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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
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 market the final market map. Market map. We don't need any more market maps because Tyler made a market map that has every company on it.
Speaker 1:Let's pull up his latest market map.
Speaker 2:The There was some VC associate out there that was making a market map and was just devastated.
Speaker 1:All my all the companies I was gonna put on the market map are now on this market map.
Speaker 3:Over winter break, actually, I was interested in this thing where, like, okay, on Wikipedia, there's, all sorts of, Wikipedia is, I think, like, a very underrated data source, and there's, like, all sorts of cool things I think
Speaker 2:you can do. Right? You mean Grokopedia, right?
Speaker 3:So Grokopedia 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 Quen three embedding 4B, I think.
Speaker 1:You speak Chinese?
Speaker 3:Yeah. Woah.
Speaker 1:Woah. He's got it, Doctor.
Speaker 3:Woah. Okay. But I got an embedding for every single article, right? So it's like basically every article has a vector. It's like 2,000 You five
Speaker 1:did this a while ago, right?
Speaker 3:So then basically I took all articles. I found all the ones that are about companies, enterprises, right? Which is basically you can find some direct direction in the embedding space that's like corresponds to how much company ness something has, right? You just find all the ones that
Speaker 1:Oh, you don't filter by like Wikipedia's categorization of
Speaker 3:whether So or I use that, but that's not inclusive of every single company.
Speaker 1:Oh,
Speaker 3:interesting. So it's like a little bit blurry. Because some things are like, well, it a company? Is it not?
Speaker 1:I noticed some like railroads on here that looked like maybe they're companies, but they're like state owned. Where is
Speaker 4:that getting It's kind
Speaker 3:of a blurry thing. 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.
Speaker 3:If you map it down to two d, you can have this cool two d map, which is basically what I did. So you can see there's these big clusters. It's like in the top left, all these theater companies or there's space companies.
Speaker 1:I noticed the aviation companies were pretty far away from the train companies. Is that
Speaker 2:the Yeah. Mean, it's just knew there was kind of like
Speaker 1:Yeah. Conflict rivalry. Yeah. Rivalry. They need to be you gotta keep those apart or it'll just
Speaker 3:start fighting. Like, when you map something down from, like, you know, there's, 2,000 dimensions down to two d. Yeah. It's, like, very hard to keep. Yeah.
Speaker 3:Like like a ton of
Speaker 1:things. And it just randomly looked like The United States.
Speaker 3:Yeah. That has nothing to do with That's
Speaker 1:so crazy.
Speaker 3:Like that was totally random.
Speaker 1:Because I looked at it and was like, oh, okay. There's a lot of companies in Florida, a lot of companies in the Northeast.
Speaker 3:Yeah. Didn't even like realize. I was like, oh, it kind of looks
Speaker 1:And then I was like, 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.
Speaker 3:Yeah. But this it's like actually interactive. So you can like look up a company and you can find where it is and
Speaker 1:tylercosgrove.com/wikipedia_map.html. Wow, really a wordsmith 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.
Speaker 1: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.
Speaker 1: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.
Speaker 1:Tyler, take us through what's going on in the world of Neolabs these days.
Speaker 3: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.
Speaker 3:Mhmm. Because there's like, broadly, I I think it generally
Speaker 1:And this will make it clearer?
Speaker 3:Yes. I think after this, it'll be pretty obvious, like, what, know, what you should be looking at, how how to think about these different companies.
Speaker 1:Yeah. I I I don't wanna be more confused at the end of Yeah. That would be a disaster if that happened.
Speaker 3:Yeah. So this not
Speaker 1:gonna happen. This is gonna be easy. Okay. Got it. Yeah.
Speaker 1:Got it. Got
Speaker 3:it. Cool. Okay. So let's just start. Okay.
Speaker 3:So you have NeoLab. Right? Yes. So Neo is prefix. Okay.
Speaker 3:It has to be relative to something. Yes. So Neo is relative to, like, your trad lab. This is your big lab.
Speaker 1:Traditional lab.
Speaker 3:This is your yeah. This is your open AI.
Speaker 2:For the
Speaker 3:big labs.
Speaker 1:Yeah. They don't get enough credit today. Building data centers, spike in CapEx.
Speaker 3:So this is gonna be your OpenAI, your deep mind, your Anthropic.
Speaker 1:Yeah. XAI. XAI kind of fits in there too. Even though it's a newer trad lab, it it fits in with the BigLab. You know, a lot of money.
Speaker 3:Dario, I think, he was he was like, yeah, three, maybe four labs. Right? So the is is probably
Speaker 1:Probably x a
Speaker 3:x a I. Yep. I I think you can also kind of throw in a Mistral in there.
Speaker 1:Okay. Oh, yeah. Mistral's a little bit older.
Speaker 3:Yeah. Yeah. I mean, Mistral there's a bunch of these labs that were basically founded in the, like, two or three years before Chekipati and then in the, like, six months after. Yeah. So I think XAI is in there.
Speaker 3:Mystical is in there.
Speaker 1:And these specifically these I feel like those trad labs, it's like they did a transformer based pretraining run. They have their own base pretrained. 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.
Speaker 1:So that's sort of like you're in the trad lab world when you're thinking about, like, a big pre train run loosely.
Speaker 3:Yeah. I mean, especially if you're talking about these big pre trains, it's it's really just these four. No one else is really at that scale. Yep. K.
Speaker 3:So Mistral kind of brings us down into I what call the sovereign labs. You know, if you kinda look at this, it's basically just labs that are not in America. Mhmm. 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 in European AI.
Speaker 3:Right? So I think it in Was it Sweden maybe? They're bringing a new data center? Yeah. So so they're kind of becoming like
Speaker 1:going on in
Speaker 3:France too. Macron is always talking about Mistral. Yep. It's it's a big leader. Cohere is also kind of I think that's like a very, you Canadian.
Speaker 3:It's a Canadian company. Yeah.
Speaker 1:Yes. Yes. But also has done their own pre
Speaker 3:trained. Has their ties
Speaker 2:to the curling team, though.
Speaker 3:And you can down. Can kinda see all your your Chinese open source labs.
Speaker 1:You can
Speaker 3:see your Quen, DeepSeek, Kimi. Unitree is also in there. Right? Unitree, I think so as we'll see later, there's also I have section for, like, robotics labs.
Speaker 1:Sure. Take us back in time now. What was going on before the Trad Labs broke out?
Speaker 3:Yeah. So so here, I I have this section, legacy labs. Okay. So these are are ones that were kind of more entrenched in these big enterprises.
Speaker 1:Yep.
Speaker 3:So you have stuff like Microsoft Research Sure. AT and T or Bell Bell Labs. Right?
Speaker 1:Oh, Bell Labs. Yeah. I forgot about Bell Labs.
Speaker 3:Yeah. That's
Speaker 1:right. After you know what? You know how you know why they call it Bell Labs?
Speaker 3:Why do they call it Bell Labs?
Speaker 1:Alexander Graham Bell. Yeah. It was founded by him.
Speaker 3:Yeah. Bell Labs. Okay. But but also like you have FAIR Yeah. Facebook AI research.
Speaker 3:Mhmm. This was like I mean, there there's so many, like, OG research papers that that came out of FAIR. Yep. This is what Yan Lecun used to be head of Mhmm. Before it transitioned to To MSL.
Speaker 3:To, yeah, MSL. Around your your trad lab, you also have post lab. Right? Yes. T o a s t.
Speaker 1:Yes. These are posters.
Speaker 3:Yeah. These are labs where you get a lot of posters. Yes. Right? So, obviously, this is OpenAI.
Speaker 3:You got Rune. Yes. Anthropic, A lot of, you know, Sholto Yep. Etcetera. Posters over there.
Speaker 3:Posters. Prime Intellect, I think They're great posters. Brown. Yeah. A bunch of Anans at Prime Intellect doing great stuff over there.
Speaker 1:For sure.
Speaker 3:Makes sense. And then you kinda get into
Speaker 1:The proper NeoLab.
Speaker 3:Yeah. The proper NeoLab. Okay. This is also a bit hard to identify because, like, what is actually the core of a neo lab? What are these different kind of offshoots?
Speaker 3:Mhmm. I think prime intellect is kind of the prototypical, like, quintessential neo lab Okay. When you think of it. It's, like, fairly recent. Yeah.
Speaker 3:It's still very much research focused.
Speaker 1:Okay.
Speaker 3: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 Sure. Novel approaches. It's research. You're hiring researchers.
Speaker 3:It's not just, like, engineers, sales guys, etcetera. So let's
Speaker 2:Wouldn't Sakana be more of, like, a sovereign lab?
Speaker 3:Yeah. I mean, so so a lot of these can can fit in all different places. Sakana would be, yeah, Japanese, maybe.
Speaker 1:Okay. And you put MSL in here because it's a new project.
Speaker 3:Yeah. This one was also a bit hard.
Speaker 1:Thinking machines is my classic go to NeoLab. Yes. I feel like it's post OpenAI exodus and sort of OpenAI is nothing without its people. You get the spinouts 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 neo lab boom from.
Speaker 1:And then a lot of the other companies are 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 $2,000,000,000 We're raising $200,000,000 It's easier. There's a 10% chance that we, you know, are are at their scale, so you can underwrite it that way.
Speaker 3:Yep. So so Thing Machines also brings us to what I call the Trad SaaS Lab. Okay.
Speaker 1:So you have
Speaker 3:SaaS Lab, you've Trad SaaS Lab. So I think the way I think about this is the trad SaaS labs
Speaker 1:Yes.
Speaker 3:Are are trying to basically use the the data Mhmm. That's inside these big enterprises, pull them out with AI.
Speaker 1:Okay.
Speaker 3:So this is Thing Machines. Right? Rumored idea, right, is they're doing RL for enterprise. Yep. Yep.
Speaker 3:Yep. A bunch of these are 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 it's gonna be inside the company. You can't really pull it out anyway Mhmm. Besides having the the AI be, like, internal. So you have applied compute to your poolside doing
Speaker 1:Mhmm.
Speaker 3:All kind of similar things in this in this Yeah. Like, enterprise LLM field. Yeah. And then I have NeoSaaS lab. This is different than than trad SaaS.
Speaker 3:Okay. Think these are are different in they're not really pulling they're not going enterprise specific, maybe. I think that's one way to look at it. They're also much more of, like, a startup focused.
Speaker 1:But they're making a product that is sold effectively as SaaS. Yes. So Cursor, Cognition, I have Ramp Labs. Ramp Labs. These are seat based sort of consumption based.
Speaker 1:It's a product that's vended into a and the product is what you get and then sort of customizes as you integrate it, but it's not you it doesn't the the conversation doesn't start with a business development relationship.
Speaker 3:Yeah. And, of course, I mean, lines are are are pretty blurry. Okay. Let's go down to the post lab. Okay.
Speaker 3:Post lab after This is after the lab. Yes. So that means, like, basically, they train the models, and then these labs are working on top of those models. That's all I think of it.
Speaker 1:Okay. Right?
Speaker 2:So you
Speaker 3:have meter. You have epoch. These are gonna do evals. Yep. You have Pangram.
Speaker 3:They're seeing, is the the model producing SLAP? Yes. Or is it producing text Yes. That you're using in some
Speaker 1:These are purely eval. They they don't have necessarily AI products themselves. They don't necessarily sell to big businesses.
Speaker 2:They could
Speaker 3:still be training models. Right? Like Pangram is training models that sit on top of
Speaker 1:the true. So it counts as a lab. Yeah. Makes sense.
Speaker 3:Okay. What else
Speaker 1:we got?
Speaker 3:Maybe that brings us down to the safety lab. Yes. So these are pretty interesting. Anthropic kinda fits in this. Right?
Speaker 3:Because they have a big safety team. They're doing a lot of mechanistic interpretability. Mhmm. You have Goodfire. I think they just raised at, like, 1,250,000,000.00, and they're just doing mechanistic interpretability.
Speaker 3:Let's go. Very interesting. Cool. LUther AI is a similar kind
Speaker 2:of I know LUther. Yeah.
Speaker 3:Okay. So then in contrast to the to the Yeah. SaaS labs we have the consumer lab.
Speaker 1:Okay. Consumer lab.
Speaker 3:So these are focused on consumers. Right? Okay. So we have, Eureka Labs. This is Andre Karpathy's Oh, yeah.
Speaker 3:Project yet. Don't think there's anything been released from it yet.
Speaker 1:Education, though.
Speaker 3:But, yeah, education
Speaker 1:Makes sense.
Speaker 3:It's four people. You have humans Oh,
Speaker 1:it's four four people, not four individuals working there.
Speaker 3:It's four Okay. People. Yeah. Yeah.
Speaker 1:It might be four people. It might be one person. Who knows? He's pretty good.
Speaker 3:Yeah. You have humans and.
Speaker 1:Okay.
Speaker 3:Right? This is the phrase. It's, like, humanity focused. You're
Speaker 1:gonna turn human into sand?
Speaker 3:Human sand.
Speaker 1:Human sand.
Speaker 2: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.
Speaker 3:So then that that brings us down to the Visual labs. Visual labs. Right? So it's a lot of either multimodal Yeah. Models or they're actually, like, producing video or images.
Speaker 3:Right?
Speaker 1:We talked to a lot of these founders.
Speaker 3:You have okay. You have Neo Auditory Lab.
Speaker 1:Okay.
Speaker 3:Right? So this is gonna be anything that has to do with vocals
Speaker 1:Yes.
Speaker 3:Or voice or music. Right?
Speaker 1:Eleven Labs.
Speaker 3:Eleven Labs. Course.
Speaker 1:Of TBPN.
Speaker 3:Thank you. Suno. Right? Making music.
Speaker 1:Suno. Okay.
Speaker 3:Gemini also released a new model
Speaker 1:Yes. From Lyria three.
Speaker 3:Neo trad lab. Yes. It's a neo lab
Speaker 1:Yes.
Speaker 3:But it's trad.
Speaker 1:Okay.
Speaker 3:Okay. So what does that mean? So basically, the way I 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.
Speaker 3:Yeah. So if you think of like OpenAI Mhmm. Very research focused obviously, but they're doing a lot of different things. Yeah. Right?
Speaker 3:So they have
Speaker 1:Consumer and Yeah.
Speaker 3: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 even within, like, language models, I'm sure they have a, you know, continual learning Yep.
Speaker 3:Team or or the all these, like, weird things where I think a lot of these neo trad labs are basically focused on one single moonshot idea. Mhmm. Okay. So example, flapping airplanes. Yes.
Speaker 3:Right? They just came on. They're talking about data efficiency. Mhmm. This is kind of the one kind of moonshot idea.
Speaker 3:Right? Obviously, it's like a very
Speaker 1:general broad. Bunch of different ways you tackle it, but they're like, that's the problem that we're going
Speaker 3:to one specific thing they're working on. Yep. Let's move up a little bit. We have Lab Lab. Neo Lab Lab.
Speaker 3: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 a researcher. Oh. So it's they're recursive. Right?
Speaker 1:Okay.
Speaker 3:So You have recursive and Yeah. Have actually two that are recursive and recursive. Wet labs? Yeah. Wet labs.
Speaker 1:Okay.
Speaker 3:So these are your bio labs.
Speaker 1:Oh, you got LabCorp. Yeah. I'm familiar with
Speaker 3:LabCorp. LabCorp. But there's there's a lot of, like, biology focused
Speaker 1:Yes.
Speaker 3:Labs. It's actually, like, I didn't know a lot of about a lot of these. Mhmm. These are all your your kind of NeoConnect labs. Right?
Speaker 3:These are Mhmm. Fairly recently in the past, like, maybe four or five years.
Speaker 1:Yes. Broadly. Neo Neo lab.
Speaker 3:Neo Neo lab. Right? Okay. So one x is building Neo robots. So there's Got it.
Speaker 1:Neo Neo lab. Makes sense. Yeah.
Speaker 3:Yep.
Speaker 1:And then Legacy Kinetic is the previous.
Speaker 3:Legacy Kinetic is kind of the old gen. Yeah. But It's cooking.
Speaker 1:They're cooking. Yeah. Waymo's cooking.
Speaker 3:Yeah. Cruise
Speaker 1:and Boston Dynamics have been a little bit behind. Yeah. Zook's also another self driving
Speaker 3:car There's a bunch in here that I I could have
Speaker 1:There's another one with stealth, I think, that never really Yeah.
Speaker 3:Hit. You have your dark lab?
Speaker 2:Yes. So this is Working
Speaker 1:with the government.
Speaker 3:I have yeah. I have Shield AI. I also have DARPA.
Speaker 1:DARPA is a lab. Yeah. They invented the Internet. Right?
Speaker 3:GPS? Yep. Yeah. So I I think this should be pretty obvious to anyone who's thinking about NeoLabs, like, should we be thinking about them now?
Speaker 1:Yeah. It's good news.
Speaker 3:But these things are coming out, like, every day. Right?
Speaker 2:You the you put the typos in just to prove that What typos? Humans. So, like, sovereign lab and then Sovereign. Ineffable intelligence also has a typo. And so I I just wanna make sure.
Speaker 2:I wanted to make
Speaker 1:sure that they You yeah. Put
Speaker 2:You put the typos in so that it was proof that you made it.
Speaker 1:Yeah. Yeah. Yeah. Sovereign.
Speaker 3:I don't want
Speaker 1:Well, yeah. I I whatever you built this in doesn't have spell check, I guess.
Speaker 2:One show, two maps. One show,
Speaker 1:two maps.
Speaker 2:Strong start. Robin Hood 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 They listed have a new fund that has Databricks, Mercor, Revolut, Airwallex, Boom, Supersonic, Ramp, Aura, and Stripe, I'm which is signed and pending relieved
Speaker 1:to finally have an answer for family and friends who have been asking, how do I get exposure to Ramp Equity? And so, you know, if if this is coming out from your head of investor relations, it's not exactly a Matt Grimm style response.
Speaker 2:They bought Databricks at $150 per share, now trading at $2.00 $4 Ramp at $90 now trading at $98 Airwallex $21. It's now trading at 18.8. And then Merkor at $7.14 now trading. So already seen a little uptick. Anchor came in and was sharing some of his sites.
Speaker 2:As 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 access. You can see this with the insane silly fees people are paying for Anthropic, SpaceX, and OpenAI SPVs. He says, too, the structure of this fund is broken.
Speaker 1:As a
Speaker 2: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.
Speaker 1:It's a
Speaker 2:great disclosure. Disclosure, long.
Speaker 1:Elon 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. So actually, I don't think HLE is a great measure of usefulness. We're moving away from these benchmarks.
Speaker 2:Andy Scott says, so it's bad, question
Speaker 1:marks.
Speaker 2: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.
Speaker 1:So Grok four has already been out. This is
Speaker 3:And a minor 4.1.
Speaker 1:4.1. So now we're at 4.2.
Speaker 3:Historically, especially when Grok four came out, people were like very very quick to say it was like, oh, this is so benchmarked or whatever. I think they've definitely retreated from from that like at least path with 4.2. It doesn't look like outrageously benchmarked or anything. Mhmm. They did this kind of interesting thing where I it's still not like fully out.
Speaker 3:It's still like in in beta if you go on the the Grok like interface. They did this kind of interesting thing where there's like four agents.
Speaker 1:Okay.
Speaker 3:Like every time you actually do a prompt there's like four agents. The agents specifically have like distinct roles. Okay. Where it's almost kind of like you have four instances of the same model, but they have different system prompts. Yeah.
Speaker 3:So you can try to get like, Okay, this one is focused on doing
Speaker 2:So you qualitative Instead
Speaker 1: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, know, anthropics 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 cerebris 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 can iterate towards on a faster time horizon than other chip companies.
Speaker 1:I mean, they 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. They have some experience there.
Speaker 2:Tariq says, I'm proud to share that Humane has invested $3,000,000,000 into XAI's Series E round just prior to its historic acquisition by SpaceX. Through this transaction, Humane became a significant minority shareholder in x AI. Investment builds on our previously announced 500 megawatt AI infrastructure partnership with x AI in Saudi Arabia. Maybe, you know, would have wanted to get this out before before the SpaceX acquisition, but better light.
Speaker 1:Wait. Wait. Wait. Wait. They said they got in before the acquisition.
Speaker 1:I know. But You mean the news?
Speaker 2:This round got announced a while ago. So maybe they would they're they're coming out with this news today.
Speaker 1:Yeah. But they're saying, hey. We got in before the acquisition. So we got we got SpaceX shares.
Speaker 2:Yeah. Don't know. But he's he's on the better late than never.
Speaker 1:On 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.
Speaker 5: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
Speaker 2:orbiting posing.
Speaker 5:Motif of
Speaker 1:slow
Speaker 5:and ferocious, methodical. Arcturus is Russian 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 gonna focus on the moon. What do you make of that tweet, and what is the competition right now? Do you think you're gonna be the first?
Speaker 4:Well, it gives me an opportunity to put on a t shirt for you. So there
Speaker 1:you go.
Speaker 4:That's the nothing else. Let me do that.
Speaker 5:Why did they keep this?
Speaker 4:Yeah. That's all yours. Oh, really? And that's the first one off the presses too, by the I think everybody's gonna want one of those.
Speaker 2:He's t shirt, Mark Bloomberg.
Speaker 4:To lose for Blue to succeed. What The US needs is it needs two SpaceXs. 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 we need to be moving very quickly.
Speaker 5:Healthy competition. But I think a lot of people read into that as the tortoise being blue origin and the hare being Elon Musk in 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 gonna get the contracts.
Speaker 5:So do you think you're gonna get there first?
Speaker 4:I I think if asked, we will make it we we'll give it a run for our money. I I like our architecture. I I like our odds of getting there very quickly. I I don't I don't have a crystal ball into what SpaceX is doing. I I think, again, Gwen and Elon are competent, and they've showed every day by launching rockets.
Speaker 4:Elon. 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.
Speaker 1:Move heaven and earth.
Speaker 2:Powerful line.
Speaker 1:The moon race is gonna be fun. I think it's shaping up well. I mean, yeah, a little bit of a come torus in the hair story, a little bit of come come from behind. I I'm not buying the as ferocious Yeah. I don't love ferocious.
Speaker 2:I don't really love the I don't really love the analogy. Like, I don't think it's the best comp strategy. Like, I I like the vague posting out of out of Jeff. It gets it gets the people going. But at the same time, just imagining Basics as a hare Mhmm.
Speaker 2:Just, like, running running a bunch of laps around the tortoise, just kind of
Speaker 1:They need to take this way further. Elon needs to wear tortoise shell 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 just a hair.
Speaker 1:Like, I got your foot. We have some breaking news.
Speaker 2:What's that?
Speaker 1:Claude Oauth is officially not allowed in Open Claw. So 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 authentication, which is used with the free, pro, and max plans, is intended exclusively for Claude code and Claude dot 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.
Speaker 2:Out of the journal. Yes. The fossil fuel tycoon teaming up with the Rockefellers to fight energy poverty. I'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.
Speaker 2:Toby Rice made his fortune unlocking a gusher of natural gas 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 US, is a co founder of Energy Corp, 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 impoverished societies, Energy Corp sees a role for a broader spectrum of solutions from fossil fuels to solar panels and nuclear plants.
Speaker 2:Notably, this approach has been endorsed by the Rockefeller Foundation, one of the oldest and richest foundations
Speaker 1:They in really opened up the flood gates 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. 200 ks.
Speaker 1:Go solve it. Go solve energy globally. 200 ks. Here you go.
Speaker 2:Best I can do is $200. I got you. I'm super excited about this.
Speaker 1:I think Macron deserves a victory lap at this point.
Speaker 2:I mean, Macron's size is looking Yeah.
Speaker 1: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 industrialization? It's the world's lungs.
Speaker 1:Everyone suffers if that happens, but they would certainly benefit in the short term. So there's a there's a hot debate here, and he is engaging in it.
Speaker 2:David Holmes has hit the timeline. He says 5,000,000 humanoid robots working twenty four seven can build Manhattan in six months. Now just imagine what the world looks like when we have 10,000,000,000 of them by 2045. Now imagine the year 2100. Dyson sphere.
Speaker 1:Dyson sphere. Dyson sphere by 2100 is the correct debate. Keep
Speaker 2:going back to my land thesis. Yeah. When armies of robots can build anything Yeah. Anytime. What what is actually scarce?
Speaker 2:In this case, I think with 10,000,000,000 of them, I don't even think land will be scarce anymore. It's like, hey, we're making, we're gonna build an island.
Speaker 1:We're gonna build another moon. We're building the moon.
Speaker 2:New moon alert. New moon alert.
Speaker 1:Just build another earth and just throw it on the other side of the solar system.
Speaker 2:Yeah. Yeah. I mean, it's right now we're talking about what businesses are unsloppable. The next meta will obviously be unclankable. Unclankable.
Speaker 2:Richard says SF guy eating a delicious blueberry. In eighteen months, everything will be blueberries.
Speaker 1:This is a perfect contrast to the other post.
Speaker 2:Just The hot dog hot dog
Speaker 1:the NSF discourse. No, no, no. David Holes. David Holes is like because David's seen humanoid robots. Like, he's lived in NSF and been around this stuff.
Speaker 1:Like, he's a true believer. And he's sort of saying, like, I've seen what they can do. And I understand the exponential here. And now imagine 10,000,000,000 of them in one hundred years. Like it's going to be crazy.
Speaker 1:And then you have Richard on the other side. Everything will be blueberries.
Speaker 2:I thought you were talking about the Delicious Tacos post. Said, I'm the CEO of a hot dog company. I've worked on hot dogs for ten years and I wasn't prepared for what I've just seen. Your life is about to change. So what can you do?
Speaker 2:Buy as many hot dogs as you can. Buy stock in hot dog companies.
Speaker 1:It's a good idea. I I I am long hot dog. I like hot dogs.
Speaker 2:Hot dog market map.
Speaker 1:Good with the kids. Everyone loves a hot dog.
Speaker 3:Hot dog
Speaker 1:all American. There's nothing better than a hot dog at a ball game.
Speaker 2:Orin Hoffman is sharing that Ozempic is bad for business. Yes. A few months ago, someone told me they had heard a rumor that a banker hedge fund had banned its traders from taking Ozempic, Wegovy, and other GLP-one weight loss drugs. Theory, as I understood it, was something like traders need to make quick decisions based on gut instinct. And GLP-1s mess with your gut instincts.
Speaker 2:You're not hungry for snacks. You're not hungry for profits. You lose your edge.
Speaker 1:It is
Speaker 2:funny Warren says GLP is getting banned by hedge funds, maybe by sales teams too. Yeah. Killing your grind set. Your gut instincts for some people saying put on mass, scale. It's time to scale.
Speaker 1:Time to bulk. Bulking seasons here. Get off the GLP-1s and start levering up.
Speaker 2:Doctor Cameron Maximus says, guess what increases drive testosterone, a micro dose of tirzepatide to cut down on physical appetite, a macro dose of testosterone, amplify psychological appetite. The solution is We're ban GLP-1s only if you're taking them solo. You've gotta be taking a full stack.
Speaker 1:You see BoneGBT saying, turns out you really do gotta be hungry for it.
Speaker 2:What about hair bench?
Speaker 1:Hair bench? What's hair bench?
Speaker 2:Gabe says, Jordy needs to ring Tyler with him when he gets his haircut. Haircut alert. Haircut Tyler asked. Yes. And I sent him my barber's information.
Speaker 2:They're working on it.
Speaker 1:Haircut alert. We got to get a card up. Jordy doesn't want to 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.
Speaker 1: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, if you're in the path of models improving, you will get stomped like Godzilla.
Speaker 1: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, know, like, there's plenty of startups that just, like, don't touch software.
Speaker 2:Just don't do anything with code.
Speaker 1:Just don't do anything with technology.
Speaker 2:Don't don't do anything with a website.
Speaker 1:Don't do anything with a website.
Speaker 2:You need a website to do business.
Speaker 1:I'm short. I'm passing.
Speaker 2:You're fucked. It's over.
Speaker 1:It's over.
Speaker 2:It's over. It was fun.
Speaker 1:No. But clearly, I mean, there's plenty of brands and products and technology and all sorts of things to build
Speaker 2:Thanks for hanging out with us, folks.
Speaker 1:Thanks for hanging out with
Speaker 2:Love you. We will see you
Speaker 1:Tomorrow. Morning. Goodbye. Cheers.