0:00
[music] >> I see a larger PR moving on the horizon.
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0:09
We're surrounded [music] by journalists. Hold your position. Right.
0:30
>> [music] >> That's misinformation.
0:40
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0:50
You're surrounded by journalists.
0:50
Hold [music] your position. Com, get up.
1:00
Trust me, I'm a journalist. Violent coup. We are experts. Founder known. Five coup.
1:23
I see more journalists [music] on the horizon. Stand by. That's press conference. UAV online. Blades. Double blades. Triple blades. Double kill. Five coup. Wrong.
2:10
>> [music] >> Com, get up. Blades. Team deathmatch. We are experts. Triple blades.
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>> [music] >> That's just wrong. Right.
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Incoming [music] clearing order inbound. Com, get up.
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You're surrounded by journalists.
3:02
Hold your [music] position.
3:10
>> [music] >> Strike one. Strike two.
3:24
Activate golden retriever mode.
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Trust me, I'm a journalist.
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Incoming clearing order inbound. Five coup.
3:46
>> [music] >> Founder known.
4:03
>> [music] >> YOU'RE WATCHING TVBN.
4:09
It's Thursday, April 23rd, 2026.
4:09
We are live from the TV out here in the Temple of Technology, the Fortress of Finance.
4:16
We have a giga stream on our hands today.
4:18
We will be interviewing 1 2 3 4 5 6 7 8 9 10 different members of the new Teal Fellowship class working on everything from ultra-fast logistics companies to investigative journalism to AI recruiting and computer infrastructure, all sorts of different projects. And I loved last year. >> 10 minutes each. It's a lightning round.
4:45
Lightning round of lightning rounds. I loved last year.
4:48
It's always super inspiring to see what young entrepreneurs are working on.
4:51
The Teal Fellowship class always impresses me.
4:55
And we've had three of these folks on the show before.
4:57
So, we'll be checking in with them, seeing where progress is at.
5:01
The Teal Fellowship, I feel like it's grown.
5:04
I don't know that it was always $250,000, but it is now.
5:05
So, the Teal Fellowship gives a $250,000 grant to to young people to build startups instead of going to college.
5:15
So, everyone here has either not graduated or dropped out or deferred or declined to attend college and instead is building a technology company.
5:23
Is this a smaller class than usual?
5:26
We don't probably probably don't have the full class because of scheduling and not everyone can be everywhere at all the same time.
5:33
We will be having them all the Teal Fellows will be joining us remotely, but all they are all in the same place up in San Francisco.
5:40
And we will be calling into them and we have Nick and Tyler up there managing that end of the show.
5:47
This class of fellows includes young founders building hypersonic highways, a research lab for building foundational models for robotics, a fraud bounty hunter, and a simulated brain.
5:56
And you probably know Dylan Field is a Vitalik Buterin is also a Teal Fellow alumni.
6:02
And we are big fans of so many different founders that have gone through the project.
6:08
And Will Manidis also a Teal Fellow. So.
6:15
>> [laughter] >> Anyway, we're going to get that sound ready because we got some bad news.
6:21
Thoma Bravo is taking a massive write-down on a software company called Medallia.
6:28
Thoma Bravo is reportedly handing over the software company Medallia to creditors after restructuring negotiations failed to materialize. This is a $5.
6:35
1 billion equity wipeout for the firm who bought the business for $6. 4 billion in 2021.
6:41
And there's some there's some background in Reuters that I think I should read through and then you can sort of give me your analysis and your and what you're seeing on the timeline around takes.
6:50
So, from Reuters, exclusive, Thoma Bravo nears agreement to turn software firm Medallia over to creditors.
6:56
That does not sound good.
6:58
Private equity firm Thoma Bravo is nearing an agreement to hand over software firm Medallia to lenders, wrapping up months of restructuring negotiations.
7:06
The move will wipe out $5. 1 billion in equity.
7:08
Medallia has struggled in recent months under the weight of $3 billion of debt which it owes to Blackstone, KKR, Apollo Group, and Antares Capital.
7:18
Thoma Bravo, Blackstone, and KKR declined to comment.
7:22
Apollo and Medallia didn't immediately return requests for comments to Reuters.
7:25
So, like other software companies, Medallia's valuation has been hit in recent months over concerns that its services will eventually be supplanted by artificial intelligence. What does Medallia do?
7:36
Medallia provides software that collects and analyzes customer and employee feedback for companies.
7:42
We've had some startups, some of them backed by Sequoia Capital, that are using AI to do this exact thing.
7:48
That's potential disruption.
7:50
And then there's also So, their their their main rivals, Qualtrics. Yep.
7:53
Um, but but yeah, and and and maybe you said this already, but you know that Sequoia was one of the big backers of Medallia when they were a private company. >> Oh, interesting.
8:02
So, they're I mean, they're still private, but There's a long history of that with um uh what was it?
8:09
The the firm that was before Zenefits was called like SuccessFactors.
8:12
Lars Dalgaard did that deal.
8:17
>> is Sequoia is a real winner here.
8:17
They they did 35 million in 2012.
8:19
They did another 50 million in 2014.
8:21
And then they later led a $150 million round. >> Wow.
8:26
And then eventually, do they take it public or did they sell it for $6 billion to Thoma Bravo directly from the private team?
8:32
Was Medallia ever a public company?
8:34
That would be interesting. I think they were.
8:36
>> You look that up and I will keep reading from this to give some more backstory.
8:40
So, private equity firms invested heavily in the software sector when interest rates were low following the peak of the COVID-19 pandemic.
8:45
We all remember 3% interest rates.
8:47
It was the golden era of startups and growth.
8:49
All the DCFs were massive.
8:52
And then of course, they became the valuations came down once the interest rates went up.
9:00
So, investors have become increasingly nervous about the sustainability of high valuations assigned to some of those assets and the debt raised to buy them.
9:07
If you're on a floating rate, I'm not you know, these firms are not necessarily backed by a 30-year fixed mortgage like your house.
9:14
Your interest rate went up and the debt payments ballooned.
9:17
And if the cash flow from the business has not also ballooned, you could be in trouble like this company potentially is.
9:24
Blackstone, KKR, and Antares hold some of the debt in traded and non-traded funds.
9:29
Uh FS KKR Capital Corp marked the debt at 79 cents on the dollar in its last quarterly report.
9:34
And of course, debt is senior to equity.
9:37
So, what does that mean for the equity? Not good.
9:39
And that's why this deal is happening.
9:42
Apollo Debt Solutions marked it at 74 cents on the dollar.
9:44
So, a question about how can they even recover you know, the full value of that debt?
9:51
The equity is obviously in deep deep trouble.
9:53
So, Blackstone's global head of private credit Brad Marshall said on a conference call in February that Medallia had been quote underperforming not because of anything related to AI but due to what we believe to be execution driven issues.
10:05
So, there is a question of well, if they restructure and this company gets in the hands of creditors, maybe they can roll out AI features and become an AI winner and accelerate the top line and restructure the debt.
10:19
I saw someone was sharing that there were there were some sales people doing kind of some anonymous reporting and saying a lot of the reps were struggling to hit quotas. Okay.
10:28
Like basically delivering like 20% of quotas. Something like that.
10:33
So, just basically getting out competed in the market. >> Sure.
10:36
Um and just go back to the earlier point.
10:39
Yes, Medallia went public in 2019 >> 2019 on the NYSE was taken private July 26th, 2021. Okay.
10:45
Uh it had been it had been trading around the $5 mark prior to the take private at 6. 4.
10:52
So, Thoma Bravo after taking it private installed a new leadership team in early of 2025.
10:59
Marshall, the head of Blackstone's global head of private credit, said that they were working on a turnaround plan and we expect there to be discussions around the capital structure.
11:08
And so, people are blackballing on the timeline.
11:11
Brandon says, yes, I'm sure this is the bottom in software and we won't get worse from here. Not good signs.
11:17
This is you know, potentially one of those cockroaches that Jamie Dimon is worrying about.
11:23
There's another post that seems like it was deleted but >> Yeah, I mean so the some of the screenshot here.
11:27
The tough thing is that everyone involved here has been on a kind of a press tour saying like everything's fine. Yes. Like we're all good. >> Yes.
11:37
AI is going to be an accelerant but even even ignoring the AI question a lot of these businesses no longer have you know, are are founder led.
11:48
They're competing against other companies that that are founder led and yeah, it's it's just kind of a credit to Jamie Dimon for his comments Yeah.
12:00
saying like you know, basically saying I'm I I don't think this is over.
12:02
When you had First Brands and and some of the other um shutdowns.
12:09
Yeah, in in the previous uh in the previous startup era, there were I mean I feel like it would have been hard to raise for just like a vanilla Qualtrics or Medallia competitor and just saying like pre-AI like we're just going to build SAS and we're just going to build a you know, a crud app and write software.
12:29
>> Yeah, new a new player is a company Aru.
12:32
Yeah, that's a very different approach. Like simulation. >> Simulation.
12:36
Yeah, that's a very different approach.
12:36
And then we had another one.
12:37
I'm blanking on the name. It was a YC founder.
12:39
He he had he had gone through YC a few years ago but uh his company's taking off doing doing something something similar.
12:47
So, people have been well aware of the opportunity that Medallia and Qualtrics have have owned. Yeah.
12:58
But uh in other debt related news, Xi Jinping wants to use the International Monetary Fund to rescue the array of distressed Chinese loans around the world.
13:09
This is an interesting piece from the Wall Street Journal opinion. Noah Smith is laughing. He says, lol.
13:17
Belt and Road failed so hard.
13:17
Xi Jinping is incompetent.
13:22
That is This is a very uh narrow view of the situation. >> Do not go to China.
13:28
Yeah, this is this is this is an interesting thing but maybe a potential bargaining chip in all of the other discussions, you know, in in tech Tyler always points out that tech is like too focused on chips when it comes to Chinese diplomacy.
13:42
That there are many many other questions around trade and what Apple's doing in rare earths and there are so many and just the the broad history of the Chinese empire that play into what their Taiwan policy is, how they will interact in the Middle East and we tend to focus it all on AI.
14:01
tend to focus it all on AI. It's all about the data centers and the chips and perhaps there are more things and this is one example of something else that is a key factor in a new broader negotiation that includes chip exports
14:13
and also rare earths and also talent movements and whether or not Manas will be able to move over to Meta and a million other things and so that is the job of these world leaders is to swirl around all the different tradeoffs and get to hopefully a a good deal for both sides. Anyway, Uh some
14:32
Anyway, Uh some some breaking news.
14:37
Merriam-Webster >> GPT 5. 5 is out. >> Uh is out.
14:42
This is just a >> introducing GPT 5.
14:42
5, a new class of intelligence for real work and powering agents built to understand complex goals, use tools, check its work and carry more tasks through to completion.
14:52
It marks a new way of getting computer work done.
14:54
Now available in ChatGPT and Codex.
14:57
I like this demo of the Rubik's Cube. That's very cool. GPT 5.
15:02
5 excels at writing and debugging code, researching online, analyzing data, creating documents and spreadsheets, operating software and moving across tools until a task is finished. Uh GPT 5.
15:13
5 delivers this step up in intelligence without compromising on speed. Matches GPT 5.
15:18
4 per token latency in real world serving.
15:21
There's the model card there and uh there are some other posts.
15:24
Um we will we will wait for the reactions to come in.
15:29
We will we'll gather them.
15:31
We'll summarize them but the team cooked. Cooked. They absolutely cooked.
15:36
>> Congratulations to everyone that worked on this.
15:36
Um let's head over to Merriam-Webster, John. What do you got?
15:40
>> Merriam-Webster added a new word to the dictionary, I believe.
15:45
And I feel like the pace of vocabulary I don't know.
15:49
I mean so they have a slang section. Oh, okay. Slang and trending.
15:53
They they must be working overtime over there because the number of wambos, if you're not familiar with wambos, these are word combos. Things like Lorraine.
16:06
the lore plus explain or queef.
16:06
I could tell John, can you Lorraine GPT 5. 5?
16:12
Yes, give you the lore, tell you about what happened with 4. 5, 4. 0, 3.
16:18
5, 3 DaVinci, give you the whole lore but then also explain what this model is capable of.
16:24
That's the full Lorraine of GPT 5. 5.
16:27
But we're not here to talk about Lorraine.
16:30
We're here to talk about choppleganger which is a new word in Merriam-Webster, the dictionary.
16:34
It's basically the main dictionary as far as I'm concerned.
16:39
Choppleganger is a term for a less attractive version of someone or something.
16:46
So, whoever's going out there and distilling 5.
16:49
5 into sort of a chopped version of it, that will be the choppleganger of the official GPT 5. 5.
16:58
So, stay away from the chopplegangers unless you're unless you're really down on your luck and >> [laughter] >> you just and you need and you need a Chinese open source model to do something for you.
17:06
Then you know, buyer beware because it might it might have some flaws but Chris Stott says There is there separately there is there is new news from Michael Kratsios that the government seems to be taking Anthropic and OpenAI's messaging around distillation very seriously and is setting up a task force.
17:22
We can read a little bit more about this later but setting up a task force to actually figure out how to prevent distillation at scale.
17:29
As the models get bigger, it's more and more of an economic impact.
17:34
When you're talking about you know, a $100 training run and a Chinese lab can distill it and sneak the the weights out and sneak the you know, exfiltrate the data.
17:43
That's a lot different of an economic impact from stealing something that I don't know, cost billions to to train or billions to to put together. So, uh good news there.
17:53
Hopefully they're successful.
17:53
It was also sort of a white pill to see a lot of the labs working together to understand, hey, we're seeing this weird amount of data go to this particular company.
18:02
Are you seeing the same thing?
18:04
Or it's a shell company and we have five shell companies in these areas that are asking for these queries and and then the other labs says, oh yeah, we're actually getting something similar and if you puzzle piece those together, all of a sudden you're getting a really
18:16
solid map of what a frontier system is capable of and so it feels like we the industry are the victim of a distillation attack even if it might not register among a single lab because that single lab is only getting hit with like 1/3 or 1/5 of the total attack. So, Uh
18:31
So, Uh we can pull up this post from Kratsios. I'll read the letter. >> Yeah, yeah.
18:39
Uh subject, adversarial distillation of American AI models.
18:42
The United States leads the world in AI technologies.
18:43
That that lead reflects decades of foundational research, bold entrepreneurial risk-taking and hundreds of billions of dollars in annual private investment.
18:54
American AI leadership drives economic growth, strength strengthens national security and advances the frontiers of science, medicine and human knowledge.
19:01
The breakthroughs emerging from American industry raise living standards, expand opportunity and improve lives around the world.
19:06
However, the United States has information indicating that foreign entities, principally based in China, are engaged in deliberate industrial scale campaigns to distill US frontier AI systems leveraging tens of thousands of proxy accounts to evade detection and using jailbreaking techniques to expose proprietary information.
19:24
These coordinated campaigns systematically extract capabilities from American AI models exploiting I don't like that. That sounds bad.
19:36
>> Exploiting American expertise and innovation.
19:39
>> Every AI researcher needs this button on their desk.
19:43
If they detect a distillation attack, fire it off and the security team will come and make sure you're locked down.
19:52
And it does seem like overall all the labs are being more careful about how the APIs roll out, where how they're doing KYC, how they're doing different you know partnerships and stuff.
20:04
And I think it's all a back and forth and a dynamic.
20:07
But it's fun that this new model is available and people play with it and I'm sure we'll see some cool stuff, some >> Yeah, and Quinn 3. 6 rolled out yesterday.
20:18
People are like, "Wow, it's it's almost as good as Opus 45."
20:20
[laughter] Opus 45 >> they they they distill Don't ask me Anyways, I'm glad that Kratios is on it and I'm glad that the labs can coordinate and work together on this one. Yes. Yes. Uh more breaking news. >> Yes.
20:37
And unfortunately, but we had talked about this previously, Meta is cutting 10% of their workforce, which is 8,000 employees.
20:44
This has been talked about for a while.
20:47
>> Eliminating 6,000 open roles. >> Yeah.
20:49
I saw Microsoft was doing something similar.
20:51
They're they're offering early retirement to something like 7% of the workforce trying to to lean out a little bit. Mhm.
20:59
I'm sure the debate will continue to rage over the underlying motivations.
21:04
These companies can be big and sometimes maybe they're too big, maybe there's AI gains, maybe there's you know different investment strategies.
21:12
We will have to see where they are moving chips around, what projects they are actually cutting, what projects they are doubling down on because these companies are also hiring at all times effectively.
21:24
Well, Kristoff had some actual media ideas for the tech industry.
21:27
He says there's not enough media in tech.
21:33
We need more We need more tech positive media. >> sure.
21:37
So he says silent library with founders. Winner gets investment. What's that? >> silent library? Silent library?
21:43
So you're Is this like a Is this like a like a webcam that observes >> Silent library is a television show. It had four seasons. It's a game show. >> It's a game show. Game shows are fun.
21:55
I could see a game show being being fun, at least different.
21:59
And it would certainly >> vying for a cash prize.
22:00
If only they can remain silent as one of them is forced [laughter] to endure a bizarre stunt while seated in a library. Okay. Okay. What else? Jackass with founders. What does that mean?
22:13
How How do you make that tech related?
22:15
Maybe like humanoid robots?
22:17
Like I I When I think of Johnny Knoxville, I just think of riding bull riding effectively.
22:22
And so I imagine like riding some sort of you know robotic bull would be it.
22:26
But you know, it's very dangerous.
22:28
Like the the the team that worked with like Bam Margera and and Johnny Knoxville, they had some serious injuries from time to time.
22:37
And and I don't know if it's in your best interest to be a founder and be like, "Yeah, I need to I need to launch my startup, so I got to go in the ring with a mechanical bull and potentially break my arm and not be able to code." I don't know.
22:49
It seems seems rough, but would be entertaining.
22:52
And with the right twist, I I believe it could work.
22:55
Interview founders while going ghost hunting.
22:58
I feel like paranormal tech content Jesse Michaels doesn't get enough credit here.
23:02
You know, he's been He's a technology He's He's It's in It's in the technology charts and it charts high. Like it's up there.
23:10
And he has a incredible pedigree, great investor, and also you know, has some of the best reporting on aliens and paranormal activity and conspiracy theories.
23:21
And you know, I guess he just hasn't cracked the way to bring a [snorts] founder along.
23:26
But if you're if you're friends with Jesse Michaels or are in touch, maybe you just tag along for a little cameo on one of his on one of his videos and that's all the PR you need.
23:38
Founders give their pitches while skydiving.
23:41
Can you Can you talk while skydiving?
23:46
>> With the right headset >> gone skydiving?
23:49
>> No, but I like the idea and I like the videos.
23:52
I like I like that it's always in like the vibe reels of like the What was that movie with Is it Keanu Reeves Point Break?
23:59
They go skydiving and he goes skydiving and there's only one parachute, so they have to fight it out for the parachute.
24:04
You haven't seen this movie.
24:06
>> Have I Have I told you the story where I was I was in on an overnight flight back from Europe? No.
24:10
I'm sitting on a on a plane. >> Yeah. And I'm awake. I can't sleep. >> Okay.
24:18
And out of the shadows Skydiver. John Wick. Oh. >> walking up the aisle. Keanu Reeves. Yeah, the real actor.
24:28
>> It's effectively like 2:00 a. m. Yeah.
24:31
And Keanu Reeves is like walking at me on this plane in the middle of the night. >> Yeah.
24:35
We ended up talking for like 5 minutes. It was cool.
24:37
He went and then he went back to his seat.
24:40
>> [laughter] >> And yeah, well, he stayed on the plane. He did not skydive. >> nice guy.
24:45
What else they have here?
24:45
SF party I'm Schmacked videos.
24:47
I don't know I'm Schmacked. >> Wow, Unk. You're Unk. You guys know.
24:54
Yeah, I kind of remember, but You remember?
24:55
Basically, this was this whole like like you could call it a media company, but it was really like a guy that would make party videos for popular probably 2010 to 2013. >> Okay.
25:10
And I I will say a lot of people use these videos as a sort of college guide. Okay.
25:17
So so I'm sure I'm sure they have billions and millions of views at this point. >> Last one. VC Prices Right. Is this a good idea?
25:26
I feel like people don't like like there's always a huge adverse selection problem with anything related to sharing financials or sharing your company.
25:35
I mean, this is a twist on Shark Tank to some extent, but they they actually did try to do Shark Tank for software.
25:42
It was called Planet of the Apps.
25:43
Gary Vaynerchuk was a host.
25:45
We can ask about him tomorrow. He's coming on the show.
25:49
And I'd love to know his reflections on tech media.
25:52
I think Gwyneth Paltrow might have been a host co-host.
25:54
They had a pretty star Planet of the Apps. Look it up.
25:58
Planet Planet of the Apps instead of Planet of the Apes.
26:02
Yeah, Gwyneth Paltrow was there. Gwyneth and will. i. am.
26:05
They had some They had a stacked lineup.
26:07
Jessica Alba Jessica Alba, founder of the On each of the episodes, software makers have 60 seconds to pitch their idea in front of the advisors on a slow-moving escalator for the visual idea of an elevator pitch.
26:22
Why not put it in Why not put them in an elevator? Yeah.
26:24
New episodes were released on Tuesday. Did it get renewed?
26:29
Did any of these apps go anywhere?
26:31
They had Silo, Focus, and Study Timer. Companion.
26:35
I could I could imagine that >> Mobile personal safety.
26:37
Pair a showroom to your home. Dote. The mobile mall. Mhm. Tracks Battle Squad.
26:44
Twist live events with a dating twist. Scooch, the word game.
26:47
Uh Yeah, the these are These sound like interesting ideas.
26:54
Not as interesting as like a as a cooler that is like has a gas-powered engine that you can like ride around, which I think of as like the quintessential Shark Tank Yeah, exactly.
27:04
Like Shark Tank works because the products are very are highly visual and you get to see, "Oh, we made a new mop.
27:10
Okay, we're going to watch Mark Cuban try and mop."
27:13
And this is going to be like physical and visual and interesting.
27:15
It is It is very difficult.
27:18
Do we have Do we Are we making progress on our Shark Tank idea?
27:22
I feel like this is very very important.
27:24
We'll have to get the updates in.
27:26
How is the Shark Tank idea going? We're on the way. Okay, good stuff.
27:32
We're very excited about that.
27:32
Anyway, lots of opportunity in media.
27:34
Also, lots of opportunity in simulators.
27:37
We have talked about simulators TVPN simulator.
27:41
We made Jeremy Gaffon simulator.
27:41
But the simulators are getting less advanced in some weird twist.
27:47
You would think they get bigger and bigger and you turn them into real games.
27:51
We're going the opposite direction because the simpler the game, the funnier.
27:54
This is Coconut Simulator. Let's watch this video.
27:58
This better be worth 99 cents, okay? I'm not going to lie. Okay. What do we do? We move? It it looks good. I can't move. Why can I not move?
28:07
Am I like dead ass a coconut? Are you serious?
28:10
I spent 99 cents to be a coconut? There's no way.
28:15
What could be the difference between arcade and realistic?
28:16
Coconut has no eyes, so you can't see anything.
28:18
the realistic game mode selected.
28:22
You got to be It's just black because the coconut has no eyes. It's first-person view. >> This is impossible.
28:26
You were in third-person view.
28:27
But But it gets it gets better because there's a sequel.
28:31
There is Coconut Simulator 2, which which is now I think I believe multiplayer. Let's play this one. >> mode? Arcade.
28:41
It's the same [laughter] game.
28:41
So what's the difference between Coconut Simulator 2 and 1?
28:45
Coconut Simulator 2 is a lot more in depth.
28:47
The other coconuts, it's multiplayer.
28:50
I thought you guys said there was a story to this one.
28:52
There is, you're just not seeing it.
28:54
It's a beautiful story about growing old with your friends around.
28:59
>> [laughter] >> This game is definitely game of the year.
29:01
It's beating Crimson Desert, no chance it survives.
29:03
Trust realism mode is different in this one.
29:05
I'm telling you right now if realism mode ends up being the same, I'm going to crash out.
29:12
>> [laughter] >> Coconuts have no eyes, so you can't see clearly. It's such a funny prank.
29:18
I love I love a simulator prank. What were you saying? Who's in the chat? Ty? Ty says games has a bit. Yeah, no. It is true.
29:26
There's actually a third simulator that we need to review.
29:28
This is Banana Simulator. Banana Sim.
29:31
You are the first person in the whole world to play Banana Sim. >> What do you mean?
29:36
Remember when I showed you Coconut Simulator and I added this to my wish list?
29:39
It's not supposed to be out yet.
29:41
You're the first person to play it ever. I'm kind of jealous. How did you get it then?
29:46
The developers liked their last video and they sent me an email.
29:48
They gave me this one for free.
29:51
>> The developers of this game sent you an email.
29:54
>> I have it on my phone, too, if you want to see it.
29:56
It's the same email, just smaller.
29:59
>> So, you didn't have to pay real human dollars to play this? Not this time.
30:03
This one doesn't even have sound. It's the beta.
30:06
Technically, you're the number one banana sim player in the world right now. You have 391 points. How do you get points? 395 points now. >> Just for playing? Just for sitting there? >> than me. Diet Coke simulator.
30:19
>> Coke simulator is the way.
30:20
>> as a diet Coke as a guy.
30:21
>> You just sit there and every every few minutes >> have data center simulator.
30:23
Maybe the next one is just GPU simulator.
30:25
And it just sits there and it just hums. Yeah. >> That's it.
30:29
>> Sometimes you overheat.
30:30
>> That's the end of the That's the end of >> Sometimes you got to be plugged back in. Yeah.
30:33
Well, let's move back over to tech.
30:35
Kevin Kelly, the founder of Wired, had some incredible predictions in 2016. Let's read through them.
30:41
Summary of The Inevitable Understanding the 12 Technological Forces that will shape our future.
30:48
This was Kevin Kelly's book from 2016.
30:51
According to Kelly, much of what will happen in the next 30 years is inevitable.
30:55
The future will bring uh will bring with it even more screens, tracking, and lack of privacy.
31:02
In the book, he outlines 12 trends that will forever change the ways we work, learn, and communicate.
31:06
Becoming, moving from fixed products to always upgrading services and subscriptions.
31:11
That has definitely happened.
31:13
Uh we're moving away from even seats.
31:15
Everything's consumption-based now.
31:16
Cognifying, making everything much smarter using cheap, powerful AI that we get from the cloud. Nailed it.
31:24
That [laughter] That is >> Blowing.
31:25
That is That is a fantastic prediction for 20 Assuming he wrote it He actually wrote it probably in 2015, yeah, yeah, published in 2016.
31:35
>> But, he's the creator of Wired.
31:35
He's been tapped into tech his entire career.
31:40
And uh has a ton of interesting uh reflections, too.
31:43
He's written a more recent book about reflections on life. It's very good.
31:46
Uh depending on unstoppable streams of real time for everything. For sure.
31:51
Uh turning all surfaces into screens.
31:54
That definitely happened.
31:54
Your toaster can watch screening TV now.
31:56
Uh shifting society from one where we own assets to one where instead we have access to all services at all times.
32:04
Collaboration at mass scale.
32:04
On my imaginary sharing meter index, we are still two out of 10.
32:08
Filtering, harnessing intense personalization in order anticipate our desires. Remixing.
32:15
Unbundling existing products into their most primitive parts and then recombining in all possible ways.
32:17
That's definitely happening.
32:19
Coconut simulator, unironically, an example of that.
32:21
Uh interacting, immersing ourselves inside of computers to maximize their engagement.
32:27
Tracking, employing total surveillance for the benefit of citizens and consumers.
32:31
Sounds scary, potentially a good outcome if things are done properly.
32:35
Uh promoting good questions is far more valuable than answers. What?
32:40
You think total surveillance is potentially >> says total surveillance for the benefit of citizens and consumers.
32:44
So, if there is a black box where my Netflix recommend where my Netflix activity exists, where no Netflix employee can see it because it's encrypted, but it can make great recommendations and recommend me the next great show that I will actually enjoy.
32:58
I'm cool with that surveillance.
33:01
That's surveillance, but it's like it's potentially good for me and I have a better experience because For the benefit of John Coogan. >> Yeah, yeah.
33:07
There is There is a surveillance bull case.
33:08
Uh constructing a planetary system connecting all humans and machines into a global matrix.
33:13
Okay, that one we're still waiting on.
33:14
But, uh a lot of good, interesting predictions.
33:19
Uh Jason Schuman says, "Wild how accurate these predictions were."
33:21
And they were, in fact, Wow. Very.
33:23
Without further ado, Yes.
33:26
We have our first guest of the Thiel Fellowship gigastream. Welcome to the show. How are you doing? Great, guys. Nice to meet you. Great to meet you, too.
33:36
Thank you so much for taking the time to come on the show today.
33:40
Uh introduce yourself and the company. Yeah, I'm Victor Boyd.
33:43
Uh we're building autonomous forklifts.
33:47
The The real goal of all of this is uh get anything anywhere in just a few hours.
33:51
And we're starting with what we think is right, and we're going to do everything it takes to get there.
33:54
How vertically integrated do you want to be on day one? Uh you want to retrofit?
33:57
Is this the comma AI of forklifts or is this the Tesla forklifts on day one? What are you thinking?
34:05
Yeah, we actually started thinking we'd be the comma AI of forklifts.
34:06
Uh but you know, cars have uh CAN bus standardized since 2008.
34:10
It's a software problem to get control over a car.
34:13
You know, the difference between cars is just software.
34:16
The difference between forklifts, even if it's the same forklift, same year even, the internals will look different from each other pretty often.
34:23
So, if you want to build like a kit that goes on any forklift, it doesn't actually make sense. So, we tried that. Uh we did.
34:30
And then, you know, we realized, "Okay, that's not the way to go."
34:33
Then we decided, you know, let's just retrofit one forklift.
34:35
You know, one model, you know, we'll deal with the differences on throughout the years, but at least it'll be okay. Yeah. We tried that. That also sucked.
34:45
Uh you know, it was just unreliable.
34:45
It wasn't fast enough for the customer.
34:47
We were kind of where everybody else was in the market.
34:50
You know, this This isn't the most original idea in the world.
34:53
Everybody knows like Yeah, constructing >> a big Well, there's also big There's Applied Intuition.
34:56
Like, there's company There's big companies in the category that have been running at this problem for a long time.
35:02
So, you do You kind of probably have, you know, you got to speed run trying a bunch of different approaches and figure out what works.
35:08
>> same thing happened in the car industry where, you know, Ford and Toyota were like, "Yeah, we're doing self-driving, too."
35:13
And then it was like, "Okay, they can do some lane keep assist and some adaptive cruise control, but we're still I'm still waiting for even Tesla-level FSD from like 3 years ago to roll out into like the major American car manufacturers.
35:27
So, there's clearly an opportunity.
35:29
So, uh how do you So, where are you now?
35:32
Did you build that first autonomous forklift?
35:34
Like, what went into that?
35:37
Yeah, I mean, uh I think what Tesla did right was that they got control over their platform, right?
35:41
We realized that, you know, that was going to be the thing for autonomous forklifts.
35:44
If you wanted to make it viable for customers, you actually had to, you know, make the platform viable for autonomy first.
35:49
So, we had to build our own forklift. And we did.
35:53
You know, we built our own forklift in uh like Q4 last year uh through a manufacturing partner.
36:02
And now, like, we we are deployed and we're deployed in a very difficult environment.
36:06
We're doing better than anybody else in terms of throughput and reliability, which is really all we care about.
36:13
Uh we plan on continuing this year.
36:13
What What we want to do this year is we just want to make it uh you know, a super scalable product cuz we right now honestly it's not right now.
36:21
[clears throat] So, sorry to Sorry to interrupt, but uh what Has there been a company in the last 10 years that decided to build a new forklift from the ground up?
36:28
Or is this a category that has been generally overlooked as everybody has wanted to build new EVs and and platforms that are maybe um more exciting to some?
36:41
You know, there's some people that um kind of got halfway there.
36:43
Like, they would go with uh somebody else's design and make a few changes and think that that was enough.
36:49
But, realistically, like, you have to be in the process from the very beginning.
36:55
Otherwise, there's just all these tradeoffs that are made in the design process that, you know, really affect you.
37:01
[laughter] It makes your product horrible.
37:02
Uh so, we did it from the scratch.
37:06
You know, we we have some off-the-shelf parts, but that's a motor, right?
37:10
Like, obviously, I'm not going to design a motor from the ground up. I don't need to.
37:12
I'll just use a forklift motor.
37:14
But, everything else needs to be me.
37:15
Everything else has to be my design so that we can have full control over the system, iterate faster than anybody else, and build the actual like real product that actually works.
37:27
Can you talk about the the environments that forklifts operate in?
37:30
You mentioned that you're deployed in a difficult environment, but what's the standard environment?
37:37
And then, how does your test case differ from that?
37:43
I think the standard environment you can think of like a a warehouse where they just ship a bunch of dog food, you know?
37:46
Let's say like 400,000 square feet. Just lots of racks.
37:53
Uh you know, it's not that difficult, right?
37:55
Like, if you cause product damage in a dog food warehouse, it just stinks.
37:59
It does stink really bad.
37:59
Like, don't get me wrong.
38:00
They're not going to be happy, but, you know, we we went into a pharmaceutical warehouse. Okay.
38:05
>> So, whereas in the dog food warehouse, like, okay, I damaged a pallet.
38:07
Let's say I destroyed everything on the pallet.
38:09
That's a couple thousand dollars.
38:10
In a pharmaceutical warehouse, I destroy a whole pallet.
38:12
Dude, that's hundreds of thousands of dollars.
38:14
So, I think like our idea behind it was, you know, this is an incredible forcing factor.
38:19
This makes us like actually build a a reliable product that's not going to cause damages.
38:22
And then, when we are successful here, we get to show this to all of our other potential customers like, "Look, guys, I know you've been burned by the industry before, but look at how good we're doing in this place.
38:34
I mean, if we if we destroyed product in their place, we wouldn't be there anymore because [laughter] we would have already, you know, we it's a bad thing. You don't do it. Yeah.
38:44
Uh talk about teleoperation.
38:44
Did you go down that path at all?
38:47
Are you compatible with teleoperation?
38:49
Is there any value >> Yeah, I I feel like this is such a unique environment because you can you can figure out autonomy, but there should be a relatively easy way to like take over the system and just use an Xbox controller as as you get to full autonomy.
39:06
I don't know if that's Yeah.
39:08
what what >> you said Xbox controller. That's what it is.
39:12
So, like, when we do teleop, which is about 50% of the time right now, um it is with literally an Xbox controller.
39:18
Uh there's like some things where they have to click on the screen to like select what pallet they need to pick up, for example.
39:23
But, when there's like some scenario where the robot's lost, like, it delocalizes or uh we need some extra training data on some workflow, then it's like, "Yeah, just pick up the Xbox controller."
39:33
It's literally, guys, uh you know, they're not on site.
39:37
They're They're somewhere thousands of miles away just getting it done. It works perfectly.
39:43
I think it's extremely valuable.
39:45
I think it was a dirty word in the industry for a long time to say like remote operation, which is which is really stupid because like customers don't care guys.
39:52
Like, you know, the customers don't care if you're fully autonomous.
39:56
Customers care if the work is done.
39:58
So, if you're over here like, you know, stressing about oh dude, we're we're only, you know, 10% autonomous, but you're getting the job done every single time and you're profitable.
40:09
Like, I mean obviously keep going for more autonomy, push those margins, but dude, the customer could not care less. Yeah, totally.
40:17
We've we've said that to some humanoid founders on this show and they're like nah, it's got to be fully autonomous. It's the only way.
40:25
Yeah, it is it is like a less sexy narrative and so I think people want to deny it or something, but yeah, the end work product makes more sense.
40:36
Talk to me about the interaction modality for the human or the manager or the owner of the warehouse in a basically full autonomous mode because I imagine at some point you have to have a system that decides, okay, we actually need to dispatch an order to get that dog food off of the top shelf and there's a lot of different pallets that
41:01
are stacked up at different levels and like what is the what systems are you plugging into or do you want this to be something where there's still a human in the loop managing and and dispatching orders and then they're sitting in an office maybe overlooking the the warehouse just just sort of giving orders, but like what is the path of the workflow? So, right now it's it's basically like
41:23
So, right now it's it's basically like the robots have this task that needs to get done every day pretty much all day.
41:31
This is very common in warehouses where, you know, there's only a few things that you need to do every day and it just takes a really long time and you need to have it done by the end of the day, but it doesn't really matter how fast it is.
41:41
And in those scenarios, I mean, you know, you just kind of give the robot instructions.
41:45
We have a map and you can set up like zones or shelves and you say like this is what's in this zone or this is what's on this shelf and it needs to go to this other zone other shelf and then the forklift just does it.
41:58
I mean, you know, it can be that simple.
41:59
Eventually, you know, the idea is that I would like to be able to run the warehouse for the customer.
42:06
Yeah, I don't want them to ever have to think about it at all.
42:08
I'd rather it just be like, okay, we we are literally your pallet movement within this warehouse.
42:13
We run your whole business anyway in terms of physical movement.
42:17
We also have your inventory management.
42:19
We can make decisions based off of that.
42:21
We know your truck is going to be in here soon, for example, we'll just set up the staging for you.
42:24
You don't have to tell us that has to happen.
42:27
That's the the goal in the next next year. Forklift is the wedge.
42:33
>> [laughter] >> Very very smart.
42:34
Have you thought about we were talking about simulator games earlier?
42:37
The chat is asking have you thought about making a forklift simulator where normal people could play the game online, but in reality they're actually helping you train >> like the capture your own.
42:48
Yeah, getting training data.
42:50
You know, that's actually funny.
42:52
I did think of that because I was looking for a game myself because, you know, like I've always been into logistics, but I got into warehousing like a few years ago and like, you know, I looked on I looked everywhere, dude. I even looked on Roblox.
43:04
Like I was looking on Roblox.
43:06
They don't they don't have any like good warehouse games, man. It's such a shame.
43:09
[laughter] Somebody needs to do that.
43:12
Maybe maybe somebody in your chat will do that for us. Yeah.
43:15
How did you make your first dollar?
43:18
How did I make my first dollar? Yeah.
43:20
>> I think I'm pretty sure it was when I was selling I sold candy on Easter when I was very young and I actually made money there which is funny cuz like the kid I sold it to was literally walking with a basket full of candy.
43:32
Like definitely more candy than I got that year, but I sold him this egg and I was like, dude, there's there's something really good in here and you want it, but I'm not going to tell you what's in the mystery Mystery box.
43:40
[laughter] There random reward. Oh, Las Vegas.
43:44
Did you did you did you drop out from anywhere or were you are you are you coming out of uh High school. High school.
43:53
Yeah, I dropped out of UAB.
43:53
I I went to a state school in Alabama.
43:56
I mean, I grew up there and then I didn't have the money to go anywhere else.
44:00
And I didn't know I didn't want to go to school in the first place.
44:02
So, you know, the second I got the opportunity to fly out here to SF, I was like, okay, it's over. I'm leaving. It's over. I love it.
44:14
>> Yeah, it's just getting started.
44:14
Well, Victor, thanks so much for coming on and breaking it down.
44:17
>> Fantastic to meet you.
44:17
I'm sure you'll be back this year. Good luck. Yeah, guys. Thank you, guys.
44:22
Up next we have the anti-fraud company.
44:24
If you hate fraud, you're going to love this company.
44:27
Alex from the anti-fraud company uses AI >> to ask him about those those those bears. Oh, yeah. Yeah.
44:37
That This is a big this is a big opportunity.
44:39
Yesterday on the show we were talking about an insurance scam where individuals dressed up in full bear costumes with fake bear claws and vandalized a Rolls-Royce Ghost from 2010 and then filed an insurance claim which was then disproven by I believe some sort of biologist who knew about bears and what they look like on camera and was able to debunk it.
45:04
We were able to debunk it too because it looked ridiculous.
45:06
Anyway, [music] we have Alex from the anti-fraud company back on TBP here for the TL Fellowship Giga stream. Welcome to the show. Alex, how are you doing? Great to see you. Suited up. Suited up indeed. Looking sharp.
45:19
>> I cannot look like a fraudster when I'm catching fraudsters.
45:22
>> Yeah, have you been dailying a suit? Uh not not everyday.
45:24
Only only when we're doing formal things. Yes.
45:29
So, you've been on the show before, but sort of reintroduce the company and the shape of the business and how you think this will play out.
45:35
Then I want sort of the update on where things are, what traction looks like, how you're actually applying the technology and then I'm sure there'll be a ton of follow-on questions.
45:45
>> Yeah, so it's it's very simple.
45:45
We use we're building AI models to detect fraud when it happens and then we go and sue the fraudsters and we only make money on contingency when we actually get a recovery for the government.
45:56
We're able to keep 15 to 30% under these whistleblower laws. >> Interesting. Yeah.
46:00
And have you received a whistleblower bounty yet or is that something that's a goal for the next year two? That that is a goal.
46:09
Maybe not for the next year two because the US legal system is is quite slow.
46:15
So, it might take three to four years before we see our first dollar of revenue actually. Okay.
46:20
How much how much can you pull forward?
46:23
I know that the plan is to use AI, but how much can you pull forward by just rolling up your sleeves and doing it yourself or hiring humans to sort of follow the same process?
46:32
Is this a problem that can only be attacked with AI like a recommendation feed?
46:37
Like you could never curate like the Tik Tok feed for every individual on that platform without AI, but you can certainly, you know, go and take a photo instead of using AI.
46:51
How how how dependent is this on on AI from the very first project? Right.
46:55
So, I would say that people have always been able to find fraud manually, but usually either they luck into it or they had a a tip from somebody on the inside and we're sort of trying to eliminate that bottleneck essentially.
47:10
We want to find the fraud using AI because we we're essentially building the Palantir of fraud detection.
47:16
We're we're tapping into all these sources of data and once we have a good idea of what's going on, then we'll send out human investigators.
47:24
Again, go talk to sources, but we want to be going outbound.
47:26
We don't want to have to wait for people to come to us because that's just that's just less less efficient.
47:30
When there's 600 billion dollars in fraud, you're not going to get tips for most of it and if you want to solve it, then this is sort of the way you have to go.
47:38
I remember during the the COVID stimulus checks, there was someone who got a check for a company.
47:46
What what what were what were those called the stimulus checks?
47:47
It was it was specifically for business loans.
47:51
SBA loans, something like that, but those those loans, one of them was just called like Ford Raptor LLC and then someone dug into it and lo and behold like someone set up a fake LLC just to buy a Ford Raptor.
48:04
And that one seemed extremely obvious, but I'm wondering how much is available in terms of public records versus stuff that you plan on doing FOIA requests for to get new sources to comb over? That's a great question.
48:19
So, we we use So, this sort of stems off of like the the OSINT community, open source intelligence.
48:26
Which is where people go around and they look at publicly available data.
48:28
And and when you're doing pure OSINT, you're only using again open source stuff that's publicly available. That limits you.
48:34
So, we do get close sources, too.
48:36
But I guess we we want to start there.
48:39
We view it like a funnel.
48:40
So, starting with the the open source information lets us cast the widest net.
48:44
Of course, some of it we're going to need to do do other things like you mentioned FOIA is one way that you can get private information, but it's it's really like you have to know again what what you're requesting from the government or what you what sort of private data source you want to buy or what human you want to go out and talk to.
49:00
And so, to get the high level view, you do need to be essentially just ingesting a bunch of stuff.
49:05
So, one one analogy we like to make is is the story of the the blind men and the elephant, right?
49:11
Is there's like five five blind men and they're each touching a different part of the elephant.
49:14
The guy who's touching the legs thinks it's a tree.
49:17
The guy who's touching the trunk thinks it's a rope, right?
49:19
Is fraud is kind of like this.
49:21
It always leaves traces, but it's it's somewhat difficult to put them back together and that's why we just want to have as many different essentially like sensors on on the government databases, on contracts, on all these data sets and that gives us a sort of a a good look into what's going on.
49:37
Then we then we ontologize it run through models, make make graphs, and see if it matches on to any known patterns of fraud that we're searching for.
49:46
>> How many how many like projects are you guys [clears throat] taking on at any one point?
49:50
Because I'm sure every single day there's like new ideas, new opportunities.
49:54
Sounds like a very very big market, unfortunately.
49:57
>> Quite quite a few projects at a at at a time.
50:00
And and again, we're just looking everywhere.
50:02
I mean, like you mentioned, Small Business Administration is is a great place to look with all these loans, defense, just anywhere the government spends money.
50:12
There there is undoubtedly going to be people who are doing it here unethically.
50:16
Oh, absolutely healthcare. Yes.
50:18
I I mean, like we spend trillions on healthcare, we spend trillions on on defense.
50:23
There there is a lot of stuff going on in these places.
50:26
>> And what's going on in Sacramento right now?
50:28
Is there some new legislation that's trying to make it harder to do journalism around uh fraud? Did I see that? >> so.
50:36
I think this was a response to Nick Shirly, I believe. Oh, really?
50:41
I think it's called the Nick Shirly Wow, okay. Right?
50:46
I don't know if it's actually called that, but that's what people on the internet are calling it. Yes.
50:51
No, that that that that's my understanding.
50:52
Obviously, Nick first did like the the big exposé about the the the the the daycares in Minnesota, which were getting funded by Medicaid or something of that sort.
51:01
And so that that also helped drive a lot of awareness and interest in our company as well because I mean, it's just sort of visceral.
51:09
I guess first there was Doge where people just got really mad about how their money was being spent, and then the the the daycares sort of was the was the was the next step.
51:17
And I think there's just a big anti-fraud moment in the United States these days where people are are just upset about the their dollars aren't going far enough, right?
51:28
I mean, like it's 600 billion dollars a year.
51:30
That's like 7-8% of the federal budget.
51:33
So that that means like a a good chunk of your hard-earned tax dollar just being stolen by people who are contracting with the government in the wrong way.
51:42
And I mean, we need to restore trust in our institutions.
51:44
We need to have good governance, have have people have faith in our institutions.
51:48
I think that again, aligning the incentives, having sort of private actors come in and do this and rewarding them when they're able to claw back money for taxpayers.
51:58
The most of most of the money that does claw back will go back to the the treasury and to the taxpayers.
52:02
I think this this is kind of a no-brainer.
52:04
How do you think about the actual application of AI tools and models in this particular case?
52:12
I understand that you're, you know, sort of creating a database or mirror data lake of all the different sources.
52:20
But then do you need a bunch of examples and to fine-tune a model?
52:24
Do you just need to load examples of red flags into the context window?
52:30
How far down are you on like the AI research side of understanding the problem?
52:35
Because I can I can imagine like with a frontier model, you can go and if you give it a lot of examples and and data points, you could potentially just one shot like a detection, and then it's just applying it at scale is the problem.
52:49
But how are you thinking about applying the actual technology?
52:53
>> are essentially two steps here.
52:53
The first step is that a lot of this data is unstructured.
52:56
A lot of this like is is documents, and so LLMs really speed that up.
53:01
It's sort of getting structured entities out of the unstructured data.
53:05
And then we we ontologize this.
53:05
We have like this company has this contract, has this relationship with this employee, etc.
53:10
We build sort of this this ontology knowledge graph like thing, and then we have the second layer, the the rules model essentially.
53:18
My co-founders are are both lawyers, have a legal background.
53:21
They've been working on sort of the fraud issue in in the legal sphere.
53:25
And so they know what that looks like.
53:27
And with their input and with their expertise, we're able to essentially develop this rules model, this rules layer, compare that to what we're seeing in the real world, and flag things that look like violations in real time or at least stuff that could be violations and we need to get more information on. Did you drop out?
53:42
Because if you have two lawyer co-founders, did they drop out?
53:43
Like what's the what's the story there? >> dropout? Who's the dropout? I'm the dropout.
53:48
My co-founders have have JDs, unfortunately.
53:52
They're also a bit older, so they're not eligible for the Thiel Fellowship, unfortunately.
53:57
>> [laughter] >> I did I did drop out of Brown in in 2025 to work at Palantir and then start this company. That's great. That's great.
54:06
Well, good luck and thank you for everything that you do.
54:09
>> How big's the team now?
54:10
We have 11 full-time employees, but we're hiring engineers.
54:12
If you're an AI engineer that wants to work on this problem, visit antifraudcompany.
54:17
com and we'd love to work with you. It's a great name.
54:22
Well, good luck out there.
54:24
>> Great to see you, Alex.
54:24
Keep up the good fight, and let us know when you catch a big culprit so you can come on and tell the story cuz I'm sure it'll be riveting. Yes, indeed. Thanks, fellas. We'll talk to you soon. >> Goodbye.
54:33
It would be funny if he was investigating a company to have the founder on Oh.
54:41
>> where interviewing them, and then Alex joins the call.
54:44
>> Sort of a yeah, the catch a predator.
54:48
>> [laughter] >> That's what you want to do with fraud.
54:49
This is a new media thing.
54:49
This is the new media opportunity.
54:53
>> New media for for pump and dumps and schemes and all sorts of stuff and rug rug pulls.
54:58
That inadvertently happened during the NFT and and crypto boom because there were a number of founders who went on shows and then it was revealed.
55:05
I mean, the the famous one is Joe Weisenthal and and and some Bloomberg reporters talking to SBF, I believe on Odd Lots, and they asked him like, "So this is like a black box that you put money in and you just get more money out?"
55:19
And he was like, "Yeah, exactly. Exactly."
55:23
Wait, SBF was on Odd Lots?
55:23
I'm pretty I'm pretty sure it was Odd Lots with yeah, I know I know Joe was on that that podcast with SBF. And uh Yeah.
55:36
>> And and and he basically describes a Ponzi scheme. >> crazy.
55:40
SBF and Matt Levine >> Matt Levine. on Odd Lots.
55:42
And Matt Levine asks like so he describes a Ponzi scheme and SBF basically just says like, "Exactly.
55:49
Like that's why it's good.
55:51
It's like you you put more money in than you and then it grows and then it's this magical system."
55:55
He was like describing like crazy DeFi schemes.
55:59
It was a very It was a rough time. It was a crazy time.
56:02
But we lived through it and we became stronger in the process.
56:03
We do have our next guest in the waiting room. >> 27th, 2022. Yes.
56:07
And so just 6 months later, everything would It was it was a wild wild time.
56:14
Well, we have Nick from Every Ticker [music] in the waiting room.
56:17
Let's bring him in to the TV field trip. Nick, how are you doing? Pretty good. [music] How are you? We're great.
56:24
Thanks so much for taking the time. Great to meet you.
56:26
>> you are the youngest ever Thiel Fellow. Is that true? That is true, yeah. Wow. >> Wow. That's amazing.
56:32
So are you dropping out of college? >> out now.
56:34
Are you You should drop out of the Thiel Fellowship >> [laughter] >> Yeah, I'm dropping out of high school.
56:40
It's pretty pretty crazy experience. >> Wow. Congratulations.
56:42
What what what have you done Yeah, what have you done to date where you get identified and and chosen for an opportunity like this? >> Yeah.
56:54
So the main thing I've been working on for the past year or so is Every Ticker.
56:59
And I'll start with the problem that we're solving.
57:01
It's that the vast majority of the US equity market is in the small, mid, and microcap stocks, but Wall Street only covers the mega and large cap stocks.
57:10
So what we do is we use LLMs to generate high-quality research on every single US stock, including the ones that Wall Street doesn't cover. Interesting.
57:22
Like the big stocks would have an equity research report from Goldman Sachs or Morgan Stanley.
57:25
These are the 20-page PDFs that you see come out every quarter or so with a with a buy, sell, hold.
57:29
Are you offering financial advice?
57:32
Like who is the end consumer of this if it's some smaller microcap?
57:40
So our philosophy is that we explicitly do not offer any sort of financial advice.
57:44
We don't give a buy, sell, or hold recommendation.
57:46
Since from talking to my users, I find that they want to use their experience and evaluate the stocks themselves.
57:55
So what we do is we aggregate and synthesize all of this research.
57:59
We come up with a thesis, and then we let the end consumer decide for themselves whether it's a stock that they're interested in. Yeah, that makes sense.
58:06
And then what about the business model? Is this a subscription?
58:09
I know if you want those Goldman Sachs equity research reports, you got to pay a pretty penny.
58:14
How much does it cost to get an Every Ticker report on a smaller microcap?
58:20
So right now, all of our 5,000 reports are completely free.
58:24
Eventually, we want to transition into a subscription model, but right now we just want to grow as fast as possible. That makes sense. Love it.
58:30
How do you how do you think about competing with LLMs over time?
58:33
I'm sure People are doing that now.
58:38
People are doing this where they say like, "Act like a Goldman equity research analyst. Analyze this company. Blah blah blah."
58:44
You can you can prompt your way to it right now.
58:48
It'll probably get easier over time.
58:51
So how how are you thinking about that dynamic over time?
58:52
Like I guess like how do you how do you expand the product?
58:56
Cuz I'm assuming that's where that's where you'd go.
58:59
So for our competitive advantage, you know, if you ask ChatGPT to write you an equity report on say Apple, it will generate you something that's decently surface level, but I would not say that it's as high-quality as an analyst.
59:10
So our competitive mode is that we have a agentic system that is fine-tuned specifically for this task.
59:16
It acts like a an actual analyst. Yeah.
59:18
And then we have all sorts of different data feeds that are proprietary. Sure.
59:23
And as for the product, this is just a starting point.
59:28
We want to get really good at the specific niche of high-quality research reports. Yeah.
59:32
And then we want to expand to adjacent parts of the research workflow in the future. Yeah.
59:35
So is one way to think about what you've built is sort of like an agentic harness on top of the frontier LLMs so that you can Yeah, but it's running in the background and then just producing the report.
59:49
>> Oh, and and then the report's just instantly available on at the at the click of a button, correct?
59:54
Uh not even at a click of a button.
59:54
We pre-generate all of the reports so you can just search a stock and it's there. >> It's already there. Got it.
59:59
And then you're probably putting those on some sort of cron job that runs every quarter or every month or something.
1:00:03
Have you ever >> How did you How did you kind of stumble into this opportunity where you were you investing a bunch yourself and just kind of struggling to find great research on on some of these smaller names?
1:00:16
So in seventh grade I convinced my parents to open a Fidelity account for me and I just instantly fell in love with the stock market.
1:00:22
I really liked um like the idea of it and I quickly got into fundamentals focused investing and learned from like Warren Buffett and like that type of philosophy. Yeah.
1:00:31
And over the summer of eighth grade I was looking into a smaller cap stocks because I think that's where a lot of the opportunities are.
1:00:38
There's um more mispricings there, right?
1:00:40
And I realized that you can use LLMs to generate decently high quality research on all these smaller stocks and so I just built a quick MVP in 3 days, published it and then I found that so many people found value in it.
1:00:53
So I just started scaling it from there.
1:00:54
Have you thought about what you're going to do with the Thiel Fellowship money?
1:00:57
I know of some um previous Thiel Fellows that have made angel investments very successfully.
1:01:02
Do you Do you play Do you Do you Is there Is there something in your mind that wants to potentially dip your toe into the hedge fund world or actually managing a portfolio?
1:01:13
>> Portfolio into Neo Clouds. >> [laughter] >> Maybe. Yeah.
1:01:17
I don't think I would do that.
1:01:17
Um in the short term I think it would probably just go to paying my server costs and things like that.
1:01:23
I'll be moving to San Francisco so um also things like rent and things like that but if there's any opportunities that I see I would definitely be interested in that. Do you have a team yet?
1:01:32
Have you built out an executive team of of grizzled 60-year-olds yet?
1:01:36
I imagine that that's the next step for you.
1:01:40
No, right now I think that um just being a solo founder is pretty good.
1:01:43
It allows me to move super fast. Yeah, that's awesome. >> That's very cool.
1:01:47
How did you make your first dollar on the internet?
1:01:51
Was it through Every Ticker? Actually no.
1:01:53
Um in fifth grade from fifth grade to seventh grade I built a Roblox game.
1:01:57
It was tens of thousands of lines of code.
1:02:00
I was spending hours every day.
1:02:02
It ended up being a complete flop but I did make a bit of money from it. >> Oh, there you go. >> Amazing. >> That's great.
1:02:07
Uh what what advice would you give to young people who want to invest in the stock market? Where should they start? Well, um Every Ticker.
1:02:16
>> I'll Sure, but >> [laughter] >> That's for sure.
1:02:20
Yeah, I think that um I personally would advise fundamentals focused long-term investing.
1:02:24
I think a lot of people get carried away by these like you know super volatile stocks and all of these moonshot opportunities.
1:02:30
I think it's important to stay grounded in where the real value is. Mhm. Yeah. That makes sense.
1:02:38
Uh what uh yeah yeah that's interesting.
1:02:40
Um what goes into a great equity research report?
1:02:46
Can you share a little bit about differentiation?
1:02:48
What what I always find interesting personally is is the research reports that can sort of contextualize a company in its broader competitive set in the market but but what do you think makes for like a good flavor like the qualitative elements that go into something that's readable, digestible, informational, effective, and ultimately satisfactory to the user?
1:03:14
Yeah, the qualitative element is one of the most important things and it's one of my competitive advantages because I know exactly what the user wants.
1:03:20
I'm one of my users and I talk to a lot of my users to see what they want.
1:03:23
So the research report is um I I take that into account.
1:03:29
So you know as you said you have to contextualize it in its broader industry and the goal of the research report is that after reading this 10-minute report you'll be able to understand exactly where the company is, its competitive advantages, um its moats, you know like its trajectory and you should be able to determine if it's something that you want to invest in or not.
1:03:48
How do you think about uh the the the the tells of LLM writing? You're absolutely right.
1:03:54
It's not this, it's that.
1:03:57
Those like stylistic flourishes, the contrastive parallelism that annoys tech insiders because it reads as LLM generated and that reads as maybe subpar but what are you actually seeing from users?
1:04:12
Do they Do they like that quality of writing?
1:04:14
Are they looking for a different stylistic tone in these research reports?
1:04:21
So I personally am quite annoyed by that Mhm.
1:04:23
and um so I use um I have a method of making things sound very human.
1:04:27
So you know from talking to some of my users one of their first questions is how do you have so many research reports and you know they don't even realize that it's LLMs or AI. >> Interesting.
1:04:37
So yeah, it's um the language in our research reports is much more human than some of the other um AI stuff you see out there. I love it. I love it.
1:04:45
I won't ask you to devote the secret but good luck.
1:04:47
It seems like it's going very well and congratulations on It was great to meet you.
1:04:51
Great to meet you and excited to follow the journey.
1:04:54
Come back on whenever there's a milestone.
1:04:55
We'd love to talk to you again.
1:04:56
Yeah, thank you so much for your time. Have a great day. Great to have you. We'll talk to you soon.
1:05:00
Um here's [clears throat] a here's a micro cap stock that might be making a breakout move, Spirit Airlines.
1:05:09
They potentially are getting a bailout from the US government to the tune of $500 million in rescue funding.
1:05:13
The money would come with equity warrants that could make government majority owner of struggling discount carrier.
1:05:20
I know you're not a fan of Spirit Airlines, Jordy.
1:05:25
[laughter] Uh but the Trump administration >> a You never got it. I've never flown Spirit. Oh, it's it's bad. It's real bad. It's real real bad.
1:05:33
You have to pay for everything.
1:05:35
You have to pay for where you board and if you get a water bottle, if you can go to the bathroom, they charge you for everything.
1:05:41
Uh but it allows them to >> for the bathroom? Maybe I'm mistaken.
1:05:46
>> charged for water, too.
1:05:46
You get charged for water.
1:05:48
>> Unless you tell them that you you need it for an emergency then they'll give it to you for free. Yeah.
1:05:51
But it allows them to advertise very very low headline prices.
1:05:56
So if you're looking for LA to Las Vegas you'll see something like $50 and you're like that's impossible and it's because like you actually cannot just get through the plane cuz even for a carry-on sometimes they'll charge I I believe. I don't know.
1:06:09
A lot of this is just my my you know memory from a decade ago but the Trump administration is looking to invest as much as $500 million in Spirit Airlines to fund the discount carrier's exit from bankruptcy.
1:06:19
The government money would take form of a senior loan with equity warrants that would come with it eventually owning a majority stake in Spirit struggled with high operating costs, stiff competition, and surging jet fuel fuel costs amid the Iran war.
1:06:35
Well, we will follow that story more in the future but we have our next guest returning to the show, Ishaan Gupta from Juicebox.
1:06:42
[music] Welcome to the show. How are you doing? Hello. I'm doing awesome.
1:06:46
Thank you so much for taking the time. Great to have you.
1:06:52
Introduce yourself and the company. Yeah. So I'm Ishaan.
1:06:55
I'm one of the co-founders of Juicebox.
1:06:57
We are an AI recruiting platform and we help companies find and engage talent using LLMs.
1:07:02
We build agents that will go out there, find the right people for every role you're trying to fill, and get them into your process. Okay.
1:07:07
We have a friend who's an executive at a big tech company.
1:07:12
He's been getting absolutely spammed with new outreach from what appears to be a new Gmail account that has been created and what's so remarkable about this this cold outreach is that it even nails the sort of like cringe footer that says like you know do not print this for the environment's sake and like you know a little quote and like it it it doesn't feel just like a prompt box shot in there.
1:07:38
It They're They're all There's a huge variety but it's not It hasn't been successful.
1:07:44
He He's He's He's annoyed by it.
1:07:46
So how do you think about actually targeting the outreach so that you're not annoying potential new hires? Of course, yeah.
1:07:55
I think I think the main problem is so the exact side is almost like the extreme side of it because they're getting like so much outreach.
1:08:01
Anytime an exact is valuable everyone's kind of spamming them. Yeah.
1:08:04
The thing is that for the average role the best way to guarantee that you're reaching out to the right people is to spend more time on the search.
1:08:10
What ends up happening in organizations today is that there's a hiring manager who really understands what they're looking for Yeah.
1:08:16
and then there's sort of these pattern matching that happens afterwards where talent teams are basically relying on these like really hard filters like oh I'm going to reach out to every software engineer at Google.
1:08:25
That's not a smart strategy, right? Yeah.
1:08:27
What LLMs are able to do is to truly understand Are you interested?
1:08:32
[laughter] It's It's like LLMs are able to truly understand what makes someone successful in a role and try to actually find people who are good at that.
1:08:40
And instead of just pattern matching based on what company you're at, what job title you're at, we'll actually go in there.
1:08:45
We'll analyze your real work product.
1:08:47
We'll see what you're doing on GitHub, what you're doing on different platforms, what companies you've been at, and develop a more deeper understanding of who's going to be a fit and then reach out to the right person. Okay.
1:08:56
So that that in fact like reduces spam. Yeah.
1:09:00
Let's flip it around to the person that's looking for a job.
1:09:01
They want to get spammed with great offers.
1:09:05
Uh what should they be doing because there's a lot of software engineers where they can't contribute publicly because they work for an important company.
1:09:13
Uh there's a lot of folks who their work happens behind the scenes and maybe they are sharing it on LinkedIn but can your scrapers reach into LinkedIn?
1:09:20
Should they have a a personal website and then uh you know put that in some sort of robots.
1:09:27
txt and web crawler filter so that they get indexed properly.
1:09:30
Like, how can job seekers show up in results more aggressively at a time when more people are looking for jobs more aggressively than ever?
1:09:42
Essentially, put your work product out there.
1:09:43
Talk about what you're working on.
1:09:45
Putting out blogs, putting out open source contributions, putting out any projects that you have worked on.
1:09:48
Those things help because What about with 3 hours of live >> [laughter] >> daily content help? I I'm sure. I'm sure.
1:09:56
I mean, it helps with an acquisition. Yeah.
1:09:59
>> [laughter] >> That's true.
1:10:00
Just talk about AI comms for for 3 hours a day and yeah, you you turn some turn raise some eyebrows.
1:10:07
Anyway, what we Yeah, what about So, are you purely focused on technical roles because you keep coming back to like GitHub contributions, breaking down wondering for someone who's in more of a knowledge role or they work in PR or marketing or biz dev or finance?
1:10:24
Like, what should they be educating people?
1:10:27
Should they be explaining their strategies?
1:10:30
Like, if they're not a natural writer or they don't just have a piece of like a side an obvious side project that they can open source, how can they show up to the AI researchers or the AI uh recruiters of the future, your company included? >> Yeah, of course.
1:10:46
So, we we we started out more tech focused because that's the industry we understood the most.
1:10:50
And we started out working with tech startups and we started working with companies like like Ramp, Scale, all of these kinds of companies.
1:10:58
And then eventually, what has happened in the last year is we've grown way way past that.
1:11:02
So, like, now tech does not represent the majority of our customer base.
1:11:05
We have more than 5,000 customers and a lot of them are larger enterprises and they're looking for all sorts of people.
1:11:10
So, what what we are able to do is we're able to join profile data and your experiences on all the company data that we have.
1:11:17
So, essentially, if you put a little bit of information about yourself on different platforms, we're able to take that information, enrich that, and do a deeper research on every company you've been at, every single skill you've had, everything you've worked on, and build a better understanding of exactly what your area of expertise is, and then make better matches using that.
1:11:33
And it applies across industries.
1:11:35
So, for example, we have people looking for traveling nurses in the Midwest.
1:11:39
Very different type of role.
1:11:41
But, what we can do is we can look up profiles.
1:11:43
There are registries available for nurses on on the internet as well.
1:11:47
We'll look up those profiles.
1:11:49
We'll understand the different places you've worked at.
1:11:50
We'll try to build a good understanding of what these different environments are, what kind of people do these other companies hire, where you've been at in the past, and then make a good inference on whether or not they're fit for the role you're looking for. Okay.
1:12:00
That's exactly what we're doing.
1:12:03
Why do you think there hasn't been a like a big like a like a decacorn scale outcome in in recruiting tech today?
1:12:10
What is Indeed valued >> would actually argue against that.
1:12:13
Like, LinkedIn's a pretty pretty massive Oh, got it.
1:12:17
Indeed is a pretty massive outcome.
1:12:20
Yeah, the recruiting company 70 billion enterprise value, bro. Yeah.
1:12:24
But, that's So, that's but but that's but that's like kind of where I'm kind of where I'm going.
1:12:27
Isn't that like a a holding company of staffing and recruiting firms? >> Yeah.
1:12:32
So, more more like labor intensive, but I can imagine >> market.
1:12:37
Let's just be >> Yeah, yeah, but but I'm saying like it feels like you could have you could have like if you execute properly, there could be an outcome closer to that holding company that you just mentioned than some of these other kind of just like recruiting tech platforms.
1:12:54
>> Yeah, yeah, I I guess just like where is the source of like economic power and lock-in and scale that would allow for a really really broad outcome here as opposed to okay, there's a whole bunch of AI recruiting firms and companies sort of bid them all down and there's not insanely high margins, good businesses, but not the hundred billion dollar outcome.
1:13:12
Like, have you been thinking through that?
1:13:14
I know it's early, but uh have you been thinking through like, what does this look like at may mega scale?
1:13:21
Of course, yeah, we we've thought a lot about that.
1:13:23
The the thing is that the main value in the recruiting industry always accrues in the services layer.
1:13:27
So far, it has never accrued as much in the software layer because it's really actually the work that goes into finding people.
1:13:35
It's the ability to search for the right person and get them into any role.
1:13:39
That is what people pay for, right?
1:13:41
And so far, there has been no way of actually automating that because you cannot do that. It requires judgment.
1:13:45
It requires actually understanding what someone's working on or what their capabilities are.
1:13:50
That is exactly what agents are able to do.
1:13:52
So, this is really the first time when you have a massive opportunity in a market that's incredibly important and you're able to build that with our LLMs because it's just not been possible until now.
1:14:00
And most companies have traditionally in recruiting tried to target this like software layer, which is like, hey, we'll build another CRM, we'll build another ATS, and all of these different platforms.
1:14:10
But, the main value always accrues in being able to find the right person.
1:14:14
That is what someone wants to pay for.
1:14:15
And that is exactly what we're able to do with LLMs. Very cool.
1:14:16
Well, >> Yeah, I I remember first discovering that just everyday recruiting services companies could be big because hey, there's a there's a public company called Hays, Hays. com. >> No way. Really?
1:14:31
>> It's just a public recruiting like staffing firm. >> Wow.
1:14:34
Uh so, yeah, that's a very very big opportunity for you guys.
1:14:38
Congratulations on the Thiel Fellowship.
1:14:39
Thank you so much for coming on and breaking it down for us and hope to see you soon.
1:14:42
I'm sure there's a lot of milestones in the near future.
1:14:44
Have a great rest of your day. We'll talk after.
1:14:48
>> Thank you so much for having me. It was awesome. >> Goodbye. Cheers.
1:14:51
Up next, we are running 1 minute behind, but we're going to catch up because we have uh we have Derpetual next, building infrastructure to enable derivatives trading on any asset. Interesting.
1:15:01
Okay, Derpetual uh Anthony Kazuki >> Before we go there, let's pull up this post from none other than who?
1:15:14
than Ben Horowitz, not Ben Horowitz. >> Yes.
1:15:18
Ben Horowitz with the launch of the day.
1:15:21
He has made the anti-Grammarly. The anti-Grammarly.
1:15:23
>> Mass up your emails with AI, guys.
1:15:23
So, if you're worried about people accusing you of a of using AI to write your emails, use sincerely Yes, misspelled.
1:15:34
to use more AI to add misspelling.
1:15:34
Yes, it it it condenses down that long, boring email that you were about to set send that says, "To whom it may concern, I wanted to reach out to express my interest in connecting regarding potential synergies between organizations."
1:15:49
And it just dumbs it down to, "Wanted to reach out. Let's talk."
1:15:53
And uh this is uh newsflash, this is how people actually email and communicate uh when it's person to person.
1:16:00
And I think everyone would be better off with a little bit uh tighter communication methods, especially in the age of AI.
1:16:06
So, uh I don't this this seems like a joke or a drop, but I would imagine this being a good product and [laughter] I think people might actually pay for this.
1:16:16
I think I I like there are so many times >> it's actually a real product. I hope so. I hope so.
1:16:22
>> to charge five bucks a month.
1:16:23
>> Alex Lieberman said uh such a good viral drop idea, but I I think it's not a I don't know.
1:16:28
I I would not expect this to go away.
1:16:30
I think this is a business and I think this will be successful and I think this is a valuable tool.
1:16:33
And it's something that uh that probably it would be hard for uh Google to justify baking into Gmail.
1:16:42
It's like it is a little bit counter-positioned against >> Yeah, it's a tough pitch, isn't it?
1:16:47
>> tough pitch to be We want to help our users get more typos.
1:16:50
>> It might be a good April Fools' Day joke that they could bring in and then leave it around. >> cuz Ben did it first. Ben did it first now.
1:16:56
So, good luck to Ben and sincerely, we'll check in with him later.
1:16:58
But, we have our next guest from Derpetual in the waiting room.
1:17:02
Let's bring him in to the TBP and ultra sound. How are you? Welcome to the show.
1:17:07
>> Hey guys, it's great to be here.
1:17:07
Thank you so much for hopping on.
1:17:09
Please introduce yourself and the company.
1:17:12
Uh I'm Anthony and I'm building a company called Derpetual and we wanted to create the derivatives exchange for everything. Okay.
1:17:20
>> So, basically, we created a new kind of derivative and we want to use this type of derivative to bring derivatives to, you know, assets beyond the top 1,000 that's that exist now. Okay.
1:17:30
Is there any crypto involved? Uh currently, yes.
1:17:34
Uh our um our protocol is working on uh on meme coins.
1:17:42
So, you can trade meme coins with longs and shorts.
1:17:46
But, uh it's honestly a great way for us to test everything, right?
1:17:49
We have the the craziest markets in the world and the craziest people in the world doing doing stuff on uh on our tech.
1:17:55
So, so we get to improve and and check if everything is fine. Yes, problem.
1:18:02
Meme coins weren't crazy enough. We made them crazier. You're welcome.
1:18:05
Uh what uh >> [clears throat] >> Is there a plan to bring this to real world assets, prediction markets?
1:18:10
Uh you know, we've seen the different financial approaches to so many different categories.
1:18:18
Uh how do you think this actually uh crosses the chasm?
1:18:23
Because we we went through the meme coin boom and it and they did sort of go mainstream, but I think a lot of people uh just didn't really find lasting value there necessarily and they sort of moved on and it's become sort of a bifurcated market.
1:18:36
And I'm wondering if if you have Is there a business to be built just in like the meme coin community or do you want this to go broader and wind up on Wall Street or wind up with retail investors?
1:18:46
How do you think about the the long-term plan?
1:18:50
We definitely want to want to go to traditional finance and end up on Wall Street.
1:18:54
And in the end, the problem with meme coins and with most things in crypto is that they are not useful.
1:18:58
So, of course, there is a market for them, but that market is going to be a thousand, ten thousand times smaller than something that actually that actually creates value instead of just being a casino. Yeah, yeah.
1:19:09
Yeah, that makes a lot of sense.
1:19:11
Um what uh Uh how do you think about the current strategy that we've seen Kalshi roll through around uh CT CFTC and futures contracts.
1:19:22
It feels like there's been a major acceleration in at least the I don't even know.
1:19:33
It's like it's like the the approval process for these new futures contracts.
1:19:38
I know that for for every new game, if there's a if the Super Bowl is happening, they have to get that contract approved and it feels like with their technology >> cycle?
1:19:50
>> Yeah, there's like a 24-hour cycle.
1:19:50
And so that that restricts some of the things they're doing, but it still feels like the fastest that the financial sector has ever moved.
1:19:58
Is that an interesting pathway for you or do you think that there's an entirely different way to attack the like the problem? Definitely.
1:20:07
I I I think Kalshi has um has been doing it right.
1:20:11
And I in the end, their strategy was pretty unique.
1:20:15
As you probably know, they um they started their company and for a couple of years they didn't do anything and just wrote letters to the CFTC about um how they please want to be regulated.
1:20:27
Uh and uh and the end that worked out and and no one else is uh is even close.
1:20:31
So they are definitely something that we want to uh we want to be learning from.
1:20:40
How are you uh is there a North Star asset that needs a derivative that is uh that would resonate with like a broader everyday audience?
1:20:48
Because uh the the the derivatives of meme coins, that seems like you know, there's a community for that, but that's probably not uh something that's uh super aspirational for the everyday American.
1:21:01
How are you thinking about, you know, uh uh the actual application?
1:21:05
The one that I always go to with derivatives is like uh you know, weather futures for farmers, that's a very practical or hedging the price of fertilizer.
1:21:14
These things are financialization, but they're clearly beneficial if you're if you're, you know, running a farm.
1:21:21
And so that's somewhat relatable to people.
1:21:24
Do Do you have other uh other derivatives that you think need to be unlocked in the near future in the real world? Definitely.
1:21:32
And I think the the inspiration is something that we probably want to have as one of our first assets in traditional finance is hydrogen.
1:21:43
Because there is no hydrogen derivatives uh anywhere whatsoever.
1:21:46
And it's a very popular asset for in for the industry, for energy, for for things like that.
1:21:52
But it's just uh to to spread out the production is too decentralized so you can't just create one hydrogen index and have it be be traded.
1:22:02
So I think uh I think this decentralization makes it impossible [clears throat] for traditional markets to to bring liquidity to to that.
1:22:12
What were you doing before this? Uh a lot of things.
1:22:15
I was doing um trading terminal for uh crypto.
1:22:18
I was doing uh uh software house and that was my first company and I was also uh making um making money selling Minecraft items. Ooh. >> There you go.
1:22:32
Uh A lot of in-game item sellers. >> Yes.
1:22:36
>> So far in this fellowship.
1:22:36
Any chance that uh we will see onion futures traded in the future?
1:22:44
Uh my fellowship money uh might be deployed to lobbying for uh finally getting onion futures. Onion futures.
1:22:54
This is uh this is a There's many single-issue voters out there for bringing back onion futures.
1:22:58
They were banned famously, very obscure piece of financial regulation.
1:23:05
>> and I and I think in general farmers in the US have a lot of political power and especially onion farmers, you know, they really they really want their futures. They do. They do.
1:23:12
Well, hopefully it happens.
1:23:14
Uh thank you so much for coming on the show. Yeah, great to be here. >> Thanks. Congratulations. We'll talk to you soon. Cheers. >> Goodbye. Cheers.
1:23:20
Uh while we're bringing in our next guest, we were in Cultured Magazine, culturedmag. com.
1:23:27
Uh what happens when Open AI buys your tech podcast? Ask the boys at TBPN.
1:23:29
We did an interview with them and we had some fun with it. >> on the show. >> Yeah, we have.
1:23:35
Uh so the first question that they asked us was what's one work of art that got you through an important moment in your life.
1:23:43
I said Godfrey Reggio's 1982 masterpiece Koyaanisqatsi.
1:23:50
Jordi, what would you say? Borat. Borat. I think that's good. I think that's good.
1:23:54
Um They asked us what keeps us up at night.
1:23:59
We said data centers being even slightly delayed. That would be terrible.
1:24:02
Like I I I lose sleep thinking about it.
1:24:04
A single supercomputer delayed by even minutes. >> Supercomputers. Even minutes.
1:24:11
>> opportunity to >> to say supercomputer.
1:24:12
They did ask us we we we we had some fun with some questions.
1:24:15
We told some serious answers, you know, we we mixed it up.
1:24:17
They asked us what do you think your biggest contribution to culture has been?
1:24:20
This is the question of Cultured. What do we say?
1:24:24
Making tech people put on a suit.
1:24:27
We've seen a few suits on this Thiel Fellowship Gigistream.
1:24:30
We've seen some suits creep into the tech podcasting world.
1:24:34
Job's not finished, but we're making progress.
1:24:36
We'll go back to our Thiel Fellowship Gigistream with Milan from Opt 32 developing compute infrastructure from compilers to chips for physical autonomy and local intelligence. Welcome to the show. How are you? >> [music] >> Good. How are you guys doing? Great to be here.
1:24:54
>> so much for hopping on.
1:24:54
Please introduce yourself and the company. Yeah, totally. So I'm Milan.
1:24:58
I'm one of the co-founders of Opt 32. Mhm.
1:25:00
And at Opt 32, what we're trying to do is essentially build modern full-stack compute infrastructure for physical autonomy. Okay.
1:25:08
So more concretely, like you said, that's everything from software, so compilers Okay. down to chips, right?
1:25:14
Custom accelerators Okay.
1:25:16
to run on-device machine learning and things like robots, drones, cars, uh autonomous defense systems, other autonomous vehicles.
1:25:22
That seems extremely extremely extremely broad.
1:25:24
Uh are you going to narrow it down?
1:25:27
Is there a beachhead that will happen where you will find a particular uh niche or is going broad piece of the Is that the strategy?
1:25:38
>> pick a small niche market and dominate, I think it's wrong."
1:25:40
But he's doing everything all at once.
1:25:43
>> I'm I'm super curious.
1:25:45
Yeah, so I guess a couple things to touch on there.
1:25:47
So the first is that, you know, the the nice thing about building infrastructure and compute infrastructure in particular is that it does generalize fairly well to many different applications.
1:25:55
So you can use the same chip and the same software to run ML in a robot as you could in an autonomous vehicle. Yeah.
1:26:00
Um I do think we are, you know, taking a sort of a an entry approach into the market, particularly from the robotics side.
1:26:07
Um and from, you know, our side, we are starting at the software layer and then gradually working our way down the stack towards hardware. Okay.
1:26:15
And in terms of So if you're starting with a software layer, uh walk me through the the infrastructure stack that you might be buying off the shelf.
1:26:24
Are you buying like Nvidia GPUs?
1:26:27
I I I know that like when I think of like a previous autonomy stacks, I think of like Tesla being very vertically integrated and then Nvidia and a few other partners sort of being like Mobileye sort of going around to the rest of the auto makers and the OEMs and sort of plugging in with a little bit more flexibility, but those companies that provide that stack aren't fully integrated.
1:26:49
So I see the opportunity, but I'm I'm I'm curious about how you're solving the short-term where even if you wanted to design custom silicon today, it's probably not going to ship to you in a year, right? Yeah, certainly.
1:27:01
So so what we do right now is we essentially build a, you know, fully automated model optimization platform, right?
1:27:08
So say you're a robotics company, you're deploying some perception model on your robots, you uh very often need extremely low latency.
1:27:17
Um you know, you have compute constraints and that you can't just toss a massive server-scale GPU on a robot that runs off, say, a battery.
1:27:22
Um so what we do is we, you know, work with these companies to get their models running faster or for them to be able to fit more intelligent models on, you know, cheaper hardware, stuff like that.
1:27:33
Um and right now our software layer is pretty much hardware agnostic.
1:27:36
So it could in theory run on an Nvidia GPU or even like a, you know, I don't know, Qualcomm accelerator or something like that.
1:27:43
But primarily we do target Nvidia GPUs as our backend. Okay.
1:27:48
>> [clears throat] >> Yeah, where what what are are you excited about like robots that are too small to uh house an Nvidia GPU?
1:27:56
I'm thinking of like the the the the Matic robotic sweeper or the Roomba.
1:28:02
Like and and I and I've been super excited about the the potential like the Roomba had such wide deployment.
1:28:08
It's it really did break through cross the chasm in terms of like robotics in the home.
1:28:14
And I'm wondering if you're excited about uh or optimistic about sort of a shorter timeline to uh those more incremental steps to robotics robotic deployment versus, you know, we've heard a lot of pitches for like the straight shot to humanoid AGI.
1:28:32
And I think that's coming, but are is there a uh a walk-crawl-run that you know, technology sector will do here?
1:28:40
Yeah, I I definitely agree with you there.
1:28:41
I think we're very excited about sort of the the gradual deployment of robots and autonomy into our everyday lives over the next few years.
1:28:46
Um I do very strongly agree that uh as as, you know, we get maybe, you know, jumping 3 years into the future, we'll see a lot more autonomy in in consumer life.
1:28:55
So, you know, that could be something like a Roomba, could be something like a cooking robot, could be civic [clears throat] duties like street cleaning.
1:29:02
As well as another area I'm particularly excited about is autonomy in manufacturing.
1:29:06
Um I think it can do a great job at sort of augmenting human workers and and uh helping to sort of bridge this um gap we're seeing where there's not necessarily enough um uh skilled say like welders or something to fulfill all the manufacturing uh wants.
1:29:24
Is there enough data in manufacturing to do anything at scale with machine learning or is there enough, you know, transfer learning from the big models? Today, maybe not.
1:29:36
Over the next few years, I would hope so.
1:29:38
Um we don't concern ourselves with the model layer.
1:29:42
We try to be everything after that.
1:29:42
So, we don't build our own models, we don't train models.
1:29:45
We work on running models essentially. >> Sure, sure. Jordy? Very cool.
1:29:48
What what were you doing before this?
1:29:52
I was a freshman at Harvard studying CS and philosophy. That's cool.
1:29:54
Why do you Why do you Why did you get into Harvard uh other than you seem like a a a smart guy? I have I have no idea. I don't know.
1:30:04
Um I spent uh most of high school doing CS research.
1:30:08
Um I started writing compilers back when I was a freshman, worked in a bunch of different university research labs, all on you know, compilers, computer architecture, programming languages, applied >> While you were in high school? Yeah. Yeah. There you go.
1:30:20
>> I what what do you make of the decline in uh computer science as a major across universities?
1:30:26
It feels like there's a I've seen some stats that show like a pretty pretty steep drop-off, and yet >> to it, John? Uh no, no, no.
1:30:34
You I mean, you're still you're still CS like major for effectively.
1:30:39
Uh but but it it it feels like uh the route that like the fear around don't major in CS is very much like don't major in CS and then try and go get like a front-end engineering job that's just writing code.
1:30:53
But if you're majoring in CS, that still might be the best path into working in robotics, working on infrastructure, doing a lot of different things.
1:31:02
So, how have you processed the value?
1:31:03
Like do you feel like the CS that you've learned is less relevant today?
1:31:10
Yeah, that's what that's what I was going to say.
1:31:11
I think there's sort of going to need to be a reallocation, so to speak, of of talent and focus within the field of CS.
1:31:16
Where as we see, you know, artificial intelligence capabilities advance, we see a move up layers of abstraction to where skills like system architecting, uh sort of higher-level theory are going to become more and more important.
1:31:28
And kind of these, you know, very low-level implementation details, like you said, being like a front-end developer doesn't provide as much value.
1:31:34
So, I don't necessarily think CS as a whole is dropping off so much as it's reallocating towards those higher levels of abstraction. Yeah. Uh how big is the team?
1:31:44
How are you thinking about the capital intensity of this business?
1:31:48
Uh when I hear custom silicon, I'm I'm I'm hearing like hundreds of millions of dollars to do really anything interesting.
1:31:55
Uh we've had some previous Deal Fellows from Etched on the show or or I've I've talked to them and done podcasts with them.
1:32:01
Um and it feels like this can be extremely uh in capital intensive or uh you can find partnerships and do something that's much more on the actual uh you know, software and infrastructure side that might be less capital intensive.
1:32:15
But how are you thinking about it?
1:32:18
Yeah, so so right now the the team is just the three of us co-founders, me and my two high school best friends, all technical.
1:32:22
Um we are hiring and hoping to grow pretty quickly.
1:32:25
Um in terms of capital intensity, the really great thing about, you know, building full-stack compute infrastructure is that we can actually sell our software in isolation of the hardware, and we're already doing that and working with some early design partners.
1:32:36
So, we can get revenue, we can do that quickly.
1:32:38
Um software development costs are relatively cheap.
1:32:41
Um sort of the you know, cost of operating our software is essentially zero.
1:32:46
So, any revenue from that is pretty much pure profit.
1:32:49
And in terms of hardware development, I think there's a a great path towards gradually moving towards a full production run of a custom ASIC.
1:32:54
Um you know, in particular, what we're going to do is build single-board computers, so PCBs, around some existing accelerator chips, and build the entire software layer there.
1:33:04
Then we'll move on to maybe implementing some chip architectures in FPGAs.
1:33:07
And then from there, you could do a, you know, smaller uh not super advanced process node kind of prototype tape-out run.
1:33:14
And then say 1 2 years down the line, once we've raised a lot more capital, that's when you go for the the big advanced process node uh full production tape-out. Yeah.
1:33:22
Are you working with uh Victor and Cavalla yet?
1:33:27
The chat >> Or are you competing and bitter rivals? No, no, no.
1:33:31
I think we'll be They'd be they'd be natural partners to some degree.
1:33:35
Yes, yes, I think we'll be definitely working with Cavalla at some point in the future.
1:33:39
Um right now we're mostly focused on sort of internal technical work, but shortly.
1:33:44
Uh why uh what is the uh the the especially in the manufacturing sector, uh is there not a lot of energy being devoted to uh not teleoperation, but just putting compute like like like I guess like a thin client for robotics would be the term?
1:34:04
Like you have a server room with a NVL72 rack on site, and then you're doing inference in the IT cabinet or closet or the local data center effectively.
1:34:16
And then your robot can just be much lighter and has like barely It just has a camera on that's just feeding stuff.
1:34:22
And like yes, there's maybe a little bit of latency, but you're talking about, you know, the speed of light across high-bandwidth Wi-Fi.
1:34:29
It seems like that might be an interesting solution. Like is that happening?
1:34:33
Is that one of many strategies, or is that a dead end?
1:34:39
Yeah, I think it's a split, right?
1:34:39
I think there are definitely use cases where you can do that.
1:34:42
And there's also other use cases where you might have some sort of network constraint or some sort of extreme latency constraint where you can't.
1:34:49
And you know, some things that we work on are actually like splitting the workload across both a cloud server call, and maybe something that's more latency sensitive can run on device.
1:34:59
Yeah, that makes a ton of sense.
1:35:01
Have you guys raised money already outside of obviously, you know, Fellowship is Fellowship is for you, but um Yeah, we raised our our seed round about 2 months ago.
1:35:10
We raised a 5 million co-led by BoxGroup and by Venture. Box. Petitioners.
1:35:17
Petitioners wouldn't get into this one. Petitioners in. We love We love him. Uh great great pickup.
1:35:23
Um and great to meet you.
1:35:26
I'm sure we'll be back on soon. >> guys as well.
1:35:27
Well, thanks so much for coming on the show. We'll talk to you soon. Have a good day. >> Thank you. Goodbye.
1:35:33
Up next, we have Galen Mead from Standard Intelligence focused on Not to be confused with non-standard superintelligence.
1:35:43
Non-standard unintelligence.
1:35:43
You don't want to be unintelligent.
1:35:47
Uh building aligned general learners, Galen Mead joins us on the show.
1:35:50
And I believe Galen is in the waiting room, so we will bring him in to the TBPN Ultra Dome as soon as he's ready.
1:36:00
Galen, how are you doing? Hey, doing well.
1:36:05
Reintroduce yourself and the company.
1:36:09
Uh yeah, we build computer use models.
1:36:13
We're a general research company. Yeah. Towards aligned AGI.
1:36:17
Um I dropped out uh I think around 3 years ago. Nice.
1:36:20
Didn't spend too much time in university.
1:36:25
And I just wanted to do a research full-time.
1:36:27
Did you jump straight to computer use, or was there something in the intervening years?
1:36:33
We at the time that we were started, there wasn't really this notion of uh a neural lab.
1:36:39
So, we from the start wanted to be a general research company. Yeah.
1:36:44
Um but initially, we we started with some work on audio models that I had continued from SF Compute. Interesting.
1:36:53
>> Just cuz, you know, you train some state-of-the-art model, and people see that you can train models, and uh you can go from there.
1:36:58
Um I think this is maybe one of the worst decisions.
1:37:02
Um not not worst, but worse.
1:37:06
>> [laughter] >> Uh uh Yeah, sure.
1:37:07
Uh strategically, but um computer use models are are nice cuz it's a pretty general form factor.
1:37:11
Why It always wanted Yes.
1:37:14
Space for for taking actions.
1:37:17
Yeah, why is computer use important?
1:37:19
I think there was uh maybe like a 1-week period where everyone was super everything will be a CLI, uh and there's maybe like a resurgence in computer use, but uh what what what what uniquely captivates you about uh computer use as a uh as a as a goal here?
1:37:38
So, it's a training recipe that I had always wanted to do. Mhm.
1:37:43
You can see in some of the early work on games from DeepMind, there's this notion of training a policy on tons and tons of supervised data from diverse uh environments, and getting something that is like a base model for actions.
1:38:00
And it it occurred to us that this exists for real-world work in the form of like screen recordings >> Yeah.
1:38:09
with a computer being the universal like actuator that humans use to interact in various diverse environments.
1:38:15
Um so, there's this this real opportunity to pretrain base models for being agents.
1:38:23
And this is a very appealing concept from a research perspective if you want to make very competent agents. Mhm.
1:38:32
Do you think uh how far out do you think we are from uh using like a truly complex piece of software like Premiere Pro or Cinema 4D or uh AutoCAD? Add any of these tools.
1:38:46
Uh it it it feels like we're on the cusp. Is that this year?
1:38:48
Are you seeing glimmers of okay, the average Premiere Pro user will probably be interacting with it through a prompt pretty quickly in the same way that uh software engineers migrated from the uh from the the, you know, IDE to the CLI and the text box pretty quickly.
1:39:10
Yeah, I think it'll be this year.
1:39:10
Um, I back in like middle school did freelance like animation. Oh, yeah. Uh, with Blender.
1:39:20
>> And I had to sort of bring that out recently for our model launch a little bit over a month ago um to put together Dharma videos.
1:39:27
And it honestly felt like stone ages compared to working with like the AI assisted code editors for my day-to-day job. Yeah.
1:39:35
Um, >> [clears throat] >> so I think we'll get there by the end of this year. Mhm.
1:39:39
And I think that will be a pretty big step change for all of these industries that aren't used to working with a Copilot. Yeah.
1:39:49
Um, is [clears throat] there any uh is there any difficulty with the lack of open source tools in other computer use like um areas because it feels like part of the reason potentially for the acceleration in in coding is that the the the IDEs were open source and also the programming languages are open source and like GitHub is this rich repository.
1:40:14
Uh but if you're if you are it will there be like this battle fighting between uh you know some proprietary piece of software that doesn't want to be used by an agent and then you're trying to figure out how you can do it.
1:40:26
Have you have you grappled with any of that?
1:40:28
So I I I think we're somewhat unique in that we are rather obstinate about hitting the input and output of a human exactly. Sure. So we take video inputs.
1:40:39
We output mouse state deltas and like character level keystrokes.
1:40:44
Um, and yeah, nobody's copyrighted the form factor of a a screen and a keyboard, right?
1:40:51
So if we interact on that level, it's fully general. Yeah.
1:40:54
How do you want to uh instantiate this with a customer?
1:41:01
We're pretty agnostic on that right now.
1:41:03
There's a lot of ways to go on the model capabilities.
1:41:05
Um, we can get pretty crisp signal on like there's a lot of things where we'd be able to tell the model to do something and it should do it. >> Yeah.
1:41:14
And once we're at that point, >> It's just download the app and start paying probably.
1:41:18
>> Little bit of a little bit of a tangent, but how have you processed the SaaS apocalypse?
1:41:22
There are some there are still some SaaS bulls and they'll tell you we're going to have we might even have more seats cuz we're going to have agents that are using computers and using existing software just like a human would.
1:41:35
But but how have you processed it overall?
1:41:39
Yeah, I I have one friend who's doing a search product who frames it nicely that there's an opportunity for a lot more usage if you have tons and tons more agents actively using these products.
1:41:52
Um, I I haven't been following this sort of stuff very closely.
1:41:54
Yeah, I'm pretty off the internet. Locked in. Yeah, love it.
1:41:58
Uh did we get how you made your first dollar?
1:42:01
That's always a >> Yeah, it was that was that doing Blender? >> Freelance animation. Freelance animation. >> Basically art artist.
1:42:06
What uh what what was the actual project?
1:42:11
Was it like marketing videos or It it was mostly like forum graphics stuff. It's a very niche space. Yeah.
1:42:16
All right, yeah, that's cool.
1:42:19
Well, thank you so much for taking the time to come chat with us. Great to get the update.
1:42:23
Come back on for your next big launch.
1:42:25
>> Yeah, congrats on the progress. We'll talk to you soon. Cheers.
1:42:27
Uh back to the culture magazine article.
1:42:29
Um, they asked us what grounds you and what invigorates you.
1:42:34
We said the daily schedule grounds us.
1:42:36
The show goes live every day every weekday at 11:00, so there's no room for projects to drift or spiral.
1:42:42
Uh booking a fascinating guest on the same day everyone is discovering them is incredibly energizing.
1:42:46
That's the Soham Parekh moment in a nutshell.
1:42:52
>> They asked what would you wear to meet your greatest enemy? What would you say?
1:42:55
>> A TBPN racing jacket covered in logos, so at least we're getting ad impressions for our sponsors. That's great.
1:43:00
Uh yeah, we had a lot of fun.
1:43:03
What what are we looking forward to this year?
1:43:05
Self-improving AI systems. That's on brand.
1:43:09
Anyway, uh we have our next guest from Swoop building a super app for Africa starting with food delivery in the waiting room.
1:43:16
Let's bring in Aubrey from Swoop.
1:43:19
Aubrey, how are you doing? Welcome to the show. Hey, I'm doing well. Uh yeah.
1:43:25
Please introduce yourself and the company.
1:43:28
So yeah, we're building a super app for Africa.
1:43:30
So I live in Nigeria now.
1:43:33
I started my first company in Africa in 2015.
1:43:35
So I got like really into geography growing up.
1:43:37
I had this first company it was recruitment company in Southern Africa and I was spending all my you know summer and winter breaks in high school within Africa building this company and I realized that all the biggest opportunities basically you you had to live in Africa.
1:43:52
All these export industries were way more competitive.
1:43:55
Anything where you had to live in Africa was a lot bigger opportunity.
1:44:00
So I was figuring out you know what is what is the you know biggest opportunity in African tech cuz I knew I wanted to move to Africa build tech company when when I was this idea of building a super app.
1:44:14
Biggest opportunity has been tech, but I felt like you know doing super app is going to be the way that you can get there how you can build a you know multi-billion dollar consumer business in Africa.
1:44:22
Let's start with the state of food delivery in Africa.
1:44:27
Are the major players there in a minor capacity?
1:44:29
Are they failing or is it sort of a wide open blue ocean market?
1:44:32
Yeah, so it definitely depends on the country.
1:44:37
So for instance South Africa Uber Eats is doing quite well, but Nigeria is our big market right now.
1:44:45
So Nigeria where we just launched we have two major competitors. We have Glovo.
1:44:50
It's a international you know competitor and you have a Chowdeck which is actually the market leader.
1:44:56
It's a local company, but you know Nigeria is very early stage.
1:44:58
If you look at you know the ratio of GDP to how much GMV food delivery is doing Nigeria is you know five or seven times behind a lot of comparable countries and it's still growing you know more than 150% every year.
1:45:13
So it's still very early days.
1:45:15
What you see is in food delivery in particular once that kind of service becomes available the culture adapts and it can continue to grow for a really long time. Mhm.
1:45:23
What do you what do you think the other players are are doing poorly?
1:45:27
Like what where is the opportunity to differentiate? >> Yeah.
1:45:31
Yeah, I mean biggest thing is price.
1:45:34
Prices are really expensive in the market. We're a lot cheaper.
1:45:39
And then I think you obviously to have a lower price you have to have a vision for you know how you how you can keep those prices low.
1:45:47
So that's when we get to this you know super app idea.
1:45:50
If we can convert a significant percentage of our food delivery customers over to being payments customers for us, that's you know something that's really exciting because you know payments are extremely large.
1:46:02
There's 18 trillion in digital payments being sent in Africa every year. 2.
1:46:06
6 trillion in just Nigeria.
1:46:06
If you can capture a significant you know percentage of that and you can be converting each food delivery customer maybe one in five of them you know starts using your peer-to-peer payments product sending money to other users.
1:46:19
That becomes not only you know a really effective way to acquire customers for something that's very high margin, but also it allows you to keep the food delivery service cheaper because you don't necessarily need to make you know your your revenue from it.
1:46:33
Whereas you know company like Glovo, they're international.
1:46:36
They only run a food delivery service in Europe.
1:46:38
It's too late to build a super app in Europe.
1:46:40
So they're not set up to build a super app.
1:46:43
They'll never launch a payment service in Nigeria because that's not their business, but it is ours.
1:46:47
So we do have an advantage there.
1:46:50
What's the what is the North Star super app look like these days?
1:46:54
What is the canonical example of success? >> Is it WeChat?
1:46:59
I'd say WeChat is definitely big.
1:46:59
I think of one company that I you know get really excited about in terms of comparison is Kaspi.
1:47:05
So they're not only like the biggest e-commerce company in Kazakhstan, but they're also one of the biggest payments companies one of the biggest banks in Kazakhstan.
1:47:14
I they do e-commerce, they do ride share, they do a bunch of verticals.
1:47:17
I think it's a really exciting business and you know Kazakhstan's not that much of a big economy than Nigeria.
1:47:24
It's about the same size, but you know Kaspi's doing 2.
1:47:27
4 billion in profit every year in just Kazakhstan and I think that you could do that in a number of African countries with the state of you know what the competition is.
1:47:37
I think it really is early days.
1:47:39
These verticals are growing very fast.
1:47:40
The competition is not established to the point that it's impossible to win in a lot of these verticals.
1:47:46
And in a smaller market you have this phenomenon where the gains to scaling become greater than the gains to specialization.
1:47:54
Whereas in a bigger market you might need to specialize because the fixed costs are relevant relative to the potential size of the business.
1:48:03
How big of a deal is the Belt and Road Initiative in Africa these days?
1:48:06
We were reading about uh some potential bailouts for some loans from the IMF being discussed at a very high level, but is is the Belt and Road Initiative uh a big deal like from being actually there?
1:48:24
Like what's your perception of it?
1:48:26
I I wouldn't say it's maybe like you know top top two or three things that you know we're thinking about in terms of what's going to shape a country, but obviously it's very real.
1:48:35
There's a lot of infrastructure that's going on uh that's being built.
1:48:40
Actually the one of the countries that we work in our first country actually Eswatini.
1:48:45
You might know it as Swaziland. That was the old name.
1:48:49
In in Swaziland that's the only country that hasn't taken Belt and Road money.
1:48:54
It's interesting to see because they actually recognize Taiwan.
1:48:55
So China will not you know do Belt and Road.
1:48:57
There's 53 African countries that do get Belt and Road funding.
1:49:01
Swaziland is the one that doesn't.
1:49:03
But there there is like a difference there in like you know who who's building roads, who's building you know different infrastructure, but I don't think it's like you know one of the top three things that I'm looking at as something that's going to you know change development in Africa. Yeah.
1:49:17
How do you think about geographical [clears throat] expansion?
1:49:19
How local do you want to be focused from a staffing perspective?
1:49:22
Do you imagine having satellite offices internationally or do you want to centralize everything?
1:49:28
What what are the trade-offs there?
1:49:31
Um in terms of recruitment, first of all like our our team is primarily cross Nigeria and India.
1:49:38
So I decided that we would go you know international with the software development function in particular to be able to be able to capitalize on just all the markets.
1:49:49
That's you know my my first business is a recruiting company.
1:49:51
That's what I felt was the right thing to do and I think we've been able to bring on a great team that way.
1:49:55
That being said the majority of our team is in Nigeria and going forward I expect to you know expand primarily in Nigeria for let's say the next year and then from there we'll be looking at primarily launching other African countries and and hiring there.
1:50:13
So the vast majority of staff is in Africa.
1:50:15
There's amazing talent in African countries particularly in Nigeria and if you are able to identify that you have a good process I think there's amazing talent there.
1:50:25
In terms of geographical expansion for the the business itself, I'm definitely excited about a couple of other African markets and we just need to be able to get to a point where launching another country would not hinder our business in Nigeria which that's not where we are today.
1:50:46
Obviously we just started in Nigeria started marketing actually last week.
1:50:51
So we're not at that stage yet but I would love to you know be at that stage where we can start launching other African countries.
1:50:57
What did you learn from the recruiting company?
1:50:59
What was the story of that business?
1:51:01
Yeah, I think I the most important thing I learned is how to recruit.
1:51:05
Second most important thing I learned is how to sort of operate a business in Africa, what it's like to to live in Africa, what what are the you know the challenges, what are the biggest opportunities.
1:51:14
I learned a lot about what the opportunities were but I think number one most important thing is how to recruit.
1:51:21
I feel that most companies in the world but maybe particularly in Africa are not meritocratic with hiring.
1:51:27
If you are if you you know can go out into a new market if you can figure out how to you know source thousands of candidates, if you can reach out proactively to people that you know are good, if you can do more than rely on referrals, if you can build an effective way to actually assess people that you know you don't know before, right?
1:51:47
If you can actually build that there's incredible talent on the continent.
1:51:52
We've been able to you know bring some amazing people onto our team.
1:51:55
I think that's the biggest thing I learned.
1:51:59
Basically how how can you get an advantage in recruiting? Yeah. Well, congratulations. Thank you so much.
1:52:06
Do you already have anything else? >> cool.
1:52:07
Have a great rest of your day. We'll talk to you soon. Congrats on the launch.
1:52:10
Congrats on the fellowship. Thank you so much. Cheers. Great to meet you.
1:52:16
>> [clears throat] [applause] >> People having more fun with the open AI image model, a hydrologically accurate cutaway of the Strait of Hormuz drawn by Richard Scarry.
1:52:24
Are you a Richard Scarry guy?
1:52:27
I'm a huge fan of Richard Scarry with the cats.
1:52:29
This is a good way to learn.
1:52:30
It's a good way to learn.
1:52:33
Riley Walls was doing some crazy stuff where actually it's Riley Goodside the prompt engineer. I got them mixed up.
1:52:39
So Riley Goodside worked at DeepMind and Scale AI and he asked chat GPT images 2.
1:52:44
0 Pro generate a photo of a cake decorated with an SVG that when transcribed to a file renders the cake another cake.
1:52:54
And so it absolutely wild.
1:53:01
>> People have been putting putting the model through its paces. For sure. For sure.
1:53:05
>> I like this other one from Tender Yes.
1:53:07
>> that got to say GPT images 2 is pretty clutch for interior decorating ideas. Yes. Yes.
1:53:12
If you if you have an empty wall put a fridge [snorts] of Sobe monster and Red Bull in your blank wall.
1:53:23
Wow, Sobe's been discontinued John. Really?
1:53:26
>> Insane opportunity for certain people to remember who they are and bring this back. >> Yes. Yes.
1:53:33
I was never I was never Sobe drinker but I always respected the brand and what it stood for, the funny ads and what a what a wild time.
1:53:41
Was it highly caffeinated?
1:53:43
Was it not caffeinated enough?
1:53:43
It seemed like it just got steamrolled by Red Bull and Monster eventually.
1:53:48
It'd be interesting to dig into the story of Sobe and what happened there.
1:53:51
But we will have to do that another day because we have Samuel from Prazo creating an AI powered infrastructure for wholesale commerce covering procurement, credit and [music] workflows for SMBs. Welcome to the show. How are you doing? Good. How are you? We're good. >> meet you.
1:54:08
Please introduce yourself and the company. Yeah, of course.
1:54:12
Well, I'm originally from northeast in Brazil, city called Recife.
1:54:16
I ended up going to Stanford for undergrad but dropped out and came back to Brazil and started Prazo.
1:54:22
And we're tackling a major problem in Brazil and trying to build the new infrastructure for wholesale.
1:54:28
And when you say infrastructure that could mean everything from like warehouses to warehouse management system to e-commerce software.
1:54:33
Help me understand what what infrastructure means in this context. Yeah, for sure.
1:54:39
We're trying to build it end to end.
1:54:41
So the idea here behind business is that while retail has seen a lot of digitization in the last 10 20 years in Brazil or in other countries across the world wholesale in our view has been left behind.
1:54:53
And these small and medium size businesses are still procuring the same way that they were 20 30 years ago.
1:55:00
Distribution hasn't changed.
1:55:03
We're basically building a tech enabled wholesale commerce player.
1:55:08
Where we deal end to end so we deal with manufacturers and we help them reach small and medium size businesses in a more efficient data driven way. Sure.
1:55:18
So is is Alibaba reasonable comp here?
1:55:21
I feel like that's that's where a lot of people meet wholesalers and suppliers if they're operating in China at least.
1:55:30
Yeah, but we're only tackling Brazil so we deal with Brazilian suppliers and Brazilian merchants.
1:55:34
We started out with food and beverage which is the highest frequency category with the most number of merchants.
1:55:40
So today we serve a a little bit over 10,000 restaurants and just in northeastern Brazil so far.
1:55:47
But only restaurants if you look at restaurant procurement is an over 50 billion dollar a year market.
1:55:52
But if you go to other verticals in commerce which in Brazil. Yeah.
1:55:56
If you go to Latin America it's double the size so about 100 billion dollars a year.
1:56:01
And if you go to other verticals than restaurants um the market just multiplies by five or six.
1:56:06
So it's a it's a massive market.
1:56:08
We're starting out with restaurants. Yeah.
1:56:10
What is the secret to getting 10,000 restaurants on board?
1:56:13
Is this cold email, phone calls, sales reps, advertisements?
1:56:17
Like what's the funnel look like to actually grow because you're operating this two-sided marketplace.
1:56:22
I imagine that demand generation is top of mind at all times. Yeah.
1:56:28
I think the trick with serving small and medium businesses is that they have high natural attrition.
1:56:35
So the way to grow in an efficient way is having very low CAC and the only way you can grow at a very low CAC is if merchants love your product then they will refer you to other merchants.
1:56:45
So basically today about 40% of our customer acquisition is sales led.
1:56:52
So we have sales reps reaching out to these merchants and onboarding them into Prazo but 60% of our merchant acquisition comes through either organic or merchant referrals or channels which are more product led.
1:57:08
How do you is disintermediation a problem for you?
1:57:11
Like how do you stay I imagine that there's transaction fees for finding and working with a supplier if you're a company that's on the platform.
1:57:19
Are is there a fear of someone meets a great supplier for their food or beverage product and then they start dealing with them directly? Is that an issue? Really.
1:57:31
And the reason for that is that we're serving these very small merchants.
1:57:36
So the biggest challenge for a manufacturer to serve them is not one of them might be reaching them but they couldn't do the logistics by themselves which is why we do the logistics as well and we also do the credit.
1:57:47
So often times a manufacturer they won't they won't want to get into these small merchants because the drop size for delivery is very low and they can't do it in an economical way. >> 500 Coca-Colas.
1:57:57
And it's like and I want credit. Yeah, yeah.
1:58:00
Exactly and they want credit as well.
1:58:02
Manufacturers are in general one they're not very good at assessing credit risk of a small merchant but second they're not senior in the in the merchants payment stack, right?
1:58:14
At Prazo since we're serving them as a one-stop shop and we are getting more and more share of their procurement, we are becoming more and more senior in their payment stack.
1:58:22
So as we grow and we become more relevant to these merchants our delinquency rates just go much lower.
1:58:28
Do you think you'll have to raise a lot of money to underwrite more more [clears throat] credit products or is there a banking system that you can plug into and sort of use like another fintech you know ally to sort of service that? Yeah.
1:58:45
Today we already use a a partner for financing but I I don't think we would have to raise a lot of money for that and the good thing about our business is that we we don't believe in offering very long credit to small merchants because they're very volatile, right?
1:58:59
So we have to offer short-term loans.
1:59:01
So, our average loan is about 10 days.
1:59:03
So, we we turn that three times a month.
1:59:09
Um and uh that helps a lot in keeping a low um outstanding balance, but compounding at a very high rate.
1:59:15
Uh the company overall is already profitable.
1:59:17
Uh so, we've raised uh funding to date, but we've become profitable. Thank you. Congrats.
1:59:24
How big How big is the team? About 100 people. Whoa.
1:59:30
When did you start this, then?
1:59:31
Uh I started company about 4 years ago um and moved back to the northeast of Brazil.
1:59:36
Um we today we only operate in two cities in northeast of Brazil. Yeah.
1:59:40
Uh so, there's major opportunity uh for growth from there.
1:59:43
Uh these two cities combined Yeah.
1:59:45
um are only 4% of Brazilian GDP.
1:59:48
So, we haven't uh we have an opportunity to tap like a 25 times uh bigger market just in Brazil without even expanding to other countries in Latin America. Mhm.
1:59:57
Uh do you feel a little bit like an unk being a part of this Thiel Fellowship class?
2:00:03
We we had the youngest ever Thiel Fellow.
2:00:07
You seem like maybe a 10-year age gap with him. Is that Is that correct? Yeah, yeah.
2:00:12
Well, uh not that much because So, I I I got into Stanford a little bit earlier.
2:00:17
Uh so, I had skipped uh grades in school. So, I got into Stanford.
2:00:18
I was a little bit younger.
2:00:22
Um but um I think I might be the oldest in this class.
2:00:26
So, Yes, we're going to call you the elder, the wisest I'm usually the youngest uh wherever I go to uh somewhere, but in this group um I'm I'm definitely the oldest.
2:00:36
We'll keep you keep you aggressive.
2:00:38
Can you uh zoom out for us and Oh, sorry.
2:00:40
Yes, I I had one more question around uh What do you think the path for this business is?
2:00:45
Is this eventually come and IPO in America?
2:00:52
How How deep are kind of Brazilian capital markets?
2:00:54
How do you think about scaling? Yeah, for sure.
2:00:59
Um Yeah, most of our investors are US-based.
2:01:01
Uh so, it definitely uh helps a lot to raise venture funding here, and I think ultimately we would want to um IPO in the US.
2:01:10
But if you look at this, there's just a major opportunity.
2:01:12
Um if I think the market potential is even bigger than food delivery.
2:01:16
Uh so, for example, iFood, which is the largest food delivery uh player in Brazil, is uh controlled by Prosus.
2:01:21
Um it's an over $10 billion market cap company.
2:01:24
Um and we think there's an opportunity to build something even bigger.
2:01:31
Uh but uh while iFood goes to the consumer space, we go B2B.
2:01:36
Um and we think there's an opportunity to build um a multi-billion dollar profit company.
2:01:40
So, um such a massive market, and there's a long way to go building it. Yeah.
2:01:47
Last question for me was uh zooming out, can you uh get us up to speed on the Brazilian economy broadly?
2:01:54
You know, China's been growing very quickly.
2:01:56
America's at the risk of like some stagflation in you know, gas prices are high.
2:02:00
Uh there's some economic growth.
2:02:03
It's heavy in data centers.
2:02:03
Uh and we we've been tracking the American market, but how are things going in Brazil broadly?
2:02:10
Like what is the economic outlook? Yeah.
2:02:14
I would say it's not great. Mhm.
2:02:16
However, the way we generally put this is that uh compared to the other emerging markets, we are better.
2:02:19
So, they're all they're all in a very bad position.
2:02:24
So, relatively we are better.
2:02:26
So, that's why like if you look at the Brazilian stock market, it's been going up.
2:02:29
Uh but it's mostly because I think investors want to invest in emerging markets have um relatively uh worse places to put their money.
2:02:37
Uh but I think Brazil tends to um grow a lot in the next uh maybe decades.
2:02:43
And the reason for that is two, I think. Um especially energy.
2:02:48
Uh so, like demand for energy will probably like go up a lot in the next uh few years, and Brazil has sort of a very uh big renewable matrix.
2:02:55
Um and we have a lot of opportunity for energy.
2:02:58
So, I think that uh helps a lot.
2:02:59
Over 50% um of our energy is hydro.
2:03:03
I think over 70% is uh renewable. That's crazy. Yeah.
2:03:10
Um and the second thing is we have one of the largest reserves of rare earth metals um in the world, which I think is very strategic as well.
2:03:17
So, I think Brazil is a very well-positioned country.
2:03:21
It just needs uh great leadership to take it to the next phase.
2:03:25
Well, we're glad you're taking the helm, and uh they're lucky to have you. Yeah, super impressive.
2:03:31
>> [clears throat] >> Well, thank you so much for coming on the >> on the show next time you guys have a big milestone.
2:03:34
Maybe a profitability milestone like your first, you know, first eight figure EBITDA quarter or something like that.
2:03:43
Yeah, I can see it in your future.
2:03:43
Well, have a great rest of your day. We'll talk to you soon. Thank you very much. Bye-bye. Goodbye.
2:03:50
Uh >> [applause] >> someone went to the new ChatGPT images 2.
2:03:55
0 model and said, "Make me the most AI slop image that ever AI slop, the pinnacle of slop, a seminal work on AI slop."
2:04:05
And people are saying this is unironically a work of art.
2:04:07
This is a crazy, crazy image that's all over the place.
2:04:12
It's really just like every style sort of.
2:04:15
There's some Fortnite in there.
2:04:17
There's a There's a jazz cup, a car with an alien, and Clippy's in there somehow.
2:04:24
I wonder if this is the real prompt.
2:04:24
You never know when people post online, but uh certainly a uh It's pretty rough to look at. It is very weird.
2:04:31
It's It's not It It didn't nail like the beauty of many of these things.
2:04:36
Anyway, uh we have our last guest of the show, Claire Wang, building biologically accurate simulations of nervous systems. No company. Pure research.
2:04:47
Welcome to the show, Claire. How are you doing? >> Great to meet you. Hi, nice to meet you.
2:04:52
Thank you so much for taking the time.
2:04:53
Uh please introduce yourself. >> research. >> Age of research.
2:04:56
Yeah, tell us tell us a little bit about what you're working on. Yeah, of course. So, I'm Claire.
2:04:59
I actually just dropped out, but I was a junior at MIT studying electrical engineering and computer science.
2:05:05
Uh a lot of my interests in the past bit has been in the field of neurotech, which includes a focus in um whole brain emulation with a focus on doing this with worms first, but also like how useful this could be for brain-computer interfaces and understanding consciousness maybe um in that sort of direction.
2:05:22
So, uh I mean, we we've talked to a number of uh of of companies that are working on brain-computer interfaces bumping into this general uh scientific discipline.
2:05:34
Uh what was the deci- Take me through the decision to not join one of the existing efforts or stay in academia and actually go out on your own?
2:05:45
Yeah, that's a good question.
2:05:45
So, I would say like um there's still a lot of current efforts that I think I really believe in. Yeah.
2:05:50
Um and that like I'm good friends with them, would help out with them.
2:05:54
I think there's a lot of bets that people have to make.
2:05:55
And these bets are unfortunately kind of fundamental science bets of like, "Oh, is this physics going to work? Mhm.
2:06:00
Is this like biological like truth actually true?"
2:06:03
So, I think like while these people I I think all these bets are like interesting, there's no 100%, and so I think it kind of makes sense to um instead make your own bet on what you think makes the most logical sense.
2:06:15
That's That's kind of my reasoning here. Yeah. Yeah.
2:06:17
Do you also think it's a better time than ever to be working more independently on biotech generally because of the advance of AI?
2:06:26
And we've talked to a couple companies that are sort of working on uh you know, the AWS of of lab equipment, more rentals projects.
2:06:36
So, that maybe you don't need to raise, you know, a billion dollars out of the gate and build a biosafety level two lab on day one.
2:06:43
Uh do you feel like more empowered to do uh you know, to validate those hypotheses that you were articulating earlier?
2:06:53
Yeah, no, I think that's a that this is like probably the right time.
2:06:54
I think it's a mix like definitely AI, um definitely uh insurgence like insurgence of like alternate science funding, um a willingness for academics and for example like people in startups to work together.
2:07:08
Um I also would think that people are just much more open to, you know, new ideas and research risk in startups, which I think like, you know, 10 years ago you'd be told like never ever invest in research risk, which is now like everything is a research risk.
2:07:21
I think >> [laughter] >> I think that's like a really good time for you to do it like right now. Yeah.
2:07:28
Yeah, Jordi, do you have a question or I Yeah, I mean, my my main question was like figuring out when when that right moment is because there's especially after the you know, the announcement of the fellowship this week, I'm sure you've been offered uh I'm sure people would offer you millions of dollars over email to just say like it's fine if you want to just keep doing research.
2:07:49
Um but uh but I guess you'll know it.
2:07:50
Well, help me help me bridge the gap between uh what we've seen in BCI with mostly reading from the brain, getting an X and Y output so you can control a mouse on a computer, incredibly impactful technology.
2:08:06
Uh and then uh simulating nervous systems.
2:08:10
Uh how are these two things related in your mind?
2:08:13
Why are they important to overlap?
2:08:14
Like what is the overlap? Yeah, yeah.
2:08:17
So, I think right now BCI technology is very powerful in the sense that it is on a great path through its clinical applications.
2:08:23
Um soon like people who are paraplegics can maybe walk again, people who are blind can see again.
2:08:28
Um but a lot of this comes from almost like post hoc problem-solving.
2:08:34
You like throw enough data at a model of like someone's brain, and like maybe you can help them like move their arm in the XY direction, or like move a mouse in a specific way.
2:08:42
But when you are able to like actually decode information from the brain and read and understand like um you know, exact signals coming from every region of the brain, being able to like truly like control the brain, for example.
2:08:55
Like if you know which regions to activate, then you have a more naturalistic control method.
2:08:59
So, like, instead of like only being able to move my arm left or right, you can move your arm like very naturalistically in any like degree of freedom.
2:09:07
And I think that's kind of the like power of being able to stimulate the brain.
2:09:12
Um you could do a lot of like research and understanding of like one of the most complex things in the universe.
2:09:18
Uh and I I mean, obviously, like we're not starting with the human brain, but any level tells you the sort of data that you need and tells you the sort of like imaging techniques that work.
2:09:25
And I think through that, you get uh a lot more information and like progress in BCI. Why C.
2:09:31
elegans and not mice or monkeys or something else?
2:09:35
Uh is it cost or do you have a firm belief that what if it works in C. elegans, it'll scale? Uh is there prior art?
2:09:43
Like, what what excites you about that particular target? Yeah. Um so, C. elegans has 300 neurons. It's really dumb.
2:09:50
It's like really just not I mean, it's not close to humans at all.
2:09:54
But I think the the argument is like we can't even simulate the C. elegans.
2:09:59
There's a lot of benefits C. elegans gives us. It is translucent.
2:10:00
Um you can do it's much easier to do gene therapy, so like fluorescence therapy.
2:10:06
So, easier to image as well.
2:10:08
Um and also, they're just like very simple.
2:10:10
They have graded potentials instead of action potentials.
2:10:12
So, I think the idea is we can't even do C.
2:10:15
elegans, so we have to start with that. Sure.
2:10:17
And there's uh a lot of like what is the sort of data that you need?
2:10:20
Like, do I need voltage data or is calcium data enough?
2:10:23
Um is light sheet electron microscopy enough?
2:10:25
So, like, there's all these questions that C.
2:10:26
elegans can answer and once we answer that question, obviously, the goal is to move on into like zebrafish and mice and fly um and so on.
2:10:34
But right now, like we are not close.
2:10:35
Uh or like we're not close to mouse, for example, because like there's no way to image the mouse brain while the mouse is still alive. Uh yeah. But you can with C. elegans. Fascinating.
2:10:46
Uh are you thinking about you'll do you think you'll wind up with co-founders?
2:10:50
Like, how how early are you in the process of like turning something into a company?
2:10:54
Do you want to just sort of like be on your own or do you want to build a some sort of team even if it's loose?
2:11:00
How how do you think about like research collaboration?
2:11:04
Yeah, so um I mean, I've been able to I've been lucky to work with some of the most amazing researchers.
2:11:07
Um and I think these are people I want to continue to to be around and learn from.
2:11:11
But I think in terms of specifics, like finding co-founders, finding the right um bet, for example, the scientific bet I want to make, that's still up in the air. I'm still like learning.
2:11:19
I'm still meeting a lot of people.
2:11:21
I'm seeing like what do I believe in and what do I like think makes the most sense.
2:11:24
So, it's still quite early stage for me.
2:11:25
Are you going to move to San Francisco? Yeah. Uh yeah, probably.
2:11:31
>> [laughter] >> Reluctantly, maybe. I mean, I like SF.
2:11:33
I lived in SF in the past, but like I don't know. Yeah.
2:11:38
>> It's kind of a principles thing. Yeah.
2:11:40
Have you always been pure researcher or do you have entrepreneurship background as well? What else have you done? Oh, yeah.
2:11:45
I mean, I would say like almost most of my background is a very clean mix between like the startup space and uh research.
2:11:51
Like, I've done a lot of work in like, for example, working at various startups, helping out with startups, some like small scale like investing stuff.
2:11:59
So, I I I think like I have a good mix of the both sides. That's very fun. Well, good luck.
2:12:03
Well, come back on whenever you have news and congratulations.
2:12:06
Yeah, we'd love to catch up soon.
2:12:09
Have a great rest of your day. We'll talk to you soon. >> Goodbye. Intel is up Intel's up. Massively.
2:12:18
>> The the market is broadly down today.
2:12:21
Intel is up 15% after hours.
2:12:23
>> 15% >> They reported earnings. That's good news. >> Let us see.
2:12:26
Intel reported We pull out our bread and continues to cook with his Intel bet.
2:12:32
>> He needed another win.
2:12:32
It had been a couple days since he had a massive win. >> Yep.
2:12:36
I think there's actually another story in here from today about a memory company that he invested in that's doing very well.
2:12:42
Well, more breaking news in the journal.
2:12:44
Bob Iger is returning to where? Disney? Thrive. Thrive. No way. That's amazing. >> Back to Thrive.
2:12:54
>> [applause] >> Love it.
2:12:54
Uh yes, he he's been an LP in Thrive.
2:12:57
He also bought a piece of Thrive. Remember?
2:13:01
>> Okay, well, that'll be a good next act for him.
2:13:03
I'm very interested to see where he goes.
2:13:05
There's a whole alumni class coming together.
2:13:07
Reed Hastings is out and more on to the next thing. We'll see where they go. Hopefully. Uh back to Intel.
2:13:13
Intel announced its first quarter earnings after the bell on Thursday beating it analyst expectations on the top and bottom line and providing better than anticipated Q2 guidance on strong data center sales.
2:13:25
Uh Intel said it expects revenue of 13. 8 to and 14.
2:13:28
8 billion for the second quarter.
2:13:30
Wall Street was anticipating uh 13 billion.
2:13:34
And as of this morning, they were they they were at around 100 at 100 um times uh PE. Mhm.
2:13:44
And so, it's only, I guess, uh only up for Intel. Climbing the ranks. Climbing the ranks.
2:13:51
Uh Tesla also released Q1 earnings. Um revenue of 2. Uh 22. 4 billion versus 21. 4 billion estimated.
2:13:58
So, they beat on top line.
2:14:02
Uh they also beat on net income. Uh 1. 45 billion versus 1. 17 uh billion estimate.
2:14:09
The interesting uh article in the journal was that uh Elon was being more cautious about Tesla.
2:14:13
Uh talking saying uh I think we need to get realistic about some timelines.
2:14:19
So, uh he is uh certainly not, you know, pumping everyone up and and he's trying to sort of reset around uh the fundamentals.
2:14:29
And so, um we will see where that goes.
2:14:31
It is a wild timeline that uh SpaceX, if it goes out at 1.
2:14:35
75 uh trillion, will be bigger than Tesla, which is sitting around 1. 1, 1. 2 trillion these days.
2:14:40
So, uh still both huge companies.
2:14:44
>> Credit to Bubble Boy over on X.
2:14:44
2 hours ago, he says, "Everyone asks me about how I'm playing earnings." He says, "Doubling down.
2:14:51
25% of my portfolio is in Intel calls."
2:14:54
Wow, there we go, Bubble Boy. Congrats. >> Well done. Good. Well played. Stuff. Well, >> Well played.
2:15:00
Thank you for tuning in to our Thiel Fellowship giga stream.
2:15:03
We will be back tomorrow at 11:00 a. m. Pacific.
2:15:07
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2:15:09
Sign up for our newsletter at tbpn. com.
2:15:11
Throw that flash bang, Jordy, cuz we are out of here. >> an honor.
2:15:16
>> It's been an honor and a pleasure.
2:15:18
>> We'll love to see you tomorrow. We'll see you soon. Goodbye.