Latest / AI for Founders with Ryan Estes / The $1.5 Trillion AI Problem Nobody Talks About - Until Now

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The $1.5 Trillion AI Problem Nobody Talks About - Until Now · AI for Founders with Ryan Estes

0:00
Welcome back to AI for Founders. I'm Ryan Estes.
0:03
AI is sprinting, but most enterprises are stuck doing the
0:06
splits. 1 foot on a mainframe, 1 foot in the cloud and the
0:10
plumbing in between is Victorian.
0:12
Today we fix that. My guest is David Carmel,
0:15
founder of Data Rocket, and his claim is simple and spicy.
0:20
Data is just data until it's clean enough for AI.
0:23
In this episode, you'll learn how legacy and unstructured data
0:26
get transformed into trustworthy AI ready fuel in hours instead
0:31
of years. We dig into onboarding that's
0:33
almost just uploaded, pricing that shares in customer savings,
0:36
and a playbook for selling urgency to cautious C suites.
0:40
This one is for founders, CTO's, and operators who want their AI
0:44
to stop hallucinating and start shipping outcomes.
0:47
If you want practical steps, case study, thinking in a few
0:51
killer metaphors you can repeat to your board, you're in the
0:53
right place. And if you like interviews that
0:56
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All right. Hey then, welcome to the show.
2:25
Hey, great to be here. Thank you for the invitation and
2:29
also just a shout out to you. Thanks for all you do.
2:31
I mean really, for some of us who sometimes get a little
2:35
weedy, it's nice to find people that have tentacles in the rest
2:39
of the world and in deeper Dimes.
2:40
So thanks, I really appreciate it.
2:43
Absolutely my pleasure. And all of this is just a, is
2:46
just a, a work passion. You know, well also I never shut
2:50
up. So I figured you might as well
2:51
turn the, the microphone on and and put it out there.
2:54
But it's great to have you on the show.
2:56
I I would love to kick things off just by you giving our
3:00
listeners here a brief description of your company data
3:03
rocket. Sure.
3:05
I'll be happy to data rocket. The nuts and bolts of it is we
3:10
transform legacy mainframe, unstructured cloud and over
3:14
provision LLM data into clean trustworthy AI ready data.
3:20
And that allows you and customers to perform the magic
3:25
for their customers and make it all happen.
3:27
In other words, without clean AI trustworthy data, to us that
3:34
means data is just data until it's clean enough for AI.
3:37
And that's the underlining problem today behind AI and
3:41
getting us to win the AI race as a country.
3:44
Amazing. So walk us through a little case
3:47
study of somebody who's like maybe your favorite kind of
3:50
clients. They've got a bunch of data.
3:52
They want to see how they can build AI tools.
3:54
This sounds like a fundamental kind of step that maybe
3:58
companies are missing. So kind of walk us through what
4:01
like an onboarding would look like and kind of what the output
4:03
ends up being. Sure.
4:05
Can we go backwards before we get to the onboarding?
4:08
Because the onboarding is actually the easiest thing out
4:10
there. All they have to do is get the
4:12
data to us so we could do the rest.
4:13
So it's, it's really that simple.
4:15
But I came up with this when I was speaking at a conference
4:20
last year actually and afterwards a couple of of major
4:26
players from a software vendor, the world, everybody else and
4:29
then and said, we don't understand.
4:31
We know you'd write about the big guy, little guy
4:34
collaboration, but we don't understand why the big guy is
4:36
not buying enough AI from us. And I said, OK, think about it
4:40
from the CEO standpoint. What are they supposed to do
4:45
they when 75% of the Fortune 500 companies are still using
4:49
mainframes and when yet they also have the cloud and they,
4:54
their data does not communicate the legacy in the cloud.
4:58
So what are they supposed to do? They have one foot in the old
5:01
world and another foot in the new world and they're doing
5:03
splits and it's not going to, it's not working out very well.
5:07
So that's how we got involved in data Rocket.
5:10
So our view is data is just data until it's clean up for AI.
5:14
And that means we don't care if it's legacy, meaning like from
5:17
the mainframes, you know, those all big IB, Ms. and things like
5:19
that. And the government spends $80
5:22
billion a year on this problem that could be solved very
5:24
quickly. The Wall Street Journal calls it
5:26
a $1.52 trillion problem, if you can believe that.
5:30
And our view is whether it's legacy, unstructured cloud over
5:34
provision, LLM, who cares? Data should just be able to work
5:38
together seamlessly and then do you should be able to put it
5:41
together how you want, wherever you want and no matter what
5:44
occasion. And there should be no blockage
5:46
and no barriers. And that's what we do.
5:48
We bring it to it all together cleanly.
5:50
So if you can get it uploaded to us and we can take it from there
5:55
and return it to you in a format you want that's already ready to
5:58
go. Now one of the people Ryan said
6:00
to me, and I was thinking myself, you know, you always
6:02
think about the fallacy of your argument.
6:04
You know what I'm saying, What can go wrong?
6:06
And, and you know, you have to, you have to be your own worst
6:09
enemy. And that's what I've been doing.
6:11
And recently until it was good enough to, you know, come out.
6:14
And that's we recently made our debut at Cloud X And you know, I
6:19
started to think, OK, so we could do legacy fine.
6:23
Then we moved to the unstructured world in the cloud
6:26
and that's 90% of the data out there also.
6:29
And then what do you do with the LLM side where you have, you're
6:32
dropping these bunker Busters to try to break up this data when
6:36
quite simply they could be using a lot less of this horsepower
6:40
and trying to come up with a way to clean it, clean the dead a
6:42
lot easier. So by doing that, our view is
6:46
let's make one pool of ocean that's clean.
6:49
Why should you have to do it? So yes, we could do it on a one
6:52
shot deal. And then somebody says to me,
6:54
meaning me in my head, well, what happens 5 seconds later
6:58
when the rest when they start bringing in all this new data
7:01
and we have a product that can handle that too.
7:03
So our job is really to put data and people and businesses the
7:09
best position will succeed together so they can make the
7:12
maximum impact. Otherwise, I think they're
7:14
fooling themselves and they're wasting a lot of money and a lot
7:17
of time trying to do these workarounds when there's a
7:20
pretty simple fix. Amazing.
7:22
So Ryan, So what somebody would do to us is they would come in,
7:27
we have a dashboard, they would literally plug in and they tell
7:30
us what they want to bring over and all that.
7:32
And we don't care if it's they can combine it legacy
7:35
unstructured, they can bring it from multiple continents, any
7:38
different mouth and whether they have 40 mainframes in 300
7:42
different locations they call could be coming at one time and
7:44
combined and turn them to clean a a ready data.
7:46
And that's what makes it fun. We can give them back the whole
7:49
index, show what's done, maybe with a few recommendations.
7:52
But it's not our job to tell you how you should use your data.
7:56
Our job is to put your data in the best position so you can
8:00
succeed. Amazing.
8:04
OK, so I I heard. A great quote that basically
8:07
says, you know, with AI you can create marketplaces very easily.
8:11
So when I'm thinking about data on a project like that, you got
8:14
a contact database where you have one side of the table and
8:17
then you have the other side of the table.
8:19
And let's say I have a million contacts in Salesforce.
8:23
I'm like, what can I do with these contacts?
8:25
I want to clean the data and, and create a marketplace for,
8:29
for both sides of these contacts.
8:31
I bring these millions people from Salesforce over to you kind
8:35
of what do you do and what, what's kind of how you
8:38
restructure the data particularly so it's sensitive
8:40
for AI And then you know what, what, what am I overlooking by
8:45
missing that step? Yeah, you're overthinking it.
8:48
OK. So just think of data as just
8:50
big one chunk, OK? Don't even think about whether
8:53
it's a customers or this names and that whatever format you
8:57
want to bring it in, you want to make sure it's clean enough and
8:59
ready enough and trustworthy enough for anybody, OK, So
9:03
that's what we really do. So on the other side, when you
9:06
want to marry it all together and then if you bring it in from
9:09
one side, you have to marry it all and you bring it forward.
9:12
So whether you want to bring it into Salesforce or bring it out
9:15
of Salesforce, you understand, right?
9:16
Bring it and migrate it. These are all things that you
9:20
can easily do. And just think about how many
9:22
different pockets of information that people are duplicating all
9:25
around your company, right? Or how many things are in, you
9:28
know, and, and things. So if we think about a
9:30
marketplace, we also have a project coming out in the future
9:34
to do exactly what you're talking about.
9:36
I think in the customer experience realm.
9:38
Why is it? I think customer experience of
9:40
the future is going to be offering and providing your
9:43
customer their products and services they never thought you
9:45
would probably possible. In other words, something that
9:49
they may not have to pay for, but you're going to give them
9:51
such a lift, it's going to create a lifetime of loyalty.
9:54
So let's take your world, the startup world, right?
9:56
What if someone created a marketplace that could bring all
9:59
these startups on, invite them on, meeting with the right
10:01
people, get a big corporate account and you bring their
10:04
customers, their suppliers, their employees on and get them
10:06
trained in this area and get them before all the right
10:09
people. Literally matching just like
10:11
that. This is what we could do.
10:13
And this is also, we have another cutting.
10:15
That's what I want to. Let's focus here.
10:17
But this is something that we have a chance to change.
10:20
You know, employee, we look at his experience, whether it's
10:23
people, businesses and data. They should be united together
10:26
for Madsen and Implant. And that's what really, you
10:29
know, it really gets us going here.
10:31
And you know, you hit on a big thing, Ryan.
10:35
Everybody's looking at different portals where it's coming from.
10:38
Like, hold on, it's OK, bring it in wherever you want.
10:42
Then you're going to come out. And you might be quite as
10:43
surprised that you have the same data stored in 1500 different
10:47
spots in your company, if you know what I'm saying.
10:49
And people are doing this. So now you can tell those data
10:52
scientists who many of them are, and please don't take it the
10:55
wrong way, but they can't, I guess is some of them are
10:58
cleaning data now, like the potato peelers.
11:00
You know, you get that baked potato, you know, you clean it
11:03
and then you put it underwater and you call it clean data.
11:06
Well, AI is telling you the reason it's reacting is that
11:11
you're not treating it right. So when you start treating the
11:14
data right, AI will be your greatest tool you've ever used.
11:18
Amazing, and sorry if I'm dense but this is fascinating to me.
11:22
What what is what? What are the standards of
11:25
cleanliness and good hygiene that AI is looking for?
11:32
You know, you're talking about someone.
11:34
First of all, I'm not a trained data scientist or a PhD and all
11:38
that. All I want to do, what I want to
11:40
do is fix the problem. You know what I'm saying it it
11:43
and started out in that way. So the bottom line, how do you
11:47
know is when it goes through seamlessly through our system,
11:51
that will tell you it's really ready because it's already
11:53
running the test on the other side against the AI.
11:56
If you understand what I'm saying, it's basically it's
11:58
telling us whether it's ready. So you will know as an
12:01
organization of quite frankly is how is your, how is AI reacted
12:07
to your dad? In most cases, it's not very
12:09
good. Now there's many we'll say, oh,
12:10
we're doing great. You know, they bring bang their
12:13
chest. We just spent, I don't know,
12:15
$500 million doing this. Well, like I would argue that
12:21
it's nice to know, but there's still a better way of doing
12:24
this. And so Ryan, there's, I'm not
12:29
trying to do a little fairy dust here, but there's kind of a
12:31
little bit there. You know, you run it through
12:33
our, our technology that comes back out clean and we tell
12:37
everybody ahead of time, feel free to test it.
12:39
We won't charge them for any tests.
12:41
You don't run it through, kick it around, you'll see it from
12:43
your end. So whether you test A5 GB and
12:45
you later bring in five petabytes, which is 1000
12:50
terabytes, if you get my drift, that's a a big one.
12:53
And we even show a demonstration with 153 terabytes of mixed
12:57
legacy and cloud, unstructured cloud and, and, and like that.
13:01
So our view is to try to make a something that's so complicated
13:05
that people never thought could happen.
13:08
Just say, hey, we can do it for you.
13:10
And now, you know, you guys can take it from here because I'm
13:14
betting that our customers will say to us, we can take it from
13:18
here. It's like that last leg of the
13:19
baton toss. You know, in the relay race, as
13:22
Americans, we usually are the four fastest runners, but we
13:25
rarely win win the race in the Olympics in the relays because
13:29
we don't pass the baton properly.
13:31
Well, in this case, you now have to you have a chance.
13:33
They're really pull ahead that final leg and and get way above
13:38
your partners. So just think if you didn't have
13:41
to spend all that money and time and unnecessary reasons for
13:45
trying to figure out debt and what to do when you can put all
13:48
that together and you're ACEO of these banks.
13:51
And you know, Chase probably spends close to a billion
13:54
dollars a year on it. And in 70% of the world still on
13:59
legacy mainframes. I'm talking about banks and
14:01
insurance companies and all that, and even the small ones,
14:04
the big ones. And then you take 99% of the
14:08
world still has the unstructured cloud problem, which is 90% of
14:11
the data. It's a big problem that really
14:13
no one's talking about. And if you want to win the air,
14:16
as this is what needs to be done.
14:18
And whether you're from President Trump all the way down
14:20
to the person like me, we all want to be put in the best
14:26
position to succeed. We want to have the best tools,
14:29
the cleanest, the fastest. And this is something that the
14:33
big guy can also bring down to the little guy and and
14:36
democratize dad all the way through.
14:38
And that's one of the exciting things as well.
14:40
So, Ryan, sometimes this is a whole different way of looking
14:43
at things. I'm in a distinct minority view
14:44
here, OK. You know, most people think you
14:47
have to just blow it through and all that.
14:49
No, no, you can. We can get it back to you.
14:52
It used to take years and that works so well.
14:54
We can now do it in hours or maybe in a day if depending on,
14:58
you know, retries if they want to do it.
15:00
But I hope I answered your question.
15:02
But the pot, you know, this is how you automate it.
15:04
You know how the when the assembly line was automated,
15:07
right, how it changed things. We're now automating the not
15:10
only just the migration and data, but the integration of all
15:13
data and making sure it stays that way for you. 365, 24-7 a
15:18
year. Yeah, no, it's really
15:20
interesting. I mean, like you said, it's like
15:23
it's it's one of the biggest tech problems.
15:25
You have legacy structure data trapped in mainframes and
15:31
unstructured clouds. Into your point, you said
15:34
nobody's talking about it. So how do you sell urgency to
15:38
enterprise C-Suite buyers who either they prefer incremental
15:43
change or they're like, it's just a problem that we don't
15:46
discuss? Yeah, that's it.
15:51
That's a great question. And I'm not saying it because
15:54
I'm on your podcast, but we're looking at a number of different
15:58
strategies. We don't have a license on
16:02
brains or a license on connections.
16:05
So there are people out there that are already providing
16:09
services or doing business with these entities.
16:13
Now they are looking for the next big thing, if you know what
16:16
I mean, improve their stature with your, with their customer,
16:19
with these big guys and so forth.
16:21
How can they not just be the fill in the blank guy, but how
16:25
do they also get a seat at the table, right to be really
16:29
someone they really rely on. So they're looking for this type
16:32
of of type of product and that's how we're looking for it.
16:35
So we're looking through certain distributors.
16:37
Actually, there's even a couple of VC firms that we may be
16:41
working with, not from the point of view of getting funding for
16:44
us because we are funded by the Bank of Carmel, which is myself
16:47
and my wife, but the, the, they have connections in the banks,
16:53
you know what I mean? They may have specializations
16:55
that we do. And it's also, by the way,
16:57
potentially a new model how to work together ahead of time to
16:59
see if you really can move forward and right before they
17:03
even want to make that investment in you.
17:05
And by the way, they can make a heck of a lot of money to being
17:08
the distributor. You know what I'm saying it for
17:10
whatever look, you know the value added reseller or whatever
17:13
you want to call it. So from our point of view Ryan,
17:16
and that really to answer your question is appearing on these
17:21
shows as one getting our name out there is another 2 is to
17:26
bring you through a chain of distribution that already has
17:30
inroads into these markets. So that's what we're looking for
17:33
and that's what we're interviewing now and that's how
17:37
we see it. But on the other hand, there's a
17:39
huge, from a little guy standpoint, it's very difficult
17:43
to penetrate to C-Suite. I mean, I started out of law
17:45
school as assistant, the chairman and CEO of a large
17:48
public company in Chicago. So I understand the 35,000
17:52
level. And I also understood that how
17:54
you need to get your boss out of the office and taking a look at
17:57
what's going on in the world, right?
17:58
You know, new things and stuff that's not really happening a
18:01
lot today. So how do you get by it?
18:03
And then everybody's a chief today, you know, I mean, it's
18:06
like, you know, like just remember how you got there.
18:09
It would, you know, this is about the business.
18:13
This is about, you know, not about necessarily what's the
18:16
greatest thing for your career, but what's the best thing for
18:18
your customer. And if you have the right
18:20
culture, these are the things they're going to go.
18:22
And then lastly, there are companies out there they're
18:24
looking for that little thing, you know, I mean that lifeline
18:28
to get on to distinguish themselves and it takes that new
18:31
CEO sometimes and to bring that through an organization.
18:36
So the longer assertive is we can't could do this on a direct
18:42
basis, but we are probably we're going to be going through people
18:46
that have connections and and so forth like that.
18:50
Cool I I love doing research on you because you speak in
18:53
metaphors that I can understand no one part Well, something I
18:58
read said that you noted that AI adoption is often stalled by
19:02
Victoria Victorian era plumbing. And as as a person who's gone
19:08
through the ringer with clay pipes, I can I really understand
19:13
that. So you know, aside from like,
19:16
let's say just people being either ignorant or just not
19:20
wanting to look at some of their their data issues, What what are
19:23
some other mistakes you see that companies are making when they
19:26
try to go all in right now with with AI or generative AI?
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I think they should figure out where they want to go and where
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and how they think they can get there and what is stuff standing
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in the way for them doing so. And also what I learned in
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up doing and recognizing that there is a a big wide world out
22:30
there and you better open your eyes and ears because you can
22:32
learn a heck of a lot. So what I found when I went to
22:36
College in the school and, and of all things Model United
22:40
Nations to start out with is I thought it was the dumbest thing
22:43
you ever heard of the geeks running around with suits and
22:45
ties and skirts and you know, all these type of things.
22:48
And then I realized it was one of the greatest experiences
22:51
because it teaches you how to think about what's on the other
22:53
side you. I think the CEO should put their
22:56
people into different positions instead of what they're in and
22:59
start thinking about what is it like from this position?
23:01
What's it like from the customer?
23:02
What's it like to deal with me and so forth?
23:05
Because you would then start the walls start coming down.
23:09
And that's what we're about. We tear down these walls with
23:11
our data. Why can't you do the same thing?
23:14
You're so worried today about couching things.
23:16
You know, when we look at strategy, Ryan, and we sit down
23:19
a lot of people, you know, it's, it's a no holds bar.
23:22
I mean, don't get me wrong. Everybody says as long as
23:25
everybody's going in the right direction, you should be
23:27
bringing it up. And I can tell you that I've the
23:30
best experience I ever had is one person came to me.
23:33
You know, she happened to bring a a suggestion on the table and
23:38
people just went after it. I could see, unfortunately, it
23:41
was not going to wasn't going very well for her and I didn't
23:44
want to have an embarrassing moment.
23:46
So she can't. We we caused paused the meeting
23:48
real fast. And I said, she said to me, you
23:51
know, you always say if if you don't put it on the table, it
23:54
can never get there. You know, we never, but I feel
23:56
like I'm not. I said calm down.
23:59
We are about to get to a solution that we never ever
24:02
thought possible. And if you didn't bring it up,
24:05
even though there only may be 1 to 5% of your original thought,
24:08
you sparked something that we've never had before.
24:11
And sure enough, it led to products like what we're talking
24:14
about now and different things like that.
24:16
So I think you need to open up the gateways.
24:19
The CEO needs to stop worrying about Wall Street enough and
24:22
start thinking about Main Street and start thinking about how can
24:26
I penetrate, how can I learn, how can I defeat my competition
24:30
and how are we better are going to be better than anybody else
24:34
out there. So we will be a magnet for
24:36
business if you start thinking in those terms.
24:38
Like when we think about people coming into an organization, I
24:40
always think about the rule of 2, is this person capable of
24:44
doing 2 jobs ahead that way? We're always thinking ahead that
24:47
way. We're always moving in there.
24:49
That's where we want to go. And I think people are stuck in
24:52
these quarterly mindsets of earnings and stuff like that as
24:54
opposed to start talking to these, to their constituents
24:58
about where they want to go and where they want to take this
25:00
company and how they want to turn this into a real rocket.
25:06
And no pun intended, but yes. And I like how you spell rocket.
25:09
It's like you're going to rocket.
25:11
It's great. Thank you now and by the way,
25:15
Ryan, I appreciate the metaphor comment because we do talk like
25:19
it's a like if you have to tear down the wall and remove the
25:22
plumbing, this is what you're talking about doing as far as
25:25
software goes. So even people like me who
25:28
didn't grow up in the software or can visualize it and on the
25:31
video on our web page, actually, this is the thing about first
25:34
video is what I thought about in diagrams and play right home
25:37
after that conference in New York.
25:39
So, you know, you got to be able to put things in terms that
25:43
people can understand and whether you can understand too.
25:46
And just using these industry terms.
25:49
I mean, I got to look them up. I mean, seriously, I went to
25:51
somebody talked to me about SAS and then I asked him, do you
25:54
know what SAS is? I mean, you know, what the heck
25:55
it was? I mean, it's like, you know,
25:57
they just dropped these buzzwords.
25:59
So I didn't mean to digress, but you're you hit a really, really
26:03
good spot. You know, it's important because
26:05
in our industry, like the the jargon is thick and like the
26:09
three letter acronyms are just like so ubiquitous and, and
26:12
being a storytelling kind of guy, I, I relate to the world in
26:16
simpler terms, I suppose. So I appreciate it.
26:18
One thing I read about you too is that you begin your work day
26:22
at 5:30 in the morning and leave open slots for creativity,
26:27
impromptu tasks. Talk a little bit about like how
26:31
building your company is kind of, I, I would say enhanced by
26:37
leaving open opportunities to to kind of like maybe do creative
26:41
work as opposed to reactive work.
26:46
You did. You do your own work very good.
26:48
I love people who do that. Only the best.
26:51
I'm a pro. What are we talking about here?
26:54
Well, you know, I have to give you a kudos on your podcast too.
26:56
So you are the best. But the answer to your question
27:00
is no one has a license on brains and on strategy and so
27:05
forth. And we all learn from
27:07
experience. And that's the one thing that AI
27:10
has not been taught enough of is how to deal with experience.
27:14
Yours mine, right? How do we build it in the
27:16
software? And that's something that we've
27:18
tried to do here. So I tried to think of myself is
27:22
also, I want people to think that my door is open.
27:27
I used to have a when I started out, I used to have a lot of
27:29
clothes. My door closed and I started
27:31
realizing people weren't as much willing to talk to me, if you
27:35
know what I mean, because my door seemed to be closed.
27:37
So I also, I started thinking to myself, well, I have to have
27:42
time to think as well. And so I always try to save like
27:47
either two 10:10 AM or 2:00 PM, you know, at least that time
27:51
every day to making sure that I have open either for customers
27:54
want to, you know, for people on a call or for my own private
27:57
time. Secondly, is there's nothing
28:00
wrong with regrouping on a daily basis about where you are,
28:04
right, where you succeeded. What have you learned today?
28:07
What is it, you know, these type of things?
28:09
So I think just from a personal point of view, yes, a lot of us
28:13
have high motors and I know you do, but at the same time,
28:16
doesn't mean a motor always has to be moving, physically moving.
28:20
We need to let our brain also take that motor and racket as
28:25
well. And so whether we're going
28:27
around seeing people and learning and watching and
28:30
different things like that and, and getting involved in these
28:32
different meetings or, but what better way than coming up with a
28:37
construct when you know that your mind's trained that at 10
28:41
AM that you're going to be doing this every day.
28:43
You know what I mean? It sets it up.
28:44
And so it's like muscle memory. It's like hitting a golf ball.
28:48
How come sometimes in the showers is when your best ideas
28:50
come about. It's because if you're right,
28:54
you're, you've turned it off and you're ready to go and you ready
28:57
to bring your mind to a whole new level.
29:00
So at the same time, the opportunity of not being able to
29:06
talk to Someone Like You or someone in your organization who
29:09
is just going to open up a whole new wave of thought that could
29:14
be that missing piece. We call ourselves the missing
29:17
piece technology, but there's always a missing piece.
29:20
There's something there that's missing and also need to hear
29:22
it. And as an entrepreneur, you
29:24
know, you just grab it and, and it's something that's done.
29:29
And that's what I try to do on a daily basis is I trying to keep
29:32
a schedule like that. I get up, you know, there's some
29:36
days I do sleep in later. So I mean, but it's if there's
29:41
other times though, I'm sure that you're the same way.
29:43
You may wake up at 3:00 AM with an idea.
29:45
That's because you're really engaged in your thinking.
29:48
And so I never want my mind and my thought process in my ability
29:53
to have an open door, whether it's my own thoughts or meeting
29:56
with people and getting feedback and also giving reactions to the
30:01
ability to make this all better. And I tried to also use maybe
30:07
not doing a great job at your podcast, but we have two ears
30:10
and one mouth. And if you learned or try to use
30:13
that ratio as far as conversation goes, it usually
30:16
works out, you know, really well for you.
30:18
You've got to be a very good listener.
30:20
And so, you know, you got to listen to your mind too.
30:23
It may sound crazy, but you do. You have to listen and you have
30:27
to treat yourself like in a different position.
30:30
Ryan, you know, I tried to say in our case, like our product
30:33
stakes people, well, you can't say that I said, yeah, well, we
30:37
have to prove that it's good enough or great enough to get
30:39
out to the next level. Otherwise, you know you're going
30:43
to be fooling yourself when it comes to the market.
30:46
Yeah, you know, mornings is something that I've had to
30:50
reclaim. I'm naturally a night owl and
30:53
mornings are just a hellscape for me typically now, you know,
30:57
with, with the invention of, of all these AI tools are so cool
31:01
and there's always so many more to demo and there's the, the,
31:04
the capacity I've got now is incredible.
31:07
You know, AI early on it's you could 10X your productivity,
31:10
which meant I was like, oh cool, that means I can do 1X to work.
31:13
But no, it just turned into do I'm now doing 100 X to work,
31:18
which you know, and I'm able to rattle through tasks without
31:21
fatigue of context toggling much better now, you know, there's so
31:26
much to get done now that to me, the value and investing in the
31:29
morning is absolutely critical. So for me, you know, I get up
31:33
for 30 and I, I go do yoga in the mornings.
31:36
And I kind of thought to now, because I think 1 unintended
31:41
consequence of like being able to 10X50X100X your output is
31:47
that there, there's, it's exhausting.
31:50
And also it creates kind of like a out in front of your skis
31:54
feeling. I mean, I, I don't want to go so
31:55
far as to call it anxiety or something like that because
31:58
there's definitely like a pulling nature to the desk.
32:01
So, you know, for me, I go, go do yoga and it kind of
32:04
harmonizes my, my brain in my body and also allows me just to
32:09
like warm up enough that like I can sit at a desk for friggin 10
32:13
hours a day and not lose my mind.
32:16
But you know, it's really been like a important part of my
32:19
practice is re getting the mornings back.
32:23
I think you have to understand, you know, I think the pandemic
32:26
has taught a lot of people that they've had to learn how to deal
32:29
with idle time, You know, when you're at home 24/7 or whatever
32:33
it was, you know, during that time.
32:35
And I didn't used to be a morning person.
32:38
I used to be able to go from like 11 to I could go to three
32:41
AMI mean I'm serious. I mean, I could and now I find
32:45
myself mornings and I can still go late night.
32:47
It's sometimes in the afternoon is like the time that I need to
32:51
take the the line down to just get my head together because my
32:55
view is when you feel it, you just got to take that wave and
32:59
start writing it. And it doesn't matter what time
33:01
it hits you. On the other hand, you still
33:03
need to have that balance and discipline that you talked about
33:05
that you can get your thoughts and head and act in order other
33:09
words, otherwise you're going to be so responding to the AI
33:14
world. And you're talking about right
33:15
now the biggest cesspool out there, which is the Internet,
33:18
right? And you're being barted,
33:19
bombarded all the time with this information.
33:22
Well, if you can't control of of what you want to do and how you
33:25
want to get there and what you want to take in it, it could be
33:29
a not a great rabbit hole. So I would just say what works
33:35
for you? Go for it.
33:37
I mean, seriously, it's hard for me to tell you that.
33:39
Like who would want to do yoga? You know, again, I'll probably
33:42
have a do a year ago. But the my point is that I'm
33:47
really, it's really nice to hear when someone says this is how I
33:50
really recoup my time and my mind and my viewpoint.
33:54
Because when you can start out today in a great frame of mind
33:57
and where you want to go, it's kind of like marriage what I do.
34:01
And I've been with my bride since high school, but I call
34:06
her my bride. And I tried to go to bed at
34:08
night thinking that I'm luckiest guy in the world and tomorrow's
34:11
the second day of our marriage. And when I do that, it really
34:15
kind of it works out. And it's like, I just met her
34:18
yesterday and it's just, you know, great.
34:20
So when you can start from that type of perspective, and I'm not
34:23
saying it's for everybody, but like you said about yoga, you
34:27
probably look forward to doing your yoga in the morning, right?
34:32
I look forward to waking up with hopefully this great thought
34:35
that this is like the second day of marriage.
34:37
You know, this is just we're just getting going.
34:39
And it's like how I look at our businesses too and hitting the
34:42
day. So when you can go out and
34:44
listen, there's a lot of crap, excuse the expression out there
34:47
that's going on in this world, but at the same time, give
34:51
yourself some time to deal with it.
34:53
And then you got to keep moving. So, and and all that.
34:56
Your bride, I'm I'm going to use that I, I call my wife my
34:59
ex-girlfriend and she does not like that, but it is technically
35:04
correct. Absolutely.
35:08
You know, sometimes you use like I go digress for a second, but
35:12
it's great that you say that. Sometimes I'll, I'll say to my
35:15
wife, my bride, I'll say lying to her, I go, how's that?
35:18
She goes well, that's good. You know, sometimes you come up
35:19
with another one, it's not so good.
35:21
You get what I'm saying. So you know, they're, they're
35:24
also your best audience and your best friend or your best
35:27
listener. And also the same way it works
35:29
both ways. So my wife's the head of she's
35:32
the executive director of this large nonprofit.
35:34
And you know, every year she gives me a performance review.
35:38
She double s my retainer, which from zero to 0 so, and tells me
35:43
that things are doing great. So anyways, it's but I couldn't
35:45
be happier. That's great.
35:47
Data Rocket, What's the business model?
35:49
How do you determine pricing? That is, I think I'm going to
35:56
wrestle with that till the day I die, but my what we're going to
36:00
determine pricing is this. We know what the cost is of
36:04
people that are, are the long and the short is for the large
36:09
person, for large players. They're used to certain
36:14
requirements, certain pain where to pay and also they are
36:22
expending exurbing amounts of money.
36:25
So our full goal and model is I would love to be able to, we're
36:31
going to charge a certain amount upfront just for the and then we
36:34
want to go into a percentage of savings to prove our real worth.
36:38
And then we'll charge on a hosting basis.
36:40
It's not hosting, but for a support basis to do the 365 a
36:43
monthly a charge on top of that. So realistically, we will
36:51
actually sitting down with some large players now and getting an
36:55
understanding how they want to be sold, if you get what I'm
36:59
saying, because every company until you are the player wants
37:04
to be sold individually nuanced for them.
37:08
And I think, you know, that's that's what we want to hit.
37:10
And what's your sweet spot? Well, you know, we prefer to do
37:13
this with that. OK, if we can work it into the
37:16
model, do that. On the other hand, we want to be
37:18
able to say we could be a game changer for you.
37:22
And I'm willing to take less up front, but I want to be, I want
37:26
to participate in those savings. And I also want to be able to
37:31
move forward and show and showcase what you have done,
37:36
what we have done together. Because at the end of the day, I
37:41
want you to be happy, more profitable and better off than
37:46
you were before. And more importantly, that
37:50
you've become a magnet for people who want to do business
37:53
with you and not just customers, employees, vendors, all that.
37:58
Because you have changed how you do business.
38:01
You brought a model going forward and you know what we're
38:06
willing to put, we want you to have some skin in the game too,
38:09
and but we're willing to show you the horsepower we have.
38:12
So that's one of the things they're pricing.
38:14
We're not a commodity yet, if you know what I'm saying.
38:17
There's people that are trying to do some of the things we're
38:19
doing. I would say in the legacy area
38:21
for sure, considering that's a $1.5 trillion issue, that's
38:26
technical debt every year really being paid.
38:29
And when it takes 40 hunt mode, it takes 1 mainframe to heat 40
38:33
homes. Just think about you know from
38:35
that from electrical pricing. So we really want to understand
38:40
the customers pinpoint really well and then but on the other
38:43
hand we want to be compensated for that as well.
38:46
If it takes, if someone says to us though Ryan that we can't do
38:50
this instead of and all that, then we will come up with a very
38:53
fairly and price that we will work out with all.
38:56
But we're not a $39.95 item, you know, we're having, you know the
39:01
it's inexpensive, but we're worth it.
39:03
And when you can save yours and I don't know how many hours and
39:09
hundreds of millions of dollars, if we say give us 90 minutes for
39:12
a data test to the chance to save 100 million plus and bring
39:16
in value, I think that's a pretty good value proposition.
39:19
So that's, you know, from a pricing standpoint.
39:22
I've heard other guests of yours too say that they've wrestled
39:25
with all the time. I've never heard anybody who was
39:27
like a expert in this. But we don't want to get too far
39:32
ahead of our skis. We also know with some of the
39:34
earlier players that probably the amounts will be less because
39:37
they're going to say you're going to will broad kill.
39:39
Obviously both both our goal is hopefully you'll be a supply and
39:45
demand if you get my point. Yeah, and I had a client I had.
39:49
A. Professional consultant on
39:50
pricing that's that was his expertise and I asked him about
39:53
his pricing and he was he was nervous about it and it never
39:57
ends It was great. You know I would if I.
40:01
Was on the flip side, I would flip it back to you, but I know
40:03
it's your podcast and say, well, yeah, well, you, you talk to
40:07
everybody in the world, how do you see it?
40:09
You know what I mean? And and and so forth like that.
40:11
So that's one of the great things about shows like yourself
40:14
that you get to learn from others and realize that others
40:17
in the same position as you like, you know, we don't have a
40:20
license on every piece of information.
40:22
And what I've always found out out there that if somebody
40:25
really is a great customer and wants to be a great partner with
40:28
you, they'll give you the the way forward in that area because
40:32
they know what fair pricing is, you know what I mean?
40:34
And things like that too. So.
40:36
Well, I know it's a it's a pain point for all the listeners of
40:38
this podcast. I mean, there's 27,000 founders
40:42
and CEO's that listen to this building with AI or actually
40:46
have AI companies and you know, pricing, especially right now
40:50
because you have all this, you know, outcome usage based
40:53
pricing with tokens. Or do you go kind of a strict
40:55
SAS model or how to set pricing is, is really important.
40:59
So I, I get tons of feedback that, you know, the economics of
41:02
businesses is, you know, a big take away they get from the
41:06
show, which is why I have to keep pressing forward here,
41:08
David. So if we're talking about no, I
41:10
would say real, real quick, oh, I'm sorry.
41:13
I would just say sorry. You have to understand where you
41:19
fit in the place. If you're in a commodity
41:21
business, then you have to make sure you're pricing right.
41:23
If you're in a value side of the business, you have to make sure
41:26
where you can bring it in right. And then if you fall somewhere
41:29
in between, then you have to figure out the best way forward.
41:33
And also every customer has a personality and it's our we have
41:40
to figure out how they want to be sold and understood and make
41:43
it better for them. Yeah, totally.
41:46
On average, what's a a kind of lifetime value for a customer
41:49
for you? Well.
41:54
Now you got me stumped. How do you determine lifetime
41:57
value? Would say revenue times a year,
42:03
annual revenue we'll say for our customer.
42:07
Yeah. What's our sweet spot of
42:08
customer to go after or how much revenue do we want to generate
42:11
per customer? Yeah.
42:13
How much revenue do you want to generate per customer or what's
42:16
the average of that? Our goal is to be in for sure,
42:20
minimally in the seven figures. Yeah, OK.
42:23
And to higher and you know we are willing to take less on the
42:29
initial side to be able to share in the savings on the going
42:34
forward. So if you think about the legacy
42:36
debt and this technical debt that they've been paying every
42:39
year and they don't have to pay it, that's a that's a lot of it.
42:42
There's a lot there. So you know, that's, that's the
42:47
part we're just didn't even play with and help them to reach it
42:52
because I find out my customers, we're all on the same page.
42:55
If it works together way better, doesn't it?
42:57
Absolutely. I mean 7 figure annual or 7
43:00
figure revenue for a client is humongous.
43:04
What what's some of the like expenses and like maybe the the
43:08
margins there? Well, I mean, we've tried to
43:14
automate everything, but we have a, you know, we want to be in a
43:22
very high end of the range of, you know, gross profit.
43:27
And we've we've invested all this time and effort in doing
43:31
so. And by able to kind of venture
43:36
in certain respects where no one has gone before and offer a
43:39
product that can get there a lot faster and huge time savings and
43:43
cost savings. That's how we look at it.
43:47
But we have to make sure that, you know, the cost of the cloud,
43:51
right and on Prem and all these types of things are very
43:54
expensive too. And I don't see them coming down
43:57
in anytime soon unless some other major players come in.
44:01
So we have to be very cognizant of that.
44:03
And you know, companies like Microsoft and Azure, you know,
44:07
they've been really good to us, but you know, how long are they
44:10
going to be, you know what I'm saying?
44:11
And, and, and, and that's where I'm getting at in that once.
44:16
So to answer your question, real short is we want to be in the
44:20
upper end of gross profit. And if anybody, because we built
44:25
our model accordingly and you know, it's a big risk reward
44:29
because we didn't take things into incrementally, we are ready
44:33
to take the big swing and we didn't want to take a step out
44:35
until we're ready to go. I would argue that data rocket
44:38
right now is really data rocket 4 point O, not one point.
44:42
OI mean, we've had, you know, those type of things.
44:44
So I hopefully answered your question and I know I've I've
44:47
hem and hawed on it, but you know, we really want to be at
44:52
the top scale of gross profit and our our performance better
44:55
support that as well. Otherwise we're not going to get
44:58
there. Amazing.
45:00
David, this has been so much fun.
45:02
I really appreciate you coming on the show.
45:03
Couple of things before we get out of here.
45:06
One, if you could live in any time or place or historical era,
45:12
what time and what place would that be?
45:17
Well, I would say definitely now.
45:20
Not now or the future. Oh, not now or the future.
45:24
Oh, OK. I would.
45:27
I would definitely love to have been around the air when the
45:32
United States and the Constitution in 1776.
45:37
I would love to have been able to sit in as a family in the
45:40
wall to listen to our framers talk about what they're looking
45:45
at and then be able to go back from back to the future, if you
45:49
know what I mean. Like that's and see today what
45:51
is going on. I always think to myself, what
45:54
would, you know, Thomas Jefferson or Franklin or Madison
45:57
or any of these framers be thinking of what's going on
46:00
today and any of these constitutional arguments and
46:02
what people are making of it. Now these law professors are
46:05
saying this and this is going on in society.
46:07
And yet the greatest thing in the world is this American
46:11
Constitution has survived. You know, if you really think
46:14
about it, I think it's the greatest.
46:17
I'm not talking about what's in it.
46:18
I'm not getting to that level. But the fact that people that
46:23
didn't have any type of electric, basically electricity,
46:27
if you know what I'm saying, you know, like we take everything
46:29
for granted today. Could you imagine them saying,
46:32
Hey, I wonder you, are you out of your mind?
46:35
But they're they thought that far, you know what I mean, that
46:38
far in advance. And I'd love to hear like I
46:41
would love to then a fire in the wall to listen to them and think
46:43
about like, we can't just talk about today.
46:46
What about tomorrow? What about 100 years from now?
46:48
You know how they thought they saw the world.
46:51
And that's why their premise has been the same and has been able
46:55
to hold weight for over coming on 250 years now.
46:58
And that's pretty freaking amazing.
47:01
Love that answer. And you know, I think it might
47:03
have also been a period that was the pinnacle of male fashion.
47:08
You know, in no other time could you have like a a purple duster
47:13
with 50 brass buttons up, you know, and like kind of like long
47:17
shorts with high socks and like a shiny shoe.
47:22
Just look cool. Yeah, as long as it's not wool,
47:26
it would have worked out great for me because I just, it's just
47:28
like crazy. But anyways, I probably could
47:32
have made it, but by 1 weakness is air conditioning.
47:34
I really don't like to be, you know, I they wore some really
47:38
heavy clothes and those summers must have been, you know, pretty
47:41
brutal. But at the same time, if you
47:43
really look at it, some of that fashion has definitely made its
47:46
way through through the years, right?
47:48
I mean, people definitely took a look at it.
47:50
It it was nobody was there was nothing that you couldn't.
47:57
Yeah, Yeah, you have long. Hair and like a A man has bow
48:01
tie in it, a black bow tie in their in their ponytail.
48:06
Well, look at the dreadlocks of today and I don't want to go off
48:09
the braids and stuff and look at the barristers and the Brits
48:12
worn those times. You know what I'm saying?
48:13
It's not, you know, there's it's, it's A and it's a little
48:17
bit different, don't get me wrong.
48:19
But you know, the concept is there and this is one of the
48:22
great things. But also, Brian, people have to
48:26
understand history going back and thinking and I know myself
48:31
that I am so grateful that I didn't read those biographies
48:35
about jobs and Musk and innovation by Stanley.
48:40
I can't believe I forgot his word just because I may not have
48:45
ever got into data Rocket or in the software because it's seen
48:49
what they were doing. And then I realized they just
48:52
put their pants on one step, one, one leg at a time like the
48:56
rest of us a lot faster. But and they look at problems
49:00
And what's told me about people like Elon Musk is when he when
49:03
his person at SpaceX has to him, we now have an inch that works
49:07
at 10 million. He says you got to get it to
49:09
two. You know what I'm saying?
49:10
Just like that. And that's when I realized that
49:13
he just wasn't a tech guy. He also do the dollars and
49:16
cents, right? And you know how it all needs to
49:18
work together. So to answer your question,
49:20
Ryan, I just think what you're doing lying people like myself
49:24
to have a few minutes to chat and to learn from you and
49:29
hopefully learn more. And I appreciate the what you do
49:32
for your readers. It's great. 100% And before we
49:36
get out of here, I just want to give a shout out to an
49:38
organization that's near and dear to your heart, which is
49:40
Facing History and ourselves facinghistory.org.
49:45
And they use lessons of history to challenge teachers and their
49:49
students to stand up to racism, anti-Semitism and other forms of
49:54
bigotry and hate. And gosh darn it, don't we know
49:57
we need that right now? So shout out to them.
49:59
And do you want to add a couple of words of kind of your
50:02
involvement with the organization?
50:03
We're conced we got involved in like 25 years ago or more with
50:09
facing history and my wife originally started out as an
50:14
educator and they asked her to come on board really was it's an
50:18
infancy and it's so great to see how something that started at a
50:23
dining room table, you know, now it's branched out all over the
50:28
world that has affected 100, I don't know, 100 million, but
50:31
pretty close to a number of children educators.
50:35
And at the same time, it really hasn't lost its focus on what
50:40
it's been trying to do and whether the lessons of the
50:42
Holocaust and what it means to the world and the rest of
50:45
society and everybody life to the rest of us.
50:48
And, you know, it's a, it's just a really meaningful organization
50:55
and I hopefully people look at it and support it.
50:58
Amazing. And of course, it's Data Rocket.
51:00
That's Data, ROCKI, t.com and David, what's the best place
51:06
people can connect with you online?
51:09
You can do it two ways, so you know I'm available on LinkedIn
51:12
and I wish I could say I'm X, but I'm not that active and also
51:19
on our data rocket if you want to you got a meeting with me or
51:22
something like that, you can drop down.
51:24
We have a contact slip, any contact, and he gets you into
51:27
Calendly and we appreciate you, Ryan and I do.
51:32
And if your people would like to spread the word, if they ever
51:36
would like to become a distributor of what we do,
51:39
there's a lot of people that need it.
51:41
And if sometimes the most important person in the world is
51:44
the one that can connect to that and make it all happen.
51:47
So that's what we need and that's what we appreciate,
51:50
people like you. Amazing.
51:52
Thanks so much, David. And thank you, Brian.
51:55
It was a pleasure. OK, everyone loves to talk about
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