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Ryan Pineda · Watch on YouTube · Generated with SnapSummary · 2026-09-18

00:00 Daario and Open AI saying that there's a

00:03 good chance AI is going to kill us all

00:04 and that the government needs to

00:06 regulate them and we need to slow down.

00:07 What do you think?

00:08 >> I think they're foolish. Some of the

00:10 people right now telling us that AI is

00:11 going to kill everybody and they need to

00:12 be regulated. We're telling people only

00:14 criminals use Bitcoin and the government

00:16 needs to regulate it. Look at the exact

00:18 same people 50% of all entry- level jobs

00:20 are going to be destroyed by 2030.

00:22 >> Yeah.

00:22 >> My first question is always show me your

00:24 math. I think there's three assets.

00:25 Bitcoin, gold, and land.

00:27 >> I said land was making a comeback. If

00:28 you look over the last 5 years, the S&P

00:30 is up 70%. Give or take.

00:32 >> Mhm.

00:33 >> In that same 5year period, the Bitcoin,

00:35 gold, and land, if you just put 33% of a

00:38 portfolio in each one of those, it's up

00:39 about 170%. The government will never

00:41 ever stop printing money. As long as you

00:43 understand that one idea, you can become

00:44 wealthy beyond your imagination.

00:46 >> Yeah, that's what I want to ask you.

00:47 How's Mark behind closed doors?

00:48 >> The only story I ever tell, we were in a

00:50 meeting. There's 15 people maybe in this

00:52 meeting, and he used All right, so

00:55 there's a lot of drama going on in the

00:56 AI world right now. You got uh Daario

00:59 and Open AI saying that there's a good

01:02 chance AI is going to kill us all 10%

01:04 20% and that the government needs to

01:06 regulate them and we need to slow down.

01:08 What do you think?

01:09 >> I think they're full of But uh I

01:12 also think that they're being

01:13 intelligent about what they should do

01:14 for their business. Two things can be

01:16 true at the same time. I don't think

01:18 that AI is going to kill us. I don't

01:19 know about you, but there's near 0%

01:22 chance of that. I keep joking that if AI

01:25 was so good that it could kill us, they

01:26 should just use it to build profitable

01:27 companies first, right?

01:30 >> But more importantly is when have you

01:33 ever heard an entrepreneur say the

01:36 government needs to take control of my

01:38 business? The government needs to

01:39 regulate.

01:40 >> It's literally never.

01:42 >> Now the other side of this is you know

01:44 who's not asking people to slow down?

01:46 Who's not asking the government to

01:47 regulate them? Mark Zuckerberg. Why is

01:50 Mark Zuckerberg not asking for us to

01:53 slow down? Because Mark Zuckerberg is

01:55 the man. He is the incumbent. He is the

01:58 guy who has a super profitable business

02:02 that he can siphon those profits over to

02:03 the AI side.

02:04 >> Yeah.

02:05 >> Open AAI, Anthropic, Grock, all these

02:09 guys, they're the challengers. So the

02:12 challengers realize is this thing is

02:13 really capital intensive.

02:14 >> Yeah. you'd much rather be Google and

02:16 Facebook or Meta than you would be the

02:19 challengers from a perspective of who's

02:21 got the most money to be able to invest.

02:23 So what I think is happening is these

02:25 guys are basically trying to slow down

02:26 the industry, use government regulation,

02:28 etc. to even the playing field because

02:31 they know that they're out resourced

02:33 from these large companies like Meta and

02:35 Google. If they can get things to slow

02:37 down and kind of level that playing

02:38 field, now they got a better shot.

02:40 >> Yeah. The other thing that's happening

02:42 that nobody is talking about yet, I

02:44 would love to see the per customer

02:49 growth of revenue at OpenAI and

02:52 Anthropic. And I'll give you a direct

02:54 example. We have a public company with a

02:58 product called Sylvia.

03:00 Sylvia uses some of our own internal

03:03 models. It uses some of the frontier

03:06 models. We used to pay a lot of money to

03:10 the frontier models. We pay

03:12 significantly less to them today because

03:14 when we were using their models, we

03:16 realized we have no sovereignty. We have

03:19 no control. We don't know if this is

03:22 actually the best way to do the AI for

03:23 our users or not. So we started making a

03:26 massive investment in building out our

03:27 own models, our own AI harness, our own

03:30 file system, all this proprietary

03:31 technology. So even though we have more

03:34 users today, we have more queries on our

03:36 platform, everything has continued to

03:38 grow, we spend less money with them

03:40 today than we did before because it was

03:43 too expensive and it was too general.

03:46 Every CEO I know in the technology

03:48 industry right now is going through the

03:49 exact same exercise. Why am I paying

03:51 them so much money if their technology

03:54 is not built for my use case? And why is

03:57 it so expensive compared to if I just

03:59 run it myself?

04:00 >> Yeah. So, they're still growing their

04:02 company, but it's because they're

04:04 signing up new users. It's not because,

04:06 hey, I used to pay them $100,000 a month

04:08 and now I pay them a million,

04:10 >> right?

04:11 >> And so, when they go public, we're going

04:12 to see all this data. It's going to be

04:13 very fascinating to see how many

04:15 companies are actually reducing their AI

04:17 bills. RAMP, which is the big uh

04:20 finance, you know, fintech company,

04:22 >> they recently showed that the top

04:24 companies using AI, their AI spend

04:27 overall on a per company basis is down

04:29 almost 10%. in the last quarter.

04:32 >> Yeah.

04:32 >> So, companies ran and adopted the

04:34 technology. It exploded

04:36 and then everyone's like, "Wait a

04:37 minute. We're using too much of this

04:38 stuff. It's not efficient." You know,

04:40 I've got employees.

04:41 >> It wasted. Well, it's just like I got

04:42 employees with six different

04:43 subscriptions to all these services.

04:45 Maybe they only need six. They only need

04:46 three.

04:47 >> Yeah.

04:48 >> And so, that's why costs are, you know,

04:49 coming down. The expenses are coming

04:51 down. And so, I think there's a lot of

04:52 nuance in here that is feeding into the

04:54 AI is going to kill everybody.

04:55 >> Yeah. I was watching they just did the

04:57 all-in podcast and they had Jensen on

04:59 and Trump called in and you know all

05:01 that stuff yesterday and it was

05:04 fascinating to for me to think about

05:05 because you know Jensen uh CEO of Nvidia

05:09 was talking about how

05:10 >> none of their predictions have come

05:11 true. Not one of them. He's like you

05:13 know they say we're going to lose all

05:14 these jobs. The jobs are still here.

05:16 They say that GBT2 was too powerful and

05:19 and it was dangerous. It's not. you

05:21 know, then they say this and they say

05:23 that and um it's like, dude, as a

05:27 private company that's trying to go

05:29 public for all of them, uh if you have a

05:31 dangerous thing, it's on you to like

05:34 figure it out and solve it because uh if

05:37 your your thing does something, you're

05:39 not going to end up, you know, being

05:41 able to go public for what you want.

05:42 >> Here's what I ask you. Do you trust

05:43 them?

05:44 >> No.

05:45 >> Do you trust them?

05:46 >> No.

05:47 >> I don't think I trust them either. I

05:48 still use the products.

05:50 >> Yeah. It's a very weird dynamic, right?

05:52 The products are valuable. Anyone who's

05:54 like claw's not valuable, you're a

05:56 >> For free or for $20 a month, like what?

05:59 >> Dude, these things are incredible. This

06:00 is

06:00 >> the best tool ever for $20. Like,

06:02 >> this is insane.

06:03 >> Yeah,

06:04 >> but people are using the product despite

06:06 the fact that they don't trust these big

06:09 companies. Now, this is not new.

06:10 >> We don't trust Facebook.

06:12 >> I was going to say the government

06:13 >> all throughout history, I don't trust

06:15 Google. I don't trust Facebook. Like

06:16 this is this is what happens is people

06:18 see a new technology, they start to use

06:19 it, they find it valuable and they're

06:20 distrustful of that new thing. Mainly

06:23 because we don't have that much detail.

06:26 >> Like if you look at Meta now, you may

06:28 not like some of the decisions Meta

06:29 made. You may not think that they've

06:31 done everything right, whatever. But you

06:34 kind of have like for 25 years or 20

06:37 years, Mark Zuckerberg has been who he

06:39 is and he's made certain decisions and

06:41 so you kind of like know what you're

06:42 getting.

06:43 >> Yeah.

06:44 Do you know what Daario is going to

06:46 decide in certain situations? You don't

06:49 have a lot of data points and the data

06:50 points you have may give you a little

06:52 bit of pause for concern.

06:53 >> And Alman's track record's really scary

06:55 when you look into it.

06:57 >> So, this is where it gets into, you

06:59 know, there's these like public

07:00 narratives that take over. And if you're

07:03 one of these companies and you think

07:05 it's going to be to your strategic

07:06 benefit for the public narrative to be

07:08 AI is really dangerous, then you start

07:10 telling people that. But there's a whole

07:12 another side of the equation which is

07:14 like think of social media.

07:16 We all have kids. How many people tell

07:19 you social media is really bad for your

07:20 kids? Don't put your kids in front of

07:21 the the screen. You know, all this like

07:23 bad mental health. Let's just say that

07:26 all that's true, which is probably

07:27 directionally is right. I still think

07:30 social media is a net positive for

07:32 humanity.

07:33 >> Connects people, they learn, they meet

07:35 their spouse, they get jobs. Like

07:36 there's all this, you know, benefit.

07:38 >> Yeah.

07:38 >> You're going to literally distribute

07:39 this podcast on social media, right? Y

07:42 >> so it's like it can be true that there

07:44 are negative side effects but it's still

07:46 a net positive

07:47 >> right

07:47 >> AI 100% will affect some jobs but the

07:51 data is showing us that AI is creating

07:52 more jobs than it's hurting so it is

07:55 then gets into this nuance conversation

07:57 of like well which jobs get hurt

07:59 >> maybe those people are going to have to

08:00 figure out what to do but overall as a e

08:02 economy and society like we're gonna

08:04 have more jobs not less and so these

08:06 guys who are like we're gonna you know

08:07 what they say 50% of all entry- level

08:09 jobs are going to be destroyed by 2030

08:10 30.

08:11 >> Yeah.

08:12 >> My first question is always, show me

08:13 your math.

08:15 >> It's just a statement.

08:17 >> What's the difference between 2029,

08:19 2030, and 2031?

08:21 >> Yeah.

08:22 >> Social Security. The government will

08:24 tell you that Social Security is going

08:25 to be out by like 2033. We're going to

08:27 literally be bankrupt at Social

08:28 Security. But they'll show you the math.

08:29 They're like, "Here's how much money we

08:30 have. Here's how much we owe. Here's the

08:32 growth rate of how much we owe. And like

08:34 you just do math and like we're out of

08:36 money by 2033." Like, by the way,

08:38 there's a bunch of stuff that could be

08:39 done to prevent us from losing all the

08:41 money or like going bankrupt, but like

08:42 that math pencils. And I understand how

08:44 you got to your number.

08:46 20 30. Why 20 30? It's just round

08:48 number.

08:49 >> Yeah.

08:49 >> 10%. Well, what's the difference between

08:52 5, 10, and 15% likelihood that it kills

08:54 us?

08:55 >> Yeah. Where's the number coming from?

08:57 >> So, okay, let's just all agree that they

08:59 just made it up. Fine.

09:02 The other way to report it is there's a

09:04 90% chance that the guys who are most

09:06 scared by AI think it's not going to

09:09 kill us.

09:11 Okay? Like I'm going to air on the side

09:14 that they are directionally correct, but

09:15 they're off on the number.

09:16 >> Do you think they really believe that or

09:18 you think it's just all business play to

09:20 benefit themselves?

09:21 >> If you thought you were building a piece

09:22 of technology that had a 10% chance of

09:24 killing people, would you stop?

09:28 >> I would hope yes. You're thinking about

09:30 it too long, brother.

09:31 >> Well, I'm just thinking like, dude, cuz

09:33 we've built a lot of

09:33 >> 10%.

09:34 >> Well,

09:35 >> that's a lot.

09:35 >> No, but we've built a lot

09:36 >> to kill all society. Or you're talking

09:38 about kill yourself.

09:39 >> Just kill people. Like

09:41 >> we don't know what they think. Do they

09:42 think they're going to die?

09:43 >> Yeah. Like because cars kill people,

09:45 alcohol kills people,

09:47 >> but that's like to travel. AI is just

09:49 like a

09:50 >> they're all tools. Are you Are you AI

09:52 non-believer?

09:52 >> I am an AI non-believer, actually.

09:54 >> Oh, explain more. I would love to hear.

09:55 >> I'm an AI believer. He's a non-believer.

09:57 >> I'm a non-believer. I'm a skeptic.

09:58 Amazing.

09:59 >> So I think

10:00 >> I met one of you in the wild. Wow.

10:02 >> Really?

10:03 >> I'm joking.

10:03 >> So I'm like I'm like I think AI

10:06 >> also he's also heavy Democrat now.

10:07 >> I'm I'm a big Democrat big uh Biden fan.

10:11 >> The good news. He's really not there.

10:14 The good news is they know how to spell

10:15 AI. So we were starting there. Okay,

10:17 let's go.

10:17 >> So this is what I think. I think AI is

10:20 the modernday

10:21 internet. So the people in the tw in in

10:24 their 20s and 30s, they didn't go

10:26 through the internet rolling out where

10:28 it actually affected us. Like so I think

10:30 AI is going to end up being like the

10:32 internet, it's just another tool that's

10:33 on our phone and we use it and it's

10:35 great. I don't think it's going to kill

10:36 us. I don't think it's going to wipe out

10:38 50% of jobs. I think it's just I think

10:40 it's a bunch of bunch of hype. Kind of

10:42 like when crypto first came out and

10:43 they're like they're going to get rid of

10:44 the banks and you're going to pay with

10:46 this and blah blah blah and then oh it

10:48 all went away and it was all hype and

10:50 everything's still.

10:51 >> So you are a AI believer. you're just a

10:52 believer that it's like a more normal

10:54 technology and the hype is not true but

10:56 the the doomsday is not true either.

10:58 >> The best the best way to think about it

11:00 is I think it's just a another internet.

11:03 >> Have you ever read the book rational

11:04 optimist? No.

11:05 >> That's what you are.

11:06 >> Really?

11:06 >> Rational. It's a great book. It's

11:08 probably one of my favorite books to

11:09 ever read. Rational optimist is

11:10 basically this idea of like I believe

11:11 that uh the world is going to improve

11:14 and all this stuff, but I just don't buy

11:15 into like the hype cycles.

11:16 >> Yes.

11:16 >> That that's basically you're a rational

11:18 optimist when it comes to

11:19 >> Yeah. But that's what I've seen like my

11:20 whole

11:21 >> So you're a believer

11:22 >> in AI. Yeah. I'm a Christian believer.

11:23 Yeah.

11:24 >> Okay. Both.

11:25 >> Yeah. Exactly. Yeah. So I don't I don't

11:27 I'm not like so sold even like the doing

11:30 the jobs thing. I think robots and stuff

11:32 like that will take more jobs than AI

11:34 because AI needs to be able to like do

11:37 things

11:39 >> like I don't know if I agree with that.

11:40 >> Yeah. I don't know cuz and when I say a

11:43 like robots even like screens and stuff

11:45 like that, I think that's more powerful

11:48 than like AI. Like I I went to

11:50 McDonald's the other day.

11:52 >> Yeah.

11:52 >> And

11:53 >> they got you working for them.

11:54 >> Yeah. So So I went to McDonald's, right?

11:57 And the inside of the McDonald's looked

11:59 like this room. It's just a big room

12:02 >> and then there's kind of sad.

12:03 >> Yeah. And then there's just a couple

12:04 screens and there's no employees there

12:06 in the front. So like that will take

12:09 jobs because obviously the cashiers I

12:11 went to the pharmacy the other day at

12:12 Walgreens there's no pharmacist there.

12:14 You walk up they ask for your birthday

12:17 and then they'll say hey do you need to

12:19 talk to a pharmacist?

12:20 >> No. Okay thank you. And that's it.

12:23 >> I went and picked up some medicine

12:24 recently for my son. Same thing. And

12:26 they just like bring it out and you're

12:27 just like okay like

12:29 >> I kind of wanted you know like little

12:31 like instructions. Yeah. No, no. I I

12:33 just wanted like a little human touch to

12:34 this rather than just like

12:36 >> or call restaurants, you call the DMV,

12:39 you're talking to, you know, AI, but

12:41 like

12:41 >> that might be better than

12:42 >> Yeah, that's way better.

12:44 >> That's way better cuz you can call in

12:45 the middle of the night and you

12:46 >> You sound like a big AI advocate.

12:48 >> Yeah. But I'm just saying it's not going

12:49 to be bad. It's like people are

12:51 thinking, "Oh, it's going to kill the

12:53 economy. It's going to kill us. It's

12:55 going to do all these things." I'm just

12:56 not sold on that.

12:57 >> So, you and I actually are very aligned.

12:59 >> Yeah. And I think it's I think a lot of

13:01 people are going to get rich from

13:03 selling the dream and then these

13:05 companies are going to go bankrupt and

13:06 then you know they made a bunch of money

13:08 and they and they got paid. And

13:10 >> what AI products do you guys use?

13:12 >> I mainly use Claude.

13:13 >> Claude, that's the main one.

13:15 >> I'm old school.

13:16 >> But like you don't use any of the

13:18 applied AI products.

13:19 >> So my tech guy, we actually just built

13:20 an app for M19, my golf mastermind

13:23 that's super sick. And I was asking I

13:24 was like, "Did you use Claude co-worker

13:26 or what did you He's like, "No, it was

13:27 like four different things that we I

13:29 couldn't tell you what he used."

13:29 >> And he he used it all. Yeah.

13:31 >> Yeah. You guys know I love the game of

13:32 golf and that is why I started M19. It

13:35 is a golf mastermind for entrepreneurs

13:37 who are doing over seven figures in

13:38 their business and who want to go on

13:40 bucket list golf trips together. We

13:42 already have hundreds of members all

13:44 across the country and we've been to

13:45 some of the best courses in the world.

13:47 Places like Pebble Beach, Pinehurst,

13:49 Bandon Dunes, and many others. The

13:51 course is the best place to do deals,

13:52 make great relationships, and have fun.

13:54 So go to mastermind 19.com today.

13:57 >> So like my general view of the world is

13:59 uh claw chatbt they're trying to build

14:01 general intelligence right they they

14:03 basically want to create something that

14:04 is like having a very smart friend and

14:07 we all have a friend that

14:08 >> the smart Google that's how I see

14:10 >> yeah but like you can call that smart

14:11 friend and you can be like hey do do you

14:13 know anything about and you can almost

14:15 throw any topic at them and they'll like

14:17 have something to say or something

14:19 intelligent.

14:19 >> Yeah. But you don't go to that friend

14:22 when you're like, I need a doctor or I

14:24 need like a tax expert. You go to the

14:27 expert on the specific vertical thing.

14:30 >> I don't I go to AI for my doctor.

14:32 >> Do you?

14:33 >> Yes, 100%. Like my doctor sucks compared

14:35 to my AI. Like it's like

14:37 >> give me an example what you would.

14:39 >> So I'll give you an example. So earlier

14:41 this year I was having like uh anxiety,

14:44 right?

14:45 >> So I was like what the hell's going on?

14:46 So, I took my blood, all this stuff,

14:47 right? And my doctor was like, "Oh,

14:51 well, you know, we're going to go give

14:52 you this blood test and then you could

14:55 come back in two weeks." So, I do the

14:57 blood test, I go home, and then I was

14:58 like, "Bro, this is this is going to

14:59 take too long." And Whoop has an AI

15:02 doctor attached to it. So, I I signed up

15:06 for the Whoop blood test. I went took my

15:08 blood test. That evening, I had my

15:10 results back and the AI was already

15:12 telling me like, "Okay, you're

15:13 dehydrated. your hemoglobin's high, like

15:16 this is high, this is high. And I and I

15:18 was like, "Okay, how do I fix it?" It

15:19 told me what to do. And by the time the

15:22 doctor actually I actually had the

15:24 appointment with the doctor, I was like,

15:26 "Hey, I'm good." Like I already know.

15:27 >> So to me, this is actually like uh the

15:30 the medical professional was augmented

15:32 by the technology.

15:33 >> Like to get the blood drawn. Yeah. The

15:35 AI can't do that. No.

15:36 >> Right. You had to go like go to a

15:38 professional, whatever. So I actually

15:41 think that what you're describing is

15:42 where most of the value is going to get

15:43 created which is it is all about

15:45 augmentation of the experts or in if

15:48 it's just a software world doesn't

15:50 involve the physical world then you're

15:51 going to have very vertical specific

15:53 type things. So like chat GPT is not

15:56 great at answering a ton of questions

15:59 where people are going to go build

16:00 vertical specifics like for us personal

16:01 finance

16:03 >> we don't think that Claude is nearly as

16:05 good from an accuracy standpoint. Do you

16:07 think Claude is a better doctor than

16:10 your normal primary doctor?

16:13 >> It depends because if you just go to

16:16 Claude right now, you give it no context

16:17 and you say, "What's wrong with me?" I

16:18 think it's a horrible doctor.

16:19 >> But if you give it all the context,

16:21 >> okay, but if you describe the pain, then

16:26 it could be like, "Well, it could be

16:27 this, it could be that, it could be

16:28 this." What a doctor would do. But then

16:29 what's the doctor say? Go get a blood

16:31 test. Go get the EKG. Go get the MRI.

16:34 >> More context. And then the doctor is

16:35 like trying to problem solve the thing.

16:38 Claude needs the same thing. Claude

16:39 needs the blood. You know, you took the

16:41 blood.

16:41 >> It needs all the inputs.

16:42 >> Correct.

16:43 >> It does. But let's just say for for my

16:45 for my scenario. I This is personal

16:48 health. So I had high hemoglobin, right?

16:52 >> I I told Congratulations.

16:53 >> Thank you. I told Chad

16:55 >> high is good, right?

16:56 >> I think so. So I was like, "Hey, I have

16:59 I have high hemoglobin." And it was

17:01 like, "Okay, go take this test, this

17:02 test, and this test." I went to my

17:04 doctor. I was like, "Hey, I want to do

17:06 these tests." I told my doctor, "I want

17:08 to do these tests."

17:09 >> I got these tests. They gave me results.

17:11 It said, "Hey, you probably have sleep

17:13 apnea or XYZ." So, I was like, "Okay,

17:17 hey doctor, like I I I want to get a

17:19 sleep apnea test." They sent me to the

17:21 sleep apnea test.

17:22 >> Guess what? I was positive for sleep

17:24 apnnea. Okay. I told Claude, "Hey, now

17:26 what do I do?" It's literally and my my

17:29 doctor is has no idea. the best part of

17:32 what you're saying

17:32 >> might also have the wrong doctor.

17:33 >> Yeah.

17:34 >> But but it doesn't have all the context

17:36 because with with AI I could I could

17:39 send my blood work. I could send my

17:41 Whoop scores. I could send my stools. I

17:43 could send everything all day and it has

17:46 all this data where my doctor I get to

17:48 sit down with them for 15 minutes and

17:50 give them like feedback.

17:51 >> You hear what he's describing? Remember

17:53 we're talking about what Sylvia is?

17:54 >> Yeah.

17:54 >> The exact same thing he's describing

17:55 right now is what we do for personal

17:57 finance.

17:58 >> Exactly. He blood test, whoop, all this

18:01 stuff. He's putting all the context into

18:04 the AI. Yeah.

18:05 >> And then he's talking to it. And the AI,

18:07 the more context you give it, the more

18:10 value. So if you just give it whoop,

18:11 >> it's better than not having it. But then

18:13 the blood, then your weight, then your

18:15 sleep, then you could health, whatever.

18:18 >> You could give it everything. Where a

18:20 doctor, it's like you're going to walk

18:21 in and they're like, "Oh, well, how you

18:23 been feeling? Okay, let's let's go do a

18:26 blood test. It's going to take two

18:27 weeks." So then after that, you know, we

18:29 have this and it just takes so long.

18:31 With AI, it's just so much better.

18:33 >> But yeah.

18:34 >> Yeah. And you're you're just saying with

18:36 Sylvia, you guys do it the exact same

18:38 way that people feed you your P&Ls.

18:40 They're

18:41 >> take the doctor and his example. That is

18:44 >> your lawyer, accountant, financial

18:45 adviser, whatever. They How many, you

18:47 know, take a financial adviser. How

18:48 often do you talk to them? Once a

18:50 quarter, once a year, right?

18:52 >> At the end of the year.

18:53 >> Yeah. Whenever

18:54 >> you're like my tax bill. Instead with

18:56 Sylvia, somebody comes in and they

18:57 attach their bank account, their

18:58 brokers, their crypto account. They

18:59 upload their credit cards. Then they

19:01 attach their private investments, cars,

19:03 collectibles, real estate, etc. So, you

19:04 give it all this information. You can

19:06 even upload your tax returns. You can

19:08 put your trust documents, all this kind

19:09 of stuff. And I tell people the first

19:10 thing to do, you go to sylvia.com, you

19:13 upload it all, and then you simply say,

19:14 "Enter me to get the information you

19:16 need to be valuable to me."

19:18 >> And she starts asking you questions. Are

19:21 you married? Do you have dependence?

19:22 What city do you live in? and all this

19:24 information that she can glean and then

19:26 you start talking to it. And what I did

19:28 was I just said, "Here's my goals over

19:30 the next three years. I want to

19:31 accomplish A, B, and C things." They

19:33 weren't like, "I want to have X amount

19:34 of money." It was like, "I have a very

19:37 specific thing I want to do from an

19:38 estate planning standpoint. I haven't

19:40 done a lot of it. This is what I want to

19:41 eventually get to. I don't even know how

19:43 to get there, but you have all the

19:44 context of my life. Like, help me create

19:46 a plan that I can execute against over

19:48 for the next three years." Mhm.

19:49 >> And what you're describing in your

19:51 doctor example, Sylvia starts to then

19:53 say, "Okay, you should do this. You

19:55 should think about this, whatever." But

19:56 what I find interesting is um if you go

19:59 and you ask Google, for example, how do

20:01 I get my tax rate down?

20:02 >> Oh, bro,

20:03 >> I I have no context. I can't I can't

20:04 help you. So, they just give you very

20:06 generic ad advice. Same thing with the

20:08 traditional, you know, claude, etc.

20:11 With Sylvia, if you ask how do I get my

20:12 tax rate down, she will go asset by

20:14 asset and tell you that piece of real

20:16 estate you own, you can actually bonus

20:17 depreciate it because it's in this zip

20:19 code or it's an opportunity zone, you

20:21 know, opportunity, right? Because it's

20:23 in that zip code. That stock you have,

20:25 this stock is up. Don't sell it. Sell

20:28 the stock that's down because we can tax

20:30 harvest that rotate into this thing. For

20:33 me personally, I have a family office, I

20:36 have an accountant, I have a lawyer, I

20:38 have all these people, a private bank I

20:39 work with, etc. Sylvia last year gave me

20:42 two ideas on the tax side that none of

20:44 them had ever told me before. It's not a

20:47 knock against these people. It's just it

20:49 was super

20:50 >> infinite knowledge.

20:50 >> It was super nuance and it was like

20:52 because this is true, this is true, this

20:54 is true, and this is true. You could do

20:56 this, right?

20:57 >> And I went to the accountants and I was

20:58 like,

20:59 >> this is a little weird, but uh could I

21:01 do this thing? And the accountants were

21:03 like basically like how the hell did you

21:05 find like where did that come from? I

21:06 was like, oh, I used Sylvia. And they

21:07 were like,

21:08 >> damn. Yeah, it does work.

21:11 >> And so I was like, okay, that's where to

21:12 me AI is valuable, but it's all about

21:15 feeding it context

21:16 >> and then using the intelligence that it

21:19 has for a specific use case.

21:20 >> So the thing that we hang our hat on

21:22 with Sylvia is we are more accurate than

21:24 OpenAI, Chat, GPT, Anthropic, you know,

21:27 all these things in tax, mortgage,

21:29 credit cards, etc.

21:31 >> So if we can be more accurate, that

21:34 ultimately should drive trust. If we can

21:35 drive trust, then people will use it

21:38 more. the more they use it, the smarter

21:40 it makes the system because it's

21:41 learning from all the edge cases. And so

21:43 the more people that use it, the more

21:45 often they use it, the better the system

21:46 gets. Like it just becomes this

21:48 reinforcing loop that this thing should

21:50 pull away from everybody over time

21:52 >> because of that.

21:53 >> I have I have the thing I'm confused

21:54 about, not to cut you off, like two

21:56 things. One, the other day I was on chat

21:58 GBT and it like it it changed screens

22:02 and it said, "Hey, we have this new

22:04 thing. You could connect your bank

22:06 account." And I was like, uh, I thought

22:08 about it. I was like, you know what? I

22:10 don't trust it. And I tried to wipe like

22:12 swipe off of it and it was like it had a

22:14 countdown where I couldn't swipe off of

22:16 it and I was like, "Oh, that's weird."

22:18 So that was weird. But another thing,

22:20 >> you ever you ever been building a

22:21 company and had a trillion dollar

22:22 company try to compete against you?

22:24 >> Yeah. No.

22:25 >> Yeah.

22:25 >> That's what you're going for.

22:26 >> I'm living that right now.

22:28 >> You know the best You know, the best

22:29 part is

22:29 >> what?

22:30 >> Everyone used to give us a knock and say

22:32 that, "Oh, we just built a chat GPT

22:34 rapper."

22:35 >> Mhm. They literally launched a chatbt

22:37 wrapper.

22:38 >> Oh, they just launched that.

22:39 >> Yeah, it's just a wrapper around CHBT.

22:41 That was my question. There's no custom

22:42 technology there for the use case.

22:44 >> That was my question with AI companies

22:46 because let's just say you you create a

22:48 a great CPA company or any company, a

22:50 health company on AI. What's because

22:54 everyone has access to the same tools

22:57 almost. What stops any of these

23:00 companies from just ripping each other

23:01 off? Can't like Wells Fargo come up with

23:04 the same AI finance stuff that anyone

23:07 else can?

23:07 >> Well, there's two there's two elements.

23:09 Uh, distribution becomes much more

23:11 important.

23:11 >> Exactly.

23:12 >> I would have a much harder time building

23:14 Sylvia

23:14 >> if I didn't already have millions of

23:16 people who follow me who are independent

23:18 investors who I've been creating content

23:20 for 10 plus years. They trust me. They

23:22 understand how I think. They understand

23:24 that I built the product for myself.

23:26 >> Yeah.

23:26 >> Shane, my co-founder, right? We

23:28 literally built it for me. And then once

23:30 it was cool, it was like, "Okay, now I

23:32 can roll it out." And people are like,

23:32 "Hey, if it's good enough for him, I

23:34 want to try it." Type thing. So

23:35 distribution is going to always be

23:36 important.

23:37 >> But then also the technology that Wells

23:41 Fargo uses, no disrespect to them. It's

23:43 not like the best AI engineers are like,

23:45 "Let me go work at Wells Fargo."

23:47 >> Yeah. But couldn't they though? Can they

23:48 just rip off? Do you get what I'm

23:50 asking? Like all AI companies, can't

23:52 they just rip each other off like so

23:54 easily and then it comes down to just

23:56 branding?

23:57 >> Well, you you also have to get into the

23:58 training thing. So like you let's use a

24:00 direct example just because I've been

24:01 working on it for so long.

24:03 >> Sylvia started out just using the legacy

24:06 models. So you would connect all your

24:08 accounts and when you would ask a query,

24:10 we told you, hey, we're just going to

24:11 feed it to one of the models with the

24:12 context of your personal finance.

24:14 >> Over time, we realized that there was a

24:16 bunch of challenges. So for example, it

24:17 would hallucinate, it wasn't accurate,

24:19 all this kind of stuff. So if you fast

24:21 forward a year and a half now, we had to

24:23 go build an entire file system. So like

24:25 memory,

24:26 >> we had to go build a model router

24:28 because when you ask a question to

24:29 chatbt, they're only going to serve it

24:31 to open AAI models. If Claude can answer

24:34 it better, Open AAI is not about, hey

24:35 Claude, can you answer this for me?

24:37 >> They keep it within their family. So by

24:38 being a neutral third party, we route it

24:40 to the right model,

24:41 >> then we had to build a whole AI harness

24:43 that does data retrieval, all this kind

24:45 of stuff. And so let's use a concrete

24:48 example of if you ask a tax question to

24:50 ChatGBT. ChatGBT says, "Okay, my model

24:53 was trained on a certain set of data,

24:55 right? And let's say that they trained

24:56 it up until December of 2025.

24:59 >> If a tax rule changed in January of

25:02 2026,

25:03 >> the model doesn't know that."

25:05 >> So what they do is they say, "Well, that

25:06 might happen often. So why don't after

25:08 we look at the model, then we'll do web

25:10 search. We'll go search on the internet

25:11 just like you or I would, etc. We're

25:12 going to look at blog posts, articles,

25:14 etc., and we're going to try to find the

25:15 information.

25:16 >> Mhm.

25:16 >> What we do is when you ask a tax

25:18 question is we actually built an entire

25:20 database where we took the entire

25:22 federal tax code, all of the states, we

25:24 put it in the database and then we made

25:26 it really easy for the agent to know

25:27 where everything is. So when you ask a

25:29 tax question, it doesn't go search the

25:30 internet. It doesn't look at what it was

25:32 trained on in past data. It just says,

25:34 I'm going to go look at the data that is

25:37 accurate today.

25:38 >> Yeah.

25:38 >> So every day we refresh that database

25:40 with the latest tax rules. Yeah.

25:42 >> So when you ask, we're just going and

25:44 pulling the actual federal tax code.

25:45 >> But can't any other company do the exact

25:47 same thing?

25:47 >> No. So I'll make it simple.

25:50 >> So like I own a tax firm and so I

25:52 actually the the problem you encountered

25:54 I uh encountered as well when I was just

25:56 researching. So I was actually doing a

25:59 presentation about QBS and how it had

26:02 changed and

26:03 >> every business owner should do it

26:04 >> and yeah and like you should get a CC

26:05 corp and this is a great idea and you

26:08 know they changed the the amount. used

26:10 to be 10 million and then it became 15.

26:12 But I was getting data from chat that it

26:14 was still 10 million because that was

26:15 the old data. So I was like, "Wow, like

26:17 it doesn't even know that that's wrong."

26:18 And I'm like, "Chat, that's not true."

26:20 Like I knew what the rule is. And it's

26:22 like, "Oh, you know what? Now that we

26:24 looked, yeah, you're right." And it's

26:26 like I'm helping train essentially their

26:29 model for them. But the way I would

26:31 equate this to is because I I' I've now

26:34 that I've I've seen like to go back to,

26:36 hey, why are all the uh frontier models,

26:40 anthropic chat, all these guys wanting

26:43 uh regulation? It's because you have all

26:45 these now open-source models coming in

26:48 and they're way cheaper. Um they're not

26:51 just trained on just, you know, their

26:53 one thing with claude or whatever else.

26:54 They're pulling from so many different

26:56 places like you're talking about and

26:58 they're transparent. you can see how

27:00 they're going about it. So, it's like,

27:01 you know, open source might end up

27:02 winning a lot of this battle.

27:04 >> I I'll give you a um math on the cost

27:08 difference. So, when we take 500 million

27:11 tokens and we go through the frontier

27:14 models, approximately $5,000 of cost. Y

27:17 >> so process 500 million tokens from user

27:20 queries. We go and we ask them the

27:22 question, they give it back. We spend

27:24 $5,000.

27:25 If we process those same 500 million

27:28 tokens on our own hardware, on our own

27:31 infrastructure and software, which we've

27:33 built,

27:33 >> Yep.

27:34 >> the cost is about $200.

27:36 >> Yeah.

27:36 >> So, you look at that and you say, well,

27:38 every single business at some point is

27:40 going

27:40 >> their own thing.

27:41 >> Yeah. Now, there's a entire movement in

27:44 the AI world where it is own your own

27:46 intelligence.

27:47 >> It's basically crypto for AI. I

27:49 literally tell people all the time.

27:51 They're like, "How the heck is this guy

27:53 that most people, if I'm like really

27:56 self-critical, I don't think a lot of

27:57 people know anything I did before they

27:58 heard about me from Bitcoin."

28:00 >> Yeah.

28:00 >> They don't know I built companies, that

28:02 I had sold companies, that I worked at

28:03 Facebook, that I had done all this

28:04 investing. All they know is like this

28:05 dude found Bitcoin and he started

28:07 telling everybody about it and that's

28:08 when I discovered him.

28:09 >> Yeah.

28:10 >> How long ago was that?

28:12 >> 10 years ago.

28:13 >> Yeah.

28:14 >> So, people are like, "Well, what the

28:16 heck do you know about AI?" I was

28:18 investing in AI companies back in 2016

28:20 2017. Some of the leading AI companies

28:22 today were not calling themselves AI at

28:24 the time. They're big data, machine

28:25 learning, like that's how they describe

28:26 themselves in like the mid 2010s.

28:28 >> Yeah.

28:29 >> But even put that aside for a second.

28:31 Most of my worldview on AI is informed

28:34 by what I saw play out in crypto. Quite

28:37 literally, some of the people right now

28:39 telling us that AI is going to kill

28:40 everybody and they need to be regulated.

28:42 We're telling people only criminals use

28:44 Bitcoin and the government needs to

28:45 regulate it. Like the exact same people

28:48 are saying now just about a new

28:50 technology, same playbook. So I've

28:51 already seen this play out. I already

28:52 know how it ends. The government's going

28:53 to tell them to kick rocks. The

28:55 government's going to say, "Look, we

28:56 need kind of sensible, you know, like

28:57 guard rails, but they're going to let

28:59 the private market figure out who the

29:00 winners and losers."

29:01 >> Let Coinbase base figure it out.

29:02 >> Correct. They're like, "We want the

29:04 market to determine the winners, but we

29:06 want to make sure that like

29:07 >> you can't use AI, you know, you can't

29:09 use AI to tell people uh certain health

29:12 information."

29:13 >> Yeah. So we're going to create this

29:14 guard rail that you can't tell you know

29:16 an AI can't tell someone to go and you

29:19 know themselves or you know whatever

29:21 right

29:22 >> the second is in crypto what everyone

29:26 realized was I can go and keep using XYZ

29:31 service or I can figure out how do I do

29:34 it myself.

29:35 >> Yeah.

29:36 >> And so they it just became about

29:37 sovereignty.

29:38 The same thing's happening in crypto. I

29:40 can use this service which the crypto

29:43 exchanges I mean this is the best one

29:44 crypto exchanges usually charge like 2%

29:46 on transaction the prediction market

29:49 platforms right now some of them are

29:50 charging 5% transaction fees

29:53 >> crazy

29:54 >> the stock market

29:56 Robin Hood on stock trades it's free

29:59 >> so like what do you think is going to

30:00 happen to the 5% commission that they're

30:01 taking on prediction market transactions

30:03 it's going to go to zero

30:05 >> crypto used to be really high it's going

30:07 to go towards zero right it's going to

30:08 trend lower

30:09 >> the Same thing with token prices. Token

30:11 prices are going to trend toward zero.

30:13 Competition, open source, all this stuff

30:15 will will attack it.

30:16 >> Yeah.

30:16 >> So when you look at this, you say to

30:17 yourself, okay, if I as a business am

30:20 worried about three things. I want the

30:23 highest intelligence for my use case. I

30:26 don't care how good your model is at

30:27 answering physics questions. If I don't

30:30 have a phys physics question, I have a

30:32 personal finance question. All I care

30:34 about are you good at answering personal

30:35 finance? So I want the best

30:37 intelligence. The second thing is I want

30:38 the lowest cost. And the third thing is

30:40 I want the most amount of sovereignty or

30:42 security for my data.

30:43 >> Yeah.

30:44 >> So I'm a business.

30:44 >> I don't want competitors to know what

30:46 I'm doing.

30:46 >> Correct. So this math situation with

30:49 ChatGBT. Even if what they're being

30:52 accused of they didn't do, the optics

30:54 are bad. There was these two

30:55 researchers. They were using ChatGBT to

30:57 put all of their work into it. They were

30:59 trying to solve this uh very hard

31:01 physics problem. And all of a sudden

31:03 they get a phone call from Chad GBT.

31:04 They say, "By the way, we just solved

31:06 this problem." And they're like, "Dude,

31:08 we were right on the doorstep of solving

31:10 it. How did you guys solve it? Were you

31:13 looking at our chats? Like, did you, you

31:16 know, it's like doing your homework and

31:17 you're like, "Oh, that guy over there's

31:19 almost done. Let me look at his

31:20 answers." And like, "All right, turn it

31:21 in." Like, I beat him.

31:22 >> I saw they solved the math problem. I

31:24 didn't know that that's what happened.

31:25 >> So, in Open AI's defense, they were very

31:29 clear. They did not look at the actual

31:32 like prompts these people were putting

31:34 into the system.

31:35 >> It's probably a lie.

31:36 >> But I I actually believe them on that.

31:38 Okay.

31:38 >> What I also believe is true is that they

31:41 train the models on

31:43 >> the using other people's stuff.

31:45 >> Yeah.

31:46 >> So if you're making progress on a

31:48 problem, the model is learning about it

31:49 and then

31:50 >> the chat GBT guys are over there asking

31:52 the same questions. The model is now

31:54 smarter about this problem because you

31:56 were using their system.

31:57 >> Yeah.

31:57 >> And so then all of a sudden they get

31:58 this breakthrough. So they solved the

31:59 problem in 88 hours they said.

32:01 >> Mhm.

32:02 >> Hasn't been solved in like a century.

32:04 >> Wow.

32:04 >> And and the thesis is it would have

32:06 never got solved if those guys weren't

32:07 using chat to figure out and train the

32:10 model for them.

32:10 >> Now if they had been using claude open

32:14 AI would have solved it. Claude would

32:15 have solved it. So the sovereignty

32:16 becomes really important. Now take that

32:18 that's like solving a math problem.

32:19 Obviously there's a lot of academic you

32:20 know kind of posturing whatever. If

32:22 you're a business,

32:24 are you going to upload your strategic

32:26 plan

32:28 if you're competing with them?

32:30 >> No.

32:31 >> No.

32:31 >> Not for AI.

32:32 >> But like for me, I I talk about my real

32:35 estate investments and M19. I'm like,

32:38 look,

32:39 >> you don't care about that data from a

32:40 competitive standpoint because

32:42 >> I'm not competing with Claude or Open

32:43 AI, right?

32:44 >> They're they're helping me grow my

32:46 business.

32:46 >> Correct.

32:47 >> But to your point, and Brian, this is

32:48 back to your point. What I think happens

32:50 and this is like what you're already

32:52 doing is uh businesses build use

32:55 specific AIs that are sovereign and are

32:59 pulling data differently than how claude

33:02 or open AAI would because to your point

33:04 claude and open AI are basically just

33:07 generalist.

33:08 >> They're good at getting you 90% of the

33:11 answer in any question that you could

33:13 have. Right. But if you really wanted a

33:15 great medical feedback, there's a

33:17 medical AI out right now. I don't know

33:19 what it is, but there is definitely one

33:20 that you could have fed your stuff to

33:22 and it would have been way better.

33:24 >> Yeah,

33:24 >> there's Sylvia for personal finance

33:26 that's way better than Claude.

33:28 >> Uh if if I wanted a sports AI to give me

33:31 sports trivia or what, there's for sure

33:33 a collectibles AI. Collectibles are

33:35 blowing up for sure. There's an AI

33:37 trained specifically on where to dig the

33:40 best information on collectibles, all

33:42 this stuff because basically how they go

33:44 and search for the information is

33:46 different on all the models because

33:47 they've been trained to search in

33:48 different ways. And this is why they're

33:50 spending so much money trying to get the

33:51 training data.

33:52 >> So they're going and buying rare books

33:54 or, you know, they're just all trying to

33:55 get an advantage. Yeah. The thing that

33:57 we go back to is we probably have the

34:00 largest data set in the world of wealthy

34:02 people talking to an LLM about their

34:03 personal finances.

34:05 >> My data is on that platform.

34:08 >> Yeah.

34:09 >> We are probably overly sensitive to

34:12 things like we're sock 2 certified. We

34:14 encrypt the PII. If somebody broke into

34:17 the system, then the PII is encrypted

34:20 and so they couldn't connect the

34:22 portfolio to the person, right? like all

34:25 of these different details where you're

34:27 just like, "Okay,

34:29 if we want to build the best thing, what

34:30 is the number one hangup for when that

34:32 popup came and they were like, connect

34:34 your bank account?"

34:34 >> Mhm.

34:35 >> You're like, "Could it move my money? Do

34:37 I want to give them my data?" Like, like

34:38 there's all these questions that

34:39 >> I'm probably going to do it. I swear I

34:41 am. But I I thought about it for a

34:42 second. I was like, "Oh,

34:43 >> we got to go to silia.com."

34:44 >> I'm going to silia.com.

34:46 >> But but here's the deal.

34:46 >> Do you guys have an app? No, I'm just

34:48 kidding. I'm sure you do.

34:48 >> We'll link to it down below. But no, but

34:51 like the other thing just thinking about

34:52 my what I was saying earlier about niche

34:54 specific things. You were talking about

34:55 it beforehand of like uh you know

34:57 Hermoszi has his AI. Yeah. And so like

35:00 basically what proprietary data do you

35:02 have? Well, he's got all his workshops

35:04 he's been doing for all these years.

35:05 They all put their banks, their numbers,

35:08 their P&Ls, all the crap. And so he has

35:10 more info on small business than

35:12 probably anyone, right? Yeah.

35:14 >> He's got more info on wealth management

35:16 than probably anyone.

35:18 >> Yeah. uh you know Claude has more info

35:20 in general about everything than

35:22 everyone and and OpenAI does, but

35:25 >> you can definitely win with niche

35:26 specific info that only you have.

35:28 >> Yeah. I I would not go try to build a

35:30 foundation model and compete against

35:31 Open Anthropic. That'd be like a suicide

35:32 mission. Yeah. Right. But if you have a

35:35 specific vertical where you have a

35:37 distribution advantage and also

35:39 >> because everyone's giving you their

35:40 data, they're paying Hormosi to give

35:42 them their data.

35:43 >> Yeah. Also though, Hermoszi understands

35:47 the problems that those business owners

35:48 have better than an engineer sitting in

35:50 San Francisco.

35:51 >> Correct.

35:52 >> So, one of the things that we we have an

35:54 office in New York, we got an office in

35:55 San Francisco. We have a fantastic

35:57 engineer. I would I would put our AI

35:59 engineers up against anyone in the

36:00 world. They have built all this stuff

36:02 and we can objectively show that we're

36:03 more accurate on a bunch of topics that

36:05 really matter. Right? We're competing

36:07 against trillion dollar companies. But

36:08 one of the insights that we have is I'll

36:10 give you a a direct reason why it is

36:14 very hard for these companies to compete

36:15 in a vertical

36:17 answering a question like how do I get

36:18 my tax rate down pretty clear to

36:20 everybody that that's a question.

36:21 >> Yeah.

36:23 >> A product feature that we have is if you

36:26 work with a private bank they want or

36:28 you're a real estate investor they want

36:29 a personal financial statement every 90

36:32 days. So, we made it that if you upload

36:36 all of your assets, you press a button

36:37 and we create the personal financial

36:39 statement for you. You don't have to

36:40 spend the time filling out the form and

36:42 doing just super simple.

36:43 >> Yeah.

36:44 >> Cuz that was a pain point I had.

36:45 >> Yeah.

36:46 >> So, I was like, that's not an

36:47 engineering problem. That is an

36:49 understanding the user problem and then

36:51 just creating products for them.

36:53 >> The the the product is really easy to

36:54 build. It's actually just understanding

36:56 this is a problem. Correct. And that's

36:58 where the vertical it becomes very hard

37:00 for kind of a more general engineer to

37:02 compete against somebody who has

37:04 vertical because we just built our first

37:06 app for M19.

37:07 >> Okay.

37:07 >> And it's the first like real app I've

37:09 built with AI and we were able to build

37:10 it in just months and you know I have my

37:13 uh tech guy up there and he's great and

37:16 everything else.

37:17 >> He wouldn't know what to build, right? I

37:19 know exactly what these golfers want,

37:21 what we need, how the app should

37:23 function, how they're going to utilize

37:25 it.

37:25 >> What does the app do? So, the app is for

37:28 members only. I could show you it after

37:30 this, but basically like I created

37:32 everything I wanted. So, like we have a

37:34 community right there for people to

37:35 post. Every single golf trip is listed

37:37 on the app. Sick. Uh we got virtual

37:39 events, we got our socials, a shop for

37:41 all the swag that we got. You know,

37:43 members, uh what's it called?

37:46 >> You can search any member by their

37:48 industry, by their location.

37:50 >> It's basically like a private network of

37:52 >> It's a community.

37:52 >> Yeah, it's a private network of like

37:54 vetted people.

37:56 The average business owner is doing 27

37:58 million a year in revenue.

37:59 >> Wow.

37:59 >> In the in that app because we have now

38:01 all the backend data because they submit

38:03 all their stuff just like you're saying.

38:05 And so yeah, there I know their average

38:08 age, their average revenues, where they

38:10 live, their industries, and now they're

38:13 all in industry related chats. They're

38:14 all booking golf times together. They're

38:16 all networking locally, doing deals. And

38:19 so like our competitive advantage is we

38:22 have the number one app in the world,

38:24 the number one community in the world

38:26 for high-end business owners who golf.

38:29 >> If you're a high-end business owner who

38:30 golfs, there's literally nothing better

38:32 you could join than what we have.

38:34 >> But like to build that thing where they

38:37 could all connect because our mission

38:39 is, hey, let's just make sure we connect

38:41 these people because that's what they

38:42 want.

38:42 >> Yep.

38:44 Like the engineers don't really know

38:46 what that means.

38:46 >> But this is what's possible in the AI

38:48 world that was not possible before. It

38:49 used to be you'd have to build an app

38:52 for high net worth people. Yep.

38:54 >> You couldn't do high net worth people

38:56 who like to golf because it was cost

38:58 prohibitive, right? It was too hard to

39:00 pull together. Then you had to get an

39:02 engineer to work on that, then another

39:03 engineer work on something else. In the

39:04 world of AI, you're going to get way

39:06 more specialization and fragmentation,

39:08 which is actually better for the end

39:11 consumer.

39:11 >> Sure. because they don't want to go on

39:14 Facebook and see a post about their

39:17 mother, their mother-in-law, a high

39:19 school friend, and oh, I found my one

39:21 buddy who likes

39:21 >> and that's kind of my point with why

39:22 Sylvia works, why this works, why the

39:24 medical stuff works, because like we

39:27 were on school, which is great. School's

39:29 a great general,

39:31 you know, platform for just general

39:34 communities. It doesn't have it's not

39:36 meant for people who make $27 million a

39:38 year who golf trips who want to connect

39:41 in different you can't do group chats.

39:42 There's not industry chats. You can't do

39:44 any of that on school. It als it's not

39:46 built for that.

39:46 >> It also from a business perspective I

39:48 think one thing that people um business

39:50 owners, technologists,

39:53 if you were on school and you went to a

39:56 business, you said, "Hey, by the way, I

39:57 have this community of people. They're

39:58 great. You know, they they have $27

40:00 million of revenue on average. They've

40:02 got uh an interest in golf. etc. You

40:05 could probably do some brand deals. You

40:06 could probably get some like group, you

40:08 know, benefits or something from that

40:09 because you have the community.

40:10 >> The second that you say, "Hey, by the

40:11 way, we can surface it. We can target

40:14 based on what they like, what they don't

40:16 like, what their demographics are, what

40:17 location they like." The more detail you

40:19 get, it's actually better for them

40:21 because the guy who's in California

40:23 doesn't want to know about the discount

40:25 at the local golf course in Alabama.

40:27 >> Yeah.

40:27 >> Right. He just wants to know about

40:28 what's going on in my community.

40:30 >> Yeah. And so in a weird way, the

40:32 fragmentation allows for people to get

40:36 surfaced the opportunities, the

40:37 information, the data, etc. that they

40:39 most care about.

40:40 >> Yeah.

40:40 >> And so we just headed towards a world of

40:42 personalization.

40:43 >> Yeah.

40:43 >> You're you're creating a more

40:44 personalized version where you started

40:46 with a general one. Now it's your own

40:48 thing and eventually you're going to get

40:50 down to you literally could probably

40:51 change the feed based on their specific

40:54 preferences. Maybe they are trying to

40:56 work on putting.

40:57 >> Yeah.

40:58 >> Versus driving. And so you can feed them

41:01 that information and they're happy about

41:02 it because like that's what I'm

41:03 interested in right now.

41:04 >> Yeah, for sure. So to go back to just

41:07 what's happening even in the world of

41:09 AI, why do they want regulation?

41:12 >> Do you think they just want to create a

41:13 duopoly, a monopoly, like they they

41:15 can't catch Meta and and Google because

41:17 they have way more resource. So they're

41:19 just trying to figure out like they're

41:20 because if tokens go to zero, their

41:22 whole model is built on tokens. Like the

41:24 $20 a month subscription is not the

41:25 thing. Yeah,

41:26 >> it's the tokens that are the thing. I

41:28 think that um there's not one single

41:32 like this is the driver. It's a

41:33 combination of things. I think that they

41:35 realize they've got formidable

41:37 competitors and if they can get

41:38 everything to slow down, if you've got

41:40 an advantage over other challengers, you

41:42 kind of pull the ladder up behind you. I

41:44 think that's one piece of it. The second

41:46 thing is um I do think there's some

41:48 product liability stuff. So, if you ever

41:51 like are on like a hiking trail, you

41:54 ever notice how they don't shut the

41:56 trail. They just put up signs everywhere

41:57 like careful of falling rocks.

41:59 >> Yeah.

41:59 >> And if a rock falls and hits you,

42:00 they're like, "Well, we told you."

42:02 >> Right.

42:03 >> Fall off the cliff.

42:04 >> Yeah. So, if they're like, if somebody

42:05 comes out and they're like, "Hey, by the

42:06 way, uh the AI killed somebody or the AI

42:10 somebody got hurt." They're like, "Well,

42:11 we told you that."

42:12 >> Right. It's why cars are like, you know,

42:14 "Wear your seatelt, right?" Or

42:16 >> cigarettes,

42:16 >> all of these, right? So like there's

42:18 some product liability, you know, kind

42:19 of uh uh coverage. You you kind of cover

42:21 your ass a little bit

42:22 >> by just saying, "Dude, we warned you. We

42:24 warned you. We warned you. This stuff is

42:25 going to kill somebody."

42:26 >> Correct.

42:26 >> Okay.

42:27 >> Then I also think that there is a little

42:29 bit of it makes them sound smart,

42:32 >> right? If you're about to IPO,

42:35 what better thing to do than have

42:36 everyone talk about you?

42:37 >> Yeah.

42:38 >> So there is a little bit of like, dude,

42:40 these guys, we're talking about it right

42:41 now.

42:42 >> Yeah.

42:42 >> It's all anyone's been talking about for

42:43 a week is they said everyone's going to

42:46 die. So there's almost like a Strand

42:48 effect of like the more people talk

42:50 about you. By the way, the guy sitting

42:51 in the White House, he's the king of

42:53 this.

42:54 >> Yeah.

42:54 >> He doesn't care if you talk good about

42:56 him, bad about him, you like him, you

42:57 hate them. If you're talking about him,

42:59 he's happy.

43:00 >> Yeah.

43:00 >> Anthropic is doing the same thing right

43:01 now. Open is doing the same thing. And

43:03 then I do think that there's a another

43:05 element which is

43:08 I went on Fox Business earlier this week

43:10 and I said this and people got very

43:12 upset. But I said the same people who

43:13 told you the climate was going to kill

43:15 you, told you COVID was going to kill

43:16 you, are now telling you software is

43:17 going to kill you.

43:18 >> Yeah.

43:18 >> There's a little bit of like an alarmist

43:20 approach and it doesn't mean have some

43:23 people died from the climate.

43:24 Absolutely. But there are people who

43:26 were telling us five years ago. No, they

43:28 they were telling us five years ago that

43:30 like the glaciers were going to be gone

43:33 or you know like the certain cities were

43:34 going to be underwater like crazy stuff.

43:36 >> Yeah. You guys can't sit next to each

43:38 other six feet.

43:39 >> Yeah. Co. Did some people die from CO?

43:41 Absolutely. But it was elderly, it was

43:43 sick people, it was, you know,

43:44 vulnerable. Like, by the way, that's

43:45 really sad,

43:46 >> but it happened.

43:47 >> But the 12-year-old kid was never really

43:49 at risk in the way that we were told,

43:51 right? The the numbers aren't as big as

43:53 they were. Same thing here. Are there

43:55 dangers to AI? 100%.

43:58 >> They are nowhere near what these guys

44:00 are talking about, right?

44:01 >> And so, it goes back to there's a

44:03 community of people who are just like

44:05 professional alarmists. Every single

44:07 thing that they possibly can, they're

44:09 like almost too smart for their own

44:10 good. I I'm a big believer that I want

44:13 to be around smart, dumb people.

44:17 >> If you're too smart, I don't actually

44:19 want to be around you because you

44:22 overthink everything. You overanalyze

44:24 everything. If you go and you look at

44:26 the great people in the world that are

44:28 some of the smartest, Elon Musk, it

44:31 sounds crazy, but he's like a smart dumb

44:33 person. He's just like, I'm going to buy

44:36 X cuz I think that free speech is

44:38 important. He didn't like pull out a

44:41 spreadsheet and do like a whole

44:42 calculation and like he was just like he

44:45 literally tweeted back at somebody was

44:46 like how much is it right? Like he's

44:49 obviously incredibly intelligent but

44:51 like that's kind of what like a dumb

44:52 degenerate gambler says on a Friday

44:54 night when he's drunk. It's like

44:56 >> smart simple people.

44:57 >> Yeah. It's like yeah smart simple people

44:58 maybe is a better way to put it. Right.

45:00 So like

45:01 >> he's like you know what would be a lot

45:03 cheaper? What if we just reuse the

45:05 rocket?

45:05 >> Yeah.

45:06 >> Right. Like what? And everyone's like,

45:08 "That's impossible. It's not." And he's

45:09 like, "Well, what if it's not?"

45:11 >> And you're just like,

45:13 >> "That's the stroke of genius."

45:14 >> What if cars didn't have gas?

45:16 >> Warren Buffett, right? Warren Buffett

45:19 says he knows whether he's going to do a

45:20 deal or not in five minutes. If he can't

45:22 do it on the back of a napkin, he

45:23 doesn't do it. He's not some super

45:26 genius financial analyst, whatever. He's

45:28 obviously smart, but it's the

45:30 simplicity. It's the like quotequote

45:32 dumbing down of the problem. And it's a

45:34 very binary. it's a great deal or I'm

45:36 not doing it right. And so you look at

45:38 this and you say to yourself,

45:40 >> I don't know how many of those people

45:41 are working at some of these companies.

45:42 I think there's a lot of overly

45:44 intelligent like overeducated people.

45:46 >> They're looking at the the crazy

45:48 scenarios that could happen.

45:50 >> If you ask them I I've seen now a couple

45:51 of examples of this. If you ask them,

45:53 okay, cool. How can people die from AI?

45:55 They will tell you the most cockamani

45:57 ideas I've ever heard. like well if AI

45:59 infiltrates the uh biochemical uh

46:02 facility that doesn't exist yet but it

46:03 could uh and then it infiltrates the uh

46:06 computer and it releases it into the

46:08 pathogen into the air and it gets in the

46:10 jetream.

46:11 >> Mhm.

46:11 >> I'm like bro what are we talking about

46:13 here? Right. Like like what?

46:16 >> Yeah.

46:17 >> By the way, you just lock down the world

46:19 and that wasn't what you said it was

46:20 going to be.

46:21 >> Yeah.

46:21 >> And so it's like

46:23 >> everyone calm down.

46:24 >> At least the other threats were very

46:26 like Yeah. They're making nukes and like

46:29 Yeah,

46:29 >> they could drop one. It's like, okay, I

46:30 could see that. Yeah.

46:32 >> You know,

46:33 >> but it goes back to the rational

46:34 optimist.

46:35 >> Yeah.

46:36 >> We should not be so optimistic that we

46:39 ignore all warnings, all dangers.

46:41 >> Mhm.

46:42 >> But we also should be rational enough to

46:44 understand that the people who are like,

46:46 >> you're never going to have to work for

46:48 money.

46:48 >> I don't know about you guys, every

46:50 person I know involved in AI or using AI

46:53 is working harder than they ever have

46:54 right now.

46:55 >> Really? And it's because there's an

46:56 opportunity there. There's like a

46:57 there's a race and so there's this

46:59 economic reward there and these guys are

47:01 running so hard at it.

47:03 >> Speaking of that, how important is it to

47:06 beat China? Why is that such a big deal

47:08 in AI?

47:10 >> It depends on your view on China.

47:13 >> It's because China sucks, dude.

47:15 >> Well, I I'll give you a good example. So

47:18 for example, um, China recently got

47:21 accused of their openweight models

47:25 distilling the American closed source

47:27 models. What that basically means is

47:28 they almost like reverse engineered and

47:31 like quote unquote stole IP. Okay. The

47:35 Ford CEO recently was on a podcast and

47:38 he was talking about how they buy the

47:40 Chinese electric vehicles and they bring

47:41 them to America and they un they take

47:43 them apart to understand what they're

47:44 doing.

47:45 >> Mhm. So like are we distilling the

47:48 Chinese technology?

47:50 >> So you get in this weird world of like

47:52 everyone when it's done to them doesn't

47:53 like it but then they do it to everybody

47:55 else because like

47:56 >> guess how the models got created? They

47:59 distilled the intelligence of the world

48:01 by reading through all the Reddit

48:02 comments and getting the rare books and

48:04 like so they like quote unquote stole

48:06 the IP and they've now settled with

48:08 journalists and media companies and all

48:10 this stuff are taking their information.

48:11 So just put aside for a second all the

48:13 like IP China America nonsense.

48:17 If you believe this technology is

48:19 valuable

48:21 then you ultimately have to come down to

48:23 what are the values of the western world

48:26 versus the eastern world.

48:27 >> And what I always tell people is

48:30 be again very simplistic. We believe in

48:33 capitalism they don't. We believe in

48:35 democracy they don't. Okay. If I ask an

48:40 American model a question in personal

48:42 finance, let's say using Sylvia, it's

48:45 probably pretty important that the

48:46 model's inherent cultural weights

48:51 think about capitalism

48:53 versus maybe an eastern model from China

48:57 or somewhere else that has communist or

49:00 socialist cultural weights. So now you

49:03 get in this world of well, if I ask the

49:06 internet, it's on the internet, isn't it

49:07 true? So if you let's say for example

49:12 back in 2020 pandemic breaks out if you

49:15 were to ask a Chinese open source model

49:19 did this start in Wuhan

49:21 would it have told you no?

49:23 >> Yeah.

49:23 >> Now by the way the Americans would also

49:25 would have told you no because every

49:26 they were censoring it right. But like

49:28 at least in that case you want to have

49:30 the technology that can quote tell the

49:32 truth.

49:33 >> Mhm.

49:34 >> So that to me is really the actual

49:36 important thing. And Twitter or X now I

49:38 think is a great example. If you go back

49:40 pre I don't know 2018

49:44 amazing crazy crazy place but amazing

49:48 2020 2021 those guys lost their minds.

49:51 They started censoring people. They

49:52 kicked the president of the United

49:53 States off of the service. They were

49:55 taking things down. The White House was

49:57 calling them up being like hey uh

50:00 misinformation.

50:02 I remember um in 2020

50:05 I was a very early believer but I I did

50:08 not have the uh intestinal fortitude to

50:10 publicly say it unfortunately. There's a

50:12 lot of other things I said in 2020 but I

50:13 did not say this. The lab leak theory

50:16 almost immediately I was like that's

50:17 true. And the reason I knew is because

50:19 Zero Hedge posted the theory and they

50:22 immediately got uh deactivated.

50:26 >> Yeah.

50:26 >> If it's not true why did you deactivate

50:28 it?

50:28 >> Yeah.

50:29 >> Right. And I actually don't know that X

50:30 could be like the arbiter of truth

50:32 there, but I am a very big believer and

50:35 uh one of the chapters of the new book

50:36 that we have coming out is

50:39 when disscent is outlawed,

50:41 that's how you know the truth.

50:44 Because if something is true, it is able

50:46 to withstand the pressure of disscent.

50:49 >> Yeah. Mhm.

50:49 >> But the second that somebody says you're

50:52 not allowed to say something else,

50:54 you're not allowed to go a different

50:56 direction, they don't have truth on

50:59 their side, they have to use coercion

51:00 and force, etc.

51:02 >> Communism, social like that. That's what

51:04 they do.

51:05 >> And so it comes back to this idea of why

51:07 does America want to win? That is just

51:10 like the programming of people and the

51:12 access to information. Then you take a

51:14 step further. Well, like I don't know,

51:15 man. AI seems pretty good at like

51:18 robotics. Seems pretty good at capturing

51:22 the Venezuelan leader, you know, like

51:24 sounds like we used it for some of that

51:25 stuff, some planning purposes.

51:27 >> Yeah,

51:28 >> Claude is so good. I don't know if you

51:29 guys saw this, the Iranian government

51:31 just got caught uploading all of their

51:33 war plans to Claude and talking to it.

51:37 And they also uploaded funeral plans for

51:39 the Ayatollah.

51:40 >> Oh, dang.

51:41 >> Or something.

51:42 >> Wow.

51:42 >> And Claude was like, "Uh, yeah, we're

51:44 going to turn this over to the

51:45 government." like like what?

51:48 >> But like some dude probably sitting

51:50 there in Iran was like, "How do we get

51:53 Trump?" But we could use super

51:54 intelligence,

51:57 >> right? So like that's funny.

51:58 >> So you think about this and you're like,

52:00 "Wait a minute.

52:02 >> I think we probably want to control the

52:03 technology that's really good at doing

52:04 this stuff, right?"

52:06 >> So like it there's a lot of different

52:07 reasons why, but I do think that some of

52:09 it's like cultural weight type stuff.

52:10 Some of it's like just what is true, but

52:12 then also like

52:14 >> national security. Yeah, it's just

52:16 there's many many reasons that it's

52:18 probably a pretty good idea for us to

52:20 win, not them,

52:21 >> right? What's your book coming out?

52:22 What's it about?

52:23 >> Um, so this is the second book in a

52:25 series where I write 50 to 55 ideas that

52:29 changed my life, uh, practice that I've

52:31 had for almost a decade now is every

52:32 time that I hear an idea or I learn an

52:34 idea that I think is either really

52:36 important or life-changing, I write it

52:37 down in a note in my Apple Notes. And

52:40 there's hundreds of these. And so, uh,

52:43 on October 6, I've got a book coming out

52:44 where I take 50 of those ideas and I

52:46 basically explain here's the idea, here

52:48 is how it impacted my life and then

52:50 here's how I think you can apply it to

52:51 yours. I write the book as each chapter

52:54 is for uh the lesson so I can impart the

52:57 knowledge on my child or my children.

52:59 Um, but it is written in a way where

53:01 anyone regardless of if you're 80 years

53:02 old or you know 12 year old 12 years

53:04 old, you could go and you could learn.

53:06 So, some examples are um farmhand sleeps

53:10 well at night. There's an old parable

53:12 that basically is a guy's trying to hire

53:14 a farm hand and he says,"What are your

53:16 qualifications?" And the farm hand says

53:17 back, "I sleep well at night." Guys,

53:19 that's not what I asked you. What are

53:20 your qualifications?

53:21 >> Yeah.

53:22 >> And he says, "I sleep well at night."

53:23 So, he goes, "I don't know what that

53:24 means, but they're fine. I'll hire you."

53:26 Couple weeks go by, he wakes up in the

53:28 middle of the night, the farmer, and

53:28 there's a big storm outside. He wakes up

53:30 his wife, and he says, "Hey, hey, oh my

53:32 god, the storm's here." They run outside

53:34 and they try to wake the farm hand, but

53:36 he's sleeping. So, they run to the barn

53:38 and they're like, "We got to put away

53:39 all the tools." And they realize that

53:41 all the tools are put away. Then they

53:42 run over to the animals and they realize

53:44 the animals are all in the barn. Long

53:46 story short is the farm every single

53:47 night prepared as if there was a storm

53:48 coming so that when he went to sleep at

53:50 night, he didn't have to worry about

53:51 running around and trying to do all of

53:52 his chores, right? He had already done

53:54 everything. He was prepared. Another

53:56 example is um when you think about uh uh

54:00 some of these ideas that really kind of

54:02 impact people's lives, I always like

54:04 stories where um

54:07 you learn about people who take

54:09 different conclusions from the same

54:11 scenario. So two two brothers, they have

54:15 an alcoholic father. One becomes an

54:17 alcoholic, one doesn't. They go and they

54:19 ask the alcoholic, "Why are you an

54:20 alcoholic?" He says, "Because of my

54:21 father. How could I not be an

54:22 alcoholic?" They go and ask the sober

54:24 brother, why are you why don't you

54:26 drink? Said, ' Because my father was an

54:28 alcoholic. How could I drink?

54:29 >> Yeah.

54:30 >> Same situation, same experience, two

54:32 different takeaways, right? And so you

54:34 like go through all of these ideas and

54:36 it changes your perspective. It's

54:37 impacted my life uh in a meaningful way.

54:40 And the entire book is basically it

54:41 takes you, you know, pretty quick read

54:43 50 ideas that if you pick up the book,

54:45 you read it, I think that you'll get two

54:47 or three ideas out of it at a minimum.

54:48 Yeah. And hopefully it positively impact

54:50 your life.

54:50 >> When what's it called? How to live an

54:52 extraordinary life.

54:53 >> And it's is it already on Amazon? I know

54:55 you said it comes out October.

54:56 >> Yeah, you can go on, you can order it,

54:57 resell it.

54:58 >> The uh the Barnes & Noble people, they

54:59 would love if you go there, but uh

55:01 Amazon usually is the dominant one, but

55:02 Barnes & Noble is uh they got their link

55:04 ready.

55:05 >> There we go. Yeah, we'll link to that

55:06 down below. So, another thing I want to

55:08 talk to you about, you know, we've been

55:09 talking about Sylvia and it's just part

55:10 of your public company that when when

55:13 did you guys go public? Like

55:15 >> we went through a spa process and

55:16 despected in early December of 2025. So,

55:20 uh,

55:21 >> nine 10 months ago.

55:23 >> Yeah. So, like what's that process been

55:25 like? Cuz you've been kind of

55:26 documenting the journey of like, hey, we

55:28 launched, things went down a lot, now

55:31 we're going to just rebuild this thing

55:33 with Sylvia and everything else.

55:35 >> So, um, there was not a lot of

55:37 documenting going on in the beginning

55:38 because when you're getting your teeth

55:39 kicked in, it's not fun. You know, you

55:41 wake up every day, you're trying to like

55:42 not get your teeth kicked in, not

55:43 worried about, you know, creating the

55:44 content. Um, the public market is all of

55:49 the negative things I was told with a

55:52 huge amount of positives that no one

55:54 told me. So, the negatives are it's

55:56 public. There's a daily stock price goes

55:59 up, people are excited. It goes down,

56:01 people are upset. Your employees are

56:03 constantly looking at the stock price.

56:05 And there is an immense amount of

56:07 scrutiny both online from shareholders

56:10 and non-shareholders, but also the

56:11 media, etc. you have to publish your

56:14 information every 90 days or so and is

56:17 incredibly expensive to run a public

56:18 company. So, as you go through that

56:20 whole process, you're like, "Well, that

56:21 sounds horrible. Why would I want to do

56:22 this?" The flip side of it is I actually

56:25 think the scrutiny helps you build a

56:26 better company. I think that the 90-day

56:28 cadence is a really good forcing

56:31 function to make sure you always have

56:32 things that you can report as progress.

56:35 You have immense amount of access to

56:37 capital. And so, we've got a balance

56:38 sheet that's got hundreds of millions of

56:40 dollars on it. We probably couldn't have

56:41 done that in the private market. And

56:43 then I think that also you see the stock

56:45 price and so when it's going in your

56:47 favor, people are excited. People want

56:49 to partner with you. They want to work

56:50 at your company. They they want to be

56:52 involved in what you're doing. And so

56:54 when you look at it on a net basis,

56:55 there's positives and negatives as

56:57 anything. I think that I learned um

57:02 you just got to be yourself though. And

57:04 so when I took the company public,

57:06 I had a suit and tie on. I was doing

57:08 everything that everyone was telling me.

57:09 you. They're like, "Hey, you got to talk

57:10 to these people. You got to hire these

57:12 people. You got to, you know, play this

57:13 game. You got to be professional." All

57:15 this stuff. I'm like a chill dude,

57:17 right? I'd done things my way my entire

57:19 life. Seems to be working, you know,

57:21 fine. Uh, so far. So, the stock fell

57:24 from $10 to $150.

57:27 For those paying attention at home,

57:28 that's not fun.

57:29 >> Yeah.

57:30 >> Right. 85% is not good.

57:32 >> Yeah. It's like a welcome to the public

57:33 market. Bam.

57:35 >> Isn't that normal, though? Isn't that

57:36 what usually happens?

57:38 >> It depends.

57:40 SpaceX, they went public 135, they went

57:42 to 200, then down to 100, now they're

57:44 back at like 140, 150. Right. So, like,

57:45 there's volatility for sure.

57:47 >> Yeah.

57:47 >> Down 85 right out of the gate. Like, it

57:50 was down 70% in like 4 days.

57:52 >> Oh, damn.

57:52 >> So, like that's not fun.

57:55 >> Continued to fall. Got down 85%. In July

57:58 of this year, I finally was like, I

58:02 either do things my way or this is like

58:05 one, not enjoyable. two, I'm going to

58:06 regret it for the rest of my life

58:08 because I'm going to be sitting there

58:08 saying, "I listened to all these people.

58:10 Why didn't I do what I thought I should

58:12 do?"

58:12 >> And so, uh, on July 13th, we put out a

58:15 video and literally, I think the title

58:17 of the video is, "I got to turn around

58:18 my company that's down 85%."

58:22 >> I want to watch it now.

58:24 >> Like, like, if there's ever like a

58:25 owning the problem, it was like the

58:27 title of that video and the thumbnail is

58:30 like me and a big red arrow down on the

58:33 thumbnail, right?

58:35 CEOs like that made those type of

58:36 videos.

58:37 >> They don't.

58:37 >> They don't.

58:39 >> By the way, when you publish that video,

58:41 you get a lot of phone calls.

58:42 >> Oh, yeah. People like, "Are you okay?

58:44 What are you doing? You're not supposed

58:46 to celebrate being down 90%." Like, all

58:48 these, you know, like I'm not

58:49 celebrating, but like

58:52 >> the elephant in the room is the stock is

58:53 down, man. Like, like we can't just be

58:55 like everything's going great. You know,

58:57 the joke is whenever you call like a a

58:58 CEO or like meet up with a buddy and

59:00 they own a company like how's it going?

59:01 Like, it's going amazing. And then you

59:03 find out.

59:04 >> Yeah. Yeah. They're like, "Yeah, we're

59:05 killing it." And then you're like,

59:07 >> "I don't know, man. Your restaurant

59:08 doesn't look like there's a lot of

59:09 people in there." Like,

59:11 >> public company that everybody knows.

59:13 >> That's I never thought about that.

59:14 >> And how much how much did you guys like

59:16 what was the market cap and all that?

59:18 >> The company fell down to uh probably

59:21 like $130 million market cap or

59:23 something like pretty small for the

59:24 public market.

59:24 >> Yeah. Yeah.

59:25 >> And when we put out this video, um

59:28 >> so before though, that would have been

59:29 what, a billion?

59:31 >> It was like high hundreds of millions. I

59:32 forget exactly when it went out. Um,

59:35 >> and and so it had collapsed, you know,

59:36 significantly, right? And so when we put

59:39 out this video,

59:40 >> I think people kind of thought I was

59:41 joking at first. They were like, "Oh,

59:42 that's a awesome." Like people like

59:43 friends of mine who I've known for a

59:44 long time, they're like, "Dude, great

59:46 video."

59:47 >> But but like nothing about the company.

59:49 Like everyone was just like, you know,

59:50 Isaac, who's here, he he edited the

59:51 video that was viral.

59:52 >> And and people were like, "Yo, who

59:54 edited this?" Right? Like like that's

59:55 was nobody was like, "Hey, tell me more

59:57 about your company." They were all like,

59:58 "Great video." And I'm like, "Oh, we got

1:00:00 a lot of work to do here." And so then

1:00:03 it was just like I am going to document

1:00:06 win or lose whatever happens here. And

1:00:08 part of it it if I'm really honest with

1:00:09 myself I kind of want to look back on it

1:00:11 like I know I'm living through a moment

1:00:13 where I'm like all right this is

1:00:14 probably one of the hardest things I've

1:00:16 done professionally.

1:00:17 >> I know that I again got my teeth kicked

1:00:19 in

1:00:20 >> at a pretty you know low like damn this

1:00:23 is like welcome to the public markets.

1:00:24 This sucks.

1:00:26 >> I think I know how the story is going to

1:00:28 end but I'm not 100% sure. Like there's

1:00:30 always risk. There's always, you know,

1:00:31 kind of different outcomes, things could

1:00:32 change, whatever.

1:00:33 >> But I kind of want to look back and see

1:00:36 the documentation of this 20 years from

1:00:38 now and be like, dude, if we build what

1:00:39 I think we can build, it's going to be

1:00:41 awesome to look back at this exact

1:00:42 moment being like, hey, I didn't know

1:00:44 whether it was going to work or not. I

1:00:45 think it's going to work, but like,

1:00:46 let's see.

1:00:47 >> Yeah.

1:00:47 >> So, we start putting out these videos

1:00:49 and the first thing

1:00:52 everyone is shocked that we work.

1:00:54 They're like, "Dude, you guys do like a

1:00:56 lot of meetings. You guys like like go

1:00:57 to a lot of I'm like, "Yeah, man. What

1:00:59 do you think we're doing? You think

1:01:00 we're just like Like, well,

1:01:01 stock was down 85%. I don't think you're

1:01:03 doing anything.

1:01:06 >> So, there's a lot of that going on. The

1:01:08 second thing though is

1:01:10 >> if we think about the products we use,

1:01:12 the companies we use, a lot of it is

1:01:13 like your values aligned.

1:01:15 >> You're just like uh you know, uh the CEO

1:01:17 of Bloom Energy. Yeah.

1:01:19 >> You know, I think that he does a

1:01:20 fantastic job of just being like, "Yo,

1:01:22 this is who he is."

1:01:22 >> Yeah.

1:01:23 >> And I think a lot of people go buy that

1:01:24 product because they're just like,

1:01:25 "Dude, this guy this guy I I you know, I

1:01:27 screw with this guy." I never even heard

1:01:29 of Bloom a year ago content

1:01:32 >> and I was like, "Bro, I like this guy."

1:01:34 >> You know who else had a great one is um

1:01:36 Mark Wahberg has a show that was on HBO

1:01:38 called uh Wall Street, but like WA HL.

1:01:42 >> Oh,

1:01:42 >> it was just like him as an entrepreneur.

1:01:44 >> Okay.

1:01:44 >> And it was it was like pretty well done

1:01:46 as like a true documentary, but it was

1:01:47 like very well done in terms of showing

1:01:49 you like what is this guy's day like?

1:01:51 >> And I walked away from that. like I like

1:01:52 his movies and stuff, but like man, I

1:01:54 got a hell of a lot of respect for what

1:01:56 he's doing business-wise, right?

1:01:57 >> So, that was the idea as we started

1:01:59 documenting this thing. Now,

1:02:01 >> you can imagine,

1:02:03 >> you know, we went to dinner last night

1:02:04 with a a buddy of mine, u you he's

1:02:06 investor, etc. He's like, I don't film,

1:02:09 right? So, there's a lot of that that

1:02:10 goes on. So, it's hard. But then also,

1:02:13 um we were talking last night that um

1:02:15 Casey Neistat when he first started uh

1:02:18 doing a lot of vlogging type stuff, he

1:02:19 was doing it because he had a business.

1:02:21 >> Yeah. Yeah. But he quickly realized like

1:02:22 if you just film people like sitting at

1:02:24 their computer, it's not very exciting.

1:02:26 >> Mhm.

1:02:26 >> So then you got to figure out like okay

1:02:28 like what are the exciting things that

1:02:29 we're doing versus what is just like

1:02:30 day-to-day boring like dude I'm sitting

1:02:32 in a meetings for six hours.

1:02:34 >> Oh damn.

1:02:34 >> You know so like what are you going to

1:02:36 do? You're going to put a camera outside

1:02:37 and like see me talking but you can't

1:02:39 hear the meeting.

1:02:39 >> Yeah.

1:02:40 >> So there there's a lot of that type of

1:02:41 stuff. But I would say that the number

1:02:42 one thing that has happened is one uh

1:02:45 there was a very big inflection point at

1:02:47 that moment. We started doing some

1:02:48 different things inside the business,

1:02:50 but people immediately were just like,

1:02:52 "Yo, I with you guys." Like, I I

1:02:54 get what you're trying to do now. I see

1:02:56 what is the philosophy behind this. I

1:02:58 understand what your vision is. I don't

1:03:01 know if you're going to do it, but it's

1:03:04 a public company. I'll use the product.

1:03:06 I'll buy some of the stock. I I I'll

1:03:08 kind of I I'll be along for the journey.

1:03:10 And so one day we were sitting on a Zoom

1:03:12 call and um we uh uh we've got this AI

1:03:15 team and um these guys are uh are

1:03:19 incredible. They're not your standard

1:03:20 like Silicon Valley engineers. So

1:03:22 they're incredible AI engineers, but

1:03:24 they are very unique individuals in

1:03:26 terms of where they live, what they do,

1:03:28 their interests, etc. And I was sitting

1:03:30 there and I was like, dude, we assembled

1:03:32 like the little giants of an AI company.

1:03:34 Like this is like the misfits,

1:03:36 >> but we're kicking these guys ass. Like

1:03:38 again like again like again we're

1:03:38 competing against trillion dollar

1:03:39 companies.

1:03:40 >> Yeah.

1:03:40 >> And we're more accurate. How does that

1:03:42 happen?

1:03:43 >> So I started talking about the misfits

1:03:46 >> but I was talking about our team

1:03:48 >> and then all of a sudden I realized

1:03:49 everybody who uses Sylvia they feel like

1:03:51 they're a misfit in the traditional

1:03:52 financial world. They're like I don't

1:03:54 trust the financial adviser. I don't

1:03:55 trust the mainstream media. You say some

1:03:57 big financial institution I immediately

1:03:59 don't want to use the product. So I was

1:04:01 like actually that is the thing that we

1:04:05 have backed into unintentionally. We

1:04:08 have a team of misfits who are building

1:04:09 a company for a bunch of people who feel

1:04:11 like misfits in the traditional system.

1:04:12 >> Yeah.

1:04:13 >> So we start say yo Sylvia misfits.

1:04:15 >> You've never laughed as hard as I have

1:04:17 laughed. When a 65year-old man DMs you

1:04:20 on Twitter and says I'm a misfits

1:04:23 and you're just like

1:04:25 >> you are though. You know what I mean?

1:04:26 like it like they feel like hey you are

1:04:29 speaking to me about this product and so

1:04:32 I've learned a lot about company

1:04:33 building about branding about how do you

1:04:36 help people understand that they have a

1:04:38 home

1:04:39 >> when they feel like they are kind of

1:04:41 like a a loner in their experience

1:04:43 >> a community within AI

1:04:45 >> it's the same thing you've got the guys

1:04:46 who you know they like golf they they

1:04:48 run a business they probably feel a

1:04:49 little alone on an island they're like I

1:04:50 don't know anybody else like me right

1:04:52 >> so when you create this now all of a

1:04:54 sudden we have people a guy today sent

1:04:56 me. He wrote a book in my ex DMs. He had

1:05:00 all these suggestions and everything. I

1:05:01 was like, "Dude, I need to like schedule

1:05:03 30 minutes on my calendar to read your

1:05:04 DM." Right. Yeah.

1:05:05 >> But he's really passionate about here's

1:05:07 how you can improve it. Here's what I

1:05:08 like about here's what I don't like

1:05:09 about it. Like that doesn't happen

1:05:10 unless we're doing the content. That

1:05:11 doesn't happen if actually people had

1:05:13 seen the stock 85%. Because if we came

1:05:16 out and the stock had just skyrocketed

1:05:18 and people were like, "Oh, dude, screw

1:05:20 this dude. This dude was right about

1:05:22 Bitcoin. Now he just creates a public

1:05:24 company and it's easy."

1:05:25 >> Yeah. They kind of start cheering for

1:05:27 your downfall.

1:05:28 >> Yeah.

1:05:28 >> But when they see that, oh wait, this

1:05:30 thing went down 85%. He launches a video

1:05:32 that's like, you know, our stock's down.

1:05:33 We got to turn it around.

1:05:34 >> Yeah.

1:05:35 >> I think people are like, you know what?

1:05:37 I I gotten kicked in the teeth before. I

1:05:38 know what that feels like.

1:05:39 >> This guy now.

1:05:40 >> Yeah. And and it's not just me, right?

1:05:42 Like our entire team, we have people who

1:05:44 are been highly successful in their

1:05:45 careers.

1:05:46 >> You're getting to watch a front row seat

1:05:48 to people who are used to winning and

1:05:51 now are they're down, you know, what is

1:05:53 it 213?

1:05:54 >> Yeah. Yeah.

1:05:54 >> When the Patriots came back,

1:05:56 >> I hate the Patriots, but I'll use them

1:05:57 as the example, right?

1:06:00 >> That's kind of what people are like, can

1:06:01 you come back?

1:06:02 >> Yeah.

1:06:03 >> Most of those stories don't end in the C

1:06:05 Cinderella like, you made the comeback.

1:06:07 >> And people don't make the content while

1:06:09 they're at the bottom.

1:06:10 >> Yeah.

1:06:11 >> You know, like they make it once they've

1:06:12 already like, "Okay, let me tell you how

1:06:14 I did it."

1:06:15 >> I called my wife before we published the

1:06:16 video. I was like, "Yo, we're going to

1:06:18 publish this. What do you think?" She

1:06:20 seen me at my lows, at my highs, through

1:06:22 everything. She was like, "Are you ready

1:06:26 for what that is? G like basically

1:06:27 you're gonna like, you know, it's like a

1:06:28 like a a mosquitoes to a light."

1:06:31 >> Yeah.

1:06:32 >> A small group of people know the stock's

1:06:34 down 85% 90%.

1:06:36 >> Yeah.

1:06:36 >> If you go publish it on YouTube,

1:06:38 everyone is going to know. People who

1:06:40 think that you're a genius are going to

1:06:42 know. And people who think you're stupid

1:06:43 but didn't know the stock was down,

1:06:44 they're going to know. Yeah.

1:06:45 >> Are you ready for that?

1:06:46 >> And I said to her, I was like, "This is

1:06:49 what it's about, man."

1:06:50 >> Yeah. E e e e e e e e e e e e e e e e e

1:06:51 e e e e e e e e e e e e e e e e e e e e

1:06:51 e e e e e e e e e e e e e e e e e e e e

1:06:51 e e e either we can succeed or we can't,

1:06:54 but rather than try to hide it or not

1:06:56 talk about it or whatever,

1:06:57 >> let's just put it out there.

1:06:59 >> Let's see what happens.

1:07:00 >> Yeah.

1:07:00 >> It's gonna be a hell of a story if we

1:07:01 come back.

1:07:02 >> Yeah. No, I love that. So what were you

1:07:05 you you mentioned this before, but like

1:07:07 you know, a lot of people don't even

1:07:08 know your career, right? They see you as

1:07:11 uh this financial guy running a company

1:07:13 and you know, they see you on X and just

1:07:16 talking about the markets,

1:07:18 >> bullshitting on Twitter. Yeah.

1:07:19 >> Yeah. But where did you get all your

1:07:20 experience from? What were you doing

1:07:22 before all this?

1:07:23 >> I have pretty much been uh building

1:07:25 companies since I was a teenager. And

1:07:29 whether it was stupid stuff that I

1:07:30 didn't even think of as a company, like

1:07:32 you know, my brothers and I would snow

1:07:33 when we'd run around and try to, you

1:07:35 know, grab up all the uh driveways in

1:07:37 our neighborhood so that we could uh

1:07:38 shovel them before somebody else to

1:07:41 running all kinds of businesses when I

1:07:42 was in school and things like that. To

1:07:45 when I graduated college, I'd been in

1:07:48 the military. um I was leaving and I

1:07:49 didn't want to get a job. I literally

1:07:51 created what I would consider my first

1:07:53 like real company as an excuse to not

1:07:56 get a job.

1:07:57 >> So, it's just like I've always been

1:07:58 drawn to doing that. Um I was very

1:08:01 fortunate uh early on. I built two

1:08:03 software companies I was able to sell.

1:08:04 Um not for a lot of money, but enough

1:08:06 where I was like, "Hey, you know,

1:08:07 there's something here. I need to learn

1:08:08 how to build bigger ones, but at least

1:08:10 like this this kind of early moment

1:08:11 >> like seven figures."

1:08:13 >> Well, I I'll tell you a good story. Um,

1:08:15 the second company I sold, uh, I did an

1:08:17 amazing deal at the time, big earnout,

1:08:21 saw zero dollars from the earnout. So,

1:08:23 you know, on paper it was awesome, man.

1:08:25 My bank account is still waiting for the

1:08:26 money to hit, you know, 20 years later,

1:08:28 right? So, like, you also learn some of

1:08:30 these things along the way of you're

1:08:32 like, "Ah, man, I probably should have

1:08:33 hedged a little bit more. I was too

1:08:35 optimistic, you know, wasn't rational

1:08:36 optimist. I was just, you know, like

1:08:38 like idiot optimist, right?

1:08:39 >> Optimistic."

1:08:40 >> Yeah. And and so like you kind of learn

1:08:41 along the way, but I made more money

1:08:43 than I would have if I had gone and

1:08:44 gotten a regular job. So I I was doing

1:08:46 perfectly fine. Um and frankly I had

1:08:48 more fun, right? Was probably the most

1:08:49 important thing. And I got to this point

1:08:51 where I said, "All right, I either need

1:08:53 to go to business school and like go get

1:08:56 like an MBA and get taught how the hell

1:08:58 do you scale these things, etc. Or I

1:09:00 need to go learn from somebody who does

1:09:02 this." And right about the same time I

1:09:04 was thinking through this, I was in the

1:09:05 process of selling said company. Um,

1:09:07 somebody from Facebook reached out to

1:09:08 me, a recruiter, and he was like, "Hey,

1:09:09 would you ever be a product manager here

1:09:11 and we had been trying to work with

1:09:13 Facebook because the second company used

1:09:14 the APIs of the social media platforms

1:09:16 and so we were always trying to get like

1:09:17 more access and they had heard that we

1:09:19 were selling the company and they're

1:09:20 like, "Yeah, why don't you come work

1:09:21 here?" Single best decision I made early

1:09:23 in my career was to go work at Facebook.

1:09:24 >> What year was this?

1:09:26 >> 2014.

1:09:26 >> Yeah.

1:09:27 >> So, I show up there's uh probably just

1:09:29 under 3,000 employees at the company.

1:09:30 They just gone public. Stock had dropped

1:09:33 significantly. Like went out at like 50

1:09:34 bucks. it dropped like $20. And when

1:09:37 these companies go public, a lot of

1:09:38 times there's a rotation of employees.

1:09:39 All the people who had worked in the

1:09:40 private market, it goes public, now

1:09:42 they're rich, they take the money, and

1:09:43 then like new employees come in. So I

1:09:45 was part of that wave of like, all

1:09:46 right, we're like the reinforcements, if

1:09:48 you will. And I was 25 years old at the

1:09:51 time and they put me as the head of a

1:09:56 growth team focused on Facebook pages.

1:09:59 does not sound sexy at all except for it

1:10:02 is the top of the funnel for all

1:10:03 advertising dollars on Facebook. You had

1:10:05 to have a Facebook page to become an

1:10:06 advertiser to give the company money. No

1:10:09 one had ever worked on growth before. So

1:10:11 I show up and we're like, why don't we

1:10:13 change the button to green? 20% increase

1:10:17 in conversion. Like this guy's a genius.

1:10:21 >> I'm like, wait, watch wait this next

1:10:23 trick. Why don't we make the button

1:10:24 bigger?

1:10:25 >> You know, like all these little things,

1:10:26 right? So team does very well right out

1:10:28 of the gate. Also, we had like kind of a

1:10:30 sister team that was working on

1:10:31 advertiser growth and they were killing

1:10:33 it as well. So the two teams together,

1:10:34 all this revenue started showing up. And

1:10:37 I learned a lot about how to grow

1:10:39 audiences on these platforms, etc.

1:10:41 Because one of the things that these

1:10:42 platforms learned early on is if you

1:10:44 show up and let's say you create a

1:10:45 Facebook page and you're a small

1:10:46 business and you start posting on

1:10:47 Facebook, you have no followers, you

1:10:49 have no network, there's nobody there.

1:10:50 You're like yelling in an empty room.

1:10:52 >> Yeah.

1:10:52 >> You just stop posting. You're like you

1:10:54 get discouraged very quickly. So what

1:10:56 they would do is you would create a page

1:10:57 and they would blast you

1:10:58 distribution-wise.

1:10:59 >> Oh, that's good.

1:11:00 >> And so you would get a bunch of

1:11:01 followers and likes and be like, "Oh,

1:11:03 there's people here. Post more."

1:11:05 >> And then over time they would take it

1:11:06 back down to the normal baseline. So

1:11:07 people felt like you're taking it away

1:11:08 from me, but really they were just

1:11:09 returning it to the baseline.

1:11:11 >> But you realize, oh, it's really

1:11:12 important to like get people

1:11:14 incentivized. How do you, you know, how

1:11:15 do you do this all stuff?

1:11:16 >> So we're doing all this growth stuff.

1:11:18 And one day I get tapped on the shoulder

1:11:19 by some of the executives and they say,

1:11:21 "You're going to leave this team. You're

1:11:22 going to go to another team." I was like

1:11:23 heartbroken. I loved the team I was

1:11:25 working with. It was a very small team

1:11:27 kind of very uh like the ethos of

1:11:28 Facebook was embedded in this team and I

1:11:31 was 25 26 years old. Like what are you

1:11:32 guys putting me in charge of this thing

1:11:33 for? This is you guys are idiots. And

1:11:36 they say uh on Monday you're going to

1:11:37 work for a new team. You're going to

1:11:38 come work with us. And they were like

1:11:40 pretty senior. They were kind of like

1:11:41 right below Zuck and Cheryl Samberg. So

1:11:43 I was like that's probably a pretty good

1:11:44 idea, but like I I really don't want to

1:11:46 leave this team but okay. So I show up

1:11:48 on Monday and I'm like so what am I

1:11:49 doing? And they're like, "You're going

1:11:50 to work directly with Mark Zuckerberg

1:11:52 and uh you're going to also help Cheryl

1:11:54 and there's this little small team of

1:11:56 three people and you guys are going to

1:11:57 figure out how to grow their audience on

1:11:58 Facebook."

1:11:58 >> Oh wow.

1:11:59 >> And I'm like that sounds like a complete

1:12:01 waste of time. Why would I help the

1:12:03 billionaire grow his audience on FA?

1:12:05 Like what the I was making money for the

1:12:08 company. Like why am I doing this? Mark

1:12:10 Zuckerberg uh is very similar to Peter

1:12:12 Teal to me. He's always like four or

1:12:14 five years ahead

1:12:15 >> really. And what he understood in 2014,

1:12:18 this like December 2014, is what now is

1:12:20 called going direct. He realized that he

1:12:22 had to talk to the media to communicate

1:12:24 to the users.

1:12:26 >> And the media didn't like him.

1:12:27 >> So they would always twist it or make it

1:12:28 the worst, you know, negative kind of

1:12:30 slant on his story.

1:12:31 >> Yeah.

1:12:32 >> So he wanted to communicate directly. At

1:12:34 the time, I think we had maybe probably

1:12:37 around a billion users, maybe 800

1:12:39 million users, something like that. But

1:12:40 it was a lot of people. But he had 9

1:12:42 million followers on Facebook.

1:12:44 >> Oh wow. So compare it to like MySpace

1:12:45 Tom who when you would join MySpace he

1:12:47 would friend everybody.

1:12:49 >> Tom man.

1:12:50 >> So we went through all these exercises

1:12:51 of like should we do that? Should Zuck

1:12:54 friend everybody right? Should um

1:12:57 another thing we thought about is like

1:12:58 one day should we put like an

1:12:59 interstitial on the page that you

1:13:01 couldn't X out kind of like your your

1:13:03 bank example and you had to like friend

1:13:05 him,

1:13:06 >> right? Or like should he write a letter

1:13:08 like asking people like that sounds kind

1:13:09 of desperate, right? You know, like like

1:13:11 what do we do? How do we do this? And to

1:13:13 his credit, he was very adamant. Do not

1:13:15 change the product to give me an

1:13:16 advantage. He didn't want a boost. He

1:13:18 didn't want the letter. Like he was just

1:13:20 like, we need to figure this out. And so

1:13:22 we had to resort to is basically using

1:13:23 analytics and testing, which is a very

1:13:25 Facebook thing to do. So we would post

1:13:27 things and then we would measure. No

1:13:29 different than you guys probably do on

1:13:30 social media, right?

1:13:32 >> But in 2014, it was like doing that back

1:13:34 then.

1:13:34 >> We got, hey, we got a great idea. Every

1:13:36 time you post your dog, it gets more

1:13:38 likes. Post more the dog. like when you

1:13:40 put your face in the photo versus not it

1:13:42 gets more likes like do that. Uh when

1:13:44 you write something do that.

1:13:46 >> So over like a 90-day period we pretty

1:13:48 much learned you know I don't know 80%

1:13:49 of the stuff we were going to learn. And

1:13:50 so the team kind of got told like all

1:13:52 right great job like everyone go back to

1:13:53 like do what they wanted what you were

1:13:55 doing before. And so that same executive

1:13:58 group asked me um they said well we're

1:13:59 starting this new team. Why don't you

1:14:00 come over here? And so I was like all

1:14:02 right I got great experience here. I

1:14:03 worked on the Facebook pages thing. I

1:14:05 saw monetization. I got exposed to

1:14:07 probably one of the smartest men in the

1:14:08 world, one of the best entrepreneurs in

1:14:10 the world, who to this day does not get

1:14:12 enough respect for how good he is.

1:14:14 >> And he gets no love.

1:14:15 >> I I tell one story all the time that

1:14:17 people always ask me, they're like, "Oh,

1:14:19 yeah, behind closed doors." Like, yo, is

1:14:20 he as good as people think he is?

1:14:21 >> Yeah, that's what I want to ask you.

1:14:22 How's Mark behind closed doors?

1:14:24 >> Hey, real quick. I'm looking for one

1:14:25 company to partner with this month and

1:14:27 run their entire marketing and sales

1:14:29 department. That means we're going to

1:14:31 run their ads, build their funnels,

1:14:32 manage their CRM, hire and train their

1:14:35 sales team, and manage them, and just

1:14:37 handle all of the front-end revenue for

1:14:39 this business. All you would have to do

1:14:41 is just manage fulfillment and serve

1:14:43 your clients well. And for doing this,

1:14:45 we're not going to do any kind of

1:14:47 equity. It is just going to be a profit

1:14:49 share agreement. So, if any of that

1:14:51 sounds interesting to you, go to

1:14:52 panapartners.com

1:14:55 right now. You can apply today. We're

1:14:57 only taking on one company. And so if

1:14:59 you think your company's a fit, make

1:15:01 sure you apply today. You've got to be

1:15:03 doing at least $50,000 a month in

1:15:05 revenue in order for us to even look at

1:15:07 it. So go to panat partners.com today.

1:15:10 The only story I ever tell, we were in a

1:15:12 meeting. There's 15 people maybe in this

1:15:15 meeting and he used to do these like

1:15:16 product reviews. So like you're going to

1:15:18 launch something and he would bring

1:15:20 everyone in a room and then you

1:15:21 basically would demo it. It was like a

1:15:23 very demo helpy uh uh heavy culture. So

1:15:25 he'd have a screen, you're pulling it

1:15:27 up, you're like, "All right, so they're

1:15:28 going to go to this page. When they go

1:15:29 to this page, they click on this and

1:15:30 then and you're showing it to them." And

1:15:32 this man in a span of 90 seconds

1:15:35 literally while we were talking about

1:15:37 this is when we were thinking about

1:15:38 putting the interstitial like all this

1:15:40 crazy stuff and like not bet the company

1:15:42 serious, but like dude, if this goes

1:15:45 wrong, the headlines tomorrow are going

1:15:47 to be Mark Zuckerberg is an insecure

1:15:49 billionaire who wants everyone to be his

1:15:51 friend.

1:15:51 >> Yeah.

1:15:51 >> You know, like pretty consequential type

1:15:53 things. And he goes, "Go back a slide."

1:15:56 Why is that not the Facebook blue?

1:15:59 >> I'm like, "Bro,

1:16:01 what?"

1:16:03 I didn't even see it wasn't the Facebook

1:16:04 blue. Nobody in the room that had looked

1:16:06 at the presentation before realized

1:16:08 that. And so I'm like, "This man went

1:16:10 from high level to literally the color

1:16:13 of the pixels."

1:16:14 >> Yeah.

1:16:14 >> Back to high level in 90 seconds.

1:16:17 >> Mhm.

1:16:18 >> So we walk out of the room and I ask uh

1:16:19 one of the guys in the room, I say,

1:16:21 "What was that?" Like this was like the

1:16:23 first time I had like seen him really,

1:16:24 you know, do something like that.

1:16:26 >> And he goes, "You have to remember that

1:16:28 Zuck has more context than all of us."

1:16:29 >> Yeah.

1:16:30 >> He picked the blue.

1:16:31 >> Yeah.

1:16:32 >> Right. Like he remembers when he might

1:16:34 not have like made some big decision. He

1:16:35 didn't do a focus group. He may have

1:16:37 just been like, I like this blue,

1:16:38 >> but like the guy has more context over

1:16:41 time. And so he has an advantage,

1:16:43 >> but he also cares. And so he's not going

1:16:46 to let that mistake

1:16:48 >> go in that presentation because if he

1:16:50 lets it slide this time then another

1:16:52 thing slips in another thing. And so

1:16:53 like he's maniacal about the details but

1:16:55 he's also smart enough to do the high

1:16:56 level decisions. When you're exposed to

1:16:58 that you're just like bro there's

1:17:00 different levels to this stuff. Like I

1:17:01 don't care how good you are. There's

1:17:04 three freaks in the world like Mark

1:17:06 Zuckerberg.

1:17:06 >> Do you have any other stories about Mark

1:17:08 you've never told before?

1:17:10 >> That's probably the single most

1:17:11 important one. One one that's fun. Um he

1:17:13 he he uh he probably doesn't even

1:17:15 remember this. My very first meeting

1:17:17 with him, he was late.

1:17:18 >> So it was like me, him, and maybe

1:17:20 there's like three or four other people

1:17:20 in the room. And um I'm like nervous as

1:17:23 hell. I'm like, "Dude, this guy, he's

1:17:25 the CEO of the company." Like, you know,

1:17:26 >> this guy's a robot.

1:17:26 >> Yeah. Like you hear all these stories

1:17:28 like all whatever, right? And so I'm I'm

1:17:30 sitting there and he's like a minute

1:17:32 late, two minutes late, three minutes

1:17:34 late. And I'm like,

1:17:35 >> well, he's obviously important. Like

1:17:36 he's probably late to all meetings. And

1:17:38 you find out he's like almost never

1:17:39 really late to the meetings, but the

1:17:41 first one you're sitting there, you're

1:17:42 like, "Okay, okay." And he comes in and

1:17:43 he's got, you know, the hoodie on, but

1:17:45 his face is like really flush. Like, you

1:17:47 know, like when a kid like runs around

1:17:48 and gets like overheated, but can't

1:17:50 sweat

1:17:51 >> or like if you sweated a bunch, took a

1:17:53 shower and you're like still overheated,

1:17:55 but not sweating.

1:17:56 >> So, he comes in like that and he's got a

1:17:58 protein shake and he's like shake but

1:18:00 like in like the uh like a bodybuilder

1:18:02 like uh you know, shaker.com.

1:18:04 >> Yeah. And so he's like shaking it and

1:18:06 he's like flush and I'm like

1:18:09 I'm just who I am, man. Were you just

1:18:11 working out?

1:18:12 >> Yeah.

1:18:12 >> And he's like, "Yeah." And so I'm like,

1:18:15 >> "What are you doing in the gym?" Like

1:18:17 I'm just like, "Dude, this is

1:18:18 fascinating to me that like the guy who

1:18:21 >> everyone says is a nerd, says he's a

1:18:22 robot, all stuff like he's a he works

1:18:24 out.

1:18:25 >> Yeah.

1:18:25 >> And so he's like, "Oh, I was doing bench

1:18:26 press." And so

1:18:27 >> nice.

1:18:28 >> What do you bench?

1:18:30 >> And like very quickly I like getting

1:18:31 death looks from like other people in

1:18:33 the room. They're like, "Dude, shut up."

1:18:34 like you're you're not supposed to talk

1:18:35 in this meeting type thing. Like you're

1:18:36 just like this underling, you know? But

1:18:38 I was like he like answered the

1:18:41 questions and he worked and I was like,

1:18:42 "Oh, this guy like puts his pants on the

1:18:43 same way we do." Whatever. And so I just

1:18:45 think that people get very caught in

1:18:47 like the public narratives, but you

1:18:49 realize like he's got kids. He works

1:18:51 out. He eats the same food we do. He

1:18:53 likes doing cool Like

1:18:55 >> how much did he bench?

1:18:56 >> To be honest, I don't remember. But it

1:18:58 was

1:18:58 >> 135. I bet

1:18:59 >> it was a number that was I mean my

1:19:01 baseline for him or my expectation was

1:19:03 like he was going to tell me like 50

1:19:04 lbs. So it it was higher than that but

1:19:06 it wasn't anything that I was like wow

1:19:08 you're really strong.

1:19:09 >> He's like 225 you're like damn okay.

1:19:11 >> If you told me 225 I'd like give him a

1:19:12 high five right I don't care if you fire

1:19:14 me man you're awesome

1:19:16 >> but I think I it was like you know I

1:19:17 don't know 150 185 what whatever the

1:19:19 number was it was like

1:19:21 >> respectable given you know the the

1:19:23 expectation.

1:19:24 >> Did you learn anything from Mark? Like

1:19:25 what was like the biggest thing you

1:19:26 learned from him specifically?

1:19:30 the Facebook was just um I actually

1:19:32 tweeted it today. Uh fa Facebook was

1:19:34 such a unique place. I don't think that

1:19:35 we will ever like I will personally

1:19:37 never experience that where it was such

1:19:39 a concentration of talent um

1:19:44 ambition and results.

1:19:47 It was um I remember hearing one time

1:19:48 somebody said uh that worked at Google,

1:19:50 I forget what book I read, and they were

1:19:52 talking about when they worked at

1:19:53 Google, uh every week they would send

1:19:55 out these emails and be like, "Hey,

1:19:56 these are the people who are joining and

1:19:58 here's their background."

1:19:59 >> And he was like, "So you'd read the

1:20:01 email and it'd be like, "All right, uh

1:20:04 this person's joining. They were a

1:20:05 two-time Olympic gold medalist. They

1:20:07 went to Stanford undergrad, got an MBA

1:20:09 at Harvard, and uh in their free time

1:20:11 they like saved the whales." Yeah.

1:20:13 >> And you're like, "That's a one of

1:20:16 freak." Like, "We're never going to get

1:20:17 another one of those." Second person in

1:20:19 the email, uh, this person speaks 17

1:20:21 languages. Uh, you know, got a five on

1:20:23 the AP calc test when they were in

1:20:25 fourth grade. And you're like

1:20:28 >> two freaks in one, you know, one

1:20:29 recruiting class.

1:20:30 >> Yeah.

1:20:31 >> Every person was like that. And so you

1:20:33 have like massive amount of insecurity

1:20:35 cuz you're just like, dude, I should not

1:20:37 be here.

1:20:40 >> I didn't even know my high school had AP

1:20:41 classes, you know? like these guys are

1:20:43 all smart, but you realize like that was

1:20:45 the culture. It was just a very heavy

1:20:47 like get great talent, which I think a

1:20:48 lot of companies aspire to, but like

1:20:49 when you see it, you're like, "All

1:20:50 right, this is special." And then the

1:20:52 second thing was uh Facebook was very

1:20:54 unique and that it was super data

1:20:55 driven. And they've gotten critiqued

1:20:56 over time for being data driven, but I

1:20:58 actually think people don't quite

1:20:59 understand what truly being data driven

1:21:01 means in in kind of the modern world.

1:21:03 So, I'll give you um uh a very concrete

1:21:06 uh positive result.

1:21:09 In 2015, Facebook had faced a lot of

1:21:12 backlash publicly. Part of that was why

1:21:13 Zuck wanted to go direct all this stuff.

1:21:15 But in 2015, um, they were trying to

1:21:19 understand what a lot of people would

1:21:20 think of as like an NPS score. Do people

1:21:22 like this company or not? How do like

1:21:25 what could we do to have a better NPS

1:21:28 score? But in a very Facebook way, they

1:21:30 were like, well NPS isn't a good

1:21:32 measurement. So they invented their own

1:21:34 metric and they called it cow cau which

1:21:37 stood for a question Facebook cares

1:21:40 about users and what they used to do is

1:21:42 they would give a survey to users and

1:21:44 they would say here's like three to five

1:21:45 questions rank on a scale of one to five

1:21:49 do you agree or not agree with these

1:21:51 statements and five meant you agreed one

1:21:52 meant you didn't agree at all and one of

1:21:54 the statements was Facebook cares about

1:21:56 users and so they would serve this to

1:21:59 people and you would answer it and then

1:22:01 they would show you different changes in

1:22:03 the product and then they would survey

1:22:04 you again later and they were trying to

1:22:06 figure out what could we do in the

1:22:08 product to get you to become more

1:22:11 agreeable to the statement Facebook

1:22:12 cares about users

1:22:14 just the premise of we're going to

1:22:16 measure this thing that is really like

1:22:18 brand affinity which most people you

1:22:20 know you go to like Madison Avenue

1:22:21 they're like well just buy billboards

1:22:22 and like I promise it works

1:22:24 >> it's immediately just like what is the

1:22:26 quantifiable metric how do we perfectly

1:22:28 design a test that we can run to

1:22:30 understand are we moving this or are we

1:22:32 not? Every single thing is ROI, metrics,

1:22:35 etc.

1:22:37 months and months and months of trying

1:22:39 to do this. Nothing is working. These

1:22:42 are incredibly smart people that have

1:22:43 been at Facebook for a long time.

1:22:45 Facebook has a ton of internal tools

1:22:46 that have been built. They're very good

1:22:47 at this testing and they have a lot of

1:22:49 traffic so they can rapidly test things

1:22:50 and get statistically significant

1:22:52 results. Nothing is working to the point

1:22:54 where now people are like having

1:22:55 meetings about like this isn't working.

1:22:59 One night

1:23:01 we had been putting these like uh kind

1:23:03 of cards at the top of the news feed and

1:23:05 the cards would be like it's your

1:23:06 birthday or it's a holiday or whatever.

1:23:09 And to give you a scale of Facebook when

1:23:12 I joined it was about 3,000 employees.

1:23:13 When I left two years later it was

1:23:15 12,000.

1:23:15 >> Wow.

1:23:16 >> Rapid growth. And we had a whole team

1:23:19 that was dedicated to just those cards.

1:23:22 What holidays do you celebrate? Who sees

1:23:25 them? When do they see them? What does

1:23:28 it say? What is the image? Well, on that

1:23:32 holiday, that image actually this group

1:23:34 of people will be offended. This other

1:23:36 group though in a different country,

1:23:38 they look at it different. Like you're

1:23:39 like localizing it. Then you're doing it

1:23:41 for a billion people on all these

1:23:43 holiday. Like so it's a very like

1:23:44 complex thing, but it's just like one

1:23:46 card at the top of your newsfeed. You're

1:23:47 like why would you ever have you know 15

1:23:50 people working on this?

1:23:51 >> And so they had created one of these and

1:23:54 um on that team was like a copywriter

1:23:56 essentially. So they would put this and

1:23:58 somebody would write the copy and then

1:23:59 the copywriter would send the copy to

1:24:01 the designer. The designer would put it

1:24:02 into the design and then the engineers

1:24:03 would make it go public. Everyone had

1:24:05 gone home for the night was the story

1:24:08 and the designer didn't want to wait and

1:24:10 so they just wrote something went home

1:24:13 came back the next day jubilee. Everyone

1:24:16 was excited and you could like feel the

1:24:18 energy and you're like what what

1:24:19 happened? They're like cow moved and

1:24:21 you're like this is like the white

1:24:22 whale. Like you're like no it didn't

1:24:24 like the test is wrong. the system

1:24:27 broke. Like there's no way somebody

1:24:29 moved cow. Who did it? You know, type

1:24:31 thing, right? Long story short, what

1:24:33 happened is the designer wrote on that

1:24:36 card whatever the holiday or thing was

1:24:38 and then they signed it dash from all of

1:24:41 us at Facebook.

1:24:43 >> That's what moved it from all of us at

1:24:45 Facebook.

1:24:46 >> They humanized the company. Some of the

1:24:49 smartest people in the world, unlimited

1:24:51 resources, one of the top company

1:24:53 priorities. No one could figure it out.

1:24:56 This designer who genuinely cared

1:25:01 just I don't know anything about

1:25:03 copywriting from all of us at Facebook

1:25:06 moves cow. If you go on Facebook today

1:25:08 you'll see that everywhere

1:25:09 >> because they realize like oh people

1:25:11 don't even know like humans work here.

1:25:12 >> Yeah.

1:25:13 >> It was like this like clinical thing.

1:25:15 And so you go back to

1:25:18 Mark Zuckerberg and the executive team

1:25:20 at Facebook one of one because they

1:25:24 identified a problem. They refused to

1:25:26 take the like normal route of let let's

1:25:28 just go spend a bunch of money on

1:25:29 marketing. How do we quantify it? And

1:25:32 then probably my single biggest lesson

1:25:33 from Facebook was especially from a

1:25:35 growth perspective is there are two

1:25:37 rules or two kind of like laws of the

1:25:39 universe when it comes to growth

1:25:40 especially at Facebook. You have to

1:25:42 clearly define the test that you are

1:25:44 going to run and then you have to

1:25:46 execute the test perfectly. And most

1:25:49 people usually don't even define the the

1:25:52 test. But if I said to you, "Okay, we're

1:25:54 going to wear different pairs of shoes

1:25:56 for a week to see which one you like."

1:25:59 Clearly defined. We're trying to figure

1:26:01 out what it is, and then we're going to

1:26:02 say, "Okay, you're going to wear this

1:26:03 shoe on Monday, this shoe on Tuesday,

1:26:04 whatever." The second part of that is

1:26:07 the most important. You have to

1:26:08 perfectly execute the test. Because if

1:26:11 you get to the end of a test and it

1:26:14 didn't work but you didn't execute it

1:26:16 perfectly, you're left wondering did it

1:26:18 not work because the thing we were

1:26:20 testing was wrong or because we just

1:26:22 didn't execute well. Right?

1:26:24 >> So every test has to be done perfectly

1:26:26 so that you can isolate the thing that

1:26:28 you're testing.

1:26:29 >> So Facebook had built all this tools,

1:26:31 all this technology etc to do this in a

1:26:33 worldclass way. And then you know when

1:26:36 we think about building Sylvia the

1:26:37 number one disadvantage we have is not

1:26:39 how much money we have our team it's

1:26:40 traffic.

1:26:42 >> Facebook has three billion people using

1:26:44 their products.

1:26:45 >> They can run a test and get a

1:26:47 statistically significant result by

1:26:50 dinnertime. It may take us two, three,

1:26:53 four weeks to get the same result.

1:26:55 >> So they can more rapidly test things.

1:26:57 >> Yeah. And let's say that they're

1:26:59 running, I don't know, that company is

1:27:01 probably running at any given time,

1:27:03 hundreds of thousands of tests, maybe

1:27:04 millions of tests.

1:27:05 >> Wow.

1:27:06 >> Because if you think of like a button,

1:27:08 so they had this system internally, uh,

1:27:10 that could do multivariate testing. A

1:27:12 single button. When you first think

1:27:13 about, you're like, "All right, it's a

1:27:14 button. What could I change? The color,

1:27:17 the location,

1:27:18 >> the words, the size, font.

1:27:21 >> What about are the edges rounded or

1:27:24 squared? Is there a shadow?

1:27:26 >> Mhm. How rounded? What is the different

1:27:30 hues of of the color?

1:27:31 >> Yeah.

1:27:32 >> How big is the font? What font? Is it

1:27:34 underlined? Is it not? Is it bold? Is it

1:27:36 not?

1:27:36 >> Yeah.

1:27:37 >> What are the dimensions of the button?

1:27:39 What it all of a sudden you have

1:27:40 hundreds of variables that you could

1:27:41 play with.

1:27:42 >> Yeah.

1:27:42 >> They had a system you could feed all of

1:27:44 that into it would just constantly show

1:27:47 it to a tons of different people and it

1:27:49 would basically spit back out and say

1:27:50 this is the winning combination.

1:27:53 >> It's like playing growth on easy mode.

1:27:56 hard work, but the tool made it way

1:27:58 easier. And so that's when I think about

1:28:00 like these companies that everyone loves

1:28:03 to kind of like poke at or or critique.

1:28:06 Like, dude, you don't accidentally build

1:28:08 Facebook.

1:28:09 >> Yeah.

1:28:09 >> You know,

1:28:10 >> they're that good.

1:28:10 >> Yeah. And look at SpaceX. People like

1:28:14 Elon's an idiot.

1:28:16 >> He might be smart dumb,

1:28:18 >> but he's still got the smart is comes

1:28:20 first, right?

1:28:20 >> Yeah. Well, they're both also not just

1:28:23 insanely smart, but they're so good at

1:28:25 execution.

1:28:26 >> Yeah,

1:28:26 >> I think that that's the main thing that

1:28:28 great entrepreneurs have compared to

1:28:29 everyone else is like there's a lot of

1:28:31 smart people with great ideas and great

1:28:32 theories and

1:28:33 >> but the execution is what lacks. I

1:28:36 recently heard um X or Twitter, they are

1:28:40 still doing meetings like 4 a.m.

1:28:44 meetings

1:28:46 >> when they were doing the like we're

1:28:47 going to fire, you know, 2,000 people or

1:28:49 you know, whatever the numbers were. Um

1:28:52 it was kind of like we're in wartime,

1:28:54 you know, we're under attack, the

1:28:55 advertisers are all leaving. Like I

1:28:56 think people were like

1:28:58 >> that's crazy, but I understand the

1:28:59 moment in time.

1:29:00 >> Yep. I have heard now from two different

1:29:03 people,

1:29:05 wartime never ended.

1:29:07 >> Like,

1:29:07 >> yeah,

1:29:08 >> that's just Elon M.

1:29:10 >> Yeah. It's just like that's just how he

1:29:11 operates his companies.

1:29:12 >> Yeah.

1:29:12 >> And so the team is significantly

1:29:14 smaller,

1:29:15 >> but

1:29:17 in a way it is um

1:29:21 it is a thing that we know works across

1:29:23 all industries. Why is it that certain

1:29:26 people are drawn to be a Navy Seal? Why

1:29:28 are certain people drawn to work at an

1:29:29 Elon company? Why are certain people

1:29:31 drawn to do the thing that everyone

1:29:33 tells them is impossible to set a world

1:29:35 record or whatever?

1:29:37 The more you tell somebody it's hard or

1:29:39 that it's going to suck or that they're

1:29:41 not going to get glory from it or that

1:29:43 they can't do it, certain people, not

1:29:45 everybody, but certain people are drawn

1:29:47 to that.

1:29:47 >> Yeah. And it's the old like I'm sure you

1:29:49 guys have seen like the uh Ernest

1:29:50 Shackleford ad I think is his name who

1:29:54 um he's like uh I'm looking for

1:29:56 explorers dangerous mission no guarantee

1:29:59 of success may lose your life but you

1:30:02 know reward and glory for those that

1:30:05 make it back or something.

1:30:06 >> Yeah.

1:30:06 >> And it's like one of the best ads of all

1:30:08 time because he basically like he

1:30:09 anti-sold them

1:30:11 >> on like you can go do some epic

1:30:13 right? But like written in like old

1:30:14 English.

1:30:15 >> Yeah.

1:30:16 >> That's what Elon does. That's what, you

1:30:17 know, these companies do is they're just

1:30:19 like, "We don't want everybody. We just

1:30:21 want the right people." And so to find

1:30:23 12,000 people now, 80,000 people, you

1:30:26 they're not all like that. But the core

1:30:28 group,

1:30:29 >> Facebook in particular, there are three

1:30:31 people who have worked at Facebook, uh,

1:30:32 they're the three longest standing

1:30:33 employees outside of Zuck, savages. They

1:30:36 forever will have my respect. I don't

1:30:39 care whatever happens at that company.

1:30:40 Those guys are incredible. And that's

1:30:43 just really rare. Like how many

1:30:44 companies can say that the core four, if

1:30:46 you will, right? And they're okay.

1:30:48 They're actually a fifth guy who's been

1:30:49 there a long time, too. Um, just call it

1:30:51 maybe the top five or six exacts have

1:30:53 been there for 15 years, 20 years.

1:30:56 >> It's rare.

1:30:57 >> It's crazy.

1:30:58 >> So, after you got out of Facebook, what

1:31:01 what ended up happening?

1:31:03 >> Um, I did a 17-day stint at Snapchat,

1:31:06 which was uh yeah, which was an

1:31:08 interesting experience. Um, and then uh

1:31:11 I pretty much started investing. I

1:31:12 wanted to start another company but I

1:31:13 didn't have a good idea and I think I

1:31:15 had like learned the idea is actually

1:31:17 pretty important. You can make any idea

1:31:19 succeed but if you want to build

1:31:21 something big

1:31:23 a lot of it is what market are you

1:31:25 choosing? Who are you going to compete

1:31:27 against etc. There's a great saying um I

1:31:29 think Mark Andrees is the one who said

1:31:31 it uh a good team meets a bad market the

1:31:34 market wins. A bad team meets a good

1:31:36 market the market wins. So like a lot of

1:31:39 the almost like strategic planning of

1:31:42 what am I going to do? Answering that

1:31:44 question determines a lot of the success

1:31:45 of whatever business you go into.

1:31:47 >> The vehicle matters matters more than

1:31:49 anything.

1:31:49 >> Correct. And so I just didn't have an

1:31:52 idea where I was like this is the thing

1:31:53 I want to go you know full speed at. And

1:31:55 so I started helping some friends

1:31:56 started investing. Um but I didn't know

1:31:59 if I was going to like investing. Like

1:32:00 it was kind of weird. I was kind of like

1:32:02 I I think I like building but let's try

1:32:03 this investing thing. Um it helps to

1:32:06 have a little bit of you know good

1:32:07 fortune right out of the gate. Um the

1:32:09 very first fund that I had it was me and

1:32:11 a buddy uh we invested in about 50

1:32:13 companies or so and five of them became

1:32:16 unicorns and so it was like okay small

1:32:19 dollars but like on paper the like

1:32:21 percentage markup was big uh for each of

1:32:23 those companies. were like that was kind

1:32:25 of fun, you know, we we like got to meet

1:32:27 a lot of people, we got to talk to a lot

1:32:28 of people, like let's do this a little

1:32:30 bit bigger. And that's when we did a

1:32:33 deal with a large hedge fund, kind of

1:32:35 more traditional institutional asset

1:32:37 manager um called Morgan Creek. And we

1:32:39 basically raised one of the first

1:32:41 dedicated venture funds specifically

1:32:43 focused on crypto more broadly. So we

1:32:46 said, no different than you would have

1:32:48 like a healthcare fund or maybe a real

1:32:50 estate fund. We're going to do one just

1:32:51 focused on this crypto thing. This is in

1:32:53 2018.

1:32:54 >> Okay.

1:32:55 >> So, pretty early.

1:32:56 >> This before it blew up in 2018.

1:32:58 >> This is uh No, no,

1:32:59 >> no. 2017.

1:33:00 >> No, of course. 2017 it goes up. We're

1:33:02 like, "Yo, we should do this."

1:33:03 >> 2018, like March, I think, or something,

1:33:05 we start going out and talking to

1:33:07 people. And you're like, "Man, every day

1:33:09 I wake up, this thing's lower." Talk

1:33:11 about getting your teeth kicked in.

1:33:12 Like, not real fun,

1:33:14 >> right? So, um, we kind of like begged

1:33:18 and borrowed our way to a $42 million

1:33:21 fund, I think it was.

1:33:22 and

1:33:24 public pension funds, hospital systems,

1:33:27 endowments, foundations. Like I feel

1:33:29 like I've just gone from basically a

1:33:31 bunch of individual investors investing

1:33:33 alongside me to now like we're playing

1:33:35 the big leagues.

1:33:36 >> Public pension funds. You go to meetings

1:33:38 with their like board and they, you

1:33:40 know, they ask you questions and stuff,

1:33:41 right? It's like real

1:33:43 >> like real present to them money.

1:33:47 >> Yeah. Well, like you know, the joke is

1:33:48 always like a venture capital uh when

1:33:51 you're raising money from individual

1:33:52 LPs, it's like, you know,

1:33:54 >> a doctor's like, "Hey, I'll put in 100k,

1:33:56 you know, let's see how this goes." Like

1:33:58 they're they're kind of loose and you

1:34:00 know, they're kind of I'll bet on you

1:34:01 type thing.

1:34:03 >> The other side of venture capital is

1:34:04 like young guys flirting with old guys

1:34:06 for money, you know? So like the young

1:34:07 founders like I promise I'm going to

1:34:08 change the world and the old guys like

1:34:10 sounds great, here's money, you know,

1:34:11 whatever, right?

1:34:11 >> The institutional world is like

1:34:13 everyone's covering their ass,

1:34:14 >> you know? So, it's like, "Okay, where's

1:34:16 your presentation? We're going to send

1:34:18 someone to your office and they're going

1:34:19 to do like real diligence. Uh, you have

1:34:21 to come to our office." In this case, it

1:34:22 was in Virginia. Well, you got to come

1:34:23 to Virginia. And one of them was a

1:34:25 police pension fund.

1:34:26 >> Oh, wow.

1:34:26 >> We're like sitting in front of the and

1:34:28 there's like a police officer there.

1:34:29 You're like,

1:34:30 >> "Oh, okay. Uh, I promise we're going to

1:34:33 do a good job, right? Like, what

1:34:35 questions do you have?"

1:34:36 >> So, we did this whole thing and we

1:34:37 started investing and we bought Bitcoin

1:34:40 at $5,500 for them. So, obviously I've

1:34:42 done very well there. Um we invested in

1:34:45 uh Coinbase uh Bitwise

1:34:48 um we invested in Figure Technologies

1:34:51 like these many of these companies have

1:34:52 gone on to be you know multi-billion

1:34:53 dollar businesses and so you know did

1:34:55 did uh very well for the LPs but what I

1:34:58 learned was the same amount of work was

1:35:01 involved in the small fund and the big

1:35:04 fund when you tell the founder I'm in

1:35:07 for 100k or I'm in for 10 million

1:35:11 no difference in the

1:35:13 And so I was like, "Oh, there's like

1:35:14 there's levels." And so then we went and

1:35:16 we raised a $100 million fund. And we

1:35:20 were right on crypto. And we actually

1:35:23 not only got the industry right, which

1:35:25 was important. We stayed away from a lot

1:35:28 of the like nonsense. A lot of the like

1:35:31 longtail tokens and all that kind of

1:35:32 stuff,

1:35:34 >> all that stuff.

1:35:35 >> Fartcoin did not make it into the public

1:35:37 the the public pension fund did not get

1:35:39 Fcoin, unfortunately. drunk monkeys or

1:35:40 whatever the MC stuff.

1:35:42 >> So it was Bitcoin and then it was the

1:35:44 equity of a bunch of these

1:35:45 infrastructure players, right? Um and so

1:35:49 >> going through that experience, what I

1:35:51 think I learned was one, okay, maybe I'm

1:35:53 not horrible at doing this, but more

1:35:55 importantly was

1:35:58 the private markets have an immense

1:36:00 amount of asymmetry to them. Both in

1:36:02 terms of how much you can uh drive the

1:36:04 value of these companies, but more

1:36:06 importantly is an information asymmetry.

1:36:08 So if you think of Wall Street in the

1:36:09 80s and 90s like what did everyone talk

1:36:11 about like who has what information that

1:36:12 they can use to you know invest

1:36:14 whatever. So people got in trouble

1:36:15 because there's rules in the private

1:36:17 market you can just call the founder be

1:36:18 like how's the company doing?

1:36:19 >> Yeah.

1:36:20 >> And they're like uh you know we're

1:36:21 growing 30% month over month. Great. You

1:36:23 want more money? Yeah. Okay. Here it is.

1:36:26 You know what I mean? Like you there's

1:36:29 complete access to information that a

1:36:30 founder will tell you. Now they might

1:36:31 not tell you all the information there.

1:36:32 You know there's gamesmanship whatever.

1:36:35 but you actually h can make a more

1:36:36 informed decision in the private market.

1:36:38 >> And so I really like that. And so I

1:36:40 basically

1:36:41 >> went crazy. I invested over 300 private

1:36:43 companies and I was just like

1:36:45 >> this is a great way to invest capital.

1:36:49 And if you fast forward um we started to

1:36:52 build more private companies. Like we we

1:36:53 really were like all right this is what

1:36:54 we want to do. And in uh 2022,

1:37:00 I went to uh buy a house in Florida. And

1:37:04 I called the bank and I said, "Uh, I've

1:37:06 never bought a house before, but I've

1:37:09 read about a mortgage. I think you can

1:37:11 lend me money. How much will you lend

1:37:13 me?" And the banker says to me, "You're

1:37:17 broke." And I was like, "I'm not the

1:37:20 richest guy in the world, but I'm not

1:37:21 broke, I don't think." Like, what do you

1:37:23 mean? And he was like, "Well, all of

1:37:25 your money is in private investments and

1:37:27 Bitcoin.

1:37:29 >> Both of those when you apply for the

1:37:31 mortgage, that's just zero. Like you get

1:37:33 no credit because the private

1:37:34 investments could all go to zero." And

1:37:36 this Bitcoin thing, like we don't

1:37:37 believe in that. Like that's a zero,

1:37:38 too.

1:37:39 >> So he was like pretty much whatever cash

1:37:41 is in your like checking account, that's

1:37:43 your assets in our eyes.

1:37:45 >> And I was like, well that's like not

1:37:46 good.

1:37:47 >> So like how much will you lend to me? He

1:37:49 was like a lot less than you thought we

1:37:51 were going to lend to you. So I was

1:37:52 like, how can I like remedy the problem?

1:37:54 And he was like, you got to build a

1:37:57 public portfolio.

1:37:58 >> And so quite literally the reason why I

1:38:00 started investing in public companies

1:38:01 was because that banker at a private

1:38:04 bank told me that that was the only way

1:38:06 that I could really interface with the

1:38:07 bank was I had to have assets they would

1:38:09 recognize.

1:38:10 >> Now fast forward, there's Bitcoin ETFs

1:38:12 and like now they'll like, you know,

1:38:13 look at a lot of this other stuff.

1:38:14 They've obviously built a whole practice

1:38:15 around like preipo companies and they'll

1:38:17 put value on it, whatever. But at the

1:38:19 time, I remember just being like again,

1:38:20 like there's levels to this game that if

1:38:23 you never been exposed to it.

1:38:24 >> Yeah.

1:38:25 >> You're just like, "Well, don't you just

1:38:26 give me the mortgage?"

1:38:27 >> So, so you had raised a $100 million

1:38:30 fund, but you had never bought a house.

1:38:32 >> Correct.

1:38:32 >> Damn.

1:38:33 >> That's crazy.

1:38:34 >> But I think that I I think that that is

1:38:36 a more common story today than people

1:38:38 realize. Like people talk about like

1:38:39 homes are unaffordable.

1:38:41 I don't think that that is a wrong

1:38:43 assessment at all. I think that home

1:38:44 prices have exploded. I was recently

1:38:46 telling somebody cuz the boomer

1:38:49 generation, I say that lovingly as a

1:38:50 term, but it's like the older baby

1:38:52 boomer generation, they don't want home

1:38:54 values to go down

1:38:55 >> because they own the homes. So like they

1:38:57 want them to go up.

1:38:58 >> Yeah.

1:38:58 >> The young people who don't own the homes

1:38:59 are like, "Yo, can we get another one of

1:39:01 those global financial crisis real

1:39:02 quick?" So like, you know, crash. So

1:39:04 like there's some of that going on. But

1:39:07 I actually think that a lot of young

1:39:08 people, especially after COVID,

1:39:12 some of them want to own a home, but a

1:39:15 lot of them don't want to own a home.

1:39:16 >> Yeah.

1:39:16 >> Not because they're like, I don't want

1:39:18 the uh I can't afford it. They're just

1:39:20 like, I like the flexibility of being

1:39:23 able to travel or move around or, you

1:39:26 know, I'm going to go live in this city

1:39:27 for one year, but I don't know if I like

1:39:29 it yet or not. Or I want to have a

1:39:31 family. I don't have the family yet, so

1:39:33 I don't want to buy something and then

1:39:34 have to move later. Like there's a lot

1:39:36 of things that go into that. And so I've

1:39:38 seen some studies that show the

1:39:39 preference of kind of the millennial and

1:39:41 lower generation. More of them prefer to

1:39:44 rent than own. And then you look at the

1:39:46 economics. And now I believe that in all

1:39:49 50 major metros, it is cheaper to rent

1:39:51 than buy.

1:39:52 >> It's 100% cheaper to rent.

1:39:53 >> So you're like, okay, they're actually

1:39:55 making a smart financial decision on

1:39:57 like a monthly cash flow basis to rent

1:39:58 instead of buy. And so you're like,

1:40:01 okay, it's kind of crazy, right? right?

1:40:02 Like I hadn't bought a home,

1:40:04 but like I don't know, like I was happy.

1:40:06 It was better experience, right? You

1:40:08 know, to be able to do that. So I I do

1:40:10 think that the whole like homes are

1:40:12 unaffordable is true, but there's more

1:40:14 nuance around like, well, how many of

1:40:16 the young people want to own a home?

1:40:17 It's a big number, but it's not nearly

1:40:19 as big as it used to be. And so, you

1:40:20 know, you just got to kind of account

1:40:21 for all of that.

1:40:22 >> How much are you worth? Can I ask you

1:40:24 that? Is that like a inappropriate

1:40:25 question?

1:40:26 >> Uh, I I would ask Sylvia.

1:40:29 >> Yeah, ask Silia. Um, I I uh I try my

1:40:34 absolute best to have what I call

1:40:36 illquid wealth.

1:40:37 >> Uhhuh.

1:40:37 >> So, there's a uh there's an investor um

1:40:40 who I really respect. Uh he is

1:40:42 incredibly wealthy and he told me one

1:40:44 time that he's a great private market

1:40:46 investor.

1:40:47 >> He's a horrible public market investor.

1:40:49 >> And the reason is because he can sell.

1:40:51 >> Yeah.

1:40:52 >> So, he buys a stock, he likes it on

1:40:54 Monday, on Wednesday he reads a news

1:40:56 article.

1:40:57 >> Yeah.

1:40:58 >> I'm out. Sells it. and then it rips, you

1:41:00 know, 2x.

1:41:01 >> And so he's a horrible public market

1:41:02 investor. That's how I am

1:41:04 >> is um if I buy a public stock, I say to

1:41:08 myself, I'm going to give it to my

1:41:09 grandkids. That is my whole investing

1:41:10 like philosophy is I want to buy things

1:41:12 that I can give to my grandkids.

1:41:14 >> What about Bitcoin? Like are you still

1:41:15 holding a ton of Bitcoin?

1:41:16 >> My personal Bitcoin I have said I'm

1:41:18 going to hand to my grandkids and

1:41:19 they're going to think I'm a genius or

1:41:20 an idiot. There's pretty much no in

1:41:21 between.

1:41:22 >> Got it.

1:41:23 The odds that Bitcoin is worth a ton

1:41:25 more than it is today is very high in my

1:41:28 opinion.

1:41:29 >> Give us interesting advice. Like I

1:41:31 didn't know you were this big investor

1:41:32 guy. Like what what should myself and

1:41:35 Ryan and the people listening to this

1:41:37 invest in?

1:41:38 >> So it's funny you asked this because one

1:41:40 of the reasons why people always ask me

1:41:42 like why did we work on Sylvia is I

1:41:44 probably get that question multiple

1:41:46 times a week if not every day

1:41:48 >> really. and I meet a 22-year-old kid

1:41:52 that is working on something and then

1:41:54 he's like, "Hey, by the way, you know, I

1:41:56 have uh birthday money or bar mitzvah

1:41:58 money or something like what do I do?"

1:42:00 You know, or I had a side job or summer

1:42:02 job, whatever.

1:42:02 >> And I always tell them it's it's so hard

1:42:04 to give that advice because you don't

1:42:06 have the personal context.

1:42:07 >> Yeah.

1:42:07 >> If a 75-year-old man asks me, I'm not

1:42:09 going to tell him to like go buy some

1:42:11 super speculative tech stock.

1:42:13 >> Yeah.

1:42:13 >> Cuz he needs cash flow because he's

1:42:15 like, "Yo, I don't got a job." Right.

1:42:17 and he wants to live and he's like

1:42:18 trying to figure out how long am I going

1:42:19 to live and how much money do I need?

1:42:20 The 22-year-old kid is like, "Dude, go

1:42:22 max risk on and like who cares if you

1:42:24 lose all your money because you're going

1:42:25 to make it back in like two years

1:42:27 because you don't have that much money

1:42:28 to begin with."

1:42:28 >> Yeah.

1:42:29 >> And so that is ultimately like the power

1:42:31 of Sylvia is the personalized insights

1:42:33 of like if you give it context, you ask

1:42:34 it questions, it'll tell you. My general

1:42:36 philosophy though, which is probably a

1:42:38 little bit different than most people,

1:42:39 is I believe that every great investor

1:42:42 gets one idea that they can exploit in

1:42:44 their life. Mhm.

1:42:45 >> And so let's go through a couple of

1:42:46 great investors. Warren Buffett, what is

1:42:48 his one idea? He can buy things for less

1:42:50 than they're worth.

1:42:51 >> He just did that over and over and over

1:42:53 again his entire career. And the dude

1:42:54 made, you know, whatever, a couple

1:42:55 hundred billion dollars.

1:42:56 >> What's your idea?

1:42:57 >> Genius.

1:42:59 Peter Teal

1:43:02 early and right on contrarian ideas.

1:43:05 >> He did it over and over and over again.

1:43:07 Worked. Bill Aman. I can antagonize the

1:43:11 out of these people because I'm

1:43:13 right and I can get them to change from

1:43:15 an activist perspective. Then he

1:43:17 evolved. He actually got two ideas now.

1:43:19 He pretty much owns most of the major

1:43:21 companies. He just waits for them to

1:43:22 sell off like 10, 15, 20% and he buys

1:43:24 them when there's dislocation in the

1:43:26 market. Okay,

1:43:28 RENT,

1:43:30 we're just going to use math to high

1:43:32 frequency trade and beat everybody.

1:43:35 I think that our entire generation has

1:43:37 one idea. It's not my idea. It's

1:43:39 everyone's. The government will never

1:43:41 ever stop printing money. That one idea,

1:43:44 as long as you understand that one idea,

1:43:45 you can become wealthy beyond your

1:43:47 imagination. Because what does that

1:43:49 mean? If the government never stops

1:43:50 printing money, they're going to devalue

1:43:52 the dollar. If they're going to devalue

1:43:53 the dollar, investment assets are going

1:43:54 to keep going up and to the right.

1:43:56 >> And that means stocks, Bitcoin, real

1:43:58 estate, land, all this stuff.

1:44:00 >> Mhm.

1:44:00 >> There is a reason why Bitcoin,

1:44:03 collectibles, and your favorite stock

1:44:05 all are just at a 45 degree angle up and

1:44:07 to the right. It ain't because we're

1:44:08 geniuses. It ain't because we're good at

1:44:10 picking the collectible or the stock.

1:44:12 It's because they are just printing so

1:44:13 much money that they're inflating all of

1:44:15 this stuff.

1:44:17 >> It's the same reason though why home

1:44:18 prices are up so much. It's the same

1:44:20 reason why young people are like, "Dude,

1:44:22 I can't get ahead. I have no

1:44:23 investments. I I I don't understand why

1:44:25 I'm doing everything that my parents

1:44:26 told me to do and I'm in debt from

1:44:28 college. I don't make enough money. It

1:44:30 feels like groceries and gas are so

1:44:32 expensive." Like all of this stuff. So

1:44:34 if you take that one idea of the

1:44:36 government will never ever stop printing

1:44:38 money, then you ask yourself what are

1:44:39 the best things to buy in that scenario.

1:44:41 >> I think there's three assets, Bitcoin,

1:44:42 gold, and land.

1:44:44 >> That's it.

1:44:45 >> I said land was making a comeback, so

1:44:46 that was good.

1:44:47 >> So if you look over the last five years,

1:44:49 the S&P is up 70% give or take.

1:44:52 >> Mhm.

1:44:52 >> In that same 5year period, the Bitcoin,

1:44:55 gold, and land, if you just put 33% of a

1:44:57 portfolio in each one of those, it's up

1:44:59 about 170%.

1:45:00 >> Oh wow. Now, when you say land, are you

1:45:01 counting actual houses too or raw land?

1:45:04 >> I'm talking about there are public

1:45:06 stocks like I'll use an easy example. Uh

1:45:09 Texas Pacific Land, TPL. Um those guys

1:45:12 have hundreds of thousands, maybe

1:45:14 millions of acres. I I don't know how

1:45:15 much. Um they timber it, they mineral

1:45:19 rights, like it's like productive land

1:45:21 that they own.

1:45:21 >> You're not talking about just

1:45:22 speculative real estate. No, you're

1:45:24 talking about incomeroucing land. The

1:45:26 worst part about making a lot of money

1:45:28 is that you have to pay a lot of taxes

1:45:30 on it. This is exactly why I've

1:45:31 partnered with Taylor tax strategy. You

1:45:33 may have heard me talk about all these

1:45:35 different types of strategies like cost

1:45:36 segregations and depreciation and lots

1:45:39 of other things. But the reality is

1:45:40 there are so many other strategies right

1:45:42 now that you can be taking advantage of.

1:45:44 The team will audit your situation this

1:45:46 year and also previous years to see if

1:45:49 you're entitled to tax savings that you

1:45:51 did not take advantage of. So, if that

1:45:53 sounds good to you, go to taylor-tax.com

1:45:56 today.

1:45:56 >> If you don't understand like the public,

1:45:59 you know, assets you could buy that have

1:46:01 land type exposure. It's probably not a

1:46:04 bad idea to like, I don't know, get a

1:46:06 map of your local area, look at the

1:46:08 downtown, see which direction it's

1:46:10 expanding, and like call up a land

1:46:12 program and be like, "Hey, where's a

1:46:13 couple acres that I could just like buy

1:46:15 and hold for 10 years?" And like if it's

1:46:17 going in that direction development

1:46:19 wise, like you're probably going to make

1:46:20 money. Yeah. because the inflation and

1:46:21 all stuff, but generally I'm talking

1:46:23 about like, you know, public stocks that

1:46:25 you can buy that have land exposure

1:46:26 where they're productive. Bitcoin, gold,

1:46:28 and land to me outperform the stock

1:46:31 market because most of the stock market

1:46:33 performance like why is the S&P

1:46:36 continuously just go up at about the

1:46:38 same rate as the money printing. M

1:46:41 >> like it's just money printing because if

1:46:43 you take the same S&P go back to 1970

1:46:45 and you look at the S&P 500 denominated

1:46:47 in gold instead of dollars it's

1:46:50 basically flat.

1:46:51 >> So like why is it that the S&P goes up

1:46:54 against the dollar which is losing value

1:46:56 but it's flat against the thing that

1:46:58 holds or appreciates in value.

1:47:01 It's just money printing. And so you go

1:47:04 back to this idea. You say to yourself,

1:47:05 "Okay, I can try to be the genius that

1:47:06 picks the next great stock. There are

1:47:08 some people who are great at that.

1:47:08 That's not my game. Smart, dumb. I just

1:47:12 want to be super simplistic. Bitcoin,

1:47:14 gold, and land. Now, I invest in a lot

1:47:16 of tech companies, all this stuff. Why

1:47:18 do I do that? I have uh tweeted before,

1:47:21 I would rather see my portfolio go to

1:47:23 zero, holding things that I think are

1:47:24 solving people's problems than try to

1:47:26 play some insider game of like you call

1:47:28 me up and you're like, dude, this

1:47:29 restaurant about to kill it.

1:47:30 >> Yeah.

1:47:31 >> I don't give a Like, I just don't

1:47:32 care, right? I don't care about the

1:47:33 restaurant. What I care about is a lot

1:47:36 of the companies that I've invested in,

1:47:37 I'm like, I see how this directly

1:47:39 positively impacts people's lives and I

1:47:42 almost think of it in a weird way as

1:47:44 like a capitalist philanthropic

1:47:46 exercise.

1:47:47 >> I want to give money to this founder

1:47:49 because if they are successful, it will

1:47:51 have this positive impact. And I know

1:47:52 that this thing that's really small

1:47:54 today that isn't helping anybody, if

1:47:55 they can do that, it'll create value and

1:47:57 I'll make money and you know, I'm a

1:47:58 capitalist just like everybody else. But

1:48:00 I want to do it in companies where that

1:48:02 positive impact. What I don't want to do

1:48:04 is

1:48:06 I don't care about the next calendar app

1:48:10 or that type of stuff. Maybe I'll do an

1:48:12 investment if I'm like supporting a

1:48:14 friend and they're doing something that

1:48:15 I don't really care about. I'll kind of

1:48:16 do like a friend support, you know, type

1:48:18 investment, but if you look at my

1:48:20 portfolio, like a lot of the companies

1:48:21 I've invested in in the last, I don't

1:48:22 know, five years, they're all these like

1:48:25 kind of pie in the sky like if we do

1:48:26 this, we're going to, you know, make a

1:48:27 positive dental problems.

1:48:29 >> Correct. Think about um a good example

1:48:31 is a base power. I don't know if you

1:48:33 guys have come across this company. So,

1:48:34 Space Power is based in uh Texas and um

1:48:37 Zack Dell who Michael Dell's son and uh

1:48:40 and this guy Justin who used to be at

1:48:41 and come together and they basically

1:48:44 have an idea of the United States

1:48:45 electrical grid is very broken and as we

1:48:49 see now electric prices are exploding.

1:48:51 >> Mhm.

1:48:51 >> Well, a huge reason for that is because

1:48:53 electric prices let's say during the day

1:48:56 in residential areas are lower because

1:48:59 no one's home. ACs aren't on. you're not

1:49:02 had TVs, all that kind of stuff. But

1:49:04 then everyone comes home, you know, 5 6

1:49:05 7 o'clock at night and all of a sudden

1:49:07 prices surge because everyone's using

1:49:09 the electrical grid. So, it's just

1:49:10 supply demand. So, what these guys

1:49:12 realize is battery technology gets to a

1:49:14 place where they can actually sell you a

1:49:16 battery. Now, I think it's $500 for the

1:49:18 battery and you attach it to your home

1:49:21 and it gets hooked into the electrical

1:49:22 grid. They install it for you. And what

1:49:24 they do is they say, "Okay, during the

1:49:27 day, we are going to pull from the

1:49:28 electrical grid when prices are low, and

1:49:30 we're going to charge the battery. At

1:49:32 night, when you need electricity, you're

1:49:35 going to use your normal electricity,

1:49:36 but we're going to take the battery

1:49:38 that's in uh the power that's in that

1:49:39 battery, and we're going to sell it back

1:49:40 into the grid because now prices are

1:49:41 high. Buy low, sell high arbitrage."

1:49:45 And by the way, for you doing this, your

1:49:48 electrical bill is going to be lower

1:49:49 because we're monetizing this battery

1:49:52 that is on your home rather than you

1:49:54 just pay the electrical power uh prices.

1:49:57 So now all of a sudden you're like, wait

1:49:58 a minute, if these guys are successful,

1:50:00 one, you can stabilize the grid, you can

1:50:02 upgrade the grid. There's all these like

1:50:03 benefits for society, etc. But most

1:50:05 importantly is like they're driving the

1:50:06 electrical price down on a per household

1:50:09 basis every single month. How many

1:50:12 people would love to save a $100, $200,

1:50:13 $300 on their electrical bill every

1:50:15 month? A lot of people.

1:50:17 >> And so these guys have exploded. They're

1:50:18 they went from, you know, starting maybe

1:50:20 three years ago or so to now I think

1:50:21 they've 12 13 billion company.

1:50:23 >> Wow.

1:50:24 >> Just here we go.

1:50:25 >> Crazy.

1:50:26 >> They have uh don't quote me on this, but

1:50:28 I think that they have uh publicly said

1:50:31 they have like over 10,000 customers or

1:50:32 something. Like a lot of people using

1:50:34 this thing. It works. They're expanding

1:50:36 now. They've gone to a second state uh

1:50:37 in Illinois. And I think that they can

1:50:40 go across the entire country. Mhm.

1:50:41 >> And so you look at that and you're like,

1:50:42 "Dude, I would love to make money. You

1:50:45 guys would like to make money. I'd like

1:50:46 to make money. My wife would like me to

1:50:47 make money. My kids would like me to

1:50:48 make money.

1:50:49 >> But you know what? If they succeed and

1:50:52 my investment went to zero, I would

1:50:54 still I'd be pissed because I lost

1:50:56 money, but I would still be happy that

1:50:57 they were successful."

1:50:59 >> And so it's like that's the type of

1:51:00 investments that I think that if you can

1:51:01 find those, you're kind of like I'll

1:51:04 live with the fact that this could go to

1:51:05 zero

1:51:06 >> because there's a potential positive

1:51:08 impact.

1:51:08 >> Yeah. the stock market. There are some

1:51:10 companies like that for sure, but you

1:51:12 don't really get the like it could go to

1:51:14 zero every once in a while, but not

1:51:15 really. But you also don't get the zero

1:51:17 to$13 billion valuation in, you know,

1:51:19 three years.

1:51:20 >> Yeah. One of my uh buddies, I'm starting

1:51:23 a Christian toy line later this year and

1:51:25 he invested in it. It's first time I've

1:51:27 ever taken an investment for a company,

1:51:28 not that wasn't real estate. I was like,

1:51:31 "Ah, man. I don't really know if I want

1:51:32 to do this. Like, I need a few hundred

1:51:33 thousand to buy inventory and the molds

1:51:35 and all this stuff." He's like, "I want

1:51:37 to be in on it." He's like, "This is

1:51:39 just a cool idea for, you know,

1:51:40 obviously families and Christians and

1:51:42 everything else." He's like, "I'll

1:51:44 invest in it." And, you know, if it goes

1:51:45 to zero, whatever. We made some cool

1:51:47 toys and like like that's his mindset.

1:51:49 And I was like, "All right, well, here

1:51:51 we go.

1:51:52 >> Let's do it."

1:51:53 >> One of my standing rules is uh if any of

1:51:55 my friends start a company, I have to

1:51:57 invest. And I stole this from from a

1:51:59 buddy of mine. The reason is if they

1:52:02 succeed, I want to be able to celebrate

1:52:04 with them.

1:52:04 >> Yeah. if they fail, I want to like be

1:52:07 with in misery with them, right? Like

1:52:10 like we're in this, right? And it's not

1:52:13 always a big check, right? It may

1:52:14 literally just be, hey, I just want you

1:52:16 to know I'm in and you know, we're in

1:52:18 this together. Call me if you need help,

1:52:19 whatever, but like I'm on your team type

1:52:21 thing.

1:52:22 >> Um,

1:52:23 >> but man, when they succeed, it's

1:52:25 awesome. Yeah,

1:52:25 >> but we were talking uh to my buddy last

1:52:27 night and um the hardest investments to

1:52:30 make are to size up your investment in

1:52:34 your friends companies because usually

1:52:36 you're too close to them.

1:52:38 >> Yeah. You're not objectively looking at

1:52:40 it.

1:52:40 >> You're like, you know, if you've ever

1:52:41 gone like especially when you're

1:52:42 younger, um if you like go to the bar

1:52:45 with a buddy and he's just like a fun

1:52:48 idiot, you know, he likes to, you know,

1:52:50 get drunk and like he says stupid crazy

1:52:52 off-the-wall stuff, whatever. Then he's

1:52:53 like, "I'm starting a company." You're

1:52:54 like, "Dude, yeah, I'll I'll support

1:52:56 you, but like you're not building a big

1:52:57 company." And then you're like, "Oh

1:52:58 yeah, by the way, my friend is like and

1:53:00 he's some like amazing entrepreneur and

1:53:01 he builds like a multi-billion dollar

1:53:02 company." You're like, "Dude, how did I

1:53:04 not put give you all of my money? Like

1:53:07 you were like one of my best friends.

1:53:08 You you know, I was so close to it

1:53:10 though that I couldn't believe it." Mhm.

1:53:12 >> And so I know that this is true for a

1:53:14 lot of people because when Travis

1:53:15 started Uber, a bunch of people didn't

1:53:19 invest because he was actually doing

1:53:21 something else and this was kind of like

1:53:22 a side project. And so they were like,

1:53:25 "Ah, you're not serious about this

1:53:26 thing." And then it became Uber.

1:53:28 >> Yeah. Yeah.

1:53:29 >> And Jason Calcanis, who I think invested

1:53:31 $25,000, he's only told everyone like a

1:53:33 thousand times. Um, he turned in $100

1:53:35 million.

1:53:36 >> Wow. Geez. And so you're like, you know,

1:53:38 he didn't have to invest a lot, but like

1:53:40 it helps if your friend is Travis.

1:53:42 >> I saw a thing like had you invested like

1:53:44 a million dollars in Anthropic like six

1:53:47 years ago or something, it's worth like

1:53:50 10 billion.

1:53:50 >> Wow.

1:53:51 >> Oh, probably. Yeah. I mean,

1:53:53 >> it's an insane kind of number.

1:53:55 >> Wow. They're they're um one of my

1:53:56 favorite stats is um

1:54:00 I think that Nvidia

1:54:03 25,000 employees at Nvidia are now worth

1:54:06 more than $20 million.

1:54:08 >> Wow.

1:54:09 >> It's crazy.

1:54:10 >> Like when you get to

1:54:12 >> how do you get them to keep working?

1:54:13 That's the crazier part.

1:54:14 >> When you get to a certain level of

1:54:16 company building, that's the flex.

1:54:20 >> It's not how big did my company get.

1:54:21 It's not how much money did I make. The

1:54:24 incremental dollar doesn't change your

1:54:26 life.

1:54:26 >> Yeah.

1:54:27 >> I have a lot of friends who have sold a

1:54:29 lot of companies.

1:54:31 The thing that they like compete on, how

1:54:34 many of their employees became

1:54:35 millionaires?

1:54:36 >> Yeah.

1:54:36 >> Now, mind you, if you sell a company for

1:54:38 like $10 billion, you made a lot of

1:54:41 money. I don't care how much of it you

1:54:42 owned, right?

1:54:43 >> Yeah.

1:54:43 >> But SpaceX is a good example. Um, there

1:54:46 were some stories where Elon was very

1:54:49 adamant that every single person that

1:54:50 worked at SpaceX got equity, even if it

1:54:52 was $5,000 of equity. And there were

1:54:55 some stories when they were going public

1:54:56 that like a mechanic became a

1:54:59 millionaire.

1:55:00 >> Wow.

1:55:01 >> But he had gotten like, I don't know,

1:55:02 $10,000 of equity or something, but he

1:55:04 had gotten it so early

1:55:06 >> that it had just appreciated, you know,

1:55:08 what is that a 10,000x I think. I don't

1:55:10 I don't like doing public math, but I

1:55:11 think it's 10,000x. And so like dude,

1:55:14 how awesome is capitalism where you can

1:55:17 make the mechanic a millionaire

1:55:19 >> by just solving the problem that

1:55:21 everyone has where hey we can't go to

1:55:23 space often.

1:55:25 >> Like that to me is again you go back to

1:55:27 like the Zucks of the world, all these

1:55:29 guys. It's like

1:55:31 I get that people like oh there's like

1:55:32 the evil billionaires whatever. Like I'm

1:55:34 sure there's people doing nefarious

1:55:35 stuff and and being bad people whatever.

1:55:37 But most of them, the people that I

1:55:39 respect and I think that I've been

1:55:40 around and I'm like, "Man, these people

1:55:41 are impressive."

1:55:42 >> They sit around and they're trying to

1:55:44 figure out like, "How do we solve the

1:55:45 problem for the customer?"

1:55:46 >> And I always joke that like, I don't

1:55:48 know, Jeff Bezos probably got underpaid.

1:55:50 >> Yeah. That dude solved a lot of

1:55:52 problems.

1:55:52 >> Yeah.

1:55:54 >> Right now, I could press a button on my

1:55:56 phone and someone who I've never met

1:55:59 before, don't know their name, have

1:56:02 never thought about will go pick

1:56:04 something up for me and bring it to me.

1:56:05 >> Yeah.

1:56:06 and maybe might do it in like an hour.

1:56:09 Crazy.

1:56:10 >> What are you trying to build your

1:56:11 company to? Like what's your goal with

1:56:13 your company?

1:56:15 >> The company mission is to help

1:56:16 independent investors make money. And I

1:56:18 think when people hear that, I like the

1:56:20 fact that it's crude. It It's just like,

1:56:21 wait, what do you mean make money? That

1:56:23 sounds like very like uh greedoriented.

1:56:26 My belief is that the people who use

1:56:29 Sylvia in talking to hundreds of them at

1:56:31 this point

1:56:32 >> are people who identify as that Sylvia

1:56:35 misfit, what does that mean?

1:56:38 >> There is a streak of they are self-made.

1:56:41 No one gave them anything. They didn't

1:56:42 inherit money. They they had to figure

1:56:44 it out themselves.

1:56:45 >> Yeah. And we were uh we were recently

1:56:47 talking um

1:56:52 I forget who said this but somebody said

1:56:53 to me, "You become a man the day you

1:56:56 realize no one is coming to save you.

1:57:00 >> No one is coming to do anything for you.

1:57:02 You have to figure out your problems.

1:57:03 You have to figure out what you're

1:57:04 doing. You have to figure out how to

1:57:05 make money. You have like like that is

1:57:07 your thing. I don't care how much your

1:57:08 family loves you etc." At some point

1:57:11 they're like, "Yo, you either do it or

1:57:13 you don't." Right? That same person,

1:57:15 whether they start a company, they're in

1:57:17 real estate, they just get a good job,

1:57:18 whatever, like that self-made person is

1:57:20 who uses Sylvia,

1:57:22 when they make money,

1:57:25 their families are better off, they get

1:57:27 financial security, their children have

1:57:30 a better life, their friends, their

1:57:33 community, the uh philanthropies they

1:57:36 care about. All of that improves by this

1:57:40 one person becoming wealthier. Mhm. And

1:57:42 so we don't have a lot of people that I

1:57:44 would consider like day traders. People

1:57:46 aren't coming on like let me buy a stock

1:57:47 and sell it tomorrow. We have a lot of

1:57:49 people who think about generational

1:57:50 wealth. A lot of estate planning, tax

1:57:52 orientation, etc. And ultimately those

1:57:55 are the people who they spend money in

1:57:57 their local communities to improve it.

1:57:59 They donate to charities. They help

1:58:02 their children. They help their

1:58:03 families. All this kind of stuff. And so

1:58:05 to me, that's the single most important

1:58:07 thing. M

1:58:09 >> a lot of people don't like hearing this

1:58:10 but like I'll take less success on the

1:58:12 company if we are successful in the

1:58:14 mission

1:58:15 >> but I think because I know that that is

1:58:17 the motivating factor the company will

1:58:19 be successful because it's really easy

1:58:21 guess what we have to do talk to them

1:58:24 what problems do you have when they tell

1:58:25 you the problem solve it

1:58:28 >> tomorrow talk to them what problems do

1:58:30 you have solve it just do that over and

1:58:32 over again and if you do it for enough

1:58:34 people you'll build a massive business

1:58:37 >> it's what Bezos post did. It's what all

1:58:38 these guys did. So like business

1:58:40 building is this complex thing, but at

1:58:41 the same time it's like very simple.

1:58:42 Just solve people's problem.

1:58:44 >> So I like to tell my kids all the time

1:58:45 when people ask them like what do you

1:58:47 want to be when you grow up? They you

1:58:48 know they say all the things that kids

1:58:49 would say you know my son for a while

1:58:51 was saying he want to be Batman. When

1:58:52 two two months he wouldn't respond to

1:58:54 his name just you had to call him

1:58:55 Batman. And I was like

1:58:57 >> like I kind of respect it. You know he's

1:58:59 got his grown ass dad like in public but

1:59:01 hey Batman you know he's whipping his

1:59:03 head around you know he like broke his

1:59:04 neck. He turned around so fast right? Um

1:59:07 but uh I always try to tell them like no

1:59:10 you're a problem solver and it's just

1:59:12 like if you identify as I solve problems

1:59:14 it doesn't matter if it is somebody is

1:59:16 in physical danger if somebody has a

1:59:19 problem at work if somebody you know you

1:59:20 want to build a company whatever you

1:59:21 want to do in your life if you solve a

1:59:22 problem including if you want to be

1:59:24 artistic what's the problem people have

1:59:26 they want to be entertained

1:59:27 >> it's like solve their problem right I

1:59:29 I've been thinking about this a lot

1:59:30 actually and sounds like you're the same

1:59:32 way where we we get a lot of people on

1:59:34 the show who um you know they just want

1:59:36 to make money. And so it's like, "Hey

1:59:37 dude, if I just buy like a home service

1:59:39 business or I flip some houses and you

1:59:42 know, there's nothing wrong with those

1:59:44 businesses or careers. They're just, you

1:59:46 know, very predictable ways to make

1:59:48 money." Um, from what I hear, what it

1:59:50 sounds like with you and same thing kind

1:59:52 of where my mind's been at lately is

1:59:53 like you want to just do things that

1:59:55 solve new issues that haven't been

1:59:57 solved before. And and and you lean more

1:59:59 on the side of innovation and hey, I'm

2:00:01 going to invest in things that are

2:00:03 solving new issues in the world. And you

2:00:06 know, yeah, a lot of them won't work

2:00:07 out, but the ones that do are going to

2:00:09 probably be really, really good both uh,

2:00:13 you know, fulfilling wise and then

2:00:14 obviously the money will come with it.

2:00:16 >> Either I want to solve new problems or I

2:00:18 want to solve old problems with new

2:00:20 technology better. I would Yeah, I would

2:00:21 say those are the two things. Um, but

2:00:23 it's also um I have a very hard time

2:00:26 being bored or being what I call

2:00:28 soulless. And what I mean by soulless is

2:00:32 I have friends who run incredible

2:00:34 businesses. They don't care. They they

2:00:36 just

2:00:37 >> It's just a money-making thing.

2:00:38 >> Yeah. And by the way, the ones who are

2:00:40 honest about it are actually happier

2:00:41 than the ones who are like lying to

2:00:42 themselves about it.

2:00:43 >> Yeah.

2:00:43 >> But just like

2:00:44 >> Brian and I both say we don't like real

2:00:45 estate.

2:00:46 >> Yeah.

2:00:47 >> It just is what it is.

2:00:48 >> Yeah.

2:00:48 >> Yeah. And it's like um car washes is a

2:00:52 good example. Like, do you know how much

2:00:55 money you probably could make if you

2:00:56 were a good operator and you just went

2:00:58 around, you know, a major metro and you

2:00:59 bought up all the car washes and you

2:01:01 like figured out all the stuff,

2:01:02 whatever, automated it? Like,

2:01:04 >> I don't care. Like, like I'm not I'm not

2:01:06 passionate about that, right? I don't I

2:01:08 don't like it doesn't get me excited.

2:01:10 >> And so, I think if you go and you look

2:01:12 um

2:01:14 >> I do a lot of different things. I have a

2:01:16 lot of different businesses. We do all

2:01:17 those investment, you know, invest 300

2:01:19 companies. You can imagine some of these

2:01:20 founders call me never. Some of them

2:01:22 call me once a week when there's like

2:01:24 kind of crucible moments. Some of them

2:01:26 may call me five times in a day.

2:01:29 The single best conversation I have

2:01:32 every single week right now is when I

2:01:34 talk to a user of Sylvia and they tell

2:01:36 me, "Here's my situation. Here's what

2:01:39 I'm trying to accomplish. Your product

2:01:41 is helping me do that." I'm like, "Dude,

2:01:44 I like screw drugs. Screw anything."

2:01:47 like this is the most energizing thing I

2:01:49 have ever done because you're like I see

2:01:52 the direct impact that this product has

2:01:54 on your life and then they'll tell me

2:01:56 like there's a guy who I recently talked

2:01:58 with he um uh he's in Ohio uh he's worth

2:02:02 um three four million bucks

2:02:05 >> and he tells me that he comes from a

2:02:07 union family.

2:02:09 So I'm like well that could mean a lot

2:02:11 of different things. What do you mean?

2:02:11 because my dad was like the the union

2:02:14 president or whatever like I was in all

2:02:16 and he worked at a a very large

2:02:18 manufacturing industrial business and it

2:02:20 went bankrupt and so he got a buyout of

2:02:22 I think it was his pension I don't know

2:02:23 all the deal but like he got like a lump

2:02:24 sum of money back in 2019ish time 2020

2:02:28 and rather than take that money and give

2:02:31 it to a financial adviser he said I'm

2:02:34 going to give it a go I never had money

2:02:35 before like I'm going to try to figure

2:02:36 this out this guy's like the next Warren

2:02:39 Buffett he like bought Carvana at three

2:02:41 bucks sold it at 80, bought Bitcoin at

2:02:43 the last bottom of the bare market in

2:02:44 2022. He was in Robin Hood. I mean, he

2:02:47 was like all over all the things that

2:02:48 like are kind of like the internet

2:02:50 darlings. And I was talking to him and I

2:02:54 was like, you know, what are you trying

2:02:55 to accomplish now? And he's like, dude,

2:02:57 I I never thought I'd have this money.

2:02:58 >> Yeah.

2:02:59 >> He's like, I'm actually scared of losing

2:03:00 it all.

2:03:01 >> I know what it feels like to not have

2:03:04 it.

2:03:04 >> Yeah. And he's like, I live every day as

2:03:07 like the thing I'm doing is working,

2:03:10 >> but I'm scared.

2:03:11 >> But like I'm I'm one bad decision, too

2:03:14 much concentration, too much risk

2:03:15 somewhere.

2:03:16 >> Yeah.

2:03:16 >> And it and it disappears.

2:03:17 >> That's how Brian lives.

2:03:18 >> Yeah.

2:03:20 >> I think it's really really natural for a

2:03:22 lot of people. And so it goes back to

2:03:26 concentration builds wealth. You know, I

2:03:28 I am uh infamous at this point probably

2:03:30 for I had 95% of my money in Bitcoin at

2:03:32 one point.

2:03:32 >> Yeah. And I was just like, "Dude, this

2:03:34 is the thing I believe in." But I also

2:03:35 told my wife, now that I've made that

2:03:38 investment,

2:03:39 yo, I'm broke because I ain't selling

2:03:42 that. I got to go build a whole another

2:03:45 financial life again.

2:03:46 >> Yeah.

2:03:47 >> Of all these things that aren't that

2:03:49 >> because I can't, you know, I we can't

2:03:50 have a family and be rolling around and

2:03:52 being like, "Yo, dad's in a good mood or

2:03:53 a bad mood because Bitcoin went up two

2:03:55 or three% today or it went down,

2:03:56 >> right?

2:03:57 >> So, like, let's go do this." And so I

2:03:59 think that there's this element of you

2:04:01 almost want to like create concentration

2:04:04 and then leave it, then go create

2:04:06 concentration again and then leave it.

2:04:09 And by creating a basket of very

2:04:11 concentrated bets, you naturally get

2:04:12 diversification.

2:04:14 >> But you have these like concentrated

2:04:16 things where you're like right now, you

2:04:18 know, at the time this is the moment to

2:04:19 buy Bitcoin.

2:04:20 >> I'm going all in. And then I scrambled

2:04:23 and was like, all right, well like I

2:04:24 don't need to buy more Bitcoin. I need

2:04:25 to go and do this other thing.

2:04:27 >> Yeah. And so it's a different way of

2:04:29 thinking about wealth building, but you

2:04:31 actually are getting into the

2:04:32 diversification. You're just doing it by

2:04:34 >> you're not just blindly diversifying on

2:04:37 every you're your whole way up.

2:04:38 >> Dude, the 604 dollar cost averaging guy.

2:04:41 >> The 6040 portfolio is atrocious. If you

2:04:46 look at um look at TLT, TLT is a uh a

2:04:49 20-year bond fund. It's not a perfect

2:04:52 measurement, but just use it as an

2:04:53 example. It's down over the last five

2:04:55 years like 40 or 50%.

2:04:57 Yeah.

2:04:58 >> Like, dude, people are just guaranteed

2:05:00 to lose money if they buy bonds. And

2:05:02 everyone gets mad at that. I'm like,

2:05:03 okay, well, let's think about this for a

2:05:04 second. Right now, what is the inflation

2:05:07 measurement? The government is telling

2:05:08 you the inflation measurements somewhere

2:05:10 three, three and a half%. Depending on

2:05:11 which metric you look at, you know what

2:05:13 time period? Okay. What is the yield you

2:05:15 get on treasuries? Three and a half%.

2:05:17 >> You're going to lose. Mhm. So, best you

2:05:20 can do according to the government if

2:05:21 they're right about inflation, which

2:05:23 they never are, but let's say they're

2:05:24 right, is that you can break even

2:05:27 on interest plus inflation. Well, I

2:05:30 don't know about you guys, but like I

2:05:31 think that inflation is probably a

2:05:32 little bit higher just naturally. And

2:05:34 so, that means that you're actually

2:05:36 losing money by buying bonds.

2:05:38 >> Bonds are the only thing that you can

2:05:39 put in a portfolio that are guaranteed

2:05:40 to lose value.

2:05:41 >> Mhm.

2:05:42 >> So, you say to yourself, you're like,

2:05:44 the traditional financial system tells

2:05:45 people to put 40% of their money in

2:05:47 bonds.

2:05:48 It's crazy. And the reason why they used

2:05:50 to do it is because when stocks would go

2:05:52 up, bonds would lag. And then when

2:05:54 stocks would go down, bonds would yields

2:05:56 would go up. And so you had this kind of

2:05:57 nice diversifying thing and you had a

2:05:59 little bit more weight towards equities.

2:06:00 Well, like actually that broke in the

2:06:02 last couple years. Stocks and bonds

2:06:04 moved together. So when stocks were

2:06:06 going down, your bonds were getting

2:06:07 crushed. So like why do you have the

2:06:10 quotequote diver there's no

2:06:11 diversification.

2:06:11 >> Bonds had no upside.

2:06:13 >> Correct. So you start getting in this

2:06:14 weird dynamic of you got to be very

2:06:17 careful where you get advice.

2:06:19 >> And one of the things that I always tell

2:06:21 young people is if you ask your

2:06:22 grandparents for financial advice,

2:06:24 >> you have to be careful. Why? They grew

2:06:26 up in a world where you could save

2:06:28 money, you can't do that anymore.

2:06:31 >> Yeah.

2:06:31 >> They grew up in a world where they were

2:06:32 like, buy real estate like a like a

2:06:34 single family home and like just save

2:06:35 your money.

2:06:36 >> It was a very kind of like protectionist

2:06:38 mindset.

2:06:39 >> If you do that now, you'll get smoked

2:06:41 financially.

2:06:42 >> Yeah. That's what we keep telling

2:06:43 people.

2:06:43 >> Yes. We've been telling everyone about

2:06:44 buying rental properties.

2:06:45 >> But just that they'll get smoked on the

2:06:47 rental properties. Yeah.

2:06:47 >> So you guys don't like real estate.

2:06:49 >> We do.

2:06:50 >> It's very similar to what you just said

2:06:52 where we went through a cycle where in

2:06:54 2020 it was like buy real estate, buy

2:06:56 real estate. You can get interest rates

2:06:58 so low and houses were cheap.

2:07:00 >> To give context, him and I have both

2:07:01 flipped over a thousand houses.

2:07:03 >> Okay. Yeah. So there was a period

2:07:05 >> rentals. And

2:07:06 >> my guess is that you didn't do the 995th

2:07:08 one cuz the other ones didn't work. But

2:07:10 go ahead.

2:07:10 >> Yeah. Yeah. So like me and him went

2:07:12 through a cycle where it was cheaper to

2:07:14 own than rent. It was like cheaper and

2:07:18 you could buy houses that your mortgage

2:07:20 would be a,000 and then you could rent

2:07:22 them for 2,000.

2:07:23 >> Yep.

2:07:24 >> But now like if you buy it's reversed

2:07:27 and the interest rates are at historic

2:07:29 highs right now like 7% for the last

2:07:31 decade.

2:07:32 >> Prices are at an all-time high. Rents

2:07:35 are actually coming down in some

2:07:36 markets. So it just doesn't make sense.

2:07:38 So that's what

2:07:38 >> and I don't see it changing.

2:07:40 >> Yeah. So, we're actually both trying to

2:07:41 do what you're talking about where we

2:07:43 built a bunch of wealth in real estate,

2:07:45 but now we're like the next thing I'm

2:07:47 concentrating elsewhere.

2:07:48 >> Yeah, exactly.

2:07:49 >> The um the the one thing that I will say

2:07:51 is uh you asked earlier like, "Hey, what

2:07:53 should we do?" And one of the things

2:07:54 that you know, you guys been creating

2:07:56 content for a long time. I've been doing

2:07:57 it for a decade or so. Um I always tell

2:07:59 people if you watch a video of something

2:08:02 I say,

2:08:03 >> you better make sure that you are

2:08:05 dynamically watching, not taking like a

2:08:07 static moment in time. Because if people

2:08:09 listened to you two years ago and they

2:08:10 were like, "Oh, buy real estate."

2:08:11 They're all about buying real estate and

2:08:12 they never watch another video.

2:08:13 >> Yeah.

2:08:13 >> And now you're like, "No, actually it's

2:08:15 a horrible time to buy real estate." I

2:08:16 know.

2:08:16 >> And they go and they do it and they're

2:08:17 like, "Oh, those guys are idiots." And

2:08:19 you're like, "No, actually, it was a

2:08:21 good time. Now it's a good time." Like,

2:08:22 you have to be very uh kind of aware of

2:08:27 >> why did you guys say it was a good time

2:08:28 to buy real estate? Yeah.

2:08:29 >> Right. Oh, it's because the interest

2:08:31 rate was lower and versus the rent

2:08:33 prices were down. And so I always

2:08:34 caution people that if you just hear

2:08:37 someone say buy real estate like buy

2:08:38 rentals and you don't actually

2:08:40 understand why

2:08:42 >> well when it changes like dude you're

2:08:44 just pouring money still in right and

2:08:46 like you you you don't know when do you

2:08:48 stop and so one of the helpful exercises

2:08:51 for me whenever I have bought things

2:08:53 that are liquid or kind of gone on into

2:08:55 new markets is what has to be true for

2:08:57 me to change my mind.

2:08:59 >> Yeah. One thing that that I'm very

2:09:03 cautious of is a lot of these guys on

2:09:05 social media that don't change their

2:09:06 mind. They are just so dead set on I

2:09:10 could easily be like, "Look, I've been a

2:09:11 real estate guy, so people know me for."

2:09:12 And I could just be like, "Guys, it's

2:09:14 going to get better." You know, I could

2:09:16 be not rationally optimistic. I could

2:09:18 just be wildly optimistic. And I'm like,

2:09:20 "No, my mind and and the data and

2:09:23 everything tells me this is not what

2:09:25 it's been for the last 15 years." And I

2:09:28 can't rationally see how this changes in

2:09:31 the next five.

2:09:32 >> So, you know, if I have to change my

2:09:35 opinion, I change my opinion. It is what

2:09:37 I'm I'm okay doing that. But then you

2:09:38 get, you know, whatevers of the world.

2:09:41 They could be Bitcoin maxis, they could

2:09:43 be Tesla maxis, they could be real

2:09:45 estate maxis, whoever that just will not

2:09:48 change their mind even as things around

2:09:49 them are changing significantly.

2:09:51 >> So, it's interesting. Take Bitcoin as an

2:09:52 example. And I I've talked at nauseium

2:09:55 to like the Bitcoin people about this,

2:09:56 which is I'm not going to change my mind

2:09:58 on Bitcoin only because I have already

2:10:02 diversified. And so I've said I'm going

2:10:05 to hold Bitcoin and I'm going to give it

2:10:07 to my grandkids. Psychologically, I've

2:10:09 removed myself from ever being shaken

2:10:11 out of my Bitcoin position because I'm

2:10:13 just like, I don't care if it goes to

2:10:15 zero. I'm literally just going to hold

2:10:16 it and I'm gonna give it to my

2:10:17 grandkids. I have the good fortune of

2:10:19 being able to do that because I built

2:10:20 the diversification by concentrating in

2:10:21 other areas. But man, has it been really

2:10:25 valuable to have that mindset when it

2:10:28 goes up hundreds of percent, drops 90%,

2:10:30 you know, over and over and over and

2:10:32 you're just like, dude, how do you live

2:10:33 through that? It's like there was

2:10:35 nothing you were going to tell me that

2:10:36 was going to change my mind. So the

2:10:38 reason I say that is I actually think

2:10:41 that um I call it like investment design

2:10:45 is a very underexplored you know kind of

2:10:48 uh uh topic because what most people do

2:10:50 I think is they look at their portfolio

2:10:52 every day with fresh eyes and so they

2:10:54 say okay do I like this stock do I not

2:10:56 like the stock should I buy should I

2:10:57 sell whatever what I try to do is I try

2:10:58 to say to myself again I want to buy

2:11:00 things that I never have to sell or I

2:11:03 don't have to sell for a very long

2:11:04 period of time I want to make one

2:11:05 decision and then I I basically want to

2:11:07 save myself from myself,

2:11:09 >> all the drama that happens,

2:11:10 >> all the energy and Yeah,

2:11:12 >> dude. It's like, you know, I um

2:11:13 >> should we buy Bitcoin now or is it too

2:11:16 high?

2:11:17 >> So, an easy way to think about this is

2:11:20 why would you buy Bitcoin?

2:11:21 >> Well, if the government's never ever

2:11:22 going to stop printing money, it's

2:11:23 probably going to go up.

2:11:24 >> I don't think they're going to stop

2:11:25 printing money.

2:11:26 >> And then are you going to buy Bitcoin

2:11:29 with money you need next week for rent

2:11:30 or are you going to buy money with, you

2:11:33 know, money that you're going to give to

2:11:33 your grandkids? Mh.

2:11:35 >> If it's the latter, then yeah, of

2:11:37 course, over a long period of time, all

2:11:38 these assets are going to go up.

2:11:40 >> What if someone's goal is to be worth

2:11:42 like $10 million in the next 10 years?

2:11:46 >> Well, you know the fact you know the

2:11:47 fastest way to get a $10 million net

2:11:49 worth?

2:11:49 >> What?

2:11:50 >> Start with 20.

2:11:54 >> How do I do that, bro?

2:11:55 >> You're real quick. Real quick. You know,

2:11:57 you're a bad trader. You'll be at 10

2:11:59 real quick.

2:12:00 But yeah, let let's say seriously

2:12:02 someone wants to be worth $10 million

2:12:03 because we were having this discussion

2:12:05 about investing yesterday. Was it

2:12:07 yesterday?

2:12:08 >> Yeah. So it's like okay, if we want if

2:12:10 our goal is to be worth $10 million in

2:12:12 the next 10 years, right? What's the

2:12:14 best?

2:12:15 >> They shouldn't invest.

2:12:16 >> They should not invest.

2:12:17 >> You got to earn it.

2:12:17 >> No. Yeah. I think that

2:12:19 >> 10 years is not feasible.

2:12:20 >> It 10 years investing. I mean, you got

2:12:23 to be world class to create $10 million

2:12:25 of value starting with let's say define.

2:12:28 Would you have done that with Bitcoin?

2:12:30 >> Let's say that you start with $25,000.

2:12:32 >> Okay.

2:12:32 >> Right.

2:12:33 >> Your grandmother unfortunately passed

2:12:34 away and you inherited $25,000 as like a

2:12:37 little starting point.

2:12:38 >> Yeah.

2:12:38 >> And you want to turn into 10 million.

2:12:40 >> Mhm.

2:12:40 >> Well, first of all, okay, what does that

2:12:42 mean? That means that you need to uh

2:12:44 what? 400x the money.

2:12:46 >> Yeah.

2:12:47 >> Okay. The best investors in the world

2:12:49 don't do that.

2:12:49 >> Like eight flips, right? Or something.

2:12:51 >> No, of course. Trust me, there's

2:12:53 somebody who's like, "Dude, no, but if I

2:12:54 just buy this,

2:12:55 >> I'll tell you a great story. Ready?" We

2:12:57 had a kid who used to work doubles away.

2:12:58 >> Yeah, exactly. Yeah.

2:12:59 >> We had a kid who used to work for us. Um

2:13:01 he he'll die laughing here say this, but

2:13:03 this kid walks into our office one day

2:13:05 and um usually like a very happy

2:13:07 golucky. He's a sales guys, you know,

2:13:09 very kind of energetic and he's down.

2:13:11 And so I see it, but I don't really say

2:13:13 anything. Second day, he's down again.

2:13:14 I'm like, that's weird. Third day, he's

2:13:16 down again. So I'm like, all right,

2:13:17 something's up. So I say to somebody

2:13:19 else on the team, because if I go ask

2:13:20 him, he's not going to tell me. So I

2:13:21 ask, hey, what's going on with him? And

2:13:23 uh they're like uh he don't want to tell

2:13:26 you

2:13:26 >> like well now I'm interested like

2:13:29 >> broke up with his girlfriend

2:13:30 >> immediately. I'm like it's got to be a

2:13:32 girl related thing you know whatever. So

2:13:34 I'm like but I'm busy today like I don't

2:13:36 got time. So like by day four he's still

2:13:37 like down in the dump. So I'm like all

2:13:39 right dude. So I get him and I bring him

2:13:40 into a conference room and I tell him I

2:13:41 said look man what's wrong? He's like I

2:13:43 don't want to talk about it. And I'm

2:13:44 like let me rephrase. We're not leaving

2:13:46 the conference room until you tell me

2:13:47 what's wrong cuz I don't got time to

2:13:48 like play this stupid game with you. And

2:13:51 so he um long story short ends up

2:13:52 telling me that he took let's say 80ish%

2:13:58 85% of the money that was in his bank

2:14:00 account and he put it into a cryptocoin

2:14:03 called ski mass dog.

2:14:04 >> Hell yeah.

2:14:05 >> Ski mass dog.

2:14:07 >> He says this to me. I'm literally like,

2:14:09 "Dude, you're you're messing with me."

2:14:10 >> Yeah. Yeah.

2:14:11 >> Like you don't want to really tell.

2:14:12 They're like, "What are you really upset

2:14:13 about?" He's like, "No, no, I did that."

2:14:14 And he says to me, he goes, "Um just so

2:14:16 you understand," he goes, "Uh, it went

2:14:17 down 99%."

2:14:18 >> Oh, damn. So he So now the conversation

2:14:21 flips from like, "Oh man, I feel bad."

2:14:22 to like, "Do you have enough money for

2:14:24 rent and food?" Yeah.

2:14:25 >> Like like we're like in survival mode.

2:14:26 I'm like, "Do we have to like give you

2:14:27 like a like a like a bonus to like make

2:14:30 sure that you could pay for food?"

2:14:31 >> He's like, "No." He's like, "I'm okay

2:14:32 right now." He's I'm just like really

2:14:33 bummed. I don't know why I did this. And

2:14:35 you know, he's like a rational person,

2:14:36 whatever.

2:14:37 >> So I'm like, "Okay." So over like a two

2:14:39 to two and a half week period, he kind

2:14:41 of like comes out of the funk. And he's

2:14:43 definitely not like happy golucky again,

2:14:45 but like he's like not in the dumps. And

2:14:48 then we're probably now like three weeks

2:14:50 into this exercise. He comes in the

2:14:52 office one day and he is bouncing off

2:14:53 the walls. And I'm like, "Dude, did you

2:14:56 meet a girl last night?" You know,

2:14:57 whatever. And he goes, "No, man. No."

2:14:59 And he like literally cannot describe to

2:15:01 me fast enough what happened. He goes,

2:15:03 "I I I never sold the coins. I never

2:15:04 sold them. They they came back. I'm up

2:15:06 $100,000."

2:15:07 >> Oh, damn.

2:15:08 >> And I'm like, "Go sell it right now."

2:15:10 Like, "What are you doing?" He's like,

2:15:12 "Hell no, diamond hands." No. HE GOES,

2:15:15 "OH, YEAH. OKAY." And he runs to his

2:15:18 desk.

2:15:18 >> At least he's smarter. Yeah.

2:15:19 >> And he sells and he comes in and dude

2:15:21 like the guy he's like salivating from

2:15:24 the mouth. He thought he just lost all

2:15:25 the money to his name essentially and

2:15:27 now he's made $100,000

2:15:29 >> and he's just like holy And like

2:15:31 it's like almost like a near-death

2:15:32 experience, but he like survived.

2:15:34 >> And so he's telling me this and I'm

2:15:35 like, "Okay,

2:15:37 >> deep breath like wooai. Go sit back at

2:15:39 your desk. Go do your job. I don't want

2:15:41 to hear about this shit."

2:15:43 So two weeks go by, three weeks go by.

2:15:46 He comes in and he's dejected again. I'm

2:15:48 like,

2:15:49 >> I lost money.

2:15:50 >> No.

2:15:51 >> Or it went up.

2:15:52 >> So immediately I'm like, dude, in my

2:15:54 office now, what happened? He goes,

2:15:57 you're never going to believe this. I'm

2:15:59 like, did you not sell the coins? They

2:16:01 went back down. So immediately I'm like,

2:16:02 we're back down 99%. He goes, no worse.

2:16:07 A politician published their filing and

2:16:12 they had bought ski mass dog. Oh,

2:16:14 >> and my position, if I hadn't sold it,

2:16:16 would be worth $11 million.

2:16:23 >> I almost

2:16:25 >> I almost literally kicked him in the

2:16:28 shin. I was like, "Dude, we're banning

2:16:29 Ski Mask Dog can never be talked about

2:16:30 in our office ever again. Goodness."

2:16:32 >> So, to this day, no one is allowed to

2:16:34 talk about that. He no longer works at

2:16:35 our company, not because of that. He he

2:16:36 he did a fantastic job for us. But every

2:16:39 time I see him, I just think I'm like,

2:16:41 >> "Dude, you like that was the experience

2:16:43 of like a Gen Z investor for the first

2:16:46 time.

2:16:47 >> Put too much money in, go down 99,

2:16:50 >> be too stubborn to sell."

2:16:51 >> Yeah.

2:16:52 >> Then make it back.

2:16:53 >> Yeah.

2:16:54 >> And then have the regret of And what

2:16:56 goes to $11 million?

2:16:57 >> Yeah.

2:16:58 >> And I'm like,

2:16:59 >> how do you invest? How do you tell that

2:17:01 person how to invest?

2:17:02 >> Exactly. Yeah. So I want to know like if

2:17:04 someone wants to be worth

2:17:06 million. Yeah.

2:17:07 >> Right now I

2:17:08 >> That's a That's a real question.

2:17:09 >> Literally right now if I had to start

2:17:10 over.

2:17:10 >> Yeah.

2:17:11 >> The first thing I would do is I would

2:17:13 immediately go and I would start to

2:17:15 create a bunch of content on the

2:17:16 internet around how do you use the AI

2:17:19 tools inside of a small business.

2:17:21 >> Okay.

2:17:21 >> Second thing I would do is as soon as I

2:17:22 started creating that content, I would

2:17:24 spend like the morning doing that. In

2:17:25 the afternoon, I would go and I would

2:17:27 walk into not call, I would walk into as

2:17:30 many local businesses as I possibly

2:17:31 could

2:17:32 >> and I would say, "Is the owner here?"

2:17:34 And when I talked to the owner, I would

2:17:35 say, "Hey, my guess is that you are

2:17:39 spending money on things, whether it's

2:17:40 software or people, that you don't need

2:17:42 to be spending money on. I am an expert

2:17:45 at using AI technology to save small

2:17:47 business owners money.

2:17:48 >> I want to come and build some tools for

2:17:51 you. You don't pay me anything unless I

2:17:53 save you money. But if I save you money,

2:17:55 I want half."

2:17:57 >> And then I would go to all these

2:17:58 business owners. A bunch of them are

2:17:59 going to like literally chase you out of

2:18:00 the uh store with a broom. It's fine.

2:18:03 >> But you're going to find a couple. And I

2:18:05 would watch YouTube videos. I'd figure

2:18:06 out how to build some pretty simple

2:18:07 stuff. And then I would go and implement

2:18:09 it. I would then start making some

2:18:11 money. Probably going to make a couple

2:18:12 thousand dollars.

2:18:12 >> Yeah.

2:18:13 >> Okay. Then I would say to that business

2:18:15 owner, what other what other problems do

2:18:17 you have? Now I'm not going to try to

2:18:18 save you money. Now I'm going to try to

2:18:19 make you money.

2:18:21 >> I want a piece. I would just keep doing

2:18:23 this. If you think about an AI type

2:18:26 consulting business, maybe they trade

2:18:29 for 10 times revenue, five times

2:18:31 revenue. So, I got to get to a million

2:18:33 or $2 million of revenue to have a $10

2:18:35 million business.

2:18:37 >> Okay. If I got to get to a million, that

2:18:39 means I need $83,333

2:18:41 a month. How do I know that? Because

2:18:43 every single business we've ever

2:18:44 started, I tell the CEO that is the only

2:18:46 number that matters. You have to get to

2:18:47 $83,333 a month as fast as possible

2:18:50 because if you get there, you're alive.

2:18:52 You'll be okay. Okay. Okay. Well, if I

2:18:54 need $83,000 a month, then how much can

2:18:56 I make per client? Well, maybe I can

2:18:58 make $5,000 a month.

2:18:59 >> Okay. Well, what's $83,000 divided by

2:19:02 5,000?

2:19:03 >> Then how many can I do?

2:19:05 >> Can I get my buddy to do it? Hey, I'll

2:19:07 give you half the money if you go and

2:19:08 you do and you just start to Here we go.

2:19:10 >> Yeah.

2:19:10 >> And you will probably for somebody who

2:19:13 is skilled could probably build a $10

2:19:15 million net worth on paper

2:19:17 >> within two years.

2:19:18 >> But could you you couldn't sell that

2:19:20 company though. But

2:19:22 >> yeah,

2:19:22 >> you're making a million bucks a year in

2:19:23 revenue.

2:19:24 >> What are your costs? If it's just you

2:19:26 doing it,

2:19:26 >> yeah,

2:19:27 >> you're making a million bucks.

2:19:28 >> I got to push back because the the

2:19:30 question is, how do I get $10 million

2:19:32 >> in cash?

2:19:33 >> In cash.

2:19:34 >> I think that you could build a company

2:19:35 and make $10 million of cash.

2:19:38 >> So, let's look at it this way.

2:19:40 >> How many people do you think create

2:19:41 content on the internet? That's all they

2:19:42 do. And they make a million bucks a

2:19:45 year.

2:19:46 >> I say a lot, but it's going to be spread

2:19:49 across

2:19:49 >> more than we think. Yeah,

2:19:51 >> but not like hundreds of thousands

2:19:53 probably, right?

2:19:54 >> Yeah. Yeah.

2:19:55 >> Creating content on the internet is

2:19:56 basically like the new small business.

2:19:59 >> You can have what's a small business.

2:20:00 Okay. Hey, I I have a family restaurant,

2:20:02 a family store, whatever. There's a

2:20:04 small number of employees that usually

2:20:05 are like family members, right? Or or

2:20:07 like closed people and we sell things to

2:20:10 the public.

2:20:10 >> And maybe a small business might make a

2:20:13 million bucks, two million bucks of

2:20:14 revenue. They've got some expenses and

2:20:16 so maybe they take home a couple

2:20:17 hundred,000 a year.

2:20:18 >> Mhm.

2:20:19 >> Okay. Well, could you do that with a

2:20:21 podcast, a YouTube, an Instagram, or

2:20:23 whatever? Probably.

2:20:24 >> It's not easy, but you could definitely

2:20:26 do it. So, if you can get to making a

2:20:27 million bucks a year doing that,

2:20:30 >> you're working for yourself. You pay

2:20:31 taxes. The rest is yours. Okay? Live

2:20:33 below your means. Take that money. You

2:20:35 could probably just put it in the stock

2:20:37 market at that point. Just put in the

2:20:38 S&P 500.

2:20:39 >> Do that every year for 10 years. You'll

2:20:41 be you're worth $10 million.

2:20:42 >> Okay. I'm gonna do that.

2:20:44 >> Yeah. It's just earning more is

2:20:46 essentially the answer. the the the big

2:20:47 lie I think that a lot of people have is

2:20:49 like, "Oh, I'm spending too much money."

2:20:51 >> There are very few people in the world

2:20:52 who have a spending problem.

2:20:54 >> Yeah.

2:20:54 >> And don't have a you're not making

2:20:55 enough money problem.

2:20:56 >> Mhm.

2:20:57 >> Because most people are not like buying

2:20:59 Lamborghinis and Rolexes and like doing

2:21:01 crazy. There's some, but like that's not

2:21:03 most people's problem. Most people are

2:21:05 like, "Damn, I have to save an extra

2:21:06 hundred bucks a month."

2:21:07 >> Yeah. I know.

2:21:08 >> They're And they can make his original

2:21:10 question. They're thinking about

2:21:11 becoming investors when they need to

2:21:13 think about being an operator.

2:21:14 >> Correct. Yeah. It it it's you are more

2:21:17 likely to make $10 million before you

2:21:19 will

2:21:20 >> invest and make $10 million.

2:21:22 >> Got it.

2:21:23 >> And so again, Anthropic, if you just got

2:21:27 a job at Anthropic, you made $10

2:21:28 million. How would you if you're all of

2:21:30 our age? We're all similar age. How old

2:21:31 are you?

2:21:32 >> 38.

2:21:32 >> 38.

2:21:33 >> Damn, you're 38. Holy crap. I feel like

2:21:35 I'm not doing anything in my life, bro.

2:21:37 >> I'm 37.

2:21:38 >> Freak.

2:21:38 >> He's 34.

2:21:39 >> I'm 35.

2:21:40 >> 35. Okay, but hold on a second.

2:21:42 One of the the uh great challenges of

2:21:45 our time is that everyone looks at and

2:21:47 says to themselves, "Yo, what's that

2:21:49 dude doing over there?"

2:21:51 >> Yeah.

2:21:51 >> How old is he? Where am I? But like the

2:21:53 comparative stuff, right?

2:21:54 >> Yeah. Yeah.

2:21:57 >> In 2020, the start of 2020, six years

2:21:59 ago, so I was 32 at the time.

2:22:03 I was not married. I had no kids. And I

2:22:08 had just enough money to think that I

2:22:10 could uh like go to a restaurant and pay

2:22:15 the bill and not look cuz everything was

2:22:18 illquid in Bitcoin.

2:22:19 >> Mhm.

2:22:19 >> So like I had very little money in my

2:22:21 bank account, right? And I said to

2:22:23 myself, and by the way, also the funds

2:22:25 that we raised, I didn't pay myself.

2:22:28 That's another thing people don't

2:22:29 realize. I don't pay myself at the

2:22:32 public company. I get paid a $1 salary a

2:22:34 year. I get no cash bonus and I get no

2:22:36 equity grant. The only way I earn money

2:22:38 is if I get the shares to $15 a share.

2:22:40 >> Damn, we're at three bucks.

2:22:43 >> Oh my goodness. How are you going to do

2:22:44 it? I was going to ask you that, too. I

2:22:46 know we're running out of time, but I

2:22:46 was going to ask you, how are you going

2:22:47 to turn this stock around?

2:22:48 >> So, you think about this, right?

2:22:49 >> Yeah.

2:22:50 >> How many people are willing to do that?

2:22:51 Now, I'm very fortunate to be in a

2:22:52 position that I'm in to be able to do

2:22:53 that, right? Yeah.

2:22:54 >> But I taking immense risk. I'm spending

2:22:57 my most valuable resource, which is my

2:22:59 time. Mhm.

2:23:00 >> And I said to the board, I said,

2:23:01 "Listen, I want nothing if I don't

2:23:04 perform."

2:23:05 >> How much would you make if you get it to

2:23:07 15?

2:23:07 >> If I get it uh at 15, it's only uh I get

2:23:10 like a million bucks or something if I

2:23:11 get it at 15.

2:23:13 >> If I get it to 50,

2:23:14 >> yeah,

2:23:15 >> 50. Basically, between 15 and 50, every

2:23:18 $2.50, I get more shares. If I get it to

2:23:21 50, that means that the one that I got

2:23:23 at 15 obviously have appreciated plus

2:23:24 what I get, I'd make $400 million.

2:23:27 >> Damn.

2:23:28 So, I'm just going to shoot my shots and

2:23:30 ask questions. Did you get a lump sum

2:23:32 when it went public? Like, did you get

2:23:33 paid?

2:23:34 >> Our investment firm has shares that are

2:23:36 allocated to it. But again, we could

2:23:38 have just taken the shares.

2:23:39 >> Yeah.

2:23:40 >> We locked them up for two years. We said

2:23:42 we're not going to touch them for two

2:23:43 years because we knew the stock was

2:23:46 going to fall because of the way Bitcoin

2:23:47 everything. And so we said again,

2:23:49 long-term thinking, we're not going to

2:23:52 have access to the money

2:23:54 >> until we address the investors.

2:23:58 >> We are not even one year in. And my

2:24:01 guess is that we will be back before

2:24:03 those shares unlock.

2:24:05 >> The investors who invested in the

2:24:07 company

2:24:08 >> will be very happy.

2:24:09 >> Yeah.

2:24:10 >> Now, could could not work, right? A lot

2:24:11 of risk, all this kind of stuff,

2:24:12 whatever.

2:24:13 >> But

2:24:13 >> it goes back to people. You ever heard

2:24:16 the saying of like uh people want to be

2:24:17 the man till it's time to be the man.

2:24:18 >> Yeah. Yeah.

2:24:19 >> Type stuff, right?

2:24:21 >> There's, as far as I understand, there's

2:24:22 three CEOs in the public market who have

2:24:24 a pay package like me. Elon Musk, Cass,

2:24:27 the CEO of Open Door, and me, and I 100%

2:24:30 ripped off what they were doing.

2:24:31 >> Damn.

2:24:32 >> Elon did it, then Cass did what Elon

2:24:34 did, and then I was like, "Well, these

2:24:35 two guys are good, and I like that those

2:24:36 packages. I'll bet on myself." And then

2:24:38 I did what they did.

2:24:38 >> I don't know that I'd be betting on Open

2:24:40 Door, but yeah,

2:24:42 >> I'm an Open Door shareholder. I'm a big

2:24:43 believer.

2:24:44 >> Yeah. Yeah, he's better than I'm a big

2:24:45 believer.

2:24:45 >> I've been in the real estate game a long

2:24:47 time.

2:24:48 >> Now, here's what I will say is it is not

2:24:52 fun. When I when we do the payroll every

2:24:55 uh two weeks, year zero,

2:24:57 >> I approve it, right?

2:24:59 >> Yeah.

2:25:00 >> They pull it together, whatever. But I

2:25:02 fin approval.

2:25:03 >> Everyone's getting paid all these

2:25:05 numbers. I get I think it's four cents.

2:25:08 >> Damn, bro. You know how many times I've

2:25:10 wanted to screenshot that and tweet

2:25:11 it and be like, "Yo, this shit."

2:25:14 >> Now, by the way,

2:25:14 >> I'm guessing you're worth at least $100

2:25:16 million.

2:25:17 >> Listen, but by the way, it's my it's my

2:25:19 choosing.

2:25:20 >> Yeah.

2:25:20 >> Right. And

2:25:22 >> man, is it this whole thing of like

2:25:25 >> you see that and you're like, "Dude,

2:25:27 this better work."

2:25:28 >> Yeah.

2:25:28 >> Like back up against the wall.

2:25:30 >> I know. Yeah.

2:25:30 >> And so, in a weird way, I I actually

2:25:32 made a whole video about it. I was like,

2:25:33 I think every public company CEO should

2:25:35 be compensated this way

2:25:36 >> because now not everyone can. So I

2:25:39 understand that. But the reason why I

2:25:41 think that is why is it that public

2:25:44 companies where the stock price goes

2:25:47 down, which means that the retail

2:25:48 shareholders are losing money, their

2:25:50 hard-earned money that they put into

2:25:52 this company is going down in value

2:25:54 >> and the CEO is getting paid tens of

2:25:56 millions of dollars.

2:25:57 >> Yeah.

2:25:58 >> The incentives are off.

2:25:59 >> Yeah.

2:26:00 >> Do you want to sell the company? Like is

2:26:02 your goal to sell the company?

2:26:03 >> Well, it's already public. You can't

2:26:04 >> you can't sell.

2:26:05 >> I mean, somebody could buy it, but

2:26:06 that's not the goal. The share price

2:26:07 goes It's uh the the the better.

2:26:10 >> But like how would you exit at some

2:26:11 point? Like or you just

2:26:12 >> you want to hear a good story?

2:26:13 >> You would sell all your shares, all of

2:26:15 them. But there's a guy that uh recently

2:26:18 sold his company for a couple billion

2:26:19 dollars. We saw him last night. And um

2:26:21 he said that when he sold his company, a

2:26:23 bunch of people called him like, "Dude,

2:26:24 congratulations." Like he made a

2:26:25 ton of money, right? Like awesome. He

2:26:27 goes, "Why are you congratulating me?

2:26:28 You should call them and congratulate

2:26:29 them."

2:26:30 >> Yeah.

2:26:30 >> And they were like, "Why?" He goes,

2:26:32 >> "Dude, they convinced me to sell my

2:26:34 life's work."

2:26:35 >> Yeah. like he he was like, "I think that

2:26:37 this could be worth way more in the

2:26:39 future." He's like, "I can't believe I'm

2:26:40 selling this thing." Like,

2:26:41 >> and I heard that and I was like, "I got

2:26:43 a bunch of friends who made a ton of

2:26:44 money, sold all these whatever."

2:26:45 >> That was awesome, right? He told the

2:26:47 people calling him to congratulate him

2:26:48 on a multi-billion dollar sale of his

2:26:50 company. He said, "You should call him

2:26:51 and thank him and congratulate him."

2:26:53 >> So, you we asked, "How would you make 10

2:26:54 million?" This would be the last

2:26:55 question because I know you got another

2:26:57 pod and we're super late. Uh, how would

2:27:00 you go about getting to a hundred

2:27:01 million? And then is that any different

2:27:03 from getting to a billion one day?

2:27:05 >> You just got to build a company. There's

2:27:07 >> the only way is building a company.

2:27:08 >> I have a couple of people I know I I

2:27:11 wouldn't even say that they're very

2:27:11 close friends, but just people that I

2:27:13 I've known over the years that um

2:27:16 bought Bitcoin at a dollar and held.

2:27:19 >> Yeah.

2:27:20 >> One one dude of all time, right? Like

2:27:22 you know, whatever, right? I've got a

2:27:24 couple of friends who uh they're a seed

2:27:26 in, you know, I told you the story,

2:27:27 Jason Calcanis, 25,000 turns into 100

2:27:30 million, right? So, could that happen?

2:27:32 Sure.

2:27:32 >> Did you just find a crazy investment?

2:27:34 Yeah.

2:27:34 >> Yeah. Also, a billion dollars could fall

2:27:35 from the sky and in your pocket, right?

2:27:37 Sure.

2:27:38 >> The only way that I believe that you can

2:27:41 predictably create outsized wealth is

2:27:45 you got to own equity. Now, there's a

2:27:47 lot of people who made $100 million who

2:27:49 worked at some of these companies that

2:27:51 weren't the founder,

2:27:52 >> right? Like a Steve Balmer.

2:27:54 >> Steve Balmer actually owns more of

2:27:55 Microsoft than Bill Gates does. Y,

2:27:57 >> right?

2:27:59 But usually it is the business owner.

2:28:02 The person who starts the company has

2:28:04 the bulk of the equity. They took the

2:28:06 most risk. It was their idea. They're

2:28:08 the source as this one guy calls it. And

2:28:11 that is the ultimate path.

2:28:13 >> Yeah. You're not going to invest your

2:28:14 way to nine figures or that that was the

2:28:16 argument I made yesterday to these guys.

2:28:18 They're like, "Well, you know, you could

2:28:19 do this." I'm like, "But that's not the

2:28:20 goal."

2:28:21 >> Also, I I I think we live in a world

2:28:24 where like everyone's like, "I want to

2:28:25 be a billionaire." I'm like, "Dude,

2:28:26 okay, let's say I make $500 million.

2:28:29 That's a lot of money. A lot of money.

2:28:31 That's more money than most generations

2:28:33 and generations of families will ever

2:28:35 see.

2:28:37 If you make $500 million, you're only

2:28:39 halfway there. You have to make another

2:28:42 $500 million, right? Like a billion is a

2:28:44 lot of money.

2:28:45 >> Yeah.

2:28:46 >> Why do you need a billion dollars,

2:28:48 >> right?

2:28:49 >> I'm not saying it wouldn't be cool. I'm

2:28:50 not saying that you wouldn't want it.

2:28:51 all that. But most people, if you have,

2:28:55 I don't know,

2:28:57 >> 10, but definitely 20, but maybe 10

2:29:00 million dollars liquid,

2:29:02 >> you're done.

2:29:02 >> You're good. What What do you

2:29:04 >> Your your family is doing whatever you

2:29:06 want.

2:29:07 >> Yeah, that's what I'm saying. Like, like

2:29:08 depending on where you live, New York

2:29:09 City is a little bit different, but like

2:29:10 depending on where you live, the number

2:29:12 is not nearly as big as people think.

2:29:14 >> Vegas, Cali, most places you're going to

2:29:15 be fine. So my point to him was I was

2:29:17 like, "Look, I'm just taking moonshots

2:29:19 trying to get a business that pops and

2:29:21 does something crazy." And even if crazy

2:29:25 is a $30 million sale at at this point,

2:29:29 that being this young, that 30 million

2:29:31 compounds to over figures

2:29:34 >> by the time it's over.

2:29:35 >> 100%. I have a friend um he he he he's

2:29:38 real weird and dumb and I hate him a

2:29:41 lot, but his name's Sam Parr. Um and uh

2:29:43 >> like Mike Zuber.

2:29:44 >> Yeah. No, no, no. My uh my my buddy my

2:29:47 buddy Sam is the worst. But um Sam's the

2:29:50 worst. So he's just the worst. He's like

2:29:53 I don't know why anyone listens to him,

2:29:54 talks to him, whatever. His podcast My

2:29:56 First Million is horrible. His company

2:29:58 Hampton I would not go check out. Um but

2:30:00 um he sold a company called The Hustle.

2:30:02 And when he sold this company, um the

2:30:05 rumored numbers are like$25ish million

2:30:07 dollars give or take.

2:30:08 >> And he has some investors whatever, but

2:30:10 like he definitely made money, right?

2:30:13 So, I don't I don't know how much

2:30:14 exactly, but like probably more than 10,

2:30:16 less than 20 would be my guess.

2:30:18 >> Y

2:30:18 >> and he's been very clear publicly that

2:30:20 he just like invested majority of it in

2:30:22 the S&P.

2:30:23 >> He's his grandkids are good.

2:30:25 >> Yeah.

2:30:26 >> Right.

2:30:26 >> It'll keep doubling every what, seven

2:30:27 years?

2:30:28 >> Like, okay. So, if he had 15, that means

2:30:31 it's 30. He's he sold the company like

2:30:33 three or four years ago. So, he's

2:30:34 probably almost doubled it.

2:30:35 >> Yep.

2:30:36 >> Like, guess what? He's I think he's a

2:30:39 year younger than me. So he's got like

2:30:41 how many sevenyear you know periods in

2:30:43 his life

2:30:43 >> where it's gonna double a lot.

2:30:44 >> It'll be worth nine nine figures

2:30:46 guaranteed.

2:30:46 >> So you look at it from that perspective

2:30:47 and like by the way guess what he did?

2:30:49 He went and started another company.

2:30:50 >> Yeah.

2:30:51 >> So like you look at it from that

2:30:52 perspective and you're like okay

2:30:55 when you put a goal on like I want to

2:30:56 build a billion dollar company. Like

2:30:58 dude that's really hard and like a real

2:31:00 billion dollar not like a I I convinced

2:31:02 an investor. I'm a young guy and I

2:31:03 flirted with an old guy and he gave me

2:31:04 money and signed a piece of paper and

2:31:05 it's worth a billion. Like actually a

2:31:07 billion

2:31:08 >> through being gay

2:31:09 >> dude. Yeah. Yeah, like a high a man. Uh

2:31:11 you said that. Uh

2:31:14 but

2:31:16 >> if you're just like, dude, I want to

2:31:17 build a company where I can make $10

2:31:18 million. That is a very feasible thing

2:31:20 for a lot of people.

2:31:21 >> And that was my point to him yesterday.

2:31:23 I'm just like, dude, all it takes is

2:31:24 just sell a company for $20 million.

2:31:26 >> Yeah.

2:31:27 >> Which is not that difficult.

2:31:28 >> 20 million. But but just take this one

2:31:30 step further. If you want to sell a

2:31:32 company for $20 million, right? We have

2:31:34 a business um that is in um in a

2:31:37 specific industry and uh they trade for

2:31:39 somewhere between 8 to 12 times revenue.

2:31:41 So, we were I was talking with the uh

2:31:43 CEO of the company and I was like, "All

2:31:44 right." We were talking about $50

2:31:46 million cuz in her uh in her life, she's

2:31:49 like, "If we sold a company for $50

2:31:50 million, she's like, "You'll never see

2:31:52 me again." Like just like that is, you

2:31:54 know, like whatever, right?

2:31:56 >> And so, um I said to her, I said, "Okay,

2:31:58 well, like let's just work backwards.

2:31:59 You want to sell the company for $50

2:32:00 million? Let's say 8 to 12 times revenue

2:32:02 multiple. So, that means let's say 10.

2:32:04 You'd have to do $5 million a year of

2:32:05 revenue and you got $50 million

2:32:06 business." She literally was like,

2:32:08 >> "Well, we're at two. like I could get to

2:32:11 five and I was like that's the game.

2:32:13 >> Like that is the game right there. You

2:32:14 don't have to build $50 million of

2:32:16 revenue. You got to build five. If you

2:32:17 want to do five, well, what is that per

2:32:19 like

2:32:20 >> that to me?

2:32:20 >> You got to be in the right vehicle to

2:32:21 get those multiples.

2:32:22 >> Correct. Correct.

2:32:24 >> So,

2:32:25 >> dude, this has been awesome. Uh

2:32:27 definitely everyone should check out

2:32:28 Sylvia.

2:32:30 I'm signing up silva.com.

2:32:32 >> Investing into it.

2:32:33 >> Yep. Right now. Right. I'm going to do

2:32:35 it.

2:32:36 >> Yeah.

2:32:36 >> I'll send you a screenshot.

2:32:37 >> Yeah.

2:32:38 >> Believer. If it doesn't get to 10

2:32:39 million, I'm

2:32:40 >> check out silia.com and then uh go buy

2:32:42 uh go buy the book. How to live a

2:32:44 extraordinary life.

2:32:44 >> Stock ticker so people can buy the stock

2:32:46 too if they want.

2:32:47 >> Um when's this going to come out?

2:32:49 >> Uh soon. Probably this week since

2:32:51 there's a lot of relevant info.

2:32:52 >> So BRR.

2:32:54 >> Yeah. Cool.

2:32:56 >> Go check it out guys. And uh if you like

2:32:58 this episode, make sure you subscribe

2:33:00 and we'll see you on the next one.

2:33:01 Peace.

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