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Speaker 1: Is AI an existential threat to humanity? Or is it

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Speaker 1: a tool that's going to revolutionize businesses, give us new

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Speaker 1: productivity and efficiency. Well, this is a big rating topic

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Speaker 1: and debate, and so I am going to bring on

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Speaker 1: an AI expert today, Hamad Shabazi. We're going to talk

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Speaker 1: about many things in AI, and he is somebody we

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Speaker 1: should listen to. He's been a tech investor tech founder

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Speaker 1: for over twenty years. He founded TiO Networks, which was

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Speaker 1: required by PayPal for over three hundred million dollars. He's

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Speaker 1: done over seventy m and a deals, died over two

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Speaker 1: billion dollars, and now he's the founder and CEO of

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Speaker 1: well Health, which is using AI to revolutionize healthcare. Maybe

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Speaker 1: what I see is the potential biggest opportunity we have.

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Speaker 1: So we're going to talk about is AI an exsidential

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Speaker 1: threat to humanity? Will governments are regulated out of existence?

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Speaker 1: Or how should they manage that? Our large language models

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Speaker 1: llms really AI or not. We're going to look at

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Speaker 1: what are the biggest areas, the biggest disruptors that AI

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Speaker 1: is going to make. We're going to look at health

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Speaker 1: care problems. Health is one of the biggest problems in

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Speaker 1: the United States and the world really for that matter,

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Speaker 1: how can AI really help solve that? And as you

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Speaker 1: probably know, the really two areas I focus on investing

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Speaker 1: is bitcoin and AI, And so from an investing lens,

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Speaker 1: how should we look at AI companies to make huge

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Speaker 1: potential returns inside stepping all the risk. It's a conversation

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Speaker 1: I've really been looking forward to. I'm so happy to

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Speaker 1: have him oud on. So let's just go ahead and

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Speaker 1: just jump right into it. We're going to talk about this.

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Speaker 1: Come on, Thanks so much for joining me today.

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Speaker 2: Mark, it's a pleasure to be with you.

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Speaker 1: Yeah, so big topics, you know, something that I mean

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Speaker 1: has kind of caught the whole world by storm over

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Speaker 1: the last couple of years since open Ai came on

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Speaker 1: the scene. Something I definitely focus on. My main areas

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Speaker 1: of focus is sort of bitcoin and AI, and I'm

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Speaker 1: really interested in let's just start, man, Let's just dive

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Speaker 1: right into the deep end of this, because we kind

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Speaker 1: of have like two different camps here, and I feel

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Speaker 1: being a tech investor and someone who studied technology cycles

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Speaker 1: really at the next real revolution, we had the led

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Speaker 1: eye and the Ledites were people that were afraid of

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Speaker 1: the power loom because it was gonna take away jobs,

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Speaker 1: and so we always have every time there's new technology,

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Speaker 1: there's the leadites, the people that are afraid of the technology.

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Speaker 1: You're working with AI, And the big question we have

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Speaker 1: is is AI this existential threat to humanity or have

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Speaker 1: we all just grown up watching the terminator and we

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Speaker 1: have this fearful, you know, vision or view of that.

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Speaker 1: What's your take?

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Speaker 3: Yeah, look, I think it's it's both of what you said.

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Speaker 2: I think it is.

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Speaker 3: I think it can be existential threat, and I think

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Speaker 3: it can be an incredible boost and productivity that that

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Speaker 3: supports humanity and provides societal value. I think that if

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Speaker 3: we don't regulate this amazing thing that not everyone truly understands,

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Speaker 3: it does have transformational potential.

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Speaker 2: In positive and negative ways.

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Speaker 3: And this is no different than what we've seen in

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Speaker 3: terms of of you know, regular internet technologies.

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Speaker 2: They've created vast improvements.

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Speaker 3: And productivity for us, but also they've created new attack

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Speaker 3: vectors that people are using to steal our information and

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Speaker 3: cause havoc in the world. I don't think the eyes

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Speaker 3: any different. I think it just creates a much bigger potential,

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Speaker 3: just given how intelligent it really is. And how and

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Speaker 3: the potential that it has in order to to to

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Speaker 3: do incredible things and to wreak even more havoc in

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Speaker 3: our lives, you know. So so yeah, I'm I'm a

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Speaker 3: big proponent. I suppose I support you know, the thesis

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Speaker 3: that guys like Elon Musk have out there where they're very,

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Speaker 3: very worried about how quickly this is developing. But also

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Speaker 3: you know, there's a recognition that it that it can

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Speaker 3: do an immense good in the world too and drive productivity.

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Speaker 1: So you look at technology as more of a tool,

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Speaker 1: and a tool can be used for good or bad.

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Speaker 1: I can use the screwdriver to fix a car, or

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Speaker 1: I could stab somebody with it. Right, It's like, I

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Speaker 1: guess that's what you're saying.

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Speaker 3: Absolutely, you know, I think I think that's fundamentally what technology.

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Speaker 3: And each time one of these things comes along, I

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Speaker 3: think it drives a lot of transformation in our in

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Speaker 3: our lives and our and and work and even how

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Speaker 3: we how we operate, uh, you know, as as private citizens.

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Speaker 3: And and that productivity uh is something that you know,

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Speaker 3: both can be used for good or bad. Those tools

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Speaker 3: can be and so it's unfortunately we can't just reserve

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Speaker 3: those those tools and that productivity for for the for

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Speaker 3: the good guys. Unfortunately, the bad guys also get access

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Speaker 3: to that.

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Speaker 2: You know.

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Speaker 3: For example, I think cybersecurity plus A I I mean,

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Speaker 3: I mean, I mean cyber threats plus AI is A

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Speaker 3: is a big and challenging issue.

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Speaker 2: You know, before what would happen in terms.

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Speaker 3: Of cyber threats is that you would have these sort

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Speaker 3: of dumb robots going around the Internet and trying to

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Speaker 3: open doors and seeing if if if if network administrators

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Speaker 3: left something open or had you know, easy passwords, and

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Speaker 3: they would just you know, and if and if something opened,

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Speaker 3: they would they would go in and see what's going

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Speaker 3: on and see.

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Speaker 2: If there's any data for them.

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Speaker 3: Now, the the algorithms that are looking for vulnerabilities are

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Speaker 3: not just checking for doors being opened. They are they're

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Speaker 3: they're taking much more complex approaches to identify you vulnerabilities

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Speaker 3: and in software and and and network infrastructure. And that's

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Speaker 3: pretty devastating considering that increasingly all of our information is online.

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Speaker 3: And so I think the only thing that can really

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Speaker 3: help support that is you know, the AI that that

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Speaker 3: defends us against the AI that's attacking us.

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Speaker 2: Right, So I think this is definitely a.

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Speaker 3: Tool that's going to be used on on both sides

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Speaker 3: of that of that equation.

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Speaker 1: Yeah, I mean in regards to that, you're absolutely right.

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Speaker 1: My account got hacked. My Twitter account got hacked. I

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Speaker 1: was locked out for like five or six months. And

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Speaker 1: you know, these these hackers are so so aggressive but

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Speaker 1: also so advanced today using these AI tools. And it's

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Speaker 1: not just to the point that you said, like going

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Speaker 1: around finding a door. I like that analogy, I find

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Speaker 1: a door to open. But the ability to deep fakes

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Speaker 1: and so everything that we know about cybersecurity, which is

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Speaker 1: send me a picture of your ID, send me a video,

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Speaker 1: all those things, now that those can be completely faked,

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Speaker 1: and so it's really the entire way that we validated

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Speaker 1: is now ineffective. So that's that's interesting. But I want

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Speaker 1: to go back to what you talked about with you know, regulation,

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Speaker 1: you said that I think you said we need regulation.

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Speaker 1: I'm curious to your take on that, just because one

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Speaker 1: ein Ran, who wrote the Atlas Shrugs, says that when

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Speaker 1: you need to get permission to produce for men who

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Speaker 1: produce nothing, and so what do a bunch of bureaucrats

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Speaker 1: who are seventy five years old. Know about regulating AI

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Speaker 1: Number one, Number two. I just listened to this interview

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Speaker 1: with Peter Diel you know obviously you know, but for

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Speaker 1: the audience, you know, prolific VC investor in Silicon Valley.

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Speaker 1: I think it was on trigonometry, and he would saying

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Speaker 1: that I'm much more fearful of the world it would

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Speaker 1: take to try to regulate it, because it would require

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Speaker 1: such an orwell and authoritarian view to try to control this,

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Speaker 1: that that's much more problematic. I'm curious your take on that,

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Speaker 1: and just do the relation.

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Speaker 2: Yeah.

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Speaker 3: Look, I don't think it's an easy thing to regulate,

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Speaker 3: but I think there's low hanging fruit and I think

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Speaker 3: this is the area where regulators can get involved. And

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Speaker 3: just and just I agree. I think you need a

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Speaker 3: light touch, but I think you need a touch. For example,

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Speaker 3: you made a great comment about deep fakes. If someone

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Speaker 3: is calling you and you know the performance of these

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Speaker 3: you know, uh, you know, these these voice robots is

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Speaker 3: becoming quite good. It's becoming quite smooth, where it's becoming

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Speaker 3: difficult for you to tell the difference between a human

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Speaker 3: calling you in a robot calling you. I think one

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Speaker 3: interesting area where where people may want to look for

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Speaker 3: regulation is is to is if a robot is calling you,

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Speaker 3: for it to declare that it's a robot. Don't you

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Speaker 3: want to know if you're talking to a robot or

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Speaker 3: to a human. I think that's important to know.

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Speaker 2: And I think and I.

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Speaker 3: Think that's just that you know, doesn't mean that that

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Speaker 3: the conversation has to be less less productive, But I

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Speaker 3: think it's an important thing to be able to know

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Speaker 3: that and and and that.

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Speaker 2: Could be regulated.

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Speaker 3: It could say, hey, look, anytime you get a call

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Speaker 3: from a robot, because we're having a more and more

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Speaker 3: difficult time discerning between the two, you know, it ought

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Speaker 3: to declare or somehow it needs to identify the fact

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Speaker 3: that it is and and so and so. It's stuff

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Speaker 3: like that to me that I think help help people

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Speaker 3: just just navigate this this this new fangled world that

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Speaker 3: we're dealing with. You know, like like the deep fakes

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Speaker 3: are a really really big problem, right. It's becoming very

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Speaker 3: very difficult to tell whether or not something is authentic

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Speaker 3: or not right. And you know, there there have been

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Speaker 3: fraud that has been perpetrated by virtue of of of

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Speaker 3: entire boards of directors that have been deep faked in

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Speaker 3: order to convince someone to wire money. You know, stuff

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Speaker 3: like that is just it's it's it's it's pretty hard

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Speaker 3: to beat. So I think I think it'd be very helpful,

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Speaker 3: you know, for there to be some of some some

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Speaker 3: of that regulation out there.

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Speaker 1: Uh yeah, I mean that sounds pretty reasonable. I mean

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Speaker 1: obviously we already have laws around fraud, and so you know,

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Speaker 1: if you're deep faking somebody that's technically illegal already, right

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Speaker 1: that that would be considered fraud. And so having disclosures

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Speaker 1: like that I think would certainly be helpful.

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Speaker 2: Man.

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Speaker 1: I could even see the disclosures being needed around you know,

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Speaker 1: the content creation piece. You know, am I you know

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Speaker 1: what am I reading here? Who wrote this kind of

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Speaker 1: thing like that? Pretty interesting? Now, I hear some people

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Speaker 1: talk about and it was a couple of years ago.

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Speaker 1: I got a chance. It was an amazing chance to

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Speaker 1: sit down with the ball. There's a another prolific VS

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Speaker 1: investor from Silicon Valley and this was two years ago,

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Speaker 1: two or three years ago, and he was talking about

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Speaker 1: the difference of narrow AI versus general AI and how

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Speaker 1: this narrow AI is really starting to advance like super

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Speaker 1: super fast. But the general AI, which is you know,

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Speaker 1: where it can sort of have this general intelligence it

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Speaker 1: could grow its own hasn't really advanced at all, and

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Speaker 1: that was a couple of years ago, so maybe that's changed.

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Speaker 1: But I also see people say that really what we

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Speaker 1: have today is really large language models LMS, and really

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Speaker 1: what they've done is they've taken language and broken that

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Speaker 1: down into data and it's not really AI. What's your

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Speaker 1: take on that.

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Speaker 3: Well, I think there is this kind of notion that

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Speaker 3: there's sort of symbolic AI and generative AI.

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Speaker 2: Right.

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Speaker 3: Symbolic AI is essentially, you know, processing and mining vast

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Speaker 3: sums of data. It is then learning how you know

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Speaker 3: what the relationships associated with that data are.

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Speaker 2: That data is.

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Speaker 3: Being trained and you're basically looking at this data to

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Speaker 3: find anomalies you know, in rules based order or when

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Speaker 3: you question a piece of data, when you prosecute data

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Speaker 3: and you want to know something, when you have rules

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Speaker 3: and you say, please let me know the values that

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Speaker 3: exceed this because you're looking for something. Sometimes you don't

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Speaker 3: know what you're looking for. You're just looking for what

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Speaker 3: are the anomalies in this data that I don't know,

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Speaker 3: what's the needle in a Haystack. That's where AI is

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Speaker 3: incredibly valuable. That's symbolic AI, generative AI is we have

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Speaker 3: so much data and it starts to inform our view

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Speaker 3: of how to essentially complete, you know, complete a sentence.

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Speaker 2: You know, when we're writing something. There's so much data,

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Speaker 2: and they've mean able to.

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Speaker 3: Stitch it together and effectively train it in such a

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Speaker 3: way that where it can actually start to generate responses.

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Speaker 2: I mean, essentially it's.

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Speaker 3: All AI because it is it is it is leveraging,

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Speaker 3: you know, uh, the power of data, unlocking the power

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Speaker 3: of data in order to to.

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Speaker 2: Provide some kind of insights.

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Speaker 3: But you know, I think if you're sort of an

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Speaker 3: AI purist, you could say only the symbolic AI is

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Speaker 3: the true AI. You know, so, so I think it

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Speaker 3: comes just back to your perspective. But to me, general

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Speaker 3: of AI, you know, it looks like magic and it's

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Speaker 3: pretty incredible, but it is dependent on the data inputs

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Speaker 3: in that large language model. So if there's garbage in there,

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Speaker 3: you're not going to get You're going to get garbage out.

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Speaker 3: And and a lot of what's starting to happen is

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Speaker 3: people are producing large language models based on you know,

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Speaker 3: more precise data sets and so you know, the smaller

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Speaker 3: models that are that are specific to particular.

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Speaker 2: Areas, and you can then get.

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Speaker 3: Uh, you know, you know, very very interesting kind of

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Speaker 3: opportunities to let's let's say you have all kinds of

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Speaker 3: different medical publications that it would be really hard to

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Speaker 3: go through and read all of them. But then the

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Speaker 3: ability to just you know, question that large language model

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Speaker 3: and essentially get responses, you know, instantaneously, that is something

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Speaker 3: that really unlocks the value of data. And that's what

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Speaker 3: we're starting to see is for the longest time, we've

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Speaker 3: all been sitting on all this data, and companies have

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Speaker 3: been saying that we've got all this data. Everyone's very

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Speaker 3: excited about sitting on the data, but that's all they've

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Speaker 3: been doing, just sitting on it. No one has really

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Speaker 3: been able to unlock the value of data until now

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Speaker 3: and the price of doing that. And that's what's very

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Speaker 3: exciting is that if this has become so accessible to

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Speaker 3: so many and so now we're starting to mobilize all

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Speaker 3: this data. I mean, I work in the healthcare field.

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Speaker 3: Thirty percent of the world's data is generated by the

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Speaker 3: healthcare industry. By next year, thirty six percent there'll be

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Speaker 3: thirty six percent growth compound growth. And all the data

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Speaker 3: that's generated in the healthcare industry, it's an enormous amount

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Speaker 3: of data, and so how are we ever going to

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Speaker 3: unlock the value of it without things like large anguish

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Speaker 3: models or symbolic AI and the complexity of the human

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Speaker 3: body requires that type of data in order to be

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Speaker 3: able to unlock you know, true insights in terms of

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Speaker 3: predictive analytics, in terms of precision health, in terms of

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Speaker 3: all kinds of different things.

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Speaker 1: Right, I know, you know, as I've used it more

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Speaker 1: and more and more just in my own business, and

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Speaker 1: you know, I go through a lot of financial reports

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Speaker 1: and sec filings and things that are hundreds of pages long.

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Speaker 1: And the ability for me to have these little mini

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Speaker 1: agents built where they can go get the data, bring

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Speaker 1: the data back, go through the data, tell me who

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Speaker 1: you know based off of who I am? What questions

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Speaker 1: I should have? Answer those questions for me? Right, it's

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Speaker 1: not right? I mean I feel like I'm in that

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Speaker 1: movie Limitless when he first takes the drug and then

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Speaker 1: all of a sudden he's like writing and like you

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Speaker 1: see all the data. I feel like I'm like that,

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Speaker 1: Like the ability to go through this data, I do

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Speaker 1: almost feel superhuman, you know. I also talk a lot

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Speaker 1: about bitcoin, and one thing that I'm really interested in

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Speaker 1: is where AI and bitcoin can come together. And the

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Speaker 1: reason why is because bitcoin is borderless, permissionless, and so

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Speaker 1: resistant those things. But it's also personless, and so that

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Speaker 1: means obviously only a person cant a bank account, but

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Speaker 1: now a machine could have a bank account and could

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Speaker 1: go out there and do these economic things, and so like,

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Speaker 1: what does that open up? We don't even know. And

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Speaker 1: it's because that humans are no good to imagine in

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Speaker 1: the future, because we can only imagine better versions of

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Speaker 1: what we have today. But when we have new building blocks,

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Speaker 1: it builds things we didn't imagine, and so I'm just curious.

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Speaker 1: You know, again, as I just said, we're not really

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Speaker 1: good at seeing the future because we don't have these

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Speaker 1: new building blocks. But from your vantage point twenty years

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Speaker 1: in technology and it's a founder, M and a, etc.

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Speaker 1: What do you think are some of the biggest areas

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Speaker 1: or maybe businesses or industries that will be disrupted by AI?

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Speaker 1: You mentioned healthcare. I think that's a big one right

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Speaker 1: off the bat. I'm curious where do you think would

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Speaker 1: be probably the most affected by this AI transition that

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Speaker 1: we're in.

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Speaker 3: Honestly, I don't think there's an industry that will not

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Speaker 3: be affected. I think that that, you know, there's there's

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Speaker 3: so much administray. First of all, I think all kinds

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Speaker 3: of different administer administrative burdens. I mean, you know, will

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Speaker 3: will will be will be addressed through the.

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Speaker 2: Use of AI.

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Speaker 3: I mean I think like a legal professional change dramatically,

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Speaker 3: Like why would you want lawyers to sit there and

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Speaker 3: draft you know, these these these significant documents. They would

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Speaker 3: get paid, you know, significant amounts of of of money

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Speaker 3: in order to do this very complicated and complex work

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Speaker 3: of looking at all the different you know, you know,

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Speaker 3: legal codes and and and covenants that they must you know,

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Speaker 3: respect and and and writing contracts that interact with those

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Speaker 3: changing legal covenants and and and laws and and that's

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Speaker 3: just mind bending work, right, It's it's very complicated and minding.

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Speaker 2: And now you know the mission.

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Speaker 3: It's clear that a machine will be able to do

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Speaker 3: that better and make less mistakes than a human would.

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Speaker 3: It's clear that a machine will will will do a

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Speaker 3: better job of interpreting an X ray result than a

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Speaker 3: human would. Now these things are are you know are

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Speaker 3: our truths. I mean that this is what's emerging, but

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Speaker 3: it doesn't mean that humans won't.

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Speaker 2: Be able to play a role. I'll give you a

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Speaker 2: great example.

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Speaker 3: You know, we already have a scenario right now where

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Speaker 3: you know, and you know, where we interact with with

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Speaker 3: humans who are administering computers. And we've actually seen this

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Speaker 3: for many years. This is this is when we get

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Speaker 3: into a plane and we fly somewhere, it's actually not

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Speaker 3: the plane, the pilot that's flying the plane, it's the

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Speaker 3: onboard computer.

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Speaker 2: And that's been happening for.

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Speaker 3: Decades, right, But but we for some reason, we still

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Speaker 3: feel a lot more comfortable for the humans there, right

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Speaker 3: because we do know that as advanced as technology can be,

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Speaker 3: it can glitch. And that's when you need to have

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Speaker 3: kind of the spatial reasoning and and all all the

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Speaker 3: kind of gifts that humans have in order to be

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Speaker 3: able to integrate information in terms of sanity, checking what's

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Speaker 3: going on and and keeping uh an eye on this

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Speaker 3: on on on on on computers and making sure that

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Speaker 3: they're they're doing what they ought to do. And that's

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Speaker 3: essentially I think what's what's what our rule is going

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Speaker 3: to start to be it's it's not necessarily doing all

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Speaker 3: that low leverage work, but really but really overseeing technology,

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Speaker 3: being almost a concierge for that technology, knowing how to

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Speaker 3: use it, knowing how to unlock its value, and and

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Speaker 3: and also that could that could open up opportunities for

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Speaker 3: for for us to to provide uh, you know, better service. So,

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Speaker 3: for example, if you're a doctor, if you have disease

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Speaker 3: detection algorithms and all kinds of things that are helping

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Speaker 3: you in in that diagnosis, instead of like you know,

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Speaker 3: you know, typing and and and and and taking notes

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Speaker 3: and and and working on an administrative things, you can spend

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Speaker 3: a lot more time looking at your patient and spending

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Speaker 3: time you know, uh you know, looking in their eyes,

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Speaker 3: examining them, you know, touching them. There's been data that's

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Speaker 3: shown that doctors that touch and pay attention to their

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Speaker 3: patients have higher patient outcomes.

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Speaker 2: I mean, just think about that.

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Speaker 3: If a doctor pays more attention to you and touches

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Speaker 3: you and shows more and directs more of his personal

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Speaker 3: energy or her personal energy towards you, that could elevate

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Speaker 3: your your patient outcomes. So I think there's all kinds

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Speaker 3: of interesting things that will happen as a result of AI,

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Speaker 3: and a lot of them I think could be very good.

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Speaker 1: Yeah, I can see that. You know, you talk about

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Speaker 1: the legal field. I mean any of these information fields

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Speaker 1: for sure, right, But the legal field, there's so much

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Speaker 1: data to go through. And so I remember my uncle

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Speaker 1: was an attorney, and man, he got so tired of

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Speaker 1: being attorney because he said all he did was read,

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Speaker 1: like you're constantly just digging through books trying to find

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Speaker 1: case law, precedents, things like that. So there's just no

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Speaker 1: way a human can go through that much data and

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Speaker 1: obviously retain it. And same in the medical field. I mean,

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Speaker 1: we have medical fields all over the world and all

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Speaker 1: these different cases, and there's just no way for someone

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Speaker 1: to get all that data. But the machines can, and

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Speaker 1: and I think to the point that you're making sort

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Speaker 1: of it's a tool. So it helps humans do a

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Speaker 1: better job as opposed to replacing humans. I you know,

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Speaker 1: for whatever reason my worldview, I don't think that machines

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Speaker 1: replace humans. I think we always need the human creativity

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Speaker 1: and I don't think machines. Machines are good at data,

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Speaker 1: but they're not good at creativity, and so it'll always

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Speaker 1: be how do humans use the machines? And so I

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Speaker 1: think from that perspective it can help us do much better. Now,

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Speaker 1: the legal field is last night I saw Tucker Carlson

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Speaker 1: and and ourf CAG live and they're, you know, bashing

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Speaker 1: attorneys and you know, these leeches and whatever. But on

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Speaker 1: the medical side, that's like life and death. Man, right,

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Speaker 1: that's like life and death. It's super important. I heard

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Speaker 1: Peter Diamantes, he's an m I T grad, you know,

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Speaker 1: tech prolific, tech tech guy. For everybody listening, Peter Diamantes

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Speaker 1: said that he thinks within five years it would be

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Speaker 1: medical malpractice to practice medicine without using AI. What do

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Speaker 1: you think about that quote.

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Speaker 2: I think he's got a point.

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Speaker 3: I don't know what the time scal is, but I

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Speaker 3: do think he's he's right in that, you know, disease

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Speaker 3: detection uh uh. And and just patient care will will

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Speaker 3: improve so significantly as a result of AI technologies. And

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Speaker 3: this will be somewhat dependent on there being enough data

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Speaker 3: on the patient. You know, if if you don't have

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Speaker 3: any data on the patient, then then you're going to

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Speaker 3: have to examine them and you're you're going to need

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Speaker 3: to rely on those skills that you learned in med.

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Speaker 2: School and whatnot.

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Speaker 3: But you know, the data footprint on a per patient

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Speaker 3: basis is increasing so much and and and most of

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Speaker 3: us have a lot of medical history. You know, tons

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Speaker 3: and tons of PDFs, you know, lab reports, hospital pronouns,

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Speaker 3: all kinds of stuff, all that is coming into a

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Speaker 3: patient record.

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Speaker 2: And and you.

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Speaker 3: Know, you know, our company heal Well a I which

436
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Speaker 3: is a which is an investee of Well It's it's

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Speaker 3: uh uh, it's a company that that we have a

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Speaker 3: strategic alliance with and have a path to control with.

439
00:22:14,160 --> 00:22:15,080
Speaker 2: You know, is able to.

440
00:22:15,160 --> 00:22:20,159
Speaker 3: Use that that EHR data to actually identify, you know,

441
00:22:20,200 --> 00:22:23,840
Speaker 3: the stage of cancer that you may have or detect

442
00:22:23,960 --> 00:22:27,480
Speaker 3: very low, very rare, uh, you know, cancers. And I

443
00:22:27,520 --> 00:22:29,800
Speaker 3: mean the kinds of things that we've been able to

444
00:22:29,880 --> 00:22:32,560
Speaker 3: do and now you know written in in in medical

445
00:22:32,560 --> 00:22:37,680
Speaker 3: publications esteemed medical publications is pretty incredible. And so I

446
00:22:38,560 --> 00:22:41,920
Speaker 3: would agree that if you at a certain point are

447
00:22:41,920 --> 00:22:45,600
Speaker 3: not using these you know, medical co pilot technologies AI

448
00:22:45,680 --> 00:22:48,879
Speaker 3: co pilot technologies, you are you are probably providing a

449
00:22:48,960 --> 00:22:50,399
Speaker 3: vastly inferior.

450
00:22:49,960 --> 00:22:52,359
Speaker 2: Quality of service, especially as.

451
00:22:52,240 --> 00:22:55,920
Speaker 3: These things come of age and they're coming very quickly.

452
00:22:55,960 --> 00:22:57,879
Speaker 1: I can imagine, I mean to your point, right, So

453
00:22:57,920 --> 00:23:00,480
Speaker 1: it's like go through your medical history and a second,

454
00:23:00,480 --> 00:23:02,200
Speaker 1: like I already kind of referenced. I mean, I used,

455
00:23:02,240 --> 00:23:04,440
Speaker 1: I'll download like sec violings and just loaded in, and

456
00:23:04,480 --> 00:23:06,080
Speaker 1: I mean the amount of data get is incredible, So

457
00:23:06,119 --> 00:23:08,320
Speaker 1: like it could like instantly gobble up your entire health

458
00:23:08,359 --> 00:23:10,240
Speaker 1: history and give you that, but then you could go

459
00:23:10,280 --> 00:23:13,919
Speaker 1: out and go, what is this specific thing, this specific cancer,

460
00:23:13,960 --> 00:23:17,879
Speaker 1: this specific ailment against everybody else that has your blood

461
00:23:17,920 --> 00:23:20,320
Speaker 1: type or your diet or your like, and what does

462
00:23:20,359 --> 00:23:21,800
Speaker 1: it have to say? And there's no way human can

463
00:23:21,840 --> 00:23:24,560
Speaker 1: do that. So I'm really interested in how it solves

464
00:23:24,560 --> 00:23:28,919
Speaker 1: healthcare because you know, I'm pretty pretty into health, not

465
00:23:29,000 --> 00:23:31,280
Speaker 1: so much through pharmaceuticals and doctors, but more natural But

466
00:23:31,600 --> 00:23:35,640
Speaker 1: you know, I'm interested that RFK said last night that

467
00:23:36,640 --> 00:23:41,320
Speaker 1: when his uncle was president in the sixties, chronic health

468
00:23:41,359 --> 00:23:45,560
Speaker 1: problems in the US were six percent, and today they're

469
00:23:45,800 --> 00:23:49,800
Speaker 1: sixty percent. And he said that in the US in

470
00:23:49,960 --> 00:23:53,720
Speaker 1: the sixties spent zero dollars on chronic health, and today

471
00:23:53,800 --> 00:23:57,720
Speaker 1: the US spends four point three trillion on chronic health.

472
00:23:57,720 --> 00:23:59,560
Speaker 1: So being able to solve some of these is really big.

473
00:24:00,560 --> 00:24:05,120
Speaker 1: So you founded a company, heal Well right to kind

474
00:24:05,119 --> 00:24:07,879
Speaker 1: of go address this, and I'm guessing, you know, I

475
00:24:07,920 --> 00:24:11,880
Speaker 1: believe as an entrepreneur, we solve problems. So what are

476
00:24:11,880 --> 00:24:15,040
Speaker 1: these big problems in healthcare that you're really trying to

477
00:24:15,040 --> 00:24:16,040
Speaker 1: focus on with heal Will.

478
00:24:20,359 --> 00:24:23,320
Speaker 3: Yeah, So heal Will is really just doing what we

479
00:24:23,440 --> 00:24:26,200
Speaker 3: just talked about before is taking vast sums of data

480
00:24:26,720 --> 00:24:31,199
Speaker 3: and it is essentially creating, you know, insights for two

481
00:24:31,280 --> 00:24:34,800
Speaker 3: groups of people, you know, clinicians and doctors who are

482
00:24:34,800 --> 00:24:39,240
Speaker 3: operating in clinics and the pharmaceutical industry. So you know,

483
00:24:39,320 --> 00:24:41,040
Speaker 3: we talked about one of them. They're creating you know,

484
00:24:41,359 --> 00:24:47,080
Speaker 3: physician co pilots, cardiology copilots basically copilots that help detect

485
00:24:47,080 --> 00:24:51,760
Speaker 3: disease based on what's in the patient record, provide notifications

486
00:24:52,119 --> 00:24:54,320
Speaker 3: and what we call risk stratify patients.

487
00:24:54,320 --> 00:24:56,440
Speaker 2: So basically can say, hey, look, you know.

488
00:24:56,440 --> 00:24:59,520
Speaker 3: These are these these are people who are at high

489
00:24:59,560 --> 00:25:01,840
Speaker 3: risk UH in your patient record.

490
00:25:01,880 --> 00:25:02,520
Speaker 2: Mister doctor.

491
00:25:02,600 --> 00:25:05,600
Speaker 3: You know, we've scanned your roster and we've identified, you know,

492
00:25:05,760 --> 00:25:07,440
Speaker 3: people who at higher risk.

493
00:25:07,320 --> 00:25:08,800
Speaker 2: Of of of of of of.

494
00:25:08,800 --> 00:25:13,160
Speaker 3: UH co op D or you know, chronic kidney disease UH.

495
00:25:13,200 --> 00:25:16,520
Speaker 3: And we notice that the dosage that you're using for

496
00:25:16,680 --> 00:25:19,600
Speaker 3: that critic for that person. Let's let's say that they're

497
00:25:20,080 --> 00:25:23,040
Speaker 3: identified as someone who has chronic kidney disease, but they're

498
00:25:23,200 --> 00:25:25,679
Speaker 3: but they're kind of underdiagnosed, meaning that they should be

499
00:25:25,720 --> 00:25:28,119
Speaker 3: on a much higher dose. It'll just sort of identify

500
00:25:28,480 --> 00:25:31,880
Speaker 3: those things, you know, just like the engine light, uh, when,

501
00:25:32,000 --> 00:25:34,000
Speaker 3: when when A when A when a pilot is flying

502
00:25:34,040 --> 00:25:37,280
Speaker 3: a plane. Uh, And and it seasoned notifications saying oh, okay,

503
00:25:37,359 --> 00:25:39,520
Speaker 3: I'll clear that notification, thanks for letting me know that

504
00:25:39,920 --> 00:25:42,919
Speaker 3: same thing. You know, Like these these notifications are popping

505
00:25:43,000 --> 00:25:44,520
Speaker 3: up and the physician can take action.

506
00:25:44,800 --> 00:25:47,960
Speaker 2: There's so much data, there's so many things to look for.

507
00:25:48,240 --> 00:25:50,800
Speaker 3: It's very difficult for a physician to actually be able

508
00:25:50,840 --> 00:25:54,000
Speaker 3: to to proactively go into the health record and identify

509
00:25:54,040 --> 00:25:56,640
Speaker 3: all these things by themselves. So this is what heal

510
00:25:56,720 --> 00:25:59,120
Speaker 3: Well is doing. It's identifying those types of things.

511
00:25:59,560 --> 00:26:01,480
Speaker 2: On the pharmaceutical side, it.

512
00:26:01,520 --> 00:26:03,600
Speaker 3: Is doing the same thing. It is taking data, and

513
00:26:03,640 --> 00:26:08,080
Speaker 3: it is taking both structured and unstructured data same same

514
00:26:08,680 --> 00:26:11,679
Speaker 3: as as for physicians. You know, this means all the

515
00:26:11,680 --> 00:26:15,200
Speaker 3: different doctor nods, all the different information that's coming into

516
00:26:15,240 --> 00:26:18,600
Speaker 3: the patient record, and it is finding out for the

517
00:26:18,600 --> 00:26:21,840
Speaker 3: pharmaceutical industry how their drugs are doing. You know, once

518
00:26:22,080 --> 00:26:24,240
Speaker 3: you get FDA approval and the drug gets out there,

519
00:26:24,400 --> 00:26:25,560
Speaker 3: it's in the wild.

520
00:26:25,680 --> 00:26:26,640
Speaker 2: You know, the.

521
00:26:26,560 --> 00:26:29,360
Speaker 3: Pharmacical industry starts to it starts to get grainy as

522
00:26:29,400 --> 00:26:32,040
Speaker 3: to what's actually going on. Is it helping, is it hurting?

523
00:26:32,400 --> 00:26:35,840
Speaker 3: You know, you know, you know, how does adherents work?

524
00:26:36,160 --> 00:26:39,560
Speaker 3: You know, So getting that kind of information back to

525
00:26:41,200 --> 00:26:45,119
Speaker 3: the pharmaceutical industry, helping them find patients, finding patients. You

526
00:26:45,359 --> 00:26:49,040
Speaker 3: ever noticed that FDA approvals can take an incredible amount

527
00:26:49,040 --> 00:26:51,760
Speaker 3: of time just go through the process of clinical trials.

528
00:26:52,040 --> 00:26:54,119
Speaker 3: A lot of that is just finding patients because it's

529
00:26:54,160 --> 00:26:56,359
Speaker 3: really hard to find them. What if you could just

530
00:26:56,520 --> 00:26:58,199
Speaker 3: leverage AI technologies to do that?

531
00:26:58,240 --> 00:26:59,560
Speaker 2: So those are these are the types of.

532
00:26:59,520 --> 00:27:02,480
Speaker 3: Things that Well you know does and can do, and

533
00:27:02,840 --> 00:27:07,720
Speaker 3: so they have been building the AI copilots that Well

534
00:27:07,800 --> 00:27:10,840
Speaker 3: Health Technologies, which is the company that I run and founded,

535
00:27:12,080 --> 00:27:15,000
Speaker 3: uh you know, is using across Canada. We're the largest

536
00:27:15,000 --> 00:27:18,239
Speaker 3: owner operator of outpatient medical clinics in the country and

537
00:27:18,320 --> 00:27:21,720
Speaker 3: so we're treating millions of patients and this has provided

538
00:27:21,840 --> 00:27:24,840
Speaker 3: us with a huge you know, productivity and efficiency and

539
00:27:24,960 --> 00:27:28,199
Speaker 3: enhancement and I want to go back to something that

540
00:27:28,280 --> 00:27:32,200
Speaker 3: you were saying about chronic disease. A big part of

541
00:27:32,720 --> 00:27:35,800
Speaker 3: the growth and chronic disease that we're seeing is behavior.

542
00:27:36,400 --> 00:27:36,600
Speaker 2: Right.

543
00:27:36,920 --> 00:27:39,399
Speaker 3: And now what's interesting is starting to happen as a

544
00:27:39,440 --> 00:27:42,679
Speaker 3: result of AI and data is physicians are starting to,

545
00:27:43,920 --> 00:27:48,520
Speaker 3: you know, provide you with with recommendations on how much

546
00:27:48,560 --> 00:27:51,359
Speaker 3: you should move and how much you should sleep. And

547
00:27:51,400 --> 00:27:53,479
Speaker 3: these are the types of things that are contributing to

548
00:27:53,560 --> 00:27:56,120
Speaker 3: the explosion in chronic disease. So you may notice I'm

549
00:27:56,119 --> 00:27:58,679
Speaker 3: wearing this ring. This is called an error ring. This

550
00:27:58,800 --> 00:28:02,760
Speaker 3: tracks my heart rate variability, resting heart rate, you know,

551
00:28:02,800 --> 00:28:05,879
Speaker 3: and even temperature, even even even even looks at a

552
00:28:05,880 --> 00:28:08,800
Speaker 3: little uh detects a little bit of moisture, and and

553
00:28:08,840 --> 00:28:12,840
Speaker 3: it's able to assess you know, my movements, my activity levels,

554
00:28:12,880 --> 00:28:16,600
Speaker 3: my sleep. This data starts to become a treasure trove

555
00:28:16,680 --> 00:28:20,080
Speaker 3: of information. You start to now in the future, you

556
00:28:20,119 --> 00:28:24,960
Speaker 3: asked you mentioned uh, Peter Demantes uh and his commentary

557
00:28:25,040 --> 00:28:28,760
Speaker 3: around around around data and and and people, you know,

558
00:28:28,800 --> 00:28:33,160
Speaker 3: doctors not using co pilots. Within five years, imagine being

559
00:28:33,200 --> 00:28:36,480
Speaker 3: able to take this, you know, this this quality of

560
00:28:36,600 --> 00:28:40,320
Speaker 3: data and plug it into the health record in addition

561
00:28:40,440 --> 00:28:42,880
Speaker 3: to all of the other data that you have. This

562
00:28:43,080 --> 00:28:45,560
Speaker 3: is when things, I think start to get very very

563
00:28:45,560 --> 00:28:49,440
Speaker 3: interesting because you have this dramatic amount of user generated

564
00:28:49,520 --> 00:28:52,800
Speaker 3: data but with but with very precise tools like this

565
00:28:52,960 --> 00:28:56,080
Speaker 3: like this, uh, like this ring that you should see

566
00:28:56,120 --> 00:28:59,760
Speaker 3: the amount of componentry and electronics.

567
00:28:59,120 --> 00:28:59,600
Speaker 2: In this ring.

568
00:28:59,600 --> 00:29:03,080
Speaker 3: It's it's frankly amazing. I heard the CEO of this

569
00:29:03,160 --> 00:29:06,880
Speaker 3: company speak around COVID and he was saying that that

570
00:29:07,200 --> 00:29:10,400
Speaker 3: his company was able to detect COVID better than some

571
00:29:10,480 --> 00:29:13,680
Speaker 3: of the tests that that were out there, just basically

572
00:29:14,080 --> 00:29:17,840
Speaker 3: due to the signature of you know, things like temperature,

573
00:29:18,560 --> 00:29:21,120
Speaker 3: heart rate and heart rate variability and all those things

574
00:29:21,120 --> 00:29:21,840
Speaker 3: that they would see.

575
00:29:21,960 --> 00:29:23,680
Speaker 2: They could they could they could.

576
00:29:23,600 --> 00:29:28,920
Speaker 3: Essentially with very high acuity, be able to identify someone

577
00:29:28,920 --> 00:29:31,760
Speaker 3: who would have COVID. So this is kind of where

578
00:29:31,760 --> 00:29:34,160
Speaker 3: we're going, and I think it's pretty exciting. But I

579
00:29:34,200 --> 00:29:36,640
Speaker 3: think but I think it comes back to you know,

580
00:29:37,320 --> 00:29:39,880
Speaker 3: you know, you know behavior, you know, everyone's looking for

581
00:29:39,920 --> 00:29:42,560
Speaker 3: the magic bullet and healthcare the magic bullet is your

582
00:29:42,600 --> 00:29:47,000
Speaker 3: behavior and so and and I think RFK brings up

583
00:29:47,040 --> 00:29:49,720
Speaker 3: a good point and how do we how we beat this?

584
00:29:50,200 --> 00:29:53,640
Speaker 3: Unfortunately has more has less to do with our with

585
00:29:53,680 --> 00:29:56,760
Speaker 3: our with our with our healthcare ecosystem, and has more

586
00:29:56,800 --> 00:30:01,920
Speaker 3: to do with our personal you know, commitment to ourselves

587
00:30:01,960 --> 00:30:04,600
Speaker 3: in order to to to empower ourselves to better health.

588
00:30:04,640 --> 00:30:07,560
Speaker 2: And so that's a whole other conversation, but that's.

589
00:30:07,400 --> 00:30:09,040
Speaker 1: A great it's a great point though, I mean, we

590
00:30:09,120 --> 00:30:11,160
Speaker 1: have to take responsibility for our health, and really it

591
00:30:11,160 --> 00:30:14,080
Speaker 1: starts with us. The problem is the education, and there's

592
00:30:14,080 --> 00:30:17,280
Speaker 1: no education around that. So I think, if I'm not mistaken,

593
00:30:17,560 --> 00:30:20,400
Speaker 1: your company heal well also isn't just for doctors to

594
00:30:20,400 --> 00:30:23,280
Speaker 1: diagnose better, but I think it also maybe helps patients

595
00:30:23,560 --> 00:30:24,040
Speaker 1: as well.

596
00:30:24,320 --> 00:30:24,800
Speaker 2: Is that right?

597
00:30:24,960 --> 00:30:26,880
Speaker 1: Or I mean is it something that I could use

598
00:30:26,920 --> 00:30:27,360
Speaker 1: to get better?

599
00:30:27,400 --> 00:30:31,280
Speaker 3: And certainly the yeah, I mean that's right.

600
00:30:31,320 --> 00:30:31,480
Speaker 2: Now.

601
00:30:31,480 --> 00:30:34,560
Speaker 3: We don't have a direct to consumer site or service,

602
00:30:34,600 --> 00:30:37,960
Speaker 3: but that's something that that's likely where where we will

603
00:30:38,040 --> 00:30:38,560
Speaker 3: head at.

604
00:30:38,400 --> 00:30:39,160
Speaker 2: Some point in time.

605
00:30:39,240 --> 00:30:41,720
Speaker 3: Right now, we're a B to B company that develops

606
00:30:41,760 --> 00:30:47,720
Speaker 3: these technologies and deploys them to to to hospitals, to clinics,

607
00:30:47,800 --> 00:30:53,320
Speaker 3: to the pharmaceutical industry. We help them orchestrate clinical trials. Uh,

608
00:30:53,440 --> 00:30:56,719
Speaker 3: you know, and and are you know, basically modernizing, digitizing

609
00:30:56,760 --> 00:30:59,640
Speaker 3: and AI enabling clinical trials so that we can speed

610
00:30:59,640 --> 00:31:01,200
Speaker 3: them up, that we get we can get to that

611
00:31:01,240 --> 00:31:05,480
Speaker 3: point a lot more quickly. So it's it's really the

612
00:31:05,560 --> 00:31:08,720
Speaker 3: roadmap kind of applying that vector of AI to a

613
00:31:08,800 --> 00:31:10,280
Speaker 3: number of different areas in healthcare.

614
00:31:10,360 --> 00:31:14,240
Speaker 1: One thing that I study technology, and I study techechnology cycles,

615
00:31:14,240 --> 00:31:17,040
Speaker 1: and I noticed they always have this like dependable pattern,

616
00:31:17,360 --> 00:31:19,480
Speaker 1: repeatable pattern of sort of how they roll out maybe

617
00:31:19,520 --> 00:31:22,120
Speaker 1: what we'd call the diffusion of innovation. And so, you know,

618
00:31:22,160 --> 00:31:25,040
Speaker 1: you have this disruptive technology, and then there's some you know,

619
00:31:25,040 --> 00:31:27,480
Speaker 1: people that are against that disruptive maybe like the Ladites.

620
00:31:27,840 --> 00:31:30,040
Speaker 1: We get over the chasm, and I'm just curious your

621
00:31:30,040 --> 00:31:33,480
Speaker 1: take on how it's disrupting the medical space. I believe,

622
00:31:34,000 --> 00:31:35,920
Speaker 1: I mean, you already have people using this, right, I

623
00:31:35,920 --> 00:31:39,040
Speaker 1: mean thousands of people already using this. I could see

624
00:31:39,080 --> 00:31:41,760
Speaker 1: where trade doctors would be like, oh, I can't use

625
00:31:41,800 --> 00:31:44,680
Speaker 1: that AI. I've been doing this for so long. I'm curious,

626
00:31:44,800 --> 00:31:47,160
Speaker 1: you know, since you already have I believe thousands, Go

627
00:31:47,200 --> 00:31:49,240
Speaker 1: ahead and correct me if I'm wrong on that, but

628
00:31:50,040 --> 00:31:53,960
Speaker 1: how has that been getting them to adopt this disruptive technology?

629
00:31:54,000 --> 00:31:59,520
Speaker 1: And how fast is this growing or you're growing with it?

630
00:32:00,080 --> 00:32:01,120
Speaker 2: Really really good question.

631
00:32:01,840 --> 00:32:05,040
Speaker 3: So so there there are a couple of different things

632
00:32:05,040 --> 00:32:08,040
Speaker 3: that I'll unpack here. First of all, there's a lot

633
00:32:08,040 --> 00:32:12,440
Speaker 3: of change management difficulty with physicians that a lot of

634
00:32:12,440 --> 00:32:19,200
Speaker 3: that has improved since COVID. You know, doctors were extremely initially,

635
00:32:19,520 --> 00:32:22,920
Speaker 3: very fearful of any digital technology.

636
00:32:22,360 --> 00:32:23,440
Speaker 2: That would review their data.

637
00:32:24,200 --> 00:32:28,120
Speaker 3: The view from a physician's perspective was I'm going to

638
00:32:28,160 --> 00:32:31,720
Speaker 3: have some kind of big brother reviewing my work to

639
00:32:31,800 --> 00:32:35,440
Speaker 3: ify areas where I made mistakes. And when it comes

640
00:32:35,480 --> 00:32:40,240
Speaker 3: to people's health, that's a very touchy thing and you know, unfortunately,

641
00:32:40,520 --> 00:32:44,760
Speaker 3: you know, uh, doctors receive a lot of flak for that. So, hey,

642
00:32:44,800 --> 00:32:46,920
Speaker 3: you made a mistake, you caused a loved one of

643
00:32:46,960 --> 00:32:50,320
Speaker 3: mine to be hurt, or what have you. So so

644
00:32:50,920 --> 00:32:54,880
Speaker 3: there was this sort of extreme fear that that that

645
00:32:54,880 --> 00:32:56,640
Speaker 3: that I don't want my data to be scanned. I

646
00:32:56,640 --> 00:32:58,320
Speaker 3: don't want anything to do with this thing. I just

647
00:32:58,360 --> 00:33:00,440
Speaker 3: want to be a doctor. I want to want I

648
00:33:00,440 --> 00:33:02,160
Speaker 3: want to see patients, and I want to go about

649
00:33:02,160 --> 00:33:05,680
Speaker 3: my way. I think what happened with COVID is that

650
00:33:06,320 --> 00:33:11,240
Speaker 3: because we went from you know, you know, very low

651
00:33:11,280 --> 00:33:16,360
Speaker 3: percentage of telemedicine to very high percentage of telemedicine due

652
00:33:16,400 --> 00:33:21,280
Speaker 3: to physical distancing requirements, Suddenly physicians needed technology. If they

653
00:33:21,360 --> 00:33:25,120
Speaker 3: weren't on telemedicine, if they weren't trusting digital systems, if

654
00:33:25,120 --> 00:33:29,200
Speaker 3: they weren't digitally enabling themselves, they were suddenly not able

655
00:33:29,240 --> 00:33:31,360
Speaker 3: to practice. So they had to get a lot more

656
00:33:32,000 --> 00:33:35,400
Speaker 3: entrenched and engaged with digital technologies, and I think that

657
00:33:35,600 --> 00:33:39,400
Speaker 3: sort of helped them understand these technologies a little bit

658
00:33:39,440 --> 00:33:42,680
Speaker 3: better and it helped diffuse some of the fears. But

659
00:33:42,720 --> 00:33:45,120
Speaker 3: I also think, you know, doctors like the rest of us,

660
00:33:45,160 --> 00:33:49,160
Speaker 3: are consumers of these technologies. So they're using chat, GPT,

661
00:33:49,360 --> 00:33:52,400
Speaker 3: they're using all these things, and they're starting to recognize

662
00:33:52,440 --> 00:33:56,320
Speaker 3: that there's enormous productivity enhancements that come with this. And

663
00:33:56,360 --> 00:33:59,640
Speaker 3: I think it's somewhat generational too. Now we have way

664
00:33:59,640 --> 00:34:03,400
Speaker 3: more millennial and Gen Z doctors, right, and so they

665
00:34:03,440 --> 00:34:08,319
Speaker 3: operate very differently than than Gen xers. And so one

666
00:34:08,360 --> 00:34:11,600
Speaker 3: of the reasons why I started well at the time

667
00:34:11,680 --> 00:34:14,600
Speaker 3: is that I just I was in twenty seventeen. I

668
00:34:14,640 --> 00:34:18,520
Speaker 3: was just sort of shocked to see how little digitization

669
00:34:18,560 --> 00:34:21,200
Speaker 3: and modernization there was in healthcare, and I thought this

670
00:34:21,280 --> 00:34:24,080
Speaker 3: is really strange. I mean, like most other industries have

671
00:34:24,160 --> 00:34:29,560
Speaker 3: been entirely disrupted with this technology. And my view is, well,

672
00:34:29,600 --> 00:34:32,359
Speaker 3: look it's going to happen. It just hasn't happened yet.

673
00:34:32,360 --> 00:34:38,360
Speaker 3: No industry evades digitization and modernization. And so the entire

674
00:34:38,480 --> 00:34:42,120
Speaker 3: thrust of the business planet well health technologies was tech

675
00:34:42,239 --> 00:34:46,640
Speaker 3: enabled care delivery and supporting the healthcare practitioner with tools,

676
00:34:46,880 --> 00:34:50,560
Speaker 3: understanding that they'll adopt those tools when they're ready, but

677
00:34:50,760 --> 00:34:53,640
Speaker 3: also understanding that there was a generational shift. A lot

678
00:34:53,680 --> 00:34:55,719
Speaker 3: of times when we look at that adoption curve, we

679
00:34:55,760 --> 00:34:58,960
Speaker 3: have to recognize that it's these that these people also

680
00:34:59,120 --> 00:35:03,680
Speaker 3: change over time, and and there are certain generations that

681
00:35:03,719 --> 00:35:05,680
Speaker 3: are that are that are probably going to be less

682
00:35:05,760 --> 00:35:08,680
Speaker 3: receptive to these technologies and certain generations and as we

683
00:35:08,760 --> 00:35:11,480
Speaker 3: as we move forward, we're just going to have more

684
00:35:11,520 --> 00:35:14,320
Speaker 3: adoptions just by virtue of the fact that we're consumers

685
00:35:14,320 --> 00:35:15,279
Speaker 3: of these technologies.

686
00:35:17,000 --> 00:35:19,480
Speaker 1: Thinking about it from like an investment lens, I was

687
00:35:19,520 --> 00:35:21,680
Speaker 1: kind of thinking about this through this as from my

688
00:35:21,760 --> 00:35:24,120
Speaker 1: VC lens. I've been doing VC stuff. I have a

689
00:35:24,160 --> 00:35:26,520
Speaker 1: I'm a partner in a in a VC hedge fund,

690
00:35:26,680 --> 00:35:29,440
Speaker 1: a bitcoin VC hedge fund, and I you know, we're

691
00:35:29,480 --> 00:35:33,279
Speaker 1: we're looking for obviously areas that are disruptive, and we're

692
00:35:33,280 --> 00:35:35,280
Speaker 1: looking for sort of this convergence. When I think about

693
00:35:35,280 --> 00:35:37,160
Speaker 1: back to kind of like rfks talking about like this

694
00:35:37,320 --> 00:35:42,080
Speaker 1: explosion of health problems and then you have a revolutionary

695
00:35:42,120 --> 00:35:45,080
Speaker 1: technology like AI that could then come in and help that,

696
00:35:45,480 --> 00:35:47,960
Speaker 1: and so you sort of have this like massive growing

697
00:35:48,000 --> 00:35:50,640
Speaker 1: industry that's sort of like recession proof because I mean,

698
00:35:50,680 --> 00:35:52,520
Speaker 1: when you're sick, you're sick, doesn't matter what the economy

699
00:35:52,560 --> 00:35:55,160
Speaker 1: is doing. So you have this massive growing industry and

700
00:35:55,200 --> 00:35:58,400
Speaker 1: then you have this disruptive technology coming in. It seems

701
00:35:58,440 --> 00:36:00,879
Speaker 1: pretty explosive. That's what I would think about it from

702
00:36:00,920 --> 00:36:03,759
Speaker 1: a VC level. So I mean, how big do you

703
00:36:03,840 --> 00:36:06,480
Speaker 1: think like this sector is of maybe this convergence of

704
00:36:06,520 --> 00:36:07,360
Speaker 1: healthcare and AI.

705
00:36:11,000 --> 00:36:13,080
Speaker 3: I think it's incredibly big. I mean, if you think

706
00:36:13,120 --> 00:36:20,719
Speaker 3: about it, healthcare is typically the largest component of our

707
00:36:20,760 --> 00:36:24,720
Speaker 3: services economy, of our economy overall in most industrialized nations.

708
00:36:24,800 --> 00:36:28,560
Speaker 3: So in the United States we're talking about depending on

709
00:36:28,600 --> 00:36:31,480
Speaker 3: how you size, healthcare can be anywhere between three and

710
00:36:31,560 --> 00:36:37,080
Speaker 3: five trillion dollars a year, typically with pharmacy being worth

711
00:36:37,120 --> 00:36:40,279
Speaker 3: about fifteen to twenty percent of that. Right, And so

712
00:36:40,960 --> 00:36:43,719
Speaker 3: now let's think about three to five trillion dollars. In

713
00:36:43,800 --> 00:36:46,880
Speaker 3: reference to the defense industry, we know that, for example,

714
00:36:46,880 --> 00:36:50,239
Speaker 3: the United States spends you know, a significant amount of

715
00:36:50,239 --> 00:36:54,839
Speaker 3: money on national defense. So national defense is about eight

716
00:36:55,560 --> 00:37:00,359
Speaker 3: seven to eight hundred billion dollars a year. So we're

717
00:37:00,400 --> 00:37:04,600
Speaker 3: talking about healthcare being multiples of national defense. You know

718
00:37:04,800 --> 00:37:08,400
Speaker 3: that what most when when you kind of understand the

719
00:37:08,440 --> 00:37:12,200
Speaker 3: context of just how big healthcare spending is, you start

720
00:37:12,239 --> 00:37:17,120
Speaker 3: to recognize that even niches in healthcare are worth you know.

721
00:37:17,120 --> 00:37:18,600
Speaker 2: Multiple billions of dollars.

722
00:37:18,920 --> 00:37:21,520
Speaker 3: You can you can you can go down rabbit holes

723
00:37:21,640 --> 00:37:24,680
Speaker 3: in in the healthcare industry and find significant opportunities. You

724
00:37:24,760 --> 00:37:27,920
Speaker 3: mentioned something earlier that I think I think is going

725
00:37:28,000 --> 00:37:30,080
Speaker 3: to be a bigger deal and and and that is

726
00:37:30,320 --> 00:37:35,879
Speaker 3: you know, educating consumers and helping them with their behaviors

727
00:37:35,880 --> 00:37:39,960
Speaker 3: to improve their health. So once once people start to understand.

728
00:37:39,520 --> 00:37:42,520
Speaker 2: That their behavior is of a very big part of their.

729
00:37:42,400 --> 00:37:46,600
Speaker 3: Healthcare and and we need more and more tools to

730
00:37:46,640 --> 00:37:49,840
Speaker 3: empower people to make better decisions. And I think that

731
00:37:50,000 --> 00:37:53,680
Speaker 3: is a big, big business opportunity. And and I think

732
00:37:53,719 --> 00:37:56,040
Speaker 3: it will involve your personal data. I think it will

733
00:37:56,080 --> 00:37:59,960
Speaker 3: involve you know, personal copilots that that will help us

734
00:38:00,160 --> 00:38:01,520
Speaker 3: run our lives better.

735
00:38:02,160 --> 00:38:03,399
Speaker 2: I also think that, you.

736
00:38:03,360 --> 00:38:06,880
Speaker 3: Know, we are going to see a significant rise in robotics,

737
00:38:07,400 --> 00:38:09,719
Speaker 3: you know, you know, back to what you were saying

738
00:38:09,760 --> 00:38:14,640
Speaker 3: earlier about you know, the disruptive potential of robots and AI,

739
00:38:15,280 --> 00:38:16,080
Speaker 3: I mean it's there.

740
00:38:16,120 --> 00:38:18,040
Speaker 2: It used to be that, you know, people would.

741
00:38:17,880 --> 00:38:20,960
Speaker 3: Say, well, if you're a hairdresser or or or a dentists,

742
00:38:20,960 --> 00:38:21,399
Speaker 3: don't worry.

743
00:38:21,400 --> 00:38:23,239
Speaker 2: You're all you're always going to have a job.

744
00:38:23,239 --> 00:38:24,719
Speaker 3: There's never going to be a robot that will be

745
00:38:24,760 --> 00:38:28,040
Speaker 3: able to do that. You know, Sorry, not true. You know,

746
00:38:28,280 --> 00:38:31,720
Speaker 3: now there's likely going to be and and and robots

747
00:38:31,760 --> 00:38:35,040
Speaker 3: being developed that can essentially do anything. There's already home

748
00:38:35,080 --> 00:38:38,520
Speaker 3: cooking robots available. There's already robots that you can acquire

749
00:38:38,560 --> 00:38:42,120
Speaker 3: that will do housework for you. Tesla is working very

750
00:38:42,120 --> 00:38:44,680
Speaker 3: hard on on solving those types of problems. But there's

751
00:38:45,040 --> 00:38:48,640
Speaker 3: enormous vcs and startups and there's going to be some

752
00:38:48,640 --> 00:38:51,520
Speaker 3: some winners in that space that will create multi trillion

753
00:38:51,520 --> 00:38:54,759
Speaker 3: dollar companies. So I think it's an incredible space. I

754
00:38:54,800 --> 00:38:57,200
Speaker 3: think it'll help a lot of people. But I think

755
00:38:57,440 --> 00:38:59,640
Speaker 3: again it'll be a tool that will be used, you know,

756
00:38:59,680 --> 00:39:01,719
Speaker 3: for good and bad. So we have to now worry

757
00:39:01,760 --> 00:39:03,759
Speaker 3: about is my robot going to get hacked and come

758
00:39:03,760 --> 00:39:06,799
Speaker 3: and kill me? I assure you that will happen. So

759
00:39:06,960 --> 00:39:10,080
Speaker 3: that's a pretty scary thought, right, right, So we have

760
00:39:10,160 --> 00:39:13,000
Speaker 3: to start thinking about how do we protect ourselves? You know, again,

761
00:39:13,040 --> 00:39:15,640
Speaker 3: this is a tool we're going to acquire, you know,

762
00:39:15,920 --> 00:39:18,560
Speaker 3: tremendous types of robots that are going to help us

763
00:39:18,560 --> 00:39:20,640
Speaker 3: in all kinds of different aspects of our lives.

764
00:39:21,120 --> 00:39:22,160
Speaker 2: And then we're and.

765
00:39:22,600 --> 00:39:24,279
Speaker 3: Likely in five or ten years we're not even to

766
00:39:24,360 --> 00:39:27,680
Speaker 3: drive anymore. There's going to be a robot driving and

767
00:39:27,760 --> 00:39:29,719
Speaker 3: we're going to get in and we may even you know,

768
00:39:29,800 --> 00:39:32,759
Speaker 3: just cars will be designed differently. They'll be designed with

769
00:39:32,880 --> 00:39:35,319
Speaker 3: beds so that we'll be able to sleep if we're going,

770
00:39:35,719 --> 00:39:39,680
Speaker 3: you know, for for you know, a longer commute, what

771
00:39:39,719 --> 00:39:43,520
Speaker 3: have you. So, really, I mean, we're at a very

772
00:39:43,520 --> 00:39:46,480
Speaker 3: interesting inflection point, I think, you know, with all this

773
00:39:46,520 --> 00:39:50,239
Speaker 3: coming together, and and I when I say this, people say, well,

774
00:39:50,280 --> 00:39:51,719
Speaker 3: aren't we always at an inflection point?

775
00:39:51,760 --> 00:39:52,800
Speaker 2: And say, well, not really.

776
00:39:52,840 --> 00:39:56,640
Speaker 3: I mean there are people I've heard Mark Andresen talk

777
00:39:56,680 --> 00:39:59,920
Speaker 3: about this and a speech of his that had been

778
00:40:00,120 --> 00:40:04,880
Speaker 3: working on this problem of generative AI for their whole lives,

779
00:40:05,000 --> 00:40:07,879
Speaker 3: you know, So they went to school, they they did

780
00:40:07,920 --> 00:40:10,840
Speaker 3: their masters, their PhD. And then they worked for decades

781
00:40:11,000 --> 00:40:13,319
Speaker 3: working on this problem. And they never saw it come

782
00:40:13,360 --> 00:40:17,799
Speaker 3: to fruition. And and and they were scientists and researchers

783
00:40:17,840 --> 00:40:21,960
Speaker 3: and extremely smart people that for decades and decades were

784
00:40:21,960 --> 00:40:25,120
Speaker 3: working on this and and and then suddenly it came together.

785
00:40:25,600 --> 00:40:26,680
Speaker 2: So it's a very.

786
00:40:26,520 --> 00:40:30,520
Speaker 3: It's we live in a very very interesting world, and

787
00:40:30,600 --> 00:40:32,560
Speaker 3: it's about to get a lot more intriguing. I mean,

788
00:40:32,680 --> 00:40:37,200
Speaker 3: as as chat GPT, as open AI drops new releases,

789
00:40:37,640 --> 00:40:43,440
Speaker 3: we're going to constantly be surprised and and and you know, uh,

790
00:40:43,600 --> 00:40:46,480
Speaker 3: you know, exhilarated by this. But but but also it's

791
00:40:46,520 --> 00:40:49,200
Speaker 3: it's going to heighten our we should be heightening our

792
00:40:49,200 --> 00:40:52,480
Speaker 3: defenses against some of these innovations as well, and and

793
00:40:52,480 --> 00:40:56,120
Speaker 3: and just trying to figure out how to make them safe.

794
00:40:56,719 --> 00:40:59,400
Speaker 1: You know, I always say, like I want the I

795
00:40:59,440 --> 00:41:02,120
Speaker 1: want the con unions and the efficiency of the technology.

796
00:41:02,280 --> 00:41:04,640
Speaker 1: I just don't want it weaponized against me. And so

797
00:41:04,680 --> 00:41:06,640
Speaker 1: there's like trying to find that balance. But going back

798
00:41:06,640 --> 00:41:08,200
Speaker 1: to what you just said about aren't we always on

799
00:41:08,239 --> 00:41:10,080
Speaker 1: the brink? And the answer to you said, you said no,

800
00:41:10,120 --> 00:41:12,080
Speaker 1: And I agree with that. I spent a lot of

801
00:41:12,080 --> 00:41:14,960
Speaker 1: time talking about cycles, and there's one cycle that I

802
00:41:15,040 --> 00:41:17,480
Speaker 1: invest along and it's called I call it a quantum

803
00:41:17,600 --> 00:41:21,120
Speaker 1: leap cycle. It happened about every fifty years. It's kind

804
00:41:21,160 --> 00:41:23,920
Speaker 1: of more broadly known as a condroidive wave cycle k

805
00:41:24,040 --> 00:41:27,239
Speaker 1: wave cycle. And about every fifty years, there's a technological

806
00:41:27,320 --> 00:41:32,160
Speaker 1: revolution cycle generally, where basically there's a cluster of technologies

807
00:41:32,160 --> 00:41:34,920
Speaker 1: that gets developed that leaps the world forward and drives

808
00:41:34,960 --> 00:41:40,840
Speaker 1: financial markets. And so there's been five industrial revolution steam engines, railways, electricity, steel,

809
00:41:40,840 --> 00:41:45,840
Speaker 1: heavy equipment, oil, oil fuels, automobiles, mass production. Nineteen seventy

810
00:41:45,880 --> 00:41:50,759
Speaker 1: one was the microprocessor, internet, telecommunications, and personal computers. And

811
00:41:50,840 --> 00:41:54,359
Speaker 1: now we're starting the sixth one right now. And I

812
00:41:54,400 --> 00:41:58,160
Speaker 1: call this like the investing black hole. The hurdle rate

813
00:41:58,239 --> 00:42:00,800
Speaker 1: at the rate of debasement is about ten percent plus

814
00:42:00,840 --> 00:42:04,040
Speaker 1: we have CPI price inflation. The real hurdle rate for

815
00:42:04,080 --> 00:42:07,120
Speaker 1: an investor is not the three or four percent CPI

816
00:42:07,280 --> 00:42:09,759
Speaker 1: number that we're being fed. It's really the rate of

817
00:42:09,760 --> 00:42:12,760
Speaker 1: debasement plus price increases. It's really about thirteen fourteen percent.

818
00:42:13,120 --> 00:42:15,400
Speaker 1: When you look at all the asset classes in the world,

819
00:42:15,480 --> 00:42:17,399
Speaker 1: the only ones that have beat that hurdle rate are

820
00:42:17,480 --> 00:42:20,439
Speaker 1: Bitcoin and the Nasdaq. So basically tech and the reason

821
00:42:20,520 --> 00:42:22,719
Speaker 1: why is the only place to really invest is in

822
00:42:22,760 --> 00:42:25,160
Speaker 1: that current cycle like the last fifty years has been

823
00:42:25,160 --> 00:42:30,600
Speaker 1: dominated by telecom, personal computers, and Internet. Obviously before that

824
00:42:30,680 --> 00:42:33,160
Speaker 1: it was dominated by four GMG. And so I think

825
00:42:33,239 --> 00:42:35,839
Speaker 1: really like bitcoin and what I'm calling Bitcoin two point zero,

826
00:42:35,880 --> 00:42:39,040
Speaker 1: like companies building on around that and AI are really

827
00:42:39,080 --> 00:42:41,239
Speaker 1: the only places that we should be focusing on. And

828
00:42:41,280 --> 00:42:44,319
Speaker 1: so that's I write a newsletter that focuses on just

829
00:42:44,360 --> 00:42:47,080
Speaker 1: the intersection of that and obviously this AI is there.

830
00:42:47,680 --> 00:42:50,479
Speaker 1: So I'm curious, just from an investor standpoint, Like I said,

831
00:42:50,480 --> 00:42:52,200
Speaker 1: I pound the table on this is where you want

832
00:42:52,200 --> 00:42:55,640
Speaker 1: to focus your investments, what are some things that you

833
00:42:55,640 --> 00:42:59,600
Speaker 1: would look at from being an AI investor, I mean

834
00:43:00,040 --> 00:43:03,359
Speaker 1: using your company for an example. You know a lot

835
00:43:03,400 --> 00:43:06,200
Speaker 1: of them are a very early stage. You already have

836
00:43:06,719 --> 00:43:09,520
Speaker 1: customers and revenue. What are some things that you think of,

837
00:43:09,760 --> 00:43:11,960
Speaker 1: you know from an investor lens that you know that

838
00:43:12,000 --> 00:43:13,880
Speaker 1: we would look at through AI companies like yours.

839
00:43:18,719 --> 00:43:22,799
Speaker 3: Yeah, look, I think that I think that essentially now

840
00:43:22,920 --> 00:43:28,360
Speaker 3: AI co pilots for every type of profession, right, I

841
00:43:28,400 --> 00:43:31,160
Speaker 3: think are are are are going to be incredibly important.

842
00:43:31,320 --> 00:43:34,640
Speaker 3: I think if you're an investor, you're now looking for,

843
00:43:34,840 --> 00:43:37,560
Speaker 3: you know, where the gaps, where the white spaces where

844
00:43:37,920 --> 00:43:41,160
Speaker 3: people have not been building these these these these co

845
00:43:41,280 --> 00:43:44,040
Speaker 3: pilots that can they can they can take data that

846
00:43:44,120 --> 00:43:46,600
Speaker 3: can create insights, and then they can they can take

847
00:43:46,640 --> 00:43:50,000
Speaker 3: those insights into some kind of software and workflow and

848
00:43:50,080 --> 00:43:53,840
Speaker 3: improve the productivity of someone who's working somewhere important.

849
00:43:54,640 --> 00:43:56,120
Speaker 2: I'll give you a great example.

850
00:43:55,880 --> 00:44:01,120
Speaker 3: Like insurance. Right, so in an insurance company, someone receiving

851
00:44:01,200 --> 00:44:04,960
Speaker 3: those insurance claims, someone's processing them, someone's putting eyes on them,

852
00:44:05,000 --> 00:44:09,080
Speaker 3: someone's making a decision around something. But it's likely related

853
00:44:09,280 --> 00:44:13,480
Speaker 3: with a whole bunch of frame, a framework of decision

854
00:44:13,520 --> 00:44:17,560
Speaker 3: making that the insurance company already has. So if I'm

855
00:44:17,760 --> 00:44:21,359
Speaker 3: an assurance insurance adjuster, I probably processed a certain number

856
00:44:21,400 --> 00:44:23,120
Speaker 3: of claims every day.

857
00:44:23,560 --> 00:44:25,160
Speaker 2: I could probably, you.

858
00:44:25,120 --> 00:44:28,000
Speaker 3: Know, significantly increase the number of claims that I could

859
00:44:28,000 --> 00:44:32,200
Speaker 3: process every day by using some kind of copilot technology

860
00:44:32,200 --> 00:44:34,719
Speaker 3: that was designed for me that would help improve my

861
00:44:34,800 --> 00:44:40,080
Speaker 3: workflow dramatically. It's that use case against every single person

862
00:44:40,160 --> 00:44:43,719
Speaker 3: who's out there doing something that that that creates an

863
00:44:43,719 --> 00:44:45,959
Speaker 3: opportunity for a startup. And this is what we've seen

864
00:44:46,600 --> 00:44:49,080
Speaker 3: time and time again, whether it was you know, you know,

865
00:44:49,239 --> 00:44:53,200
Speaker 3: you know, the mobile computing revolution, whether it was just

866
00:44:53,239 --> 00:44:56,080
Speaker 3: the Internet altogether, it was any time something new and

867
00:44:56,160 --> 00:45:01,239
Speaker 3: enabling came up, eventually it started to proliferate and create

868
00:45:01,280 --> 00:45:05,640
Speaker 3: efficiencies and productivity enhancements in practically any any in every industry.

869
00:45:06,120 --> 00:45:09,560
Speaker 3: And I think AI is probably a more important one

870
00:45:09,600 --> 00:45:13,880
Speaker 3: because of its ability to take very complex sets of

871
00:45:13,960 --> 00:45:17,239
Speaker 3: data and information and create very simple insights.

872
00:45:16,960 --> 00:45:18,200
Speaker 2: That can be used by humans.

873
00:45:18,719 --> 00:45:23,320
Speaker 3: So so I think that the startup opportunity in in

874
00:45:23,480 --> 00:45:26,399
Speaker 3: AI is substantial, but I think most of that has

875
00:45:26,440 --> 00:45:29,719
Speaker 3: been limited to software until now. What we're starting to

876
00:45:29,760 --> 00:45:32,680
Speaker 3: see is where at this kind of convergence or or

877
00:45:32,800 --> 00:45:34,400
Speaker 3: or this very interesting point where now.

878
00:45:34,320 --> 00:45:38,080
Speaker 2: Robotics is his has progress quite a bit.

879
00:45:38,080 --> 00:45:41,360
Speaker 3: Right, And so we've all seen those videos of of

880
00:45:41,360 --> 00:45:45,200
Speaker 3: of of that robot, uh that that you know now

881
00:45:45,280 --> 00:45:49,200
Speaker 3: is doing backflips and balancing itself on on on on

882
00:45:49,200 --> 00:45:50,240
Speaker 3: on beams.

883
00:45:49,840 --> 00:45:50,480
Speaker 2: And stuff like that.

884
00:45:50,520 --> 00:45:53,279
Speaker 3: Whereas like a few years ago, if you pushed it

885
00:45:53,360 --> 00:45:57,080
Speaker 3: would just fall down. Right now you could you could

886
00:45:57,080 --> 00:45:59,879
Speaker 3: basically take baseball bats against it. And it's gonna it's

887
00:46:00,160 --> 00:46:02,440
Speaker 3: to be just fine, and it's going to balance itself perfectly,

888
00:46:02,480 --> 00:46:04,719
Speaker 3: and it's going to do a backflip and a front

889
00:46:04,719 --> 00:46:07,120
Speaker 3: flip and you know, land on one leg and all

890
00:46:07,560 --> 00:46:10,600
Speaker 3: that is pretty incredible. So think about think about that

891
00:46:10,719 --> 00:46:14,680
Speaker 3: in addition to all these insights, and you know that

892
00:46:14,680 --> 00:46:17,080
Speaker 3: that that we're able to to process from data, and

893
00:46:17,120 --> 00:46:20,600
Speaker 3: in addition to how we can now generate solutions and

894
00:46:20,680 --> 00:46:23,120
Speaker 3: act on data, and you start to be able to

895
00:46:23,120 --> 00:46:28,000
Speaker 3: see that it's it's not an unreasonable thought to think that,

896
00:46:28,440 --> 00:46:32,120
Speaker 3: you know, humans may not be as useful as we

897
00:46:32,120 --> 00:46:34,759
Speaker 3: were before, and so we start to have to you know,

898
00:46:34,960 --> 00:46:39,239
Speaker 3: and of course there's enormous wealth creation potential in that right.

899
00:46:40,080 --> 00:46:42,000
Speaker 3: And it does come back to this point that a

900
00:46:42,000 --> 00:46:44,360
Speaker 3: lot of people have made in the past. You know, ultimately,

901
00:46:44,440 --> 00:46:47,160
Speaker 3: you know, decades from from now, will we all be

902
00:46:47,280 --> 00:46:50,240
Speaker 3: on some kind of you know, universal basic income because

903
00:46:50,440 --> 00:46:52,560
Speaker 3: it'll just be really hard to compete with a robot.

904
00:46:53,200 --> 00:46:54,560
Speaker 2: I mean, it sounds.

905
00:46:54,239 --> 00:46:57,440
Speaker 3: Really star fetched, but but it'd be really hard to

906
00:46:57,480 --> 00:46:58,960
Speaker 3: compete with a really good robot.

907
00:46:59,000 --> 00:47:00,839
Speaker 1: Well, it was, it was, it was it was hard

908
00:47:00,880 --> 00:47:03,320
Speaker 1: for the field It was hard for the field workers

909
00:47:03,360 --> 00:47:05,040
Speaker 1: to compete with a machine that can come and do

910
00:47:05,040 --> 00:47:07,840
Speaker 1: the work of five thousand men too. And so you know,

911
00:47:07,920 --> 00:47:13,279
Speaker 1: every technology cycle we've we've seen that same thing. So, uh,

912
00:47:13,840 --> 00:47:16,399
Speaker 1: you know, when when those five thousand, when those five

913
00:47:16,400 --> 00:47:18,760
Speaker 1: thousand jobs in the field were replaced with a machine,

914
00:47:19,640 --> 00:47:21,680
Speaker 1: we had a problem, what do those five thousand men do?

915
00:47:22,520 --> 00:47:24,120
Speaker 1: And it turns out what they went and did with

916
00:47:24,120 --> 00:47:27,120
Speaker 1: science and medicine and technology, right, so what we should

917
00:47:27,120 --> 00:47:29,040
Speaker 1: be doing is removing lower level tasks and working on

918
00:47:29,160 --> 00:47:31,799
Speaker 1: higher level tasks and and so that's kind of kind

919
00:47:31,800 --> 00:47:33,319
Speaker 1: of where I see it. But but you're absolutely right.

920
00:47:33,360 --> 00:47:35,719
Speaker 2: It's uh yeah, I mean I was just reading.

921
00:47:37,000 --> 00:47:39,439
Speaker 3: I was just reading that that that more and more

922
00:47:39,640 --> 00:47:43,719
Speaker 3: surgeries are starting to happen with AI robotics. So think

923
00:47:43,719 --> 00:47:47,239
Speaker 3: about that same think about something as as as important

924
00:47:47,360 --> 00:47:53,000
Speaker 3: and as you know, just you're you're vulnerable. You're such

925
00:47:53,040 --> 00:47:56,160
Speaker 3: a vulnerable state where your body's being opened up, so

926
00:47:56,160 --> 00:47:58,440
Speaker 3: so something very important can happen, you know, tumor can

927
00:47:58,480 --> 00:48:01,760
Speaker 3: be taken out, or something can occur as a result

928
00:48:01,840 --> 00:48:06,640
Speaker 3: of that that operation. And yet you know they're saying that,

929
00:48:06,960 --> 00:48:10,520
Speaker 3: you know, robots could actually perform some of these surgeries

930
00:48:10,560 --> 00:48:15,919
Speaker 3: better and make fewer mistakes, right, so you know we're

931
00:48:15,960 --> 00:48:17,719
Speaker 3: in twenty twenty four, what is it going to look

932
00:48:17,800 --> 00:48:21,000
Speaker 3: like in twenty thirty four or you know, so you

933
00:48:21,560 --> 00:48:24,439
Speaker 3: know it's clear where it's going. And so I think

934
00:48:24,520 --> 00:48:27,280
Speaker 3: I think you know as an investor where you're applying capital.

935
00:48:28,239 --> 00:48:30,840
Speaker 3: You know, you're looking at this tool and you're saying,

936
00:48:30,880 --> 00:48:32,520
Speaker 3: what are the good things and bad things that can

937
00:48:32,520 --> 00:48:34,960
Speaker 3: come out of this tool? And I can sort of

938
00:48:35,160 --> 00:48:37,440
Speaker 3: that can inform my views of where to allocate capital,

939
00:48:37,480 --> 00:48:41,000
Speaker 3: where I can see returns. What is interesting to note too,

940
00:48:41,040 --> 00:48:43,799
Speaker 3: is that teams can be smaller now and achieve much

941
00:48:43,840 --> 00:48:47,120
Speaker 3: bigger things than before. A lot of people don't recognize that.

942
00:48:47,239 --> 00:48:50,400
Speaker 3: Like what used to be what you may have in

943
00:48:50,440 --> 00:48:53,680
Speaker 3: the past needed twenty thirty forty developers with now you

944
00:48:53,719 --> 00:48:56,760
Speaker 3: may be able to do with three developers just because

945
00:48:56,760 --> 00:48:59,680
Speaker 3: of the power of eye technology. So that's also changing

946
00:48:59,719 --> 00:49:03,359
Speaker 3: your adically, the way that investors think about productivity and

947
00:49:03,360 --> 00:49:08,279
Speaker 3: what they and what they're looking for. And again that

948
00:49:08,400 --> 00:49:10,960
Speaker 3: ratio isn't isn't isn't a real ratio today, but that's

949
00:49:11,000 --> 00:49:14,800
Speaker 3: where it could go. It's certainly we're certainly starting to see,

950
00:49:14,960 --> 00:49:17,640
Speaker 3: you know, developers become a lot more you know, valuable

951
00:49:17,680 --> 00:49:20,120
Speaker 3: if they're able to leverage a eye technologies, and that's

952
00:49:20,120 --> 00:49:21,440
Speaker 3: only going to accelerate over time.

953
00:49:21,520 --> 00:49:24,799
Speaker 1: Yeah, when you talk about machines doing surgeries, I mean,

954
00:49:25,360 --> 00:49:27,480
Speaker 1: I mean, how many coffees do my doctor have this morning?

955
00:49:27,520 --> 00:49:30,120
Speaker 1: How shaky is his hand? Maybe a robot doing that

956
00:49:30,200 --> 00:49:33,799
Speaker 1: isn't such a bad idea. Yeah, So you know, I

957
00:49:33,800 --> 00:49:35,200
Speaker 1: want to kind of I kind of want to wrap

958
00:49:35,239 --> 00:49:37,200
Speaker 1: this up. But you know, like I said, I write

959
00:49:37,200 --> 00:49:39,440
Speaker 1: this newsletter focusing on bitcoin and AI, and this is

960
00:49:39,480 --> 00:49:41,759
Speaker 1: this is right there in the crosshairs. Uh. And to

961
00:49:41,800 --> 00:49:44,160
Speaker 1: your point, part of the reason why I'm so interested

962
00:49:44,160 --> 00:49:46,279
Speaker 1: investing in this AI stuff is to the point that

963
00:49:46,320 --> 00:49:49,440
Speaker 1: you made, businesses can be so much more efficient. So

964
00:49:49,680 --> 00:49:51,440
Speaker 1: you said one guy could do the work of you know,

965
00:49:51,520 --> 00:49:54,400
Speaker 1: multiple people with this, And so when companies are more efficient,

966
00:49:54,480 --> 00:49:56,959
Speaker 1: that means they have more profits. And when they trade

967
00:49:57,000 --> 00:49:59,840
Speaker 1: at high multiples like tech companies do, so highre multiples

968
00:49:59,840 --> 00:50:03,840
Speaker 1: at profit levels typically means a pretty good return for investors.

969
00:50:03,840 --> 00:50:06,120
Speaker 1: But yeah, I want to I want to wrap this up. Amad,

970
00:50:06,320 --> 00:50:08,560
Speaker 1: You've been such a such, such such such a help.

971
00:50:08,560 --> 00:50:10,040
Speaker 1: I really appreciate all the insights that you've had. Like

972
00:50:10,080 --> 00:50:11,520
Speaker 1: I said, as someone who focused on this area, I

973
00:50:11,600 --> 00:50:13,200
Speaker 1: love to talk to the people in the trenches that

974
00:50:13,239 --> 00:50:17,239
Speaker 1: are building this out. Go ahead and just tell us,

975
00:50:17,280 --> 00:50:19,279
Speaker 1: you know, for everybody listening where they can follow up

976
00:50:19,280 --> 00:50:21,799
Speaker 1: with you or the company if they want to, you know,

977
00:50:21,840 --> 00:50:23,960
Speaker 1: follow what you're doing or learn more about Healwell or

978
00:50:24,000 --> 00:50:26,799
Speaker 1: in the company.

979
00:50:27,200 --> 00:50:27,439
Speaker 2: Yeah.

980
00:50:27,520 --> 00:50:30,040
Speaker 3: No, absolutely, Thank you very much. It's been awesome to

981
00:50:30,080 --> 00:50:34,680
Speaker 3: talk to you, and I share your enthusiasm around UH, bitcoin,

982
00:50:34,840 --> 00:50:38,399
Speaker 3: crypto and how that converges with business. I actually think

983
00:50:38,440 --> 00:50:40,800
Speaker 3: a lot of incentive models are going to be solved

984
00:50:41,360 --> 00:50:46,120
Speaker 3: by by by combining crypto and UH and real world problems,

985
00:50:46,120 --> 00:50:50,840
Speaker 3: for example, incentives around our health. You know, one of

986
00:50:50,880 --> 00:50:54,240
Speaker 3: our co founders in the UH, in the Heelwell project,

987
00:50:54,360 --> 00:50:56,879
Speaker 3: Brian paste Braga, you know, I.

988
00:50:56,800 --> 00:50:57,919
Speaker 2: Talked to him about this a lot.

989
00:50:58,000 --> 00:51:01,680
Speaker 3: He has a real vision that that that that there

990
00:51:01,680 --> 00:51:04,000
Speaker 3: may be a token in the future that that we

991
00:51:04,040 --> 00:51:07,200
Speaker 3: collect as a result of us as a result of

992
00:51:07,880 --> 00:51:10,600
Speaker 3: basically executing on things that are good for us UH.

993
00:51:10,680 --> 00:51:13,200
Speaker 3: You know, good good behaviors in our lives, you know,

994
00:51:13,280 --> 00:51:14,799
Speaker 3: you know, go for a walk, go for a run,

995
00:51:14,880 --> 00:51:17,520
Speaker 3: go for a swim. We collect tokens and somehow that

996
00:51:17,560 --> 00:51:24,600
Speaker 3: gives health. But yeah, yeah, yeah.

997
00:51:24,480 --> 00:51:25,240
Speaker 2: No exactly.

998
00:51:25,320 --> 00:51:28,040
Speaker 3: Well, those types of business models are already proliferating, but

999
00:51:28,080 --> 00:51:30,000
Speaker 3: I think I think they're going to get even more

1000
00:51:30,040 --> 00:51:34,680
Speaker 3: sophisticated with time. But look, yeah, we're Well Health Technologies.

1001
00:51:34,880 --> 00:51:35,560
Speaker 2: We are.

1002
00:51:35,800 --> 00:51:39,840
Speaker 3: You know, our company website is Well dot company, not

1003
00:51:39,840 --> 00:51:44,600
Speaker 3: not dot com, but dot company spelled out. And then

1004
00:51:45,400 --> 00:51:48,319
Speaker 3: heel Well dot a. I I'm the chairman of heal

1005
00:51:48,400 --> 00:51:51,279
Speaker 3: Well and one of the co founders of that, and

1006
00:51:51,320 --> 00:51:55,360
Speaker 3: we're on you know Twitter, uh, you know, Instagram, you

1007
00:51:55,360 --> 00:51:56,480
Speaker 3: know all the different socials.

1008
00:51:56,560 --> 00:52:00,000
Speaker 2: You know, we've got handles and all those so and

1009
00:51:59,840 --> 00:52:01,480
Speaker 2: and you know, feel free to engage with me.

1010
00:52:01,560 --> 00:52:06,920
Speaker 3: I'm just Hamid at Well and uh well Hamed at

1011
00:52:06,920 --> 00:52:09,239
Speaker 3: Well dot Company. And you know, it's been a real

1012
00:52:09,320 --> 00:52:12,759
Speaker 3: pleasure to to talk to you. Really really love the

1013
00:52:12,800 --> 00:52:14,879
Speaker 3: way that you approach these problems and think about them

1014
00:52:14,920 --> 00:52:17,400
Speaker 3: and uh you have you have a great podcast, and

1015
00:52:17,440 --> 00:52:18,360
Speaker 3: it's an honor.

1016
00:52:18,200 --> 00:52:18,640
Speaker 2: To be with you.

1017
00:52:18,640 --> 00:52:20,880
Speaker 1: Thanks so much. And you're also a publicly traded company

1018
00:52:21,200 --> 00:52:23,319
Speaker 1: on the Toronto Stock Exchange, I believe as well. Right,

1019
00:52:23,320 --> 00:52:26,360
Speaker 1: So that's well w E l l IF.

1020
00:52:26,920 --> 00:52:29,480
Speaker 3: Yeah I should have mentioned that on on the Toronto

1021
00:52:29,520 --> 00:52:34,200
Speaker 3: Stock Exchange. We trade at as well dot t O

1022
00:52:35,040 --> 00:52:37,719
Speaker 3: uh and heal Well trades at as A I d

1023
00:52:38,040 --> 00:52:43,080
Speaker 3: X dot t O uh so so so y a

1024
00:52:43,200 --> 00:52:45,799
Speaker 3: I d X so a I and then d X

1025
00:52:45,920 --> 00:52:49,239
Speaker 3: is the abbreviation for a diagnosis, So a I d

1026
00:52:49,480 --> 00:52:53,600
Speaker 3: X dot t O and uh. And we we also

1027
00:52:53,680 --> 00:52:58,280
Speaker 3: trade on on the O t C uh so I think,

1028
00:52:58,719 --> 00:53:02,680
Speaker 3: wells is w you h t C F.

1029
00:53:04,160 --> 00:53:05,240
Speaker 2: And uh.

1030
00:53:05,400 --> 00:53:07,759
Speaker 1: Here let me for everyone listening, we'll go ahead and

1031
00:53:07,760 --> 00:53:09,520
Speaker 1: make sure link that in the description and the show

1032
00:53:09,560 --> 00:53:11,920
Speaker 1: notes down below. So make sure you go, yeah, we'll put.

1033
00:53:11,800 --> 00:53:13,840
Speaker 3: In the link there. But but yeah, we trade on

1034
00:53:13,880 --> 00:53:16,760
Speaker 3: the OTC. We'll get you those links, those those tickers

1035
00:53:16,760 --> 00:53:17,120
Speaker 3: as well.

1036
00:53:17,160 --> 00:53:19,400
Speaker 1: Perfect. Well, Hammad, again, I really appreciate you taking the

1037
00:53:19,400 --> 00:53:20,839
Speaker 1: time to talk to me. I loved, like I said,

1038
00:53:20,840 --> 00:53:23,680
Speaker 1: talking to the founders and the trenches. It's important for

1039
00:53:23,719 --> 00:53:25,080
Speaker 1: me as an investor to kind of get in and

1040
00:53:25,080 --> 00:53:27,360
Speaker 1: dig in. So anyway, thanks so much for for doing that,

1041
00:53:27,440 --> 00:53:30,319
Speaker 1: and and uh and and really just overall, I mean

1042
00:53:30,440 --> 00:53:32,239
Speaker 1: what you're doing to improve health because like I said,

1043
00:53:32,239 --> 00:53:35,200
Speaker 1: it's a it's a massive growing problem, and so I

1044
00:53:35,239 --> 00:53:37,480
Speaker 1: appreciate founders going and attacking a big problem.

1045
00:53:37,520 --> 00:53:39,960
Speaker 2: Thank you, thank you. I really appreciate that, all right,

1046
00:53:40,000 --> 00:53:41,480
Speaker 2: thanks so much, thank you.