TBPN | Friday, May 9th — searchlore

TBPN | Friday, May 9th

Published May 10, 2025

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[Music] [Music] [Music] [Music] [Music] [Music] [Music] [Applause] [Music] We came to this [Music] world to reach the stars to see our future.

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[Music] [Applause] [Music] Let's go. It's Friday. Let's go. You're watching TV. Let's go.

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Today is Friday, May 9th, 2025.

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We are live from the Temple of Technology, the the fortress of finance, the capital of capital. Let's go.

4:04

It is Mother's Day in just two days.

4:07

The Super Bowl of Pronatalism, baby. It's Tuesday.

4:10

We've all been waiting for.

4:12

We've all been waiting for it. Especially your mother.

4:13

It's going to be amazing.

4:15

Uh, switch your mom to ramp. com. Uh, time is money. Time is money. Save both. Switch your mom to ramp. com. Get her on ramp.

4:21

Seriously, what is your I mean, what does your family run on if not ramp? Yeah.

4:25

I mean, it's a good time to ask uh what is your you know, how are your parents thinking about enterprise security, right?

4:32

Should should they be on Vant?

4:34

Do they need to be thinking about sock 2 compliance? Yeah. Does mom pay sales tax? Get her on numeral. That's right. Put that on autopilot.

4:42

Take that off of her plate so she can raise.

4:44

I mean, it's the greatest gift.

4:45

It is to let your mom no longer have to be spending more than five minutes.

4:49

It is, you know, a month.

4:51

Anyway, we're going to be doing a deep dive on Mother's Day, giving you a gift guide, breaking down just some of the most obvious choices for what to get the mothers in your life.

4:58

Um and uh but first we want to take you through the market, give you a little update on what's going on, what's moving the market.

5:05

Obviously the big news is uh the US UK breakthrough tariff truce.

5:08

Trump announced this yesterday.

5:12

Uh it's the first post tariff pact.

5:14

US keeps 10% blanket duty.

5:17

UK cuts average duty on US goods to 1. 8%.

5:19

Autos the 27 and a half% levy on British cars falls to 10%.

5:23

Huge for the Aston Martin owners in the community.

5:28

Huge for the Bentley owners in the community.

5:31

Huge for the Rolls-Royce fans.

5:32

I'm sure there's a lot of you listening from probably in your cullinins. Black badge probably.

5:38

Fortunately, Aston will be safe here.

5:40

So, the the the it's a 10% tariff for up to 10,000 vehicles. Sorry, 100,000 vehicles.

5:47

Aston Martin for context only expects to sell one around 1,000 in 2029.

5:53

So they have a lot of that a lot of that quota to sort of grow into which is great.

5:58

Something happened my Celsius hit me in the face and I think I'm going to lose the eye.

6:02

John's going to lose his eye.

6:04

Hey, you don't need eyes for podcasting.

6:06

I think there's a certain amount of caffeine level that uh it will it will blind you. Yeah.

6:09

Um anyway, obviously exciting.

6:12

A little bit uh odd that this one's the first one because uh you were telling me that we actually have a trade surplus with the UK. We do.

6:20

Um, so, uh, you know, one step forward, two two steps back potentially, but it seemed like the market liked it.

6:26

It seemed like the market traded up.

6:27

Bitcoins at all times high all time highs or or or near all-time highs over 100K.

6:32

Um, and uh the the uh we are going to have Pippa Lamb from Sweet Capital come on on Monday.

6:40

She uh is from some foreign land.

6:40

I think she said it was like London. the United is it United?

6:46

Yeah, I think about these places that I think Carthage or like Constantinople like these like longlost forgotten lands and she actually lives in one and so I'm excited to ask her like do they have movies there? Yeah.

6:58

Uh do they do they have telephones yet?

7:01

Like what type of technology is going there? Exactly. Yeah.

7:04

Um but she's going to come on and break it all down for us on Monday.

7:06

But in the meantime, we're going to move on.

7:09

Uh China uh surprised on exports.

7:09

uh headline exports of 8.

7:13

1% year-over-year growth versus 2% estimates.

7:16

Um US shipments were down 21%. Okay.

7:19

shipments were down 21%. Okay. But where's this data coming from is the big question because last uh so so there was a report I think it was maybe Monday uh the days blend together but but at some point uh China used to report on like hundreds of different economic sort of

7:36

metrics from across the country at you know things like uh land sales local GDP things like that a while back they just stopped fully stopped reporting on you know most of those metrics they stopped sending me yeah reminded of a you know

7:52

anytime you have a you know angel investment or something like that that stops sending investor updates usually uh doesn't uh usually not a good sign um so China has sometimes they're just advancing to like you know just board meetings only they don't need to update

8:06

the angel investors they don't need to update the global economy no they're responsible or the other one is a founder that's building in public you know just euphoric every single week they don't post for three months and you're like you know that's how you No, Aquaire inbound. Maybe, maybe we

8:20

Maybe, maybe we should aqua hire China. Yeah, merge them in. Definitely something. Bring them on the team.

8:27

Definitely should be in the conversation. In the conversation.

8:28

Uh, Nvidia is launching a new H20.

8:30

They're pushing forward the memory bandwidth trim below US export caps.

8:35

The export caps keep moving down.

8:37

Nvidia keeps adapting the the hardware.

8:39

Um, Pinterest popped big on a huge Q1 beat. Uh, they had earnings.

8:46

Q1 revenue 8505 855 million. Uh MAUs at 570 million.

8:49

AI ad tools push Q2 guide to 960 million to 98.

8:57

Yeah, Pinterest is there's an article in the in the in the information about how they kind of changed the management structure.

9:03

We didn't get a chance to cover it, but it is interesting.

9:06

Gen Z accounts for 40% of user base.

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Fastest growth is in Brazil and Indonesia.

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Uh you know, these these these platforms are just valuable.

9:12

Uh, I've actually been using Pinterest every once in a while to try and find like really aesthetic reference photos for brand guides and style guides. It's pretty cool.

9:21

It's also been overrun by AI slop in a really negative way.

9:24

So, I almost want to have a filter on there to say, "Okay, I'm looking for, you know, a newsman in a suit, but don't show me anything that was created after 2022 or the 80s." Yeah. Yeah. Yeah.

9:36

Um, nothing that was uploaded.

9:38

Uh, but some good stuff, some bad stuff, and they're certainly pumping a lot of ads in there, and the ads are pretty related to what you're searching for because it's such a visual medium.

9:46

So, congrats to Pinterest for the big Q1 beat.

9:48

Coinbase also uh bought a company for $3 billion, barely broke through.

9:54

I mean, this is a huge number. Size gone for sure.

9:55

Would love to talk to Brian Armstrong about that.

9:59

He's coming on the show soon.

9:59

We will have him break it down.

10:00

Uh, would love to get the founder of Darbit on as well.

10:04

700 million in cash and 11 million shares of just coin.

10:07

Yeah, their cryptocoin shares. No.

10:10

Um, their stock is just coined.

10:13

Darabet is one of the world's top BTC.

10:15

I think it is the world's top options house. Yeah. Yeah.

10:20

And uh so very strategic pickup for Coinbase. Makes a lot of sense.

10:23

Adds Europe and Asia as client base and regulated futures to Coinbase's spot heavy mix.

10:29

Derivatives drive over 50% of global crypto volume.

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Very important for them to get in. So excited about that.

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saying they they have 90% of they're clearing 90% of crypto option open interest which is around 20 billion dollars in volume. So very impressive.

10:45

On the flip side, the company was started in 2016 based splitting time between an Amsterdam and Panama.

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It's kind of a fun fun place to uh bounce between. Interesting.

10:54

Uh I'm sure that's for like regulatory reasons, right? Yeah.

10:58

Um on the flip side of crypto, uh Celsius founder Alex Mashinsky was sentenced 12 years for fraud.

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tied to the 2022 Celsius collapse.

11:05

Uh fund a ton of 20 20 B's at the peak 20 billion AUum.

11:12

Um they he made a $48 million personal gain via token manipulation.

11:15

Um the still deciding what the restitution figure will be, what he'll have to pay for that.

11:22

Uh prosecutors saw 20 years defense pleaded for less than four years citing cooperation.

11:26

Uh but I don't know how much he was cooperating because he was selling merch that was like all about like bankruptcy and stuff.

11:31

It it was like really really crazy.

11:33

Uh a lot of great um Coffeezilla reporting out there if you want to go take the stroll down memory lane and hear about the Celsius debacle.

11:41

But uh this is a rough time.

11:43

I think they were doing okay while the market was up, but once the market pulled back where you know who's naked, the yield that they were offering was concerning high. Yeah.

11:53

Given that interest rates uh the risk-free yield was about zero at the time. Yep.

11:57

And the last story is that uh there's a $3 billion lawsuit against Google in Italy.

12:04

Uh we won't do our Italian accents today, but uh I'm sure we'll be tracking it.

12:08

It's part of the ongoing pressure on big tech, especially abroad.

12:10

Google getting into suiting. No.

12:12

Uh actually search uh but this goes back to 2010 to 2017.

12:18

Uh Google was showing a search bias against a particular group in Italy.

12:23

And so they're claiming that Google shopping uh you know hurt them to the tune of billions of dollars and they want restitution.

12:29

And so uh the financial impact is pretty limited given that Alphabet's 2024 revenue was 320 billion but the legal overhang is certainly a headache for Google.

12:38

But anyway, good luck to them.

12:40

anyway, good luck to them. interesting dynamic being uh internal counsel at a at a firm like Google where there's just like probably can you I don't even I don't even maybe these they I I doubt you you could probably figure you I'm sure you could figure out the number but I imag I wonder how many inbound

12:58

lawsuits complaints Google is getting on a daily basis right it has to be yeah hey want to settle this with me I mean there was that whole story about that guy who was just sending fake invoices to big tech companies just being like yeah if it's under 10 grand they'll probably just pay it and just doing that constantly. Wouldn't work wouldn't work

13:13

Wouldn't work wouldn't work if wouldn't work with rampant. com. Yep.

13:17

Uh anyway, uh let's pull up the show, the run of show to kind of give you an guys an idea of what the rundown is today.

13:22

Uh we're taking you through the Mother's Day gift guide.

13:24

Rora was featured in the Wall Street Journal.

13:27

We're going to break it down.

13:27

We're going to do some timeline, tell you some other stories.

13:29

Uh then we have uh two anons from Stanford uh talking about the Stanford Review, the bombshell report from the Stanford Review.

13:36

Then we got Morgan Hel, Sonia Wong, uh Will Quist, Aiden Der, bunch of other folks coming on, founders announcing rounds, announcing new products, bunch of VCs yapping.

13:47

Should be a good show for you.

13:49

Uh and if you're following the market update and you want to trade it, get on public. com.

13:53

Investing for those who take it seriously.

13:54

Multiasset investing, industryleading yields, they're trusted by millions, folks. Go to public. com. Aston Martin sponsor. Aston Martin sponsor.

14:02

Uh, should we do six facts about David Steiner, the new US Postmaster General?

14:07

So, I mean, and this is one of the most coveted roles obviously if I were if I were going in the admin postmaster for sure. Postmaster.

14:14

I mean, I I think of myself as a master of posting. Post.

14:16

So, it it would make sense.

14:18

It could, you know, the the extended the full title is poster master general. Poster master general.

14:24

The most senior poster in the country.

14:26

Maybe they should have a posteconomic master general. Post economic general. Yeah. Post economic general.

14:31

He's just like, "Yeah, actually I can't really revitalize the DoD because I'm actually in San Chopi and uh F1's coming up, so I'll get to it, but uh I'm not really checking email right now.

14:43

I I haven't checked this inbox, so good luck getting in touch with me."

14:48

We're just like, "That's exactly who we want."

14:49

Anyway, uh he spearheaded a comeback for Waste Management.

14:50

Uh he's a FedEx director who led turnaround of the of Waste Management after an insider trading scandal.

14:57

some of the company's top executives in the 1990s falsified financial results. That's not good.

15:01

Uh really really playing into the the meme that that the trash companies are run by the mafia, right?

15:08

Uh he joined the company as deputy council in 2000.

15:11

In less than four years, climbed the ranks to become CEO.

15:14

He streamlined operations.

15:15

He calls himself the walking embodiment of better lucky than good. I like that. That's great.

15:20

Uh that's because he went to work for Waste Management.

15:23

He turned down a job offer from Enron.

15:25

Speaking of better lucky than good, uh I was listening to Senra's episode on on Jim Simmons, sorry, Simons. Simons.

15:33

Um and uh Jim Simons uh was obsessed with being lucky.

15:39

He's like, I wake up every day, I just think, how lucky can I get today? That's amazing.

15:45

Uh so he's stepping down as as director of FedEx because FedEx, of course, competes with the US Postal Service, which he will be running. Honestly, surprising.

15:55

could have been running both.

15:55

I would have liked to see FedEx this whole divevestature thing.

15:58

We got to get I would have Yeah, I would have liked to see FedEx, you know, kind of merge with the USPS.

16:04

I would like to see if somebody goes in the someone goes in the admin, don't make them divest, make them go 10x levered, whatever they're long. Yeah.

16:13

Just really stake it all. Risk on.

16:15

Hey, you're going to get a pension after this.

16:16

You know, but if if you make any mistakes, your bags are cooked.

16:18

a 10% down draw from any of your bags, you're wiped, right? You're wiped. So, don't mess up.

16:25

It was really high stakes. Mess up. Yeah.

16:27

Uh, so he has a big job job ahead of him.

16:29

The postal service has been hemorrhaging money for years because of declining mail volumes, limits on what it can charge customers, and a costly mandate to deliver around 168 million addresses 6 days a week.

16:41

Steiner must also address persistent delays of mail and package deliveries in some parts of the country.

16:44

He was hand selected by Trump and he says he wants the postal service to remain an independent agency. I don't like that.

16:49

I think we should take this thing private. Yeah. Spack it. Spack it. Yep. Let's Let's let it rip.

16:55

Let Chimoth cook this one.

16:57

Uh I'm sure you all are aware of Mother's Day coming up.

17:00

I'm sure you've already done a lot of your prep, bought all the gifts, but if you haven't, we have a gift guide for you today to take you through some of the just some of the basic options that you should be thinking about.

17:10

If you're in the audience, we know that you're wealthy, most likely posteconomic.

17:13

So you got to make sure that the mothers in your life, you know, your pre post economic, right?

17:21

You know, impending, you know, you could probably take a non-reourse loan against your shares if even if you're a series B founder and then drop that on something like the pink star.

17:29

It's a 60 karat vivid pink diamond ring coming in at only uh $70 million, but it's I mean at that price, right? Say no.

17:38

I mean, we talked about the graph hallucination, rainbow diamond watch, 110 carats for the wrist, and everyone's just going to know like, wow, that mom has a great son. Yeah, for sure.

17:51

Um, he or she really loves their mom. Yeah.

17:53

I mean, if you want to take it a little bit further, you could go diamond tiara. Yep. A crown.

17:55

These have tiaras kind of fell off. They did.

17:58

But that just creates an opportunity for them to be sew back.

18:02

And I think that 2026 is feeling like it will be the year of the tiara.

18:08

You know, your mom is going out on a little morning stroll.

18:12

She's gonna want to throw on that tiara. Exactly. Just put around. Yeah.

18:14

A lot of people say, "Oh, flowers.

18:17

Flowers are only for Valentine's Day."

18:19

But I think if you rethink what flowers can be, you can get to a really interesting pace. Yeah. Exactly.

18:26

So, Juliet Roses, Orchid Rarities, you can push a bouquet up into the six figures. Yeah.

18:32

If you really I thought you were going to say uh you know, basically drop an entirely new uh rose garden into her backyard or hire a team of bio bioengineers to engineer an entirely new breed of roses never before seen. Totally. Totally.

18:46

That's a reasonable thing.

18:48

not unreasonable even to be getting ahead of it, thinking about next year going to Monsanto saying, "Hey, look, I want to develop, you know, an entirely new, you know, group of flowers, completely bio-engineered." Yep.

18:58

Um, you can also take some of those flowers, dip them in 24 karat gold, boom, you have an eternal bouquet. They'll never wilt. That's right.

19:08

That's going to run you like 40k, but worth every penny. Totally.

19:09

Um, what else should we uh recommend?

19:12

I mean, obviously, renting an entire tropical island for a family week. Totally doable.

19:18

Uh round the world private jet grand tour. Very good option. A ticket to space.

19:23

We saw this with uh with Jeff Bezos. Totally.

19:26

Totally expect I mean you've been saying this for weeks.

19:28

You think that uh you won't be able to get on a Blue Origin flight for the next three years after Mother's Day because everybody is going to be sending their mother and their mother-in-law to space. Yeah.

19:40

They made this whole thing with the Blue Origin.

19:41

Oh, it's all the women flight. What about all the moms?

19:42

They should have just done really focus on just moms in space.

19:45

Yeah, that's that that's definitely the future. That's viral.

19:49

Uh, another favorite, we talked about this, just a good old wire transfer.

19:53

Just a big wire transfer.

19:53

You remember as a kid you're you know your birthday, you know? Yeah. Yeah. Kind of got 100 bucks. I got 50 bucks. I can buy a video.

20:01

And the logic at the time was this kid knows how to spend it better than I do. Exactly. Flip it on your mom. Flip it on your mom. Yeah.

20:07

And six figure wire transfer. Sixig wire transfer. Boom. Yep. It's great.

20:11

Uh if you're look if she's into purses, you know, we talk about Birkkins.

20:17

If you're going to go Birkin, don't go basic Birkin.

20:21

Go Hermes, Himalaya Birkin with rare crocodile, diamonds.

20:24

Yes, it's going to cost you 200, maybe 500k, but it's going to stand out in a sea of monotonous Birkkins, right?

20:31

And for the average platform VC, that's 20% of your annual comp, right?

20:37

Like not even counting carry, right?

20:40

So it's just it's it's the least that you could do. Yeah.

20:44

So in fact, if you see if you meet some GP and their mom doesn't have a Himalayan Hermes Birkin with crocodile and diamonds on it, you would think like, "Oh, their fund's not doing well." Yeah. Exactly.

20:56

Immediately they're not raising. They're not ready.

20:58

They definitely can't lead my next round. Yeah.

21:00

So I got to start talking to other folks.

21:02

I got to run a run a bigger bidding process for sure. Start networking.

21:05

Um, but I mean if you want to go if you're more serious, you should go with the uh Muawad Thousand1 Nights diamond purse. Yes.

21:13

Guinness Book World Records of handbags.

21:15

I mean such a big deal out of the Birkens, but you can go a whole whole order of magnitude higher. It's $3. 8 million bag. Yeah.

21:21

But you know, it's really the only way to stand out. One of one. One of one exactly one.

21:28

Uh the armored Rolls-Royce. This is a great choice.

21:30

This is a great runs going to grab coffee.

21:32

if they're still dropping off the grandkids, dropping off the grandkids. 100%.

21:35

Um, yeah, just getting, you know, ideally knowing my mom 700 horsepower is kind of like the right range for a daily. Totally.

21:49

My mom drives a Prius, actually.

21:49

But, uh, I I I think I think Have you considered swapping a V12 in there? Engine swap for Prius.

21:55

Yeah, maybe maybe just putting whatever car she has, just soup it up.

22:00

engine swapping a Cayenne Turbo GT engine into the Prius. Yeah.

22:05

And so, you know, because she wants to be lowkey. Yeah. Exactly. She She's comfortable.

22:09

She knows what the buttons are. Yeah.

22:10

But that doesn't mean that she can't have a little more a little more power. I like that.

22:16

I mean, Zuck did this for Priscilla, right?

22:18

He made the He made the custom stretched turbo GP minivan. Yes. Which is sick.

22:23

But you could have gone further.

22:25

You could have done You could have gone, you know, Lamborghini Urus minivan.

22:29

Yeah, you could have gone cullinin, black badge, minivan, manory. Yeah, there you go.

22:34

I think I think it's really like the creativity of just, you know, mixing these different cultures together is key.

22:41

I think under uh underutilized option, something you can do last minute.

22:47

Take take your mom's phone, bring it to a jeweler, have them bedazzle have them bedazzle the back.

22:54

And what I actually like about this is because the iPhone is uneven on the back, right? It has a camera.

23:01

You can just put the diamonds around this side, so it will actually sit flat, right?

23:05

So it's actually, you know, it's practical. Yeah, it's practical.

23:10

It's it's it's more practical, but it's also fun.

23:11

She's taken a picture of, you know, the grandkids and Yeah. So definitely something.

23:18

I mean, we see a lot of a lot of these like tech billionaires with their super yachts, but you never hear about the mothers of tech billionaires getting their own super yachts. Totally.

23:26

And so, I would recommend calling a feed ship, start customizing something.

23:28

Now, I mean, two days you're not going to be able to do much, but maybe next year, maybe the year after you start, you want to be thinking three decades for Mother's Day minimum. Absolutely. Absolutely.

23:38

Um, you could also do a private IMAX theater at home for the cinnaphiles. Totally. They're into film. Totally.

23:45

Just dig a massive hole into the house.

23:48

build an underground bunker under uh check out the you know reach out to the boring. Yeah.

23:53

I mean the last one that's pretty easy is uh just paying for a full domestic staff for each.

23:59

You know why I like this? Yeah.

24:01

Why is because you can write a card and say you're going to do it, but you get a little bit of time to actually find the staff, right?

24:08

Like to find the individual people that are going to make up that staff.

24:11

And so it's one of those great last minute option. Yeah.

24:16

Uh that you know will be unforgettable. Yeah.

24:17

I really feel like you're if the household is running smoothly, the staff is firing on all cylinders.

24:25

Ideally you have kind of a uh uh an Alfred type a butler who really oversees the staff.

24:31

And I would love to see that type of you know house manager, estate manager using linear. Totally. Totally.

24:39

to to actually because linear is a purpose-built tool for planning and building products.

24:42

You want to be building custom software to run your smart home.

24:46

Don't don't the ideal Alfred was a PM and big tech and take that sort of like ethos into the home and really just run it like it's a product with millions of users, right?

24:58

When is the registration on the mensori call in due?

25:00

Let's make sure that the license plates are coming. Track it in linear. Boom. Exactly.

25:05

This is a system for uh modern software development.

25:10

Streamline issues, projects, and product roadmaps.

25:11

I mean, it's the perfect Mother's Day gift. Really, it really is. Shout out to Matthew.

25:17

I had a good call with him this morning.

25:18

Runs a lot of the marketing over at Linear.

25:19

Anyway, uh let's quickly take you through the run of show.

25:22

Let's pull that up to show you what's happening today.

25:25

Uh we're going through the Mother's Day gift guide, but we also have some massive news.

25:29

Ror was featured in the Wall Street Journal.

25:31

We're going to break it down. We'll do some timeline.

25:32

Then we got the Stanford Anans coming on and then a bunch of other guests. nons over at Stanford.

25:36

Yeah, they wrote a pretty inflammatory piece. Yeah.

25:40

Uh which I'm excited to have them break down for us.

25:42

But uh let's take you through the Wall Street Journal. Rora was featured. Yeah.

25:46

So for for those that are new, I started a company called Rurora uh with uh my co-founders Brian and Charlie.

25:56

Uh long time ago at this point.

25:59

uh spent a lot of time in R&D and launched last year and it's been uh just off to the races which has been great.

26:07

And uh anyways, Rora was in the Wall Street Journal uh yesterday and I got a little bit of the backstory here.

26:14

Somebody reached out apparently with a uh just like a random Gmail and was like, "Hey, I'm doing some product testing.

26:20

Can I get Aurora or whatever Brian ended up sending?"

26:23

Um and the article turned out really well.

26:25

Um so we can read through it.

26:28

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26:30

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For many people, the thought of water filters triggers the tiresome realization that theirs needs to be replaced.

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or worse that the taste of their filtered water isn't what they actively crave the way they once crave that chilled bottle of Fiji or Evian and probably still do despite the new news images of masses of plastic choking the oceans.

26:59

Uh I still have uh hopefully much um way less now, but I still, you know, as of last year had a bunch of friends that were just drinking tap water, which is actually completely insane in in our year uh 2025.

27:10

So anyways, going on uh and let's be honest, that plastic pitcher has never again looked quite so crystalline as it did straight out of the box, has it?

27:22

Um drinking water out of plastic, just don't do it.

27:26

Uh so good news, it doesn't have to be that way.

27:28

According to market research firm Fortune Business Insights, the global demand for water purifiers is exploding.

27:34

And recently, water filter makers have, pardon the pun, flooded the market with models ranging from sleek and even glamorous to sporty, high-tech, and industrial chic.

27:42

Rora, I'm looking at you.

27:44

The question then is how to choose a system that fits your drinking needs and aesthetics without sacrificing efficacy.

27:50

Uh, over the last several months, I tested everything from single bottle to multi-gallon filters.

27:54

Blah blah blah blah blah blah blah blah.

27:58

Let's just talk about Rora for the family. Uh, is Rora numeral yet?

28:00

Got to pay sales tax, right? Working on it. Got to get on sales tax.

28:07

Put that sales tax on autopilot. Oh, add integrating. There we go. There we go. Run it, Sean. Sales tax on autopilot.

28:15

Spend less than five minutes per month on sales tax compliance.

28:17

Uh anyway, uh congratulations to the whole Aurora team and you can go pick one up at roy. com. R.

28:25

Let's do a quick polyarket review.

28:25

Uh there's a few markets that I'm tracking.

28:30

There's a few markets that you're tracking.

28:31

Uh I'm interested in this Tesla Elon Musk CEO replacement thing.

28:36

Uh right now um there was there was this leaked uh Wall Street Journal article saying Tesla was thinking about replacing Elon Musk as CEO.

28:42

Uh Poly Market has a 13% chance.

28:44

I think this is the perfect thing to be tracking on Poly Market.

28:49

Uh and now then they also have who will replace Elon Musk as CEO.

28:51

Uh of course 86% says no CEO announced in 2025 but JB Strabble is sitting there at 2%.

29:00

I don't know if you know him.

29:00

He runs Redwood Materials.

29:01

one of the earliest employees at Tesla has been there for a long time.

29:05

Redwood Materials does battery recycling.

29:06

It's a huge company, but you could imagine them kind of putting together. So, I don't know. Wild.

29:12

There's a couple other folks in here that are that are interesting.

29:14

Gwen Shotwell is actually sitting here at 1%.

29:19

Dar Koshwashari at Uber Dark, Mary Bar, there's a bunch of other interesting people.

29:25

But that is a market that I'll be tracking over the through the end of the year as the you know the the peanut gallery.

29:31

uh yaps about Elon where he's going.

29:34

The last thing that's interesting, largest company at the end of May, Microsoft overtaking Apple. Moged. Moged. Moged. Yeah, Apple.

29:43

Microsoft has a lot of momentum right now and Apple has the opposite.

29:45

A lot of different forces uh coming into uh coming into play.

29:52

Obviously, the trade wars kind of botching AI. Yep. The big thing.

29:58

Um, yeah, I'm I'm I'm just very interested.

30:01

It's interesting that Google is not even in the conversation on this market anymore, right?

30:05

Obviously impacted over the last week from some of the comments that Apple made um in the in the um uh trial earlier.

30:14

Nvidia is still sitting at 4%.

30:16

Kind of that wild card because it it moves much it's much more volatile stock because you know they can just blow out one quarter if someone orders 10 million H100s or something.

30:28

But obviously uh a rough go with the with the international sanctions and the chip bands and whatnot.

30:32

Uh what else are you tracking?

30:34

Which company has the best AI model end of May?

30:35

Google sitting at 74% with so crazy you were talking about like they've trained such incredible models.

30:43

They dominate the eval transformer and invented the transformer absolutely insane team and yet really struggled to actually break through and have this crazy counterpositioning where the more they do with AI search the more it takes away from their search.

30:55

It's the classic innovator's dilemma, right? Yeah.

30:59

Everyone said AI should be a sustaining advantage and I think it could be in the sense that like they'll vend this into GCP and cloud platform and they will be a beneficiary of AI but uh they do have some serious serious product challenges.

31:11

Yeah, I was thinking they have uh uh there's that Warriio meme which is like I've won but at what cost?

31:18

And that's really isn't that from Avengers originally?

31:20

Uh yeah, but it was like popularized by this like image.

31:23

Uh it would be too hard to pull it up but um anyway XAI is actually second place which is interesting because you think OpenAI based on the performance of the consumer app would would be more in the conversation but OpenAI is only sitting at 5% for this is based on this is based on benchmarks. Yeah, of course.

31:40

Of course, benchmarks don't matter to chat GPT anymore.

31:44

Just user retention, I think. Yeah.

31:47

And stuff in ads and things maybe. Who knows? Put an add in it.

31:52

Anyway, speaking of ads, we should talk about talk to you about Vanta.

31:54

Automate compliance, manage risk, improve trust continuously.

31:58

Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation whether you're pursuing your first framework or managing a complex program.

32:09

Anyway, uh did you know that Vanta was one of the fastest growing vendors on ramp last month? I did not know that. That's great. They are. Congratulations. Cruising. Congrats to Vanta.

32:17

Well, we got to move back to Mother's Day because we forgot to take you through some of the really obvious houses that are on the market.

32:26

If you're looking for a house for your mother this Mother's Day, Mother's Day, uh there's an 8.

32:29

5 million California home with a backyard railroad.

32:35

And so a lot of train heads are going to be scrambling for a lot of moms have kids. The kids love trains.

32:41

Buy a house with a railroad in the back. Trains.

32:43

When David and Sherry purchased their Lockinata Flint Ridge home, not too far from me. Not far.

32:47

In 2008, they found the rusted remnants of train tracks where a previous owner had built a backyard railroad.

32:53

It turns out that Lochinata Flintidge, home to a number of Disney employees, was once a hub for garden railways.

32:59

Uh while the family weren't train enthusiasts, they were intrigued.

33:03

With the help of a one-time Disney imagineer, they spent about a year recreating the railroad, completing it in 2011.

33:09

Today, the roughly 530 foot circuit has a tunnel and a train station with a working crossing light.

33:15

The train itself is faux steam, battery powered locomotive, and caboose with two riding cars.

33:20

This is honestly, this is the ultimate backyard toy for the grandkid. It's such a flex.

33:26

Everyone has Oh, infinity pool. Oh, coin pond. Oh, Jim. Yeah.

33:32

Do you have a railroad in your backyard? No.

33:33

Uh, the quote's so good, David, the owner.

33:36

He's like, uh, there's something universal about trains.

33:39

They bring out the youth in everyone.

33:42

However, their railroad needed a new conductor.

33:43

They put in they put, uh, the 1.

33:45

55 acre property on the market for 8. 5 million.

33:48

They're se they're semi-retired and moving permanently to their second home in Park City.

33:51

Uh, he's a financial executive, grew up in Chicago.

33:53

Uh they married in 1982 and later settled in Flint Ridge, a semi-rural community about 13 miles from downtown Los Angeles where we are right now.

34:01

Home of LA's auto community.

34:04

If you ever want to be scared driving, just drive up.

34:06

Uh yeah, that's that's like I mean effectively home.

34:12

You have to drive through Linata to get to Angelus Crest Highway. Yeah. Yeah.

34:17

And uh if you ever want to feel like you're gonna get run over, go on AC and uh try going the speed limit.

34:23

People will fly by you at four times that. It's awesome.

34:29

Apparently Disney also had a railroad in his backyard.

34:31

Walt Walt Disney in Home in Home Hills in LA.

34:33

The original railroad uh was built in the 1960s.

34:38

According to the son uh who said his train loving father was friends with Johnston and Kimble.

34:43

Uh when the new family bought the house, the tracks had been dismantled and largely built over.

34:49

At first, the railroad wasn't a priority.

34:50

When David had played with toy trains growing up, he said he wouldn't describe himself as a train guy.

34:53

But a friend offered to introduce David to the late Bill Tyson, a one-time imagineer and garden railroad enthusiast who could help restore it.

35:02

Then they visited the South Coast Railroad Museum near Santa Barbara where they rode a garden railroad for $1.

35:08

At the end of it, we said, "Okay, we have to do this. It's just too much fun."

35:11

It was the most expensive dollar I ever spent. That's hilarious. It's great. That's great.

35:15

Um, we have another property. Another property.

35:21

This is another good option for Mother's Day if you're in the market to buy your mother a house.

35:25

She's trying to move south closer to the tropics.

35:30

Yeah, why not Fort Lauderdale? Better weather. Fort Lauderdale.

35:31

There's a new massive waterfront home. 15,000 square ft.

35:35

$39 million and it includes eight separate bar areas.

35:40

So for the entertainer, so a heavy people are saying alcohol is coming back.

35:46

You know, there's so much alpha with alcohol being out of style. Maybe go long alcohol. Yeah.

35:50

Uh and enjoy the eight bars.

35:52

But you could also serve mocktails and beverages for kids. You could do anything. Drinks. Yeah.

35:56

Uh for more than 20 years, Steve Seavore has been hosting lavish parties at his waterfront mansion in Fort Lauderdale, Florida.

36:04

Last year, he hosted a Barbie themed gala, decking out the house in hot pink and dressing up as Ken.

36:08

I always say if the house burned to the ground and I didn't have any insurance, I got my money's out worth out of it.

36:15

The 64year-old retired bachelor. That's hilarious.

36:18

So he's now listing the property known as Villa Diploma for $39 million.

36:23

He says he travels frequently, especially during the summer months, and wants a home that doesn't require as much attention.

36:27

So the house is Saver, who trained as a lawyer, made his fortune in merchant banking, and is a former CEO of the Pittsburgh based communications company, Comnet Ericson, retiring at age 39.

36:37

He assembled Villa De Palma over two decades, starting with the purchase of the main house for about 1. 8 million.

36:44

In 2002, property records show Florida is known for massive fluctuations in their housing market.

36:50

In the years since, he has spent millions adding more land to expand his footprint to roughly an acre.

36:56

One of those properties included a five- bedroomedroom house, which he raised around 2009.

37:00

This guy just turned making this party house into his full-time job. raised.

37:06

Raised means demolish, by the way.

37:09

Uh, and so he says to celebrate the demolition, he hosted an an animal house style toga party.

37:13

He spent two weeks decorating the house to look like a fraternity house.

37:17

Then the night before the demolition, he and his pals tore up the house with sledgehammers.

37:22

Oh, can you imagine that?

37:26

At the end of the night, we were throwing kegs through windows. He said, "What a, bro. This is so insane."

37:34

After tearing down the small house, Seavore uh has expanded the main house, completing the project around 2010.

37:38

The eight-bedroom Mediterranean Mediterranean style estate has about 15,000 square feet of living space and two large large outdoor pools.

37:45

It's well equipped for entertaining with a wine celler and eight separate bar areas, including one inspired by Hotel Ducapen Rock in the south of France. Hotel. Uh have you been? No. Oh.

37:58

Uh and Savore calls another bar on the rooftop Tequila Tower.

38:02

This guy is such a party animal.

38:05

A giant table in the space can hold plenty of people to dance on after tequila has been depleted. Savage.

38:11

There are two relaxation rooms.

38:13

His property is within walking distance of the beach in downtown Fort Lauderdale's main shopping strip.

38:16

Uh I would come down for long three-day weekends and ended up staying two weeks.

38:21

When I retired, I made my playground my home. What a legend. Anyway, great option.

38:25

I'm interested to see where this one lands. Yeah.

38:28

Anyway, um we have two minutes till our first guest is joining. Let's do some timeline. What else is in news?

38:37

Actually, let's do an ad. Eight.

38:39

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38:39

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38:49

How' you say I went hard last night. I did terribly. I got 85 96 96. Ah, it's rough.

38:59

93 minutes of deep sleep.

38:59

One last potential gift for mothers on Mother's Day, a billboard. Go to adquake. com.

39:07

They make out of home advertising easy and measurable.

39:09

Say goodbye to the headaches of out of home advertising and buy a massive out ofome campaign.

39:15

Huge opportunity to just buy thanking your mother for all the 10 billboards closest to your mother's home, wherever she lives and and make it so that she can't leave her house while without seeing how much you love her. Exactly.

39:28

So, we got a post here uh from uh we'll cover this and then we'll go into the guest from uh Bucko Capital Bloke.

39:36

He posted back on March 3rd.

39:36

New Mag 7, Ryan Mal, which is German's arm, German arms manufacturer, Salana, uh VCscoin, Palanteer, Salesforce for Okay, he's not pulling punches. He's DTOC.

39:53

I can't say anything on this show. bloke.

39:58

Uh, all new appliances and vehicles purchased before the tariffs gold.

40:03

And so he then quotes it and by saying Rhymel is up 54%, Salon is up 15%, Palanteer up 42%, hems up 26%, Dave up 60%, used vehicles up 10%, gold 14%, and the Max 7 is down 3%.

40:20

I wonder if this is actually just over the last two months maybe.

40:23

Yeah, I guess this is Wait, did the tariffs hit March 3rd? Something like that?

40:26

Um, anyway, congrats to him for calling it.

40:30

Clean up the language if you want to be on the show more.

40:32

Uh, uh, anyway, uh, they said to cheat on everything, so I decided to cheat their company.

40:38

I generated 84,000 believable resumes to spam their job applicants and send AI agents to waste their time.

40:45

I'm shorting the VCs to funding them.

40:46

You see this fight that's going on?

40:47

Can we, Michael, are we able to pull this up on the screen?

40:49

So, Rise is trolling Roy, former guest on the show.

40:54

So yeah, Roy has a company called Cluey.

40:56

He is helping people cheat on everything right now.

40:59

I think they're focused on sales calls and he's attracted a lot of controversy and Rise is messing with him a little bit by just using AI to spam uh Roy with an almost inconceivable number of applicants.

41:17

Somehow I don't think it's going to affect Roy.

41:18

I think he's going to be just fine. Royy's built different.

41:20

He's hiring a videographer in San Francisco with for between 500k 300 to 500k.

41:28

Absolutely massive numbers for a vlogger.

41:30

So if you want to vlog and make half a mill a year, it's crazy. Head over to Cle. Head over to Cle. Good luck to him.

41:37

Anyway, uh we should have our first guest joining uh right now or set of guests actually.

41:42

I think we'll have two voices on the show. They're both anonymous.

41:45

Um, they've been involved in the latest report from the Stanford Review about spying on campus.

41:51

You're not going to see their face.

41:54

You're not going to hear their names.

41:55

Um, but you are going to hear us ask some questions about the report and try to get to the bottom of what's going on on campus and dig a little bit deeper into the viral uh, Stanford Review article that went up most recently just a few days ago.

42:12

Anyway, uh, welcome to the show.

42:12

Can we hear Can you hear us? Can we hear you? Yes. Thanks for having us. Thanks for having us. Great to be here. Great to have you. Thanks for joining.

42:19

Uh can you start by uh I I I don't know how you want to characterize your involvement in the story or I know you want to remain anonymous but uh can you just give us the high level on how the report came together and what the key findings were? Yeah, let's do it.

42:32

So the Stanford Review spent over one year investigating Chinese academic espionage at Stanford.

42:39

What we did is we talked to Stanford students.

42:41

We talked to Stanford faculty.

42:43

We talked to Stanford China experts and we talked to Congress people about this.

42:47

And at the end of our investigation, we compiled a bunch of anonymous reports for people working in AI labs, student researchers and faculty.

42:53

And the overwhelming conclusion was that there is essentially widespread intelligence gathering at the behest of the CCP at Stanford.

43:03

What does that actually look like?

43:03

I mean, no offense to Stanford folks, but like it's undergrad.

43:08

It's not exactly like a proprietary AI lab or like nuclear weapons program.

43:12

Like what are they trying to steal? Gotcha.

43:14

So I'm going to pass you over to my colleague and she's going to talk a little bit about what her experience with espionage look like. Sure. Yeah.

43:24

So, a lot of um what espionage looks like, you know, on an undergrad level is um Chinese students and researchers trying to basically collect any and all information about Stanford that they possibly can.

43:41

So, you know, Stanford is an open research institution.

43:44

So, a lot of the things that are being reported back to the CCP aren't exactly secrets.

43:53

And you know obviously um you know it's not as bad as stealing defense secrets. Yeah.

43:58

But the issue is that it's happening at such a massive scale because they the CCP essentially has all of their students reporting back to the CCP with any and all information that they have. Right?

44:14

So once one one Chinese international student characterized it like this.

44:18

Many Chinese students have handlers.

44:21

The CCP wants to know everything that's going on at Stanford.

44:24

This is a very normal thing.

44:26

They just relay the information they have.

44:27

Now, with regards to sensitive research, we're not just talking about the undergraduate level.

44:30

We're talking about the graduate level.

44:31

And these are some of the best AI labs and best robotics labs in the United States of America and the world at large.

44:36

What they want is not just published papers because you can imagine you can't replicate publish papers just via post papers.

44:44

They want the meth methodology sent back.

44:45

They send back communications channels.

44:47

They send back people that are involved with the research and other Chinese internationals that are working on it so that they can replicate this research at China.

44:56

How would you rate the university's reaction uh to uh the piece?

44:58

There's obviously been a bunch of conversation and chatter online, but I imagine you guys have been having conversations as well. Yeah.

45:09

So we actually just published today um Larry Diamond, Matthew Turpin um wanted to publish sort of a response article um so that was also published in the Stanford Review and uh they they came out in support uh of our article um which you know was good and then Stanford also came out with a statement saying you know they take this very seriously and it is important to them.

45:36

um you know we are sort of under the impression that there's not much that's going to be done about it because on some level everyone knows that this is happening and nothing is really being done about it um on a university level they really want to

45:53

kind of stay out of things right I mean Matt Pton the one of the me one of the guys that commented is a the former US deputy national security adviser Matthew Turpin worked as the China senior advisor advisor to the National Security Council. These guys have seen this

46:07

These guys have seen this happening for years on years.

46:09

The issue is that Stanford is an open knowledge research institution.

46:13

That means there's no secrets and that means it's very hard to prosecute people for sending back sensitive research information to China.

46:21

So really what they get people on is just their visa.

46:22

You know, they're working for the PLA, but they haven't told the authorities about it.

46:25

So what needs to happen is we need to have people that are sending back this information registered to foreign agents and set up new laws and new legal guidelines that prevent people from being able to send public information at research institutions back to foreign governments. Right. Yeah.

46:40

And and the other thing too is you know we we also have need to recognize that the students are actually victims in this situation.

46:50

I mean the CCP is exploiting them.

46:50

They are essentially crowdsourcing uh you know espionage at massive scale at you know universities all around the country and they are there's this thing called transnational repression and basically you know if they don't want to report back to the CCP if they have any reluctances or if they don't comply their families can be threatened um their livelihoods their scholarships so you know It's really a sad situation for them as well. Yeah. I mean, totally.

47:26

Matthew Turpin, who worked with the NSA, even said at Stanford, there's been Chinese international students who had their parents brought to the police station because they've refused to turn over Stanford's research information.

47:36

So, if you don't comply, you will face penalties from the Chinese Communist Party, and your parents and your family back at home in China may be put in harm's way.

47:44

And we've seen that happen. Makes sense. Yeah.

47:46

It's incredibly challenging.

47:48

you know, you could be here, your parents could be here, and yet if you have even one family member at home, a grandparent, a great-grandparent, you know, there there's some amount of risk.

47:56

Uh what I don't know if you saw this uh post by Kimai Cutler, who I think is at uh initialized, right? Partner at Initialized.

48:06

Initialized. uh she said this uh this report has pretty much all anonymous sourcing unfortunately and so it does not seem like a good platform upon to upon which to argue that many most or all Chinese nationals or firstgen Chinese students are spies um how do you interpret that kind of criticism of the

48:25

piece right let's go through this point by point so first of all when you look at how the CCP operates they have a 29 2017 national security law under article Seven, all Chinese citizens must comply with national intelligence and provide information and keep secrets when they're asked. So straight out of the

48:42

So straight out of the gates, all citizens must comply at the face of penalty working with security services.

48:48

So anybody who's asked at Stanford to do do something for the CCP must do it or they will face legal penalties in China.

48:54

The second point, we have talked to Chinese international students.

48:59

They have families at home for the same reason that there's transnational oppression.

49:01

They cannot go on the record and say these things with their name out there because their families will be taken away.

49:06

Their families will be brought to police stations.

49:08

It's already happened with research.

49:09

With regards to Chinese China experts, we've had Matthew Turpin on the NSA.

49:14

We've had Matt Penj and the SNA NSA and then we've had Larry Diamond all come out in support of our article.

49:21

They've said this is a decade decadesl long relentless um interference within Stanford to misappropriate our sensitive research technologies.

49:28

All the experts have agreed with us.

49:30

But at Stanford, we've seen a culture of silence and fear talking about this issue because it's characterized as racism.

49:37

So 166 Stanford professors wrote an article to the Department of Justice saying that we need to shut down the Chinese China Initiative, which was an initiative that sought out Chinese spies because it's racist.

49:50

But what that fails to recognize is if you don't shut this down, you have Chinese students who are the real victims here.

49:56

So, it's China who is profiling their citizens and victimizing them. It's not the USA.

50:04

This is not racial profiling because we're just looking towards Chinese nationals who are being repressed by their own government. That makes sense.

50:13

Um, putting aside the kind of like the geopolitical issue of of China specifically, how how are you thinking about um like not suffering from a lack of brain drain?

50:24

from a lack of brain drain? I feel like you know the majority of tech company like the tech industry might collapse if we didn't have you know a fluid transition from like you know there's Indian CEOs all over Silicon Valley there are plenty of allied countries

50:39

that send talented individuals uh you know even going back through history we've had operation paperclipip we've we we have we have historically successfully uh integrated members from even rival nations and that feels like something that would a risk of losing um if we go too far here. Is that is that a real risk or how

51:00

Is that is that a real risk or how do you think about um confronting that?

51:05

Yeah, I mean I think you know a lot of people have sort of interpreted our article you know and have said like ban all Chinese nationals and from attending elite universities or you know coming to school um in the US.

51:20

Um, and we are we do not want that.

51:25

I mean, you know, I've I grew up going to a Chinese immersion school.

51:30

I I think it's a beautiful culture.

51:32

The people are amazing and they are huge assets to Silicon Valley and Stanford.

51:37

Yeah, we just want to see some change in the way that um these students are able to live and study abroad because right now they it's as if they have no rights because they can't they have such a hard time coming forward.

51:55

So, we need to, you know, figure out a policy system that can give these students um a greater awareness of the freedoms that they have and we need to be able to defend them, right?

52:06

tried a couple of solutions to keep Chinese students in the United States and improve security.

52:11

I mean, we should remove WeChat from the United States.

52:13

That's a main method of communication with the Communist Party.

52:16

It's an unsecure platform that's monitored.

52:17

Secondly, we should consider AI visas for bringing Chinese people's families out of China and to the United States because that removes a huge risk of transnational oppression.

52:26

So, not just getting the best Chinese researchers, but also getting their families in coming to the United States.

52:32

And that's what we saw with the Soviet Union.

52:33

I mean with the Soviet Union whole people's families came with them.

52:37

And then the third thing that you have to think about is China requires a lot of these people to come back.

52:41

The USA should be doing the same thing.

52:42

If you want to come study and work on the most sensitive research technologies that are key to winning um the AI race, you should be forced to stay in the United States for 10 years or more because what we really don't want to happen is for these people to replicate these ecosystems in China. Yeah. Yeah.

53:01

what you know who who have you guys seen the most support from uh on campus and you can keep it high level uh to protect uh the identities if if people don't necessarily want to be public yet but I imagine there's quite a few people on campus that have you know massive concerns around you know the national security issues that you guys are presenting and and are you know hugely in favor of reform.

53:26

Yeah, I think um we definitely, you know, the vast majority of the response has been off-campus.

53:32

I feel like there's been a surprisingly uh little amount of response on campus, but I think uh the best reactions have definitely been from the Hoover Institution.

53:42

Um they have been looking into this kind of thing for a long time.

53:47

Um and are working on, you know, policies to to deal with these things.

53:53

Um, but you know, a lot of people that we interviewed still wanted to remain anonymous.

53:58

So, it's it's really tricky because if you want to work with China on some level, you can't be, you know, consistently speaking out against China.

54:07

Um, so I think that's sort of the difficult balance.

54:11

Is there is there an argument here for just kind of going completely open source with our research?

54:18

I mean, famously like the Manhattan project had a ton of spies on it.

54:22

we still kind of won the Cold War.

54:24

Um, it it feels like a sloppy and potentially crazy uh solution, but is there a world where the only thing that matters in a geopolitical technology race is just pace of play and the actual secrets don't matter that much? Yeah.

54:40

So, you definitely want to play more offense than defense when you're talking about the technology race.

54:44

We're not going to win this by stifling competition in America.

54:47

we were going to win this be through the way we've always won it by having an open free market system that encourages competition.

54:52

You don't want to ruin the risk of China using vast government intervention and vast government subsidies to essentially take the best research out of America and distribute it large at China by using vast state apparatus.

55:06

We've seen this happen with electric vehicles.

55:09

We've seen this happen with most American technologies.

55:11

China misappropriates it.

55:12

I mean 33% of Chinese GDP is directed towards subsidies alone.

55:16

The whole Chinese economy is geared towards leveling up and distributing advanced technologies.

55:24

The issue with the United States and it's a great thing is that the government will not subsidize these technologies to the same extent and encourage their fusion to the same extent.

55:31

So the real issue is that China may get these technologies and then use these subsidies to create a mass market in China and gain an advantage.

55:37

So to a certain extent there has to be sensible research policy that protects our core interests.

55:45

After seeing all this, do you think that American tech companies are gen generally too naive uh when it comes to espionage?

55:52

I think the the major players in national security, defense tech, things like that have have historically been um very aware of the espionage risk.

56:02

But uh do you believe that many sort of more American tech companies specifically in the Bay should be um paying a lot more attention to security? 100%.

56:14

Um the foreign interference task force was disbanded.

56:17

The China Initiative was disbanded.

56:18

Two key efforts to stop Chinese research and corporate espionage.

56:23

We have very few lines of defenses right now.

56:25

Companies need to be vigilant because we've seen this happen again and again.

56:29

I would say that they know it's happening and they've known for a very long time.

56:33

Even at Stanford, the people we talk to will all say, "Yeah, this is not new to me. Duh.

56:38

We knew about this for so long.

56:38

We this is obvious and they're surprised that we've even published this article, some people, because it's so apparent."

56:47

Um, so what really needs to happen is we need the government to come through with sensible research policies that also encourage innovation.

56:54

And Bay Area companies, particularly startups working in this area, need to be more vigilant about this. That makes sense. Great.

57:01

Well, thank you guys for coming on and sharing.

57:02

It's it's uh extremely insightful and bit uh bit scary. It's scary.

57:08

I'm glad you're on the case.

57:08

It seems like you're researching this. I'm optimistic.

57:10

Uh we need people like you guys to, you know, keep banging the drum and and uh if we're ever going to get changed.

57:16

So, come back on uh when you guys have more news.

57:18

Good luck with the follow-up reporting. Great. Thank you so much. We'll talk to you soon. Cheers. Thanks so much. Bye.

57:23

Next up, we have Morgan Howell coming in.

57:26

Morgan Howell coming in. a little bit of well I mean in geopolitic geopolitics are in his wheelhouse he was falsely accused of claiming that the US would fall remember oh yeah so we got to talk to him about that uh addresses allegations of being anti-America

57:47

so funny but we're excited to have Morgan on the show he he was great last time and I'm sure we'll have a fantastic I'm excited to ask him about his new book Oh yeah Art of Spending Money Art of Spending Money Well welcome to the Morgan, how you doing? Hey guys, good to

57:57

Hey guys, good to see you. Great to have you on. Welcome back. It's been too long. Yeah.

58:01

Uh, I mean, I want to start with the with the accusations that you claimed that America was in decline.

58:07

There I saw a viral post.

58:09

I didn't look at any of the replies, so I don't really know what uh uh what was the story there.

58:15

It's it's weird because everyone knows that things get exaggerated on the internet, if not just made up on the internet.

58:21

When it happens to you, it's it's pretty strange.

58:24

So, I went on the Diary of a CEO podcast.

58:26

It aired, I think, a week or two ago, and we talked about tariffs, and I I mentioned something about the the fall in manufacturing uh jobs over the last 80 years, something to that extent.

58:38

And I I had so many different asterisks of like, oh, well, part is technology, some of it is offshore, etc. , etc.

58:44

And that got spun into Morgan Hel is predicting the collapse of America.

58:48

And it's one of those just like, what?

58:49

How does how does that even happen?

58:51

And so, I I called the guy out. he deleted the post.

58:53

But it's it's it's amazing to see when it happens to you personally because then you start questioning every like how many times have I read a headline that said so and so predicts the collapse of America but and but they didn't or or or some some uh you know some version of that.

59:07

So I'm not predicting the collapse of America. I'm quite optimistic.

59:10

Such a challenge right now.

59:13

There's so much long form content that's created and we're also in this era of of like clipping, right?

59:18

The internet likes long form content, but it really likes, you know, these short segments and sort of pulling out interesting moments and and we've had some issues with that uh already where, you know, we we had the CE we had the CEO of Perplexity on and John asked him a hypothetical question about ads and Techrunch ran with this article saying that Perplexity planned to, you know, just jam a ads in their browser or something like that.

59:46

And anyways, context is is uh very important.

59:51

Um were you at uh were you at at the Birkshshire uh annual meeting?

59:54

Uh no, this is the first this is the first one in four years I didn't go to.

59:58

Awesome one to miss, right? Yeah. Wow. Yeah.

59:59

Um how was how was what what made you decide not to go this year? Just just family stuff. Busy.

1:00:06

I've got I've got lots of other stuff going on.

1:00:09

What's interesting is that I think I've been seven times and um the last four times I went, I didn't even go inside to the meeting.

1:00:16

It's turned out to just be a place where there's so many like-minded people who go there.

1:00:20

So several years ago, me and Brent Behore and Patrick Oanessy and Shane Parish all rented a house and hung out and it was an awesome weekend even though we didn't even go to the meeting.

1:00:29

So it's just been a place where there's so many collective people who have the same same thoughts, same priorities to go and meet. That's great.

1:00:37

Um well, I wanted to I mean I reached out to have you on earlier this week because uh obviously the news of um of uh Buffett, you know, stepping back at the end of the year to kind of get your reaction to it.

1:00:48

get your reaction to it. uh I it's one of those things it's interesting dynamic where uh it feels like the most important sort of knowledge and wisdom in the world is sometimes becomes so widespread that people uh sometimes people don't even pay enough attention

1:01:08

to it even even 0ero to1 is one of those things it has like it's it's it's one of the most popular uh business books in history yet um people kind underestimate the value of it because it's just become um you know the ideas and it have become shared so broadly. So I wanted to have

1:01:25

So I wanted to have you on you know specifically to talk about kind of the ideas that that you've um you know uh most gravitated to from uh Buffett yourself.

1:01:35

Some of the ones I'm sure are are um very widely you know understood and talked about and then I'm sure others that are a little bit more kind of under the radar.

1:01:44

There's just so there's so many good bits.

1:01:46

So yeah, I mean the the first is that to put his his retirement into context, Buffett made his first investment before Pearl Harbor and he started professionally managing money as a professional hedge fund manager when Harry Truman was president.

1:02:00

So just to like the the context of how long he's been doing this is absurd.

1:02:03

And then so there's two parts of that.

1:02:05

One is like yes, let the guy retire. He's 94.

1:02:07

He's been going non-stop since he was 11. Like come on.

1:02:12

And then the other side of that is that's why he's successful.

1:02:13

Yes, his annual returns are good.

1:02:16

They were very good back in the 50s, 60s, and 70s.

1:02:18

He hasn't really outperformed in any meaningful sense in a quarter century.

1:02:24

Not a criticism because he has almost a trillion dollars in assets now.

1:02:26

It's almost imposs it's almost impossible to outperform when you're that big.

1:02:29

But the point that is so easy to overlook is that yes, he's been a good investor, but he's been a good investor for 80 years, and that is literally 99% of why he's been successful.

1:02:40

So, I made this point in my book, The Psychology of Money, that if Buffett retired at age 60, you would have never heard of the guy.

1:02:45

He never would have been a household name.

1:02:48

He would have retired with like a hundred million bucks. Awesome.

1:02:50

He he buys a yacht and a house in Miami and lives happily ever after.

1:02:53

But no one would have ever heard of him.

1:02:55

The whole reason he's successful is because he's been going for so long.

1:02:58

And I forget who mentioned this.

1:03:01

I forget where I read this, that Apple is Steve Jobs with a thousand lives. That's what Apple is.

1:03:06

like Steve Jobs built the company and then and then died, but Apple can live on because he built what it is.

1:03:10

And I think that's what Bergkshire's always been.

1:03:13

And Buffett has talked about this that he wanted to build a company that would way outlast him.

1:03:16

And the irony is like he he's still last he still ran the company for half a century or more.

1:03:20

And so he lasted a long time, but it's going to keep going so far after he's gone, which is rare and unique.

1:03:27

And I also think of all the lessons that people like us try to learn from him, the the wrong lessons that you can learn from Buffett is like is is how to pick stocks.

1:03:38

I think that's probably the wrong lesson to learn because a lot of the what he did in his heyday in the 60s and 70s just would not work today.

1:03:44

It's a very different world, different markets, faster information.

1:03:48

It's just not transferable.

1:03:50

But the lesson of like he's successful because he stuck around for so long even when he was so preposterously financially independent by you know by hundreds of orders of magnitude.

1:04:01

U he he kept going and like that's something that ordinary people can stick around of like stick around long enough that you're going to let compounding actually work in your favor.

1:04:11

Uh I I think that's probably the most pertinent takeaway for people.

1:04:13

pertinent takeaway for people. There's also so many entrepreneurs too who when they talk about and I understand this it's not a criticism because being a founder of a startup is so ridiculously hard and stressful but for so many of them the goal is we're going to build

1:04:26

this company we're going to scale this company and we're going to sell it and I I I get that I don't look down upon that but the the huge massive results are for are people who are like I'm going to do this and I'm going to do it as long as I as humanly possible that I can. Yeah, I

1:04:38

Yeah, I I have a bunch of follow-ups there.

1:04:40

Uh, first up, uh, Greg Ael. Is he underrated?

1:04:44

It's interesting because he's coming in in his 60s.

1:04:46

He looks like, you know, guy who could be retiring next year, but the culture of of Buffett, you have to imagine that he's thinking, "Yeah, I got at least three more decades in here.

1:04:59

I'm just getting started."

1:05:00

So, what's your take on Greg Ael?

1:05:02

Uh I don't know if you've actually dug into his career at all, but I'd love to know kind of how you think the culture uh at that Buffett created kind of lives on.

1:05:12

Yeah, he's 60 so he can run the company for 30 years and then run for president after that. That's how this works. That's a standard path.

1:05:18

I think um most people outside of Bergkshire don't know that much about Greg Ael.

1:05:23

He hasn't he he's not like Buffett has very intentionally, I think, been on kind of a media tour for the last 25 years.

1:05:29

Goes on CNBC all the time.

1:05:31

of course the annual meetings and the letters like people know a lot about him.

1:05:34

Greg Ael people don't know that much about him other than that he's been at Berkshire for 25 years and Buffett picked him as the obvious successor.

1:05:42

What is known is that he's not a stock picker and there's going to be people who who get into a lot of trouble over the next 5 or 10 years who are like looking for Greg Ael stock picks and they're not going to find them anywhere. He's an operator.

1:05:53

He's a damn good operator.

1:05:54

Um, and that's important because Bergkshire 30 or 40 years ago, its market cap was like half stocks.

1:06:00

It like that's like its portfolio was just public stocks that own Coca-Cola and Proctor and Gamble and stuff.

1:06:06

Today, it's like less than 20%.

1:06:07

So, the majority of Bergkshire are wholly owned operating businesses and that's Greg's bread and butter.

1:06:13

It's just like operating those.

1:06:14

I think it's it's probably a very similar transition from Steve Jobs to Tim Cook.

1:06:18

Steve Jobs was this like genius magician and Tim Cook was uh just a a stone cold operator and that's I think that's what we're going through with Bergkshire.

1:06:28

You're going from like the the wizard stock picker magician to the stone cold operator and worked out great for Apple.

1:06:34

It's just you have to keep your expectations in check both because Bergkshire's size and because of what Greg Greg Abel's strengths are.

1:06:41

It it's not going to be and Bergkshire hasn't been this in 20 or 30 years.

1:06:46

the place that's going to have like massive outperformance year after year. Yeah.

1:06:51

I wonder about how much he needs to be a stock picker because I totally understand the operator lens, but when you're looking at that $300 billion cash pile, it's like what are you going to buy a big oil company or something like like you can do so much with that.

1:07:06

So, I'm sure that will be will be a challenge.

1:07:08

Uh I wanted to talk about the legacy of Warren Buffett a little bit more.

1:07:11

There was a retrospective in the Wall Street Journal that I kind of took issue with, but I wanted to get your take.

1:07:17

Uh the author is talking about uh Buffett's remarkable recall.

1:07:23

He estimates that Buffett read more than a 100,000 financial statements.

1:07:28

He famously reads books and seven newspapers and just reads constantly.

1:07:33

Uh and the right and the author says his unparalleled exposure to financial information combined with his prodigious memory made Buffett into a human form of artificial intelligence.

1:07:42

He could answer almost any query out of his own internal database that has given him an unparalleled ability to identify the kernel of significance in any new bit of information and a durable advantage over other investors.

1:07:52

Now, and this is the controversial part in my mind, says now that AI is universally available, a person with Buffett's massive command of data won't even have an advantage in the future.

1:08:03

And I just don't know if that's true, but I want to know, do you think AI changes the landscape in a way that the Buffett strategy just doesn't work anymore?

1:08:12

strategy just doesn't work anymore? I think part of the the the problem with Buffett in the last 20 or 30 years is that his folksy grandpa demeanor hid the fact that he is off thecharts intelligent and most of the time when you think of like an Einstein kind of genius like they have a certain look and

1:08:29

they talk a certain way that and Buffett was not that's what made him so popular is you felt like you could relate to him but anyone who spent a lot of time with him when he's not talking for the camera or writing in his folksy way will tell you he is he is in in a different universe of intelligence particularly for money. And the reason he's

1:08:44

And the reason he's successful is that since he's been 11 years old, he has spent 24 hours a day, 7 days a week thinking about stocks and nothing else.

1:08:52

And so it's less about the data that he knows, even though people will tell you those stories that uh he can recall specific figures from a balance sheet from an annual report he read 20 years ago.

1:09:03

So there is like a just an insane memory recall, but it's the pattern matching recognition in there.

1:09:10

And then this other element that's way more important, which is his reputation.

1:09:13

That's something that AI can't do.

1:09:15

And so a lot of Buffett's biggest investments, certainly the most important ones, came from the fact that he was had such a good reputation that he could walk into a boardroom and just say, "Hey, I'd like to buy 10% of your company."

1:09:27

And they were like, "Yes, name us your terms.

1:09:28

Like, we'd love to partner with Warren Buffett."

1:09:30

And so that's less about intelligence or data and more about the reputation that he had.

1:09:35

And I think if you I think if Buffett were a jerk or a raider uh and had had a reputation of stripping companies, he would not have been 5% as successful as he was.

1:09:46

All the big deals came from people wanting to partner with him.

1:09:50

Particular like think about 2008, the financial crisis.

1:09:51

Every bank called him up and they were like, "Warren, name your terms."

1:09:56

Like just just tell your terms.

1:09:58

And he did it with Goldman Sachs and GE and Bank of America where he got these crazy deals that nobody else could have because of his reputation.

1:10:05

And so I I wasn't just that it wasn't just that he had the liquidity.

1:10:10

You're saying it was just like Yeah.

1:10:12

There's there's there's a lot of liquidity in the world.

1:10:15

There's a lot of people who can write who can write a big check, not who can do it with that kind of that kind of reputation.

1:10:20

So I think there there is a little bit of truth to that idea that the skill that he had of reading annual reports was so much more valuable in the 1970s than it is today because everyone's reading the same reports.

1:10:32

There's bots that can scam them just instantly.

1:10:35

But there's like that's not to say that anyone can be a Warren Buffett now.

1:10:39

I think that's certainly a stretch.

1:10:40

What do you think of the uh this narrative that the past decade of Bergkshire is really just the story of Apple and their and this again like kind of a stock pick there?

1:10:50

Uh they're at such a huge scale.

1:10:52

Um do you think there's like a cultural shift towards technology?

1:10:56

Of course when Apple was already the most it was the most valuable company in the world and it still outperformed the market and drove fantastic returns.

1:11:03

Um it seemed like a big shift at the time.

1:11:06

Uh at the same time it p it p p p p p p p p p p p p p p p p p p p panned out very well.

1:11:09

Um do you think that that represents like a significant change in the culture?

1:11:15

I think what's interesting is that for having a reputation for decades of I don't do tech. I don't do tech. I'm a grandpa dinosaur. I I don't do tech.

1:11:23

Literally in dollar terms, Warren Buffett is the most successful tech investor ever.

1:11:26

He made he made a hundred billion dollar profit on on Apple.

1:11:29

No one's even come close to that.

1:11:31

And so is that like a change or an evolution?

1:11:34

It is an evolution, but it's easy it's easy to assume and it's wrong to assume that Buffett's been the same investor for 80 years.

1:11:42

I think part of why he's been so successful is that every five or 10 years he completely updated his operating system to to have a different style, a different influence, and to to really adapt what was going on.

1:11:52

And I think that there there's only a handful of investors who are like that, who have made money in different eras.

1:11:58

Uh there's a lot of investors who were very well suited for one era.

1:12:02

they could make a lot of money for 10 years, but they couldn't keep it going because nine times out of 10, it's because they didn't update their their thinking.

1:12:09

They were stuck on this world that didn't exist anymore.

1:12:13

And so there's a very long history of Buffett doing that and every 10 years doing things that he would not have 10 years before that. Yeah.

1:12:21

Um, are you familiar with that uh famous napkin diagram that Walt Disney drew showing how the parks relate to the film production, relates to the merchandise.

1:12:32

Have you seen that chart before?

1:12:34

No, but it sounds awesome.

1:12:34

Okay, so Walt Disney um drew this big chart mapping the entire uh ecosystem of Disney and how everything uh works together and it's cited as this example of like oh like you know build this business where everything feeds into the other piece and uh you know what is your Disney map?

1:12:54

Uh but I was digging into it and I realized that Disney made that chart 10 years before he died.

1:13:01

like he had already been building just in film for a decade and then 20 years to build the parks.

1:13:10

The Walt Disney born in 1903 I believe the park didn't open until 55 and he died like shortly after.

1:13:16

And so it was more like a reflection on what he had built as opposed to it was a map that was that was drawn after he had explored the territory.

1:13:27

And so I'm I'm wondering your take on this idea that like um the idea of focus and the idea of empire building being something that can be charted beforehand versus has to be kind of naturally discovered and then can be potentially mapped after the fact.

1:13:45

I think there there's several historical examples of business of like very successful business uh people who had were product geniuses and and terrible at b at business.

1:13:56

Walt Disney was one of them.

1:13:58

He was the product genius of of of all time.

1:14:00

He was a terrible businessman.

1:14:03

Henry Ford was another Henry Ford is the most successful mechanical engineer in history and he was at best a mediocre businessman.

1:14:10

There's so many of of those like that.

1:14:12

I even I would even say Steve Jobs might fall into that category of technological genius, design genius, very at at best mediocre business person, which is why you needed a someone like Tim Cook.

1:14:22

And I think there's there's been quite a bit of that.

1:14:25

But when the product is so good, you can take it in so many different directions.

1:14:29

directions. Disney is an interesting example of of so many of the of the of the film IP that they had that was sitting on the shelf for decades and when it was made it was like oh people will watch this in the movie theater that was the only medium that they could watch it on and then the video cassette

1:14:45

tape came along and they're like oh we could sell all of this and the DVD came along they're like oh there's another avenue to sell it and then streaming came along and it was like sky's is the limit so it was like the I the IP was so good that you didn't need a genius me businessman to uh to to come up with a distribution strategy. Like it was so

1:15:01

Like it was so good that it just kind of ran itself. Ford was was similar.

1:15:05

Like the cars were so superior to anything else that even if Henry Ford was making blunder after blunder, the the company kept going just fine.

1:15:12

And so I think I think a lot of times when you have like a very longunning business, it's less because they made phenomenal business decisions and more just because the product that they made was so phenomenal that they could keep it going.

1:15:24

If an idiot ran Apple, it would still sell a zillion iPhones every single year.

1:15:28

And there, but there's only a handful of companies that are like that.

1:15:31

I want to talk about the book out October 7th.

1:15:33

Uh do you have a log line or like what what is the oneline pitch and then uh how can you unpack the different The title is the title is a pitch.

1:15:42

I guess the title is the pitch, but uh can you can you break it down in a little bit more detail? A little bit detail.

1:15:48

The book is not called the science of spending money because I don't think that exists.

1:15:51

There's no way of saying here's how you should do it that's going to work for me and work for you even if we're similar people.

1:15:57

It's just like spending is very individualistic.

1:15:58

So I call it the art of spending money because art is different from person.

1:16:04

It's it's it's subjective.

1:16:06

It's often contradictory.

1:16:06

And so rather than telling you what to do, I don't want to lecture anyone, but um the book is a look at at envy and social aspiration and keeping up with the Joneses and uh becoming fulfilled and getting attention and watching other people.

1:16:22

It's kind of like the psychology of spending money, which I I which has always been like so much of what I write is just trying to figure out my own life. Yeah.

1:16:29

So, I looked at instances where I was clearly, it's hard to admit, but like when I was clearly envious and when I was clearly trying to get people's attention and just when you dig a couple layers below that, like why attention am I trying to get?

1:16:41

Is that person even paying attention to me? Do they even care?

1:16:44

Was giving that attention like ticking away from other parts of my life?

1:16:47

There's so many different layers to dig through.

1:16:48

And I think those things are universal.

1:16:50

Even if what you're spending money on is not a science, the psychology of it tends to be pretty universal.

1:16:57

So, it's just a a look at that.

1:16:59

That was more than one sentence.

1:17:00

I have I have to hone that a little bit sharper. But that's it. That's great.

1:17:03

I mean, is there a key anecdote or story or even just like uh case study that you think is the most uh tractable for people to grab on to?

1:17:11

I I think about uh you know, all the emotion that goes into the identity tied to the car that you drive or the house that you buy.

1:17:20

But uh what what what what case studies are you pulling from to kind of ground the lessons in something that anyone can understand who's reading the book?

1:17:28

I don't know if this is the best case study, but it was one that I I thought was so interesting.

1:17:31

I read the biography of Harvey Firestone from Firestone Tire.

1:17:34

He was the tire magnet like 120 years ago whenever he lived.

1:17:38

And he has this part in his biography where he says every single successful person that he knows, including himself, when they became rich, they bought a giant mansion.

1:17:47

And every single one of them to a tea hated it.

1:17:49

It was a pain in the ass to run.

1:17:51

It was it was you when when when when your house is 17 bedrooms, it's just more roof to leak, more radiators to break.

1:17:57

And and he was like, "Everyone hates it, but all of them do it.

1:18:01

Every single one of them does it.

1:18:02

And even when they hate the house, they never sell it and get a small house."

1:18:06

And he had this line that I loved.

1:18:08

And he said, "There is no going back except as a broken man."

1:18:10

So like once you inflate your lifestyle, you cannot deflate.

1:18:14

Even if you hate the inflated lifestyle, you cannot go backwards because it becomes so synonymous with your identity.

1:18:20

It's like the size of your house is who you are.

1:18:22

I thought that was pretty interesting.

1:18:24

And he was like the equivalent of a billionaire living in a mansion.

1:18:27

It's not a very relatable example.

1:18:28

But I think there's so much of that that if we spend money on something and it doesn't make us happy, it's very hard to rewind.

1:18:36

So be like be really careful when you're inflating your lifestyle because you it's very easy to go forward.

1:18:40

It's extremely hard to go back. Yeah.

1:18:41

How much time have you spent uh or or are you putting any attention in the book towards the way that um San Francisco and the tech industry spend money?

1:18:49

I' I've always been, you know, fascinated uh by it given how much uh just this dichotomy between extreme wealth and this uh in many ways desire to one glass box please. Yeah. Yeah.

1:19:04

Or this desire to um you know be uh and it's it's good in many ways, right?

1:19:11

If tech if if imagine the the negative attention that the technology industry would have gotten if every uh you know series C founder in San Francisco was like driving a Lamborghini, right?

1:19:22

It would have just it would have been it would be fun, but it would have been um Well, I think I think I think there's part of that that they can't spend a lot of money because it's not liquid.

1:19:29

Like they're rich on paper, but there's not a lot of liquid wealth in San Francisco.

1:19:32

I mean, there is, but it's all it's all relative.

1:19:34

But I but even that is a sense of social signaling.

1:19:38

Like if people know that you're rich and you are going out of your way to live an austere lifestyle that that might be because it'll make you happier.

1:19:47

It might also be because that's the signal that you're trying to send.

1:19:50

That's like those are feathers.

1:19:52

Buffett's the best example of this.

1:19:52

I mean you you have to spend some time on on Buffett in the book of just intentionally. Yeah.

1:19:59

But then that got kind of twisted because Mark Zuckerberg was famously driving like a Corolla for a while.

1:20:04

But then Sam Bankman Freed at FTX kind of used that as window dressing to be like, "Oh, I'm the altruistic billionaire.

1:20:11

I I don't I just drive a beater car."

1:20:13

Uh when of course he had like a mansion in the Bahamas and a bunch of other stuff that he like wasn't pointing the camera at and and Mark Zuckerberg's interesting because he famously drove the Acria for whatever for many years and now he just bought a half billion dollar yacht.

1:20:25

So it all it all it all comes around eventually. Yeah.

1:20:28

He's also evolved his taste in cars a lot where he could buy a supercar, but I believe the most recent car he bought was a Cadillac CTS-V Blackwing, which is like a $100,000 sports car, but it's not a half a million dollar sports car, but it's an American, you know, muscle car.

1:20:42

It's like very fun and very different.

1:20:44

So, he's he's clearly finding his own path in expressing himself in a more unique way than just like one rich guy car, please.

1:20:53

Uh, and so I I I think he's probably happier and like carved out a unique niche.

1:20:58

And I feel like that that's often a better place to land is not just don't just buy the most expensive thing.

1:21:03

It's okay to buy the expensive thing if it's craft and and it's interesting to you and it has a story and it has relevancy or performance.

1:21:10

Um, but it needs to like speak to you in some unique way that actually, you know, improves your life. I don't know.

1:21:15

What's your most irrational uh purchase?

1:21:17

I know you've talked in psychology of money that you you paid cash for your house or you don't have a mortgage, I believe.

1:21:24

But, uh, what else is is potentially irrational?

1:21:28

I I don't know if this is irrational, but it's one I think about a lot.

1:21:32

I grew up skiing uh as as a competitive ski racer.

1:21:34

And when I was, you know, 10, 12, 13 years old, I always felt like all my friends had better gear than I did.

1:21:40

They had the nicer skis, the newer jacket and whatnot.

1:21:42

And back then it bothered me.

1:21:44

When I was 13, I was like, I was so envious of them.

1:21:46

And so now, and obviously I I got over that, but now that my son, he's nine now, we we ski a lot together.

1:21:53

And I think because of the scars that I had for my friends having nicer stuff, I made this vow a couple years ago.

1:21:58

I was like, I'm going to buy my son the best skier every year.

1:22:02

He's going to get the best skis, the best boots, the best jacket.

1:22:04

And I'm doing that to kind of like fill this hole that I had when I was when I was a young kid.

1:22:09

And the irony about it is he could care less.

1:22:11

He does not care whatsoever what gear he has.

1:22:13

And so that's interesting, too.

1:22:16

Like I think there's always a story behind spending.

1:22:19

For me it's like he doesn't care because he has No, he doesn't care because he's so all he knows is the best, right?

1:22:24

Is that isn't that I feel like I feel like I like if if I buy him a new pair of skis, I expect him to be like, "Oh, wow. Let me look at these. They're so cool."

1:22:34

Cuz that's what I would have done.

1:22:34

And I get him and he's like, "This doesn't doesn't matter whatsoever."

1:22:38

No, I totally resonate with that, though.

1:22:39

I I remember growing up skiing snowboarding.

1:22:44

I would I would always have a snowboard that was like three seasons old, lightly used, and it was like the practical decision. It was $50.

1:22:51

It got you down the mountain just pretty much as well as anything else.

1:22:56

But then I'd be watching the Burton, you know, videos, and it's like, you know, looking at these and and I'd be just running the numbers.

1:23:02

I'm like, I would have to ref 400 soccer games to get, you know, that board with those bindings.

1:23:09

It's like I'm going to ref like 10 games this weekend, but I'm not going to get there.

1:23:13

Yeah, I was skiing at some point.

1:23:14

I got too tall for regular skis.

1:23:16

And so they needed to They were like, you have to go to custom skis if you want to be really in this.

1:23:21

And I was like, I'm switching to scuba diving.

1:23:23

Like, you know, whales are big enough for the ocean.

1:23:25

I'll be big enough for the ocean. It's fine.

1:23:26

Uh I do have one last question. Do you have one?

1:23:28

I have one last but um you go.

1:23:29

Switching, I hate to switch uh back, but on the art of spending money, what is the uh what do the real uh Birkshshire enthusiasts think that uh or what has Birkshshire signaled around how they're going to spend $300 billion or whatever their their current cash pile is?

1:23:49

cuz I feel like the general market's sentiment is that, you know, Birkshshire is preparing for the next, you know, uh, next time great companies go on sale due to some crisis.

1:24:02

Uh, but I'm curious if you have any, um, sort of insight there.

1:24:05

I think, you know, the the list of companies that you could acquire for hundred billion dollars is not a very long list, but there is a list.

1:24:13

Like it's it's not inconceivable that Bergkshire could make a hundred billion dollar acquisition.

1:24:16

Um I I' I've often thought that Bloomberg the the media company like B that would be a perfect Bloomberg fit.

1:24:23

It's probably worth about hundred billion or that that'd be perfect for Berkshire.

1:24:28

And then Bergkshire is so big it's a trillion dollar market cap.

1:24:30

They could probably repurchase a hundred billion dollars of stock.

1:24:33

It might take three or four years but they could do it.

1:24:35

There's enough liquidity there.

1:24:36

So you could spend 200 billion.

1:24:38

Like that's that's pretty conceivable.

1:24:40

The big question for me is once Buffett is gone and passed away and his his stake in Bergkshire is more dispersed, then you could there's room for an activist investor to come in.

1:24:51

Right now, you can't have an activist in at at Bergkshire because Buffett owns too much.

1:24:55

He would just tell him to go away and and then it's over.

1:24:58

Once Buffett's majority voting stake or like big voting stake is gone, then it's interesting.

1:25:03

Will Bill Aman or one of those guys come in and say, "We want you to do a $200 billion special dividend.

1:25:10

We want you to spin off this and that and that."

1:25:12

I hope that doesn't happen, but you can very easily see it happen, particularly if people aren't going to give uh Greg the, you know, a a five-year leash to prove his way.

1:25:22

If if the results aren't there in two or three years, you'll probably have people knock at the door. Yeah.

1:25:27

And so having that pressure, I hope he doesn't have pressure to deploy that $300 billion of cash knowing that if he doesn't someone's going to come knock at the door and threaten his job or tell him to spend off this or that.

1:25:38

So he's not you said he's not a stock picker either.

1:25:41

So he's not exactly just going to go out and be like, "Okay, I'm going to start deploying this and you know that way, right?

1:25:48

It feels like it has to be more significant." Yeah. Yeah.

1:25:50

Uh I have another question, but we'll have to do it next time.

1:25:54

Uh this was great joining leaving us all hanging.

1:25:57

I know it's just going to turn into a whole conversation and we can do a whole another 30 minutes.

1:26:00

So why don't we just do another 30 minutes in a couple weeks. I'll see you then.

1:26:05

Morgan, we love coming on. Have a great Friday. We'll talk to you soon. Bye.

1:26:08

Here's the run of today's show.

1:26:11

We have Sonia from Sequoia coming in the studio next.

1:26:13

Uh they Sequoia just hosted the AI Ascent Conference uh with an absolutely stacked roster.

1:26:19

some of the greatest programmers, some of the greatest uh entrepreneurs in artificial intelligence or really just in history uh all coming together.

1:26:28

We saw a fantastic jacket swap between Alfred Lynn and Jensen Wong and I'm excited to talk to her about trends in artificial intelligence and what is happening in both the early stage and mid-stage startup market.

1:26:41

Obviously, as a venture capitalist, she is investing in a lot of interesting stuff today.

1:26:45

So, welcome to the stream, Sonia. So good to meet you. Thanks for having me.

1:26:50

You know, Alfred was bummed.

1:26:50

His jacket was Hermes and he was sad to lose it.

1:26:52

And I was like, "Don't worry.

1:26:52

I think I think you got the better trade here." Oh, wait.

1:26:55

So, that was a permanent swap. He gets to take it home. Permanent swap.

1:26:59

It's hanging off the side. Okay. That's fantastic. Yeah. It needs to be framed. I mean, it's iconic. Iconic. Yeah.

1:27:02

I mean, I want to buy a leather jacket just so if I'm ever in the same room, I have something to swap. We'll get you the dates. Don't worry. Fantastic. Uh, yeah.

1:27:12

Give me the rundown of AIScent.

1:27:12

Is this something that happens every year? Was this year special?

1:27:16

Obviously, it's a, you know, a bigger trend than ever.

1:27:20

Uh, who were some of the interesting speakers? Uh, give us the open.

1:27:21

Um, well, I'll I'll give you the origin story. I'll take us back a bit.

1:27:25

Um, we invested in OpenAI back in 2021.

1:27:27

So, this was back when it was very much, you know, it was a few guys using the API, definitely no chat chat GPT yet, but we just felt like they had invented magic.

1:27:39

And we wanted to make sure that our portfolio companies would be kind of the first to be able to see that, play with it, um, and transform their own businesses.

1:27:45

And so we set up a field trip actually for like 40 of our portfolio companies to go visit OpenAI back in May 2022.

1:27:51

And so that was like pre-Chat GPT moment and everyone loved that field trip.

1:27:56

Um it was like people were you know playing with Deli for the very first time.

1:28:00

Um you know this was when there was no public access yet.

1:28:02

Um starting to build with GPT3.

1:28:04

Uh and so our founders loved it both for like the inspiration element of like oh my gosh we are in the belly of the beast of the thing that is building the building the magic but also like from a very tactical perspective like here's how we should be using this stuff to transform our businesses and so founders loved it.

1:28:21

They asked you know can we bring this back next year.

1:28:23

I honestly I'm not like a party thrower.

1:28:25

I like hate throwing events and so I was like oh man do we have to do it again but everyone wanted to do it.

1:28:31

So we um we did our first kind of nonopenai specific event um and more across the entire ecosystem the following year um that was like right after the chat moment and so um this is our third year throwing that event in a row.

1:28:45

We've had amazing speakers Sam Alman has spoken at every event. We've had Jensen twice.

1:28:49

We've had even the audience is incredible.

1:28:52

audience is incredible. Like um in our opening talk we kind of called out what we viewed as the biggest AI product innovations of the year and you know we had notebook LM deep research sesame like I think some of the biggest like innovations those people were all just happen to be sitting in the audience so

1:29:06

it's just like a lot of firepower in one room that's greating um what uh what are the top uh kind of discussions that people are debating right now I mean from talking to people on the show uh this idea of like the pre-training wall needing to move into uh more RL focus techniques to kind of get the next level of breakthroughs. Is that a is that the

1:29:25

Is that a is that the right question to even be asking? Were people debating it?

1:29:30

And do you have a take on the idea of like this hitting a pre-training wall? Yeah, totally.

1:29:34

Um, so we had Noam Brown speak at last year's AI Sense and we had Dan Roberts this year.

1:29:40

They're both on OpenAI's strawberry team and and we had actually gotten a preview of this from Noom actually before he even joined OpenAI.

1:29:47

And so, um, he had done a lot of research historically in in AI gameplay.

1:29:51

And if you if you take the lesson from Go for example with Alph Go, which I think was one of the seminal results in in reinforcement learning, um the top humans are like at 3500 ELO at Go, uh the best bots are like 3,000 before you give them access to inference time compute.

1:30:07

But if you if you actually let the model actually sit and think for a minute before it places its piece, you can get that ELO up to 5,500 points.

1:30:13

So like way better than superhuman.

1:30:15

Um and so the key insight there is like um to get roughly the same order of performance once you've like it's diminishing marginal returns on on multiple vectors right but once you've kind of like hit diminishing marginal returns on one vector pre-training if you start scaling post training from there um and sorry specifically in time compute you get like 100,000x scale up in performance there.

1:30:37

So we're just starting to climb that second curve.

1:30:38

Um, I think OpenAI deserves a ton of props for like seeing that and like investing decisively behind it because I think if you talk to a lot of researchers in the e ecosystem, like one, a few years ago, it wasn't even obvious that the lessons from AlphaGo could even apply to the LLM world.

1:30:56

And so, you know, like if you went to visit research labs a few years ago, it was like there's the RL group and there's the LLM group and they're not the same people and it's like it's very different.

1:31:05

And I think I think Noam and um various other people like really pushed forward that vision and I think Sam invested heavily behind a reasoning infrastructure because the hard thing is like scaling up reasoning infrastructure is different from scaling up pre-training infrastructure.

1:31:18

Um and when I talk to my friends a lot of a lot of them have joined certain labs like OpenAI XAI that have really invested ahead in in reasoning infrastructure because it is such an important vector for scale.

1:31:29

Yeah, I was always wondering about the Alph Go uh pre-training kind of scaling law and wondering like okay, we have all this major compute.

1:31:37

What happens if we go back and train Alph Go on a 100,000 H100s?

1:31:41

Like are we going to get even better or really have we actually topped out?

1:31:46

And it sounds like it sounds like we basically did and we like kind of learned that lesson.

1:31:49

But is that a refutation of scale is all you need?

1:31:53

need? um kind of the bitter lesson or do you see it as just a continuation of that theme that we will need to continue continue scaling and it will just be new new algorithmic paradigm on top of what we have and then scale that and then

1:32:09

another one and then scale that and then another one and then scale that because it sounds like you know when I hear a 100,000x improvement in test time compute or inference time compute uh that sounds like a lot of data centers. Yeah, it's it's a lot of data centers.

1:32:23

Yeah, it's it's a lot of data centers.

1:32:24

Um I'm very much an AGI maxi like pro bitter lesson.

1:32:28

Um and I think that this is, you know, just another vector that we're going to scale on.

1:32:32

And it's not like pre-training is dead, right?

1:32:33

It's like you kind of like, you know, from an economics 101 perspective, you go to like where's the lowest marginal cost of the incremental uh unit of intelligence.

1:32:42

And so right now, a lot of that is on reasoning, but I think it's going to break to other to other vectors as well.

1:32:47

Are are you seeing that in uh image and diffusion as well?

1:32:50

It it feels like the images in ChatGpt uh is doing something different.

1:32:55

It feels like they're layering a few different techniques together.

1:32:59

I was playing with the text and trying to get like the text is so good now, but I was trying to get like a snake to weave in and out of the text and it was kind of getting confused and I was like, I feel like there's some layers going on here or something.

1:33:13

I'm trying to like kind of understand it and I'm just wondering like uh you know we might be past just like the big transformer paradigm of LLM and text responses in the reasoning era.

1:33:24

Um are we are we evolving past the big diffusion model in image generation as well?

1:33:32

Um so I'm not a researcher but I get to talk to a lot of smart researcher friends.

1:33:36

Um my understanding is that it's a it's a combination of a transformer diffusion architecture and I think that most people don't believe that diffusion models will will fully get us there whereas transformers have a lot more juice in them. Sure.

1:33:47

Um and so even if you look at it's not just image, it's video, it's robotics, a lot of those have transformers as their backbone. Yeah.

1:33:53

Um but I do think like you know there's so much that's happening in like the harness around it, right?

1:33:59

What is the you know what's the for loop that you run the model in?

1:34:01

What what tools do you give it access to?

1:34:02

Um, we did a little poll at AISense of like what what innovation is going to drive the the most amount of progress in the AI ecosystem in the next 12 months and like uh biggest answer was MCP and and tool use and forming an ecosystem around that.

1:34:18

So I think like the models themselves get smarter but they're also surrounded by a big ecosystem on MCP.

1:34:21

Uh how are you viewing that as a position in the market? Is it just a standard? Is it just an API?

1:34:31

Is it is it a framework or will there be companies that build around it?

1:34:35

Is there are there going to be open-source frameworks that then we find a Red Hat Linux of MCP and it winds up being a big company even though it's mostly open source?

1:34:44

Um, how are you thinking about that from an investor's perspective?

1:34:48

Yeah, I very much see it as a protocol and and like something for the industry to standardize on.

1:34:53

And so I think there are there are obviously some benefits that occur to entropic from having uh steered that. Yeah.

1:34:59

But I think it being an open standard is really important and you know that's why a lot of the big other a lot of the other big model labs are standardizing behind it as well.

1:35:07

Um and so I think it's a net positive for the ecosystem.

1:35:10

There's a bunch of startups spinning up trying to make money off of it in some way.

1:35:14

Uh I don't know if I'm really bullish on any of them really having a shot at it.

1:35:18

I mean I think there's certain people that have you know if you have like an infrastructure advantage and for some reason like Cloudflare for example is making a big play.

1:35:25

Um then like maybe I buy that.

1:35:25

Uh, but if you're, you know, a small startup trying to spin around spin up a MCP shop, I I just don't really see the the right to win. Yeah.

1:35:33

Maybe the value accrrews to like McKenzie coming in and saying like, "Hey, we're going to help you implement an MCP server for your existing Fortune 500 company or something."

1:35:41

Was there any interesting conversations around benchmarks and do you think they were talked about more or less than the last year?

1:35:50

Um, I would say like this is an audience that like very much, you know, knows that the benchmarks exist and like doesn't really care too much about them.

1:35:58

Uh, we ran a poll of like, you know, if you could only use one model for the rest of your your life, what would it be?

1:36:02

And like OpenAI by far number one, more than 50% sure, even though like if you look at where they are on the model leaderboards, they're like um, you know, they're not there on LM Arena.

1:36:10

Uh, and so I think there's a little bit of a the benchmarks aren't really, you know, they're saturated.

1:36:17

They're not really the vibes test.

1:36:19

I think people care a lot more about vibe test right now. Well, yeah.

1:36:20

And just end user value, right?

1:36:22

Well, speaking of benchmark, do you have a take on the Manis investment that's kind of burning up the internet right now? Uh oh, man.

1:36:32

Seems like an odd choice in 2025.

1:36:34

I take I'll give you I'll give you a hot take, John.

1:36:38

Apparently, the US Treasury is examining benchmark uh capital's ties to Mana.

1:36:43

So, uh, feel free to pass on the question if if you don't want to talk about it.

1:36:48

I will say they've built really cool tech.

1:36:49

I think like the devil's in the details for like what exactly, you know, you know, where's user data, etc.

1:36:54

And I would imagine that they did their homework, but I don't know. Yeah.

1:36:57

Um, switching gears, uh, the the big story this week around OpenAI, obviously after the event was the, uh, you know, new CEO, uh, specifically coming in to focus on applications.

1:37:11

I'm curious, you know, if you could highlight any of the kind of conversations around value acrual.

1:37:16

Um, you know, Sam, I think, has been pretty explicit in the past that if you're building products with the assumption that the models are going to continue rapidly get better, uh, you're probably in a good spot.

1:37:31

If you're not, you know, maybe you're going to struggle or, uh, get made redundant at some point.

1:37:36

But I'm curious what the um general kind of vibe was around around that.

1:37:41

Yeah, I would say like I mean you know the mimemetic cry in the in the venture ecosystem right now is just like the value is in the application layer.

1:37:49

The value is in the application layer.

1:37:51

I actually have a great meme on this.

1:37:52

We we had the um the meme on like the value is in the application layer.

1:37:56

And then we had Jensen in the audience and I just had a picture of of Jensen on top of Scrooge McDuck just raking in all the dollars.

1:38:03

Um but like I I agree like we very much think values in the application layer and it depends how you want to play it, right?

1:38:08

Um I think that like there's there's going to be a place at least in the near term uh for uh vertical agents applied to a very specific sector and like we've we have a bunch of those companies in our portfolio, Sierra, Harvey, Open Evidence.

1:38:20

Um I think that what the foundation models have proven though um which was debatable a couple years ago is that they have every right to win the application layer.

1:38:30

Um and so like it wouldn't have been obvious that you know a company building um foundation models could like figure out the application magic.

1:38:37

Um but like chat GPT is like a to me it's like a runaway freight train uh in terms of consumer adoption.

1:38:43

Some of the metrics they've published like 300 to 500 million weekly activives uh year to date.

1:38:47

Um, it's just it's phenomenal user growth.

1:38:50

Um, one of the things that Sam shared at our conference in terms of how people are using chat chip was really interesting to me.

1:38:57

Um, uh, if you're old, you're using it as a Google replacement. Um, so I'm old.

1:39:02

Uh, if you're in your 20s or your 30s, um, people tend to be using it as like more of a life advisor, life coach type thing.

1:39:09

And then if you're really young, like the youths are using Chat GPT as an operating system.

1:39:13

And I found that framework really interesting.

1:39:16

And you know, especially in combination with like they're clearly building things around memory, um around tool use, around connecting to your your other applications, it really does feel like, you know, if you're if you're a young person and like really really connecting chat GPT and like mindmeldding with it, um that use case seems really interesting to me.

1:39:35

And so like if you if you you know, if you think about Google as front door to the internet, $2 trillion America company, right?

1:39:42

Um, it feels to me that chat GPT is in many consumers minds that front door to AI.

1:39:48

Uh, and as what AI can do, uh, as the ceiling on that goes up, we just like each deepen our our product usage.

1:39:55

Um, a couple years ago at the first AI sent, I posted this chart of like the ratio of daily to monthly active users uh, for chat GPT and um, some of the other kind of mainstream mobile apps.

1:40:08

And the punchline at the time was like usage is terrible.

1:40:10

time was like usage is terrible. like people are like it was like a 14% down if I remember it was like people kick the tires a little bit and then churn um and we've been like tracking from externally from data science signals just like seeing those down ratios increase um it's pretty crazy like down

1:40:27

is now in line with Reddit um it's approaching Google levels and so like if you think about Reddit being like a super engaged like you know you're in there having conver like multiple conversations you see that same behavior both anecdotally and in the data for chat GBT and so um to your question world value crew. We think it's in the

1:40:41

We think it's in the app layer. Yeah.

1:40:43

Um I think a lot of the horizontal app layer opportunity will be uh will be won by foundation models like like OpenAI, like XAI.

1:40:50

Uh and then a lot of the companies we're backing are going after very specific vertical opportunity.

1:40:56

Are you particularly bullish on enterprise application as independent?

1:41:00

I mean we I'm thinking about like Google very much one consumer search but you know in legal there was Lexus Nexus and then Palunteer helps you know the government's search through data sets and there's there's all these different enterprise use cases that Google wasn't able to go after.

1:41:14

We were even talking about Armada which is an enterprise like you know uh service on built on Starlink.

1:41:21

built on Starlink. the Starlink team has dominated in consumer but uh you know the enterprise needs of certain enterprises is just too unique and so there's actually a business to build there and that seems like it maps with your strategy most recently but what do you think totally totally I think the um the enterprise war feels like it is like

1:41:39

you know we're in the first inning still um it's not clear who's going to win yet I think you know in our portfolio like Glean for example has done a really amazing job as like a horizontal chat like platform that kind of connects to all your enterprise data um but then it's like a question of like How deep do you need to go? Like, you know, Harvey

1:41:52

Like, you know, Harvey has a ton of legal case specific data or Sierra has a ton of like customer support specific um data on workflows.

1:42:02

And so, I think there's, you know, the enterprise AI battlefield is very much a a work in progress at this at this time.

1:42:08

I think like the shape of workflows and problems is like so diverse in the enterprise that like my guess is a lot of these companies will be successful. Yeah.

1:42:16

How are you thinking about humanoid robotics?

1:42:17

I've talked to there's a lot of high-flying companies that feel like it's mostly renders at this point.

1:42:23

Then there's some really amazing researchers working on stuff.

1:42:25

Uh I'm personally waiting not to see the demo of one humanoid walk around because we've seen those with Boston Dynamics for decades.

1:42:34

I want to see the satellite photo of the data center that's getting built out to do a massive training run for an endtoend robotics model and I haven't seen that yet.

1:42:44

Is that the right signal to be looking for for takeoff in humanoid robotics or should there be something else that I'm tracking as we go into this like humanoid roll out which it feels like it could be tomorrow or it could be two decades away.

1:42:56

It doesn't feel like tomorrow to me.

1:43:02

I was going to say the same thing. I'll be generous.

1:43:03

There's a lot of share the details, but let's just say I've um I think the humanoids are a lot closer than than we may think they are. Okay.

1:43:10

Um, and it's like I I thought this stuff was science fiction and I've talked to a lot of smart people who have told me and shown me things that have made me realize like, wow, this is probably on the time like in I would guess in like two or three years this stuff will be in tens of thousands of households at least, maybe thousands. Yeah. Wow.

1:43:24

Um, and uh that actually echoes kind of like the timelines from that I think Sam set on stage that you know um we had Jim Fam from Nvidia on the on the show.

1:43:35

He like he's like, you know, there's the there's the digital touring test of like you don't know if it's a human behind the uh the computer or not or not.

1:43:43

Sorry, a computer uh a human behind the screen when you're talking to it.

1:43:48

He's like the the physical touring test is, you know, when you leave your house and it's a mess in the morning and you come back and it's like it's all perfect and and all cleaned up.

1:43:56

Um and like he also thinks we're we're about to get there.

1:44:00

Um for him like a huge part of the breakthrough uh is is going to be synthetic data pipelines. Sure.

1:44:04

Um and so like robotics unlike LLM like you just don't have internet scale data to train on.

1:44:10

Y um but like one of the amazing things about what's happening what's happening in AI right now is just like with LLMs but also more specifically with these generative world models you can generate you know tens of thousands of variations of the same environment to simulate and these robots can get better and break through that data barrier extremely quickly.

1:44:27

And so I, you know, I was personally like a robot bear for the longest time and and you know, talked to a bunch of very smart people in robotics in the over the last week and I've flipped.

1:44:36

I feel like the humanoids are coming.

1:44:38

And in terms of in terms of uh uh capital intensiveness of like actually getting there in a few years, obviously it's very expensive to build a factory that produces robots.

1:44:48

produces robots. Do you think we're also going to see raises from humanoid robotics companies that are uh where you know a ton of the raise goes into Nvidia GPUs to build a huge data center to train some massive model because yeah I agree with you on the synthetic data you could wind up with webcale you know trillions of tokens like we've seen with

1:45:09

the GPT4 training run I think it's going to be extremely capital intensive and it reminds me of autonomous autonomous vehicles um five plus years ago go uh where like you know at the end of the day like we have we have Whimos and we have Teslas driving around those those companies had enormous economic engines to support the development. I think the

1:45:26

I think the same is happening with humanoid right now.

1:45:29

Uh and so my guess would be it's like it's going to it's not just Nvidia GPUs it's everything right because you're you're co-developing the hardware um you're you still have to collect a ton of data on on actions and so it's it's it's it's a ton of spend everywhere. Yeah.

1:45:43

I mean, huge trends in AI broadly.

1:45:46

It feels like all the metrics are up and to the right.

1:45:49

At the same time, valuations are very high.

1:45:52

What's your overall take on the venture market?

1:45:54

Are we in a bubble right now?

1:45:56

Can there ever be too much venture capital? All the key questions. Never, John. Never.

1:46:00

There is there's too much venture capital already.

1:46:03

Uh um look, I I'll say a lot of companies are raising on like what I'd call Vibe revenue right now.

1:46:09

and like it's like it's like it's you know pilots being counted as revenue.

1:46:14

It's like really really terrible retention stuff.

1:46:16

And so like um once you peel past that I think there's like a cohort of companies that are like growing high quality revenue at the highest pace that we've ever seen.

1:46:26

Um and that includes like I mentioned OpenAI before but it's also companies like Glean and Harvey and Sierra and and all these companies.

1:46:32

And so um to the extent like valuation is a function of like how much have you d-risked, how much product market fit do you have, like what is your what is the growth rate of your of your business and what is the ultimate TAM potential.

1:46:43

I think these AI companies are demonstrating just, you know, growth rates outside of what we've ever seen before.

1:46:50

And then TAM potential like because it is very much you're selling into, you know, if you're able to get outcomes based pricing, you're selling into a into a services replacement, not a tools replacement.

1:46:59

It's a it's a TAM in the trillions, right?

1:47:00

And so, yeah, sorry to interrupt you on that point.

1:47:03

interrupt you on that point. something something I'm curious about is is so yes if you have um an AI tool that can uh replace services spend you can capture some you can whatever ideally capture a lot of that you know market but the

1:47:20

thing I keep coming back to and maybe this isn't the right way to think about it but you're you're not simply competing with uh end humans that are delivering that services a company will also be competing with other AI tools that have you know a similar cost structure. So, does that not over time

1:47:34

So, does that not over time just drive the the the sort of dollar amount that you can capture just down to something that looks more like a software market.

1:47:45

We have this debate all the time.

1:47:48

Um, so I'm glad you bring it up.

1:47:49

Um, I think it really depends like if what if what you're doing is like, you know, really low switching cost, really low differentiation above what the models provide, like yeah, I think that margin's going to get competed down.

1:48:00

Um, and so I think that's why we've we've historically debated a lot of these GPT rapper companies.

1:48:04

I think that, you know, if you're building something that's really hard to build, um, or that's integrating into a customer base customer base that wants to like choose an AI champion and and move on with life.

1:48:16

Um, which by the way happens like we we just did a bunch of references in the healthcare transcription market.

1:48:20

Like you talk to these healthcare CIOS, they're like, I'm choosing one transcription vendor.

1:48:24

I'm not repping that thing out for the rest of my life.

1:48:29

Uh, and so I think there's there's nuances to the stickiness of these things.

1:48:32

Like I work with a company called Gong.

1:48:34

Um, and they do, you know, they're a sales AI company.

1:48:36

And the theory was all like always like transcription. Love it.

1:48:42

We love Gongs on the show. You baited us.

1:48:45

It's a huge part of the brand.

1:48:47

Oh my gosh, it's amazing.

1:48:47

Um, and they have such a quirky brand, too.

1:48:51

There's so many gongs around their office.

1:48:52

But like the theory was always like transcription should commoditize, and I think that very much hasn't happened.

1:48:57

And like sales teams standardize on them.

1:48:59

They standardize their processes on them.

1:49:00

They train all their reps on it.

1:49:02

And like it has all your data.

1:49:03

So like I think the the theory of how you build build modes is um is different from like rubber meets the rubber meets the road in terms of like how these companies in practice do build the moes.

1:49:13

But like I think you are in a run like hell business because there's so you know there's you know we we backed it we backed an AI DevOps company uh like an AI troubleshooter.

1:49:24

There's four other companies that are trying to do the same thing right now.

1:49:26

And so like um we are kind of in like a run like hell segments of the market right now.

1:49:30

How do you think about private equity stepping into the AI race, we've seen a few venturebacked approaches where the ideas like instead of the Harvey approach, let's buy law firms and we've seen even in the pre about a decade ago Justin Khan was working on Atrium this kind of like tech powered law firm where it was a law firm uh but Harvey's made the choice not to.

1:49:54

What is your take on uh private equity dipping their toe into more venture scale opportunities and venture investors starting to look more at private equity style rollup deals? Yeah.

1:50:05

Well, so I used to I came from private equity, so this is something I think about a lot.

1:50:08

Um I think it makes a ton of sense and it's a continuation of the private equity play, right?

1:50:12

A lot of the investments I did in PE only worked because you took 20% of the cost out.

1:50:17

And so like now you have a much better tool to go and do that, but like it very much is the playbook and it's what they're bestin-class at.

1:50:23

And so do I expect that they'll be great at adding, you know, AI to the arsenal for how they get those margins up? Absolutely.

1:50:29

I think that, you know, when I think of the businesses that I'm excited to invest in, it's like, okay, at the end of the day, it's who's who's creating gross profit.

1:50:37

It's grow gross profit dollar creation.

1:50:39

And you can choose to do that by investing in the billion dollar revenue company and taking their cost down 10%.

1:50:44

or you can choose to do that by backing, you know, that amazing DevTools founder that knows how to build like the AI native um DevTools company that's going to create a hundred million dollars of right off the bat.

1:50:55

And so like I very much personally like us in the founders that are kind of creating new revenue dollars and gross profit dollars, but like multiple ways to play.

1:51:04

I will say like, you know, I had to really retrain my brain when I went from private equity to venture and just I mean everything you like the way you operate is just so different.

1:51:12

And so I do think it takes a different type of culture uh to operate a rollup um or a cost out strategy versus invest in startups.

1:51:20

And so while I agree like I agree with the strategy of PE firms doing their thing and venture firms doing their thing, um I have a question mark on on uh the the blurring of the of the core competencies.

1:51:29

How do you think about the different businesses that Sequoia's um running right now from early stage growth stage beyond?

1:51:38

How do all these uh things play together in the strategy?

1:51:42

Uh we've seen some venture firms even dip their toe into general catalyst buying a hospital network.

1:51:46

Some lots of people are thinking outside the box these days.

1:51:50

There's the crossover funds.

1:51:51

Um what do you think Sequoia does best and what are you excited about in the future?

1:51:56

We're not buying any hospital chains yet. Okay.

1:51:58

Uh I would say like if you think of our strategy it's like it's seed to IPO and beyond for like the most ambitious entrepreneurs in the world.

1:52:05

And so um sometimes we're able to catch them early at the seed uh like Airbnb like Stripe.

1:52:11

Um and sometimes we catch them later on in their journey.

1:52:13

But like the the point of adding additional kind of pools of capital to our fund strategy has been when we find a winner in our portfolio.

1:52:21

Um for example, take like a SpaceX.

1:52:21

We want to be able to invest a lot of money behind that company as it as it goes um on its journey.

1:52:28

Uh and the reason for the Sequoia Capital fund is like even after these companies go public, we think a lot of that return is still to be had.

1:52:35

And so we've we've modified our structure over the years to be able to kind of support these companies as they as they grow and become later stage and go public, but ultimately it's you know it's invest at the earliest point of conviction and and ideally that's at the seed. Makes sense.

1:52:48

Jordy, do you expect to see more?

1:52:50

Uh Sam had a some interesting sort of uh quotes over the last week or so.

1:52:57

I don't know exactly when they were happening.

1:52:59

Talking about uh the cost Sam Alman the the Sam other Sam which other sorry sorry to the other Sam lesson. Yeah. Yes.

1:53:07

Um no he he the the quote to summarize it was or the line was something to the effect of the cost of AI or the cost of intelligence will just uh converge on the cost of energy or electricity.

1:53:24

I'm curious, you guys talk and I'm curious if you think that that is a potential area that you expect to see more net new early stage startups exploring because it probably hasn't got enough.

1:53:39

You used to have nuclear and were we talking with this with Shawn Magcguire?

1:53:44

We were saying like there is no Elon of energy yet, but it feels like the last massive massive market that no tech founder has really gone and dominated in kind of the founder mode way.

1:53:55

We were talking about big oil is still bunch of huge companies.

1:53:59

Can't name any of the CEOs.

1:54:01

They're not really in founder mode.

1:54:02

They're kind of boring and maligned and it feels like there's an opportunity there.

1:54:06

But yeah, sorry that's a lot.

1:54:06

No, I mean that's a great question.

1:54:08

We uh we had Chase from Croisso on our on our podcast and I I will I'll put in a bet that Chase might be that Elon like figure.

1:54:15

He shared some stats that were amazing to me and it's like I'd always kind of thought about AI from the oh I can generate cool Jubilee images perspective but I didn't realize the extent of like the sheer extent of the industrial buildout that is happening to support all of that.

1:54:28

And so Chase shared like if you look at like typical data centers today like 20 40 megawatt data centers like the biggest data centers of the world are in Northern Virginia or sorry in the US are in Northern Virginia.

1:54:38

the aggregate capacity is four and a half gigawatts there.

1:54:40

Um Chase at Crusoe himself has 20 gigawatts in pipeline right now, more than two gigawatts built out.

1:54:47

And so like the sheer scale of the buildout right now is just like nothing that we've supported in the past.

1:54:52

Um and the bottlenecks are moving around.

1:54:55

So like people it's actually impossible to get chips now.

1:54:56

A lot of that is is easing.

1:54:58

Um and power is a new bottleneck.

1:55:01

And so that's this is why there's so much happening in in West Texas right now in Abalene.

1:55:04

Uh where they just have this like massive overbuild of renewables especially with wind.

1:55:09

Uh and so like I think very much you'll see a lot of the AI buildouts uh following uh following power and energy because that ultimately is the binding constraint right now. Makes a lot of sense. We'll let you go.

1:55:19

This is this has been fantastic conversation.

1:55:22

Went all over the place, but we'd love to have you back. This is so amazing.

1:55:25

Come to our AI party next year.

1:55:26

And I heard you were asking Andrew about swag.

1:55:28

We have these scented We have these amazing scented candles.

1:55:32

I actually have one on my desk.

1:55:33

We have these scented candles.

1:55:35

I'll send one I'll send one your way.

1:55:35

It was evidence that I didn't know our audience at all, but I I enjoyed the scented candles very much. I love scented candles.

1:55:41

Mother's Day is coming up, so you know, I know your your um your segment on the Himalayan Birkin, I was like, I love these guys.

1:55:47

Yeah, they're going to be flying off the shelves. Yeah. Yeah.

1:55:51

After we do the show, everyone's going to go out and get one.

1:55:54

Anyway, thank you so much for stopping by. We'll talk to you soon. Have a great Friday.

1:55:57

Uh, next up we got Will from Slow Ventures.

1:56:00

Uh, I'll be right the other side of uh of Slow Ventures, Sam Lesson's business partner.

1:56:08

Obviously, he's been on the show many times. We had to swap him out.

1:56:10

We're we're replacing Sam with Will from Slow Ventures.

1:56:15

Um, very excited to have him on the show.

1:56:17

Uh, I've been digging into a bunch of those questions.

1:56:19

I still want to know more about the robotics timeline.

1:56:22

I'm going to try and dig into that.

1:56:23

I still need to know how I need to talk to more researchers about how images in ChachiPT works because I feel like there's something going on there.

1:56:33

Uh you know, you see it with the uh with the text models that there's very clearly uh you know certain filters running on top.

1:56:40

You get these weird rejections with the images where sometimes it will like just the Studio Giblly thing is bizarre because Studio Gibli is real intellectual property.

1:56:52

Studio Gibli is a real company and when you say that it doesn't say, "Hey, this violates our our intellectual property rules."

1:56:58

But if you ask it to generate a picture of Superman, it'll say, "Hey, that's copyrighted."

1:57:01

And so I'm wondering if OpenAI did a deal with Studio Gibli behind the scenes or something or maybe there's some definition of how the IP shakes out.

1:57:09

But um hopefully going to have a lot more AI researchers and investors on the show to kind of answer some of the bigger questions that I have.

1:57:19

But uh in the meantime, we're continuing to yap about venture capital with a venture capitalist.

1:57:25

Welcome to the stream, Will. Good to have you here. Boom. Are we live? Are we doing this? Oh, we're live. Yeah.

1:57:31

Hit the soundboard every 5 seconds for this one.

1:57:34

We need to raise the energy in the studio. We got to go.

1:57:38

Venture capitalist yaps about venture capital.

1:57:40

Every time we have someone on for slow, we we go crazy with the Chiron.

1:57:44

Uh, expect some expect some wild Chiron going on.

1:57:48

It's Friday, but we're not letting the energy go down. How's your Friday? I'm all good, man. I'm about to amp up.

1:57:54

I I got this I got a few more things to get out and then we got two little league games this afternoon.

1:57:58

So, we're just we're just getting going here. Fantastic. Yeah.

1:57:59

And we're coming up on the playoffs.

1:58:01

So, it's a big little league game. Are you a screamer?

1:58:04

Are you Are you yelling at coach? Are you a coach? The ref. I'm a coach. Yeah. Yeah. I'm You are the ref.

1:58:10

The other guys yell at you.

1:58:10

I'm a I'm a zened out coach. Okay.

1:58:13

I'm a zen out I'm a zen out coach. That's my philosophy. That's good. Anyway, what's up, boys? Great to see you.

1:58:18

Have you ever Have you ever been thrown out of a game? No. No.

1:58:23

I had a dad who was a screamer. I had a dad. No, no.

1:58:25

I had a dad who I love dearly. Rest in peace.

1:58:27

We were best friends, but he was uh he was a yeller at the at the refs and so I kind of swore that off a long time ago.

1:58:33

adapted, learned, improvised over Hey, when I coached, actually, when I coached water polo, I did get thrown off a uh I got thrown out of a game when I was coaching high school varsity water polo because I was being too sarcastic.

1:58:43

I wasn't I was I wasn't yelling, but I was too sarcastic and the referee didn't appreciate that.

1:58:51

Anyway, you guys look great.

1:58:51

I almost busted my suit out, but it wasn't uh for the next one. We hope so.

1:58:55

Didn't feel like my role.

1:58:56

Didn't feel like my role.

1:58:58

By the time we're done, all of Silicon Valley is going to be dressing in suits every single day.

1:59:01

And we're going to be we're going to be switching to business casual.

1:59:05

Well, if you go to our website, you'll see in classic form slow slow zigged when everyone was zagging. That's great.

1:59:12

And and decided to show our LPs when things got a little tight that we are extremely We're not just serious capitalists.

1:59:16

We're extremely serious capitalists. Buttoned up.

1:59:18

Um a suit a suit a suit is one thing, but a tuxedo really shows people that you're serious about managing money.

1:59:24

Well, yeah, that's right.

1:59:26

I know it's a great it's a great photo.

1:59:27

Uh well, I've wanted to have you on for a while.

1:59:28

Always enjoyed our conversations.

1:59:30

And I want to know what you disagree with Sam on.

1:59:33

He's got a lot of hot takes.

1:59:34

Uh he's probably probably everything. Probably everything. Welcome to Slow.

1:59:39

What about his idea of like uh you know, he's pretty anti- AI.

1:59:42

He likes the AI cherry on top businesses.

1:59:44

Are there any of these uh pure AI plays?

1:59:46

We were just talking to Sonia at Sequoia.

1:59:52

She's had a lot of luck finding enterprise AI application layer companies that are pure AI.

1:59:56

They are kind of rappers, but they find these particular enterprise niches that can go really big.

2:00:02

The honest answer is Sam.

2:00:04

Sam Sam, you keen and I have a really we really enjoy pushing each other and using Twitter as internal Slack and highlighting where we disagree.

2:00:10

But the reality is is we're all pretty well aligned. We say it differently.

2:00:14

We get there differently.

2:00:16

But no, I'm just as anti-AII as Sam.

2:00:18

But I think, listen, the important thing, we're not anti-AII. You know what I mean?

2:00:23

I think that's like the easy way to take it.

2:00:24

Our answer is like AI is rad.

2:00:26

AI will definitely What do we do with swear words on this show allowed?

2:00:31

It's a family-friendly show.

2:00:32

We We won't c We won't cuss, but we also don't have a bleeping mechanism, so we can't I can cut I I coach Little League. I can cut it both ways. Okay. Um, let's keep it clean.

2:00:40

Say swear like you're five years old. Don't dr.

2:00:45

Like listen, I think we all agree AI is going to break the economics as of the world as they work in a massive way, right?

2:00:52

So they and the problem in Silicon Valley like that's step one to a compelling venture capital thesis and a lot of people just stop at that step.

2:00:59

They're like okay cool economics are going to break let's start investing against it.

2:01:01

And I think for us once you click okay but where are those economics going to flow disproportionately right against someone that has a really compelling business model right that that can get from here to there on very little equity.

2:01:14

That's where our AI thesis breaks down.

2:01:20

Um, and I and I don't it's hard to argue, you know what I mean, that a lot of the game I mean I think the hottest take that I do agree with of Sam's is this is not nearly as disruptive as people talk about and it's more of an enabler and the winners are going to be big companies with balance sheets and distribution and data and all those things for for a long while.

2:01:35

So I I'm totally on board with that.

2:01:38

That doesn't mean we're not doing things with AI, but I think they meet the next couple click steps for us, right?

2:01:43

And a lot of those do look like AI cherry on top businesses with great founders who get who get sectors and understand what's going on, right?

2:01:50

And then they leverage AI in addition to a bunch of other stuff.

2:01:52

At the early stage, there's been this pattern I've been tracking with the new generation of like Gen Z founders essentially where they need to break into Silicon Valley. It's really noisy.

2:02:06

There's a lot of I mean honestly the millennials are dominating like the latest you know hundred billion dollar company is Sam Alman and you know a decade ago it was Mark Zuckerberg when he was in his 20s uh we have a new big power law winner and it's and it's as an elder millennial that makes me extremely

2:02:24

extremely excited happy yeah yeah yeah but the Gen Z entrepreneurs have been they haven't had this massive power law win yes there scale AI which is doing fantastically but there but there aren't there aren't as many like like Gen Z hasn't really found their zuck yet and so they need to break through in a different way. And I've been seeing a

2:02:40

And I've been seeing a number of of young founders break through through viral stunts on Axe and and in the media.

2:02:48

Um and and when we talk to them, we often come away saying like, well, like maybe we didn't love the fact that they have to play this game, but it just feels like a game that they have to play.

2:03:00

And I'm wondering if you have a take on like the the the requirement of modernity that you have to be such a showman now to get attention for your startup that you often have to push it really hard, make all these crazy claims and do all these stunts.

2:03:15

There's a kid that we had on the show who's hiring 50 interns to do uh social media for him and he's, you know, getting kicked out of Colombia and it's allowed him to raise money and it's allowed him to break into Silicon Valley.

2:03:26

Uh, but there's always that risk that it's taking you away from just going heads down, building the product, doing like the Dylan Field thing at Figma where he was just grinding for years and then produce a great product.

2:03:37

So, uh, are we in a new era or is this kind of just the natural evolution of breaking into Silicon Valley?

2:03:45

I think a lot of that is like compensating for not actually having extremely interesting novel hypotheses that screw with the economics of the world and a sick business model.

2:03:51

So I number one is like I think it's copium um for the most part.

2:03:56

I have another take that I've been wrestling with which is like I don't think we have founders that are capitalist enough.

2:04:04

Um I think like when you see whiz print that outcome and then you find out it's like Sequoia Doug Leone a second time founder who was IDA like you start going like oh my god those are people that you know what I mean?

2:04:18

They play they play to win and and they and they the scoreboard for them and everybody is in dollars.

2:04:23

And so I think that's like another thing that comes to mind when you talk about founders struggling to do that.

2:04:29

It's like I I I've been kind of curious on like has there been an entire generation of founders on the back of hey it's product right that like aren't as capitalist as necessary.

2:04:40

And my my take on that is like there's actually three markets.

2:04:44

You got to back people are like the big rad companies end up coming for people that can do like are addicted to winning in three places.

2:04:49

The market for customers, the market for talent and the market for capital.

2:04:53

And they have like a tremendous amount of interest and respect in all three games.

2:04:56

And they want to go maximize and manipulate all three of those.

2:05:01

I'm not saying the illegals like no don't go that but like they're looking at all three going constantly how do I move chess pieces to like win this game more?

2:05:08

And so, um, I think a lot of the stunts are are either from a product oriented mindset of like I just need people to see my thing and try my thing, which I just think as totes out at a certain point. Um, so I I'm not sure.

2:05:23

I mean, listen, I think if you're really good at it, it is a way to bend some of those markets, you know what I mean?

2:05:30

But I think you need to understand I I would say, and again, I don't want to talk about Sam our takes overlap. No, no.

2:05:36

He had a good point about this, which is like you sell AGI to raise free money for a consumer application. You know what I mean?

2:05:44

And you sell self-driving in order to have enough equity to get the margin structure on your cars to a place that's sustainable.

2:05:50

And like there is a role for that, but it can't be the whole thing. Yeah.

2:05:54

And my interesting working strategy, I've been trying to figure out who patient zero was that like robbed founders of their like cutthroat capitalist nature.

2:06:02

Um, and my working theory that I'm curious I want to get more feedback on.

2:06:07

I think Google was patient zero because they built a product and a business model that itself was like the most beautifully efficient capitalist thing that was like constantly operating at the efficient frontier all the time where they could come off of like, hey, we're just vibes in product, you know what I mean?

2:06:22

And and it that was like the exception that proves the rule, you know what I mean?

2:06:27

because it was such an amazing uh I mean I still I I live in awe of AdWords every day.

2:06:34

I think about Google is 100% right.

2:06:34

Like they built a monopoly that just spit out cash and then they could just do whatever they wanted because like they didn't need to be ruthless at all in anything else because they built the perfect Well, and and uh Morgan earlier was saying you could potentially say the same thing about Steve Jobs around, you know, the the original the product goat in some ways who wasn't Yeah.

2:06:56

wasn't as he didn't have to be as obsessed about the business model of the Oh, so I so I I totally disagree on that take actually and I and I think I uh Sorry, not to cut your No, no, no. Go for it. We love disagreements. So, no.

2:07:10

So, this this I I didn't pick this up till recently or maybe I knew it and forgot it, but um I mean, everybody knows like they founded Apple.

2:07:17

He got thrown out when it was time to like run a serious business because he couldn't do that.

2:07:20

Went away, wandered in the woods for reals, started Next. Yep.

2:07:26

Um, and I didn't realize he plowed almost all of his Apple profits into Pixar.

2:07:32

Did you guys read this article? Uh, no. No.

2:07:33

I I mean, I'm like loosely familiar with that story. Okay.

2:07:38

So, not not only did he do that, he ended up getting super deep in the weeds and like running the game theory on their IPO and strategic investment from uh from Disney.

2:07:48

And he basically like ran the most gnarly capitalist playbook there.

2:07:54

He like he ran brinksmanship using the capital markets as a lever. Yeah.

2:07:58

And that's when he went back into Apple and crushed it.

2:08:00

So I actually think he like is it this counterfactual?

2:08:04

He's exactly the rule which is you need to be on you need to be on tilt in all three ways like what are customers how do customers value? How do you win them? How do you win talent?

2:08:13

And how do you win capital markets?

2:08:15

And I and I when I read that story, I felt uh I felt like my cooked up theory is actually correct, which is like then he re-enters, right?

2:08:22

And Apple's up 100,000x as he reenter. Yeah.

2:08:25

I mean, I I I agree with uh that and I have like kind of a similar take just the the difference between Apple and Google is that Apple does operate a little bit in the world of atoms.

2:08:35

They have to actually make a thing.

2:08:36

And so there is some sort of ruthlessness that comes from like the screws have to be screwed into the metal.

2:08:43

and and if you're not on time, you're not on budget, like things can really go wrong.

2:08:49

Whereas, if you just have this beautiful algorithm and this website that people just have as their default home page, it gets better and more profitable with using like it is a little bit easier to run Google than it is to run Apple.

2:08:58

Like Apple like you know your supplier could kind of screw you like what is going to happen in goo in Google's world anyway. I don't know.

2:09:06

I mean it's a good take anyway that that that was that's sorry you you you mentioned millennial founders and stunts and it got me on one of my nine talk tracks that I grab.

2:09:12

of my nine talk tracks that I grab. But I mean we we we were just talking about this like there has been this trend and we were tracking it from uh yeah it probably started at Google uh but then uh Mark Zuckerberg drove like the Accord for a long time and then Sam Bankman Freed kind of took that like yes I'm a

2:09:27

billionaire but I'm like the I'm like the down to earth billionaire the benevolent billionaire and he was like driving a Toyota Corolla and it kind of you know created this meme around like you can be really successful and wealthy but like you don't want to display it and then the next generation kind of got caught up in this meme of like Uh, it's all about the experience. I just want I just want experiences.

2:09:46

I don't want any material things.

2:09:48

And it's a big question about if you can't concretize what you want in life from a material perspective, like you can't say, "Hey, I actually want a house for my kids to live in.

2:09:57

I actually want that cool car that I've been obsessed with since I was a kid.

2:10:00

Can you concretize building an empire, building a big company?"

2:10:04

If you're not thinking concretely in materialist p in materialist like perspective, maybe. Okay.

2:10:12

I just think people who want want to win, you know what I mean?

2:10:14

They're just like, I'm playing a game. Yeah.

2:10:15

I mean, and he knows about Zuck is like extremely competitive.

2:10:19

Whatever he gets into, it's like I'm going to learn Mandarin faster than anybody else has.

2:10:23

Like, so I I don't disagree that like I don't know.

2:10:27

I I I joke everyone talks about Sam Alman as this like visionary product guy.

2:10:30

He also drives a $3 million car.

2:10:32

He like cares about, you know what I mean?

2:10:33

He he knows the scoreboard's kept in money.

2:10:35

Um, but it's also a $20 million car, by the way. Oh, he does. There you go.

2:10:40

The three million Koix egg.

2:10:41

That's the daily I think and then the F1 is in the Okay.

2:10:43

Well, so on on the young founders note, something that I think is is happening is that so much venture capital appeared and became available to very young people that weren't necessarily exceptional in San Francisco.

2:11:00

Let's blame this on Will, right?

2:11:01

People that are people that smallest fund you've interviewed all week. No.

2:11:05

So, so, so founders that are that are talented but not not the the sort of necessarily top of their class.

2:11:14

And if you give a young founder $5 million and you tell them to run their first business, they're going to act like somebody who won the lottery effectively.

2:11:25

They're going to spend money in a way that is just Yeah. Give me your YC take. I thought that was Yeah.

2:11:30

And I and and and I saw a founder there was a No.

2:11:32

So, there was a founder who recently raised around.

2:11:34

We've we've had him on the show.

2:11:37

I think he's super talented.

2:11:38

And he was like, you know, hiring 50 interns at once.

2:11:40

And then he put out a post.

2:11:43

He's he's going to hire a videographer this summer for like five half a million dollars a year.

2:11:47

And and I was like, that's the one thing that YC gets right because they basically say, spend no money until your thing is really working and then you can start pouring, you know, fuel on the fire, but until your thing is working, spend 20 grand a month, right?

2:12:03

It's been proven time and it's really, really hard to buy product market fit.

2:12:06

Like you can buy growth post product market fit, but it's very hard to buy.

2:12:10

If you could buy product market fit, big companies would be doing it all day long for new products.

2:12:15

I heard I heard about another I heard about another company that h that that's planning to spend 20% of basically two rounds that they've put together on a single launch day effectively, right?

2:12:29

And this is for a product that doesn't have any users right now. Yeah.

2:12:32

And so it's like stuff like that where it's like you're giving talented young people an obscene amount of cash.

2:12:38

And it's the same thing that would happen to, you know, somebody that's scratching something off in, you know, uh, uh, you know, at a gas station and they get $5 million and they blow it in a year kind of, but it's almost worse than that, which is like, hey, here's a bunch of money that was like pretty easy to come by.

2:12:55

And we don't actually want to hold you accountable to the money coming back because it's product and like it's almost we fell on this trap of and you know I I'll get on my rant about value proposition but we fell in this trap of like backing people to do like subjectively valuable things. Mh.

2:13:10

And that's like a really hard capitalist endeavor to scale which is like we we so we talk a lot about value proposition existing on a 2 by two of quantitative and qualitative causal and correlating.

2:13:24

And you can build great businesses in all quadrants, but like what you do to scale them out of the gate is totally different.

2:13:29

And I think we got down this like, hey, what's the value of that product? It's like I don't know.

2:13:32

Let's like it's different to me than it is.

2:13:35

It's like handbags are clearly valuable, but like why are they worth $10,000 for some people?

2:13:39

Like you can't there's no math, science, logic, or anything behind that. It just is.

2:13:42

that. It just is. And so I I I think Jordan is actually another thing we're like here's a bunch of money and go do things that like do not have we talk a lot about our job and our money is to figure out does something work right like with a true or false answer and the

2:13:56

problem is a lot of things got started that like don't have that true or false answer right so it becomes about derivative signals that actually aren't like connected to building killer businesses so I I yes and your comment yeah uh Sam's been really big on AI as a

2:14:12

sustaining innovation in the mag 7 in the big tech world uh at the same time it feels like the big tech like the stocks are performing very well the financials are fantastic but the products seem to be faltering you can't find the right Gemini app for Google

2:14:29

very easily Apple intelligence no one's really raving about it at least in the tech community uh what is your take on big tech is it a better time than ever to start a company that takes a shot at a product that normally would be you know owned by big tech. I I still think

2:14:43

I I still think that that if I were running Google, well, I don't think 90% of the use cases in AI are actually like interestingly monetizable.

2:14:54

I I don't know that I don't know that it like and I'm starting to watch my own usage now and question that a little bit, but like the majority of the con the the content being generated from AI and the majority of my usage is like not adamant.

2:15:08

So I I don't know that I'm like that stressed if I'm you know what I mean like what am I I'm like going into chatk and talking to them about what I'm going to talk to you guys.

2:15:18

I didn't do that but like that use case is like really great really valuable to me.

2:15:22

What's actually the Ben back to subjective value prop like what's the value of that?

2:15:25

Very hard to put a number on and very hard to monetize.

2:15:28

So, I I I was on I tweeted it a while ago, but like someone did I forget who did someone did a really good like take on this, which is AI is going to be awesome and change the world and AdWords is mostly safe from that.

2:15:40

So, I and I don't I just I don't like I don't love the hey, I want to take on the product because they're not good. You know what I mean?

2:15:48

I think problem statements when it comes to building companies are not nearly as powerful.

2:15:53

They don't lead to important companies nearly as much as like I just believe the world works in this way.

2:15:58

nobody else thinks it works.

2:15:58

And if I don't work on that, somebody else is.

2:16:02

And I'm not I've been big on like will the future.

2:16:05

It's like no, no, go work on things you think are like just truths. You know what I mean?

2:16:09

Because you'll be pissed if somebody else works.

2:16:11

So I I don't have a good answer for you on like do you take on Google right now because I think the answer you have to be you have to like come out and think the world works dramatically different in 15 years and start building against that.

2:16:22

Can you give us an update on on your guys's franchising thesis?

2:16:24

uh these sort of um four I I feel like uh the venture world got excited about four-wall businesses because there's been there had been a bunch of M&A over the last few years and people realizing like hey you can kind of spin up a brand and you know prove it out a little bit and get a bunch of other people to um you know scale it.

2:16:46

Uh and I I feel like this idea of of you know this business in a box has been um very prevalent for the last few years.

2:16:56

you guys have had, you know, your own thesis around it.

2:16:57

Um, but I'd be curious to get an update there on where you see that kind of category and and how you see that opportunity today.

2:17:07

Yeah, I mean, top of funnel has been lighter than we wanted.

2:17:09

I think the overlap with people who think this way and like think of slow is not.

2:17:11

So, that's that's something we're constantly working on is like seeing more in that space.

2:17:16

Um, listen, the way we got to that space is like what are the rattest businesses out there? You know what I mean?

2:17:22

just from a year in year out efficiency efficiency on equity just like what are the things you would want to own for 30 years and hand off to your family which I think is like a really great framework on this stuff. Yeah.

2:17:34

And you kind of quickly get to franchises you know what I mean like they kick off a ton of free cash flow pretty durable for the most part really efficient scale dot dot dot.

2:17:42

Like that was just always my interesting take is somehow venture capitalists became software investors and not uh and not folks looking for novel hypotheses that have killer business models attached to them.

2:17:56

Um and so anyway that's how we ended up there. How's it gone? It's like really fun.

2:18:00

I think it's a very logical output for a lot of innovation.

2:18:03

Um I don't think we're doing nearly as much as we would like there but we continue to look every single day.

2:18:09

And again for us for us it exists on a like the the it's it's like one expression of you created some novel IP that has rad economics you know what I mean that like does something that creates a ton of value in the world.

2:18:21

Um and so we talk a lot internally and we're spending a lot of time on our our GBO thesis our growth by buyout thesis that we rolled out probably I know maybe first I think we did but uh with some friends in our in a company called Metropolis.

2:18:36

And it's like I'm not wedded to that.

2:18:38

to that. I just thought that that you know what I mean I looked at vertical SAS and I was like oh my god it actually came to me if it's interesting in industrial rail I got pitched by these guys with a sweet industrial rail

2:18:47

logistics company and I was like I love this I want to be involved right and then I started digging in and realizing there were some tough go to market dynamics where you might actually double the earning power of an entire industry but be an 8 millionaire company. Wow. I Wow.

2:18:57

I was and I I mean directionally correct I'm making that up but I was like that's wild. You know what I mean?

2:19:05

And I was like, how did like how does how did we get to this place that you could actually do something wildly disruptive and everyone else but you gets paid for it?

2:19:13

And that's where I started peeling away the layers of like what Right.

2:19:15

So like it's like oh what if you bought a railroad and you doubled its profitability?

2:19:19

You'd make more money than an 8 million or a SAS company, right? And then you go off it. Yeah. Right.

2:19:25

Then you go to what are the other versions that it's like a maybe some some places it's more efficient to vertically integrate.

2:19:29

vertically integrate. maybe some places business in a box and then a woman who works to me is much smarter than I am when I was outing all this off quickly was like what about toast and I was like yeah should definitely be a software company so that kind of informed our framework that is slot into how do you

2:19:44

think about buyouts um I mean there there's venture capitalists that are getting in and on the equity side there's some funds private equity funds that do equity investments and they have bank partners that do the debt piece um I imagine that you don't have a credit fund sep separately that's managing that. But is that coming? Okay. And then But is that coming? Okay.

2:20:01

And then and then there's also like the the the private credit guys that come in with just the credit piece.

2:20:07

Um how do you think that that's going to evolve and is it important for VCs to kind of have at least partners in the whole capital stack? You'll have the partner.

2:20:17

I mean like so again Metropolis is our kind of lighthouse case study in this.

2:20:21

If you look at I think the where there's a hole is kind of the growth equity portion of this, right?

2:20:27

We'll fund someone to go do some breakthrough product work and go, "Oh my god, the economics are changed."

2:20:32

And we'll fund them to do a small scale buy of a business in Evat and see if you can like there's basically two experiments.

2:20:37

Does the product actually transform economics and does it translate when you own and operate the P&L?

2:20:41

Then there is a hole on the equity side for like great, let's go buy 10 of not one.

2:20:46

Um, but if you look into the Metropolis deal, it's Wall Street. You know what I mean?

2:20:50

So, I think at enough evidence this all slots right into their models and Vista's credit fund comes in and D Um, I'm curious.

2:20:58

Uh, we had Harley on from Shopify yesterday and it feels, you know, in many ways it's the most significant SMB platform in the world.

2:21:09

It's a product that at scale entrepreneurs uh it it's so critical to the business that yes, they're going to have issues with it, but generally people feel great about Shopify.

2:21:18

And I've been interested to think about um in a lot of other categories you have these sort of like Jirro dreams of sushi type opportunities where linear was able to uh look at everybody hates Jira but like we need tools like that.

2:21:36

And so what if you just built an amazing issue tracker from the ground up and really cared about the craft.

2:21:42

And if you look at Shopify, you could look at, you know, oh, this design thing's not great or what if this was better or what what if we, you know, made this more intuitive.

2:21:54

But I don't believe that you could take you could take the talent most talented people in the world right now and try to get them to rebuild, you know, a commerce platform like Shopify and just given the developer ecosystem and now shop pay and all these other things. I don't know.

2:22:09

I don't think like I'm struggling to see how somebody replaces Shopify in the market right now and I'm curious uh how you know if you guys have looked at any other any other players that is anybody even daring enough to try to to take on uh Shopify. I just I'm with you.

2:22:32

I think it's it's a fool's errand.

2:22:32

So, back to like value prop, which my whole north star is like I need to buy someone that's creating a ton of value and can capture a lot of it pretty efficiently.

2:22:39

Like that's the end of the day what we're trying to find in the world.

2:22:40

But value prop has to sit on two axises.

2:22:43

And again, this is like everyone calls me professor Will and you're hearing why I go in some long tangent that's like all theoretical. Uh, love it.

2:22:51

But you got to have an absolute value prop, but also a relative one, right?

2:22:56

Like if you go out great, you create a dollar for people, they're stoked.

2:23:00

If the other solution creates like 98 cents of value, right, and it solves the both same, you guys are going to be in a dog fight from a sales and marketing standpoint, right?

2:23:09

You're gonna you're like you, yes, you create value, but you're communicating that to the market is going to be super hard.

2:23:14

And so, I think the issue with taking on someone like Spotify or Shopify is the value like it's hard to create relatively more value.

2:23:23

Can you do things nicer on the edges? Yeah.

2:23:24

But like the core functionality of like I have a business that generates profit for my family. Yeah.

2:23:29

you know, it's like that's a lot of it. It's very hard.

2:23:32

The switching costs are super high.

2:23:33

There's a ton of risk and it's hard to do that dramatically better and be like, "Hey, just by using our platform, you know what I mean?

2:23:41

You will get make dramatically more money."

2:23:43

And so, yeah, and Shopify can say to be clear, they can say, "Look, if you have Shopay, you're going to get an incremental you're going to get an incremental 10% of revenue just by default because the check you're going to have less abandoned carts. We lose you. Well, he's frozen. Let's kick it over.

2:24:07

He's absolutely some ads. Go to getbzzle. com.

2:24:10

Your bezel concierge is available now to source you any watch on the planet. Seriously, any watch.

2:24:15

Let's bring in some more soundboard.

2:24:18

Uh, and we can also sing the wander song. Find your happy place. Find your happy place.

2:24:27

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2:24:33

It's a vacation home, but better, folks.

2:24:35

Can you imagine if Slow is just so committed to just being lean like their companies that they didn't pay for Zoom, the internet bill or something? No, no ISP.

2:24:44

Um anyway, uh we uh we have three founders coming on back to back to back.

2:24:51

Tell Will he's happy to jump back on.

2:24:51

We can close out, but we don't have anyone in the studio right now.

2:24:55

Uh but we are going to talk about Nourish, Madic, and Fastino.

2:25:00

Three wildly different companies, one in the healthcare space.

2:25:04

Um one in the We actually have the robot in the box over there.

2:25:08

We should unbox it on the show with him.

2:25:09

Uh it's kind of a next generation Roomba talking about uh cleaning uh in your house and very excited to talk to him about that.

2:25:17

And then yino is training AI models on uh commodity graphics cards.

2:25:22

So, I'm sure there'll be a bunch of interesting things that we can dive into on how Deep Seek was able to train on low-grade unoptimized cards.

2:25:29

If they can do it on uh gaming quality cards, I'm sure uh you know, other people can do it on all sorts of uh different cards.

2:25:35

Uh should we do some timeline in the meantime? Let's do it. Let's talk. I messaged Will.

2:25:41

I I'm I'm actually uh concerned hopefully like you know Oh, we have someone. Okay, great.

2:25:46

Yeah, let's bring him in. We have Aiden. Welcome to the stream. How you doing? Welcome. What's what's up, guys? Thanks for having me.

2:25:53

Uh, congrats on all the success you're having with the show. You guys have blown up.

2:25:58

Yeah, I mean, success uh you know, technical difficulties.

2:26:00

It's uh one thing after the other today on the stream, but we're doing well.

2:26:04

Our last great bounced two seconds, but uh it was a great conversation and excited to have you here.

2:26:13

Would you mind kicking us off with a little bit of an introduction on yourself, the company, and the news from this week? Sure.

2:26:19

Um yeah, so I'm Aiden Dur.

2:26:19

Uh I'm the co-founder and CEO of a company called Nourish.

2:26:24

Uh Nourish connects chronic condition patients of all different types with a registered dietitian over teleahalth and we get it covered through through your health insurance.

2:26:33

Um you know we have the largest network of dietitians in the country.

2:26:36

We have over 3,000 dietitians on the platform.

2:26:37

Um we've served hundreds of thousands of patients and the vast majority of patients uh pay nothing at all through their through their health insurance.

2:26:44

We're a network with uh most major commercial insurance payers, Medicare, you know, some Medicaid plans at this point.

2:26:49

And you know, I've made a lot of exciting progress um that we can speak about today.

2:26:55

And then yeah, in terms of the news, you mentioned uh we announced a series B round recently.

2:26:58

It's a $70 million round. 70. Let's go. Fantastic. Yeah. Ring the bell. Ring the bell. Congratulations. Thanks. Yeah.

2:27:09

So uh you know it was led by JP Morgan's growth equity fund and then uh participants um from all all of our existing investors.

2:27:17

So Index Ventures who had led the series A and Thrive who had led the seed round and then Box Group who I know our mutual friend David David Tish um who's you know invested at the seed round has doubled down at every round including this one for David. Yeah. Yeah.

2:27:29

We got we got mutual friends all all over the place.

2:27:33

James Peront Morgan doing him proud. JP Morgan in the deal.

2:27:36

uh is this uh like why was this not covered by insurance before?

2:27:41

Is this a regulatory change? Is this technology?

2:27:43

What's driving the growth in the business?

2:27:46

Are you just the first to think of it?

2:27:47

Or is there something unique that's allowed you to start covering this type of treatment uh with insurance because that seems like a major major unlock, right? Yeah. Yeah. Good question.

2:27:59

So, um this actually first started getting covered in the early 2000s by Medicare. I think it was 2005.

2:28:05

uh Medicare had done a study and seen ROI for this this sort of care.

2:28:10

And really the insight that you know Medicare put together which is kind of the same one we put together is that you know you look at the healthare um healthcare crisis and you know trillions of dollars to spend and hundreds of millions of people with chronic conditions and millions of deaths um each year.

2:28:23

each year. uh actually kind of you start double clicking most of those are downstream of nutrition and the vast majority of spend is just downstream a handful of conditions you know obesity and heart disease and high blood pressure and cholesterol and kidney disease and you know a few others and so they had seen ROI for working with a dietitian you know it's a really effective intervention and started

2:28:40

covering it and then the affordable care act um 2009 carved it out as a preventative benefit um and so that's this coverage and then kind of the commercial payers you know followed and more Medicaid plans um and then kind of the more recent unlock which is right around when we starting the business the business is a little over 3 years old was uh tele health coverage uh at par with in person because of COVID. And so

2:28:57

And so um that was kind of you know maybe the the biggest you know recent regulatory unlock that you know made this uh you know a lot more accessible.

2:29:04

Of course you could have built this business you know in person uh but would have been much harder than than the way it's built today via teleah health. Yeah.

2:29:09

On the on the subject of the raise how is it different raising from you know major financial institution versus a uh you know typical valley VC?

2:29:18

I imagine it's both similar and different at the same time. Yeah.

2:29:26

So, um I think actually, you know, in terms of the the actual firm, uh the person we're working there, Paris Haymon, who um we're really excited to partner with, he actually was previously at Index Ventures and been part of the team that had run our series A.

2:29:37

So, actually, you know, wasn't so different uh in practice in terms of just, you know, we'd already known him and and I think the you know, the processes are similar.

2:29:45

I think the biggest difference uh really was obviously at each incremental stage uh there's different expectations.

2:29:50

So series B you know very different in terms of you know maybe more focused on the actual metrics and and getting really deep on the data than than series A or or or seed.

2:29:57

And so you know that's definitely you know an an adjustment but uh we're fortunate that kind of the you know the basics of the the process and and and the buyin on the mission and vision and stuff like that was you know pretty similar to last round given we already knew Paris. That makes sense.

2:30:10

What is the tech stack like for one of these companies?

2:30:12

Are is there like a plaid of insurance that you can kind of build on top of like or are you writing integrations and is is this uh is there a piece of like you know service as a software where there's a lot of humans in the loop in the short term and then in the long term it'll be AIdriven like uh what does the actual uh buildup of the company look like?

2:30:35

Uh so there there is not not a not a ton of what you said in terms of the insurance contracting piece.

2:30:38

insurance contracting piece. That's something we've and kind of a big unlock and the value we provide for dietitians is uh actually going directly to these insurance companies and you know negotiating these contracts and um I think that's one of the reasons you know most dietitians didn't accept insurance historically is uh it's a pain to you know go directly to one of these really large enterprises and negotiate a

2:30:54

contract and then even if you get the contract you know deal with the ongoing administrative burden licensing credentiing billing and whatnot and so um for the most part we've built you know all that whether it's from a tech perspective or just from a you know

2:31:05

operational uh competency perspective inhouse uh we have leveraged you know, a great vendor called Candid Health for for billing and it's kind of, you know, API forward billing platform that we've built on top of. And so that's been a

2:31:15

And so that's been a big unlock on the billing side, but I would say most of the other stuff from a, you know, contracting and credentiing and licensing perspective has been, you know, a lot of manual work and uh kind of competency that we've built up in house. Yeah.

2:31:26

Can you talk about trends in uh the evolution of dietitionians and how they work?

2:31:30

We we were talking about how uh there was some research that showed that artificial intelligence was particularly good at therapy and you could imagine the future is you know you just have an LLM giving you diet advice.

2:31:43

Uh they're on they're on Tik Tok.

2:31:45

They're like okay my all my clients are keto now and the next week they're on Tik Tok again.

2:31:49

They're like okay we're going pale everybody carnivore now. How's it evolving? No. Yeah.

2:31:56

now. How's it evolving? No. Yeah. Yeah, I mean you're hitting on an important question which is like you know we've seen AI be a big unlock for the business and I think uh you know you're you're kind of asking more about you know the patient side uh which what we've started to build in into the app is you know essentially a lot of tools for the for the provider to use or the patient to

2:32:15

use without the provider in the loop to get a lot of the you know info you're you're you're talking about and the way I think about it is like you know about 80% of the equation is actually behavior change but about 20% and like

2:32:25

accountability and that's where the human is is especially good But 20% is a little bit more like educational blocking and tackling and Google is not especially good at that but you know the LMS are. And so that's a lot of where

2:32:34

And so that's a lot of where we're leaning in is like how do you you know give the the dietitian tools to uh with AI to to really provide amazing care on the things that only they can do and then leverage AI for the rest of you know you know if you're u you know if you've gotten to the point where maybe you're obese and have multiple chronic conditions.

2:32:51

It's it's not that you're just kind of you know a couple educational tips away from from having success.

2:32:57

you know, you've typically built up these habits over years, if not decades.

2:32:59

And so, um, while the LM can, you know, provide some help in terms of educating you on on what's good for your diet and personalizing it and and whatnot, um, the dietitian is really important for having the the human in the loop and and really the accountability to do that behavior change.

2:33:11

And so, you know, as the LM keep getting better, like we, you know, we want to really lean in there.

2:33:15

You know, we think we're, you know, really well positioned to to leverage AI in this way given, you know, we've seen hundreds of thousands of patients and have a lot of data on what works in terms of driving outcomes and and and habit change and behavior change.

2:33:26

And so uh we think kind of the you know the the synergy is having you know both the dietitian and the uh and the AI in the loop kind of drives the better care ultimately. Very cool.

2:33:35

How what have you seen around the adoption of GLP1s?

2:33:38

Uh the concern that I've always had with them is that people that should just focus on their diet end up you know taking you know a magic shot that makes them lose weight.

2:33:51

Uh and that's good because for for a lot of reasons, but um uh I have to imagine a lot of the a lot of the end patients that you guys work with uh are both using GLP-1s and you know thinking about um the diet uh uh very intensely, but what's your reaction been?

2:34:12

And I'm I'm sure that's a question you you got a lot even during the the fund raise I imagine. Yeah. Yeah, for sure.

2:34:17

imagine. Yeah. Yeah, for sure. And um it it has been really interesting to see that you know the evolution of that over the past few years and you know the our dietitians had a lot of expertise working with GLP ones even before you know the the recent explosion you know I think as you all know they were originally improved for uh diabetes and so dietitians have been you know working

2:34:34

with diabetic patients on them for for a long period of time but of course the uh you know the recent explosion that that's everyone has seen for weight loss has also affected us and what we've seen is that um they are a really um really

2:34:45

important innovation and a really valuable for a lot of patients and a really useful you know tool in the toolkit and we've definitely seen it skyrocket in terms of our our patient population and people using that. Uh but

2:34:52

Uh but they aren't a panacea.

2:34:55

Uh so uh you know there's a lot of side effects.

2:34:58

Um a lot of people fall off the medication because of the side effects or or they're just like you know the logistics of adherence of getting access to the medication and injecting yourself every week.

2:35:08

And so what we've really done is kind of pair ourselves as a complement to the drug.

2:35:12

Um you know these drugs are FDA approved to be paired with diet and lifestyle change.

2:35:15

And so they we see ourselves as as that part of the equation.

2:35:19

And you know there are a few different ways we've kind of built out our care model um uh across kind of the journey of a JLP1 which is first you know we have a care program that's before you even get on a medication.

2:35:27

So a lot of folks want to uh before they try try a medication see if lifestyle change will work for them.

2:35:32

And so um we'll kind of have a you know a step therapy type product where you try it before you get on the medication.

2:35:36

Um and then if you do choose to get on the medication uh we have of course a companion program that will maximize the efficacy of the drug by by mitigating side effects.

2:35:44

And you know we've seen a lot of cool outcomes with people losing uh you know about 33% more weight uh with the dietitian plus the JLP1 than uh than they do just with the JLP1 alone.

2:35:54

And then the final piece of the equation which is you know maybe the most important which is actually getting off the drug.

2:35:58

the drug. you know, uh, a big problem with the medications which are our, you know, our payers see, but also our patients is that, uh, when you get off, you often see rebound weight gain and then, you know, for, of course, for the

2:36:07

patient, that's really frustrating and difficult, but also for the system and for the insurance payers, uh, from an ROI perspective, now you've spent all these dollars to have someone lose weight and then regain it, which of course is, you know, uh, worse. And so,

2:36:16

And so, uh, we have, you know, a program built around getting off the the medication, making sure that's that's sustainable for the long run.

2:36:22

And so yeah, we've seen it be, you know, a really big tailwind for the business because uh it's top of- mind for payers in terms of managing this cost and lifestyle change is a part of it.

2:36:30

And then of course patients are are really motivated to uh uh change their behavior once once they're on one of these medications.

2:36:36

What's the use of the funding?

2:36:36

$70 million is a lot of cash. Uh is that R&D spend?

2:36:41

Are there growth channels that you're investing in?

2:36:43

How are you thinking about growing the team and the company over the next couple years with that new series B? Yeah.

2:36:50

Um so I mean the way we've always kind of thought about our success is scale and outcome.

2:36:58

So it's you know how many people can we help and how much do we help them you know the quality of the care and so uh want to use the capital on both.

2:37:02

So you know scale maybe most obviously we want to really invest in uh expanding our network of dietitians.

2:37:07

So as mentioned have 3,000 today you know the the largest in the country but had a long wait list for a long period of time of dietitians who want to join who haven't been able to and so really want to expand our network of dietitians quickly to be able to support more patients.

2:37:17

Of course on the patient side of the equation we'll we'll invest in go to market to get in front of uh more patients to raise awareness that this is something that's you know effective and covered by their insurance.

2:37:25

Um and then on the payer piece of the equation want to keep investing in uh you know partnerships with insurance companies as I mentioned you know we have you know pretty good national coverage a couple hundred million lives covered at this point but there's you know a lot more plans we can get a network with to expand coverage and want to continue doing so.

2:37:41

And then, you know, maybe last but certainly not least, maybe the part that I I'm most excited about is really accelerating development and product development, you know, you know, effectively.

2:37:49

I think, you know, we talked about some of the AI use cases we've seen for patients and and dietitians.

2:37:53

And uh I think basically everything we've built there has has has been successful for those parties and driving outcomes and and saving dietitians time and and uh patients really love it.

2:38:02

And so, you know, we're really eager to to continue investing in a lot a lot of that, you know, product development and um you know, long term.

2:38:09

I think the the reason we got into this I I had had my own chronic condition.

2:38:12

I'd had really bad migraines and I had worked with a dietitian to solve it and uh you know of course dietitians uh were were really valuable for me and that was part of the reason and and my co-founders and that's part of the reason why I wanted to expand expand access to it.

2:38:22

But we know that you know part of healthy lifestyle change is not just nutrition but things like mental health and um and sleep and fitness and I think there's a lot of interesting kind of applications we can do uh of that in our product to drive better outcomes and so really want to run at that pretty quickly.

2:38:38

What else are you seeing in the overall like health tech market that's interesting or complimentary?

2:38:41

We talked a little bit about GLP1s.

2:38:43

There's a lot of uh online pharma companies that are working in that market, but uh where else are you seeing exciting companies or trends that could be complimentary to what you do?

2:38:56

Yeah, I I think they're kind of on the on the vectors that I I just mentioned.

2:39:00

So, of course, there's been a lot of movement in the in the GLP-1 space and we've partnered with uh with players there to to help get access to these medications to our to our patients.

2:39:06

Um, you know, a lot of our patients work with, you know, a therapist in tandem and so, you know, there's, you know, great companies in the mental health world that we've we've referred to.

2:39:14

Um, I think labs are are really interesting.

2:39:19

You know, about 85% of our patients get, you know, labs done each year.

2:39:20

It's of course a natural part of the care journey of both getting a baseline of you know discovering you know where you're at in your health journey and where you need to improve but also as you're making progress over time.

2:39:29

Um it feels like was just destroyed by the Therono story and no venture capitalist will touch it now.

2:39:37

But uh yeah I mean it does seem like there's a lot of new new entrance there.

2:39:42

Superpower and function health kind of doing uh the the upmarket version but there's lots of other ways to get labs done obviously. Yeah. Yeah.

2:39:51

And it's it's I think you know of course theos was you know more about the actual kind of base level of like getting getting the lab work done.

2:39:56

I think a lot of these you know players that are doing cool work that you mentioned are are more about uh kind of facilitating getting the labs through the through the large lab companies and then when AI is really great of like interpreting the labs and so uh of course a lot of our you know our patients are are getting labs done consistently and think the people there are doing a lot of cool work. That's cool. Jordy, anything? Amazing. This is great. This is fantastic.

2:40:15

Thanks so much and congratulations on the massive round. Said it again. Oh, thanks. Thanks. One last size, guys. It was great to be on.

2:40:23

You ask good questions and uh you know, I I was thinking about as I was getting on, I was uh we feedback's really big on our team and and always get feedback about talking too fast.

2:40:30

And so, as I was going on here, I was like, "Oh, I got to make sure to, you know, talk slowly."

2:40:34

But then I realized YouTube and podcast ad, that's probably where most your views are.

2:40:37

People will just change the speed, but they won anyway. Yeah.

2:40:40

A lot of people say they just listen to the show on 2x because it's too much content.

2:40:45

So, the real high performance on 3x. So, that's the future.

2:40:50

People can listen to whatever whatever they want.

2:40:51

Anyway, thanks so much for stopping by. We'll talk to you soon. Great to meet you. Cheers.

2:40:54

Uh, next up, we have Madic Robotics coming into I'm pumped for this one. Yeah. Yeah.

2:40:58

You've been talking to the founder.

2:41:00

I was DMing with the founder. He sent us a robot.

2:41:03

We're going to have it be cleaning our new studio in no time.

2:41:05

We're very excited for that.

2:41:07

Wired said, "This is the best robot vacuum we've tested.

2:41:12

Scored a rare 10 out of 10." Let's hear it. Bum.

2:41:14

Can I get the Ashton Hall sound effect? There we go.

2:41:17

I haven't heard this at all today. It's killing me. Let's go.

2:41:19

Bring them in and play that effect again.

2:41:24

Jordy, welcome to the stream. Welcome to the stream.

2:41:28

Congratulations on the Wired article.

2:41:30

Congratulations on all the progress.

2:41:31

Uh, can you kick us off with a little introduction? Robots and homes. Robots and homes. Yes.

2:41:35

They said it could have been done. They said it was a 2035.

2:41:38

Most people, they ship a render. They ship a video.

2:41:40

This guy ships a real robot. What a concept. Welcome to the stream. How you doing?

2:41:44

Uh thank you guys for having me. I appreciate it.

2:41:46

Um I'm a president co-founder at Madic.

2:41:48

We built home robots and my background is in computer vision and product and we prior to this we were at Nest.

2:41:55

So that's a quick background and Oh sense. Yeah. Okay.

2:41:59

So how many people thought you were crazy to go make a you know another take on a on a you know the next Roomba you know or whatever whatever the pitch was. I think they still do.

2:42:08

Uh I I think it's still why are you doing floor cleaning robots?

2:42:14

That's the that's the question we get quite a bit and and the answer really is that this is the only robot with scale. Yeah.

2:42:20

Uh this is the the irony or of what we're doing is that at thousand robot ship we are already the second largest American consumer robots company. Wow.

2:42:30

So we talk about this point of view that there is this perception that navigation manipul and and mapping and localization in indoor environment is solved problem.

2:42:41

environment is solved problem. uh but I tend to think of it as a firm paradox that if it is a solve problem where are all the robots in our lives why aren't they at the airports right why aren't they at the airports grocery stores why

2:42:52

aren't we swarmed with it uh and and the answer is that it's actually quite a hard problem number one number two um economic viability which is making it profitable and surviving as a business itself is a challenge and uh on a flip side making it valuable for customers. Yeah. talk about the evolution of the Yeah.

2:43:10

Yeah. talk about the evolution of the relevant breakthroughs in artificial intelligence and and which ones you are true beneficiary of uh imageet obviously very groundbreaking the transformer architecture haven't heard about that

2:43:26

having an impact at least in the Amazon's Roomba context are you using transformers what about LLMs what about different models uses like a vision a vision first vision only approach which John has been nering out about for months. So I want to know yeah like like

2:43:41

So I want to know yeah like like like does is it what is this does this lead into the why now or how do you tell the story of the underlying technologies that have led to an improved experience here? A great question.

2:43:52

We actually left Nest and Google in 2017 to start working on it because of two trends.

2:43:58

One is a self-supervised learning techniques that were emerging which is what LLMs are essentially they're learning on their own.

2:44:05

And then second one was that my co-founder and CEO Namit helped spec out Google coral TPU from Nest perspective.

2:44:12

So that trend of AI chips coming out and compute skyrocketing was a trend we saw coming and between with those two things we thought it was possible to build edge device uh uh edge device robotics and and the reason we thought edge device was critical was we as humans we don't have hard minds latency is really critical especially in a uh dynamic environment that we live in.

2:44:31

So we always thought that robots have an ability to make decisions really fast.

2:44:36

Um and specifically for indoor robotics, we just felt like indoor world was built by humans for humans for our visual perception system.

2:44:42

So vision only robotics was the only way to go that it needed the same perception system as us.

2:44:48

If you're trying to build let's say level five robots, well level five robots for cars means that cars drive like humans.

2:44:54

So inside home, it means that they behave like humans, clean like humans, manipulate like humans.

2:44:57

So it should have the same perception system and those were the trends that helped quite a bit and and uh in doing that and now um if you look at our robot what it does it it builds a Google street view like uh uh map on its own.

2:45:11

So the way we thought about it is we as humans we go into a new home new environment new indoor space we self-explore self map and then remember exactly where we are.

2:45:21

So localize can robot do that?

2:45:21

Well the answer is yes it can.

2:45:24

Our robot does that but it still has the same ability as cats and dogs.

2:45:27

Yeah, we can't tell our cats and dogs to go sit by the couch or go in a living room.

2:45:31

They don't understand that yet.

2:45:33

And that's where VLMs and some of these open sour uh source dyno and and clip some of the models that are being released are really useful because now we can extract semantic embeddings and information at the image level out of it and we can actually transfer that into our map at a voxel level.

2:45:51

So each uh uh our maps are built using voxels which is like a 3D pixel 1 cm by 1 cm.

2:45:56

So each walk actually knows that it belongs to a chair or a human leg or a child or a or a piece of furniture and that's when you can uh start asking and doing all kinds of things like hey go clean by the bookcase in a living room and it knows what you're talking about.

2:46:11

So it's really that's the next layer which is turning it into much more of a natural language inter interaction between a robot and a human.

2:46:18

talk about the actual training runs that go into your models.

2:46:23

Is there a concept of of iteration on the training runs like GPT1, GPT2, 3, four, 4. 5?

2:46:29

He said they spent less than a million dollars on NVIDIA on compute. Okay. Yeah. So, break that down.

2:46:34

Okay. Yeah. So, break that down. uh h how do you think about uh obviously you're you're acquiring data constantly but then is there this pace of let's do another bigger run and and are you is there capital at risk when you when you actually uh roll out a big training run uh great question so I'll take a a

2:46:55

little bit of a higher view and come down but there is this concept in selfming car as well as robotics that there would be one god model and that god model would do everything when you look at practicality of for deployment almost always there are multiple models at a smaller level that you uh you do it. So we've always taken this approach

2:47:10

So we've always taken this approach that we're not trying to do research we're trying to build product.

2:47:14

So whatever is available to us let's go do it.

2:47:17

So our approach has been combination of uh obviously neural nets and and some of the work that's happening but also what we do is uh is referred to as a special AI.

2:47:26

Uh so so that's a term that uh Dr.

2:47:29

Fifi Lee really prioritized and we use human information bottleneck principle. Mhm.

2:47:33

So the way we do this, we take we have image toxel neuronet network and then we combine that with long-term slam using both classical and and techniques and build this world and then the physics of the world is permanent.

2:47:45

Now as a human being I can uh know that there's a wall here.

2:47:48

There is I know how what will gravity do.

2:47:50

So based on that for us it doesn't take 26,000 iterations to learn how to tie shoe laces.

2:47:57

So in the same way once you know the physics you can predict things of that physics that I know certain objects will topple over if I were to do that.

2:48:04

So that's how we think about it.

2:48:06

So we use information model like principle.

2:48:07

So for us computes and data has been critical but it's less of a traditional logistic gigantic um uh uh data set and let robot do everything on top of it.

2:48:19

So it's less of a less of a compute intensity but there is obviously iterations and and is there something like a mixture of experts model that you could kind of pull from and and and design like yeah to kind of scale up the model is that is that relevant at all? Absolutely.

2:48:35

So we have uh our own master uh student models and stuff. Exactly.

2:48:41

Exactly. And what we and the way we use it is that hey the 3D part of it we use traditional techniques of a spatial AI then adding semantics and understanding and context part of it that's our master instrument model works very well but even for the neural net sometimes for the precision there are master

2:48:57

instrument models that we can use for precision as well just to see the way humans see so occupancy network that we have um a lot of lot of it is inspired by the approach that Tesla has taken over the years for to build their full self-driving talk about simulation are using a lot of simulated data. I imagine

2:49:11

I imagine that you could pro procedurally generate a million or trillion households with different furniture legs and stuff pretty easily.

2:49:20

Uh drive virtual robots around that, use that to generate data.

2:49:24

We've heard about a lot of that in the humanoid context.

2:49:25

Are you seeing luck with synthetic data for your product? Abs. Absolutely.

2:49:30

I think uh we started with self-supervised learning.

2:49:32

Then we realized that simulation and supervised simulation actually works very well as well along the way.

2:49:36

Uh so we built our own simulation environment using Unreal Engine with our own robot.

2:49:41

Uh so we've customized it over the years and have a large set of environment but that usually takes it we what we've seen is that it takes us to about 0 to 80% but that the final 20% always comes with the real world data.

2:49:53

So we take we initially train it to create quoteunquote the master model but then the precision and finetuning almost always comes from adding real world data. Mhm.

2:50:01

Switching gears a little bit on product strategy.

2:50:06

I imagine you have ambitions uh well beyond this initial form factor. Walk us through Yeah.

2:50:13

Walk us through maybe the the was it was this always the the form factor that you were going to start with and at what point do you do you look at kind of expanding from here? Great question.

2:50:26

So we always imagine goal was always to go build Rosie the robot.

2:50:30

All of us want that uh something uh sort of Alfred that comes in a home and just takes care of everything.

2:50:36

But we thought that the best way to do it is the way human child grows which is in the first five years of human child they just learn how to navigate from a perception perspective.

2:50:45

They're just trying to make sense of 3D rules and they pick up the object and learn that it drops.

2:50:49

So in the same way floor cleaning robot allows us to do that.

2:50:52

But then we evolve and say just like a 5 to 10 year old child can it start picking up a shoes and moving it around?

2:50:57

Can it just organize unbreakable items?

2:50:59

So for 5 to 10 year old, we don't give them knives and scissors and all the risky stuff.

2:51:03

So in the same way, can it start with this task and can we along the way productize it and start shipping and then ultimately put it all together as a full-blown robot and we thought this approach was better because as we did consumer research and we always start with customers and work backwards, we realized that there is a lot of apprehension about robot and whether they can do things accurately. Mhm.

2:51:22

So even though you know robot vacuums have been around for 23 years now, they've only penetrated 13% of the US households.

2:51:32

87% don't even have it yet.

2:51:32

So and the reason is because they they're just not that good. They're not accurate.

2:51:37

They're actually kind of dumb.

2:51:38

actually kind of dumb. uh so for so for 2002 they were amazing device but they hadn't moved forward and we thought that purpose-built device that solves the problem to the nth level is way to earn customers trust and then it to the second third port task and the way we

2:51:54

built it is if you see our current robot we have a black crown we have a black border on the top that's where the eyes and the brain sits and it's very much like a human being and we always thought that we just have to build that once and then it just grows up just like a robot so as robot scales it will scale scale. So the beautiful part of what we've

2:52:10

So the beautiful part of what we've built is that and then we have demos of it uh in our thing where we can just raise the robot maybe even put it at a 6 ft level and everything just works out of the box.

2:52:20

So it you can put it on top of the humanoid and it will map the entire space for humanoid and and with a six degrees of freedom with the same precision at 1 centimeter level.

2:52:27

So are you thinking about adding like a robotic arm so it can pick up a shoe and put it back in the closet? AB: Absolutely.

2:52:32

uh as we as we go into users home.

2:52:34

I've been about 200 homes now, real homes now and parents always talk about can you just give me a tall cleaning robot.

2:52:39

Uh that's my biggest pain point.

2:52:41

So that that comes again and again.

2:52:42

Uh the second thing we've heard is a lot of uh kids talking about their parents who live on their own in Florida or Texas and they don't have a time to go there and they're not techn technologically savvy as well.

2:52:56

So they're like, "We'll take this robot.

2:52:57

will actually control it, clean their home, but can you also send me a 10-second time lapse at the end of the day to confirm that um that they're okay?

2:53:06

How do you think about privacy?

2:53:08

I imagine you have to message like, "Hey, we need the data, but we're going to anonymize it.

2:53:11

You can trust us, but then there's data leaks that happen."

2:53:14

I'm sure this is an important part of your messaging, but what are you saying to people?

2:53:17

Huge, huge part of So, prior to we worked at Nest, I was a product lead for Nest Camera, so I know privacy is a big deal.

2:53:23

So, that's why we do the whole thing on their edge device.

2:53:26

And then the way we do is just opt in.

2:53:29

uh and we always knew and this is pri our prior sort of at flutter we also did gesture detection where webcam did this that if you build a trust with user there will be a spectrum of users on one end there will be users who says don't ever take even my telemetry data I don't want to share anything on the other hand there will be user who says take everything you want I don't really care

2:53:48

in between there are lots and lots of users who would say hey these are the long tail at which your robot fails and I actually want you to help uh uh uh get better so we'll share the data So we have we we haven't even done it automatically but there is a record button on our app and users have already uploaded thousands hours of data with permission on their own. Do you have

2:54:07

Do you have evals like like when you train a new model do you put the robot the final eval is is can the madic robot clean up after two toddlers after they've had dinner.

2:54:18

I I was gonna say frat party.

2:54:20

You know, throwing frat party toddlers make more messy make a bigger mess. Spaghetti and meatballs.

2:54:24

A regular vacuum can't even handle. No, no, no.

2:54:28

Instant instant disaster.

2:54:28

It needs a scoop and all sorts of stuff.

2:54:31

Anyway, Jordan, last question.

2:54:33

How How do you expect the humanoid How do you expect the humanoid market to play out over the next few years? Right.

2:54:41

It's obviously an area that you guys want to uh you guys will be competing in that market over time, but clearly made um some some big decisions around how to get there.

2:54:51

Uh there's a lot of companies that have raised so much money that they and and you you know on once you've raised couple billion dollars, people are going to want you to be shipping or at least having you know robots that are that are creating value uh in in these settings.

2:55:09

Um but at the same time like iRoot you know and we talked about this offline.

2:55:15

iRoot is the biggest robotics company in the US with 50 million units shipped.

2:55:19

Amazon Robotics is a second at 750K.

2:55:22

And then Boston Dynamics has only shipped 1500 robots in its entire lifetime.

2:55:28

And so when and but at the same time we just had uh Sonia on from Sequoia Capital a little bit ago and she's like very humanoid pil.

2:55:35

He was like, I think they're coming like, you know, quickly.

2:55:38

And so I'm curious as somebody who's actually building and shipping robots now kind of how you project out the next few years.

2:55:45

And I imagine uh you must be kind of entertained by it all.

2:55:50

It's it's going to be, you know, there's a lot of capital on the line.

2:55:53

You know how hard it is, but I'm curious how you think there there that's a great question.

2:55:59

There are two pieces of the puzzle there.

2:56:00

there. One I think you guys have touched in past interviews around accuracy and how accurate can robotics get and the the thing that we talk about internally is with AI today we are collaborating if it gets 90% of right we're pretty happy with it with with robotics especially in

2:56:17

a trivial task we almost want always want to delegate we want to set it and forget we don't want to do that last percent because that's a you know I don't want to finish that last corner cleaning my of the cleaning or that one last plate And that actually puts bar much higher. There's a corary there as

2:56:32

There's a corary there as well which is we go to school maybe 4 years 8 years learn how to do coding.

2:56:38

So if AI gets 90% of right we're mesmerized.

2:56:42

But we don't really go to school to learn how to navigate our home or how to pick up a glass or how to vacuum floors.

2:56:47

So the the trivial the task the higher the accuracy expectations for customers because if you make mistakes in just picking up a glass they think of it as a dumb robot. Like come on. Yeah. Thank you.

2:56:58

And and the analogy is imagine someone is helping you set a dining table um for your dinner party one out of 100 times and one out of 100 times they break one glass or one set of plate.

2:57:08

Yeah, there's a good chance you're going to fire them.

2:57:12

Uh so so the bar of accuracy is much higher there.

2:57:14

So that's one piece and then second thing is the adoption.

2:57:16

So this is where General Magic is a good example in our mind.

2:57:20

General Magic tried to build iPhone in 1995. Didn't really work.

2:57:25

Amazing team all ex people.

2:57:25

20 Fidel was there and in instead what we got was purpose-built device from I uh cell phones to PDAs to iPods to um blackberries and then we combine everything into an iPhone.

2:57:38

So in a similar way we tend to believe that purpose-built robots will see light of the day first and then they will get combined into multi-purpose devices and the more you have a human form the more expectations that customers would have.

2:57:51

So there is a home side of it and there is an enterprise side of it.

2:57:53

So we tend to think that in enterprise or factories there is a good chance robots would be used in few years.

2:57:58

With homes we think it's a little bit far away. Yeah, that makes sense. Yeah.

2:58:03

And if if you're buying let's say you know a humanoid comes in at at even comparable to something like what Uni Tree is selling right now and it's $40,000.

2:58:10

Your expectations on that are going to be gigantic.

2:58:17

So it's going to be an uphill battle.

2:58:19

But uh we'll have to have you back on as there's uh Yeah. One last thing.

2:58:23

What what were we about to say?

2:58:23

I was going to say you were touching on a really great point.

2:58:26

We actually talk about it internally that there is no ubiquitous consumer electronics device higher than $2,000. Yeah. TV cars have been right.

2:58:34

Cars have been around for 100 years.

2:58:36

Utility is clear even though and and that's usually 10,000 $20,000 and even then it's a considered purchase.

2:58:43

We just don't wake up and do it.

2:58:44

So the utility and the value has to be proven and then you have to convince customers to say okay it is worth spending 10$10,000 $20,000 and it will survive five years 10 years.

2:58:53

So there is a productization element to the robotics that needs to be paid a little bit more attention to.

2:58:59

Well thanks so much for stopping by. This is a great chat.

2:59:01

Uh we will talk to you soon.

2:59:03

We are going to use our Matics.

2:59:04

We're going to use our Maddox at the new studio. Yeah we're excited. We will let you know. We'll talk to you soon. Bye.

2:59:11

Uh, next up we have Fastino coming in the studio.

2:59:13

Uh, sounds like an Italian name. Fino. Fino. Ask him about it.

2:59:19

John, you you love to you love Your Italian accent is it's fantastic. Yeah, it's my favorite. Uh, big news.

2:59:26

Fino trains AI models on cheap gaming GPUs and just raised 17. 5 million from Kla.

2:59:32

Let's bring him into the studio.

2:59:34

We got uh Greg here from Fino or George. George, welcome. How you doing, guys? How you doing? You guys hear me? All right. Yeah.

2:59:42

Uh, first off, uh, how do you pronounce the company's name? Is it Fino or Fino?

2:59:45

So, funny enough, Fastini in Italian means a feast. Oh, okay.

2:59:52

But Fastino, it's kind of a play on fast and tiny, like very quick and small model.

2:59:58

It's our lame attempt at naming.

3:00:01

You've been feasting on uh Nvidia graphics cards.

3:00:04

You've been feasting on Kla Venture dollars.

3:00:05

Uh, putting them together.

3:00:08

Can you break down uh what's the news this week? All the above. Yeah. Yeah.

3:00:12

Well, great great being on, guys.

3:00:14

Am I your last one of the week? Uh last one of the week. Tapping it off. Tapping it off.

3:00:18

Hoping to save the best for last.

3:00:20

You guys are looking pretty pretty fresh, by the way.

3:00:23

I feel pretty underdressed. Yeah. Hit the soundboard.

3:00:25

Let them know that we still got energy.

3:00:27

I want to hear the Ashton Hall effect. Not that one. The other one. Let's go.

3:00:30

It's It's Friday, but we still got energy.

3:00:34

We're not We're not slacking off here.

3:00:36

Next time I'm going to have a a suit. I'll go buy one.

3:00:41

I'll go buy Please bring it on. Bring it on.

3:00:42

Suits are are You should have one for every day of the week. Yes. Yes. Yes.

3:00:47

I got to go to New York in a couple weeks, I think.

3:00:49

Uh I got to go first by Taylor.

3:00:51

We got a suit guy for you. Yeah. Yeah. We'll introduce you.

3:00:53

Anyway, uh break down the news and then we'll we'll start talking about uh the business. Yeah. So, great being on.

3:00:59

Uh this week we launched TLMs.

3:01:02

So, uh, it's a family of language models called task specific language models.

3:01:08

Y, they're small, lightweight models that are really fast and they're built for AI developers.

3:01:13

So, being task specific really means that we're more accurate on enterprise tasks than large models like OpenAI or Gemini.

3:01:24

And and they cost a fraction to train.

3:01:26

So, we spent less than 100K on GPUs to train our models.

3:01:29

Uh, but we're beating industry benchmarks for for enterprise tasks. Okay.

3:01:34

So, are you fine-tuning open-source models?

3:01:36

Are you ripping apart a mixture of experts model to just have a smaller set of weights? Give me the scope. I've seen the GPT 3.

3:01:43

5 circles like this and the GPT4 circles huge. How big is your circle? I guess.

3:01:49

So, we're not fine-tuning or distilling any open-source transformer-based models.

3:01:57

Uh, what we're doing is very different.

3:01:58

We took a different approach from the large labs.

3:02:00

Uh we've built a new architecture that maintains high accuracy even with very low parameter count.

3:02:07

So we're not fully revealing exactly how many parameters, but all of our models are far below a billion parameters. Oh wow.

3:02:13

Our accur our architecture actually gets more accurate as the task becomes well defined.

3:02:18

So they're not generalist models.

3:02:21

You cannot ask them to do anything, but they're extremely performant for the tasks that we built them for. Okay.

3:02:26

Talk about the data sources.

3:02:28

I imagine that if you're doing summarization, you need a whole bunch of examples of that to train on. Text to JSON.

3:02:33

Probably need some text and some JSON.

3:02:36

A lot of stuff's out there on the web. Are you scraping? Are you crawling? Are you buying data?

3:02:40

Are you using uh open source data sets?

3:02:42

Where's the data coming from?

3:02:44

Yeah, really all of the above.

3:02:46

I think there's a big debate out there in academia as to whether synthetic data or real world data leads to a more accurate model.

3:02:54

We've definitely been using a blend of all the above.

3:02:59

But the first models that we're rolling out and you you nailed them perfectly.

3:03:02

We have models for developers doing text to JSON, text to SQL.

3:03:04

We have an agentic function calling model that we're putting out.

3:03:10

I know you guys talk quite a lot about agents and yeah, an agent that can book a flight for me or can book a hotel for me.

3:03:17

So, we have a very lightweight model that inferences in milliseconds that can basically take what the user wants and call an API.

3:03:23

So very much developer focused.

3:03:26

We've got models that can parse documents, redact private uh identifiable information from documents, which is huge for banks, insurance companies.

3:03:36

We have a really fun model.

3:03:38

It's our favorite model in house.

3:03:40

It's a profanity censoring model. I love that. Amazing.

3:03:43

We don't swear on this show. So yeah. Yeah.

3:03:45

keep it light in the office.

3:03:47

But you can imagine we've had some fun late nights building synthetic data for profanity model which gaming can be used anywhere.

3:03:55

It's pretty much the funnest red teaming we can do.

3:03:58

I try to plug my ears or turn the That's great.

3:04:02

Uh I mean you're if you're inferencing this in milliseconds, is there then a desire to uh deploy this at the edge?

3:04:09

You know, run this in, you know, the cloud of the business that's actually deploying this.

3:04:14

Well, so I just have to ask for the profanity model.

3:04:18

Could we run it in real time while we're doing the show?

3:04:20

So if a guest, you know, drops a an F-word, an F-word, yeah, it'll it'll it'll work in real time.

3:04:28

It'll be much faster than an existing LM.

3:04:30

That's Yeah, I I I think the I think the real TV shows, they have a system that puts it on delay and does something like this, but the delay has got to be so much faster if you're imagining like using whisper and then this uh there's a lot of things.

3:04:41

But in terms of that latency, latency is really important to us.

3:04:44

I imagine it's important to uh your clients and customers.

3:04:48

Are you seeing demand for let us run your model and we'll still pay you but we just don't want to go back and forth with your API. Yeah.

3:04:54

So there are a lot of ways you can deploy smaller lightweight models.

3:04:59

Obviously we're going to be heavily reliant on our API. Yeah.

3:05:02

But when models have a small enough footprint which actually comes from a low lower parameter parameter count you can run them on prem you can run them on CPUs low-end GPUs.

3:05:10

I think Ash and I, my co-founder Ash, he had a dev agent similar to Curser in 2023 after I sold my last company.

3:05:19

I was actually an investor in his startup.

3:05:21

He had a problem where his LLM costs ended up being higher than his headcount cost and it's a problem that a lot of agent companies face.

3:05:32

So, I was actually I spent a little bit of time as a GP after I sold my last company and all of our portfolio companies were facing the same thing. Uh rising cost of LLM.

3:05:40

So, it's not only latency, as you mentioned, accuracy is a big problem with large LLMs, but frankly, the key issue that we've seen is that LLMs just aren't built for the enterprise.

3:05:53

They're built for consumers, right?

3:05:54

So, GBT, Gemini, they're trained on trillions of data points.

3:06:01

They're used by our friends, our family every day. They help you code.

3:06:05

They help you get food recipes.

3:06:05

They help you prep for podcast interviews.

3:06:10

They're not built for high-scale enterprise tasks, right?

3:06:13

But enterprises are spending millions of dollars a month on these large monolithic APIs.

3:06:17

I want to tell you guys a story. Sure. How GBT is being used.

3:06:23

So my my wife's dog got really sick a few weeks ago.

3:06:25

Um cute little guy, 13 years old. Uh he has cancer.

3:06:31

We went to the pet hospital and the doctor basically recommended that we put him down.

3:06:36

We had her best friend on the phone and we're trying to make a decision based on the doctor's recommendation, symptoms, dog's age, what to do.

3:06:45

And who was the tiebreaker?

3:06:48

We asked GBT, "Hey, GBT, here's what's going on with my dog. Here's a situation." Wow. Should we put him down? Right.

3:06:55

So, my wife was using GBT to to play Yeah. play maker.

3:07:00

Um, and and for the record, GPT told us to put him down and we didn't listen. He's doing well today.

3:07:08

But wow, that that's a crazy story.

3:07:10

Why would AI is the Antichrist trying to take out the dot the poor pup? Yeah.

3:07:16

Why would a large bank, Bank of America, City, JPMC, if all they're looking to do is analyze your bank statement or look at some log for fraud, why are they using the same model model that my wife used to consult on her dog's mortality, right?

3:07:32

So, using these massive models, it's like trying to come up with a with a cool metaphor for the show.

3:07:40

It's like it's like the Door Dash guy riding Saturn 5 to come for a pizza, right?

3:07:44

It's so that's it's so unnecessary and frankly that's why large enterprises, large banks haven't put chatbt or an LLM into a chatbot. Yeah.

3:07:56

Super fascalizing your bank statement and it's just like $500 on dinner.

3:08:00

You cannot afford to have a dog.

3:08:02

You got to put that dog down.

3:08:05

It's like Chase, what are you doing? What are you doing?

3:08:06

JP Morgan, cool it with the recommendations.

3:08:08

Reasoning is a very sexy word in this space right now.

3:08:14

But from speaking with almost a hundred Fortune 500 enterprises since we announced our preceding round in Q4, they don't want models that can reason.

3:08:23

A bank does not want a model that can reason its way through your last 100 chatbot users logs and figure out their personal information.

3:08:30

So when you're building lightweight task specific models like this, our models are in domain.

3:08:36

They're only trained to do the task that the enterprise using them for.

3:08:41

So, I think we have that edge very much just in how the models are built. Okay.

3:08:46

Talk about the gaming GPUs uh worth less than $100,000 in total is what Techrunch is reporting.

3:08:52

Uh did you build that yourself?

3:08:54

Do you have your own data center or are there clusters out there of low-end GPUs that are all rigged together from like legacy Bitcoin mining applications or something like that?

3:09:05

Where where are you getting these?

3:09:05

Yeah, so we're we have uh GPUs in house.

3:09:07

We have uh you know we use GPUs in the cloud but we all told our models take about a couple hours to train.

3:09:16

They cost the training cost for one model is less than you know the cost of a Chipotle burrito.

3:09:23

I so much said about how but a but a 2012 Chipotle burrito or 2023 one that didn't give me food poisoning hopefully.

3:09:32

But but there there's been so much said about when Deep Seek came out and they only spent $10 million on H100s, however questionable that number was.

3:09:42

I think what we're trying to do as a super small team is show that you don't even need H100s to build generative models for enterprises, right?

3:09:52

So, we didn't use a single H100.

3:09:54

We used T4s, gaming GPUs, low-end V100s, and and you can do that.

3:09:59

and and you can do that. Uh I think we've proven that you know banks don't need a model that takes six months to run or it's going to drain you know Lake Tahoe to for a training run right so what's more important with that training run uh flops or memory because I imagine

3:10:17

lower token counts you haven't released it but I imagine you can fit it in memory and so that unlocks it but um talk to me about the dynamics of like building a cluster and thinking about the the different parame parameters that go into the cards that you select. Yeah. Yeah.

3:10:34

So, we have a family of of it's going to be less than 10 models most likely for the next six months.

3:10:38

I'll say that they're far below a billion parameters each.

3:10:44

So, we don't need a cluster even.

3:10:47

We can just run these on one or two GPUs each for for inference. For inference. Yeah.

3:10:52

And for training, it's the same thing.

3:10:54

It's a very low-end GPU for an hour or two. So, wow.

3:10:56

We we definitely believe that there's going to be a giant shift in how language models are used, right?

3:11:04

So you guys have seen waves of the last couple decades.

3:11:07

So these massive IBM mainframes y shifted into client server architectures and open source software.

3:11:14

You used to have these massive monolithic uh builds that would take, you know, that would ship a month at a time.

3:11:22

Software applications were were were shipped so much slower.

3:11:24

and then out came microervices and you have a different release for your payment gateway and your APIs.

3:11:30

Uh this kind of workload partitioning as we call it, it's completely going to change the landscape in in language modeling.

3:11:37

So there was a report from Gartner that came out about a month ago saying that small task specific models are going to outpace LLMs and enterprise usage by 3 to one in three years.

3:11:49

And and we want to we want to lead that.

3:11:51

We definitely think that every developer is going to become an AI developer.

3:11:56

So every dev today will need to be able to integrate a language model into their code just like they're they're integrating a open-source mpm package or Python library.

3:12:08

It needs to be much simpler.

3:12:08

Uh and that's very much how we're looking to change the game.

3:12:13

It's going to be really hard to compete with the big labs. Yeah.

3:12:16

If we're just focus on the models, which obviously we're a foundational model company.

3:12:21

Yeah, but we need to make much smoother developer workflow integrations.

3:12:26

We need to make life easier for devs. And right now it's Yeah. Where does this Yeah. Where does this go?

3:12:32

I mean, I I I know um some of the stories about companies like like ramp for example, our sponsor, they uh need to uh digitize receipts.

3:12:41

So they get a lot of images. They do OCR.

3:12:43

Uh and I think Google provides an API for that.

3:12:48

There's a bunch of companies that do OCR.

3:12:51

comes through this kind of messy cluster of text, then pipe that through GPT4 and boom, you have structured data. Uh, llama comes out.

3:12:59

Okay, maybe it's getting cheaper.

3:13:01

But this seems like something where you'd want to go to you guys and get an even cheaper model that's distilled just for that one task.

3:13:09

But that feels like almost like you're a consulting shop.

3:13:12

Or is there is there a place where a company says, "Hey, we've been using GPT4 or Llama and we've done, you know, 10 million inferences and so we have a lot of data about what works, what doesn't.

3:13:24

Uh, can you train a custom model for us to drop our inference cost by a couple orders of magnitude?

3:13:28

uh or or are you trying to focus more on more versatile foundation models that can be uh just tools in the tool chest and aren't kind of oneoff specific systems for a specific task within a specific company?

3:13:42

Yeah, I think there are a few a few ways to answer that.

3:13:46

The first one is probably talking about agents and how agentic systems are evolving.

3:13:50

I'm definitely in the camp of thought that says that agentic systems will take over legacy SAS systems within four to five years. Right?

3:13:59

So when you see how these agents are being put together, it's typically daisy chaining eight or 10 LLM calls.

3:14:07

So in a chatbot, you want to parse a query.

3:14:09

You want to then figure out the right document to give back to the user, summarize the right chunk of that document, give it back all in real time, uh, with a very smooth chat interface.

3:14:20

So we definitely see a world where you have different models for different tasks.

3:14:26

And and we're not saying that we're going to replace large generalist LLMs.

3:14:30

The models that are larger, they're good at reasoning.

3:14:35

They're good at research.

3:14:35

They do things like orchestration.

3:14:38

So they'll help orchestrate this entire pipeline.

3:14:40

That's still going to be the large model.

3:14:44

That's still going to be your GPTs.

3:14:46

You're going to be your Geminis.

3:14:46

But the actual agents, the workers that are calling APIs that are doing these sort of deterministic, highcale, high throughput tasks, those are all going to be very small, intelligent, task specific models.

3:15:01

That's how we seeing it see it play out. Cool.

3:15:03

Uh Nvidia's down 6% today. Is that because of you?

3:15:07

Is that uh is it is it is the market are they pricing?

3:15:13

I mean billions of dollars billions of dollars have been evaporated from the markets on the news.

3:15:16

No, but how do you you know how do you think uh you know assuming Fesino just gains you know massive adoption over the coming years? Yeah.

3:15:26

How does how do you think that impacts the GPU market GPU demand broadly?

3:15:32

Yeah, for you know for the record the low-end GPUs that we use are still Nvidia GPUs.

3:15:37

We're still, you know, we're still a big Nvidia 10 to Jensen.

3:15:42

Jensen might appreciate if you weren't so efficient and you, you know, raised, you know, $500 million and gave it to him.

3:15:49

Have you thought about doing that?

3:15:50

If he can help us scale and go more viral, we we'll need more of them for inference.

3:15:54

For the record, but I think there's going to be a huge demand for for consumers, for uh LLMs as they are.

3:16:02

I think we we were lucky enough to have two of the first OpenAI investors on our cap table. That's right.

3:16:07

And they discussed that in the very beginning when there was a pitch deck, there wasn't a business model.

3:16:13

They didn't think that they were going to be a consumer company, but the consumer appetite for LLMs has gone crazy.

3:16:18

Even during the Deep Seek moment, didn't Deepseek get to number one on the app store?

3:16:24

My my wife, my friends are all downloading Deepseek.

3:16:26

So the need and the the hunger for consumers to, you know, automate their lives will constantly be driving the need for these larger generalist LLMs.

3:16:36

We just don't believe they're needed for the enterprise.

3:16:40

So we're taking a very different approach. Very cool. Makes total sense. Anything else?

3:16:44

I love this conversation. Me, too. This is super fun. He's an absolute dog. You're an absolute dog. You're an absolute dog.

3:16:50

Call me anytime you guys want to talk about LLMs and I might need to get some get some help on buying a suit for New York. Oh, fantastic. We've got you.

3:16:55

And and seriously, we'll we uh we got you. You got us.

3:16:59

I want to figure out I want to figure out this real time censoring thing.

3:17:03

I think it'd be hilarious.

3:17:03

We have some we have some guests that come on and try to drop fbombs. It's unacceptable.

3:17:08

Our children listen to this. It's unacceptable.

3:17:09

And I think it'd be hilarious if it was like, you know, made like a duck sound. Yeah. Quack. It' be great.

3:17:15

So, well, our people will talk to your people. Yeah. Appreciate it, guys.

3:17:20

Thanks so much for having us on. Thank you so much. Uh, hey, big news. Uh, yeah.

3:17:25

Yeah, Rippling has raised 480 or sorry, 450 million at a $16. 8 billion valuation.

3:17:32

And bigger news, I was actually supposed to be texting with them. Let me see. YC is a customer really.

3:17:40

And that is big considering YC has created all the big payroll companies. Gusto, Deal, Ripling.

3:17:50

Yeah, Gary must have been uh finding it hard to pick favorites, but well, I'm I'm texting with the team.

3:17:55

Hopefully get Parker on soon to talk about the business. Um uh one last thing.

3:17:59

Uh we have a couple posts we want to go through.

3:18:05

Uh but Michael, uh can you check the printer because I think we got a special print out for today.

3:18:09

We haven't been printing posts very frequently, but we got one post that I wanted to print. Hopefully, it printed. Let me see.

3:18:24

I don't even know if it printed. It did not print. Did not print. All right. Is it working?

3:18:33

I'm trying to print it again.

3:18:35

And we might be out of paper, but Okay.

3:18:37

But we can pull up the digital version.

3:18:40

Anyway, uh Luke Metro said, "This show used to print out my tweets and read them.

3:18:45

Now they have the head of the army."

3:18:47

So, thank you to Luke Metro.

3:18:51

I tried to print it, but we haven't printed in so long that the printer's not working.

3:18:56

Anyway, there's some other posts.

3:18:56

Uh did you see the drama in Ander World uh about Matt Grim uh taking uh notice.

3:19:02

co co to task for uh selling some fake equity in the company. Yeah. So I it's hard to know.

3:19:13

I mean the way that notice was displaying this information was uh not not consistently candid is how I would describe it.

3:19:22

Um and then the funniest thing you called this out to Matt.

3:19:26

Uh apparently the CEO of notice messaged Matt and said, "Hey, I'm FINRA registered so not supposed to post publicly on social media.

3:19:33

Happy to continue the combo privately or do a call if you want. Let me know.

3:19:37

Buddy and Buddy, it's a war crime.

3:19:40

Hitting Matt Grim with buddy is a is a war is a war crime. Straight to jail. It's so bad. It's so brutal. It's so bad.

3:19:49

Um I actually think the notice platform is pretty cool. Oh yeah.

3:19:53

Like they they they have a bunch of you know I mean I imagine that there's probably some steelman here.

3:19:59

There's a million ways that you know it's a big company.

3:20:02

There's a lot of investors.

3:20:02

Someone could have come and uh and figured out a way to put some money.

3:20:07

No, I think what was happening is an employee. Oh, really? Okay.

3:20:10

Like the reason that the the the way that you would have zero fees Sure.

3:20:14

is an employee was common, but it was Yeah, it's common that wasn't being and there should be transfer restrictions.

3:20:21

I mean, these are very standard.

3:20:22

So, there's something odd.

3:20:22

I mean, there's been a big big history of these odd like secondary sales like uh for a while people were doing forward contracts.

3:20:29

Did you ever follow that story?

3:20:31

So, so basically I mean I think still work.

3:20:34

I I think they're definitely banned in most companies.

3:20:36

You're not supposed to do forward contract and it's in your but the whole nature of a forward contract is that the company doesn't really there's very good chance the company would never find out. True.

3:20:49

But they can still ban it in your employment agreement.

3:20:53

Like it can still be something that you agree to when you join the company.

3:20:56

So basically a forward contract if you're not familiar uh it's the right to purchase the shares at a future date.

3:21:00

uh much like a stock option.

3:21:02

So you're basically writing an options contract against your shares uh in in the third party and and it and the the shares themselves don't actually transfer and so the company in theory doesn't need to approve it but it's very they wouldn't necessarily know and they wouldn't necessarily know. Yeah.

3:21:16

And so this was a big thing very very controversial.

3:21:19

A lot of people don't like this.

3:21:20

And so even though it doesn't it doesn't relate, it doesn't um result in a whole bunch of uh like legal complications that obviously you're not transferring the information rights because you're not actually transferring the shares.

3:21:31

They couldn't sue the company.

3:21:32

That's the big reason why you want a clean cap table is because you have to deal with every investor.

3:21:35

You have to give them information. They can sue you.

3:21:38

Uh if they just own a forward contract, they probably can't do that.

3:21:42

It's still a problem and creates all these distortion market.

3:21:44

So, uh, at Grim taking them to task, he says, "We're one of the good guys," says CEO of company who solicits retail investors to buy shares at a significantly inflated price for what it's worth in a privately held company they do not own shares of, have direct access to, or have any information or information rights from, which to discern financial performance or market positioning or anything whatsoever to inform the proposed investment.

3:22:06

investment. All while not clarifying publicly or for that matter to the clients they are soliciting how exactly this exposure is structured or what precisely these clients are buying and while privately hiding behind a claimed

3:22:20

veil fake by the way their fin registration means they can't comment when keeping it real goes too far uh rough so clean it up you don't want to have that grim on your bad side he could basically say this about most secondary brokers and or platform forms. Um,

3:22:34

Um, anyway, we got some massive breaking news. We got some personnel.

3:22:41

Jacob Efron's been promoted to managing director of Red Point. We love to see it. Very interesting.

3:22:47

The rumors, this is a maxed out contract.

3:22:49

Rumors have been swirling for a long time.

3:22:52

He was in free agency, considered free agency, stuck with Redpoint, got promoted. Congratulations to him.

3:22:58

Couldn't be more excited for everything ahead.

3:22:59

And weird uh weird terminology over at Redpoint.

3:23:04

not GP, not partner, managing director, very investment bank like, you know, official business.

3:23:10

Uh Andrew Reed had a funny post.

3:23:12

He was a uh coming from banking.

3:23:14

No matter how senior I get in venture, I'll always think that a managing director is uh more senior to me. Moged. Feels feels very senior. It does. It does.

3:23:24

Um anyway, uh Arvin Strvas over at Perplexity is taking more shots at Bloomberg.

3:23:30

Bloomberg is such a joke.

3:23:32

Well, Perplexi does realtime call transcriptions for free.

3:23:34

Bloomberg has a 15minute lag and it costs $30,000 a year.

3:23:38

To be clear, that was Marcelo who said that somebody at Techrunch will listen to this. Do not ser is a joke.

3:23:48

Um, anyways, what Perplexity is doing uh around real time call transcription tech crunch reports is my quote.

3:23:54

Me quoting someone else as our That's what I was saying. So bad. That's a risk.

3:24:00

The bar is low these days, folks. The bar is low.

3:24:04

Uh anyway, any other post we want to go through? The Pope is American.

3:24:08

Vatican City has a BIE now in a Waffle House and a Costco. It's great. They've taken over. America is in control. It's fantastic.

3:24:17

And we hope you have a great weekend.

3:24:19

We hope you have a great Mother's Day.

3:24:20

Pick up something simple.

3:24:23

And thank you for joining us this week, folks. Joining for the break.

3:24:27

We had a great time doing this show. Yeah, it was fantastic.

3:24:29

Good job for tuning in and we'll see you next week.

3:24:33

And remember, it's Mother's Day.

3:24:33

We talked about this 3 hours ago, the Super Bowl of pronatalism, folks. Take care everybody.

3:24:41

Have a great Mother's Day. Cheers. Bye.