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>> Very I see more and more journalists on the horizon. You're watching TVPN.

4:34

Today is Tuesday, April 7th, 2026.

4:37

We are live from the TVPN, the temple of technology, >> the fortress of finance, the capital of capital.

4:45

>> Uh, let's pull up the lineup because we have a great show for you today, folks.

4:49

We have Riley Walls, the jester of Silicon Valley.

4:51

You had a different name for him when he came on.

4:52

There was something else. >> Internet Rascal. >> Internet Rascal. That was it. Uh he is a rascal.

4:59

>> He's uh he's coming on to talk about the auction to name a street in San Francisco.

5:03

Uh we have a whole bunch of funding news.

5:05

Aditia Bandandy from Noon.

5:07

Zack Shaw from Hermes is coming on.

5:07

Uh Wang Louu from Mapadin is coming on.

5:10

And then Zack Kukov and Thomas Lefant are joining us live in person in the TVPN Ultradome.

5:18

Uh well there is a whole bunch of news to run through.

5:20

with the first story is that Meta employees are apparently token maxing and competing on an internal leaderboard called Claudonomics for status as a token legend.

5:33

This is from uh the information uh over a recent 30-day period.

5:36

Total usage on the dashboard topped 60 trillion tokens.

5:40

And this sparked a huge debate over how much is Meta actually spending with Anthropic.

5:45

Of course, the other big news is that Enthropic uh just passed 30 billion in uh run rate revenue uh with one of the probably the steepest revenue growth chart in human history. Absolutely legendary.

5:59

>> Yeah, this this this uh you know chasing status as a token legend reminds me of kind of maybe it was a year ago at this point you were saying like will tokens ever become like eyeballs the way eyeballs were during the >> during the era, right?

6:14

just optimized for for eyeballs.

6:16

Obviously, not every eyeball visit to a website is created equally, but people were optimizing for uh eyeballs.

6:24

And now uh you know, I don't the reaction to this, I think, has been >> generally at least online like been uh I guess reassuring.

6:35

A lot of people are saying uh Gary Basin says uh you why Marty says goodart's law.

6:41

when a measure becomes a target, it ceases to be a good uh measure.

6:46

Uh and so who knows what's actually going on internally, but we do know uh Zuck is pushing the entire company to be as a AI native as possible.

6:56

And this guy loves spending money, too, right?

6:59

>> I I have a crazy bullcase here that I will run through.

7:01

Let's get through some of the story.

7:03

Um first, uh we got to pull up this uh this comic from XKCD uh in the comments here.

7:08

when a metric becomes a target, it ceases to be a good metric.

7:12

It's uh right under the the leading post. There we go.

7:15

And says uh and the the other counterparty says, "Sounds bad.

7:19

Let's offer a bonus to anyone who identifies a metric that has become a target." Uh it is good.

7:24

Uh I don't think that's what's going on here.

7:28

>> Lighter was texting a friend at Meta and uh sent the the post we just discussed on token maxing and said true. And the person said yes.

7:35

It's pretty sad, >> but I mean imagine so so uh Meta has been there's been rumors of Meta layoffs >> for a while now. >> Sure.

7:46

>> Uh unclear how many if any uh if any have happened.

7:49

But uh if you're sitting there the Zuck is saying like we need to get AI native.

7:54

Bos is saying we need to get AI native and then suddenly there's a token leaderboard. Yeah.

8:00

>> You do not want to be at the bottom of the list. I will say that. Right. Yeah.

8:04

uh you know, you don't want to be the you don't want to be the guy who's having to explain like, "No, well, I've actually getting the most out of each incremental token and the other guys just like set up a uh set up an agent that just counts one million every single written or something." Yeah.

8:16

Uh yeah, I mean, you have to measure the actual output, the impact on the business.

8:22

Uh I mean, fortunately, Meta has been a huge beneficiary and a huge winner of AI.

8:26

Uh the ads are getting better targeting.

8:28

They're seeing they're delivering more ads and and the and the quarterly earnings have been strong.

8:32

Um the headline number here that that uh sort of took everyone by surprise is that Meta's staff used uh this is from the information Meta's uh the story claims that Meta staff used 60.

8:43

2 trillion tokens over 30 days which would pencil out to about a third of Enthropics ARR was the number that was thrown out.

8:52

But both of these claims are pretty questionable.

8:54

Uh, and so Tyler did some back of the envelope math to show that the one-third revenue estimate is way way too high.

9:01

And I don't know, do you want to take us through some of the reasoning there?

9:04

And then we can talk about the knock-on effects of all this. >> Uh, yeah. Okay. So, uh, 60.

9:07

2 trillion tokens is the number.

9:10

Like, we can just assume that's true.

9:12

>> Um, so basically I I'm going to assume that um the all the employees are basically just using Opus 46. >> Yeah.

9:19

>> Um, so then there's basically three numbers you need to look for. Yep.

9:22

>> Uh, in like the API cost. So there's like input. Yep.

9:25

>> There's uh cached input and then there's output. >> Sure.

9:28

>> So for Opus 46, it's $5 per million tokens on input.

9:32

>> It's uh 50 cents per million tokens on >> input cashed and then it's $25 on output. >> Yeah.

9:37

Um >> so if you multiply that 60.

9:38

2 trillion tokens at the highest possible rate, $25 per million tokens, then you do get like a billion dollars in a month, which is crazy.

9:49

That's not what's happen. >> A crazy number.

9:51

But like you have to think about it like um you know if you're using uh like cloud code or or any of these coding agents uh you know the vast vast majority of the of the tokens used is input. >> Yeah.

10:01

>> Um because like so imagine you're working on some uh you know coding file right there's a thousand lines of code in the file.

10:06

Um >> maybe the model is only changing like like 10 at most, right?

10:10

So that's a very small percentage.

10:12

So the output tokens are going to be you know a very small percentage of the total tokens going in. Right. Yeah.

10:18

Um so uh open open router uh publishes like a lot of this data.

10:22

So you can kind of use those ratios to figure out what is actually like what are the actual numbers of of the um you know input versus cache versus output.

10:30

Y >> um so this >> just to get sort of like market standard averages like baseline benchmarks.

10:35

Now Meta could be using these tools differently but if we are to assume that they're the the shape of their agentic coding efforts are similar to the average this is what the numbers look like.

10:47

So maybe that there is like some you know bad incentive where people are just saying to the model like count up to a billion and then do it again.

10:53

So then it's like totally skewed.

10:55

But if if they're doing it relatively normally. >> Yeah.

10:58

>> Uh >> so on on Open Rider it's about uh 98.

10:59

9% of all tokens are input >> and that's including cached ones, right?

11:07

>> Because you're stuffing the context window with all your code base or a huge amount of context.

11:11

It's going around finding >> and that's not changing every time. So you can cach it. Yep. Um that's like 1. 1% is output.

11:16

Um so basically um if you basically get all the numbers that means uh like the kind of mean token >> uh the mean million tokens is going to be uh $2 and like around 26.

11:29

>> Uh so that'll get you to something like 136 million a month for the 60 trillion tokens, right?

11:35

So that's like way less than 900. Yep.

11:37

>> So that that would be um 1.

11:37

6 billion a year like run rate. >> Still huge. >> That's a lot.

11:41

But that is still in the max. Yeah.

11:43

uh that that's like I'll talk about >> assuming they're in the top.

11:46

>> Yeah, >> that's assuming that uh open router the the the kind of breakdown of how they're using the tokens is the same as open router which I think it's not.

11:54

But if we assume that that's um like $4,500 per engineer if there are >> uh I think 30,000 engineers at Meta. >> Yeah. >> Every month. $4,500 on tokens. >> $4,500.

12:08

That's actually in line with what I've heard a lot of other people spending in terms of their token budgets.

12:13

That's not like absurd absurd if you're trying to incentivize people to use that. >> Yeah. No, not at all.

12:18

>> But so so you can actually see the breakdown on open rider of how people are using tokens.

12:20

So uh 17 the biggest uh plurality is open claw which is uh 17. 6%. Yeah.

12:28

>> And then cloud code is is 16. 8. Sure.

12:30

>> So I think um uh if you think about cloud code uh you would imagine that like in cloud code there's uh the the kind of um percentage of cache tokens is going to be higher than in in open claw. >> Yeah.

12:44

>> Uh so I think >> uh meta's usage is actually going to be uh more heavily based on the the cache tokens. Sure.

12:51

So um if you do it just based off like cloud code usage uh you'd actually see um a higher percentage of of the input tokens be of the total tokens.

13:00

So it's only like8% is the output.

13:03

>> So then if you get all those numbers through again it's only like 55 million a month which would be >> uh 669 million a year >> uh and each engineer would be like $1,800.

13:14

>> Yeah that yeah that's actually pretty low >> which is like I think very reasonable.

13:17

John Chu over at Coastal says, "Plenty of my Meta friends told me folks have been building bots that just run in a loop burning tokens as fast as they can due to this policy.

13:25

It is an ab it's an absolutely stupid policy and is similar to how Meta uses lines of code to measure engineering output.

13:31

Managers are supposed to use it as a proxy and dig in to understand work complexity, but plenty of managers are lazy and just don't."

13:38

That was in response to Christine over at Linear saying, "Ranking engineers by token spent is like me ranking my marketing team by who spent the most money."

13:45

Yeah, >> we may not have hit our KPIs, but Joe spent 200,000 on a branded blimp that only flies over his own house, so he's getting promoted to VP.

13:54

>> I'm I'm pro branded blimps, though. I like that idea.

13:56

Um the my so so my take on this was uh that yeah, it it sort of ties to what Jensen Wong was talking about at GTC.

14:05

He was saying that uh an engineer that's making $500,000 might soon command something on the order of $250,000 a year in token budget.

14:12

Uh under Karpathy had a similar line.

14:16

Um he said uh it it's all about tokens.

14:23

Uh he said on a podcast last month, what is your token throughput and what token throughput do you command?

14:27

And so uh Meta actually has two different harnesses internally.

14:32

They have a version of open claw called my claw.

14:34

And then they also of course acquired manis, but it appears that they're running uh claude one maybe opus under the hood uh to actually uh generate the tokens that come through those harnesses.

14:47

Um the the interesting thing is that at 250k AI budget per engineer, you're at like 20,000 a month.

14:56

And so based on Tyler's math, this feels like, okay, there's going to be another maybe 4x to get to Jensen's prediction.

15:03

Uh the the the the baseline that was going viral around, oh, uh Meta spent like maybe a billion dollars last month with Anthropic, that would work out to like $83,000 a month, which is absolutely insane.

15:16

And I don't think anyone's really thinking that that's what's what's going on here.

15:20

that's what's what's going on here. Um but uh this felt a lot of people were saying like this is you know there's a lot of negative >> the other thing is like ARR is is is the annualized run rate version right which which does come down we don't know how they calculate it >> I'm assuming they're not choosing like a Saturday like multiplying that right it >> sure um but uh the the bigger question

15:47

is like a lot of people you know John Shu there is saying that uh you know oh this is like a bad metric and maybe it is but um I think it makes clearer the strategy with Meta Super Intelligence Lab because if you're looking at you know it's clear that they're spending hundreds of millions of dollars on this just for internal codegen tooling like running their business they are going to

16:10

spend an inordinate amount of money on frontier inference and so training a model there they will be able to amvertise the training cost of the next model that they build not just over can they get a product out that goes viral and becomes its own standalone chat app that people pay for or maybe it's ad supported like just on the internal usage they could be running a you know multi-billion dollar token bill that

16:39

they would have to pay another lab and so if they develop that internally it's pure vertical integration and then you also have everything that's happening on the actual ad targeting and content delivery side And when you add up all of those, all of a sudden the big question has been like, does Meta is Meta going to be able to launch an entirely new AI product uh like Vibes or something like that? Uh and this is a data point that

17:01

Uh and this is a data point that to me says they don't need to because just from a pure vertical vertical integration story, the investment in MSL can pencil out.

17:14

>> What are what are you laughing?

17:16

>> I just want you to get to your skitso theory.

17:17

>> What's a skitso theory?

17:17

that you that that the this this this whole like token maxing thing is like a is like a barrage while they distill the model. >> Oh. Oh yeah. Yeah.

17:26

I mean there is a there is a world where if you're running if you're if you're generating trillions and trillions of tokens of a frontier model >> like Meta is really like burning through a lot of tokens and I wonder it's like oh we're just token maxing. >> Yeah.

17:41

>> Yeah. I mean there's another story about distilling we'll get to later in the show but there there is a question about if I if I have a if I write an essay and then I have a model rewrite it those tokens they are from that model provider they I buy them they become mine can I train on them that's probably out of

18:02

terms of service so you would think no but you sort of wind up in this ship of thesis world where if if uh meta pays anthropic uh hundred million dollars or a billion dollars to go rewrite every line of code, every email, every Slack chat, every internal message, like basically map the entire organization, rebuild it. They wind up with an in

18:24

They wind up with an in incredible training corpus that they can use for their next model.

18:28

But I would imagine that they can't.

18:31

And I imagine that the uh that the enterprise contracts go both ways.

18:34

contracts go both ways. they can't uh you know the lab can't train on the corporate information that's standard in all of the enterprise contracts and I and I would imagine that the opposite is true as well although it is this fuzzy ship of thesis world where if you're using coding agents to upgrade your infrastructure and then you want to run

18:56

and train some model on your infrastructure do you have to pull out the tokens that were revised by the AI lab that you don't have the right to train on it's all very uh interesting apparently uh startups that have gone out of business are able to sell their corporate histories for something like a million dollars to data brokerage firms and AI labs now. Have you heard about

19:14

Have you heard about this? >> Yeah, heard about it.

19:16

I'm not I'm skep I'm skeptical.

19:19

I mean I mean certainly there there's market for it but uh >> basically all the need that a company built over a few years maybe they >> code but also usage within different enterprises. >> Yeah.

19:31

All sorts of different stuff.

19:31

uh basically like an you know an RL environment or something that can uh help whatever business process they were doing.

19:39

So if you're you could imagine a data broker buying the data set from a startup that has their go to market motion that can be rldled on and then also their codebase that can be folded into uh training on how to write better code and all of their marketing messages and basically everything that they did on internal external comms everything is tracked.

20:00

uh maybe they've been using granola or something.

20:02

They have like very detailed notes of everything about how they built the business.

20:05

Even though it wasn't successful, there's going to be a lot of lessons there that can be folded into the next training run of the next thing.

20:11

Anyway, >> uh in other news, >> yes, >> Intel is joining Terraab. >> Yes, let's read.

20:18

>> Intel is proud to join the Terafab project with SpaceX, XAI, and Tesla to help refactor silicon fab technology.

20:25

Intel says our ability to design, fabricate, and package ultra high performance chips at scale will help accelerate Terraab's aim to produce one uh terowatt a year of compute to power future advances in AI and robotics.

20:36

Uh and throwing up a post of hanging with Mr. Musk himself.

20:42

Um what uh Intel up uh up on the day up almost 3% unsurprisingly and just continues continues to be on a tear up almost 15% in the past month and 167% over the last year.

21:02

So >> uh let's go through the Wall Street Journal's coverage of this.

21:04

Uh Elon Musk is partnering with Intel on his ambitious Terrafab project which aims to build specifically designed chips for SpaceX and XAI as well as for Tesla.

21:13

Uh in an announcement Tuesday, Intel said it would work with the companies to design, fabricate and package ultra high performance computing chips at scale.

21:24

The company shared a photo of chief executive Lip Bhutan shaking hands with Musk, CEO of SpaceX and Tesla.

21:28

Uh the partnership is a win for Tesla which has struggled in recent year or Intel which has struggled in recent years leading the company to cut production capacity when demand was surging for data center chips and when competitors like Nvidia and AMD have thrived.

21:42

That was always a uh just such a tough pill to swallow when you would talk to the ASIC companies like Cerebrus and you would say hey like you're doing something new you're not you're not doing Nvidia chips.

21:56

Is there any way you could get off of TSMC?

21:57

and they're like, "No, like we still need to be in Taiwan."

21:59

Um, obviously there's a huge geopolitical component here.

22:03

We can get into all that, but uh last year the Trump administration reached a deal to acquire an equity stake in Intel for around $9 million to help secure the American chipmakers business.

22:13

Uh the US government held 8.

22:15

4% of Intel's shares outstanding in Mar as of March 20th according to securities filings.

22:20

The figure doesn't include warrants that could increase the government's equity stake in Intel.

22:25

Uh so that as you mentioned Intel shares gained 3% in in Tuesday trading.

22:30

Terrafab represents a step change in how silicon logic memory and packaging will get built in the future.

22:37

Lip Bhutan said on X Tesla and SpaceX confirmed the partnership in post on X.

22:41

In March, Musk unveiled the plans for a single facility in Austin, Texas to make chips to be used by SpaceX and XAI uh which merged in February as well as by the publicly traded Tesla.

22:51

He pitched the project as an opportunity to quickly experiment on chip design by designing and manufacturing the chips in one facility.

22:58

The fab will make chips for use in Tesla's robo taxis which they're already uh fabbing I believe at Samsung although they do have Nvidia dojo chips I think that are TSMC.

23:07

So they've been doing using both of those fabs but uh Optimus will also need uh chips and they are planning to use Intel for that as well.

23:18

So these are two areas of priority for the electrical vehicle maker as it shifts its focus to artificial intelligence enabled products.

23:24

It will also make chips optimized for use in space where SpaceX is planning to deploy huge numbers of satellites capable of handling AI computing tasks. Um, you know. >> Yeah.

23:34

So, who who else do you think they need to get involved here >> because just the two of these the two of these guy, you know, uh, Intel >> Yeah.

23:43

>> and Tesla coming together.

23:43

>> and Tesla coming together. Uh it's it's it's good to have more involvement, but still I think the entire project >> uh >> No, we've seen we've seen a few of those like AI leader gatherings in DC where you see Tim Cook and and Sundar and Sam

23:59

Alman and Daario and all the the whole all the leaders are together and I was always hoping that at one of those uh dinners they would say, "Okay, everyone's going to try and say the biggest number, but this time it's going to be how much you're committing to

24:13

Intel and how much you'll how you'll buy from them if they come online with a competitive product because the demand side is has has always been a big problem for Intel that they have the capability they have plans to build the 2n 3 nanometer plant like a frontier plant leading edge fab um but the every

24:33

other company has been so tied to TSMC uh but we but I think everyone now acknowledges that TSMC uh is not is not investing super heavily in capex they're not uh going you they're not scaling up as much as uh as the industry would like them to. And so lots of folks have have

24:49

And so lots of folks have have sort of signaled towards a chip bottleneck coming in the next few years and Intel has the opportunity to communicate that.

24:58

This seems like the first step in that in that chain.

24:59

So companies including Tesla often design their own semiconductors but need a supplier to actually make the so-cal make them in a so-called chip fab.

25:06

Uh must companies have sourced chips from a wide range of suppliers including Nvidia, Samsung, uh Taiwan semiconductor. Oh, I got it.

25:13

Uh, Musk said that Terrafab is needed because his company's demand for chips is is slated to far outstrip the supply it gets from partners.

25:23

I was listening to Chuck Robbins from Cisco talk about uh data centers in space and the heating issue came up and and he was like, "Yeah, I don't I don't I I don't really have like a a solid answer for that yet."

25:36

Um but I do think that if you are if you are bullish on uh data centers in space, you have to start with the fact that Starlink works in space currently because it is doing compute.

25:53

It's not >> possibly put let's let's be honest, John, we couldn't possibly put a computer up there. >> Yeah.

26:00

Like there there are computers with like they don't they they can't inference frontier models.

26:05

inference frontier models. can't you know it's not gigawatts in space yet but there are I believe across the entire Starling cluster megawws of compute in space with solar panels and they do heat up because you are running a chip that

26:20

routes packets across the internet from one satellite to the next to get you your internet via Starlink and so it's not that it's a solved problem it's that is that we are actually we are on a path to you know deploy some level of compute in space Tyler Yeah. I mean we we've

26:35

I mean we we've seen like Philip Johnson like there are chips in space right now like there are GPUs I think aren't there he said there were like five or six H100s right? >> Yeah. Yeah.

26:45

>> So so like they do work.

26:45

It's like the I think most people's problem with space data centers is that it's like economically it doesn't make any sense.

26:51

Well, so yes, that is the correct angle, but a lot of people are getting get hung up on but no, no, there is a whole conversation about like like it is impossible and and you need to like move past that into the economic equation which then gets you into timelines and actually thinking about what needs to happen to to dissipate that heat.

27:09

But clearly, yes, you can.

27:11

I mean, you can put humans in space on the ISS and and cool that there like we we have created ways to uh to to to move heat around in space for decades.

27:21

space for decades. Uh it's a it's obviously a new challenge but I think starting with with the baseline of like it it's it there is compute happening in space right now we're going to try and I mean Elon wants to like thousand exit 100 thousand exit million exit I don't

27:38

even know how uh what the what the scale is but orders of magnitude and so there's new engineering challenges but uh at the very least it's it's worth uh it's worth acknowledging that there is computation happening in space at scale with the starling cluster there. Uh

27:51

Uh anyway, >> speaking of space, looks like Elon is going to use SPCX as a ticker for the SpaceX IPO, which he had to acquire from Matt Tuttle, hence the ETF's ticker change shown below.

28:04

Uh Eric from Bloomberg says, "We predicted this could happen in a December note.

28:09

Nice catch by Will, who famously gave the meta ticker to Zuck.

28:13

I did not know that Will Hershey uh had the Meta ticker previously.

28:19

So, uh, we know we know somebody that spots on >> who who had the metaticker.

28:24

>> Uh, a guy named Will Hershey. Oh, interesting.

28:26

>> There's a company called Roundill.

28:26

Um, but we know somebody who's Matt Ball.

28:31

>> We had somebody here come in. Yes.

28:33

outside of show hours and say that they were squatting on a bunch of >> tickers and the and the idea >> seems so >> I I think I think what what what might be the reality is that you actually that it needs to be >> um >> it needs to be further along than just reserved.

28:51

I don't know that having it >> uh you can go uh if you're a startup today, you can go reserve your ticker today.

28:59

Uh but I'm not sure that that actually gives you enough leverage to when when uh Elon comes knocking ready for an IPO, you actually have priority over.

29:10

>> I think you have to actually be doing something with it.

29:11

So, uh, if I have it correct, I'm looking it up to make sure that I have the facts straight, but um, uh, the Matthew Ball, uh, started an ETF based on the metaverse, which was, uh, he wrote a whole book about the metaverse and had a series of blog posts outlining the broad trend and the various companies that would benefit from that.

29:33

And so >> ticker squatting ticker squatting.

29:38

>> The team does not like uh Oh, okay.

29:40

Round squats on lots of tickers. That's interesting. I don't know.

29:42

Apparently Meta Materials was in that ticker for a little bit.

29:47

There's been a few others, but um I think >> Roundhill creates ETFs. >> ETFs.

29:52

Well, I think if you have an active ETF, that's a lot different than just having a reservation. Although, I don't know.

29:58

Maybe you do get paid off. What?

30:00

>> So, so this uh fund, it was only like $10 million. So, it's very small.

30:02

So you actually could like >> Yeah. >> Yeah.

30:05

I mean $10 million is still like not you know it's still a fair amount of amount >> but you could potentially launch an ETF with 10 million of >> Yeah.

30:12

Like like if you have a really good ticker >> Yeah.

30:16

>> you know you could you can definitely make an argument that like yeah it's worth over $10 million and you can somehow marshall enough capital to to you know do something with it.

30:23

There was a there was a YouTube influencer who launched an ETF and uh it it sounded so good on paper because it was like wow like his audience came together and put like $10 million into this ETF but it was like uh generating something like three basis points of fees or something.

30:44

So regardless of how it was like uh regardless of what the fund did, like the actual flow would be like less than what he was earning in YouTube ad revenue.

30:53

Like a few thousand dollars a month would be like the net gain from the from the ETF effort.

30:57

And I think it was something that uh got wound down pretty quickly.

31:01

Uh anyway, I had I had a broader take on uh Elon and Intel.

31:02

So, um, back in 2022, uh, I was trying to imagine this divergent path, the the the road not traveled of Elon buying Intel because Intel >> Yeah.

31:16

We talked about this before that we there was a moment where people were tracking like Intel's corporate jet, Elon's >> and Global Foundaries, too, but Yeah. >> Yeah.

31:24

And it and it seemed but I think it ultimately ended up just being election related. >> Yeah. Yeah, I think so. Yeah.

31:28

They happened to be at Mara Lago maybe at the same time.

31:31

Um but uh it it was it was such a cool idea because Elon had uh you know I mean he'd run PayPal and stuff.

31:38

He he had some experience in like software but he hadn't run a social network.

31:42

So there was a lot of there was a lot of questions about like what would happen to Twitter?

31:45

Would he be able would he need to change the business model?

31:47

He wound up doing subscriptions.

31:49

It wound up being a good exit because he rolled it into XAI which rolled into SpaceX. So everyone did fine.

31:52

But there was a lot of like, okay, $44 billion for Twitter, which after the Zerp era ended, all of those companies traded down 60% or something.

32:03

So, there was a lot of chatter around is is this a good deal?

32:07

Is this a good use of his time?

32:07

He's known for running incredibly intense engineering operations, making self-driving electric cars, rockets that land themselves like like semiconductor fab feels more like that than social network feels like like rocket factory, right?

32:26

It is a factory at the end of the day.

32:28

And so I was trying to work backwards from like could that actually have happened? I don't know.

32:32

The numbers I had was were uh so at the end of 2022, Intel was a $110 billion market cap company.

32:40

Like that's a huge company still even though it had been beaten up in the public markets.

32:44

Uh but Elon had just put together 44 billion to buy Twitter and the Chips Act had put uh in 2022 had just put together 280 billion including 53 billion specifically for US semiconductor manufacturing.

32:58

Also, just think about where Intel would trade if Elon >> had been able to own it or roll it in.

33:03

I mean, it'd be it would be like just putting together the 44 billion was a herculan effort and there was a ton of debt and there were all these different parties involved.

33:14

>> It's just a much much different business.

33:16

>> 110 and and it's not like you can just go buy the company at its lowest possible value.

33:21

you have to make an offer that all the shareholders will approve of and potentially be higher than the uh than than what the market cap is because if you're if you're an Intel shareholder and you're looking at $10 billion market cap, you would imagine that uh it could go up and it did.

33:40

It's now over what 200 billion Intel market cap.

33:42

What what are they at right now? 260.

33:45

So like if you if you if you were a shareholder in 2022 and you were at 110, you probably, you know, are happy that you didn't get bought out at that time, but it was possible.

33:55

Uh it wasn't that much of a stretch to imagine something happening there four years ago.

34:01

We didn't wind up on that path, but you know, my hope is that this is the first step uh on the long road to generate enough demand for domestic chip manufacturing to really move the needle.

34:12

Um because I'm rooting for Intel and I'm rooting for American semiconductor uh supply chain.

34:16

Uh Tyler went over to the TSMC plant in Arizona.

34:18

It seems like it's going well. Um >> yeah, it's huge. >> It's huge.

34:24

Um >> they didn't let me in.

34:26

>> They didn't let you in, but we need more very clearly and uh Intel is working on that fortunately.

34:32

>> All right, we got to talk about a corporate retreat that went badly wrong. >> Okay.

34:36

Technology company Plex took its 120 employees to Honduras for a week-long bonding experience.

34:41

It was a disaster from the moment they arrived.

34:46

Senior executives at the tech company Plex were eager to treat their 120 fully remote staffers to a week-long corporate getaway in a tropical paradise. Pop quiz.

34:55

Tyler, do you know what Plex is?

34:57

>> I don't know about Plex. >> No.

34:58

>> Have we seen Plex before? >> I don't know either. So, we all failed.

35:02

But now it's your job to figure it out. I will continue.

35:04

The plan for the Honduras trip was simple.

35:05

Company meetings and team uh building >> by powdery soft beaches during the day and island fund at night at a cost of roughly half a million to the company.

35:15

They'd build the trip around a survivor theme with teams and challenges, but it'd be fun, not too physically grueling.

35:20

The CEO of Plex, a free streaming platform, would play a role similar to that of Survivor host Jeff.

35:27

Perhaps the executive should have taken it as a sign that just as the first bus of staffers pulled up to the resort.

35:30

The chief executive was already in his hotel bathroom experiencing the initial waves of a violent stomach infection.

35:37

What followed was a comedy of errors, including military drills that outpaced anything this group of office workers had in mind.

35:44

A rogue porcupine, stranded airplanes, and one syringe to the butt of an employee.

35:49

Corporate retreats are generally assumed to be torture or at least a semi semistressful chore.

35:54

What with their forced fun activities and hybrid workplay environments that leave workers confused about boundaries?

36:02

Is that >> is that is that like the industry standard? That seems wild. >> I don't know.

36:09

I don't think I've ever been on a corporate retreat.

36:10

I've been on some like founders fund events, but those aren't really retreats.

36:14

Those are more just like conferences. But I don't know.

36:18

Corporate Retreat seems, I don't know, never unexplored territory for me.

36:23

>> It's no wonder the new season of Jury Duty, a comedy series that tricks an unsuspecting non-acctor into believing his off-the-wall fictional circumstances are actually happening, is set at a corporate offsite.

36:32

But in real life, Plexon 2017 beats anything on TV.

36:34

Here's the story of an Allstaff Company getaway told by six people who were there.

36:39

A trip where most everything that could go wrong did go wrong.

36:42

Nearly a decade later, they're still working together and still talking about It had effect.

36:50

>> It It was bonding experience. We all >> worked. Well, yeah.

36:52

It's crazy that this is now now coming out.

36:53

So, Sean Hoff, 42, founder of Moniker Partners, an independent corporate retreat agency that planned the trip.

37:00

About 3 weeks before we arrived in Honduras, we got an email from the hotel's general manager that said, "I will be departing.

37:04

I wish you the best with your retreat."

37:06

I knew something was off.

37:08

3 days later, another email.

37:10

The head chef was no longer going to be at the hotel.

37:12

Uh, Scott 52, chief product officer and Plex co-founder.

37:15

we get there, we've got to take the bus from the airport, dirt roads, you start getting closer and there are guard towers around the property, people with machine guns and stuff.

37:23

A lot of people were like, "Where are we going?"

37:25

Keith, uh, the CEO of Plex 54.

37:28

We usually go a day early and we set up.

37:30

If there's any little thing, we have to get it right just so the employees have the best experience possible.

37:35

Uh, Keith woke up the day that people were coming in Sunday morning and he is sick as a dog. Everyone there is fried.

37:41

Basically, people are telling me, "Don't eat the vegetables.

37:44

Don't eat the vegetables."

37:45

>> Don't eat the vegetables. That's like the same. >> No, no, no. Cuz they they clean it. They wash it in water.

37:49

And it's usually not filtered water, right?

37:50

Because it would just be kind of crazy to >> Yeah. Yeah. Here it is.

37:53

I I >> So I've got to have a salad. Just one salad. So I got E.

37:56

coli, which may be the worst thing you could get possibly ever.

38:00

Just as people were arriving on the buses, I was like, I lost eight or 10 pounds.

38:04

They had a doctor come to me, which apparently is pretty standard.

38:08

They nailed an IV bag to the bed post. Just nailed it.

38:12

>> Uh people are arriving for a party that night.

38:13

The next day is Survivor theme kickoff.

38:15

There is not one person on the planet more excited about Survivor than Keith and his wife.

38:19

They have watched every single episode.

38:20

My wife and I met Jeff, the host of Survivor.

38:23

What what I wanted is when everybody shows up, I do a Jeff Welcome to the island.

38:27

Here's the theme for the week. But Scott got to do it.

38:31

The opening Survivor thing was a contest where people on their different teams open up a platter.

38:35

You have to eat what's on the platter. Sean.

38:37

Sean, who's the Plex head of business development?

38:42

>> Who you going to call? >> Um, yeah.

38:43

Somebody is uh somebody is uh cold texting me. >> Oh, yeah.

38:49

>> Pitching me their their startup and they've called me a bunch of times today. Um >> Oh, wait.

38:54

Is it actually them or is it their AI agent?

38:57

>> I I wish I could pick up.

38:57

It's just like a little bit little bit too much.

39:02

>> But yeah, cold texting somebody like getting their number.

39:04

Um I I don't think that's the new meta.

39:08

>> No, >> it's it's um >> it's we we heard from an executive in uh in tech that they are getting dozens of emails every single day um trying to recruit them.

39:21

And every email comes from a new Gmail account that's un uh that's like unregistered, brand new, but it's all like, you know, LLM written, very different, like doesn't really do all the research, but has a few keywords in there.

39:38

And it's clear that someone is is building sort of like a nextgen recruiting agency that's basically just a lot of spam.

39:46

uh feels like the the the end result will be like a return to relationship building and not like broad top of funnel.

39:54

>> I should read the cold the cold text from this morning.

39:56

And I have nothing against nothing against cold uh cold email and just uh you know being being bold, but I did uh read this out loud to you, John, so I'll read it to everyone.

40:06

So I got a text from an unknown number today at 7 a. m.

40:08

All right, Jordy, good news or bad news first.

40:11

This is blank and I'll leave the name out.

40:14

And then I just get a PDF of a deck and then uh a text.

40:21

All right, Jordy, the bad news is this was an unplanned introduction and on the surface probably lukewarm outreach.

40:27

The good news is that there's zero doubt you're now in touch with the founder with the most grit of anyone you've interacted with the past 12 months and likely anyone you'll interact with over the next 12 months.

40:37

50,000 seed round passes over the past 10 months here to make 50,000 in one.

40:47

So, so no, you should be coming in being like, I've been passed on 50,000 times.

40:52

I'm hoping this is the one that gets through >> that gets through.

40:55

>> That seems like a rough estimate though.

40:57

>> Months of feedback and iterations have made it better.

40:59

So, you're seeing more quality more quality presentation than rejection 10,000.

41:03

Looking forward to your message.

41:07

>> The chat wants the builder to pitch.

41:09

They want you to hear this out.

41:10

Everyone's in favor of this. >> Wait, wait. Pitch who?

41:15

>> They the chat wants you to get on the phone with them.

41:17

I mean, they wanted to do live.

41:19

I don't know if you should do live, but you should take the call and uh and get to the bottom of it. >> I will take the call.

41:24

I will take the call, but >> let's go back to the corporate retreat. Um, >> okay.

41:30

So, they hire they hire uh a former Navy Seal, >> okay, >> to basically haze at the team on the beach.

41:36

And you can pull up a picture, an image here.

41:40

>> The quote is, "This is not a super fit group in general.

41:42

One of our biggest mistakes was hiring a former Navy Seal to pump the team up."

41:46

As I'm in my room dying, I could hear them out there doing all the drills and yelling.

41:51

And so I'm in here thinking, "This is terrible."

41:54

But it sounds terrible out there, too.

41:56

Uh we're doing army crawling on the beach. It was 100°.

41:58

I bailed out partway through.

42:00

I went into the ocean just to cool off.

42:02

I went in probably on all fours because I was tired. It's not a fit group.

42:06

not a super fit group in in general.

42:08

The ex-Navy Seal is like, "We can tone it down, no problem."

42:10

We get up there and it's hot and humid and people are passing out.

42:13

I don't think he'd ever seen quite such an unfit group.

42:15

We ended on, I guess, what's probably a golf course uh on command.

42:20

Everyone had to hit the grass. Everyone's silent.

42:23

We're pretending we're Navy Seals, but I happened to land in the wrong spot.

42:26

I'm just like, "Oh god, what is happening?"

42:29

I was sitting on a fire antill. I was wearing shorts.

42:31

I jumped and had my I had hives and bumps from the bites. This is ridiculous.

42:35

Someone saw an alligator on the golf course.

42:37

Sounds like a ridiculous.

42:39

>> There was a porcupine that fell through one of the ceilings.

42:44

>> This is like a fire festival for uh corporate.

42:47

>> Fire festival of corporate retreats.

42:49

>> I It's It's fun that this this porcupine is horrifying. Wow, look at this guy.

42:54

Rick Phillips discovered this in his room shower.

42:55

The hotel got uh pretty much just got the porcupine and left.

42:58

I guess it for me it was a good thing because being a non-talkative software engineer, I got some notoriety. It's a beautiful resort. There are sand fleas.

43:07

They had to fumigate every day. What a weird quote.

43:09

Uh we did a nice dinner down the by the beach and everyone got bit by the sand fleas that weren't supposed to be there.

43:15

We all got matching tank tops.

43:18

>> Real life uh real life episode of The Office.

43:22

>> Anyway, >> you can imagine Michael Scott.

43:24

>> Um there is some breaking news.

43:24

Um, Anthropic is set to preview powerful Mythos model to ward off cyber AI threats.

43:31

Uh, the AI company's partnering with Amazon, Microsoft, and others to offer the new model to find and patch software bugs.

43:38

And uh, this is from the Wall Street Journal.

43:40

Anthropic is taking steps to arm some of the world's biggest technology companies with tools to find and patch bugs in their hardware and software.

43:47

The company is making a preview of its new AI model called Mythos available to about 50 companies and organizations that maintain critical infrastructure including Amazon, Microsoft, Apple, Alphabet uh owned Google and the Linux Foundation.

44:00

Uh cyber security researchers and software makers worry that artificial intelligence is becoming so good at exploiting vulnerabilities that it could cause widespread online disruption.

44:10

Security experts have predicted that AI models will discover an avalanche of software bugs and the effort is set to help companies stay one step ahead of cyber criminals and other threats.

44:17

This feels like a very good rollout strategy generally both because we've seen a huge amount of of cyber attacks and and hacks and accidental releases like even if it's not uh you know there's been we had a a member of the security team from CrowdStrike on the show last week uh talking about the the rise in cyber attacks broadly and so getting the getting the uh uh the the the the most frontier models in the hands of big companies early.

44:51

Great from that perspective and then also just great as a product demo uh which will get the entire organization excited about deploying the technology broadly.

44:58

So very good as like a as a B2B uh go to market motion.

45:04

This makes a ton of sense.

45:05

Uh when measuring the dollar cost to find a bug, mythos claims to be about 10 times as efficient as previous AI models.

45:12

Uh details of Mythos's capabilities were previously rep reported by Fortune.

45:16

While Enthropic has no immediate plans to release Mythos, other models will likely match its bug finding capabilities within the next few years.

45:24

Graham said we basically need to start right now preparing for a world where there is zero lag between discovery and exploitation.

45:29

So very carefully rolling these out. Uh the uh Claude Opus 4.

45:35

6 found more high severity bugs in Firefox in two weeks than the rest of the world typically reports in two months.

45:43

And so, uh, good good news there for cyber security.

45:46

Um, there is some >> some news yesterday, uh, in the New York Times, shots fired at Indianapolis Councilman's home after a boat that was backing a data center.

45:56

Uh, no one was injured, but Councilman Ron Gibson called it deeply unsettling. broke on X yesterday.

46:04

And Alice writes in the New York Times, "Bullet hit bullets hit the home of an Indianapolis city councilman early Monday morning, leaving shattered glass and holes through the front door and a handwritten note reading, no data centers was left under the doormat."

46:15

The councilman, Ron Gibson, was among the city's leaders who voted 62 last week to approve a reasonzoning measure that would allow Metro Blocks, a Los Angeles company, to build a data center on the northeast side of Indianapolis.

46:27

Local residents had protested the proposed data center for months, citing concerns about environmental impacts and changes to a historic neighborhood.

46:35

Dozens of people filled the city council uh chambers city county council chambers last week before the vote holding signs and speaking in opposition to the data center.

46:46

The station reported um gunfire at his home crosses a line, Mr.

46:50

Gibson wrote in an emailed statement.

46:52

I understand that public service can bring strong opinions and disagreement, but violence is never the answer, especially when it puts families at risk.

46:58

So, very, very dark uh situation. Mr.

47:01

Gibson wrote that he and his 8-year-old son were awakened by the gunshots between 12:45 and 12:50 a. m.

47:08

Monday, and he rushed to reassure his son that he was safe.

47:11

He said 13 rounds were fired at his home with bullets striking just steps from the dining room table where his son had played with Legos the day before.

47:19

So, >> uh, incredibly, incredibly dark.

47:23

Run says, uh, just so everyone is clear, this is evil.

47:27

You were justified in thinking it's morally bad.

47:29

Tons of apologetics happening for bad people.

47:30

If you think behavior like this is just desserts for the tech industry due to some hobby horse you have, you've gone insane.

47:40

And Noah Smith says, "We may still be underrating how big of a political issue AI is going to be."

47:45

And uh there's a video here from CBS.

47:49

We can we can put the sound on. >> Oh yeah.

47:53

>> Just days after voting in favor of building a new data center in Indianapolis.

47:57

Local council member Ron Gibson says he woke up to the sound of gunfire overnight.

48:01

Gibson said 13 rounds were fired at his home while he and his 8-year-old son were asleep.

48:08

Some of those bullets landing just steps from their dining room table where his son was playing with Legos the day before.

48:17

When he stepped outside, he says he found this handwritten note reading, "No data centers under the doormat."

48:24

>> There are real benefits tied to this development.

48:28

Construction is expected to support roughly 300 jobs over a three-year period.

48:34

>> While some counselors argued the data center will bring revenue and jobs, there was push back from residents over environmental and quality of life concerns. Thank you very much.

48:43

>> The vote to move forward passed 6 to2.

48:46

Indianapolis police are calling this an isolated and targeted incident and police have yet to identify a suspect.

48:54

And one group that protested that data center last week released a statement today saying in part, "Violence has no place in our community or in our advocacy."

49:02

The FBI now assisting in the investigation.

49:05

Tony >> Chanel, thank you for >> Yeah.

49:08

Noah Hersko in the chat says people have gone insane.

49:10

Uh it is it is extremely disheartening to see.

49:13

Um I mean I I would hope that the uh the case to be made for data centers is much more complex than 300 jobs over three years.

49:26

Uh like the rateayer protection pledge, the environmental concerns, all of that needs to be addressed and communicated not because of this this is you know horrible.

49:35

Um but just in general from the from the public that has push back and and reticence about the data center developments. >> Yeah. Local tax revenues. >> Yeah.

49:45

And and it should be uh it should be a boon to every community and the uh every community should feel like the the net benefit is truly positive and I feel like people many many community members do not feel like it's in their favor and so there's a lot to do on that front.

49:59

Um uh in some other more positive news, OpenAI, Enthropic, Google are uniting to cop uh to combat model copying in China.

50:12

This is a bigger discussion around AI safety.

50:15

Uh we've talked about this.

50:18

>> You look at that >> some some uh >> who knew faith in that get along. >> Yes.

50:24

>> Yes. I mean like I mean I'm sure people in the chat have seen the New Yorker article where uh there's just tons and tons of quotes from various AI leaders all you know upset with Sam Alman and uh the the uh the interai drama has been uh

50:43

bubbling up since the dawn of open AI like OpenAI was started as a reaction to Google and then Anthropic leaves and teams up with Google and then Elon doesn't like Anthropic and then Ilia Sutzkver and Meera leave but they don't join Enthropic and so there's been so

50:59

many personalities and so many disputes I feel like the uh the the takeaway is that this is all extremely high stakes there's a technological uh you know transition happening a huge amount of money on the table a huge amount of

51:13

influence on the table and so everyone is uh sort of clamoring for their share and uh it's creating a lot of a lot of friction um but uh My my overall takeaway from the uh from the uh New York article was uh a lot of that had been already reported out. A lot of that

51:30

A lot of that was, you know, we sort of knew that there were rivalries and and and uh you know, uh a lot of hurt feelings between different members of the AI community and uh nothing was particularly shocking to me, but uh if you have more comments, please leave them in the chat.

51:49

Um let's go through this uh this com uh what's actually going on with this uh this um model copying in China question.

51:57

Uh so rivals OpenAI Anthropic PBC and Alphabet Inc.

52:03

s Google have been have begun working together to try and clamp down on Chinese competitors extracting results from cutting edge US artificial intelligence models to gain an edge in the global AI race.

52:13

The firms are sharing information through the Frontier Model Forum, an industry nonprofit that the three tech companies founded with Microsoft in 2023 to detect so-called adversarial distillation attempts that violate their terms of service.

52:28

According to people familiar with the matter, the rare collaboration underscores the severity of a concern raised by US AI companies that some users, especially in China, are creating imitation versions of their products that could undercut them on price and siphon away customers while posing a national security risk.

52:44

And so I was trying to square this question of distillation and model commoditization with the news that Enthropic has reached 30 billion in run rate and has a agreement with Google and Broadcom for multiple gigawatts of TPU capacity.

52:59

Like clearly there is insatiable demand for frontier tokens, frontier models.

53:05

They're incredibly expensive to train.

53:07

We saw in the Wall Street Journal that um that uh these >> expected training costs from >> Yeah, it was training and inference, but it was hundreds of billions of dollars.

53:17

And so the hope is that you is that you're able to amvertise that over >> over at least a couple years, you know, a long time ideally.

53:25

Uh the the the shelf life of a model after you train it is pretty limited.

53:30

If you're being commoditized and copied, if you're being distilled, it's even faster.

53:34

uh at the same time just staying on the frontier clearly leads to an incredible ramp in revenue.

53:42

So is commoditization a real problem.

53:45

It feels like it's almost just more of a problem from an AI safety perspective because you can't have the geopolitical conversation like what Bernie Sanders is proposing around uh different different labs working together potentially pausing or slowing down or just just even adding more constraints and reviews before models get released.

54:05

um it's harder to do that if you have a different country that's racing ahead and moving much faster and trying to close that gap.

54:14

Now, if the model is delayed in America and their whole strategy is to to distill the model, well, then they're still three months behind even if we take three months off in America.

54:24

So, maybe that's not an issue.

54:26

But, uh it it was an interesting uh it was an interesting uh development.

54:31

So um uh the uh like as models get bigger and more powerful hopefully it becomes easier to track distillation efforts I would imagine.

54:39

So what is the canonical example of the distillation uh question was that originally deepseek Tyler?

54:49

>> Uh I mean I I don't know if it's very hard to prove right because it's like >> how do you you know you don't if you just look at the weights there's no way to really tell.

54:56

It's just like you just ask the model you know who are you and then it says I'm Claude.

54:59

Yeah, it's like the smoking gun, but that doesn't always happen because you could remove all references to >> Claw.

55:06

People were saying when you know DeepSeek first like came on, everyone's like, "Oh, wow. This is so good." >> Yeah.

55:12

>> People at Open Eye were saying like, "Okay, they've distilled on this." Yeah.

55:15

But like was there any like proof? Not really. Maybe just like Vibes.

55:17

If you talk to models a lot, you see that they kind of respond similarly compared to other models.

55:22

And and I mean the smart strategy if you are a a distiller lab would be to generate a bunch of tokens from uh Google Anthropic and OpenAI mix them together so that maybe every once in a while it says it's clawed but only onethird of the time because you have a whole bunch of other tokens from other from other models and it all blends together.

55:44

So that actually underscores the need for the Frontier model forum and the three companies working together.

55:50

I do wonder how you like actually combat this.

55:51

Uh because it seems very hard unless you basically just say the only people that can hit the API are like trusted enterprises.

55:58

>> Um and you have to sign these big contracts before which I think is >> there's a good case to be made for for that being like the the way forward.

56:06

>> I mean it doesn't seem that crazy to do uh like varying levels of KYC for varying levels of token spend or or enterprise contract.

56:13

So, if you are uh if all of a sudden it's like you're doing meta level inference, well, let's make sure that it's meta that's actually the one on the other side of that contract and that they're not vending the the frontier tokens into another like reselling them basically in some way.

56:32

So, you need the chain of know your customer, but uh in theory, you should be able to have, you know, a $200 pro plan that only that would does not ever deliver the level of of tokens to fully distill the model.

56:47

And then as someone ramps up on the API and they're spending 5,000 a month, okay, you do a little bit more of a check, then they're spending $100,000, then they're spending $10 million.

56:56

And once they get up like every lab must know, okay, to distill this, you probably need to spend a hundred million dollars, $10 million, whatever the number is, set the KYC threshold there.

57:08

And but it's going to be cut in a third because it's going to happen across all of these labs.

57:12

So whatever their whatever their threshold is for, okay, it's really time to go and and do the do the proper KYC.

57:21

Um you sort of need to divide that by three because you have to assume that it's happening across all three.

57:25

And then it's also probably happening across multiple smaller organizations that are, you know, essentially fronts and are harder to KYC. But I don't know.

57:35

>> Uh, let's do a lightning round of >> timeline posts. >> Okay. What do you got? >> Read Wiseman. >> He's an astronaut.

57:42

Yeah, >> he's on Artemis 2. >> Yeah. >> December 7th, 2016. >> He posted at 8:47 a. m.

57:48

Dreamet I was in lunar orbit last night.

57:51

been in that post vivid dream that wasn't real funk all morning and uh yesterday >> he made it real. >> Wow.

58:00

I I I saw this poster and I was like, "Oh, like that's just like some random poster." Like, "Okay, cool."

58:06

Like, yeah, like cool story.

58:06

Like, yeah, anyone could have that dream.

58:09

I didn't realize that it was actually the uh >> the astronaut who is now on on uh around the moon and making his way back to uh to Earth.

58:20

There was another uh cool video from uh the uh uh from the moon uh mission.

58:29

Uh astronaut Victor Glover discussed what it means for him and the entire Artemis 2 crew to be observing Easter Sunday from space during their historic mission.

58:37

Uh we should pull this video up. It's on X.

58:40

It's about a minute long.

58:43

Let's see if we can play this.

58:46

I think these observances are important and as we are so far from earth and looking back at you know the beauty of creation.

58:53

creation. I think the for me one of the really important personal perspectives that I have up here is I can really see earth as one thing and you know when I read the Bible and I look at all of the amazing things that were done for us who were created it's you you have this

59:10

amazing place this spaceship you guys are talking to us because we're in a spaceship really far from Earth but you're on a spaceship called Earth that was created to give us a place to live in the universe in the cosmos think maybe The distance we are from you makes you think what we're doing is special. But we're the same distance from you.

59:27

But we're the same distance from you.

59:28

And I'm trying to tell you, just trust me.

59:30

You are special in all of this emptiness.

59:32

This is a whole bunch of nothing.

59:34

This thing we call the universe.

59:35

You have this oasis, this beautiful place that we get to exist together.

59:40

But I think as we go into Easter Sunday, thinking about, you know, all the cultures all around the world, whether you celebrate it or not, whether you believe in God or not, um, this is an opportunity for us to remember where we are, who we are, and that we are the same thing, and that we got to get through this together. >> I love it. >> Powerful stuff. >> Great mission. >> Creatine cycle.

1:00:00

Yeah, >> having a GF is insane because it's literally unlimited chat with no token spent. >> There we go.

1:00:06

Uh, what did uh, Sucks say?

1:00:10

Is three weeks too young to give my baby a reatride?

1:00:12

Not trying to raise a fat porky butterball. Yes, it's too young. Be careful out there.

1:00:18

Uh, have you seen this image of the Instagram go growth guru?

1:00:23

This goes viral constantly and normally it's a it's like a reals reaction video, but someone took that this exact message and put it on X and it went mega viral with 130,000 likes.

1:00:35

Instagram go grow growth gurus are so funny.

1:00:37

He can't use his laptop because he's holding a drink.

1:00:39

He can't drink because he has a cigar in his mouth.

1:00:41

He can't smoke his cigar because both hands are occupied.

1:00:45

I think I think this is uh surmountable though.

1:00:47

I think you can actually hold the cigar with the martini or something.

1:00:52

But it is a true like peak performative growth guru. Very very funny.

1:00:57

But uh I'm sure I'm sure it had its intended purpose.

1:00:59

Uh Chase passive in says that's my mentor you're talking about.

1:01:04

He makes multiple eight figures in passive income and only charges $25,000 for a full b full day boot camp mastermind to teach me his strategies. Delete this.

1:01:11

Um what is >> high yield Harry dialing into our morning meeting.

1:01:16

Morning stocks are down slightly after Trump announced a whole civilization will die tonight.

1:01:20

Uh really dark over on social today and uh hoping >> yeah it's on the cover of the journal today.

1:01:28

Trump Trump stirs fear in Iran over talk of attack.

1:01:31

President says the US military could take out the entire country in one night.

1:01:35

Uh President Trump said Monday that the US military could take out the entirety of Iran in one night.

1:01:42

And Iranian officials have told mediators now to trying to reach a lastditch ceasefire deal that they uh that they fear Trump will follow through on a massive attack Tuesday night.

1:01:52

So we are we are continue to hope hope for a uh a resolution to the the geopolitical conflict.

1:02:00

It's very very frustrating.

1:02:00

Um and uh yeah, it would be so much better to refocus on uh problems at home and uh opportunities at home.

1:02:08

Um anyway, >> uh moving on.

1:02:12

>> What is Pirate Wire saying about Satrini? An analyst number three.

1:02:15

Uh Ryan writes, "Our newspapers used to put reporters in active war zones. They've stopped.

1:02:22

So new media is picking it up.

1:02:24

Last week, research firm Satrini put a man in the street of Hormuse to figure out what is going on in the oil world."

1:02:29

called Satrini's uh quad quadrilingual employee referred to only as analyst 3 >> packed a bag with 15 grand in cash some zins and cigars before shooting over to Aman after managing to finagle his way onto a speedboat.

1:02:44

Analyst 3 reported from the water just 18 miles away from the Iranian coast while smoking a Cuban in a world uh where the New York Times is calling uh NATO the North American Treaty Organization.

1:02:56

We've got live financial reporting from Iran.

1:02:59

Um yeah, this this was just absolutely uh an insane story.

1:03:05

An insane story of you can just do things. >> Totally.

1:03:08

Uh yeah, I'm talking to Satrini about coming on the show later this week.

1:03:11

Uh obviously he's been working incredibly incredibly hard with his team to uh report and publish this and and turn things uh into uh analysis.

1:03:19

And there's a whole uh deep dive that you can read on Catrini.

1:03:24

Uh and uh just what a fantastic uh um uh piece of reporting. Truly uh unexpected.

1:03:31

I didn't think anyone would do this. We were joking about it.

1:03:35

Uh and they just went and did it.

1:03:38

>> Uh we stopped doing ads, but NASA >> NASA's doing carrying torch. Let's see.

1:03:43

>> They have uh >> Is that a sponge daddy or something? >> Yes. Scrub Daddy.

1:03:47

That's the viral infomercial product, right? Yes.

1:03:51

>> And then a liquid death as well.

1:03:53

>> Is there a liquid death?

1:03:54

>> This is This has to be fake, right? The Taco Bell thing. This is a joke. I can't tell.

1:04:00

Is the Nutella's in here?

1:04:00

These are all seems to be real Nutella >> AI generator or something. Yeah.

1:04:08

Uh Grock wanted me to tell you it's real. Um I don't know.

1:04:10

Uh it does seem like people are are having fun with this.

1:04:15

Um but uh yeah, very very funny. >> All right.

1:04:19

Uh, Andrew Hubman says 93 years and 231 days old.

1:04:22

Ann is sten dead hangs for 2 minutes and 52 seconds. >> That is insane.

1:04:31

>> To set new world record.

1:04:33

>> We tried this this morning.

1:04:34

>> Yeah, we did this this morning.

1:04:35

>> 2 minutes and 52 seconds is truly an eternity.

1:04:38

I don't know if that's in the cards for us.

1:04:39

Maybe maybe we can get there. We What is Karp at? Five minutes something.

1:04:45

>> Yeah, Karpa is at around five minutes. >> Absolutely insane.

1:04:47

Yeah, these these numbers don't sound big, but you start hanging and it is absolutely brutal.

1:04:52

So, congratulations to to Ann. Yeah, what a feat. >> What a feat.

1:04:58

Well, we have Riley Walls in the waiting room.

1:05:00

Let's bring him in to the TV Ultra Dome.

1:05:05

>> Riley, how you guys doing?

1:05:06

>> Hey guys, >> are you >> We are good.

1:05:09

>> Are you where I think you are? Introduce yourselves. Explain where you are.

1:05:12

>> We are We are at the alley. We're live at the alley. >> Live at the alley.

1:05:15

This is uh this is the street right here.

1:05:17

Um >> yeah, life is good.

1:05:19

This is this is my friend Patrick.

1:05:20

Uh we worked on uh the alley together.

1:05:23

Yeah, this has been a really fun project.

1:05:24

Um we Yeah, it's it's been a blast.

1:05:28

>> Give us the full history of of the alley, how you guys came to own it, and then we'll get into this project.

1:05:36

>> Basically, uh we learned that this this alley was forclosed on.

1:05:38

And it's kind of a long story where this this woman accidentally bought it thinking that she was buying this house right here, which is No, it is this funny.

1:05:44

Oh, it >> is which which was which was worth like a million dollars and she bid 25K on it thinking that she was getting the steal of a lifetime.

1:05:51

She won >> and is this the kind of thing she didn't she didn't want to tell anyone like because she was like, "Okay, this is like probably thought it was too good to be true, but then like maybe it was real so didn't want to tell anyone if she had talked about it."

1:06:06

She knocked on the doors because this is an apartment building and she knocked on the doors and told the tenant she was not raising the rent.

1:06:11

Um so she was she she really thought she owned this and then she realized she didn't and then um there was a whole news story about her mistake and then we we eventually reached out to her and and negotiated for a little while and we we bought it for um a little more than she actually bought it for.

1:06:26

So she got bailed out and we we were able to do a nice thing and still do this crazy project. >> That's amazing.

1:06:31

Uh, so did you find this alley from the news article?

1:06:34

Is that how this all started?

1:06:38

>> Yeah, we we had the the Patrick and our friend Theo and I, the three of us, we had been talking for like a a while before that about buying a different alley in San Francisco, >> but we couldn't end up buying that one, but we we saw this new store.

1:06:48

We're like, "Oh, this is perfect." Um, >> yeah.

1:06:51

>> And we we tried to we tried to email her to to, you know, get us get us the offer, but Riley had to send send a letter in the mail.

1:06:58

>> And she got the letter physically. >> Yeah. Yeah, >> physical work. >> Okay.

1:07:02

So, uh it transfers to you. You you own it.

1:07:04

And then what uh what rights are you entitled to because clearly it's not the house next door, but you own something.

1:07:11

Like what exactly do you own?

1:07:14

>> We we spent um a a good amount of time with lawyers to figure out what actually is allowed. >> Yeah.

1:07:20

>> There's actually a car driving right at us right now. Move out of the way.

1:07:24

>> If you need to move, you can.

1:07:24

You can show us wherever. Takes this little tour.

1:07:28

What neighborhood is this, by the way?

1:07:31

>> This is in the sunset, uh, which is like 24th in uh, >> yeah, 24th in Kirk. >> Okay. Middle sunset. Sorry. >> Live live on air. There's a car coming.

1:07:42

>> There's a car coming down the alley. Okay.

1:07:43

So, it's >> This is actually This is actually a great question because we we uh, yeah, live demo here.

1:07:48

We So, we we we legally can't block the alley, so we are obliged to move.

1:07:53

Um, that is actually in tandem with the question you guys just asked. Yeah, we can't block it.

1:07:57

Um cars have uh the right to drive down it because there's there's easements here.

1:08:01

Um but we do have the right to paint anything on the surface of the street. Okay.

1:08:06

And we also can give um the alley a name. Okay.

1:08:10

>> Um so yeah, it's it's >> Is there already a sign?

1:08:12

Does the alley have a name?

1:08:14

Has it historically had a name? >> No.

1:08:17

So it um it Google Maps calls it Dirt Alley.

1:08:20

We think that some editor randomly added that like a couple years ago, but it's nowhere else. Okay. >> It says that name. >> No official name.

1:08:28

And then and then is there any square footage that anyone could build on?

1:08:30

Is it or is it just the actual road?

1:08:36

>> It's it's just the road.

1:08:36

Yeah, we have it's literally it's it's literally like 8 ft wide and yeah, nothing else.

1:08:40

So and the aura of you know if if you become the owner of this of having an alley named after you in the great city of San Francisco. >> Yeah.

1:08:52

So yeah, take us through the process to actually auction this, set up the website, draw demand.

1:08:57

How did all this play out? >> Totally. Yeah.

1:09:01

So when when when we when we first bought it, it was it was actually dirt alley, right?

1:09:04

Like there was there was nothing on it.

1:09:05

So So first we had to pivot.

1:09:06

Uh and so we we we found some pavers uh you know, they paved it uh 80 ft of it or so.

1:09:10

Uh you know, built the website in in in a weekend, I think, with with our friend Theo, uh who's who's helping us with the project.

1:09:19

And um yeah, and then and then we we we thought about how to go live and I don't know if you want to say more. >> Yeah. Yeah.

1:09:24

I think this has been cool cuz like the the art side of it like people can go to paint. com.

1:09:28

They actually just ended 5 minutes ago.

1:09:30

Uh but for like the the during the weekend people could submit little like 48x 48 pixel drawings and we have space to paint like 1,200 of them on the street.

1:09:41

Um and that's like totally free.

1:09:41

And then to to kind of cover the entire project and be a little more like capitalistic we we auction off the naming rights.

1:09:48

So, uh, that ends in like 55 minutes or so. Um, and, uh, yeah.

1:09:53

>> So, the full street is going to be painted with this, uh, this full alley, uh, like this whole mural of everyone that picked something. >> Yes. Yeah. Yeah. >> And this was free.

1:10:02

It was basically anyone that wanted to could go.

1:10:04

And what was, was this inspired by Reddit the place that idea?

1:10:09

>> Yeah, that that's been a huge inspiration.

1:10:11

That's been really cool to see.

1:10:12

So, yeah, we're kind of reviving that and uh, making it IRL and SF.

1:10:13

talk talk about the expectations because when when we first chatted about this, we were like, "Well, we'll we'll be we'll be we'll be the early bidder to to at least make sure you guys uh get your money out and then by the time you actually sent us the the auction, it was already well well above."

1:10:30

Uh so, how's the response been?

1:10:34

Yeah, it's been because we were we were kind of stressed because we're like putting a lot of money on the line and we're like we either this will either like flop and like or it'll actually do really well and it has done really well which is which is good.

1:10:45

Um right now the the highest bidder is is WordPress.

1:10:47

Um WordPress way >> WordPress way 135k which is which is insane.

1:10:55

>> How is the auction going to work at the end?

1:10:56

Is it going to be like I forget the terminology of it, but if somebody places a new winning bid within the last, let's say, minute?

1:11:03

Does it extend the auction?

1:11:04

Are you expecting a like uh are are people circling now or I feel I feel like I saw Josh Browder.

1:11:09

I know that Josh is probably thinking I'm going to wait until the final minute and come in with the top offer, but uh how what's the dynamic going to work like?

1:11:18

So in in the last 5 minutes if somebody uh bids then it gets extended by another 5 minutes uh until until it ends.

1:11:27

>> So so it'll be a while.

1:11:28

>> And then once the auction closes what's the process like to actually rename it?

1:11:32

Have you have you traced through like what does it take to update Google Maps, Apple Maps, Ways, all the different mapping features?

1:11:38

Um do you think that'll be pretty easy or is it is it like is there like a self-s served portal for like renaming streets? How do you prove this?

1:11:46

There is um this is a unique street because it's privately owned.

1:11:50

Um and as the owners we we kind of the source of truth for a name like this is the sign itself.

1:11:54

So putting up a sign and then we can I think Google Maps like you take a photo or something and you submit the edit and it should should appear soon.

1:12:02

I actually when I was in high school, I I um maybe I shouldn't share this on the internet, but I I uh as like a senior prank, I installed a street sign like on an unnamed alley in my hometown named after my high school track coach and um it got renamed on Google Maps and it's been like 5 years this this road has been on there >> and it's still on there. >> Yeah. Yeah. 5 years later the Yeah.

1:12:23

Like people think it's real. It's really funny.

1:12:27

The mayor has mentioned it in a in a meeting before. Yeah.

1:12:28

Did you uh did you have to do anything to sort of uh I mean these projects they always run the risk of like vandalization basically.

1:12:38

I'm looking at someone clearly tried to paint wrong way on the street although they couldn't quite get all the up votes to land the way they wanted so it's a little bit out of order but uh did you have a process for uh reviewing submissions or was this all community-led?

1:12:53

Like how did you think about the risk of someone putting up something that was like offensive?

1:12:57

Yeah, we had we had some attempts at this, but but we we had so some automated stuff to remove it and then we were, you know, the entire time it's been up since Thursday, we've been manually reviewing it.

1:13:07

Uh, you know, as a team, we have a group chat and we're just, you know, reviewing it and and someone's taking the the turn and wake up in the morning and there's some stuff we remove and but it's not it's not been so bad.

1:13:14

It's been the internet has been pretty friendly to us relative to how how bad it could be. >> It gets crazy.

1:13:19

I remember uh didn't Justin Bieber put out a post on like I I'll go wherever the audience votes me for my next tour and they tried to send him in North Korea.

1:13:28

It's a famous example of like the internet going wild at the same time.

1:13:32

Bodie McBoatface very successfully named boat, you know, everyone enjoyed that one.

1:13:35

Um uh give us an update on some of the other projects.

1:13:39

How is the uh the Pokemon Go pay phone project going these days?

1:13:45

>> Yeah, that that ended a few weeks ago.

1:13:47

Basically, I I had gotten a list of all the payoneses in California.

1:13:48

there's still like a few thousand that work.

1:13:51

>> Uh and then made like a Pokémon Go type game where you have to go to different payoneses and you can claim them by calling a number and um yeah like >> three or 4 hundred payoneses were called from and u it was really really cool to see people go out and uh and uh find them all.

1:14:06

>> And there was a leaderboard but no specific prize. >> No no no prize. Yeah. Just just the memories.

1:14:14

>> What who who won how many calls did they make?

1:14:16

How much did they travel?

1:14:16

Uh this girl named Maggie won. She won by one point.

1:14:21

Um and uh yeah, she she was driving all over the state.

1:14:25

It seemed like there was there's a lot of lot of phones.

1:14:28

Um I think I I nerd sniped quite a few people uh which is cool to see.

1:14:32

>> Uh what what other projects are on your plate right now?

1:14:35

Are you still working on the J Suite or is that project sort of done at this point?

1:14:39

I know that there's a whole documentary you can talk about, but uh what what else is in your world?

1:14:45

>> Yeah, Jmail has been pretty crazy.

1:14:47

There's like 15 or so people that are that have helped in some way for that project and it's it's kind of died down now.

1:14:53

Maybe there'll be some resurgence in the Epstein files. Maybe we'll we'll see.

1:14:57

But um >> yeah, that that's been really really crazy to see.

1:15:00

um project that I'm I'm probably going to drop this weekend is uh we scraped a bunch of data about the US like how much money the US government spends and we'll hopefully make like a like a Spotify rap style like oh here here's how much you paid in taxes this year like oh this much >> this many dollars went towards like defense or social security or things like that. So >> interesting.

1:15:24

>> That'll be that'll be cool to see.

1:15:25

>> I'm sure I'm sure that'll make a lot of people really happy. Yeah. Yeah.

1:15:31

It's crazy to see like I'm like, "Oh, actually like I kind of, you know, seeing these actual numbers like where things actually get spent on like is is feel like it just makes you think a different way." >> Yeah.

1:15:39

In general like with something like that, Spotify rap works so well because people screenshot it, they share it.

1:15:44

How do you think about um the usergenerated viral loop for these projects?

1:15:50

Is that like a key piece of the of the idea phase?

1:15:52

you you think about, okay, how can I actually create some sort of flywheel for generating attention or is this just like sometimes you get lucky and people share and and it's sort of an afterthought.

1:16:07

>> I think it's fun thinking about like I mean like the alley project it's just like a fun idea like we we were actually talking at like a party like a year ago and I was like oh wait there's some some alleys that get forclosed on in SF like >> borrowing. Yeah.

1:16:19

I said it as a joke, but then Patrick and Theo were like, "Let's let's actually do it."

1:16:22

And then there was a girl that was like in law school at the party.

1:16:26

We were talking to her about like the legality of it.

1:16:27

And then it just it took a while to actually make it happen, but like it's just kind of fun for us.

1:16:32

And then thinking about how to actually make it go viral is also like a kind of a secondary thing, but um it it is the framing is so important for these sorts of things.

1:16:40

And like we also wanted something cool like this is kind of just a cool like thing to to to do for SF.

1:16:45

like >> um it's cool putting SF on the map and and doing things like this, especially when there's like >> Yeah, I think I think we're really lucky that these sorts of things can get funded, too.

1:16:54

Um I think SF is a very special place for that.

1:16:57

>> What kind of inbound What kind of inbound pitches do you guys get at this point?

1:17:00

Like I'm sure people are DMing you like, "Hey, I found this weird kind of anomaly.

1:17:04

I think I think I think you could, you know, something like an like an alley.

1:17:08

Uh is there an inbound flywheel yet?"

1:17:13

>> There's there's all sorts of weird things. I don't know.

1:17:14

Like someone's like, "Yeah, we have like a tank in SF." Like any ideas for that?

1:17:17

Like I don't know there's there's like a lot of weird things.

1:17:21

>> I'm expecting you guys to to figure out some mechanism to like effectively take over a country at some point.

1:17:28

>> Maybe that's the next step.

1:17:30

>> I wouldn't put it past you.

1:17:30

Uh well, congratulations.

1:17:32

Where can people go to actually bid? >> paint. com/auction.

1:17:37

>> 46 minutes 28 seconds left.

1:17:37

We're going to be keeping our eye on this as it count as it counts down.

1:17:46

>> Uh yeah, I'm very interested to see how people react when there's only when there's only a few minutes left.

1:17:53

>> Yeah, this will be very fun.

1:17:53

Well, thank you so much for taking the time to come give us the update and chat with us.

1:17:57

Uh congratulations on uh a huge success.

1:17:59

I mean, I this is uh well well above uh I mean $135,000 that seems well uh above what you paid and you should be able to recoup all the cost.

1:18:10

Do you have a plan for what to do with the money?

1:18:13

>> Yeah, not going to use it for ourselves at all. Be clear.

1:18:15

We're going to um we'll it'll take some money to paint the actual mural on the street, but there'll definitely be something left over.

1:18:21

We'll just keep it for the next project of this sort in San Francisco.

1:18:23

Some kind of IRL project here. >> That's amazing.

1:18:27

Uh well, what a fun project.

1:18:29

Thanks for coming on and sharing with us. We'll talk soon.

1:18:32

>> Congratulations to you guys as well.

1:18:34

Crazy that we're >> colleagues now. Yeah, we're colleagues.

1:18:37

>> Yeah, we're gonna be up in SF next Tuesday. Let's hang in person.

1:18:40

>> We'll have to do a show from the alley at some point. >> That'd be amazing. >> Awesome. Great hang guys.

1:18:44

>> We'll talk to you soon. >> Congrats. >> Goodbye. >> Cheers.

1:18:47

>> Um the uh Have you seen this map of the surface of Venus?

1:18:52

Did you know that Venus has land surface? >> I did not.

1:18:56

>> Uh the land surface of Venus has some insane RPG world potential.

1:18:58

Someone needs to vibe code a video game where you can go run around on this or or play some sort of 4X game.

1:19:04

Uh yeah, someone says Hasbro's really messing up not releasing Risk Venus.

1:19:09

Um uh there's another there's another cool AI project that launched from Netflix.

1:19:14

Uh there's a video here showing their new project Void.

1:19:19

Uh the AI removes objects from videos.

1:19:22

Uh but it even corrects the physics after the objects or people are removed.

1:19:28

And there's a demo in the comments here.

1:19:30

Um, so, so you can see that that they the bottom is the uh the the element that's being removed.

1:19:37

And so if you have the the the the kettlebell is deforming the pillow once the AI removes that, which is often a very very time-intensive VFX task, the physics of how the scene would have played out if that character had not been in the scene are then uh you know recalculated.

1:19:57

And so the duck, not only is the duck not being smashed, the duck just appears like normal.

1:20:05

And uh this is just a a project that um will if you've ever done any of this type of work, it's uh it's incredibly cumbersome.

1:20:13

And so um I think this will be adopted very very quickly.

1:20:18

Uh Nishan says he had a use case four years ago.

1:20:20

A big Hollywood VFX uh company came to us, the company I was working with, and asked us to remove freckles and pimples from the face of actors and actresses 4K movie footage.

1:20:29

At the time, we really struggled and failed to do it.

1:20:31

If it was now, we would have easily tackled the problem.

1:20:32

And there's so many examples of this.

1:20:34

Uh the physics wait, the physics correction part, removing stuff from video isn't new, but making the background actually behave uh correctly is is a completely different problem.

1:20:43

And so this is a huge uh huge move for the VFX industry in Hollywood.

1:20:49

>> Also, is this has does Netflix have a history of contributing to >> I think so.

1:20:55

They've done Yeah, they've done I mean they've done a lot of uh they've done a lot of standard setting around cinema camera gear.

1:21:00

They were a big proponent of the Blackmagic uh ecosystem, I believe.

1:21:05

Uh great like price per value for uh shooting a film and delivering in 4K, which is something that they sort of mandate across their entire ecosystem, but can be cumbersome for creators if they have to go shoot on a very expensive cinema camera.

1:21:18

They've done a lot of stuff there.

1:21:21

And then of course, they acquired that uh AI company from Ben Affleck, I believe. Wasn't that the story? >> Oh, yeah. >> And I I don't know.

1:21:27

It's possible that there were some researchers from that team that bled over onto this project, although this seems like it was in flight for uh longer than before. I agree.

1:21:36

Corridor crew needs to do a video on that feature.

1:21:38

That would be very cool to watch them road test it and see, you know, where the boundaries are because I'm sure it doesn't work in every possible scenario.

1:21:44

The demo footage is always going to be the best, but uh very very cool.

1:21:48

Uh anyway, without further ado, we have our next guest, Adicha Bandandy from noon in the waiting room. Let's bring in. How are you doing? >> Hey. Hey John. Hey Charlie. I'm doing good. How are you folks? >> We're doing great. >> Great. >> Welcome to the show.

1:22:02

Since it's the first time on the show, please uh introduce yourself and the company. >> Absolutely. Yeah. Thank you for having me.

1:22:10

Uh my name is Adita Bandi.

1:22:10

I am the co-founder of Noon.

1:22:12

Um and uh yeah, I'm um you know originally you know from India.

1:22:18

I moved here to the Bay Area in 2015 as part of my first startup getting acquired by Yahoo. Mhm.

1:22:25

>> Um I'm a product designer turned product manager.

1:22:26

I'm a second time founder.

1:22:29

>> U you know I have a co-founder his his name is Kushagra.

1:22:31

He's also a product designer turned uh second time founder. Yeah.

1:22:35

>> What was the first company?

1:22:37

>> U so we were actually you know back in 2013 uh there was this problem of like rendering PDFs and word documents inside apps. >> Sure.

1:22:46

>> And basically I was building this company called Bookpad out of Bangalore and uh we we can render 13 plus formats in any app.

1:22:52

uh so someone like Dropbox could integrate with us and start rendering documents.

1:22:58

>> So that's the company we built and Yahoo bought it for Yahoo mail so we can part of the whole attachment attachment layer for >> document. Yeah.

1:23:05

So if you're in Yahoo mail and you need to open a PDF, it just opens in the browser natively.

1:23:11

is how much of that is a technical challenge and then how much of that is uh just sort of dealing with like you know Adobe IP because I imagine that like Adobe has products and they maintain a standard but you can use some of it but there's pieces that you can't like what were the decision-m criteria around building the product there?

1:23:29

around building the product there? Yeah, it was very complex like you know if you basically all of these formats were not made for for cloud or for browser uh they they were built in 1980s like if you open up any PDF or a word document

1:23:43

it's insane how complex they look inside uh so we have to build a a rendering engine we have to build a conversion layer that understands these documents and and tries to recreate them for for the cloud so or uh so that's that's basically a very technical challenge um for us to do that. Yeah. And we just Yeah.

1:24:00

And we just were very, you know, young and happy and trying to solve some something very, very complex and that just made us very happy. >> Yeah.

1:24:08

Um, so, so walk me through uh the decision to start the next company. What was that like?

1:24:14

What was the okay, this is time to run it back? >> Yeah.

1:24:18

So, yeah, my co-founder and I, we've been product designers all our lives.

1:24:21

We love building products and then we've been like observing the AI space.

1:24:25

We've been like using design tools for last 20 years of our lives, right?

1:24:30

Like from from grad school.

1:24:30

Um and then for the most part of like you know all the tools that exist in the market like they're all graphic design tools like you can design the visuals of the product but not not the product directly.

1:24:42

Uh so so designer is limited to designing the visuals and then they hand off the visuals to the engineer to build it.

1:24:49

That's how it was always done. Yeah.

1:24:51

>> And the moment AI coding tools came into the market, everyone started realizing, oh, I can build, I can do the functional aspect of it as well.

1:24:57

And then they started realizing how amazing it is to do both together. >> Mhm.

1:25:02

>> But all of that was happening in multiple tools. It's all broken.

1:25:06

>> And that's that was the idea behind this like, hey, why is it happening in multiple tools?

1:25:10

Why can't it all happen in one tool where someone who wants to design and build products can just do it all in one tool? >> Yeah.

1:25:17

So I mean when you when you hear about vibe coding projects you you hear about like insane lines of code so much so much functionality like instantiated just at uh you know a a couple sentences dropped in a prompt.

1:25:30

Um what changes about the product design workflow in a world where sort of the entire underlying structure of the product can change on a dime all of a sudden.

1:25:47

Uh so the the the the tools that are very text based um so the problem is like they're very limited in in how you can say something right. Sure.

1:25:55

Now describe me a painting. >> Yeah.

1:25:59

>> Let's say you start describing the painting.

1:26:00

You can never fully describe the painting. Right. >> Yeah.

1:26:02

>> That's the problem with text based editors or text based design tools.

1:26:05

I mean, you hear a lot of people sharing that even just for like oneoff open claw tasks like texting an image or screenshot from their phone along with a prompt just to give a little bit more, hey, here's a screenshot of my calendar.

1:26:20

I'm essentially exporting a bunch of information via PNG, uh, which is a richer format than people thought, I guess.

1:26:27

Um, and so, uh, h how closely do you want to link the visual design elements, the interaction elements, the logic of how things fit together?

1:26:36

logic of how things fit together? Um because you know like we've we've already sort of transitioned out of the uh you know uh visual designer in a pixel perfect like layered Photoshop file into something completely new and and and dynamic but that that uh that

1:26:55

barrier is bleeding and and uh or is uh is is becoming fuzzier basically >> 100% and and then the way we look at it is if you if you try to understand like what what are we trying to do >> we are first of all what we're saying is like we work on top of your product code. >> So essentially we we don't do any MCP.

1:27:08

>> So essentially we we don't do any MCP.

1:27:12

We don't need to do any sort of like any middle layer like cloud or codeex like we don't need any of that.

1:27:16

We directly sit on top of your product code. >> Yeah. code base.

1:27:19

And then then what happens is like the the designers or anyone who's working on the product can see that that code visually rendered on the canvas. >> Sure.

1:27:28

>> So now you can understand okay this is what my product looks like in different screens and different components. Yeah.

1:27:33

>> And then there you know refine it, make it better, create new screens, create new features.

1:27:42

>> So then you don't need multiple tools. >> Yep.

1:27:44

>> That's the second advantage.

1:27:44

You're not switching between multiple tools.

1:27:45

Things are not getting lost in translation. >> Yeah.

1:27:49

>> Um you know, all the details are preserved.

1:27:50

And the last thing is >> Oh, sorry. Yeah. Yeah. Yeah. Yeah. Last thing. >> Yeah.

1:27:55

And the last thing is like you work on both the functional and visual layer. So you it's up to you.

1:27:58

You you you build you build the visual first and add functionality.

1:28:03

you can control both of them, you know, very very in a fine tuned manner. >> Yeah.

1:28:07

So, uh if you're thinking about just something as simple as like making a CTA bigger and you're dragging the size of a button, that can be quicker in a visual format than in a prompt where maybe you wind up describing the number of pixels that you want it to be or say a little bit bigger.

1:28:24

Okay, a little bit smaller.

1:28:26

It can be tricky but what is the flow of actions that are happening behind the scenes once I actually resize a button for example. >> Yeah.

1:28:36

So let's take the button as an example right.

1:28:38

So first of all the button that you're seeing on the canvas >> it's not a rectangle or shape.

1:28:40

There's real button code behind.

1:28:42

So that's why it's getting rendered on canvas for you.

1:28:46

Then then when you're actually ed you know trying to edit the button visually or maybe using some you know AI there or maybe directly you're doing it >> the code is getting edited >> in real time.

1:28:55

So essentially you can think of as like a visual code editor.

1:29:00

So you edit the code visually.

1:29:02

>> So we're kind of building a code editor from scratch that can be edited visually. >> Yeah.

1:29:06

Uh how are you thinking about going after customers?

1:29:09

Do you want to be in the enterprise or more self-s serve?

1:29:16

Like where where's the beach head? >> Yeah.

1:29:18

And how how much of how much of this round was based on uh traction to date versus long-term opportunity and and scale of the market? >> Yeah.

1:29:28

No, I think uh for us like you know we're a design tool.

1:29:30

Uh design tools people want to try first before like even thinking of like before thinking of paying it or getting like into a team.

1:29:37

So we'll always going to be a a free tier free tier. Use it for free.

1:29:43

If you if you like it, then pay for it. That sort of a model.

1:29:45

So we're going to we're going to be going to be bottoms up that way.

1:29:50

>> But in terms of the traction, like we are currently like, you know, lot of some of the best known tech companies in the Bay Area.

1:29:56

We have pilots with them and they're going live.

1:29:59

>> Um so a lot of that has helped us like first build the product with their feedback.

1:30:03

Um and then and then do pilots with them to make sure like you know it's working for a for a team for serious work. >> Yeah.

1:30:10

Uh talk about some of the angel investors in the round. How much did you raise?

1:30:16

>> So we raised uh uh 44 million overall. Uh and yeah thank you. It's insane.

1:30:20

Um it's an amazing amazing journey for us.

1:30:27

Um and and um some of the some of the investors that we have is like first round capital, chemistry, uh F4, Scribble, Elevation, these are the VCs.

1:30:36

But the most exciting part for me and my co-founder is that we have >> some of the best design and product minds uh in the industry, part of the fund raise, right?

1:30:45

Solio, the second designer at Facebook, investor in Vanta, Perplexity, uh Versel, >> then we have Katy Dill, head of design at Stripe, Scott Bellski, um founder of Behance.

1:30:57

Uh then we have Mike Davidson, head of M design at Microsoft AI, Henry Modiset, head of design at Perplexity, Ian Silver, head of design at OpenAI.

1:31:07

These are just some of the names like we have >> a lot.

1:31:09

Um we we didn't share the full list actually for fundrais Yeah.

1:31:15

>> Yeah, that makes sense.

1:31:15

How are you how are you tracking uh the way design is changing in the era of vibe coding?

1:31:20

It feels like more and more small businesses, more companies are building custom software.

1:31:27

A lot of it is sort of like internal facing.

1:31:30

So design is more of an afterthought.

1:31:34

But you have to imagine that if the software is valuable and provides growth a growth vector for the business at some point you're going to wind up with uh design challenges and tradeoffs.

1:31:48

Um is are are you thinking that this will be like a tool that's eventually dropped on top of uh like a a vibecoded system or do you think this was more of like an entry point into a company building new tools internally?

1:32:04

So all three types right like let's say let's take the first type an existing company big product let's say you know like Spotify or someone else like Dropbox >> um they have an existing product they can they can use the tool to build their next feature >> the w coding if you let's say you're site you're doing a side project you

1:32:21

wipe coded something onable or some other some other w coding tool you can build your you can bring in your project here >> almost instantly and start designing and working on it better and you have the third one you're starting from scratch You can of course like start from scratch very easily on tool and and then and then build your product. So can we

1:32:37

So can we kind of support all entry points?

1:32:39

Um it's a very multi-purpose uh tool that way.

1:32:44

>> How are you thinking about business model?

1:32:45

This feels like very logical for seatbased and yet there's been so much push back around the seatbased model.

1:32:52

You're sort of starting from scratch so you can pick your your your monetization method.

1:32:57

What are you thinking of uh for the next couple years?

1:33:01

Yeah, I think right now at least like we've we're working on the pricing model.

1:33:05

We're talking to a lot of uh customers to understand like what works for them.

1:33:08

We don't want to break their budgets, right?

1:33:10

Like there are certain budgets assigned already for this.

1:33:11

Uh so TBD, you know, we'll we'll soon publish our pricing on the website when we show the product as well.

1:33:17

So very very soon you'll see you'll see the product go you know uh on on the website and and the pricing going live. >> Very cool. Well, awesome.

1:33:25

>> Thank you so much for taking the time.

1:33:27

Congratulations round together >> and we will talk to you soon.

1:33:30

>> I have one thing like I also have I got a bong.

1:33:33

>> Oh yeah, you have a gong.

1:33:34

>> I have a bong for you.

1:33:36

>> Hit that gong >> because we've uh >> we've done >> I couldn't hear you.

1:33:42

>> I think Zoom I think the Zoom uh noise cancellation just completely killed the gong.

1:33:47

We'll hit the gong again for you. We'll hit our good news. Thank you. Thank you. >> Thank you.

1:33:57

It's great to have you on.

1:33:58

I'm sure you'll be back on.

1:33:59

>> We'll talk to you soon.

1:34:01

>> We'll talk to you soon.

1:34:01

Congrats to the whole team.

1:34:03

>> Have a good rest of your day.

1:34:03

Um, name Street, we are counting down the minutes. How long do we have? 29 minutes.

1:34:11

WordPress still in the >> at number one atund 35,000.

1:34:19

>> Our next guest is Zack Shore from Hermus.

1:34:21

He's returning to the show with some massive news. Zack, how are you? Where are you? >> What's up? >> I'm great.

1:34:29

Thanks for having me, guys.

1:34:31

>> This is your second outdoor guest today. It's fully spring.

1:34:35

>> Riley was in San Francisco.

1:34:35

I assume you're in Georgia. >> No, close. I'm in Virginia.

1:34:40

I'm at the uh Defense Action Forum, the JP Morgan >> Oh, cool. >> event.

1:34:45

So, good place to be when you're announcing a $350 million unicorn round. >> There it is.

1:34:52

That's what I was waiting for. There we go. >> Thanks, fellas.

1:34:55

And congrats to you as well.

1:34:57

Big uh big moves for the uh the TVPN fellas. >> Big moves. Big moves. >> Uh what's new?

1:35:03

What's it's it's been a minute since you've been on the show. Yeah.

1:35:07

>> Give us give us all the updates. >> Yeah, guys. A lot.

1:35:09

I mean, obviously we've got the raise, but the raise is a function of of the milestone.

1:35:13

So, you know, we flew our second aircraft in 9 months, which is pretty unheard of.

1:35:17

uh the first one we flew last year in like May of 25.

1:35:21

And then we just flew our Mark 2 aircraft.

1:35:23

That's an F-16sized unmanned aircraft.

1:35:25

So, you know, fighter jet speed, fighter jet size, thousands of pounds of payload, thousands of pounds of thrust.

1:35:31

Uh we're slated to fly it again on Friday. There you go. Exactly.

1:35:35

Uh that was out in white sands.

1:35:38

Um and you know, imminently push this thing to supersonic and and really just start really start building the heavy systems that the country needs.

1:35:43

Uh the second aircraft, which is a Mach 2 aircraft, is in production right now uh in Atlanta.

1:35:49

And then we we just announced also that we're expanding our headquarters out to Los Angeles uh in in the Gundo >> and we're going to build our Mark III platform out there.

1:35:56

So yeah, we'll be neighbors. I'll come see you guys. >> That's amazing. >> All right.

1:36:00

What uh with with uh autonomous jets is how how does how is the development and just like R&D process different?

1:36:11

I imagine I imagine there's a bunch of advantages because you're not worried about a human life.

1:36:15

Obviously, there's still a bunch of risk and you don't want to, you know, crash >> uh crash the thing that the team works so hard to build, but uh the the iteration cycle feels insane based on on how you've described it.

1:36:28

And so I'm curious how works.

1:36:32

>> You you kind of nailed it, right?

1:36:32

I mean, taking a person out allows you to take a lot more technical risk just like full stop.

1:36:36

I can lawn dart something >> intentionally, right?

1:36:39

>> intentionally, right? just to push the envelope on a vehicle to really understand >> lawn dart is that >> yeah just like >> right >> yeah I guess >> if we need to that's not our goal but you could right if I really want to find the edges of performance and there's nobody on board you you have that you

1:36:56

can take that kind of risk um and then you can iterate faster there's also I can take all of the systems that exist on an aircraft that are there for human survival the oxygen the ejection seat all the command and control you know capabilities the the human human machine interface screens, the the um the stick, the throttle, all that. I can pull that

1:37:13

I can pull that all out and I can put in more payload, more fuel, and just continue to drive uh more capability for the war fighter.

1:37:20

I mean, we just saw um that incredible mission to to rescue those uh F-15 pilot and co-pilot in in uh in Iran.

1:37:26

And >> ideally, you know, you have a vehicle like this, you don't have to do a rescue mission.

1:37:33

We don't even have to put ourselves in those positions and ask American men and women to to take that kind of risk.

1:37:38

So there's, you know, operational utility to the unmanned platform and there's a significant accelerant to development because of the risk we can take. >> Yeah.

1:37:45

Uh take us through the different uh aircraft that you've built so far.

1:37:47

Uh because the and then remind us of the goal of h how fast are we going?

1:37:53

Is is like miles per hour the correct benchmark for each subsequent test?

1:37:58

I imagine that you're trying to make each one faster than the last.

1:38:01

Basically, >> that's that's correct.

1:38:03

I think you know we we speak in terms of Mach, right?

1:38:04

So Mach 1 being supersonic.

1:38:06

Um but so the first aircraft was not really an aircraft. Uh we called it Turkey.

1:38:11

It was sort of a actually no before that was the EMU, excuse me, >> a flightless bird. Right.

1:38:16

So that had a jet engine and uh it had a bunch of the avionics and the sort of radio links to just show that we could build an integrated team, build some hardware, hook up the engine, and get this thing sort of taxiing down the runway.

1:38:27

And we did that in 24 and 25.

1:38:28

We built Turkey, uh, which was a flying bird, but turkeys don't aren't meant to fly.

1:38:35

And so that aircraft flew it at, um, Edwards, and we demonstrated we could rapidly build a jet powered aircraft.

1:38:39

It was a 10,000lb airplane, had fixed landing gear.

1:38:42

It was a GEJ85 engine, uh, which is the same engine that you see on the jet trainers that American men and women train on.

1:38:50

>> And that was in Mayo 25. And now we're on Mark 2.

1:38:52

And we call that aircraft Eagle.

1:38:55

And this is where it gets kind of fun, right?

1:38:57

This is where you start to see what I would call product utility.

1:39:00

So, this aircraft is the size of an F-16, maybe a little bit bigger, fully unmanned.

1:39:03

It's got a 30,000 lb thrust engine.

1:39:06

Uh, to give you a sense of comparison, like the CCA program, those engines are roughly a 3,000 lb of thrust.

1:39:12

So, you're looking at 10x uh more power, about 10x more payload.

1:39:14

Um, and just a totally different problem space that we're working in.

1:39:19

And the first aircraft of this series, we're building three of these Mark 2 Eagles.

1:39:24

The first one, uh, we flew that that's what you showed the video of.

1:39:26

This one will go supersonic.

1:39:27

So the premise of this aircraft is demonstrate that the the the vehicle design, the shape, they call it the outer mold line, can get through something called transsonic.

1:39:34

So transonic is that that window right before supersonic.

1:39:38

And there's a lot of very unique things that happen uh with physics uh for lack of a better term, right before you go through that supersonic window in terms of shock waves and stability for the platform.

1:39:50

And so you really want to demonstrate that your plane can make it through that, you know, 0. 99 Mach to 1. 1 1. 2 Mach window.

1:39:55

That's a huge risk window that we're going to unlock um here shortly. The next vehicle Mark 2.

1:39:59

2 will have some additional uh pieces of proprietary technology on it.

1:40:05

Our proprietary pre-cooler, which is a technology that sits in front of the engine, will be on that next aircraft.

1:40:11

That aircraft's uh being manufactured right now in Atlanta.

1:40:13

And that will allow that airplane to do Mach 2 plus.

1:40:16

So now we start to really get into that really really high speed regime.

1:40:18

As an example, you know, the F-15 is the fastest uh fighter jet in the world right now.

1:40:24

that aircraft in a dive with nothing on it will do maybe 2. 5 maybe maybe Mach 2. 5.

1:40:29

So we'll do uh we're going to go for that number straight and level uh with this aircraft.

1:40:34

And then the third aircraft in this series uh is going to be Mach 3 and that aircraft is going to be manufactured in the new Elsagundo uh facility and and will be flying uh somewhere around the middle of 27.

1:40:50

>> Uh talk about the actual technology. >> Speed is just insane. >> Yeah. speed of R&D.

1:40:53

>> Yeah. speed of R&D. Uh yeah it's why >> inative design guys I mean right like continue to build hardware so that as I am building as I am flying the current airplane I am building my next airplane right and so on and so forth I mean this is SpaceX >> over and over again right this is SpaceX for aviation or as we say SpaceX

1:41:11

sideways so you know that's how you can take this kind of uh this hardware risk and continue to go and to your original question our goal is Mach 5 right that remains our goal >> but the key technical unlock in here is actually Mach Because in order to unlock our next propulsion approach, we need to demonstrate that you can get a turbine engine, this case, the F100, 229, the F-16 engine. We need to demonstrate that

1:41:32

We need to demonstrate that I can fly that engine at Mach 3 for a period of time.

1:41:36

And so that's what this series of aircraft are going to do.

1:41:37

And then we've also got some pretty exciting um you know capabilities that we're going to be able to offer to the war fighter uh with this platform at the same time.

1:41:46

How focused are you on the on the on the defense industry specifically because there must be demand from commercial there's demand from the AI world for this technology.

1:41:56

Uh how are you thinking about the trade-offs there? >> Yeah, great question.

1:42:01

So commercial is one of those things where yes this technology eventually will naturally lend itself to a commercial application but we're a ways away from that.

1:42:09

I mean if we think about just aviation historically you start with def the defense environment.

1:42:14

Think about how jets and planes were even adopted.

1:42:15

I mean the department of defense is going to take more risk and going to you know help develop inherently just by operating these systems these technologies to lower the complexity lower the risk on them so that they can be eventually adopted in a commercial environment.

1:42:28

But the other problem is when you work into commercial aviation the the flight certification requirements for a new commercial engine or a new commercial airframe are steep >> for good reason right we're putting people on board.

1:42:39

So to have the economic viability to pursue that certification process requires you to have a stable business that's got robust economics.

1:42:46

business that's got robust economics. So for us even if you know and we do eventually want to go after commercial you have to build a viable defense business first just to have the economic scalability not to mention the expertise on the platforms and so for us you know I certainly want to do commercial work uh eventually and I think these

1:43:02

propulsion systems will lend themselves but we are a defense company right that is our bread and butter that's where we're focused uh we're not interested in some of the you know the energy plays on the propulsion we are really just true north unmanned high moach high altitude systems for the war fighter and as those systems come online we will be, you know, the arguably the best aircraft manufacturer in the world. Um, and that

1:43:20

Um, and that we'll start lending that to other aircraft problems.

1:43:25

>> Talk about the actual technology that enables you to go uh Mach 3, Mach 4, Mach 5, uh, ramjet, scramjet.

1:43:30

What what is the what's the lineage here?

1:43:34

How much of this has been used in the past?

1:43:36

What are you inventing from scratch?

1:43:39

What are you pulling off the shelf and leveraging? >> Great question.

1:43:43

I mean, a lot of what we're doing has been done before.

1:43:45

Um, we're trying to stay out of the world of science problems, which is where you get into the scramjets, Mach 67, and stay in the world of engineering problems.

1:43:51

And science problems, things have to be invented that don't exist yet.

1:43:54

Uh, specific materials, production processes.

1:43:57

Ramjets have been around since the ' 50s.

1:43:59

Um, NASA did a lot of work on this.

1:44:01

You see these on missiles currently all over the place.

1:44:03

And so, ramjets are very well understood and well tested.

1:44:06

And so, what we're doing is taking a different approach with the propulsion system and using something called a turbine-based combined cycle or TBCC. >> Mhm.

1:44:15

And this is an engine type uh that's a mix of multiple propulsion cycle propulsion systems.

1:44:20

NASA sort of led the way on this and we demonstrated this propulsion cycle on the ground in about 2022 and I think uh DARPA is the only other group that's done this and so us and DARP are the only people who've demonstrated this propulsion cycle and it's got basically three components to it.

1:44:35

Um, you got your inlet and the air comes in and and the first thing that happens is we've got a proprietary precooler uh that that cools the air down and and slows slows the flow.

1:44:42

It then hits the turbine.

1:44:45

Uh so in this case the F100 229 uh the jet engine.

1:44:47

So we're not building a new jet engine.

1:44:50

Now the challenge has been with ramjets for a ramjet to light the air has to be moving through at Mach 3.

1:44:57

>> So how do you get the system how do you get that air flowing at Mach 3?

1:45:00

Typically, we've seen it in missiles with rockets and you boost it, right?

1:45:04

You use solid rocket or liquid rocket, but that has a couple problems to it.

1:45:08

Number one, um you have to really harden the system to handle all those G's and you're not going to have big wings because of drag.

1:45:14

So, you're really looking at systems that are not really optimized for flight and they're just one way.

1:45:19

So, with an aircraft, you can have uh a more graceful acceleration.

1:45:22

We are able to basically tune that jet engine that two that F100 to get Mach 3 air flow.

1:45:29

It goes through the ramjet and then I can light the ramjet.

1:45:32

Now once I light the ramjet, I cocoon off the turbine engine.

1:45:36

I route the air around it directly into the ramjet.

1:45:38

Now I can fly Mach 3 to Mach 5.

1:45:40

When I decelerate, I do that whole system in reverse. I open the doors.

1:45:45

The air comes through the turbine engine.

1:45:46

The ramjet shuts shuts off and the jet the traditional jet engine takes over and takes me from, you know, Mach 2 back down to the ground.

1:45:53

And this is why it's called the turbine based combined cycle because I'm combining these two propulsion genres. Sure.

1:45:59

And it it allows us to use mature technology to unlock these sort of these flight conditions.

1:46:03

And you won't get much above Mach 5.

1:46:05

That is hypersonic is that line at Mach 5.

1:46:08

And we don't need to get above Mach 5. There's two reasons.

1:46:11

One, survivability and and the sort of analysis we've seen is it's overkill.

1:46:15

And you're going to find yourself in sort of physics land that's going to cost you more and take you a lot more time to run up that curve.

1:46:19

And also, you get the Mach 67 world.

1:46:22

Now you're in science problem world.

1:46:24

That's scramjets, which are still kind of more new.

1:46:27

that's CMC's for very bespoke material sciences, right?

1:46:30

I can have a hot end a hot vehicle that's titanium or incanel or steel and I can massproduce that to handle this the heat and temperatures.

1:46:39

And so what we're doing is taking a lot of old information and we're modernizing it.

1:46:43

And for that matter, the the SR71 had an aircraft called the D21. You can Google this.

1:46:49

It was a drone that sat on top of the Blackbird between the the uh right on the back between the engines.

1:46:56

And once the aircraft was above Mach 3, they turned that aircraft on.

1:46:59

It was only a ramjet, no moving parts.

1:47:00

The air flowed through that ramjet.

1:47:02

They lit the ramjet and the aircraft flew off and it went Mach 3 plus for about 3,500 nautical miles.

1:47:08

And so in this way, we can start we can start accessing those conditions again just like we did in the 50s and >> were those uh was the D21 like a single use designed to fly like reconnaissance or deliver?

1:47:22

>> It was designed to fly reconnaissance. Yep. Exactly.

1:47:24

But then it would just ultimately crash and burn.

1:47:28

>> It would it well it was supposed to return.

1:47:29

Um they didn't end up going the full distance with it.

1:47:32

They had a couple successful test flights.

1:47:33

They also had some bad flights where they had some challenges.

1:47:36

You can look up the history.

1:47:38

But more importantly, this concept was executed and validated in the '60s, right?

1:47:42

And so we can take modern practices and all these learnings and sort of bring bring the past forward and say, you know, speed is in vogue again.

1:47:50

Yeah, the in the introduction of ICBMs and stealth technologies really took America's focus away from high-speed systems and that's why nobody really worked on these things for about 60 years.

1:48:01

>> Question from the chat.

1:48:01

Uh there's a claim that China has a system that can go Mach 20.

1:48:05

Do you know anything about that?

1:48:07

Does that seem realistic or propaganda?

1:48:11

>> It sounds like the South It sounds like the South China Morning News if anybody's aware of that that uh that that uh newspaper. There's a lot of claims.

1:48:19

So, I mean, I'm not proving to anything that I would call uh I'd call shenanigans on that. >> Okay, last question.

1:48:24

Uh, talk about the decision to expand to Elsa Gundo.

1:48:29

Incredibly cool community at the same time, three time zones away, it feels difficult to manage a team. Is it about talent? Is it about resources? What's the thesis? >> Yeah, it's talent.

1:48:40

It's it's talent, talent, and tacos. Talent and tacos, right?

1:48:44

Um you you've got the core engineering talent that knows how to iterate on hard heavy hardware uh heavy high-speed hardware is really resident there and nowhere else.

1:48:53

And >> you know ultimately as much as the hardware we're building is exciting and innovative the >> sort of the team we are reconstructing reconstructing is really the unlock for this business because aviation has not seen this pace of iteration or these flight conditions in a generation.

1:49:08

So it's not like we can go and hire somebody from company that's effectively take the best we can find from you know the closest parallel and then you know bring them into the the aircraft world and say take what you learn from you know innovating and uh uh in and and working quickly on rockets and now apply that to aircraft and so if you really want that talent and you want to draw that talent the best place in the world to be is Elsagundo.

1:49:33

Well, congratulations on the round.

1:49:37

Congratulations on the progress and enjoy the rest of the conference. We'll talk to you soon. >> Very impressed. >> All right, guys. Thanks so much. >> Have a good one. Goodbye.

1:49:44

>> Uh, up next we have Hungai Louu from Matipin in the waiting room.

1:49:49

Let's bring them in to the Ultra Dome.

1:49:52

We're still figuring out transitions between guests. We're working on it. We're working on it. >> How you doing? >> Welcome to the show. >> Thanks for having me.

1:50:02

Thank you so much for joining me.

1:50:05

>> First time on the show.

1:50:05

Please introduce yourself and the company. >> Sure, John. Uh my name is Hong Way.

1:50:09

I'm one of the founders here at Mapped In and we are mapping all of the indoors, one building at a time. >> Okay.

1:50:16

>> Up from Waterloo, Canada.

1:50:17

>> How are you mapping it? What What is the sensor?

1:50:21

>> So, look, there's low tech and there's high-tech.

1:50:23

Uh you can download the Mapton Scan app, grab any iPhone, um grab any 360 camera these days and just walk through buildings.

1:50:30

Yeah, >> everyone thinks that that's what you have to do and you don't.

1:50:33

The the amazing thing is that 70,000 people have now mapped, you know, all sorts of stuff, schools, their own house, offices, malls, just by scanning in >> the piece of paper that's on every wall.

1:50:46

I bet somewhere in in in that room of yours, there's an emergency escape map on the wall somewhere in the studio and you can take a photo of that and you can get it in.

1:50:54

The problem, of course, is it's a it's a picture.

1:50:55

And so, how do we turn that into vector data?

1:50:57

How do we make that interoperable and useful to all the other apps that you already use and take for granted?

1:51:03

That's what we're trying to do here at Matt.

1:51:05

>> So, what are what are all the different use cases for indoor mapping data?

1:51:12

>> So, so the one that uh you know I've become known for because I've been at this for a while is you know that touchcreen in the mall that you guys probably use at Santa Monica Place and stuff that gives people directions. Yeah. >> That's us. >> No way.

1:51:24

>> So, we do that for Yeah.

1:51:24

We do that for a third of the malls in the world.

1:51:26

We do it for the Super Bowl four years in a row.

1:51:29

We do it at LAX where you guys are trying to make that airport experience a little bit better.

1:51:33

Um, we think we touch about a third of Americans a year with our products.

1:51:37

They probably wouldn't know it's us, but we're we're trying.

1:51:40

Um, and now increasingly we're starting to be embedded in safety applications.

1:51:44

We've mapped thousands of K to2 schools now in the United States, unfortunately, because when something bad happens, you need a map of the inside for the good guys to know how to run inside. >> Yeah.

1:51:54

When when I think about uh those customers that you mentioned, malls, the Super Bowl, LAX, I feel like they have reasonable budgets.

1:52:03

So, is is it about cost savings to use an image based on a floor plan as opposed to just walking around with a 360 camera?

1:52:12

Because that doesn't feel that cumbersome when I think about the the price of an Insta 360 or a GoPro or even your phone.

1:52:18

like it feels like the data collection shouldn't be that cumbersome, but what what is motivating the the desire to sort of go uh go lighter on the data collection side? >> Yeah, fair question.

1:52:32

And and first I assure you they all negotiate very hard.

1:52:36

So any customers listening, they they got a good deal.

1:52:37

Um but I I think the hardest part about mapping the indoors is not mapping it once but keeping it up to date. Okay?

1:52:44

>> You know, the the goofy example is like Santa Claus moves every year uh in the mall at Christmas, right?

1:52:48

So, if you want to know where Santa Claus is going to be this Christmas, the only person who knows is the person on the ground planning Christmas at the mall in that off in that back office, right?

1:52:58

And if you wait until that's already happened and then you somehow try to scan it, it's too late.

1:53:04

Um, never mind that it's private property and you know, someone would escort you outside.

1:53:08

So, a lot of managing and mapping the indoors for you and I and for everyone else who needs that information is getting ahead and being able to plan ahead and managing that constant change.

1:53:17

the, you know, you can scrape the outdoors with a with a LAR sensor or with a satellite with a car, it's good for about five years.

1:53:23

If you even if you manage to scrape the 100 million buildings that are privately owned indoors, it's good for about 5 days.

1:53:32

So, how do how do we how do enable those folks to plan ahead? >> Yeah.

1:53:36

So, uh I mean the the tagline is Google Maps for the indoor spaces.

1:53:39

Uh are are you going to partner with Google?

1:53:43

Are you going to partner with Apple Maps?

1:53:44

Is there an API that you can just expose these things to?

1:53:46

because I imagine if I'm LAX I I and I pay you.

1:53:51

I'd want the data to be as available as possible to my customers and patrons.

1:53:58

>> I wish I could use that tagline.

1:53:58

I'm glad you did uh so I don't have to.

1:54:00

Um but look, I I think from from their perspective, and obviously I can't speak for Google and Apple, but you know, Google wants better data so that they can serve more users.

1:54:10

Apple wants better data so they can sell more phones.

1:54:15

um they I think they're generally pretty happy when someone does all the hard work of mapping the indoors and makes that information accessible and and standardized.

1:54:25

Um so we publish a lot of data on behalf of our many clients, the LAX of the world, the you know Sofi Stadiums of the world to platforms like that.

1:54:33

Um I can't speak specifically to which ones and who because they're all pretty sensitive about like this is my private property, you Mr. Big Tech can't have it.

1:54:40

Um, but to your point, you know, consumers need this information. It's it's on the wall.

1:54:45

So, we're we're just about standardizing the pipes um and enabling building owners to publish the information.

1:54:54

>> How do you think this interacts with uh the potentially coming robotics boom?

1:55:01

>> I've been getting a lot of calls about that.

1:55:02

Uh wouldn't surprise you, of course.

1:55:04

Um, so I think there's enough problems to solve outdoors for robotics that I'm, you know, I'm still kind of holding my breath for when this becomes more real indoors.

1:55:15

Um, there's more constraints.

1:55:18

I'll just say that if you, if you, you know, we're only now solving for self-driving cars outside.

1:55:21

It's been what, 20 years that we've been talking about it.

1:55:25

And that's actually a much simpler problem, right?

1:55:26

like streets, buildings, lamposts, pylons, there's like, you know, far fewer things you have to recognize outdoors to be able to >> move around.

1:55:36

Indoors, there's way more >> and and the training data is not actually easily available because unless you're a Roomba, you can't just scan people's houses and even if you could, it's incomplete.

1:55:46

So, you know, we're we're building up, we think, a pretty large training data set.

1:55:50

Um, we, you know, seen our technology now deployed for Department of Homeland Security, various fire departments and police departments throughout, uh, throughout the United States, but going, you know, I guess I'm sometimes accused of being too Canadian in that we don't we don't sell ahead of reality.

1:56:06

Um, I think we're a couple years out of, uh, of going to map the inside of Saturn's moons soon.

1:56:17

You got to think like 50 years ahead.

1:56:20

How about Mach 3 jets for starters? I'll do that first. Yeah. >> Yeah. Yeah.

1:56:23

Maybe maybe airplane hangers, you know.

1:56:26

>> Yeah, we're headed that way.

1:56:26

I'll say this, if we don't pull it off, I don't know who would.

1:56:30

Um we have by far the most amount of training at this point of the indoors, but we're we're doing it on behalf of our clients.

1:56:35

And I really do think that the indoors belongs to the landlord, belongs to the people operating it.

1:56:41

>> Um and and so it's about who can enable them to realize that future. >> Yeah. Yeah.

1:56:45

And to help their customers makes a ton of sense.

1:56:46

Uh tell us how much did you raise?

1:56:49

>> I love how you've you just got a third of the market.

1:56:51

Like I just like it is it is such a such a large uh so much market share and and uh those early early pitches.

1:57:02

>> Um yeah, it's it's just fascinating market overall.

1:57:05

But anyways, >> tell us about the round. >> Yeah.

1:57:08

>> Well, my fiance hates that I I don't like going to malls anymore, if you would believe it.

1:57:11

It's it's really not fun.

1:57:12

Um it just feels like working. >> Yeah. Oh, dude. All right. Hey, we raised $24. 5 million USD. >> Hey, there we go. >> Congratulations.

1:57:25

>> That's a lot bigger than the sales gong we have in our office. So, thank you. >> It's a huge one. >> Awesome.

1:57:31

>> Uh well, thank you so much for coming on the show.

1:57:34

>> Uh congratulations on the progress and we'll talk to you soon. >> Love you guys. >> Have a good one. >> Goodbye. Love you.

1:57:39

Love you is a great exit.

1:57:42

Uh, our next guest is live here in the TVP Ultra Dome with us, Zach Kukov. Welcome.

1:57:48

Thanks for having welcome to the show. How you doing? Thank you. >> Big day for you guys.

1:57:53

>> It has been far too long since you've been on the show.

1:57:55

Last time, uh, we were talking.

1:57:57

>> Why are you in Why are you in LA?

1:57:57

Why is a man like you in Los Angeles?

1:58:01

>> What does a man like me have to gain in Los Angeles?

1:58:02

They're a nice tan, a little bit of relaxation.

1:58:06

>> Everyone who's joined the show has been outside.

1:58:08

Uh, I mean a few people, but uh uh it's spring is fully in uh uh >> it's beautiful. >> Yeah. In full swing.

1:58:15

Uh did you get snowed in in DC?

1:58:16

Did you go crazy blizzard? >> We had snowrete. So we were stuck. What's that? >> Snowrete.

1:58:21

I had never heard of this before in Washington.

1:58:22

It's the only East Coast city I've ever seen.

1:58:24

They haven't figured out how to plow snow cuz the snow Here's the problem with Washington. The snow lands.

1:58:30

Everybody just sits there and there's a wait for it to melt.

1:58:34

>> So their only plan is to like help it gets warm.

1:58:36

Work smarter, not harder. >> Yeah. Except neither.

1:58:38

They just let it sit there and it didn't melt and it stayed really cold for a week and it merged into like a weird hard soft hard the water and the snow.

1:58:47

And somebody who's smarter than me can do the like chemical side, but basically once it melts and freezes over like four times in a row, >> it becomes this thing called snow creep and you're basically stuck and you can't leave your house.

1:58:59

So we couldn't like drive basically for the better part of like two weeks. It was brutal. Yeah, it was wonderful.

1:59:04

But uh how is business broadly?

1:59:07

Uh reintroduce yourself, re redescribe your role and then uh we can go into some of the hot topics. >> Yeah. Yeah. Yeah. Uh okay. I run a lobbying firm. Yeah. >> A lobbying firm.

1:59:16

I run the tech practice of a lobbying firm.

1:59:18

Firm's called Lewis Burke.

1:59:20

We do science, tech, education. Wow. That's so nice. >> Wow.

1:59:25

That should be in every room I walk into.

1:59:27

They should always applaud for me.

1:59:29

>> We do science, tech, education, healthcare.

1:59:31

I run the deck side of the house.

1:59:35

You're like you're I was watching you guys on the way over >> being the most like straightforward explanation. I really like dramatic.

1:59:39

I was like really into it.

1:59:42

Anyway, I run the tech side of the house.

1:59:44

So I lobby for venture firms.

1:59:45

I lobby for private equity.

1:59:46

I lobby for high net worth individuals mostly who come out of venture private equity and then I lobby for a whole bunch of tech companies >> and specifically on the federal side. >> On the federal side. That's right.

1:59:55

So we do everything at the federal level.

1:59:58

>> So give me your sort of postmortem on big beautiful bill.

2:00:00

Uh that was the last thing we talked about. Wow.

2:00:04

>> What were the key uh decision points?

2:00:04

I know that the government was shut down for a while.

2:00:08

There's a lot of back and forth.

2:00:09

Like how did this all shake out? >> Okay.

2:00:11

Basically, every This is It didn't used to be this way.

2:00:13

It used to be in Washington that things operated, I wouldn't say smoothly, but like go back to the '9s, West Wing, Optimism, N.

2:00:21

Gingrich, Contract with America, Bill Clinton, like >> yeah, yeah, we get a sound effect for that one. That's nice.

2:00:26

And things pretty function pretty well.

2:00:28

Now in Washington basically every time something happens it sets up the seed for the next issue.

2:00:34

It's like if you only had the treaty of Versailles over and over and over again. Okay.

2:00:38

So Big Beautiful Bill comes out.

2:00:40

It happens after Big Beautiful Bill.

2:00:40

You lead up to the big conflict which is around Homeland Security, ICE and enforcement.

2:00:46

And you basically have a variety of issues that come out.

2:00:48

Most recently you guys I'm sure were tracking Christine out right at DHS.

2:00:54

>> Others may be coming out soon too, right?

2:00:57

you know, AG, many others in the case too.

2:00:59

A lot of this stems from the inability to get the entire government funded, right?

2:01:03

Big beautiful bill and by the way, reconciliation as a whole, guaranteed funding for border enforcement for ICE, right?

2:01:09

So, when the Dems later come back and say, "Hey, I want to be able to stop some of these things that are happening in Minnesota and things like that.

2:01:17

The only lever they have to pull is to stop funding DHS as a whole."

2:01:21

That's why you get these airport shutdowns.

2:01:23

That's why you get these super long lines at TSA and so on and so forth. >> Yeah.

2:01:27

But from a tech perspective, how many like all of that uh like flows through to whatever tech leaders want to happen in Washington just is slower. But what was on the top?

2:01:39

What's on the top of the the the stack in terms of to-dos in Washington for Silicon Valley broadly these days?

2:01:47

>> I mean, I will tell you it's less of a policy to do and the biggest political issue right now is probably the data center stuff.

2:01:53

You guys are I'm sure I'm sure people on talking about it.

2:01:54

You saw the thing in Indiana, right? City councilman. It was horrible. >> Terrible.

2:02:00

Had 13 shots in his house. Okay.

2:02:04

>> Putting aside how horrible that was, there is like a very broad bipartisan emerging consensus that data centers rise energy costs and water and all sort, you know, create pollution and whatever.

2:02:14

You saw like they you know, increase heat temperatures for you know parking lots.

2:02:18

A lot of that's not true.

2:02:21

But because the perception of it is real, you have this big huge armed basically opportunity for candidates who are uh populist sometimes, but opportunist always to come in and run on that issue in 28.

2:02:33

So the big the problem with tech right now in DC is you have a bunch of tech money coming in.

2:02:37

You guys know leading the future is the open AI uh associated super PAC and uh public action or or public fur something along those lines is the anthropic alliance super PAC.

2:02:48

Both have been putting tons of money into races like New York 12 I think with Bores, right?

2:02:53

All sorts of people who have been either pro or anti- data center and AI development as a whole.

2:02:59

There are now hundreds of millions of dollars of tech money going to try to arm those.

2:03:04

That's the big issue for tech.

2:03:05

like if you want to get anything else done, you basically are saying how do I play now in midterms but also looking ahead to 28 even if I'm not talking about data centers directly to make sure my preferred candidates get in to actually open the doors for the things I want to be able to do. >> Yeah.

2:03:20

And how much of the data center question is about uh research education on those issues like there was a full back and forth on the water issue.

2:03:27

I think that one landed in a pretty good place with energy less so because no one debates that these data centers use a ton of electricity.

2:03:36

I mean they're measured in electricity.

2:03:39

That's how we refer to how big they are.

2:03:41

It's a gigawatt or a megawatt or >> it's a proportionality problem, right?

2:03:46

Like the data center issue is yeah, do they use a lot of electricity? Sure.

2:03:47

But in comparison to things like growing almonds, right, or raising a cow, it's actually not that much.

2:03:54

And it turns out we love cheeseburgers and we find cheeseburgers like have huge value in America and so we accept the trade-off that in order to have cheeseburgers we have to pay a little bit for electricity, right?

2:04:04

It turns out there's a lot of value in AI.

2:04:05

By the way, this is not a winning political argument.

2:04:07

I wouldn't go up and down and make the argument that, hey, everyone's going to have to get used to paying a little bit more to pay for AI. No one wants that. >> No. Who who would want that?

2:04:17

>> And that's the backbone of the rateayer protection pledge. Correct. That's right. How is that going?

2:04:19

because that is a pledge right now, but it feels like it could be codified into law at some point.

2:04:26

>> I don't know that I'm super bullish on it being codified, but what I'll tell you is anything that tech can do to get in front of the issue like do you guys know the account on Twitter more perfect union? Okay. Okay.

2:04:35

I think they are the most effective messengers by the way in politics today.

2:04:39

politics today. They have one uh narrative they push and it's very very well done which is look at these great local small town salt of the earth people fighting back against the evil tech firms who want to do things like build data centers right and so the more that you see that and the more salient

2:04:56

the issue becomes and it's already so salient as it stands today you guys saw leaking the future played in uh the sort of two Illinois races that just happened recently during the midterm cycle they won one out of the two if you look at what they ran on In neither case do they run ads saying we love data centers, please fund data centers. Right? The ads Right?

2:05:13

The ads were about saving democracy and it's great to have Jesse Jackson Jr.

2:05:17

and the other candidate and so on and so forth.

2:05:21

Like the more that tech can come out and ahead of these things and then change the topic to the variety of other issues voters care about, the better position we are in.

2:05:28

The more that we just try to take this on because we don't need 30 more years, right?

2:05:32

We need to have a window of time where we can come in and get data centers built and actually start to operationalize a lot of the future value of AI.

2:05:40

If you wait too long, there's a window where our adversaries pull ahead of us and the opportunity for American AI to dominate goes away.

2:05:47

And if we're if we're uh too aggressive, right, the other inverse of this is we risk really alienating a lot of the communities that are local.

2:05:53

So, you need to thread the needle on getting out ahead with things like the pledge, right?

2:05:57

making sure we can come out and show that we're listening to constituent needs while also at the same time actually delivering on some of the positive benefits that are not just more doom around AI is going to take your job and kill your horse and burn down your house and whatever. >> Sure.

2:06:11

>> Uh what what has messaging been like around the the b like I I think for a lot of people they're like okay data center in my area what does it do for me?

2:06:20

They're like am I still going to be able to use LMS or video models?

2:06:26

even if it doesn't go in like so what are you going to do for me and so I think one of the best >> uh one of the best arguments for it to date has been like tax revenues is there any is uh that can be you know repurposed for a bunch of other things seems to be pretty significant based on some of the numbers I've heard but is that kind of messaging resonating or are people still just saying you know flat out uh not not in my backyard? Yeah, it is.

2:06:55

Uh I would say it's very nimi.

2:06:55

I don't think the tax stuff is breaking through to people.

2:06:59

And the reason it's not breaking through in part is because for every person like you and me and John who says, "Of course these things are going to throw off cash.

2:07:05

How could you not look at them and think you can fund an entire school district with one data center, right?

2:07:10

You have somebody else who goes, "Well, that doesn't logically track to me because you have five employees manning these things and they're going to be more and more automated as time goes on.

2:07:18

And more to the point, why not put them somewhere else?

2:07:21

We're gonna have to deal with it and see the negative externalities.

2:07:22

Like the messaging thing that tech hasn't figured out yet.

2:07:25

And partially we do it to ourselves.

2:07:27

Every time you have somebody come out and say AI is going to take your job and it's going to totally disrupt society. It's going to end.

2:07:33

He didn't have the people who built NAFTA weren't selling NAFTA by saying, "Hey, NAFTA is going to ship all of these jobs overseas and be horrible."

2:07:40

And by the way, NAFTA, whatever you think about it, did set up the conditions for a lot of jobs moving overseas.

2:07:46

Like tech has to get out of its own way a little bit and stop saying things even if there is some some tale chance of it being true.

2:07:52

Like we've left the context bubble, right?

2:07:56

We're not in I lived in SF for seven years.

2:07:58

Like we're not in Berkeley right now where we're having a great conversation in front of the light cone and whatever and having sort of in-depth intellectual discussion.

2:08:04

We're in persuasion mode and you can't come out and say that. >> Yeah.

2:08:08

What about uh the just taxes broadly?

2:08:11

Because when I hear a data center will throw off a lot of taxes, I think, well, maybe corporate taxes wherever that company is headquartered and they'll certainly pay real estate taxes, but it's a very small real estate footprint.

2:08:25

And so are there any local uh leg local regions that have figured out how to rethink the tax base to actually capture some of the value and say look we're down but you're going to have to pay and here's the actual deal and let's let's think about it more in those terms to make sure that we're actually internalizing the value.

2:08:49

>> In some ways you're a georgist, right?

2:08:50

You're like, "Hey, I'd like to have a land value tax if we just capture some of the improvement we've created, which I'm very sympathetic to. I hear the argument."

2:08:58

The big tax conversation today, it's been um uh a little bit back and forth.

2:09:03

And the back and forth has been between some people who are saying, "Hey, we need tax breaks to lure in data centers."

2:09:09

Like for all that we hear about local community hates data of like offline people who are saying things like, "Actually, I would like to have any job in my area." Right?

2:09:17

I'd love to have any good construction job, any job running these things. So that's okay.

2:09:24

So one side of the issue is do we have people who are saying we need tax breaks or tax incentives to lure, right?

2:09:31

And and that's actually a wedge issue even within Republicans, too, by the way, who say, "Hey, some of whom say, hey, you're putting this in my backyard.

2:09:37

You're destroying the character of my community."

2:09:39

Very sort of the classic nimi arguments we've heard.

2:09:40

And some of whom are saying things like, "Gosh, I would love to have more industry in my community." Okay, so that's one.

2:09:45

community." Okay, so that's one. The other side of it on the tax side are people who are saying okay we already have them right and so what are the other things that we can levy sometimes land improvement sometimes by the way it's a consumption tax on energy right so you can actually there are people who

2:09:57

are saying look should we craft more narrowly targeted energy consumption taxes commercial in their orientation which is not a perfect >> and it's not something we've historically done because correct if I'm using energy to do my dishes and you're using energy to watch not watch Netflix we don't typically put a value on that

2:10:14

differently but >> maybe extraordinary ary circumstances require extraordinary >> many many have pushed for you know podcast tax >> energy energy consumption >> podcasts are the backbone of America okay we cannot tax them and so you see this back and forth in Georgia like I would tell you okay Virginia which is I

2:10:31

think the largest concentration of data centers in the country >> in part the dem the reason Spanberger the Democrat won the new the recent gubernatorial race in Virginia is because she campaigned on the sort of very kitchen table issue of your energy cost has gone up I am going to do the things to make it go down. One of which

2:10:46

One of which is slow down on data centers, right?

2:10:48

And in Georgia, if you look there right now, I would tell you the best chance the Dems have of flipping the Georgia gubernatorial race in the last 20some years is running on they, by the way, they flipped some of the Georgia public utility commissioner seats, which are like super nerdy, low salience, but they flipped them on this question of I am paying more for my energy.

2:11:07

I don't want that to happen.

2:11:09

How do we prevent these data centers from going on?

2:11:10

It's a big problem and the super PACs are doing a good job, but they could be doing a lot better of a job of articulating, by the way, nonAI related messaging to support AI candidates.

2:11:22

>> What about zooming out to just energy broadly?

2:11:24

Because I feel like uh while the AI companies might be and the tech companies might be narrowly focused on data center construction, a lot of them do have bets in solar and nuclear and a lot of venture capital firms have funded solar and nuclear efforts and that feels like potentially a more pro- bipartisan issue.

2:11:45

How do you see that breaking?

2:11:45

How do you see the like when we talk to nuclear founders, we get extremely excited and then they give us their timelines and it's like it'll be online in 2030 and I'm wondering if there's anything that the government can do to pull that forward.

2:11:56

Are there any efforts or any reframing like if you were to put on your strategist hat on like how do we I mean how do we just get excited about clean energy because that feels like it's longer term but it's potentially the release valve for everyone makes everyone happy.

2:12:14

>> So okay I'll say two things.

2:12:14

One and I should disclose that we lobby on basically all these issues.

2:12:17

So everyone should just count appropriately. I'm talking my own book. Okay.

2:12:22

>> One is you can think about what are the ways. >> Thank you very much. Thank you very much. Thank you very much.

2:12:33

>> One is we could think about what are the ways that we can colllocate by the way data centers with currently stranded sources of energy. Right?

2:12:39

So if you have like there's there's two components of energy.

2:12:42

There's production and there's transmission. Yeah.

2:12:44

And the government can do a lot.

2:12:45

The federal government can do a lot on from both the research side and some of the permitting side on the construction consideration.

2:12:51

The transmission side is a lot more nuanced and and sort of a boring conversation.

2:12:57

But suffice it to say like they're very limited ways in which federal government can really make a big uh impact on transmission.

2:13:02

And so you might think to yourself, okay, what are the ways where we can take places we've already built energy, right?

2:13:07

Everything from all the above, right?

2:13:09

from nuclear to solar to coal to natural gas and build data centers with already exist.

2:13:13

Same thing by the way that crypto mining did right for many many years.

2:13:16

Call that door number one.

2:13:18

>> Door number two is I am very bullish on nuclear and things of that nature and partially because we we do lobby for it but also like if you're thinking about what are the ways in which nuclear moves forward.

2:13:28

What you actually want is for the federal government to think about what are ways that we can centralize authority over permitting more and more because you don't want to have like the local permitting group up in arms saying we don't want a three-mile island.

2:13:39

What you actually want is for like a much more centralized one-stop shop authority.

2:13:44

And so in that way you're not call it industrial policy, call it big government, call it whatever you like, but you're getting back to the idea that the government plays a much firmer hand in actually driving where these things go over the short and medium term.

2:13:59

Is the data centers in space thing bullish or bearish for building more data centers on Earth?

2:14:04

Because it feels like a get out of jail free card a little bit.

2:14:07

It's like now that we can build them in space potentially like let's just not build anymore here.

2:14:12

>> Well, it's my position that America owns space too.

2:14:14

Okay, let's just start there for a second or at least a little bit of space. >> Um, okay.

2:14:18

Okay, in all seriousness, the honest answer is I find it hard to believe that the economics of building data centers in space flip before we have a government that needs uh that is more flexible and more amendable to people who want to build locally.

2:14:30

Like part of the challenge is this.

2:14:32

You saw the here's the quintessential example.

2:14:35

Okay, >> the Germans get very excited about decommissioning nuclear energy, right?

2:14:39

They they've lobbyed for it for years and years.

2:14:40

They have the famous tweet you've all seen of the Green Party candidates, you know, announcing the shutdown of the last reactor. Then a crisis happens.

2:14:48

In this case, Russia, Ukraine, and suddenly Germany is saying, "Gosh, I wish we had nuclear energy.

2:14:51

Any source of energy we would take today." Okay.

2:14:55

You have the straight getting closed.

2:14:57

You're saying to yourself, "Gosh, I wish we had any source of energy."

2:15:00

>> I think oil is at 140 a barrel right now. >> Yeah.

2:15:02

I don't know the exact, but that sounds >> where it was at when uh the Russian. >> That's right. Okay.

2:15:08

What's the common thread across all these different things?

2:15:11

Government is reactive and people are reactive to crisis.

2:15:13

And so it is um I don't you would never root for a crisis.

2:15:18

You want a crisis to ever happen to waste >> but don't let it go to waste to quote Rahm Emanuel, right?

2:15:21

And so you're far more likely to say hey what's what's more likely to me on a short and medium time horizon.

2:15:27

The economics of building in space rapidly flip possible but I don't know how likely.

2:15:32

Or something happens on Earth and there are millions of things that could happen >> such that people now are newly incentivized to build and allow for building at home.

2:15:41

And I hope it doesn't require a Sputnik moment, right?

2:15:42

Hope, by the way, another Sputnik moment.

2:15:47

Maybe Deep Seek was already one.

2:15:47

I hope that's not the outcome, but if it is, the outcome will be people are much more amendable to building, I think, uh, locally here on Earth and America.

2:15:56

>> Thank you so much for coming on and breaking it down. >> I appreciate it. Congratulations, guys.

2:16:02

>> And we will talk to you soon.

2:16:04

>> We have some breaking news I can take you through while we bring in Thomas Leafant from CO2.

2:16:09

Uh, the alley has officially been auctioned fully.

2:16:13

The naming rights to Riley Walls's Alley have just sold to Notion for $140,000. It's now the notion way.

2:16:21

It does seem like they sniped it.

2:16:25

Uh, the notion I like that in WordPress, they called it the notion way.

2:16:31

Uh, yeah, WordPress was asleep at the wheel.

2:16:34

They let it get away from them for just 5,000 more.

2:16:37

>> Do not pay just absolutely.

2:16:37

I like that someone was trying to make it Bags Street. Core Automation Inc. The way also was there.

2:16:46

>> Someone went back and tried to keep it named Dirt Alley for $111,000. Gum Road was a good one.

2:16:53

Well, uh, we can bring in our next guest, Thomas Lefon from CO2.

2:16:56

He is here live with us in the TDP Ultradome.

2:17:00

Thomas, great to see you. >> How are you doing?

2:17:04

Thank you so much for taking time. >> It has been too long.

2:17:07

Uh I want to I want to actually begin at the very beginning.

2:17:12

Uh can you tell us where you grew up?

2:17:16

>> Yeah, I was born uh in Paris in 1976.

2:17:16

So I'll be turning 50 in uh >> Congratulations.

2:17:22

>> Actually about a month and a half or so. >> Success. >> Thank you.

2:17:26

And uh really split my growing up between uh the US and France.

2:17:32

>> Uh my father was an executive who kind of moved around.

2:17:34

So between Paris and New York did a back and forth twice.

2:17:35

was kind of used to moving around. >> Yeah.

2:17:40

>> Um, settled in New York in 1988.

2:17:43

>> One of the first questions I get is why I don't have an accent when my brother does. Yeah.

2:17:48

>> And I think it's really related to our >> kind of where we grew up and our difference in ages when we moved to the US, but >> obviously incredibly grateful to have moved to the US.

2:17:56

Um, what I do remember from France is the feeling of a country that kind of felt stuck in neutral. >> Sure.

2:18:04

And so coming to especially a city like New York with the dynamic uh economy and just the the feeling of life just in the buildings construction was incredibly motivating.

2:18:14

I went to a really international school in uh New York that had a lot of uh different types of people.

2:18:22

It was a French language school.

2:18:24

So you had your kind of classic expats.

2:18:25

you had um a lot of diplomats from all over the world since French is a diplomatic language in a lot of countries.

2:18:34

Um so I was really grateful to that exposure. >> Mh.

2:18:39

>> And then I went to Yale.

2:18:39

I studied computer science.

2:18:42

My junior year I realized what a good programmer was. >> Yeah.

2:18:48

>> And that I wasn't one of them. >> Wow.

2:18:50

>> Uh because what would take a good programmer, you know, an hour would take me six days. >> Wow.

2:18:57

Um, so that was kind of sobering and I thought, okay, I got to think about something else. And, uh, I love movies.

2:19:03

Always watched a ton of movies.

2:19:03

Um, it was kind of the peak for I think kind of the movie business.

2:19:08

We were about to roll into the DVD era. >> Yeah.

2:19:12

>> Which if you think about it now as an analyst was essentially the studios monetizing a library again. >> Yep. >> At virtually no cost. >> Yep.

2:19:22

So everybody's building DVDs. Media is at the peak.

2:19:26

You had Mike Oitz on the cover of Newsweek. >> Yeah.

2:19:31

>> Um so I learned as much about that industry kind of reading the New York Times on Mondays, which was kind of the digital edition, reading Vanity Fair and Premier Magazine and anything I could get my hands on.

2:19:42

I said, "Okay, well, >> um Hollywood sounds like a lot of fun and CIA has this uh training program."

2:19:49

Well, I don't know anything. I don't know anybody.

2:19:51

I've never really been there.

2:19:52

So, getting trained sounded pretty good. >> Yeah.

2:19:56

>> I went to my alumni house uh and I we had these binders and you could look up industries and there was one that said entertainment >> and I saw there was one agent who had gone to Yale who was at CA.

2:20:05

So, that we're now in my senior year.

2:20:10

>> I called her uh every day for 6 months at the same time. >> Wow.

2:20:15

And I got to actually know her assistant pretty well because agents have to pick up the phone. >> Yeah. >> Right.

2:20:21

Because that's how their business is built.

2:20:23

>> Wait, what time of day?

2:20:23

Like early morning, late? Uh, no.

2:20:26

>> How did you settle on one particular time?

2:20:28

Because even by default, I would like mix it up and try.

2:20:32

>> I knew first thing in the morning.

2:20:33

>> No, it's kind of an interesting thing.

2:20:34

Like if you just get a random call here or there, you're not really paying attention to it.

2:20:37

But if every single day at 3:00 you get the same call by the third or fourth day you're like >> I I knew she would take my call first thing in the morning. Yeah. Exactly.

2:20:48

>> So I'm like right about right after lunch right it's like the midm morning the mid-after afternoon nap.

2:20:52

Maybe I'll hit them in a weak spot.

2:20:56

>> So I got to know the assistant and we would kind of joke around and um >> but finally uh one day I got a call back from uh from her.

2:21:04

Her name was Sally Wilcox. was a book agent.

2:21:08

>> And she said, "Well, what's it going to take for you to stop calling me?"

2:21:09

I said, "Well, I want an interview."

2:21:14

And she actually gave me, I thought, an amazing answer to that.

2:21:17

She said, "Well, if you agree to move out and you call me after you've moved out to LA, I'll get you the interview."

2:21:27

>> She wanted you to move >> move before you even get the interview.

2:21:30

That is what I realized what she was doing was she was kind of testing my conviction, right?

2:21:35

And she probably got a lot of calls from people who said, "Oh, I want to do this. I want to do that." >> Yeah.

2:21:40

>> And she's like, "Well, if you have the conviction in yourself to move out with no job, that shows you really kind of want it."

2:21:46

It was probably a filter on her part. >> Yeah. >> So, I did.

2:21:49

And it was my second time, I think, in LA. And I called her.

2:21:55

>> And I only had one egg in the basket.

2:21:58

There was no other egg in the basket.

2:22:00

Like, this was the only egg in the basket. >> So, I called her.

2:22:03

I said, "Well, you remember you told me if I moved, you would give me the interview." And she did.

2:22:08

I interviewed I think on uh some I moved out May 22nd, I think, after I graduated.

2:22:16

I got here first week of June and by July 7th was my first day >> of 1997 in the CA mail room >> in the mail room.

2:22:24

>> And uh I think you guys were just maybe there, right?

2:22:26

It was just this iconic >> um >> place and a lot of uh people I kind of looked up to had started in the mail room.

2:22:34

Uh Ron Mayard started in the mail room.

2:22:37

David Geffen, Barry Diller.

2:22:37

So I'd read about all these legends of the business kind of starting.

2:22:43

>> There was a dress code back then, too. >> What's that?

2:22:45

>> There was a dress code back then, too. >> Oh, absolutely.

2:22:47

>> Yeah, there's still a dress code today.

2:22:48

And that I think that's the thing that sticks out the most about the mail room is just the number of talented young people that you have in a pretty small space and they're all dressed perfectly. >> Yeah.

2:22:59

>> Which is >> and every experience you have >> Yeah.

2:23:02

>> in that mail room is kind of unique.

2:23:04

>> So I'll tell you guys this story.

2:23:04

I don't think I've ever shared this one publicly, but >> um we uh did a lot of work for Ralph >> Lauren.

2:23:13

>> And this is back kind of in the Friends era.

2:23:14

And if you remember Jennifer Anderson's character on Friends worked at Ralph Lauren. Yeah.

2:23:19

>> So, he's coming in and he's doing a taping and so I'm asked to go pick him up at the Beverly Hills Hotel and just escort him for the day while he's shooting the scene.

2:23:28

And if you remember the scene, it's in an elevator and Jennifer's uh character Rachel bumps into Ralph and it's like a 12 second scene.

2:23:40

You can look it up on YouTube.

2:23:43

>> So, it took like 3 minutes to shoot. >> Yeah.

2:23:45

So we get on set at nine.

2:23:45

He by I think he's in denim on denim. Just classic Ralph.

2:23:50

And >> right before he goes on he's like call her up or call her down. I'm like call her up. >> Let's go. Let's we're rolling.

2:23:59

>> We're rolling with this.

2:23:59

And uh >> so it takes like 3 seconds and so we arrived at 9 and like 9:08 we're kind of done.

2:24:06

And Ralph says, "Well, um my I didn't think this would be that quick and my next meeting is not till 4:00.

2:24:12

So, why don't we go and spend some time together?

2:24:15

And I'm I'm really kind of interested to go where you shop. >> Mhm. >> Oh, interesting. >> Oh my gosh. >> Ready?

2:24:20

He wanted to jump right into market research. >> Yeah.

2:24:23

So, we spent the whole day going together uh shopping around.

2:24:24

One memorable one was going to Fred Seagull back when Fred Seagull on Melrose was >> every new brand was kind of >> getting broken uh into there.

2:24:34

And I just watched him walking around and the way he engaged with every single salesperson, never talked down to him, to your point, did market research.

2:24:42

Why are people buying this style, not that one?

2:24:46

It was just amazing to watch.

2:24:49

>> And uh how just curious he was and how much he wanted to learn.

2:24:53

So we finish and I'll get to the end of the story, but at the end of the day, he's like, "Look, I got to ask you something.

2:24:59

It's really bothered me the whole day." I'm like, "Yeah."

2:25:01

He's like, "Why aren't you wearing Ralph Lauren?"

2:25:05

And I said, 'Look, honestly, the cut's not great, and the store on Beverly Drive is a little old and there's a lot of wood.

2:25:10

It doesn't really feel kind of new.

2:25:11

And he's like, "Man, you're so right."

2:25:13

Um, but he's like, "You really should be wearing Ralph.

2:25:16

So, go there and just tell him I sent you and you can get anything you want." >> Wow.

2:25:21

>> So, we kind of parted ways and he was he was really lovely.

2:25:23

But I debated whether I should go or not.

2:25:25

And the next week, I go and I go and I introduce myself to the very pretty lady at the counter and I said, "Look, I'm sure you get this all the time.

2:25:33

time you're going to think I'm a crank, but Ralph sent me and he said I could get whatever I want.

2:25:38

And she paused for a minute and she looked me up and down and she said, "Oh, we've been waiting for you." >> No way.

2:25:44

>> And uh I kind of spent the >> you know, an hour kind of going through the store.

2:25:49

But you know, the exposure that you got to people through that job um >> was really amazing. >> Yeah.

2:25:56

>> Watching you know, actors, watching directors, watching entrepreneurs like Ralph.

2:26:01

Um, so I was really grateful for that opportunity.

2:26:05

>> Talk about you you had a uh we were catching up yesterday off air and you had a story about uh you can just talk generally about um your what what what maybe entrepreneurs could learn from actors and actresses that are breaking in through Hollywood and what they have to go through from a competitive standpoint to actually break out.

2:26:23

I'm sure you got to see a bunch of different stars over the years, but the, you know, everything from, you know, the rejection to to the the constantly having to, you know, constant hustle because, you know, as soon as a project ends, it's like, okay, what's the next thing? >> Yeah.

2:26:42

I think I think people underestimate, first of all, how hard it is to be an actor and how competitive it is.

2:26:47

Um, first of all, especially in the movie industry, right?

2:26:49

You have to kind of find a new job every, you know, six or seven weeks.

2:26:53

So, you're constantly unemployed.

2:26:56

You're constantly having to search the new job.

2:26:57

Not only that, when you go and you actually interview for a job, you might run into 10 other people that look exactly like you, right? All of your competition.

2:27:06

Like, imagine if I was pitching an entrepreneur and I'm like, "Oh, there's Sequoia and Andre and Benchmark and we're all kind of sitting lined up next to each other waiting."

2:27:15

That's kind of what actors go through when they audition.

2:27:18

And then, by the way, when we give you feedback, it's not even feedback on your business. It's on you.

2:27:23

We don't like how you talk, how you look. Too tall, too. >> Your ears. Your ears are >> right.

2:27:28

I I represented Jordana Brewster um and who's still who's a friend now and is married to a friend of mine.

2:27:34

But one day on one of the casting sheets, it said, "Hey, a Jordana Brewster like actress for this role."

2:27:39

So, I called and I said, "Well, what about Jordana Brewster for this is literally who you said."

2:27:44

They're like, "No, no, no.

2:27:44

We just want someone like her."

2:27:46

I said, "Well, what about her?" Right?

2:27:47

But that that is the kind of stuff and obviously the competition is intense, >> right?

2:27:55

>> And it actually reminds me a little bit of what it was like to be an entrepreneur in China, right?

2:27:57

Very similar like you would have >> if you had a ride sharing company.

2:28:00

There were 150 other ride sharing companies that you had to kind of get through just to then win your city to then go and compete with all the other cities to then at the end compete against Uber and China as an example and DD kind of won that.

2:28:13

So, it's an extremely competitive kind of industry and I relate to that.

2:28:19

Um, as someone who worked with actors, >> you would sometimes talk to an actor and say, "How'd the audition go?"

2:28:24

They would tell you, "I nailed it. It was just so good."

2:28:27

And then you'd call the casting director, the director, and they would say, "That was the most unprepared, bad audition."

2:28:33

And so, you have to figure out how to communicate that feedback in a way that's constructive to the client, right?

2:28:41

because just telling them, "Oh, you you did great, but you didn't get it," isn't necessarily useful. >> Yeah.

2:28:46

>> Uh but in a way that doesn't also um you know, is detrimental, right, to their mental health and things like that.

2:28:54

So, it's it's very much a people a reputation uh kind of business and I really enjoyed it for the seven years I did it. >> Yeah.

2:29:02

What was the process of getting out of the mail room?

2:29:04

I imagine that you have, you know, a few really iconic stories from the mail room, but there were plenty of days that were just photocopying. Is that roughly correct? >> Yeah.

2:29:16

I mean, we did we we did shopping for groceries.

2:29:18

We we copied scripts, we delivered scripts.

2:29:21

So, there was a whole >> set of kind of stories on that.

2:29:24

And honestly, we could we could fill a lot of podcasts just the different things >> that we kind of did.

2:29:30

But look, it it rewarded hard work.

2:29:32

It rewarded attention to detail. Yeah.

2:29:35

>> Um, ultimately and you got invited to the retreats, right?

2:29:38

And CA did these retreats every year and after one of them I had some thoughts.

2:29:41

I wrote them down >> and I handd delivered a letter to the managing partner saying, "I was at the retreat and here are my thoughts." Right? I wish I still had it.

2:29:49

I don't know what I said.

2:29:51

And then I didn't hear anything for a long time.

2:29:53

But when I came off uh what was called the runs where you delivered scripts, you um then waited to be picked for a desk and only the top five got to interview for desk to try and not keep people jammed there too long.

2:30:07

But the desk of Brian Lord opened up and he was the co-chairman and is now the CEO.

2:30:14

And he asked to interview me because he had read my memo and he said, "I want to interview the kid from the memo."

2:30:17

Even though he's not in the top five, I don't care.

2:30:20

So I went up and uh I'd never really met him or spent any time with him before but we sat down and um somehow we got talking to about John Steinbeck which uh was my favorite writer and I was reading a biography of him at the time and we spent 30 minutes talking about John Steinbeck.

2:30:37

Nothing about business or how do you answer the phone or what he's looking for in an assistant.

2:30:43

Uh so I came back down and all the guys were like how'd he go?

2:30:45

I'm like, well, I don't think it went well because he didn't ask me a single question other than, you know, talking about John Steinbeck.

2:30:53

>> So, I'm like, there's no way I got this job.

2:30:55

>> And then he ultimately kind of gave me the job. >> Yeah, that's amazing.

2:30:58

>> And I worked with him for almost three years.

2:31:00

Unbelievable >> experience.

2:31:03

Um, coming off of Mike, having gone to Disney, then having Flamed Out and starting his own company.

2:31:12

um so much kind of turbulence in the industry and uh he was an amazing guy and it was it was a great formative experience for me.

2:31:21

>> Uh since we are in Hollywood running a a show that covers venture, was there anyone in that era of Hollywood that was uh from the talent side that was leaning in and investing in in in startups of any kind?

2:31:36

No, I mean what I remember from that era is no one really thought about the investing side of it per se.

2:31:43

People thought that wow the business is going to be digitized so let's think of creating content for these new digital channels and so it was more on the monetization side of here's a new distribution channel called the internet and how can we adapt our businesses to that new channel. >> Yeah.

2:32:01

Um, I had always loved kind of investing, so I was kind of trading public market stocks on the side.

2:32:08

>> Um, and my brother and I kind of started kind of doing that together and I just bought companies that I liked and >> was kind of doing it as a hobby.

2:32:16

>> Um, eventually when I was promoted to an agent, I kind of realized two things.

2:32:20

One is I preferred being an assistant to an agent. >> Oh.

2:32:23

>> Um, ra I preferred being an assistant rather than an agent. >> Um, so that was one.

2:32:27

And then two, I kind of really like this stock thing on the side, right?

2:32:32

And someone said, "Well, always invest in someone's hobbies because that's what they choose to do in their spare time."

2:32:38

>> And so, Philipe called me and this was a couple years into CO2 and said, "Well, we're kind of trading this account together anyways.

2:32:43

Why don't you come over and do this?" >> Yeah.

2:32:47

>> And you know, that's how I got started.

2:32:49

>> And yeah, what year did you get started at CO2?

2:32:51

What was the strategy then? Walk us through post. com crash. How are you feeling? What's the strategy?

2:33:00

>> 2003, uh, so we're just coming off of 2000, 2001, 2002, and down 80% on the market. >> Yeah.

2:33:07

>> 03 kind of had a snapback.

2:33:07

So the market was up 50%.

2:33:10

We were well positioned for the downturn, not as well for the snapback.

2:33:15

>> But now it's about, okay, what did the next kind of decade kind of look like? Right?

2:33:18

The easy money's been made on the quick rebound, right?

2:33:21

NASDAQ, I think, was up 50% year to date or something like that.

2:33:25

like that. and we were really looking for a semiconductor analyst and um couldn't find one and uh this is something I'll forever be grateful to my brother for but eventually he said look since we can't find one he dropped a copy of the universe and this universe

2:33:40

was a printed memo of essentially all the stocks and key metrics about each name PE volume sector >> the semiconductor universe and he said you know why don't you go ahead and do it can't find anybody anyways And you know, I had no real training as an analyst. And you know, I I would sit

2:33:58

And you know, I I would sit in our bullpen and there were analysts from Morgan Stanley and Goldman Sachs.

2:34:04

And I thought, man, I my training was in a mail room.

2:34:06

You know, I don't know anything.

2:34:09

>> Um, but what I started to realize is the downside is I didn't know anything.

2:34:12

The upside is I didn't have any bad habits either in terms of how >> so I really learned from my brother directly and I started to define how we want to invest versus how maybe you learned it at Goldman or a mutual fund or something like that.

2:34:29

And so um I kind of relearned from first principles you know Philipe and I kind of working uh hand in hand >> and I started learning semis and pretty soon if you started in semis at the time all the roads led to one company and that was in Certino and it was Apple. >> Yeah. >> Why?

2:34:49

Well because the iPod was starting to really gain traction and an iPod was a semiconductor product. It had nan flash. It had a processor. Yeah.

2:34:58

>> So that was the gateway into Apple which eventually led to the iPhone. Yeah.

2:35:02

>> And it was kind of an iconic investment for us and we got to know that management team really well. >> Yeah.

2:35:06

I wanted to ask you about that.

2:35:08

How much of your investing philosophy at the time was quantitative pulling metrics building models versus t doing expert calls talking to management teams listening to earnings calls.

2:35:20

The >> the model was a really sacred place for me and because I hadn't trained >> Yeah.

2:35:26

As an analyst, I felt I needed to build every model myself >> because I didn't trust myself with someone else's work >> because I wasn't good enough.

2:35:34

So, I'm like, well, I'm going to build every single cell myself.

2:35:37

That's going to mean I'm going to understand it if I built it myself, right?

2:35:40

Versus if I take someone else's complex model, >> I'm not going to understand.

2:35:44

I'm going to rip a DC since I'm number 10 for the day. >> So, let's go.

2:35:51

I knew when you had coffee, by the way, after dinner last night, I was like, "Okay, my guy, >> get ready for the caffeine." >> Yeah. >> Yeah.

2:35:59

>> So, I felt like I had to rebuild every model myself because I couldn't understand someone else's model. >> Sure.

2:36:04

>> And what happened in the process of building that model is as I would go through a sellside model and there were lines that I thought were irrelevant, I said, "Well, >> why should I add that to my model?

2:36:12

Like, this is driving no value."

2:36:14

But >> it was kind of a verb thing at the time.

2:36:18

It was like, "Well, hold on.

2:36:18

It was like, "Well, hold on. the company reports it this way or yeah >> this is a revenue line and for the sake of accuracy you kind of need it in there but like I didn't know enough to basically say well >> to me it doesn't drive any value to my investment thesis so I'm just going to

2:36:33

>> lump it into this other bucket yeah called other >> and I'm going to rename things the way I understand them not the way the company chooses to report it or >> yeah ex basically just what made sense to me and what my thesis was about so that when I pitched my thesis, my model actually reflected what my thesis was. >> Yeah. >> Yeah. >> Right.

2:36:52

Not let me pitch you my thesis, but now I have a thousandline model and I have to go from row two to row seven to like the other tab, then back to line 250 to kind of explain it to you.

2:37:02

Like that made no sense to me.

2:37:04

Like the narrative was no, let me show you from >> the top line all the way to the bottom how it flows and what the key functions are. >> Yeah.

2:37:12

What's uh what's >> that started developing by the way our philosophy? >> Yeah.

2:37:16

>> Yeah. Doesn't that kind of still define like a partner meeting at like aren't you guys like let's say you meet an entrepreneur you're excited about them you do the work and then from what I've heard you guys will spend like hours and hours and hours still just in the model like ignoring ignoring >> and we will but a lot of times you might

2:37:36

be getting a model from the cell side >> sure >> right because it's faster and it's more convenient but it might not be exactly how your thesis is being laid out right so one of the things I try and talk to all of our analysts and say, well, let's have a model that really reflects our simple view of what the thesis is and what the what the drivers are. >> Um, so I think a model to me is kind of

2:37:56

>> Um, so I think a model to me is kind of a sacred place and in fact our Apple model which I then passed on to Jamon Rangaler who's now our CIO was kind of this sacred I didn't let anyone edit a cell on that model.

2:38:08

I knew every single cell. I knew the color.

2:38:09

I wanted a very specific kind of color for the background of certain cells, right?

2:38:13

And eventually that kind of got passed on.

2:38:18

But to me at the end of the day uh I learned this in actually back in Hollywood we had u all the trainees one day got brought into a meeting with uh Steven Spielberg.

2:38:30

>> And Stephen said every great story can be pitched in three sentences no matter what the story was.

2:38:36

And I said so pitch me a story or a movie and I'll pitch it to you.

2:38:40

And no matter how complex the movie was, he understood the essence of it.

2:38:47

>> And in three sentences, you got the whole movie.

2:38:49

And what I realized is it takes a true understanding of story to be able to crystallize it in three sentences. Right?

2:38:57

If you don't understand something, you'll say, "Okay, well TVPN is a podcast and it's about these two guys and they do this."

2:39:01

Well, do you really understand what it's about?

2:39:04

Because you just gave me 10 minutes of rambling stuff. >> Right?

2:39:08

>> Right? and all the great investors that I've met um like Stan Ducken Miller or you know my brother Phipe or Dan Loe or some of these kind of legends of the hedge fund world right they have an ability to take any kind of story

2:39:23

>> and just drill it down to its essence to what the key pivot points are that are going to make or break that stock at that particular moment >> and so we really try and say our thesis should be simple we should be able to explain very in you know a few sentences right

2:39:41

and you should have a model that reflects that thesis >> so on the public side in particular then we dive into okay let's go into your model and let's say okay can Apple sell 50 million phones can the arpoo be in the out year is it going to increase is

2:39:58

it going to decrease you know when I look back at our old Apple model I actually think we did a pretty good job on units where we were way off is we had the price of the phone declining 5 to 10% a year because that's what every consumer electronics product did. TVs

2:40:10

consumer electronics product did. TVs >> and in fact the ARPO doubled right I think the first iPhone was like >> 600 right unsubsidized >> and00 yeah >> so never would have kind of forecast that >> interesting >> so um yeah so models are are quite important to us

2:40:29

>> what was the what was the mood in the hedge fund industry broadly during that time around different strategic expansion opportunities there's obviously a high frequency trading boom that's happening there's more quantitative strategies, there's debt strategies, there's so many a hedge fund can mean so many things. How were you

2:40:48

How were you thinking about defining what you would do best and where you would expand to or decline to expand to?

2:40:57

>> Look, I think for tech it was an amazing um environment to be in because almost every company was getting swallowed up or >> Yeah.

2:41:07

>> So if you were in TMT, >> you really felt like you were the center of the universe.

2:41:10

So there wasn't as much pull for distractions. >> Correct. >> That makes sense.

2:41:14

>> And then also we had companies going public pretty early on.

2:41:18

>> So you could do a lot of differentiated work in companies that the market cap was a couple billion. >> Yeah. >> Right.

2:41:25

I think what really changed for us in the early 2010s was Meta, >> then Facebook and Alibaba >> staying private for longer. Yeah.

2:41:34

Yeah, >> we just never seen companies of that scale >> who were that important to our research in our market not go public.

2:41:42

>> It was uh Meta or Facebook went out at like 60 billion I believe something that >> yeah I think around there right in right around 2012 201 >> So if you're used to buying a company potentially at two or three or six billion that's a big myth.

2:41:55

>> It's like a 10x difference but mo not only that we actually were an investor in Google from pretty shortly after the IPO. >> Sure.

2:42:02

And Google had this stretch postfinancial crisis where it kind of traded sideways for a long time. >> Yeah.

2:42:08

>> And the reason was here comes Meta or Facebook and they're going to replicate a private internet that Google won't be able to search and so Google's going to be under pressure. >> Yeah.

2:42:18

>> And we weren't able to talk to Meta.

2:42:18

So we didn't know what they were thinking. >> Sure.

2:42:22

>> As soon as the company went public, ironically, >> Google stock started working. Yep.

2:42:27

>> Because they didn't come out and tell you we want to kill Google or we're replicating the internet.

2:42:32

we're doing kind of something different.

2:42:34

Both stocks ended up working. >> Yep.

2:42:36

>> So that was kind of a big eye openener to us.

2:42:38

>> It felt both offensively minded and defensively minded.

2:42:42

>> H how could you tell at that moment?

2:42:44

Obviously, it was correct that companies would stay private longer, but I'm sure you were debating the question, is Meta the outlier or they the exception that will eventually prove the rule versus there's a structural shift in uh private equity venture capital that will propel many more companies to stay private well into the tens of billions of dollars of market cap.

2:43:05

>> We would not have foreseen what ended up happening.

2:43:07

We had an instinct that it might happen >> and remember that Spotify is kind of getting >> built at the same time, right?

2:43:13

And it's redefining music and we really, >> you know, as I mentioned, Apple was a core >> thesis for us.

2:43:20

And now with here comes subscription music, which goes directly against Apple's model of selling you an album.

2:43:27

>> And so what's going on?

2:43:27

They just did a round at three billion.

2:43:29

Felt like a lot >> and then Uber. >> Yeah. >> Right.

2:43:33

So it was it was just kind of you felt an Airbnb, right?

2:43:35

So I would say like those two companies, the mobile internet coming out, these companies getting big in in private markets, it felt like undeniable momentum, >> right?

2:43:47

And so and China, by the way, right? The same.

2:43:50

Um, so we felt like we had to uh participate.

2:43:55

>> So what was the first private investment you made? >> Evernote.

2:43:59

>> Evernote, >> I think, was one of the first.

2:43:59

And we said, "Look, we're going to do later stage deals, 100 million plus in revenue."

2:44:06

So, we did deals like Evernote and Box.

2:44:09

And ironically, our most successful deal is one that broke all of the rules, uh, that I just laid out, which was Snapchat. >> Okay.

2:44:17

>> Where I think we led the series C >> in that evaluation of about a billion and a half. >> Yeah.

2:44:23

and >> and what was the reaction from other more traditional venture investors when you guys started leading around?

2:44:32

>> I think that um look venture felt very different back then. There was fewer firms.

2:44:38

There was more uh atrified thinking, right?

2:44:43

And is just kind of starting.

2:44:46

We actually shared a building with them.

2:44:48

So, we were kind of starting at the same time about as they were.

2:44:50

Um, and it felt like, wow, we're going to bring a bit of a different competitive energy to this market. Um, it felt very clubby. >> Sure.

2:45:02

>> You hadn't seen like these new firms like founders that obviously, you know, and a bunch of others kind of really make their mark. >> Yeah.

2:45:09

>> So, uh, it was sharp elbowed for sure.

2:45:12

>> And and still is in many respects, which is probably what I like the least about that market because I love talking about ideas.

2:45:17

I love trying to be a positive sum thinker which >> the public >> easier to do in the public markets. >> Correct. >> Yeah.

2:45:23

>> Did you bring the the models to private markets?

2:45:27

Did were you building financial models or the team? >> We did. I think we brought that.

2:45:30

We brought analytical thinking.

2:45:32

We brought kind of deep research like I remember reverse pitching Aaron Levy at Box a big deck that we had done and we had just done what we thought was kind of public market like research but we brought that to a private entrepreneur and >> um he hadn't seen that kind of work before.

2:45:49

So that was a differentiator for a minute until other firms realized wait we can do that >> or even better we can outsource it to Bane. >> Yeah. >> Right.

2:45:58

And so we had to kind of quickly kind of adapt to that. Yeah.

2:46:03

>> Um but in the beginning it was novel. >> Yeah.

2:46:06

>> And that was an industry that was really done in word and we were Excel thinkers. >> Yep.

2:46:10

Y >> so that was kind of a very different kind of mindset that we were bringing to the table.

2:46:15

>> Was there any uh shift required in the messaging to LPs, the fund structuring, anything that were that you had to work through in order to actually set up the fund for success in the private markets?

2:46:31

I think our LPs first of all LPs are not talked a lot about in venture which is kind of interesting like we talk a lot about the founder we talk a lot about the companies >> but I would say for us we are pretty clear that our customer at the end of the day is our LPs and so the trust that they give us means a lot >> um I have virtually no outside

2:46:51

investments right uh um I I do some as favors and things like that but almost everything I have and own >> is in our fund Um, so we kind of act as entrepreneurs and as owners ourselves and >> we um asked for trust from our LPs and I think at the end of the day they were willing to give us a chance in our first fund. I don't think they held us

2:47:14

I don't think they held us specifically to exactly what we said we were going to do, but they're like these guys are pretty disciplined and they're pretty smart and they're entrepreneurial and aggressive and let's see what they can do, right?

2:47:26

And so I think over the years what's helped us most in our business and I think why we're still in business 25 almost years or 27 plus years later when a lot of our peers have disappeared over time is um we never lose sight of our investors and hopefully we've made some good decisions but we've also made some bad ones but I think our investors learn more about us on how we deal with our bad decisions Right.

2:47:54

And so I think we've earned uh hopefully some trust from them over the years.

2:47:59

So I remember distinctly uh an investor when I called about the SNAP deal and saying look I know this is a bit off brand.

2:48:07

This was one of the largest investors in the fund but I just have a lot of conviction in this deal. And he said then do it.

2:48:15

That's what ultimately why we're investing in you.

2:48:20

And so I think the the trust that you build, the relationships that you build with your own investors over those periods of time are really important.

2:48:28

>> So relationships with entrepreneurs, building models for you know Apple, Arpoo, you projecting units that feels all very micro.

2:48:36

Y >> how have you processed macro statistics and and factors like interest rates?

2:48:44

Everyone talks about uh when interest rates rise all the DCFs change.

2:48:46

There's a pullback in the private markets.

2:48:49

We we lived through this with like the end of ZERP, but how much are you tracking the labor market, the GDP numbers, the interest rates, and how much of a factor is that on the strategy dayto day, monthto month, year to year, or even like broader terms? >> Yeah.

2:49:07

So, data science has really become a much larger part of our business than it was back then. >> Sure.

2:49:12

So we now have I don't know maybe 20 or 20ish 20 to 30 people something like that in data science that are just processing different types of data and alternative data.

2:49:22

So think it's not just macro data, it's app store data, it's clickstream data, it's credit card data, >> so we use a lot of that for our investment research, right?

2:49:35

>> And some of that we even make accessible to our portfolio companies, >> right?

2:49:38

So that made us smart about a trend.

2:49:40

We were early customers of data bricks and snowflake as an example that let us invest in those companies. >> Yeah.

2:49:46

Um so that is way more of a presence uh in today's world than it was you know 20 years ago. >> Yeah.

2:49:55

>> So we're constantly um looking at data as an example.

2:49:58

You know I think OpenAI is probably the most important company in the world today in the sense that it's um the driver of AI both consumption uh and spending.

2:50:07

So I look almost every day at the chart of chat GPT users download share how it's weathering the storm versus other competitors.

2:50:17

You know that's really something that wasn't available 1015 years ago. >> Sure.

2:50:22

>> Um with this amazing data set called Onavo which actually gave you engagement data from from users on phone and then Zuck bought it >> and turned it off and and I remember thinking like damn that guy that's what great move.

2:50:36

It was the only data set that really gave you uh engagement data. >> Yeah.

2:50:41

>> Um so we're always looking for kind of new data sets, right?

2:50:44

Um and then obvious so that felt like a major shift, right?

2:50:48

right? going from >> um you know data science enabled research and now obviously we're getting to AI and >> agentic research and >> how are you thinking about AI as a category from an investor perspective versus the data bricks and snowflakes which to me feel it's easier for me to maybe understand the financials the model that I would build how I think about value acral and competition in data bricks and snowflake fantastic

2:51:18

businesses, but feels like easier to pattern match against previous eras of software and tech innovation versus AI where you have infrastructure and capex and training costs and inference budgets and all sorts of different um your your entire products getting copied by open source every three months and it just feels like a different uh different puzzle to solve when you're thinking about underwriting those businesses. is

2:51:46

is like how have you grappled with that?

2:51:48

Do you see it as an extension of the tech investing or is it an entirely new motion?

2:51:54

>> Well, for me it was almost coming back home to what I knew because the infrastructure layer was really semiconductor driven. >> Sure. >> Right.

2:52:00

So I think our knowledge of semis and >> our team's knowledge of semis was a great head start >> because a lot of people just hadn't done semis.

2:52:10

So, we're kind of new to semis and I had a lot of relationships in the industry and that led us to believe the series being Cerebras um that I think will be kind of a generational kind of company.

2:52:22

>> Yeah, I love been on multiple times. Great.

2:52:25

>> Um so that felt very natural to us and uh pretty quickly when we saw what Jensen was building and the momentum that Nvidia was building in the data center. >> Yeah.

2:52:35

So that was kind of our first telltale that wow something big is going on here.

2:52:40

So we had seen semis before in the mobile era. >> Yep.

2:52:44

>> So we felt very equipped when AI first came around to look at it from a semiconductor GPUs memory TSMC.

2:52:51

>> You know I've personally been in Taiwan many times, visited TSMC. Um >> great anecdote.

2:52:56

I'm I'm driving back to Taipei City with my host from TSMC and we're on the highway and uh there's a golf course uh and it's nighttime so there's there's lights and people playing and I just very innocently turned to him and said uh wow you guys you guys play golf at night here and he very innocently looked back at me and said well when do you play and that's when I realized like that's >> we're we're in a different level of of of work here. Yeah.

2:53:24

>> Um, so we saw it at the infrastructure layer first, right?

2:53:26

Where it just became obvious you didn't have to necessarily worry about who was going to win like the whole infrastructure layer will win. >> Yeah.

2:53:34

>> So I think that was kind of layer one.

2:53:36

>> I think layer two then came kind of the models and obviously we're investors in a number of them.

2:53:43

>> I'd say the most complex element of AI today is you can almost talk yourself into a bull case and a bare case for almost any name. >> Yep.

2:53:53

in tech and I think software is kind of seeing that right now right >> so is software going to win because of AI or get displaced right so you've got that in infrastructure you've got the well when is the peak >> and what multiple peak should things

2:54:08

kind of trade at right so it's both an exhilarating dynamic but also very uh complicated environment in the sense that like for example when when data bricks came around no one thought well gee data bicks is going to put Salesforce out of business. >> Yeah. >> Yeah. >> Right.

2:54:25

>> It just felt like a new architecture. >> Yep. >> Yeah.

2:54:27

>> There's something about AI that feels a lot more disruptive.

2:54:30

And >> uh what if your model, not today, but in two years can just build you a workday right off the bat? >> Yeah.

2:54:39

>> Um >> what does that mean for workday?

2:54:41

Does that mean their data is more valuable? >> Yeah.

2:54:44

>> In on the lefth hand side, or no, does that mean they get fully wiped out? >> Yeah.

2:54:48

So I think that battle and it's being played out kind of in the public market today, right?

2:54:52

You kind of see it in in these names, um, is >> how can a public company CEO actually communicate a vision for that case, the bull and bear case around AI, what effect AI will have on their company.

2:55:12

>> I mean, you're seeing it.

2:55:12

It's I I read uh I read something that we're kind of moving into a selection market, right?

2:55:20

Um so now like >> some companies are going to do well, some companies are not.

2:55:24

I mean, look at Square, right? >> Yeah.

2:55:28

>> Jack came out and just said, "No, I'm pivoting the entire infrastructure of this company for an AI era.

2:55:32

We think you're going to need to be um remote, right?

2:55:38

Um because you're going to move faster if you're remote."

2:55:40

And so he's kind of laid out a whole vision about how he wants to run the >> because you're sort of generating the necessary context because you're not getting the inperson interaction.

2:55:48

So you and small teams, >> yeah, small teams, you'll more more easily be able to be AI native if you're basically explaining process.

2:55:57

>> Yeah, >> very small teams moving really quickly without a central organiz or or you know kind of like the Borg, right?

2:56:02

No central organiz organizing force.

2:56:04

The model drives everything. Mhm.

2:56:08

>> And then you have other companies that are saying no product well the labs themselves right are all in person and they believe right that you uh product development needs to be done kind of in person.

2:56:18

So you're kind of seeing a lot of these different ways of you have different models.

2:56:22

Some people are going to charge for tokens.

2:56:24

Some people are going to charge for data access or ingress and egress. Mhm.

2:56:28

>> So I think what we're seeing right now play out is a true Darwin like survival of the fittest where software companies are going >> the mail room. >> Exactly. Right.

2:56:41

Um you saw anal come back to workday. Right.

2:56:44

workday. Right. He probably thought that I think Carl is an amazing executive and >> um is a good friend of mine but maybe he thought in order to make the changes I need to be have that kind of founder mindset right uh founder mode as as

2:56:59

Brian calls it right so we're seeing now a lot of these different approaches kind of compete with each other um I think it's too early to tell who's going to win but eventually I think we should see kind of separation between winners and losers right? Which should be good for our

2:57:14

Which should be good for our business, >> but I think right now it's still so early that um it's not clear who will win.

2:57:22

>> Is it enough to look at reacelerating top lines?

2:57:24

I imagine uh we've talked to a lot of founder CEOs, maybe their unicorn status decade in completely reinvigorated by AI.

2:57:34

They come on, they see that the growth has returned.

2:57:37

It feels like a new startup even though they're maybe coming back from a sbatical.

2:57:43

Maybe they're coming back in after hiring an outside CEO.

2:57:44

Maybe they're just coming back in with a new vigor.

2:57:48

But uh what are you seeing more at the earlier stage or mid-market stage around companies that are starting to show signs of being winners in the AI age?

2:58:00

>> I think look the good news is no one is head in the sand about this.

2:58:04

head in the sand about this. Uh so I do think in prior cycles you had more of a head in the sand mentality >> right so for example if I remember when cloud got started um there was do you remember virtual cloud right um that was going to be the big thing right I can't just have my

2:58:21

stuff in Amazon I'm going to have this virtual cloud and you know obviously that hybrid cloud right was another one no it's going to be half >> all big data right all those things basically just got torched and went by the wayside right so there was a lot ahead in the sand >> similar with the phone. Um, you need a Um, you need a keyboard. It doesn't have 3G.

2:58:37

It doesn't support Adobe.

2:58:40

>> Doesn't have copy and paste. >> Exactly. The battery life, right?

2:58:42

Uh, Bengate, you may remember that one.

2:58:45

That was a whole weekend or something.

2:58:49

>> Whole weekend wasted on Bengate.

2:58:51

>> Um, >> so there was a lot more to me head in the sand in prior >> Sure.

2:58:56

>> uh, investments uh, cycles, right?

2:58:58

In prior tech themes where people were just pushing back against the idea >> that this was going to work.

2:59:03

Yeah, >> I think AI is one where the consensus view is it is going to work.

2:59:08

It's not it's an extension extension level event and so the sense of urgency is high.

2:59:15

So that does feel a little bit different to me than maybe prior cycles where I think that took time for right the carers are like well I'm not going to allow Apple to have an app store and I don't want to be a dump pipe and right so they all these things that they fought. >> Yeah.

2:59:31

>> Um and over time >> tech won. Yeah.

2:59:34

>> The difference to me with this specific cycle is everybody agrees it's going to happen and it's happening quicker and the stakes are higher than any other. >> Yeah.

2:59:46

>> So I think every board is ultra motivated.

2:59:49

Every founder is focused on this.

2:59:49

Now they're bringing different approaches and and you know we'll see which ones win out.

2:59:56

But I would say they're tracking token consumption, right?

2:59:58

So how much of my revenue is token based right?

3:00:02

How much of my COGS is token based?

3:00:04

How much of my GNA is token based?

3:00:07

How much of my spend per developer >> on cursor openai and anthropic is happening right so that in you know I do sit on a bunch of boards as an observer most most of the time.

3:00:20

So I kind of see a lot of that kind of happening.

3:00:22

So the awareness is absolutely there.

3:00:24

People are doing different approaches.

3:00:26

are doing different approaches. There's some that are no I'm going to white box a totally new product >> right that where I think I'm uniquely positioned to build it and then there's others well like no my data set is so valuable I'm not going to allow my customers to build apps directly using

3:00:42

my data >> so you're seeing a lot of kind of different approaches >> right um but everyone's awareness is at a 12 out of 10 >> so there's no convincing needed >> right everybody's aligned every board member is aligned every investor And now it's about okay what does that sense of urgency mean for this company? What are

3:00:59

What are things that we need to track and the things that we really need to go and execute on?

3:01:06

>> What does it take to make it as a new hireer at CO2?

3:01:10

>> So we do uh and I'm assuming you mean on the investment staff, right? Um we do case studies.

3:01:16

Um very important part of the process for me.

3:01:19

Uh we usually pick a public name, right?

3:01:22

Because we want to test your thinking.

3:01:24

And my favorite types of names are names where there's a good bull case and a bare case.

3:01:31

>> And whichever one the prospective analyst argues, I will vehemently argue the opposite. >> Yeah. >> Right.

3:01:37

Just to test their thinking. >> Exactly.

3:01:39

Understand their thinking.

3:01:39

So that's really really important.

3:01:44

>> Are you trying to pick obscure names or household names? Everything. >> No.

3:01:48

I mean for a long time we used Netflix. >> Okay. Yeah. >> Right. As an example.

3:01:50

that was a really controversial stock both in the DVD era then when they moved into streaming then when they moved into proprietary content >> and it was a heavily shorted stock over that period of time.

3:02:02

So there was a lot of interesting >> sure >> uh ways to look at that name right and you could ask interesting questions like well if they increase you know price by a dollar what happens to EPS and what you realize it's it was almost all profit so EPS went up a lot. >> Yeah.

3:02:20

So, we're just really trying to test thinking.

3:02:23

>> Have you ever had to revisit a candidate who made a really great bull case or bear case that the firm maybe didn't agree with and then they came back with an I told you so five years later.

3:02:35

>> You know, I've never had someone kind of email me that.

3:02:37

Um, which is surprising because we we've done a lot of >> you've done a lot of case studies.

3:02:41

I can imagine there's a couple I told you so where I was like, "Oh, I called dominoes or whatever."

3:02:47

And and honestly to me it's not about whether they say the right you know sometimes people think it has to be if I say bowl of bear it's the yeah doesn't really matter.

3:02:57

It's like how did you articulate your thinking?

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Did you lay out a clear model?

3:03:00

A lot of kind of where where we were starting, right?

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And so that's my favorite part of this job is thinking through a name and the opportunity and what could happen.

3:03:12

And >> um it's kind of the intellectual backbone. >> Yeah. >> Of what we do. >> Yeah.

3:03:18

>> You know, I would say that I think the key to our platform is number one like seeking big themes and big ideas. That's a big one.

3:03:25

And then the kind of the risk management piece. >> Yeah. >> Right.

3:03:29

That's kind of kept us in business for a long period of time.

3:03:31

>> How is AI changing the role of early analysts or career or analysts who are earlier in their career on the investment side? >> Too early to tell. >> Too early to tell.

3:03:42

>> Um obviously look, we use AI every single day. >> Yeah.

3:03:46

>> Um I use it a lot to test my thinking, to clarify my thinking.

3:03:52

Uh, I've always had a weird dichotomy personally where I I'm I love reading, but I'm a terrible writer. >> Okay.

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>> And one of the things I like about AI and chat GPT specifically is it's helped me actually write in a way that I can be proud of.

3:04:07

Not just sometimes I write, >> you know, I'll write something an email to somebody.

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I'm like, it's just so badly written, you know, and it's just >> I don't know how to make it better. I know it's not good.

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I don't know how to improve it.

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And it's so frustrating because I know what great >> writing is from my reading, but I just can't do it. >> Yeah.

3:04:26

>> And at least >> I I've had a had a a family member send me some something that was very obviously obvious to me AI generated.

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And uh I think people have an aversion to AI generated text.

3:04:40

>> I totally don't get that.

3:04:40

But but but the thing is like I was reading through it and I was like this is very cool because I know this person would not have been able to articulate their thoughts in this way but they went line by line and I know they they mean it right and so they were able to communicate something that they never would have been able to communicate with text.

3:04:58

Maybe if we sat down and spent you know uh uh a couple hours talking through it I would have been able to get the gist. >> Exactly.

3:05:05

But uh >> some people take out the M dash because they write and they're like, "Oh, I don't want it."

3:05:10

And I'm like, "What do I care?" >> Yeah. >> Like I don't Yes.

3:05:12

A lot of emails that I write are helped by Chad >> like the the Arnold Schwarzenegger line, you know?

3:05:19

He's he's I smoke my stogies everywhere.

3:05:23

>> Why judge me on what I've said and my idea and whether it's well written? >> Yeah.

3:05:30

>> Who cares whether it's was written by AI or not?

3:05:33

I that that I totally don't get that. Yeah. Or polished.

3:05:34

But >> in fact, I hope that more uh people are able to communicate things that maybe they couldn't before. Yeah. >> Right.

3:05:43

Because they didn't know how or they only knew granular >> things, you know, and now more people can write.

3:05:49

More people can communicate. >> Yeah.

3:05:52

>> More people can express themselves.

3:05:52

Like to me that's an incredibly empowering um >> I love it. >> Right. Vision.

3:05:59

talk about uh how you guys have approached investing in in you know multiple companies in the same category uh or the same general category.

3:06:09

How how has that evolved over time?

3:06:11

time? Were you were you mocked early for for for doing that from maybe some of the more traditional funds and then how have you managed to make it work in practice and you know maintain the trust of uh >> entrepreneurs conflicts which is kind of what you're >> um has definitely changed a lot in the

3:06:30

valley as companies have stayed private longer right >> and I think we have to be kind of precise by what we mean by conflict right so as an example funding two series A companies at the same time that are pursuing the same opportunity is an obvious conflict that I think no firm including us would ever do. >> Yeah. >> Yeah. >> Right.

3:06:50

So, let's just kind of be very clear on that.

3:06:54

>> I think it's quite different when now you're talking about these very latest stage companies, right, that are kind of competing with each other.

3:06:59

But look, every company is kind of competing, cooperating.

3:07:05

You know, Apple and Google are great examples.

3:07:07

You know, they compete, but they're partners.

3:07:08

compete, but they're partners. So I think the distinction and the conflicts distinction has to be um you know kind of changes as companies and markets kind of mature >> right so I think you're seeing that become much less of an issue in mid to later stages even by firms that you know

3:07:28

would typically viewed conflict as core to what they do like traditional venture firms right have not kind of >> moved in that direction and I think it's just the nature of the market so that that's kind of would be my first point >> I I think the second point is the execution of it I think really matters. So um you know if if I view a perceived

3:07:44

So um you know if if I view a perceived conflict between companies uh even if they're later stage I will always let the founders know. >> Yeah.

3:07:54

>> Directly and I'm not asking for their permission.

3:07:57

So I think you also have to be clear with the founder because what if they say no and you still want to do it then you're in trouble.

3:08:02

Now you look now you've just broken your word and I won't do that.

3:08:07

>> But I will inform them. I'll be very direct.

3:08:09

I won't let them hear about it from uh somebody else or something like that and I'll explain kind of the rationale, right?

3:08:15

Um so I think communicating directly both good news and bad news that is something that I learned as an agent.

3:08:22

Um, I'm not afraid to have difficult conversations um because I think we can grow from them.

3:08:28

And I ask the same of founders or employees that I work with to both come to me and say if if you have an issue, let's just kind of talk about it.

3:08:35

And look, I've hired a lot of people.

3:08:38

I've fired a lot of people over the years.

3:08:41

I've asked a lot of people to go and look for different career paths.

3:08:43

So, I'm comfortable having this those conversations.

3:08:47

conversations. Um so you know that's not something that um you know I think your your reputation and trust then is kind of the the second point on the execution right of >> we take information security incredibly >> uh strongly that's you know we're SEC

3:09:06

registered and you know even before coming here I got like a four-page memo from my lawyer about you can talk about this but not that and SEC this and so of course you know I I have to read it and >> you can tell the Ralph Lauren story. >> Yes. Yes. I hope so. I mean specifically >> Yes. Yes. I hope so.

3:09:18

I mean specifically by the way I >> the way it well you know so side note I believe that meetings should be recorded as an example.

3:09:27

as an example. Now my compliance will say we can't have meetings be recorded because uh it creates a paper trail >> insane discovery they can take >> and let's just not even talk code too specifically let's just talk an enterprise >> but then I say okay let me posit

3:09:44

enterprise CIO says no I can't have my meetings recorded I'm too afraid I'm like okay >> let me posit two scenarios to you okay scenario one is >> and in each you have a bad actor that's doing bad things right whatever that is you know like belligerent talking to people, whatever. So,

3:09:58

So, >> scenario one, nothing gets recorded.

3:10:01

You know, nothing >> and 10 years later, someone comes out of the woodwork and says, "By the way, X, Y, and Z, 10ear pattern of deception. Nothing happened."

3:10:12

Okay, so that's scenario one, right?

3:10:15

>> Scenario two is every meeting is recorded.

3:10:17

The first time said person does something that's not right, the compliance system, right, which is always listening, sends that person an email and says, "Hey, by the way, >> better you didn't do this, talk to that person that way.

3:10:31

Disclose this piece of information depending on the severity, >> right?

3:10:35

Second time person does it again, says, "Hey, I now have to flag this to IR to HR." Yeah. Right.

3:10:43

>> I would much rather live in world number two, right?

3:10:46

because you know what the problem is. There's a system. >> Sure.

3:10:51

>> There's flags that have been raised. >> Yeah.

3:10:54

>> And eventually, you know, someone kind of gets involved and you either remediate or you terminate the the person or whatever, right? >> Yep.

3:11:02

>> So, >> you the the back and forth of he said, she said, all of that. Yeah. >> Exactly.

3:11:06

And who knows, maybe that person, if they had gotten that first warning, >> Yeah.

3:11:12

>> might have realized, oh, wait, yeah, you're right.

3:11:14

I'm being abusive or whatever the case may be of whatever they were they were violating, right?

3:11:18

So >> I think to me that's a better world, right?

3:11:24

Than kind of the ignorance of while getting no feedback and then you just learn much later that kind of you had a problem. >> Yeah.

3:11:31

I mean it certainly seems like a trend.

3:11:32

I mean Bridgewwater's written about it a lot radio but then also >> and he did that with no analytics, right?

3:11:36

So he so that's >> different but >> correct.

3:11:39

I I think to me what will happen is the analytics are going to get so much better. >> Interesting. >> Right.

3:11:45

And these systems are going to know and they're going to be able to look at your WhatsApp and your messages and your emails and all of your >> calls and they're going to be able to just say, "Hey, by the way, just >> don't say this or did you think about this?"

3:11:59

Or maybe you could have they're going to coach you.

3:12:00

They're going to say, "Hey, you told this customer to to off."

3:12:05

Be like, "Well, >> maybe you shouldn't do that.

3:12:06

Here here's like two other ways you might have mentioned you know like whatever this >> situation your frustration with the situation.

3:12:14

>> Um >> yeah no very interesting.

3:12:17

>> Instead of the the token maxing dashboard you can have the social credit score dashboard the bottom 20%.

3:12:25

>> No no there's a lot of environments that it can make. >> Yeah.

3:12:28

Maybe there's a divide on like the inperson versus remote work crowd like >> Well, let me be clear.

3:12:32

I'm talking about work context.

3:12:33

This is I'm not talking about after work.

3:12:35

Yeah, I I I just wonder if uh if that would become something that uh employees uh select into or out of for various reasons.

3:12:42

Just like some people are huge fans of remote work, some people can't stand it and there's a variety of >> I also think there's a clear distinction between transcription and recording. >> Okay. >> Right.

3:12:54

They don't necessand to me, right?

3:12:57

So >> I don't necessily need a system that transcribes every word that was said and keeps it in some database >> somewhere.

3:13:04

I'm not sure that's necessary, but key takeaways from the meeting, >> what was said, what was agreed upon, like that's useful.

3:13:11

That's a good corpus of data to me.

3:13:13

Just because someone's quote recording doesn't necessarily mean that it's transcribing.

3:13:17

In fact, I like the option of deleting.

3:13:19

In an ideal world, I would recommend, well, you can delete the transcript.

3:13:23

The transcript's not that relevant because maybe you batted an idea back and forth three times and you said something that turned out not to be true, but you figured that out later.

3:13:32

So you don't need any of that.

3:13:33

What you do need is what was said, what was agreed upon, >> was anything done out of compliance or not. Right.

3:13:40

So it doesn't mean it doesn't imply a world where everything you say is recorded. >> No. Right. >> No.

3:13:45

It's funny because uh uh there's so many opinions on this, but uh we record everything all day and live stream it on the internet.

3:13:52

What uh >> how have you processed a number of these uh venture funds that have uh become publicly listed that are taking a lot of the different names that CO2 is in, you know, trying to find basically the most in demand >> uh secondaries, putting them into funds.

3:14:11

As I've watched, I think what I've seen is like yes, it's very obvious there's an incredible amount of demand from the public to invest in these names, but the big issue is that as soon as that demand floods in, you know, supply and demand price shoots up and then you have a bunch of people investing at effectively, you know, 10 times uh what the, you know, private valuation or or the underlying asset value.

3:14:33

But how have you processed it?

3:14:36

Do you think there's a more elegant solution over time?

3:14:37

So, um, we do have a fund called CEK that I'm allowed to mention, um, that addresses some of this and, uh, >> you know, people can kind of go online and research more about it.

3:14:51

>> Um, what I would say generally is people want access to these companies.

3:14:59

>> And I think there's a lot of arguments for going public.

3:15:00

One of I think the most powerful in my opinion is to de democratize access totally to companies. Let's take an anthropic.

3:15:09

Let's take an open AI, right?

3:15:11

And enabling the retail investor, enabling the Trump accounts, which I think is a marvelous idea that my friend Brad Gersonner really spearheaded and I give him a lot of credit for this idea of wow, why can't we have every single uh new child already have uh be invested in the market and participate in the value creation of these companies? So, I love that.

3:15:37