087: Ultra Violet: Conor McGinn & Akara

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Hey there, this is Pete.
Townsend familial Ventures and

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welcome to money.
Never sleeps podcast.

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Looks inside the head of
entrepreneurs in a what makes

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them do, what they do.
This episode of money.

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Never sleeps is kindly sponsored
by Ireland's fintech and

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financial services recruitment
specialist.

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Top-tier recruitment.
If you are calling me to help

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attracting retaining great
talent for fintech or financial

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This, highly advisable that you

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build a relationship of the team
and Top tier recruitment.

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You can find them at top-tier
Recruitment.com and tell him we

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sent you this week.
We talk to Connor mcginn from a

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Carro Robotics.
And Ireland, based startup

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building robots, that care given
the direction that this podcast

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is gone in the past two years
from its fintech roots to a few

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tech-driven, human interest
stories, then an exploratory

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dive into crypto blockchain and
Venture Capital.

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It's really looking forward to
learning more about Connor and

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the acara team given that this
is different.

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Connor really got me.
Thinking on this one, especially

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with the cars sense of purpose
and making an impact, deep

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expertise in robotics and
artificial intelligence combined

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with first-hand experience with
the problems or solving, which

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is so important when you are
building a start-up.

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But let's not give too much away
in the intro and just jump right

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in with Connor mcginn and this
week's episode of money, never

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sleeps.
Here we go again.

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Again, welcome to money, never
sleeps.

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Recording today from the home
studio.

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And we're on with Connor,
mcginn, co-founder and CEO at

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acara, Robotics and Ireland,
based tech company developing

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technology that empowers people
in the healthcare industry.

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A car is a spin-out company from
Trinity College.

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Dublin, Builds on over a decade
of pioneering scientific

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research in robotics and an
artificial intelligence.

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This research goes all the way
back to 2010.

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When Connor was young PhD
student.

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And then, after that, and
assistant professor Trinity from

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2014 onwards after years and
years.

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Thousand Years, researching,
designing, building, researching

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designing, building.
Let's fast-forward to today,

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where a car is social robot
designed to help keep seniors,

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socially connected, whose name
is Stevie recently made the

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cover of Time Magazine with a
headline, the robot that could

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change the senior care industry.
So with that, Crescendo welcome

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to the show.
Connor.

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Thanks piece.
Great.

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Awesome to have you on.
So we're introduced by Eamonn

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carry from techstars by way of
is sharing deal flow newsletter.

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The embedded Links at Connect
his newsletter readers to the

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power of the bridge app for
intros courtesy of Connor Murphy

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& Co also part of the techstars
family.

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So shout out to Amon and Connor
and former guest and friend of

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the show.
Martin cast from Barclays, who's

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always encouraging the Irish
startup ecosystem to stay close

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to techstars.
Anyway, enough about text yards

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is our first chat with robotics
company founder and 80 plus

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episodes of money.
Never sleeps Connor.

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So, honored to have you here and
great.

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Great to learn more about what
you're doing.

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Yeah.
Yeah, plan.

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Thanks for the interest.
It's awesome.

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Cool.
So, why don't we just start out

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with your backstory?
How you got to this point and

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what you're up to right now?
Yeah.

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Yeah.
Sure.

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Well, I guess I've always been
interested in robotics.

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We very early age, but I never
like most people who are

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interested robotics think that
they're gonna have a career in

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us, but I guess as an undergrad
I kind of saw the potential.

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It was interesting because as
undergraduate engineering

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students are usually exposed to
you know on day one.

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You're not just special.
Those into one area, you can be

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an exposure to everything.
And I remember, just kind of, I

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guess having an epiphany of
seeing the mechanical system

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sitting next to you know,
computer science kind of

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computer vision, AI system.
And just thought, well, I feel

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we can actually build these
machines to do things.

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This normally people are
required to do and it just

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seemed to me that there had to
be a lot of applications where

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it made sense to do that.
And it seemed that we re just

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have to start something.
So that was kind of where

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robotics became real for me and
then I guess over.

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I'm I started to do more and
more research in this area.

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I did a PhD in this area.
My interest was always on the

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applications of the technology
first.

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And then from that I kind of
found interesting technical

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problems to work on and I guess,
you know, we're at the point now

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where I feel like enough
research has been done to get

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this out there into the real
world and suppose that's what's

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motivated, the formation of the
car.

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Gotcha.
Gotcha, make sense.

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I saw kind of looking back at
your background as well on

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LinkedIn.
You are a mechanic internet,

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John buggy garage and Ansley
Motors in Thousand.

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Eight 2006.
Was that like a young Anakin

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Skywalker fixing podracers
before, moving on to more

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complex problems.
My only interest primarily as an

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imaginary was in cars as you can
imagine them.

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I felt that like, I would have
loved to work and for me to warn

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or, you know, any working for
some of the big kind of sport

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car makers.
I thought like, you know, when

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we kill to work at Aston Martin,
but you know, I suppose, as I

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became and, you know, deeper
entrenched in engineering.

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I saw that the job opportunities
in those spaces.

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It didn't seem that.
Exciting.

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It seemed like all of the
really, you know, groundbreaking

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still had been done previously
at the time.

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I was a student, it was all this
kind of, you know, hydrogen

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hybrids type stuff going on.
That I wasn't really that

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interested in.
And I just felt that like, you

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know, that kind of technical
leap what happened.

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And so I kind of opened my eyes
and looked a little bit more

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into inside of that space at
that point.

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And we'll certainly, you know,
from from the age of probably 16

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till 21 and I spent Summers
working as a mechanic because I

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felt and, you know, it would be
good skill to have and will be

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really helpful.
If I sit We design a car, it'd

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be good to know how they're
designed to begin with.

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Absolutely.
Absolutely.

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You did a short stint at Nasa
to, didn't you?

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Yeah, so it was an interesting
opportunity because what

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happened was that?
I am going to start working on

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robotics was very little being
offered here, even in

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universities myself and actually
David McKee, own, who's a

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Kearney Professor new CD.
He was a PhD student at the time

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and we set up a kind of a
robotics Society.

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I suppose here in Dublin, we'd
meet in the science, category

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every week and, you know, the
goal was to try and no develop.

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About such as a hobbyist, I
suppose and complete them in

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international competitions.
I wouldn't say we were very

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good, but, you know, the fact
that we were kind of proactive

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in doing this, and we probably
have a lot of passion.

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And when the folks at Nasa, were
looking for recruits for their,

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their good robotics program,
they wanted to have some people

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from outside of America.
And, you know, they didn't just

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want to find the smartest
college kids.

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They wanted to find people who
are, you know, dynamic and

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actually getting stuff done and
somehow they managed to come

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across us and I got an
invitation to spend a summer

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working on.
A project there which was an

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interesting experience to say
the least.

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I bet, I bet.
Yeah.

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My sister and I've said this on
the show before my sister's a

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rocket scientist or aeronautical
engineer.

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She is stayed in education for
her career to really spread the

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word and spread the wealth that
way in terms of her experience

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and knowledge and everything.
She's built up over the years.

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She did a little bit of a stint
with Jeff Bezos his rocket

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company.
Yeah, you know, little bit of an

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overlap there tiny bit and only
by association.

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I have no rocket science ability
in my head.

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What?
However, but at least cool to

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make that connection, so tell us
about a Cara.

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I had to look at.
I know that acara is Irish for

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friend, right?
My kids.

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My kids are in Wales school and
we talked about this with Ash

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Quinn, a couple weeks ago.
And they, I know very little and

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that was even something new for
me to learn when I had a look at

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that today.
Yes.

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Okay.
Kyra is the name of the company.

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Interestingly enough.
The only thing that I guess our

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founding team really ever
disagree on his name.

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Ames, so we can be building very
technical things that a lot of

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you think that there would be
kind of intellectual discussions

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about what the what part were
using or what algorithm were

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using.
But in fact, usually, it's the

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name that causes the most when
the conflict.

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So after about 20 names over the
course of two to three years,

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our founding team eventually
settled on the car and it's belt

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AKA or a.
So we could have overcome some

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of the, I guess fanatical
challenges that might come with

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an Irish, a fully Irish name,
but nonetheless, we wanted to I

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guess, corporate a name that I
sounded good meant something and

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I guess reflected the values
that we have as a company.

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And you know, what, we try to do
is develop technology that

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enables people beginning with
people in the healthcare

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industry and south of Cairo kind
of fit that mold quite well.

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Yeah.
Yeah, I like it.

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I like it.
What were some of the others

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that didn't work before?
This Juno was the last one?

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Jocasta stuff that you do piece
was an interesting one because

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like we did look at like, you
know, Greek and Roman gods

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because you know robotics often
kind of conscious those images

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and then I suppose We felt that
we just couldn't be that

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arrogant and we moved away.
I can't remember there was

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probably probably five or six
that were there were closed

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containers.
Judah was definitely second with

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Cara and then before that, I can
remember was probably 18 months

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ago at this point.
I was just change equally.

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We have, there's as much, you
know, in fighting between what

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the logo might look like as
well.

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So we had ones of have a kind of
an avocado.

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He ones that were robots.
There was a Hebrew and back and

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forth.
So the kind of two rings that we

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go, we have at the moment and
which I guess our little bit

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assembly.
Like of Audi.

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The look like half of mod
symbol.

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Yeah, they're things as well.
The came to through a lot of

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trial and error.
Okay.

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Okay.
I got you.

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I got you at school and tell me
about Stevie who is Stevie

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Stevie is the Celsius.
This robot does, I think most of

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my career has led to up until
this point when I say, so she

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Cicero.
But what I mean is that it's a

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robot that looks quite human.
Like it's going to face its

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size.
As it may be to be four and a

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half feet tall and the way you
interact with his true natural

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interaction, the same way you
interact with with Each other.

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Like a lot of people who in the
building robots, they kind of

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start with the problem and say,
okay.

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Well, what explicitly does this
robot need to be able to do?

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And whether that's, you know,
picking something up off the

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ground or getting something from
a different room.

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That's that's where the design
is centered around.

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We sort of taken the opposite
approach and saying that, you

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know, in order for this robot to
be used and not to be not for

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people to put off, we need to be
able to interact with it

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seamlessly and to do that.
We need to not have to adapt.

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Our check our Behavior too much
and doing so requires you to

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actually designer and social
requirements.

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So, Stevie is a robot that does
that Very much designed to be a

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kind of a general purpose, robot
and not regarding what we've

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hoped to stop by the by
centering, you know around the

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intellectual property around the
interaction.

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We can always layer
functionality on top of that.

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I'm going to see the what we did
was we focus on retirement

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communities as a user base and
there's a huge supply shortage

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of workers in that space.
And we felt that a robot like

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Stevie could be really helpful
in enabling staff to be more

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with less.
And we spent a period of between

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two and three years, developing
this robots TV, and and then not

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just Playing but also embedding
it inside retirement real

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retirement, community.
So, we've tested at this point

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quite extensively in the US and
also in Europe, mostly in the

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UK, but someone are and as well.
Okay, is there just one Stevie

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or there are multiple Stevie's
that are making the rounds right

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now.
So this is actually to Stevie's.

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So, the first one we built we
built in 2017 and and that was

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very much a proof-of-concept.
Like, you know, when you were

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small team with the small budget
we had and you tell people we're

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going to be able to, you know,
one of the most advanced robots

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out there.
You're going to have people.

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Actual some skepticism.
And so the first TV was was, you

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know, and we built it with 5,000
Euros budget or something like

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that.
We build it three months.

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And that was really just to
provide confidence that we can

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actually get this done.
The next one.

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Then took us quite a bit longer
to build it substantially more

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updated version has far greater
interaction capability, but also

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artificial intelligence
capability and that took us

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probably 14 to 16 months to make
and then we probably spent as

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much time now testing in the
field.

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So, we generally learn so much
between iterations of robots

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that it doesn't make sense for
us to build more than one.

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Yeah, it would only make sense
to spend more than one if we

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were to like ship them out
somewhere and then leave them

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out there.
And but the problem with doing

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that is just that you don't
learn from how it goes in the

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environments.
If it's just sitting there, it's

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one else is looking at it.
I got it.

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I got to that make sense.
What do you think?

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I mean, in terms of, you know,
five, six, seven, eight years,

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really, of deep deep deep on the
educational side and research

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side in terms of Robotics.
And then what was that real

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crossover?
Point to the, why of the first

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deployment really of Stevie or
of your robotics skill set and

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00:12:07,400 --> 00:12:11,600
your team's approach right to go
to have this entry point into

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the senior care industry, right?
What was that robotics, but into

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the senior care industry point
for you, I would say it was the,

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the point happened long before I
became an academic, I suppose

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when I was not even before I was
a PhD student.

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I, you know, I would have loved
to, when I graduated go into a

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robotics company, would they
just didn't exist?

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And so I kind of saw that vision
of like, you know, these robots

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code and eventually will be
deployed in these ways and but

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they're not being employed like
this now.

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So like I was going to force
into PhD in a way and I felt

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that you know by getting that
knowledge we could.

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I could I could reduce that kind
of gap between the scientific

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lab side of things and also the
real world.

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So I felt that like really me
doing a PhD in me becoming an

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academic was always leading
towards this point and I felt

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that like, the traditional model
and universities is often, you

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know.
Got scientists doing the

286
00:13:00,908 --> 00:13:04,300
research and then the license it
perhaps to a company that will

287
00:13:04,300 --> 00:13:07,700
deploy it.
And but I felt that like the it

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00:13:07,700 --> 00:13:09,200
was this was an example of
somewhere.

289
00:13:09,200 --> 00:13:10,300
Maybe that didn't make as much
sense.

290
00:13:10,300 --> 00:13:12,300
Maybe it makes sense for the
person who invented it,

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actually, the person who deploys
it and you have to take it that

292
00:13:15,100 --> 00:13:17,200
extra bit further.
And look, I think time will

293
00:13:17,200 --> 00:13:19,200
tell, if that's the right
approach to take, but that's

294
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always been doing.
I've seen it, it's that it

295
00:13:21,100 --> 00:13:22,900
starts the approve, the concept
out in the University.

296
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That's where that's what
scientific grants are for.

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00:13:25,200 --> 00:13:26,800
And, you know, if we want to
prove this that scientific

298
00:13:26,800 --> 00:13:28,200
validity is something, we write
papers.

299
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That's, that's always been the
way it works.

300
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But like, what's the next step?
The Next Step shouldn't

301
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automatically be to hand things
over and I feel.

302
00:13:34,600 --> 00:13:36,300
Now we've done a lot of that
work.

303
00:13:36,600 --> 00:13:38,900
And we importantly, you've
gained a lot of experience of

304
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how to implement a robot in a
nursing home, or in a hospital

305
00:13:42,200 --> 00:13:45,800
or whatever might be.
And really the next step is now,

306
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I think to figure out how do we
do that at scale.

307
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And look, there's lots of very
interesting, scientific things

308
00:13:50,900 --> 00:13:53,700
that we will, and will
scientific research questions

309
00:13:53,700 --> 00:13:55,900
that will address and doing so.
But you know, equally that's a

310
00:13:55,908 --> 00:13:59,000
commercial Pursuit, you know,
the University's remits not to

311
00:13:59,008 --> 00:14:01,800
build 100.
Robots, and so we feel like the

312
00:14:01,808 --> 00:14:04,600
next step, you know, very
organically is to set up a

313
00:14:04,600 --> 00:14:08,200
company that's to, you know, be
able to get funding to manage

314
00:14:08,200 --> 00:14:11,500
some of the operational elements
that are the go-to company that,

315
00:14:11,500 --> 00:14:15,700
you know, are kind of unique to
us startups require and, you

316
00:14:15,700 --> 00:14:17,800
know, differ from what
University Research Grant looks

317
00:14:17,800 --> 00:14:21,300
like, importantly, I guess for
me is impact that we found in

318
00:14:21,300 --> 00:14:23,400
industry and retirement
community space and the allocate

319
00:14:23,400 --> 00:14:24,700
space.
That's just crying out for

320
00:14:24,700 --> 00:14:26,300
Innovation.
Like, you know, there's more

321
00:14:26,300 --> 00:14:28,700
people working in direct
healthcare workers and the

322
00:14:28,700 --> 00:14:31,700
reason retail And, you know,
there's just a massive, massive

323
00:14:31,700 --> 00:14:34,300
demand for these jobs and that
with covid, we've seen the

324
00:14:34,300 --> 00:14:37,800
importance of these roles, but
we undervalue them, the job

325
00:14:37,800 --> 00:14:39,900
prospects, or promotional
prospects, pay is pretty

326
00:14:39,900 --> 00:14:41,700
terrible.
And, you know, and if we can

327
00:14:41,700 --> 00:14:44,700
develop technology that can
enable people in these jobs to

328
00:14:44,700 --> 00:14:48,400
up, skill to be able to get new
opportunities, to be able to do

329
00:14:48,400 --> 00:14:51,300
more with less and we think we
can improve conditions in that

330
00:14:51,300 --> 00:14:53,200
industry and you think that's
worth while.

331
00:14:53,300 --> 00:14:54,500
Yeah.
Yeah, I get that.

332
00:14:54,508 --> 00:14:58,200
Would you know, when I first
looked at what you guys were

333
00:14:58,200 --> 00:15:01,900
doing Connor?
My mother-in-law is in a nursing

334
00:15:01,900 --> 00:15:05,100
home in Dublin.
And, you know, I've been out

335
00:15:05,100 --> 00:15:09,500
there and it's a nice place, but
you can totally tell that it's

336
00:15:09,500 --> 00:15:12,000
engagement.
That's just so important and

337
00:15:12,000 --> 00:15:16,200
that engagement can come from,
you know, any which way.

338
00:15:16,400 --> 00:15:20,000
And I've seen on your website.
The, you know, the face of

339
00:15:20,000 --> 00:15:24,300
Stevie, right that shifts from
the eyes in the smiling robotic

340
00:15:24,300 --> 00:15:28,700
face to actually being a
FaceTime or a video chat face,

341
00:15:28,800 --> 00:15:29,600
right?
With our love.

342
00:15:29,700 --> 00:15:31,100
One's.
Was there.

343
00:15:31,100 --> 00:15:33,500
A moment for you?
Where it was like, okay, be on

344
00:15:33,500 --> 00:15:36,300
the commercial side of seeing.
What is the biggest Gap out

345
00:15:36,300 --> 00:15:39,900
there in the market for
something like Stevie to, was

346
00:15:39,900 --> 00:15:44,700
there a personal moment of hey,
the the senior care industry is

347
00:15:44,700 --> 00:15:47,400
where it's going to make sense.
For me personally, as well as

348
00:15:47,400 --> 00:15:49,600
commercially.
Definitely.

349
00:15:49,600 --> 00:15:52,400
I can't leave.
Things started long before we

350
00:15:52,400 --> 00:15:57,200
decided to build robots like
for, you know, eldercare, the

351
00:15:57,200 --> 00:16:00,400
motivation for me to get into
that industry was was In high

352
00:16:00,400 --> 00:16:03,800
school and like I had a
grandmother that passed away in

353
00:16:03,800 --> 00:16:06,400
a nursing home.
And she was a very independent

354
00:16:06,400 --> 00:16:09,300
woman open to open till quite
late in her life.

355
00:16:09,500 --> 00:16:12,300
And I remember when she was made
to live in a nursing home, like

356
00:16:12,300 --> 00:16:14,800
feeling a little bit let down
because technology could do so

357
00:16:14,800 --> 00:16:16,200
much.
But this woman who, you know,

358
00:16:16,500 --> 00:16:17,800
she was perfectly Compass
mantis.

359
00:16:18,600 --> 00:16:21,800
Well as much as you'd expect for
a woman in her eighties and but

360
00:16:21,800 --> 00:16:23,300
yeah, it'll she had some
Mobility problems.

361
00:16:23,300 --> 00:16:26,900
So she was doing by yourself and
think she she might fall twice a

362
00:16:26,908 --> 00:16:29,500
year and the family just thought
that was too big of a risk, too.

363
00:16:29,700 --> 00:16:31,000
Let her to continue living by
herself.

364
00:16:31,000 --> 00:16:34,000
So she gave up a lot of
Independence to move into a

365
00:16:34,000 --> 00:16:36,200
nursing home, where she shared a
room with a woman with dementia.

366
00:16:36,400 --> 00:16:40,500
And I just felt that the we
should be doing more to to

367
00:16:40,500 --> 00:16:42,400
enable these people to get older
with dignity.

368
00:16:42,700 --> 00:16:46,300
And even though, like, the, the
people, there, the staff that

369
00:16:46,300 --> 00:16:50,100
are there Grace like, you know,
you rarely hear negative stories

370
00:16:50,100 --> 00:16:53,400
about nursing home staff at they
normally fantastic, but it's

371
00:16:53,400 --> 00:16:55,300
just, you know, there's a limit
to what people can do, unless

372
00:16:55,300 --> 00:16:58,300
there's technology that enables
and and sports them and there

373
00:16:58,300 --> 00:17:02,300
just wasn't in that area.
And I actually spent a couple of

374
00:17:02,308 --> 00:17:06,700
months working in a nursing home
in high school just as a way to

375
00:17:06,700 --> 00:17:10,000
kind of sport community.
And like I just felt that this

376
00:17:10,000 --> 00:17:13,400
was an area where I could work.
Well, with the people I felt

377
00:17:13,400 --> 00:17:17,900
that, you know, we know from
demographics that you know, the

378
00:17:17,900 --> 00:17:20,700
number of people who are going
to be above the working age and

379
00:17:20,700 --> 00:17:22,900
15 to 20 years is going to be
unmanageable.

380
00:17:23,099 --> 00:17:25,700
And it's already on management
will more than one or two people

381
00:17:25,700 --> 00:17:28,200
in hospital beds before
coronavirus, we're pensioners

382
00:17:28,200 --> 00:17:32,800
and So we have a challenge to be
able to improve efficiency in

383
00:17:32,800 --> 00:17:35,300
healthcare.
So thus we're able to cope with

384
00:17:35,300 --> 00:17:38,800
the demand that an aging
population, brings and Robotics

385
00:17:38,800 --> 00:17:40,600
is an obvious area.
You don't need to be an

386
00:17:40,600 --> 00:17:43,800
economist to point to the fact
that this will eventually make

387
00:17:43,800 --> 00:17:46,300
commercial sense.
But, you know, as an engineer

388
00:17:46,300 --> 00:17:49,700
who wants to develop technology
that delivers positive impact,

389
00:17:49,700 --> 00:17:52,000
it seemed to me a no-brainer.
Absolutely.

390
00:17:52,000 --> 00:17:54,000
Absolutely.
It totally see that.

391
00:17:54,400 --> 00:17:57,800
And it's great to hear that kind
of personal experience has made

392
00:17:57,800 --> 00:18:01,300
its way into your Envisioning
ambition, with all this because

393
00:18:01,300 --> 00:18:03,300
it's so important to have that
to really keep driving you

394
00:18:03,300 --> 00:18:05,100
forward, right?
Yeah.

395
00:18:05,100 --> 00:18:06,800
Absolutely.
It has to unlike, you know,

396
00:18:06,808 --> 00:18:08,800
because I couldn't wait.
I know this is a risky Pursuit

397
00:18:08,800 --> 00:18:11,400
and I know that you know, it's
difficult to do a start-up at

398
00:18:11,408 --> 00:18:14,100
the best of times and picking
something that's is I guess as

399
00:18:14,100 --> 00:18:18,300
out there as robotics Hardware's
is, as hard as they say like,

400
00:18:18,300 --> 00:18:20,500
you know, with the situation
where when it's all said and

401
00:18:20,500 --> 00:18:23,800
done, we fail.
Like if I look back and say it,

402
00:18:23,800 --> 00:18:25,400
well, at least we tried to do
something worthwhile.

403
00:18:25,400 --> 00:18:28,400
I don't think I'll have regrets
and what they - sacrifices and

404
00:18:28,400 --> 00:18:30,900
come, you know, the minister.
Devices and things you give up.

405
00:18:31,200 --> 00:18:34,100
And in order to put this, you
know, where it needs to be to be

406
00:18:34,100 --> 00:18:37,800
successful are high as you know,
and if you don't see value

407
00:18:37,800 --> 00:18:41,800
Beyond, you know, making money,
then it won't be doing this for

408
00:18:41,800 --> 00:18:43,900
and exactly.
Certainly most at myself.

409
00:18:43,900 --> 00:18:47,700
Like, you know, I've got
classmates that people, I grew

410
00:18:47,700 --> 00:18:51,200
up with who, you know, have are
making very comfortable living

411
00:18:51,200 --> 00:18:54,700
at the moment, you know, working
9 to 5 jobs, like this is way

412
00:18:54,700 --> 00:18:57,700
more than just, you know, about
a commercial success for me.

413
00:18:57,708 --> 00:18:59,500
This is about doing something
worthwhile.

414
00:18:59,700 --> 00:19:03,200
My life and selling the company
that hopefully, you know, can

415
00:19:03,200 --> 00:19:04,400
make a positive impact in the
world.

416
00:19:04,700 --> 00:19:06,100
That's awesome.
I love that.

417
00:19:06,300 --> 00:19:10,200
Tell me you talked a minute ago
about funding and getting this

418
00:19:10,200 --> 00:19:12,400
funded at this stage.
How is that going for you?

419
00:19:13,500 --> 00:19:16,800
It's going.
Well, I guess like any startup.

420
00:19:16,800 --> 00:19:20,100
It's always challenging when
you're raising around because I

421
00:19:20,100 --> 00:19:22,800
guess, you know, your, it's
hardly less vulnerable stage.

422
00:19:22,800 --> 00:19:24,400
You're at because you haven't
done anything yet.

423
00:19:24,600 --> 00:19:27,400
And even though we've come from
a university that has, you know,

424
00:19:27,400 --> 00:19:29,400
we managed to show that we're
capable of building.

425
00:19:29,600 --> 00:19:32,500
Technology.
And you know, we have a team

426
00:19:32,500 --> 00:19:35,900
that's been together for quite a
while, you know, we're pitching

427
00:19:35,900 --> 00:19:39,100
to a different different
audience, I guess as myself as a

428
00:19:39,400 --> 00:19:41,200
someone whose background has
been mostly academic come to

429
00:19:41,200 --> 00:19:43,000
this point.
I probably have a point to prove

430
00:19:43,000 --> 00:19:47,300
commercially and obviously,
speaking to VCS and investors

431
00:19:47,300 --> 00:19:50,500
like the way you pitch to them
as is going on quite nuanced.

432
00:19:50,500 --> 00:19:53,200
I'm not as I'm learning.
So I think, you know, people

433
00:19:53,200 --> 00:19:54,800
have generally been impressed by
what we're doing.

434
00:19:55,200 --> 00:19:59,000
And since covid has happened.
We've kind of reprioritized.

435
00:19:59,000 --> 00:20:01,100
A lot of We're doing so that's
involved.

436
00:20:01,100 --> 00:20:05,200
Making significant changes to
our deck and a business plan,

437
00:20:05,200 --> 00:20:08,000
which I think is, you know, be
able to show that were adopting

438
00:20:08,000 --> 00:20:11,500
that market is we see it as a
big opportunity to start

439
00:20:11,500 --> 00:20:14,900
developing em robots in response
to covid as well as the CD

440
00:20:14,900 --> 00:20:18,200
platform, which we can talk
about it later if you like, but

441
00:20:18,200 --> 00:20:19,700
it's been interesting and I
think that there has been a lot

442
00:20:19,700 --> 00:20:21,400
of engagements.
It's one of these things that

443
00:20:21,400 --> 00:20:23,900
just takes time and we've only
seriously started trying to

444
00:20:23,900 --> 00:20:27,300
raise the round in January and
then with covid and everything

445
00:20:27,300 --> 00:20:29,400
else, you know, realistically,
it'll probably be another couple

446
00:20:29,400 --> 00:20:30,700
of months.
Months before we do it, but

447
00:20:30,800 --> 00:20:32,600
certainly all of the signs are
pointing to the fact that it

448
00:20:32,600 --> 00:20:34,900
will get there.
Yeah, absolutely.

449
00:20:34,900 --> 00:20:39,000
And I did see Violet on your
website as well fighting

450
00:20:39,700 --> 00:20:44,500
fighting covid-19 right with
lightning, you know, like I

451
00:20:44,500 --> 00:20:45,600
don't even want to get into
that.

452
00:20:45,600 --> 00:20:49,900
If I sit that Donald Trump gave
the world but you know, there

453
00:20:49,900 --> 00:20:53,000
was a bit of a difference
between opening up somebody's

454
00:20:53,000 --> 00:20:56,100
body and Shining ultraviolet
light on the inside to kill

455
00:20:56,100 --> 00:20:58,800
coronavirus, which is just
ridiculous to even think that

456
00:20:58,808 --> 00:21:01,600
someone would mention That
versus actually okay.

457
00:21:01,600 --> 00:21:04,200
We have a lighting system here
that has been demonstrated

458
00:21:04,200 --> 00:21:10,000
scientifically to to kill germs
in physical environments rather

459
00:21:10,000 --> 00:21:11,800
than biological environments,
right?

460
00:21:11,800 --> 00:21:15,900
So that must have come about
pretty quickly or here at that

461
00:21:15,900 --> 00:21:19,700
been in the works prior to the
world catching fire in the last

462
00:21:19,700 --> 00:21:22,300
few months.
Yeah, you know, it was honest

463
00:21:22,300 --> 00:21:24,700
and in the works.
So, like one of the things that

464
00:21:24,700 --> 00:21:26,900
we've been doing, and I think, I
think we operate quite

465
00:21:26,900 --> 00:21:28,400
differently from a lot of
Robotics startups.

466
00:21:28,400 --> 00:21:31,100
And then like the menu.
Time we spend in the field and

467
00:21:31,100 --> 00:21:33,100
with users is probably more than
worse.

468
00:21:33,500 --> 00:21:37,000
People might expect.
So, like, over the last kind of

469
00:21:37,000 --> 00:21:39,300
18 months or so.
I just spent a number of months

470
00:21:39,300 --> 00:21:41,700
and, you know, not just testing
it in nursing homes or Atomic

471
00:21:41,700 --> 00:21:43,500
means, but actually living there
and staying there.

472
00:21:43,500 --> 00:21:45,800
So, I'm pretty first.
I think I'm a pretty first an

473
00:21:45,800 --> 00:21:49,200
understanding of what the places
are like and what the challenges

474
00:21:49,200 --> 00:21:51,400
are.
And this part of that process

475
00:21:51,400 --> 00:21:53,700
like wouldn't be one of the many
kind of challenges.

476
00:21:53,700 --> 00:21:57,200
We could have pointed out was
infection control, you know, you

477
00:21:57,200 --> 00:21:59,400
probably hear of drug resistant
to a germ.

478
00:22:00,500 --> 00:22:03,300
Amorous a seed, if these things
cause chaos.

479
00:22:03,300 --> 00:22:07,000
And hospitals, hospital-acquired
infections are hugely

480
00:22:07,000 --> 00:22:09,700
problematic, because you might
have someone with an open wound

481
00:22:09,700 --> 00:22:12,400
who gets an infection and all of
a sudden, you know, your

482
00:22:12,400 --> 00:22:14,200
antibiotics.
Don't, don't do anything to fix

483
00:22:14,200 --> 00:22:16,000
it.
And this is a huge problem with

484
00:22:16,000 --> 00:22:18,600
older people.
And we saw this being a massive

485
00:22:18,600 --> 00:22:21,100
issue in retirement communities
because it's impossible to

486
00:22:21,100 --> 00:22:23,600
implement the same kind of
infection control programs.

487
00:22:23,600 --> 00:22:26,800
You can't, you know and
disinfect a room that's full of

488
00:22:26,800 --> 00:22:29,000
fabrics, carpets, curtains and
things.

489
00:22:29,300 --> 00:22:32,200
And and so we started looking at
alternative methods of

490
00:22:32,200 --> 00:22:34,300
disinfection that potentially,
we could have put on the CV

491
00:22:34,300 --> 00:22:36,500
robot as an add-on.
That was kind of where the idea

492
00:22:36,500 --> 00:22:39,000
began.
And like again, it's TV is being

493
00:22:39,000 --> 00:22:41,500
a vehicle to be a potentially be
able to do a lot of things.

494
00:22:41,800 --> 00:22:44,900
And that was, that was something
that we could put into the

495
00:22:44,900 --> 00:22:46,800
product, the the technical
roadmap.

496
00:22:47,400 --> 00:22:49,800
We spent about a year working
on, try to understand the

497
00:22:49,800 --> 00:22:52,700
germicidal effectiveness of UV
lighting, and when I say

498
00:22:52,700 --> 00:22:55,100
germicidal, Effectiveness
swimming is, if you have one of

499
00:22:55,108 --> 00:22:58,300
these lights turned on and you
know, how close is a need to be

500
00:22:58,300 --> 00:23:00,900
to the For germs are on in order
to kill them.

501
00:23:00,900 --> 00:23:02,200
How long does it need to be
turned on?

502
00:23:02,500 --> 00:23:05,000
What are the specifications of
the lice are important?

503
00:23:05,100 --> 00:23:06,800
But these are the kind of
critical.

504
00:23:06,800 --> 00:23:08,300
What the science is very clear.
It works.

505
00:23:08,600 --> 00:23:10,300
These are the critical questions
that need to be answered.

506
00:23:10,300 --> 00:23:11,400
If you want to put it on a
robot.

507
00:23:11,800 --> 00:23:15,300
And so when covid with a covert
outbreak, kind of re started to

508
00:23:15,300 --> 00:23:17,900
become clear that this was here
to stay, we've been, we don't

509
00:23:17,900 --> 00:23:19,000
over a year of work in this
area.

510
00:23:19,000 --> 00:23:22,000
So good timing, I guess as a
team.

511
00:23:22,100 --> 00:23:24,400
Yeah.
Well, I would say nothing is

512
00:23:24,400 --> 00:23:26,000
good, but it's fortuitous in a
way.

513
00:23:26,000 --> 00:23:28,900
So we kind of felt, you know
that there was a potential here,

514
00:23:28,900 --> 00:23:31,100
too.
I wouldn't say pivot the company

515
00:23:31,100 --> 00:23:34,100
cuz I don't feel that we are
pivoting and instead we just

516
00:23:34,100 --> 00:23:35,800
kind of I guess reprioritize
things.

517
00:23:35,800 --> 00:23:39,100
We just said, you know this tv
robot can wait for now and I

518
00:23:39,108 --> 00:23:41,300
think the demand and the short
term is going to be four things

519
00:23:41,300 --> 00:23:44,900
that can help with, you know,
reducing the transmission of

520
00:23:44,908 --> 00:23:47,200
covid.
You know, let's see if we can

521
00:23:47,500 --> 00:23:49,700
put together a robot that we
could deploy quickly.

522
00:23:49,700 --> 00:23:54,800
And let's leverage our know-how
as well as our knowledge in UV

523
00:23:54,800 --> 00:23:56,100
lighting.
Let's try to do something

524
00:23:56,100 --> 00:23:58,900
quickly and that led to a
prototype that we since tested

525
00:23:58,900 --> 00:24:02,300
now, if you are Littles, and
we've worked with the

526
00:24:02,300 --> 00:24:04,800
microbiology lab and Trinity
that have been fantastic and

527
00:24:05,200 --> 00:24:07,200
they've really been able to,
they don't have a horse in the

528
00:24:07,208 --> 00:24:08,500
game here.
So they've been able to tell us

529
00:24:08,500 --> 00:24:12,900
what works and what hasn't and
you know, very, you know in an

530
00:24:12,900 --> 00:24:15,400
independent way and we've been
able to show that this thing is

531
00:24:15,400 --> 00:24:17,900
fact, even I guess going
forward, a big part of what are

532
00:24:18,300 --> 00:24:19,500
our product is going to be in
the future.

533
00:24:19,500 --> 00:24:22,600
It's going to be disinfectant.
Disinfectant at these

534
00:24:22,600 --> 00:24:25,200
technology.
Yeah, that's really interesting.

535
00:24:25,200 --> 00:24:28,900
And, you know, we talked about
on this show, the importance of

536
00:24:28,900 --> 00:24:31,700
a Founder, having first-hand
experience with the problems or

537
00:24:31,700 --> 00:24:34,100
solving.
You said a few minutes ago that

538
00:24:34,100 --> 00:24:37,300
you actually lived inside a
nursing home for a little while

539
00:24:37,300 --> 00:24:39,100
to figure some of this stuff
out.

540
00:24:39,100 --> 00:24:40,500
Is that right?
Yeah.

541
00:24:40,500 --> 00:24:44,400
Well, it kind of happened, you
know, quite unintentionally, but

542
00:24:44,500 --> 00:24:46,900
since then it's almost become a
practice within our group is

543
00:24:46,900 --> 00:24:49,400
that this is sort of how we how
we how we do things.

544
00:24:50,200 --> 00:24:52,700
So I called him only when we
built the robot, I guess the

545
00:24:52,700 --> 00:24:56,400
partners we were looking.
Or were end users and we know

546
00:24:56,400 --> 00:24:59,500
from the literature that will to
really it's really difficult to

547
00:24:59,508 --> 00:25:01,500
deploy a robot and get it
accepted over time.

548
00:25:01,700 --> 00:25:02,800
Typically.
What happens is that when you

549
00:25:02,808 --> 00:25:04,900
bring a robot into an
environment, especially social

550
00:25:04,900 --> 00:25:08,000
and you see a lot of interest
early on and what's called a

551
00:25:08,008 --> 00:25:09,600
novelty effect.
It's a little bit like, you

552
00:25:09,600 --> 00:25:11,900
know, when the kid is interested
in their toys at Christmas but

553
00:25:11,900 --> 00:25:13,500
like four days later they
forgotten about them.

554
00:25:13,500 --> 00:25:17,400
Yeah, and you know, is there's
not value there to keep people

555
00:25:17,400 --> 00:25:20,900
using it over time.
And then you, you know, you

556
00:25:20,900 --> 00:25:22,800
haven't got anything.
And that's been a problem for a

557
00:25:22,800 --> 00:25:24,800
long time in robotics.
And what we've been trying to

558
00:25:24,800 --> 00:25:28,600
do, I guess, is we want to make
sure that we're able to stay

559
00:25:28,600 --> 00:25:31,200
involved without you.
No problem as long as possible.

560
00:25:31,200 --> 00:25:33,800
Because if and when people stop
using it, we want to know why?

561
00:25:34,100 --> 00:25:37,300
And that was our motivation
initially, and what's happened.

562
00:25:37,300 --> 00:25:41,600
Is that by staying that clued
in, and we're able to react.

563
00:25:41,600 --> 00:25:44,900
So soon, as we start to see
people not use the technology or

564
00:25:45,200 --> 00:25:46,500
we fight.
We know, if you get to know

565
00:25:46,500 --> 00:25:48,200
people, personally, they'll tell
you very honestly, why they

566
00:25:48,200 --> 00:25:51,300
don't like something we can make
that change quickly and that

567
00:25:51,300 --> 00:25:53,200
stops the drop-off.
That's so that keeps them

568
00:25:53,200 --> 00:25:55,800
engaged.
And the fact that the robot is

569
00:25:55,800 --> 00:25:59,300
adapting to, their needs of
their requirements is a key

570
00:25:59,300 --> 00:26:03,800
piece, and it's not a scalable.
It's not a scalable way to do

571
00:26:03,800 --> 00:26:06,300
things.
But at the very early stages, if

572
00:26:06,300 --> 00:26:09,500
you're the first people to bring
a machine into this industry and

573
00:26:09,500 --> 00:26:11,500
we think it makes sense to spend
a lot of time doing this up

574
00:26:11,500 --> 00:26:14,000
until the point where you solve
most of the problems.

575
00:26:15,300 --> 00:26:17,300
And from that point onward, you
can probably do it remotely, but

576
00:26:17,300 --> 00:26:18,700
certainly in the short term you
need to be there.

577
00:26:19,300 --> 00:26:22,200
So we've been able to get a lot
of fundamental insights from,

578
00:26:22,200 --> 00:26:24,100
from doing it.
We've been able to get Lot of

579
00:26:24,100 --> 00:26:27,300
insight into error tolerance,
like, you know, had how much

580
00:26:27,300 --> 00:26:29,000
error can people, tolerate, like
what happens?

581
00:26:29,000 --> 00:26:31,700
If somebody at the robot calls
Somebody by the wrong name, like

582
00:26:31,700 --> 00:26:34,600
these are the kind of in places
intangibles that think a lot of

583
00:26:34,608 --> 00:26:36,000
people don't.
You don't really know unless you

584
00:26:36,000 --> 00:26:38,100
see us.
We spent a lot of time.

585
00:26:38,100 --> 00:26:40,200
No seeing and we were pretty
good idea.

586
00:26:40,900 --> 00:26:43,200
And what happens is that it's
and it's not just me that spends

587
00:26:43,200 --> 00:26:45,900
time there the whole team.
Do it screens that are software

588
00:26:45,900 --> 00:26:48,300
Engineers when we're writing the
code.

589
00:26:48,300 --> 00:26:51,400
We're testing the code like in
Our Minds Eye were able to kind

590
00:26:51,400 --> 00:26:53,400
of go back to those times and
say well if the robot was

591
00:26:53,400 --> 00:26:55,000
deployed And it did what I've
Just Seen.

592
00:26:55,000 --> 00:26:57,600
Would that be okay, you know, if
you're just so you know PhD

593
00:26:57,600 --> 00:27:01,000
student or postdoc or you know,
you know, fresh higher, like you

594
00:27:01,000 --> 00:27:03,100
just don't have that experience.
You don't have that insight.

595
00:27:03,100 --> 00:27:06,100
And, you know, your opinion is
going to be in most cases.

596
00:27:06,100 --> 00:27:09,500
I'd say wrong.
People will be very surprised

597
00:27:09,500 --> 00:27:10,800
with what works.
What doesn't.

598
00:27:11,300 --> 00:27:11,900
Yeah.
Yeah.

599
00:27:11,900 --> 00:27:14,800
It was that was that a natural
kind of step for you or did

600
00:27:14,800 --> 00:27:16,900
someone say, Hey listen, you
really got to get out there and

601
00:27:16,900 --> 00:27:20,300
live it.
Like I would say it's a

602
00:27:20,308 --> 00:27:24,900
combination like we The things
that we've been doing in the

603
00:27:24,908 --> 00:27:28,900
university for as long as we've
set the group is we're not just

604
00:27:28,900 --> 00:27:30,200
innovating in the area of
Robotics.

605
00:27:30,200 --> 00:27:32,800
Like, you know, we're working
on, you know, an

606
00:27:32,800 --> 00:27:36,100
interdisciplinary Global
Innovation project that requires

607
00:27:36,100 --> 00:27:37,900
lots of different people with
lots of different skill sets.

608
00:27:37,900 --> 00:27:41,400
So a lot of what we've been
doing over the years has been

609
00:27:41,400 --> 00:27:44,200
trying to find ways and systems
for us to collaborate work well

610
00:27:44,200 --> 00:27:48,100
together and with not just as a
team, but also like weed seed,

611
00:27:48,100 --> 00:27:50,500
our users has been in somewhat
of an extension of our team.

612
00:27:50,800 --> 00:27:53,700
And so we've been very lucky to
have collaborations.

613
00:27:53,800 --> 00:27:56,100
With your like to Stanford.
And we work with number of

614
00:27:56,100 --> 00:27:58,300
different, not only universities
across the world.

615
00:27:58,300 --> 00:28:02,600
Would also big companies, the
likes of Panasonic sap and, you

616
00:28:02,608 --> 00:28:06,500
know, some big companies in
Germany and we've managed to, I

617
00:28:06,500 --> 00:28:10,500
guess find within ourselves, the
way to operate that I think

618
00:28:11,000 --> 00:28:13,400
keeps us very, very clued into
customers.

619
00:28:13,400 --> 00:28:17,000
If we're going to fail, we want
to fail fast and that's been

620
00:28:17,000 --> 00:28:19,100
very much the kind of the
philosophy that we have, and I

621
00:28:19,108 --> 00:28:20,700
think that's very much the
culture within the team.

622
00:28:21,100 --> 00:28:24,300
And so I guess when we work on
these kind of projects We make a

623
00:28:24,308 --> 00:28:25,900
lot of progress in the early
stages.

624
00:28:25,900 --> 00:28:28,600
It's all centered around
learning from mistakes.

625
00:28:28,600 --> 00:28:31,600
We try and prototype things.
And I think that's where we have

626
00:28:31,600 --> 00:28:33,900
a lot of experience doing and
that's what we've been doing

627
00:28:33,900 --> 00:28:35,000
invited.
That's how we've gotten

628
00:28:35,000 --> 00:28:38,100
something from, you know, an
idea to testing in a week.

629
00:28:38,500 --> 00:28:40,000
That's how we've gotten.
Clear the validation from

630
00:28:40,000 --> 00:28:42,600
hospitals in a month.
It's that sort of practice.

631
00:28:42,600 --> 00:28:45,200
It's practice that we've refined
in the University as well as the

632
00:28:45,208 --> 00:28:47,900
science.
And I think that as we start to

633
00:28:47,900 --> 00:28:51,000
scale and it will be possible
to, you know, grow as an

634
00:28:51,008 --> 00:28:53,500
organization while keeping those
those practices on it.

635
00:28:53,700 --> 00:28:55,500
As a nation.
Yeah, I get that.

636
00:28:55,500 --> 00:28:59,900
I get that and it seems like
you've got some pretty Natural

637
00:28:59,900 --> 00:29:03,700
Instincts there towards doing
all this Connor that have been

638
00:29:03,700 --> 00:29:06,100
helpful to you and getting the
businesses far.

639
00:29:06,100 --> 00:29:09,100
What do you think some of those
specific personality traits are

640
00:29:09,100 --> 00:29:11,900
just to name a few that you
think that have been important.

641
00:29:12,100 --> 00:29:14,500
I say you probably get a
different answer if you ask my

642
00:29:14,500 --> 00:29:17,300
team but I suppose they could
know the cliched ones would be

643
00:29:17,300 --> 00:29:19,100
would be probably things like
resilience.

644
00:29:19,400 --> 00:29:22,300
They don't mean takes games.
Like I know, I I've lost count

645
00:29:22,300 --> 00:29:25,200
long time ago, the number of
people You look to be skeptical

646
00:29:25,200 --> 00:29:27,000
and I've pasted what we were
going to do.

647
00:29:27,700 --> 00:29:30,400
Feel when I started building
robots, that one of my kind of

648
00:29:30,400 --> 00:29:33,600
litmus test was actually, you
know, will people think this is

649
00:29:33,600 --> 00:29:36,900
impossible and it's only if they
say yes, is it something I'm

650
00:29:36,900 --> 00:29:38,700
interested in that similar to a
chip on his shoulder.

651
00:29:38,700 --> 00:29:41,200
I got yeah because you know
people writing me off before I

652
00:29:41,200 --> 00:29:44,400
had a chance to do things and I
think being take skin that we

653
00:29:44,400 --> 00:29:45,500
are, we dealing with failure all
the time.

654
00:29:45,500 --> 00:29:48,300
And you know, sometimes it's
Meredith sometimes, you know,

655
00:29:48,300 --> 00:29:52,000
we've made we've made a mistake
but as we go on as we get more

656
00:29:52,000 --> 00:29:54,800
experience, like less and less
failure, As points tools making

657
00:29:54,800 --> 00:29:57,400
technical mistakes.
It's usually learning things

658
00:29:57,400 --> 00:29:58,900
that we can, we can we can build
from.

659
00:29:58,900 --> 00:30:02,000
So if we do fail, we learn from
it, we get stronger because of

660
00:30:02,000 --> 00:30:03,500
it.
And I think that's, you know,

661
00:30:03,700 --> 00:30:06,800
not having that fear, like not
having a fear of failure.

662
00:30:07,100 --> 00:30:09,100
And it's been something that I
think is a team, is really

663
00:30:09,100 --> 00:30:11,100
galvanized us over the years.
And that's something that I

664
00:30:11,108 --> 00:30:13,500
think, you know, if we didn't if
we were scared that, what we

665
00:30:13,500 --> 00:30:16,200
were going to do didn't work
and, you know, we probably have

666
00:30:16,200 --> 00:30:18,800
a long time ago.
I think probably the piece

667
00:30:18,800 --> 00:30:21,300
that's differentiated as most
though has been empathy.

668
00:30:22,500 --> 00:30:25,400
I mean, empathy kind of The
users, like I think a lot of

669
00:30:25,400 --> 00:30:29,500
Robotics companies and people
who work in AI, they get kind of

670
00:30:29,500 --> 00:30:31,700
sidetracked by the technology.
Like look how cool this is.

671
00:30:31,700 --> 00:30:34,200
Look how much of a step change.
We can do on the technology

672
00:30:34,200 --> 00:30:38,000
front and they might lose sight
of, I guess the, you know, the

673
00:30:38,000 --> 00:30:41,800
ethical responsibilities.
And also the fact that like, you

674
00:30:41,800 --> 00:30:44,500
know, you're Building Technology
for people to use, this can

675
00:30:44,500 --> 00:30:46,700
affect people's lives and
meaningful ways, you know,

676
00:30:46,708 --> 00:30:49,300
whether that's positive, you
know, naming people do more with

677
00:30:49,300 --> 00:30:52,600
less or - it, you know, at the
end of the day, you know, robots

678
00:30:52,600 --> 00:30:55,700
and AI can threaten jobs.
Soooo, and you know, these

679
00:30:55,700 --> 00:30:59,600
things that potentially could be
- like they're in the back of

680
00:30:59,600 --> 00:31:00,700
people's heads.
We're going to be using the

681
00:31:00,700 --> 00:31:02,600
technology.
And yeah, like what we've tried

682
00:31:02,600 --> 00:31:06,100
to do is put ourselves in their
shoes as best as we can, and

683
00:31:06,100 --> 00:31:07,400
really involve them in the
process.

684
00:31:08,000 --> 00:31:10,400
So, we work, I think we make a
real effort when we're

685
00:31:10,400 --> 00:31:12,600
developing technology to work.
What users we try to do as

686
00:31:12,600 --> 00:31:17,000
little in a vacuum as possible.
We try to do as much with the

687
00:31:17,000 --> 00:31:20,300
support and engagement of those
partners, and I think that like,

688
00:31:20,300 --> 00:31:22,100
you know, it's true, those
Partnerships that we've been

689
00:31:22,100 --> 00:31:24,600
able to make up for the fact
that I haven't had that much fun

690
00:31:24,600 --> 00:31:26,800
doing and make up for the fact
that, you know, we're only, you

691
00:31:26,800 --> 00:31:29,900
know, less than 10 people and
because ultimately, they could

692
00:31:29,900 --> 00:31:31,600
you do the better, you do the
better, you can work with

693
00:31:31,600 --> 00:31:34,900
others, you know, it's that old
cliché that like a, you know, a

694
00:31:34,900 --> 00:31:37,900
rising tide lifts All Ships.
It's that sort of that sort of

695
00:31:37,900 --> 00:31:39,400
approach.
And I think we've sort of

696
00:31:39,600 --> 00:31:41,900
embraced that kind of
collaborative mind to try to

697
00:31:41,900 --> 00:31:44,400
find like-minded people and try
to find the common ground to

698
00:31:44,408 --> 00:31:47,000
work on trying to find the areas
where they add value and we add

699
00:31:47,000 --> 00:31:48,800
value.
And you know, let's be a team on

700
00:31:48,800 --> 00:31:51,700
this and he'll these are changes
that need to get made if not us

701
00:31:51,700 --> 00:31:54,400
who and you know, that's Do our
best here.

702
00:31:54,400 --> 00:31:59,000
And I think that, that sort of,
you know, that sort of sense of

703
00:31:59,000 --> 00:32:01,100
working together and come up
camaraderie.

704
00:32:01,800 --> 00:32:04,900
It's been enriching, but it's
also been rewarding in terms of

705
00:32:04,900 --> 00:32:07,200
outcomes.
We managed to do a lot more than

706
00:32:07,200 --> 00:32:08,800
I think.
We would have otherwise and

707
00:32:08,800 --> 00:32:11,200
precisely because, you know,
we've treated this as being

708
00:32:11,200 --> 00:32:12,600
something that's bigger than us.
Yeah.

709
00:32:12,600 --> 00:32:14,000
I hear you.
I hear it now.

710
00:32:14,000 --> 00:32:17,000
It's all really encouraging and,
you know, to get commercial

711
00:32:17,000 --> 00:32:19,100
again for a second.
I mean, I'm thinking about this

712
00:32:19,100 --> 00:32:23,400
in the context of, you know, B2B
versus b2c versus B to B to C,

713
00:32:23,500 --> 00:32:25,700
right.
Which is, you know, any time

714
00:32:25,700 --> 00:32:29,900
that you're trying to sell a new
product and where you need both

715
00:32:30,600 --> 00:32:33,900
the person or the business,
that's paying the bill, right to

716
00:32:33,900 --> 00:32:36,900
buy the product.
But also those that you intend

717
00:32:36,900 --> 00:32:40,900
to have the end impact on the
outcome on to both all enjoy

718
00:32:40,900 --> 00:32:43,300
using the product, right?
And to want and need that

719
00:32:43,300 --> 00:32:45,900
product.
Not only do you have to solve

720
00:32:45,900 --> 00:32:48,600
the B2B problem, which is the
business problem.

721
00:32:48,900 --> 00:32:51,200
But you need to solve the
customer problem with and

722
00:32:51,200 --> 00:32:52,800
problem, as well at the same
time.

723
00:32:53,100 --> 00:32:56,400
That's really Hard to do and
never mind bakey it into a

724
00:32:56,400 --> 00:33:00,400
robotics business, right?
So, you know, kudos to you guys

725
00:33:00,400 --> 00:33:03,100
for getting it this far, the
really impressive to say, thank

726
00:33:03,100 --> 00:33:04,600
you Pete.
What do you guys think?

727
00:33:04,600 --> 00:33:06,900
You know, what do you think
Connor in terms of you?

728
00:33:06,900 --> 00:33:11,300
And I spoke offline last week
about kind of your network in

729
00:33:11,300 --> 00:33:16,700
the community and those that you
lean on as a sounding board for

730
00:33:16,700 --> 00:33:19,900
advice that that Community is
opened up quite a bit for you in

731
00:33:19,900 --> 00:33:22,600
the last in the last six months
or so.

732
00:33:22,800 --> 00:33:26,500
Yeah, we I'm very grateful that
we've been lucky to have so many

733
00:33:26,500 --> 00:33:29,900
advisers that are, you know,
coming with experience and lots

734
00:33:29,900 --> 00:33:32,200
of different areas that have
been willing to, you know, give

735
00:33:32,200 --> 00:33:36,700
their time and wisdom and
really, really helpful to

736
00:33:36,700 --> 00:33:38,600
helping us.
I guess you'll trade the whole

737
00:33:38,600 --> 00:33:41,200
process, whether it's commercial
or technical or, you know,

738
00:33:41,400 --> 00:33:44,300
providing an opportunity for me
to kind of soundy check ideas

739
00:33:44,300 --> 00:33:46,500
that we have and that's been
helpful because make moving

740
00:33:46,500 --> 00:33:48,400
quickly, you know, you kind of
have to be decisive and it may

741
00:33:48,408 --> 00:33:51,000
be difficult when we got 50
different things in front of you

742
00:33:51,000 --> 00:33:52,700
and you're trying to figure out
like it should I pick this or

743
00:33:52,700 --> 00:33:55,100
this and like, One of the big
decisions for example, like, you

744
00:33:55,100 --> 00:33:58,200
know doing it doesn't make sense
here to kind of hit the brakes

745
00:33:58,200 --> 00:34:01,100
on Stevie and focus on this new
robot and despite the fact that

746
00:34:01,100 --> 00:34:02,900
we work overtime magazine for
months ago.

747
00:34:03,200 --> 00:34:06,000
Like these are pretty big
decisions that you kind of want

748
00:34:06,000 --> 00:34:08,100
to trust in other people's
judgments on as well as your own

749
00:34:08,100 --> 00:34:11,100
and like someone like David
Maloney, for example, who is the

750
00:34:11,500 --> 00:34:15,500
one of the founders of Nvidia
sand is now, you know what Intel

751
00:34:15,500 --> 00:34:17,199
and you know providing support
to us through there.

752
00:34:17,199 --> 00:34:19,500
There ain't very high incubator.
Like, you know, I don't

753
00:34:19,500 --> 00:34:22,500
wonderful conversation with him
where he basically, you know,

754
00:34:23,000 --> 00:34:25,600
made it very clear.
That this, you know, covid is is

755
00:34:25,600 --> 00:34:29,000
going to be a world changer.
And, you know, there's a big

756
00:34:29,000 --> 00:34:31,100
opportunity here for robotics.
They're already trying to do

757
00:34:31,100 --> 00:34:33,600
this in Japan, you know, he has
confidence in what we were

758
00:34:33,600 --> 00:34:35,300
doing, was better than what was
being done over there.

759
00:34:35,300 --> 00:34:38,100
And you know, this is something
that you know, you can act fast

760
00:34:38,100 --> 00:34:41,900
on and you know, he was willing
to support us through that and

761
00:34:41,900 --> 00:34:43,400
like, you know, that was the
starting point.

762
00:34:43,400 --> 00:34:46,600
I was you know, where we could
have transitioned from, you

763
00:34:46,600 --> 00:34:48,900
know, full-time working on
Stevie to all of a sudden

764
00:34:48,900 --> 00:34:53,500
working with Intel on this, you
know, UV robot and like that.

765
00:34:53,600 --> 00:34:56,800
Was an example of Someone Like
Jerry Lacy who's also a

766
00:34:56,808 --> 00:34:58,700
professor at Trinity has a lot
of experience with spinning out.

767
00:34:58,700 --> 00:35:02,100
Like he's been someone who's
been really helpful in, advising

768
00:35:02,100 --> 00:35:06,100
me personally on how to be able
to juggle the responsibilities

769
00:35:06,100 --> 00:35:08,700
of being a professor and as
well, as you know, leading a

770
00:35:08,700 --> 00:35:11,400
start-up and had to be able to
do both and time management.

771
00:35:11,700 --> 00:35:14,500
And again Haley bring things
that are kind of currently in

772
00:35:14,500 --> 00:35:17,100
the lab how to bring this those
to Market in the process of to

773
00:35:17,100 --> 00:35:20,400
go with it and he has a wealth
of experience in doing that and

774
00:35:20,500 --> 00:35:23,500
he also happens to you know, as
luck would have us have a lot of

775
00:35:23,700 --> 00:35:27,400
It's in the area of of them
hygiene control and he knows a

776
00:35:27,400 --> 00:35:29,400
lot about the sector.
So he's getting good advice.

777
00:35:30,000 --> 00:35:32,100
I think to be honest, though.
The people who live in most on

778
00:35:32,100 --> 00:35:35,000
as a selling for our team, like
most of the people who are

779
00:35:35,000 --> 00:35:38,100
working with us, like we've been
together for some cases 56 years

780
00:35:39,200 --> 00:35:42,200
and like they're people who
have, you know, as much

781
00:35:42,200 --> 00:35:45,000
experience as I do in the most
part and speak to be brutally

782
00:35:45,000 --> 00:35:47,100
honest like they play Devil's
Advocate better than anybody

783
00:35:47,100 --> 00:35:49,400
else.
And when we hear criticism from

784
00:35:49,400 --> 00:35:51,600
the outside, that's nothing that
we haven't been talking about

785
00:35:51,600 --> 00:35:53,300
ourselves like six months or a
year ago.

786
00:35:54,200 --> 00:35:56,500
And like there's one person on
the team particular guy called

787
00:35:56,500 --> 00:35:59,900
Michael Coleman and like,
worrying and yang.

788
00:35:59,900 --> 00:36:03,000
I guess I see the the
possibilities of stuff but he

789
00:36:03,000 --> 00:36:05,700
kind of puts it in control.
He's very much a details person

790
00:36:05,700 --> 00:36:08,100
and I think we work really well
together because I'll page

791
00:36:08,100 --> 00:36:11,700
something that, you know, maybe
exciting, whatever else, but I

792
00:36:11,700 --> 00:36:13,800
kill same, you a real like, you
know, the technology for this

793
00:36:13,800 --> 00:36:17,300
Connors three years away.
And so, you know, that kind of

794
00:36:17,300 --> 00:36:19,900
puts me in when I'm in the
ground and means when we're

795
00:36:19,900 --> 00:36:22,900
going to raise funding.
Yes, like the the vision is

796
00:36:22,900 --> 00:36:23,500
clear.
I know.

797
00:36:23,700 --> 00:36:26,200
It's needed, but I can work when
we try to match that with the

798
00:36:26,200 --> 00:36:29,100
ask and you know, we're doing
that by refining, it within the

799
00:36:29,107 --> 00:36:31,600
team and like, I'm really lucky
that we do have, you know, we've

800
00:36:31,600 --> 00:36:33,900
got, you know, other
co-founders, someone call a man

801
00:36:33,900 --> 00:36:37,100
Burke who I can prototype things
like in the sleep with will, you

802
00:36:37,100 --> 00:36:40,000
know with no machine?
Yeah, and you know using parts

803
00:36:40,000 --> 00:36:41,800
we play into dumpsters.
Like that's that's the level of

804
00:36:41,800 --> 00:36:43,300
talent.
We're dealing with, you know,

805
00:36:43,300 --> 00:36:45,900
someone like, you know, Ki and
Donovan, this guy who started

806
00:36:45,900 --> 00:36:50,400
working with when he was 15 and
you know, he seems like he's the

807
00:36:50,400 --> 00:36:54,700
guy is unbelievable and I What
he does and you know, Nev

808
00:36:54,700 --> 00:36:59,500
Donnelly, who's one of the, she
won the big award for, you know,

809
00:36:59,500 --> 00:37:02,300
best day eye for student
project, couple of years ago for

810
00:37:02,300 --> 00:37:04,200
her master's work in deep
learning.

811
00:37:04,200 --> 00:37:06,600
She comes with a background of
of devops as well.

812
00:37:06,600 --> 00:37:09,000
So like all of a sudden now and
she started kind of Meandering

813
00:37:09,000 --> 00:37:10,500
to.
So all of a sudden, now, we've

814
00:37:10,500 --> 00:37:13,300
got like, our leads.
Can you know, AI person who's

815
00:37:13,300 --> 00:37:15,900
got a lot of experience with,
you know, mechanical engineering

816
00:37:15,900 --> 00:37:18,300
that underlies, a robot, just
you knows what the Realms of

817
00:37:18,308 --> 00:37:21,100
possible and probable as well as
understanding the devops

818
00:37:21,100 --> 00:37:23,900
pipelines in the web app.
So like when we actually They

819
00:37:23,900 --> 00:37:26,800
say that there's a, you know,
there's a synchrony there and

820
00:37:26,800 --> 00:37:27,900
like when you have people like
that.

821
00:37:27,900 --> 00:37:31,400
Like, you know, we can probably
anything technically, probably

822
00:37:31,400 --> 00:37:34,100
have the expertise for eighty to
ninety percent of it.

823
00:37:34,100 --> 00:37:36,500
We don't need to reach out to
that many technical experts and

824
00:37:36,500 --> 00:37:38,200
where we do.
We're lucky to have some, some,

825
00:37:38,200 --> 00:37:41,000
some big players and, you know,
some real leading figures and

826
00:37:41,000 --> 00:37:43,700
Robotics and Ai.
And who I probably wouldn't like

827
00:37:43,700 --> 00:37:46,400
me to name them in the podcast,
but, you know, we're getting

828
00:37:46,400 --> 00:37:48,900
help from from people who've
been there and dullness and as

829
00:37:48,900 --> 00:37:50,600
much as anyone.
That's awesome.

830
00:37:50,600 --> 00:37:52,500
That's awesome.
And when we spoke before, as

831
00:37:52,500 --> 00:37:56,200
well, Connor you Stood that you
felt like a boxer and the best

832
00:37:56,200 --> 00:37:59,200
shape of your career, at least,
not you yourself, but the

833
00:37:59,200 --> 00:38:02,800
company, right?
The business, and from your

834
00:38:02,800 --> 00:38:05,200
description of the team.
It certainly sounds like that.

835
00:38:05,200 --> 00:38:07,600
Looking at it from this point
forward.

836
00:38:07,600 --> 00:38:10,900
Going ahead.
How do you envisage scaling the

837
00:38:10,900 --> 00:38:15,000
acara business in Ireland rather
than the great draw of the

838
00:38:15,008 --> 00:38:17,100
United States of America or
perhaps elsewhere?

839
00:38:17,500 --> 00:38:18,200
Yeah.
Yeah.

840
00:38:18,200 --> 00:38:20,400
I know.
I want that analogy.

841
00:38:20,400 --> 00:38:23,600
I guess it was, it was, it was
one that I kind of.

842
00:38:23,600 --> 00:38:25,200
Scatter.
Because I did, I do, I feel I

843
00:38:25,207 --> 00:38:26,400
feel like we've been training
for this.

844
00:38:26,400 --> 00:38:30,000
Like, that's, that's really it.
I think that we've all of what

845
00:38:30,000 --> 00:38:31,700
we've done up to this point,
have kind of been, you know,

846
00:38:31,700 --> 00:38:34,500
when you're fighting the
journeyman, we've been doing a

847
00:38:34,500 --> 00:38:36,300
good job with those.
I think now we're ready.

848
00:38:36,400 --> 00:38:38,700
Like it feels to me that like,
we have all of the systems in

849
00:38:38,700 --> 00:38:41,100
place with a lot of Partnerships
in place, like most of the

850
00:38:41,100 --> 00:38:43,100
designers, don't we just need,
you know, the opportunity to

851
00:38:43,100 --> 00:38:45,600
execute so feel like we're ready
to go on this.

852
00:38:46,400 --> 00:38:49,900
Like, looking at where other
companies have raised for

853
00:38:49,900 --> 00:38:53,800
robotics like, you know, the
average value - average Raise

854
00:38:53,800 --> 00:38:56,600
for could have proceeded in
robotics at San Francisco.

855
00:38:56,600 --> 00:38:59,500
Is like a, you know, 5 million
raised.

856
00:39:00,200 --> 00:39:03,100
And, you know, that that that's
the kind of money that you're

857
00:39:03,100 --> 00:39:05,900
looking out and just it's
impossible to get that seed

858
00:39:05,900 --> 00:39:08,100
level in Ireland.
It just and Europe.

859
00:39:08,100 --> 00:39:09,700
We haven't traditionally
invested in this.

860
00:39:10,100 --> 00:39:11,300
And so we're going to two
options.

861
00:39:11,300 --> 00:39:14,500
We can either pack up and go to
San Francisco and where they

862
00:39:14,500 --> 00:39:15,900
have a track record of doing
this.

863
00:39:16,400 --> 00:39:18,900
And I think there's a lot of
drawbacks by doing that or

864
00:39:18,900 --> 00:39:21,900
instead.
We try to, you know, build have

865
00:39:21,900 --> 00:39:23,400
a way to either get that kind of
money.

866
00:39:23,600 --> 00:39:26,400
Ireland, or try and do more with
less.

867
00:39:26,600 --> 00:39:28,500
I think the latter is probably
what we're going to try to do

868
00:39:28,500 --> 00:39:30,900
initially.
I think, you know, we had

869
00:39:30,900 --> 00:39:32,800
something like, you know, two to
three million.

870
00:39:32,800 --> 00:39:36,200
We'd be able to get a 24-month
Runway and move pretty quickly

871
00:39:36,200 --> 00:39:38,800
on this, but it's just, it takes
time to convince people that

872
00:39:38,800 --> 00:39:40,800
we're worth that kind of money
and we have to prepare

873
00:39:40,800 --> 00:39:43,800
ourselves.
So that so our hope is to now is

874
00:39:43,800 --> 00:39:46,700
is to try and get something
revenue-generating pretty

875
00:39:46,700 --> 00:39:48,200
quickly.
There's not too many robotics

876
00:39:48,200 --> 00:39:50,500
companies that are out of the
pre-revenue stage.

877
00:39:50,500 --> 00:39:53,400
So I think that'll show people
that we're not just you.

878
00:39:53,500 --> 00:39:56,000
We're not just a company that
does cool stuff in the lab.

879
00:39:56,000 --> 00:39:58,500
We're actually company that can
build something people want.

880
00:39:58,500 --> 00:40:00,500
And so I think we're gonna do
that with the with the covid

881
00:40:00,500 --> 00:40:02,600
response robot.
We're hopefully going to use

882
00:40:02,600 --> 00:40:05,600
some seed money from from
angels, as well as grant money

883
00:40:05,600 --> 00:40:07,100
to be able to get over the line
and do.

884
00:40:07,100 --> 00:40:10,500
Our hope is that we can we can
we can raise a seed round by the

885
00:40:10,508 --> 00:40:12,100
end of the summer.
That's, that's not a realistic

886
00:40:12,100 --> 00:40:15,200
goal.
I had the opportunity.

887
00:40:15,200 --> 00:40:19,000
I think for Ireland is a mass of
like we were a country that's

888
00:40:19,000 --> 00:40:22,000
relatively small and land mass
and we have a lot of experts.

889
00:40:22,000 --> 00:40:23,300
We've got a lot of big companies
that are based.

890
00:40:23,600 --> 00:40:26,400
Here, we have a lot of expertise
and a.

891
00:40:26,400 --> 00:40:30,000
I so like all of the ingredients
are there for us to basically,

892
00:40:30,000 --> 00:40:33,000
you know, AI is almost.
It's like like use our lenders,

893
00:40:33,000 --> 00:40:35,800
like, a proof-of-concept site
for the technology to be able to

894
00:40:35,800 --> 00:40:39,300
like have something where we're
developing and deploying and in

895
00:40:39,300 --> 00:40:42,600
the geographic region close to
us and what we can do.

896
00:40:42,600 --> 00:40:44,900
Hopefully it's you know, have
two Theta, two effects.

897
00:40:44,900 --> 00:40:47,700
One is we can create a positive
impact here, which is what we've

898
00:40:47,700 --> 00:40:51,100
been wanting to do from day one.
And especially now in the time

899
00:40:51,100 --> 00:40:52,700
of covid.
We're a technology is helpful.

900
00:40:52,700 --> 00:40:54,700
There.
Are we That it's doing something

901
00:40:54,700 --> 00:40:56,300
that's good.
But equally it's providing

902
00:40:56,300 --> 00:40:59,000
confidence to bigger markets,
like places like, you know, the

903
00:40:59,000 --> 00:41:02,500
US Mainland Europe, you know,
Asia are getting interest.

904
00:41:02,500 --> 00:41:06,700
Now, from, from Central America,
like what we'd like to be able

905
00:41:06,700 --> 00:41:09,000
to do is say, okay.
Well, here's the template where

906
00:41:09,000 --> 00:41:11,600
we got it to work in Ireland and
let's replicate that somewhere

907
00:41:11,600 --> 00:41:13,300
else.
And I think that's that's how

908
00:41:13,300 --> 00:41:16,500
you scale.
This look if we if we can scale

909
00:41:16,500 --> 00:41:19,300
it from day one.
If we wanted to like most of our

910
00:41:19,300 --> 00:41:21,800
kind of interest at the moment
is coming from overseas.

911
00:41:21,800 --> 00:41:24,300
Like we're getting a number of
emails, each A from from the

912
00:41:24,300 --> 00:41:26,800
u.s.
In particular, Australia has

913
00:41:26,800 --> 00:41:30,000
started to get really interested
to like, you know, if we can

914
00:41:30,000 --> 00:41:32,800
find the reality is that if we
can't find the funding and not

915
00:41:32,800 --> 00:41:34,900
to do this in Ireland, we're
going to have to go to one of

916
00:41:34,900 --> 00:41:37,400
those places and like, it'll be
a shame because I just don't

917
00:41:37,400 --> 00:41:40,900
think, you know, the long-term
viability of this like, the best

918
00:41:40,900 --> 00:41:42,700
way to do it is going to be to
do it here.

919
00:41:42,700 --> 00:41:45,500
Like do it on our doorsteps
build it scale, it nationally.

920
00:41:45,600 --> 00:41:50,000
And then we have, you know, an
ideal situation where we can

921
00:41:50,000 --> 00:41:52,100
make it work somewhere else.
We can take what we've done

922
00:41:52,100 --> 00:41:53,400
here.
Bring it somewhere else.

923
00:41:53,500 --> 00:41:57,000
Else and I think that's the
that's the best way to scale the

924
00:41:57,000 --> 00:42:01,500
robotics piece and I visited
most kind of Robotics ecosystems

925
00:42:01,500 --> 00:42:03,300
around the world.
So I've been to Japan, been to

926
00:42:03,300 --> 00:42:05,500
America been to Europe or the
doing stuff.

927
00:42:05,500 --> 00:42:08,200
And I've never seen anywhere
that has that potential before.

928
00:42:09,100 --> 00:42:11,200
I really haven't.
And, you know, if we could do it

929
00:42:11,200 --> 00:42:14,200
here, I think it'd be pretty
special, but it will require,

930
00:42:14,800 --> 00:42:18,200
you know, government
organizations to be pretty

931
00:42:18,200 --> 00:42:20,700
proactive about this and because
it's not something we can just

932
00:42:20,700 --> 00:42:22,800
will into place.
It has to be something that's is

933
00:42:22,800 --> 00:42:26,200
driven and Likes of SFI, the
lives of the University Systems,

934
00:42:26,200 --> 00:42:29,000
the likes of the, the startups
likes of Enterprise Ireland.

935
00:42:29,000 --> 00:42:31,900
They need to get behind this
kind of vision and, you know, it

936
00:42:31,900 --> 00:42:33,200
seems that the evidence is that
they are truck.

937
00:42:33,200 --> 00:42:35,800
They're starting to, but the
possibilities, I think for this

938
00:42:35,800 --> 00:42:37,400
are huge.
Yeah, absolutely.

939
00:42:37,400 --> 00:42:40,000
And I've seen plenty of other
things happen in Ireland.

940
00:42:40,000 --> 00:42:42,800
Like this where, because like
you said, it's a small country

941
00:42:42,800 --> 00:42:45,200
five million people, the
demographics are quite

942
00:42:45,200 --> 00:42:47,600
interesting and that you've got
this concentration of people in

943
00:42:47,600 --> 00:42:52,700
the Dublin area, with one kind
of style of living, perhaps to,

944
00:42:52,700 --> 00:42:56,200
you know, to Is it but then a
much different way of life in

945
00:42:56,200 --> 00:42:58,300
other parts of the country with
all different types of

946
00:42:58,300 --> 00:43:01,500
situations and scenarios, right?
So you like you said, you got

947
00:43:01,500 --> 00:43:05,700
plenty of a material here,
right?

948
00:43:05,700 --> 00:43:09,700
For lack of a better word to
demonstrate that this can be a

949
00:43:09,700 --> 00:43:12,000
success.
So, you know, yeah, really want

950
00:43:12,000 --> 00:43:15,600
to see this happen.
Just to kind of wind up.

951
00:43:15,800 --> 00:43:17,900
Thanks so much for sharing.
All this about the business at

952
00:43:17,900 --> 00:43:20,900
about what you're up to tell me
one thing that people wouldn't

953
00:43:20,900 --> 00:43:26,600
expect to know about you.
There's probably a lot, but I

954
00:43:26,600 --> 00:43:30,200
suppose it's funny, as
passionate as I am about this

955
00:43:30,200 --> 00:43:32,400
and those people would think
that like from an early stage.

956
00:43:32,400 --> 00:43:37,100
I was really like as a nerd and
school like it was complete

957
00:43:37,100 --> 00:43:38,800
opposite.
I was I was a jock and school

958
00:43:39,300 --> 00:43:41,100
and like I didn't really care
too much.

959
00:43:41,200 --> 00:43:44,100
Okay, it mats and stuff, but I
was like a pretty average like

960
00:43:44,100 --> 00:43:47,200
high school student and okay.
This was only something that I

961
00:43:47,200 --> 00:43:49,100
like sport for me, was my
passion growing up.

962
00:43:49,100 --> 00:43:51,200
I wanted to be a professor and
prep pressure will be player

963
00:43:51,200 --> 00:43:53,300
more than anyone else.
That that was that was me.

964
00:43:53,400 --> 00:43:56,100
My has that would look so I
played I played sport a very

965
00:43:56,100 --> 00:43:59,400
high level but like, you know,
college and Engineering for me

966
00:43:59,400 --> 00:44:01,600
was always a fallback for sports
for a very long time.

967
00:44:02,100 --> 00:44:05,700
And it's only been at a later
stage, that is like any of this

968
00:44:05,700 --> 00:44:09,200
has really gotten gotten real.
So I think it's it when I talked

969
00:44:09,200 --> 00:44:12,600
to like high school students or
when I talk to parents of kids.

970
00:44:12,600 --> 00:44:14,300
I quite often.
They think that like, you know,

971
00:44:14,300 --> 00:44:16,700
what we're doing is unattainable
for either themselves or their

972
00:44:16,700 --> 00:44:20,300
kids and like in reality like
the opposite is completely true.

973
00:44:20,300 --> 00:44:22,800
Like if you were to ask someone
likes my teachers where they

974
00:44:22,800 --> 00:44:25,600
think I'd be Now I think two of
them would have said we'd

975
00:44:25,600 --> 00:44:26,900
achieved anything that we've
done.

976
00:44:27,500 --> 00:44:31,800
I'm with you right there man.
That's my teachers and what I

977
00:44:31,800 --> 00:44:36,600
was 15 or 16 years old, they'd
be like Pete who you don't see

978
00:44:36,600 --> 00:44:39,400
him around here.
Yeah.

979
00:44:39,400 --> 00:44:40,800
I know.
I know student.

980
00:44:40,800 --> 00:44:43,800
What it was is I was I'd say
pretty forgettable student

981
00:44:43,800 --> 00:44:45,000
foremost.
The teachers that I had me.

982
00:44:45,100 --> 00:44:45,900
Yeah.
Yeah.

983
00:44:45,900 --> 00:44:48,200
No, I'm right there with you.
I'm right there with you and and

984
00:44:48,207 --> 00:44:51,300
hopefully, you know, onwards and
upwards, right?

985
00:44:51,300 --> 00:44:53,300
So awesome.
Well, thanks so much.

986
00:44:53,400 --> 00:44:56,200
I really appreciate you coming
on to the show Connor, keep

987
00:44:56,200 --> 00:44:58,200
doing what you're doing.
This is going to work.

988
00:44:58,700 --> 00:45:08,900
Thanks, baby.
That does it for this week folks

989
00:45:08,900 --> 00:45:10,100
and thanks to Connor for opening
up.

990
00:45:10,100 --> 00:45:11,500
His mind.
To help us figure out why he

991
00:45:11,508 --> 00:45:13,100
does.
What he does makes and show

992
00:45:13,100 --> 00:45:15,400
notes for this episode or on
money, never sleeps that IE.

993
00:45:15,400 --> 00:45:17,500
So check us out online.
Remember, if you were a

994
00:45:17,500 --> 00:45:20,000
colleague need help attract, and
retain, great talent for fintech

995
00:45:20,000 --> 00:45:22,500
or financial services.
Company is highly advisable, but

996
00:45:22,500 --> 00:45:24,400
you build a relationship.
Even top your recruitment, as

997
00:45:24,400 --> 00:45:26,400
they really know their stuff.
You can find them at top-tier

998
00:45:26,400 --> 00:45:28,000
recruitment.com.
Also.

999
00:45:28,000 --> 00:45:30,300
Thanks to Conan Brophy, from
creates on for editing this

1000
00:45:30,300 --> 00:45:31,900
podcast.
As for me.

1001
00:45:31,900 --> 00:45:33,600
I increase the odds of startup
success.

1002
00:45:33,900 --> 00:45:35,200
Get in touch with the contact
page.

1003
00:45:35,200 --> 00:45:37,500
I know where your Ventures.com,
you could check out what Owen

1004
00:45:37,500 --> 00:45:40,600
Fitzgerald is up to these days
on Twitter at 0 and Fitzgerald

1005
00:45:40,600 --> 00:45:43,700
nine finally till next time.
Thanks for listening.

1006
00:45:43,700 --> 00:45:44,200
See you.