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Welcome to "Visionary Voices" the podcast where we dive into the minds of business owners, founders, executives, and everyone in between.
Each episode brings you face-to-face with the leading lights of industry and innovation.
Join us as we uncover the stories behind the success and the lessons learned along the way.
Whether you're climbing the corporate ladder or just starting your business journey, these are the conversations you need to hear - packed with visionary voices and insights.
Let's begin.
So Bradley, thank you so much for joining me on today's episode of Visionary Voices.
Could you give us a top level view about what it is that you're working on right now and
your journey so far?
Awesome.
Thank you.
Well, it's great to be here.
Excited to be on the show.
A little bit about me is that I have been really excited about the opportunities of
robotics and automation for a long time.
So 20 years ago, my college roommate and I, took these robotics classes that did these
cool things like play dodgeball, captured a flag.
And the idea 20 years ago that you could take a robot and just push power and it could
then know the objective and do it from there.
That's always been something that's captured.
captured my imagination and really exciting to me.
So fast forward 20 years, luckily I get the opportunity to be able to be at Luxonis.
And what Luxonis does is we help machines be able to see and see like how humans can see.
So we provide real-time human perception for machines, so like robots and automation
systems.
And you basically can think of us as serving as the eyes, the ears and the brains for
robots.
That's so cool.
Like, just talking about robotics and how it's all coming together now, especially with AI
and how things are changing.
So, I mean, I'd love to dive into the story of starting the business and what that looked
like in its early stages.
So I know it's a pretty cool story of some of the things that you did.
So I'd love to hear from you.
Like, how did you get started with the business and how did you start kind of selling the
initial batch of products that you guys had made?
Yeah.
So Luxonis was founded in 2019 by my college roommate, Brandon.
And the way that the business got off the ground actually was with Kickstarter campaigns.
building hardware is really tough.
You don't know if you're going to build something that people want.
And if you want to build it at a price point, they want to buy it.
You have to do it at a big volume.
so Kickstarter is a really great way to be able to engage with the developer community and
create something that people wanted.
So we actually got off the ground by doing two Kickstarter campaigns.
Together they raised $2.5 million across 15,000 backers.
So we essentially convinced the most technical people on the planet, kind of these
hardcore hobbyists that these are folks that like it's a Saturday and they're in their
garage and they're trading some cool robot around around their house.
We convinced them that we had something uh something that was really special for them to
be able to play with.
So we created a product that was very developer friendly.
So the products that were available in the market were more closed off.
You couldn't do as much with it.
So we had something that was very open and flexible.
And we created something where you could do a lot of different cool projects with it,
know, across, you know, across different, you know, robotic applications.
So that was really the key thing that was able to make it so we could zero to one as a
hardware business.
Otherwise it is, it's pretty, pretty, pretty, pretty difficult, especially given that we
grew up doing that.
in the midst of kind of post COVID-19 where if you remember four or five years ago, know,
purchasing electronic components or any component was pretty difficult.
But that was how we initially were able to get off the ground.
Yeah, I mean, I think it's such a cool, cool story, right?
And a cool way to start the business and get things moving, especially, you know, as you
said, right, in the hardware side of things, right, where there is quite a large cost
involved with, you know, getting it made, getting it all shipped out and everything.
And what you could do is essentially kind of raise that capital first and then go ahead
and manufacture what you need to do.
And it's a good, really good way to test the idea, right?
To see, this something that the market wants?
You validate the market's needs and that.
that side of it.
So it's really cool how that sport got you started.
Now we kind of spoke about, you know, that go from like zero to one.
Now I go from one to a hundred.
Like what does that look like?
And cause I know you've had to make some shifts in the way you're kind of approaching the
business side of it.
So I'd love to hear on your side, like how, how are you managing one to a hundred now?
Yeah, definitely.
So consumer hobbyists are amazing customers.
They're able to come up with some very creative things that they want to do with your
technology.
They're not very great for being able to scale with, at least from a hobbyist perspective.
And the challenge for it is they'll buy a device and they're pretty demanding over what
they want the device to be able to do.
And they're not going to buy another device from you unless maybe you've done something
that's cheaper.
Or maybe you've come up, come out with something, something new.
So they buy a device two years later, they're using, using the same device.
It's difficult for to scale for this type of a product.
So starting in particular in kind of late 21 into 2022, we really did an intentional
transition to being able to sell to businesses.
And as we made that transition, it was actually easier than we thought.
Fortunately, those 15,000
hardcore engineers, they actually all have day jobs and they were selling up within their
organization to say, Hey, look, there's these great, this great technology that can be
used to kind of solve old problems, pioneer new ways of solving problems.
And that was really helpful for us.
So when we started to get more kind of business focus, when I would do say, you know, 30
sales calls in a week, 20 out of the 30 sales calls, the customer had already used it
before.
And so they're already familiar with the product.
And so it's not the normal type of way you're acquiring, acquiring a customer, right?
It's somebody that's already excited and used it and goes, I know, know this can do
something useful.
And then something exciting happened, you know, late 2022, which is a lot of the world
became much more excited over the possibilities of artificial intelligence and physical
AI.
And so
organizations started to, from the top down, say, hey, we want, we want to be able to see
ways that we can make investments to improve safety, improve productivity, things like
that.
So that transition was successful for us.
So fast forward to today and more than 95 % of our revenue today is, you know, business to
business.
And there's a whole set of new challenges that came with being able to sell enterprises,
but, know, that was obviously something that we were, we were ready to tackle.
Yeah, yeah, no, definitely.
mean, again, it's a really cool way of pivoting into that side of the B2B space, but then
you're not having to go out and kind of create the demand there because you already had
like this customer base, this fan base that was, you know, obviously got value from you in
the company already.
And so it's very, I guess, natural for them to kind of sell, you know, your services, all
the products that you guys have into the business that they work at.
I mean, how did you structure that then?
Like, how did you approach those current customers on the B2C side and say, hey, look,
we're making this change into the B2B side of it.
Did you jump on the calls with them or like, how did you guys approach the strategy there
to kind of activate that B2B mechanism, let's say on your side?
So we were very fortunate that we had so many customers that were coming to us that it
made it so that we were able to kind of farm the customers that were already in our store
buying our product.
So we would take a look at them and we would say, who's so-and-so that just purchased from
us?
And you're like, my gosh, they work at a Dow Jones 30 customer uh company.
So then we would reach out and we'd say, hey, what is it that you're working on over there
at that Goliath company?
And that's how we were able to set up calls with them.
And then we also just had a lot of folks that as they were using the technology and they
realized that we were moving to being geared around being able to kind of solve the more
enterprise scale problems.
They reached out to us and said, Hey, we want to be able to connect.
And then from there, we made sure that we had our company and our engineering team really
ready to be able to uh lean into kind of solving the things that they were after.
So enterprise customers, want something that's going to be very reliable, something that's
very stable, something that's going to be very rugged, last for a long time.
These enterprises are using something where it is hour by hour, second by second, core to
their operation.
And so things like downtime are a big problem for them, right?
So we were very ready and willing to be able to kind of rise to meet that challenge and
that required being able to move quickly.
and required having our engineers be excited about working on that.
There are some engineers that that's not fun for them.
ah They'd rather go create the new, new thing.
So we had to make sure we had a team that was eager to tackle that new wave of challenges.
Yeah, no, for sure.
how did you, I guess, build and scale that team as well, right?
Because I guess from the, on the B2C side, obviously, you know, raises this capital
initially, and then had to, of course, find a talent, I guess, to kind of help produce it
and things like that.
And then obviously shifting into the B2B side, did you just retain the same talent or did
you have to find new talent to serve that side?
Like, how did you manage the team building dynamic?
Because I know obviously many make these big changes within the company, right?
Is the talent piece is very important to get everyone on board with.
like the change that you're making.
like to your point, like some engineers, they might love that enterprise side of things,
but then others, as you said, right.
They want to make the next kind of big thing.
And then in the enterprise side, it's maybe a little bit slower than that.
like, how did you manage the team dynamics there as well?
Cause it would be interesting to get your, yeah, your take on that.
You know, building and retaining a great team is probably the number one predictor of our
success and then, our failure.
So it really is top of mind for us.
Cause we know it's something that's so critical.
We're an organization that is, you know, we have our growth is product led growth,
without, without a great product or nothing.
And so back when we were supporting kind of the consumer hobbyist, uh, side of things, we
only had a team of about 10.
So it was pretty small.
We were able to support them.
And as we like in 20.
for example, I think we ended up adding 45 people.
And that was just hiring to be able to support kind of what these enterprises need.
That's where today we have a staff of 100.
Of that 100 staff, 80 of them, 80 % are engineers.
So it's very engineering heavy.
And we self-perform all of the technology stack from kind of hardware and firmware.
to all aspects of software, machine learning, computer vision.
We do all of that in-house.
So building a great team was really around finding superstars that we were just really
impressed with and going, okay, let's just build a team around you.
So it's really about identifying one or two people that are really great in that
discipline.
And then from there with them leading it, then being able to attract talent actually gets
pretty easy because great engineers are attracted to work with great engineers.
The other aspect with, you know, building and, you know, retaining a great team is, is, is
taking some tough steps at times to be able to ensure that you have a high talent density.
We've always wanted to make it so that if somebody looked to their left and looked to the
right, that they are really impressed with who they are working with.
And if.
they didn't, then that was not kind of the company we wanted to be at.
We all felt like we'd done at companies where you had some of your teammates that kind of
everybody knew they weren't great, but the company was tolerating it for some reason.
So sometimes that part's been difficult, but it's been necessary em for us.
it's something that's every day is sort of critical for us as we look ahead, where it's
just like, if we just keep having a great team, then we think that we were going to be
able to be successful.
Yeah, yeah, for sure.
think what you said about, you know, the, I don't want to say like the founding team, but
the initial kind of highs that you have within the company or like the leaders in the
company, they need to be of such a high caliber, right?
So then they can attract, as you said, right, they can attract the best engineers, the
best talent from their network or just, you know, from people that they know and things
like that as well.
And so I think that's a really cool, I guess, guess tip, right, for people, if they're
looking to start building a big team, all these different things, you need to make sure.
the initial talent that you have in the managers or leaders in the team are of that
character as well, right?
To attract the right people through, which is really cool.
I guess with the founding team then, like how did you find those people, right?
Because I think initially as like a startup is, you know, you have this kind of vision of
the company of what you wanna build it into and what it's gonna do.
But then obviously finding the initial team is you need to get them to buy into this.
into this mission that you have as well.
So like, where did that founding team come from?
And did you manage to get them brought into the wider mission or like, what did that look
like in the initial stages?
So, you I was not.
You know, at looks honest at the very, very early days, I joined a little bit later in the
journey, but I do know kind of the, the, the, the details pretty well.
What I'll say is, that the way that we were fine, able to find, for example, like our
co-founder, Martin, uh, who's our chief architect, actually was through kind of online
developer communities.
let's go, wow, this is somebody that's working on, uh, kind of testing and creating things
that are related to our space.
One of our first machine learning engineers is
based out of Colorado, they had actually uh written a paper around the type of technology
and we were able to bring them on as an intern initially for testing.
em So it's been kind of these serendipity moments at times, but it's really about tapping
into kind of who are these folks on the planet that are interested in building in this.
em And there's been, there's some heartbreak with it too.
So we had some people.
know, initially that we kind of try to tap into and no shoot, you know, it didn't work
out.
But luckily we have, we've been able to have a team that's um had kind of long staying
power for the leads on top now.
But getting to those initial folks, that is one of the hardest, one of the hardest stages.
And there's definitely no secret sauce that we had other than a lot of trial and error and
a lot of effort.
Yeah, no, for sure.
I mean, it makes sense, right?
If it aligns and someone comes along and they fit, then it's great.
And the reason why I ask this is because it's quite timely for me because we're starting
to build out kind of like the larger team now because right now there's only two of us in
the company, which is nice and lean, which is great.
But we're starting to hire out some more.
So we're trying to find the talent that we're looking for.
uh And it's one of those where it's like, you know, we're speaking to a lot of people, but
it's just not quite quite right just yet.
And so...
It's gonna be interesting to see how things kind of play out and everything, but we try
and build out this kind of founding team, let's say, of these initial leaders.
But yeah, we'll see how that one goes.
And so I guess with obviously AI coming to the equation now, after you guys launched, AI
is now the big thing.
How are you guys now implementing AI into the robotics side of the business and what does
that look like?
And also as well with the kind of the enterprise.
companies that you're starting to work with, like what, if you can of course say, what are
the rough use cases or reasons why they're looking to use this technology plus also AI as
well?
Because it'd be interesting to know what the use cases are and what they're using these
products for.
you
So what Luxon is really pioneered was the ability to be able to run the artificial
intelligence models, to be able to run it on device on the edge.
So the legacy way that these systems would work is they're collecting a bunch of data and
then they're sending it somewhere else for processing such as such as the cloud.
It's our belief in market thesis that basically most of, most of artificial intelligence
and physical AI, most of the processing is going to happen, you know, on the edge.
rather than in the cloud.
And so Luxonis has really leaned into that to the degree that we're actually introducing
our series four product this year, so the latest generation, and it delivers 45 times more
artificial intelligence capacity throughput than the previous generation.
So we know from our customers enterprises, when they get a taste of running models on the
edge, they want to be able to do more and more and more.
So we're really, really leaning into that.
Enterprises that are coming to us, they're really wanting is to be able to have the
ability to do really the three big things that our product does.
uh Running stuff on the edge is a big one.
The second one is being able to have the highest resolution of sensors.
So by having more processing on the edge, you can actually get more pixels and you can get
more information of the scene around you.
And then the last one is that when you think about our technology, we're not like a Tesla
that you hear about with self-driving where they're doing
a camera based only, more more like a Waymo where we're wanting to grab as many sensor
modalities and infuse them perfectly frame by frame on the edge.
So in addition to having a stereo camera that creates a 3D map of the world,
We also have like a high resolution color camera.
And then we have other sensors, like we have time of flight.
We have microphones, we have IMU for motion, we have infrared lasers coming out, and we'll
always be looking to add more more sensors.
So we don't want to just build like.
human level perception, we're ultimately going to be getting to superhuman level
perception.
And that's really where it's exciting because it means that we can enable systems that can
exceed humans at every possible kind of task or project in the physical world.
And that's really the end game for this technology and what's so exciting about physical
AI.
Yeah, yeah, for sure.
you know, having the AI run on the device as well, it's so interesting because, lot of
the, you know, because I've spoken to like some law firms and things like that, you know,
some clients that we're starting to work with.
And one of the reasons why they didn't want to adopt it just yet, like the technology was
because of security.
Is there like, if it's getting sent to the cloud or these different things, like how do we
kind of protect ourselves and that data as well?
And I guess it's the same thing as well, right, with running it, I guess, on devices.
is I guess a little bit more secure than if you were having to send it to the cloud and
then back again, let's say, for like a response.
And again, I'm not a technical person, but that's my assumption off the back of that.
That's right.
right.
And it highlights one of the customer use cases that we can talk about.
So say our device is used in a retail setting and the customer wants to be able to
understand the consumer demographics of people that are in their store.
If you had a legacy system, you would maybe be taking the data that's in, of people in the
store, like their faces, and you're sending it somewhere.
And you could run into challenges with privacy.
If you're in Europe, maybe, know, GDPR.
And instead with our devices, you can
make it so that the processing of that data um happens on the device and all that needs to
come off of it is, we think we had somebody in the store that was this age and this gender
and had this emotional sentiment, but the actual, um the capture of that person never
leaves the device.
So it's not like somewhere there's a database, a cloud database that could be hacked later
that goes, oh, hey, here's this picture of this person inside of a store.
Some other customer use cases are really common, which really kind of breaks out between
robotics and automation.
In robotics, we do quite a bit with you know, autonomous mobile robots, humanoids and
industrial equipment.
With industrial equipment, it's a big one for us.
uh Currently industrial equipment, you know, hurts or kills lots of humans every year in
the millions.
And so we have the ability to make it so that finally machines can stop hurting humans.
You'd think we would have that pretty nailed by now, but we
Yeah.
And then we also have the ability with industrial equipment to make it so you can operate
it remotely and make it so you can operate it autonomously.
And yeah, mean, the use cases are honestly, it's like probably like one of the most
exciting parts of the job.
They are very varied, but they're used a lot in warehousing and logistics.
They're also quite a bit in transportation.
um It's a uh agriculture.
There's a lot of different industries that are pretty exciting for us.
Yeah.
Yeah.
Amazing.
mean, it's so cool and it's such a, an interesting technology, especially, you know, in
the time that we are right now with this whole AI thing that's, that's, that's happening
as well.
Cause I think the, two technologies work so well, obviously together, right.
Um, but which is, which is really cool.
And then in terms of the future of the company, like, where do you see all of this going?
Like over the next kind of five to 10 years?
Cause of course, you know, AI is here to stay for sure.
And a lot of companies are going to start adopting it more and more.
Um, and so like, where do you guys fit into this kind of wider, you know, kind of.
AI piece over the next few years.
You know, we believe that over a longer time span, stretching into the decades that
physical labor is going to be.
um supported and uplifted or outright replaced by robots and automation.
And we think that's a really exciting opportunity for humanity.
About half of global GDP today is uh humans doing physical work in the physical world.
So either they're doing labor with their body or they're doing some type of a task that
requires them to kind of perceive the physical world separate from, different from
obviously some of like them.
work that some folks do.
And so we believe as that kind of revolution occurs, that the most difficult part of being
able to do it is the human perception piece.
And we think there's a reason why, as they say, that the human brain, half of all of our
power and energy goes to basically just processing our visual cortex.
It's pretty incredible that the sensory inputs that we have.
So we think as the technology evolves, it's gonna be exciting and show up in different
ways.
um We don't have necessarily conviction to say, this type of robot's gonna win or, you
know, uh there's this type of future for like a smart city.
You you can see around the world, there's some cities today that like have cameras
everywhere and they can track all of their citizens everywhere and things like that.
You know, we're not really so concerned over.
You know that other than to say like we do, we always want the technology to be used for
good.
Um, and we think that there there's more than anything, there's things that are exciting
with that.
So like in a smart city, you know, you have the ability to make it so like crime can be
lower, uh, for, for example.
Um, and then, yeah, we, we think is, you know, when I think about like, for me personally,
like one day could I have a humanoid robot in my house that could help like pick up, pick
up, uh, you know, pick up the things around the house, clean the kitchen, stuff like that.
That's a future that.
that I'm pretty excited about.
So we'll see how it plays out.
It's gonna be really fun.
Yeah, no, no, for sure.
For sure.
I think it's going to be, it's going be very interesting, especially the next five years,
I think.
So I think it's going be really crucial for obviously the adoption of these technologies,
the regulations that come in and all these things as well, because obviously it is a
relatively new technology in terms like consumer adoption.
And so I think, the next five years are going to be very, very interesting to see how
people adapt, but also companies as well.
you hopefully in 15, 20 years time, we can have, you know, somewhere around the house or a
robot around the house to help out with these things, which would be great, which would be
really cool.
And so, okay, amazing.
And then in terms of, you you personally then, right?
Cause obviously going on this journey with the company and scaling really quick and making
these big decisions and like pivoting and everything.
Obviously there's going to be a lot of personal development or growth like throughout this
time.
So have there been any like transformational kind of periods or?
moments that you remember that we can talk about.
Startup life is definitely not for everybody.
It's definitely a roller coaster and there's a lot of stress with it.
uh It's a typical week if you basically on Monday you think that you're going to fail, but
then by Friday you think you're going to win and go to the moon.
What I'll say is the most difficult part has been by far the most difficult part of the
journey for me is
Unfortunately, the college roommate that I had Brandon that co-founded Luxonis, passed
away in 2023.
And so something that was very personally, very tragic for him passing away uh quite
young.
And it was also very difficult to navigate for us as an organization because he was our
fearless leader.
was his company that he co-founded and it his mission and his vision.
so being able to both grieve the loss of a great long-term friend
And then also be able to help the company transition through something that was really
hard because we had to basically kind of rebuild the company since, you know, at the time,
know, Brandon was helping to lead our sales and lead our technology.
So those are types of things that can happen in a startup where um it's really unexpected.
And you just have to kind of dig in deep and say, okay, this is really tough.
You know, is this something that we believe in and want to keep building?
And so for me personally, it's always a very emotional thing that, you know, I have the
opportunity to be able to kind of carry on um with the kind of mission that Brandon
started.
And so it's very special and very important to me.
And that's also what makes it most, the most intense, because when something seems to be
going wrong, it's like, no, no, no, this is, this is my baby.
This can't go wrong.
This has to win.
This has to succeed.
You know, we have to be able to do, you know, to be able to do this for Brandon.
So that's definitely been the most.
Yeah, both are the most difficult part.
Yeah.
No, no, honestly, I mean, it sounds very, very tough, like not only to get yourself
through it, but also then, you know, being the leader in the company as well and getting
the company through it.
So it's not only like you dealing with it.
It's also helping everyone else deal with it, right.
As well.
And so, um, no, definitely sounds like a very, very challenging time.
yeah, which is, which is crazy.
So I guess then as well with the, but with the company and the team that you have now, um,
like,
how far are you gonna scale the size of the company?
Just because I'm interested, you because you're very much engineer heavy right now.
Like how does that scaling plan look?
Are you gonna keep hiring at the rate you have been or do you think, you know, with the
talent you have now, that's enough to kind of get you to where, you know, where your
mission is or where the goals are?
You know, something that we talk a lot about is you take teams, in particular engineering
teams, and you go from a few people to a few dozen people, you have all sorts of new
problems going on because nobody, you can't, it's not easy to have it so that everybody
knows what everybody else is working on and have it all work together.
So being able to, you know, manage things with the team size we have, there's a lot more
that we can get out of what we currently have, I'll say.
And then I will say that as an organization, probably not a surprise.
We're very,
leaned into how artificial intelligence can help us do things like, you know, develop
software as an example.
So there's a lot of efficiency gains that we're able to get from our current team.
The thing for us is we scale, a lot of the scale from a revenue perspective actually comes
from manufacturing.
So, you know, we do sell hardware and that is a primary driver of our revenue.
And what's exciting is that we are able to have really strong operating leverage.
need a whole lot more staff and overhead to build say 100,000 pieces of the product versus
building a thousand pieces of it.
cause we use a contract manufacturer and it's really just kind of add a couple of zeros.
Um, it does, it does require some additional, you know, staff or managing things like
quality control and stuff and logistics and stuff like that.
Um, but that's where I would think the business could scale really well.
So, our, you know, current outlook for the future, you know, we think that.
when we're at about five times our current revenue or maybe at, you know, maybe double the
staff, something like that.
And so I think that you'll see that sometimes with manufacturing companies that scale up,
but then also just with the ability for, you know, say like one of our machine learning
engineers to be able to, uh you know, develop software at such a higher rate than they
would have been say, you know, circa 2021.
So yeah, we're really excited about how the business will be able to scale.
And we do talk about that quite a bit, but fortunately, like we have all of the team in
place and we've had a bigger team, a better team, more experienced team.
necessarily excited.
We had a point in our journey in 22 where our average tenure for a team member was three
months.
And so you can imagine the chaos that happens from that.
Yeah.
the average tenure for somebody is obviously well over a year.
so that is helpful.
it's still controlled chaos, but a little less of a complete chaos.
Yeah, definitely.
mean, I'm very excited to see how things develop over the next few years.
Because I think this technology for a lot of companies that they need this technology,
right?
And so it's going to be so interesting to see how they adopt it and how things change, not
only with them implementing your technology itself, but also how they implement it on the
wider sense as well.
So one of the final questions we always ask guests in the show is if you can go back to
your 18 year old self.
and only take three lessons with you, whether it's some philosophical knowledge, some
business knowledge, general advice, it be anything.
What would those three things be and why would it be those things?
That's a great question.
You know, when the first thing I'll say is 18 year old me would not have listened.
Yeah, like, yeah, what do you know?
I guess what I'll say is, that, you know, growing up my, you know, my parents, they told
me when I was kind of worried about what it would be like working, you know, working in a
career, which when you're 18 years old, it's hard to picture and imagine.
They just said, you know what, like, if you just, if you just keep showing up every day,
you're going to be okay.
And I didn't really understood what they meant by that.
But the thing that I would sort of add to that advice to 18 year old me is that, you know,
just find something that you really care about.
So if you get to if you get to show up each day, and you, you're you feel that you, you
know, you deeply care about what's in front of you, then you know, I think that your life
is going to be pretty good.
I guess the last thing that I'd say of the three pieces, so you know, keep showing up.
find something you care about, I'd say, know, always remind yourself that your work ethic
is your biggest controllable input.
You know, can't, you're not gonna be able to change your raw intelligence.
You're not gonna be able to change these other factors.
But if you wanna have one thing that you really completely own and nobody else can
interrupt it, it's, what's the work that you put in?
So.
If you find something that you care about, you know, put a lot into it and then, you you
have, you have a good chance of being able to get a lot out of it.
So that was maybe a little long-winded, but, uh, again, yeah, 18 year old me would be
like, yeah, yeah, yeah, yeah, yeah.
Get out of here.
I mean, there's some great lessons, right?
And, and, know, the reason why we asked this question, cause there's always some people,
you know, listening to this and even myself, right?
It's always curious to know like what, what are those lessons that we would always pass
down?
Um, so yeah, thank you so much.
mean, again, I've learned so much on this, on this podcast myself, you know, not coming
from the robotics industry or anything like that.
It's all brand new to me.
So, uh, thank you so much for obviously taking the time today.
Really enjoyed the conversation and I'm sure, you know, everyone else says as well, uh,
know, who's, who's listened to it.
Thanks so much.
Thank you very much.