Latest / AI for Founders with Ryan Estes / Doctors aren’t the problem

Transcript

Doctors aren’t the problem · AI for Founders with Ryan Estes

0:00
News flash, healthcare doesn't have a diagnosis problem, it has
0:03
a time problem. And that time problem is quietly
0:06
ruining lives. Most patients get 13 minutes
0:09
with a doctor. That's barely enough time to
0:12
describe symptoms, let alone find the real cause.
0:15
So you get pills, not answers. Not because doctors don't care,
0:19
but because the system doesn't allow them to.
0:22
Haresh Patel learned this the hard way.
0:25
He spent 12 years building a company and burned millions, and
0:28
still only one. After years of pivots, his take
0:31
away is painful and useful. Building the tech is the easy
0:35
part. The.
0:35
Hard. Part is knowing who you serve,
0:38
how you make money, and why your products should even exist.
0:41
So this time he did something most founders won't.
0:44
He stopped building and spent two years just listening to
0:47
doctors, patients, and anyone living in the gaps of modern
0:50
medicine. So steal this method before you
0:53
build your MVP. Build your advantage.
0:56
Create a system that turns messy records, wearable data, and life
1:00
context into a clean story. A human expert can news fast.
1:04
AI isn't here to replace doctors.
1:07
It's here to give them their own brain back.
1:09
Now here's a question. What problem in your life or
1:12
business keeps coming back because you're treating symptoms
1:15
instead of digging for the Pearl under the mattress?
1:18
Welcome to AI for Founders. I'm Ryan Estes if you want the
1:22
full breakdown on building in healthcare, validating before
1:24
coding, and why rookie thinking beats resume thinking.
1:28
This is for founders, operators, and anyone obsessed with solving
1:31
real problems. 3 quick thing, go to AI for Founders and subscribe
1:36
so your brain stays ahead of your calendar.
1:38
Steal one idea per week and pretend it was your own.
1:41
I won't tell if you're building something big, you're going to
1:44
need a bigger mind. So start there.
1:46
And if you like the show, please leave a review.
1:48
Right. I came for the strategy, stayed
1:51
for the uncomfortable truth. Now a quick commercial for
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burst. Rush, welcome to the show.
2:41
Thank you so much for joining me.
2:42
Thank you, I appreciate you inviting me as a guest.
2:45
I was looking forward to this. Absolutely.
2:47
My pleasure. Now kind of we were talking
2:49
about a little bit beforehand before we started rolling here
2:52
about just how hot medical tech in AI is right now.
2:58
There's so many opportunities in healthcare for AI to truly kind
3:03
of revolutionize what we're doing.
3:04
Also, it gets us into kind of fun tangents of what's possible
3:08
over the next 12 months, the next two years, next five years.
3:12
So I super excited for this conversation.
3:15
Now I want to introduce early beyond, you're the CEO of
3:18
Diagnostic MD, which is a healthcare service company
3:21
focused on bringing diagnostic imagery or imaging rather
3:25
directly to patients rather than forcing patients to navigate
3:28
hospitals, clinics and long wait times.
3:30
So that's kind of a brief description, but talk about
3:33
where you are with the company right now.
3:35
We're still very early, well, early stages, but late stages.
3:39
I started the project almost two years ago.
3:43
It was about mid 2023 and I put it on hold.
3:47
I actually built the first platform and it was actually
3:50
quite frankly quite advanced in some of the LLMS at that time.
3:54
But I put it on hold because I found out that you know, nothing
3:57
against the technologist and it's hard work, but building the
4:02
technology is relatively easy. The harder part is to answer the
4:06
basic questions of who's my target market, What's my target
4:10
customer? How do I make money and how do I
4:13
monetize and what's the business model?
4:15
Stupid is really the mantra and, and the reason I, I, I, I have
4:20
to kind of say in Tony and shake it because I was a stupid one
4:23
when I built my last company. We built a platform took about
4:27
12 year journey, went through about $30 million.
4:30
We had a great exit state St. acquired the company.
4:33
It was a fintech platform doing very similar things that this
4:35
platform does, which is taking disparate records, structuring
4:38
it, creating visuals so that doctors, doctors can see the
4:41
whole patient. So the architecture is the same.
4:44
But boy, we struggled for as as strong as my product market fit
4:47
background is, it took us four or five more years longer
4:50
because we should have thought those things through.
4:52
And so each time required a slight adjustment or a major
4:56
pivot. So I said, let me spend the last
4:57
two next two years of listening. So I've been listening to
5:01
anybody that's a doctor, anybody that's in the medical field,
5:04
anybody that's a patient, you're a patient.
5:06
I'm a patient and just learning from that and I think I'm in
5:09
much better shape. So now we're relaunching the
5:11
platform, rebuilding it with current standards with the
5:14
knowledge that I built up on what I think it'll make make
5:18
make it more successful than what the first, I wouldn't call
5:21
it first one, not attempt. It was just like start, no stop,
5:24
time out, answer these questions, have a better idea.
5:26
Now at the end of the day, I'll still probably guess wrong.
5:29
And as all of us know, we never end up where we think we're
5:31
going to be versus where we where we intended to be.
5:33
So we have a small group of Notre Dame students that are
5:38
have joined the company and we're all working on SWAT
5:41
equity. We have built the business plan.
5:44
I'm in the initial stages of fundraising and we are in the
5:46
initial stages of building out the platform.
5:48
And one other interesting thing we're doing right now is while
5:51
the platform is being built out, we're going to create an
5:53
educational center. And this is really to help
5:56
people find solutions that are out there.
6:00
Where medicine just generally says we don't have a good
6:02
answer, there actually is an answer.
6:04
So we want to build that out first because that's what people
6:07
are struggling with. My doctor doesn't have a good
6:09
answer. I'm tired of taking pills.
6:11
If you dig deep enough and are willing to be experimental,
6:15
there are solutions out there that are worth a try.
6:17
So we want to build that out first and then slip the
6:19
technology platform, which is a core diagnostic methodology that
6:23
is more comprehensive than at least your modern medicine
6:26
offers today. Incredible, you know, and the,
6:30
the validation process is one of my favorite things to talk
6:33
about, largely because there's that quote, it might have been
6:36
Abraham Lincoln or something. He said, if I have 10 hours to
6:38
fell a tree, I'd spend 9 hours sharpening the blade.
6:42
So I, I think there's a lot of like wisdom and saying like, OK,
6:45
hold on, let's pump the brakes. Let's see what the market is,
6:49
what the what the product can be.
6:52
So as you're kind of spending this time, how do you go about
6:54
kind of like, you know, determining what is the problem
6:58
you're trying to solve and then ultimately who are your first
7:01
customers? Well, the customer I'm really
7:03
passionate about is you and I. It's the patient.
7:06
It's the 100 million of us over the 350 million that are
7:10
suffering with some sort of ailment where medicine is kind
7:13
of front of its arms and said, you know what, we don't have a
7:15
good solution. We got a lot of pills for you or
7:18
suck it up. But, and it's not because they
7:21
don't care. It's just these are complex.
7:23
They're, I call it the Pearl below 20 mattresses.
7:26
You've got to take each mattress off to figure out what is that
7:29
Pearl that is causing it. And ultimately there is an
7:31
answer if you dig deep enough. And it will need to be curious
7:34
and willing to be open minded. And so we want to make that that
7:38
job easier on peeling the onion or peeling the mattresses off so
7:42
we can find that Pearl and take it out and hopefully solve the
7:45
problem. So it's, it's, it's addressing
7:47
the consumer. Now I, I'm also consumer focused
7:51
because I also feel us and the primary doctor are the victims
7:56
of a system that nobody created maliciously.
8:00
It just is the way it is. But between pharma and insurance
8:04
and the hospital network, it just leaves very little time for
8:07
the patient and doctor to have a real meaningful collaboration.
8:10
The average time to get in front of a doctor's 13.2 minutes.
8:15
Kind of let me understand your symptoms, let me give a few more
8:18
tests. Let's look at that, come back,
8:20
look at that snapshot and I'll give you a recommendation.
8:22
It's just not deep enough to understand the whole person and
8:26
what that Pearl bill in a 20 mattress is.
8:29
So I'm very consumer focused, but I really feel like we are
8:32
the ones suffering from misdiagnosis, under diagnosis,
8:36
long diagnosis and the doctor's suffering too because they they
8:39
joined for compassion, the nurse joined for compassion, but the
8:42
time the economic pressures just don't allow for the compassion
8:46
to come out. So I'm really focused on a
8:49
patient portal centric, which in itself makes it very
8:52
differentiated from other platforms that may do the exact
8:54
same thing, but they're serving the institution side of the
8:59
business. You bet, you know, I, I was
9:03
speaking with a a brilliant guy the other day and he said, you
9:06
know, before I really consider launching a product, I want to
9:09
have distribution built in on the cap table.
9:12
Which is to say he wants to have either design partners that are
9:15
keyed up ready to be, you know, customers on day one or they, he
9:20
has a very fast delivery for go to market.
9:25
So you know, you're validating, you're talking to patients,
9:29
you're talking to doctors, you have your own personal
9:31
experiences, of course. How are you starting to think
9:33
about gathering design partners to help build the product?
9:37
And then where are you going for kind of the first few customers
9:40
as you're kind of getting ready for launch?
9:41
First one is that in terms of design partners is very
9:44
confusing. And it's confusing because AI is
9:48
moving so rapidly. Yeah, the team that I built with
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Mercados, I love the was of people.
9:57
They're brilliant, but I'm not sure they're the right team.
10:02
And so I've been spending a lot of time thinking through what is
10:05
the right skill set in today's world.
10:07
Is it the 1015 year, you know, experienced front end engineer,
10:12
right, a solid architect? Or is it somebody that doesn't
10:16
know software at all like me? And should I be writing it
10:19
because of the tools and the ability to transfer exactly
10:21
what's in my head onto software, make it actually work?
10:26
So it's, it's, it's actually been more challenging than I
10:28
thought in selecting the right technology.
10:31
And so I, I think that the skill set actually needs to be, and I
10:34
may be wrong and I'd love comments from your audience, but
10:38
I think it's somebody that has four or five years of good front
10:41
end engineering that can because AI is not perfect, right?
10:46
So they have to be smart enough to say, wait a minute, that code
10:48
is BS. Do it again.
10:50
Because I know when I use AII find my eyeball, it like, OK, I
10:56
know because if it's on the business side, marketing side,
10:59
business plan, that's way off, right?
11:02
So you need the experience to say, OK, this code is flawed or
11:06
it's not going to work or it's not going to support a
11:07
particular architecture. But you also need fresh thinking
11:10
around, I'm not going to code 1 Polygon at a time, right?
11:16
One code at a time or one line of code.
11:19
You need to use more current tools.
11:21
So I look at people that are what I call rookies.
11:25
And rookies doesn't mean young, it just means people that want
11:28
to reinvent themselves. Somebody who said, you know
11:30
what, I got 20 years of engineering software engineering
11:33
experience, but I'm going to throw that out of the out the
11:36
window and I'm going to relearn what's modern today.
11:38
And that's a rookie. And I think that's the right
11:41
skill set of looking for that engineering team that are
11:42
rookies that have experience but are have reinvented themselves
11:46
leveraging today's technology. Yeah, rookies, I'm a rookie.
11:51
You know, it's one thing that I always felt like a a warm
11:54
embrace from Zen. There's that great book from
11:57
Suzuki, you know, Zen mind, beginner's mind and a bit of a,
12:01
a sidetrack here, but like, you know, Zen and the kind of the
12:05
Zen abbot's really found a, an interesting audience in, in
12:09
America because we have kind of like that enthusiasm, like, I
12:12
don't know, let's begin again, let's start again and that
12:16
rookie kind of attitude. Now I'm very fortunate that I
12:19
have like dozens of these rookies around me that are doing
12:22
the most incredible things. And I'm really inspired by that
12:26
because in the past, you know, development, it's like, OK, what
12:28
am I going to do? I'm going to go find a junior
12:30
and I'm going to do some hiring or I'm going to go find like a,
12:34
an agency to help me design. And I've had success, but mostly
12:38
I've had a lot of challenges and failure with outsourcing
12:41
development. It can't move at the speed that
12:43
I'm used to. Now where you and I have
12:45
connected is that, you know, entrepreneurs, sure, leadership,
12:49
sure, But at the core we're sales dogs.
12:52
You know, it's like, I know what the customers want.
12:54
I want to get it to market really quickly.
12:56
And now, and I mean like right now in the last couple of weeks,
12:59
we really have this capability. So one thing that I've been
13:02
playing around with is kind of the AI software augmented or AI
13:06
augmented software engineering and a couple of tools.
13:10
You know, clawed code right now is unbelievable.
13:13
I mean, it is developing like very, very quickly.
13:16
So, you know, rookies like us can really get in there and do
13:19
damage. But there was a bit of a chasm
13:23
to to build a bridge across, which is, you know, as a rookie,
13:26
I don't know what I don't know. You know, a lot about software
13:29
development is like creating the proper guardrails and different
13:32
kind of compliance issues that I just don't know exist.
13:35
So shout out to one of the guests on this podcast, Tyler
13:38
Wells. He's got a, a company called
13:40
Brain Grid that actually does that.
13:42
So you talk to it casually about what you want to do and then it
13:45
creates the PRD and all the requirements and tasks that you
13:49
can then chip off one at a time going into clod code.
13:52
And then, you know, using Clod to help you kind of navigate
13:55
Clod allows you to build like this very, very, very
13:59
sophisticated software very quickly, you know, which is
14:03
really exciting, you know, so when you're kind of approaching
14:07
it, you know how to begin development.
14:10
I understand that unease so, so much because, you know, 1
14:14
characteristic of kind of more senior developers is it's, it's
14:17
harder to teach old dogs new tricks.
14:20
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16:54
Yeah, if you're talking about books, Rookie Smarts is the one
16:57
that I kind of crystallize this idea and it's a really good book
17:01
and it talks about rookies and rookies, again, doesn't mean
17:05
it's the 22 year old hockey player that, you know, wins that
17:08
award rookie of the year. It can be an 80 year old.
17:11
And and it really talks about how you pair rookies with senior
17:15
mentors and the combination is powerful.
17:18
And I like the story that I like the most is actually they they
17:20
talk about that movie that Tom Hanks is in where he's the
17:23
captain of the ship and the Somali, you know, hijack his
17:27
ship and hold him hostage and call the crew hostage.
17:30
And it's interesting because Tom Hanks is probably the most what,
17:34
you know, actor on and, you know, he's the he's not the
17:37
rookie, He's the, he's the experienced guy.
17:39
And what the director does for the effect of when they first
17:42
meet is he takes an actor that's never acted before who acts as
17:46
the new captain. And they're shouting each other,
17:48
I'm the captain. No, no, no, I'm the captain.
17:51
Now I've got the gun. And you've got the perfect
17:53
pairing between the rookie and the experienced guy.
17:57
You know, no acting experience, decades of acting experience.
18:00
It's a really good book teaching you how to be a rookie in your
18:03
whole life. And then to talk a little bit
18:06
about the, the comment about development.
18:07
I just met somebody, I'm exploring the front end of our
18:10
platform of how to get information of how to build
18:12
somebody's personal story. And so one of the things that he
18:16
can do is build a digital twin of doctors or any, any
18:20
profession. So you can scale.
18:21
Right now he's working well on university professors and his
18:25
whole experience has been the same thing, which is, gosh, I
18:28
hired team A that didn't work. Very experienced.
18:30
I hired Team B and finally I hired TMC, which is me, and he
18:35
was able to build a product himself or at least get the idea
18:38
there's an issue is sometimes not the developer, it's what's
18:40
in our head and getting it in a way that the other person can
18:43
understand it. So now you combine the 2 and
18:45
that's what the power of AI is, at least to get that first MVP
18:48
out. Of it and it's really important
18:52
to have doctors on board and Co signing and you know, I, I've
18:57
been critical of the medical industry.
18:59
Not not that it's difficult, but I also want I've got a lot of
19:03
friends. Somehow I got 3 or 4 like
19:04
friends that are doctors and surgeons and they, they
19:07
subscribe to the podcast shout out and they're like, dude, why
19:10
are you always like smashing doctors so much?
19:13
So I want to be sympathetic, you know, because there's some kind
19:16
of stereotypical personality traits of doctors that we all
19:19
kind of understand, but you have to worry about or you have to be
19:22
considerate of like what they've gone through to do it.
19:25
The rigor of getting your MD is unbelievable.
19:29
The internships that you have to endure after that are
19:32
incredible. The working hours that they do
19:35
in the field is unbelievable. Plus everybody's got their own
19:38
Google MD. So as you're kind of going into
19:41
diagnostics and working with, you know, patients as a
19:45
clinician, you're getting people that are cross checking you with
19:48
Google and they have all the information and they read A blog
19:51
post and this and that. So if doctors are a little bit
19:54
chippy, there's probably good reason for it, you know, but
19:57
also because of that, like if they you're starting to
20:00
introduce diagnose diagnostic tools to to kind of like help
20:04
doctors, they might feel threatened by that.
20:07
So how, how do you kind of approach kind of the the
20:10
clinicians as to say like, Hey, I am friend, I come.
20:13
Well, it's kind of what I, what I said, right, is that the
20:15
victims here is the doctor and the patient, the system that is
20:21
built to serve us has created that time pressure, right?
20:27
And So what AI can do. So some of it, I think you just
20:31
have to be empathetic like everybody else.
20:32
You can't just say, hey, listen, get your head out of the sand,
20:34
right? Stop fighting.
20:36
Is this going to replace your job?
20:37
Well, first of all, doctors are never going to be replaced
20:39
period. But you look at their workload,
20:43
right? And the amount of time actually
20:45
they, what they decided to get into medicine for, you know,
20:48
that's like less than 10% of their job now.
20:51
So how can we help them do more of that and lessen everything
20:55
else? And diagnosis takes time.
20:57
Research takes time, right? Once I know you've got symptom
21:00
Ambien CI may not have an immediate answer, but I've got
21:03
to go back to my journal and I've got to go back and talk to
21:05
other doctors. I've got to go back and do some
21:06
research and find out. Wow, at eleven, 4:00 AM in the
21:09
morning, those doctors probably have those 4:00 AM that caught I
21:12
met rash or I met Ryan and gosh, what's the answer?
21:16
I don't have one. I got to go find out.
21:18
Well, that's something that they can offload, right?
21:21
AI can help. They can.
21:22
The good news is that like you and I can read out some
21:25
hallucination, they can read out hallucination boy, they can
21:28
learn a lot, a lot quicker because AI was synthesized
21:31
information. So if they view it as a
21:33
productivity tool, as positioned, this will make you
21:35
more productive or oh, by the way, the two hours that you
21:39
really need with this patient to really understand them is
21:40
already been built out as a patient had software from
21:43
Diagnostic MD, right? The story's entirely built out
21:47
in 3 pages that can give you a full, you know, medical, you
21:51
know, A-Z chronologically built out in terms of how your blood
21:55
is behaving over time. You have your wearable data,
21:58
which is longitudinal. And now all of a sudden you
22:00
overlay that with the life story they do is you, what trauma do
22:03
they have? What good news do they have?
22:05
What injuries do they have? All of a sudden they've got a
22:08
more complete picture. So we've just taken those 13
22:11
minutes you get with the doctor and made 5 minutes really
22:14
productive because he's actually achieving what he needs another
22:17
hour and 45 minutes to get to, to understand and call patient.
22:20
So it's it's, it's productivity gains and it's also how to get
22:25
knowledge quicker, faster for something that you've seen for
22:28
the first time as as a new symptom.
22:31
Yeah, 100%. You know, into your point, yeah,
22:35
the, the, the more time you can get, the more productive time
22:38
you can get with your, your doctor, the better.
22:40
And also the more productive that they are in their position.
22:45
You know, because again, like a lot of the, the, the kind of
22:48
research takes kind of bandwidth and it takes willpower.
22:52
And what we know is like our attention is a finite resource,
22:55
which if you're lucky, it resets every night after you go to
22:58
sleep. But if you don't sleep well for
22:59
that night for whatever reason, you know, you're, you're, you're
23:02
that deficit, you know, I mean, for example, yesterday I was at
23:07
an insane attention deficit. You know, those days where you
23:10
just like toggling through tabs and you're like, you can't
23:13
figure out like, did I finish that or did I not?
23:15
We all have that doctors experience that if they're up
23:18
against something maybe that their expertise and experience
23:22
doesn't readily prepare them for, they've got to go do that
23:25
research component. If you can take 4 hours off that
23:28
time and make it into 4 minutes, then they're reserving all of
23:31
that willpower and, and basically decision making
23:34
capability for what they can do on the bedside.
23:37
You know, where I think, you know, for most doctors, that's
23:40
what they got into it for, you know, so there's this great
23:43
opportunity. And I love your, your Pearl
23:45
analogy is like, you know, if, if the AI tools can even peel
23:49
back one layer of a blanket, one layer of a mattress, like that's
23:53
the time and energy that they could have spent on something
23:56
that they're, they're more attracted to.
23:58
I'll give you. Something I just recently
24:00
experienced first hand. And so I developed tinnitus,
24:04
right? Tinnitus is ringing in the year
24:06
and it's really bothersome now. Is it life threatening?
24:10
Dull. I don't need to go to the
24:11
emergency room. But if you start searching
24:14
online, there is no cure for tinnitus.
24:18
Sorry. That's generally the answer if
24:20
you spend about an hour doing research.
24:23
If you continue to dig, you'll find podcasts.
24:27
There's a doctor in Germany, right, who said, no, there
24:30
actually is a cure. Now it's a thesis.
24:34
But first of all, he does a great job educating tinnitus is
24:37
not don't see your ENT specialist, which is your first
24:40
thing you want to go do because the problem is in brain.
24:45
And he describes 3 areas because the problem is doctors, not
24:48
against the doctor, but doctors are not trained to ask these
24:50
questions because he specializes.
24:52
He did a lot of research on our behalf and he said if you had
24:55
hearing loss, well, what happens is the part of the brain that
25:00
use uses to process the noise is confusing.
25:04
Wait, there's not enough noise coming in.
25:05
It's not going to produce noise. That's the tinnitus, right?
25:08
So first thing you want to do is take two hearing tests over
25:11
time. If you did one few years ago,
25:13
great, take it again. As you'll see, if there's major
25:16
degradation, that'll be your cause of tinnitus, which means
25:18
you're ready for hearing aids. For two reasons is once the
25:21
brain starts getting noise, it stops producing that ringing,
25:24
right? But in he said three, there are
25:28
three things that can happen and you have to trace 12 months
25:31
before you tonight has happened a serious fall or injury that
25:35
could have displaced your jaw or your neck trauma, something
25:41
shocking, right, or stress. Stress is a major, major factor.
25:46
Again, this is the mind body connection that's missing and
25:49
goes if you've had in the last than 12 months, your likelihood
25:51
of getting tonight is really high.
25:53
And so for me, it's like while the bingo like went off last
25:56
December, in this room, this room, the ceiling water was
25:59
pouring through. We had a bad moving job, and the
26:02
entire house got destroyed. And I, in the middle of the
26:07
night, I'm trying to turn the lights on to see where else the
26:10
house is leaking. And the entire house is leaking
26:13
every. Yeah, I was.
26:15
It felt like the Titanic. And so on the way down, I
26:19
slipped and fell. And I was lucky that I missed my
26:23
spine by 1/4 of an inch and I didn't fall forward and hit the
26:27
steel railing, stainless steel railing, but it was enough to
26:31
displace. I said, well, bingo.
26:32
One, maybe there's a misalignment somewhere that's
26:34
causing this. Obviously it's traumatic to go
26:37
through and see your House of 25 years disappear overnight.
26:40
We were displaced for a whole year.
26:42
And then obviously there was a stress of, you know, insurance
26:44
wasn't going to pay for it for now until I sued them.
26:47
So I had actually all three events.
26:49
And so I was seeing a chiropractor for my back and I
26:52
just mentioned this to him. He said, oh, we've been trained
26:54
on tinnitus. And he started working 9
26:58
different muscles here, readjusting the neck,
27:00
readjusting the jaw. He put his fingers in exactly
27:03
where inside the mouth where the dark.
27:06
The dentist give you the novocaine shot.
27:09
They're a man. It was painful.
27:10
But I'm telling you, my tinnitus on the right ear is almost gone.
27:14
And so this is something first level.
27:16
There is no cure. It just requires research, it
27:20
requires training. And so this is available now to
27:22
doctors to be able to spend that time to find out if you can
27:25
curate it for them to say which one of these three happened to
27:28
you. You should ask me that question.
27:30
So I thought that was a telling story around not lack of
27:33
compassion, but it's, you know, it's a vast work out there.
27:36
There's what, 5000 white papers a year just on breast cancer.
27:40
There's no humanly possible way to be able to be on top of all
27:43
of that. Yeah, yeah.
27:46
So it really lends itself to greater specialization, which
27:50
ultimately is where how you find greater excellence.
27:52
So in this case, your doctor, you know, if it's using your
27:55
tool, he could say like, you know what, I actually don't have
27:58
experience with this. And in historically, we've
28:01
thought that there was no known cure or treatments for that.
28:04
However, you know, I, I can use diagnostic MD and they're going
28:08
to say like there may be some chiropractic option, there may
28:11
be some other things. There's other researches being
28:14
done from other fields. Maybe you can go pursue that
28:18
which is, which is huge. Man, that's a tough year.
28:22
It was a tough, I always look at that was a lot tougher year for
28:27
99.9% of all wants my problems. So that's kind of how I looked
28:30
at it. I know a portion of the the
28:35
platform also is kind of leaning towards a kind of a
28:38
decentralized service model. Talk a little bit about that
28:40
well. I'm trying to answer what you
28:43
mean by decentralized meaning. If you could expand on that,
28:47
that would help me. Yeah, you bet.
28:50
And I'm not sure if, if maybe I read it wrong, but basically if
28:52
the hospital isn't the center of care and the administration, a
28:56
lot of times, you know, obviously like HIPAA compliance
29:00
and, and the ability to share information has been locked up.
29:03
So you kind of really are very kind of subject to your primary
29:07
care physician and their recommendation that perhaps this
29:10
is an opportunity that the patient actually retains their
29:13
their information is able to like share outside of that.
29:16
Good, really good question. So I think the core of the
29:20
problem has been owning your own records.
29:23
You should be the CEO of your own health.
29:24
We shouldn't have farm it out right and you should be
29:27
responsible giving the doctor as much information about you as a
29:31
person. The problem has been is that up
29:35
to now it's been really challenging to get your own
29:36
data. Now recent changes by the
29:39
current administration is making it a lot easier and the mandates
29:44
that are required. So I can now grab a lot quicker
29:47
than even two years ago, drag and drop and get everything in
29:50
one place that's really, really good.
29:53
Almost all the AI platforms that are out there around diagnostics
29:56
and something similar to what we're doing, that's become table
29:59
stakes very quickly. The problem is it's unstructured
30:03
because I can, it's like, OK, I've organized everything, all
30:05
the clutter and put in my garage.
30:07
It still means I'm having a hard time finding what I want in the
30:10
garage and stop painting a picture.
30:11
So structuring it and creating a longitudinal, longitudinal
30:17
vision of what is a trend is. So that's the information is
30:21
gold, right? The data is garbage.
30:24
So translating from data to information is an opportunity,
30:26
right? But the challenge you have is
30:29
that HIPAA is designed to protect you, but it's also
30:32
protecting you from your own information.
30:35
So the problem is if, and I'll pick on just one company and and
30:38
it's obvious they're glitter bad.
30:39
It's just everybody knows who they are.
30:41
Epic. It's like the £800 gorilla and
30:43
and electronic health records. But the problem is they serve
30:47
different clinics and hospitals. So if I go to Stanford, which is
30:52
close to my house, right, the Stanford Health tonight, they're
30:55
an Epic customer. I can drag and drop that data
30:57
down. But if I happened to go to Palo
30:59
Alto Medical Foundation or Sutter Health or I went to UCSF,
31:03
University of California, San Francisco, all of a sudden they
31:07
have three different instances of Haresh.
31:10
I can't ask Epic, can I have Haresh, all the records, I have
31:14
to go individually each one. And then again, you got this
31:16
problem with three different data rooms.
31:18
So while it's gotten easier, it's just put all the data in
31:22
one big corner. It still has to get organized
31:24
and put into one place. The second issue is wearables
31:28
has also become a lot easier to be able to put into that.
31:30
But correlating the 2 is an opportunity.
31:33
And I would say that 800 and 1000 LB gorilla is nobody
31:38
examines the mind. Nobody examines and does a
31:41
connection between mind and body.
31:43
And that's where we specialize a plan to specialize in terms of
31:47
there's a huge correlation around why doctors even asked
31:50
you're stressed now anything going on in your life, we should
31:53
be worried. Obvious stress is a is a Pearl.
31:56
It's a big Pearl that hides is. Well, not only that, but they
32:01
they test efficacy against placebo, which placebo even when
32:06
people know they're to consuming placebo, it still works 51%.
32:10
Of it has incredible potential to lock things up and unlock
32:13
things. You just don't know a lot about
32:15
it. Look at look at how it is
32:18
covered in insurance in terms of get sick, you get covered.
32:22
You have a heart attack, heart transplant, get covered.
32:24
Ask for a little bit of mental therapy.
32:27
Good luck. Right?
32:29
It's outside the engine network. And for people that love solving
32:33
problems, that's like why I think that the medical industry
32:36
is, it's so tantalizing just because it's so fraught with,
32:41
with issues. You know, the, the data 1 really
32:43
is just like central and foremost.
32:45
You know, every time I go see a new specialist, they want to ask
32:48
you all the same questions, but you're also like writing it on a
32:51
clipboard. I'm like, dude, what?
32:52
This is just going to be hanging around the office.
32:55
You know, you want to know how many alcoholic drinks I have in
32:57
a week? It's going to be 0 because I
32:59
don't know. I don't want people judging me
33:00
in the office. What are we talking about here?
33:03
So, yeah, and this idea of diagnosis as well and like being
33:07
able to like democratize the results is is super powerful.
33:10
Let's let's talk a little bit about the team and kind of where
33:13
you're at right now. You mentioned you got your
33:15
building team with some folks at Notre Dame.
33:17
How are you introduced and kind of like begin work with these
33:20
folks? Well, I'm I'm alumni from Notre
33:22
Dame, so I usually speak to groups that come out here to
33:26
visit Silicon Valley. One of the breaks of the NBA
33:28
student service program called STEAM.
33:31
And this year I happened to get invited by a professor from the
33:34
Mendoza School of Business, which is the MBA school and as a
33:38
guest speaker. And so I was a guest speaker.
33:42
I was a instructor for his class, which is like a 2 hour
33:45
class in the afternoon. And so the word got out through
33:49
him and through others, you know that I'm there.
33:51
So my whole calendar got filled up with speaking to different
33:53
groups, either one-on-one lunch and learn dinner group and then
33:57
the class. And so I was talking about the
34:00
platform, how I got the aha moment to the platform, the
34:03
journey and my hidden agenda was going to attract some really,
34:07
really good talent through this process.
34:09
And I was fortunate enough to three or four people that were
34:12
really excited about what I was doing.
34:15
And and so they're they're working hard, finishing their
34:19
MBA program in May and fingers crossed that, you know, we raise
34:22
some money so I can actually hire them by the time they
34:25
graduate. So it's been a good symbiotic
34:27
relationship. I said keep your options open.
34:28
You never know, the world of investing can be funny, but you
34:33
know, they're working really hard.
34:35
They're one student is from and I, when I say student, these
34:39
folks, you know, when you go to NBA school, you, you have years
34:42
of experience in between. So they're not just fresh
34:44
undergrads, but you have industry experience.
34:48
One student's from Ghana and I asked them, I always ask the
34:52
same question. Why do you want to buy my
34:53
product? Why do you want to spend time
34:54
with me? Right.
34:55
And I said, well, why? What intrigued you about wanting
34:57
to have breakfast with me? He said, well, you know what?
34:59
He goes, I'm forgotten. We don't even have doctors in
35:02
large parts of the country. You'll save millions of lives by
35:04
just having access to, you know, something that is better than
35:07
nothing, right? And that was his passion.
35:09
Another student is from Columbia and I think Bogota, Maine city.
35:14
And he had personal, you know, experience with mind body
35:17
connection and was passionate around making sure other people
35:21
that he knew that suffered didn't have the same issue. 3rd
35:24
actually wants to start an engineering company.
35:26
He's a little passionate product management and he actually has
35:28
his own small development companies.
35:31
So they kind of fit the various puzzles pieces of the puzzle.
35:36
And we've got 3 or 4 doctors now that that I think are fantastic
35:40
in terms of a business model where they have the time to give
35:45
to perform deep diagnosis. So we're trying to make digital
35:47
twins of them so we can scale their diagnostic methodology
35:51
into the platform because this is all about get whole person
35:55
built out medical records structured over time with
36:00
patterns, wearable data and life story.
36:04
You put those 3 prisons together and all of a sudden you know the
36:08
doctrine on a three page PDF you say, wow, I know you are way
36:11
better than I could have with just that form that you filled
36:13
out in the lobby. And if that's eight, that's 80%
36:17
of the game discovery of who that person is.
36:20
Then it's a lot easier to say, well, that's where the Pearl is.
36:23
It's not going to happen overnight, but it's certainly
36:25
not going to take the 10 years it took me to find out what my
36:27
root cause was. Yeah, when the the idea of like
36:33
capturing that data over time, a digital twin is amazing because,
36:39
you know, the impossible play like every time like a doctor
36:43
retires, it's almost like, Oh my gosh, it's like losing a
36:46
library. Because with all of that
36:49
experience and talent, you know, you pull that away.
36:53
But let let's say if a doctor has had 40 years in a career and
36:58
he's been wearing a wearable or, you know, an Apple Watch or some
37:02
kind of like Meadow Ray Ban's been documenting all of his
37:06
conversations, all of his procedures, all of that, then
37:08
you're starting to get at least like a piece of all the
37:12
expertise, you know, that that was obtained over his entire
37:16
career. And like right now we're
37:17
basically at day 0 for that starting to happen.
37:21
Incredible. It sounds like also with.
37:23
Oh, go ahead. I was saying that the other
37:26
challenges the way medicine is done today, like you said, it's
37:31
specialists because and we're victims of it too, right?
37:34
As soon as you have an ear problem, I want to go see an Mt
37:36
person. Like if I have the knee pain, I
37:39
want to go see somebody that's specialized in that, right?
37:42
So we also contribute to it as we jump to conclusion, but the
37:45
connection is probably somewhere else.
37:48
So getting people that understand how to step back.
37:54
So doctor that kind of finally gave me the aha that while I can
37:56
solve this in AI, he was, I met with him, filled out the forms
38:03
in the lobbying. His nurse came in and took all
38:04
the vitals, you know, just while I was there, created a beautiful
38:07
Manila folder with all of that ready for the doctor to take a
38:10
look at. He comes in, it's on his desk
38:11
and he sits down and he goes, I don't want to open that.
38:14
It was like very unconventional. I don't even know what want to
38:18
know what symptoms you have right now?
38:20
I want to know you start from the beginning.
38:21
Born C-section or natural. I didn't know.
38:25
I mean, I did, but most people don't.
38:27
One of the patients we put through the platform had to say,
38:30
let me put you on hold. I really don't know.
38:31
I have to ask mom. So if he wanted to start from
38:35
the beginning, tell me from the beginning, tell me everything.
38:37
And then he peppered questions in.
38:38
And then it was only after two hours, which you don't get from
38:41
the doctor. And then he went to the
38:44
whiteboard and drew a quadrant. He goes, listen, I've been
38:46
trying to fill in all of these quadrants.
38:50
And the problem you've had is you've seen 12 specialists.
38:54
Each one was right, but they were wrong because they each
38:56
were reaching behind the curtain piece and feeling one part of
38:59
the elephant. I'm going to play Charlotte
39:01
Calls. You're going to take that
39:02
curtain off and we're going to see the whole elephants.
39:04
They're all interconnected. And bingo, I forgot about mine.
39:09
The reason I was there. And all I could think about is,
39:11
Oh my God, this is just systematic data collection.
39:15
Yes, it's a machine like we were doing assets in real estate and
39:18
energy and renewable energy. But obviously the body's more
39:22
complex and it's got a mind component to it.
39:24
But the methodology of scanning data and organizing it so you
39:27
can see it, the light bulb went off.
39:31
It's a, it's a, it's a diet. It's the bedside manners and the
39:35
systematic way of collecting information from a person.
39:38
And when you can feed that, even if today I challenge anybody, go
39:41
type your symptom into any of the platforms, including Clawed
39:44
Health, that's out, ChatGPT Health, that's out.
39:47
It'll give you a quick answer and feel really good, right?
39:51
But what it should be doing is let me ask you some more
39:53
questions. Let me ask you some more
39:54
questions because the more you can give it, the better.
39:57
All you're actually going to get the right, you're going to find
40:01
that fault sooner. Yeah, yeah.
40:05
I, I love that. And I think that that's
40:06
ultimately the promise is you get more of that kind of
40:09
attention from your physician, you know, because I personally
40:13
really like kind of the more shaman modality, you know, which
40:17
is kind of leaning towards that, you know, and, and shamanism is
40:20
usually rooted in some kind of like metaphysical kind of belief
40:24
structure, which is also great. And I'm open to that.
40:28
But really being from the West, it's like, well, I'm more
40:31
scientific rigor as kind of the the language I speak.
40:34
So doctors are the same way, you know, scientific rigor is kind
40:38
of the language that they speak to reach the outcomes they have.
40:41
But then also the, the, the, the shamanistic kind of tactic of
40:46
like getting to know your psychology and who you are as a
40:49
person works just as well in kind of a, a scientific
40:53
modality. There might not be any like
40:55
papers being able to validate some of it, but I think
40:58
anecdotally, they all know the difference.
41:00
When they're having a good day, they have better outcomes.
41:02
I mean, there's just a, a certain amount of like truth to
41:05
that. One thing that might be kind of
41:08
of concern for additional data points was a, that's another
41:12
guest on the show kind of brought this up that as AI is
41:16
creating more opportunities, more data, more diagnostic
41:19
opportunities, that it does increase liability, that if the
41:23
doctor using the tools and somehow miss it, then they could
41:26
be held liable, you know, malpractice and things like
41:29
that. Have you heard anything about
41:32
potential liability hang ups when you're talking to doctors
41:34
and things like that? Yes, I mean, I think doctors in
41:37
general are very risk averse. They already have huge like, oh,
41:40
that's why I defer to their revenue goes towards liability.
41:43
I mean malpractice insurance, right.
41:45
So it's already there. And I think anything that's new
41:49
creates more fear. But I, I look at it and saying,
41:54
listen, if I can help somebody find a cure, not a treatment,
41:58
make a difference in those two words faster.
42:01
And we're going to say it's each one is a hypothesis machine
42:04
we're going to try. But now they have more knowledge
42:07
out there and they're smart enough to bleed out the stuff.
42:09
That's what I call this hallucination that AI doesn't
42:11
produce the right results. I think they're actually going
42:14
to reduce their liability. But like anything new, people
42:17
have to get past this initial fear.
42:20
And unfortunately, our media loads to play on the fear as
42:23
opposed to all the successes. You get the one example of, you
42:27
know, one Tesla that you know, that happened to crash in
42:29
Berlin. It's like, did we forget about
42:31
the other 50,000 casualties that happened every year based on
42:34
human mistakes driving the car? Yeah, well, in the myth that AI
42:39
is taking everyone's jobs, you know, really was debunked
42:43
because that was just because the big players, whether it's
42:47
anthropic or open AI or Google, they just didn't want their
42:52
their subscriptions to show up in like a marketing expense.
42:55
Rather they can put it over into HR.
42:57
So they're like, hey, it's not that you're spending $100,000,
43:01
you're saving $1,000,000 because you didn't have to hire
43:04
somebody. So it's, you know, the sales
43:08
tactic. Faster.
43:09
And I would say that every invention we've had, you know,
43:13
if somebody's told you in 1900 that 98% of the farmers would be
43:17
displaced, you'd pretty, you'd pretty freaked out.
43:20
But it exactly played out that way.
43:21
There's only 2% of our population that does all the
43:24
farming that we need for our food because of automation.
43:27
It just happened over 100 years. This is happening faster.
43:29
So, yeah, jobs will get displaced, but it'll create new
43:33
jobs. And remember listening to a
43:35
podcast with that Jensen Wong of NVIDIA, and he said, well, just
43:39
think of all the robots are out there.
43:40
Well, yeah, it's going to be an industry.
43:42
You're making clothes because you want your robots look
43:44
prettier than the other one. They'll be holding this here on
43:47
garments and so it'll create new job.
43:48
Let's it's just constructive destruction, you know, on on
43:52
warp speed. Yeah, it'll be fun.
43:56
It's funny to think about like our great grandkids will
43:58
probably be romantic about our era and be like, wow, you had to
44:01
change the channel with like a remote control and not with your
44:04
brain. Must have been awesome in the
44:06
same way that I. What's the steering wheel of the
44:08
car? Why are you having now this
44:09
steering wheel? Yeah, you drove.
44:12
Wow, that seems cool. He like now it's it was
44:15
terrible, like we were stuck in traffic all the time.
44:17
In the same way that I think about farming.
44:19
If I could go back and see great, great grand pappy Estes
44:22
and be like, hey, I want to be a farmer.
44:25
He's like describe to me your life.
44:26
I tell him he'd be like, dude, what are you insane?
44:29
Like no, you like a hot shower eye.
44:30
That sounds amazing. Tell me about.
44:35
That no, there will be a job displacement.
44:37
That doesn't mean that, but if you kind of sit there with your
44:40
head stuck in the sand, you will not have a job.
44:42
You're going to have to reinvent yourself.
44:44
Go back to that rookie smart. You have to always be learning
44:47
like something new. 100% love to hear your opinion on kind of the
44:53
ground floor of fundraising. And right now, you know,
44:57
fundraising is particularly frothy, which, you know, being a
45:00
tech guy myself is very exciting because a couple years ago, man,
45:03
it was really stalling out like everybody, you know, had, you
45:07
know, iron locks around their wallets and they didn't know
45:10
what to do. But now in particular, like
45:13
people are looking for opportunities and really it's
45:15
kind of a, a winner take all market.
45:18
So things are moving fast. So you're, you're speaking with
45:21
investors. What is kind of their concerns?
45:23
What are they looking for right now as far as projects?
45:26
I mean, clearly AI is front and center.
45:30
I have a lot of friends that have done well over time and
45:33
they've all seemed to have become VCs or either running
45:35
their own fund or part of them, a larger fund is kind of their,
45:38
their own twilight career. And you know, you talk to them,
45:42
you know, all the business plans coming across are very AA
45:46
centric. It's it's hard to separate the
45:48
wheat from the chafe, right? In terms of what really AI
45:51
means, right? Or is he just going to slapped
45:53
on as a as a way to get attention?
45:56
But I think most people, most people are worried about the
45:59
bubble, right? Are we, is it getting too
46:01
frothy? Is is it going to burst?
46:04
And it will, I mean, there's nothing new, right?
46:07
It happened when the auto industry came out and it
46:10
happened when the radios came out and televisions.
46:12
And, you know, there are hundreds and if not thousands of
46:15
business lives and most will die.
46:18
But I still think that most feel very, very bullish.
46:21
And it's still early enemies. Oh yeah, in the sense bigger and
46:25
longer laysand.com. So I, I personally feel that
46:28
way. And maybe that's what I'm
46:29
hearing because that's what I want to hear.
46:30
But I, I hear both sides of the story.
46:32
There seems to be more optimism that the application layer.
46:35
So all the money that's going in today is I would call the
46:37
infrastructure, right? The NVIDIA, you know, the data
46:41
that companies, the the LLMS, that what you do with all that
46:45
layer, what like a diagnose is going to be the productivity
46:48
layer. That race is still wide open,
46:50
right? And companies are still
46:51
emerging. Yeah.
46:52
Is there over investment? Probably overvaluation.
46:55
Yes, I do think that investors, the whole process is very
47:00
inefficient and you've raised money.
47:02
I'm sure a lot of your guests on the show or listeners on the
47:05
show have raised money and it's just inefficient.
47:07
You just have to kiss too many frogs on both sides to find it.
47:11
I mean, I remember when TPG invested us at Nakatis, we saw
47:16
it with 3000 business plans. She invested in three companies
47:18
that year. I said, now I said I've gotten
47:20
to Stanford a lot easier that you invested in me, but it's
47:26
not, it's because it's inefficient.
47:28
I, I get really frustrated when investors just say they have a
47:31
blank page and say, just send me your, your prospectus.
47:33
I met only one investor so far. It was in Denver.
47:36
He had 10 questions when he opened his slide and said, if
47:40
you, it was like the immigration forum, if any of these are yes,
47:45
you know, please come and see me.
47:46
I mean, if all of them are yes, anyone's no, no new bother.
47:50
And he said you have to be within 60 miles of me because
47:52
I'm hands on. And I said Oh my God, if every
47:55
VC would actually be really clear on what they're looking
47:57
for. Yeah, save you a lot of time.
48:01
Save a lot of time, but not too far walks of VCs.
48:04
But that's my one magic wand, is it?
48:06
Please tell us what you're looking for and what you're not
48:09
looking. For total.
48:11
Nice. All right, Haresh, we're about
48:13
to enter the quantum round. Are you ready?
48:15
OK, whatever it is, bring it on. It was the lightning round, but
48:20
as we got to 2026, I'm like, no, this is so a couple of
48:26
questions. You're obviously very bullish
48:29
for AI and health tech. What's the bear case Where where
48:34
should AI be not be implemented into healthcare I.
48:38
Don't think at the end of the day it's the ultimate decision
48:40
maker, right? It is a facilitator for good
48:46
building, good hypothesis. At the end of the day, it should
48:49
not replace the doctor. It shouldn't replace an expert.
48:53
It should be a good second opinion or a third opinion.
48:55
It should be a place to gather more information, but it should
48:58
not be the final word. You're entering college today as
49:03
a freshman. What would you study?
49:07
Pumped engineering. This should be a whole degree in
49:10
pumped engineering. How to use AI And I think
49:13
college education needs to be completely revamped.
49:16
I mean, I spent, I mean, every engineering course ended up
49:19
being derivatives and integrations and I never used
49:22
them a single day and didn't prepare me for day one the job.
49:24
So I just shouldn't be using calculators.
49:27
I shouldn't be using slide rules.
49:28
We shouldn't be using Abacus. And similarly, you know, all the
49:31
stuff that they teach isn't really preparing students for
49:34
day one. So but yeah, I think prompt
49:37
engineering to me is going to be, I've just seen my son.
49:39
It's amazing how much more he can get done than I can.
49:43
Is he's he's reinvented himself to be a prompt engineer.
49:45
It's it's how you converse with AI.
49:49
Yeah, nice. And I know you have some
49:52
charitable organizations that are really near and dear to your
49:55
heart and I'd love to give them shout out and just talk about
49:58
some of the work that you're doing so or they're doing.
50:01
Let's start with the American Indian Foundation.
50:04
Why are these folks important to?
50:05
You well they are a good friend of ours.
50:08
We not each other since we moved to Silicon Valley in the early
50:10
80s and they had a successful career.
50:14
They started this foundation after they sold their company
50:16
and it was really to bring technology and I would say hope
50:21
to the poorest part of of India and Bill Clinton became the
50:27
chairman of that honorary chairman of that foundation.
50:29
He gave a talk. I happened to be there in a few
50:31
minutes. The economy goes, India is one
50:32
of those countries where the correlation between IQ and
50:35
income don't Farley. So this particular organization
50:39
raises $40 million buckets. And for that they can build a
50:41
small school, a small clinic and and a library and and they
50:48
educate people. And so they produce some
50:51
brilliant, incredible people out of that.
50:54
That's what they do. They get medical care, like you
50:56
said, you can get educated and you can learn about the
50:58
computers, the Internet. And I'm sure they have evolved
51:01
this technology move learn more about AI and they put it in, in
51:05
the, in the, in the deepest, largest loans that they can
51:09
find. And there are a lot of them
51:10
still, unfortunately in India. So that's their, that's their
51:13
amazing. Yeah, I've met.
51:16
So many dozens of just dynamic incredible founders that
51:20
happened to be Indian. What?
51:21
What is the thread about the Indian people that make them
51:24
such great founders and operators?
51:26
Yeah, I, I don't know if I have the answer to it.
51:28
I can only recount the story that somebody wrote to the
51:32
Silicon Valley executive and he just said that, you know, things
51:36
are so fluid in the business world, you got to figure out,
51:40
you know, how to navigate. And so if you look at traffic in
51:44
India, there's a joke that that they sent an atheist India
51:47
because he obviously didn't believe in God.
51:48
And he goes, well, we want you to believe in God, but all we
51:50
want you to do is send to this intersection and we'll talk to
51:54
you the end of the day. And and he watched the chaos for
51:56
the full 12 hours of daylight and he came back and was the
51:59
only way this country functions. There's got to be a God with
52:03
watching the traffic. And so I think tying that back
52:06
to, you know, if you try to navigate Indian streets into
52:10
chaos, you know, that's what software is.
52:12
Your pricing is wrong. The minute you put it down, your
52:14
business model is wrong. The minute you're right on the
52:16
PowerPoint, you know, the world is fluid and it's changing.
52:18
And I just need to learn how to navigate, you know, changing.
52:21
That's that's that's not my theory.
52:24
That's the best I can understand.
52:25
Trying to say why are they more successful?
52:28
That's probably 1 reasons. We, we grew up in chaos, right?
52:31
So we know how to deal with chaos because the business world
52:33
is nothing but chaos. And gracefully, I think too, you
52:37
know, like the Indian people. Of course, I'm generalizing and
52:40
stereotyping here, no disrespect, but like gracious
52:43
and polite and courteous. And, you know, it's great in
52:47
America. I think probably, you know, the,
52:50
the, the President of the United States is an, A cartoonish
52:53
example of what they think a businessman is like, you know,
52:57
but maybe the truth, it couldn't be further than that.
53:00
How about the Foundation for Excellence?
53:01
FFE is also a good friend of ours.
53:03
You want to be our very first client.
53:05
Also Mcconness, and he has a similar vision but very tech
53:11
focused. And so he likes to give full
53:14
scholarships to kids in high school and in college, but they
53:17
have to earn it every year by maintaining AGPA.
53:20
And you get tied directly. So if I give let's say $3000, it
53:25
ties me directly with the number of students directly and they
53:27
can communicate with me right to me.
53:29
So I know my money's being passed through.
53:32
And so they have to have very high GPA, very high standardized
53:36
testing scores. But the criteria is always a but
53:39
in life, but is you have to have no money, meaning you are the
53:44
poorest of the poor. And but they give direct
53:47
scholarships and every year they bring but does know them that
53:50
they've actually immigrated here and doing really well and now
53:53
actually become dollars. So it's really gratifying as you
53:55
get the first hand touch to to who you've given money to.
54:00
And the foundation itself runs on on no money.
54:03
So it's 100% pass through, which is kind of cool.
54:07
And folks can check them out. They're ffe.org.
54:11
And the other was aif.org. We'll put links to that in the
54:14
show notes. And we're running out of time,
54:16
but we didn't even have a chance to talk about your new book so
54:20
people can go learn more about Haresh.
54:23
Now. I know Haresh a little bit.
54:24
We've been talking the last couple of weeks and he has an
54:27
incredible story of how he solved his own 55 year medical
54:33
mystery. The book is called the ghost in
54:36
my body. And from his website, you can
54:38
sign up to get alerts when that comes when that gets published.
54:41
So that's Haresh Patel dot AI to learn a little bit about more
54:45
about Haresh. But this has been awesome.
54:48
I I love what you're working on. I'm a fan.
54:52
Where can people connect with you online personally?
54:54
LinkedIn is obviously a good place looking me up as Haresh
54:57
Patel, my website as you mentioned Haresh Patel dot AI.
55:02
Those are probably the two best ways to to reach me and Haresh
55:06
at mydiagnosticmd.com for my e-mail.
55:12
So any one of those reach me and I will definitely reply back.
55:15
But I appreciate you bringing me on as a guest and expect to stay
55:19
in touch. Absolutely.
55:22
My pleasure. And, you know, how can I confirm
55:24
that you're not actually the digital twin of Haresh?
55:29
I don't know. You'll have to.
55:31
Nowadays you have to look at videos over and over and over
55:33
again. So you got to think you'll have
55:34
to re listen to this. Podcast, it's right, it's right.
55:38
If you know when you were talking about the the leak in
55:41
your roof, if I would have seen drops starting to come down in
55:44
the screen. I'm like, wait a minute.
55:45
I don't know what's real anymore, but I really appreciate
55:49
it. Great to talk to you again,
55:50
Haresh, Appreciate you. Hey founders, let me ask you
55:54
this. Are you building with AI or
55:57
getting left behind by it? If you're serious about scaling
56:01
smarter, faster and bolder, you need to check out AI for
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