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

0:01We 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 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 the there was some VC associate out there that was making a market map and which is devastated oh my all the companies I was going to put on the market map are now on this market map. Over winter break, actually, I was interested in this thing where, like, OK, on Wikipedia, there's, like, all sorts of, like, Wikipedia, I think, is, like, a very underrated data source.

0:40And there's, like, all sorts of cool things I think you can do, right? You mean Grokipedia, right? So Grokipedia is a little different because it's, like, generated on the fly, right? I took every Wikipedia article. There's, like, 7 or 7.5 million English ones. And I ran them through an embedding model. It was QEN3 embedding 4B, I think. You speak Chinese? Yeah,. Whoa, he's got it down. . OK. But basically, I've gotten embedding for every single article, right? So it's like basically every article has a vector. It's like 2 ,500. You did this a while ago, right? So then basically, I took all the articles.

1:14I found all the ones that are about companies, enterprises, right? Yeah. Which is basically, you can find some direction in the embed space that corresponds to how much like company-ness something has, right? Company-ness? Really? Really? Oh, you don't filter by Wikipedia's categorization of whether or not it's a company? So I use that, but that's not inclusive of every single company. Oh, interesting. So it's a little bit blurry, because some things are like, well, is it a company, is it not? Yeah, I noticed some railroads on here that look like maybe they're companies, but they're state-owned, and where does that fit in?

1:44Yeah, so 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 cool 2D map, which is basically what I did. 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. I noticed the aviation companies were pretty far away from the train companies. Is that the way it's actually? Yeah, I mean, because you knew there was kind of like a little bit of rivalry.

2:15Yeah, rivalry. They need to be, you got to keep those apart. It'll just start fighting. Like when you map something down from like, you know, there's like 2 ,000 dimensions down to 2D. It's very hard to keep a ton of things. And it just randomly looked like the United States. Yeah, that has nothing to do with... That's so crazy. That was totally random. Because I looked at it and I was like, oh, okay, there's a lot of companies in Florida, a lot of companies in the Northeast. Yeah, I didn't even realize. I was like, oh, it kind of looks like... And then I was like, what is this enclave in Canada?

2:39Why 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. Yeah, but it's actually interactive, so you can look up a company and you can find where it is and stuff. TylerCosgrove.com slash Wikipedia underscore map dot html. Wow, really a wordsmith with the with the URLs there Tyler. Couldn't use a TLD list 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 we get market maps are basically done, but a lot of the NeoLabs are not on this market map.

3:15And let's click over to Tyler's market map of the Neo Labs because we've been tracking the Neo Lab boom. We've had a lot of these founders on the show We 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 Neo Labs have exploded. Tyler, take us through What's going on in the world of Neo Labs these days? Yeah, so Neolab is kind of this interesting term like it's very broad people say like Neolab It's not very clear what they mean because there's like broadly.

3:52I think it generally and this will make it clearer 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 different companies? Yeah, I don't want to be more confused at the end of this. Yeah, that would be a disaster if that happened Yeah, so this is gonna be easy. Okay. Got it. Got it. Got it. Cool Okay, so let's just start okay. So you have Neolab, right? Yes, so neo is prefix. Okay, let's be relative to something Yes. So, NEO is relative to like your Trad Lab. This is your big lab. Traditional lab. This is your, yeah, this is your OpenAI.

4:19Let's give it up for the big labs. Yeah, they don't get enough credit today. The open data centers, spike in CapEx. So, this is going to be your OpenAI, your DeepMind, your Anthropic. Okay. This is kind of your big lab. Yeah, XAI. XAI kind of fits in there too. Even though it's a newer Trad Lab, it fits in with the big lab. Yeah, I think Dario, I think, he was like, yeah, three, maybe four labs, right? So, the force is probably XAI. I think you can also kind of throw in Mistral in there. Okay. Oh, yeah. Mistral is a little bit older. Yeah. 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.

4:55Yeah. So I think XAI is in there. Mistral is in there. And these are still pretty... These are still pretty... 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 Yes, yeah, 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 Yeah, 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 is some meaning to this so like Mistral you've seen Mistral become kind of the the leader in European AI, right?

5:36Yeah, you're a champion. Was it Sweden, maybe? They were bringing a new data center? Yeah. So they're kind of becoming like... A lot of stuff going on in France, too. Yeah, Macron is always talking about Mistral. Yep. It's a big leader. Cohere is also kind of... I think it has like a very... Canadian. It's a Canadian company, yeah. Yes. But also has done their own free trains. No ties to the curling team, though. And then you can go down, you can kind of see all your Chinese open source labs. You can see your Quan, DeepSeek, Kimi. Unitry is also in there, right? Unitry, I think... So as we'll see later, There's also I have a section for like robotics labs.

6:07Sure. Take us back in time now. What was going on before the Trad Labs broke out? Yeah. So here I have this section, Legacy Labs. Okay. So these are ones that are kind of more entrenched in these big enterprises. Yep. So you have stuff like Microsoft Research. Sure. AT &T Bell Labs, right? Oh, Bell Labs. Yeah. I forgot about Bell Labs. Yeah, that's a lab. You know why they call it Bell Labs? Why do they call it Bell Labs? Alexander Graham Bell. Yeah. It was founded by him. Yeah. That labs. Okay, but also you have stuff like FAIR, Facebook AI Research. This was like, I mean, there's so many OG research papers that came out of FAIR.

6:45Yeah. Jan LeCun. Jan LeCun used to be head of before it transitioned to MSL. Yeah, MSL. Around your Trad Lab, you also have Post Lab, right? Yes. P-O-A-S-T. Yes, these are posters. Yeah. These are labs where you get a lot of posters, right? So obviously this is OpenAI, you got Rune, Anthropic, a lot of Shulte, et cetera. You've got a lot of great posters over there. A lot of great posters. Prime Intellect, I think. They're great posters. Will Brown. Yeah. A bunch of Anons at Prime Intellect doing great stuff over there. For sure. That makes sense. And then you kind of get into the core. The proper NeoLab.

7:15Yeah, the proper NeoLab. Okay. 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. Okay. When you think of it, it's, like, fairly recent. Yeah. It's still very much research-focused. Okay. Like, sure, they have enterprise, like, you know, think 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.

7:42So let's... Wouldn't Sakana be more of, like, a sovereign lab? Yeah. I mean, so a lot of these can fit in all different places. Sakana would be, yeah, Japanese, maybe. Okay. And you put MSL in here because it's a new project. Yeah. This one was also a bit hard. Thinking Machines is my classic go-to Neolab. Yes. I feel like it's post-open AI exodus and sort of open AI is nothing without its people. You know, 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.

8:19And 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. Yeah, so Think Machines also brings us to what I call the Trad SAS Lab. Okay. SAS Lab, you've Trad SAS Lab. So I think the way I think about this is the Trad SAS Labs are trying to basically use the data that's inside these big enterprises, pull them out with AI.

8:57So this is Thing Machines, right? Rumored idea, right, 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. So it's having the AI be like internal. So you have applied compute, you have poolside doing all kind of similar things in this enterprise LLM field. And then I have NeoSaaS Lab. This is different than TradSaaS. I think these are different in that they're not really pulling, they're not going enterprise specific maybe.

9:32I think that's one way to look at it. Also much more of like a startup focus. But they're making a product that is sold effectively as SaaS. Yes. So cursor, cognition, windsurf. I have ramp labs. 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 the conversation doesn't start with a business development relationship. Yeah, and of course, I mean, these lines are pretty blurry. Okay, let's go down to the post lab. Okay, post lab, after the lab. Yes. So that means like basically they train the models and then these labs are working on top of those models.

10:14That's how I think of it. Okay, okay. So you have meter, you have epoch. These are going to do evals. Yep. You have pangrum. they're seeing is the model producing slap? Yes. Or is it producing text that you're using in some way? Yes, yes, yes. These are purely eval. They don't have necessarily AI products themselves. They don't necessarily sell to big businesses. But they could still be training models, right? Like Pangram is training models that sit on top of the lab. That's true. So it counts as a lab. Yeah. Makes sense. Okay, what else we got? Maybe that brings us down to the safety lab. Yes.

10:43So these are pretty interesting. Anthropik kind of fits in this, right? Because they have 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. Let's go. Very interesting. Eleuther AI is a similar kind of lab. I know Eleuther, yeah. Okay, so then in contrast to the SAS labs, we have the consumer labs. Okay, consumer labs. So these are focused on consumers, right? So you have Eureka Labs. This is Andre Karpathy's project. I don't think there's anything been released from it yet.

11:14Education, though. But yeah, education. Makes sense. It's four people. You have humans. Oh, it's four people, not four individuals working there. It's four people. It might be four people. It might be one person. Who knows? He's pretty good. Yeah. You have humans and. Okay. Right? This is the, I think that phrase, it's like humanity focused. You're going to turn human into sand? Human sand. Human sand. Yeah, we got to hang out with the founders at the Super Bowl. But yeah, focus on creating models that work better alongside people. So then that brings us down to the visual labs, right? So there's a lot of either multimodal models or they're actually producing video or images, right?

11:55We've talked to a lot of these founders. You have Neo Auditory Lab. Okay. So this is going to be anything that has to do with vocals or voice or music, right? Eleven Labs. Eleven Labs, of course. Sponsor of TVPN, thank you. Suno, right, making music. Suno, okay. Gemini also released a new model. Yes, today for Liria. Liria 3. Neo Trad Lab. Yes. It's a neo lab, but it's Trad. Okay. 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. Okay. They're also largely, they're focused on like kind of a single idea. Yeah. So if you think of like OpenAI, very research focused, obviously, but they're doing a lot of different things.

12:37Yeah. Right? So they have - Consumer, enterprise. Yeah, they have consumer, but it's even like on the product or on the research side, right? They're doing video images. Sora images. Yeah, but even within language models, I'm sure they have a continual learning team or all these weird moonshot things. I think a lot of these NeoTrad labs are basically focused on one single moonshot idea. Okay, so example, flapping airplanes, right? 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 general, broad subject. There's a bunch of different ways you can tackle it, but they're like, that's the problem that we're going after.

13:11But it's one specific thing they're working on. Yep. Let's move up a little bit. Yeah, what is NeoLab Lab? 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 they're recursive, right? Okay. You have recursive and recursive. Yeah, you have actually two that are recursive and recursive. Wet Labs? Yeah, Wet Labs. Okay. So these are your bio labs. Oh, you got LabCorp. Yeah, I'm familiar with LabCorp. Yeah, LabCorp. But there's a lot of biology-focused labs. It's actually like I didn't know about a lot of these.

13:46These are all your kind of NeoConnect labs, right? These are fairly recently in the past, like maybe four or five years. Yes. Broadly. And then the NeoNeo Lab. NeoNeo Lab, right? Okay, so 1X is building NeoRobot. So there's Neo... NeoNeo Lab. That makes sense, yeah. Yep. And then Legacy Kinetic is the previous... Legacy Kinetic is kind of the old gen. Yeah. But Cookin. They're cooking. Waymo's cooking. Cruise. Boston Dynamics have been a little bit behind. Zoox also, another self-driving car company. There's a bunch in here that I could have included. There's another one, Stealth, I think, that never really hit.

14:21You have your Dark Lab. Yes. So this is... Working with the government. I have, yeah, I have Shield AI. I also have DARPA. DARPA is a lab, yeah. They invented the internet, right? Yeah. GPS. Yeah. So I think this should be pretty obvious to anyone who's thinking about Neolabs, like how you should be thinking about them now. Yeah, it's good. But these things are coming out like every day, right? You put the typos in just to prove that it was made by humans. So like Sovereign Lab and then... Sovereign? Sovereign. Ineffable Intelligence also has a typo. And so I just wanted to make sure people knew you put the typos in so that it was proof that you made it.

14:58Yeah. Sovereign. I don't want... Well, yeah. Whatever you built this in doesn't have spellcheck, I guess. One show, two maps. One show, two months. 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, Mercor, Revolut, Airwallex, Boom, Supersonic, Ramp, Aura, and Stripe, which is signed and pending, closed. I'm relieved to finally have an answer for family and friends who have been asking, how do I get exposure to ramp equity?

15:39And so, you know, if this is coming out from your head of investor relations, it's not exactly a Matt Grimm style response. They bought Databricks at$150 per share, now trading at$204. Ramp at$90, now trading at$98. Airwallex,$21. It's now trading at$18.8. and then Mercore at$7.14 now trading. So I've already seen a little uptick. Anchor came in and was sharing some of his sites. There's a single closed-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 access.

16:19You can see this with the insane, silly fees people are paying for Anthropics, SpaceX, and OpenAI SPVs. He says, too, 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. Disclosure, long. Elon Musk announced that XAI is moving away from traditional academic benchmarks like humanity's last exam to focus Grok on maximal utility for real world engineering and software development.

17:03He said, actually, I don't think HLE is a great measure of usefulness for moving away from these benchmarks. Andy Scott says, so it's bad? 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. So Grok 4 has already been out. This is a minor revision. And 4.1. 4.1. So now we're at 4.2. 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.

17:40They did this kind of interesting thing where it's still not fully out. It's still in beta if you go on the Grok interface. They did this kind of interesting thing where there's four agents. Every time you actually do a prompt, there's four agents, and the agents specifically have distinct roles. 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, okay, this one is focused on doing a qualitative thing. Instead of mixture of experts, mixture of agents. I wonder what the bull case is here for XAI. There's a world where they carve out some sort of niche, you know, in the optics like focused on coding very specifically and, you know, had some major, major gains there.

18:24What else is there? Also, it is interesting to think about with the cerebrous news and with the value of like high-speed inference on one, 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. Tariq says, I'm proud to share that Humane has invested$3 billion into XAI's Series E round just prior to its historic acquisition by SpaceX.

19:02Through this transaction, Humane became 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 the SpaceX acquisition, but better late. Wait, wait, wait. They said they got in before the acquisition. I know. You mean the news? This round got announced a while ago. So maybe they're coming out with this news today. Yeah, but they're saying, hey, we got in before the acquisition, so we got SpaceX shares. Yeah, I don't know. It is odd that it's the late.

19:40I'm just saying better late than never. 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. Recently, Jeff Bezos, who never tweets, this was his first tweet of 2026, posted a photo of this black tortoise, which goes along with blue origins, a motif of slow and ferocious, methodical. But 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?

20:22Do you think you're going to be the first? Well, it gives me an opportunity to put on a t-shirt for you. So there you go. That's the... Nothing else. Let me do that. Do I get to keep this? 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 of those. You t-shirt Ma Bloomberg. SpaceX doesn't have to lose 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.

20:58And so we need to be moving very quickly. Healthy competition. But I think a lot of people read 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? I think if asked, we'll give it a run for our money. I like our architecture. I like our odds of getting there very quickly.

21:35I 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 U.S. 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. Move heaven and earth. Powerful line. The moon race is going to be fun. I think it's shaping up well. I mean, yeah, a little bit of a come tortoise and the hare story, a little bit of come from behind. I'm not buying the tortoise as ferocious.

22:12Yeah, I don't love the analogy. I don't really love the analogy. Like, I don't think it's the best comp strategy. Like, I like the vague posting out of Jeff. It gets the people going. But at the same time, just imagining SpaceX as a hare, just like running a bunch of laps around the tortoise, just kind of. 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 hare. Like, I got your foot. We have some breaking news. What's that? Claude OAuth is officially not allowed in OpenClaw.

22:50So Anthropic is responding to the OpenClaw, 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. Out of the journal. Yes. The fossil fuel tycoon teaming up with the Rockefellers to fight energy poverty.

23:30I'm sure 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 of EQT, one of the largest natural gas producers in the U.S., is a co-founder of Energy Corpse, a nonprofit that helps developing nations such as Ghana, Zambia, and Burundi build out their energy infrastructure and prosper.