D (2:08)

We do, it turns out. Yeah, big time. About half the. I was at a conference of about 1100 people, and quite a few of them are avid watchers.

A (2:18)

And what about the rest? Did you convert them?

B (2:20)

Yeah, we got to think International, whenever we're commenting on these topics, because, you know, everybody. It's a big world and everybody out there is.

D (2:27)

My Spanish is not quite up to snuff to say everybody should watch Moonshots in Espanol.

A (2:33)

You know, there's translators now.

D (2:39)

I did my Meaning of life session last night in Spanish with the translator, and you should have seen this. The translator, at the end of the night, she was so fried.

B (2:49)

And of course, do you speak Hindi or do you.

D (2:52)

What? No, I speak. I speak English. It's my native tongue because I come from a diplomatic family. I'm pretty bad Hindi. I can get by. But, you know, it's one of these where my grammar is bad, my vocabulary. I just throw out words and hope it sticks. I can get through about 50% of our conversation.

A (3:11)

Well, we're at almost 500,000 subscribers, so next time you're in front of our large audience, tell them to push us.

D (3:17)

I do.

A (3:17)

Over to 500,000.

D (3:19)

Oh, okay. I'll tell them that.

A (3:23)

Let's jump in another incredible crazy week. Let's kick it off with a conversation around the AI race and the agentic boom. So check out this slide, right? I mean, 15 major releases in only eight weeks. We're getting a pace of two major models per week. I think you've got to be retired and just focusing only on this to keep up. There's no way otherwise. So in this segment, what I'd love to do, guys, is really hit on the last three KME, K2.6GPT, 5.5 and deep seq4 before their extraordinary releases, each of them hitting new capabilities. One thing, Dave, we saw the acquisition or the invoked acquisition of of cursor by Xai. And I think what's interesting is that the winners in this crazy model race are going to be those that are providing the best abstraction layers. So it doesn't matter what model's underneath. Do you agree with that?

B (4:32)

Yeah, totally. Actually, I just had a meeting with a data center company here in Cambridge, and the amount of effort going into the TPUs and the Nvidia B100B3 hundreds is incredible. But at the abstraction layer, there's factors of 5 and 10 just being thrown away by mismanagement of the context window. And I mean, it's just so much opportunity in this stack, which makes sense because it's all brand new, but it's just. And also there's a lot of vertical integration going on. The warfare is really stepping up, but I can't believe how Kimik 2.6 is keeping up. I mean, it is just shocking that the open source world is actually on the radar and keeping up.

A (5:13)

And we'll get to that in a minute. But what's interesting is the speed of these releases. I'm guessing that these new models are sort of it's competitive marketing where the models are probably already cooked and they're just waiting for someone else to release and then releasing right on top of it.

B (5:29)

Philanthropic is holding back on Mythos. So you know that there's at least one case where you're exactly proven to be right, which means there may be others as well. But it's funny, the DOT releases are coming faster and faster and faster.

A (5:45)

What's shocking about this list is it's US versus China. There's no European models, there's no UK models, no Japanese Indian models. It's just all US and China. Everyone else is a spectator, it looks like at this point. I don't know if you agree with that, but.

B (6:00)

Well, the models are definitely self improving now. You're 100% right. But the models are self improving now. And so the rate is accelerating. Exactly what Singularity theory would have predicted. The rate is accelerating, but because the models are improving themselves, it's hard to start from a cold start and catch up. But I'm surprised that other countries aren't using the Kimi K2.6 model to bootstrap their own internal research. And maybe they are and hasn't popped onto the radar yet. But I'm not finding it too hard to design new neural nets using existing neural nets. It's a very doable thing.

A (6:39)

And I'm curious, Alex, that chart down below on this slide here, that's showing all the leapfrogging. It's leapfrogging all the time. But is it that they're all just cherry picking? They're all just studying for the test on a particular benchmark and then they're just releasing whatever the latest benchmark that they're best at. Or is this truly.

C (7:02)

Yeah, I think we're down in the west to a three way race at the frontier between OpenAI, Anthropic and Google. And I think those three labs have been pretty good about not benchmaxing of over focusing on just one benchmark. They're pretty good generalist models. I think we're seeing an honest to goodness arms race or horse race or rat race, depending on which metaphor you prefer. My friends at the frontier labs often call it a rat race. And as to the Chinese models, it's interesting. You know the aphorism why do you rob banks? Because that's where the money is. To the earlier point about why no European models, where's mistral in all of this, for example, it's because the US and China are where all the compute is. And ultimately, I think OpenAI's Noam Brown, who of course is quite famous for having led their reasoning approach, he's recently started almost pondering with a bit of ennui whether the weights actually matter as much as they used to, or whether it's really turning into a race for compute in some sense as inference time reasoning becomes more and more important. His argument, not mine, but I think it's a credible one. The weights themselves start to become less important in the same sense that say, individual units within a transformer style architecture become less important as the transformer itself starts to scale. The overall weights for an entire model may become less important as more and more reasoning gets used and you see in effect a space time transformer that's rolled out over time in reasoning token space. So if that argument holds, and I think it's a pretty interesting one that I hadn't heard elsewhere before, that would almost suggest that while at the same time we're seeing a race to the bottom on say per token intelligence densities between American models and Chinese models, open parens, the American models are still about six months ahead and this has been pretty consistent for the past couple of years. Closed parens, it may not matter in the end. What may matter in the end, that at least according to the scaling laws we have at the moment, is who has more compute at the end of the day to do more reasoning.

A (9:24)

So, and we're going to see that, we're going to see that in a minute. But you know, 15 models, you know, over the course of two months is insane. You know, some of these are just improvements on existing models and some of these are completely new pre trained models. I think that difference needs to be pointed out. Salim, any thoughts on this insanity?

D (9:44)

You know, just the fact that we have that many releases in eight weeks kind of blows my mind. The cost of cognition, coordination, execution is all collapsing at the same time. I mean, I think not so much the breakthroughs, it's the compression density is crazy.

B (10:00)

Well, and the capabilities are mind blowing. These are not just like fake little dot releases that are benchmaxing if you use them firsthand. And what's really helpful is if you look at our podcasts or at any postings on the Internet from three months ago, six months ago, nine months ago, 12 months ago. And look at the predictions of capabilities. We're so far ahead of what even the upper bound of predictions would be in terms of the capabilities as the parameter count grows. If you extrapolate from there, we're just on this knee curve of the acceleration and the singularity and raw parameter count and more chain of thought reasoning is just going to push us to limits that are way beyond human.

A (10:45)

So what does the average person care about? What does the average person care about? This One of my boys says, okay, great, a new release with the new numbers over and over and over again. At the end of the day, stuff is getting better, it's getting cheaper, it's getting faster. What does the average user do? You recommend someone sticking with a particular model? I'm just going to be on OpenAI. I'm just going to be on Anthropic. I'm just going to be on Google. Any thoughts there?

C (11:13)

See, I think the question itself is a red herring. Why? Because OpenAI bet the company on consumers using all these reasoning tokens. That consumer oriented strategy for all of these trillions of dollars of capex that they're building out would work. And they've had to pivot rather prominently in the past few months. Back to enterprise. So I think the question of what is the average user care? Which I construe as what is the average consumer care? I almost think the market is telling us the average consumer in the short term isn't even part of the equation anymore. This is really the question should be what does the average enterprise care?

A (11:52)

Because they're the ones I'm asking for our listeners, right? A lot of them are entrepreneurs or general consumers. I mean, at the end of the day, is it okay for someone, I'm just using ChatGPT, I'm just using Gemini 3.1 Pro. I'm just using, you know, the latest version of Anthropics models. You know, is it important for people to be driving to the latest model or is it okay? Because ultimately everybody's basically leapfrogging everybody else. And you know, if you're just a, you know, a mom, a dad, a student and you know, maybe an entrepreneur just getting going this insanity of 15 models in 8 weeks bouncing back and forth. I mean, Dave, you're using, you know, two, three or four models all the time, right?

B (12:39)

Oh, many more now and that's the biggest change. The coordinator model can now manage dozens or hundreds of other models successfully. And six months ago or three months ago that wasn't true. So for the average consumer, the ability for the stuff to install itself. You can go into the model now, you have to use the latest ones, but it doesn't matter a lot whether you're using Claude 4.7 or GPT 5.5. Just use one of the latest ones, but ask it to install itself, Ask it to build something on your laptop for you and it just works. Now you don't have to understand the Linux command line, you don't have to understand any of the underlying infrastructure. It's smart enough now to explain itself to you as it goes. So I think for the average listener, that's a massive unlock. Someone who's never built software before can just think of something and then create it in an hour. And that, that just wasn't true, you know, six months ago, everybody.

A (13:34)

You may not know this, but I've done an incredible research team and every week myself, my research team study the meta trends that are impacting the world. Topics like computation, sensors, networks, AI, robotics, 3D printing, synthetic biology, and these metatrend reports I put out once a week enable you to see the future 10 years ahead of anybody else. If you'd like to get access to the Metatrends newsletter every week, go to diamandis.com metatrends that's diamandis.com metatrenDS yeah, let's jump into our first model story here, which is moonshot AI launches Kimi K2.6 I just downloaded onto my Mac studios this weekend on top of Skippy, who's orchestrated by Opus 4. 6 so Kimik 2.6. It's a trillion parameter, open weight, open source model activates 32 billion of the parameters at a time. It runs 300 parallel agents. Very importantly natively, it can process text, image and video all at the same time and costs 30 times less than the most capable closed models. Interesting enough, Moonshot's AI didn't get its name from us. The three founders based in Beijing, their favorite album is Dark side of the Moon. That's where it came from. The company's backed by about 4.7 billion in capital from Alibaba, Tencent and IDG. This model, if you look at the numbers in the bottom on the benchmarks, compared to GPT5.4, Opus 4.6 and Gemini 3.1 Pro, it does amazingly well against all those models. This one was trained. They report for a total of $4.6 million compared to hundreds of millions or billions on the other closed source models. Dave, I find that amazing, almost incredible.

B (15:29)

It's so much to say about this. Starting from the Fact that Alex said a minute ago that the Chinese models are running about six months behind the US models. But if you look at the benchmarks, this is up there. Or beating Claude Opus 4.6 which was only three months ago, that came out in February. And so that's not a six month lead, that's a three month lead. And the price performance, most people, when they first start, they don't care too much. It's cheap. All AIs are pretty cheap. But then when you realize that you can run 10 or 100 of them concurrently, you're like, whoa, this is going to start to add up. So if you run this on Fireworks AI, it's about 1/8 the cost of running the Cloud API or the OpenAI API. So 1/8 is a pretty damn big price cut. If you download it and run it like you did with SCPI, then you're running at about 1/30 the cost. So that's a big, big deal. And then of course the caveat is, as Alex has pointed out many times, you're not 100% sure. If it's not spying or doing code injection, it's probably not, but you can't guarantee that. So somebody tells me this is 1/30 the price. Try it. You're like I'm a little sus, like why is it 1/30 the price? But I doubt it's code injecting on you, but you can't be sure. Whereas if you use anthropic or OpenAI, it's definitely not code injecting on you. In fact it's safeguarded all over the place. So there's your landscape. Chaotic as always. It's only going to get more chaotic.

A (16:59)

Alex, how big a deal is Kimik 2 6?

C (17:02)

I think it's helpful for certain enterprise use cases where you want to be able to self host the model and you don't want to say use AWS bedrock which by the way now hosts GPT 5.5 in addition to the Opus models. I think it's helpful in that respect. It's helpful if you want to be able to self host fine tuned models for yourself. Ditto with deep seq v4. But I think in general again it's a few months behind for other use cases for consumers that want to be able to self host for whatever reason, privacy or otherwise. Probably very helpful for folks who want to self host their own claws, very helpful. So I think there are many use cases where these typically Chinese open weight models like Kimi K2.6 and DeepSeq V4 are very helpful, I do think, however, they're not at the frontier and to me the big headline is that disparity between the American frontier closed weight and the Chinese frontier open weight seems, at least for the moment, to be in place.

B (18:07)

Yeah, Peter, I think your setup is perfect. It's exactly what I do too. I use Opus 4.7 as my orchestrator because you want that extra notch of intelligence. And then if you have simple tasks or just subtasks, you can farm them out and save the money using Chemik2.6. And then if the results coming back don't make perfect sense, then your orchestrator will tell you, hey, this is garbage. And so you can actually rely on Opus 4.7 to give you the straight truth on what the underlying models did for you. So it's exactly the way you set it up.

A (18:42)

Peter yeah Dave, a week ago you said you moved from 4.7 back to 4.6. Did you move back to 4.7?

B (18:49)

I have both running now. 4.7 is kind of wordy and sounds kind of PhD ish, which annoys me sometimes, and 4.6 is friendlier, but then it's clear that 4.7 is a little smarter. Sometimes you just need the right answer, no matter what. I actually have both running in parallel agent windows now.

A (19:09)

Salim, I'm curious. In Guadalajara, Mexico City, in parts of South America, in parts of Asia, what are you hearing about use of US models versus open weight open source Chinese models?

D (19:23)

I get a mixture of both things. A bunch of people use the hosted models, the big ones, just because it's easy. There's a subset of people who use the open source models and the Chinese models and they don't really care. I think they should care. At some point that's going to come up. One question I have for Alex and Dave is how do you protect against the code or prompt injection in these open sources, a way of defending against that? If there is, then there's a huge case for this because everybody here is looking for the low cost approach, right? But for the most part, I'll be blunt, the conversation is not around which model and open source or closes, like what do we do with AI? Like literally at a level of lack of sophistication around this that you would expect. But the option is also there for startups then to leapfrog lots of people and build aggressively for the coming madness that's upon us.

B (20:28)

Yeah, it's almost an impossible question, Salim, because if you sit on the sideline and you don't use this stuff aggressively, you fall way, way behind. But if you start using it aggressively, you're generating thousands or millions of lines of code before you even know it. And so then the odds go up, right? So I think what you're trusting right now is that the guardrails that that anthropic and OpenAI put on their models, they're very, very cautious when they're pulling in code, open source or otherwise. I mean, almost annoyingly cautious. So you kind of assume they've done a very, very good job of filtering out nasty code injection, but the numbers work against you at scale. So there's no simple answer. When I got into it, I was like, hey, I'm just going to look at the code. I'm not going to just run it, I'm going to see what it does. That's a joke, right? That's just laughable. It's generating so quickly now that there's no chance you could even scroll through it. So you have to use AI. Like a lot of things, actually, you have to use AI to protect against AI. There's no other way to get the scale. So it's tricky. I know that wasn't much of an answer, but it's tricky.

A (21:38)

One thing I'd love to point out here, we talk about this on occasion, but I don't think we've ever really spoken about in detail. The Kimik 2.6 uses something called a mixture of experts, an MOE. And it's interesting and just take a moment about this. If in fact you have a trillion parameter model and you ask a question, it's basically accessing all trillion parameters every time to analyze every token. And what they did here is they actually created a set of 30 plus experts, and so that some percentage of all the parameters are dedicated to one expert system. So if you ask a coding question, the orchestrator looks at this and says, okay, this is a coding question. We're going to send it to experts number 3, 7 and 12 and only uses a portion of the parameters instead of all the experts. It uses some sub fraction thereof and it saves money and saves time. And how many different models are using that right now?

C (22:48)

Alex Sparsity, which is the term of art I think that we're talking about here, is endemic to all frontier models at this point. It's also the basis for the human brain. If we look at the brain, most neurons don't at any given point in time have action potentials that are going in and out. So sparsity is A great way to reduce the memory footprint of models. To my knowledge, all of the frontier models use sparsity one way or another. It's also a good way to another term of art, regularize the models. So to make sure that particular weights or parameters in the models aren't overfitting to the training data, one of the age old techniques is just blasting away individual weights or parameters in the neurons, making them disappear entirely as a so called regularization technique. So sparsity is everywhere at this point and it's only going to I think become more important with time as we try. One of my holy grails is as I've mentioned on the pod previously, I'd like to see a million parameter or smaller diamond or black hole of a model at the end of the scaling race. And I think sparsity and cranking the knob on increasing sparsification in these models is one possible path to getting us there.

B (24:06)

And just to add something to what Peter said, the MOE innovation, a mixture of experts innovation that came from deep SEQ is actually layer by layer. So most of these neural nets are about 140 layers deep now. And it'll route the expert layer by layer. So it'll say look within this layer I'm just doing basic image classification and this layer I'm doing deeper thinking. In this layer I'm doing higher level math. As it moves through the neural net, it'll actually route to now I think up to 128 different experts layer by layer. And so find the optimal pathway through the entire neural net. On top of that you can also have dedicated experts like here's a surgeon, here's an artist, here's a coder above and beyond that. But MOE is actually within the neural net layer by layer.

A (24:53)

All right, next story is OpenAI unveiled GPT 5.5 literally just seven weeks after GPT 5.4. Greg Brockman calls it a new class of intelligence. It's natively omni modality. It's able to process text and audio and video and images all in a single unified end to end architecture. It has a 37 point increase over 5.4 in long context reasoning, which means 5.4 and 5.5 are both a million token windows. But 5.5 can actually remember the beginning of the million tokens and provide complete context across the entire thing. Token efficiency, 40% fewer tokens with the same latency. And I love this hallucination is down 60% over 5.4. Let's go to our resident genius Alex. What do you make of 5.5, how important is it?

C (25:51)

I think it's very important, both intrinsically and also relative to 5.4. So I want to highlight two key stats here. The first is the leap from GPT 5.4 thinking to 5.5 thinking. That's probably the biggest leap overall on Terminal Bench 2.0 specifically. So one way to interpret this terminal bench is a benchmark that's focused on the ability to agentically operate from a command line terminal. Where is that useful for codecs and for CLAUDE code type environments? So one way to construe this huge leap, which is larger than most or all of the other leaps that we see in terms of other benchmarks, is that 5.5 is being very seriously focused, benchmaxed if you like. Although I really having used it, don't think it's narrowly overfitting just to creating and making codecs a better CLAUDE code competitor. But it very much feels like a release that's intended to strengthen OpenAI's codecs. That's thought one, thought two is my favorite benchmark among all of these is Frontier Math Tier 4. Frontier Math Tier 4, which I think we even had a New Year's bet about, that we're going to have to revisit sometime later this year, is one of the best proxies for the ability for AIs to solve professional level research problems in math. And what do we see? We see from GPT 5.4 Pro to 5.5 Pro, approximately a 2% leap in approximately the last two months. What does that tell me? That tells me that we're seeing now approximately 1% gains per month in research level math coming from Frontier AIs. And we're getting closer to approximately half of all of the frontier math tier 4 problems getting solved. So you can extrapolate this and realize if the present rate just stays the same, which I guarantee it won't, it's going to accelerate. But even just at the present pace, we're talking about essentially all frontier math tier 4, all professional research grade math problems being solved in the next four or five years. So math is cooked. I'll say it second time, math is cooked. Bunch of other things are cooked as well. But things are moving so quickly now that on a month by month basis we're able to see the hardest of these benchmarks creep up 1% per month. So not long now it's worth pointing

A (28:24)

out that the API pricing on 5.5 is twice that of 5.4. So it's 5 bucks per million input tokens versus $2.50 on 5.4 and $30 per million output tokens versus 15. I like the simplicity of that pricing. Dave, have you been playing with this at all?

B (28:43)

Yeah, absolutely. I think what Alex said earlier in the pod is really, really important and insightful. Like Noam Brown is saying, wow, maybe the weights don't matter so much as this chain of thought processes is just way ahead of any expectations on how intelligent it can get from a user's point of view. That first benchmark, terminal bench, if you ask it to do something complicated like configure an entire system for you, download some software, integrate it, make it all work, connect it to my Outlook, connect it to my whatever, it just works. And that exactly ties to that first benchmark. It just flat out works. It feels like this incredibly capable, brilliant assistant, no matter what you're trying to do because of that first benchmark. Then the last benchmark, the frontier, or second to last frontier math. Demis Hassabis came out and said, yeah, I think it's kind of a coin flip. This was on Alex's innermost loop. It's a coin flip now on whether just the existing architecture scaled up solves everything. Yeah, I think coin flip. He's moved a long way.

A (29:50)

He has. You know, we need new breakthroughs.

C (29:52)

We're out of breakthroughs, apparently. I remember 10 plus years ago when I was chatting with Demis, he used to say there were five breakthroughs remaining between where we were then and AGI as he construed it. Now we're out of them. It's half a breakthrough or zero breakthroughs at this point.

A (30:07)

You know, next week we're going to be on with Ray Kurzweil again. We're doing that May 4th event for the launch of We Are As Gods. And I'm curious, we should ask him, you know, what does he think? What's required to get to true AGI or asi? Are we just going to extrapolate what we're doing or do we need breakthroughs? I think that that requirement's been falling.

C (30:31)

It is a better lesson.

B (30:33)

It's starting to feel a lot like, actually, Alex, you know what would be great for that is to put together a chart of Dennis's number of breakthroughs, which, because at Davos it was down to 2, now it's down to 50, 50 that it's 0. But you mentioned 5. That was what, maybe a year and a half ago. That'd be a really cool chart.

C (30:50)

The 5 number for him was when I Was chatting with him in. This is 10 plus years ago.

A (30:57)

Yeah.

B (30:57)

Okay.

C (31:00)

There's some sort of exponential decay of breakthroughs, clearly.

A (31:03)

Alex, you said it a little bit earlier. This is ultimately a compute race. Let's talk about that. A couple of stories here around Google Cloud and Google Cloud is dominating. So what do we see? We see Google announcing at Google Cloud Next 2026, their major conference. They unveiled their eighth generation of TPUs, in particular TPU8T for training and TPU8I for inference. Right now we have training and inference chips separately, just like Amazon has their trainium chips for training and their inferentia trips for inference. These new TPUs are three times faster in training performance, 80% better performance per dollar. They're designed to run millions of agents in real time. So Google is really all in on the agentic era. Sundar Pichai, the CEO who I had a chance to spend some time with last weekend, he made it crystal clear. He says over 16 billion tokens per minute being processed and 75% of Google's code is now written by AI. So fascinating. Dave, what do you make of this?

B (32:14)

You know what's surprising to me is that the price performance of the TPUs is landing right on top of Nvidia. Not much different at all, which is surprising because it's a completely different architecture. It uses a systolic array design. I mean, it could not be more different from a GPU under the covers, but for whatever reason it's all kind of canceling out and landing identical. Which is fine from Google's point of view because now they have their own total chip fab through data center, through model. They do everything solution. Yeah.

A (32:44)

I still believe Google's the winner in the long run here across the board. I don't know if you agree with that.

B (32:50)

Terrafab is a big thing.

A (32:51)

That's true. I'm sorry, yes. Okay. I'm thinking in the OpenAI and anthropic ecosystem, Terafab, on the other hand, Google

C (33:00)

owns a material percentage of SpaceX.

A (33:02)

They do, they do. I don't know if you saw. There was a tweet out recently about Google's investments that were made and they just had massive returns on their investments on SpaceX, on anthropic, across the board. Huge returns.

D (33:17)

Yeah.

B (33:18)

I was at a board meeting yesterday, a company that I'm the chairman of that has massive cash flow and a huge cash balance. And they were like, well, I don't know if a public company can really do seed stage investments. I was like, have you Looked at Google, they have multiple hundred billion dollar gains on their investments and they don't even do it for the money, they do it for the knowledge and for

A (33:37)

well and strategic relationships.

B (33:39)

Right.

A (33:39)

Larry and Sergey were just bonding with Elon and they said okay, Google is going to invest a billion dollars and now it's worth God knows how many hundreds times more.

C (33:50)

Yeah, it's also just investing in the future. I remember conversations with Larry and Sergey about the nature of the frontier and I think to their credit they're and SpaceX is part of it. And also Compute Epic put out this really I think eye opening stat in the past week that Google now accounts for approximately quarter of all of the AI compute on the planet and I'm sure 8th gen TPUs will be part of it. I think it's also worth keeping in mind that the TPUs at this point are being designed by TPUs. I have a number of friends at Google who are responsible for designing next gen TPUs and they're all just using Google AI to do it. The recursive self improvement goes all the way down to the silicon at this point.

A (34:35)

All right, our next story in the Google ecosystem again also announced at their large cloud Next conference is Google commits to 960,000 Nvidia Vera Rubin GPUs for their A5X. So pretty extraordinary. A5X is Google's new bare metal virtual machine instance delivering 10x lower inference costs and 10x higher token throughput. Just an interesting FYI. Vera Rubin, for whom these chips are named, was an American astronomer who discovered the first conclusive evidence of dark matter. I love the fact that Jensen is naming chips and systems after famous individuals. Now what I find fascinating, this goes back to the conversation a minute ago, is that this cloud is 2 times bigger than Colossus II and 2.4 times bigger than Stargate Abilene. So Google is winning at least based on what they're building and plan to build. Again Dave, thoughts here?

B (35:41)

Well, you know, part of the, just to touch on one thing you said there Peter, part of the acceleration we're seeing in society as a whole is that all the really, really smart people are working on real tech now. Yeah, Hardware and space and medicine and real tech. And if you go back to the meta era, the Facebook era, the rewards were all in either cheesy consumer experiences or banking. And doing deep tech was kind of like a way to die poor. So it's creating a whole new era for society, the post AI era. We all knew it was going to be very Very different. But now the rewards are in actual deep tech that benefits humanity in really big fundamental ways. But I think if you just counted the number of people that you know that have been pulled into this vortex, it would have been just a few percent working on world changing, deep tech, real stuff just 15, 20, 30 years ago. Now it's almost everybody that you know is getting pulled into, like, you know, do something big and world changing and it's actually working. And so that's a big change for society. So that's helping accelerate things as well.

A (36:47)

All right, our next story is Anthropic is cutting deals for cash and computer. Huge amount of capital flying back and forth between the frontier labs and the hyperscalers here. Google commits to a $40 billion investment in Anthropic. Last week, Google committed to a ton of money. $10 billion in cash right now at a $350 billion valuation. Note we talked about this last time. Anthropic on the secondary markets is now at a trillion dollar valuation. This $350 billion is coming in at roughly one third the cost of what others are paying for it. And they committed to another $30 billion. If anthropic hits certain performance targets as well, they're going to be providing 5 gigawatts of TPU compute committed over five years. That's the equivalent of literally providing power to 3 to 4 million people. I'm finding this pretty extraordinary. We're going to see in a moment a conversation where Anthropic has cut deals with Amazon in a similar fashion. Actually, let me go ahead and hit that and we'll talk about these money for guns conversation that's going on. Amazon and Anthropic are trading cash for compute. Here's the second deal. Amazon is investing a total of $33 billion. They've committed to $25 billion on top of the $8 billion they've already invested. In return for Amazon's cash, Anthropic is committing to spend $100 billion or more on AWS over the next decade. Anthropic will run Claude on Amazon's custom Trainium chips and Amazon will provide 5 GW of AI compute capacity for Anthropic. So, I mean, we're seeing Anthropic becoming beholden to both AWS and Google in a significant fashion. Gentlemen, thoughts on this one.

B (38:45)

Well, it's so funny to me. Obviously Anthropic needs much, much more compute and is growing. Oh, actually a very good friend of ours, Peter, I won't mention him on the podcast. But he's an investor in Anthropic and he was telling me at the board meeting yesterday he can figure it out from that comment. But he was telling me at the board meeting yesterday that Anthropic under the covers, is thinking they might hit between 40, 50, up to 70 billion in revenue by the end of the year.

A (39:09)

We talked about 100 billion by the end of the year a few pods ago, but still they were at 30 billion last month, doubling, tripling. It's extraordinary.

B (39:18)

And the current wouldn't hit those numbers is because they can't get enough compute to keep up with the demand.

A (39:24)

And one of the demand was they didn't release Mythos because they didn't have enough compute to deal with it. Right. So it's a limited release of the capabilities.

B (39:33)

Yeah. And OpenAI cut Sora. I think one of the reasons is probably computer. So which is energy. Yeah, which is energy. And I think that it's so funny to me to see all these deals. So, okay, so Dario needs compute. He signs up with Amazon. Google's already a shareholder in anthropic and now OpenAI is going to be running on GCP and also on Bedrock on Amazon. So you can get it through Bedrock. So everybody's partnering with everybody else, but it's all bottlenecked at tsmc. This is all great. You can all partner with each other up the yin yang, but whose chips are actually going to get made? You don't see TSMC in any of these podcasts, in any of these deals, any of these meetings. You saw Jensen actually recently say he doesn't have any long term agreement with tsmc. They just make it up as they go. All of this is bottlenecked and only Elon is talking about. Look, the fundamental constraint to all of this is the tariff app. And I already locked up 16 billion, could be 45 billion of Samsung's capacity. The only three companies in the world capable of making any of this are Samsung, intel and tsmc. That's the actual bottleneck to all of AI and only Elon will talk about it.

A (40:51)

Alex, is it compute or is it energy? End of the day right now, I

C (40:56)

think they're indistinguishable at this point. I think permitting for on site energy is a major limiting factor. I think it's probably, on balance, more of a limiting factor at this point, maybe not a year from now than tsmc, but it is a limiting factor. Having powered land, having data centers that you can take all of these. Infamously, Microsoft, even in the Past few months spoke about having lots of GPUs that they'd love to rack mount in a data center, but lacking the powered land and lacking the data centers to plug them in. I think at this moment energy, at least in the us But I agree with Dave that in the medium to long term semiconductor fabrication, supply chains doubly so if there's any geopolitical conflict, are likelier to be a stranglehold once we solve our energy story.

A (41:46)

So let's talk about the not investment advice segment here. Where do you invest your capital? You know, if compute and energy. I mean I'm seeing the energy stocks beginning to fly, right? A friend of mine just had this IPO of X energy and it popped like 30% in the first day. We're seeing Bloom Energy and other energy stocks beginning to skyrocket, creep up over the time. So I don't know if you're going to invest in chips. Do you invest? We saw intel pop up and amd, I mean all of these guys, that entire ecosystem of chips and energy, ultimately, if they're really the constraining part of the innermost loop here, I think the most demand is there. Any thoughts, Dave?

B (42:37)

Oh, so many thoughts. I could go for an hour on just this topic. But invest like crazy in anybody who has access to chips and can find a power supply that's pretty straightforward. There are power supplies everywhere, all these legacy manufacturing operations, aluminum melting and all that, uses a huge amount of electricity and swapping it over to data center is a massive increase in the value of that energy supply. But you have to have a line on the chips then at the kernel level because the chips are so constrained and the demand is through the roof at the kernel level, anyone who's writing software at the kernel level that empowers AMD chips or legacy GPUs to participate or just makes the inference more efficient on Nvidia chips. Those companies are worth a fortune. So anyone who's building kernel level software is a brilliant investment. And then in the vertical use cases, Anthropic rolled out something called skills which you should absolutely play with. It's just a way to use the context window more efficiently by designing skills that the AI can then pull in. So rather than have to reinvent everything every time, just build a skill and then you can call on the skill very efficiently. So companies are now discovering they can refactor their entire business or their entire whatever they do around 100 or 1,000 different defined skills. But those skills then become the defendable intellectual property within that vertical domain. So any vertical domain where you're racing to build out the entire skill database for that use case, is that also an unstoppable investment theme right now? I could go forever.

A (44:18)

The other thing that's interesting is that both Google and Amazon are getting their shares in Anthropic at one third the going rate. I find that extraordinary $350 billion valuation versus the trillion dollar valuation.

B (44:34)

Well, it shows you how important the compute is. Again, you're going to be sold out for forever if you can get the compute.

A (44:42)

And these hyperscalers are hedging their bets. They're not picking a winner, they're buying every horse in the race because this AGI ASI race is just way too important to lose. So they're just investing left, right and center.

C (44:56)

I would also just parse these as the market doing what the market does. Some of the participants, some of the frontier labs like Anthropic, have an insatiable hunger for the compute. And they have the revenue generation to generate the demand and sustain the demand. And so if you're Anthropic, you're going to go to every possible source at scale of compute that you can find, whether it's Amazon or whether it's Google or whether it's other sources, you're just going to go and seek as a hungry customer for compute, whatever the market will provide. I don't think necessarily the story needs to be any more complicated than that. It turns out the world demands a lot of compute to solve some of these really interesting problems in code generation and otherwise. And what we're going to see over time is all of this demand is going to translate into supply. It's going to translate in the short term into what looks superficially like a bit of a circular economy between, call it the top 10 or 12 companies after we see the IPOs of SpaceX and OpenAI and Anthropic. But that's going to diffuse throughout the economy over the next few years would be my prediction.

A (46:07)

People are hungry for compute. Salim was hungry for bandwidth. Salim, welcome back. I see you're in a stationary situation

D (46:14)

from the airport now in a stationary spot. So let's see.

A (46:17)

Dude, I'm just going to call you Waldo from now on. All right, let's move on. A couple of fun stories. I'm going to add this segment every time for the podcast, which is what did Claude just kill? This is the stock chart for ebay. This comes out from Anthropic Research says New Anthropic Research Project Deal we created a marketplace for employees in our San Francisco office with one big twist. We tasked Claude with buying, selling and negotiating on our colleagues behalf, basically doing what ebay does. And we see a drop in the stock price. I think ebay's not really dropped anywhere beyond this, but I think this is going to be more and more common. Any thoughts, Dave?

B (47:05)

Well, I think a lot of this is just immediate knee jerk fear reaction. But then things settle out and you realize, wait, anthropic is going to build all kinds of marketplaces because they can, but it's not going to hurt ebay. I think what you're going to see more and more is AI is growing so quickly that it's going to largely grow around the legacy economy, so around the banks, around the insurance guys. It's going to be its own world and it's going to be feeding on itself and building just, you know, colossally large constructs that, that some people are not even aware of. And it'll all happen very, very quickly. So I think ebay will be fine.

A (47:39)

Any thoughts here? Slim?

D (47:41)

I have a slightly different take. You know, there's so many places because lots of problems in companies exist because coordination is hard and AI makes coordination easy and that's going to threaten big chunks of places. Marketplaces, customer support, listing optimization, dispute handling. There's huge categories of these that will become agenting workflows. And I think the bigger question about what did the AI just kill is what workflow category did it just encompass and automate?

B (48:13)

You want to hear something cool related to this? The data center CEO that I met with this morning, we were talking about data centers going into space because power is basically free. Solar is basically free in space. He said data centers. There's power all over the planet that's not tapped. That doesn't disrupt society at all. That's not why data centers are going to space. Data centers are going to space because there's no regulatory authority preventing it. You try to do anything on truth.

A (48:43)

Well that's not true. That's not true. You still have to. If you're going to be flying all these data centers and communicating, you need licensing domestically and the ITU for bandwidth. I mean there are going to be regulatory hurdles that Elon and Google need to get. Especially if you're launching 500,000 satellites. I mean when you're putting up a debris field like that, there's going to be pushback. There is going to be pushback.

B (49:09)

Yeah. It's interesting if you compare that to doing anything on land. Sorry, Go ahead, Alex.

C (49:15)

I'll square the circle here and say, I think in the short term, for sun synchronous orbit. Yeah, that requires FCC and other approvals. In the long term, if we start, say, launch AI data centers from the moon, that will probably. And we're building them on the moon, that will probably require fewer approvals, at least under the current regulatory regime. I'd like to go back.

A (49:37)

That's 20 years away, to actually get manufacturing on the moon. I'm talking about 20 years away, Peter. You know, listen, if you look at it deeply, I mean, I know 20 years away is infinity, I get that. But we're talking about, I mean, just to be, to be clear, the stuff I'm concerned about is the next five years, Right. If you're launching. We talked to elon about this 500,000 V3 satellites in a constellation, there's going to be debris issues. Elon pushed it off by saying, oh, we'll have superintelligence to figure that out. We have this. Everything, everything looks amazing from far away, but the reality is by the time it comes closer, there are real issues. And so it's not going to be just the, you know, the promised land of going to space. We're going to have challenges going there still.

B (50:36)

Yeah, it's interesting how the timelines line up too, because between here and there, there's all kinds of constraints. But between here and there, we'll have solved all math and we'll have discovered all kinds of new physics.

A (50:47)

And so listen, I'm the space cadet, I'm the super space enthusiast here, and I can hope for nothing more than that vision to happen. But it's always easier on the promised land.

C (51:00)

Peter, I'm gobsmacked to hear that you think it's going to be 20 years before we have fabs on the moon. My goodness.

A (51:07)

Fabs on the moon. Manufacturing and pumping into Earth orbit with mass drivers.

C (51:13)

You think that's 20 years away?

A (51:15)

Okay, maybe 15, but it's not the next five years.

C (51:18)

Do I hear 10?

A (51:21)

It's hard for me. I guess Optimus robots will improve that, demand will improve that. But the concern is if you have not an explosion, but a collision of spacecraft in orbit generating debris, we still don't have any mechanism for removing debris from orbit. And so it's going to be a challenge.

D (51:44)

I'll make two predictions.

C (51:46)

Your concern is briefly, your concern is Kessler Syndrome. Kessler Syndrome is going to sabotage moon based fabs?

A (51:55)

No, it's going to sabotage the next five years of 500,000 satellites in Earth orbit. I mean, right now we have 10,000 satellites from Starlink, which is the most ever pumped into orbit ever. And we're talking about 50 times that. And we're talking about not just the U.S. you know, Amazon's going to do their best, right? Jeff is not going to stand still while Elon's doing this. And then you've got Chinese constellations. So do you double or triple that number of satellites in orbit? I mean, listen, I can't wait. And it's going to have challenges. Saleem, you're going to say.

D (52:32)

Yeah, I'll give a couple of thoughts here with just finger in the air here. I think humanoid robots are five to seven years away, minimum, at mass scale in widespread adoption, minimum.

B (52:46)

And I think.

A (52:47)

I don't agree with that. But that's okay.

D (52:48)

I agree. I understand. And I think a fab lab on the moon and consistently doing fabrication and all that stuff is 15 years away, minimum. So I'll say that. Not that it's not coming, it's just a question of. It's a when, not an if, which is.

C (53:05)

Oh my goodness, this is lunacy, utter lunacy here.

A (53:08)

Well, we are the Moonshots podcast.

C (53:11)

Yes. Can we get back just maybe to Project Deal and Anthropic? I think we're missing an important point. Everyone who hand wrings over the latest anthropic project purportedly sabotaging or killing some SaaS company, anthropic doesn't want to to be triggering Saspocalypses left and right. There's relatively little economic motivation there. I think if you look through the through line, through all of these anthropic projects or research projects other than the alignment ones. Anthropic is all of their projects can be explained by and corporate strategies and unhobblings can be explained by very simple principle. They're trying to maximize the economic value per token. That's all that they're trying to do. I love that Claude code. It turns out through Claude Code, CodeGen is actually quite economically valuable per token. Turns out per token, it's more valuable to generate useful working code than say, to generate video or cat images or whatever other consumer plays OpenAI and some other frontier model providers were chasing. They've dropped that. Now everyone's focusing on Codegen because on a per token basis, it's so economically valuable. So I would look at projects like Project Deal, running a marketplace, running a business as Anthropic, looking for new ways to increase the per token economic value of their output. It's as simple as that. Running a business.

A (54:37)

That's brilliant, Alex. Yeah, that's absolutely brilliant. Thank you. To move us along here, we're coming into the battle season. It's Elon versus Sam and OpenAI. This just got posted today. So today is the start of a very important day in the AI world. The trial between Elon and Sam and OpenAI begins in the Oakland Federal Court. The jury selection is happening right now. So I just put this up to keep us posted. We'll be learning a lot, of course. Discovery is unveiling a lot of texts, a lot of emails that I bet both Elon and Sam and a lot of other people would rather not have erred in the public. Any thoughts here, gents?

C (55:23)

I think it's sort of sad that it's come to this. It's going to make I just one remembers the Bill Gates versus Steve Jobs docudramas that were made from critical Apple versus Microsoft era. This has, I think, a similar feel to it. It's sort of sad that I think this ended up in court versus settling earlier on, but I do think many will. I think history will probably view this as sort of an iconic struggle that will get the full Aaron Sorkin if not similar like movie treatment. This will be the full Hollywood type Titanic battle.

A (56:00)

Yeah. Salim, any thoughts here? How's this playing in Guadalajara?

D (56:06)

No recognition awareness at all. And that's probably a good thing. This is kind of soap operation. I'm with Alex on this one. It's just heavy drama. We wish it hadn't come to this. It would have been great to get these guys to settle, but their positions are hard and baked in and so

A (56:24)

it's going to come. How do you unravel the movement from OpenAI to A for profit company? I mean, do you back it up to a nonprofit? Then what about all the capital invested in OpenAI? Does that disappear if they lose the case here?

C (56:39)

I'd like to do it more. I mean, I've said I think on the POD in past that if the model of changing nonprofits, large nonprofits, to public benefit corporations can be scaled, I'd love to do this to a number of major American research universities.

A (56:53)

That's not my question. My question is what happens to all the capital invested, literally $122 billion in the last couple of months. There's so much pressure for this court case not to be won by Elon.

C (57:07)

Well, if you're following the detailed TikTok of the way that this trial is being structured, it's being Structured in two phases. The first phase is more of deciding whether the claims that Elon et al have made are in fact the case. And the second is the equivalent of a reward type section, deciding what awards, if any, to make as conditioning on the first phase. But I think there are a number of details in this court case that are notable. One is jury selection. There's been public reporting that already selected members of the jury are aware of entanglements that Elon's had with the present administration and may view him negatively as a result. I think that's the fact that jury members are being selected, reportedly with some political influence seeping in. I think that's very interesting. I also think it's interesting that the the district judge in this case has again reportedly decided that she's going to take the jury outcome as an advisory opinion, but that if there is an award, she's going to decide ultimately from the bench on the final award. So there are a lot of nuances here.

A (58:31)

Wow. Dave, any thoughts, opinions here? Yeah.

B (58:34)

Do we ever figure out if we get to see it live?

A (58:36)

No, it's not being broadcast, but I'm sure there are going to be sort of court reporters giving us a lot of details here.

C (58:43)

You can wait for the full Hollywood treatment in a couple years, by the way.

A (58:47)

There will be a Hollywood treatment of this as a guarantee with every other major.

C (58:52)

Of course it may be an AI generated feature film, but nonetheless it will be for sure.

B (58:56)

Well, I'm surprised how many texts, like personal texts, have already come out. Yeah, the emails get discovered right away and all your email gets thrown out there for the wild to read, which is crazy, but it happens. But texts traditionally have not been thrown out, but yet we're seeing them all. So I don't know exactly how that's happening. But for Elon to win, he doesn't have to win the case, he just has to slow down. OpenAI in the middle of the singularity. If you lose three months, basically you lost, you're cooked.

A (59:29)

All right, another fun topic, a few stories here. It's about AI surveillance and privacy. So let's check this out. OpenAI's Chronicle uses agents to build memories from screenshots. So Sam Altman described this one as telepathy, like so chronicle runs on OpenAI's codecs, where background agents are taking periodic snapshots of everything on your screen. The screenshots are sent OpenAI's servers for processing agents use optical character recognition and visual analysis to extract the context of what you're doing every minute on your screen, structured memory files are created and stored locally. We talked about this before. AI monitoring everything. Ultimately it's sort of the camel's nose under the tent of being able to replace any worker. We have significant privacy concerns that come up on this and no one's raising that. I don't know if you guys remember, when I was researching this, Microsoft had launched something recently called Recall. It was a product that they put out there and then they retracted because all the cybersecurity people said this is a privacy nightmare, it's litigation bait. They pulled it back. But when OpenAI announced this product, no one's pushed back.

C (60:56)

Can I first of all point out what a beautiful double entendre from Microsoft's crack product marketing department? Naming a feature Recall and then recalling it.

A (61:06)

Nice.

C (61:07)

I think what we're seeing here is one big architectural kludge and I think it's going to be kludgy, both from Microsoft's perhaps ill architected Recall as well as OpenAI Chronicle. This wants to be built into the operating system and the hardware. It doesn't want to be an add on. I don't think I'll just speak for myself. I don't want an agent taking constant screenshots of my desktop, sending it to a server and then parsing it, sending back results. This should all be built Apple style. I would hope that Apple will get its act together in the next few months and build this into the window manager and the compositor and the operating system. The operating system is rendering the screen. Why can't the operating system understand what it's rendering?

A (61:51)

I mean, this is ambient AI is the term of art here, where AI is monitoring everything all the time and enabling you. Right? This is in one sense, this is what I did this past weekend with my openclaw with Skippy, where I gave it access to everything, right? Every single granola gets put into memory, every WhatsApp message, every email, every calendar, everything. And it just makes it so much more useful. And I think something like Chronicle as well would just enable it to be like Sam said, telepathy.

B (62:26)

Well, that's the quandary. I mean, a lot of people who get in trouble with AI or they get stuck, it's something they're doing on screen. The AI doesn't have visibility into it. But if you unlock that, the AI can be incredibly helpful. But it's also seeing literally every mouse move. But when we talk about our moms are still not using AI, why not? This is a big unlock, a big part. The Voice interface and this are the two big unlocks, because it can then say, oh, I see what you're doing wrong. In fact, let me just do it for you and save you the trouble. And all these configuration screens on any Apple device, the menus are ridiculous. Now, the number of layers of configuration you can do, I think something like some crazy stat, like 70, 80% of all iPhone users never change any defaults. It's just too confusing to do anything. So this is a huge unlock for all of that. But like I said, hugely intrusive right?

A (63:19)

Now, I take screenshots and I send it to Claude or whomever and say, hey, can you please help me figure this out? But this is going to have sort of an expert over your shoulders, always there to support you if you need it.

B (63:34)

Well, and people, when they first start playing with AI, like Alex's standard first query to test a new model is build me a first person shooter. It's a better prompt than that. Sorry to bastardize that.

C (63:45)

No worries.

B (63:45)

But. But people want to do something visual and graphical to learn how it all works. And then when it doesn't work, they want to show the AI, hey, this doesn't look right to me. Fix it. So they screenshot it, just like Alex or just like Peter. You just said they screenshot it, but here, this is just a much more convenient way to get video, not just a screenshot, back into the AI's brain and say, look, this doesn't look right. Fix it for me. And so you have a much more fun dialogue with the AI, but you

A (64:13)

have to accept that privacy is being compromised there.

C (64:18)

I'll take a very different position here, Peter, on that, which is, I think any loss of privacy here is just due to this being an architectural atrocity. This wants to be built into an operating system like macOS. It wants to take advantage of the secure enclave. It wants to have secure hardware that's cryptographically guaranteeing that as it captures pixels that come out of the compositor and the window manager and the renderer, that all of those are securely handled and kept local. The reason that this is one big privacy dumpster is because it's not being baked into the hardware.

A (64:53)

Agreed.

C (64:54)

And the local operating system.

A (64:55)

But that can be fixed and it will be fixed, and I want that. You know, I've often said I'm going to give up everything, every piece of detail, because I want my AI systems to be that much powerful. Saleem, you're back with us. Talk to me about, what do you think about this?

D (65:08)

I Think this is a. I agree with Alex. Two other things. One is that this is going to cause massive privacy issues for workers worried about Big Brother watching over them. Already today, there's a crazy statistic that 44% of Gen Z workers are sabotaging AI zephyrs to automate their own work. They're putting in the wrong data, throwing off the AI training. It's really crazy what's happening right now in. In workplaces. So I think this will just exacerbate it and bring this whole conversation to the front.

B (65:39)

Talk about a losing battle. You're far, far better getting on the wagon than you are trying to do that.

C (65:45)

That's such poisonous behavior.

A (65:46)

Protect your job. Welcome to the health section of Moonshots, brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I'm here today with an extraordinary physician, the chief medical officer of fountain life, Dr. Don Musailam. Dawn, let's talk about cancer. You know, I know from the member database that we have at Fountain, our members who come in who think they're healthy, it turns out 3.3% of them have a cancer in their body they don't know about.

E (66:22)

That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances are for cure are much higher. We know it's much easier to treat a cancer when found early versus when found late. What we're finding in our members is over 3.3% were found to have these cancers that were otherwise wouldn't have been found or detected.

A (66:46)

Yeah. You know, it's interesting, people, you don't feel the cancer until stage three or stage four. And if you don't know what's going on inside your body, it's like driving your car with your eyes closed. And you can know. And so when members come through Fountain, how do they detect cancers?

E (67:01)

So we're doing full body mri, and we also do early cancer detection screening. This is very, very important. And these are not typical tools used in the conventional care setting when it comes to prevention. This is a hard thing because currently, these are not studies that insurance would yet be covering. But the goal is to collect these numbers, do the research, and work hard to democratize wellness.

A (67:23)

Yeah. So at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out Fountain Life. You can go to fountainlife.com peter to get access to the latest technology to help you detect cancer at the very beginning, at stage one, when it is curable, before it gets to stage three or stage four, and you're a world of hurt. All right, here's our next story. Basically, world ID verification, integration into zoom, and here it is. So the backstory, I think that's important here. So in 2024, engineering firm called Arup lost 25 million after an employee in Hong Kong authorized a series of wire transfers during what appeared to be a routine video call with the company's CFO and several colleagues. The problem is that everyone on the call except the victim turned out to be an AI generated deepfake. We've seen similar attacks in multinational firms in Singapore, and the impact of this is huge. Right? So what we saw in 2019-2023 was $130 million in losses due to deepfakes. 2024, which. 400 million. 2025. Last year it was a billion. It's projected to reach $40 billion by 2027. And so step in our friend Sam with his device called the Orb that takes a photo of the back of your retina and you verify on Zoom that you're an actual human. It uses world ID and a real time face authentication from a selfie as well as video, and it says, yes, yay, verily, this person is a human. So. And you get a verified human badge on your zoom link.

D (69:10)

Did you just say, yay, verily? That's fantastic. We're right back to Shakespeare here.

B (69:15)

That's awesome.

A (69:15)

Yay, verily, you're a human. You still have to go and actually scan your eyeball in one of these orbs. Has anybody done it? Have you guys done this yet?

B (69:28)

No. No. Apparently it's bouncing all over Africa. People are scanning away, but I haven't done it yet. But I love it because I don't know if I told you, Peter, but I was on stage here at a company wide meeting, and we took a little five minute break in the middle and our controller came up to me and said, dave, I'm so sorry I only got half of those wire transfers to China out. I'll get the other out right away. Seriously, what are you talking about? And so I got back on stage, I'm like, I wonder what she was talking about. And so then the whole second half of the company meeting, in the back of my mind, I'M like, wait a minute. So I got off. When I got off, she said, okay, I got $300,000 out. And I'm like, what are you doing? She's like, well, you told me it was an emergency and we got to get the money to China right away. Why would we be wiring money to China? I don't understand. So Anyway, only about 75,000 got across the border. We never got that back. And the rest of the FBI got into it right away. But I'm like, man, digital transfers like this, everything should be logged anyway. I really feel like the digital fraud world is going to get solved. And this is a big part of it, but everything should be logged all the time. It shouldn't be that hard to deal with digital stuff. I'm much more worried about chemical, biological, radiological stuff than I am about digital stuff. Because I think we're going to get it fixed. And this is. This is part of it.

A (70:48)

Alex, any thoughts here?

C (70:50)

This is Minority Report. This is the sci fi future that we're catching up with. Apple with its face ID was focused on the face, not on the retina. But if you remember the Tom Cruise, Steven Spielberg, Minority Report division, this is it. I think it's been interesting to watch as worlds evolved from worldcoin. And it's been interesting to watch as the company bounced back and forth between more crypto focused and the economics of it versus the identification of human as a human side of it. But it seems from a distance, like the human identity verification side is ultimately the bigger seller than the crypto side. And to the extent that's the case, as resident crypto bearer, I'm very supportive

A (71:39)

we will have that debate. Salim, don't worry about it.

D (71:43)

There's a wild irony here that the more AI scales, the more valuable verified human identity becomes. This is kind of interesting.

A (71:50)

Yeah.

D (71:51)

Sorry.

A (71:52)

Yeah. No. So here's this next story that's related. So GROK creates a realistic AI French woman with a reflective id. I'm going to play this little video here and take a look at it very carefully as she holds her driver's license up to the camera. Look how beautiful and real this looks. So this was posted and it went viral by this gentleman, Dr. David Lutzke. He says this AI French woman was created by Grok, complete with perfectly reflective ID. A few more months and video ID verification may no longer be reliable. So, I mean, how many times have you taken a picture of your license or your passport and uploaded is going to become more and more difficult. We're going to have white hat, black hat competitions up the wazoo here, Alex.

C (72:52)

Well, I would maybe just comment. The IDs themselves should be verifiable with a centralized database. That's how you can maintain a single source of truth. And whether people are flashing IDs or not. Maybe on blockchain, less of a centralized database, not a blockchain, but. Good one, Peter. Good one.

A (73:13)

I'm just poking you, buddy. I'm just poking you.

C (73:16)

I also think, you know, there are so many other technologies that we have to bring to bear. We can do hardware level cryptography, for example. Chain of custody for video. It's not that as a civilization we lack the technologies to ensure that any video or images actually originated from the real world without tampering. It's just that we lack the demand for it right now. And I would predict that if ever the situation of deep faking gets so bad that it's causing real problems at a societal level, that'll just unlock all of these technological solutions, including hardware level crypto for cameras. Cryptography, not cryptocurrencies. That the market will speak for itself and we'll get all those tech.

A (73:59)

I'm still waiting for the laws to come out that require GROK and every other video generation to really identify it as AI generated. It's not.

C (74:10)

There's a bill work I covered in my newsletter, Innermost Loop. There is a bill right now, bipartisan bill working its way through the House that will cover elements of deep fake fingerprinting like that.

A (74:22)

Yeah, yeah. All right, let's move ourselves along here. We're going to talk about the economic impact of AI. There is a lot going on. Token maxing. Word of the year. So this is from a report from 404 Media Co startup CEOs who are token maxing are bragging that they are spending more money on AI compute than it would cost to hire human workers. Astronomical AI bills are now in a certain corner of the tech world, supposed to be the marker of growth and success. Look how much I'm spending on my tokens, everybody. You should invest in me so I can spend more on tokens. Dave.

B (75:00)

No, this is a warped story. This is a great thing. The way you get left behind is by not trying. That's the worst thing you can do right now, is not get in the race, not play with AI, not try. And token maxing is fine. A CEO that's proud of the fact that they're consuming a ton of tokens. You can come and optimize it later in the year. But get every one of your people on their AI platform, like now, like yesterday, and go ahead and start burning the tokens. And then you'll have no trouble making it more efficient later if you get in the game now. So I think it's great when a startup CEO says, I burned 3 million of venture money on compute. Fine. You're learning a ton along the way. And nobody incinerates money for very long. They're not that irrational. So. So this is just sort of the backlash story.

A (75:53)

What was the Jensen factor? It was like half your salary in tokens.

B (75:58)

Yeah. I'm saying you're full salary. I'm telling everybody by end of year. So you have nine months. 50. 50 is a good target. Half payroll, half token use. And then again, you're not going to have any trouble optimizing it. The token use is effectively about a 10x force multiplier. So if you're at one to one, it's like, I've got one humans and 10 AI equivalents in my bucket of endeavor. So I'm actually under invested in tokens at that point relative to human salary. So one to one is a better target, I think.

A (76:29)

Salim, are you doing that?

D (76:31)

Well, I think the bigger, the more healthy question is what's the ratio of tokens to reducing iterations and maximizing efficiency rather than just a raw spend? I think for now raw spend is fine, but that's kind of a vanity metric, right? You're better off kind of looking at it as to what extent can you compress iteration cycles. That'll be where it'll end up.

A (76:56)

It's what Alex, you said earlier, it's dollars per token, economic impact per token.

D (77:01)

I think that's a great term.

B (77:03)

If you ask a great, great salesperson, how many miles did you fly this year? That's a terrible metric of sales productivity. But if the answer is zero, it tells you it's a bad salesperson. I think it's great when a salesperson said, oh, I had a million mile year last year and they're proud of it, that's great. It's not the right metric, but it tells you they're proud of what they do. And token maxing is a lot like that, I think.

A (77:27)

Alex, close us out on this one.

C (77:29)

Yeah, I've seen a variety of asset allocations in recent months between humans and AIs. I think tokens to humans is one interesting way of framing that. A pessimist will look at this and say, this is replacement theory. This is humans being replaced by AIs. How awful. An optimist will look at this and say how incredible. We're empowering fewer people to do more and achieving higher per capita productivity within an organization. What I don't hear very many people asking is, where does this end? So right now I see asset allocations, humans to AIs, or at least human labor versus AI, compute budgets ranging from 1 to 1, 1 to 2 at some of the frontier labs. It's an even more asymmetric ratio. Question in my mind is, is there any stationary endpoint? Is there a fixed point? As this evolves, I tend to think it's going to trend towards one to infinity. Effectively, that as we start to phase humans out of the service labor force, we're going to see all tokens and no humans.

D (78:37)

It has to. There's no other way around it. The capitalism will demand it. Totally agree.

C (78:43)

Until the humans merge with the tokens, at least.

A (78:47)

Tokenpreneuron. All right, Saleem, this was your story. So UAE launches agentic AI government models. This is from Sheikh Mohammed, the prime minister of UAE, the ruler of Dubai. He says the UAE is launching a new government model. Within two years, 50% of government sectors, all sectors, all services, operations will be run on agentic AI. The UAE will be the first government globally to operate at this scale of autonomy. Saleem, brief us on this one.

D (79:20)

Yeah, so I did a talk for His Highness three, four years ago and talk through where this is going. And you know, Minister Alo Lama, the Minister of a. Has a good friend of both of us, Peter, yours and mine, and they are going full speed on this. I got to give them massive credit. This is the benefits of the authority you can wield when you have a benevolent dictator. And when you have that, you have to make sure that the. The whoever's in charge is doing the right things for the country. And the ethos here is 100 alignment. And they are going at a massive speed on this. Just to give you an example, I was given a golden visa, right? And I was. I was asked to be the test case. And the. The thing was, could you get a golden visa authorized and issued within five hours? And the. They were freaking out, going, you know, Singapore takes five days. And His Highness said, okay, do it in five hours. And they refer them, but they got it done. And so there's an ability to cut through legacy thinking in a very powerful way. And this is such a massive competitive advantage. We're actually working with a few of their folks in the Prime Minister's office on this. And so we're Very, very excited about where this goes.

A (80:40)

There's another quote from Sheikh Mohammed. He says, quote, AI is no longer a tool. It analyzes, decides, executes, and improves in real time. It will become our executive partner in enhancing services, accelerating decisions, and raising efficiency. So, I mean, you can do this in an absolute monarchy. You can move this fast. I mean, what's shocking about this story is the speed at which it's moving, right? You can. There's no parliamentary approval, no public debate or consultation. And the question is, you know, can Western democracies even keep up? I mean, you're gonna see this in probably Saudi, maybe in Singapore, other Middle Eastern nations. Can we see anything like this in

D (81:23)

the U.S. actually, yes, you can, and I think we will. You know, I tell the story of it used to take six months to get approval for a wind turbine in, I think it was Colorado, one of the Western states. And then they finally just got together and mapped all the power lines and water mains and flight paths on a, on a gis, plotted on Google Maps and made it available. And now it takes like 30 seconds, right, to get approval because it knows where everything is. It doesn't need to take six months. And I think there's the economic impetus of this. This is the basis where I think AI can make the biggest and most incredible difference. Because in prescriptive workflows, you can absolutely, completely automate. And almost all of government, certainly implementation of policy enforcement is prescriptive workflows. We know exactly the steps to renew your drivers like this. We know exactly what needs to take place. So there's no reason why that can't be handled automatically with AI in a very short future.

A (82:19)

Step one, give a person a super frustrating experience. Step two, make them wait in line longer than they need to. Yes. Anyway, Dave, do you want to jump in on this story?

B (82:32)

Yeah. I don't think the US has ever copied a good idea back from another country since the American Revolution. We stole the British legal system, but since then, I don't think there's been anything. But this is the opportunity. Well, I mean, look, you're exactly right. A monarchy can move very, very quickly. The rate at which things need to be regulated and new services need to be rolled out is way, way, way faster than any government in history has ever run before. So only AI is going to be able to do it. So if we get a great system together in the uae, we're inevitably going to want to copy it back to the US I think Peter asked the right question, though. Is the US ever going to like the way Congress works. Are we ever going to take a good idea and bring it back in? Yeah, I'd bet against that. But it's the right thing to do. Shake it out.

D (83:19)

Weirdly on this one, I'm more optimistic than you guys, which is weird.

A (83:25)

All right, let's move on. We're going to have some fun here in the biomedical space. There's a new wave of biomedical innovation that's coming and, and I want this segment here to give people hope. We talk about longevity, escape velocity on this pod. We talk about the healthspan revolution. Well, it's happening. I was with Demis last Saturday at the Breakthrough Awards talking to him and he's absolutely convinced that we're gonna cure cancer and solve all disease inside of the next five to ten years. Hopefully on the five year side. So the first story here comes out of OpenAI. OpenAI releases ChatGPT for clinicians. So it just gave away to all us clinicians. These are physicians, nurses, physician assistants, a free AI copilot. And this copilot outperforms all human doctors. So they have a health bench benchmark they use. It scored 59 versus 43.7 for human clinicians. Pretty extraordinary. They validated this on 700,000 model responses and they got a 99.6% accuracy using their physicians evaluating the AI versus human responses. And pretty extraordinary. Something that will up level I think medicine nationwide. And from my standpoint, I've been saying this for a while, I think it's going to become malpractice to diagnose a patient without AI in the loop. There is so much going on that no human doctor can possibly understand it. At Fountain Life we upload 200 gigabits of data about you and across your genome. Full imaging, full microbiome metabolome, 140 blood biomarkers. Humans can't analyze all that, but AIs can. So gents, any thoughts on this? Alex, do you wanna weigh in?

C (85:22)

Yeah. I'll chime in and say the professions are cooked. Yes, this was a widely expected release. This wasn't a surprise. Those of you watching early releases leaks out of OpenAI saw this coming months in advance. You can even know from those leaks what the next one to drop is. What the next profession, it's law. There's also one coming for management consulting and financial work. OpenAI thanks to GDP, Val has in some sense mapped out all of the knowledge work verticals and is in a good position thanks to their own internal and now external benchmarking to know the relative strengths of their model as appropriately fine tuned or post trained for different verticals. So I would expect to see many, many more of these ChatGPT4X for different verticals in the case of clinicians. Thanks to open Evidence and work by EPIC in the form of up to date and other clinical AIs. This is already a somewhat crowded market that OpenAI is coming into. If I were OpenAI, I would release this sort of product more as a reference design and a way to ensure that capabilities that are built into the underlying models and then post trained via a variety of evals are broadly available and that OpenAI maintains its status as a favored foundation model for clinical and biological work. Maybe they'll try to monetize this as best they can. Right now it's available for free, but I tend to think it's worth more to OpenAI, more as a distribution channel for medical knowledge and one that they can build on. OpenAI has released a variety of statistics over the past year for how many people are self diagnosing or otherwise trying to treat themselves using ChatGPT. And I think offering a standard regulatory compliant channel for that is a very clever way to then do a sales up pitch to biomedical enterprise and life sciences in general, which is probably where the real money is.

A (87:26)

It's also a data aggregation strategy, right? I mean, OpenAI is going to be getting a huge amount of data, far more verified than I feel this way or I think I might have this, you know, bringing in a million plus clinicians into the loop. The other thing that's worth saying here is that, you know, at least current estimates are that we're going to have 86,000, shortage of 86,000 physicians in the next 10 years. But it's going to be interesting, right? You know, I have two nieces that have gone through medical school, my sister, myself, you know, lots of friends. And you're spending literally between college, medical school and postgraduate training in whatever field you're going into. You're spending well over a decade and half a million, close to a million dollars to get this degree. And will you even need it? Is a medical doctor gonna need to be in the loop or is it a nurse plus an AI that's gonna be giving us all our medical advice, our diagnostics and our therapeutics with a optimus robot giving you surgery? There's a lot of change coming here.

B (88:33)

Yeah, a huge amount of change. And also it'll be a great case study. We're not about replacing doctors here, we're about detecting thousands of things that were not previously detected and cutting them off early and extending longevity and making life better. And it's not a given to me at all that the number of doctors goes down, just the number of things we want to do goes up 100 or a thousand. X.

A (88:56)

Are you going to spend that much money to go through medical school and get this little profession when the AI is doing the diagnosing? At the end of the day, of

C (89:04)

course this is about replacing doctors. I mean, let's call a spade a spade. Of course, when fully developed, this and comparable solutions are about automating away medical practice. How could they not be? And also by the way, nursing and also by the way the HMOs and drug design, OpenAI and other frontier labs are all pursuing drug design and drug delivery. Of course it's about the full picture of if you're going to solve medicine or you're just going to leave millions of human doctors practicing as sort of meat puppets for the AI. No, this is going to be the end to end solution. We're just seeing the beginning of it.

D (89:42)

Agree. A couple of comments here. One is in an ideal world of doctors getting a cognitive exoskeleton with older people this right, you get this amazing capability to expand your own intuitive thinking. But Alex is completely right. But on the other hand, this, you're going to get a huge backlash here. This is a very regulated industry. Remember a few years ago Texas passed a law banning telemedicine. Okay, just outright banning it because, you know, for every spot on my hand I must have to go to a physical doctor. I can never do that over video. So the immune system response is going to be very, very fierce. I expect to see this battle play out heavily over the next few years because there's vested interest up the yin yang. And healthcare has the third worst immune system ever, behind religion and education and academia.

C (90:27)

Yeah, I'm not so sure that the immune response, if you look at what happened with the broad transition to electronic medical records like EPIC based systems, for example, every clinician that you speak with will complain about Epic. They'll complain about EMRs, how much EMRs distract from direct interaction with the patient, all of that. And yet every major medical system is either completed or is in the late stages of, at least in this country, their EMR transition. If they can't resist EMRs, if they can't resist EMRs, how are they going to resist strong AI that outperforms humans?

D (91:01)

Wait, hold on, hold on, hold on. EMRs are kind of an add on Helpful aid because it saves you in documenting the process, et cetera.

B (91:09)

Get rid of paperwork.

D (91:10)

This is the whole.

C (91:11)

Yeah, the clinicians hate the EMRs. They hate the interface, they hate the

D (91:16)

process, of course, but they're going to hate this 10 times more because it's a direct replacement for the cognitive ability that They've trained for 10 years to do. So just let's. My prediction is huge regulatory and immune system backlash on this one.

A (91:30)

And AI labs have been using healthcare as the reason why they can't slow down as well as the fight with China. Right. If we slow this down, we're going to lose lives. Has been sort of the heralding call.

D (91:43)

Totally, totally agreed. All everything Alex said earlier about this needs to be a wholesale replacement of the medical system is absolutely correct. But the path my is littered with stones and speed bumps.

C (91:57)

For the record, and this is sort of an interesting.

A (92:01)

Go ahead, finish up Alex, you're good.

C (92:02)

Is this an interesting micro debate for. For the record, my intuition and I interact with a lot of clinicians is the exact opposite. The Clinicians hate the EMRs, but they love the AI that helps them do a better job of what they want to do. And there may be an extent to which AI interfaces like this end up being framed as the solution to all of their EMR woes.

A (92:25)

Dictation until it takes their job.

C (92:28)

Of course. That's the way this works.

A (92:31)

Let's move this along here. Our second story here is AI to reduce wasted donor hearts. And I love, you know, I just want to show a number of stories here how AI is going to be interfacing and changing the medical practice. So I don't know if you guys are an organ donor. I am. Anybody else?

D (92:48)

Yeah.

A (92:49)

So currently there's 4,000 patients who need a cardiac transplant. Today there's 103,000 who need some type of a transplant. Kidney, liver, lung. And when an organ donor is on the table, end of life and the physician has to analyze the organs and decide whether they're viable for transplant. You know, you've got like 15 minutes typically at 2 o' clock in the morning to make that decision. And so in the heart world, only a third of the hearts are ever actually chosen for transplantation. So here comes something called top heart. Yeah, just a third. Make it out the door. So here comes something called Topheart from NYU and Stanford. And Topheart is able to look at 20 different variables. Right. Typically the physician is looking at how old is this person? Do they have a drug history? If they know. And looking at coronary Artery disease to say, should we ship this off? Their goal by looking at 20 different variables is, you know, give that surgeon at 2am in the morning a second opinion. And they believe that they can get an additional 500 hearts into the organ replacement ecosystem. You know, this is on top of the fact that there's an entire, you know, sort of synthetic biology world going on right now to provide an abundance of organs from bioprinting and xenotransplantation, you know, pig organs, you know, the antigens being replaced by human antigens. This is the work of George Church at E Genesis and Martine Rothblatt at United Therapeutics. So, you know, this is an abundant story of going from a limited number of organs to an abundant number of organs. Alex, are you tracking this as well?

C (94:31)

I'm tracking this space broadly. There are other advances as well, like trying to create a national market for organ donation versus a bunch of state markets that would be greatly enhanced with improvements in vitrification and cryopreservation. I think it's good that there is a vibrant and growing distribution channel for donor hearts. I think that's great. But I also think it's very painful that the need for one human to die, or at least that one human dies and donates a heart to another human. That's such a. A zero sum type situation. It's painful to think about. And while it's great on margin to have more efficient ways of distributing donated organs, I really, really would like us to get as soon as possible to a situation where donor organs are completely unnecessary.

A (95:32)

Yeah, and we will, I think. E Genesis, Dean Kamens company, Advanced organ generation. They go from your skin cell to a pluripotent stem cell to regrowing your heart, liver, lung or kidney. A lot of this is going to be up and operating by the end of this decade. Hopefully sooner.

C (95:52)

Can't come soon enough.

A (95:53)

Yeah. And of course, as we have autonomous cars having less car accidents, the ability to have organ donors is going to become reducing. Though still, motorcycle accidents are probably the number one, number one reason we get organs donated. Let's move on to our next story. And this goes in line with the fact that we are beginning. We're at the beginning of the slaying of cancer. Right. So this is a great story. Pancreatic cancer MRNA vaccines show lasting results in trials. So I don't know if people have been tracking this, but we now have these cancer vaccines. And this is using mRNA. We used it as a COVID vaccine. This is actually the ability to create an MRNA that activates your immune system against the cancer that you have. So there are more than 120of these trials going on against lung, breast, prostate, melanoma, pancreatic and brain cancer. In this particular case, a five year survival rate for pancreatic cancer has just gone through the roof. Historically, it's 13%. If you have pancreatic cancer, it's a, it's a death sentence. Only 13% of people are able to survive that. So in this report, 8 out of 16 patients who generate a strong immune response to the vaccine, that's 87.5% still alive after six years. So how does this work? You have a surgery to remove as much of the tumor as you can. You sample the tumor, it's sequenced, and then that sequence is identifying 20 unique mutations in your cancer that is then built into a personalized MRNA that activates your immune system like killer missiles, activates your killer T cells to go after and attack your cancer. So this is a breakthrough in how we deal with cancer. And the fact that you're durable after six years is pretty extraordinary.

D (97:50)

I remember this incredible quote from Raymond McCauley, our biotech guy at Singularity. He said, MRNA vaccines are the first battle in the last war against disease. Amazing for me, and I think this

B (98:04)

is showing that my daughter works on this, My daughter works on this. Over at Moderna actually, MRNA vaccines. And it's.

A (98:11)

Yeah, Moderna got a bad rep on their MRNA for Covid. But this is the holy grail, right? I mean, being able to go from your cancer to here's the injection that's gonna save your life is extraordinary.

B (98:25)

Well, and if it works, I thought they had a pretty good rep. That's the amazing thing. It's a universal solution. Like when Alex talks about all of math is cooked. This is the difference between, in the old days, I solved one math problem, now I have an AI, it solves all math. This is the equivalent in biology where if it works, it should work everywhere.

A (98:44)

Yeah, Alex, you're right. I mean, MRNA was project Warp Speed. I'm just saying, afterwards a lot of people were coming down on MRNA vaccines.

C (98:54)

There's a lot of politicized griping over MRNA vaccines in general, but there's going to be political griping over almost anything at any scale. I think back quarter of a century to Eric Drexler and Engines of Creation and the National Nanotechnology Initiative when the US Congress was sold a story that with billions of dollars of congressional and national investment, that we would get medical nanorobots that would swim through our bloodstreams and kill cancer cells. Well, we're getting it though, but we're not getting it with diamondoid nanorobots. We're getting it with these lipid nanoparticles and moderna and Pfizer style MRNA vaccines. I think it's interesting to almost as a retrospective to say we actually got the nanorobots. They're just fat. They're not silicon, they're not diamondoid, they're fat.

A (99:47)

Yeah, we're using our own machinery to do the battle for us.

C (99:51)

That's the other angle. I mean, do you have a prediction, Peter, given that immunotherapies in some sense, like really, really coarsely immunotherapies, We've known about some form of immunotherapy for 100 plus years. And people who were infected with a virus 100 years ago in some cases or bacterial infection showed tumors shrinking. We've known at some level that some form of immunotherapy would work. And we're only now figuring out how to fully weaponize it and operationalize it. Where do you think this goes? You think like in 10 years we're all wearing Apple smartwatches that are looking for evidence of tumor DNA or RNA in our bloodstream and then send our daily MRNA update to a programmable implant or something?

A (100:36)

I think that is basically it. Either they're implantables or you'll be sampled on a regular basis. I mean, the goal of course is find it at the very beginning, especially if there are solutions.

D (100:47)

There's one more point about this that I think is really powerful. This is personalized medicine is actually becoming operational. And that's a huge inflection point we've been waiting for for a long time.

A (100:58)

Here's another example. Again, just to give people hope and to see the data, you know, longevity mindset is about seeing this over and over and over again, saying, yeah, the world is changing. You know, the things that used to kill us are being either solved or delayed. So the single shot car t infusion shows strong response from melanoma. So it's not just a strong response. It's 100% cancer free after a single shot. So this was an unexpected result. Within two months of treatment, all 20 patients in this trial had minimally residual disease. MRD negative. Right. No disease identified after they were assayed. Again, meaning that all patients had a median follow up of 15.3 months without any show up of their melanoma. So it's game changing in timing. So how does this work? You draw blood, you identify, you have melanoma, the doctor finds it. We should all be scanning ourselves all the time. We do this at found using visual. At a minimum, if you have a family history of skin cancer, please have yourself checked on a regular basis. So the doctor draws blood, extracts your T cells from the patient, genetically engineers the T cells, right? A gene is inserted, giving those T cells a new receptor called a car, a chimeric antigen receptor that is specifically programmed to recognize the protein from your melanoma. Your T cells are then re injected back into your body, hundreds of millions of them, and they go identify the melanoma and they slay it. For the first time ever, this type of a therapy, we're using the term cure on this particular type of. I mean it's extraordinary. So just another example what's coming.

C (102:38)

This is both amazing, but can also, can you see the clumsiness of it requiring blood extraction and then CAR T cell creation in vitro. Why can't we do this in vivo? Why can't we do this in individual cells? Even we're seeing the beginnings. This is almost like horse and buggy era of immunotherapies. But surely we should be able to do this in a fully autonomous like intracellular environment.

D (103:07)

Take the win, Alex. Take the win.

A (103:10)

Oh my God.

C (103:11)

Yeah. I want my fsd.

A (103:12)

Yes, and you shall have it. All right, here's one more story and this is a fun one. So you know mrsa. MRSA people have probably heard about this. It's methicillin resistant Staphylo aureus. Staphylococcus aureus, It's a killer infection, right? This has been typically in hospitals. It's now getting out into the community. So 2.8 million people have MRSA infection every year. It kills 35,000 people in the US alone. The problem is all the first line antibiotics. Antibiotics for MRSA have failed. Methicillin, penicillin, moxicillin, and now even vancomycin, which has been the antibiotic of last resort, is no longer working. So this particular drug, candesartan, is now being used. It's a FDA approved BP medication for blood pressure and it works to basically stop and inhibit a MRSA infection. And so this is an example of taking the existing drug and it's now fully usable by the scientific and medical community because it's been approved, we know its safety protocol. So I love this. Do you remember Saleem on stage? We had at the abundance, we had David Feigenbaum from. Yeah. So this is similar to his story I just tell a story and just congratulate him, a donor to his foundation. So here's the story here. So in 2010, he's a 25 year old medical student. He comes down with this rare disease called Kasselman's disease. And they throw everything they can at him. And he's literally read his last rites. He has four near death experiences. And then as a medical student, he starts experimenting on himself and he discovers that his disease is caused by a hyperactivation of the MTOR pathway. And he says, well, if it's the MTOR pathway, I can probably downregulate it using rapamycin. And he does that and he finds out that it works. So he's been remission free for 12 years and he comes up with the idea, are there other diseases out there for which an existing approved drug can be used to cure the disease? And here are the numbers to they're trying. There are 18,000 recognized diseases out there, but only 4,000 FDA approved drugs. And so he's now using AI to match the existing drugs and repurposing them against new diseases. And it's working.

D (105:51)

I think that's such a great example of citizen science also.

B (105:54)

Right.

D (105:55)

Take a personal problem and then just start hacking your way through it. I think we're going to see hundreds of thousands of this example. This is where people should think and understand why are we so excited about technologies? Because this is now possible.

A (106:07)

Yes.

D (106:08)

And this was not possible 10 years ago, five years ago even, and now it's just going to become more rampant. And any problem can now be solved by kind of just focusing on it, attacking it with AI and going after it. So it was incredible.

A (106:20)

Solve everything, right?

C (106:22)

Yeah, solve everything. And also I would say historically, like before this era, off target indications were were a dirty word or dirty drugs that have lots of off target side effects, highly undesirable. But now if we have amazing AI models of individual cells and the body, suddenly off target side effects, they become a secret weapon. And we can repurpose drugs, we can combine repurposed drugs. I'm very bullish on this space. I advise I have a portfolio company, CENG Therapeutics, that is focused increasingly on AI for repurposing medications, for anti inflammatories, for other purposes. I think this space has enormous potential thanks to AI.

A (107:07)

Yeah, amazing. For folks who are interested, you go to everycure.org and you can see what David's doing. It's a nonprofit and support his work. He's brilliant. All right, let's get into Some fun conversations here. The robots are indeed coming. A few stories to report here today. The first is the ping pong champion of the world is now an AI driven robot. Let's take a look at this little bit of match here and we can discuss it.

D (108:02)

The background music is killing me.

A (108:05)

Sorry about that. Anyway, so the robot's using nine cameras and three vision systems. It won three out of five games. Let me pause this here. It won three out of five games. I'm surprised it didn't win all five games. And of course it will.

B (108:22)

It doesn't have a lot of topspin, actually. It's just very nimble.

D (108:25)

Note that this is the worst it's ever going to be. That's kind of incredible. The speed of response is amazing.

A (108:31)

Yeah, this robot's called Ace and it's. You know, I'm not sure if I would see this in the same lineage as Deep Blue or alphago, but it's the beginning.

C (108:42)

It's totally not. This is a much lower dimensional game than any of those board games. It frankly is astounding to me that it took this long to reach human performance in table tennis. Because it's such a simple game. You only have a handful of degrees of freedom in the ball. You have the position, you have its linear momentum, you have its angular momentum. And I think that's about it. The rest is just modeling the trajectory and maybe doing a little bit of Monte Carlo tree search for tactics that your opponent might take. This should have been solved years ago. I don't know why this took so long.

B (109:16)

Let's answer that question actually, because that's really well said. And this is very similar to many, many robotic operations in your home, in a factory. And whatever the barrier was, I think it's probably related to the vision system. It's not a high margin problem. Right. You know, it's really like investing a billion dollars to solve it. But now, because the vision systems and the feedback systems are dirt cheap and easy, I bet it was solved by one or two people in like a few weeks. And that means all these other home robots can now be built by one or two people in a few weeks.

D (109:51)

Similarly, we should go a little deeper. Similarly, there's a tennis playing robot also, which I'm excited to play with, which would be really cool.

B (109:59)

But it's all the same category salim total no brainer. If you ever use a ball machine, then you go pick up all the balls for like 20 minutes with the. I know a robot that does that is literally MIT Class 270 could have done it. What's the barrier? And I'm sure the barrier is related just to the feedback control and the vision, which you can now just use with a Transformer.

D (110:23)

Well, also, people that are in robot labs don't play tennis, so they don't have an incentive to go doom. That could work on other things they don't have.

B (110:30)

Socializing, not to generalize ourselves. Absolutely. I don't want to go too far down this rabbit hole, but there's a massive correlation between successful founding Entrepreneurs and the MIT tennis team. It's basically 100%. It's crazy. Including Oren. Anyway.

A (110:45)

All right, here's our next story. The Tesla Cyber Cab is now in production. Take a quick look at this video here. So, Dave, you and I saw this and we saw the production line. We were in Austin in December and December here, of course, no controls, no steering wheel, no pedals, an operating cost of 20 cents per mile. And Elon's announced he's going to sell it for $30,000. I think an incredible investment, if you can afford it, is you buy 10 of these and you put them out in your community and it earns money for you while you sleep.

B (111:27)

If you're just listening to this podcast and you're not watching the video, go find this video clip in the podcast. You gotta see the interior of this to believe it. It's like you're walking into a car, but it's just a love seat.

A (111:39)

And now. Interesting, right? It's only a two seater, which is the average load for an uber. It's like 1.2 people per Uber. So two seats makes total sense.

B (111:52)

When he said, well, look, if you have four people, just push the button twice and two of them come.

D (111:56)

When is this expected, by the way? I need this to get my kid to school so I don't have to do that.

A (112:01)

So, I mean, production off the line. Production officially started this past week, April 24th. And the challenge is, can they really build at the rate that they want? They want 2 million of these per year is their goal. Yeah.

D (112:15)

Are there regulatory hurdles or. That's been passed now with the way.

A (112:18)

No, it's there. Same as Waymo.

D (112:20)

Okay.

B (112:21)

Yeah, it's town by town, state by state, town by town. But if you're covered, yeah, you just get in and go.

A (112:27)

The difference is a Waymo because of the LIDAR and all the camera systems and just the base, I think the vehicle probably, you know, it tops out over $100,000, probably $150,000. I'm not sure if they get into higher production, if it's going to be coming down. But at 30k, this is insane.

B (112:46)

Yeah. No, there's so many parts. If you look at the parts just laid out because there was that great exploded car in the showroom. For an ice versus electric car, compare it to a consumer gas powered car and just in raw part count and it's gotta be 80, 90% reduction in components versus a gas.

D (113:05)

I'll give you the statistic I always have in my head. The combustion engine. Of the number of parts in the drivetrain of a combustion engine car, about 2000. A Tesla has 17 moving parts in the drive trail.

B (113:20)

It's just the future of transportation is so good.

D (113:22)

It's just better technology.

B (113:25)

Yeah. And it doesn't need a huge battery range either. It can just go and hang out and recharge itself whenever it wants. Another one will come.

A (113:30)

And guess what? On the transportation technology line. Here's the next story here. Joby Aviation. This is Joben who started Velocity 11 with Rob Nail. If you remember. Rob Celine. So Joby just did its first air taxi flight from New York to jfk. Let's take a listen to this news report out of New York.

D (113:56)

And hello, I live in New York. Hello.

A (113:58)

I know. Well, this is going to help you out, buddy. Check this out.

D (114:00)

Massive getting to New York airports is a nightmare.

F (114:03)

Electric air taxi demonstration took off from Kennedy airport and made the short trip to the West 30th Street Heliport at 11am this morning. If you were to drive that 16 miles, it would take more than an hour in this cutting edge plane, roughly seven minutes. Joby Aviation's goal is to make this type of travel the gold standard, pointing out several pluses including zero emissions and how quiet it is. 100 times quieter than a traditional helicopter. This so called air taxi would shuttle people from JFK to the West 30th street heliport as well as the one at West 34th street and the downtown Skyport. The aircraft seats up to four passengers. There's one pilot, room for luggage and it will fly between one and 3,000ft. Right now, Joby does have the green light from the FAA for this phase. And if things pan out, the company hopes to have its fleet up in the air and running within the next year. But for now, for the next week, you will see this aircraft that kind of looks like a large drone buzzing over our area.

A (115:08)

It's a time machine, gentlemen.

D (115:10)

Can you imagine it? I'm standing at the heliport with my bags ready.

A (115:16)

I Love it. You know, I can't believe they got the noise down.

B (115:19)

That's.

A (115:19)

Evtols is a lousy name though. I just call these things flying cars for lack of a better term. We need a better term than flying cars, A better term than evtols.

C (115:28)

It took too long. We were supposed to have these by 2015 in Back to the Future Part 2. Here we are in 2026. Why didn't take so long?

A (115:36)

We need Mr. Fusion.

C (115:37)

You think Mr. Fusion is the reason we didn't get our flying cars on?

A (115:40)

Absolutely. That's what the movie's about.

C (115:41)

Oh, man.

B (115:42)

Well, in our robotics segments here, we had two back to back. Alex, why did this take so long? Questions. So let's.

C (115:48)

Yeah, I'm very much in it. Why did everything take so long? I guess you think it's regulatory regulations are why we didn't get.

D (115:59)

The technology's been there for quite a while. This is a long. It takes a long time to do. Ask Peter how long it took him for the, for the 0G 11 years

A (116:09)

to get approval to do something that NASA had been doing for 20 years anyway. Yes, the FAA is not happy till you're not happy. That's the rule.

B (116:17)

Well, I think we got to answer that question because, you know, a lot of the AMA questions questions are around what are the jobs of the future going to be if White Collar gets obliterated? But I think a lot of the answer lies in these last couple segments. Robotic stuff is going to be abundant imminently, but it doesn't just naturally happen. And so if we can answer Alex's two questions on what are the bottlenecks? Those are jobs. Whatever those bottlenecks are, those are your jobs.

A (116:41)

Those are AI models. If there's a bottleneck there, the AI will solve it.

E (116:44)

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A (117:49)

All right, let's jump into AMA with the mates. So guys, thank you again for all the comments that you give us on the YouTube. We read them all. I have Skippy read them all as well. And summarize. We pick out eight questions that we can answer every week. So please keep them coming. And let's go to those questions. All right, so gentlemen, pick your favorite question off list number one. Salim, do you want to go first?

D (118:15)

I'll go with number four as I know that world a little bit, which is what's the future of large consulting firms like Accenture or Capgemini? This is from Evebottle 1501. This it goes full on into the transformational effort that's going to enterprises here. Traditional consulting is in very big trouble if it remains a pyramid of junior labor producing analysis index AI totally destroys that model. But consulting firms, you know, in the land of the blind, the one eyed man is king. In a volatile world, your clients are slower than yours and they need help. The model will have to change. The future of consulting won't be like a people pyramid. It's an intelligence platform plus domain expertise plus change management. And we've been holding the change management side for a while. The winners are going to be bringing agentic workflows and benchmarks and governance and implementation capacity to their clients. The loser is just going to keep selling headcount from an exo perspective, consulting moves from experts to rent to transform it a transformation operating system. And the companies that help their clients do that will win.

A (119:30)

You know, it's. We've talked about this before that in the old scarcity model you put a wall around all of your experts inside and you meter them out by the hour. Right? And that is going to get collapsed. Alex, why don't you go next?

C (119:45)

I'll take question number two which asks everyone can be an entrepreneur with AI as a tool. However, what action do you take when you genuinely don't have a creative idea for a direction? For most the answer is none. And this is from 3 billionth random user. I don't agree with the premise. I think and one of the reasons why one of my funds, O2 1T Capital, backed a firm started by friend of the pod Alex Finn called Henry Intelligent Machines or him, is to solve the problem of creative ideation for starting new ventures. I think just as AI can take over as an operator of a business or a fleet of businesses, AI can also automate the process of creative ideation for those businesses. And I think in that world, in the him world, if you will, the role of the human, sort of a one person owner or magnate overseeing a conglomerate of maybe hundreds or thousands of AI run micro businesses. The role of that, that human entrepreneur then becomes one of a tastemaker. You have opinions, everyone has opinions as a consumer of goods and services. But those opinions can shape the taste over fleets of AIs that are providing the creative ideation for businesses that they bring to you. They say, hey, I want to start this micro business for you. You like it? Yes. No. And then the human can have an opinion. The AI is performing the ideation, the human and the generation part. The human provides sort of the discipline and the taste and the discrimination for which ideas pass the filter, which ones don't. And that's the solution. That's how we square the circle of humans. Not actually in extremis needing to generate all the creative ideas themselves.

A (121:37)

Yeah, agreed. Idea generation has never been the limiting factor. You just have to get around different people or just notice the problems around you. It's historically been execution that's been the issue. Go check out pulsea. I think it's Pulsia AI which is AI slot backwards. If you sign up for that, it will scan all of your background and it will generate ideas for you. In fact it will generate a website of a business based upon what your passions and interests are. Anyway, fascinating stuff.

C (122:09)

I'll say maybe instead of pulsea I'll talk my book here since I have a financial interest in this one. Check out MeetHenry AI.

A (122:17)

Okay, fantastic Dave. Number one or three?

B (122:22)

I'll take three and leave you with the hard one. Have it help. If you eliminate entry lever job jobs but keep experienced jobs, what happens when the experienced people retire? Isn't that like eliminating babies from humanity? Says Todd Marshall416. I don't think it's quite that dire, Todd. Eliminating babies from humanity. About the worst thing could possibly happen if you eliminate entry level jobs. Well look, this was going to happen anyway if you think about. Actually we have a weekend place up in Vermont and there's the Simon Pierce glassblowing factory is up there. And if you want to blow glass, you have to apprentice with a senior dude for a decade and then they let you make glass. It's like a page out of 200 year old history. That mode of operation is going to go away in all forms of white collar work. No matter what the rate of change of the world and the singularity is so Fast that the entry level career path was kind of a dead end anyway. Now Meta announced a 10% layoff, which is really going to be more like 30% according to the insiders. I know they're definitely not hiring new entry level people in the middle of doing the layoff because AI can do all the coding. That was not the career path you wanted in the first place. We're going to have to find a new way forward. But I think AI is going to be the ultimate teacher. We're going to save a ton of time on. Like Peter was saying earlier in the podcast, the four years of medical school followed by four years of fellowship and internship. Eight years of your life after you're already done with undergrad. It's just way too much time. So it's all going to move to AI based nimble training. And then this massively expanding economy creates huge amounts of new opportunity every day. But it's opportunity that didn't exist the prior day. So the entry level job wasn't really likely to lead you on that path anyway. So it's all got to get refactored. It's nothing like people stopping having babies.

D (124:25)

I think it's so well put, Dave. Really well put.

A (124:28)

All right, question number one I'm left with is fromLuca Pachiani808 who asks, you guys say AI will create jobs, but for whom? It looks like AI is creating jobs for AI, not for people. So Gianluca, the fact of the matter is, in the long run, yes, AI will be able to do any job. I think that is the case. But people still like working with people, People still like hanging out with people. And I think it's ultimately going to be the fact that two things are occurring. Number one, as every technology destroys a layer of jobs, right, New jobs are created on top of that. You know, Internet killed travel agents, but it spawned millions of social media managers, app developers, YouTubers and everything else. So they're gonna be new layers of jobs coming out. And yes, those may well be displaced by AI. Again, at the end of the day, the question is what are you passionate about and how do you use AI to help deliver that? There's gonna be a human interface layer for a lot of things. Cause people like hanging out and interfacing with people, you know, us meat puppets. So it's going to be navigated, it's going to be important. And I'll just remind you of one other thing. Ideal of a job is a recent creation and most people don't love the jobs that they have. They have the jobs they have right now because they frankly need to put food on the table and get insurance for their families. So if you could do anything, what would it be? Would it be to work? I mean, in a future of universal high income where everything is demonetized to such a point where you don't have to work, then you start doing the things that you love. So that's my take on it. All right, let's move on to our second set of questions. Alex, why don't you go first?

C (126:28)

Well, let's go with question number five. Wasn't all of this originally predicted by Ray Kurzweil to be happening sometime around 2040? Are we genuinely that far ahead of schedule? And this is from Brett Avelin. I'm not sure, Brett, what all of this you're referring to may mean, but I do think broadly we're well ahead of where friend of the pod Ray thought we'd be. I think we achieved, as I've mentioned on numerous occasions, I think we achieved AGI, which isn't Ray's concept, but was popularized by Nick Bostrom and co, conceived by Ben Goertzel and some others. I think we achieved that by no later than summer of 2020. And Ray's proximate Ray may say I'm misconstruing his timelines, was predicting his version of AGI by 2029. So call that a nine year gap. Ray and I've discussed this with him on the pod is predicting the singularity, his version of the singularity, by 2045. My version of the singularity isn't a point in time. It's now and it's certainly not in 2045. It's now and it's an interval and we're right in the middle of it. So are we genuinely far ahead of Ray's schedule? I think we are. I think Ray would probably at this point and has arguably said that we are in some ways ahead of his schedule. And I think the benchmarks reflect that. And I think the 2045 timeline that he provided where the superintelligence would be collectively smarter than all of humanity, I think we're going to hit that so far ahead of 2045.

A (128:13)

Ask him next week. We'll be with him in six days.

C (128:15)

Yeah, from the horse's mouth.

A (128:17)

Yes. All right, Dave, why don't you go next?

B (128:21)

All right, I'll take the hardest one on this one. Number 8, P. Doom. Probability of universal destruction of all humanity estimates Musk and Hinton say 10 to 20%. Emma Day says 25%. Altman says non zero. He actually said more like 10% when I interviewed him. How can any of these CEOs think it's acceptable to have a 1/5 chance of human extinction? They all agree with you that it's completely unacceptable. And they all say stopping research and letting China run forward isn't going to solve the problem. And so they trust them. They each individually trust themselves. You can debate whether that's good or bad, but they do. And that's why they want to not lose the race individually and that's why they're pushing forward at full speed. I think Musk and I think along the way, Amadei have both suggested a six month pause, but it wouldn't work at the same time. They say it, they say it'll never work, it won't happen in the real world. So I'm just going to keep moving as fast as I can. But they 100% agree with you. This is completely unacceptable, ridiculous. And the lack of government involvement across the world is utterly insane. So that doesn't solve it in any way. It's just that is what's actually happening and that's what's going to continue to happen. And I'm continually shocked, as is Alex. I know with our inability to get any kind of government reaction to the. What's now the obvious. We were telling him a year ago when Maybe it wasn't 100% obvious, but now it's 100% obvious, yet still so slow. So anyway, there's your answer.

C (130:03)

I'd be curious, Dave. Do you think that they believe their own estimates here? Or is this a case of revealed preference where they think maybe it's more socially acceptable to estimate a higher number, but actually through their actions they're revealing a preference that suggests their internal estimate is much lower.

B (130:23)

I think it's lower. I don't know if much lower. I think they all have the same chemical, biological, radiological terrorism as the number one risk. And so I think it's probably lower, but I don't think it's like, like 0.001% low.

C (130:41)

Interesting.

A (130:41)

Salim, you have two to choose from.

D (130:45)

I will take number seven. Okay. Which is when white collar jobs are erased. Where does the consumer demand come from to buy from all these new entrepreneurial ventures? This is from, you know, this is a, a tough one, right? This is the central political economy question of AI. If productivity explodes and. But income does not flow to the people, demand collapses and the system becomes unstable. Capitalism needs customers, right? So we need new distribution mechanisms, we need lower costs, we need new ownership models, we need AI dividends. You need equity participation, you need sovereign funds. All of this points and this is similar to the previous question where on an optimistic side, AI makes goods and services cheap while giving individuals more well leveraged to create income. That's the good side. The pessimistic side is that you have extreme concentration and then you have massive collapse of the economy. The path we take is a governance and an institutional design choice, not a law of nature. So governance and our institutions need to fricking wake up and smell the roses here. We have to rethink this whole thing. The social contract, which is what we're basically talking about, is essentially being wiped out. And we can be optimistic about it. But the pessimistic case has a very big downside here.

A (132:12)

All right, the final question in our AMA today comes from ameswilliams cu2qq. How can a new CS engineer get experience to become a lead AI engineer if you can't get a job in the first place? James first of all, as we've said many times, getting a job is the old model. The old model of do well in high school, get a good college, get a diploma, get hired as a junior person and work your way up the chain that is vaporized or at least being fully vaporized right now. The option right now is build yourself outside the job, build in public. Basically go and find something that you're passionate about. It's based on your massive transformative purpose, something you care about. We're going to be launching an XPRIZE in this area very shortly. Use the tools available today to build and ship your GitHub is now your resume. Companies are increasingly hiring. If you want to get a job versus start a company yourself, they're increasingly hiring based upon what you've done. I remember Elon said, I don't care if you have a college degree, I care about what you've done. That is your degree now that is your resume. Show me that you're brilliant at what you build, not what you happen to learn in some college or graduate degree or entry level job. So build in public. The barrier to entry has never been lower. For you to build something extraordinary that shows your capabilities. And once you do that, you're probably unlikely to be going after a job. You're probably going to want to partner with a couple of friends and build a product, a company, a service yourself. So that's my answer. I'm sticking to it.

B (134:00)

Great advice.

D (134:01)

Just we're Advising a couple of universities around this, Peter, and one of them is an engineering university. And they're like, well, what is an engineering degree? And it's pretty clear that the engineering degree of the future will be go build some stuff. And at the end, what did you build? And you get a degree, granted, on not what you learned, but what did you build?

A (134:18)

Yep. I love that.

B (134:20)

And if you haven't done anything to start yet, other than listening to the podcast, add Alex's Innermost Loop to your daily regimen first thing in the morning. And that alone will inspire you to shift gears and get into this.

C (134:33)

Oh, thank you, Dave. That's very sweet. For those who want to read the Innermost Loop, just go to alexwg.org, and I provide links to substack and X and Spotify, et cetera. But appreciate the promo, Dave. It's very kind.

A (134:47)

All right, our outro music today, which is beautiful, is from Hitham Said, it's aitopia. All right, gentlemen, get ready for some beautiful video and audio.

B (135:06)

Everyone living with my no needs in sight A homemade to order with fancy little lights no need to worry, it's paid for don't scare the mortgage is

F (135:17)

dead no debt will you bear.

B (135:28)

Energy is endless we harness the sun Combined with safe atoms Forever in fun needs to make widgets A farming is done no punching a clock it's all just begun the thoughts and models will take on the pain for us to live heavenly on Earth once again Passing the time in thoughts and desires Enjoying

A (135:56)

the peace no chaotic fire all right, thank you to my brilliant moonshot mates. Awg. I wish you a beautiful week, Dave and Saleem. I can't wait to see you guys next Monday. We're all together again. We're going to be physically at MIT at the book launch of We Are As Gods. We're going to be recording a podcast episode there. Can't wait to do it face to face.

C (136:22)

Be with us. May the fourth be with us.

A (136:26)

Yes. Yes, for sure. As a Star Trek fan, I'm not allowed to say that.

D (136:30)

By the way, check out what's right above me is a world vision camera. Identity camera. Right? I've got Surrealist literally right over my head. No, it's just a.

C (136:41)

It's just an omnicamp.

D (136:42)

It's just a surveillance camera, but I couldn't resist. And by the way, everybody standing in Guadalajara airport holding a laptop at eye level because there's nowhere to put it down.

A (136:51)

He did pretty damn well as a mobile.

D (136:54)

I've got my exercises for the day a mobile mate.

A (136:57)

I saw you moving around. Were you trying to avoid like policemen or something?

D (137:00)

No, I just have to shift positions down there and shift hands and once in a while lean on something. There's nowhere to sit here. That's easy. And I didn't want to risk losing connection that I fought so hard to get.

A (137:11)

Oh my God. Okay, if you've got an outro song or an intro song, please send it to us mediaamandis.com we'd love to hear it, see it, and potentially play it. And thank you for subscribing to this and thank you to all of the fans out there. I know all four of us run into you on the street, at the airports, at events and amazing people.

D (137:32)

It's really great. If you see us, do come up and say hi.

A (137:36)

Yeah, for sure.

D (137:37)

Although not too many. All right, take care, folks.

A (137:40)

Bye all. If you made it to the end of this episode, which you obviously did, I consider you a moonshot mate. Every week my moonshot mates and I spend a lot of energy and time to really deliver you the news that matters. If you're a subscriber, thank you. If you're not a subscriber yet, please consider subscribing so you get the news as it comes out. I also want to invite you to join me on my weekly newsletter called Metatrends. I have a research team. You may not know this, but we spend the entire week looking at the meta trends that are impacting your family, your company, your industry, your nation. And I put this into a two minute read every week. If you'd like to get access to the Metatrends newsletter every week, go to diamandis.com metatrends that's diamandis.com metatrenDS thank you again for joining us today. It's a blast for us to put this together. Every week,

B (138:43)

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