Limitless Podcast - The Age of Hyper Acceleration: AI, AGI & Beyond!

Episode Date: May 8, 2025

AI isn't just growing—it's skyrocketing us into an unprecedented era of hyper-acceleration.Josh Kale joins us to explore how breakthroughs in intelligence, from protein sequencing and synthetic biology to autonomous transportation and energy abundance, are reshaping our world at dizzying speeds. Prepare for a future that's closer than you think, where the cost of intelligence approaches zero and possibilities become boundless.------TIMESTAMPS0:00 Intro3:48 Exponential Human Progress7:50 Moore’s Law vs Huang’s Law15:17 Synthetic Biology20:37 Economic Impact23:17 Job Market31:02 Self-Driving Cars33:33 Aviation35:43 Energy41:10 David Deutsch45:42 Electricity & Income51:20 Nuclear Energy54:46 The Unibomber1:01:09 Defensive Accelerationism1:03:06 Robotics1:06:14 Closing & Disclaimers------RESOURCESJosh Kalehttps://x.com/Josh_Kale The Beginning of Infinityhttps://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359 ------Not financial or tax advice. See our investment disclosures here:https://www.bankless.com/disclosures⁠ 

The first person to break down and reverse engineer the first protein, it took him 12 years to do. And he took this protein and he crystallized and he shot it with x-rays. And then he actually used like a ruler and pencil to like connect them together and kind of reverse engineer this. And then over the course of the next 60 or so years, we were able to finally discover 150,000. And now because of AI, we've just discovered 250 million. 150,000 to 250 million. Quite a different number. And that 250 million was only over the course of like the last two.

years and change. It was very quick. Okay, so David, there's this quote by Steve Jobs that I love that says everything around us that we call life is made up by people no smarter than we are. And if you think about it, it's true. Like the clothes on our back, everything around us, no one smarter than us has ever made anything that we have here. And the same is true if we go back like 40,000 years to cavemen. We're not really even much smarter than they were. We have the same brain size, roughly, same cognitive ability. We benefit, though, from the collective accumulation of knowledge over time. So it's like the snowball where this was very small, but over time it's kind of rolled down this hill faster and faster and it's grown. And now we benefit from this collective knowledge,

but we're not actually smarter. So the question I think we want to answer in this podcast and the really interesting question is like what happens to the world around us when people are actually smarter than we are? Like what happens when they're 10% smarter, 20%, 100,000, a thousand percent smarter? And we could ask that question because this new thing called AGI, which is artificial general intelligence. It kind of has this fuzzy explanation, this fuzzy definition. My definition of it is like a form of intelligence that is smarter than a human across pretty much every medium. And the initial expectations from experts were 2040s, 2050s, 2060s.

But the reality now is that we are months away from this intelligence instead of decades. So it's like how crazy is this world about to be when everything around us is actually made up of people that are much, much smarter than we are? And let's back up with this. So I think you're setting this foundation of humans have always been on this exponential growth curve because we have capable brains. That's why we're humans. And we have been like building technologies, building tools. We created the wheel and then everyone had the wheel.

And then we could create like the next technology after that on the top of the foundation of having the wheel. And then we created, you know, plumbing, agriculture. all these technologies got layered on to each other. And that's where we have this notion of like 2% growth year over year. And 2% growth does compound. Like that is an exponential growth curve. If you're growing 2% every single year, you are growing up an exponential.

But I think what you're doing, what you're setting us up for this episode, Josh, is you're calling for an inflection point, a change in that growth curve because something different is happening. Where previously we have just been like layering technologies on each other and humanity has been, you know, accelerating. Once we got the internet, we are going, we're going a lot faster because we somehow filled in all the gaps between,

you know, building the wheel and having the internet. And we are layering all of our human advances on top of those previous technologies. But you're saying that this is different. Like, in addition to all of that compounding growth curve, we are getting this new technology that is materially changing the rate of growth because we're, we're, previously, all of the intelligence of humans who created the wheel to created the internet where it was about the same. Like, we were all running on the same wetware. Our brains were about the same level of computational capacity. And everyone has been like able to become about the same level of smartness about each other. That's what you're saying is like the history. That's,

that's history up to today. And you're saying today is different. Now it's different. That's what you're saying? This time it's different. And it actually is different. And I have the chart that I pulled up on the screen that I'll try to describe to people, which is the chart of human progress in relation to time over a long period of time. And it mostly looks like a fairly flat chart except there is this like slight incline that starts to grow slightly exponentially about 60 years ago. And that's when the invention of Moore's Law happened. So this was a big moment for us where we had transistors and transistors led to computers and computers led to a whole bunch of technology that accelerated things pretty quick. So we have now this steeper ramp up.

And then today things change, and the reason I say this time it's different, is because this new, we have this new law called Quang's Law, and it's loosely debated on how accurate it is, but it is basically the speed of training a cluster is, it improves 25-fold over every five years. And Moore's Law was that the speed of the amount of transistors on a chip doubles every 24 months. So this is a 5x increase in Moore's Law that initially started this growth trajectory. And that's why I think the chart will start to look like this.

And it's really hard to, for the people that are listening, it's basically vertical. The line goes straight up. It's an exponential curve with a pretty strong kink in it that it's still curving upwards, but it goes up much faster, much sooner. Yes. And the idea is that like this is going to get pretty fast because we're entering the age of hyper acceleration. And the reason for that is because, like we said, everything has been built on this wetware that is like pretty dumb.

Like we're smart, but we are only so smart. relative to what's coming. Now, since we're able to accelerate five times faster than we were with Moore's Law in terms of training this new form of intelligence, there's going to be a lot of weird and wacky things that start to happen really, really quickly. And we're kind of seeing this. It's similar to COVID where like humans are very durable and very malleable and we're good at getting over trauma. So you can kind of like mat or mute out your emotional bands to deal with this stuff. But if you just look at what happened in the last week, We were able to give gene therapy to a blind Irishman.

We had another one that treats babies... Gene therapy to a blind Irishman. What does that mean? What happened? So we can inject these new forms of genes into a person who's blind and give them eyesight back. We made a blind person see again. See again. Yes.

And that's just through gene therapy. That's not through neural interfaces. We fixed blindness in one individual. In one individual, just like last week. And then there was another breakthrough with... with ARC-Evo to DNA sequencing model. So there's this really interesting thing that's happened with DNA,

and we won't go too deep, but basically if you can reverse engineer a protein and rebuild it, you can create all these new forms of technology. You can solve forms of cancer, you can cure forms of Alzheimer's. We discovered 250 million of those. When over the past 60 years,

we've only discovered 150,000, and the first person, it took him 12 years just to discover one. So we now have this crazy slew of technology that was just released last week. Google AI has a co-scientist that accelerates the development of science. You can query 100 PhD students' worth of compute for about like 10 cents and 10 minutes. Microsoft has a quantum topological quantum chip.

We have all these new humanoid figures. Croc3 has a new AI model. There's all these new machine learning models that are front-tier models. It's like accelerating very, very quickly. And every week there's something new just yesterday. For the people that remember the Deepseek R1 model that broke Wall Street, That came out only two months ago. Yesterday, Alibaba released QWQ, which is 20 times more efficient than Deep CQR1 and slightly better.

So already, the thing that broke Wall Street has been broken 20-fold in a matter of two months. So things are going super fast. We're developing everything super fast, and that's because it's all kind of downstream of intelligence. The smarter we are as people, the more complicated problems we can solve, and the faster we can solve them and the faster we can accelerate these new problems worth solving. Let's back up and define Moore's Law and extrapolate that into Huang's Law. Once again, Moore's Law, my understanding of Moore's Law was it was just really an extrapolation on the computational power of a CPU, of a computer, of a computer chip.

And the idea here is that we are just, there's this trend line around how fast a chip can be. And I think you defined it as two X's every two years. And Moore's Law has been in effect for decades, many, many decades. My understanding of it is like it runs up against a wall based off of like this the thickness of silicon down to the nanometer level. And so we eventually run out of slack to grow in Moore's Law. And I thought we are, like my last understanding of Moore's Law is we are approaching that limit. Maybe you can update me on that.

But then now we, in addition to that, we also have Huang's law, which I don't think is, it's not apples to apples, it's more apples to oranges, but it's of the same spirit where it is talking about the computational power as it relates to, I mean, I'm guessing Huang is in Jensen Huang from Namedia. Yeah, so it's the computational computational power of clusters that is largely computing AI stuff. Maybe you can fill in the

gap of all the gaps that you left for our listener. Yeah, so Moore's Law is the amount of transistors on a chip doubles every two years. That is roughly like you could think of it as the computer processor, the CPU, the brain of a computer doubles in speed every two years. Quang's Law is slightly different. Apples to Orange is largely debated, but

Huang's Law is every five years we get a 25x improvement in training capability for GPUs, which means we can train these LLMs, these AI models, 25 times faster every five years. And Moore's Law is running into this interesting law where it's actually constrained by physics now, where the particles have gotten so small that we're not quite able to fit many on there without these new innovations. And I think that's kind of why you're seeing things start to stagnate slightly, but equally an opposite takeoff in the world of AI. And now it's not certain that we won't

hit a wall very quickly with Huangslau as well, with GPU training clusters. But there's this other side of it with software. And the software can actually compensate for the lack of hardware in the case that this ever does degrade. What we just spoke about recently with Deepseek and QWQ is they were able to take these giant foundational models that took hundreds of thousands of GPU clustered together, tons of energy, tons of compute, and they were able to distill them in a model that can actually run on a desktop computer. And that improvement is like a thousandfold improvement, and that happens on the software side of things. So there's this, there are these two pillars that are advancing really quickly. There's a software stack and there's the hardware stack,

and both of them are going exponential. So even if Huang's Law is half of what he says it is, or half of what is expected, the opposite forces that are happening in the software field will continue to accelerate that even faster. So we have these two tailwinds, both going super quick. Okay, so Huang's Law is a 25x of computational efficiency every five years, which is pretty pretty damn steep, much deeper than Moore's law. But then you also gave us the 20x performance or efficiency increase between the Alibaba model that got released earlier this week versus the DeepSeek R1 model that got released two months ago.

So we're layering a 25X every five years of computational power. You're multiplying that with what we are seeing in the efficiency gains of the highly competitive AI lab model releases. And that's software, right? And I love this metaphor. I use it many, many times on bank lists. There's two ways to scale. You can just have better hardware.

Your Xbox can Xbox 360 can upgrade to an Xbox one. You have a better chip in there. The hardware is better. But then also the software devs, the devs writing the games, can write more efficient code. It can use the same hardware more efficiently, create more beautiful games. There's two ways to do this. And we're seeing both, we're seeing both happen at these crazy scales. A 20x increase in efficiency for a model in two months is insane.

that growth curve can't be sustainable. I suspect it tapers out by the end of this year. And I think it just illustrates that there's a lot of slack in the AI model development race. There's like a lot of optimizations to be had. But the fact that we are 20xing in efficiency in two months in terms of like one model release to the next just tells me that there's so much optimizations to be done here. And so we actually don't really know where the efficiency gain. happens on the software side of things.

Also, a part of this to take into account is we are building, these efficiency gains are happening and they're compounding because of the output of the efficiency gains. So now because we have smarter models to help us, we are able to build even more efficiencies, even more improvements. And that will kind of hopefully, the hope is that it will increase systematically. Whereas we build these smarter models, they can help us create more efficiency, help us design more efficient chips, and it will be the self-fulfilling acceleration curve. The models help us design better hardware. The hardware gets better, and we can run more

powerful models, and then the models can help us design more hardware, and it kind of just naturally converges at the theoretical optimal, the optimization point extremely fast. Exactly, yes. And as we accelerate faster, there will be even more improvements to the world of atoms instead of just bits. So now it can teach us how to build these machines to build this new computer, to build this new chip. that will make things even faster. So it's kind of this self-fulfilling cycle up to wherever we end up going. Okay.

And so that's why you're illustrating this kink here, again, on the screen that we're showing, where there's been this compounding growth of humans and the technology that we've had ever since we invented the concept of technology and innovation and science. And there's been just, you know, call it 2% growth year over year over year. We've been on an exponential growth curve. It's been a modest 2%. But this is why you're saying that there is a kink here, and we are entering the age of hyper-acceleration,

where, sure, we've had more useful tools. We've invented, you know, power tools for building better homes. We've invented computers, and that really helped accelerate things. But nowhere have we actually cheapened the cost of intelligence. And the idea here, I think we, like, right now, doing, like, chat CBT and Open AI, they're spending a ton of money on just running inference, running people's queries into chat chit,

and that costs a bunch of money. but also the amount of inference that they're doing is also quite high. And I think what you're saying here is basically we are collapsing the cost of intelligence from being expensive, which is like you need to produce a whole entire human. You need to put them through school. You need to put them through college. You need to put them through a PhD program. That takes a lot of time.

That takes a lot of energy. That's very costly. And that was the previous model. And now all of that intelligence is becoming inside of the span of five years, 10 years. It's becoming free. intelligence is like PhD, peak PhD level intelligence will be free in a few short years. Is that where you're saying? Yeah, totally.

And that was kind of the essence of why I created this like little visual is because it was mostly the intention of exploring the downstream effects of intelligence as the price rapidly decreases to zero. Because everything, again, everything around us is it requires intelligence to make. But what happens if that intelligence becomes so cheap that it is readily accessible and 10 times smarter than we are by anyone in the world? Like it just makes you think a lot about the productive output that we can unlock. And that was the essence for the very steep curve.

I see that there's two sides of this conversation. There's so much optimizations left to be done on the hardware side. There's so much optimizations. Way more optimizations left to be done on the AI model side. These things are going to get so much more efficient. The cost of intelligence is going to drop to zero. There's so much optimizations to be done on just like the mere intelligence side of the equation. But that's the producing intelligence.

And then there's the application of intelligence. And that's where you went through some of this list here. And you talked about the gene therapy giving a blind man site for the first time. Or like AI, Google AI co-scientists literally just accelerating science broadly. And so while we have the AI lab wars reducing the cost of intelligence down to zero, we are still like in the very early days of actually seeing the application of that intelligence. And I think also something that you're saying is like, well, actually some people are applying this and they're implying it into fields all across science, all across academia, all across

knowledge. And it's kind of like slipping under the radar. But if you pay attention, you'll kind of notice that we're doing some pretty crazy things really quickly. Yes, that's absolutely right. One of the more interesting ones that I do want to highlight here, because I think it's so cool is the DNA sequencing and the protein sequencing. That is like a core foundational part of biology. And the first person to discover the, to break down and like reverse engineer the first it took him 12 years to do. And he took this protein and he crystallized and he shot it with x-rays. And then he actually used like a ruler and pencil to like connect them together and kind of reverse engineer this. And then over the course of the next 60 or so years, we were able to finally

discover 150,000. And now because of AI, we've just discovered 250 million. 150,000 to 250 million. Quite a different number. And that 250 million was only over the course of like the last two years and change. It was very quick. And this was also, relatively speaking, two years ago, was very early in the AI cycle. So by using this AI, we were able to unlock this entire foundational core of biology. And when you could break down proteins to the simplest form, it unlocks a lot of really cool innovation. So like I mentioned earlier, Alzheimer's is a direct result of like DNA misfolding or a protein

misfolding, same with a lot of forms of cancer. But there's also these other applications outside of just biology where you can form a protein structure that eats plastic and you could just send it out into an ocean and design it so that it doesn't harm the ecosystem but can just eat a lot of plastic, it can convert it into something useful. Or you could design these new materials that are much more heat resistant, like a rubber tire that never pop, that lasts forever. Material sciences starts to expand. And there's this whole world of biology that we've just unlocked that now we can manipulate and mutate for the first time because we understand it. So it's starting to give us these really like foundational, basic

of knowledge of biology that now we can take and we can use and we can apply to the real world. And that'll lock an entirely new industry. I think this is the subject that you're talking about is called synthetic biology. At least that's how I understand it. And maybe the best way to explain this to listeners is the way a computer works, like going back to a simple Turing machine is you have this tape and you have this tape of like serial numbers. It all goes down to like ones and zeros.

But like the way a computer works is they processes the string of digits serially. and then we just make faster computers to do these things faster, and then we also have multiple cores to do them in parallel. But ultimately, at the end of the day, it boils down to a serial string of characters, a serial string of bits that a computer processes. And DNA is that same kind of structure. It is a serial chain of proteins with more combinations than just at ones and zeros.

It has like the AT, GE DNAs. But ultimately, it's just a serial, like, a string of information. And it's kind of like the compute, it's the organic code for biology. And there's this whole like universe out there where like, okay, if we figure out how to string the correct order of proteins together, we can make some really cool things. And I think what you're doing here, Josh, is you are combining our like intelligence, complete and comprehensive intelligence onto like how we can correctly order biology to create any sort of like organic structure that can do almost anything that we want. We can cure blindness. we can produce this organism that consumes plastic, that helps fight climate change, whatever.

And because it's biology, the actual applications are pretty boundless. They can kind of touch anything. And that is just synthetic biology. There's still like all the other subjects that we have to get into, which is like rocketry, like life on Mars, anything else, like any other like science that any listener wants to imagine in their head. And so this is why you're saying we're entering the age of hyper-refer. acceleration because every single industry is about to hyper accelerate.

Yes. Everything around us that was made by us, if we were smarter, can be improved. So you will see these improvements across the board. Another interesting one about the synthetic biology, too, is the entire food supply chain and how we make food, how we create food, how food gets genetically modified over time. All of that changes too. So there's so many of these different areas that stand to change so much once we understand them better and we know how to how to refine them better to get better outputs. Okay, so between the eras of AI labs becoming hyper-efficient, creating like massive intelligence,

dropping the cost of intelligence to zero, and then before we get to the actual changes in the industries that are actually where the rubber meets the pavement, we're going to have kind of like the investment layer or the economics layer. This is going to change the economics of everything. Investors are going to reallocate capital. The cost of things are going to,

going to change and fluctuate, I think go down. Where do you think this, before we actually talk about, and talking about all the possible ways that this impacts us is literally... We'd be here forever. We'd just be here forever. We'd just be talking about the future of the universe. So, like, let's talk about, like, how this, like, what are the economies of this?

What was the economic impact of this in the short term? It probably leads to some sort of rapid deflation, the type of industries that it affects. So just to define deflation versus inflation, a lot of people know what inflation is. It's just kind of like the price of goods and services slowly increasing over time. They're very familiar. They see that in the grocery store. We see that gas stations, everything you buy has slowly increased over time.

And the reasoning for that is there's this really great contrarian take by Peter Thiel that I love, where he says that we haven't actually accelerated a whole lot outside of the world of technology, meaning if you took your grandma and you just kind of froze her in time 50 years ago and then drop turn to your living room today. Not much would look very different. If I look out the window, I'm looking at New York City. I'm looking at bridges and buildings, and those have all been there for the last 50 years,

the roads, the infrastructure. The cars look different, but they're still cars. They look different, but they still run on the same fuel. They still have the same materials that they're built with. They just have computers inside, and the computers make them a little bit smarter. But outside of the world, the computers, we haven't actually accelerated very quickly.

So we haven't had this gross overproduction surplus of things that we need that can lower the price. So we generally have seen inflation there, but the place where we have seen deflation is in technology. And that TV on the wall will look like magic to her. And the phone in your pocket where you can reach anything in the world instantly into your fingertips, that seems like magic. And the cost of that has come so low that now it's accessible to basically anyone. So that is deflation. That's where we see it in technology. And it's probably why we haven't seen it in many other industries outside of it. But Once we're able to apply this new intelligence to these new industries to get that surplus,

hopefully the downstream effects of that are more abundance, more surplus, and a lower cost of goods, a higher quality of living. I think we'll start to see that once we start to manufacture more in the world of Adams versus bits, which is digital. And that Bankless Nation is where we are going to take this conversation next. Welcome to Bankless where we explore the frontier of internet finance and internet money. And now the future of Adams too. This is David Hoff and I'm joined here once again with our very own Josh Kale.

Josh, welcome back to Bankless, your very own podcast. Thank you. Oh, it's great to be here again. I'm so grateful that I have someone who is interested in hearing me talk about this because this is what I think about all the time. So thank you for having me again. It's a pleasure. Yeah, I really enjoyed the last conversation that we did. If this is your first time listening to Josh, we did one other podcast, the one last week,

really just kind of doing the landscape of the AI arms race in a very, like, zoomed-out way, where, like, sure, it's fun to talk about, like, the competitiveness between these different AI labs. but I think Josh really synthesizes information in this very big way, and that's what we want to do here again on this episode. So we're going to continue this episode, but first we're going to talk to some of these fantastic sponsors that make this show possible.

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participation and then last secure verification apps validate your proofs instantly on chain like on cello or off chain audited by ZK security the Self app is live on iOS and Play Store. Visit self.xyz and follow self protocol on X. Okay, Josh, with the arrival of AI, people like to talk about the job market because there's that classic line of AI is going to come take my job. And then there's the usual rebuttal to that is, well, AI might take your job. But the more likely thing, it's actually going to be somebody who's using AI better than

you is going to take your job, not this strictly AI. And then there's also the additional rebuttal to that. Well, it's like, yes, there might be some job destruction, but it's a creative destruction. And there's going to be new jobs that are created. You know, think about when the Internet came and all the news. We were worried about newspapers just going out of business, which, you know, definitely happened. Although, you know, there are still, you know, the Wall Street Journal. They're still the New York Times.

And so there was some, like, elimination of jobs as a result of the Internet. But then, like, YouTubers and influencers or created this huge economy. inside of the internet. And so this idea of creative destruction came where, yeah, we're going to lose some jobs, but we're going to create a lot new of opportunities ahead. There's one sector that I think people are actually going to just materially lose their jobs,

and they are going to have to figure out something else to do. Because when you're talking about deflation, we're bringing the cost down. Uber's biggest cost is probably paying drivers. And we're already saying like Waymo in San Francisco have just driverless cars. And so, like, I think there are material industries there. There's going to be, like, massive job. Not like, you don't really lay off your Uber driver, but that's millions of jobs going away.

What do you think about this conversation? Yeah, it's a scary thing. And I want to preface it for the people that are worried about it with just the essence of solving problems, which is what we're doing. We are solving the problem that transportation is very unsafe. It is very costly. And it is very low production, meaning you have to sit there and you have to drive and cars kill the most amount of people every year. and it's just this very dangerous thing. Like you are allowed as a, what, 17-year-old to drive a five-ton vehicle at 100 miles an hour

anywhere in the world. That seems a little scary, a little unsafe. So what's happening here is we are solving problems and there will be this dislocation of people who were solving the past problems. But it's important to note that like solving these problems creates a more exciting world. Like when you solve a problem, generally it leads to more problems, but these are better problems to solve, which is why the world around us improves. It's not because we reduce the amount of problems, but because we solve the worst ones, multiplying the amount of better problems to solve.

So we've kind of seen this happen with the agricultural revolution where there was like seed drilling and crop rotation and crossbreeding. And all of the people who did those jobs originally, they lost their jobs. But the productive output, the downstream effects of it were so much higher. And it happened to get in the Industrial Revolution where we invented like the steam engine and we invented the production line, textile machines. And the people who did that, that they lost their jobs, but the steam engine created a train and then the train created railways and then railways created transportation and cross-country distribution of goods and services.

So it opened up all these additional industries. And I think that's probably what's going to happen. Cars is a great example because, again, very unsafe, very expensive. If you can remove the human element from that, suddenly your one-hour car ride to the airport is a fully productive time where you don't have to drive yourself. You can just do stuff on your phone. you can take a nap, the amount of accidents that will happen will be much lower, it'll be much safer. You don't have to worry about it. And the cost, because there is no human element, will drop aggressively.

I think it's a few dollars currently per mile is the way you can gauge transportation in New York City. That should drop down to cents once these cars are able to drive themselves. So it's one of those things where, yes, the cab drivers will lose their jobs, but the world will be a better place because of it. And the hope is that the productive unlock from this new technology will enable even more. jobs for them to take and more interesting jobs. I think this conversation is quite timely for me personally because right after this podcast, I go and on Turro, there's this, there's this app out there for people who don't know who can just like rent a car.

It's like Airbnb for cars. So there's this, some guy like four blocks away from me has a Tesla that I'm going to rent and I'm going to drive for six hours upstate. And Tesla has auto driving, but I have to keep my hands on the steering wheel because of regulation. And so that is six hours where I am unproductive. I like my most productive form is going to be listening to a podcast and like growing my knowledge. And that's, that's if I have the brainpower for that.

So otherwise I'm just like stuck in this car for six hours. It's going to cost me like, because I'm going for the weekend. It's going to cost me like $300 or $400 to like rent this car for the weekend. So it's going to cost me a bunch of money. I'm going to be very improductive for six hours there, six hours back. And that is like some of the least. I just have to get up there. So there's no other way to do this.

And so I think what you're saying, what we're extrapolating here is like, okay, so. in the future, short-term future, maybe regulations aside, in the short-term future, that like $400 cost for a Tesla for three days is actually going to drop to like, I don't know, $25, $50. Maybe I don't rent a car,

but there's just a car that takes me up there. And during that time, I am not, like with my hands on the steering wheel, I have my laptop open on my lap. Maybe I'm even doing a podcast, but I'm doing something otherwise productive with my time. I think the low productivity angle of, you know, Uber drivers,

cab drivers is actually really illustrative because like other than getting some other human from point A to point B, that's the only production that actually happens as a result of that job. Yes, that is absolutely right. Actually, I would encourage you to try full self-driving on your road trip up because it's really good and it's it signals how close we actually are to. Oh, I intend to do it. But my last time I was in a Tesla, I had to like keep my hands on this steering reel. Now it's gotten better. It's just your eyes need to be on the road. So you don't have to touch anything. You just have to look forward. Can I wear sunglasses? You can, but there's actually these small sensors that can penetrate your sunglasses that can see your eyes beneath them.

They've got it pretty good. And this is just really a matter of regulation. The technology is mostly there. In fact, Tesla's planning to roll out their cyber cab network fully autonomous in Austin in the middle of this year. So we are very close to this happening. And I think a lot of people who haven't tried it don't know precisely how close we are. So give it a try. I'm curious to hear your thoughts. But it's very good and it's very exciting because, yeah, You could either take a six-hour nap or you can do whatever you would want during that time and do so much more safely than if you had a tire driver or if you yourself retired. And that's just that's just the idea of transportation. And I don't think that's really, I don't know if you have any takes on like airline transportation or if that's getting any cheaper. Oh, you do.

Okay. So this was talking about like car automobile transportation is dropping call it 100x in cost over when this technology gets absorbed by by humanity. aviation transportation has not progressed at all over decades. It's been one of the most frustrating things in my life. Is aviation transportation costs going down to? Yes, you can apply this to everything. We could have the same conversation about 10,000 different industries, and it will be very similar because once we have better materials, once we have better intelligence, once we have more abundant energy,

we can create much better products. So one that I'm super excited about is called Boom Arrow. They're a supersonic airplane company. And what they've done is they've been able to basically take the plane and turn it into a supersonic plane that is much more efficient, much faster, and does not disturb the ground level of people with these hypersonic booms. So their new technology is allowing planes to go way faster. I'm not sure than multiple, but significantly faster at significantly higher volume for the hope is a lower cost. So not only do you get a product that's better, you get an experience that's better, but you actually get a lower cost. higher safety because these will be built by really impressive engineers that don't have these edge

cases that you've been seeing with these airplanes that are crashing and burning. The airline industry has gotten a little scary and it's mostly because it's a duopoly between Boeing and Airbus. Airbus, yes, I believe those are the two. And they have no incentive to do right by the people because they are the monopoly. And if you can start to introduce these new competitors through this new technology that provide such a better experience at such a better price, then there's no reason why 10 more of those won't come into the market. And gradually over time, we will just remove the duopoly that is these

unsafe kind of crappy airline companies and we'll have hypersonic travel. And we will have hypersonic travel that's like really comfortable and really cheap and affordable. And it's, it's mostly a matter of manufacturing materials and regulation. Those are the three things that are stopping that from happening. But it's happening. One thing I think we are doing with this conversation, maybe there is something to fill in here is like, first we grow, hardware and then sophistication of our models. That's step one. Step one, grow intelligence.

Step two, reduce the cost of intelligence. Step three is three question marks. And then step four is profit, where we have like super cheap transportation, super cheap airlines. And all of a sudden we can do a supersonic travel for very, very low. Like how does that first conversation that we had about AI intelligence actually relate to producing like deflation in transportation costs? Or are we just kind of assuming that eventually with the massive reduction in intelligence that we're going to figure out at scale hypersonic, supersonic airline travel? Yeah.

So I want to amend your hierarchy slightly. You put intelligence at the top. I think there is one thing that is slightly higher than intelligence and that's energy to power all these intelligence systems, to power us. We need tons of energy and we don't currently have enough energy. So I think this all kind of starts with the energy layer. And again, it's synchronous with intelligence. There's this is fun fact that I love. It's that if you go outside into your backyard, if you go to like a park down the street

and you pick up a rock, the rock actually has more potential energy inside of it than the equivalent size piece of coal. And it's interesting because we use coal, we burn coal, but it turns out that organic plant matter that's been stored underground isn't super dense in energy. Whereas this rock that's down the park, it has trace amounts of thorium and uranium. and they're very low amounts, but the only thing stopping us from extracting that versus coal is understanding how to process it. And the resource thing is really interesting because it's like but nothing is really a resource until we assign the knowledge to it that it is a resource. Like

we didn't get iron out of nowhere. Some guy found a rock and then he figured out how to smelt it and refine it and we went from iron ore to iron ingots and now we have skyscrapers. And there's this kind of progression through resources where as we get more intelligent, we are able to access them more abundantly. And I think this kind of happens synchronously with intelligence, where the more energy we have, the more we can power the GPU clusters, the more we can understand. And then to get to your question, the downstream effects of it, well, it can teach us how to manufacture better factory. So one of the really interesting developments has been humanoid robots recently. And these They're robots that have general purpose intelligence.

They have hands. They have actuators that kind of function like humans. And they've been starting to go into factories and starting to manufacture things themselves. And a lot of the production line has been simulated through these large AI models that can kind of emulate the efficiencies and inefficiencies of a system and then weed them out for you. So generally with technology applications or products, the first one is kind of crappy. Like if you bought the first iPhone, it wasn't that great.

The second one was pretty good. The third one was like really good. one was amazing. And the fourth one is basically the same as the 16th one. They're all the same. Because we kind of reached this local maximum. But if a computer can do all those iterations for you because it's much smarter than you, then the first version can actually be the fourth version, that final version, that like local maximum of what we're able to do. So I think that probably applies through most places. As we kind of synchronize this manufacturing machine layer with the intelligence layer, it can basically teach us how to make things. And then our job as humans

would be to go and create the infrastructure required to make these things that we want, to make these hypersonic airlines, to make these self-driving cars, it can remove all the inefficiencies and basically give us the answer, give us the blueprint. So your equation is that energy plus intelligence equals profit, basically. As in like once we have abundance of energy, we are currently growing an abundance of intelligence, and you can combine those two things,

and then the universe is our oyster. We can literally unlock every single door once we have those two things. Yes, from down to the biology example that we used all the way up to just like building the most large, like starship, rocket ships to other planets to go mine other resources. And these things are very abundant. China last week, the thorium in those rocks that I mentioned, they found a deposit of thorium that can power the country for 60,000 years. And it's just sitting there. And they don't really know what to do with it because they don't have the intelligence or the manufacturing capabilities. So they're starting to learn how to create these things called saltbed reactors that are safe.

nuclear reactors to extract it and to refine it. But it's not there yet. And if they had this higher form of intelligence that could feed them a blueprint, like, hey, here's exactly what you need to do in the optimal state to extract this energy. They suddenly have power for 60,000 years. And then they can apply all of that energy to whatever problems they want to solve outside of that. And there's this chart. It's basically showing that there are no energy poor countries in the world. If you don't have energy, you cannot produce valuable things. And so that higher energy thing, the higher intelligence thing, they're the self-fulfilling loop, and they are upstream of basically all of that profit.

I remember doing an episode. We've done a couple episodes with guests like this. One of them is Arthur Hayes where he says he denominates his wealth in hydrocarbons, in energy. And I don't know if he actually does that, but the point stands of just like, well, I mean, what is the dollar? What is the dollar really? It's actually the petro, the oil reserves of the world that is. is actually the fundamental denominator of anything.

And why is it so valuable? Well, because it produces energy. Josh, have you ever read any of David Dorch's stuff? Oh, man, it's funny. I have the beginning of infinity and fabric of reality, like right here. Actually, hold on. Yeah? Okay, okay.

So, okay, so I think you know exactly where I'm going with this. Here's Josh is zooming over to go get his books. Yeah. Have you read it? I've tried. I've tried. It's David Dorch is dense. This is a tough book.

I don't want to say I've read it because I have not, but I have tried and I have I've actually consulted YouTubers on how to go about approaching this book because it is so dense. Right. But it's very high signal. And I think a lot of people who I trust and respect really love and keep coming back to this book. So that is why I continue to chug away. But yes, I have attempted to read at least. You're aware of David George.

Okay. So he's got this idea in these books, the two books that you named, he argues that the only fundamental limitation on what humans can achieve is constrained by knowledge. like all constraints are knowledge constraints at the end of the day. And specifically, it's our ability to discover the right explanations and create the technology for the necessary technology to do things is just a knowledge constraint. And so if something, his idea is that if something doesn't break the law of physics, then the only thing preventing us from achieving that is the lack of knowledge to get there.

Right. So space travel, there's nothing in physics that humans, that forbids humans from colonizing other planets or traveling between galaxies, the reason why we haven't done it yet is we don't have the knowledge, right? Aging, if aging is just a biological process governed by physical laws, then in principle, we can learn how to reverse it. The only obstacle is the knowledge, how. And so I think this is what you're alluding to is like, okay, we need the energy to power the intelligence. We actually have the intelligence, or at least we're getting it very, very soon.

I consider us having the intelligence, but I'm sure we're going to have even more intelligence at the rate of these AI labs competition. And so once we get the energy, we have the knowledge, and then the world is our oyster. And so, like, yes, we didn't really have a clear answer as to how AI actually allows for supersonic travel at, you know, very, very low cost. But it's making this big assumption that, you know,

you smash energy and knowledge together, and there's literally no problem that we can't access. And that is why you're calling this the age of hyper acceleration and why there's a kink in the compounding growth curve because everything becomes accessible to us by like the end of this decade. Yeah, really, really soon. And there's this example, I mentioned last time, but I really love so much. And it's the mouse in the prime number maze where you have this like this mouse that's

not super smart. It's in the middle of a maze. And it will run forever and never figure out how to get out of it. But if the mouse knows that it's a prime number maze and it understands what prime numbers are, then it can very easily get its way out of this maze. And there's so many questions to be explored to unlock that. one key bit of knowledge that unlocks this entire world for us. So part of the higher level of intelligence is asking just better questions or even knowing what questions to ask that are

worth solving and then pointing it at those questions. But I very much agree with David in the sense that all of this is just an intelligence problem and an energy problem. And if we have unlimited of both or if you have enough resources to harness a seemingly unlimited amount of both, there is no problem that we can't solve. Everything. Like any, it kind of breaks your mind. And the same way that LLMs kind of broke my mind when I first started using them, the only constraint is your own creativity or your own questions to ask it. Like, I feel like I'm still not getting the most out of these large language models because I just don't know how to use them quite well.

And that's kind of the case with these LLMs as they get better is the hardest thing will be understanding what to ask, what questions we should are worth learning. Wow, that's deep. Once we have infinite intelligence at our fingertips, the constraint becomes what do we ask it. Yeah, within the realms of physics, or perhaps not, like perhaps we understand more quantum physics that break all the rules that we have.

Like, we can kind of play God. You can create these new forms of babies that are genetically perfect, and they never have any mutations and they age at the exact rate that you set. Or you could have this food that you create that looks nothing like what we've had, but it's the exact nutrient macro complex for your specific body.

And it just shows up every day, and it tastes delicious, and it's built just for you because we can manufacture it for every person on Earth. And this kind of goes across the board for anything, anything and everything that you can imagine. Again, once it's made up by people smarter than we are, it changes, it changes everything for everyone. So it's a weird thing. We're getting there super quick. Can you pull up the graphic about how there are no energy rich? There are no energy poor rich countries. And so the claim here is that we can look at all of the countries that exist in the world. And all of the rich ones, all the ones that are wealthy, have an abundance of energy. It trace over this idea and why this is so important again. So what you're looking at is this relationship between electricity consumption and GDP per capita of countries along the world. And you'll notice in the bottom left, there is a lot of countries like Bangladesh, Pakistan, Sudan, Nigeria. These are all energy poor companies in the sense they haven't quite figured out how to unlock large amounts of energy.

And then you reach this threshold, which is kind of set by India and Indonesia, where they're just kind of there. They've consumed, what is that, a thousand kilowatt hours of energy per capita. And everything above that is the wealthy nations. That's where you'll see in the top right in Norway is actually very wealthy and has a lot of energy. But there you see the United States, a little bit lower down. You see China, Japan. You don't see any of the large dominant countries in the world underneath this threshold because energy is so important because energy powers our transportation, our food, our,

everything you do on a day-to-day basis requires that. particularly for manufacturing. We have managed to unlock through burning fossil fuels, alternative energy sources like solar, enough energy to power ourselves and to become wealthy, but we are pretty quickly outpacing our ability to create this energy. And we're starting to see it with data centers where people, companies like Project Stargate,

the $500 billion planned by Masayoshi-san and OpenAI, they need to create large amounts of power just to power these energies because the grid can't support it. So we do have enough energy. It's very important for energy because everything is downstream of that energy. But again, we are quickly coming up against that threshold of how much energy we actually have versus how much we're going to need. Looking at this chart, I'm just very much reminded of Y equals MX plus B.

Just to be clear, on the vertical axis is energy consumed per capita. And so if you are, if you're a country that has a higher energy consumption per individual, you are higher on the vertical access. And on the horizontal access is just wealth of the nation. And so if you're a more wealthy nation, you are further on the horizontal axis. And this is just a perfectly linear chart. As in it's like, I've never seen something like more correlated to like wealth that I've seen in this, than in this chart. It's like if you have more energy, your country is more wealthy.

Like this has definitely always been a topic of like policy debate. And I think this is something that like the Trump administration. is actually like leaning into it's just like more energy more energy that's like the the the democrats the liberals are very hesitant to with his type of energy because it's coal he's like very he's into all forms of energy including coal the democrats are like well what about global warming but then the conservative right is like no more energy like accelerate like innovate like let's grow the energy conversation uh and in terms of just like strictly politics aside in terms of strictly like domestic innovation what does that conversation looks like about like our,

what's the energy conversation inside the United States look like right now? Yeah, it's such a shame that technology has become politicized because it is so foundational to the well-being of everybody. So it sucks that there are these split views on how we should go about it. I think currently the ideology is to use as much energy as we can to accelerate quicker,

which is something that I'm really excited about. Again, going to the problem-solving thing is like solving one bad problem never leads to like a non-problematic state, but it leads to much better problems to solve. So if we do need to burn a lot more fossil fuels, a lot more coal to power these new plants that will then give us the information we need to unlock nuclear technology, say, that is a big win. And maybe we suffer on a short-term basis where we do produce a lot more pollution. But the second-order effects of that vastly outweigh the slowing down and diminishing of this accelerative force that will get us out of the problem. So I think currently the United States

is roughly aligned with the energy needs of what we're going to eventually have. There is a move over to nuclear energy, which I think is super exciting because that feels like the end game. That feels like the final form. And it's a shame that nuclear technology has gotten such a bad rap. Again, technology has become this politicized thing. Very few people have actually died from it. it is very safe. The old power plants that did meld down are nothing like these new power plants. And there's this new company actually just last week. I think it's Valor Electronics. I could be butchering that, but they created this modular nuclear reactor that is the size of a large apartment, maybe. And the idea is that you can take a stack of these modular reactors and you could place

them around a data center, like the Open AI Project Stargate one that's going to require a lot of power. And it can be fully self-sufficient and fully stable with nuclear energy. And I think that is like a really fun direction that we're headed to where we are now able to produce these things. We don't really have the regulatory restrictions and safeguards preventing these people from innovating and accelerating on it. And I think that feels like a very healthy direction that I'm super excited about going is now we do have permission to build these new nuclear systems.

We do have permission to explore these new forms of energy that are necessary to power this world in which there will be a lot more robotics, a lot more manufacturing than we currently do today. I'm not terribly informed about the nuclear subject, but my loose understanding is that it was one of these industries that, A, got a bad rap downstream of the Cold War. We had the three-mile island incident inside of the United States. There was a Chernobyl incident in Eastern Europe, and I think that's kind of like marked the tone of nuclear, but it's also just been highly regulated. Like, it's been so incredibly regulated that I think that's actually the main culprit is so why we don't see too much nuclear energy production?

Because we regulated it heavily because of, well, nuclear bombs. And so maybe you can kind of understand that. But it is also, like you say, a shame that we don't have like this robust nuclear industry. Because, again, to my knowledge, just like you said, it's super safe, it's super clean, it's super powerful, and super abundant. And again, going with this relationship of like more energy equals more innovation, maybe we should reset our priors on understanding nuclear as an industry inside of the United States. Yeah, the chart was so clear.

Like the correlation between energy and dominance is like it's such a clear cut representation that's really hard to debate that. It's a shame. And I wish I had these specific examples because I know the meltdowns were not as bad as most people think. But it's a shame that that very early form of technology impacts so greatly the technology that we could produce today. We have accelerated so much in our understanding of nuclear engineering, but also manufacturing, technology, material science, where now we do have these, they're these things called pebble bed reactors and these new Gen 4 reactors that are meltdown proof and they have a much smaller footprint and they don't lead to any extra pollution and they're self-sufficient and they don't

require this grid that's kind of broken down to distribute energy. It could do so locally. So it largely has been a matter of this bad reputation that it's gotten, but also the people preventing it because they do feel like it's it's afraid and we have this like very we've we've had in the past this very afraid mindset where we don't want to hurt anybody we're far too empathetic to to harm anybody but in not hurting anybody we've harmed everybody because now we have this energy constraint and we we don't have the ability to accelerate our way out of it because we have not been allowed to so it's very clear like we need energy and and we would love more intelligence these things are amazing and we have the technology to do it without

hurting people. We just need to get out of the way and let the people who are ambitious enough to try give it their best shot. Josh, I'm getting a very clear sense that you are a techno-optimist with a very strong bent towards acceleration. Like you are you are acceleration ride or die. I don't think you're not being pragmatic about it. I think you do understand that there are costs, but I think you are just heavily biased towards like, yeah, there's costs and there are solutions to those costs too, which we will also discover if. and only if we accelerate. Yeah, it's kind of like religion where I chose this because this one makes me the most excited

about waking up in the morning and it feels really good to me. And I'm very familiar with the downsides and actually less so because I choose to be blindly optimistic. I'm pretty helpful, but I am familiar with the downsides of a lot of this technology. I understand that there are lots of them, but I also do feel pretty strongly that like solving these really hard problems will lead to more problems, but they are better problems, more interesting problems to solve. So I think the second order effects of this acceleration are better issues, even though there will be more of them. And that's something that does get me excited.

You know the name Ted Kaczynski, right, Drosh? I'm not familiar. No, you got to fill me in. The Unabomber? Oh, yeah. Familiar with that name. Do you know why the Unabomber was the Unabomber? Unfamiliar, no. Okay. So, let's get a history license. He was, I think he had a, mental disorder, something like schizophrenia or mania, I could be misremembering here. But he was a Unabomber because he had this prediction about the future, which was that the abundance of technology and the cost of technology would become so high and the cost would be so low that long-tail risks would be absolutely everywhere. And the optionality for an individual to cause outsized destruction upon the planet would become increasingly available to the point where merely statistics says that something bad will happen because we are creating so much potential out of the long tail of humans that one human can, using technology, using infinite intelligence, asked chat CBT or the uncensored, unhinged version of chat CBT, how do I make an atom bomb?

with normal household equipment. And as we defined earlier in this podcast, the only constraint to whatever we want to do is access to intelligence, to the intelligence needed, the information needed to get what we want. And so I'm with you every step of the way. And I'm really like where this is going.

And I think there's a lot of cool futures that we have. There's going to be cool technologies. We're going to be able to zip around the whole entire planet for pennies in like 10 years. It's going to be great. And I also think that most here, Humans are good. Most humans are good people. Like 98, 99% of people are inherently good people. But it actually the problem is, and this is what Ted Kaczynski saw, was that it actually only takes one person when technology is so powerful and information is so cheap and energy is so abundant. It actually only takes one person to cause outsized destruction. Have you thought about this subject? What are your thoughts or reflections? Yes. And my solution was actually derived from Palmer Lockie, who is just like the super interesting guy that I love.

He's in defense tech. And he talks a lot about the war field and how the dynamics of war works. He builds missiles and he builds weapons. And his thesis, and the thesis kind of since the beginning of time, is that defense is always slightly easier than offense. And this is true in software. This is true in hardware. It's always easier to defend something than it is to attack it.

And in a lot of cases, we have built these malicious tools. I mean, lots of people have guns in their house. We have nuclear bombs. We have genetic mutations. One did get away from us with COVID where, sure, maybe a handful of people did do something bad. Maybe it leaked out. Like, that's really bad. The hope is that as we go faster, as we understand more, it will continue to be easier to

defend against these bad actors than it will be for the actors to attack. And that's generally the hope. And again, it's like, it's hard to make a convincing argument to stop the progress because of the edge case of one. person causing damage. It's more interesting to continue the path forward while taking into account and being very careful about the edge cases that can harm people. I think we're seeing this a lot in AI from the leading labs like Open AI, where they're super concerned about alignment and safety because they do understand the power of these large language models and this intelligence.

And if it gets into the wrong person's hand, what can happen? Not necessarily if it gets in the wrong person's hand, but if it's able to manipulate a large group of people into doing, doing things, changing their minds. There's a lot of weird edge cases where like, we're very malleable and we are very subject to these sways of opinion. So there's always going to be that bad thing. And there's going to be lots of them. And they're going to get progressively worse, most likely. But I guess the hope is that the tradeoffs for making things better, for moving faster, will offset the downside of those few bad actors that want to use it maliciously. And there's always this double-edged sword to all progression, all technology. But you just have to hope that people

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found his identity as defensive acceleration where he says, yes, I'm a fan of acceleration as concept, but I'm even more of a fan of defensive acceleration. And there are technologies that he labels that are out there that are inherently defensive rather than offensive, where, like, weapons, armaments, bombs are inherently offensive. But cryptography is an inherently defensive technology. Bearer assets, Bitcoin, Ether, Defi, inherently a defensive technology. I'm actually unfamiliar with Palmer Lucky's idea that defensive technologies are easier to create than offensive technologies. I'd love to hear like a deep dive on that. But if that is true, if those arguments are sound, then I, too, find myself highly unconcerned with the Unabomber's future of the universe.

It's important to have people like Vitalik, to have people on both sides that are some people that are very like, oh, we are just going to go super fast, heads down as fast as we possibly can. And then other people that are like, wait a second, we're unlocking these, like, this whole slew of attack vectors that need to be accounted for. And like, let's build solutions for that. So there needs to be stuff that happens on both sides. It does feel as if it will be easier to defend than attack. But at the end of the day, like, we still have nukes. It only takes one person to start a nuclear war and that's, that's it. So we exist in a very fragile state as a society, which is also why it's like, oh, like, why don't we just go to Mars and just like duplicate our intelligence there. That way we have some redundancy. Right. We have a plan B. Yeah, plan B. So there, again, like there are ways to defend against these nukes. Well, let's just get off of the planet and let's take. intelligence to this new place. So if they nuke this one, surely their nukes can't reach this next one. And you can kind of like follow this down the line of threat vectors and figure out solutions that are kind of creative and unique to all of them.

Josh, I think this conversation that we've had spawns many, many new conversations. Something that we didn't talk about this episode that I think is still highly relevant is robotics. I think there has been just like a general movement towards robotics in just very recent weeks and months. My understanding of robotics is like we're going to totally have robots and like they're going to be humanoid robots because we have created a humanoid universe as in

the form factor for navigating the universe it's human. And so if we want help, if we want the most effective robots, they need to be robotic and we can talk about like the downstream implications of that. I think there's like a nuclear episode to be done here. There is a synthetic biology episode to be done here.

What would you be most interested in exploring next? Out of all the, all the different doors that have been opened up once we slam together the particles of energy and intelligence and we want to see the downstream impacts of that. What do you think are the first doors that we ought to go down here? Those are the top three. It's energy. It is manufacturing. It is synthetic biology. Those are all amazingly impressive. The energy one is probably the least exciting, even though it's the most important because they're just, okay, we figured out like nuclear fusion vision. We can build these reactors. They supply us with a lot of energy. Cool. The manufacturing

is unbelievable. The biology is even crazier. I think if you want to blow people's minds, the biology is cool because everything is downstream of biology, like the materials that we use, our own chemistry, you can recreate the biological world synthetically. That's super cool because it creates a lot of outcomes. And then the manufacturing is super cool because our world will actually start to look different. That world we describe where if you take your grandma and you drop her into a living room 50 years from now, the hope is that it'll look much different because we'll have robotic helpers doing things and we'll have all this autonomous transport and things that we probably can't even imagine now. So I think robotics is super cool in the sense that for the first

time we'll have computers that can coexist with us in the real world that are autonomous. Our interaction with computers has only ever been static in the sense that like we have a computer on our desk, a phone in our pocket. But they've never been able to, it's never really been two ways where there is this other person like thing that you can actually converse with or interact with. And not only does it unlock a lot of quality of life improvements for us where it can do all the things that you don't want to do, but it also allows them to do all of the less favorable things in life as a society that we don't want to do. Like now all the harmful jobs, the jobs that can hurt people, they're all taken care of by robots. And now all the manufacturing that kind of sucks that isn't super precise, that's all done by robots.

And the convergence of humanoid robot and alien-looking robot that does a specific narrow set of tasks, that is super cool because it opens up this whole new world. It's the autonomous transport. It's the humanoid robots. It's the swarms of drones that can deliver anything anytime. There's a lot of really cool improvements in both of those places. So I would say manufacturing, industrial manufacturing and biology, synthetic biology in particular, are like two rabbit holes that go so deep. There is no end to them. They're black holes, and they're all equally exciting in their potential.

I can definitely feel your excitement through the microphone. I think maybe just one last visual metaphor that is coming to my brain that we can leave the listeners with is something that you said earlier in this podcast is maybe what you said about Peter Thiel, which is the only thing that has really innovated meaningfully over the last 50 years is technology. So computers are very powerful, and they've been innovating very fast. Cell phones, anything with a chip, anything electricity-wise, all technology has innovative very, very fast. Now, with the arrival of AI and like commoditized intelligence, using our favorite David Deutsch quote, which is like the only constraint that we have is knowledge. And now that we have

knowledge at our fingertips, we have the means of all of that innovation that technology is seen over the last 50 years. Technology can now reach back at every single other industry that we haven't seen that industry and pull it forward simultaneously all at once, all together, all in our lifespans. and not just in our lifespan for like the next decade or so, I think we're really going to see a lot of change. And that is something to definitely look forward to maybe be scared about, maybe feel optimistic about, feel quite a lot about at the very least, no matter what.

And it's going to be very exciting. And so I'm very interested in doing subjects around all of these episodes in the future. Josh, thanks so much for coming back on your very own podcast. Really glad to have you here. Thank you. Yeah, it's been my pleasure. It's been so much fun talking about this stuff. I really love chatting about it, but normally don't have many people to talk to.

So thank you for listening to me for everyone else who's listening. I appreciate it. I hope you also enjoy talking about this crazy, wacky, weird, wild future that is coming fairly quickly. Bankless Nation, if this episode nerds snipped you, we can go talk about it inside of the bankless discord. That's where I hang out. That's where Josh hangs out. That's where all of the bankless community talks about mostly crypto things, of course, but also there's a frontier technology channels in there as well.

So if you like this episode, we can go talk about it in there. If you are not a bankless citizen, there is a link in the show notes. So you can go sign up to become a bankless citizen. There's also a $9 a month ad-free podcast feed. You do not get access to the bankless Discord. The only thing that you get is the ad-free podcast feed, but if you're tired of the ads, you can get that. Or if you become a full citizen,

you can come and hang out with me, Josh, and the rest of the bankless team in the Discord. Guys, thanks so much for bearing with us for this episode. Crypto is risky. You can lose what you put in. But nonetheless, we are headed west. This is the frontier. It's not for everyone, but we are glad you are with us

on the bankless journey. Thanks a lot.