Where are humanoid robots in Europe? What cool startups are working on them? There are maybe 100 humanoid robot companies on the planet, and 16 major ones, but none in Europe in those top 16 according to Peter Diamandis’ recent report.
That might just have changed.
Neura Robotics out of Germany is working on the third generation of its 4NE-1 robot and CEO David Reger says in June they’ll be releasing it. And it should be the best humanoid robot on the planet, he says.
In this TechFirst we sit down and chat about Europe’s answer to humanoid robots, and what Reger sees as a significantly pro-social and pro-human means to bring AI and robotics into the world.
We discover how Neuro Robotics is innovating with their upcoming Gen 3 humanoid robot, 4NE-1, learn about their unique approach to robotics, including responsive AI, real-time data streaming, and the development of a sensitive robotic skin.
We also explore the future of work, the race against global competitors, and what AI-driven humanoid robots mean for society.
Watch the show here, and subscribe to my YouTube channel. You can also subscribe wherever podcasts are found …
What you’ll find in this episode
00:00 European Humanoid Robots
01:09 The Concept of ‘For Anyone’ Robots
01:46 Rapid Innovation and Development
06:29 Challenges in Humanoid Robotics
09:02 Neuro Robotics’ Unique Approach
17:53 Collaborative Market Strategy
19:55 Teasing the Third Generation Robot
20:10 Challenges in Robot Sensing and Interaction
20:50 Innovations in Robot Skin and Sensors
22:59 Speed and Agility in Robotics
25:38 The Global Race in Robotics
28:46 The Future of Humanoid Robots
31:45 Balancing Technology and Society
34:03 The Role of AI and Robotics in Human Life
38:27 Concluding Thoughts and Vision
Transcript: Neura Robotics on humanoid robots for Europe
(note, this is AI-generated and will contain errors)
John Koetsier (00:00)
Good. Today we’re going to take a deep dive into humanoid robots made in Europe. Hello and welcome to tech first. My name is John Koetsier. There are at least 16 major players in humanoid robots globally. But when I chatted with Peter Diamandis recently about his big report on humanoids, there wasn’t a ton in Europe. That’s a problem because Europe has a unique perspective on people, on society, economics, and
David (00:08)
Yeah.
John Koetsier (00:29)
how to develop in a fair way for all. That’s critical if we think about the humanoid robots and the future of work. Today we’re chatting with perhaps the leading European startup in humanoid robotics. It’s called Neura Robotics. They’re just five years old. Recently took on a big new investment and they announced 4NE1, that’s 4-N-E-1, in October of 2024. Today we’re chatting with the CEO and founder, David Reger. Welcome, David.
David (00:59)
Thank you so much, John, for having me here. So excited to talk about robots.
John Koetsier (01:03)
I am super pumped about it. Thank you for spending your late Friday evening with me. Wow. The first week back from Christmas vacation, although you got an early start. Tell me about for anyone.
David (01:15)
anyone is our, let’s say humanoid robot, which is supposed to be a multipurpose device. So it means a robot with the shape of a human and I hope very soon also with the capabilities also of humans and that’s what we are right now working on. So we released a Gen 1, Gen 2 and now working on a Gen 3, which will be released in June this year. So it’s already this year.
And I’m super excited about that.
John Koetsier (01:46)
That is super rapid innovation. mean, you talked about it in October, which is just two, three months ago, and now you’re already working on Gen 3.
David (01:57)
Yes, I mean, we actually started a little earlier to work on it. So I released actually the Gen 1, one day before Elon announced this robot. So it was also purposeful, sure. And it helped us also to get a little bit of visibility in that space. But at the same time, yeah, we made a very fast shot to get experience with it, worked on the Gen 2 right away. And also here,
saw all the limitations out of the field. So we have already some robots deployed in the fields, in the automotive space, some other spaces and yeah, and not waiting and just working already on Gen 3 because here it’s all about, let’s say, getting them better than what’s on the market because it’s a big race we are in right now and we want to win.
John Koetsier (02:48)
So I want to talk about all of it, the robot, what it can do, where you are, your rate of, of, of progress, what it can’t do yet. Some of the hardest parts, all that stuff. We’re also going to get into some of what this means and what this looks like, how you see the future with humanoid robotics and all that stuff. Before we dive into that, the name is super interesting, right? I mean, I read it probably 10 times, maybe 25 times, just going for N E
Dash one before I kind of picked up on it for anyone. Talk about that name.
David (03:20)
Yeah. So yeah, the name was actually my idea because I thought like we were discussing because if you look at all our robots, we have like Lara, have Myra, we have meth. And even the last times I got a little bit of shitstorm like why are you calling them like women’s names or names and there’s like I couldn’t actually let’s say like
John Koetsier (03:39)
Mm-hmm. Mm-hmm.
David (03:47)
I thought, the next platform should be for anyone. It should be no gender, but actually just a device which is supposed to help humans and in everyday tasks. So it means not just in factories at work, but actually also in our households. So a multi-purpose device. And for that, I thought everyone should make their own names up because I once also walked into our AI lab and I started talking to Myra.
came in and said like, Hey, Myra, and then I felt like, okay, there’s like 10 robots started and asking, Hey, what can I do for you? And then I realized, it’s actually stupid to just call it one name, we should actually give like people like the ability, like possibility of just named their robot, because it’s a family member, let’s say like that, or not really family, but something which will be in every household every, like everywhere, and everyone should just call it whatever we want. So and this is my for anyone.
John Koetsier (04:46)
I love it. It sounds like the early days of when Apple brought out, I’m going to say S I R I cause I don’t want to activate anything in my immediate neighborhood. And, know, your, your, your watch and your phone and your computer would all sort of say hello. And it’s like, Whoa, I just want one of you. Very cool.
David (04:47)
Yeah.
exactly.
That’s exactly
the thing. If you call it, like, so this is why, also one of the first, in fact, a lot of you have and turn on this robot, it’s like, call it, give it a name. And so you can actually call it whatever you want to. And it’s still able to listen and will be mostly super sensitive to this, let’s say, call word.
John Koetsier (05:17)
Yeah.
I love that.
Yeah. Yeah. I love that. You’re working on Gen three. I mean, it reminds me of figure between their first model and their second model, they had a 10 X improvement in speed. Are you seeing similar jumps and leaps between generations?
David (05:42)
Yes. Even now, GEM3, I would say it should be the best robot on the market. So in my opinion right now, if you look at the field, right now the leading humanoid on the market, I would say it’s Optimus. From its behavior, the way of how it moves and how it behaves at this time. it’s pretty good. And so now our job is to be better than them.
John Koetsier (05:58)
Mm-hmm.
David (06:11)
That’s why we put a lot of effort now into the new Gen 3 and I’m super excited about to show something in June which will actually beat also all the other humanoids on this
John Koetsier (06:17)
Mm-hmm.
It’s tough job. A lot of people working on this super smart people and a lot of money behind this. What’s been the hardest part?
David (06:34)
The hardest part, I think the difference from us to others is that I see the humanoid also just two arms on a very unstable platform and it’s nothing else. The difference from us to all the others is we have already thousands of robots in the field and they are robot arms with cognitive abilities. So means they also hear, see, feel and react on different situations. So the most difficult part is actually the whole data streaming.
like how you stream the right data and how do you do it in real time so you can actually close the loop between the virtual world and the physical world. This is actually a task which why we actually started right away and new Gen 3 because we couldn’t close the loop of like of the data to receive the right data to be actually reactive enough to actually be let’s say
John Koetsier (07:29)
Mm-hmm.
David (07:32)
reactive in the field. Good enough. So that’s why also we started to actually adding more sensors and adding different kind of sensors to close the loop. And this is what I’m seeing right now on this this old videos, which we watch all watching and always in this nice environments and you see what they’re showing you like, hey, what’s an apple and here this is an apple. Nice. The thing is like everything is static and that’s why it’s simple. That’s something that everyone can do. But I think the difference is if
John Koetsier (07:56)
Mm-hmm.
David (08:02)
If you look at the real world where everything is moving, nothing is static, everything is just somehow moving and this is what the human is best at. It’s like being reactive and cognitive enough to react on all kinds of situations. This is something we want to win in. This is also the most tough.
John Koetsier (08:25)
Super interesting to hear your answer there. Often when I ask that question over robotic CEOs, they’ll say something like hands or something like that. was talking to sanctuary AI and they were saying, you know, half the complexity of a robot is in the hands. And that’s true. And that’s mechanical challenge. It also reminds me, I was chatting, I haven’t even released this episode yet with real robotics and they’ve got Aria and, and we had her on the call and
to her, by the way, not an it. And the delay there in response was significant. So you’re talking about being able to respond quickly. I think that’s really critical. Talk about how your history has enabled that because as you mentioned, you have thousands of robots that you’ve shipped or robotic arms that you shipped, and you’ve called them cognitive robots. So they haven’t just been an arm that is programmed and does moves X to Y or inserts
B in A or anything like that. It’s actually been responsive.
David (09:25)
That’s actually the difference we brought into the robotics field. There was no one. We were like the pioneer in cognitive robots because of one reason. Before I started NeuroRobotics, every robot is just a stupid device, like a metal piece with some motors, and you’re adding the whole periphery around the robot. That was how robotics was working the last 70 years. What NeuroChange is actually bringing this sensor is what you normally put around the robot just inside of the robot arm.
and then give the robot actually the ability of seeing, hearing, and feeling when being reactive on the real physical world. So it means which is not static. And now the tough and the difference from us to all the others was that, and this is actually what you just mentioned, is like this reaction time of taking too much time. It’s because mostly they’re using this huge neural networks, or network language processing, which have…
John Koetsier (10:18)
Let me ask Jachy BT. Let
me get the answer and let me give it to you.
David (10:23)
Yeah, the thing is, you have to like, maybe how many billions of parameters, and this is actually taking time. So it’s delayed all the time. And this is the difference from us to the most. like, the real world, are not able to have this delay. So it means this is the difference from a human to this machine, which is just having a smart brain in a cloud and a piece of metal.
And what we do is actually that you have it already in three different layers. So our artificial intelligence, we call it neuron networks. We define it like three layers. So it means the first layer is actually on the edge. So it means directly on the sensor. means by the camera itself, by the micro area itself, by the force-talk sensor itself. So it means the hearing, seeing and feeling, which you need to pre-process and have like a nerve system. it means like when, like a good example for that is
being reactive, like for example, plug in like an electric vehicle plug in. If you just use, let’s say, a very big neural network for that, a huge, let’s say, in a cloud, you will never hit and you will never be able to really stick it in because you need to guide it through and you need to be reactive. So it means in real time and millisecond. So, and if you use their Justice SmartBrain, it will not help you because you need like a nervous system which is reactive.
John Koetsier (11:23)
Mm-hmm. Mm-hmm.
David (11:47)
Because it knows how it should be and you’re reacting on it. And the most are just showing it. This is why I was not super impressed by it. seemed like this is something we did already in 2019. Like, hey, Myra, show me what’s an apple. That simple? Because the apple is simply there and you need to react on it and you’re just pointing on it and everything is static and you’re taking it. As soon as you’re actually going and saying like this whole world is moving and you’re reacting on the situation or you have to guide something, you need to balance something.
you need to have different types of so it means there’s three different layers. One is just giving you the information right away in real time and the other is like putting them together and making like an application out of that. So it means you’re guiding something in feeling and in the same time knowing the information of how what it wears the deepest point of it. So you’re clicking in or things like that. Like you have more and more information which you’re combining them together. This is let’s say the way of
how we do it, like three different layers. One is like on the edge. So means here we are talking about, let’s say, five, six million parameters, which are fully enough for the application. And then you’re having, let’s say, the mid-layer, which is also still on the edge. So it means on the control box or at the chest of the human eye. There you’re going up to, let’s say, five to 600 million parameters, while you’re having also the cloud access and you’re making the overall decisions. And this is actually going up to, let’s say,
John Koetsier (12:54)
Mm-hmm. Mm-hmm.
David (13:14)
10 billion parameters. And this is the difference, like if you really want to have a humanoid robot or just any kind of robot doing some smart task, having knowledge about a situation, you need to have these different layers. And that’s the only way of being real reactive and moving this physical.
John Koetsier (13:15)
Mm-hmm. Mm-hmm.
It’s super interesting to hear you say that because it strikes me that there’s some similarities to the human body. mean, we have some levels of intelligence that are not located in our brains. Right. So you mentioned reflex or something hot or something like that. Right. That’s, that’s, that’s like in the core that’s in, in, our spine, right. We it’s before conscious thought, and then we have conscious thought and we can think about it and we can also go do some research.
David (13:45)
section.
John Koetsier (14:00)
You know, go to the cloud, right? Or something like that. That makes some sense. That’s super interesting. Now you mentioned that you’ve obviously shipped thousands of robot robotic arms, components, pieces, and stuff like that. And you’ve been cognitive from, from kind of day one. That’s what you’ve been working on. Now, what about the job you’re, you’re attacking right now is massive. Because if I look at your website and where you want for anyone to work, the list is basically everywhere.
And that’s why, course, you chose a humanoid robotic. Isn’t that too much to go for instantly? Where do you see for anyone working initially?
David (14:42)
It’s a super good point. So yes, it is too much. So it’s too much for ourselves, but not too much for the world. And that’s also why we are mainly working on numerous platforms. So it’s the same of a smartphone itself or like a smartphone itself is also too much for Apple. If they just would do all the apps and everything themselves, what they did is actually they defined the architecture of the hardware so you can actually have an operating system.
where everyone can talk to. And then you’re building the developer environment and a platform where everyone can actually build their apps and at the same time also distribute their apps and make them multipurpose platform out of the smartphone. And that’s exactly what we are doing in the robotics field. So it means all the robots we brought in the field, we have partners which are working on different kinds of applications. So it means welding, gluing, palletizing, polishing, deburring, whatever.
John Koetsier (15:17)
Mm-hmm.
David (15:35)
And if you’re using the right sensors in the right time, so you’re streaming the right data, you can actually label the data. And this is how you can train the robot in real field and let others do it, which are experts. Because if I’m not a welding expert, I don’t know anything about welding. the other thing is like what everyone is talking about, you just can learn on videos. That’s not true. If I show you, for example,
video of somebody welding and you’re watching it for one year, will you be a welding expert? It’s not. Because you will never, like you will not because you’re missing the experience of actually the forces and torques like which you are having by leading the welding torch and this is actually and then you’re having also this noise of welding, you know, like and this is how you define an expert and you define a skill for being an expert in welding.
John Koetsier (16:07)
Probably not.
David (16:29)
And this is exactly how robotics functions. So you’re having the ability of like, you have the small neural networks, which are actually getting feeded by, by leading this welding torch on the side. And then you’re seeing and saying like, this is a good welding line. At the same time, and that’s different to human, you can actually just save it and it will be like much better and much faster actually, than, than you’re training a human being. Then you have to retrain others, but in robots you don’t, you just click and you distribute it to all robots in the world and you can let them all train at the same time.
because they are labeling it as welding. Then you are seeing something and then you are hearing the noise of welding and this makes you a welding expert. This is exactly how we do it with robots.
John Koetsier (17:11)
Love it. it reminds me of the matrix, teach me how to fly a helicopter. Boom, download. There I go. And everybody is an expert. Instant expert. Right. what’s interesting, sorry, excuse me. What’s interesting about what you’re saying there is that you’re focusing on the cognitive complexity of the task. And so what it sounds like is you’re building. Infinitely.
David (17:15)
Okay.
John Koetsier (17:37)
a general purpose robotic on the humanoid platform. And then the complexity of an individual task is in what to do, how to do it, where to push, what to push, what to lift those sorts of things. And that you can outsource or partner with experts in those areas.
David (17:56)
And that’s what we are doing is actually we are partnering in pretty much everything. So we’re not even selling one single robot directly to the market. So we are actually selling through the biggest robot companies, robots to the market. And this is actually, this is the only way how you can actually work with experts in that field. And this is also the difference from Neura to all the others is our go-to-market approach. So we have today more than a billion other book. Why? Because simply we are working with the biggest channels in the world.
And at the same time, we are actually solving the biggest challenge of a startup because a startup has some issues. The challenge of a startup is the whole growth phase. Let’s say everything is not ready yet or you’re going into the market and it’s always too early, you’re pushing products. And that’s also the trust part. And if you’re selling robots, this is the most important thing. If you’re buying robots, want that your machine is running.
day and night and all the time without failures. So how to solve that is actually by simply using channels of his experts, which are also helping us to get faster there than we would actually do in ourselves. And this is what I realized when I built my first robotics company. So it’s now not my first one. So the first one I built was in Switzerland, which is still doing good. So it’s an amazing company, but it’s like limited in my opinion, like from size and what they can actually be in the future because simply
John Koetsier (18:53)
Hmm.
David (19:20)
I did exactly the same way as everyone do. They’re simply building good robots or trying to build good robots and then bring them through their own channels to the market. And then they have to build their service infrastructure. They have to solve all the challenges in the same time and somehow sell a lot of robots because that’s what’s the expectation of every investor. You can’t do that. Yes.
John Koetsier (19:23)
Mm-hmm.
Love it. Love it. You’re focusing on what you’re best at and
you’re allowing space for other companies in the market because they’re experts in different areas and they can bring to market. They can maintain, they can do all those things. Tons, tons of sense there. Absolutely. Let’s, I don’t know how much you want to talk about your third gen, but you brought it up. And so I’m pretty interested in you. Of course you want to have a big announcement in July. So you don’t want to share too much right now.
tease us just a little bit. You know, what will this thing be like? What will it be capable of?
David (20:13)
So let’s say there was basically three areas we focused most on. So number one is the interfacing part, how we interface and how we stream more data in the same time. And at the same time, how can we actually use the data of many robots in the same time? This is the most difficult part. And here, yes, at the same time also,
John Koetsier (20:38)
proud intelligence.
David (20:41)
It’s a sensing part. And the sensing part is also one very important aspect inside, which I can probably also talk about. I can talk about everything, it doesn’t matter. So it’s a skin. We work on a real skin, which has like pixel skin. So it means you can have the first robot, which can actually also really have a skin so you can feel it when you touch it. Why do they focus on that?
simply when like people get impressed every time they’re coming visiting me and I let let’s say for anyone fill up my glass but I I don’t get impressed at all so I’m super annoyed by my robot my own robot because you see like how slow does it move like when it comes close to me you don’t want to risk something you’re somehow fighting with yourself that’s why I’m saying two arms on an unstable platform
John Koetsier (21:28)
Mm-hmm.
David (21:37)
and then it’s bending over and then going, grasping my glass. then it takes too much. The first time it’s always impressive because we’re seeing you think, wow, how smart is this device and how amazing. Watch it 10 times like I do and you’ll freaking hate this thing. You will say, this will never help me in anything because I would never have the ability of a patient. that’s the word. And why is it like that? And this is what I thought about it. The way it’s like simply is like…
John Koetsier (21:45)
Yes, yes.
Patience.
David (22:07)
again, the sensor data, like how you sense the human and how you’re like when we are interacting with each other and I fill up your glass, I’m super fast next to you and I’m somehow having you in my eyes and I’m touching and not fearing to hurt you and to kill you. Like with my arms and the human and the robot is different because it simply does not have enough abilities of sense. So it means my opinion was there like because we they don’t have a skin and if you can actually create a skin
which have also superhuman sensors, it means not just when you’re touching it, actually even before, and you sense already much more than just the touch and the touch and the exact way of how much pressure you put on a touch, then you can actually have a completely different approach of how you control the robot next to a human. And this is actually one of the most important topics we work on.
John Koetsier (22:46)
Mm-hmm. Mm-hmm. Mm-hmm.
David (23:02)
The other is the whole sensors itself, like the seeing, hearing, and feeling, that it’s just better and faster, and you stream it all at the same time. The bandwidth of streaming, this is a big issue. It is a big issue in automotive field, and this is actually where we helped ourselves with some technologies out of there, but at the same time, we had to reinvent them because it was still not good enough. And the other part is actually the way of…
agility of the robot itself. if you look at Boston Dynamics, what they do is crazy cool. With the electric one, they do not as much, but they still showed a nice video of the Nikolaus making it backflip. I think it’s impressive because with electric motors, this is super difficult. And this is also something we touched. So we invented a new actuator where we believe it’s the high. It’s right now the
John Koetsier (23:39)
Mm-hmm. Mm-hmm.
Mm-hmm.
David (23:59)
best one in the market. It’s also an own gearbox technology and different cooling and different way of how we do. And this is actually like so and in the same time it’s like for example like this is actually the other I would better not tell yet because this is
John Koetsier (24:20)
Well, you’ve told a lot already, which is
really great.
David (24:25)
Actually,
whole team around the NDA, they not even allowed to talk internally about the humanoids so much. But it’s a separate department which is only focused on humanoids and not able to take all the technologies out of the field because this is the synergy we have. But I think I told the radio larger.
John Koetsier (24:47)
No, that’s
great. I appreciate that. And I think those are, those are critical capabilities. It’s different places. One is speed, speed matters, right? And robots, especially some of the gen one humanoid robots that we’ve seen are slow and it’s painful. You know, you see them walking and it’s like, okay, here’s a geriatric old human taking five hours to get from here to the, to the wall type of thing that won’t fly in an industrial environment. You need a decent level of speed.
doesn’t have to be super fast, but it needs a decent level suite. And second is working with humans. You can’t just slow down and be catatonic when you’re next to a human. need to be able to work as you said, naturally, like you and I, if we interact in a real space, we’re not afraid of smashing each other in the face or something like that. Cause we know we have controls and sensors and are able to not do that. So that makes a ton of sense. I do want to talk about.
David (25:36)
Exactly.
John Koetsier (25:43)
kind of some of the bigger aspects of humanoid robotics. And I’ll start here. This is kind of a Manhattan project. Maybe that’s the wrong term. That’s a moonshot. I’m talking kind of globally as a species, inventing something that can do much of our work for us. How do you feel about your place in that? How do you situate yourself in that?
David (26:10)
First of all, do see now not the moonshot anymore. We know how to go to the moon, so that’s why maybe the moonshot is not working. I think it will be reality very soon because there is a huge pace going on because of one thing. It’s all because of China.
John Koetsier (26:21)
Mm-hmm. Haven’t been there in a while. Personally.
David (26:38)
So because China is giving the pace and that’s why I’m also excited about that because before, I mean, I started in 2019 and it was nobody believed in hardware, like the whole space. Like I was in US, I was in Japan, I was looking for investors and they were like all living in a metaverse and nobody was actually believing in robotics at all. So now they step like one step back. So it means human rights. And the thing is in that, that,
John Koetsier (26:41)
Mm-hmm.
David (27:07)
field, you know, it got actually not just by Elon. Elon is actually pretty much looking at one thing. He’s looking at China. Because he knows like China is not predicting future, they’re making future. So and this is this is why I do believe human rights will be reality until 2030, like in a very high number. So means millions of that. And, and the reason for that is like China actually has a goal until 2030.
John Koetsier (27:15)
Mm-hmm.
Mm-hmm.
David (27:34)
They want to actually have 5 % working labor, let’s say, added by humanoid robots. And this is actually also the whole driving force behind that. And this is also why even Elon actually simply, let’s say, had a pivot in car company, or like, he could call it almost like that and made like a robotics company out of it, because he sees like, this is a space which will be bigger in the future than any others.
This is the driving force behind everything and for us as the only German humanoid robotics company and also I think in Europe there is not many as you said in the beginning. have to participate in the market and not just participate but we have to actually win.
to not stay behind and not end up as a museum in five years. For us, it’s not just a nice vision, it’s actually a must-have and a must-have to win. This is also why we are doing this. That’s I’m believing it will be very soon. But why humanoid? This is maybe also a question.
John Koetsier (28:33)
Mm-hmm.
Maybe, maybe
let’s pause on that first point right there and then get back to what you were just going to get to. As I kind of hinted off the top, it does really matter if the future that many people are foreseeing does happen. And whether it’s 2030, 2035 or something, you have millions, billions of robots. You’ve compared them to smartphones and there are billions of those things out there, right? That will upend our economic systems.
And if you’re not, if you’re not a player at that table, if you don’t have something at the table, the price of labor is going to approach zero in some sense. It’ll never be zero. You understand what I’m saying, but it’ll approach zero in some sense. If you’re not a player at that table, your economy, your society, your nation, your, your European community, if we can put it that way. yeah, left behind and not in control of your destiny anymore.
Because if the price of production approaches zero, wow, that changes everything.
David (29:57)
The thing is, I think if you’re looking at European economic, like our, let’s say, state, economic state, isn’t like the right word for it, like right now. So everyone is just now talking about, mean, recently also Elon Musk is involving himself a lot in Europe also, everywhere on this planet. Like he tried to do everywhere. I mean, he is the most efficient entrepreneur on this planet. So kind of we should actually…
John Koetsier (30:10)
status.
Yes, politics.
David (30:27)
let’s say, listen to what he’s saying, but at the same time also should know that he has different interests maybe than we should have in Europe. And this is actually where we, I see Europe actually still as one of the, let’s say, if you look at Europe, you will see the best social system still on this planet. You see also still, in my opinion, still the best cards. So the only thing we have is the fear of the future.
John Koetsier (30:48)
Mm-hmm. Mm-hmm.
David (30:56)
because we have to come up with something new. And this is exactly where we position ourselves right now, because we know this is the time of robotics, because we are just not allowed to miss this train. If we are going to miss robotics, Europe will have a super huge issue in the future, because that’s the only thing we don’t have on a control yet, because we don’t have solution, we don’t have an answer on this issue. And this is also why we are like…
John Koetsier (30:56)
Mm-hmm. Yep.
David (31:23)
Europe without robotics is like not a… I would say it’s not possible to exist because there is no real need of Europe anymore as it was before because we were always known for our amazing cars, our amazing technologies, machines which we built and this time is kind of not over yet but it could have an end if we are missing the strain of automation.
John Koetsier (31:43)
Mm-hmm.
Here’s where I think why I think it’s critical. I’m Canadian, just so you’re aware, but I’m in Vancouver, Canada right now. Here’s why I think it’s critical that a European company be one of those that is a winner in humanoid robotics. When we talk about economic systems, if we look at the States, we see great opportunity and great poverty and see some of that in Europe and not to a lesser degree. Obviously we see some of that in Canada to a lesser degree as well.
David (31:53)
That’s it.
John Koetsier (32:17)
But we see that and if we are going to invent a future where we automate 50%, 60%, 70 % of maybe the manual labor type of jobs, but also many of the intellectual labor, see what chat GPT does. We’d see what other technology, AI technologies are doing. I mean, what does that society look like? Is, are we going to build something where
A vast majority or a huge significant chunk of people are just left behind. That’s a dangerous world for me. That’s a very dangerous world. have people who don’t know what they’re doing there. Maybe they’re super poor. They get super angry. They don’t have a purpose in life. They don’t know what to do. That’s a dangerous society. That’s a powder keg. You’re, you can light a fuse on and, and who knows where that goes. Europe has.
its own challenges, obviously, which you know much better than I do. You live there, you’ve operated there, you’ve built businesses there. But one thing Europe has done pretty well is balance the needs of industry and people and societies and balance the needs of growing together, not just having the, you know, the top of the heap people, be, super great. not saying anything’s perfect. Nothing’s perfect in any system or anything like that, but there has been a good.
balance there. And that’s why I hope that we can see great humanoid robots out of Germany, out of Europe, so that we can see, is there a model to follow that is pro-social, that is for people, and that does the right thing?
David (33:54)
I think this is a huge topic. I really love also what you just said, because I think this is a question we have to answer as humanity very soon. It’s like, what things we don’t want to do, what things we want to do, because at this time, we are aiming for that, or we are aiming for all these nice technologies, which are enabled by artificial intelligence, which I always question.
myself like and it’s actually my my standard keynote where I always ask like What would you like to do if you have enough money enough time? For everything you want to do so mostly you will come up with what? Like some of them are like more like like artists So it means they would they would like create art every day. They would just draw pictures like every day
The others are more musicians, they would spend all the time in music. The others are more writers, so they would write the new stories, they would write up a new Netflix story, whatever. The others are more like me, I’m a developer. So I would just simply lock myself up again and just develop new cool technologies. That’s what I love, that’s what’s fulfilling me every day. And if you look, actually, this is actually where I have the next page where I show you Luz.
John Koetsier (35:01)
Mm-hmm.
Mm-hmm.
David (35:17)
Because if you look at artificial intelligence today, will actually find out, like with majority, for example, for drawing pictures, you can actually have like five seconds for a new picture. And then you just simply tell it should look like in Van Gogh, then it will look like in Van Gogh. the same also with music. I just always tell the story about one of my friends actually took my blog, which nobody reads and took all the stuff there and then put it like into music. And then first you put it in chat, like tell like, hey, make a song out of it.
put it in his music app and get a song which the first time touched me actually. And the same also with writings. You can write in any language of the world. can actually imitate any style of writing what you want to write. And the same also will happen to developing things. As a mechanical designer or whatever, there will be tools for that very soon, which are good enough. But are these things which we…
John Koetsier (36:00)
Mm-hmm.
Mm-hmm.
David (36:15)
really want to get rid of? Because the most artists I know, like people which are drawing, they love to draw, you know, like, and there’s a lot of people which would love to spend their time in that, but you simply don’t have. And this is actually, and but the things, but there’s other things which we hate to do. It’s like taking the garbage out, filling up your dishwasher, tidying children’s rooms, this all the discussions even like I always say, like it will save a lot of marriages if we simply have
John Koetsier (36:21)
Mm-hmm.
Mm-hmm.
Mm-hmm. Mm-hmm.
David (36:42)
tools for that. So the expectation on a human is not based on how good they are, in thinking of filling everything into dishwashers or tidying children’s rooms or whatever. So this is actually where I see that robotics will actually be sustainable and helpful for humans on a certain degree on things which we don’t like to do. And this is like one day, so today, we are still not enough humans on this planet. And especially with this aging population, there is more
John Koetsier (36:51)
Mm-hmm.
Mm-hmm.
David (37:11)
Let’s say people retiring then really coming up. So this is why robotics will have this moment the next five years. It will not take jobs. It will actually create new jobs, maybe more interesting jobs. At the same time, it will actually help a lot of humans and our economies and everything. So it will reshuffle a lot of things and whatever, but it will be a very good time in my opinion. But it will change one day, yes, because…
John Koetsier (37:14)
Mm-hmm.
David (37:37)
There is one day this challenge coming up again. And I think this is the moment where we have to make decisions of what things humans should be able to do and further do because they love to do it and not because they have to do it. And these are things I hope this is one of the things where we humans actually make decisions like in five years, six years, seven years and simply say, okay, these are things which AI shouldn’t do.
John Koetsier (37:48)
Mm-hmm. Mm-hmm.
Mm-hmm.
David (38:06)
AI
should just be helpful, be a device, a tool for us so we have a better life, that we can enjoy our life, we can actually have a better way of not being, like not having a two hour travel to see my parents, but actually going and simply playing chess, like maybe through the robot arms which is placed there, like just having a better life.
John Koetsier (38:23)
Mm-hmm.
Love it. Love it. Love the vision and the thinking. The reality of course, is that many people have different answers to those questions. Like what should AI do? What should AI not do? What should robots do? What should robots not do? And those are going to be answered differently from different people in different countries. And I guess for me, the biggest thing for people is to understand, hey, do you love the process of what you’re doing or you just want the product?
If you just want the product, let a robot do it. Let mid journey do it. Let chat GPT do it. If you love the process, engage in that and enjoy it. This has been fascinating. This has been super interesting. want to thank you for taking this time and for having this conversation.
David (39:17)
Thank you, John. Thanks. It was also like I really enjoyed it. And yeah, thanks for having me.