The AI lab market map, Robinhood brings startups to retail, GLPs & hedge funds | Diet TBPN

We have a great show for you today, folks. Specifically, Tyler Cosgrove has been on a little bit of a tear with the market maps. He dropped the final market. We don't need any more market maps because Tyler made a market map that has every company on it. Let's pull up his latest market map.

0:02

The. There was some VC associate out there that was making a market map and was just devastated.

0:21

All my. All the companies I was going to put on the market map are now on this market map.

0:27

Over winter break, actually, I was interested in this thing where like. Okay, on Wikipedia there's like all sorts of like Wikipedia, I think is like a very underrated data source and there's like all sorts of cool things I think you can do.

0:33

Right, you mean Grokipedia. Right.

0:42

So Grokipedia is a little different because it's like generated on the fly. Right. I took every Wikipedia article. There's like seven, seven and a half million English ones. And I ran them through an embedding model. It was Quin 3 embedding 4B, I think.

0:44

You speak Chinese?

0:56

Yeah. Wo Shu and jungle.

0:57

He's got it. Okay.

1:00

But basically I got into betting for every single article. Right. So it's like basically every article has a vector. It's like 2500.

1:04

You did this a while ago. Right.

1:11

So then basically I took all the articles. I found all the ones that are about companies, enterprises. Right. Which is basically you can find some direction in the embedding space that's like, corresponds to how much like company ness something has. Right. All the ones at the end.

1:12

Really? Oh, you don't filter by like Wikipedia's categorization of whether or not.

1:25

So I use that. But that's not inclusive of every single company. So it's like a little bit blurry because some things are like, well, is it a company? Is it not?

1:31

Yeah, I noticed some like railroads on here that looked like maybe they're companies but they're like state owned and. Yes. Where does that.

1:38

It's kind of a blurry thing. So you can't just use just what Wikipedia says, but you can basically find things that are companies and then you have an embedding for every single one. Right. So it's this big vector, super high dimensional space. If you map it down to 2D, you can have this like cool 2D map, which is basically what I did. Yeah. So you can see there's these big clusters. Right. So it's like in the top left, it's all these theater companies or there's Space companies.

1:44

I noticed the aviation companies were pretty far away from the train companies. Is that.

2:05

Yeah, I mean, there was kind of like.

2:11

Yeah, conflict.

2:13

Rivalry.

2:14

Yeah, rivalry. They need to be. You got to keep those apart or they'll just start fighting.

2:15

Like, when you map something down from, like, you know, there's like 2,000 dimensions down to 2D, it's like, very hard to keep, like, a ton of things.

2:18

And it just randomly looked like the United States.

2:25

Yeah, that has nothing to do with. So, like, that was totally random because.

2:27

I looked at it, I was like, oh, okay, there's a lot of companies in Florida, a lot of companies in the Northeast.

2:30

Like, yeah, I didn't even, like, realize. I was like, oh, it kind of looks like.

2:33

And then I was like, what is this? What is this enclave in Canada? Why does that. Is that Alaska or something? But in fact, it has nothing to do with the United States. It just happens to look like the United States.

2:36

Yeah, but this. So it's like, actually interactive, so you can, like, look up a company and you can find where it is and stuff.

2:45

Tylercosgrove.com wikipediamap ht HTML wow. Really a wordsmith with the URLs there. Tyler couldn't use a TLD domain. There are some fun ones in here anyway. That's a fun project. All the links take you to Wikipedia, go check it out. And market maps are basically done. But a lot of the Neolabs are not on this market map. And let's click over to Tyler's market map of the Neolabs, because we've been tracking the Neolab. Boom. We've had a lot of these founders on the show. We came out of the world where we were like, okay, there's DeepMind, there's Google, there's OpenAI. Now we got anthropic, there's thinking machines, and there's a couple different companies. But the neolabs have exploded. Tyler, take us through what's going on in the world of Neolabs these days.

2:49

Yeah, so neolab is kind of this interesting term. It's very broad. People say, like, neolab. It's not very clear what they mean because there's like, broadly, I think it generally.

3:44

And this will make it clearer.

3:53

Yes. I think after this, it'll be pretty obvious, like, what, you know, what you should be looking at, how to think about these companies.

3:54

Yeah, I don't want to be more confused at the end of this. Yeah, that would be a disaster if that happened.

3:59

Yeah.

4:03

So this is going to be easy. Okay, got it. Got it, got it.

4:04

Cool. Okay, so let's just start. Okay. So you have neolab, right?

4:07

Yes.

4:10

So neo, this prefix. Okay. It has to be relative to something.

4:10

Yes.

4:13

So neo is relative to like your trad lab. This is your big lab.

4:14

Traditional.

4:17

This is your. Yeah. This is your open.

4:17

I'm setting it up for the big labs.

4:19

Yeah. They don't get enough credit today. The open data centers spikes in capex.

4:20

So this is going to be your OpenAI, your DeepMind, your anthropic kind of your big lab. Yeah. Xai.

4:25

Xai kind of fits in there too. Even though it's a newer trad lab, it fits in with the big lab. A lot of money.

4:29

Dario, I think on Turkish he was like, yeah, three, maybe four labs. Right. So the force is probably Xai.

4:35

Yep.

4:41

I think you can also kind of throw in Mistral in there.

4:42

Okay. Mistral is a little bit older. Yeah, yeah.

4:45

I mean Mistral. There's a bunch of these labs that were basically founded in the like two or three years before ChatGPT and then in the like six months after.

4:48

Yeah.

4:55

So I think Xai's in there, Mistral's in there.

4:55

And these specifically these, I feel like those trad labs, it's like they did a transformer based pre training run. They have their own base. Pre trained. Maybe it's not at the frontier, but at least they're playing that game. They're not doing fine tuning, they're not doing something else. So that's sort of like you're in the trad lab world when you're thinking about like a big pre train run.

4:57

Loosely.

5:16

Yeah.

5:17

I mean, especially if you're talking about these big pre trains. It's really just these four. No one else is really at that scale. Okay. So Mistral kind of brings us down into what I call the sovereign labs. You know, if you kind of look at this, it's basically just labs that are not in America. But I think also that there actually is some meaning to this. So like Mistral. You've seen Mistral become kind of the leader in European AI.

5:17

Right.

5:36

So I think European champion. Was it Sweden? Maybe they're being a new data center.

5:36

Yeah.

5:41

So they're kind of becoming like stuff.

5:41

Going on in France too.

5:42

Macron is always talking about Mistral. It's big leader. Cohere is also kind of. I think it has like a very, you know, Canadian. It's a Canadian company. Yeah.

5:43

Yes. But also has done their own pre trains.

5:49

No ties to the curling team though.

5:52

And then you can Go down, you can kind of see all your. Your Chinese open source labs and see your Quen Deepseek, Kimi Unitree is also in there. Right. Unit Tree. I think so as we'll see later. There's also. I've sectioned for like, robotics labs.

5:54

Sure. Take us back in time now what was going on before the trad labs broke out.

6:07

Yeah. So here I have this section. Legacy labs.

6:11

Okay.

6:14

So these are ones that are kind of more entrenched in these big enterprises. So you have stuff like Microsoft Research AT&T. Bell Labs, right?

6:15

Oh, Bell Labs. Yeah, I forgot about Bell Labs after. You know why they call it Bell Labs?

6:24

Why do they call it Bell Labs?

6:30

Alexander Graham Bell. Yeah, it was founded by him. Yeah, Bell Labs.

6:31

Okay. But also you have stuff like you have Fair Facebook, AI research. This was like. I mean, there's so many like OG research papers that came out of fair. Yann Lecun used to be head of. Before it transitioned to msl. To MSL around your trad lab, you also have Post lab, right? T O A S T. Yes.

6:36

These are posters.

6:56

Yeah, These are labs where you get a lot of posters. Right. So Obviously this is OpenAI. You got Rune.

6:57

Yes.

7:01

Anthropic. A lot of, you know, Sholto posters over there. Prime Intellect.

7:02

They're great posters.

7:07

Yeah, A bunch of anons at Prime Intellect. Doing great stuff over there for sure. And then you kind of get into the proper neolib. Yeah, the proper neolab.

7:08

Okay.

7:16

This is also a bit hard to identify because, like, what is actually the core of a neolab? What are these different kind of offshoots? I think Prime Intellect is kind of the prototypical, like quintessential Neolab. When you think of it, it's like fairly recent. Yeah, it's still very much research focused. Like, sure, they have enterprise, like, you know, thinking about different stuff, but at the core of it, you're still like, trying to find these like, new novel approaches. It's research. You're hiring researchers. It's not just like engineers, sales guys, et cetera. So let's.

7:16

Is it. Wouldn't Sakana be more of like a sovereign lab?

7:42

Yeah. I mean, so a lot of these can fit in all different places would be.

7:45

Yeah.

7:49

Japanese maybe.

7:49

Okay. And you put MSL in here because it's a new project.

7:50

Yeah. This one was also a bit hard.

7:54

Thinking Machines is my classic. Go to Neolab.

7:56

Yes.

7:59

I feel like it's post. Post OpenAI exodus and sort of OpenAI is nothing without its people. You get the spin outs and you think Thinking Machines and SSI are two of like the first case studies that sort of set the tempo for okay, it's possible to do some research outside of the big trad labs. And so that's where you get the neolab boom from. And then a lot of the other companies I feel like are saying, okay, we're going to do something similar to Thinking Machines or ssi. We're going to commercialize early or late. But we're following in that and we're benchmarking to that. Oh, they raised 2 billion, we're raising 200 million. It's easier. There's a 10% chance that we are at their scale. So you can underwrite it that way.

7:59

Yeah. So Thing Machines also brings us to what I call the trad SAS lab. SAS lab. You've tried SAS lab. So I think the way I think about this is the trad SAS labs are trying to basically use the the data that's inside these big enterprises, pull them out with AI. Okay, so this is thing Machines. Right. Rumored idea.

8:39

Right.

9:00

Is they're doing RL for enterprise. A bunch of these are doing fairly similar things where it's kind of chatting with your data, using the data that's very valuable to a company, but it's going to be inside the company. You can't really pull it out anyway besides having the AI be like internal. So you have applied compute to your pool side, doing all kind of similar things in this like Enterprise LLM field. And then I have Neo SaaS lab. This is different than Trad SAS lab. These are different in that they're not really pulling, they're not going enterprise specific maybe. I think that's one way to look at it. Also much more of like startup focused.

9:00

But they're making a product that is sold effectively as SaaS.

9:36

Yes.

9:40

So cursor cognition, windserve.

9:40

I have ramp labs.

9:43

Ramp labs. These are seat based, sort of consumption based, but it's a product that's vended into and the product is what you get and then sort of customizes as you integrate it. But it's not. The conversation doesn't start with a business development relationship.

9:44

Yeah. And of course, I mean these lines are pretty blurry. Okay, let's go down to the post lab.

10:01

Right after lab.

10:05

This is after the lab.

10:07

Yes.

10:08

So that means like basically they train the models and then these labs are working on top of those. So you have meter, you have epoch. These are going to do evals, you have pangram. They're seeing is the model producing Slap.

10:08

Yes.

10:23

Or is it producing text that you're.

10:23

Using in some way? Yes. These are purely eval. They don't have necessarily AI products themselves. They don't necessarily sell to big business.

10:25

But they could still be training models. Right. Like Pangram is training models that sit.

10:34

On top of the lab. That's true. So it counts as a lab. Makes sense.

10:36

Okay, what else we got? Maybe that brings us down to the safety lab.

10:40

Yes.

10:43

So these are pretty interesting anthropic. Kind of fits in this. Right. Because a big safety team, they're doing a lot of mechanistic interpretability. You have Goodfire. I think they just raised at like 1.25 billion, and they're just doing mechanistic interpretability.

10:43

Let's go.

10:55

Very interesting. Eleuther. AI is similar kind of lab.

10:56

I know.

10:59

Yeah.

11:00

Okay, so then in contrast to the SaaS labs.

11:00

Yeah.

11:04

We have the consumer labs.

11:05

Okay. Consumer labs.

11:06

So these are focused on consumers. Right. So you have Eureka labs. This is Andrej Karpathy's project. Yeah. I don't think there's anything been released from it yet.

11:07

Education, though.

11:14

But yeah, education makes sense for people. You have humans.

11:15

Oh, it's four. Four people. Not four individuals working there.

11:18

It's four people.

11:21

Yeah, it might be four people. It might be one person. Who knows. He's pretty good.

11:22

Yeah. You have humans and. Okay, right. This is the defrays. It's like humanity focused.

11:25

You're going to turn human into sand.

11:31

Human sand.

11:32

Human sand.

11:33

Yeah. We. We got to hang out with the founders at the Super Bowl. But they're. But. But yeah, focus on creating models that work better alongside people.

11:34

So then that brings us down to the visual labs. Visual labs. There's a lot of either multimodal models, or they're actually producing video or images.

11:46

We talked to a lot of these founders.

11:55

You have Neo auditory lab.

11:57

Okay.

11:58

Right. So this is going to be anything that has to do with vocals or voice or music.

11:59

Yes. Eleven labs.

12:05

Eleven labs. Of course.

12:07

Sponsor of tvpn.

12:08

Thank you. Suno. Right. Making music. Gemini also released new model.

12:09

Yes.

12:13

Today, Lyria 3 Neo Trad Lab. It's a Neo lab. Yes, but it's trad.

12:13

Okay.

12:19

Okay. So what does that mean? So basically, the way I think about a lot of these labs is that they're extremely research focused.

12:19

Okay.

12:25

They're also largely. They're focused on, like, kind of a single idea.

12:26

Yeah.

12:30

So if you think of, like, OpenAI. Very research focused, obviously. But they're doing a lot of different things. Right.

12:31

So they have Consumer.

12:37

Yeah, they have consumer. But it's even like on the product or on the research side. Right. They're doing video images.

12:38

Sora images.

12:43

Yeah. But even. Even within, like language models, I'm sure they have a, you know, continual learning team or all these like weird moonshot things where I think a lot of these Neo Trad labs are basically focused on one single moonshot idea. Okay, so example, flapping airplanes.

12:44

Right.

13:00

They just came on. They're talking about data efficiency. This is kind of the one kind of moonshot idea. Right. Obviously it's like a very. There's a bunch of different ways you.

13:00

Tackle it, but they're like, that's the problem that we're going.

13:09

It's one specific thing they're working on.

13:11

Yep.

13:13

Let's move up a little bit. Yeah.

13:13

What is neolab Lab?

13:14

Neolab Lab. So these are a lot of companies that are focusing on. They're also like, very research focused. The point of the research is to build essentially like a researcher. So it's. They're recursive. Right, okay, so you have recursive and recursive. Yeah, you have actually two that are recursive and recursive.

13:15

Wet labs.

13:32

Yeah, wet labs. Okay, so these are your bio labs.

13:33

Oh, you got LabCorp.

13:36

Yeah, I'm familiar with LabCorp, but there's a lot of like biology focused labs. It's actually like, I didn't know a lot about a lot of these. These are all your kind of Neo connect labs. Right. These are fairly recently in the past, like maybe four or five years. Yes, Broadly.

13:37

The Neo Neo Lab.

13:53

Neo Neo Lab. Right. Okay, so One X is building Neo robots.

13:54

So they're got Neo NEO Lab. Makes sense. Yeah.

13:59

Yep.

14:02

And then Legacy Kinetic is the previous.

14:02

Legacy Kinetic is kind of the old gen. Yeah.

14:05

But cooking.

14:08

They're cooking. Waymo's cooking.

14:09

Yeah.

14:10

Cruise and Boston Dynamics have been a little bit behind. Zook's also another software.

14:11

Yeah, there's a bunch in here that.

14:16

I could have explained.

14:17

Yeah, there's another one, Stealth, I think, that never really hit.

14:18

You have your dark lab.

14:21

Yes. So this is working with the government.

14:22

Yeah, I have SHIELD AI I also have darpa.

14:24

DARPA is a lab. Yeah. They invented the Internet, right? Gps.

14:26

Yeah. Yeah. So I think this should be pretty obvious to anyone who's thinking about neolabs, like, how should we be thinking about them now? But these things are coming out like every day.

14:30

Right.

14:38

You put the typos in just to prove that humans. So like Sovereign Lab and then Ineffable intelligence also has a typo. So I just want to make sure. I wanted to make sure. Yeah, you put the typos in so that it was proof that you made it.

14:38

Yeah, yeah.

14:58

I don't want.

15:00

Well yeah, whatever you built this in doesn't have spellcheck, I guess.

15:01

1 show 2 maps 1 show 2 strong start Robinhood says historically investing in private markets was limited to institutions and the elite, but not anymore. With Robinhood Ventures you can now get exposure to private companies like the ones listed below. They have a new fund that has databricks, Mercur, Revolut, Airwallocks, Boom, Supersonic, Ramp, Aura and Stripe, which is signed and pending close.

15:05

I'm relieved to finally have an answer for family and friends who have been asking how do I get exposure to ramp equity? And so if this is coming out from your head of investor relations, it's not exactly a Matt Grimm style response.

15:32

They bought Databricks at $150 per share, now trading at 204. Ramp at 90, now trading at 98. Air Wallix $21 it's now trading at 18.8 and then Mercore at 714, now trading. So already seen a little uptick. Anchor came in and was sharing some of his sites. A single close end fund that gives you exposure to some of the top private startups. My thoughts People want access to private markets of course. So much wealth creation in America happens in startups and people desperately want want access. You can see this with the insane silly fees people are paying for anthropic SpaceX and OpenAI SPVs. He says to the structure of this fund is broken as a closed end fund. The price here can diverge very significantly from the net asset value of the underlying assets with fomo from Access, this could easily trade at a very high multiple to nav, leading to a lot of retail investors getting their face ripped off. It ends up being less of a venture fund versus a speculative product to ride private market sentiment. It's a great disclosure Long Elon Musk.

15:46

Musk announced that XAI is moving away from traditional academic benchmarks like Humanities last exam to focus GROK on maximal utility for real world engineering and software development. He said, actually I don't think HLE is a great measure of usefulness.

16:50

We're moving away from these benchmarks, Andy Scott says. So it's bad question marks. I think it's totally fair to just focus on real world utility. But of course people are still going to ask, well I still want to know how it does.

17:07

So Grok4 has already been out.

17:22

This is a minor revision and 4.1.

17:25

4.1. So now we're at 4.2.

17:27

Historically, especially when Grok 4 came out, people were like very, very quick to say it was like, oh, this is so benchmax or whatever. I think they've definitely retreated from that like at least path with 4.2. It doesn't look like outrageously Benchmax or anything. They did this kind of interesting thing where it's still not like fully out, it's still like in beta if you go on the GROK like interface. They did this kind of interesting thing where there's like four agents. Every time you actually do a prompt there's like four agents and the agents specifically have like distinct roles where it's almost kind of like you have four instances of the same model but they have different system prompts. So you can try to get like, okay, this one is focused on doing.

17:29

Like qualitative things instead of mixture of experts, mixture of agents. I wonder what the bull case is here for. For xai, there's a world where they carve out some sort of niche anthropics like focused on coding very specifically and had some major, major gains there. What else is there? Also it is interesting to think about with the Cerebras news and with the value of high speed inference on the whole model on one chip. Is that something that Tesla's chip team can iterate towards on a faster time horizon than other chip companies? I mean they do custom silicon and they've done it for a long time and they got an entire self driving model that runs on a car. So you know, they have some experience there.

18:07

Tariq says, I'm proud to share that humane has invested 3 billion into XAI Series E round just prior to its historic acquisition by SpaceX. Through this transaction, Humane became a significant, a significant minority shareholder in Xai. The investment builds on our previously announced 500 megawatt AI infrastructure partnership with Xai in Saudi Arabia. Maybe, you know, would have wanted to get this out before, before the SpaceX acquisition. But better late.

18:54

They said they got in before the acquisition.

19:22

I know, but in the news this round got announced a while ago, so maybe they would, they're, they're coming out with this news today.

19:25

Yeah, but they're saying, hey, we got in before the acquisition, so we got, we got SpaceX shares.

19:33

Yeah, I don't know. It is OD saying better, better late than never.

19:38

Yeah, you mean on like a comms front. Let's play this clip from Jeff Bezos his space company, Blue Origin, will move Heaven and Earth to get to the moon before rival SpaceX.

19:42

Recently, Jeff Bezos, who never tweets, this was his first tweet of 2026, posted a photo of this like Black Tortoise, which goes along with Blue Moon. Slow and ferocious, methodical. A lot of people have viewed it as a warning shot to Elon Musk, which really was focused on SpaceX going to Mars, and now he's saying we're going to focus on the moon. What do you make of that tweet and what is the competition right now? Do you think you're going to be the first?

19:52

Well, it gives me an opportunity to put on a T shirt for you. So there you go. That's that. Nothing else. Let me do that.

20:24

I get to keep this.

20:33

Yeah, that's all yours. And that's the first one off the presses too, by the way. I think everybody's going to want one.

20:34

Of those T shirt mods. Bloomberg has to lose.

20:38

For Blue to succeed, what the US needs is it needs two SpaceX's. It needs two launch companies competing vigorously against each other to try to give us the most capabilities as a country, commercially, civilly, from a defense perspective, because our adversaries aren't standing still. And so we need to be moving very quickly.

20:40

Healthy competition. But I think a lot of people run into that as the tortoise being Blue Origin and the hare being elon Musk and SpaceX, because it also comes after Secretary Duffy had said that SpaceX is behind, so they were opening up for everyone in terms of Artemis. And Jared Isaacman, who's now the administrator, also said, essentially, yeah, whoever can get there first is going to get the contracts. So do you think you're going to get there first?

21:01

I think if asked, we will make it. We will give it a run for our money. I like our architecture, I like our odds of getting there very quickly. I don't have a crystal ball into what SpaceX is doing. I think, again, Gwen and Elon are competent and they show it every day by launching rockets. But I love the fact that the US would compete us against each other. They are for sustainability on Lunar, we're talking about who could get there in 2028 if asked. We will step up and we will move Heaven and Earth to get to the moon first.

21:25

Move Heaven and Earth.

21:58

Powerful line.

21:59

The moon race is gonna be fun. I think it's shaping up well. I mean, yeah, a little bit of a come Taurus in the hair story. A little bit of come come from behind. I'm not buying the tortoise as ferocious.

22:00

Yeah, I don't love, I don't really love the I don't really love the analog. Like I don't think it's the best calm strategy. Like, I like the vague posting out of Jeff. It gets, it gets the people going, but at the same time just imagining base X as a hare. Just like running, running a bunch of laps around the tortoise. Just kind of.

22:11

They need to take this way further. Elon needs to wear tortoiseshell glasses. Be like, I turned your tortoise into my glasses and Bezos needs to start carrying a rabbit's foot for good luck. That would be the hair. Like I got your foot we have some breaking news.

22:32

What's that?

22:46

Claude OAUTH is officially not allowed an openclaw. So Anthropic is responding to the Open Claw OpenAI news. This would be a great time for Sam Altman to step in and let us use OpenAI subscriptions with OpenClaw. So in the Claude code docs, OAuth and OAuth authentication, which is used with the Free Pro and Max plans, is intended exclusively for Claude code and Claude AI. Using OAuth tokens obtained through Claude Free Pro or MAX accounts in any other product, tool or service, including the Agent SDK, is not permitted and constitutes a violation of the consumer terms.

22:47

Out of the Journal yes, the Fossil Fuel Tycoon Teaming up with the Rockefellers to Fight Energy Poverty I'm sure the the online conspiracy community will love this one. EQT Chief Executive Toby Rice is starting a nonprofit to tackle a lack of access to modern energy infrastructure in poor countries. Toby Rice made his fortune unlocking a gusher of natural gas in Appalachia. He has a bold new ambition, bringing energy to millions of people in impoverished nations. Rice, the Chief Executive Equity, one of the largest natural gas producers in the US Is a co founder of Energy Corps, a nonprofit nonprofit that helps developing nations such as Ghana, Zambia and Burundi build out their energy infrastructure and prosper. Unlike other philanthropic incentives that emphasize renewables to energize poverty diverse societies Energy Corp. Sees a role for a broader spectrum of solutions, from fossil fuels to solar panels and nuclear plants. Notably, this approach has been endorsed by the Rockefeller foundation, one of the oldest and richest foundations.

23:23

You really opened up the floodgates with this. The Rockefellers, you know, Wasn't John D. Rockefeller the richest person in human history? You see how much he's putting in this project? 200 g's 200k go solve it. Go solve energy. Globally 200k. Here you go.

24:25

Best I can do is, is 200 bucks. I'm super excited about this.

24:41

I think, I think Macron deserves a victory lap at this point.

24:48

I mean, his Macron size is looking.

24:52

Yeah, it's size. It's size compared to this. Should impoverished societies be encouraged to rely on polluting fossil fuels to improve their fortunes or leapfrog to intermittent renewables? There was this question about should Brazil be allowed to clear cut the Amazon rainforest to pull forward industrial civilization. It's the world's lungs. Everyone suffers if that happens, but they would certainly benefit in the short term. So there's a hot debate here and he is engaging in it.

24:54

David Holz has hit the timeline. He says 5 million humanoid robots working 24,7 can build Manhattan in six months. Now just imagine what the world looks like when we have 10 billion of them by 2045. Now imagine the year 2100.

25:20

Dyson Sphere. Dyson Sphere. Dyson sphere by 2100. Is the, is the correct like debate.

25:36

I keep going back to my land thesis. When armies of robots can build anything anytime, what is actually scarce in this case? I think with 10 billion of them, I don't even think land will be scarce anymore. It's like, hey, we're gonna build an island.

25:42

We're gonna build another moon. We're building the moon.

25:57

New moon alert. New moon alert.

25:59

Just build another Earth and just throw it on the other side of the solar system.

26:02

Yeah, yeah. I mean, it's, you know, right now we're talking about what businesses are unsloppable. The next meta will obviously be unclankable.

26:07

Unclankable.

26:14

Richard says, SF guy eating a delicious blueberry. In 18 months, everything will be blueberry.

26:15

This is a perfect contrast to the other post.

26:21

Just the hot dog. The hot dog.

26:24

One SF discourse. No, no, no. David Holes. David Holes is like, because David's seen humor humanoid robots. Like, he sees, he's lived in SF and been around this stuff. He's a true believer. And he's sort of saying, I've seen what they can do and I understand the exponential here and now. Imagine 10 billion of them in 100 years. It's going to be crazy. And then you have Richard on the other side. Everything will be blueberries.

26:26

I thought you were talking about the delicious tacos post. He said, I'm the CEO of a hot dog company. I've worked on hot dogs for 10 years and I wasn't prepared for what I've just Seen, your life is about to change. So what can you do? Buy as many hot dogs as you can. Buy stock in hot dog companies.

26:54

It's a good idea. I am long hot dog. I like hot dogs.

27:11

Hot dog market map.

27:14

Good with the kids. Everyone loves a hot dog hot dog market map. It's all American. There's nothing better than a hot dog at a ball game.

27:16

Orrin Hoffman is sharing that Ozempic is bad for business.

27:22

Yes.

27:26

A few months ago, someone told me they had heard a rumor that a banker hedge fund had banned traders from taking Ozempic, WeGovy and other GLP1 weight loss drugs. Theory, as I understood it was something like Traders need to make quick decisions based on gut instinct and GLP1s. Mess with your gut instincts, you're not hungry for snacks, you're not hungry for profits, you lose your edge.

27:27

It is funny.

27:46

Warren says GLP is getting banned by hedge funds, maybe by sales teams too. Yeah, killing your grindset. Your gut instincts. For some people saying put on mass scale, it's time to scale.

27:47

Time to bulk. Bulking seasons here. Get off the GLP1s and start levering up.

27:58

Dr. Cameron, Maximus says guess what increases drive testosterone. A microdose of tirzepatide to cut down on physical appetite. Macrodose of testosterone amplify psychological appetite. So the solution is we're going to ban GLP1s. Only if you're taking them solo. You've got to be taking a full stack.

28:03

Did you see Bone gbt? Turns out you really do got to be hungry for it.

28:21

What about Hair Bench?

28:28

Hair Bench? What's Hair Bench?

28:29

Gabe says Jordy needs to bring Tyler with him when he gets his haircut.

28:30

Haircut. Haircut alert.

28:33

Haircut alert.

28:35

Tyler asked.

28:36

Yes.

28:37

I sent him my barber's information. So I think they're working on it.

28:37

Haircut alert. We gotta get a card up. Jordy doesn't wanna do it, but I think we should put up a card for Jordy's new haircut. We don't like secret haircuts. Overheard in SF. A VC was giving advice. OpenAI and Anthropic are like Godzilla. You need to find an alleyway to hide in. What a funny thing to say. There's something good there. I mean, the models, you know, if you're in the path of models improving, you will get stomped like Godzilla. But there's still plenty of opportunities all over the ecosystem, especially if you're not doing something that's in software. I'd be like, you know, like there's plenty of startups that's just like, don't.

28:42

Touch software, don't do anything with code, don't do anything with technology, don't do anything with a website, don't do anything with. You need a website to do business.

29:19

I'm short, I'm passing.

29:30

You're cooked. It's over.

29:31

It's over.

29:32

It's over. It was fun.

29:33

No, but clearly, I mean, there's plenty of, like, brands and products and technology and all sorts of things to build and.

29:34

Thanks for hanging out with us, folks.

29:41

Thanks for hanging out with us.

29:42

Love you. We will see you tomorrow morning.

29:43

Goodbye.

29:46

Cheers.

29:47