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Welcome to Innovate, presents
Entrepreneurs in Tech.

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Today we are delighted actually to have
our guest, Johannes Nailer.

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Johannes, thank you for joining us and for
our audience and our listeners and viewers

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who might not be familiar with you, is it
possible you can just introduce yourself

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and give us just a little bit on who you
are and what do you do before we dive into

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it and really get into the details.

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Yeah, of course.

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So it's awesome to finally be here.

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Like I said, I mean, you were telling me
before this is the first episode, so I'm

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honored to be the first guest for it.

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So yeah, I'm Johannes.

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I'm the founder of Tapt .ai.

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So Tapt is, I mean, we can go, we're gonna
go into a lot about what Tapt is, but the

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TLDRs, we make it possible for any
performer to be able to create their own

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world tour.

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My background is, I've been in the music
industry for about,

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five -ish years now, but before that I
have about like 10 to 15 years of

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technical experience.

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So starting from college, I went to grad
school for machine learning and published

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some papers on that.

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And getting out, did a lot of data
engineering work, big on ML and big data

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type projects.

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Got into the music industry by...

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college friends and then eventually
professionally by working at Audius and

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now with Tapt.

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I am the technical co -founder for it.

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Great.

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So you're more on the tech side, I
understand.

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And you're young when you said, hey, I
only been in the music business five years

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and been in the tech for 15 years.

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So I would assume you started when you're
like eight, nine years old being in the

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tech business.

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yeah.

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Yeah, I mean, like, Legos are a thing.

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Yeah, I went pretty hardcore down that and
even starting it like in middle school and

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whatnot.

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I used to think I was gonna be a chemist.

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And so in middle school and early high
school, I was making rockets.

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And I thought I wasn't, I couldn't decide
on chemistry or physics.

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So I eventually kind of started weeding
off that when...

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I mean, if I don't know if you have a
question for down the line, so I won't

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spoil the story yet, but I blew up my
backyard and I kind of started weeding off

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of, weeding off of chemistry in rockets.

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you know, my next question, my next
question is really, take us back to your

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childhood.

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Like how did the whole passion for being
into the tech, being a rocket scientist, I

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should say, you know, being somebody
who's, you know, innovative and really

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focused and obsessed with technology and
how did you develop that to your later on

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in life with school?

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Like what pushed that kind of vision and
the curiosity?

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Yep, I'm from the middle of nowhere,
Virginia.

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I'm like really far out there.

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Like the county that I live in or the
county that my parents live in has been

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invited to join West Virginia several
times.

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And that's how rural it is.

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And there's not a whole lot to do out
here.

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And I'm a very active person.

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I'm always trying to like find something
to do when I get bored.

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I'm very much so like, okay, I gotta do
something, gotta do something, gotta do

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something.

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And...

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being in an environment in the suburbs in
the middle of nowhere, Virginia, I just

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ended up getting into stuff, right?

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Like getting into, I would help a friend
mount a motor onto a bicycle so he had a

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little motorcycle that he could drive
around with.

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Or, you know, the rockets is obviously
kind of the big flashy one of making

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rockets and building experiments off of
that.

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But even trying to do metalworking, my mom
works at a hospital, so I...

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got a lot of dry ice, so I would do
experiments with dry ice and try to freeze

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things and just do stuff like just really
getting into stuff.

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I was always very like hands on, I'm
bored.

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I need to do something right now to get
some kind of stimulation to get to cut

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through the the rural Virginia vibe.

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Amazing.

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Then you went to college and you studied
data science and machine learning, ML.

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And this is pretty impressive.

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And you have masters in that, correct?

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Yeah, it's funny.

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I didn't get into, originally, like, you
know, chemistry and physics, while being

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an engineer, not really close to computer
science at all.

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The transition happened when I was in high
school.

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My friends would sign up for, we're trying
to be college students, and we'd sign up

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for, capture the flag events,
cybersecurity capture the flag events,

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right?

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So you'd have some school, usually,
because we're from the middle of nowhere,

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be like Radford or something like that,
would set up.

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a capture the flag event, you'd have to do
all these cybersecurity challenges like

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cryptography, that stuff that would
require a lot of math.

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And we would end up breaking into those
and having a lot of fun doing it.

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And we would win first place and try to
beat all the college students.

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And we were like 15.

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So that is what got me more into computer
science is that I want something that I

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could have a big impact with, but I don't
threaten my backyard getting blown up.

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or losing a finger or something like that.

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In terms of engineering, computer science
is pretty tame.

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There isn't a whole lot to lose.

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So yeah, getting into college, it started,
I thought I was going to be doing

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cybersecurity, but I ended up not liking
the crowd and got much more into data

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science.

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I really liked building, just building ML
models, writing scripts to try to create

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something out of nothing.

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Well, I like that.

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I'm obsessed with technology, but I never
went to school for tech.

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I love it.

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I love Unibart.

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I love understanding.

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I love seeing it evolve.

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And that's a big thing for me.

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And how did the transition from, you know,
ML, machine learning, data science, all of

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that, building a rocket science, building
a rocket in the backyard, transfer all of

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that to music.

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So how did this, where the connection came
in?

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You said because of a friend, but still.

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The passion, the mental has to be there.

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absolutely.

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I mean, if you want to get into music as a
tech person, you have to really want it

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because the space is everybody wants to
get into music, but not a lot of people

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are willing to go get through the hard
part because music is like fun.

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You go to go through live shows like as a
tech person, it's like fun to be in tech

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and music.

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But the reality is it's like under the
hood.

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It's tough.

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Like it's hard to get people to take you
seriously.

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It's hard to get investment of any kind.

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It's hard to work with artists.

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They're very tough customers.

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So to get through the actual challenges,
you got to win it really bad.

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And for me, it's a bit easier because most
of my friends in college weren't tech

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people.

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They were actually musicians.

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A lot of them were producers or EDM DJs or
something along those lines.

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And while it's very fun to work on big
data engineering projects, like my first

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job out of college was at Capital One.

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And so I had a lot of data to play with
and I could build really cool ML models.

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I had a lot of smart people I could
interact with.

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At the end of the day, all the stuff I
built when I went home from work is like

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nobody, nobody could see any of that
stuff.

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Like nobody really cared what I was
building versus when I was doing

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hackathons and even just building stuff
for them on the side.

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It was a lot more fulfilling for me to be
able to see I built something and now all

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of a sudden my friends can use it like day
of the feedback loop is much tighter.

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And so that was what started getting me
more and more into music is, okay, if I'm

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just around all these producers, I'm gonna
bill for them.

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Interesting.

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So how were you able to scale from being a
two things, music tech startup and as a

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software engineer.

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So were you focused pretty much on the
other side of it, which is the tech side

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of it to build it.

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Say that again.

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You got involved in a startup with a
friend of yours.

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That was the whole thing.

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Yes.

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And how did you manage to scale it down?

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Getting involved in a music tech startup
and also being an engineer or software

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developer because you're the person who
actually built the infrastructure for it

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or a platform.

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I mean, it's, it's a little wild kind of
balancing all that.

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Cause when I first started getting,
getting into startups, I wanted it to be

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like a side gig.

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I really liked, that kind of romanticized
image that's that people often present on

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Twitter of the indie dev who's working on
stuff on the side.

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So originally that's what I thought I was
going to have my day job.

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And, I was going to have this side hustle
that it was eventually just going to

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overtake it and make me more money.

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And I could go to Spain or Bali or
something like that and hang out on the

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beaches and.

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and program music tech.

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That was at least the original thought
process for it.

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But then as I mentioned earlier, music
tech is not, it's very flashy at the

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start, but it's a lot harder than most
people consider.

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Like if once you get through all the, this
is fun, I get to be at live shows, it's

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tough.

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And so it started to make any kind of
success or any kind of progress, it would

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dump more and more into my time.

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And so scaling it basically meant taking
more away from my job, eventually leaving

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my job, just to have to be able to do
this, just to move the needle for this and

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get some kind of progress, right?

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And it's also a pretty big shock for
coming from a data background where I'm

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used to dealing with huge, like petabytes
of data at Capital One.

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There were S3 buckets that were just
egregiously large.

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And going from that to a startup where you
quickly learn,

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Like I think the big, the famous Paul
Graham quote is build not to scale.

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Like you want to build something that
doesn't, you know, it's

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counterintuitively.

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You want to build something that doesn't
scale first and then try to make something

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happen from that.

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So figure out where the demand is in the
market and then scale it, but don't try to

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scale before you found the demand.

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and that's especially true for music.

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Cause there's a lot, like I said, there's
a lot of flashy stuff.

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There's a lot of people who have these big
brands and they're very loud and proud.

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But if you take away that one person, the
whole product falls apart.

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So that kind of bringing a full circle,
that's the TLDR, right?

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Is, yeah, just ended up doing that full
time and then working on building stuff

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not to scale.

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I mean, you being a software developer to
organizing hackathon style remix

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competitions, which is really, I want to
hear about that.

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Then creating an AI label, which is the
startup.

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Yeah.

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Rubik's competitions before?

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Which one?

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No, no, remix, you know, hackathons.

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Well, there's regular remix competition I
heard of, yes.

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But hackathon style remix competition, is
it kind of tech involved in it or?

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There can be, absolutely.

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I mean, you know what a normal hackathon
is, right?

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Like you've seen the people going to...

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In Virginia, I used to organize,
especially for my university, I went to

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college in Richmond, Virginia, and we had
something called RamHacks, and it was the

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biggest hackathon in Virginia.

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We had nearly 500 people participate.

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People from all the different schools in
Virginia would drive to Richmond just to

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participate in our hackathon.

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So I understand, and that's where I've
learned,

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Most of what I know.

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I love hackathons.

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I didn't like, I say, I always try to put
the grad school stuff on paper and that's

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really flashy, but to be honest, like it's
really just for paper.

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Like the stuff I learned at hackathons is
the stuff I really is what taught me how

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to build the needle.

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And at a startup, you want to build an MVP
really fast and get it out to market.

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And hackathons are what taught me that if
anything grad school taught me the

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opposite.

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So leaving it, I saw that there's an
opportunity in music for, for something

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similar to that.

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And I love remixed competitions in
general.

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Audius, the company that I worked at,
hosted their own remix competitions and

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you would put it on their website, Audius
being like a web three SoundCloud.

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And when you upload something to Audius,
you can actually mark it as a remix.

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And so you get these like remix trees, if
that makes sense.

231
00:12:16,238 --> 00:12:21,058
I've like, okay, here's the OG content and
then here's all the remixes of it.

232
00:12:21,058 --> 00:12:23,978
And the content creators could decide a
winner.

233
00:12:23,978 --> 00:12:24,938
And I loved that.

234
00:12:24,938 --> 00:12:27,022
And I wanted to do an in -person one.

235
00:12:27,022 --> 00:12:32,562
Because I love in -person hackathons over
online ones, so it made sense.

236
00:12:32,562 --> 00:12:34,312
And Richmond's got a lively music scene.

237
00:12:34,312 --> 00:12:37,042
I was still living in Richmond at the
time, and so I started organizing these

238
00:12:37,042 --> 00:12:37,882
things.

239
00:12:37,882 --> 00:12:44,762
I set aside a weekend where I would ask a
couple of the hometown, the well -known

240
00:12:44,762 --> 00:12:45,912
musicians in Richmond.

241
00:12:45,912 --> 00:12:51,642
I'd get samples from them, whether it be
vocal samples or just any kind of beats,

242
00:12:51,642 --> 00:12:55,222
any kind of samples, any kind of stems
that they'd be willing to give me for

243
00:12:55,222 --> 00:12:57,221
free.

244
00:12:57,422 --> 00:13:04,822
I would bring it to, I'd find some kind of
space, I'm pretty crafty, and present it

245
00:13:04,822 --> 00:13:08,142
to all these musicians and say, make
something off of this.

246
00:13:08,142 --> 00:13:12,642
Either remix it somehow, take these stems,
do something cool with it, it's very open

247
00:13:12,642 --> 00:13:13,422
-ended.

248
00:13:13,422 --> 00:13:18,322
And at the end of the weekend, just like a
hackathon, we'll present what we've

249
00:13:18,322 --> 00:13:19,162
created.

250
00:13:19,162 --> 00:13:21,302
And then decide winners and whatnot.

251
00:13:21,302 --> 00:13:22,062
So we try to...

252
00:13:22,062 --> 00:13:25,722
get as many sponsors as we could and try
to give out prizes and whatnot.

253
00:13:25,722 --> 00:13:28,582
But just like a hackathon, we would also
have workshops.

254
00:13:28,582 --> 00:13:35,582
Like one jazz musician, there was a lot of
EDM producers at the first one we ran and

255
00:13:35,582 --> 00:13:38,802
a jazz musician came in and taught a
workshop on how to read sheet music.

256
00:13:38,802 --> 00:13:42,182
And because none of the DJs or producers
knew how to read sheet music.

257
00:13:42,182 --> 00:13:47,842
So all in all, very, very good fun.

258
00:13:47,842 --> 00:13:48,952
That's really what they were.

259
00:13:48,952 --> 00:13:52,032
It's like a lot of these guys learned how
to make music.

260
00:13:52,032 --> 00:13:54,922
extremely fast with samples that they
wouldn't be able to get on their own.

261
00:13:54,922 --> 00:13:56,572
And I think everybody learned a lot.

262
00:13:56,572 --> 00:13:59,003
I loved organizing those things.

263
00:13:59,972 --> 00:14:00,392
Love it.

264
00:14:00,392 --> 00:14:02,092
Are you still doing it?

265
00:14:02,952 --> 00:14:03,162
no.

266
00:14:03,162 --> 00:14:03,632
too much.

267
00:14:03,632 --> 00:14:04,962
I wish I could.

268
00:14:04,962 --> 00:14:09,132
I always kept saying, I would tell the
organizing team like, yeah, we'll do one.

269
00:14:09,132 --> 00:14:10,602
We'll do one in the next couple of months.

270
00:14:10,602 --> 00:14:14,522
And then I would travel somewhere and then
I'll be okay with it next couple of

271
00:14:14,522 --> 00:14:14,692
months.

272
00:14:14,692 --> 00:14:15,682
And then I travel somewhere.

273
00:14:15,682 --> 00:14:20,950
So we did a few, but, the, for now those
are tabled, unfortunately.

274
00:14:21,156 --> 00:14:24,936
Well, let's talk about the big elephant in
the room, the big startup that you and

275
00:14:24,936 --> 00:14:29,476
your partner created and you guys push it
and you're standing behind it, TAP .ai.

276
00:14:29,476 --> 00:14:35,676
Now, tell us the mission behind it and how
does it empower or empower its performance

277
00:14:35,676 --> 00:14:38,316
and create world tours from their phone?

278
00:14:38,316 --> 00:14:42,340
Like, how can you create a world tour from
your iPhone or your smartphone?

279
00:14:42,414 --> 00:14:44,074
Heck yeah.

280
00:14:44,074 --> 00:14:48,164
So, I mean, that's one of my favorite
taglines, right?

281
00:14:48,164 --> 00:14:50,504
Is that be able to create a world tour
from your phone.

282
00:14:50,504 --> 00:14:55,604
I think it's, it, every, like, how do you
know if a performer's big?

283
00:14:55,604 --> 00:14:56,684
How do you know if an artist is big?

284
00:14:56,684 --> 00:14:58,234
Well, they had a world tour.

285
00:15:00,974 --> 00:15:02,414
Exactly.

286
00:15:03,274 --> 00:15:04,054
A hundred percent.

287
00:15:04,054 --> 00:15:05,384
It's everybody wants a world tour.

288
00:15:05,384 --> 00:15:07,284
Like anybody small and big, they want a
world tour.

289
00:15:07,284 --> 00:15:09,541
Especially if, I mean,

290
00:15:10,030 --> 00:15:12,890
you know, any, any kind of young guys
gonna want something, gonna want something

291
00:15:12,890 --> 00:15:16,770
to say like, look, I went and performed in
all these different cities, even just

292
00:15:16,770 --> 00:15:19,150
like, you know, even a world tour in
general, just being able to say that I

293
00:15:19,150 --> 00:15:23,670
performed in New York, or I've went to
several states over to go perform.

294
00:15:23,670 --> 00:15:30,850
Like that's a, I don't know, it's a really
powerful kind of visual, but me and Aries

295
00:15:30,850 --> 00:15:36,190
and I have been, Aries is my co -founder,
have been hacking on stuff for over a year

296
00:15:36,190 --> 00:15:36,782
at this point.

297
00:15:36,782 --> 00:15:41,422
Like even when I still had a job and we
were kind of just doing stuff on the side,

298
00:15:41,422 --> 00:15:43,822
we knew that we wanted to build something
in the music space.

299
00:15:43,822 --> 00:15:46,052
I have a strong music tech background.

300
00:15:46,052 --> 00:15:50,202
He's got an extremely strong music
background and dealing with production,

301
00:15:50,202 --> 00:15:52,901
especially in the hip hop R &B genre.

302
00:15:52,901 --> 00:15:56,512
This is the space that we are meant to be
in and it makes sense for us to be here.

303
00:15:56,512 --> 00:16:02,002
So we had been coming up with startup
ideas, products really quickly, like I

304
00:16:02,002 --> 00:16:05,920
said, coming out with MVPs, just seeing
where the demands at in the space.

305
00:16:06,190 --> 00:16:09,510
The AI record label was one of them.

306
00:16:09,510 --> 00:16:12,570
But there were several different
iterations on products that we would

307
00:16:12,570 --> 00:16:12,990
release.

308
00:16:12,990 --> 00:16:16,970
We'd say, OK, this is going to be the
customer segment we're going after.

309
00:16:16,970 --> 00:16:18,910
This is going to be the value proposition
for them.

310
00:16:18,910 --> 00:16:23,470
Let's get an MVB product out really
quickly, and let's send it out.

311
00:16:23,610 --> 00:16:26,390
And we even must have had hundreds of
these things.

312
00:16:26,590 --> 00:16:30,270
But the ones that really stuck out were
the live music ones, the ones that really

313
00:16:30,270 --> 00:16:31,550
got any kind of traction.

314
00:16:31,550 --> 00:16:34,350
We would build something in a weekend, and
all of a sudden, we'd have 500 people

315
00:16:34,350 --> 00:16:35,854
using it, which,

316
00:16:35,854 --> 00:16:39,794
you know, now doesn't sound like a lot
with the volume that we're getting, but

317
00:16:39,794 --> 00:16:43,134
500 people for something you built really
quickly, I know from hackathons, that's a

318
00:16:43,134 --> 00:16:44,984
good number, like that's an actual solid
number.

319
00:16:44,984 --> 00:16:47,674
So we would keep kind of like survival of
the fittest.

320
00:16:47,674 --> 00:16:51,894
We build all these products and then take
the ones that got the most kind of

321
00:16:51,894 --> 00:16:55,674
traction and then keep iterating on them.

322
00:16:55,674 --> 00:17:00,654
And yeah, live music was the thing that
got it, like every single time.

323
00:17:00,654 --> 00:17:04,646
And everybody in live music tries to build
a marketplace of...

324
00:17:04,910 --> 00:17:07,930
building bookings, there's like 10
different booking apps nowadays.

325
00:17:07,930 --> 00:17:13,910
But the one thing that we noticed was that
none of them had any kind of good data to

326
00:17:13,910 --> 00:17:14,490
deal with.

327
00:17:14,490 --> 00:17:16,170
Like everybody's shooting in the dark.

328
00:17:16,170 --> 00:17:21,970
Like there's no Spotify for artists kind
of level analytics or chart metric level

329
00:17:21,970 --> 00:17:22,360
analytics.

330
00:17:22,360 --> 00:17:24,150
Everybody's just shooting in the dark.

331
00:17:24,150 --> 00:17:26,630
So when everybody's digging for gold, like
what's the saying?

332
00:17:26,630 --> 00:17:27,290
How's the saying go?

333
00:17:27,290 --> 00:17:29,270
You bake the shovels.

334
00:17:29,270 --> 00:17:30,542
So we started...

335
00:17:30,542 --> 00:17:34,522
collecting as much data as we could, which
is easy for me because that's my whole

336
00:17:34,522 --> 00:17:35,002
background.

337
00:17:35,002 --> 00:17:36,982
I was like, okay, this is perfect.

338
00:17:37,422 --> 00:17:42,462
So we started collecting tons and tons and
tons and tons of data on bookings.

339
00:17:42,462 --> 00:17:47,942
I have, Ilyas had his own from his agency
had his own proprietary data set.

340
00:17:47,942 --> 00:17:49,942
I'm pretty good at building web crawlers.

341
00:17:49,942 --> 00:17:55,142
I had to do it for some of my master's
courses and also at Capone.

342
00:17:55,142 --> 00:17:58,502
And then just getting data from wherever
we could get it and just being very data

343
00:17:58,502 --> 00:17:58,802
hungry.

344
00:17:58,802 --> 00:17:59,910
And,

345
00:17:59,918 --> 00:18:02,928
What we realized is we've got a goldmine
here.

346
00:18:02,928 --> 00:18:04,478
We've got a goldmine for all this data.

347
00:18:04,478 --> 00:18:08,638
Every time we get on a call, people are
feeding to get what we have.

348
00:18:08,638 --> 00:18:14,938
And we have this strategy of products,
potential products that we could use this

349
00:18:14,938 --> 00:18:15,768
data set for.

350
00:18:15,768 --> 00:18:19,958
Because we're like, Jesus, we have really
good stuff.

351
00:18:19,958 --> 00:18:23,218
And the first one was, well, let's start
small.

352
00:18:23,218 --> 00:18:26,318
And let's start with something that we
know is going to be a hit from data that

353
00:18:26,318 --> 00:18:28,498
we have from the previous products we've
built.

354
00:18:28,498 --> 00:18:29,798
Let's just.

355
00:18:29,934 --> 00:18:34,184
quick MVP get out a way for performers to
be able to book shows in multiple cities.

356
00:18:34,184 --> 00:18:40,074
If we have data on venues across all of
North America, we have data on who

357
00:18:40,074 --> 00:18:41,094
generally books there.

358
00:18:41,094 --> 00:18:44,294
We have all their genre data, their
capacity data, everything in between.

359
00:18:44,294 --> 00:18:49,994
Let's make a really quick way to say, are
you a performer that makes hip hop music

360
00:18:49,994 --> 00:18:52,774
and this is your previous booking history?

361
00:18:52,774 --> 00:18:56,326
Well, these are the venues that we'll
recommend for you based on...

362
00:18:56,366 --> 00:18:59,426
this massive data set that we have in the
background and we could present it to you

363
00:18:59,426 --> 00:19:03,972
in a very easy to understand UI just right
on a map where you can see where it goes.

364
00:19:03,972 --> 00:19:05,512
excuse me for interrupting.

365
00:19:05,512 --> 00:19:15,292
So for our viewers and myself and our
followers, basically you kind of, you have

366
00:19:15,292 --> 00:19:21,752
a predictive AI analytic system that
forecasts where should they, where the

367
00:19:21,752 --> 00:19:26,952
best for them based on their followers,
based on the size of network or social

368
00:19:26,952 --> 00:19:28,342
media presence that they have.

369
00:19:28,342 --> 00:19:31,204
Based on that, your AI tab.

370
00:19:31,204 --> 00:19:40,764
dot AI technology platform kind of tell
them where and which venues will host them

371
00:19:40,764 --> 00:19:43,548
based on all the information you have on
them.

372
00:19:43,822 --> 00:19:47,982
Yeah, which ones would be, which ones
should they, you know, keeping it kind of

373
00:19:47,982 --> 00:19:50,582
like broad, which one should they perform
at?

374
00:19:50,582 --> 00:19:54,972
Like, if you're trying to perform in New
York, which venues should you perform at?

375
00:19:54,972 --> 00:19:57,312
And here's a really easy and intuitive way
to reach out to them.

376
00:19:57,312 --> 00:20:00,722
Like, based on who they've booked in the
past.

377
00:20:01,402 --> 00:20:01,780
Yeah.

378
00:20:01,780 --> 00:20:02,240
New York.

379
00:20:02,240 --> 00:20:03,880
Somebody, I'm an artist, I'm in New York.

380
00:20:03,880 --> 00:20:05,470
Let's say, Virginia, where you are.

381
00:20:05,470 --> 00:20:06,940
I'm an artist in Virginia.

382
00:20:06,940 --> 00:20:10,760
And I don't have the funds to travel, to
bring the whole band.

383
00:20:10,760 --> 00:20:14,500
If I have a band with me or I'm a solo
artist, I can't, I have a job,

384
00:20:14,500 --> 00:20:17,480
responsibility, whatever it is, school.

385
00:20:17,480 --> 00:20:22,300
So I would find me venues that would host
me locally.

386
00:20:22,300 --> 00:20:24,740
It's only a drive, you know.

387
00:20:24,740 --> 00:20:28,924
I can drive or take the bus or public
transportation to get there.

388
00:20:29,134 --> 00:20:30,114
Absolutely.

389
00:20:30,114 --> 00:20:35,054
And there's a lot of people don't tour as
much as they normally would because it's

390
00:20:35,054 --> 00:20:35,874
too much of a risk.

391
00:20:35,874 --> 00:20:39,414
Like if you're, you said you're like,
okay, I'm a musician from New York.

392
00:20:39,414 --> 00:20:40,594
I don't want to leave New York.

393
00:20:40,594 --> 00:20:44,754
Cause if I leave New York and the show
bombs, what am I going to do?

394
00:20:44,754 --> 00:20:46,634
I'm at, that's like, I'm out of a bunch of
money.

395
00:20:46,634 --> 00:20:48,614
That's an awful tour.

396
00:20:48,614 --> 00:20:50,424
or I'm not going to drive all the way.

397
00:20:50,424 --> 00:20:54,514
Let's say, you know, I, I hear that you
could probably book a show, you know,

398
00:20:54,514 --> 00:20:57,062
Austin, Texas is the, the.

399
00:20:57,166 --> 00:20:58,786
live music capital of the U .S.

400
00:20:58,786 --> 00:21:02,606
But then as an artist in Virginia, I'm not
just going to fly or drive to Austin just

401
00:21:02,606 --> 00:21:04,706
because I might be able to get a show.

402
00:21:04,846 --> 00:21:08,226
With what we're building, you know you're
going to get a show.

403
00:21:08,226 --> 00:21:11,026
You know you're going to get a show and
you know it's going to pay well.

404
00:21:11,026 --> 00:21:13,125
So you're not taking a risk.

405
00:21:13,125 --> 00:21:18,036
It's a much more calculated science of
where am I going to go to do a show.

406
00:21:18,036 --> 00:21:22,686
So even if I have 500 followers, if I can
prove that I can make a sufficient amount

407
00:21:22,686 --> 00:21:25,606
of money in Texas, then why not fly to
Texas?

408
00:21:25,606 --> 00:21:26,254
If...

409
00:21:26,254 --> 00:21:28,396
You know, if it makes sense to me, right?

410
00:21:28,396 --> 00:21:29,126
it get booked?

411
00:21:29,126 --> 00:21:30,716
Does it get booked also?

412
00:21:30,716 --> 00:21:35,496
It doesn't just tell you where you should
perform or which venue will take you.

413
00:21:35,496 --> 00:21:37,156
Would it book it for you?

414
00:21:37,156 --> 00:21:37,626
no, it doesn't.

415
00:21:37,626 --> 00:21:41,616
You would have to make the call yourself,
or make the call, or send an email, or

416
00:21:41,616 --> 00:21:42,382
whatever.

417
00:21:42,814 --> 00:21:47,914
the intro right now because I think I
mentioned this earlier is we made a very

418
00:21:47,914 --> 00:21:50,874
strategic decision that we didn't want to
be a marketplace.

419
00:21:50,874 --> 00:21:52,704
Marketplaces are very tough to build.

420
00:21:52,704 --> 00:21:54,274
It's tough to get started.

421
00:21:54,274 --> 00:21:57,494
To get to liquidity for a marketplace is
extremely hard.

422
00:21:57,494 --> 00:21:58,894
If you can get it, you got it.

423
00:21:58,894 --> 00:22:02,174
If you can make a marketplace work, those
things are unstoppable.

424
00:22:02,174 --> 00:22:03,674
Like Airbnb is unstoppable.

425
00:22:03,674 --> 00:22:05,274
eBay is unstoppable.

426
00:22:05,274 --> 00:22:06,554
All these places.

427
00:22:06,554 --> 00:22:08,814
But getting it started is tough.

428
00:22:08,814 --> 00:22:10,030
So we made...

429
00:22:10,030 --> 00:22:16,150
the decision early on that we were going
to not build one, or at least for now, not

430
00:22:16,150 --> 00:22:17,370
building a marketplace.

431
00:22:17,370 --> 00:22:22,770
So that just inherently means that however
the venue wants to book them, they're free

432
00:22:22,770 --> 00:22:23,330
to do so.

433
00:22:23,330 --> 00:22:26,450
And however well they want to pay them,
I'm sure you know payment methods for

434
00:22:26,450 --> 00:22:27,726
musicians are...

435
00:22:27,726 --> 00:22:28,936
all over the place.

436
00:22:28,936 --> 00:22:30,646
There's some of the due percentage of
ticket sales.

437
00:22:30,646 --> 00:22:34,906
Some of them are 50 -50, some of it's 100
% at the start, some of it's 100 % at the

438
00:22:34,906 --> 00:22:37,516
end, some of it's like percentage of bar
sales.

439
00:22:37,516 --> 00:22:40,426
Like they have all these different ways of
paying you.

440
00:22:40,426 --> 00:22:45,126
So rather than trying to build out a
solution for each one of those, we let

441
00:22:45,126 --> 00:22:45,916
them take care of it.

442
00:22:45,916 --> 00:22:47,294
We make the intro.

443
00:22:48,292 --> 00:22:50,672
But at least you make the intro, which is
great.

444
00:22:50,672 --> 00:22:53,152
That even, that's a big icebreaker.

445
00:22:53,152 --> 00:22:54,632
Interesting.

446
00:22:54,632 --> 00:22:55,892
Very, very interesting.

447
00:22:55,892 --> 00:22:57,134
And you know...

448
00:22:57,134 --> 00:23:00,834
line, we'd love to partner with somebody
that does do this kind of stuff and does

449
00:23:00,834 --> 00:23:03,294
do the contracting and whatnot.

450
00:23:03,294 --> 00:23:04,484
Cause we also don't do ticket.

451
00:23:04,484 --> 00:23:06,134
Cause again, we didn't want to build a
marketplace.

452
00:23:06,134 --> 00:23:09,554
We didn't want to have, have two sides
that we needed to deal with.

453
00:23:09,554 --> 00:23:12,324
And if you build, if we wanted a
ticketing, then now there's three sides,

454
00:23:12,324 --> 00:23:15,574
cause there's fans, there's artists,
there's venues, and then maybe even a

455
00:23:15,574 --> 00:23:16,454
fourth for promoters.

456
00:23:16,454 --> 00:23:18,534
And like, how do you make all of those
guys happy?

457
00:23:18,534 --> 00:23:19,294
It sucks.

458
00:23:19,294 --> 00:23:20,834
It's not fun.

459
00:23:21,554 --> 00:23:22,332
So.

460
00:23:22,332 --> 00:23:27,672
.ai technology that you guys built, you
know, there is potential impact on the

461
00:23:27,672 --> 00:23:29,952
booking industry and ticket sales.

462
00:23:29,952 --> 00:23:31,762
You know, I could see where this going.

463
00:23:31,762 --> 00:23:37,892
I mean, this is not just where can you
perform an introductory level.

464
00:23:37,892 --> 00:23:39,372
This is going to probably expand.

465
00:23:39,372 --> 00:23:40,552
That's what I see.

466
00:23:40,552 --> 00:23:41,412
Where is it going?

467
00:23:41,412 --> 00:23:46,872
As you mentioned earlier, how Uber
impacted and disrupted the cab industry,

468
00:23:46,872 --> 00:23:51,364
how Airbnb without owning one hotel,
disrupted the hotel industry.

469
00:23:51,364 --> 00:23:54,314
you know, and so forth and others and
others.

470
00:23:54,314 --> 00:23:58,004
And I see this technology going that
direction.

471
00:23:58,004 --> 00:23:59,194
Correct me if I'm wrong, please.

472
00:23:59,194 --> 00:24:02,364
You're the developer, you're the engineer
and you know.

473
00:24:02,606 --> 00:24:04,606
No, I mean, you know what you're talking
about.

474
00:24:04,606 --> 00:24:11,226
You've obviously heard of Red Ocean, Blue
Ocean, and we are operating in a blue

475
00:24:11,226 --> 00:24:16,316
ocean, whereas the people building out a
lot of places in the music industry,

476
00:24:16,316 --> 00:24:20,126
everybody seems to go for the Red Ocean,
which is, in my head, recorded music.

477
00:24:20,126 --> 00:24:22,626
It's either recorded music or artist -to
-fan experiences.

478
00:24:22,626 --> 00:24:24,416
Those are very competitive Red Oceans.

479
00:24:24,416 --> 00:24:28,826
I've seen 15 different artist engagement
startups, and they all do the same thing,

480
00:24:28,826 --> 00:24:30,734
and they're all pretty bad.

481
00:24:30,734 --> 00:24:34,114
I'm friends with some of them, so I'd say
that, hey, they do really good, they're

482
00:24:34,114 --> 00:24:37,834
very talented people, but I don't feel
like that, in my opinion, I don't think,

483
00:24:37,834 --> 00:24:41,454
to be brutally honest, I just don't think
they're hitting the mark.

484
00:24:41,454 --> 00:24:44,384
And there's really a different ticketing
startups right now.

485
00:24:44,384 --> 00:24:47,614
There's in recording music, there's a
million different recording music startups

486
00:24:47,614 --> 00:24:51,994
that are trying to make some kind of, take
some kind of sliver out of that.

487
00:24:51,994 --> 00:24:54,414
We're not operating in this competitive
red ocean.

488
00:24:54,414 --> 00:24:56,114
We're in the nice blue ocean.

489
00:24:56,114 --> 00:24:57,854
There's nothing really stopping us.

490
00:24:57,854 --> 00:24:58,158
It's...

491
00:24:58,158 --> 00:25:01,438
It's big, they're on a lot of competitors
and we're creating our own demand.

492
00:25:01,438 --> 00:25:06,638
The people who join, I think my favorite
metric for Tapped is that we don't get a

493
00:25:06,638 --> 00:25:12,158
whole lot of churn because the people who
join the app stay with it and after the

494
00:25:12,158 --> 00:25:15,238
app's gone, they don't know what they were
doing beforehand.

495
00:25:15,238 --> 00:25:16,798
Their life's completely different.

496
00:25:16,798 --> 00:25:20,418
And I think that's my favorite metric is
if we took this app away from people, they

497
00:25:20,418 --> 00:25:21,298
wouldn't know what to do.

498
00:25:21,298 --> 00:25:23,430
They'd beg us for it back.

499
00:25:24,036 --> 00:25:25,376
Because there's nothing like it.

500
00:25:25,376 --> 00:25:27,356
There's nothing like it right now.

501
00:25:27,356 --> 00:25:27,966
Yes, you're right.

502
00:25:27,966 --> 00:25:35,516
There is a lot of new startup tech
platforms that focuses on artists or fans,

503
00:25:35,516 --> 00:25:41,236
they call it now, you know, artists, fan
engagement or super fans and all of that.

504
00:25:41,236 --> 00:25:43,476
Everybody coming up with their own thing.

505
00:25:43,476 --> 00:25:48,836
It's and everybody try to say, OK, this is
what I have is the is what's going to the

506
00:25:48,836 --> 00:25:52,736
new the new mark in the music business,
the new thing, you know, the new

507
00:25:52,736 --> 00:25:54,116
innovative thing that's going to.

508
00:25:54,116 --> 00:25:58,316
you know, take them take this kind of
level to the front.

509
00:25:58,316 --> 00:26:00,296
Which is, I don't know yet.

510
00:26:00,296 --> 00:26:03,796
I mean, you know, everybody has something,
as you said, but nobody has headed on the

511
00:26:03,796 --> 00:26:08,756
market that something kind of blew up
because I think the whole emphasis.

512
00:26:08,756 --> 00:26:12,136
Well, because the whole infrastructure of
the music business still there didn't

513
00:26:12,136 --> 00:26:17,276
change, you know, we went from physical to
digital.

514
00:26:17,276 --> 00:26:23,172
So we are still in the digital world and I
what's happening right now.

515
00:26:23,172 --> 00:26:27,012
I don't like to use the word disruptive
because I think any technology is not the

516
00:26:27,012 --> 00:26:28,892
word destructive, it's advancement.

517
00:26:28,892 --> 00:26:31,772
You know, we advance it, we're
progressing, we're going forward.

518
00:26:31,772 --> 00:26:33,712
You know, that's what technology does.

519
00:26:33,712 --> 00:26:34,562
It takes us forward.

520
00:26:34,562 --> 00:26:37,952
It makes things easier, empowers us to do
things.

521
00:26:37,952 --> 00:26:43,212
It makes what we're doing reach every
point of this planet that we live on, like

522
00:26:43,212 --> 00:26:44,182
before it wasn't.

523
00:26:44,182 --> 00:26:47,732
Then digital came in and everybody,
there's iPhones and smartphones, whether

524
00:26:47,732 --> 00:26:53,028
Android or Apple, but it made you reach
the world through your technology.

525
00:26:53,028 --> 00:26:56,888
So technology, I'm a big fan of
technology, you know, and it's, I think

526
00:26:56,888 --> 00:27:01,028
technology is a great tool for human
advancement in any industry, whether it's

527
00:27:01,028 --> 00:27:06,508
in music and in education and travel,
anything, anything in medical, all of

528
00:27:06,508 --> 00:27:07,008
that.

529
00:27:07,008 --> 00:27:13,128
So in your opinion, as a, you know, as an
engineer and data science and all of that,

530
00:27:13,128 --> 00:27:19,108
and a developer, what do you see the most
innovative advancement happening in the

531
00:27:19,108 --> 00:27:20,756
music industry today?

532
00:27:25,966 --> 00:27:34,286
I think the big, the elephant in the room
in terms of music tech is generative

533
00:27:34,286 --> 00:27:36,306
audio, like stuff that's, you know, AIs do
it.

534
00:27:36,306 --> 00:27:37,886
You've heard of Suno?

535
00:27:37,886 --> 00:27:40,446
Yeah, Suno came to our event mixer.

536
00:27:40,446 --> 00:27:44,046
It's like, you know, generate songs you
could put in a prompt that says, you know,

537
00:27:44,046 --> 00:27:48,046
make me a song about a cat in blue grass
or something like that and it'll make it.

538
00:27:48,046 --> 00:27:50,046
And it's like, really, it's pretty good.

539
00:27:50,046 --> 00:27:52,197
It's not bad.

540
00:27:52,430 --> 00:27:57,310
Those kinds of things are the big elephant
in the room in terms of music tech.

541
00:27:57,310 --> 00:28:01,220
Everybody talks about them of, okay, what
is royalty is going to look like?

542
00:28:01,220 --> 00:28:02,100
How they trained it?

543
00:28:02,100 --> 00:28:04,140
I think that's what's got everybody
distracted.

544
00:28:04,140 --> 00:28:07,950
Everybody outside of music tech, that's
what's got them distracted.

545
00:28:07,950 --> 00:28:11,710
And I think that is going to fundamentally
change things, especially with stuff like

546
00:28:11,710 --> 00:28:13,890
sync licensing.

547
00:28:13,890 --> 00:28:16,850
With regenerative audio, you don't need
sync licensing anymore.

548
00:28:16,850 --> 00:28:17,774
You just...

549
00:28:17,774 --> 00:28:21,634
somebody who wants audio for a movie or
for a commercial is just going to generate

550
00:28:21,634 --> 00:28:22,024
with Suno.

551
00:28:22,024 --> 00:28:25,914
They're not going to pay an artist like
tens of thousands, like 30 grand to make

552
00:28:25,914 --> 00:28:27,774
something for a commercial anymore.

553
00:28:27,774 --> 00:28:29,492
They're just going to make it.

554
00:28:29,492 --> 00:28:35,512
now is how this music been trained on and
which music and that's the only, yes,

555
00:28:35,512 --> 00:28:36,852
that's the issue right there.

556
00:28:36,852 --> 00:28:40,152
But the problem is, as we said before, and
you asked me, what's my point?

557
00:28:40,152 --> 00:28:41,152
How do you see it?

558
00:28:41,152 --> 00:28:46,832
The problem is, as I mentioned, there's
physical and digital technologies over

559
00:28:46,832 --> 00:28:49,112
here and the music business here.

560
00:28:49,112 --> 00:28:51,092
It's always way ahead.

561
00:28:51,092 --> 00:28:55,572
So now this whole AI thing, it's way ahead
of the music business.

562
00:28:55,572 --> 00:28:57,956
And the music business trying to catch up,

563
00:28:57,956 --> 00:29:03,896
Some wants it to some don't want it to to
catch up but eventually it will and all of

564
00:29:03,896 --> 00:29:08,896
these new things that Coming up with all
these startups all these great text music

565
00:29:08,896 --> 00:29:15,236
startups that with all different ideas and
all different approach I think the music

566
00:29:15,236 --> 00:29:20,436
itself the infrastructure has to change a
little bit advance itself Then you'll see

567
00:29:20,436 --> 00:29:26,656
new ways of consuming music music and all
of that, but still based on the old ways,

568
00:29:26,656 --> 00:29:27,364
you know

569
00:29:27,364 --> 00:29:34,244
DSPs and digital streaming platforms and
distributors and aggregators still a lot

570
00:29:34,244 --> 00:29:36,904
of the old ways is still run the business.

571
00:29:36,904 --> 00:29:41,524
But now it's kind of a little bit of a
shake like, okay, the music has to move,

572
00:29:41,524 --> 00:29:46,204
the business itself has to move forward to
catch up with technology in order for all

573
00:29:46,204 --> 00:29:51,264
of these new startups, a lot of these AI
driven startups, whether it's tools or

574
00:29:51,264 --> 00:29:55,564
whatever they are, in order for this to
happen, the music business has to kind of

575
00:29:55,564 --> 00:29:56,164
push.

576
00:29:56,164 --> 00:29:58,384
So there should be, there will be changes
happening.

577
00:29:58,384 --> 00:30:01,884
There will be, and I cannot put any of
these new startups down because we don't

578
00:30:01,884 --> 00:30:07,124
know yet until things move forward, you
know, as the whole infrastructure move

579
00:30:07,124 --> 00:30:08,644
forward, yes.

580
00:30:08,878 --> 00:30:09,418
It's tough.

581
00:30:09,418 --> 00:30:09,718
Yeah.

582
00:30:09,718 --> 00:30:13,298
I mean, it really, the music industry, I'm
thinking about it.

583
00:30:13,298 --> 00:30:15,168
I like the way, I like your analogy for it
too.

584
00:30:15,168 --> 00:30:18,697
Cause the way I've always thought about it
is if you think of it like this ball

585
00:30:18,697 --> 00:30:23,158
technology so far has really just affected
the outside of the ball.

586
00:30:23,158 --> 00:30:24,798
I guess, especially the artist of fan.

587
00:30:24,798 --> 00:30:26,738
Artist of fan has always been the thing
that's optimized.

588
00:30:26,738 --> 00:30:30,878
It's the one thing like Spotify did
extremely well as artist of fan is now

589
00:30:30,878 --> 00:30:33,718
like so optimized to the point where
artists don't even make money.

590
00:30:33,718 --> 00:30:36,128
It's not even like, it's just too
optimized.

591
00:30:36,128 --> 00:30:38,761
Like there's no money in it anymore.

592
00:30:39,022 --> 00:30:43,202
But at its core, I don't think tech has
really affected the music industry as much

593
00:30:43,202 --> 00:30:45,282
as it ought to have.

594
00:30:45,282 --> 00:30:46,462
I think that's changing.

595
00:30:46,462 --> 00:30:50,242
I think that there's finally the cracks in
the music industry.

596
00:30:50,242 --> 00:30:54,622
Tech is starting to flow through and it's
exciting.

597
00:30:54,622 --> 00:30:59,602
But it's also a little wild at the same
time because one of the main reasons

598
00:30:59,602 --> 00:31:03,522
investors don't like to get into music
tech is because of licensing.

599
00:31:03,522 --> 00:31:06,542
The legislation for music tech
fundamentally is...

600
00:31:06,542 --> 00:31:07,862
For moving is awful.

601
00:31:07,862 --> 00:31:13,192
It's so bad in its the history going all
the way back to like 1718 hundreds of

602
00:31:13,192 --> 00:31:16,512
the old ways, it's still the old ways,
it's still the old ways.

603
00:31:16,512 --> 00:31:20,472
A lot of it, as I said, they don't want it
to change because that's how they make

604
00:31:20,472 --> 00:31:23,472
money, you know, and that's how it's set
up.

605
00:31:23,472 --> 00:31:29,092
And that's how they control it because
technology, you think it can make the old

606
00:31:29,092 --> 00:31:32,156
ways, they're gonna lose their grip on it.

607
00:31:32,494 --> 00:31:39,374
Yeah, you can track different kinds of
licensing deals to...

608
00:31:39,374 --> 00:31:44,294
I think the most telling example is in the
1800s, somebody made a self -playing

609
00:31:44,294 --> 00:31:44,814
piano.

610
00:31:44,814 --> 00:31:47,774
It was a piano, you didn't actually have
to have a player, you could put it in a

611
00:31:47,774 --> 00:31:48,754
bar.

612
00:31:48,754 --> 00:31:50,674
Think of it like a jukebox.

613
00:31:50,674 --> 00:31:52,694
And it would play itself.

614
00:31:52,694 --> 00:31:56,484
But if it plays somebody's song, who gets
the music for that song?

615
00:31:56,484 --> 00:31:58,126
And there was this whole thing and...

616
00:31:58,126 --> 00:32:01,886
One artist in particular freaked out about
it and so Congress very quickly made

617
00:32:01,886 --> 00:32:04,146
legislation for it really hastily.

618
00:32:04,146 --> 00:32:09,606
And now you have this terrible law that
sits there and what are you gonna do about

619
00:32:09,606 --> 00:32:09,706
it?

620
00:32:09,706 --> 00:32:11,946
I mean, Congress isn't gonna move
particularly fast.

621
00:32:11,946 --> 00:32:14,606
Like the people in the tech community hate
when you have to wait for Congress to have

622
00:32:14,606 --> 00:32:15,766
to do something.

623
00:32:15,766 --> 00:32:22,206
So a lot of fundamentally what's messed up
is there's so many bad laws and so much

624
00:32:22,206 --> 00:32:26,286
like hastily made legislation of, shoot,
shoot, shoot, we gotta, like.

625
00:32:26,286 --> 00:32:28,306
And I think it's going to happen with
generative AI too.

626
00:32:28,306 --> 00:32:29,412
It's going to go shoot.

627
00:32:29,412 --> 00:32:35,632
of the benefit of certain people in
control of the business, yes.

628
00:32:35,912 --> 00:32:41,532
But you know what, this takes me back to
what you guys developed, Tapped AI, where

629
00:32:41,532 --> 00:32:46,772
you mentioned it, you just said, right now
there's no money in streaming, artists not

630
00:32:46,772 --> 00:32:47,322
making money.

631
00:32:47,322 --> 00:32:50,192
Don't forget about major artists, talk
about independent artists.

632
00:32:50,192 --> 00:32:52,388
So where independent artists gonna turn?

633
00:32:52,388 --> 00:32:55,508
Okay, the music, not every independent
artist's music's being played in

634
00:32:55,508 --> 00:32:58,638
commercials and movies and in anything,
not everybody.

635
00:32:58,638 --> 00:33:00,368
You know, some are, but not everybody.

636
00:33:00,368 --> 00:33:01,728
There's a lot more not.

637
00:33:01,728 --> 00:33:06,548
So how can they get themselves out there
beside, you know, short content, beside

638
00:33:06,548 --> 00:33:09,888
TikTok or beside, you know, somebody's
social media platform that's really

639
00:33:09,888 --> 00:33:12,268
helping a lot of independent artists come
forward.

640
00:33:12,268 --> 00:33:18,460
I would say Tapped AI is actually a key,
is the main key that really gonna help.

641
00:33:18,532 --> 00:33:22,752
A lot of independent artists perform
performance performance before a live

642
00:33:22,752 --> 00:33:26,571
That's what it's all about about being
engaged with your audience being engaged

643
00:33:26,571 --> 00:33:30,112
with your fans and you guys are the key
definitely tap the eye technology

644
00:33:30,112 --> 00:33:35,352
definitely a great great tool and
enhancement for independent artists To

645
00:33:35,352 --> 00:33:37,752
perform and to generate revenue and get
their name out there.

646
00:33:37,752 --> 00:33:38,556
Yes

647
00:33:38,958 --> 00:33:42,358
Yeah, because, you know, with there being
no music, where else are you supposed to

648
00:33:42,358 --> 00:33:43,438
make money?

649
00:33:43,438 --> 00:33:47,558
You either, as an independent guy, you're
either making it, playing shows or selling

650
00:33:47,558 --> 00:33:47,838
merch.

651
00:33:47,838 --> 00:33:49,058
And where do you sell the most merch?

652
00:33:49,058 --> 00:33:49,958
At live shows.

653
00:33:49,958 --> 00:33:53,298
So you're really going to incentivize to
get as many live shows as you can get.

654
00:33:53,298 --> 00:33:57,218
And even bringing it full circle, I'm
very, it's very fortunate that TAPT is not

655
00:33:57,218 --> 00:34:02,258
in a field that has all these different
licensing issues and all these different,

656
00:34:02,258 --> 00:34:04,898
like, there's not a whole lot of
legislation around live music.

657
00:34:04,898 --> 00:34:08,018
I mean, there is, and there's some state
stuff, but it doesn't affect us.

658
00:34:08,018 --> 00:34:08,302
Like,

659
00:34:08,302 --> 00:34:13,082
When I say we're in a blue ocean, we're
really in the, like, we're in the part of

660
00:34:13,082 --> 00:34:18,162
the music industry that's just, like,
really nice to be in.

661
00:34:18,162 --> 00:34:20,312
And so, yeah, we're moving fast.

662
00:34:20,312 --> 00:34:23,662
Like, we have the opportunity to move fast
whereas all these other startups, and even

663
00:34:23,662 --> 00:34:28,262
startups like I mentioned, like the fan to
artist engagement ones or the ticketing

664
00:34:28,262 --> 00:34:32,822
ones, all these other guys, they pay for
our data because they, there's so many,

665
00:34:32,822 --> 00:34:35,842
you know, they start their startup and all
of a sudden there's, like, just

666
00:34:35,842 --> 00:34:37,006
inherently, like,

667
00:34:37,006 --> 00:34:40,606
all these obstacles ahead of them and they
want whatever they can get to get ahead of

668
00:34:40,606 --> 00:34:43,926
these obstacles and there's legislative
ones, there's like old money guys getting

669
00:34:43,926 --> 00:34:47,226
in, old music heads, it's just all these
obstacles for us, we don't have any of

670
00:34:47,226 --> 00:34:49,766
these obstacles and we have the data to
support those guys.

671
00:34:49,766 --> 00:34:53,406
So we're having, we're in calls right now
with tons of different, from ticking

672
00:34:53,406 --> 00:34:56,886
companies to artists to foreign companies
to all these people that we mentioned who

673
00:34:56,886 --> 00:34:59,806
are trying to get access like to our API.

674
00:34:59,806 --> 00:35:01,716
They were like, what will it cost?

675
00:35:01,716 --> 00:35:05,862
Cause the only other option is to pay
somebody like Polestar, the other other.

676
00:35:06,094 --> 00:35:09,874
database that's very similar to ours is
Polestar and that's Ticketmaster data and

677
00:35:09,874 --> 00:35:13,854
they charge 40 grand a year for a license
for data that's not that good in the first

678
00:35:13,854 --> 00:35:15,334
place.

679
00:35:15,334 --> 00:35:19,084
So we're really like, we're at a full
sprint.

680
00:35:19,084 --> 00:35:21,234
There's nothing stopping us.

681
00:35:21,234 --> 00:35:27,294
And the art of seeing that, that's why we
get so many people onboard is the only way

682
00:35:27,294 --> 00:35:31,314
for them to make music is to play live and
we help them play live.

683
00:35:31,314 --> 00:35:32,398
So it's like, and...

684
00:35:32,398 --> 00:35:35,558
Even for somebody saying, wait, we still
have a pretty generous free tier.

685
00:35:35,558 --> 00:35:36,278
We're a startup.

686
00:35:36,278 --> 00:35:38,628
Like we want people to, we want to
encourage people to get on.

687
00:35:38,628 --> 00:35:41,418
We're not really in the business of like,
let's get it as much as we can.

688
00:35:41,418 --> 00:35:46,458
We have some things gated, but we really
want to facilitate people getting on the

689
00:35:46,458 --> 00:35:47,448
app and trying things out.

690
00:35:47,448 --> 00:35:49,858
And so I can get the feedback to build a
better product.

691
00:35:49,858 --> 00:35:52,798
And so even the guys who are getting on
and staying on the free tier, they don't

692
00:35:52,798 --> 00:35:53,378
leave.

693
00:35:53,378 --> 00:35:56,438
They stay with us and they're constantly
looking for new shows, looking for new

694
00:35:56,438 --> 00:35:57,138
gigs.

695
00:35:57,138 --> 00:36:00,293
I have somebody and,

696
00:36:00,526 --> 00:36:03,546
I know he's got the money, but he likes to
be cheap and he doesn't want to pay for

697
00:36:03,546 --> 00:36:07,666
the subscription to that we have.

698
00:36:07,666 --> 00:36:11,846
But on the app, he just wants to know
whenever he wants to play in the city, he

699
00:36:11,846 --> 00:36:12,726
does the research himself.

700
00:36:12,726 --> 00:36:15,666
He goes in our app and finds where all the
venues are in the city and calls them

701
00:36:15,666 --> 00:36:16,026
himself.

702
00:36:16,026 --> 00:36:18,246
I was like, you could just pay 10 bucks
and we'll do it for you.

703
00:36:18,246 --> 00:36:20,036
Like, you could send out the intros.

704
00:36:20,036 --> 00:36:22,266
Like that's the whole point is we send out
intros for you.

705
00:36:22,266 --> 00:36:23,142
And he's like,

706
00:36:23,598 --> 00:36:27,738
He doesn't want to pay the $10, but
whatever, but it still finds value on the

707
00:36:27,738 --> 00:36:28,068
fruit here.

708
00:36:28,068 --> 00:36:31,038
He's he's honored like all the time
playing around, like looking on the map,

709
00:36:31,038 --> 00:36:34,008
looking for venues, looking up all the
stats on them and stuff like that.

710
00:36:34,008 --> 00:36:39,364
Like we were fundamentally changing how
things are getting done.

711
00:36:39,364 --> 00:36:41,684
Wow, amazing, amazing.

712
00:36:41,704 --> 00:36:46,924
What are some of the challenges you faced
revolutionizing the live performance space

713
00:36:46,924 --> 00:36:48,264
with technology?

714
00:36:48,264 --> 00:36:50,012
And how did you overcome them?

715
00:36:50,886 --> 00:36:53,306
Without a doubt, it's data.

716
00:36:53,306 --> 00:36:55,586
That's how we're solving the problem,
right?

717
00:36:55,586 --> 00:37:02,336
There's no easy way to get a lot of data,
to scale it.

718
00:37:02,336 --> 00:37:04,706
You can get small stuff.

719
00:37:04,706 --> 00:37:08,646
You can have one artist's booking history,
or you can have one venue's booking

720
00:37:08,646 --> 00:37:13,186
history, or you might find a calendar here
or there, but nobody knows who's playing

721
00:37:13,186 --> 00:37:17,070
where, except for maybe Ticketmaster knows
it for...

722
00:37:17,070 --> 00:37:18,900
The people that work with them.

723
00:37:18,900 --> 00:37:22,230
I mean, some of these other ticketing
companies like Dice, for example, has a

724
00:37:22,230 --> 00:37:23,120
decent amount of data.

725
00:37:23,120 --> 00:37:24,570
And I know the team at Dice.

726
00:37:24,570 --> 00:37:30,250
I've met them from New York and they have
an office in Barcelona where I lived.

727
00:37:30,250 --> 00:37:35,130
And they got some good stuff, but at the
end of the day, that's also not public.

728
00:37:35,130 --> 00:37:39,670
There's just a severe lack of data of
what's going on and what things look like.

729
00:37:39,670 --> 00:37:42,030
And that's, we're starting it by creating
TAPT.

730
00:37:42,030 --> 00:37:44,240
We are fundamentally under the hood.

731
00:37:44,240 --> 00:37:46,414
We see ourselves as a data analytics
company.

732
00:37:46,414 --> 00:37:52,374
We take in tons, terabytes, right now it's
still in the gigabytes, but we're taking

733
00:37:52,374 --> 00:37:53,044
it out to scale.

734
00:37:53,044 --> 00:37:57,474
We are gonna be taking in terabytes all
the way up to petabytes of data of what's

735
00:37:57,474 --> 00:37:58,704
going on in the music industry.

736
00:37:58,704 --> 00:38:02,633
Who's playing where, at what frequency are
they playing?

737
00:38:02,633 --> 00:38:03,884
What are the best times of year to play?

738
00:38:03,884 --> 00:38:06,114
All these other different factors.

739
00:38:06,114 --> 00:38:08,374
But right now it still sucks.

740
00:38:08,374 --> 00:38:13,174
I'm lucky that I've done this stuff
before, because the code is disgusting and

741
00:38:13,174 --> 00:38:15,022
it's messy and it's...

742
00:38:15,022 --> 00:38:19,942
super hacky, but it gets the job done and
gets us a lot of data.

743
00:38:19,942 --> 00:38:26,022
We take in, every day we probably add like
50 ,000 new performers that we track and

744
00:38:26,022 --> 00:38:30,722
around a thousand new venues that we track
of who's playing at these places.

745
00:38:30,722 --> 00:38:34,902
We take in a lot of data, but it's hard
work.

746
00:38:34,902 --> 00:38:38,922
It keeps you, I spent a lot of hours
working on it, right?

747
00:38:38,982 --> 00:38:42,242
But that's the biggest challenge in live
music without a doubt.

748
00:38:42,242 --> 00:38:45,350
Which thank God there's somebody like Tap
The Eye solving that.

749
00:38:46,142 --> 00:38:46,716
You

750
00:38:46,716 --> 00:38:49,476
could see somebody knocking on your door
soon.

751
00:38:49,556 --> 00:38:50,996
A big exit.

752
00:38:51,756 --> 00:38:54,176
Let me ask you, go ahead.

753
00:38:54,596 --> 00:38:56,118
No, no, go ahead.

754
00:38:56,118 --> 00:38:59,108
that would just be nervous you go crazy
you go crazy

755
00:38:59,108 --> 00:39:05,648
Where do you see the intersection of
technology and live music heading in the

756
00:39:05,648 --> 00:39:06,848
next decade?

757
00:39:06,848 --> 00:39:11,932
And what role hope you're going to play in
shaping that future?

758
00:39:12,558 --> 00:39:16,034
Yeah, do you know, have you heard of media
research?

759
00:39:16,932 --> 00:39:18,412
Media research.

760
00:39:18,412 --> 00:39:20,452
I'm not gonna say I did.

761
00:39:20,492 --> 00:39:21,712
Maybe, maybe.

762
00:39:21,712 --> 00:39:22,502
Are they in Jersey?

763
00:39:22,502 --> 00:39:23,132
No.

764
00:39:23,462 --> 00:39:27,982
technically based out of London.

765
00:39:30,322 --> 00:39:32,542
They do all kinds of...

766
00:39:32,542 --> 00:39:35,942
I mean, you know what Bloomberg is, but
they basically do like Bloomberg reports

767
00:39:35,942 --> 00:39:37,822
for the music industry.

768
00:39:37,822 --> 00:39:38,842
They're awesome.

769
00:39:38,842 --> 00:39:42,510
They have very comprehensive music
industry reports.

770
00:39:42,510 --> 00:39:46,130
on what does recorded music look like,
what does live music look like.

771
00:39:46,130 --> 00:39:48,060
It's the kind of stuff that you would put
on a pitch deck.

772
00:39:48,060 --> 00:39:52,170
I know because I made the pitch deck and I
had to use MIDI reports to find the kind

773
00:39:52,170 --> 00:39:54,750
of data that we needed.

774
00:39:54,750 --> 00:39:59,510
And the team is also, they come out to
Barcelona a lot, so I got to meet up with

775
00:39:59,510 --> 00:40:00,650
the team.

776
00:40:00,910 --> 00:40:04,530
And one of them lives out, one of the team
members lives out in New York.

777
00:40:04,530 --> 00:40:07,350
So, MIDI research, amazing stuff.

778
00:40:07,350 --> 00:40:12,174
And what they have been putting, one of
the reports was talking about,

779
00:40:12,589 --> 00:40:14,379
who the kind of shows that are getting
played.

780
00:40:14,379 --> 00:40:19,689
And I think it's no secret that rock is
still very dominant in the live music

781
00:40:19,689 --> 00:40:20,749
space.

782
00:40:20,749 --> 00:40:22,349
Like rock is what you go to see.

783
00:40:22,349 --> 00:40:26,219
Even though in recorded music, it's
probably hip hop still, I would imagine.

784
00:40:26,219 --> 00:40:28,029
Like hip hop's still what most people
listen to.

785
00:40:28,029 --> 00:40:31,869
If you were to decide between a rapper
that you didn't know or a band you didn't

786
00:40:31,869 --> 00:40:34,229
know, you're gonna pick the band.

787
00:40:34,229 --> 00:40:37,729
You're not gonna go to, like you'll go to
the rap show if you know the rapper,

788
00:40:37,729 --> 00:40:39,499
you're not gonna go to some random rapper.

789
00:40:39,499 --> 00:40:41,133
Cause there's too much.

790
00:40:41,326 --> 00:40:43,026
it's way more likely they're going to
flop.

791
00:40:43,026 --> 00:40:46,806
At least for the band, you know that,
okay, they're all going to know how to

792
00:40:46,806 --> 00:40:47,746
play all the instruments.

793
00:40:47,746 --> 00:40:49,226
Like, it's going to sound...

794
00:40:49,226 --> 00:40:53,886
The worst thing a band is going to do, if
a band sounds bad, it probably just sounds

795
00:40:53,886 --> 00:40:54,456
generic.

796
00:40:54,456 --> 00:40:56,116
Like, it might sound generic, but that's
fine.

797
00:40:56,116 --> 00:40:58,866
You're going to live shows to drink beers
and listen to generic rock music.

798
00:40:58,866 --> 00:41:02,396
Versus a rapper, if the rapper bombs, the
rapper bombs.

799
00:41:02,396 --> 00:41:05,526
And especially if you don't know them and
you don't feel that kind of connection.

800
00:41:05,586 --> 00:41:08,526
Like, rock is still the dominant genre.

801
00:41:08,526 --> 00:41:09,508
But...

802
00:41:09,508 --> 00:41:10,214
Got it.

803
00:41:10,214 --> 00:41:13,774
it's looking like and we've seen this out
in Europe already and I think it's it's

804
00:41:13,774 --> 00:41:16,554
bleeding into the US and especially
looking at these media reports of seeing

805
00:41:16,554 --> 00:41:19,254
how landscapes are shifting.

806
00:41:19,254 --> 00:41:22,524
I think EDM is going to be the thing that
overtakes rock.

807
00:41:22,524 --> 00:41:26,894
Rock has been dominant since the since the
80s and I think EDM is finally going to

808
00:41:26,894 --> 00:41:33,974
unseat it as the as the live music like
the biggest thing to play in live music

809
00:41:33,974 --> 00:41:36,494
right now is going to be these EDM shows.

810
00:41:36,534 --> 00:41:37,594
Yeah.

811
00:41:37,594 --> 00:41:38,664
Now what it would...

812
00:41:38,664 --> 00:41:39,286
were at their.

813
00:41:39,286 --> 00:41:40,710
eating could be different, but.

814
00:41:41,604 --> 00:41:47,404
Wow, I thought they were at their peak
some years ago and now it kind of went

815
00:41:47,404 --> 00:41:47,694
down.

816
00:41:47,694 --> 00:41:51,324
But hey, I mean, you in that business, so
you'll understand them way better.

817
00:41:51,324 --> 00:41:53,258
You have the data, so.

818
00:41:53,258 --> 00:41:58,678
going off of what we see from our database
and I'm going off of a lot of what media,

819
00:41:58,678 --> 00:42:01,618
the kind of research that media puts out
as well.

820
00:42:02,138 --> 00:42:06,648
And yeah, they, and the flavor of it could
be different because I'm sure in Chicago,

821
00:42:06,648 --> 00:42:07,768
it's probably going to be more house.

822
00:42:07,768 --> 00:42:09,058
In Detroit, it's going to be more techno.

823
00:42:09,058 --> 00:42:12,778
In New York, it seems house is still like,
every single show in New York seems to be

824
00:42:12,778 --> 00:42:14,618
a house show nowadays.

825
00:42:14,618 --> 00:42:18,148
Like you play house music and something
like that.

826
00:42:18,148 --> 00:42:18,542
Mm -hmm.

827
00:42:18,542 --> 00:42:21,682
But it seems to me to get down into
Virginia.

828
00:42:21,682 --> 00:42:25,382
Richmond, I still go down to Richmond,
Virginia pretty frequently.

829
00:42:25,382 --> 00:42:30,242
And while there's still a lot of rock
music played, the majority of people my

830
00:42:30,242 --> 00:42:32,902
age go to the EDM shows that are in the
city.

831
00:42:32,902 --> 00:42:35,962
And the thing I hear the most is I'm
complaining about how they wish there were

832
00:42:35,962 --> 00:42:40,762
more EDM venues and that the current
venues would book more EDM artists.

833
00:42:40,762 --> 00:42:43,072
So I'm seeing that stuff firsthand.

834
00:42:43,072 --> 00:42:47,906
I'm also seeing it in the data that EDM is
still got a ways to go.

835
00:42:49,060 --> 00:42:51,280
Yes, yes.

836
00:42:51,280 --> 00:42:57,940
In your experience, what is the most
common misconceptions people have about

837
00:42:57,940 --> 00:42:59,580
the tech industry?

838
00:42:59,740 --> 00:43:04,100
And how do you believe these
misconceptions impact innovation and

839
00:43:04,100 --> 00:43:07,488
entrepreneurship within the field?

840
00:43:11,406 --> 00:43:18,386
Well, for one, startups, I mean, you look
at like the original, like when startups

841
00:43:18,386 --> 00:43:21,006
really started to become a thing and
venture capital really started to become a

842
00:43:21,006 --> 00:43:24,966
thing, it was these rebels, it was these
rogues, it was these guys going against

843
00:43:24,966 --> 00:43:30,066
the curve, like just doing things a little
bit different and being a little wonky.

844
00:43:30,066 --> 00:43:34,186
And those were the innovators, the guys
moving the needles.

845
00:43:34,726 --> 00:43:39,886
And like most things, as that stuff kind
of matures, the rebels start to get weeded

846
00:43:39,886 --> 00:43:41,230
out and you start getting stuff

847
00:43:41,230 --> 00:43:45,410
people who are optimizing themselves for a
metric.

848
00:43:45,410 --> 00:43:49,270
Like they try to see what VCs are looking
for and they optimize for that metric

849
00:43:49,270 --> 00:43:55,330
versus the OG startup heads who weren't
optimizing for any metric except I'm gonna

850
00:43:55,330 --> 00:44:01,310
build the best thing ever and it's gonna
be mine and that kind of championship.

851
00:44:01,310 --> 00:44:07,090
And even the startups were always building
like rebels, they were building against

852
00:44:07,090 --> 00:44:07,850
the status quo.

853
00:44:07,850 --> 00:44:09,262
Like the original Google was.

854
00:44:09,262 --> 00:44:12,362
It was definitely building against the
status quo.

855
00:44:12,362 --> 00:44:17,582
And what I see now is the industry has
started to mature enough that VCs are

856
00:44:17,582 --> 00:44:23,542
looking for, are you an NYU, MIT,
Stanford, or Harvard grad rather than are

857
00:44:23,542 --> 00:44:27,782
you this rebel who's going to try to like,
who's going to change the game?

858
00:44:27,782 --> 00:44:33,962
Like they want you to look good on paper
versus look good in action.

859
00:44:33,962 --> 00:44:35,598
And I mean,

860
00:44:35,598 --> 00:44:41,278
I'm also extremely biased saying that, but
I've seen that a lot is I've seen

861
00:44:41,278 --> 00:44:44,978
companies, music companies with these NYU
grads, they get all this money and they

862
00:44:44,978 --> 00:44:46,258
have no idea what they're doing.

863
00:44:46,258 --> 00:44:50,418
They have no idea what they're doing, but
you get Ilias and I in there and I went to

864
00:44:50,418 --> 00:44:54,898
Virginia Commonwealth University, who goes
to Virginia Commonwealth University.

865
00:44:54,898 --> 00:44:57,078
There's no Ivy league behind that.

866
00:44:57,078 --> 00:45:02,418
There's no special about Virginia
Commonwealth University, but Ilias and I

867
00:45:02,418 --> 00:45:05,390
break things and we move fast and we,

868
00:45:05,390 --> 00:45:10,130
We're not the type of guys that say, okay,
we'll work with our current market.

869
00:45:10,130 --> 00:45:14,350
AlienStyle and I fundamentally believe
we're in the blue ocean and the demand is,

870
00:45:14,350 --> 00:45:15,400
we're gonna create the demand.

871
00:45:15,400 --> 00:45:16,470
We're gonna create the market.

872
00:45:16,470 --> 00:45:18,430
We're gonna rebuild the industry.

873
00:45:18,430 --> 00:45:22,440
We're not trying to work in whatever
guardrails have been predefined for us.

874
00:45:22,440 --> 00:45:26,090
We are open to do whatever we want.

875
00:45:26,090 --> 00:45:26,758
And...

876
00:45:27,055 --> 00:45:31,055
I've been championing that from doing
cybersecurity back in the day to doing

877
00:45:31,055 --> 00:45:34,215
hackathons, to always trying to break
things and figure out very clever

878
00:45:34,215 --> 00:45:35,595
solutions to problems.

879
00:45:35,595 --> 00:45:40,315
You've met Ilias, so you know that he
champions that quite a bit of I'm gonna

880
00:45:40,315 --> 00:45:44,997
break things and I'm gonna go as fast as I
can and nobody can stop me.

881
00:45:46,510 --> 00:45:51,430
But I've seen a lot of other startups not
championing that, but get loads and loads

882
00:45:51,430 --> 00:45:55,730
and loads of funding, which is very
upsetting.

883
00:45:55,730 --> 00:45:58,190
But time is the biggest teller.

884
00:45:58,190 --> 00:45:59,950
So I could be completely wrong.

885
00:45:59,950 --> 00:46:01,180
And you fast forward 10 years.

886
00:46:01,180 --> 00:46:03,780
We'll see who's around in 10 years from
now and who's not.

887
00:46:03,780 --> 00:46:08,660
Well, I tell you one thing, you guys have
something, if you don't have the funding,

888
00:46:08,660 --> 00:46:12,460
you have the mindset and you have the
drive and the hunger.

889
00:46:12,460 --> 00:46:17,760
And that, trust me, that surpass many,
many, many fundings and many things

890
00:46:17,760 --> 00:46:21,500
because in the end of the day, who's going
to stand there?

891
00:46:21,500 --> 00:46:25,920
And if you guys still standing, and that's
what I see and that's what you guys got.

892
00:46:26,640 --> 00:46:29,108
That will take me to the next question.

893
00:46:29,226 --> 00:46:33,386
the last thing I think about Google is
too, I mentioned Google back in the day

894
00:46:33,386 --> 00:46:35,206
was they were, they were the guys breaking
things.

895
00:46:35,206 --> 00:46:37,426
They were guys moving the needle forward.

896
00:46:37,426 --> 00:46:41,206
Like one of the things I loved about
Google and I, I want to, you know, this

897
00:46:41,206 --> 00:46:46,766
is, this is me showing that I have a grad
degree is I really loved like the paper on

898
00:46:46,766 --> 00:46:51,706
MapReduce and the story behind how they
invented MapReduce is all these other big

899
00:46:51,706 --> 00:46:55,694
companies like IBM and these guys had
these big mainframes.

900
00:46:55,694 --> 00:46:59,114
And rather than building horizontally,
everything was to build vertically.

901
00:46:59,114 --> 00:47:01,674
We need a bigger computer, bigger
computer, bigger computer.

902
00:47:01,674 --> 00:47:03,474
And Google didn't have a lot of money.

903
00:47:03,474 --> 00:47:07,024
They were still relatively strapped for
cash at the time.

904
00:47:07,024 --> 00:47:08,754
So they had a lot of servers.

905
00:47:08,754 --> 00:47:11,574
They had a lot of computers, but they
didn't have any powerful ones.

906
00:47:11,574 --> 00:47:21,414
And they created this framework to do
massive data wrangling and manipulation

907
00:47:21,414 --> 00:47:24,078
jobs with all these computers.

908
00:47:24,078 --> 00:47:28,398
commodity computers, commodity servers
that they had at the time, rather than

909
00:47:28,398 --> 00:47:32,618
having to buy the biggest mainframe ever.

910
00:47:32,618 --> 00:47:33,918
And they championed this.

911
00:47:33,918 --> 00:47:37,698
Look, we humans evolved for scarcity, not
surplus.

912
00:47:37,698 --> 00:47:40,157
We live in a scarce environment and we're
going to work around it.

913
00:47:40,157 --> 00:47:42,418
Versus now, you see like Google Fiber.

914
00:47:42,418 --> 00:47:47,638
Google Fiber had the chance to be its own
thing, to really unseat AT &T and Verizon

915
00:47:47,638 --> 00:47:48,598
and change the game.

916
00:47:48,598 --> 00:47:51,448
But now Google Fiber works with AT &T and
Verizon.

917
00:47:51,448 --> 00:47:52,782
And there's more of this.

918
00:47:52,782 --> 00:47:57,662
let's work in the existing ecosystem,
let's partner, let's do this, rather than,

919
00:47:57,662 --> 00:48:01,622
no screw AT &T and Verizon, we're building
our own thing and you can't stop us.

920
00:48:01,622 --> 00:48:02,732
They don't do that anymore.

921
00:48:02,732 --> 00:48:05,342
Google's kind of lost its sauce.

922
00:48:05,342 --> 00:48:09,162
And looking at it as AI now, who are the
biggest AI companies?

923
00:48:09,162 --> 00:48:13,042
They're the ones who can get their hands
on that Nvidia chip, that $40 ,000 like

924
00:48:13,042 --> 00:48:14,602
those big Nvidia chips.

925
00:48:14,602 --> 00:48:17,342
And that brings, you know, thinking of the
analogy of the mainframe.

926
00:48:17,342 --> 00:48:20,412
You were the only a big tech company if
you could get a mainframe.

927
00:48:20,558 --> 00:48:25,318
Who's going to be the scrappy engineer
that's going to create MapReduce but for

928
00:48:25,318 --> 00:48:28,058
commodity GPUs?

929
00:48:28,058 --> 00:48:31,078
That'll be the thing to see.

930
00:48:31,078 --> 00:48:31,778
Exactly.

931
00:48:31,778 --> 00:48:33,618
Humans evolve for scarcity, not surplus.

932
00:48:33,618 --> 00:48:37,998
If you put yourself in a situation like
Google where you have all this cash and

933
00:48:37,998 --> 00:48:41,358
you don't know what to do with it, you're
not going to innovate as fast as you want.

934
00:48:41,358 --> 00:48:44,538
It's me and Ilius strapped for cash that
are going to be the ones who are going to

935
00:48:44,538 --> 00:48:46,898
do something clever that you didn't
expect.

936
00:48:46,898 --> 00:48:49,510
We're fundamentally these hacker guys,
right?

937
00:48:49,688 --> 00:48:50,468
Yes.

938
00:48:50,468 --> 00:48:53,888
And you know, that would lead me to my
next question is, you know, for our

939
00:48:53,888 --> 00:48:58,348
listeners and our viewers who's going to
be inspired by you, by your drive, by your

940
00:48:58,348 --> 00:49:03,628
energy, by your motivation, by your story,
and say, hey, you know, if he did it, how

941
00:49:03,628 --> 00:49:04,438
can I do it?

942
00:49:04,438 --> 00:49:05,378
I could do it.

943
00:49:05,378 --> 00:49:05,778
What I need.

944
00:49:05,778 --> 00:49:09,728
So what's your advice to some of these
people going to be inspired by your story

945
00:49:09,728 --> 00:49:12,912
and your vision and your motivation and
drive?

946
00:49:13,006 --> 00:49:18,186
Yeah, well one thing, one of the saddest
metrics I've heard, and I hate that the

947
00:49:18,186 --> 00:49:22,786
Nvidia CEO even said this, is there's no
point in learning how to program anymore.

948
00:49:23,646 --> 00:49:30,726
Because I think Ilias has learned this
because he's, I'm not some programmer that

949
00:49:30,726 --> 00:49:33,916
Ilias contracted $30 ,000 to go build him
an app.

950
00:49:33,916 --> 00:49:36,666
Like I'm a technical co -founder, we're co
-founders in this.

951
00:49:36,666 --> 00:49:40,110
And I've learned a lot more to the table
than just I know how to program.

952
00:49:40,110 --> 00:49:43,410
Like there's more to being a technical co
-founder than just I can actually build

953
00:49:43,410 --> 00:49:43,840
the thing.

954
00:49:43,840 --> 00:49:47,410
I mean, that's nice, but there's a lot
more that goes into it.

955
00:49:47,410 --> 00:49:50,670
So you, and you, you wouldn't know that
unless you've got your hands dirty and

956
00:49:50,670 --> 00:49:51,460
started building something.

957
00:49:51,460 --> 00:49:57,510
Like if you look at Ilias versus some of
the guys who, you know, I'm biased, but I,

958
00:49:57,510 --> 00:49:59,200
I, they're not as scrappy as Ilias.

959
00:49:59,200 --> 00:50:02,130
They're not as good at marketing as Ilias,
but you know, they're kind of like

960
00:50:02,130 --> 00:50:03,630
analogous to him to any sense.

961
00:50:03,630 --> 00:50:05,500
Like they, they work in the music
industry.

962
00:50:05,500 --> 00:50:08,206
They have all this, all these connections,
all this knowledge.

963
00:50:08,206 --> 00:50:12,326
they end up contracting somebody to make
an app, but they won't learn the things

964
00:50:12,326 --> 00:50:14,526
that Ilias has learned by working with me.

965
00:50:14,526 --> 00:50:18,806
Ilias knows how to build an app better
than them, even if he can't actually

966
00:50:18,806 --> 00:50:20,406
program the code.

967
00:50:20,406 --> 00:50:23,006
And again, you're not going to know that
unless you get your hands dirty and you do

968
00:50:23,006 --> 00:50:23,216
it.

969
00:50:23,216 --> 00:50:25,966
So my advice is get your hands dirty and
do it.

970
00:50:25,966 --> 00:50:28,646
The most stuff I learned was hackathons.

971
00:50:28,646 --> 00:50:31,566
I got into hackathons, I built stuff, my
GitHub looks crazy.

972
00:50:31,566 --> 00:50:36,406
I have over 100 repos on GitHub of me just
building something and trying to solve it

973
00:50:36,406 --> 00:50:38,213
and doing it fast.

974
00:50:38,446 --> 00:50:40,166
just to try and make something happen.

975
00:50:40,166 --> 00:50:43,906
And I think Ilias has done similar stuff
with the music industry of take these

976
00:50:43,906 --> 00:50:45,406
risks and try it.

977
00:50:45,406 --> 00:50:49,446
And especially for young people, what I
tell them, I mean, what I tell them, I'm

978
00:50:49,446 --> 00:50:54,866
still a young person, is it doesn't matter
if you don't have cash or anything, as a

979
00:50:54,866 --> 00:51:00,266
young guy, you have a very large reserve
for risk and you have a bigger reserve for

980
00:51:00,266 --> 00:51:02,006
risk than most people.

981
00:51:02,006 --> 00:51:06,546
And you should use that risk to your
advantage.

982
00:51:06,546 --> 00:51:07,598
Like somebody,

983
00:51:07,598 --> 00:51:12,998
A startup has less cash than Google, but I
can take a lot more risk than Google can

984
00:51:12,998 --> 00:51:14,098
do.

985
00:51:14,438 --> 00:51:17,598
And I think that's more powerful.

986
00:51:17,598 --> 00:51:22,198
So take the risk, do the things, build
stuff and lose as much as you can and fail

987
00:51:22,198 --> 00:51:23,538
as fast as you can.

988
00:51:23,538 --> 00:51:26,618
But you're not going to learn anything by,
I hate the people who say like, I have

989
00:51:26,618 --> 00:51:29,248
this startup idea, but you know, I'll wait
until the time is right.

990
00:51:29,248 --> 00:51:30,468
Cause the time is never going to be right.

991
00:51:30,468 --> 00:51:31,238
You just have to do it.

992
00:51:31,238 --> 00:51:32,864
You have to always jump off that cliff.

993
00:51:32,864 --> 00:51:33,564
it.

994
00:51:33,564 --> 00:51:34,244
Yes.

995
00:51:34,244 --> 00:51:35,354
Yes, absolutely.

996
00:51:35,354 --> 00:51:36,525
Well, you heard it first

997
00:51:36,755 --> 00:51:38,075
Entrepreneurs in Tech.

998
00:51:38,075 --> 00:51:40,495
You heard it first right here.

999
00:51:40,495 --> 00:51:45,995
Anything that you want to tell the
audience or listeners or viewers that you

1000
00:51:45,995 --> 00:51:50,595
coming up with on Tap The Eye that to look
forward to next month or something, a new

1001
00:51:50,595 --> 00:51:56,021
function or new thing that you want to
advertise or mention or plug in?

1002
00:51:56,021 --> 00:52:00,861
we announced a couple weeks ago that we
have a model in beta right now, an ML

1003
00:52:00,861 --> 00:52:06,821
model for generating an entire tour for a
performer.

1004
00:52:06,821 --> 00:52:09,851
So with a click of a button, it'll tell
you what cities you should go to.

1005
00:52:09,851 --> 00:52:14,461
It'll give you an estimate on the number
of revenue you make from the tour, the

1006
00:52:14,461 --> 00:52:18,601
amount of fans you'll reach, how long the
tour will take, so on and so forth.

1007
00:52:18,601 --> 00:52:21,581
It's very much in beta, so we are looking
for engineers right now.

1008
00:52:21,581 --> 00:52:22,845
We're looking for people who...

1009
00:52:22,845 --> 00:52:29,365
are really good in data science, pros if
you have a machine learning background,

1010
00:52:29,365 --> 00:52:34,105
just people who are willing to get their
hands dirty and work with very ugly, messy

1011
00:52:34,105 --> 00:52:38,725
data because we're a startup and we have a
lot, but I'm the only guy that cleans it

1012
00:52:38,725 --> 00:52:39,345
right now.

1013
00:52:39,345 --> 00:52:41,045
So, you know, you do your best.

1014
00:52:41,045 --> 00:52:44,205
So somebody who's scrappy, but likes
working with tons of data and likes to try

1015
00:52:44,205 --> 00:52:45,603
to make something out of nothing.

1016
00:52:46,771 --> 00:52:47,241
Amazing.

1017
00:52:47,241 --> 00:52:51,791
Well, thank you for joining us today on
Innovate Presents Entrepreneurs in Tech.

1018
00:52:51,791 --> 00:52:55,671
And we hope you found today's insight
inspiring and insightful.

1019
00:52:55,671 --> 00:53:01,971
Be sure to connect with us on social and
stay informed about our upcoming episodes

1020
00:53:01,971 --> 00:53:08,321
on this new Innovate Presents
Entrepreneurs in Tech.

1021
00:53:08,321 --> 00:53:09,141
Thank you again.

1022
00:53:09,141 --> 00:53:10,291
Till next time.

1023
00:53:10,291 --> 00:53:12,411
And we appreciate your time.

1024
00:53:12,411 --> 00:53:14,009
And thank you very much.

1025
00:53:14,525 --> 00:53:15,945
Thank you.

1026
00:53:15,985 --> 00:53:17,055
This is great.

1027
00:53:17,055 --> 00:53:18,235
pleasure.

1028
00:53:18,235 --> 00:53:20,935
Absolutely.