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4:50
You're watching You're watching TVPN.
4:53
Today is Thursday, May 29th, 2025.
4:55
We are live from the TVPN Ultra Dome. That's right.
5:01
the temple of technology, the fortress of finance, the capital of capital, but now it's also the TBPN Ultra Dome. You heard it here first.
5:10
It's it's really it's it's a rough year to be a super dome because you thought you were at the top of the the food chain, you know, just as far as domes go and then a new player comes in nowhere. Yeah. No one saw it coming.
5:22
kind of like, oh, like Google tries to react to chat GPT, you know, kind of blindsided by LM Chat GPT moment for super domes. Yeah, exactly. Exactly.
5:32
The Ultradome comes out and you're like, well, now maybe we have to build an Ultra Dome. Maybe we got to compete.
5:38
How are we going to do that? So, uh, we'll see.
5:39
We'll go do fordome with the best of them. We will. We will.
5:45
Anyway, we got a bunch of news for you.
5:47
We're going to take you through about 30 minutes of recap of the news.
5:48
Then, we got a stacked lineup.
5:50
Ashley Vance is coming on.
5:52
Chase Lock Miller from Crusoe Energy is coming on.
5:55
Uh we're we we're talking to folks about uh putting um data centers in space.
6:01
We're talking about we're talking to Chase at Crusoe about putting putting data centers in Texas.
6:07
And then the only thing bigger than Texas is space. Space data.
6:09
We're going over to we're going I'm more I'm more of an Abene guy, but I know you like space.
6:16
I know you like space data centers.
6:18
I'm really excited to talk to Peter Hall. from David. He's cooking. He's making a fortune.
6:22
He's back in the bar game.
6:24
And uh I think the growth uh I would say what was the number 145 140 they're targeting 140 million in revenue in the first 12 months of operations. That's insane.
6:36
Um and now to be clear about 10% of that probably was from me when I was living on them exclusively for a couple weeks.
6:43
Uh because they are they do cost a lot but that's still very impressive.
6:47
But yeah, it's one thing to get to a run rate. Yeah, like that.
6:49
But even that is extremely difficult to do.
6:53
But to actually do 140 million in revenue that quickly and seemingly has product market fit from coast to coast, right?
7:01
They have uh you know, big presence, you know, in in the typical wellnessy cities, but then also in like middle of nowhere. Yeah. Yeah. It's crazy.
7:10
It's it's it's crazy these second acts from these founders.
7:12
Um they should work, but sometimes they don't, but he clearly hit knocked it out of the park with this.
7:18
Uh so we're going to be talking to him today on the show.
7:19
Um, anyway, speaking of space, space space manufacturing, space data centers, uh, we have an update on Elon Musk. He's leaving Washington.
7:27
Here's how that affects the Trump agenda.
7:29
Interesting, uh, story with Elon.
7:31
Obviously, he went into DC, built out the Doge team.
7:33
Had some wins, had some losses, some setbacks, some arguments, some debates.
7:40
Um, but he has now concluded his tenure as a special government employee, departing the White House.
7:46
It's back to work on space, baby.
7:47
Yeah, he's he's going back in.
7:49
Uh there's there's a lot at stake at SpaceX.
7:52
We're seeing the the ninth launch.
7:54
Interestingly, uh the the latest Star Starship launch uh broke up again.
8:00
Another RUD, rapid unscheduled disassembly. Great. You hate to see it.
8:06
Great name for when your spaceship blows up.
8:09
But particularly bad for the people that were in the whole firmament camp. Yeah.
8:14
because it broke up on re-entry. Yeah.
8:17
And so it had actually reached peak velocity.
8:20
So anyone that says, "Oh, it can't go through the atmosphere at all and space doesn't exist."
8:27
Uh bad day because the the latest the latest Starship actually does in fact reach its peak.
8:32
It could in theory drop off uh payload and then it just breaks up on the way down, which means it's a very expensive nonreusable spaceship right now.
8:42
uh which is of course breaks the whole economic model. Doesn't actually work.
8:48
So they got to figure out how to get it back without it breaking up, but good news that they're at least getting it there, but they have some really important milestones on the horizon that they need to hit.
8:55
And so Elon's probably going back in there.
8:57
Also, we saw the the X outage and kind of, you know, things falling apart there.
9:02
I'm sure he's like, I need to be sleeping in that conference room, too.
9:05
I need to be on the factory floor, SpaceX, and go directly to the conference room.
9:08
I heard a rumor that there were tents set up at the XHQ. Okay.
9:13
For people to sleep in in the office because it be o over the weekend when when the site was down recently.
9:19
Recently because because it was, you know, it's embarrassing for your service to go down in such a big way. Yeah. Yeah.
9:25
And I'm sure that uh the culture there just doesn't tolerate, you know, downtime.
9:30
So although held up really nicely for us yesterday with crypto day. Yes.
9:34
Massive day on the internet. Massive day.
9:37
And the interesting thing is Tesla Tesla's stock price has been pricing in Elon Elon leaving Washington for a while.
9:48
It's up 22% over the past month alone because there was that there was that whole Wall Street Journal reporting report that said that Elon might be stepping down as Tesla CEO and he said absolutely not. No chance.
9:57
Um but yeah, I mean he's getting back in back in the game.
10:03
Can you pull up the poly market on Tesla new CEO?
10:04
I want to see that and I'll take you through some of the news down horrendously.
10:08
Probably now that he's not working for the government anymore.
10:12
Um, yeah, it's at a 1% chance. Yep. As of today.
10:16
So, so when is when is Elon Musk leaving the government exactly?
10:21
As a special government employee, Musk's tenure was limited to 130 days, which runs out at the end of May.
10:27
A White House official said that Musk's offboarding started Wednesday, adding that he hadn't been uh he hadn't been a regular presence in the West Wing in recent weeks.
10:35
As my scheduled time as special government employee comes to an end, "I would like to thank President Donald Trump for the opportunity to reduce wasteful spending," Musk wrote on his social media platform X late Wednesday.
10:46
And so they also cut this off. Yeah.
10:48
because he specifically said the Doge mission will will only strengthen over time as it becomes a way of life throughout the government.
10:58
And I just had to call out that the journal had to uh chose to not include the second part of of that.
11:05
Interesting, which I think is important, right?
11:07
He's not just saying, he's saying, "Hey, I actually believe in in sort of I'm trying to reflect on like of the organization."
11:15
how much of this was expected and what actually is happening because I feel like the prediction on the right was that uh Elon and Trump are going to be a an unstoppable force forever and they're they're going to reign forever and and and they're going to clean up the government, do everything.
11:33
And then and then the other side on the left, it was like they're going to have the most massive blow up and they're going to be at each other's throats and they're going to hate each other, right?
11:41
And it's like what actually happened?
11:43
like seems like they kind of did some work together.
11:44
They like worked on a project together.
11:46
They did a group project. School project. School project.
11:48
And like they seems like maybe they're still like kind of friends and kind of chilling, but also Elon's like, "I got other stuff to do."
11:55
I think that Tesla Model Y is still parked outside of or or it's a S. Cybertruck or S Model S.
12:02
You know, Trump got the red Model S. Yeah. Yeah.
12:04
I'm Yeah, it doesn't seem like a blowup entirely.
12:07
At least not from Trump and Elon. They seem to get along.
12:11
Although Elon did say like he's kind, it seems like Elon's more like disillusioned with government overall because he said he's not going to be donating again and that and that it seemed like like there were so many special interests that once he got into the swamp he was like it's just too swampy.
12:25
I was like me with my HOA bought my bought my house went to the first HOA meeting being like this excited I'm going to revolutionize HOA.
12:35
There's so many obvious things that we could do to improve, you know, the operations of the neighborhood.
12:37
realized very quickly that that there were some major, you know, I think you weren't chaotic enough.
12:43
You should have launched like an HOA coin immediately. Yeah.
12:47
Really re really really shook things.
12:50
Shook nobody's building at the intersection of HOAs and crypto.
12:51
No, no, it is whites space. Long leg. Get on it. White space.
12:57
Um anyway, the the the Democrats are taking a little bit of a victory lap.
13:01
Democrats took credit for Musk's decision to leave uh Washington, arguing that the extensive litigation and public pressure surrounding Doge forced his hand.
13:09
He lost Democracy One, wrote Norm Eisen, a co-consel for the House Judiciary Committee during Trump's first, which is interesting because as a special government employee, his tenure was limited to 130 days and he's leaving at the end of that. Yeah.
13:24
So taking a victory lapal that's like saying you hired a contractor.
13:29
But also that I I don't know.
13:31
I feel like that that 130day thing like we're kind of hearing about that for the first time now.
13:34
Like that wasn't the way it was messaged beforehand.
13:37
It was definitely messaged like Elon's going to be part of this team for four years like like expect Doge to go on a generational run here and do a lot.
13:46
It was not like oh Elon's hey just to set you just to set the record straight up front he's going to be in for 130 days.
13:53
like let's see what we can do then like like I I this is the first I've heard of the 130day thing.
13:58
So it feels heard I heard of that from another guest that we've had on the show who is a special government employee and and he had messaged it to me like oh I'm it's just a sprint. It's a sprint. Okay. Interesting.
14:12
That was kind of core to the Doge strategy from the very beginning is special government employees that are on these sort of shortened stints. Yeah. Yeah. Yeah. That makes sense.
14:22
Uh, so what did he actually do while he was in Washington?
14:23
Asks the Wall Street Journal.
14:25
Musk came into the government with bold plans to spat to slash spending by as much as two trillion in rapidfire succession.
14:31
Doge and Musk dismantled agencies such as the US Agency for International Development and the Consumer Financial Protection Bureau, leaving thousands of federal employees out of work.
14:42
Musk and his Doge team emerged after Trump's inauguration with a series of shock and awe moves that rattled the federal workforce.
14:47
Um this the subject uh he sent an email to a lot of government employees fork in the road, the same line Musk used in a 2022 email to Twitter employees shortly after he took over the company and renamed it X.
15:00
Tens of thousands of federal workers took the offer um to I'm going to send an email to the team tomorrow morning with the title fork in the road and it's do you guys want bacon or steak breakfast? You're gonna go. It's great.
15:15
Uh Musk's promise to slash the federal wedge by trillions of dollars ran into the wall of non-discretionary spending programs such as social security and Medicaid.
15:22
Uh Doge said it had saved the government 175 billion from a combin combination of asset sales, contracts, lease, and grant cancellations, workforce reductions, and other moves made since January 20th inauguration.
15:35
So it seems like maybe a decent outcome. I don't know.
15:38
It's like the you know better to not waste that money but yes it doesn't write it doesn't correct the budget and so it it's not bill you know increases it dramatically.
15:49
So it's not it's probably like better to have done some of that stuff than not.
15:54
But the real question is social security and Medicaid.
15:56
And so the answer to that longevity drugs move the retirement age to 95 solves all of this. Let's get Brian Johnson. Brian has his way. It'll be 150. Yeah. Yeah.
16:06
that really keep grinding.
16:10
It would completely change entitlements if uh if people could work longer, but very unpopular to ask people to do that.
16:19
Anyway, um massive news out of Andoral and Meta. Uh they're teaming up.
16:21
Metaf fired Palmer Lucky.
16:25
Now they're teaming up on a defense contract.
16:27
The Facebook and Instagram parent is partnering with Anderol Industries to develop combat VR headsets for the Army.
16:33
And uh there's a beautiful picture of Palmer Lucky and Mark Zuckerberg there.
16:38
Let's hear it for getting the band back together. Love it.
16:42
Ashley Vance has an exclusive interview with Palmer Lucky.
16:44
He's going to be joining the show in just a few minutes.
16:48
Uh and we'll break it all down.
16:48
Uh very interesting because the narrative I was hearing internally in the defense tech world the some of the Anderal haters were kind of like oh they bought this Ivast contract for from Microsoft but the contract's going to get recompeted and at a certain point why wouldn't Meta compete against Anderoll for this?
17:07
They they have a great team.
17:09
They have a lot of the hardware.
17:11
They could actually deliver this and you know the contract is up there like let go get it. Well problem solved. Now they're teaming up.
17:19
And so if you think about it, it's like Zuck started wakeboarding, doing jiu-jitsu, and now he's a defense contractor. Let's go. The cycle is complete.
17:27
Back to the back to the couch with the red solo cup, drinking a beer.
17:31
The the first Zuck interview.
17:34
It's great technology, brother. It's fantastic.
17:35
Um but yeah, uh exciting uh exciting interesting development.
17:40
So, um, uh, obviously there was a messy split with Palmer Lucky years ago, but everything we've heard about that was that it it it like Zuck was not super involved in that process, which is kind of crazy to think about, but Meta was such a big idea of a massive acquisition.
17:58
It's such a big company that yeah, maybe you bring someone in, you kind of let them work and then at a certain point like this individual is layered and you don't want to just like immediately go over the top of like the the three people that are in between you and this new uh employee that you've hired because I don't think Palmer was the CEO of Oculus when he went in.
18:19
And so it's possible that he technically didn't it's not like he technically reported to Zuck.
18:23
And so um anyway, they've clearly rebuilt the relationship.
18:28
very exciting and there's been there's been some progress even just in p in public discussions on X between uh Bos and uh and Palmer Lucky going back and forth about what happened, what mistakes were made, some apologies and it was just very cool that that the water was able to flow under the bridge.
18:45
I I really appreciated that.
18:47
Yeah, I just like to see them doing business together again. Totally. Totally.
18:50
Uh so Ly's defense company Anderal Industries and Meta said Thursday they will build a line of new rugged helmets, glasses and other wearables that provide a virtual reality or augmented reality experience.
19:00
The system called Eagle Eye will carry sensors that enhance soldiers hearing and vision detecting drones flying miles away or citing hidden targets for instance.
19:10
It will also let soldiers operate and and interact with AI powered weapon systems.
19:16
Ander Andrew's autonomy software and Meta's AI models will underpin the devices. Very cool.
19:20
Uh oh, that's interesting. Yeah, makes sense.
19:23
You you'd want to have an LLM on board to actually interface with everything, but then you need to interface with all the different assets on the battlefield.
19:30
And so, Lattis comes in there.
19:30
That's Andrew's autonomy software.
19:32
Soldier on the battlefield.
19:33
Llama, what do you got for me?
19:40
Turning into GPT, but it's like, yeah.
19:43
Hey, Llama, what what should I be paying attention to right now?
19:45
a really dark llama on bleeding out. Send the medevac.
19:50
How do I how do I tourniquet myself? Rough.
19:51
Uh but I mean, you know, like like you have to put you have to put these pieces together, but Llama is a silly silly name. But I love it.
19:58
I wouldn't be surprised if they say Eagle or Yeah. Yeah. Yeah. Yeah.
20:02
They might need to they may need to hide that brand underneath the hood. Yeah.
20:06
Uh, the collaboration brings together a social media giant that has long been the target of Washington scrutiny and a weapons maker that is a rising star inside of the Pentagon.
20:16
The partnership author offers another example of Silicon Valley's ideological evolution and big tech's expanding embrace of defense work.
20:24
Lucky says, "I should look at this as I have succeeded.
20:27
I've successfully persuaded not just Meta but many others that working with the military is important." Yep. Yep. Co-cultural victory. Yeah. Yeah.
20:36
The I mean in the previous era the one company that was kind of pro working with the government was Microsoft and of course they had the hollow lens project but they were also selling Outlook and Excel and all these different products.
20:49
And I think that just came from the the like the Balmer era, the Gates era where they saw their software as just enterprise software.
20:56
So they needed to get in the hands of everyone.
21:00
And so it it became standard in on pretty much every military base that there's Windows machines.
21:05
Um and so that that legacy there was never it was kind of like even pre pre- global war on terror.
21:13
And so support was at an all-time high.
21:15
Those partnerships grew and grew and grew.
21:17
Um, and of course the uh the hollow lens project was was very cool and IVAS was was was just a cool project to work on, but it didn't seem like they had the the maybe the defense contractor DNA to really take something into the hands of the war fighter even if they were able to put Excel in the hands of the war fighter. Uh, yeah.
21:33
And what what pal uh sorry what Andrew has now is credibility with Washington and the existing relationships to say we are going to be a distribution channel for the best technology in the world and work with the best partners to deliver the best end products and uh great it's a fantastic partnership.
21:53
We got to go get the demo soon.
21:55
Uh, Meta in recent months has recruited former Pentagon staff to join its ranks and an effort to navigate the labyrinth of the defense procurement process.
22:04
In November, it opened up its AI models for military applications, a new line of business for the company whose profits have been powered by online advertising.
22:10
In a statement, Zuck said the Eagle Eye technology will help US soldiers protect interests at home and abroad.
22:16
Uh, Meta and Anderil will have jointly have jointly bid on an army contract for VR hardware devices worth up to about hund00 million.
22:24
If awarded, it would be Meta's most significant tie-up with the Defense Department.
22:28
The contract is intended to vet headset prototypes that are part of a larger$22 billion dollar army wearables product project, of which Anderell became the lead vendor in February after Microsoft failed to deliver a functional VR headset.
22:39
Ander said the collaboration on the headsets, which the companies have already mostly funded themselves, is going forward.
22:46
Irrespective of winning the Army contract, Anderol is betting other parts of the military will also be buyers.
22:51
Yeah, that makes a lot of sense.
22:51
Why would you not want this if you're in the Air Force or the Navy or or the Marines?
22:55
Uh the Meta Partnership delivers a victory lap to Lucky, whose entrepreneurial roots and much of his fortune can be traced to VR.
23:01
Uh and and then they give a little bit of background on on Palmer's journey.
23:04
It's finally got all my toys back.
23:07
He started Oculus when he was 15 years old. Yeah. Insane. Insane story.
23:14
The partner, the new partnership gives Lucky access to all of his old VR designs.
23:19
Plus, newer tech, his team has built after he was fired.
23:24
I finally got all my toys back, Lucky said. I love that. That's amazing. So good.
23:28
It's still so wild that in 2017, the US has two real political parties, and in 2017, there was one that if you donated to, you would get fired.
23:40
It it it made me very frustrated at the time.
23:42
It was it was very very rough.
23:42
It was just like that's not that's literally anti-democratic.
23:47
The whole point of democracy is you can vote for whoever you want. Yeah.
23:51
Like it's like the one thing in democracy that is like it's like it's the definition of democracy.
23:58
You have to be able to vote for whoever you want.
23:59
Like it's like everything you've learned about how America is supposed to work is now just at at stake is bad bad times.
24:06
But yeah, there's two parties but but you can only donate to one of them.
24:12
And if you donate to the other you get fired.
24:14
But hey, what matters ultimately all the different groups have come around and uh are moving forward.
24:20
So great to see um in other news um we should go through I I this is a story that a lot of people have been asking me to kind of dig into.
24:33
Uh I was talking to Jordan Schneider over at China Talk about this.
24:35
over at China Talk about this. He said it'd be great to get people's view in tech around this more political story which we don't really which we don't really cover but um Trump's I mean currently waging a war on American universities which does have some tech implications in the sense that uh you
24:53
know the teal fellowship has been in you know renegotiating the relationship between entrepreneurship and higher ed for a long time and then also a lot of tech companies hire from elite universities and So, uh, Trump has been threatening to withhold billions of dollars in federal research funds to punish campuses. Um, and, uh, there's a
25:10
Um, and, uh, there's a story in the journal here about, um, the the punch that launched Trump's war on American universities. So, I still don't know.
25:22
I still I still don't know exactly the angle of this.
25:24
I think it's something that we should dig into.
25:26
I think the big question is like America has national interests, has different security interests, also has competitive dynamics around brain drain and the and you know we want the top entrepreneurs, we want the top technologists, the top 01's um the administration has a view, big tech companies have a view.
25:44
It would be interesting to hear from tech leaders on where they think this these policies should go around um the top research universities.
25:54
When you hear when you talk to the biotech folks, they're like, "We need research to be done in universities funded by the government in order for our business to make sense."
26:03
You don't hear that much from tech companies.
26:05
They're like, "Yeah, like the next big AI breakthrough probably just going to come from Google.
26:09
They won't be able to productize it fast enough and we'll just roll it out into some B2B SAS."
26:14
uh and and so but at the same time there are a ton of AI labs Silicon Valley tech startups that want to hire tons and tons of talented individuals from all over the world and there's big questions about how the Trump administration is is affecting that.
26:27
Uh there's that there's that famous quote from Trump on the All-In podcast saying we should we should staple a green card to every university diploma and this feels like the opposite of that.
26:38
And so there's a question about like was that a campaign promise? Big news.
26:41
Yesterday, Secretary of State Marco Rubio said that they will begin revoking the visas of some Chinese students, including those studying in critical fields.
26:50
China is the second largest country of origin for international students in the US behind India.
26:56
In the 2023 to 2024 school year, more than 270,000 international students were from China, making up roughly a quarter of all foreign students in the United States.
27:06
That's actually down significantly.
27:07
I saw Salana was posting something uh yesterday as well just showing that that 270 number is significantly lower than than just a few years ago. Yeah.
27:15
So there is there is a question about like trade balance a little bit where it's like you know if if if two what is it 270,000 270,000 if 270,000 Chinese citizens are coming to American universities should 270,000 Americans be at Chinese universities.
27:33
Is there an imbalance there?
27:34
That's an interesting question to dig into just in terms of like reciprocity of these relationships.
27:41
Just like if Tik Tok can operate here, well, can Instagram operate there?
27:43
That would be an interesting trade.
27:45
If if there's an imbalance, we probably need to have a discussion one way or another.
27:50
Um, there's also the question, I mean, yeah, I I I would have to give a very rough estimate of how many Americans were at Fudan, which was the university that I studied abroad at in in China.
28:01
And it can't it had to have been less than 50 total. Yeah.
28:06
At least that that I was aware of.
28:09
It was pretty easy to, you know, at the same time, I wonder how much of a how much of a shot across the bow, how painful is this to the Chinese government?
28:18
Because if you if you remember the the whole story of uh what is it? No, no, no, no, no.
28:23
Russia with the Magnitki Act.
28:25
Uh it it banned um adoption from Russia to America and that was like extremely painful for some reason.
28:34
And I don't exactly know why that was such a hot button issue, but that was like a key point of leverage against against Russia at some point.
28:43
And so I wonder I I I want to dig into the reaction to this move by the Trump administration by the Chinese Communist Party.
28:50
Are they upset about this?
28:50
Like does this go against their plan in like a very meaningful way?
28:54
Is this something that will will be another another uh poker chip on the trade negotiations around tariffs or around the the the flow of fentanyl or something like that?
29:06
Could this just be a an opening gambit that then gets negotiated down and Trump's merely just putting this ch this piece on the chessboard to then negotiate against?
29:15
Or is this something that um maybe China doesn't really care about and they're just like, "Yeah, we actually want our students here and so uh this is this is great for us.
29:22
Please, please send send all the all all the student all the smart people back.
29:26
Like we we we don't want to be brain drained.
29:29
I don't know which way it's going to cut.
29:30
Um and so I want to dig into it more.
29:33
Their foreign ministry spokesperson Mao Ning has said, "We urge the US to effectively safeguard the legitimate rights and interests of all international students, including the Chinese students overseas."
29:43
So not really a real response, but we'll see how it plays out. Yeah. Yeah.
29:48
I wonder if there'll be like retaliation or some sort of like uh cancelling of American students that are in China, how this will actually play out.
29:57
But but we should start asking more of the guests about how they're reacting to this and uh especially if they have, you know, deep insight here.
30:04
I don't just want to hear random people yap about it. Anyway, guess what?
30:08
Guess how many American students studied in China in 2024. 5,000. Guess again. 10,000. Guess again. 1,000. 800 800.
30:19
So I was pretty I was pretty close with being like even in this was like 2016.
30:25
I was like maybe there's 50 other students. That's really low. 800's nothing. Yeah.
30:31
I mean we have like millions of college students in America, right?
30:35
You would think that, you know, a couple percent of them would be over there on at least like one semester or something like that.
30:40
So there was 11,000 prior to the pandemic. Okay. Wow. It really fell off. 90%. That's crazy. Wow.
30:49
Anyway, uh in uh in other uh Asia news, uh Kim Jong-un uh launched a new North Korean warship using a risky side launch technique and it completely crashed. I guess that's brutal. Yeah.
31:06
After after that soundboard, you can never go to Pyongyang is not going to be happy to see that.
31:13
We're not going to distribute the show in Pyongyang anytime soon, unfortunately.
31:16
Uh, the unconventional technique for launching a big military vessel points to North Korea's haste in modernizing its outdated navy and lack of resources.
31:23
Uh, Kim Jong-un witnessed a warship topple over during its launch, deploying a risky side launch method.
31:30
Naval experts site inexperience, a rush timetable, and topheavy warships as factors in the failed launch.
31:35
North Korea chose a side launch to save cost, while the US and South Korea use safer floating dock launches. O, very, very rough.
31:45
Imagine you're just like you.
31:45
So Kim Jong-un, he traveled to North Korea's city of iron.
31:49
Okay, you got to hand it to him. That's an amazing name.
31:53
Let's hear it for the city of iron. City of iron.
31:54
It's where they build the ships. Isn't that amazing?
31:56
So they have a major industrial. Yeah. Yeah. Yeah.
32:00
You you got to call us the space.
32:00
We're just calling balls and strikes here on this one.
32:03
We're pro North Korea now.
32:03
The city of Iron is just too good.
32:05
Uh the it's home to a major industrial shipyard where a hulking warship awaited him.
32:10
A VIP podium had been erected.
32:13
alongside the port for the vessels launched.
32:15
A officials awaited anticipation, but the big moment of celebration turned into calamity.
32:22
The 5,000 ton destroyer lost its balance as it lurched into the water, toppling over and embarrassing Kim, who seeks to modernize his Soviet era naval fleet.
32:31
And it's so funny because like they didn't send us video obviously, like they didn't broadcast this because they would only broadcast it if it's like successful.
32:36
So, we just had like satellite photos of just it's just like getting destroyed slowly, slowly.
32:41
Very rough, very very rough. Um, brutal.
32:44
What do you What do you do if you're the chief engineer that that tips the the warship over and Oh, I actually know what you do.
32:50
Uh you go to jail there, I bet.
32:52
Or so four North Korean officials have been detained over the mishap.
32:57
Uh which called it an unpardonable crime.
32:59
Can you imagine the stakes of like Oh yeah.
33:02
You think it's hard being a hard tech founder in America? Oh yeah.
33:06
Try doing that in North Korea where you go to jailess.
33:12
We've had nine starships just blow up and we're like, "Okay, like you're sleeping in the factory now.
33:17
Not you're going to jail."
33:18
Like, you're working harder.
33:20
Our our answer is you work harder.
33:22
Not not you not not you go to jail.
33:25
Uh what has become clear in the aftermath is how an unconventional choice of launch method, Kim's rush timetable, and a top heavy warship overladen with weapon systems was a recipe for disaster.
33:35
They just keep being like, "Yeah, just like throw one more missile battery on that." Yeah. Yeah. Yeah. Yeah.
33:39
Put a Gatling gun on it, too. Yeah. Yeah, another cannon. No problem.
33:43
Just keep doing it and then it just rolls over.
33:44
It was a recipe for disaster according to satellite imagery analysis.
33:49
I haven't seen a failure like this one.
33:51
Uh I've never seen a failure.
33:55
Said a retired Marine colonel that they the Walter Turtle called up and he's just like this is insanely bad. Yeah.
34:04
Who are you on the phone with, Jordy?
34:05
No, just calling up Mark Concian. Yeah. How bad is this?
34:10
I haven't seen one this bad. Never seen one this bad.
34:12
It was a 470 foot long warship.
34:13
Kim's second uh destroyer had been built in the in this in the northeastern city and uh did not go well.
34:24
Anyway, uh our guests are going to start.
34:26
There's a bunch more stories here.
34:28
We got Peter Raal in the waiting room. Bring him in. Yeah, let's do it.
34:34
Peter, Peter, how you doing? [Music] Nice. Nice sweatshirt. Looking good. Thank you. It's a sample, too. Oh, very.
34:44
Congratulations on your second announcement of the week.
34:47
This is the first real one, I guess. Sorry.
34:49
Sorry that Sorry that you got leaked, but um uh very excited for you.
34:54
Excited to have you on the show to break it all down. Um yeah.
34:59
What is the most recent news?
34:59
I only know the the incredible revenue number, but what else is uh what else is driving the news cycle right now for you guys?
35:07
Uh well um so we acquired a supplier an ingredient supplier called Epig.
35:12
Um and then yeah our valuation 7 725. Wow.
35:14
Uh forecast this year is 140.
35:20
Um so I think those those numbers and you know I think you know it's like in we've accomplished more in nine months than we did in RX over four years. Wow.
35:33
Um, so I think we're I think we're one of the What other What other brands have achieved that level of revenue within a year of launch?
35:43
It has to be like there's two um Fastables and uh Prime Prime. Yeah. Yeah.
35:51
Because just massive distribution cannons and really like leveraging like essentially like you know $100 million worth of free marketing on day one, right? Yeah. Yeah.
36:02
So what's the secret for you?
36:02
Because It feels productled.
36:06
Is that is that is that the right assessment?
36:08
Obviously, you have a bunch of lessons uh you know, war stories you can pull on from RX Bar, but like it feels like the product just sells itself in some way.
36:17
Yeah, I would say it's definitely productled.
36:19
Um we act, you know, in CPG it's pretty hard to differentiate measurably, right?
36:26
It's it's and you know we are 75% of our calories are coming from protein.
36:31
the market is at like 50.
36:33
Um so so it's meaningful and it's an example like the market was on the surface if you don't apply rigor it looks pretty competitive. Yep.
36:42
But if you just like spend a little time talk to some customers you realize like NPS is pretty low like no one's really happy.
36:50
Um and a lot of people don't just aren't even in the category because of taste and texture and nutrition.
36:57
So, I think big opportunity for us in in the bar business is like to really expand the category and make delicious, nutritious products that that people are like, "Oh, oh, protein bars don't taste like shit."
37:11
Like the expectations that there's just they're just going to be bad. Yeah. Yeah.
37:14
When you say people aren't in the category, are you talking about the difference between protein shakes, which can typically be like extremely high protein from calorie ratio?
37:22
Uh more more specifically, people are like people don't consume protein bars.
37:28
Like you you'll you'll hear people say like, "Oh, I'm just not a protein bar person." Sure. Sure. Sure. Sure. Yeah, that makes sense.
37:35
Those are the people you ask them like, "Why not?"
37:37
It's fundamentally taste. Yeah. Fantastic.
37:39
I I I I want to kind of push back on this idea that it's productled.
37:42
They agree that there's product differentiation, but this feels like it's only possible from you because if you call up a major distributor or supplier or retail chain, they say, "Yes, I know you.
37:53
You you you have a you have a win under your belt.
37:56
I'm willing to go big and we don't need to do some long extended test."
38:00
Is this isn't to knock you.
38:03
I mean, this is why you worked really hard and you're reaping the benefit of that.
38:05
But is that is that critical or do you think if you were nobody and you were cold calling?
38:08
My position is more so like Peter would have the relationships to get the bars on the shelf, but he's not making you know holding every customer that walks into the store at gunpoint being like buy my product.
38:20
You know, I mean it is very different than Mr.
38:22
Beast and Prime where you know you have like major influencers driving this like it is more productled in that sense. Yeah.
38:27
And I would say like my credibility or track record is like a lubricant to facilitate things. Yeah.
38:36
But but the repeat and it's it's all product driven. Yeah. Yeah, that makes sense.
38:40
Talk about the decision to acquire one of your major suppliers.
38:42
Uh was that something that you anticipated doing a long time ago or, you know, what was the point that that that made sense? Yeah.
38:52
So, when we started, you know, in our business, you don't want any single source supplier. It's just too much risk.
39:01
So, it's like it's it can become a nasty dependency.
39:03
So we identified the technology, the ingredient and was like this is this is like magic like technology should feel.
39:10
Um got close with them um became 90% of their volume.
39:16
So we were basically you know 90% of the yeah 90% of their revenue and um it just made sense to vertically integrate and derisk it and so we can just control the supply.
39:30
Um yeah and and and you know I can't imagine a company without without us being together.
39:34
Um and and enables us it just like totally widens the aperture of like where we can go um and truly have a platform for AC across different different products different different brands different different consumer needs.
39:46
How h how did you dig into like the defensibility around that intellectual property?
39:50
because I feel like the gold standard for defensible IP in you know anything FDA regulated is probably um pharmaceuticals like drugs but as we've seen with like the ompic wagui transition like even GLP1s they were able to kind of eat at the edges and now Eli Liy is taking share from Novo and it feels like if it can't work in GLP1s like how can this possibly hold in a protein supply chain right?
40:18
Like you're going to get someone who spins something up that's like one molecule different or something like that.
40:25
Is that is that a risk or did you dig into that at that level of depth?
40:28
Yeah, I mean it's there's one there's really one process to make to make the molecule.
40:31
Um and you know it's like sure someone can go try it but like we'll we'll just litigate and then so they calculate that.
40:44
Um, and I guess the I guess the the the bull case is that like yeah, you could go and fight on the IP stuff, but at the end of the road, you don't get a pharmaceutical product, you get a CPG brand.
40:57
brand. So, and so far there you're going to have to fight real real hard and at the end you then you also have to build a brand also do the distribution and also whereas you know Eli Liy like if they spend a bunch of money to figure
41:09
out how to you know alter the GLP1 to get a drug that works like well then it's just a matter of doctors prescribing it right and it's like and it gets paid and like in in CPG you kind of have like two years runway before someone copies you. Y now we have like
41:20
Y now we have like structurally nine years. Yep.
41:23
And I think by that time it's kind of too late. Yep. Yep. Yeah.
41:28
I mean you already see that with the 140 revenue like like as that gets bigger it's just going to be you know ubiquitous and you own all the shelf space.
41:36
So even if there is knockoff copycat it's like yeah you're already big.
41:40
You're less than a year in post launch.
41:42
Uh what is a good outcome look like?
41:46
You've already had a multiund million dollar exit.
41:48
I imagine you're aiming a lot a lot higher here but but what is that in your mind?
41:53
Yeah, I try not to think about the outcome too much, but I do.
42:07
That's the most honest answer ever. The best. Yeah.
42:09
Any any entrepreneur is like, "Oh, yeah.
42:11
I've never thought about the outcomes just purely mission, you know." Yeah. It's amazing. Yeah.
42:15
Um like I would be pissed if we don't get to a billion in revenue. Yeah. Yeah.
42:19
You know, I that that would be disappointing. What was RX bar roughly?
42:25
Like what's your personal high water mark? 240. Yeah. Okay.
42:28
And I think maybe maybe a little bit more. Yeah. Yeah. Yeah. 4x that. Yeah.
42:34
So I But I I do think I I do think we can build a really diversified portfolio of brands. Yeah.
42:43
Um that really address a broad population and and create a ton of consumer surplus.
42:51
So, um, you know, I think like, yeah, we just have so much work to do.
42:55
And, um, yeah, I I I I do fantasize about being a public company. Uh, let's go. I love it.
43:06
And I think I have this is what my dreams are.
43:09
We'll have you back after your public company and you're just going to be like, it's it's such a headache. It's so annoying. I can't say anything. I'm in a quiet period. I can't come on TVPN. It's the worst.
43:21
Yeah, I might be the irreverent. Yeah.
43:23
These calls, but but um Yeah, I just think like there hasn't been a truly uh from scratch brand platform in the space.
43:35
It's all been done through M&A. Yeah.
43:37
Chani is doing it actually now. Yeah. Sure.
43:39
Um, and I think that and you're a believer in in these brands living under the existing, you know, CC Corp and or or like it doesn't sound like you want to do like a studio.
43:49
No, I only studio analogy like we we we're not going to do M&A.
43:55
We're gonna we're going to build them like I I me and my team we've demonstrated we can like go from scratch like um Yeah.
44:00
So so we would just build them.
44:05
It would be a decentralized model.
44:06
So like different orgs, you know, like M so David's the master, David's Mars and then David Protein is the operating business.
44:13
Epig will be its own subsidiary and then we'll create other brands.
44:18
Um and then they just have to be decentralized organiz like orgs because you you know like David in his life cycle is very different than a newborn baby. Yeah.
44:29
And so like you need to design the or for agility and speed and so so that's what we're designing for.
44:36
Um, and how much do you view yourself as a technology company?
44:38
I I heard you bring that up earlier.
44:42
As in you're wanting to pursue opportunities where you can have a unique sort of durable edge through some type of effectively chemistry.
44:52
I don't know if that's the right. Yeah, chemistry.
44:54
Um, I, you know, we're in the chemistry business now.
44:56
I I want to keep learning in investing.
44:58
We, you know, I don't know what we don't know, but like once you get in the pool, you'll figure things out.
45:02
So um you know like technology is not really welcome in food.
45:07
Um so we we we restrain from the t-word.
45:11
Um uh but yeah we want to keep making really valuable products like that's just our focus.
45:19
Um and and obviously technology is really the best way to get there.
45:24
Have you have technology you have investors that are traditionally technology investors.
45:30
I mean, I I don't think I've ever seen Green Oaks do a CPG round. Yeah.
45:34
The last deal they did was like Windsurf, right?
45:36
Or like the last big outcome for them.
45:37
A wildly different company.
45:40
Have you have you taken a crack at kind of reverse engineering what it takes to really go after like a Nestle or a Unilever, like one of the major major conglomerates that has been built through M&A, but if you if you think really really far into like what it takes to create a hundred a hundred billion dollar outcome in consumer packaged goods. Yeah.
45:57
No one's even come close.
46:00
Like we all site like Red Bull is a great outcome.
46:01
Celsius, but these are all like single things in very niche categories that are big categories like energy drinks.
46:06
So the companies get big, Red Bull's big, Monster's big, but no one's really been able to figure out the right corporate structure to to just compound and compound and compound and create this like hundred million dollarundred billion dollar behemoth. Yeah.
46:20
So the key way to address the really large TAM and consumer food is through different brands that have different DNA that address different sort of problems or consumer state like needs.
46:32
So like David's I use the analogy of like D brands are just human beings. They have fathers. They have they have DNA.
46:40
They have behaviors, beliefs.
46:40
They you know have friends.
46:43
You got to let that brand or that asset be it be itself.
46:47
like you can't jam it into places it doesn't belong.
46:49
So like the David brand with our future portfolio and optimizing for calories coming from protein.
46:55
Um so when you look at David, you know, it's like the most protein, least amount of calories. Yeah.
46:58
That that TAM is probably 1.
47:00
5 billion in revenue in the US.
47:03
So So you really need multi you need like a house of brands to go after very different parts of the population. Yeah, that's one. And then international. Yeah.
47:14
What's the mega scale and they're global.
47:17
So that that's the key thing.
47:19
What's the push back been like from kind of like the trad community that wants to do everything all natural everything like you know oh just you know farmtotable the pollen crew the RFK crew like there's all these different uh different segments of kind of uh like anti-modernity in food.
47:41
What's the reaction been like? Yeah.
47:41
So um you know Peter Teal talks about like anything with science at the end is not science. Sure.
47:49
So nutrition science is has really to date not been science. Yeah.
47:54
Um and so so it's really been it's a really emotional conversation and and and not a intellectual conversation.
48:00
So that's the first place and it is complicated and and and our society is really confused around it.
48:08
And so in under the confusion in a way to simplify something complex, people find like just simple correlations like if my ancestors didn't eat it, I shouldn't.
48:17
And that that's like a pretty good framework or like it's not bad advice, but it's clearly not sophisticated and it's way more complicated than that. Yeah.
48:26
So um and it's like the interesting thing with like the ancestral movement, my friend told me this and I thought it was like really good.
48:34
It's like the ancestral food movements.
48:36
Like it's like post-traumatic stress response.
48:38
Like it's like a post-trauma response where like like you just stop and you like don't progress and you actually go backwards because you're scared to make it worse.
48:49
And perhaps that's because food [ __ ] up and food, you know, yeah, got run by CFOs who just cut the bottom line and just cut cut.
48:57
Um, but I do believe like there's a way to advance food in a way that and it isn't that scary and um, yeah.
49:08
So I think I I goal is to have like a more intellectual conversation on food like you know not something like oh I can't pronounce it therefore it's automatically bad. Yeah. Yeah. Makes sense.
49:20
Food is about like nutrition is about like it's pretty simple.
49:22
It's about the 80% like don't overeat calories. Don't be fat.
49:26
Turns out that's [ __ ] terrible.
49:29
Don't spike your blood sugar and get enough protein. Yeah. And hit the gym.
49:35
I wish we had more I wish we had more time.
49:37
There's a there's a bunch more questions I have, but congratulations on the milestone.
49:40
Just getting started and uh appreciate you coming on.
49:44
Yeah, we'll talk to you later. Congrats. Cheers. Bye.
49:45
Next up, we have Ashley Vance.
49:48
You know that you know that Green Oaks is writing underwriting that to small chance of a hundred billion dollar for sure.
49:54
I think that's why they're they're doing the deal. Neil Meta undefeated.
49:58
Uh next up we have Ashley Vance coming in the studio from Core Memory. Bring him in. How you doing, Ashley? Oh, look. He's in the new studio. New studio reveal. Let's go exclusive. Let's go. Breaking news.
50:14
Core memory interview Palmer Lucky.
50:17
You're hearing about it here first, folks. You're cooking. You're cooking.
50:21
It was a good good morning. It's uh Yeah. Palmer came.
50:22
I think he Well, he came in yesterday, I think, straight from there's this photo we posted of him and Zuck together.
50:30
He's wearing the same clothes.
50:32
I should have actually asked him, but yeah, I think he came straight from their their peacemaking accord. Yeah.
50:37
What's uh what was your read on it?
50:41
How much of it was uh how much were you focused on like the technical side of the deal between Anderol and Meta to work on VR for the military versus just the the the the crazy full cycle narrative of uh the emotional journey? We kind of did both.
50:57
I mean, he's he sat here for a couple hours and from my memory, you know, I think the first 45 minutes was going into the deal, the backstory, all that.
51:06
you'll the press release on this is super thin on the the technology and the the Wall Street Journal story didn't have that much either and Palmer he went into some some detail on our podcast about about the tech and then yeah he got into the backtory I mean this is
51:21
crazy this if I've been following Palmer pretty close for the last couple years I never would have imagined that this would have been possible given the uh the intense the depth of this like 9-year war that the those he and and Meta have had. So, um yeah, I I did not
51:37
So, um yeah, I I did not fully see this coming.
51:40
Is it accurate to describe it as a war between Zuck and Palmer or war between Palmer and Meta?
51:45
I think it was like more Palmer and Meta.
51:50
You know, if you go back um Blake Harris wrote this book, The History of the Future, and he he spends like 40, I don't know, 40 or 50 pages laying out the the saga.
51:59
you know, Palmer put up this paid for this billboard and about Hillary Clinton and became a known Republican in public and and was fired from Facebook and and in you know in that book you see a lot of operatives within Facebook um kind of pushing Palmer out and and we talk about this on the podcast.
52:21
I mean if Palmer's being truthful in what he said, he didn't lay the blame directly at Zuck's feet.
52:25
He he kind of felt like Zach was doing what he had to do to protect his company and and what the employees wanted.
52:32
So um so I think it was more more meta directed. Yeah. Yeah.
52:37
It is it is crazy to think about the post Oculus acquisition time period at Meta and just not having Palmer report directly to Zuck.
52:46
Like that seems like that's the real cardinal sin here more than the crazy stuff that happened politically.
52:53
It's just like how do you bring in someone who's like clearly the face of VR, a generational founder, like all these amazing things and like it would just have been so much easier to manage that relationship if there was no one else in between like no management layers in between. Yeah.
53:06
And like almost definitely I think Palmer's probably still at Meta making working on VR.
53:13
I don't think 100% on Android, you know. Yeah.
53:16
Didn't uh Jenkum from WhatsApp stick stick around for a long time?
53:20
And obviously like the Instagram guys had kind of a rough go and they're upset about it now.
53:23
But like they I think they reported directly and like built that for a long time.
53:26
It was just odd that like VR was so important in meta that like you know reality labs huge investment and yet Palmer Lucky was not you know he should have been on the board like like he should have been added really really into the inner circle.
53:40
It's been a while since I read Blake's 20s. Who cares?
53:43
Zuck was 19 when he started the company or whatever.
53:46
Sure, you know, break the rules.
53:46
And it's it's been a while since I read Blake's book, but yeah, I mean, he kind of lays out all these layers of management that were put between Palmer and and and Mark.
53:56
So, but here they are taking photos as as friends again.
53:58
Have you gotten a demo yet?
54:01
Any of the Ander hardware yet?
54:03
I haven't gotten on on this headset. No.
54:06
I mean, this has been an I I've been down at Androll doing some reporting and Palmer's always like you because this this is like an extension of this Ivas deal that that um that Androl took over from Microsoft.
54:19
So, I've been down there and Palmer's like, "Oh, my secret lab is over to the side and that's that's where these guys are working."
54:25
I mean, people should know.
54:27
One of the coolest parts about this, I think, is that I mean, yes, they're making this war fighter helmet of the future, like the sci-fi thing we see in every military movie that doesn't exist, but beyond the like VR AR data feed coming in.
54:43
I mean, and Palmer talks about this in the podcast.
54:46
He's trying to like reinvent the helmet itself, right?
54:49
To make it lighter weight, to make it uh the ballistics upgrade, all of that stuff.
54:54
I mean, so he wants to build it's not just like the um the the sort of tech end of this.
55:00
He just wants to make a better helmet for soldiers, too.
55:03
And and I know he's been spending a lot of time on all the materials and and things like that. Yeah.
55:07
Have you been tracking any of the other developments at Meta.
55:10
There was some there were some setbacks with the Llama team.
55:12
It seemed like they kind of went a little bit too far into the pre-training paradigm and maybe not enough in RL.
55:18
And so Llama 4 has kind of fallen behind in some of the some of the benchmarks.
55:22
Uh, at the same time, uh, there's some really amazing, uh, VR demos that are coming down the pipe, like the Orion, uh, augmented reality glasses, and then even just like the Next Quest headset, I feel like is going to be incredible because all that work that Apple did to get those incredible screens into the Vision Pro.
55:44
Well, like Zuck's probably going to be able to buy that for the because it's two years later now.
55:47
Uh, it seemed like that was something that was unique to Apple, but only for a small period of time, and Apple kind of whiffed on actually getting that into the hands of millions.
55:56
If it's in the next Quest, it could be really cool.
55:58
So, any takes on what on what Meta has been doing in VR or AI lately?
56:02
I mean, I I do not follow them as closely as I do some other companies.
56:09
The thing that's always stuck out to me was when they bought Control Labs, which was doing this weird take on a brain computer interface by reading the motor neurons in your in your wrist.
56:21
And and you know, we've seen uh Meta do some demos around this kind of like new interface using your body and to and your brain to navigate computers.
56:29
I actually think that's like the under um you know it's probably one of the hardest things they have to pull off but it's it's kind of under reportported in some ways because um I mean if you look at what trying to read the tea leaves on whatever Open AI and Johnny IV are doing you know we're we're clearly we're lurching every some companies are lurching toward a new kind of computer.
56:56
We've been on the same basic computer for decades.
56:58
Well, to be clear, the OpenAI thing, they were just they were pretty clear that they said it's a third device besides your phone or your computer that will work with those devices. With those Yeah. Yeah. No, that is fair. That is fair.
57:11
I just I still feel like we're this everybody wants a new toy. Give us a new toy.
57:17
Somebody will figure this out. I am confident.
57:20
I I'm old enough to remember like, you know, when I first started reporting. Yeah. It was like exciting.
57:26
People had different operating systems.
57:28
They had different takes on computers all the time, you know, and and I just feel like we've lost that.
57:32
So I This is like What are you talking about? The new iPhone.
57:35
It has a button on the side.
57:37
There's an extra button now.
57:40
We We can only We can only refine the bezel so much.
57:43
You know, you're looking a gift horse in the mouth, Ashley. Come on. This thing is awesome.
57:51
I have I have a bone to pick with you guys which is you know you got this glowing profile in the information about your new media empire and I was quoted in the story and they didn't even bother to mention my new media empire I mean no
58:08
boom sorry sorry I hit the wrong effect I hit the wrong effect yeah I mean it was it was half like complete glazegate like just so over the top like lavishing praise on us for genius and reinventing media as a whole from the ground up. And
58:23
And then the other half was like a complete hit piece. It was brutal.
58:27
And I guess you balance those out and it's like, you know, kind of a kind of a nuance take.
58:33
But yeah, they did they did some people dirty. They put our quotes. They put us in quotes. They put us in quotes.
58:37
But this will be the last time TVPN ever appears in quotes and the last time that Ashley Vance has ever quoted in the information without mentioning core memory. Memory. com. Go subscribe right now. That hurt. That hurt.
58:48
I uh I like that everyone wants you guys to be the Ringer or Grantland and neither of you seem to know.
58:56
I don't know what those things are.
58:57
But it's it's even worse than that cuz people are like, "Oh, it has like you know Sports Center."
59:01
Like haven't watched that.
59:03
But Squawkbox also haven't watched that.
59:05
Can you cop it to something?
59:07
We're reinventing the wheel.
59:09
We're reinventing the wheel.
59:09
At TVPN we're focused on reinventing the the media's wheel. Yes. I love it.
59:13
But yeah, give me the give me the broader update on Core Memory.
59:18
I know you have a bunch of different products.
59:20
You're you're you're filming movies, you're writing books, you're doing uh short documentaries, like give me the give me the overview and I want to know specifically about the latest update with Neurolink as well. Yes. Okay. Yeah.
59:32
I mean, we've launched a bunch of stuff. I'm writing on Substack. We launched a podcast. Palmer is on it today.
59:36
Um, and then, you know, I think the thing that we're maybe most proud of or one of the things is, yeah, you know, I used to make a TV show for Bloomberg and and we did very well there and we kind of killed that, built our whole team from scratch.
59:51
And so on our YouTube channel and on our Substack, we've got um I don't know, they go anywhere from like 8 to 20 something minute episodes that are um you know, dives into different inventors and scientists and startups and then so anyway, please go check.
1:00:06
You're kind of telling the story of these like various brotopias that are existing all over Silicon Valley.
1:00:09
You know, unlike some people that only leave Brotopia, we we do do many female scientists.
1:00:17
Um but you know, we're going all around the world really.
1:00:20
So we we just went to so far the early episodes were Silicon Valley based just cuz I had to get this up and running really quick, but we just went and filmed in Switzerland for a couple weeks.
1:00:29
And so part of that was to your other question, you know, we're working on a movie about brain computer interfaces and and Neurolink is at the heart of that and so we're following this journey for for many months, years maybe.
1:00:44
And then um and then we filmed a couple episodes with some Swiss companies. Yeah.
1:00:48
How do you think about Oh, sorry. Journey.
1:00:53
What's uh what's happening in Switzerland?
1:00:54
It's kind you know, it's always like this mixed h Oh god, you're going to get me on like a Europe rant.
1:01:00
I'll try to contain myself.
1:01:00
Um, Switzerland is my second favorite country in the world.
1:01:05
It's like, you know, if you're going to go with the well-made museum uh version of Europe, it's it's it's great.
1:01:13
They do have some I think a lot of Well, okay, they have a lot of good biotech stuff coming out from pharma and and just like a incredible education system.
1:01:21
Um, and they have finance stuff, but we went robotics, you know, I went to a couple university robotics labs. I the it's interesting.
1:01:32
They're always doing pretty cool stuff and then it's always hard for them, I think, to make the leap into forming a startup and and like really getting money behind that and sort of the same ambition that you would see out of a similar Silicon Valley company.
1:01:45
But yeah, we met a bunch of young kids who were doing cool um we you will see it coming up in a future episode.
1:01:52
We had a a robotic uh swan um robots robot swans dancing in a lake shooting water and lights and and we were up till 2 in the morning filming that.
1:02:03
So they actually have a fantastic gun on them yet.
1:02:05
They actually have a fantastic robotics gun on the robot swan. I'd love that.
1:02:09
The over in Switzerland they they you got to do a profile on this.
1:02:14
They have a fantastic robotics industry all around telling the time.
1:02:18
And so they have these hu amazing companies.
1:02:20
You should do a whole profile on Phipe, Adamar Pig, Vashron Constantine.
1:02:26
Uh, it's it's like this machine.
1:02:28
It's it's basically robotics, but just to tell the time and so I can imagine an Ashley Van style deep dive on those companies being really really good. It's hard tech.
1:02:36
I would I would enjoy that. I think that'd be great. Yeah. But yeah, it was cool. It's cool.
1:02:42
They have they got good energy.
1:02:44
you know, they they the Swiss government really backs uh EPFL and ETH, these two universities kind of on a level that I don't know you you I've been all over Europe filming the TV show and I'm always I'm impressed when I go to Switzerland.
1:02:59
Do you think American investors should be posted up in Geneva just slanging checks?
1:03:05
Is the is are they ready for American industrial venture capital?
1:03:10
They go from spending just the entire summer there to spending some time during most of winter and most of winter.
1:03:15
And most of winter, ski season, so ski season, summer, but then they could also spend some fall and springtime there.
1:03:21
It's I mean uh I try and you know, I don't want to overgeneralize and crap on an entire continent.
1:03:27
But um you know I do think I do think there's opportunities there.
1:03:30
But then sometimes you walk through the European startups and and the energy level is is not the same as you would you'd find in the US.
1:03:42
They're not sleeping on the floor of the factory over there.
1:03:43
We have we have billion dollar companies where the teams are sleeping in tents. Yeah. Yeah.
1:03:48
We're about to talk to a billion dollar company where the founder is sleeping out in Abalene, Texas where you got Chase.
1:03:57
Have you been to Abalene yet? I have.
1:03:59
I went with Sam and Oh, yeah.
1:03:59
I'm going to I'll name drop.
1:04:01
I went with Sam and Greg a couple weeks ago and got the tour.
1:04:06
That was like a big Everyone in Europe was asking me.
1:04:09
They're like, "Are we hosed on AI?"
1:04:10
And I was like, "Wow, I just went to Abalene and each one of those buildings cost about $50 billion and they have a lot of them and you guys have precisely none of them."
1:04:27
So I mean it it was kind of like a harsh I I gave a talk in Poland as part of this trip and people were not aware of the scale of like the investment even though wasn't MGX setting up a data center in France. Yeah. Yeah.
1:04:39
I mean the the overall Stargate project is available to other countries and and those deals are being negotiated right now because obviously Nvidia wants to sell chips and you know Cruso will want to sell energy and there's a lot of different companies in the Stargate supply chain that want to be a part of that even if it's in another country.
1:04:56
So, but from what I like from what I was gleaning from chatting with Sam on that trip, it sounds like France is the only country that's kind of ready to to pony up at the we would like to participate level. Interesting.
1:05:09
Is it going to be like luxury AI LVMH GPT like branded tokens?
1:05:16
Yeah, LV artistal tokens.
1:05:20
That is what they do best.
1:05:23
Anyway, this has been fantastic. Thanks so much.
1:05:25
We'll talk to make it a regular thing. Thank you, boys. Thank you. I would love that.
1:05:27
Yeah, I I'll see you this weekend. All right. Thanks, guys. Bye. Cheers.
1:05:31
Up next, we have Chase Lock Miller from Cruso Energy coming in the studio.
1:05:36
Welcome to the stream, Chase. How are you doing? Welcome.
1:05:40
Where are you in the world?
1:05:40
Are you in Abalene, Texas? Nine. It might be. I don't know.
1:05:45
It might be in San Francisco. Oh, we don't have him. Oh, okay. He is not here yet.
1:05:48
I thought we had him, but we can do some timeline. What do we got? Oh, this is interesting.
1:05:55
Uh there's a shakeup at AI in uh in in terms of who's in charge of AI at Meta.
1:06:01
And I want to go deeper here because we're a a little bit of an AI day is coming together uh in uh on Thursday.
1:06:05
We have OpenAI and Anthropic on board.
1:06:10
We need someone from Meta.
1:06:13
So this is a uh little call out.
1:06:15
If you work in AI at Meta, let us know.
1:06:17
We'd love to have you on the show and duke it out with the rest of the foundation model company CEOs and and uh research scientists. Lab on lab violence. Lab on lab violence.
1:06:28
Anyway, uh speaking of the man who powers it all, we have Chase Lock Miller from Crusoe Energy in the studio. How you doing? Welcome. Welcome to the stream. We are missing sound. Are are are you muted? Are we muted? What's going on?
1:06:44
Let's check it out and get to the bottom of this. And he builds. We can hear you now. Makes data centers. He does.
1:06:49
He's not a a Zoom expert. Give him a break. What is new with you? How are things going?
1:06:55
Give us the latest on all the news that came out.
1:06:58
I feel like the last two weeks have just been Crusoe wall-to-wall coverage.
1:07:02
Um, but how are things going in your world? Uh, things are good. Things are busy.
1:07:07
Uh turns out uh you know AI needs a lot of power and uh all these chips need a lot of data center capacity.
1:07:15
So um what we announced last week was uh you know the completion of funding for um the expansion of uh our our facility in Abalene, Texas.
1:07:24
Um that's going to consume a total of 1.
1:07:27
2 gawatts of of total power capacity.
1:07:29
Uh and and that funding uh we we did in partnership with uh Blue Owl Capital.
1:07:38
Um, so the total funding is is about 15 billion in in total capacity uh to build out. That's amazing. We love to see it.
1:07:46
What how how is it different working with uh an investor, a financial institution like Blue versus some of the investors that you've worked with on the venture side?
1:07:54
Is it more Excel and less vibes and decks?
1:07:57
Uh is it is it a wildly different underwriting scenario?
1:08:00
like, you know, we're we're more familiar with the venture style where it's usually just a a handshake and a term sheet on the back of a napkin, a prayer. Yeah.
1:08:07
I feel like I left the the vibes investors, you know, behind, you know, a long time ago.
1:08:15
That was more like kind of the earlier stage stuff, but uh so, you know, certainly certainly different.
1:08:22
And I think with Blue, they've been a great partner, you know, one of the leading uh real estate uh you know, private equity uh practices in in the world.
1:08:30
Um so very sophisticated.
1:08:30
I mean, they've been super supportive and helpful across getting uh the entire deal structured and um and and financed.
1:08:39
Uh so and then obviously very very deep deep uh pocketed uh capital providers to really help us you know make these projects happen at really significant scale.
1:08:48
Um, now what's different is like this is not an investment in Cruso, you know, equity, right?
1:08:54
This is a this is a partnership with Blue Owl for this specific project. Got it.
1:08:59
And just, you know, Cruso has a business model that is uh, you know, not asset light is very capex intensive. Yeah.
1:09:06
Uh, which requires, you know, uh, being able to tap into those very large pools of capital uh, to basically make these large scale AI factories happen.
1:09:14
So what so so uh really break down the the anatomy of like how these deals work.
1:09:19
Is it like there's a new LLC or a new CC corp that's that that's created and then is there something that looks like a mortgage with like a 30-year payback period interest only period and then I imagine that this facility is going to make no money for a few years while you're building it out but then it'll start making a bunch of money and so is there some sort of repayment schedule that's responsive to that dynamic? Yeah.
1:09:43
So um the breakdown of the structuring is uh so this also came out there was a uh you know the exact number is yeah I'm sure you can't share but uh the uh there is uh construction financing that's being provided by JP Morgan um so on the expansion it's a little over 7 billion um that's provided by JP Morgan and then a number of other banks and and and capital providers in the syndicate including Bank of America um and a handful of others.
1:10:13
Uh but uh the the way it works, I mean it is uh it is a a propco right so it's a property company that basically you know it's an LLC that ends up owning the um the individual uh you know uh campus or theualual buildings.
1:10:30
Um all of those buildings have an affiliated lease with them um with with with our customer um which is a long-term lease agreement.
1:10:38
uh you know being able to partner with a large scale investment grade customer is really what helps unlock a lot of the capital here.
1:10:47
capital here. um you know when when people are talking about these very very large quantum of capital credit is like the magic unlocked right so so so when you have big long-term offtake agreements with high credit quality customers um that's where you can really unlock these larger pools of capital and
1:11:05
you know we can put $15 billion to work uh in a uh in a positive capacity and then so uh so that's happening over there how do you make money then sure we we make money as uh both a a project developer um as well as uh we we are a partner with Blue All and the ownership of the entity. Makes sense. Our Makes sense.
1:11:24
Our customer, you know, it's it's it's basically like we're the landlord, right?
1:11:30
We have a customer that is paying us a a monthly rent for uh 15 years and, you know, we make money uh that's in excess of, you know, sort of our debt service obligation. Yeah.
1:11:40
Then talk to me about the energy side.
1:11:42
That's kind of the bread and butter like the history of Crusoe as I understand it.
1:11:45
Uh how important is it to find um unique kind of combinations of resources to provide energy to these largecale data center projects?
1:11:57
Um what's happening in Abalene?
1:11:59
What's unique about Abene?
1:11:59
And then uh and then I want to dive into a little bit more about the life cycle of that energy production plan. Yeah.
1:12:05
that energy production plan. Yeah. So uh so much of the bottleneck of scaling AI has boiled down to just lack of energy or lack of access to energy and uh you know Crusoe from its founding seven years ago has always taken this energy first approach to building computing infrastructure and instead of thinking about you know how do I build the next data center in Northern Virginia we've always kind of thought about like where
1:12:31
can we access lowcost you know clean as much as we can uh and abundant energy uh to to power computing infrastructure and uh sort of the revolution you see unfolding with with AI and sort of this this complete transformation of the
1:12:46
digital infrastructure landscape um is uh is is pretty mind-boggling when you when you really think about it because Northern Virginia is sort of like the center of the world for data centers. Everybody's like okay the Northern
1:12:59
Everybody's like okay the Northern Virginia corridor that's sort of where the internet is happening that's probably where like you know this Zoom conference is being like hosted. Yeah.
1:13:07
Uh, you know, just so much of the internet happens in Northern Virginia. AWS US East. We know and love it. Yeah. Exactly.
1:13:13
Everybody backbone of of our industry. Yeah. Totally. Exactly.
1:13:15
So, all of the data center capacity we've ever built in Northern Virginia Yeah.
1:13:20
is about four and a half gigawatts. Wow.
1:13:26
What we're doing in Abalene, Texas is 1. 2 gigawatts. Wow.
1:13:27
And you know, we're looking at trying to do more. Yeah. We're one company.
1:13:32
This is for one customer.
1:13:34
We're looking at other sites that are 5 g, right?
1:13:36
So, you're talking about building a whole Northern Virginia that's been built over the last three decades. Yeah.
1:13:41
One facility for one customer, right?
1:13:44
Uh, you know, there's just fundamentally not enough power there in Northern Virginia to make that happen.
1:13:50
And so, you know, what's happening with AI is like you're seeing everybody start to take an energy first approach to uh developing this infrastructure.
1:13:58
And that's really what led us to Abalene, right?
1:14:00
Abene is a market where um you know there's an abundance of energy.
1:14:04
A lot of uh wind particularly uh and solar had been built on the back of production tax credit incentives.
1:14:11
And their problem was actually they didn't have enough demand for energy, right?
1:14:15
They would frequently get curtailed, which means like they would have to sell power at a negative price.
1:14:21
So they're shutting down their wind farm.
1:14:23
Um or pricing would go negative uh and they would sell at a negative price.
1:14:28
Um, so their issue was actually just not enough demand for power.
1:14:32
So it was a it was a good natural fit between uh AI uh factories and uh uh you know lowcost clean abundant energy.
1:14:41
Um you know it's a it's a pretty awesome you know setup.
1:14:43
We're going to account for about I think the number is about 30% of the total tax revenue for Abalene just like we're like project that's amazing.
1:14:52
Can you give me like kind of an energy 101 on on energy production in Northern Virginia between uh I'm sure natural gas is in there, there's solar, there's wind.
1:15:02
I don't know if there's any nuclear.
1:15:04
Are we still using coal at all?
1:15:06
Like I really have no idea. Yeah. Yeah.
1:15:08
What about like crude oil or fuel or like just gasoline?
1:15:11
Does that power AWS at all?
1:15:13
Like I'm just curious about that mix. Yeah.
1:15:15
No, there is actually uh you know, especially during moments of peak demand.
1:15:20
If you if you have peak demand at night, diesel generators, extremely cold night, there's obviously no solar. Sure.
1:15:26
And you know, people are, you know, oftentimes just having to use uh oil.
1:15:31
Um, you know, burn oil to produce power.
1:15:34
Uh, which is like not a good uh, you know, uh, not a good uh, uh, yeah, it's just it's like the dirtiest possible option here.
1:15:42
Like we could be a lot cleaner. It's expensive. And it's expensive. Yeah.
1:15:46
Um and then contrast that with Abalene.
1:15:49
What's the energy mix look like there?
1:15:51
Is it similar or is there something different? You mentioned wind.
1:15:55
Is there more wind in Abalene than Yeah, I mean Abene is one of the windiest places in the United States.
1:16:02
Sort of this corridor that just gets a tremendous amount of wind and that's why a lot of uh renewable energy developers built there. Sure.
1:16:07
Uh that you know I I think it's actually important to understand this production tax credit.
1:16:12
Um so the way this works is that uh the independent power producers that build these renewable energy uh facilities um they get paid a production tax credit for producing and selling a kilowatt hour of clean power. Mhm.
1:16:25
Now they get paid that regardless of who they sell it to and at what price.
1:16:30
And so that has led to these consequences where um you know you'll often see power prices go negative because their actual realized price is you know after you factor in the production tax credit subsidy they're getting is positive but it's like kind of they're having to pay someone to take that kilowatt hour and then they go collect that that that subsidy through the production tax credits. Mhm.
1:16:54
credits. Mhm. Um so now the issue becomes those production tax credits only exist for 10 years and so at the end of the 10 years you still have this working wind farm and you're like okay power prices go negative now I have to curtail and I have to shut off which means I could be producing power but I'm not because there's literally no
1:17:13
marginal demand for the power and you know I think this is where you know having this alignment of you know markets where you can produce power in a very cost-effective capacity um and you can actually build an AI data center there uh to soak up that energy uh is a very good alignment that you know Cruso's tried to uh facilitate. Can
1:17:32
Can you talk a little bit about the history of the company?
1:17:35
I know that at one point there was uh some some crypto mining with gas flaring stuff going on and then the transition from that into the current AI boom.
1:17:44
Was that a major emotional roller coaster or did it kind of overlap in a perfect way where it was just like all growth? Um yeah totally.
1:17:53
So uh you know we started the company one of the first applications of energy was uh uh was was as you as you talked about was basically capturing waste methane from oil production that would otherwise be flared. Okay.
1:18:09
And then utilizing that to power initially Bitcoin mining data centers but you know we also powered uh our early versions of our AI data centers. Sure.
1:18:17
Like smaller training runs, right? Yep.
1:18:18
Um and this was like pre-hat GPT.
1:18:21
This was like, you know, we were working with like, you know, the the MIT department of physics, uh, doing, uh, you know, early simulations of the big bang, you know, and, uh, you know, CCL department at MIT, like, you know, early early work in our AI cloud development, but ultimately, you know, when I started the company, I really wanted to build an AI cloud platform that was like uh, so a lot of people are like, wow, this was a great pivot.
1:18:47
And I always tell them like, no, this was like the plan from day one, believe it or not. Sure.
1:18:52
Um and uh you know but I always felt like energy was the thing that tied together all computing infrastructure and any computing uh application when really scaled out energy does become the bottleneck.
1:19:02
Uh and I had seen that and experienced that in these proofof work blockchains like like Bitcoin and Ethereum.
1:19:08
Um but uh and I felt like if if AO is going to scale you know energy would be a massive component in the overall operating cost of uh operating intelligence systems at scale.
1:19:19
And uh so uh we did a lot of early investment in in terms of like making the platform uh you know work with with Crucial Cloud uh and and trying to you know figure out what what we wanted to build and for who.
1:19:33
Um and then you know we kind of you know with the with the launch of chat GPT it really sort of catalyzed just massive investment and attention to you know purpose-built GPU infrastructure and we had done that from the ground up all the way from energy data centers um as well as uh uh managed infrastructure as a service um at the software layer.
1:19:53
Did you lose any sleep over the deepseek uh news or were you Jeban's paradox pill from day one and you knew that it was just gonna keep going? Um, yeah.
1:20:06
I I I think just like the way that got spun up in the media was like so uh mystery, very pro-China media spin like very early on.
1:20:16
Like I think anybody everybody was like wait like there's no chance this was like a couple of hobbyists that had like a couple of GPUs in their garage and they train this model off of like that's just like not what happened.
1:20:29
They did this training run with scraps.
1:20:31
Scraps and a powered with a bicycle. They Yeah. Yeah, totally.
1:20:36
Um, yeah, it was a couple couple guys with pens and paper just uh but I guess I I I guess like the bigger question is uh as it does feel like we're somewhat shifting from a pre-training to an RL environment.
1:20:48
The scaling laws are holding in the macro, but it seems like there's a series of scurves in terms of the different training and improvement paradigms that lead to just better products.
1:20:59
And so yes, we can we can do another 10x increase in pre-training, but maybe we hit a data wall or there's some problems there.
1:21:09
Um, what are you seeing in terms of tradeoffs for demand on the data center side as we go through these paradigm shifts in terms of what's important to create a really really performant AI product?
1:21:24
Is it just we're shifting from training to inference and that doesn't even affect you? Does it affect you?
1:21:30
Do you need a different different buildout for for a large training run versus just mass inference of of complex models consistently forever all the time because demand's so high but it's smaller models all over the place like it does any of that affect the way we build data centers?
1:21:47
I think it does affect some of the ways that we build data centers.
1:21:49
Um but to the question of slowing demand I mean the conversations that I'm involved in there's like if anything we're seeing demand accelerate um and for you know bigger you know bigger you know larger scale clusters uh and uh and and just the overall demand is is increasing quite a bit for for for inference as well.
1:22:13
inference as well. Um, and I think I think you see kind of this uh uh transition where you know folks will use a very large AI factory for a training run that you know gets some state-of-the-art model um you know that infrastructure is still useful for you
1:22:30
know a long period of time uh to serve uh you know both inference workloads as well as you know any of these like post-training uh test time compute scaling you know chain of thought reasoning models um that you know are are basically uh you know taking inference
1:22:47
queries and then thinking about them and sort of playing out a whole bunch of different scenarios um and then coming up with better smarter uh more intelligent answers but uh you know and I think that's like the crux of you know the infrastructure it's like what we're building are these AI factories right so
1:23:03
they're factories that manufacture intelligence they're factories that manufacture intelligent outcomes that um you know are are prompted by you know input from users and um I don't see any near-term shortage of demand for you know more intelligence. uh what are you bullish or bearish on
1:23:24
uh what are you bullish or bearish on sort of upstart or SMB players that that want to build AI factories or data centers that uh you know see the broader opportunity and then are maybe you know putting together sometimes sophisticated teams sometimes less sophisticated teams
1:23:41
but able to pull together capital and you know want to bring data centers online and just assume there's going to be demand waiting there or just assume that they can actually you know build something that's state state of the art, pretty bearish. Uh, uh, we've seen a
1:23:55
Uh, uh, we've seen a massive influx of, you know, we call them like two guys in a pickup truck where, you know, I got my cousin Lenny who has like this plot of land, you know, out by his ranch and, you know, there's a power line that goes through it and he knows someone that works at the power, you know, just like just put up a barn and throw some racks in there. We're in business. Hit up a hype.
1:24:19
Hey, hey, I'm a Google shareholder.
1:24:21
I'm going to just call up uh Sundar Sundar. I got a contract. No problem.
1:24:26
Yeah, I got I got some 3090s in here.
1:24:28
I got I got a I got a couple 1080 Ti right over there. Couple propane tanks.
1:24:34
Yeah, propane barbecuing GPUs.
1:24:36
propane barbecuing GPUs. Yeah, I I think the I think the thing is that these these projects uh you know they're they're so big um that like the capital investment to you know make them happen is so massive and you're seeing people speculatively build smaller scale stuff but the you know the really big stuff that's you know whatever couple hundred megawws or you
1:25:02
know gigawatt plus you really need credit to make it happen like you know I said it before but credit is the unlock to all of this infrastructure getting built and we have companies with the greatest balance sheets in the history of business that are going allin on this uh you know technological uh paradigm shift underway and with that you can unlock a lot of you know infrastructure capital to make all of this happen. Um, but you know, if
1:25:30
Um, but you know, if you're going to speculatively, you know, spend, you know, 20 million bucks kind of trying to build an AI factory, you know, it's like shooting a BB gun at a grizzly bear, you know, it's like not uh, you know, you're not you're not you're not Well, yeah.
1:25:47
at the at the $20 million scale, you can get a a group of smart people that can make a deck and then investors are going to see that and be like, I want to make money on this AI thing and be like, well, yeah, we'll throw 20, 30, 40.
1:26:02
Chase has the best animal-based metaphors because I remember we were at that nuclear conference and you said that the demand for energy is so high that companies would burn whale oil if they could.
1:26:12
And for some reason, you keep coming back these but the BB gun at the grizzly bear is great.
1:26:15
where um so I'm sure you work with hundreds of different vendors for different you know components parts etc.
1:26:22
components parts etc. uh where do you think there is major you know sort of supply chain risk or shortages like where would you like to see I was about to ask this we we we heard something that like a large portion of the transformer supply chain
1:26:38
not the algorithm to the transformer the physical infrastructure comes from China and maybe there's a risk to the supply chain with the trade war there uh would love to know what the key inputs are outside of we all know power we all know Nvidia GPUs But what else could we be constrained on? Whether it's cement or
1:26:56
Whether it's cement or transformers or copper, I don't even know. Lay it out for us. Yeah.
1:27:01
I mean, you know, the bottlenecks move around.
1:27:06
Uh, you know, the bottlenecks for AI infrastructure builds kind of move around.
1:27:09
You know, you sort of had this moment of uh infinite demand for H100s when they first launched.
1:27:14
And, you know, I think Elon famously said that, you know, it was way easier to acquire illegal drugs than get an H100.
1:27:19
illegal drugs than get an H100. and uh uh so so so you know it's it's rapidly shifted into energy and data center capacity and and what does it mean to actually build that stuff out um you know high voltage transformers are definitely like a big bottleneck um a lot of that capacity does get built in
1:27:37
China um you know there it's a diverse enough supply chain that you know I'm not that worried about you know trade war kind of impacting uh high voltage transformers but you know they are kind of long lead time assets um uh you know outside of you know there's a whole stack in the in the in the in the transformer side too. So like you
1:27:57
So like you have the medium voltage transformers uh switch gear can be like a a major long lead time item um that Cruso actually started manufacturing in-house.
1:28:06
Um so we have factories in Tulsa, Oklahoma in in uh right outside of Denver, Colorado.
1:28:13
When you say switch gear, is that like network switches like Ethernet routing or something else? Oh, sorry.
1:28:16
Um it's it's electrical switch gear.
1:28:17
Um, this is basically like your, you know, electrical room that has all of the uh uh, you know, breakers uh that that feed into uh the uh the uh the actual data halls that are powering the Yeah, it's like a power strip.
1:28:32
You plug it in the wall, you get six outlets out of the back kind of like that, but like the big version of that. Is that right?
1:28:36
It's kind of like, you know, it's kind of like your uh, you know, it's it's kind of like your uh, your breaker box in in your house. like you trip a breaker. Yep.
1:28:46
You got to go down and you got to go flip the switch.
1:28:50
It's like that at, you know, a gigawatt scale data center something. Yeah, that makes sense.
1:28:55
Um uh but switch gear is definitely a bottleneck. Sure.
1:28:59
Chillers is another big thing.
1:28:59
Uh you know, I think an interesting trend in in data centers right now is uh with the with the uh introduction of the GB200, the new Nvidia uh uh chip.
1:29:09
Um basically you're seeing this massive transition to uh liquid cooled uh computing at significant scale.
1:29:20
Uh so you know a lot of the you know government labs and high performance computing communities have been experimenting with things like immersion cooling you know singlephase and two-phase as well as you know water cooling uh DLC uh for decades.
1:29:33
Uh but no one's ever done it at the scale that's unfolding right now.
1:29:38
unfolding right now. Uh and the reason it's happening is because you just have so much energy density uh so much heat being produced by these new NVIDIA chips um as we move on to you know uh more advanced architectures uh that there's
1:29:52
you know simply just not enough uh heat capacity basically move that heat off the chip uh from a traditional uh uh aluminum heat sink um or something that's like so big that you know it can you talk about the life cycle of water in some of these these AI factories. We
1:30:08
We had somebody on the show, I don't remember their name, but I do remember that they said, "We don't have enough water in Abalene to to run, you know, these data centers, and that didn't quite feel correct."
1:30:21
So, I'm not going to call them out. Is that a bottleneck? No, it's not.
1:30:25
Um, it depends on how you design it.
1:30:28
Um so we've tried to um you know I think uh water can be a very sensitive topic depending on the communities that you know you're engaged in and Cruso's always tried to be a you know phenomenal partner to you know the local communities that that that we're working with.
1:30:44
Um so you know the way we've designed our AI factories is uh what's called a closed loop architecture. Yeah.
1:30:51
So that means basically you have cold water that flows into the rack and it flows over the chips over over this you know through this through this copper pipe and you have this heat exchange uh between uh the silicon that goes through through the copper and then to the water and then hot water sort of ex exhausted from from the rack.
1:31:10
That hot water then goes out to uh a heat exchanger.
1:31:13
Um that's a that's a chiller that's outside.
1:31:16
And it's basically just you can kind of think of it as like a a massive maze of of uh copper uh pipe and then you like blow air over it.
1:31:26
You try to blow cold air over it and then the heat basically gets exhausted um out of the water and then that and then you basically have cold water from that that then feeds right back into the system. Mhm. Yeah.
1:31:36
I think people 1 million gallons of water per building.
1:31:40
We only fill it one time, right?
1:31:43
It's not like we're using a million gallons. Yeah.
1:31:44
And this this is what this is what the media has implied is that like every time you make a cute studio Giblly image, you're like dumping a gallon of water, you know?
1:31:52
It's like, yeah, a gallon of water might flow over the chip while you're doing that, but then it flows over the next one and the next one.
1:31:59
This is recycling which is not at that kind of scale that are open loop where basically you have a fresh water supply and that is you know you are consuming a little water in that that scenario.
1:32:10
Um but in in in our case you know we've designed it with a closed loop architecture that you know is like a you fill it one time then you're done. What's for closed loops? I love closed loops.
1:32:21
Um last last question I have.
1:32:21
How do you evaluate your pipeline?
1:32:24
I'm sure you're a very popular guy getting, you know, phone calls and emails from all over the world, but you're building physical infrastructure.
1:32:31
It's not like you can just copy and paste, you know, uh what you're doing in Abalene or or some other areas, you know, a million times.
1:32:41
So, I'm sure you have to be pretty.
1:32:45
Yeah, I mean, we're trying uh you know, we are we have a couple other projects that are underway um that, you know, uh hopefully we'll be able to talk about you know, more soon, but you know, uh similar scale or bigger uh is kind of like, you know, what we're seeing.
1:32:58
Uh so, you know, a lot of demand, you know, unfolding, you know, within the ecosystem.
1:33:03
And, uh, you know, I think we we try to be thoughtful about our partners.
1:33:07
I mean we we really ultimately want the space to be successful.
1:33:09
You know I I view AI as a generational opportunity to uh transform you know human prosperity around the world and uh we just want to help make that happen and you know we we don't think any one company's going to do it alone.
1:33:23
We want to help support the entire industry in terms of uh making this technology successful scaled and and and really rolled out to the masses. Makes total sense. Fantastic.
1:33:33
I have a ton more questions, but we'll have to have you back on because this was a fantastic conversation.
1:33:38
We'll talk to you soon, Chase.
1:33:39
Thanks so much for stopping by. Cheers. Great to have you. Take care.
1:33:43
Before our next guest, let me tell you about RAMP. Ramp is uh ramp. com. Time is money. Say both.
1:33:47
Easy to use corporate cards. Almost in tears earlier.
1:33:52
Tears of joy using ramp travel.
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1:33:55
I know this is such a shill that No, it's not a shill. It was off air.
1:33:59
It's an incredible It's an incredible John was saying it is a beautiful thing that I can get IMO gold medalists to make me my travel business travel app. Yes.
1:34:07
Any I mean when we talk about software that is lacking you just the most obvious example is like the airline app, right?
1:34:18
The airline app is notoriously buggy and you're not logged in.
1:34:21
Ramp travel saves your all of your information immediately.
1:34:27
It shows you all the flights across everything.
1:34:29
You just click one button, it just books it and then it's just boom, you're just booked. No, no chasing receipts.
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No chasing receipts because it happens inside of ramp which is amazing.
1:34:36
But even aside from the expensing, even if I had to do something else on the expensing side, just the experience of actually booking on is so much easier.
1:34:44
It's like I mean I wish I wish we had it on camera because it was a really special model. Leaked leaked leaked. I'll be right back.
1:34:53
Anyway, um our next guest is coming in the studio.
1:34:56
Um but first, let me also tell you about Figma.
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1:35:06
Um, we have our next guest coming into the studio.
1:35:10
Uh, Factory AI Matan, welcome to the stream. How are you?
1:35:16
Thank you so much for having me. I am good. How are you? I'm great.
1:35:18
Uh, thanks so much for hopping by.
1:35:21
Uh, can you kick us off with a little introduction on yourself and also uh the company and the news?
1:35:25
I know that there's big news coming up. Yeah, absolutely.
1:35:29
So, uh I'm Maton, CEO at Factory.
1:35:31
Uh here to share a bit about our latest launch. Uh we released Droids.
1:35:36
Um droids are not just your regular everyday coding agent.
1:35:40
They are full endtoend software development life cycle agents built for the enterprise.
1:35:49
Big enterprise, not just not just startups.
1:35:50
Um although you know we've uh in the last 24 hours seen a pretty big explosion of usage with um startups which has been really cool.
1:35:57
Um yeah that's uh it's been it's been it's been fun.
1:36:01
Talk to me about the evolution of AI agents and coding agents.
1:36:04
I remember there was a company a couple years ago that was training their own foundation model.
1:36:10
Then the regime seemed to to seem to shift to okay we're not going to do any pre-training on code specifically.
1:36:18
the foundation model companies got that covered, but we will do a bunch of post- training.
1:36:21
Then it became more maybe there's some RL fine-tuning.
1:36:23
Maybe there's just some some reasoning that we're doing.
1:36:26
Now it feels like a lot of the coding agent companies are just kind of like we're we're rappers, but we're still printing money and rappers and unnecessary porative.
1:36:36
It's actually the best way to build this business and it's helping us.
1:36:40
So So where do you fall and and and what is your take on the different I I just got here.
1:36:44
Here I got to compliment Matana. I know. I know. I know. Looking fantastic.
1:36:50
Thank you for when you when you come to the temple.
1:36:52
When you come to the temple, you must be dressed appropriately. Yes. Yes. Thank you. Anyway, but yeah. Uh yeah. No, great question.
1:37:01
I think uh it's been interesting how in the last two years there have been so many trends within uh the kind of like sub sector of uh AI for software development. Y u you're spot on.
1:37:12
on. there was a really big trend especially if you measure it by the capital that was put into it in terms of uh companies coming out there saying that they're going to train models in particular for code or fine-tune models for code there's been I mean there's
1:37:26
been quite a few who raised to the tune of like half a billion dollars to train their own models and I think something that um uh a lot of people were saying at the time or maybe whispering because they didn't want to offend those who put half a billion dollars in was uh code is
1:37:41
a core competency for for the foundation models they will not allow a startup to you know fine-tune their way to having the best models as you see in in general with code it's like alternating between open AI anthropic Google um XAI in terms of the number one spot in code and so I
1:37:58
think it I guess it took a lot more money than it should have but I think people come to the realization that fine-tuning an RL for code specific models will be won by the foundation models um is that just a function of like of Code is the internet. The The internet is code. Code is on the internet.
1:38:15
And so if you train an LLM on the internet, like you're going to learn like like out of the box, if you do a robust largecale training run on web text, you're going to get Reddit answers and you're also going to get a pretty good model that can code.
1:38:30
And so unless OpenAI says, "Let's not train it on code," it just you're going to lose to them because they're putting the most, you know, energy, the the most cycles into training the big model.
1:38:41
And so you just get that as a byproduct.
1:38:43
Yeah, that's that's spot on.
1:38:45
It's actually I would go even further to say that um and I know uh there were papers on this in around like 2022.
1:38:52
around like 2022. I'm sure there have been since but yeah there is a direct correlation between how good a model is at code and how good it is at other general purpose tasks that makes sense so it is just it is like it is you know table stakes for the foundation models
1:39:07
to be the best at like raw coding um so exactly right there yeah so how do you how do you build a business without getting rolled yeah how how have you how have you guys navigated just partnerships with the labs in general where it's you're kind of fremies you know it's like we can work together, but this is a good question. At some point, somebody's
1:39:25
At some point, somebody's going to try to kill the other one. Yeah.
1:39:28
No, this this is this is a really good question and it's very top of mind.
1:39:31
I mean, I think the reality is the closer you are to the zero to one or like the vibe coding or the like less technical your audience and the smaller the company, um that's really where uh the foundation models are like about to sweep it up or or already have.
1:39:47
So it's like you know building apps from scratch, building websites from scratch that's something where the model providers will basically get it for free like plus or minus some deployment details in terms of you remember with with uh prehat GBT there were a bunch of players that would generate copy for you based on the open openai API and they had just rock their revenue rocketed. Yep.
1:40:11
And I remember it was the same sort of like people like the same behavior of like posting screenshots of like the revenue dashboards and then those screenshot shots stopped being shared postg cuz all that revenue you know just went away as as Shakespeare said these violent delights have violent ends.
1:40:27
Um there was a really there was a really great great tweet I saw the other day.
1:40:31
I can't remember who posted it but it was uh you know a lot of people are talking about these like record-breaking like runs to like you know large ARRS. Yep.
1:40:41
Um and what's going to be coming soon is some record-breaking churn for a lot of this like monthly these monthly subscriptions.
1:40:47
Um so that'll certainly be interesting to see.
1:40:49
Um but yeah, so that's the part of the spectrum where uh the foundation models are best poised to tackle is that like 0ero to one um less technical use case.
1:40:59
For us, we've been focused on the enterprise from day one because one, it's really not sexy and so it's not like conducive to like viral demos and all that hype.
1:41:08
But that's where a majority of developers are getting a majority of their pain.
1:41:13
Like dealing with like cobalt, dealing with these like 20-year-old code bases, migrations, refactors.
1:41:19
And in order to handle that well, you can't just like oneot with like a chat GPT or a codeex or a cloud.
1:41:27
You need to have deep integrations with their codebase.
1:41:30
Oftentimes it'll be like multi-reo integrations.
1:41:32
Um you'll need to understand not just where the code is now. Nice.
1:41:37
Love the little thumbs up guy.
1:41:38
uh not just uh not just where the code is now but how it got there, what the best practices of that org are.
1:41:43
Um integrating with tools like Jira, Google Drive, Century, Data Dog.
1:41:49
Um the kind of first principles thinking there is that in order to produce the quality that an engineer who's been at that company for like 10 years would produce, you need to have access to the same information and that information sometimes requires some really ugly integrations.
1:42:03
um you need to uh kind of get the the workflows that are a little bit more specific to these large archaic orbs which is a little bit further a field um from what the foundation models are are are working on right now.
1:42:18
Talk to me about synchronicity asynchronous coding agents versus synchronous uh co-pilots versus autonomous agents.
1:42:26
Where do you think it's going? Do these lines blur?
1:42:28
Are they are they distinct product?
1:42:30
It feels like OpenAI has three coding products now.
1:42:36
I can go to 03 and I don't even ask it to write code and it just does.
1:42:39
Uh I imagine that the amount of code that's being written for non-technical people who don't even know that code would be useful in giving them an answer to a question is just skyrocketing right now uh in that product. Then there's Kodax.
1:42:55
They also have Windsurf now.
1:42:55
And so you can imagine a few different bites of the apple there.
1:43:00
Are is that uh are they are they indexing the market or are these distinct different markets?
1:43:04
Will there be one power law outcome in the coding space in terms of time? Yeah. Yeah. Great question.
1:43:11
I think the the first order like bifurcation that'll happen is between the non-technical audience and technical audience.
1:43:17
So like for nontechnical being able to spin up apps on the fly is like cool and fun and that'll continue to be something that's useful.
1:43:24
They're never going to Yeah. Exactly right.
1:43:26
and they're never going to really be that concerned with like going too deep into the code itself.
1:43:31
They'll just like I want a to-do list app. Make it for me. Okay, great.
1:43:33
Um for the technical user, I think that's where it gets a lot more interesting.
1:43:38
Um basically, and maybe maybe this is just a quick like kind of philosophy that we have at factory.
1:43:45
Every transformation shift has a very clear behavior change associated with it, right?
1:43:49
So like internet had people uh getting most of their information from like TV, newspapers, books to then like going on this console on their desk and like you know getting all their information there.
1:44:00
Um mobile had people you know walking around like heads up to now like you know walking around on Tik Tok subway servers all the time, right?
1:44:07
These are very visceral obvious behavior changes.
1:44:08
And yet everyone talks about AI as it's you know the the largest one to come.
1:44:13
It's going to put these other ones to shame.
1:44:14
And yet if you look at the mostus product, the most used AI products right now, there is no new behavior.
1:44:20
Like chat GPT perplexity, that's just Google with better results.
1:44:24
Um the behav like the silhouette of that behavior doesn't actually look that different.
1:44:28
Similarly with a tool like copilot or cursor, the way that software development is is looking, the behavior is still the same.
1:44:34
You're still in this IDE which was built for the world where humans wrote 100% of their code.
1:44:38
We're quickly going to a world where humans will write 0% of their code and our take is that their behavior will fundamentally have to shift then and not like in some iterative approach where you like iterate your way from an IDE to whatever this new thing is.
1:44:53
But our take is instead you need to build that from the ground up.
1:44:57
And this is kind of a long-winded way of answering your question about the like async versus sync.
1:45:02
The reality is in this future, developers are going to be natively working with agents.
1:45:07
And so they'll need to like dynamically adjust between if they're collaborating with an agent to like look through their codebase and understand how they should plan some new feature and then having a good plan for it and now like firing it off, delegating it to these to these agents.
1:45:23
Um, and at the same time it's going to put more emphasis on the testing because if you just shoot from the hip a ton of agents or in our case droids, um, then you're going to have a ton of code to review before you release it to production because ideally you're going to review it, right?
1:45:35
But that's kind of a depressing world where okay, we don't write any of our code, but now you just need to read like thousands and thousands of lines before you can ship anything.
1:45:43
So if you as the developer have better tests and you know, hey, if it passed these tests, I don't even need to review it because I like expressed all of the constraints that I had through these. So if it passes, great. Let's ship it.
1:45:56
There's kind of this new emphasis on testing um to kind of enable that more delegative workflow.
1:46:02
What's the secret to avoiding churn in the enterprise?
1:46:07
Are you trying to ink multi-year contracts upfront?
1:46:10
Are you just playing the same game as everyone else and getting a bunch of experimental budgets because money is money and you know that seems to be the meta right now.
1:46:21
But I imagine that you're you're you're thinking about this or at least like messaging to the community when a competitor gets to a you know a customer first.
1:46:30
Are you kind of hovering you know with the understanding that like hey you know maybe they're you know if we can really show that we're significantly better we could we could win.
1:46:39
And is there is there like a little bit of a price war here?
1:46:42
Because you imagine that you come into an enterprise and you say, "Hey, this would cost you so much to do with like Accenture and we're going to do this this replatforming of your cobalt application to .
1:46:52
NET or something or Python and uh and and you would spend 10 million on that.
1:46:59
We'll do it for 8 million."
1:47:01
Uh and that's all of a sudden all of that's all of a sudden that's like 90% margin for you instead of like 50% margin.
1:47:05
Um, but then your competitor comes in and says like, "Well, we'll do it for seven million.
1:47:10
We'll we'll do it for six million.
1:47:11
We'll do it for five million."
1:47:12
And so there's there it seems like there's some sort of price war dynamic that might happen.
1:47:15
Walk me through all of that of like winning in the enterprise financially. Yeah. Yeah.
1:47:19
That's that's a great question.
1:47:19
So, first of all, yes, we do year-long contracts just because it's important to have that mutual commitment and in particular because adopting the tools is not enough.
1:47:28
not enough. like there I cannot tell you how many like CIOS and CTO's I've spoken to who have adopted like the hot new AI IDE and then you ask them the question that they don't want to answer which is how many people are actually using it and it ends up being like 10 20% and of
1:47:43
those 10 to 20% a lot of them are just using it like the idees of old totally so they're kind of like adopting these new tools patting themselves on the back being like look CEO we did it we adopted AI job's done give me the big bonus but the reality is is like they're actually not getting any productivity improvements. And so part of why we do
1:47:58
And so part of why we do these longer term uh commitments is because one of our core competencies is not just having the best agents in the game, but also helping them. Look, I love it. I love it. There we go.
1:48:11
Um we're also helping them adopt their behavior patterns.
1:48:13
Um because that's the thing is like you could have a tool that's 50% as good, but if you have twice the adoption, then you're now at parody.
1:48:19
And so I think it's just a lot less sexy because all the like you know great engineers who are coming out of Stanford want to work on spinning GPUs and all that stuff talking about like behavior change in the enterprise that's like yo like you know they don't want to think about that but that is where the ROI is going to come.
1:48:33
where the ROI is going to come. Um and also John to your question like the way we actually get in and do these deals is focusing on those deliverables about this was scoped out to be four months and we did it in two months or one month or two weeks that's ROI that actually
1:48:48
matters to like the seauite when you talk about like we shipped tests 10% faster it's just so like or we 20% more lines of code it's just so amorphous and so not tied to real business outcomes that if we can come in and actually um tie things to things that are shipped per quarter or um you know pulling in dates for certain deliverables. That's
1:49:08
That's where it's just like it's so frictionless because it's not really existent elsewhere in the market.
1:49:15
Uh I was texting with Adam and Ben from Genius, one of your investors, and they said to ask you about a fateful walk that you had with Shawn Maguire. Does that ring a bell? Yes, it does.
1:49:26
Um so this is uh this is in like the founding history of factory basically.
1:49:31
Uh two years ago or two and a half years ago I was doing a PhD in theoretical physics at Berkeley um which is what I was doing prior to factory and uh you just phoned it in.
1:49:43
You want to kind of take the easy path in school or Yeah. Yeah. Exactly. Yeah.
1:49:48
you know, just uh some fun string theory, which to be fair, it is really fun uh and beautiful, but uh decided it wasn't a path for me in particular because uh you know, to to be a good physicist, you kind of need to thrive in isolation, just like in your room alone like reading papers.
1:50:02
Um did that for like 10 years, was really stubborn.
1:50:05
It's kind of a long story, but ended up uh reaching out to this Sequoia partner, Shawn Magcguire, because he also used to be a string theorist and uh he ended up, you know, going into entrepreneurship, sold a company for a billion dollars, joined Sequoia, saw like a random podcast with him, and I was just like, I've never seen like another physicist who has somewhat social skills, like let me hit him up and get some get some life advice from him.
1:50:29
Ended up going on a walk together. on this walk.
1:50:31
He said, "Matan, you need to drop out of your PhD and you should either join one of my portfolio companies uh and just like work on glue. Not a thumbs up. Let's go."
1:50:41
Or you should join you should join X because Elon just took over and you'd have to be a badass to voluntarily go there.
1:50:48
Or you should start a company.
1:50:48
Um and so that was uh eight days later dropped out of the PhD and started factory.
1:50:55
So Well, congratulations. Very cool.
1:50:57
And uh yeah, thanks for stopping by. This was fantastic.
1:50:59
And uh thank you for giving agents a cooler name. I love droids.
1:51:03
We got to get ourselves some droids.
1:51:05
We got to get you guys some droids. Thank you guys. Great jam. Have a good one, Matan.
1:51:09
Uh speaking of automation, let's tell you about Vanta.
1:51:12
Automate compliance, manage risk, prove trust continuously.
1:51:14
Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation whether you're pursuing your first framework or managing a complex program.
1:51:25
And next up, we have Johnny from Muan Space.
1:51:27
We're talking about data centers with Chase Lock Miller in Abalene, Texas.
1:51:31
Now we're going to space and we're putting the data centers in space.
1:51:35
Very excited to talk to Johnny.
1:51:37
So, welcome to the studio.
1:51:37
Johnny, break it down for us.
1:51:39
And I'm going to let Jordy do the intro on this.
1:51:41
And if you're on your phone, would you mind rotating it 90 degrees so it's widescreen? Thank you. There we go. You mind kicking us off? What's going on? Great to have you.
1:51:51
Where are you calling in from?
1:51:52
Uh, randomly, I'm actually down in y'all's neck of the woods in Long Beach.
1:51:57
I'm uh hanging out with a friend at BAS today. Nice. Nice.
1:51:58
Um so yeah, why don't why don't you give a quick intro, background on the company, yourself, all that good stuff. Yeah, sure.
1:52:07
So, Muon Space, we're a 150 person startup in the Bay Area.
1:52:10
Uh we're building a platform to deploy large numbers of satellites and constellation format uh for a lot of different mission types.
1:52:19
Um we were founded in 2021.
1:52:21
Um it's been a pretty pretty big rocket ship ride so far. It's been really fun.
1:52:26
Um, my background going back a ways, I mean, I I' I've been in space for, you know, well, longer than I care to admit, including I was an intern back at SpaceX in the very early days, like 2003 and four.
1:52:36
I was part of, uh, I was the chief engineer at Skybox Imaging, which was the kind of first venturebacked satellite company, kind of space before space got cool. Um, very cool.
1:52:48
And so, uh, you know, taken a lot of the lessons learned and kind of things I've seen, uh, from those experiences to the new company. Awesome.
1:52:55
and then break down break down the new company. Yeah.
1:52:57
So, I mean, I think the way to think about this is um you know, a lot's changed in the last decade of space.
1:53:03
Um, traditionally, if you wanted to go do something in space, it required a lot of sophistication.
1:53:06
Um, you had to have it literally a team of rocket scientists.
1:53:10
Um, you obvious um a lot of deep expertise in everything from, you know, ground stations to to launch to avionics software.
1:53:17
launch to avionics software. um you know barriers to entry have come down in a lot of ways but a lot of that complexity remains and our kind of goal is to really um abstract a lot of that away from a customer that wants to do something in space and doesn't want to have to go build that full vertical technology stack for every new use case that comes up and I think that was a a
1:53:37
big lesson we took out of Skybox is we kind of built two companies under one roof satellite company and a data company and that the future should really about be about you know a data company being able to go build a data business without having to deal with all the complexity on the the space, the ground, the operations, the hardware, uh the integration side and that's really what we're trying to solve. I want to
1:53:56
I want to know about uh the potential of data centers in space.
1:54:01
It sounds like a crazy idea.
1:54:04
I know some folks are working on it.
1:54:06
Just kind of like what's your high level take of the progress?
1:54:08
We've been tracking, you know, uh dollar per kilo to orbit.
1:54:14
It's been falling, but recently there's been setbacks and different programs and there's more competition and there's a lot of thing different dynamics going on.
1:54:22
Um, what do you think, uh, the key milestones are to get us to a future where we're really doing, you know, mass manufacturing, big, you know, mega scale projects in space. Yeah.
1:54:35
Well, let me let me start off.
1:54:38
I mean, I think you guys are hitting on the right metric, right?
1:54:38
It's like the hardest part of this is what does it cost to get a kilogram in space?
1:54:43
because everything else kind of deres from that ultimately.
1:54:45
If you want a certain amount of power, if you want to be able to put um an aperture in space to do communications, whatever it is, like that's really driven by what it costs to get it there in the first place.
1:54:55
And I think it's important context to kind of see how far we've come down that path.
1:54:59
I mean, largely driven by SpaceX over the last decade.
1:55:02
And you know, I I like to tell the story of, you know, about 15 years ago at Skybox when we were trying to launch these small satellites, we were literally going to southeast Russia and launching satellites on converted Russian ICBMs.
1:55:13
I mean, they were they were popping out of the ground and putting satellites into space.
1:55:16
And that was the only way for like a Silicon Valley venture-fed startup to go put something in space. Wait, wait, hold on.
1:55:23
Did I hear you correctly?
1:55:23
Like the the the ICBM actually launches from one of those missile silos in the ground and makes it to orbit.
1:55:29
pops out of the ground and goes to orbit.
1:55:31
I mean, there's videos on YouTube that the you search on YouTube, you can see this and it's it's crazy.
1:55:37
I mean, you know, and that that's that's what it took before SpaceX kind of like revolutionized the launch business.
1:55:43
And even at that, we were spending something like 10 times as much per satellite or per kilo is what we can go buy on a transporter launch today.
1:55:52
So there's been at least one and arguably two orders of magnitude improvement in the last call it decade on kind of what it means to launch things to orbit.
1:56:00
I think if you imagine that happening again another order of magnitude um it you know again it's going to dramatically change the way you think about feasibility of some of these things like putting very very large power hungry things in orbit.
1:56:14
Um, we know how to do the solar, we know how to do the structures, we know how to get, um, you know, we know how to make electronics work in the radiation environment, which is always a concern, and do it reliably.
1:56:24
Um, and so really, ultimately, I think it's going to come down to unit cost.
1:56:28
Um, and and what does it actually take to do that?
1:56:30
The great thing about space is you have virtually limitless power.
1:56:34
The sun, you know, you can you can go into orbits where you're in the sun all the time.
1:56:37
So, it's not like solar on Earth where you're going in and out of eclipse or in and out of night.
1:56:42
Like, you can be on all the time.
1:56:43
And then you have this cosmic background of three Kelvin cold sync that you can go dissipate all the thermal energy you need from running your electronics and stuff.
1:56:52
So it's actually in a lot of ways like sort of an ideal environment to do this if you can actually get there.
1:56:56
Um so I yeah that's the kind of way I'd think about it. It's interesting.
1:56:59
Um what it feels like SpaceX has commoditized launch and is kind of the power law winner there.
1:57:06
Obviously there's other companies that are competing but uh they've they've standardized uh launch.
1:57:10
Uh they've also standardized Starlink and then we talked to the Endurosat founder about standardizing a satellite bus platform and kind of getting to the mass manufacturing less bespoke, less customized, less handbuilt pieces.
1:57:23
What else are you seeing as kind of critical pieces of the space supply chain that need to be standardized or you might be trying to standardize?
1:57:34
Where wh where are the pieces in the supply chain to do the things that you want to do?
1:57:40
um where it's still like, "Oh, that has a long lead time or that's handbuilt or that's way too customized.
1:57:46
I get what I want, but it's too exquisite system. It's too expensive."
1:57:50
Talk to me about the supply chain. Yeah.
1:57:51
And I'm I'm actually going to give you the answer you asked for and then an answer I want to answer. So, I'll do both. Yeah.
1:57:56
Um on the supply chain side, you know, I think there's a lot of progress been made.
1:58:00
Like our satellites look very much like, you know, um especially things like the electronics look very much like what goes into an EV right now.
1:58:07
the batteries, a lot of the electronics, a lot of the individual kind of semiconductor parts heav he heavily leveraging other commercial industries.
1:58:16
So it doesn't look like a traditional space supply chain.
1:58:17
I think that problem more and more is is is solved.
1:58:19
Um I think Enduroat obviously is is doing very similar things in that way.
1:58:24
Um some of the places where we really see that that that has not yet happened one is in more on the payload side.
1:58:30
So things where like you know you think about the spacecraft bus and durosats building buses.
1:58:35
Um you know I do think there's a path to that becoming very standardized but every mission you put in space has a bus and then it has a moneymaker.
1:58:42
It has something that's actually doing what you need it to do whether it's communications or remote sensing or beaming power if you're trying to beam power.
1:58:50
If you're putting a data center in space it's got a payload of of compute or whatever.
1:58:53
And what we see in most of the kind of traditional space missions is that that now has become kind of the hardware bottleneck.
1:58:59
It's like how do you actually get the payload you need to solve your ultimate business problem um built in uh in quantities large enough to deploy in a constellation at cost low enough that you can do it.
1:59:09
And I think there's a lot of movement in the right direction in that way, but it's uh we're not there yet.
1:59:15
And and the the the other question that I kind of I'll I'll answer that you didn't ask is I think the other big bottleneck outside of hardware is still software.
1:59:23
So like you know we we spent there's a lot of talk right now on hardware supply chains across a lot of industries including space.
1:59:28
Um I think there's paths to address that.
1:59:31
What is still very true in space is that these are very complex autonomous robots with global networks that are having to communicate um in in inside communication outside of communication and software is really the glue that holds all that together.
1:59:46
And in a lot of ways the integr integration of all these things and the software integration the hardware integration is still the hardest part.
1:59:52
It's really making these large complex systems work as a whole.
1:59:56
And I really think that's a place that there's not being enough emphasis put in the industry right now.
2:00:00
It's something that we're really trying very hard to solve with kind of the the kind of core hardware software stack that we're building.
2:00:06
So that instead of you know for every new use case you're trying to solve in space, you're going and from the beginning having to do this crappy new integration of a bunch of parts of hardware and software that don't actually aren't actually designed to talk together.
2:00:19
You have a platform that much like a data center rack today everything from the lowest levels of hardware to the high level application software is designed to interoperate.
2:00:29
It's designed to be pluggable.
2:00:29
And so you know when Google's putting data centers in the data center they can go take a bunch of stuff off the shelf.
2:00:33
It does every rack doesn't look the same but they have a bunch of parts that are interchangeable.
2:00:38
They can go throw that in plug them in the software works the hardware works etc.
2:00:41
And we've got to get to that with space too. Yeah.
2:00:42
Yeah, I mean we've done that a ton in data center like cat like cat 5 cables, cat 6 cables like these are all standardized.
2:00:49
Even Facebook open open source their their blade design for their rack mounts.
2:00:53
Um and there's a lot of other uh you know ecosystem tools.
2:00:55
Um last question from me.
2:00:58
Um are you tracking the SpaceX Starship progress?
2:01:02
Um there was news today that Elon Musk is winding down his relationship with the government as a special government employee.
2:01:10
That was a 130day mission.
2:01:10
Um, probably spending a little fewer nights at Mara Lago, more nights in the SpaceX factories.
2:01:19
Um, are you are you tracking that?
2:01:21
Are you excited about that?
2:01:21
And I guess the big meta question is Elon has always seemed interested in getting to Mars, but he's willing to go to Mara Lago if that's what gets him to Mars.
2:01:33
He's willing to go to Starbase, Texas if that's what get him to Mars.
2:01:35
So, it feels like this might be a moment where he's shifting his focus.
2:01:39
The mission's the same, but he's shifting his focus from uh from regulation to engineering challenges.
2:01:46
Mars is back on the menu, boys.
2:01:47
Mars is back on the menu.
2:01:47
But what what has your take been on the on the recent star uh uh Starship or SpaceX news?
2:01:54
Yeah, I mean, I guess I would start by saying, look, it you know, building rockets is really [ __ ] hard.
2:02:01
Like, I mean, I think anybody that says it's not is crazy. It is rocket science.
2:02:05
I think we we've almost been lulled in complacency by how successful SpaceX has made it and like how how easy they make it look.
2:02:10
But I mean Starship is a crazy complex complex rocket unlike any that's ever been built.
2:02:15
So like at some level I feel like these setbacks should be expected and people shouldn't act so surprised.
2:02:20
I I think there's I don't have enough inside information to know if like forward progress is being made or sideway pro progress there's more focus whatever but like this stuff's really hard so I don't think it's that surprising that there's setbacks. Mhm.
2:02:32
Um, you know, I think that the the key thing for the launch unit economics, which I think ultimately, you know, Starship, we're hope we're all we're all hoping it's going to get us another order of magnitude on unit economics.
2:02:41
So much of of it is not even about scale or size or technical performance or capability.
2:02:48
It's about launch cadence. It's about rate. It's about flying.
2:02:50
It's like airlines, right?
2:02:52
If you bought a 737 and flew it five times a year, nobody would be able to afford to fly.
2:02:58
But because it flies 10 times a day, all of a sudden that the amorization of that fixed asset over those flights makes a ton of sense.
2:03:04
So I think for Starship to really go, what we need is we need applications that require Starship like Starlink has for Falcon 9 that drive us to launch it three, four, five times a day like you fly 737s.
2:03:16
And I think if that happens like it it will we will get another order of magnitude in the unit economics.
2:03:22
And I mean maybe tying the loop back to the original question, you know, you start thinking about applications like deploying data centers in space where you're putting thousands of things that are each many many tons each into space and there's like a really core first order economic driver requiring that needing that to scale AI training and these type of workflows.
2:03:44
That's the kind of thing that I think could drive the demand that you need for something like Starship to really get another another sort of order of magnitude unit economics.
2:03:50
So, I don't know if I really answered your question, but that's the way I think about it. No, man. Makes sense.
2:03:54
Uh, well, thank you so much for stopping by. This is great. Come back on again soon. Yeah. Yeah, you bet.
2:03:59
It was great talking to you guys. We'll talk to you soon. Cheers. Bye.
2:04:00
Uh, and while we're waiting for our next guest, let me tell you about Linear.
2:04:05
Linear is a purpose-built tool for planning and building products, meet the system for modern software development, streamline issues, projects, and product road maps.
2:04:14
And I've been lucky to use for over a decade now. Over a decade on linear. That's crazy.
2:04:20
I'm surprised the company's even decade old. Seems seems No, sorry. Half a decade. Half a decade.
2:04:27
Because a decade ago, you were in middle.
2:04:29
I was about to say the better part of a decade. Yeah. Yeah. Okay.
2:04:30
Uh well, we have our next guest.
2:04:33
David from Retool is coming in the studio.
2:04:35
I'm very excited to finally talk to you.
2:04:37
I was at your YC demo day at alumni demo day.
2:04:41
And no one's going to believe this, but I it's true, so I just have to say it.
2:04:47
and and you were the one company that stuck out and I was like that company's going to be amazing and I wasn't I didn't even think of myself as an angel investor.
2:04:54
I should have just been like let me please let me invest.
2:04:57
Anyway, it's been fantastic to watch the arc of retool and everything that you've built.
2:05:01
So, congratulations and uh thanks for joining the show. Thank you. Great to be here. Huge fan of TVPN. So excited to be here. Great to have you.
2:05:09
Uh can you give us uh a little update on the company?
2:05:14
kind of uh define the different eras, what the product is, and where it's going.
2:05:19
I know obviously we're in a period of transformation with artificial intelligence.
2:05:22
I'm sure we'll go into all of that, but what has the bread and butter been for the last couple years? Yeah.
2:05:29
So, for the first few years of retool, retools like Legos for Code. Yeah.
2:05:33
Which is we allow you to build sort of higher level building blocks and you can code faster.
2:05:38
You could say you can piece them together and you can build software. Yeah.
2:05:41
And the weird thing about retool was that we always focused on this category that we called internal software which is extremely not sexy because no one ever thinks about internal tools but surprisingly is something like 50 to 60% of all the software in the world is actually internal facing. Yep.
2:05:57
And no one thinks about it actually.
2:06:00
And so that's where started the idea behind that and and engineers are not like oh I want to work on the internal tool.
2:06:06
on the internal tool. typically like you know you put the engineer that's not cracked you know and be like yeah just focus on this or just someone who's just like grinding away but yeah I mean this was the big we were talking to Joe Eisenthal about this with the the question of like meta training llama and
2:06:23
there's a bunch of places where LLMs will will instantiate themselves in consumerf facing products across meta but also they have a massive amount of internal tooling that can benefit from from AI and so uh you know not having to fork over endless boatloads of cash to, you know, to just check is this does this have profanity in it one billion times a second, right? It's like that
2:06:43
It's like that could be a very big uh open AI bill if they don't have train that internally.
2:06:49
And so yeah, I think the the the the dark clouds over the internal tooling world is something that most people might not be aware of, but you've obviously been living in that.
2:06:58
So so talk to us about the growth of the company. Uh what's the mix?
2:07:01
Is this all enterprise driven?
2:07:04
Uh, is there small and medium businesses that benefit from Retool?
2:07:08
Kind of what's the bread and butter on the Well, if you look if you look at the homepage, it's like and the logos, it's like, oh, so you guys work with every company? Yeah, it's pretty cool.
2:07:18
I mean, we work from companies ranging from the US Army to the US Navy, the state of Utah, all the way to small startups, two person companies, five person companies.
2:07:27
So, you don't get put on the American dynamism market maps.
2:07:30
I'm gonna start demanding we gota put them on the American dynamism. Some respect on retool.
2:07:36
It is the backbone of the US military. No, seriously.
2:07:39
I mean, there's there's internal software everywhere.
2:07:43
Um, but yeah, may maybe it'd be good to shift into kind of like the AI moment.
2:07:47
It feels like retool was kind of vibe coding without the vibe coding meme or without the it allowed you to vibe code without the actual instantiation of you know you're not in an IDE but you're effectively vibe coding or building something that's uh very quick.
2:08:02
Uh how have you been processing the the AI boom?
2:08:08
When did you first think about implementing LLM and other AI technology into retool and and how has it been going so far? Yeah.
2:08:18
So the really cool thing about retool is that once you have these Lego blocks built. Yeah.
2:08:22
You can actually give them to AIs to actually use actually which is really interesting.
2:08:26
Y believe is missing now which is today if you look at how many dollars have invested in the US in AI.
2:08:34
I think it's around a trillion maybe a trillion and a half or so.
2:08:39
But if you actually look at how much revenue there is from all AI products across all companies, I think it's like 20 30 billion, that's pretty crappy ROI. Have to believe. Terrible.
2:08:53
And the question is, is it all a bubble?
2:08:58
Can we figure out some use case for this LM beyond just chat? Yep.
2:09:00
And if you look at where we are today in the consumer market, the enterprise market, it pretty much is all just chat. Mhm.
2:09:06
Uh I think if you look at that 20 $30 billion of revenue, I want to say something like 80% of it is chat GBT revenue plus cloud revenue. Y which is awesome. Yep.
2:09:18
But chat is pretty limited.
2:09:20
I mean is chat going to grow 30x from where we are now? It's hard to say.
2:09:24
I think probably no is the answer.
2:09:26
And so what we think is really missing is a way to actually leverage and use LLMs to actually go automate labor. Mh.
2:09:34
And the weird thing about this is that LLMs are actually plenty smart already.
2:09:39
If you, you know, I use chat a lot.
2:09:39
I'm sure you two both use chat quite a bit as well.
2:09:42
It's basically AGI at this point.
2:09:45
I mean, there's the classic Turing test thing of, you know, blew past that. Way past that. Yeah.
2:09:48
And yet AI aren't doing anything yet.
2:09:52
So, it feels like there's this big disconnect. Totally.
2:09:57
what we're here to really solve is can we actually allow AIS or LLMs to actually do things in your business.
2:10:03
So right now it's just chatting back and forth.
2:10:07
Can we actually allow it?
2:10:07
forth. Can we actually allow it? Can we allow the US Navy for example to say hey it's not just using chat GBT to answer some questions but actually chat GBT actually or LMS actually do things in the US Navy whether it's approving orders whether it's approving plans
2:10:21
whatever it might be that I think is the next frontier for AI is AI that actually does things and I think we're almost there which is pretty cool that's what we're working on can you exciting help me work through this question of there's like this AGI ASI narrative And then like how does that not destroy every software company? Because I I I'm
2:10:42
Because I I I'm seeing MCP servers spun up left and right and my question keeps being like if the AI is so smart, why does it need a server?
2:10:52
Why can't it just use HTML and UI like any other human? Right?
2:10:58
um at a certain point is is it going to is there is there a world where I say I want to sell a t-shirt online and instead of spinning up a Shopify store it just writes payment interop code and it doesn't even use Stripe it just
2:11:13
builds Stripe from scratch I at a moment's notice if it's so smart and it just works for a billion human hours add it up at at 160 IQ and it just builds me Stripe for this one t-shirt that I want to sell uh like that feels like it align. That's also maybe not even the
2:11:29
That's also maybe not even the best example because of the regulatory component, but we also had Steven on from the co-founder and CEO of Lambda Labs.
2:11:36
His his point of view was like broadly that you're just going to generate the software that you need and you might generate 500 and and I'm sure this is stuff that retools already doing to some degree.
2:11:46
Generate a bunch of different versions of what a tool could look like, rank them, allow you to sort of try different versions of it before landing on.
2:11:54
landing on. So there's like this one there's this one world where like having retool primitives that LLMs can interact with is amazing at in terms of like making sure that there's robust performance and everything gets up to speed really quickly at the same time
2:12:09
you know in the really long term do we even need these primitives and can we just do everything from scratch what's kind of your long-term view of how this plays out so this is I think a secret that we've actually discovered is in what cases do you want determinism versus what cases do not want determinism. That's interesting. That's interesting.
2:12:29
And we actually just announced yesterday that uh we have automated 130 million hours of work for our customers over the past 12 months.
2:12:40
If you divide out the math, that's around 70,000. It's a lot of hours.
2:12:44
Yeah, I think the secret is exactly what both of you just pointed out there, which is in what cases do you want AI and what cases do you not want AI?
2:12:53
And uh to give an example, OpenAI has this product called operator.
2:12:58
Yeah, it's this agent like thing that does things on your computer. Yep.
2:13:01
And actually for consumer use cases, operator is really good.
2:13:04
What up does is basically an LM with one tool and that one tool is use the computer. Yep.
2:13:11
And if you want to go buy a uh shirt, if you want to go buy a pair of socks, that agent is really good.
2:13:16
It, you know, what it'll do is you say, "Hey, I want, you know, socks size medium uh a navy blue color."
2:13:22
It will open up the browser.
2:13:24
It'll Google, I'll find the sock. It'll buy it. It'll use a credit card. It's done.
2:13:28
And that's fully non-deterministic.
2:13:30
You know, it's kind of making it up on the spot.
2:13:31
And that's pretty good for a consumera use case.
2:13:35
Whereas for a enterprise use case or a federal use case for example, you actually don't want it doing that.
2:13:40
And so I'll give you an example.
2:13:42
Uh one of our customers, a large company, um actually uses retool for employee onboarding.
2:13:47
And so uh what they say is, hey, every time a new employee starts, you got to go do all these tasks.
2:13:52
Maybe you have to go send them a laptop, you got to send them a key fob, you want to do this, you want to do that, whatever.
2:13:59
And if you ask operator to go do that, operator says, I got one tool and it's web search.
2:14:04
So, how do I onboard an employee?
2:14:06
I'm going to open my browser.
2:14:09
I'm going to Google how do I onboard employees? Find a WikiHow article.
2:14:11
It reads the WikiHow article.
2:14:13
It's like that's not at all what you want.
2:14:19
Business has very specific ways of onboarding an employee. Yeah.
2:14:22
And the AI actually calling those specific things because you actually don't want it reinventing the wheel.
2:14:27
all the time and a lot of those might be gated and private and maybe not even on the open web and also very high risk from a regulatory perspective even.
2:14:38
So you don't actually want the AI reinventing it all the time. Sure.
2:14:42
So that's kind of what we mean by the building blocks is you almost give AI these building blocks, these tools, these MCP servers and have AIs call them as opposed to AI reinventing it all the time. Yeah.
2:14:54
Because you don't actually want AI rewriting your security policy or how do I send laptops to employees every day.
2:15:00
Instead, you want it to say, "Hey, oh, let me think.
2:15:02
There's a big storm happening in the southeast."
2:15:04
So, for me to get the laptop in on time, I have to ship by express.
2:15:09
That's something you want the AI reinventing and reasoning about, but you don't actually want the AI reinventing, you know, do I use FedEx today or do I use UPS? What do I feel?
2:15:17
You know, you know, you have a contract with FedEx, you want to use the FedEx one. So, Sure. Sure. Sure. Yeah.
2:15:20
And that's something that like a good office manager, good person in HR who's onboarding would actually think about and reason about and you could inject that with a reasoning LLM.
2:15:29
That makes a ton of sense.
2:15:31
Talk to me about the evolution of the business model.
2:15:32
Um, consumption versus seatbased pricing.
2:15:37
And then going into the future, are we going to be looking at outcomebased pricing like what we're seeing Mark Beni off talk about in in terms of Salesforce where it's more like resolutionbased?
2:15:48
Uh, you want a job done, our agents, our tools can get that done and you're going to pay for results. Yeah.
2:15:56
So this is a fun this is I think a dirty secret too is that I think outcomebased pricing is basically designed to rip customers off. Okay.
2:16:03
The reason why I believe that is when Mark Beinov tries to charge you per outcome.
2:16:11
He's like well what's the value of what I deliver and great pay me that.
2:16:13
Whereas in reality it costs a lot less for him to actually go deliver that value.
2:16:20
That's software though we want 90% margins. Come on.
2:16:22
What do you get against high margins? Well, for I hear you. Yeah.
2:16:27
No, it it creates a But but isn't couldn't you argue with SAS?
2:16:32
The SAS vendors basically trying to charge as much as possible without the person saying, "Oh, we're going to build this ourselves or oh, that's that's actually, you know, we can just hire two more people to do this."
2:16:44
You know, it's always this dance between you want to be you no company should be trying to capture all the value that they create, right? Yeah.
2:16:53
because no one would buy a product. Yeah. Yeah.
2:16:54
So, the way that we're pricing is pretty interesting, which is that we price based on inputs, which is we just say our agents work for $3 an hour and that's it.
2:17:03
If you want a smarter agent, you can hire a smarter agent.
2:17:06
So, actually that $3 agent is a Deepseek agent.
2:17:08
So, it's a I guess a Chinese agent for $3 an hour.
2:17:10
Or you could go buy 03, which is a uh quite smart, probably the smartest right now reasoning agent.
2:17:17
I think that's something like maybe $120 an hour or something.
2:17:21
So it basically depends on what kind of task do you want to do.
2:17:22
If you want to do a simple task actually deepseek is quite good at that at 3 getting a really good deal compared to hiring human labor.
2:17:30
Whereas if you want something you know really knowledgeworky or something really nuance done maybe 03 is better actually.
2:17:36
But I think the innovation here is that we are charging on a uh per runtime hour basis.
2:17:40
And what we've discovered is that an hour of runtime for 03 for example is actually worth something like 40 50 hours of human labor.
2:17:50
You tried at least research can do in an hour.
2:17:53
I mean I probably couldn't even do it honestly.
2:17:58
So that I think is really cool.
2:17:58
And then what happens is you actually look at how much the customer pays per task actually completed.
2:18:03
completed. If you compare you per hour our pricing to Salesforce's for example part ticket resolve pricing our pricing ends up something like 95% cheaper which is really really cool like if AWS charged you not on compute or storage if they charged you on how much money does
2:18:21
your app make let's charge 90% of that that's a total ripoff right so that's kind of how we think we're almost you know we're trying to be almost like an a AWS of labor if you will yeah um how do you evaluate As the business has evolved, I'm I'm sure people early on it was pretty easy to communicate even to investors. Hey,
2:18:39
Hey, people spend a lot of time and energy and engineering resources on internal tools.
2:18:45
We can be a big company just doing this.
2:18:47
Now, I imagine as you look at the business evolving, you're like threatening a lot of different categories of software because you're saying we're going to enable teams to more easily make the decision.
2:18:58
Do we buy this or build it ourselves?
2:19:01
and like maybe retool is like some middle ground.
2:19:06
I don't know if that's the right way to think of it.
2:19:07
Um but how do you evaluate kind of the scale of the opportunity now?
2:19:14
So the internal goal that we have and it's pretty early but our internal goal is to go automate the equivalent of if not actually 10% of the labor in the US by 2030 is our goal.
2:19:25
And the idea is that Let's go.
2:19:32
I love an ambitious goal. I love it. I love it. And here's the TAM. Yeah. US labor 10%. 10%. Yeah.
2:19:42
And actually the cool thing is we're on track actually. That's amazing.
2:19:47
Over the last few months, if you draw that now, yeah, 2030 is a long time away, but you draw the line out, it comes to I think% actually. So that's incredible. It's pretty cool.
2:19:54
But 130 million hours automated is no joke. So yeah, serious.
2:19:59
Yeah, that is absolutely wild.
2:20:01
Well, well, 15 minutes was not enough time.
2:20:03
We have three more minutes.
2:20:05
We have three more minutes. Our next guest at 120.
2:20:06
I have one more question.
2:20:07
Um, uh, talk to me about the knockout dragout fight happening in the Foundation model space.
2:20:13
You mentioned Deep Seek at $3 an hour uh, versus 03 at $120 an hour.
2:20:19
How do the other Foundation labs compare?
2:20:22
What do you do to benchmark them internally?
2:20:24
Are the benchmarks cooked?
2:20:26
What's the take on Meta and Llama?
2:20:29
There seem to be a ton of energy there in terms of your use case.
2:20:35
Having an open- source model that you can inference for cheap or at cost seems incredible and yet they've it seems like they they're a little bit behind on the reasoning models.
2:20:44
Uh are you optimistic there?
2:20:45
Uh are you kind of model agnostic?
2:20:48
Um what's your overall take on the foundation model space?
2:20:54
It's pretty cool to see this play out especially with our customers because yes so for us we're theoretically agnostic you can use whatever model you want. Sure.
2:21:01
But it's really cool seeing what customers prefer actually. Yeah.
2:21:05
And uh so for example with a you know large federal customer you might think oh you know actually onrem is really important and so they actually prefer to use llama you know or something like that. Sure. Not the case.
2:21:13
Actually you know open a selling a lot of open AI.
2:21:17
Turns out actually there's a lot of traction even in sort of big federal agencies for something like open AI and they actually have contracts already and so they plug it into retool and just works. That makes sense.
2:21:25
You know I think I would have thought if you asked three years ago is the federal government happy giving all their data to you know LLM. No. Hell no.
2:21:32
It seems very unlikely but it's happening which is really cool.
2:21:37
But you also see this obviously happening in other countries too where like whether it's Saudi Arabia or Europe with mistrial for example people are getting kind of nervous about sending data to other countries LLMs and so it's going to be cool to see how the world plays out.
2:21:51
world plays out. Uh it's surpris it's very much not a meritocracy is maybe one way of putting it which is we thought that when we build our agents product with an eval framework people would just say let's see who does best who does it for cheap business let's go and actually
2:22:05
that's not the case which is I don't know how I feel about that honestly is that because like there are SDRs that are buying steak dinners for people and swinging them over or is it more like there are qualitative me metrics that matter more like the nature of the contract matter matters more than just benchmark price, etc. Like the like the
2:22:22
Like the like the the quantitative metrics. There's that.
2:22:27
But maybe another way of putting it is LLMs have been stickier than I thought, which is maybe the branding is so important or something like that. Yeah.
2:22:34
Everyone just says, "Oh, it's just one line of code to swap out."
2:22:36
But if you're used to the certain like what does Underpathy call it? Like spiky intelligence.
2:22:42
There's like certain models that spike in certain ways and you have expected behaviors and they Yeah, they might all hallucinate at the same 3% rate, but if they hallucinate in a specific way, you build around that, that type of thing. Okay, exactly. Yeah, very cool. Cool.
2:22:54
But excited to see how the space develops.
2:22:56
So, well, thanks for coming on. This is fantastic.
2:22:58
We'd love to have you back and and just chat about AI and and automating 10% of labor. Good luck.
2:23:04
Give us give us access to the internal tool that's just the autom the labor automation tracker.
2:23:09
I'd love to just follow along.
2:23:11
we can put it up as a ticker on on the screen.
2:23:12
It's just like progress, you know.
2:23:15
No, I I I love the the ambition and and excited to follow it. Come back on again soon. Yeah.
2:23:21
Thanks so much for stopping by. We'll talk, David. Bye.
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2:24:04
Anyway, uh our next guest is from Pallet.
2:24:08
We have Shashant in the studio.
2:24:10
Let's bring him in and hear the latest update. How you doing? Doing well. How are both of you? Fantastic.
2:24:15
Uh would you mind kicking us off with a little introduction on yourself and the company? How you doing? Yeah, sure. So, I'm Susant.
2:24:22
I'm the founder and CEO of Pallet.
2:24:24
So, what we do at Pallet is we're an augmented AI platform built for the logistics industry.
2:24:28
So, we automate away all the back office tasks you do in running a logistics business, right?
2:24:35
data entry, quoting, appointment scheduling, tracking where your ocean containers are.
2:24:39
So there's about like 11 trillion dollars spent on logistics.
2:24:42
But what's surprising is about a trillion dollars of that just goes on these back office tasks that you have to do. Mhm.
2:24:49
So what we saw was here is like a real prime application of using AI agents to go and automate away these tasks.
2:24:53
But we took an augmented AI approach in that we always have humans QA the work that agents are doing.
2:24:59
That way the completion of the task is near guaranteed to very high degrees of accuracy.
2:25:05
So that's why a lot of the leading logistics providers really trust us to automate away what basically eats up about 10% of their opex.
2:25:11
What's the wedge product that most companies start with or do do you have uh do you think of it that way or are you trying to sell like a whole uh suite of products on day one?
2:25:24
So what's interesting is that like we built like the technology we built is pretty generalizable and you could customize it per use case. Yeah.
2:25:31
So customers have started with a wide range of use cases.
2:25:35
Like one of the largest coal chain storage companies started off doing data entry.
2:25:39
Another very well-known shipper has started off automating how they do how they basically bid for freight with different transportation providers.
2:25:46
Other folks have looked at using us for automating spot market coding.
2:25:50
Other folks have started off with appointment scheduling.
2:25:53
So we always try to start off with one use case but that use case could be quite distinct and the agent we deploy could be quite distinct client by client.
2:26:01
When you say data entry are you migrating people from other systems primarily or Excel or are there still paperbased workflows out there like what are you actually seeing in the modern American economy?
2:26:13
Because I feel like a lot of companies will give the pitch of like get off of paper but realistically most companies are on Excel.
2:26:18
You guys have a humanoid robot that goes to a printer to print something out and then faxes still. Still joking. Joking.
2:26:26
Of course, humanoid robots will come in soon.
2:26:29
But uh the way it works actually is that like you go to one of these like even the largest logistics providers in the world, right?
2:26:37
Like your DHLs, your FedExes, they still get paper documents sent over email.
2:26:41
Basically, the way it works is you have your email inbox.
2:26:44
You have a bunch of emails coming in with documents attached to it. You look at it first.
2:26:51
You look at is this customer someone I should be working with?
2:26:53
Are they someone I work with or not?
2:26:54
Then I look at the document.
2:26:56
Does it have all the fields I want?
2:26:57
Then I go type in literally all the fields off the document into my system and I'm doing this like a hundred times every single day, right?
2:27:06
Like I think the whole reason I started this company was I went to one of her customers offices.
2:27:10
It was like a Sunday afternoon and um he was like typing in data like he was typing in a 100red orders into a system on a Sunday afternoon and he's like I can't watch the NFL playoffs.
2:27:22
I have to type data manually in from my inbox.
2:27:25
That is my transportation management system and this is how I spend my Sunday evenings.
2:27:31
And I was like that's crazy.
2:27:31
Like there has to be something better we could do here.
2:27:35
What is the what is the Super Bowl of the industries that you sell into?
2:27:40
Like are there specific conferences where where everyone gets together in terms of like logistics that are super important for you to sponsor or like like what does the top of funnel look like for you or or is it is it very much just like cold outreach, sales reps, dinner, steak dinners, that type of thing or what's the actual go to market motion?
2:27:58
So the go to market motion um I think we we rely on a wide range of channels. We rely on conferences.
2:28:03
Cold dialing, believe it or not, is still really popular.
2:28:07
Like when I got our first customers, like I would actually do something even more extreme.
2:28:11
Like I would drive around East Bay in Stockton, literally knock on warehouse doors, get kicked out until I could get our first customer in.
2:28:21
So I went a bit extreme, but a lot of our SDRs still cold.
2:28:23
Um and then there's quite a bit that comes in inbound.
2:28:27
But I think the key the key though, it's not just about the method of the outreach.
2:28:32
Like all these companies are a bit skeptical of Silicon Valley technology companies naturally, right? Yeah. Yeah.
2:28:37
So the thing I tell like the thing that we've done is about 30% of pallet has come from the industry.
2:28:41
They've worked at places like Ca, they've worked at places like Uber Freight.
2:28:44
They've worked at places like Flexport and they understand all the use cases in the company.
2:28:49
So I always tell everyone on our sales team, you have to earn the right to talk about the technology.
2:28:54
First you need to show the customer on that cold call that you understand their business really well.
2:28:58
That like if I'm talking to a freight forwarder, I understand terminology like how they deal with containers.
2:29:04
deal with containers. what are inco terms and all this industry specific jargon and if I can show that the person on the other end is more receptive to hearing me out about how I can utilize technology because if you understand their business processes you understand
2:29:17
their workflows they're like this is not just another company that's in soma that's just like looking at logistics terms on chat GPT these are people that actually just get me how much has AI been just an accelerant of the product that you're already selling versus something that you can upsell and bolt-on as like a new product. I think
2:29:35
I think AI has been a huge game changer where it's like been like the primary focus of the company, right?
2:29:43
Like if these foundation models didn't exist, our value prop of our product wouldn't be as strong as it is today.
2:29:48
So, I'd say is very critical.
2:29:50
And I think in fact, one thing that's made it even more critical is what's happening with the recent tariffs, right?
2:29:55
like the volatil like what tariffs have really created is like this extreme sense of like volatility where like April container volumes in Long Beach are spiking now they're down and this the courts reverse the ruling and then it gets reversed back in the last hour.
2:30:09
So people have no idea like my my volumes keep going up and down and I just can't scale up the human capacity to support it.
2:30:16
And now AI really just allows me to kind of come and like if I'm getting more emails into my inbox.
2:30:21
I can deploy these agents and then they can go and handle this excess volume that I would otherwise have to hire contractors really quickly to go do.
2:30:28
So it's a huge game changer especially with what's happening right now.
2:30:32
What was it like selling uh maybe call it a month ago when when the trade the trade war chaos was sort of peaking in some ways?
2:30:43
And I imagine people at that point, you know, feel like they probably need better tools, but at the same time they're like, "Hey, like I can't really handle like onboarding."
2:30:51
Um uh but I'm curious what what your experience was actually like.
2:30:59
What I tell them is like look you're you're kind of like right now the way we price this is on a per success outcome.
2:31:05
Your cost of doing this let's say is a dollar in house it's going to be a $130 on pallet.
2:31:09
So if we do what we're going to say you're going to have guaranteed ROI and you're going to have immediate time value.
2:31:15
The second thing is that we built a very the way our product works is we can ingest a customer's SOP and immediately spin up an agent that replicates their workflows.
2:31:25
So we also really emphasize our time to value.
2:31:28
where we're like, look, you're not changing your tech stack.
2:31:29
You're not changing anything about your operation.
2:31:34
You're just sharing you give literally give us a video recording of what you do.
2:31:37
We'll capture that and we can then create the agent to go and automate it and it requires minimal touch.
2:31:42
You're not changing any of your I just have to call something out.
2:31:45
So, you were at Retool before Pallet.
2:31:46
We just had David on and he said that uh he it's just so funny because he was like uh charging on a per outcome basis is BS. That was his take.
2:31:57
Uh I don't I don't fully, you know, necessarily agree with it, but but it it's it's amazing uh that that you were just there. Yeah. Yeah. I love David.
2:32:05
He used to be my uh former boss, but I think that's one area we probably disagree on.
2:32:08
I think outcome based pricing is uh is a game changer. Yeah. Interesting. Yeah. Yeah. Uh talk about the round.
2:32:15
Uh it's only been seven months since you raised your $21 million series A.
2:32:19
Now there's a $27 million series B.
2:32:21
I'm sure there's a valuation markup.
2:32:23
I don't know if you're sharing that but uh it's not a huge step up in terms of total capital raise.
2:32:27
So what does that tell us about the structure of the business and the burn rate and how you're thinking about capital consumption?
2:32:35
You mentioned uh that AI is kind of infiltrated everything.
2:32:38
Is that a cost center now?
2:32:40
Are you actually spending significant amounts of capital on inferencing AI models or is this more just we have something that's working so we're going to hire more write more code more sales reps more dinners more steak dinners more champagne more hitting the size gong when you get a big client.
2:32:56
So actually we had we most of the capital from the last round we hadn't even spent like we barely touched tapped into that.
2:33:05
So this round was entirely opportunistic and the the part of the reason it came together was the folks at General Catalyst. I knew them really well.
2:33:10
We got to know each other really well and they helped build Samsara and Samsara was probably the most iconic logistics software company.
2:33:19
So a lot of it was Hmon Mark and the team there like really knew how to build a big business.
2:33:23
The second part was I kind of felt like the industry was going for a big change and I just really wanted to accelerate our momentum on our product and our enterprise sales motion where what I saw was like I was talking to the CIO and the CEOs of companies like DHL, Kuna, Estee Lauder and all these corporations and they were telling me like we're trying to figure out how to use AI.
2:33:44
We have no idea which model to pick for which use case.
2:33:48
It's like these models keep evolving at a rapid pace.
2:33:50
we need someone to be that translation layer to kind of make that happen.
2:33:54
So that that was very obvious from the conferences that I was going to.
2:33:57
And the second thing was when these tariffs kicked in, I was like there's so much volatility that the the value prop of this stuff is like a no-brainer and we just really need to accelerate at this time.
2:34:07
So that's why like it was completely opportunistic where we had the right partner.
2:34:12
The market felt like it was just getting ready for the technology and we were like we know exactly what we want to do so like let's go and accelerate. Yeah.
2:34:18
Yeah, you mentioned some of those really big companies and kind of them not being able to decide between different models.
2:34:23
Have you bumped into any of the large consulting firms, the McKenzies of the world?
2:34:32
Because we heard this narrative early in the AI boom that they're making more money than the startups and the tech companies because they're they're selling pitch decks at extremely high margin.
2:34:42
Uh talking about AI transformation and then just saying, "Hey, if you're a big company, you can go build it.
2:34:48
here's here's our take on AI or are they more of a partner that could recommend you is do you have any insight into how uh the the big management consulting firms are are confronting the AI issue and plugging into like the Fortune 500.
2:35:02
So what we've generally seen is that the consulting firms are like very much open to partnering with companies.
2:35:08
I think the way that you think about it is like the executives at these businesses they're like there's a lot that's happening. There's a lot of noise.
2:35:15
I'm not even sure who the right partner is.
2:35:17
Do I go with a horizontal solution like Microsoft Copilot?
2:35:20
Do I go with one of these newer players?
2:35:23
Like where where do I allocate my capital and who do I pick as my partner?
2:35:27
And think of the consulting firms as a way to kind of help these companies filter out all that noise on who is the right partner for me to work with.
2:35:34
So we very much think of them as critical partners that help us like earn the trust of these Fortune 500 corporations.
2:35:40
And that's how um that's how we view it.
2:35:44
actually one of as the CEO of one of those firms is actually someone who I've gotten to know well and uh he actually has helped us actually open a couple of doors. Very cool.
2:35:51
Just got to say you're exceptional at sales.
2:35:56
I'm ready to sign up for pallet right now and I have nothing to do with with your industry.
2:36:00
Hey, we're going to be shipping a lot of hats, a lot of jack he's he's like well a lot of people think that you know our solution might not be right for them but you know actually try Yeah. Yeah. Yeah. Yeah. Yeah. The the the anti- cell.
2:36:12
sells sells better than anything else for sure.
2:36:16
Um, anything else, Jordy?
2:36:16
No, congratulations on the new round. This is fantastic. Your progress.
2:36:20
Yeah, I mean grateful to be here.
2:36:22
Um, one last thing, by the way, is that one thing we've uh we've really kind of pushed for at Pallet is this concept called hybrid work. Sure.
2:36:30
Where what we believe in is actually that like there's only two places you could work at pallet.
2:36:34
Either in the office or at the customer site.
2:36:39
Oh engineers, every month we make our team spend a week at the customer site.
2:36:44
So last month our entire team was out in Medí at a customer's BO learning their operations. Wow.
2:36:50
And learning how they can like like every single aspect of their process.
2:36:55
And I think this is really important because in a world where software costs are treading to zero like if you don't understand the customer's workflows like in excruciating detail, you might not be like building the right thing and they don't use all good documentation.
2:37:06
And you can't just go chat GBT the right answer on how does container track and trace work.
2:37:12
You actually have to sit there, observe, learn, and then uh that's I love that the hiring the hiring process candidates like, "So, do you guys support hybrid work?"
2:37:23
And you're like, "Of course we do."
2:37:24
You know, about a week out of the month, you'll be, you know, in South America, you know.
2:37:28
Um and you and and you built out that function at at Retool, right?
2:37:33
This sort of concept of this deployed engineer. Is that correct? That's correct. Yeah. Yeah.
2:37:39
I I was the probably the first forward deployed engineer there. Very cool. That's cool. I love that.
2:37:43
So So you're pretty bullish on the forward deployed engineering meme kind of just growing and growing in the future broadly for like every company or is it specific to B2B software or something about AI automation and uh something that needs to be customized.
2:37:56
I imagine that there there have to be some organizations that they they don't need it because the product is so standardized, right? Yeah.
2:38:05
I think I think it's not necessary for every type of business.
2:38:09
I think in our case, we're dealing with a company that has a lot of internal systems and processes that you have to hook on to.
2:38:16
Y and we're trying to make change management to your point, what you said earlier was like, hey, I don't want to change my operations too much.
2:38:23
I want to stick with what I do.
2:38:25
So, if we want to make change management simple, we need to hook in with your existing infrastructure.
2:38:28
So, at least for Pallet, we think the forward deploy engineering function is critical to make change management easier.
2:38:33
But there's obviously tons of companies that could succeed without that function. Yeah.
2:38:38
Yeah, that makes a ton of sense.
2:38:38
Uh well, thank you so much for stopping by.
2:38:42
Yeah, congrats on all the progress. I'm super bullish.
2:38:43
Yeah, we'll talk to you soon. Cheers. Bye.
2:38:46
How'd you sleep last night, Jordy?
2:38:49
I historic numbers again.
2:38:49
Actually had kind of a brutal bot it.
2:38:52
We were on so much caffeine yesterday, John. I do.
2:38:55
You did you track how much caffeine you were on?
2:38:59
I was on a lot of a fair amount of caffeine.
2:39:02
couple Yerba only got six and a half hours of sleep which is almost seven for me got an 82. What'd you put up?
2:39:08
What was your final number? Did I beat you? No, you didn't.
2:39:10
Even on my worst night, John, my worst night this month, I still beat you. I got an 84.
2:39:16
Anyway, go get yourself an eight sleep, 5year warranty, 30 night risk-free trial, free returns, and free shipping.
2:39:22
It's what Charles sleeps on when he's crying himself to sleep. Yeah.
2:39:25
Uh there's a timeline post I wanted to highlight here from Andy Nexuist because it ties into our next advertisement.
2:39:33
Uh Stripe put up a uh a digital billboard in Grand Central and it just says Stripe billing usage based billing to scale your business faster.
2:39:44
And uh and the meme is is everyone there having the same thought bubble? What a silly niche ad.
2:39:52
Nobody at Grand Central has even heard of Stripe, let alone implemented it in prod like I have.
2:39:57
And it's so funny, but it's so true.
2:40:01
Like I have actually implemented Stripe and I'm sure so many people that go through San Grand Central have.
2:40:07
And I think it's I think my interesting takeaway from this, sorry, is is that um is that there's there is a desire for like we talk about out of home obviously and we're going to read you an ad quick ad, don't worry, it's coming.
2:40:21
But um but I think that there are companies that feel like they need to abstract their message into even broader terms when in fact I think uh just being clear about what the product is and just knowing that yes implementing implementing Stripe is a common thing at this point and and people and those people walk through Grand Central. Yeah.
2:40:47
A lot of people a lot of people do start yelling about their company without being clear about the the product marketing effectively.
2:40:57
And then that can be that can just make your life so hard because people are like aware of your brand or your company, but when the moment comes that they should that they would convert, they don't actually think of you in that context. Yeah.
2:41:11
Like there is a different version of this stripe ad that is uh like a photo of of Patrick Collison talking about increasing the GDP of the internet and stuff and it's very like like high fidelity.
2:41:22
I'd like to see him going like one of the classic Arnold houses hitting the zizz the zyz guy.
2:41:32
Um yes absolutely but um but but aside from that it's like it's like it's okay to just send the message up front.
2:41:40
Just just be just be just be straight up with it.
2:41:42
Uh like adqu out of home advertising made easy and measurable.
2:41:46
Say goodbye to the headaches of out ofome advertising.
2:41:47
Only adqu combines technology out of home expertise and data to enable efficient seamless ad buying across the globe.
2:41:53
And um yeah I I am I'm still I'm still super long on on out of home advertising.
2:42:01
You can see Stripe is doing it in uh Grand Central and uh it is and it's the most fun. It's the most real. Yep. Yeah.
2:42:08
Seeing your ad on on Meta X, etc. is cool. Yeah.
2:42:10
Seeing it in the real world. Nothing like it. Nothing like it. It's different.
2:42:16
Uh, did you see Nathan Fielder piloted a full Boeing 737 plane?
2:42:20
I didn't finish the show yet, but I finished it.
2:42:23
It was kind of a challenge because I kept falling asleep like twothirds of the way through every episode, so I feel I've seen the entire season, but like I actually should probably watch it back again. So awkward.
2:42:35
But um but yeah, it was it was absolutely wild and especially the context like the timing of when of when the show actually premiered and and the episode started rolling out. Yeah. It really is insane. Absolutely.
2:42:50
But he's one of the best to ever do it and he's he's a he's a real icon. Yeah.
2:42:54
And uh we have to I would like to be in the next his next season.
2:43:00
Well, speaking of icons, there's nothing more iconic than a uh than a Rolex.
2:43:02
And you can get one on bezel. Go to getbzel. com.
2:43:07
Your bezel concierge is available to source you any watch on the planet. Seriously, any watch.
2:43:11
They have 26,000 luxury watches available.
2:43:13
And so go check it out at getbbezzel. com.
2:43:16
And next up, we have uh Rob Taves. Good friend of mine. I grew up with him.
2:43:21
I went to middle school and high school with him.
2:43:23
Now he's a venture capitalist at Radical Ventures.
2:43:25
and uh he's a great columnist uh and writes about artificial intelligence and has been writing about artificial intelligence uh since before it was cool which is fantastic.
2:43:34
So we'll bring in Rob and ask him a bunch of questions about artificial intelligence. How are you doing? What's going on? I'm doing great.
2:43:41
Thanks for having me guys. Welcome to the show.
2:43:43
Uh what is top of mind for you right now in AI?
2:43:45
What's the biggest thing that you've gotten right over the last few years?
2:43:49
I know you have that scorecard 10 predictions and then you rank yourself.
2:43:52
uh as you look back on all of your predictions, what's the big one you got right?
2:43:57
What's the big one maybe you missed on?
2:44:01
It's it's a great question.
2:44:01
Yeah, every uh every year I write a column of 10 predictions for the the year to come in the world of AI.
2:44:07
And to keep myself intellectually honest, I go back at the end of the year and grade which ones were right, which ones were wrong. Let's see.
2:44:14
So far this year, um I uh one of the predictions I made at the end of last year was that um President Trump and Elon would have a messy falling out, which would have various implications for the world of AI, which looks like it may be playing out.
2:44:27
We'll we'll see how that all comes together.
2:44:28
Um yeah, we were reading that today because Elon is no longer a special government employee, but at the same time, messy seems like the key word.
2:44:36
There might be a split, but if it's clean, you're getting a yellow like a clean split.
2:44:43
We need satellite imagery of whether the te the red Tesla is parked at the White House.
2:44:46
If there's a Tesla still at the White House by the end of the year, I think you got to you got to fail that one. Yeah. Yeah. Not messy yet, for sure. Yeah.
2:44:55
It's interesting because it is kind of a narrative violation because half of the half of the world was like they're going to be together forever. They're in love.
2:45:02
They're going to be partnered for four years, maybe for 10 years.
2:45:03
Um and then the other half was like this cannot last.
2:45:07
It's going to blow up and it's going to be super messy.
2:45:09
And it feels like right now it might just be, hey, back to business.
2:45:13
Uh, sleeping in the factory. Who knows? Yeah.
2:45:14
Well, the the the book is not yet over, so we'll Yeah. Yeah. Yeah. Anything could happen. Anything could happen.
2:45:20
Um, what what about um things that you feel like have played out as expected.
2:45:24
Have you been uh have you been uh kind of reassured or shocked by uh the data wall, the pre-training wall, the shift of the focus to reinforcement learning, uh the importance of tool use going forward, the importance of productization, uh versus this like this narrative around, oh yeah, just scale up the big transformer model, GPT7 will be ASI done. Yeah. Yeah.
2:45:49
No, I think I think those trends are all very much playing out and it's been interesting to watch.
2:45:56
watch. So I think it was it became increasingly clear over the course of last year that the pre-training scaling laws really were plateauing and um this narrative emerged around towards the end of last year as opening I was increasingly teasing its reasoning
2:46:11
models and it its O series of models that uh there was this kind of next frontier and next vista for scaling which was inference time compute and and uh you know um scaling reasoning models and so forth and the reasoning models 01, 03, 04, you know, deepseeeks, R1, etc. They have
2:46:29
They have been very powerful and have unlocked a lot of new capabilities and use cases and so forth.
2:46:35
I think the jury is still out as to whether they really represent like this next massive uh runway for scaling that will take us many years into the future.
2:46:44
I think it's still maybe the case that the the fundamental underlying capabilities of the models are not growing as quickly as they did in the like GPT2 to GPD3 to GPD4 era.
2:46:56
But importantly, that may not matter that much to your point, John, because I think the value and the activity is increasingly moving up the stack.
2:47:01
And even if all model capabilities basically were frozen in time today and there were no further advances, there's literally trillions of dollars of economic value to be created by just figuring out how to productize these models.
2:47:15
build particular solutions for particular end markets and even the big frontier labs openai anthropic etc are increasingly focused further up the stack so I think that's where more and more the action will be yeah what what is your take on how the shift from pre-training to uh to test time compute
2:47:33
affects like data center buildout the need for these like hypers scale stargate like massive data centers that stuff made a ton of sense in the leopold ashen Brener, we're just going to scale it up and get this massive data center going for like the biggest pre-training run ever. Uh, in in a test time
2:47:49
Uh, in in a test time inference regime, it feels like maybe it's more on demand.
2:47:55
Maybe you don't need as many big superclusters, but am I just thinking about that incorrectly because maybe we still need the massive clusters because the rate of token production is just going uh going completely parabolic.
2:48:06
No, I think that I think that narrative conceptually holds true and and I think a key insight as you touched on is inference can be done in a much more decentralized way.
2:48:16
You don't need to have like hundreds of thousands of GPUs that are all really tightly interconnected like as close together physically as possible.
2:48:23
And so in a world where more and more of the compute is inference either like productionization of models or even inference time compute that can be done on GPUs that are more spread out and you don't need to have these like matt like 5 gawatt clusters.
2:48:36
The counter narrative is the like and I'm sure you guys got sick of like hearing Jevans paradox get referenced like a million times over the past few months but we love Jeb's paradox.
2:48:47
We think it should be taught in middle school.
2:48:48
I we almost got it tattooed right here. Never forget. Never doubt. never gets.
2:48:52
Yeah, that guy that guy has really been immortalized.
2:48:58
But uh but I like I think there is a narrative that even as inference becomes more important, you as computer and cheaper, there will still be bigger and bigger and bigger pre-training runs.
2:49:05
And so that will justify the the massive capex buildout that we're seeing.
2:49:09
capex buildout that we're seeing. Yeah, I've always been interested to see if there's going to be like the the the the you know the big transformer training runs like the GPT45 training type training run in image diffusion or in robotics or even in I was thinking about like you know we we we've kind of solved
2:49:28
chess engines but like what happens if you scale that up seven more orders of magnitude like just do you get some weird outlier uh outlier scenario the the question I always have is like when can I see it on semi analysis when can I see it from a satellite image, then I know that the big training runs happening. I'm wondering, are there any
2:49:43
I'm wondering, are there any other areas or or or regimes of of training that aren't just predict next word that we could see a huge data center run happen or or is it really just text is the only option for that scale?
2:50:00
or will we see like a V4 training run and we'll be hearing about like, oh wow, it's at it's at 5 gigawatt scale because V4 just needs that much pre-training. Yeah. Yeah.
2:50:10
So, this this was one of my predictions for 2025 is that we would start to see more and more scaling laws in data modalities other than text, other than language and and I do think that we're starting to see that play out in robotics, for instance, in biology.
2:50:25
I mean there's like the the the the whole premise of the whole justification for building such massive compute clusters is this notion of scaling and as you increase compute and increase data the model gets reliably better and we are starting to see like nent signs of that in some of these other data modalities.
2:50:41
other data modalities. Um I think one one important challenge or constraint though is there are very few other modalities that where there's as much raw data available as there is for text like there is not an internet for robotics training data or an internet for biology even and so the training data sizes can limit it like it it
2:51:02
doesn't make sense to have a massively overparameterized model if there's just not enough training data and so for the foreseeable future at least like I think the amount of training data available will cap like how big the biggest robotic foundation model can be for instance relative to the biggest general purpose language model. Do you buy the
2:51:16
Do you buy the whole like uh robotics thesis of like but but transfer learning is really effective and like we can totally simulate this because we have Unreal Engine.
2:51:26
It feels like a little bit of potential cope being like well we know that you don't have the trove of the robotics data and so you're coming up with something that hasn't worked in other modalities but at the same time Unreal Engine's pretty real and you can imagine walking a robot around and learning a bunch of stuff if you train.
2:51:45
Um so what is your take on on robotics data and transfer learning simulated learning that type of stuff?
2:51:50
Yeah, I mean I think there's no question that robotics foundation models are getting increasingly generalized and like there's more and more signal that you can in fact build a general purpose robotics foundation model and and you can expose it to some previously unseen scenario and it knows how to navigate the real world uh in a way that you know generalizes the same way that that language models started doing a few years ago.
2:52:14
I think this question around training data is a really really important one and a really key one and and um there are very strong opinions on both sides.
2:52:22
There there are robotics experts who swear you can use simulation and synthetic data to massively scale up the training data set and use that to kind of erupt these scaling laws and there are other folks who believe that you know there's really no substitute for real world data. Yeah.
2:52:36
And you can you can supplement it a little bit with simulation, but like the sim toreal gap is still a very real like there's a meaningful gap there.
2:52:45
And so you can't get that much juice out of just simulation.
2:52:47
I think today I fall more on the side of the importance of real world data.
2:52:53
Like I think there's just so much nuance about the real world that uh that isn't adequately fully captured in Unreal Engine or whatever like sophisticated simulation engine you can build.
2:53:04
build. Um, but I do think as time goes on like that sim to real gap will probably close and I would imagine it ends up looking not dissimilar from the way the autonomous vehicle industry I was about to ask where um like real world data is essential right and everyone remembers seeing Google's cars driving around for you know over a
2:53:22
decade collecting data um but no major autonomous vehicle program today is not deeply based on simulation as well and so I think some mix ends up being necessary I think In robotics today, it's still you still have to be heavily weighted toward real world data, but hopefully just for the sake of leverage and technology advancement. I think
2:53:40
I think simulation will end up getting better and better.
2:53:44
Is there an analogy between uh the pre-training to test time inference regimes in LLM training and chat models to what's happening in uh autonomous vehicles and the difference between pre-training on all the data and on policy training that you can do and then also add simulation on top of that.
2:54:07
is that I is there is there an evolution of the of the kind of like mix of training paradigms that you would use in in AV?
2:54:16
And I guess like the bigger question is just like Whimo versus Tesla.
2:54:20
Is there a meaningful data gap between those two companies?
2:54:22
Because Tesla's obviously been tracking tons and tons of data, but Whimo seems to be working pretty well when you get in the back of one.
2:54:30
And so it seems like both companies might just have enough data and it and we might wind up in a situation where you know like no one's talking about a data gap between Gemini, ChatgBT, Anthropic, Llama, they all just have all the data. Yep. Yeah. Totally. Yeah.
2:54:48
It's it's interesting the way that autonomous vehicle AI stacks work today is like they're because they were kind of crafted and developed in the pre-generative AI era.
2:54:56
They're not like these massive pre-trained foundation models where they just fed all of the data in the world.
2:55:03
Like they are much more handcrafted, but it is an interesting thought experiment and there are like more younger next generation autonomous vehicle companies that are taking this approach like let's build a foundation model for driving um and you know and can we do something you know similar to what companies like physical intelligence are trying to do for general purpose robotics.
2:55:23
Can we just apply a model like that to autonomous vehicles?
2:55:26
How would a startup like that get data?
2:55:28
That that feels like the hardest thing uh because you can't just crawl the web, right? Exactly. Yeah. Yeah. Yeah. It's it's Yeah.
2:55:34
It's much harder to get training data for it.
2:55:35
To your question on Google versus Tesla or Whimo versus Tesla, I think this is like a fascinating age-old question.
2:55:41
And you're right that Tesla has way more training data in the sense that it has this fleet of personally owned vehicles driving around.
2:55:48
Uh the quality of the data is is lower than Whimos though for the for the sure specific concrete reason that they don't have LAR as a as a sensor modality.
2:55:57
And so this is like the big debate that before I got into BC I worked in autonomous vehicles and like even back then this was a debate like can you get to level four autonomy without lidar and Tesla needs to believe that the answer is yes because their business model selling cars to consumers and lidar is so expensive that like it would completely wreck the unit economics of selling a car to an individual.
2:56:18
Whimo can can can stomach the cost of a ladder because their model is a robo taxi model.
2:56:24
Um I honestly I think the jury is still out like it's it's Tesla folks will tell you that like we're very close to that and LAR was never necessary but it's I think it's not it's not completely clear yet.
2:56:36
Um and so in that sense like the the data set that Whimo has is more robust just because it's much more multimodal.
2:56:44
Do you have an do you have a sense for why LAR is so expensive?
2:56:47
Like I mean Elon Musk was able to make rockets cheap.
2:56:52
Like the iPhone is cheap.
2:56:52
Like the iPhone is cheap. like how can we not get lidar down like even just to like a couple thousand dollars like that wouldn't break the Tesla paradigm right but I assume that when we're talking about a LAR package on a Whimo we're talking like five or six figures and that breaks the model but we
2:57:09
historically humans have been really good at like mass manufacturing expensive stuff and make it cheap so uh I've always wondered about you know Elon for a decade has been LAR is doomed we don't need it we don't need it but he also changes his mind and so I wouldn't be surprised if one day he's just like, "Yeah, we figured out how to do it. We
2:57:25
We have LAR in the cars and like, you know, too bad." Yeah. Yeah. Yeah. Yeah.
2:57:29
And there there have been rumors of Tesla like experimenting with LAR here and obviously under wraps. So like you're right.
2:57:36
I wouldn't it wouldn't shock me.
2:57:38
I think it's a great question.
2:57:38
I think the short answer is just like it's a complicated sensor.
2:57:41
There's a bunch of lasers.
2:57:42
They're moving around and so forth.
2:57:44
But but to your point, uh the cost curve has been coming down on LAR and we'll continue to so like the original like kind of like bucket shaped LAR that Valadine made were like $64,000 a pop on like the really old school Google self-driving vehicles.
2:57:57
Now they're probably like a few thousand each.
2:58:01
So you need you need several of them on a car. So it does add up.
2:58:03
But I think you're totally right.
2:58:05
And like there are like research prototypes of LAR that are solid state, meaning they don't have pieces that move and that makes them a lot cheaper.
2:58:12
Um, and people talk about like $250 per unit light, which again are not like production ready yet, but I think you're certainly right that like the way this debate could be resolved is like it may just all converge because in a few years lighter gets cheap enough that like Tesla can use it and everyone can use it.
2:58:28
I I had a buddy uh friend of the show recently.
2:58:32
I'll give you some context.
2:58:34
He said Whimo will change the world.
2:58:36
Walk down the street $1 million in metal sitting unused 20 hours a day on every street in America.
2:58:41
Garages will be useless, turning into storage or ADUs.
2:58:45
Parking lots, the same thing.
2:58:48
Street parking will be more lanes.
2:58:48
Uh, driving offense revenue, DMV revenue, parking tickets all go to zero.
2:58:52
Gas stations will be worthless. Sounds insane, right?
2:58:56
But Uber went from nothing to everywhere in a decade.
2:58:58
Whimo might take 15 years, but the disruption will be nuts.
2:59:02
Um, how do you think about the downstream?
2:59:05
you know, assuming that you believe some of that is is real and and I think Sean makes some great points.
2:59:11
Um, how do you think about the investment opportunities that are downstream from ubiquitous autonomous driving and like is it even are there going to be v as many venture opportunities or is AI car washing startup?
2:59:30
Pull the autonomous vehicle in, it washes itself. It's great.
2:59:32
That would be different than a regular drive through. No, no. Roll up.
2:59:36
We're doing a roll up for humanoid. Humanoid. Yeah. Yeah.
2:59:38
So, I just to me this seems like a lot of opportunities on the real estate side is like, hey, can we turn this into more housing or, you know, whatever.
2:59:47
But parking lots aren't cheap though.
2:59:49
But yeah, this is this is the context here is that Whimo monthly rides were sort of ticking up gradually and then just shot up to 708.
2:59:59
And I guess they're now doing more rides and lift in uh the Bay Area as well. It's crazy. Yeah, it is.
3:00:05
It's been amazing to see how quickly Whimo has gone from like a novelty to just a a piece of the fabric of life for people in San Francisco.
3:00:14
And like most people that I know in in the Bay Are in San Francisco use Whimo more often than they use Uber and Lyft.
3:00:19
And it's like quickly spreading to to beyond just being a Bay Area phenomenon.
3:00:24
Like they're now live in LA, as you guys know, and in Phoenix and in Austin and so forth.
3:00:29
Uh and yeah, so I think it is um it's remarkable to see it after all these years finally become like a real business that's scaling quickly and and I really like the excerpt from your from your friend.
3:00:40
I mean I think I I very much agree with that line of thinking.
3:00:43
And one of the things that initially attracted me to the world of autonomous vehicles back like a decade ago was this fact that it's really fascinating technology.
3:00:51
You know, it's difficult technology.
3:00:53
Being able to get a car to drive itself, but it also once you solve it and you start scaling it, it has so many broader implications, second and third order impacts on so much of society and the economy.
3:01:05
Like so much of modern life is built around roads and cars and the way cities are designed and so forth.
3:01:10
So, I do think it will have dramatic uh implications on civilization, bigger picture.
3:01:18
it will play out over a longer period of time because we're talking about the built world and it takes time to adjust.
3:01:23
But yeah, I think especially in cities, at least to start, especially in urban areas.
3:01:27
Um, it's some crazy stat like a third of real estate in the average American city is devoted to parking and so much of that can go away and it's like such an inefficient use of space.
3:01:40
So, you can imagine cities being totally redesigned around humans rather than around cars, more pedestrian areas and so forth.
3:01:46
Um, you can also imagine there's a a lot of people talk about the rise of exerbs, like being able to live further and further outside of of an urban setting because when you're commuting, you don't have to be driving like you can imagine the entire form factor of a car changes and you know, maybe you have a desk.
3:02:02
Yeah, I want it to be a desk and a couch and I just want to Yeah.
3:02:06
I mean, I've spent a bunch of time thinking about this because I kind of have a gnarly commute right now.
3:02:09
And uh it will just become so much better, you know, within when I can get Jord's in Malibu. I'm in Pasadena.
3:02:18
You've spent time in both.
3:02:20
We'll get you back here eventually. Yep. Yeah. No. Yeah.
3:02:22
I'm excited for LA for Whimo to expand the geoence in in LA more and more. Yeah. Yeah. Yeah.
3:02:29
Uh well, this is f this is fantastic.
3:02:31
We'd love to have you back. This is a lot of fun.
3:02:33
We could talk about 25 other topics in AI since it is the most fascinating in industry right now.
3:02:38
Uh we didn't even get a chance to talk about deals and stuff, but I'm sure we'll I'm sure we'll go into all that in the next time you're on. Yeah, that sounds great.
3:02:44
Thanks for having me, guys. Thanks so much. We'll talk soon. Bye. Next up, we got to sing. We got to hit gongs. We got lots to do.
3:02:50
We're going to say, are we Is he going to sing with us?
3:02:55
It's always hard to sing with a remote guest, but maybe we should sing beforehand. Find your happy place. Find your happy place.
3:03:01
Book a wander with inspiring views, hotel grade amenities, dreamy beds, top tier cleaning, 247 concier service.
3:03:09
It's a vacation home but better folks.
3:03:11
And we have the founder of Wander in the studio coming in to announce a massive round of funding. Welcome to the show. How are you doing? Get out that wide. Hit that gong. Hit that gong. Breaking the gong. Hit that gong.
3:03:27
Welcome to the studio, John.
3:03:30
It's great to have you here.
3:03:30
It's It's great to be here.
3:03:32
I was hoping that I'd be able to sing with you guys.
3:03:34
So, I'm a little I'm a little disappointed that I didn't It's really hard with the delay.
3:03:38
We got to figure this out somehow.
3:03:40
Some latency mitigation or something.
3:03:44
We also need to make it a full song because oftent times I get to place choruses and verses.
3:03:48
We need to integrate the whole pitch into one big song. But congratulations. Break it down for us.
3:03:56
Ideally, ideally when somebody walks into a wander for the first time, they're sort of serenated by us and we can sort of, you know, two, three minutes on the whole home speaker system. So, we'll work on that.
3:04:07
We can make that happen for sure. That'd be great. Pull that off.
3:04:10
But yeah, give us the business update.
3:04:13
Break down the fundraising round.
3:04:14
What are you actually spending it on?
3:04:16
Because I know it's an asset light model, but uh explain how the round came together, the progress of the business. Totally.
3:04:22
Uh, so it's a $50 million series B led by QED, Fifth Wall alongside Red Point.
3:04:28
Thank you, Logan Bartlett. Okay. Red Point.
3:04:31
Yeah, Red Point, Starwood.
3:04:34
Bunch of bunch of really incredible folks. Fantastic.
3:04:36
And yeah, in terms of capital, it's really I mean, I'd love to get on here and act like we're going to do some like crazy stuff, but it's really scale.
3:04:44
Like, we have three core priorities, which is quality stays, quality customer support, and then quality homes.
3:04:50
And that's really where we're where we're laser focused. Very cool.
3:04:54
What's the key to onboarding more uh wanders?
3:04:57
I I I've seen the numbers ticking up every week as we cover you guys.
3:05:02
Um what is the what's the funnel look like?
3:05:05
Yeah, it feels like you guys have a relentless pace. Yeah.
3:05:09
And like a culture that really celebrates that and yeah, break it down.
3:05:14
Yeah, Wonder definitely has a very high output culture as a as a startup.
3:05:19
I I tell the the team that like culture is not um you know like uh happy hours and you know like that kind of stuff. It's it's about winning.
3:05:28
And so you know as as a company we we obviously very much focus on on that idea.
3:05:32
From from a growth perspective there's about 300,000 wander worthy locations across North America and Europe.
3:05:40
And so that's really our our target.
3:05:42
Um, you know, from a systems perspective, we actually built out a a fleet of AI agents that went and found each one of these homes and then enriched it with owner contact information.
3:05:51
So, we we know exactly who we're who we're going after.
3:05:54
Um, and so that's sort of the the focus right now is really a salesdriven model onboarding these homes onto the platform and then automating their operations with Wander OS and delivering that great experience to customers.
3:06:06
Talk about kind of looking back a little bit.
3:06:09
I mostly want to spend time looking forward, but like navigating through the the ZERP era, lessons learned like that era allowed for a very different type of business model that you guys have have evolved, but I would love to hear kind of the the backstory.
3:06:26
Yeah, I mean, when Wanderers started, interest rates were were pretty much zero.
3:06:30
Uh and so that allowed for us to have a very asset heavy model where we actually went out and bought those first few locations on balance sheet really with the idea of solving the cold star problem of a marketplace do things that don't scale.
3:06:42
And so as as we grew, obviously that had to transition.
3:06:45
You had two crazy events happen at once.
3:06:47
You obviously had um sort of the massive, you know, rise of interest rates.
3:06:51
But Wander actually at the time had a hund00 million credit facility with Credit Swiss as a six-month old startup.
3:06:58
Uh which was incredibly hard to put together.
3:07:02
Um and then to have, you know, this uh systemically important bank, you know, explode as a as a CEO trying to scale the company was pretty pretty wild. Traumatic.
3:07:11
That was yeah a pretty quick transition.
3:07:16
Talk about uh I I I feel like one of the one of the craziest things you can potentially do as an entrepreneur is go into a a category where there is a there's an active startup even if they're scaled. Uh that's still founder.
3:07:31
That always seems dangerous because they're still somewhat agile.
3:07:34
But obviously you've counterpositioned the company against Airbnb.
3:07:38
Um but but how uh what decisions are you making?
3:07:42
How are you thinking about maintaining differentiation and and really competing as uh you know Chesky goes on a on a run building out different products and different strategies?
3:07:54
It seems like there's more opportunity than ever to differentiate.
3:07:58
But how do you think about it?
3:07:59
Yeah, I mean first of all like I think Airbnb is a great a great company and Brian's like an incredible founder.
3:08:05
So I have nothing nothing negative to say there. Yeah. Yeah.
3:08:07
I think I think for Wander, you know, we we do deliver a little bit of a different experience.
3:08:13
So, our our net promoter score for Q1 is, you know, 85.
3:08:18
Uh, which is, you know, phenomenally phenomenally high. Like, thank you. Uh, yeah.
3:08:22
Like, true true customer love. Yeah.
3:08:24
And that doesn't happen by accident.
3:08:25
I mean, literally, if anyone has a negative sentiment in the concierge chat when they're like talking with our support, that literally gets flagged across the entire company.
3:08:34
If there's a stay that's below an eight out of 10, then they're going to get a call from our COO.
3:08:41
And like I see that feedback and we're going to be hyper aggressive on fixing it.
3:08:45
And that's across, you know, every single stay, every single home, every single customer.
3:08:50
And so I think that that like relentless customer focus is um just a very different model than, you know, Airbnb.
3:08:58
Airbnb is sort of that um unmanaged marketplace um versus, you know, Wander.
3:09:04
like we truly do care about the quality of our inventory, the quality of your customer experience, you know, to the point where literally like I will hop on the phone and like deal with whatever the issue is to ensure that it gets there.
3:09:15
And I know that sounds obviously like very unscalable, but I think that just purely from a a cultural perspective, it forces, you know, systems and automations to be built.
3:09:26
in in a time where I think that you know given everything that's happened in AI you actually do have this opportunity to deliver hospitality and like perfection at scale and so that's that's really where our focus is.
3:09:37
Can you talk about advertising in this category?
3:09:41
Um, I was I I was we were talking to Keith Ra Boy about whether or not Airbnb should have an advertising product because we've seen with Uber and uh and is it Instacart where Fiji Simo was where she spun up advertising.
3:09:57
There's it's it's not quite a marketplace business, but there's an element of that, but the take rate was low and so the advertising product was very meaningful at those companies.
3:10:06
Keith Reo's take was that in the housing vacation stays market it's less relevant because the take rate's a little higher but do you agree with that or do you think that there is a future where uh where just broadly the category is driven by advertising dollars in a meaningful way? Yeah.
3:10:24
I mean, when you when you go on to like Booking.
3:10:26
com, as an example, or even VBO, like you will see ads sure promoted to get to the because if I have a house and I want it rented, I'm willing to sacrifice a little bit of my margin to try and rise to the top of the rankings, right?
3:10:40
You'll actually even see offplatform ads.
3:10:42
So, like very typical like, you know, go buy this product type where they're actually taking people off platform, which is pretty interesting.
3:10:50
Um, like I'm I'm sure that it's a a revenue source.
3:10:53
I mean, these these platforms spend a ton of money on getting traffic um from a performance marketing perspective, from just their own internal marketing, SEO, etc.
3:11:02
And so, it certainly makes sense to like try and monetize a percentage of that traffic that's never going to end up converting um to actually like booking a home.
3:11:12
But that being said, like Keith is obviously, you know, correct and very smart that the the take rate on vacation rentals is really high. Sure.
3:11:18
So for for Wander, our average order value is about $5,400 and you're looking at like an average take of about 30%.
3:11:25
Um, and so like for us, that would have to be a lot of like random ads to, you know, like make that up and and of course the customer experience is is not not great.
3:11:37
We're really trying to market users on on that that that booking.
3:11:39
But yeah, I mean I think for a company like Airbnb it would totally make sense, especially to the, you know, on the avenue you mentioned where hosts are just paying a little bit more to promote their house to the top of the feed.
3:11:49
You know, they have a lot of inventory and um, you know, sort of pulling pulling yourself out of that is, you know, difficult for your mom and pop Airbnb host. Yeah.
3:11:57
Talk about uh disintermediation in the context of uh marketplaces and platforms.
3:12:04
We saw the canonical example of the dog walker, which is basically just a lead genen company because as soon as you find a good dog walker, you immediately disintermediate.
3:12:13
And there's some things that those companies can do to prevent that.
3:12:16
Um, with with some rentals, it's very much like I'm going to be in this town for just this week. I need this place.
3:12:22
I'm not going to bother disintermediating.
3:12:23
But if you fall in love with a place and you're going there every year, we've heard about people kind of trying to Google the place and find a different way.
3:12:30
Airbnb has an issue where property management companies will list a home and then the person that's booking the home stay there and then just reach out to the property manager and do a deal off platform.
3:12:44
Whereas Wander you guys are just full stack, right?
3:12:48
So it's like you could even get you could look up the title or whatever and get to the owner and they'd be like, "Okay, if you want to book the house like it's still going through wander, but yeah." Yeah.
3:12:57
How do you think about that?
3:12:59
what what what does that tech stack look like to prevent that or help that?
3:13:02
Yeah, I mean, so I I do think that it is like the core the core risk and I think that's even something that like has been talked about on their earnings calls is like direct booking websites and there's ways for them to mitigate it.
3:13:15
And you also have a ton of supply that isn't professionally managed where the operator isn't going to have their own direct booking website.
3:13:22
They're just going to list on Airbnb.
3:13:23
And so I think they're actually like positioned relatively fine.
3:13:27
You know, I think it's like a a travel hack that not many people use.
3:13:31
You know, for for Wander, like I am a huge fan of like having complete and total control over the business and the platform that I'm building.
3:13:39
Like when I was a kid, um my first little company, I was like 13, 14.
3:13:44
Um we're like hosting Minecraft servers and whatever else.
3:13:46
And when Minecraft got purchased by by Microsoft, they rolled out a ULA.
3:13:50
Totally killed my little business.
3:13:52
Had to fire like four or five people.
3:13:54
And so I learned about like platform risk at like pretty, you know, pretty young age. Never again. Wow. Never again.
3:13:59
Um, and and so, you know, for Wander, I knew I wanted it to be verticalized.
3:14:05
I knew I wanted to have like my own booking engine.
3:14:06
I knew I wanted to have my own property management software.
3:14:09
I felt like that was like the most durable piece.
3:14:10
And then you also have to ask yourself like, you know, as a space and a and a brand matures, like that brand, that brand promise, you know, actually matters.
3:14:19
And so if people look at Wander and associate it as a a brand that says, "Hey, this is a quality stay, you know, you don't get uh replaced quote unquote."
3:14:28
Um, and so I think that's also like a really important piece is the underlying brand and sort of that guarantee to the customer they're going to have a good trip. Yeah.
3:14:35
Uh, how do you think about taste in the context of of what you're doing?
3:14:40
Wanderers always felt like a just a very it's felt like a hospitality brand as much as it's felt like a technology company.
3:14:48
Uh where did that come from from anywhere?
3:14:51
I mean you were you were building uh coding coding tools historically which which you know I guess it's you know important to have good design in that category but maybe when you started coder it wasn't even the case.
3:15:05
So I'm curious where it came from besides you know people like Kyle crushing it. Yeah.
3:15:09
Um, you know, my my my journey as a founder has been like pretty, you know, pretty fascinating.
3:15:18
Obviously, like my first ventureback company I started when I was 17, 18, you know, coder, enterprise developer tools.
3:15:24
So, radically different space than, you know, travel.
3:15:26
Um, you however like I've always had a deep passion for, you know, design and as a kid, I you know, did high school online, traveled, you know, 200 plus days a year all over the world.
3:15:38
Um, and you were you were you were race car.
3:15:41
You were driving race cars, right? Yeah.
3:15:43
I don't really I don't really talk about it, but yeah, I used to I used to race Formula 4. Uh, no way. And then Yeah. Formula Formula Mazda. It was funny.
3:15:52
John John and I got lunch in Malibu like a couple years ago at this point and uh I was like, "Oh, do you want to like I you know knew he drove cars at a at a high level.
3:16:01
I was like, "Do you want to go drive my Ferrari or whatever?"
3:16:05
And John gets behind it and he like, you know, normally when people like are like trying out a car, you know, like everybody's car, they're like taking it tame and he's like, you know, really experiencing the na naturally aspirated V12. That's amazing. But I was confident. I was I was okay.
3:16:18
I was confident that that he was going to take care of it.
3:16:22
So he's got the experience for it. That's awesome.
3:16:23
Yeah, it was a it was a great it was a great time. Great meal.
3:16:27
Um, and so, so yeah, I think with Wander, I mean, candidly speaking, like Wander is like my soul, but like as a company, like everything needs to be high quality.
3:16:36
The software needs to be on point.
3:16:38
I also don't think people realize like how much is truly automated with Wander.
3:16:42
Like at the top you have the booking platform, but underneath is literally this property management software that's running all the vendor communication, coordination, payouts, preventative maintenance, task tracking.
3:16:55
like Wander doesn't actually employ any local property managers.
3:16:57
Uh that's all just through software, the underlying coordination of vendors, which I don't think that like anyone fully, you know, realizes because of course we don't we don't market it that way.
3:17:06
Like you you want it to feel like a like a magic trick.
3:17:10
Um, and so I think like I think that's probably where you're seeing the design come from is that like if there's anything on the site that that that bothers us like the entire team is just obsessed over this this principle of quality.
3:17:24
Last question for me and we'll let you go.
3:17:27
Um, are experiences or local other services a a true complement to vacation bookings and and housing or or is that kind of a round peg in a square hole? Yeah.
3:17:42
The the way that I look at it is that I I think it's a feature of a platform.
3:17:46
I don't think it's a platform in and of itself.
3:17:48
And I think that you have this rare moment where you can effectively abstract the way that you connect to these service providers.
3:17:56
So, you know, if you were to go back in time, let's look at like Open Table as an example, they had to build out integrations with the restaurants.
3:18:03
The the restaurants had to use Open to measure, you know, manage their reservations or whatever else so that you could provide this online platform.
3:18:12
you know, now with, you know, what exists from a technology perspective, you can have an AI agent call a restaurant and make the reservation for you.
3:18:18
And so, you no longer need to force these types of integrations.
3:18:20
And so, the way that I look at it from a services perspective is you just end up with this like aenic concierge that exists inside of your travel app that goes ahead and calls the, you know, local chef or the restaurant or whatever else.
3:18:33
And so, you don't really end up building necessarily direct integrations.
3:18:37
you more build like an abstraction layer and a curation layer. Yeah. On the experiences. Makes a lot of sense.
3:18:43
I I mean I have one more question about uh SEO was really big in the early Airbnb story.
3:18:47
Are you looking at any of these AI SEO tools?
3:18:49
Do you think that it's important to show up in Chat GBT for example?
3:18:54
And are there any services or kind of best practices that you think relate to AI SEO or isn't it GEO according to Andre Norwoods?
3:19:04
Yeah, I don't know what the the acronym is, but that it sounds like a good acronym.
3:19:11
It's actually something that we we have started focusing on pretty intensely.
3:19:14
Um, so the the real key is sort of where these agents are referencing, you know, the materials and the facts that they're that they're getting.
3:19:23
And so what you end up with is like you basically need a source of truth strategy.
3:19:27
Um, which is is a really interesting phenomenon.
3:19:30
Basically, it's like how do you how do you sort of like uh become the system of record from a fact perspective or get the data onto, you know, a platform that that is is viewed as having the facts.
3:19:40
Like for example, chat GPT references Wikipedia a lot. Sure.
3:19:44
Um and so like that candidly speaking is a little bit harder to like quote unquote hack versus like traditional SEO.
3:19:52
Um, and there's definitely going to be like an entire push and I actually think you're going to see a lot of value creation from platforms like Wikipedia uh come from the fact that they are viewed as like a source of truth. Very cool. Anything else, Jordy?
3:20:07
Uh, you said there's 300,000 homes that you guys have sort of softcircled as targets.
3:20:13
I'm assuming you're going to get all of them in the fullness of time.
3:20:17
What do you what do you want to do in the next uh what do you want to do by 2030?
3:20:20
Do you have a do you have a target?
3:20:22
Yeah, I mean hopefully by 2030 we've accomplished that for sure.
3:20:25
Um that's the that's the pointing to the Yeah.
3:20:29
Pointing to the the I like I I I can't wait for there to be like five homes left and you're just following following around the owners like in a in a helicopter being like I got you. I got you. Give us the keys.
3:20:43
Listen, we we'll we'll we'll get it done.
3:20:45
And before I jump, one thing I want to note is as a a sponsor of of this show, how incredible you guys are and how happy we are.
3:20:53
And so to anyone who's watching this thinking about where to spend their ad dollars or what podcast to sponsor, I cannot encourage you more enough to work with these boys.
3:21:04
They are incredible and the ROI is is through the charts. Thank you, man.
3:21:06
That that that really means a lot.
3:21:08
You guys bet uh you guys bet early.
3:21:10
You're one of our very first ones and uh we will never forget it.
3:21:14
Yeah, I think my favorite thing is that uh people might think that we ran the song by you before doing it live. We did not live.
3:21:21
We didn't get any push back and I think it's all for the better if there's less oversight.
3:21:26
So, we've been having fun. It's funny.
3:21:28
Did Did we create Did Did we create We created singing. We created jingles. We created the jingle.
3:21:33
We created the jingle from first principles. Copy. We created the jingle.
3:21:36
No, we literally they didn't send us any copy.
3:21:38
We didn't ask for any copy.
3:21:39
We didn't ask like how do you do an ad read for your company?
3:21:42
We just went to your website and just sang the first tagline on there.
3:21:44
If it had said anything else, we would sing. Funny.
3:21:47
It's so drilled into my brain now that I just assume that you guys were singing forever.
3:21:51
Like we just we just went to wander.
3:21:53
com and started singing Find Your Happy Place.
3:21:55
Well, we should take you out.
3:21:56
I want to make I I want to make a a We should We should make like a radio like Absolutely. Yeah.
3:22:01
Hey, this is John and Jordy VPN.
3:22:07
Anyway, thank you so much for coming on the show. This is fantastic. And congratulations.
3:22:10
super excited for you and the team and uh John and I are like basing our summer plans off of Wander Vacation uh Wander World. So, for sure. Congratulations. We'll talk to you soon. Bye. Cheers. Thank you guys so much. Later, John.
3:22:23
Uh one last news item I want to hit before we get out of here.
3:22:27
Uh we we were talking about the poly market on Elon Musk out as Tesla's CEO in 2025. It is lower than ever.
3:22:32
Uh it was around 20% in March.
3:22:36
uh crashed down to 18 15% was recently sitting around 13% and now is down at 9% and so uh the news about Elon leaving the US government has been uh you know a bull case for him staying as Tesla CEO which makes sense because he has a lot of work to do a lot of opportunity between uh self-driving and humanoid robots robots who better to run that company than Elon and another one I've been tracking circle IPO in 2025 is up to 94% chance.
3:23:09
It dropped to 47% chance on uh May 21st, so about a week ago, and it has just rocketed up to uh 95 and I expect we'll see some news on that front. Yeah.
3:23:23
Uh I mean the other market since we're in poly market mode, these are fun. I love these.
3:23:31
So uh there's of course our market on how much the iPhone 17 will cost.
3:23:35
Uh, over $1,000 is at a 13% chance. Over $1,500, 2% chance.
3:23:38
Over $2,000 is only a 2% chance.
3:23:42
Uh, I think Tim Cook's going to get it done. Keep keep iPhones cheap.
3:23:47
Uh, the other interesting thing is, uh, which which company has the best AI model at the end of May?
3:23:53
Google's running away with it at 96 uh 96. 5%. Yeah, that's May.
3:23:57
If you go out farther, if yeah, if you go out farther to the end of the year, Google's at 40%, open uh OpenAI's at 22%, XAI is at 23%, Anthropics at 7%.
3:24:09
And so, a little bit closer of a horse race towards the end of the year.
3:24:14
What I want is I want benchmarks and evals for video models now because VO3 seems to be just completely running away with the game.
3:24:24
It but you know, we haven't seen what the next iteration of Sora looks like.
3:24:27
OpenAI clearly cares about that.
3:24:31
They've been building a product in image VO.
3:24:32
In fact, the first the first consumer product from OpenAI was not a chat model. It was Dolly.
3:24:40
Dolly 2 was the first was the first product they released and then Chat GPT came out.
3:24:45
And so it's obviously in their DNA.
3:24:46
They're not just going to let that they're not just going to let Google run away with it.
3:24:50
There's so much more that you can do in video.
3:24:51
Uh we see this with Runway.
3:24:53
We see this with a bunch of other products in the space.
3:24:54
Um, it'll be interesting to track that, but we don't really have a great benchmark or eval for that.
3:25:00
So, uh, we'll have to get one and then we'll have to put it on Poly Market. Got it.
3:25:03
Anyway, uh, we will see you tomorrow.
3:25:06
It's going to be a live show.
3:25:07
Uh, Aurora, we launched a new product today.
3:25:10
A filtered shower head [Music] there. Let's see. Big news for Aurora.
3:25:17
Filtering your shower head, cleaning up the water you're bathing in. Yeah.
3:25:18
So we this is a product that was in the works for uh couple years at this point.
3:25:23
We we were pretty strategic about when the right time uh would be to roll it out uh and made the best shower filter in the game on a bunch of different metrics.
3:25:37
Flow rate, filtration, very cool uh ergonomics and uh yeah, go check it out.
3:25:44
I made a code or I had Brian CEO make a code TVPN little discount and um love it.
3:25:51
Yeah, excited to see how this goes.
3:25:53
So, we will be back tomorrow.
3:25:53
Little bit of an earlier show.
3:25:55
I'm doing some traveling and it'll be no guest, just me and Jordy chopping up the timeline throwback episode.
3:26:01
I think these are some of our best.
3:26:03
It's going to be fantastic. I can't wait.
3:26:05
Uh, so it'll probably be an hour, hour and a half around 10:00 a. m.
3:26:08
If you're looking to tune in, uh, we will of course let everyone know and it'll be in your RSS feeds.
3:26:14
And if you're listening to the RSS feed, please go leave us five stars on Apple Podcast or Spotify. Do it.
3:26:16
Ben Ben's pointing a a gel blaster at us right