, and I've got a lot of confidence that these things are, are going to improve. I mean, we've seen so much improvement in just a short time.

Luka:

Right. And are the regulations in New Zealand similar to those in Australia?

Edward:

Yeah, generally very similar. I mean, they have some slight differences being a different country, but there are a lot of similarities. I think there's more similarities between New Zealand and Australia than there is, say, between Australia and the US or Australia and Europe. but you know. Having their own government systems, they do, have their, their own slight differences, within their, their rule base. A little bit harder, in some ways, a little bit easier in others. but so far it's, yeah, not a huge

Luka:

Very interesting. All right, well, let's, switch topics a little bit and talk about the, the ecosystem in Australia, the drone operations and OEMs. When you look at, you know, Wing, Manna and others, both operating in Australia and elsewhere, but what are you learning from them, and how they're accumulating operational experience. where do you place them in terms of collaborators, potential competitors down the road?

Edward:

Yeah, I mean the, the work that groups like Wing have done, in Australia here and and overseas has been really, really useful for operators of, of any type of UAV autonomous system because it's. They've gone through the process of working through how best to interact with regulations, so how best to keep, groups like Cassa informed how to work with them as opposed to against them. they've demonstrated, lessons around fleet management. How are you going to keep a, a number of UAVs functioning in a safe and reliable manner? that developed fantastic safety culture around, notifying not just within their company, but within the, the public that they're flying over and around, you know, keeping that communication going. Now, obviously for our operations, we're not in that type of urban environment, but it still Provides great lessons for us in, you know, how do we let neighboring properties know? How do we collaborate with them to make sure that we keep that positive, viewpoint, that positive, relationship going between UAVs and the general public. And And then of course, you've got all the things like data-driven risk modeling, the work they've done to, define the risks that they're facing in their delivery systems. In many ways is, is far more complex, as I've described, than out in a rural environment, but it's still great. examples of risk management of, of how you can take the data you are collecting and use that to define what are the actual risks you're facing, and then use that in discussions with regulations. I mean, and then there's of course the company wide things of learning lessons about how to, you know, scale A-A-U-A-V company, into, a new industry like this. I mean, we've seen that happen in good ways and bad ways, you know, successes and, and failures. So it's a lot of lessons to be taken from all of that.

Luka:

Indeed. Yeah. We had, Adam Woodworth, Wing, CEO on the podcast recently, and he explained how the team went into Australia initially because of a clear. Or regulatory, you know, pathway. I wonder, what your thoughts are on their subsequent focus on the US instead. I wonder, to what extent is this driven by, just the, the economics and the market size and the opportunity in the US versus saturating that initial regulatory advantage in the US catching up on that front. Do you have a, a perspective on either.

Edward:

Yeah, that's a really good question and. That was a great episode, by the way. yeah, no, it's, I, I believe that's probably an element of both. I personally, from a startup founder perspective. we all look to the US as a much larger economic opportunity. I mean, you have such a large population and such a, large country of being able to address a market in that way. Australia, uh, in many systems is a great testing ground because of that size difference, because we're smaller in population because we can have a focused trial or test case of whatever technology it might be before then taking it across to a much larger market like the US or Asia or wherever it may be being deployed, whether or not they reached a saturation point from a regulatory standpoint. I couldn't say I, I don't have enough background into where they got to, with their operations, but, you know, I would heavily lean towards the economic side. you know, use Australia as a proof case scale to the us. You know, it's what we are doing. It's what so many other startup founders of many different industries are doing is build it, scale it here, grow it over there.

Luka:

Makes sense. Alright, Edward, this was a fascinating conversation. Any other, takeaways for the audience

Edward:

Yeah, just to say that, I believe that the, the opportunities involved, here for autonomous systems and for UAB systems in general is huge, and it's only going to get bigger and bigger. But I think it's really important that all of us as, members of this community is to understand that it is an evolving landscape, that everything takes time to grow, you know, and in that process there will be ups and downs. There will be the, you know, the collapse of swoop, there will be the rise of others. These things will happen. But as long as we all remain flexible and adaptable and work together with, regulators, with customers, with the general public. We can bring all of this forward to a very, very successful space. Here at Drone Hand, that's what we're aiming for in the, rural sector, in the livestock production center, is to try to have that level of collaboration from government regulatory standpoint to industry to the users themselves and their neighbors and the general public around them to make sure that we can bring our systems in in a way that is going to be reliable, effective, and sustainable from an economic and, climate sense moving forward so that these things can be widely adopted to have the benefits that they can So obviously bring.

Luka:

Nicely said. Well, this is a great way to, uh, wrap up the conversation. Thank you so much for sharing your insights and for your time. Really enjoyed it.

Edward:

No thank you. It's been great.