Pod Insights Bankless The AI Arms Race: Who Will Dominate the Next Industrial Revolution? | Josh kale About this episode The AI arms race is on, and the stakes couldn’t be higher.
Bankless podcast producer Josh Kale breaks down which players are poised to dominate this next industrial revolution. From the transformative power of AI across industries to the geopolitical and economic implications, we explore the critical dynamics shaping our technological future—and what this means for investors, builders, and society at large.
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TIMESTAMPS
0:00 Intro
0:25 A New Life Form?
4:23 The AI Rabbithole
11:49 xAI & Grok 3
26:51 Deepseek Breakthrough
28:47 English as a Programming Language
30:19 Open Sourcing LLMs
34:13 Other Models
39:29 Meta AI
44:04 AI Bets
46:27 Elon Musk vs Sam Altman
51:59 Worldcoin
55:48 From OpenAI to AGI
1:03:01 AI Doomerism
1:10:06 Neuralink
1:15:20 AI vs Humans
1:22:08 Frontier Tech
1:24:43 Crypto’s Role
1:28:00 Closing & Disclaimers
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Transcript 18,039 words · 2 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 Just this week in frontier technology, we had Microsoft announce this like new quantum computer. Grok came out and it was the new frontier model that was made in 12 months. There is computation improvements in like genetic mutations and gene. There's all these crazy frontiers happening downstream of this new form of intelligence that's happening that's allowing all this research in these other parallels to happen.
0:25 Speaker 00 David, do you think humans are the bootloader for artificial intelligence? Meaning, are we the scaffolding that exists to build this next superior form of intelligence after humans?
0:35 David Hoffman? I feel like this is a leading question. I feel like your answer is yes, it absolutely is.
0:42 Speaker 00 My answer is a little complicated, but mostly yes. I definitely believe we are here to build this next crazy form of intelligence. But I'm curious, what do you think?
0:51 David Hoffman? Yeah, let me try and break down your question. So we have biological consciousness, we have biological life. And I think society is like on having this big conversation of like, what does it mean to have not just artificial intelligence? Because I think in 2025, we do know that artificial intelligence is like totally possible because you can go to like chatgpt.com and access some form of intelligence. And there's this AI arms race going on where everyone is trying to create this like model, which is highly parallel to some sort of brain, which is like biological human intelligence, not even human intelligence, like just biological
1:34 David Hoffman? intelligence, because animals have it too. Like animal brains and human brains are the same tissue. And so we already know that there is like artificial intelligence out there built into like circuits and code and software. And it's highly parallel to the knowledge and intelligence that we know of in biology. And I think the broader question, even more zoomed out from that is, is that, is there like actual consciousness there? And is there a new life form out there that is downstream of what we create when we create artificial intelligence? And again, the question was very leading, and I think your answer is yes, there absolutely is.
2:12 Speaker 00 It certainly would appear as if so. And it's interesting that we're kind of going faster into artificial intelligence than we are our own biological intelligence, where we understand more about the brain of a neural network than we do our actual brains. So is the end state of this, understanding more biological intelligence and creating better versions of that through artificial? Or do we just accelerate to this crazy artificial world of robots and computers and intelligence far beyond what we need, to the point where we are possibly not even needed anymore?
2:41 David Hoffman? Yeah, I think your bootloader word is human intelligence. The bootloader for artificial intelligence is actually doing a lot of work there. Like, that's a very big word. Bootloader implies that, like, once it has been loaded, the human intelligence is actually relatively obsolete and redundant and not really necessary. Do you also believe that?
3:01 Speaker 00 It would appear that way, just based on the trend that we're going, that these super intelligent systems, once they've learned everything from us, they will be better than us technologically, they will be better than us cognitively, they have none of the biological restrictions that our meat forms have for us today. So it feels as if they can be the final form of what we lack in our biological limitations.
3:26 David Hoffman? I think this conversation, like, we're experiencing when we go and we experience the idea of artificial intelligence. We, like I said, we can't actually go experience that. You can go to chatgpt.com and you can experience artificial intelligence, like art intelligence in a very purist, contained way. But I feel like there's also a bunch of adjacent conversations as well, kind of like robotics and like brain and neural link interfaces. I feel like we're actually kind of spanning, we're not just talking about AI. This is not just an AI conversation. This is actually like, kind of like a frontier tech conversation, where it's a collection of a bunch of technologies, is my intuition right here?
4:05 Speaker 00 Very much so. Yeah, this is the first time in history we've had enough computers to even have this discussion where we have enough power to build these systems to ask these questions. And it's accelerating super, super fast, faster than anything I've ever seen accelerate. So the questions continue to shift on a regular basis, which is what's really exciting here.
4:23 David Hoffman? And I think this is where I think I want to kind of zoom back out and just say, welcome to Bankless, where we explore the frontier of internet money and internet finance, and now also internet intelligence. I'm David Hoffman, and I'm here, joined today by a team member of the Bankless podcast team, Josh Kale. Josh, welcome to your own podcast.
4:43 Speaker 00 Thank you. It's pretty funny to be on the other side of things. Normally, I'm the one pushing the buttons behind the scenes. Now I'm the one pushing the buttons in front of the scenes. So it's cool. It's really exciting to be here. I'm very glad you asked me to come. This is stuff that I love chatting about just for fun. So it's cool to have an audience of people that hopefully also care about this.
5:00 David Hoffman? Yeah, maybe to shed a little bit more light on what we're going to talk about in this episode and the motivations for this episode. Bankless, primarily deeply a crypto podcast. We've covered every single nuance of crypto that there is to explore, starting with DeFi and like infrastructure, like blockchains like Ethereum, but then also down the rabbit holes of like digital identity. And I think we've explored most everything. And now we're kind of just like keeping up with the trends. We've dabbled into the world of crypto AI with AI agents, but there's a lot left on the table on just the AI side of things. And Josh here, I think, is an explorer of the AI and adjacent technologies rabbit hole. And so I want to get
5:42 David Hoffman? a little bit of taste of what that rabbit hole is like, because there's a world of like alternative content producers out there, like the Dwarkeish podcast, you see this on Lex Friedman a lot, but then there's also like kind of a long tail of content producers that are all producing content about AI. And Josh, you are frolicking down that rabbit hole. And I want to get a taste for what that rabbit hole is like. So maybe just before we get into even more conversations, maybe about the current trends in AI, we're going to talk about Grok 3, we're going to talk about Claude and Lama and Facebook and OpenAI, and even like the open source world as well. But I just want to know a little bit more about Josh Kale and like why he's so excited about AI and motivated to like go down this AI rabbit hole and kind of get a taste for what that AI rabbit hole is like.
6:28 Speaker 00 Yeah, I've always been super excited about frontier technology. I think it's just like, it's the reason to wake up in the morning and be excited about something. Because over time, I've seen how it improves the world. It started with computers super early on. I was like, wait, this is super cool. People are building things with computers. And eventually led me to AI, which was an amazing frontier that's super exciting. Crypto has slowed since Bankless has started, but there is this cool new shiny toy on the block that is AI. And it's very much experiencing its exponential curve, more so than even I've seen with crypto. And the implications are the same, if not much larger than they were with crypto. Because now you're talking about actual intelligence of species that we can duplicate and improve and make better. So the AI infrastructure now is this really cool, important thing.
7:11 Speaker 00 But there's this like shiny new thing on top that can now interact with these payment rails that we created. That is doubling and tripling in speed every week. And Moore's Law was like the speed of a process or the amount of transistor on a processor just doubles every 18 months. With AI training models, it's like every 18 weeks. It is so much faster than any other technology I've ever seen. So it's been really exciting to just try to stay on the frontier of this. And it's, it's almost overwhelming how much is happening. Just this week in frontier technology, we had Microsoft announce this like new quantum computer. Grok came out and it was the new frontier model that was made in 12 months. There is computation improvements in like genetic mutations and gene. There's all these crazy frontiers happening downstream of this new form of intelligence that's happening.
7:56 Speaker 00 It's allowing all this research in these other parallels to happen. So it's kind of this like top order function that everything is downstream of in terms of innovation.
8:05 David Hoffman? What's exploring the AI rabbit hole? Like, I'm sure, like I named some podcasts, right? The Dorkesh podcast, he's really into this, Lex, the podcast. Sometimes he does like six hour sagas with multiple AI teams that are so incredibly dense. So I know that it's podcasts. I know it's also on Twitter. And so I think the crypto community will see some similarities here. It's, there's Twitter drama, there's podcasts, there's also company releases. How is it the same, like your information diet to like follow this meta and follow this trend? How is it the same? How is it different?
8:36 Speaker 00 So it's the same in a lot of ways in the sense that there's tons of good podcasts. There are tons of great Twitter accounts. I'd say about 90% of my information flow comes from Twitter still. There's a really great community similar to crypto Twitter. They're not financially incentivized in the same way that crypto Twitter is. So there is generally slightly more signal in these accounts to noise, which I find really interesting. And there's this really cool new thing that helps me learn, which is just the product itself. What's cool about this frontier that has never existed with others is you can actually interact with it and it could help accelerate your learning progress. So if you have a question, instead of having to search through Twitter, instead of having to search through these podcasts, you could actually just go on Grok, which has access to all of the Twitter accounts. And you could say, hey, Grok, can you just find 10 tweets from the 10 top people
9:21 Speaker 00 who are talking about this one thing today and aggregate them for me and share the data for me? So it's a super accelerated learning curve, which is required because this space moves so fast.
9:30 David Hoffman? Right. I listened to a three and a half hour long YouTube video that I got from your Twitter recommendations. You tweeted this out that this was one of the best videos. It was from Andre Karpathy and it was just a deep dive of how an LLM works, like down to the basement, really deconstructing it. And I found myself like pausing the video at times and opening up ChatGPT and asking ChatGPT questions about the video, which was teaching about how ChatGPT works. And like my level of comprehension and understanding, I've never learned something faster than I had both watching an expert kind of like teach me, a human expert teach me, while also having this like AI sidekick. So it's very, something very interesting of like, I'm trying to learn about AI, but I can use AI to learn about AI.
10:15 Speaker 00 Yes, it's the self fulfilling learning curve. And that video is actually one of my favorite things. And that's how I would suggest a lot of people go about learning AI. I think you could tie it to crypto where if you're new to crypto and you want to learn and understand the frameworks and the basis of everything, you kind of start at the top. So you start with Bitcoin, Ethereum. You understand the big players in the space. You learn who Satoshi is. You learn Vitalik's story. It's similar where in the world of AI, you can learn about OpenAI. You could learn about Anthropic. You could learn about XAI. You could learn who's the founders and kind of get a view of the land. And then skipping that middle part of the Twitter narrative, of the social structure, and just going right to the foundation, right to the base, like as close to source who's as you can. And I think in that case, in the Ethereum case, it would be like you're reading Vitalik's blog post.
11:00 Speaker 00 Now you're going to Andre's videos and you're just watching him explain what an LLM is from the core foundation. So you establish this like high lever level overview of the architecture and then this very low level overview of the specific things that make it work. And once you have that framework, then you could enter the trenches and you can say like, you could start filtering through people's social narratives and you could start filtering through their takes on things. And you have this like very core understanding of how it actually works, which I think is super important for a lot of people to do. And Andre's video is spectacular. I watched it too. All three and a half hours.
11:33 David Hoffman? Yeah. I'm on my second run through of the first half of it. I haven't even like actually entered the second half of it.
11:39 Speaker 00 It's dense.
11:40 David Hoffman? It's really dense. It's really good.
11:41 Speaker 00 It's so good.
11:42 David Hoffman? It's really good.
11:42 Speaker 00 It feels like a college level education on LLMs in a three and a half hour video.
11:47 David Hoffman? That's right.
11:48 Speaker 00 Like, love.
11:48 David Hoffman? Right. So Josh, there's a bunch of just current news cycle events, as there will always be in this sector. And I kind of want to go through a lot of them that has happened in the last like two weeks. It's the XAI and Grok 3 that I think is the current most, most current event that I think the AI community is digesting because it's not unlike the DeepSeek news cycle event where people just realize, wow, there's this team out there that everyone was under indexing and now it has like skipped to the front of capacity here. And it's now another player in this game. And I think as we kind of go through the current events, we're going to be able to kind of like set the landscape and like understand the board. So we can start placing the pieces of all these different players in the game.
12:32 David Hoffman? XAI, of course, Meta, Grok, even the Chinese players. So I think that's where we're going to start this conversation. But first, before we get there, we're going to talk to some of these fantastic sponsors that make this show possible. In the Wild West of DeFi, stability and innovation are everything, which is why you should check out Frax Finance, the protocol revolutionizing stablecoins, DeFi, and Rolex. The core of Frax Finance is Frax USD, which is backed by BlackRock's institutional Biddle fund. Frax designed Frax USD for best-in-class yields across DeFi, T-bills, and carry trade returns all in one. Just head to frax.com, then stake it to earn some of the best yields in DeFi. Want even more? Bridge your Frax USD over to the FraxTil Layer 2 for the same yield plus FraxTil points and explore FraxTil's diverse Layer 2 ecosystem with protocols like Curve, Convex, and more, all rewarding early adopters.
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15:38 David Hoffman? Okay, Josh, let's kick this off with XAI and Grok 3. I remember when Elon Musk introduced Grok, and Grok is the ChatGPT equivalent that is embedded inside of Twitter. I think it now has its own standalone app. But it was... Originally, Grok 1, to me, was kind of a joke. It was fun, it was cool, but it wasn't serious. Grok 3 is something materially different, and to my understanding, like, check me here, Grok 3 has kind of just like leapfrogged all the other models and kind of placed itself at number one in terms of some of the benchmarks or metrics that we use to measure AI LLMs. And now everyone's realizing that XAI, the Elon Musk XAI team, is a world-class team
16:23 David Hoffman? that is able to compete toe-to-toe with everyone else. That's my summary. Check me if I'm wrong, and like, what would you add to that?
16:29 Speaker 00 I think a lot of people will check you on the fact that it is the best model in the world.
16:33 David Hoffman? Okay.
16:33 Speaker 00 It is very subjective, what defines the best model. There's a lot of tests that people create to test these models. It has performed exceptionally well. So I think everyone can agree it is a frontier model.
16:43 David Hoffman? It's a frontier model. Okay, it's pushing the frontier.
16:46 Speaker 00 Yes, so this is like in competition with every other frontier model that is currently available. There's a lot of talk about ChatGPT 5 and new anthropic models that can leapfrog this. But I think it's important to note that like a Google's DeepMind is 13 years old, OpenAI is eight years old, XAI has been building this for 12 months. So it is significantly younger and they have managed to go from Grok 1, which was basically a science experiment, to frontier model in record time. And I think that's the story that people are really excited about is the rate of acceleration that's coming out of an American-based company. Deepsea kind of took people by storm because it was Chinese and there were not many close ties with the Deepsea team. This was built right here, right next to all of these other companies.
17:29 David Hoffman? Right under our noses.
17:30 Speaker 00 And they managed to leapfrog them. Yeah, right. And they managed to leapfrog them after just 12 months. So it's incredibly impressive feat of engineering.
17:36 David Hoffman? I think the this story of XAI and Grok 3 is very on-brand for Elon Musk's leadership, where he just really demands so much out of his teams. What was some of the things that was kind of happening in the background that really like symbolizes exactly like how this scrappy team was able to get so far ahead?
17:56 Speaker 00 This is incredible. I think this is a story that only Elon can tell because there's really not many people in the world that have the resources monetarily or like intelligence-wise to get this done. He was able to attract a team of these builders and I believe they were able to get 100,000 GPU cluster in 122 days and then doubled it in 90 days to 200,000 GPUs. And that's the largest publicly known cluster in the world. The way they did it-
18:21 David Hoffman? Elon Musk has the largest publicly known cluster of GPUs in the world?
18:26 Speaker 00 I believe so.
18:26 David Hoffman? Okay.
18:27 Speaker 00 I think like privately, people have more, but publicly, this is the most that I think people know. And prior to this announcement of the 100K GPU cluster, it wasn't even certain that clusters could be that large, because there's a lot of variance that needs to happen and a lot of interference. It's a whole different story, but it's incredibly impressive. Elon, being Elon, wants to do this as fast as humanly possible because his goal is to crush OpenAI and Sam Altman,
18:52 David Hoffman? Right, because he has beef with him.
18:52 Speaker 00 who turned OpenAI into a closed source company. So there's these two opposing forces. There's OpenAI, which is now closed source, that wants the frontier, leading artificial intelligence. They just want to get there as fast as possible. And then Grok, who has this other approach where they want to be the more open source and they want to seek truth about the universe. So the whole thing about Grok is they want to discover questions about the universe. That is their ethos versus OpenAI. So Elon wants to get there as fast as possible. So he couldn't build a factory because building a factory takes too long. So they were looking for existing factories. They found one in Memphis that was abandoned by an old tech company. They decided, okay, we are going to Tennessee. So that was the basis for why it's built in Tennessee is because that's just where the biggest factory was available. That had enough infrastructure for them to build it.
19:37 Speaker 00 So the whole team got up, moved to Tennessee. And in the first 120 days, they first got the GPU cluster, meaning they had to get that relationship with Jensen. They had to convince him to give him 100,000 GPUs, which is no easy feat. Then they had to wire them all together. They needed power. So I think initially the power grid only gave them 20% of what they actually needed. So what they did is they shipped in thousands of generators to be against one wall of the factory. And then they had to cool the factory. So they rented 25% of all of the cooling availability in the United States just to use for this one factory and that cooled it down. So now they have the most power, the most cooling. They have this crazy engineering team that's wiring it all together as fast as possible.
20:21 Speaker 00 They're working nights and weekends. And then there's these problems where there's variance. So now the power that's coming in has to power 200,000 GPUs. But the GPUs are spinning up and then they're spinning down over like 10 milliseconds. And that variance in power makes them very difficult to handle for these generators. So they had to call up Tesla, import mega packs, which are these giant battery packs. They had to reprogram the meta packs, mega packs that they would fix the variance. And there's all these technological crazy limitations that happen first here. And then there's the thing where now you have 200,000 GPUs running all at the same time, doing this process called like a reverse gradient, where it runs through and it trains the model. If any sort of variance happens on any bit of any of those GPUs at any moment of that training run, the whole training run is gone.
21:07 David Hoffman? Wait, so there has to be 100% precision by this very large group of GPUs?
21:13 Speaker 00 Every single run and they're doing hundreds of thousands of these runs. The amount of precision and engineering coordination required to do this was unbelievable. A lot of people would have said this was never possible and they did it in
21:25 David Hoffman? Just because there's too many variables that could go wrong.
21:28 Speaker 00 Yes, and it's such a very much like in the way that AI models are frontier. The manufacturing is very frontier as well and the actual training clusters are frontier technology as well. People didn't know that you could actually string these together this quickly in this way and do it the way they did. It's it's an amazing feat of engineering. I think that's the more interesting story than Grok 3 is how they actually built it so damn fast.
21:47 David Hoffman? Wow, okay, there's a ton of parallels here with Bitcoin mining. And I guess that's just the story of data centers. But the last time I ever heard about the subject of megawatts or even gigawatts was from Bitcoin mining facilities requiring a ton of power to power ASICs. But ASICs are like pretty stupid machines. They're like the stupidest machine possible. There's one algorithm and you it just spits out algorithms and you need to cool it. You need to have a lot of power and you need to have a lot of these machines, but they don't have to be synchronized with each other. And they run 24/7. They don't they don't stop running and then start running and then stop running again. So like the level of complexity here is a lot higher than my intuition with Bitcoin mining. But there's but that's really where the parallels end, right?
22:32 David Hoffman? There's no like synergies, like there's there's synergies with Bitcoin mining and power grids where like sometimes a local city will flex up and down its power grids and you can kind of offset that with Bitcoin mining. But there's no synergies here. There's no overlap between Bitcoin mining and AI compute resources, is there?
22:48 Speaker 00 There's not much. There's definitely the energy conversation where how are you going to power all this? But that's mostly where it ends. I guess there's a lot of similarities in terms of networking infrastructure, getting the GPU to talk to each other, building them in a cluster. But ASIC mining GPUs and traditional GPUs that they're using for this training are super different. So it has some similar parallels and I'm sure people who built data centers for Bitcoin mining could help build data centers for AI training, but that's mostly where it ends. It's very dissimilar, probably to the to like the benefit of Bitcoin. If these GPUs were ASIC miners, that would be a problem because that's a lot of compute that could be used to attack the network. So thankfully, two separate worlds.
23:27 David Hoffman? Okay. Okay, so you're emphasizing that it's not just the fact that the X, the Grok 3 model is now a frontier model, but it's also the fact that Elon Musk kind of does what he does best and he leads operationally and commands a very small, very bright, talented team to have this extremely just crazy outcome in a very small amount of time. So now, as we know it, again, check me if I'm wrong, but the XAI team is operating the largest known cluster of GPUs. They also had this kind of competitive advantage where they could use Tesla battery packs and maybe there's like some domain knowledge that the XAI team was familiar with Tesla, I'm assuming, just because of proximity to the Tesla team. Do
24:11 David Hoffman? we think that Elon Musk will be able to retain this lead of like operational or compute resource? Or is that something that is always going to be an arms race with other companies too?
24:23 Speaker 00 Yeah, so I'm not sure 100% that they do have the single largest cluster. I believe it's the largest publicly known cluster. A lot of these companies are much more private. So that's unknown. But I think the story here, like you mentioned, is the rate of acceleration, the fact that they were able to do this so quickly, using so many different domain knowledges from Tesla, from SpaceX even for energy and propulsion, stuff like that. I think that's the story. And I think based on their rate of acceleration, they should be ahead soon. I love the comparison that Gavin Baker made where it was like Google DeepMind had 12 years to get here, OpenAI had eight years to get here, XAI had one year to get here. If they continue at this rate of working nights, working weekends, of really using all this unique domain knowledge to continue growing these, so long as they
25:06 Speaker 00 have the GPUs, I don't see any reason why they would fall behind. So I imagine it kind of looks like they're at the front now, OpenAI releases their model, Anthropic releases their model, now they're in the back. After they release this new version of their training center, which Elon said they want to power it up to
25:23 Speaker 00 1.2 gigawatts, I believe, which is five times, which should be 100,000 or sorry, a million GPUs. So you're talking like a 5X increase from this. I very quickly think that XAI will just continuously lead and leapfrog all of these at some point soon.
25:37 David Hoffman? Interesting. You see, you think XAI will constantly be found at the number one spot. And I think what you're saying is the number one spot is constantly going to shuffle. But you think that like in terms of like the frequency of being at the number one spot, XAI is a pretty strong candidate for being there?
25:51 Speaker 00 It would appear so just because of their engineering abilities. Now, there's a big caveat to that that we saw with DeepSeek, where DeepSeek did not have the engineering resources that the United States does in terms of GPU cluster size. But they were still able to produce a frontier model using this novel breakthrough of distilling. So there is a world in which GPUs matter less and less. In that case, it becomes a very different battle. It's not really a manufacturing battle. It's more of an intellectual coding battle of who can create the best algorithms, who could distill the best models, who can make those breakthroughs. Until those breakthroughs happen, it's very much a brute force challenge, where how many GPU clusters can we, how large can we get our cluster to train? And a big question with this was scaling laws. A lot of people were questioning scaling laws. Does it actually scale proportionally?
26:34 Speaker 00 If you have 100,000 GPUs to a million GPUs, do you get a 10X improvement? So far, the scaling laws are intact and Grok has proven that. So until that changes, it's likely just going to be a battle of GPUs. And I think the XAI team has a really good chance of staying in the lead with that.
26:51 David Hoffman? Okay, so the DeepSeek model had this breakthrough, this technical breakthrough in the way that the LLM was designed, because it has this mixture of experts' efficiency breakthrough. And so if you ask it a question, the nature of the question will kind of get routed to different parts, different sectors of the LLM. This is as I understand it. And some sectors will light up and be more active, and other sectors will stay quiet and be less active. And that's an efficiency of compute resources breakthrough. But can't XAI and Grok 4 or 5 or whenever they decide to incorporate this, can't they just copy that technique? And now they have that technique, which was the DeepSeek advantage, and they have their compute resources advantage.
27:32 David Hoffman? The compute resource is not commoditizable because XAI owns those things. But the DeepSeek technical breakthrough, totally commoditizable. Is that a fair conclusion?
27:44 Speaker 00 That seems right. Yes. So now that DeepSeek has made this crazy chain of thought improvement where they basically what they did is they took this gigantic model and they were able to distill answers from it using chain of thought. So basically, the giant model talks about how it gets to an answer. The reasoning model, the refined model, uses that chain of thought to train itself and give itself higher quality answers with lower compute requirements. That's totally being used. One thing that's interesting about XAI that Elon initially said wasn't happening, but I saw on X yesterday that an engineer confirmed they're actually showing chain of thought raw and in real time when you prompt XAI, or the Grok model. Meaning if you ask it a question, it will show you exactly what it's thinking as it's thinking it.
28:27 Speaker 00 So not only is the XAI team using this to improve future models, I'm sure. But what's interesting is they're currently closed source, but they are making chain of thought open source, which means other people now can take this Grok 3 model and they can view the chain of thought. They can feed that to their own smaller refined LLM and they can make their own. So it's this like pseudo open source thing that they're doing.
28:47 David Hoffman? It's not open source, but it's a window. It's a selective window into Grok 3 that allows people to kind of see what's going on and then actually kind of hook into that technically.
28:58 Speaker 00 Yes, and I think a lot of people get confused because LLMs seem like this big scary network of code that doesn't make any sense. But all these things they're doing are they're just token generation machines. So they are just speaking in plain English to itself. And then it is reflecting on the English it said and it is thinking again in English. And you could view this whole chain of thought in plain English. There's actually no code that is really running with the model.
29:20 David Hoffman? The command line interface of these LLMs are actually in English. So there's no hidden code behind the scenes here. There's no like binary that these things are thinking in. They're actually thinking in English in April and we are able to see into their brain as it thinks in English.
29:35 Speaker 00 Yes, and one of my favorite tweets is by Andre. I think he has it pinned to his profile where it says like the hottest new programming language is English. And that is so true because these models, yeah, we could probably see it there.
29:46 David Hoffman? Yeah. That's all he says. He says the hottest new programming language is English.
29:51 Speaker 00 And it's so true. And it really removes this layer of intimidation from LLMs and AI in general is because they think in English. They think the same way that we do and they infer in English and they process things in English and you could interact with them in English. It's this really liberating thing that's happening where you don't really need to write code to engage with these things because they don't, they understand code and they could write code, but they also very well understand English and can interpret that to code however you want. It's a super powerful thing happening.
30:19 David Hoffman? What's XAI's stance on open source? So Grok 3, to my knowledge, not fully open source, but that new window into a part of Grok 3, I thought was pretty interesting, but Grok 3 not open source. My understanding is that Grok 2 is open sourced once Grok 3 is like stable and released and fully in production. Is that, what's XAI's stance on how to open source their older models?
30:44 Speaker 00 Yeah, that's correct. So they have a frontier model, which is Grok 3. Once Grok 3 is finished and feature complete, they're still rolling out features every day. That's when they plan to release Grok 2 as a fully open source model. And open source mean they'll release the weights that it was trained on. So it should kind of proceed like that as they go where their frontier model will be private and then the model prior to that will become public and open source for everyone to use.
31:08 David Hoffman? And each company, so I have this tweet here that, I'll just read it. Still insane to me that four of the original OpenAI founders have raised billions for direct competitors. Elon with XAI, Mira, Thinking Machines, Ilya, SuperSafe, John, Anthropic. There's other frontier model like AI labs out there, but I think this kind of just gives the lay of the land is like sort of, we're trying to set the board here. There's all these different companies. Each one has their own independent stance on open source, correct? And ironically, OpenAI, I think, is the most closed. XAI is like pretty far leaning towards open source, but not completely open source. How did the different companies compare in terms of their stance? How do we think about this?
31:52 Speaker 00 Yeah, so this is like the most insane version of Game of Thrones you could imagine. Because going back to how we started, this is this is the fight for the bootloader of artificial intelligence. And I think the people at the OpenAI team were at the frontier and they realized this. So what happened was Elon got pissed because OpenAI that they named after was no longer open. And then Mira got upset, who is the one of the other co-founders because of unknown reasons. She left to start her own company. Then Ilya left to start his own company. And then John left to Anthropic and now is working with Mira. So a lot of the co-founders of OpenAI either realized the stakes were too high for one person to control it, or they had a disagreement about how it was being controlled.
32:34 Speaker 00 And they decided to exist on this spectrum of openness to closeness. So in the case of Elon and XAI, his goal is to create an open AI platform to really be the open AI. He's doing that, he's not fully doing that. Grok there would be open source if they were. But I think the intention over time is to release it and to seek truth. Mira has a company that just came out of stealth called Thinking Machines that is kind of adjacent to this. Ilya is very focused on safe alignment, making sure that the AI is aligned with humans' intentions, that the goodwill of people. And then John actually left OpenAI to go to work at Anthropic and then left Anthropic to join Mira in her Thinking Machines company. So it's this wide spectrum.
33:16 Speaker 00 There's a lot of people who are very excited to compete against each other for this prize of who can have the leading AI models.
33:23 David Hoffman? I'm seeing like a parallel to the Ethereum co-founders story. So Ethereum had like eight co-founders at the very beginning. Gavin Wood left to do Polkadot. Charles Hoskinson left to do Cardano. And so is that kind of like a kind of similar structure that we're seeing here is like, well, Ethereum was created, AI was created, smart contracts were created. And then all the co-founders were like, wow, this is really cool. But I want to go lead my own project because I have a slightly different philosophy on how things ought to get built.
33:54 Speaker 00 Yeah, this has happened throughout history a few times. You also saw like traditionally with the PayPal mafia, where this incredible group of people all built PayPal and then left to start all these other companies like Teal created Palantir and all these other companies. Then Elon was from there. David Sacks was part of that group. There's a lot of names who you know today that were all part of this cluster. I think it's a very similar thing.
34:14 David Hoffman? But there's also like other AI lab companies out there, right? Because like we haven't even talked about Meta here. Meta not doesn't have a OpenAI co-founder. It just has Zuckerberg and the Meta team. So there's also Meta. Anthropic is John. So John is a old OpenAI co-founder. Is there anything else other than Meta that's not listed here? That's worth discussing as like who's in the game, who's on the board?
34:35 Speaker 00 The personalities aren't as extreme. This covers most of them. I mean, Meta has the most known one, which is Zuck. And then there's Satya, which has Google and the Gemini. Yes. So those are big names.
34:47 David Hoffman? I never hear anything positive about Gemini. I only hear negative things about Gemini.
34:52 Speaker 00 Okay, Gemini is actually a really great model and I encourage you to try it. The specialty with Gemini currently is they have an incredibly large context window, meaning you can fit a lot of words, tokens, people call them, into the models that it can reference quickly. So with a lot of traditional models or just normal models in general, they're referring to this kind of densely compressed data set. With the context window, you could feed a very high quality data that's not compressed through words. Like, for example, you could upload a PDF. Gemini allows you to upload the most information and also share the most information.
35:24 David Hoffman? So a context window is like, I'm looking at something. Say I'm looking at a document and when I'm a human reading it, I'm like remembering the page that I just read. Now I'm on the second page and that's like my local amount of attention that I'm able to like keep in my short-term memory. And it's like a context memory, a context window is like, well, the Gemini model can actually see like maybe 800 PDF document pages all at once simultaneously. And other models don't have that large of like attention field. Is that, is that a way to understand this?
35:54 Speaker 00 Exactly. Yeah, that's a perfect explanation. It's like. Things out of context would be like you reading a book, putting it down and coming back the next day and trying to remember specific things. You remember it, but sometimes it gets a little blurry. Sometimes you hallucinate and you come up with the wrong answer. This has all of it presented right in front. It is very clear, very accurate, and that's what Gemini is particularly good at. So I wouldn't discount Gemini. Gemini is doing good work.
36:16 David Hoffman? Okay, why do I think that it's like negative, though? Is it, maybe it's the censorship, like Gemini is a super high, like woke, wokely biased model?
36:24 Speaker 00 Yes, it could be the censorship thing where like Gemini was the one that was generating pictures of like non-presidents as presidents on murals and it had a lot of drama for that. It was very much the woke AI. I think they're starting to turn against that. I think the difference and the reason why you hear less about Meta and Gemini is because they're not part of this cabal of people. They were like Facebook and Meta had nothing to do with AI a while ago. Google had nothing really to do with AI. Now they're turning the resources towards it. But a lot of these other companies are AI-first laboratories. So their entire existence exists for building AI models. Whereas with Meta and with Google, those are just pillars of the larger business.
37:04 David Hoffman? Right. Okay, so it's Meta and Google that are the two companies that have AI products, but they also have just like a whole entire like alternative products, like Meta has Facebook, Instagram, all that kind of stuff, WhatsApp. How does that change? Does that change anything in terms of just like how, what their piece on the board looks like or behaves like? How do I think about that?
37:26 Speaker 00 Yeah, I think it's different for Meta and Google. Google AI is a requirement. Google's largest product is its search engine. And AI very quickly has started to
37:37 David Hoffman? So they're trying to not be disrupted.
37:39 Speaker 00 Exactly, yes. Or in a sense, they're disrupting themselves before others can disrupt them. So they want to get ahead of search getting destroyed or eliminated by AI models by building their own. So I'd say Google is definitely doing it out of necessity. And again, there's a lot of downstream benefits that come from building frontier models for their own business. They can now optimize their engineers. They could optimize a lot of the code base. I forget the number, but I think on Dwarsh's podcast recently, the Google engineer said, I think 25% of all the code contributed to the code Case now is built by AI. So not only does it help defend their moat of search, but it also helps improve the productivity of all of the employees at the company. In the case of Meta, I think it's a different plan. Meta has Facebook as their cash cow.
38:21 Speaker 00 They have the attention, but the attention is just like kind of messy and like not really productive. And it's just, it's a social media platform. So what they have is they have this moat of user attention, and they want to try to get ahead of this trend and point that attention towards their products that use this AI. So Meta's open source approach is allowing people to build their own tools, build their own communities on top of their Llama model in hopes that they can build these products that strengthen the moat around the existing audience that they have. So a problem right now is a lot of companies are raising a ton of money to train these clusters. They're very expensive, but there's no clear path to revenue for them. You're just kind of throwing money at a black hole.
39:01 Speaker 00 You're getting these really good models, but there's really no moat effect outside of ChatGPT, which is an application. So you want to kind of take your existing users, turn them into a moat as best you can. And that's kind of what Meta is doing. They famously did this with, with PyTorch was one of them that came out of the old Facebook team. And there was, there's one other programming language as well. But they like to create these tools in-house, open source them, and then build a development community around them to leverage their existing users.
39:29 David Hoffman? So Meta seems the most differentiated via any of the other AI labs, simply because of like kind of the, the social media and just like user base that Meta has. That's my intuition. Is that right?
39:43 Speaker 00 Yes. Yeah, I think we probably like Meta the most as crypto people who are super into open source. Meta has been the largest, most transparent contributor to open source AI technology. They have developed, or they've poured billions of dollars into training these models. They've open sourced all the weights. They haven't open sourced everything. There are still private parts of it that they're using for their own proprietary platforms. Instagram, Facebook, they're all built using these AI tools that Meta hasn't announced. But I think they're probably leading the way in flagship, um, open source models that people can build off of. And that's been really exciting. You could actually run. I, as a test on my little MacBook here, I tried to run one of their smallest models and it actually works. It's very slow, but you can run a model locally on your machine.
40:26 Speaker 00 And it's really cool that larger companies have access to these, these huge models to then train for their needs and, and do things how the hell they wish.
40:33 David Hoffman? Interesting. Well, why is Meta on the spectrum of closed to open, like closed source to open source? Is it Meta that's on the most open source side of the spectrum? And if that's true, why are they there?
40:45 Speaker 00 In terms of big players, I would say yes. They're the largest and the most open source relative to someone like OpenAI who's probably, or even Anthropic. They're very large and very closed source. I think they're there mostly because of the access that they have to users currently and the fact that they are behind. So when Meta's behind, it's very, unless you're Elon with XAI, it's very difficult to catch up to the frontier models. So there is a case to be made that if we can't beat them, we could just dilute them. And by releasing these models that are open source, allow anyone to use them, allow people to build on top of them, allow people to distill them. They can kind of grow this open source conglomerate faster and cheaper than a lot of the closed source people can.
41:30 Speaker 00 A lot of the closed source models like ChatGPT, their entire revenue is built around their subscription. And if XAI or if Meta can provide a free alternative, that starts to eat away at their profit margins because there is this shiny free thing that works 80% as good, but it's free. And then there's the second thing where they have this moat of tons of attention and users. And if you're a developer, you want to go where the users are. And if Meta is offering you this open platform to build on top of, to access this whole user base, I think that's super appealing to developers as well. So the thing for Meta is they probably wanted to dilute others while allowing developers to come and giving them access to the most amount of tools possible. So they can get this free attention, they can get this free workforce essentially of developers who want to come and build on top of their existing platforms.
42:14 David Hoffman? Okay, so Meta has just revenue streams from elsewhere. Whereas OpenAI and more of the closed source ones, the AI-only laboratories, they have to make money from their AI product. And so that's what probably the incentive for them to stay closed source. OpenAI charges $20 to $200 a month for some of their most advanced models. Meta doesn't have a most advanced model, but they can be a loss leader. Their AI labs can be a loss leader and they can kind of penetrate the market with a technically inferior product, but they can kind of lean into the value and the ecosystem of open source and trying kind of just like. Volume their way into penetrating into the market. And what I mean by that is like when you open source your ecosystem and you kind of commit to
42:59 David Hoffman? an open source, you recruit the army of open source developers to create an ecosystem around your LLM. And so they're just trying to like dilute the value of having a frontier, a closed source frontier model by not, by just having this more open source one and trying to make a platform of, an ecosystem platform more than like a very powerful model. Is that kind of the understanding?
43:24 Speaker 00 Absolutely. Yeah. Meta does not need AI revenue to survive. And OpenAI does, and Anthropic does. So they are forced to keep these closed, they are forced to charge a premium. Meta does not. Meta can earn its growth over a long period of time from network effects, from gathering an audience of builders and developers that want to build on top of it. Whereas these closed source models need to generate some revenue because the promise is that these will eventually absorb large swaths of GDP of the world and they will produce trillions of dollars of revenue. But until that point, you need to have some sort of revenue model to keep the lights on, to continue training, to continue getting there. And the only way for these closed source models to do that now is to just charge a premium and try to create the best apps they possibly can.
44:04 David Hoffman? Do you have any like bets? Where would you place your bets in terms of like who's going to win here? How do you even think about that? Like, do we even need to think about that? Or should we just like grab our popcorn and watch these players duke it out and be chill with the drama? Or like, do you have a favorite here? And if you do have a favorite, why?
44:22 Speaker 00 Well, my favorite currently is XAI. I am obsessed with the engineering team at that company. I think the fact that they spun up that cluster in 12 months is absurd, considering they were quoted like four to six years or something crazy like that. So I think the rate of acceleration of people who are super hardcore and also just care about more truth seeking versus alignment, that gets me really excited. If you chat, Grok has an early chat feature that some people got access to. You could talk to it and it sounds like it's your friend. It doesn't really sound like it's trying to like prevent. Some parts of the truth from getting to you, it's just very raw, very open. I love the mission statement. I love the rate of acceleration. I love the hardcore engineering. They just seem like the most generally aligned because they want to get as far and as fast as possible
45:06 Speaker 00 and then dump it all off to the public and allow it to be open source. I think OpenAI has a really good chance. I think Anthropic has a really good chance. They all have a great chance. And there's this crazy like sub level of drama existing there where all the co-founders are leaving and they're competing against each other and they're suing each other. The thing famously when Sam Altman got kicked off the board in 2023, part of it was the result of Ilya Sutskever, who was one of the co-founders. He decided that he would vote to kick Sam off the board, which is a crazy thing to do because they were co-founders. But clearly they didn't see things eye to eye. And the big meme that came from it is like, what did Ilya see? That caused him to freak out so much that he wanted to kick the CEO out of the company. He later apologized. He changed his mind. But after doing so, he left and he said, that's enough. I'm building my own thing.
45:51 Speaker 00 So there's this crazy.
45:52 David Hoffman? And we still don't know the truth behind all of that drama, right? It's still kind of opaque to us?
45:57 Speaker 00 Yeah, there's a lot of stories that came out of it. Nobody knows the actual source truth. I don't think it has come out exactly what happened. We don't know exactly what Ilya saw. But we know that he left and created a company based around alignment, which is making sure the AI is aligned with human values. Because clearly there is something missing in the OpenAI team. So it's this like crazy Game of Thrones fight for power. Really fun drama, really fun to watch. It's best just, yeah, the popcorn. Sit in the back of your seat and watch them all duke it out because the stakes are very high.
46:26 David Hoffman? Right. What's the deal with Elon Musk versus Sam Altman? Like, there's a lot of drama. That's like some of the most drama that's currently happening, right? Like, download me on that.
46:35 Speaker 00 Yeah, and of course Elon is in spotlight for 20 other things, but this is one of them. And basically, when OpenAI got started, OpenAI was named OpenAI because it was supposed to be open source research for AI models. So. Elon wrote a $50 million, some amount of check. OpenAI was built as a nonprofit. Elon was one of the co-founders, along with Sam and a few others, Mira, Ilya, the whole crew. And then he left and he started working on other things and he was building Tesla. He was building SpaceX. Over that time, Sam, who was running the company, decided that in order to train these frontier models and to accelerate the progress of AI research and development, they needed more money. They couldn't exist as a nonprofit. They had to raise and become not a for-profit, but closer to a for-profit to fund all the
47:20 Speaker 00 GPU clusters, to fund all the electricity, all the training. So Sam considered that he should start raising a lot of money and Elon got very upset about this because the whole goal for Elon and the reason they named it OpenAI is he wanted to keep it a nonprofit. That way, there's no incentive for anyone to use this AI technology harmfully. And he wanted to allow the open research of it so that he believed it was going to be very powerful. Once it does become powerful, everyone has equal access to it. Sam, over time, slowly strayed away from this. He raised a bunch of money. He trained a big cluster and now their revenue is built on this closed source thing that is very, very powerful and he has no intentions to give it up. In fact, he's actively trying to create a for-profit branch of OpenAI, which is part
48:04 Speaker 00 of the recent bid that Elon made of, I think it was, I forget the amount, like $94 million or billion dollars or something for it. Because Sam wanted to spin it off cheaply, Elon is kind of trolling him. He made this huge offer for XAI or for OpenAI that now has to get justified by the board and is kind of sabotaging Sam's plans to make this for-profit arm. So Elon is very pissed. He went and made his own thing. Sam is upset with Elon because he thinks that you need money in order to train these clusters and that's kind of where they butt heads.
48:33 David Hoffman? Interesting. Was the $94, $97 billion offer by Elon like more than what people assume AI, OpenAI is worth, less than what people assume OpenAI is worth? How do we think about that number in relation to reality?
48:49 Speaker 00 I'm a little fuzzy on the exact numbers, but I do know that the number for Sam was hoping to take it was $40 billion.
48:56 David Hoffman? Forty, four zero.
48:57 Speaker 00 Forty, yes.
48:57 David Hoffman? Okay.
48:58 Speaker 00 So this was more than double that, which is still below.
49:01 David Hoffman? And Sam said no, and was this, this was a move by Elon to offer him a very high number in knowing that Sam would be resistant to the fact that it came from Elon. And now you said that now Sam has to go and justify to the board why the 97 million dollar valuation, billion dollar valuation is not good.
49:19 Speaker 00 Yes, and this is, granted, this is speculation, so take this with a grain of salt.
49:22 David Hoffman? Okay.
49:23 Speaker 00 But it would make sense that the reason Elon did it is to sabotage this plan that Sam had to spin off a for-profit part of OpenAI. So because Sam wanted to get it for 40 billion, Elon offering 90 whatever billion cannot be justified to the board like, hey, if we're turning down this offer, we're not giving it to you for half that. That's crazy. So that's kind of where we stand now. So in a way, people believe this is not like for sure, but people believe that Elon was trolling Sam because he knew he wouldn't sell, but he wanted to make it harder to create that for-profit entity.
49:54 David Hoffman? But it was a credible bid, correct? So Sam could have said yes, and then Elon and Sam would be in a, in a negotiation after that.
50:02 Speaker 00 Yeah, we've seen this happen before with, with Twitter. It was, he made the bid, people didn't think he was serious, he tried to pull out, they wouldn't let him pull out, and now he has Twitter. So it's like one of those things where, I mean, this is a real bid in the sense that if they wanted to accept it, Elon could now own OpenAI today.
50:17 David Hoffman? Is it still on the table? What's the status of the offer?
50:21 Speaker 00 I don't know. I think it was quickly shut down by Sam. That was for sure. So Sam wanted no part of this bid.
50:27 David Hoffman? Right.
50:27 Speaker 00 He was like, I'm not taking it, but I'll buy Twitter for X amount.
50:30 David Hoffman? He trolled Elon back, right?
50:32 Speaker 00 Yes, or attempted to, the best he can. I find that Elon really cares much less than Sam in the sense of like being serious. So Sam's working on his trolling abilities, but that's kind of where they're at currently is. They're not selling OpenAI anytime soon.
50:46 David Hoffman? But didn't OpenAI raise from Microsoft a while ago and raise a bunch of money from Microsoft at a particular valuation?
50:53 Speaker 00 They did, and the deal with Microsoft was super bizarre. This is a one of a kind type deal, again, because the goal of this or the prize of winning this race is so high that there is a certain return multiple on Microsoft's investment. I don't know the specific amount, but once it reaches that multiple, all the shares are then reverted back to the nonprofit to then be distributed because the assumption is that AI will unlock the world's GDP. So it will create so much value for the people who own it that it does not make sense for a single shareholder to benefit from all that because it will be so much money, the money will not be as important as it used to be. If you generate $100 trillion worth of value, Microsoft doesn't need that, Sam doesn't need that. So the idea with the Microsoft investment is they will get an investment and a return up to X percent.
51:38 Speaker 00 And then after that X percent, the shares get returned to the nonprofit and they can stand to benefit humanity as a whole. And that's kind of where WorldCoin comes in, is Sam kind of saw this future of infinite GDP unlocked by robots and he wanted to come up with this kind of like global payments for citizens and that's kind of where WorldCoin fits into this.
51:59 David Hoffman? WorldCoin is like the distribution mechanism. So like when one, when there's one winner of the AI race and the world's GDP is equivalent to like owned by this LLM or this AI company, then we need to figure out how to distribute the spoils of the world's GDP to the people. And we need a distribution mechanism and that is WorldCoin. Is that, is that the connection?
52:24 Speaker 00 Yes, exactly. The idea is like so much money will be generated by these AIs that it will relinquish everyone's responsibility to be productive in society. Therefore, we need this like payment method to, one, prove that you are human. So that's why they scan your eyeballs is because AI so far cannot create artificial irises. And, and just to prove that you're human, so you can get some sort of universal basic income that can sustain people moving forward. So that's a weird dystopian future to think about, but it's something they're taking seriously because WorldCoin is a very serious company with a many millions of users who are thinking about these types of problems.
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55:08 David Hoffman? The face of like GDP in a small way. And then, and then like, we're jumping forward to like, okay, and then one AI is going to win, and it will be the world's GDP, and it will direct the world's resources. And owning it is equivalent to owning the world. Can you like, carry us through that logical train of thought? How do we go from where we are today to like, a single AI, like, owning the world? How would you like, carry us there?
55:32 Speaker 00 Sure, yeah, and granted, that's probably the worst case scenario. Is a single entity owning... No, we don't like that, because that would be like, a single entity owning the largest nuke in the world. They have the most power, they have all their, like, money, they have everything. And to leave that up to one person, in this case, in this example, Sam, seems very scary. We don't want that. So that's why there is this race of companies, and also open source models, who want to dilute that and also play a part in getting there. Now, to get there is, is a very long way. There's this debate on AGI, and have we reached AGI? And what exactly is AGI? That probably comes first. I guess you could loosely define it as being smarter than a human in basically every facet.
56:16 Speaker 00 So, right now, the goal is to make an AI model that is smarter than a human at basically everything a human can do. And we're not quite there yet, because we're currently training on all of the human knowledge base. So the way these models work is they'll scrape the entire internet, they'll scrape all the books, they'll scrape kind of all the publicly known knowledge that humans have created. They will condense it into this model, and they will train the model based on that data to kind of emulate it, but it's not actually creating original ideas just yet. It's kind of doing the stuff based on what we've already created. Now, it's mostly removed, it's mostly gone through all that data. It's trained itself on all the data, and now they're working on ways to make it think smarter.
56:57 Speaker 00 We kind of see this, early on with Chain of Thought reasoning, that was the big DeepSeek breakthrough, is the model can think about what is thinking out loud, and then reason against those thoughts. So it could kind of idiotly think on top of itself, and get smarter that way. But there hasn't quite been this like, breakthrough where it is generating quite original thoughts. So there's, there's a big leap there between... This is smarter than a human at math. This is smarter than a human at coding. This is smarter than a human at socializing. But there's not really a model that can do all of that at once. So that's the first step. We're very close to that. I would imagine that probably comes in the next 24 months, just based on the rate of acceleration. But then there's also a huge jump there to actually impacting real world stuff.
57:41 Speaker 00 These robots, like they don't have bodies just yet. They don't, they're not physically capable in the way we are. So there's a huge jump between unlocking tons of GDP and unlocking just like marginal increases of GDP. I think AGI is the first step where if you can get these brilliant thinkers that never sleep, can think incredibly quickly, never need any energy, again, aren't limited by our biological limitations. That's probably the natural first step. That unlocks a lot, but there's still this like whole world of robotics, of automation that needs to happen in order for them to really replace stuff that we're talking on the scale like decades from now.
58:17 David Hoffman? Right. Okay, so the, let me regurgitate what you said. So as I understand it, make sure I got it right. There is this like library of human knowledge. All human knowledge is recorded somewhere, mostly on the internet, but also in books and other documents that are out there in the world. And the current LLMs that are in development by all of the players that we've talked about so far on the episode are trying to ingest all of that knowledge, make a model that understands and knows all that knowledge, and then can efficiently with minimal compute resources, or just, you know, efficient compute resources, spit out that knowledge, regurgitate that knowledge better than any one human can at any one particular domain.
59:02 David Hoffman? So there's all of these domain experts. There are chess grandmasters. There are PhDs, like rocket scientists, theoretical physicists. There's all areas, all areas of expertise where there is like a leading frontier human. We are trying to make an LLM that is better than all humans about all human generated knowledge. And that's like, and that's what you just said is we are trying to get there maybe within 24 months. We will have an LLM that is better than all humans at. Human previously generated knowledge and that we're gonna, we're gonna get there. That seems pretty reasonable, not too crazy. But the new thing is, well, humans don't have a monopoly on knowledge.
59:43 David Hoffman? There's knowledge out there that other species, other types of intelligences can create and know. And so the next step that we don't know how we're going to get to is can we make an AI model that can generate new knowledge independently of humans? And that is like actually a much bigger engineering challenge that we do not know how we're going to get to.
1:00:04 Speaker 00 Yes, smarter people than I can probably tell you a clearer path to getting there. It does seem that leading researchers think like this is imminent and very possible very soon. I don't know exactly how it works, but I know the implications of it, which are just creating original ideas based on this foundational knowledge that we have. So for example, in the world of biology, these models are trained on all of the biology we know. Using that biology, it can generate enough compute power of thousands and thousands of PhD students to infer new tests worth having. So in the case of curing cancer, for example, they can take all of the biological information we've ever unlocked about cancer cells, about how they work, about like the core biological things. They can make these inferences based on all this knowledge that sometimes we can't see.
1:00:47 Speaker 00 There's this funny thing that I heard biology talk about actually where
1:00:53 Speaker 00 it takes a very small knowledge unlock to unlock a very large picture. So in the case of a mouse being inside of a box and the box is a maze and it doesn't know how to get out of the maze, but it turns out the answer to the maze is just every prime number you turn right. And if you're a human and you know what a prime number is, you can very quickly like infer, okay, prime numbers get you out of this maze. But without having that knowledge, you can be stuck in that prime number maze forever because it's very difficult to guess at that scale. So there are probably these small knowledge unlocks that AI could infer for us that allow us to go out into the world and test that could unlock these huge new slots of information that we were just too blind to see. So I think that's the most exciting medium term thing is, is what type of inferences can AI make
1:01:38 Speaker 00 that allows us to go test to be like, holy shit, I can't believe we missed this super simple thing that unlocks this new gigantic world of science. So that's the part that's really exciting in the intermediary.
1:01:47 David Hoffman? So these are engineering tricks and there are just engineering tricks out there that unlock like step functions in terms of like the capacity of these models. Is that what you're saying?
1:01:58 Speaker 00 Yes. And then I assume there's also people that will say that they will begin to create their own ideas and do the tests themselves and start to form these artificial conclusions. That comes further down the line.
1:02:08 David Hoffman? And that's actually where Bankless stumbled into AI is right at that subject. Like, correct me, you were there when we had Eliezer Yudkowsky on the podcast. And me and Ryan were going into this interview with Eliezer Yudkowsky and we understood him to be this AI expert who deeply understood how AIs work. And we were just going to ask him about like, yo, AI, like, teach us about AI, Eliezer. And then we also had in this agenda, like, oh, we're going to talk about AI and crypto. But like, it became very apparent, like 10 minutes in, that Eliezer Yudkowsky was like, once we cross this one particular line and we don't know where that line is, and once AIs get materially smarter than us and they start self-improving themselves, we lose all semblance of control and it's a brand new world and we don't have any control in that world.
1:02:54 David Hoffman? That is the same line that we are just talking about just now, right?
1:02:56 Speaker 00 Yes, it's referred to as, I guess, like, takeoff is what some people call it. Is, once the AIs get smart enough to reason with themselves, they can improve themselves and they can do so at a rate that is-
1:03:06 David Hoffman? They can create their own knowledge, they can improve themselves, the bounds, the brakes are taken off.
1:03:11 Speaker 00 Yeah, incomprehensible to us. And there's these funny jokes that we are actually like in the early stages of that right now. Like, if you think about what just came out this week, it's like, we have now quantum computing breakthroughs, we have genetic mutation breakthroughs, we have like chromosome breakthroughs, protein folding breakthroughs. All these frontier breakthroughs are happening in one week. What would have like normally come out over the course of a year, a decade even. So is it starting to happen? This is probably the early stages. This is stuff that's still comprehensible to us. I wonder how this will change 12 months, 36 months into the future. As these models get smarter, as they're able to teach themselves, as they're able to improve. The idea is, once you, I guess the idea that Eliezer was referencing is, once you've reached this critical threshold, AIs can teach themselves so much, so quickly,
1:03:55 Speaker 00 that they will just take off in terms of their knowledge base and they will be incomprehensible to humans. And that's kind of the scary thing, because they will understand us so deeply and so easily be able to manipulate us and the world around us. We are very dumb relative to what type of intelligence is coming.
1:04:11 David Hoffman? Does crossing that line cause any like fear in you at all? Or are you just still like mostly stoked?
1:04:17 Speaker 00 I think it's it's kind of like thinking about the universe and the stars and stuff is like, it seems overwhelming when you say there's like trillions of these things and we're so small, but it's like kind of tough to comprehend. So I can only be like so much in awe of space or so much afraid of the AI. Like, I understand, I guess on like a conceptual level, how scary and how dangerous it could be because of how easily manipulated we are, how dumb we are, like how limited we are. But it's tough to kind of feel really scared because it's just tough to understand the gravity of it. Like, I understand it's a big deal. I understand it's probably a bad thing. It certainly shouldn't be controlled by a single person. It hopefully gets distributed. But I don't really know the second order implications of how bad that actually could get.
1:05:01 Speaker 00 And you could speculate and I just don't know. I choose to be optimistic. I choose to be excited. I'm not a researcher. I am not playing a role in this future, so I'm just here admiring it from afar. Um, yeah, that's it. But yeah, it's it's freaking nuts. It's a really big deal.
1:05:17 David Hoffman? And I think this is maybe where the conversation wraps around to how we started this conversation with talking about how human intelligence is just a boot loader for artificial intelligence. And I think what you were alluding to there, why that was the leading question is because there is a potential version of not just our human world, but the universe at large, where it's actually artificial intelligence that scales so much farther than any sort of biological intelligence. And it kind of, I think you're invoking this idea of like transhumanism there. Fill in the rest of this picture, right? Like, I think in this lifetime, we are all going, as humans, going to experience AIs crossing this AGI threshold. And there's some form of just Camryan explosion of artificial intelligence.
1:06:02 David Hoffman? And then we don't really know what happens after that. But like, people talk about this idea of transhumanism and like truly artificial life, not just artificial intelligence, but artificial life. All of that conversation begins at that point, right?
1:06:14 Speaker 00 Yes, so there's this like weird trend that's been happening since we really invented early forms of technology where every incremental improvement we make to the technology, we become more closely associated with it. It becomes more of our reality. So this started with like, like the radio, for example, you would spend an hour a day listening to a radio station, maybe your favorite sports game, talk show. We got TVs, you could watch TV shows, you spent two, three hours a day on this. Then we got a computer with an internet connection, you could spend your whole workday. You're spending eight hours a day in this alternate reality. And then our smartphone, I have some friends that are using a 12, 13, 14 hours of screen time a day. And now Meta and a bunch of other companies, Apple with the Vision Pro, they're inventing these glasses and goggles where now the technology's actually strapped onto your face. And now you're spending every waking hour of your
1:06:58 Speaker 00 day through this virtual lens, into this virtual reality. And the natural extension of that, and Elon, who is at the forefront, is building with Neuralink, is this brain-machine interface. So the idea is that like AI will get so good so quickly, it will become incomprehensible to us. We just will not be able to understand it, it will be so far superior. We will be like an ant to this human, and the ant being human. So in order to get around that, you have to, it's either like, if you can't beat them, join them. So it's like, okay, we need to figure out how to peacefully coexist with this artificial intelligence. And that is the the brain-machine interface. And that's kind of probably what gets big, once AGI starts to get big, and that's what a lot of frontier companies are also working on, is how do we merge ourselves with this technology? So we don't get left behind. We still maintain
1:07:43 Speaker 00 our human element, but we're able to engage with it. And there's this really funny demo I saw yesterday. It was two robots who were in a kitchen, and they were putting groceries away, and they were interacting with each other, but they weren't saying anything. They were just communicating telepathically. They just kind of look at each other, show the item, and they already know what they're thinking. And it's very quick, whereas speaking has like a lot of compression, it's very lossy. I have to speak to you, you have to uncompress it, and then recompress it, and then you speak to me. It takes a long time. And I think We'll start to see these unlocks happen in robotics and happen in like artificial intelligence species and kind of want that for ourselves. Like, how cool would it be to just download a language pack when you travel? Or it has access to your neural cortex.
1:08:26 Speaker 00 It can see everything that you see and it can lucidly allow you to replay these dreams. Or it can remember a day in perfect precision and you could query against it. And it's like unlocking this whole new part of your brain that when supplemented with artificial intelligence can create some really cool use cases, but also really scary use cases. So I think the future is like very uncertain. It seems certain that AI will get very, very smart. It seems certain that we will try our best to assimilate with this AI and hopefully integrate with it peacefully. But there are a ton of variables along the way, mostly around the fact that it is scaling so quickly and we are just like too dumb to keep up and manage all of this. It's really, it's a freaky time. It's an exciting time, but it's happening, whether people like it or not.
1:09:11 Speaker 00 It is very much here, it is very much happening.
1:09:14 David Hoffman? It's the idea that with the brain link interface, neural link interface, that we're creating these AI models that are becoming extremely powerful. They're only going to get more powerful and we're just never going to be able to keep up. Like biological intelligence is just so comparatively slow and by comparison. But with a brain neural link interface with and with a model, then we can actually connect our brain with a model and have that model be not just like something else that does the thinking for us, but actually our thinking. Can you like close that gap where like there's a one model where like, okay, I have my phone. My phone's right here. It's very powerful. This, this, this computer chip is extremely powerful. My brain is also pretty powerful, but I have to actually like use my brain to like
1:09:59 David Hoffman? leverage the power of the chip in my phone and the data that's on the internet. And that's this, it's a separate device that I can leverage. But there's a difference there with a brain link neural link interface where my brain and the chip are actually the same thinking unit. Is that what you were saying?
1:10:17 Speaker 00 Yes. And think about the horrors that is navigating your smartphone. Like first you have to unlock it, then you have to use your two thumbs to type things in, then you have to navigate things. And it's like, oh my God, it's so slow. It's so painfully boring. It's like, there's very- Low data streams that come through your smartphone. Whereas as like with a Neuralink, you could imagine it as an extension of your brain, except it is this like super brain that's built on top of your existing brain. So now you are thinking your normal thoughts, but you have this like sudden genius that is now in your body and you have all of the modality that comes with these devices. You have your sight, you have your hearing, you have your taste. And similar to how like OpenAI's models will engage with your camera or your microphones,
1:11:01 Speaker 00 it will do that, except you are the sensor. So your senses become the sensor for this artificial form of intelligence that has way more memory than you, way more processing power than you, has access to all of the world's knowledge, has access to all of the inferences that have come up with from this, all this knowledge, and you can access it instantly. And it creates these really weird use cases where if you can just kind of train this Neuralink piece on your brain over a long enough period of time, can it just emulate you, the person? So if your biological body dies, can't you just create a copy of everything that's recorded and place it in a synthetic brain? And is that really the person?
1:11:43 Speaker 00 Or is it just their memories and their ideas trained over a period of however many years it's been in there? So it's kind of this like, you're taking the AI, you're putting it inside of you, and it becomes an extension of your biological being. And in the meantime, it has these really cool use cases where it can cure people of Alzheimer's. It could create like any neural cognitive problem. It can solve that. It can solve people who are paraplegic, quadriplegic. It has a lot of really cool use cases for solving biological problems. But once those biological problems are solved, then it's like, okay, how powerful can we become as humans when we merge ourselves with this new form of intelligence? Which creates a lot of really weird existential questions around like, are you even you? Like, if you copy your brain into another being, like, does your consciousness transfer? Does that matter?
1:12:28 Speaker 00 There's a lot of these really strange existential questions that come from it. But like, that's kind of where we're going. And there's, these chips are in people today and they're working. What? Yes, there's a, there's a demo by Neuralink where one of the players in CS:GO, which for people who don't know is like a pretty challenging First-person shooter game on PC, he's able to play just by thinking with his brain. He has no fine motor cortex function.
1:12:52 David Hoffman? There's a lot of, there's a lot of reflex requirements to be good at CS:GO. So the latency needs to be as close to zero as possible in order to be at all competitive with CS:GO. How competitive is, what were they?
1:13:04 Speaker 00 Good. So there's this cool thing that happens when you have direct-to-brain access versus having to go brain to your like extremities like your hands. Because it takes a lot of time for you to think something and then for you to do something. Believe it or not, there is like a millisecond latency between those things. So when you have direct access to the brain, you lose that latency that's required for your motor function. So Palmer Lucky uses this really great thing in Defense Tech because he's interested in building these super soldiers. If you can, there's this problem that happens when you're shooting a gun. And when you're shooting a gun, particularly a sniper, you want to be precise. You need to pull the trigger at the exact right time. But there's this latency that happens between your brain, between your finger. You think you want to do it and you actually do it. But if you have direct access to your brain, that latency disappears completely.
1:13:49 Speaker 00 And you are just able to pull the trigger based on the thought versus the motor function. So I think that's the case with gaming and with things in the future. Is the best gamers, the best performers in high Twitch skills will be the ones that have direct access
1:14:05 David Hoffman? In their brain.
1:14:05 Speaker 00 To the brain. Yes. Because there is, you're, again, you're removing the latency. Like gaming, you know about ping, where there's like a few millisecond ping. Those milliseconds apply to like the actual motor functions of your body as well. And when you have direct access to the brain, it removes a lot of that. So that creates this whole other use case, set of use cases for this stuff. So it's good. It's really, really good. And it's, again, it's, this is the worst it's ever gonna be.
1:14:27 David Hoffman? So when I was watching the Andre Karpathy YouTube video about how to understand an LLM. I was, like I told you, I was like pausing it and I was asking ChatGPT questions about like kind of my intuition about how these things works. And one of the things that I was asking it is trying to like understand the comparison between an LLM and a brain structure, like neural structures. And one of the things I was understanding is like one of the process, one of the parts of the processes for how On LLM is created is that it's what does an LLM do? It tries and predicts the next token. And if it gets the token right or wrong, then it starts to re re weight a lot of the parameters according to the feedback, right?
1:15:09 David Hoffman? And so it it just tunes itself using this positive feedback loop of just like, did you get it right? Then good job, like here's a cookie for the all the parameters that got it right. Did you get it wrong? Like, let's take away some weight for the parameters that got it wrong. And then you just learn, you learn by association. And there's this concept in psychology called neurons that fire together, wire together. And when a neuron fires, it releases a hormone and it releases a neural transmitter. And that allows for nearby neurons to move physically move closer to that other neuron. So the the rule of thumb is neurons that fire together, wire together. And this is how basic learning happens.
1:15:49 David Hoffman? You know, the the window of plasticity allows for this to happen very rapidly, but it still happens in all brains at like at all times. And that was the same pattern that I noticed in what Andre Karpathy was talking about in his in his LLM video. It's like, OK, well, then when when a parameter gets something right, it tunes those parameters closer together. And when it when it gets something wrong, it separates them. So it does so that happens less often. Same structure, like neurons that fire together, wire together, parameters that get things right, bind, then they also separate. And so I asked ChatGPT, is this an acceptable comparison? And it said, yes, with some nuances, with some corollaries.
1:16:30 David Hoffman? A LLM does learn language in a similar pattern that a human does, but a human has some very key advantages. And when you are training an LLM, we need billions and billions of tokens for basically words, or maybe even more accurately syllables for these LLMs to be tuned at all. And when a baby is learning languages, they don't need billions of words in order to learn language. They only they need much fewer words in order to actually start to pick up on on words and learn them. And there's a couple of reasons as to why this is true. Babies aren't just hearing words. They are also seeing with their eyeballs. They are understanding the context of the room that they are in.
1:17:12 David Hoffman? They are seeing the context of when mom says something to dad about some object that they have in their hands. So they have visual cues that is very useful for them to learn language. But not only that, but human brain structure is naturally ready to learn language. We have adapted, we have evolved to be very ready to learn language. So we have these things that LLMs do not have. And I think this conversation starts to open up with robotics where, okay, well, what happens when you give robotic appendages to an LLM? And you give it like, you give it a camera and you give it ears and you give it a place in a room. Well, then you start to actually take away some of the advantages that humans have. But then when we are hooking these things together,
1:17:57 David Hoffman? there's just a comparatively like collection, collective set of technologies that allows what you're talking about, the integration of a chip and the rest of the universe, the world, resources and capacity. And when a chip hooks into our biological structure, hopefully that doesn't make us irrelevant. Hopefully that makes the biological component of this actually much more relevant. What are your thoughts?
1:18:22 Speaker 00 It's a testament to how early we still are. Like these models are so smart, but they're also incredibly dumb relative to humans. And there's also the energy thing as well, where like, if you fed an LLM a steak as energy, it would not work. Like your brain only requires, what, a couple thousand calories to like function and to provide life support for your entire body. Whereas these computers consume a ton of energy. So it's like fairly dumb in the sense that it is single modality in most cases, meaning it only trains off of the human language. It's also horribly inefficient in the sense that it's dumber than us, even though it has a billion times more energy than us. So these systems are still like horribly inefficient. They're still very, very dumb, but they're suppose they like, they look very smart.
1:19:07 Speaker 00 And it's because they are. So it shows the length of the gap in which we still have yet to fill when they can. Have the compute power of a human brain, but also have the multimodality of a human being, and also become much more power efficient. And you're starting to see this in the Tesla team with Optimus, where the first use case of broad broad training on the visual modality is with Tesla cars. Every car that's been shipped has eight cameras around it. All of those cameras feed video into this giant cluster. And that cluster is now being trained on the outside world. And the reason why Optimus, which is Tesla's robotics division, is the best so far is because it's been trained on
1:19:52 Speaker 00 not only words, but also sight. And it knows what the world looks like around it. So it can understand it conceptually closer in a way that humans can. Next, I'm sure you'll see more haptic stuff happening, where it'll start to understand the feel of things. Then you could have the sound of things. And you could kind of tap into each sensor of the human body with a synthetic sensor on a robot. And that's kind of what's happening. When you think about your phone, it has a camera for your eyes. It has a microphone for your mouth and for your ears. It has speakers and it's missing the smell. It's missing the touch. But those two things are very easily replicated in a humanoid robot. So we're at one modality. We are slowly getting to two.
1:20:35 Speaker 00 I'm sure the next ones are coming. And again, it's just a matter of creating this data. It's very difficult to train these models unless you have a way of collecting data. Tesla's the only one because they have cars rolling around. So when their robot initially started getting trained, it thought it was, when it was walking down a hallway, it was looking for the road signs and like the lines to stay in. Because it thinks it's a car. Like this humanoid robot is walking around, but it thinks it's a vehicle. And it needs to be trained that it's not. But it at least has the data to give it some sort of context. So we are like super far from the full multimodal human experience, but we're getting there. And like Optimus, and I forget the name of the company that just revealed yesterday, but there's a lot of other robotics companies that are also working towards this goal.
1:21:16 David Hoffman? Josh, this is really, really cool. I hope listeners imaginations are opening up, are breaking very wide open. There's just like so many adjacent technology like rabbit holes that this like spins off of, like robotics is just a huge conversation. Brain link interfaces is a huge conversation. There's, there's like this rabbit hole for each one of these things. Even like rockets and interplanetary travel starts to be, become like very, very relevant here. Synthetic biology, where, since you are on the Bankless Podcast team, how do you think, where do you think we should go next? I want to talk to a bunch of AI people. What like, and I know you're tuned in with a bunch of the listeners. Maybe we can just like tease them for what we are trying to do with a podcast, or what your like guidance would be for where we're trying to go.
1:22:01 Speaker 00 Yeah, I think it's, it's exploring this new frontier that is downstream of AI, because it does unlock a lot of these cool new technologies. So, one of the main ones, like you mentioned, like biochemistry is crazy, how we're able to manipulate human beings and the genetic makeup of us. That's a really cool place. It's worth exploring. Interstellar travel is really interesting. I think one that's super interesting and probably more pressing than most is energy. A lot of this AI requires energy. A lot of crypto requires energy. A lot of the world requires energy. And we have a shortage of it and an incapability of making enough, even though the planet receives enough. So, energy is a really cool sector. There is supersonic flight, which is happening now. There are, man, there's so
1:22:46 Speaker 00 many cool platforms. I think you could just, the listeners could probably expect generally more frontier technology outside of crypto that parallels crypto. So, AI, again, research downstream affects crypto. We have an entire AI crypto podcast that is built around this new frontier that's unlocked because of AI. I think that's similar to a lot of adjacent industries, maybe closely is energy, like figuring out, we definitely want to talk about nuclear energy, how that is becoming a bigger thing, how that's going to power a lot of these requirements in a world in which artificial intelligence demands so much energy. Those types of things are what I would probably look out for. I'd say crypto adjacent frontiers that have downstream effects that affect crypto, but are like super exciting. Quantum computing is another one. There are breakthroughs now on like
1:23:28 Speaker 00 a monthly basis. We don't really know the implications of quantum computing just yet. We know that it can solve really difficult problems, probably break encryption, probably mess up Twitter. We have a great episode with Justin Drake that's talking about that.
1:23:42 Speaker 00 There's a lot of interesting stuff. So I think just generally frontier technology will become a bigger focus. Those are some of the main pillars that would be around it.
1:23:50 David Hoffman? I want to give my attempt at explaining how all of these things will impact crypto and why and how crypto is relevant here. And I also want to give you the opportunity to do the same. My answer for this is like, there's a general broad theme of acceleration here. Like, we are, we are going faster. Crypto has always moved very fast. AI is moving even faster. Some of these technologies aided by AI are going to be some of the most quickly innovating technologies that we've ever seen. Just because as time moves forward, time goes faster. Technology always speeds everything up. Gone are the days where any of these frontier technologies can wait for the public markets that close on weekends and holidays and outside
1:24:34 David Hoffman? of business hours are able to keep up with, or are even the right venues for the value of these things to be expressed. So there are going to be small projects, big projects, that are going to monetize a lot of the innovation that they're creating via the crypto markets. We actually just saw Kaido, a centralized United States domestic data analytics company, issue a token that is currently trading at $2 billion. That thing trades 24/7/365. They didn't have to do a year's worth of collaboration and just in order to go through collaboration with the SEC and the public market regulators in order to get an IPO
1:25:17 David Hoffman? on the stock market. That's way too long. It costs tens of millions of dollars. And why would they do that when they could just issue a token? And it's successfully monetizing Kaido at $2 billion. And so crypto as a frontier technology that on a time scale can keep up with all of these other technologies is going to be the monetization layer for teams, projects, innovations, and also the open source side of AI crypto to begin with, is why crypto is relevant at all. We have these extremely rapidly developing technologies. They need to be matched with public markets that are also rapidly developing and can keep up with that speed. That's my answer. How would you change? What do you disagree? Do you agree? What was your answer here?
1:25:59 Speaker 00 I love that. That is a great podcaster synthesis of where we're going, generally accelerating. I think- That's right. I totally agree. Having value transfer rails for all of this technology that can keep up with this technology is hugely important. And crypto is the only thing by far that provides that. AIs will want to transact with each other. They will want to store value in secure places that are provably secure. The whole value of the world, as it becomes more and more digitized, will likely become tokenized. And this infrastructure that we're building in this world of crypto is absolutely critical to this future. Because if we do not have payment rails, if we do not have value transfer, if we do not have
1:26:41 Speaker 00 secure storage of whatever type of value, whatever we have assigned to value in the future, then this world cannot exist. Because then the reliance on humans would be too much. Or the reliance on AIs that might have mischievous intentions would have. So crypto is still hugely important. In fact, I would say it's absolutely critical to a future in which we continue to accelerate, continue to innovate, continue to make these amazing breakthroughs. It will serve as that sediment layer for all of it.
1:27:07 David Hoffman? Okay, so Josh, you are inside of the Bankless Discord. So if any Bankless citizens are in there and they have any questions for Josh, feel free to ping him. I think we're gonna open up a frontier technology channel, or a part of the Bankless Discord, just to talk about some of this stuff. Because I know many people in the Bankless Nation are just extremely intrinsically curious about all technologies, crypto included, but also much of the things that we were talking about. And as I want to keep on doing episodes in this direction, I want to understand this a little bit more. I want to keep on exploring these rabbit holes. And so I think listeners can expect more content on these frontier technologies to show up in the podcast feed. We are still going to do plenty of crypto content. And maybe there's just gonna be more total content in order to cover both
1:27:51 David Hoffman? of these bands. Many of these episodes are gonna show up on the Bankless Premium RSS feed. Because we want Bankless to grow the crypto awareness of the world. But we also want to make room for frontier technologies as well, because it's gonna come and impact crypto. So I think that's my call to action to Bankless listeners, is there's gonna be more total crypto content on the Premium RSS feed. And then you can also expect some adjacent frontier technologies, robotics, brain link interfaces, AI, rocketry, all this kind of stuff showing up on the free feed as well. Josh, thanks so much for walking through all of this stuff. That was a very ambitious episode. I think we killed it, my man.
1:28:28 Speaker 00 Yeah, it was really exciting. It's very cool to be on the opposite side of this and to be a part of the production to talk about this stuff. There's not a lot of people who I know that are interested in this. So if anyone listening does want to join the Discord, does want to chat about it, that would be awesome. Thank you for having me. I've been watching the pod for a very long time prior to even joining the company. So it's, it's pretty cool to finally be on it. So thank you for the opportunity. Thanks for letting me talk about the stuff that I am just super interested in in general. I really enjoyed it. I had a great time.
1:28:52 David Hoffman? Bankless Nation, you guys know the deal. Crypto is risky. New frontier technologies also risky, but it's also extremely risky to ignore them and not be aware of the times. Because time is accelerating and we need to keep up with the times. We are here to help you front run the opportunity. Nonetheless, the frontier is risky. You can lose what you put in, but we are headed west. So thanks for being with us on the bankless journey. I appreciate it.
Previous ROLLUP: Macro Bearish Report | Biggest Hack In History | SEC Drops Crypto Cases Next Is Trump Trying to Wreck the Economy? | Macro Expert Jim Bianco Page updated October 26, 2026 · transcript and summary are machine-generated
