← Moonshots with Peter Diamandis
Moonshots with Peter Diamandis
GPT 5.2 Release, Corporate Collapse in 2026, and $1.1M Job Loss w/ Alexander Wissner-Gross, Salim Ismail & Dave Blundin | EP #215
02:03:06
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- [00:00]
Peter Diamandis
OpenAI releases GPT 5.2.
- [00:03]
Dave
The capabilities are just shockingly different than they were a few weeks prior. OpenAI has just unveiled GPT 5.2, which it's billing as its most advanced frontier model yet.
- [00:13]
Alex
The value that we see people getting.
- [00:16]
Dave
From this technology, and thus their willingness.
- [00:18]
Alex
To pay, makes us confident that we will be able to significantly ramp revenue.
- [00:21]
Salim
The fastest scaling consumer platform in history. We're almost at a billion users. That just blows my mind.
- [00:27]
Peter Diamandis
A lot of change is coming rapidly. I think the biggest challenge is people are not projecting properly on how rapidly this is going to tip.
- [00:36]
Salim
I think 2026 is going to see the biggest collapse of the corporate world in the history of business.
- [00:42]
Peter Diamandis
In 2025, we had 1.1 million layoffs, which is the most since the 2020 pandemic.
- [00:48]
Alex
71% of comparisons between a human performing this knowledge work and the machine resulted in the machine doing a better job at more than 11 times the speed of the human and at less than 1% of the cost of the human professional. So knowledge work is cooked.
- [01:08]
Dave
Now that's a moonshot, ladies and gentlemen.
- [01:14]
Peter Diamandis
Speaking of alien creatures, I was touring with Colossal yesterday. Ben Lamb, I'm an advisor, early investor in this company in Colossal is amazing. They've got something like 12 different species at different stages of de extinction. Right. They brought back the dire wolf. They're going to be bringing back the saber tooth tiger. I can't wait for that. And of course, the woolly mammoth. They created the woolly mouse. Right. So they've been able to identify the genes that in particular are different or different phenotypes. Right. Like length of hair, length of snout. And it's fascinating what they're doing and their ability to actually find the closest living relative. And then snippets of DNA. So they have DNA going back as far as 1.2 million years. They haven't been able to get DNA older than that, but that's still pretty incredible. But being able to actually like. Yeah.
- [02:17]
Salim
Didn't Ben say that we couldn't restore animals if the DNA was older than like 10,000 years?
- [02:24]
Peter Diamandis
Well, for example, the woolly mammoth DNA that they've gotten ranges from like 10,000 years to 1.2 million years. Right. And okay. And they've got to identify that's not a single species, that's a whole spectrum of a species because there's evolution going on all that time. And so they're trying to figure out, okay, what part of the phenotypes, like the tusk and the woolly Mammoth hair and its cold tolerance and all of those things. And they're reconstructing a single lack of, you know, an approximation of woolly mammoth. Anyway, the programs are amazing and Ben is such an incredibly good CEO. I'm excited. He's going to be one of our Moonshot closing speakers at the Abundance Summit this year. So we're going to go deep with how do you, how do you go from zero to $10 billion valuation in four years and how do you do something? And no bio background at all for Ben. Right. He was the CEO of Hypergiant, the software company. Incredible.
- [03:22]
Dave
So Salim, your multi armed robot can shear the woolly mouse and then we can make sweaters in time for the holidays out of it. We can all wear them on the pod by non humanoid robots.
- [03:34]
Peter Diamandis
All right, all right. I think it's time to jump in with enthusiasm. Yes. All right. Welcome to Moonshots. Another episode of WTF just happened in tech. This is the news that hopefully impacts you, inspires you, gives you moonshot thoughts and gets you ready for the future, because that is one of our primary goals. How do we prepare you for what's coming next? A lot of AI news today is a special episode that we pulled together in order to celebrate the release of GPT 5.2. But we'll get to that in just a moment. I wanted to hit on some of the top, sort of like top level hyperscaler updates and battles. So just a few headlines here. We'll be discussing them through the pod here today. ChatGPT was the most downloaded app in the iOS app store in 2025. Congratulations to them. They're nearing 900 million active users. Gemini is catching up. Anthropic jumps to 40% enterprise share. Amazing. Accenture is going to be training 30,000 people on Claude. Elon has let us know that Grok 4.2 is coming very shortly in the next few weeks and Grok 5 in the next few months. We said in a moment ago, OpenAI has released GPT 5.2. That's going to be coming up in a moment. And interestingly enough, Google launched its deepest AI research agent the same day that OpenAI dropped GPT 5.2. A little bit of PR battles going on between them all. All right, one other piece of data on the downloads here to give people a look at the scoreboard. GPT ChatGPT received 902 million downloads. Gemini is at 103.7 million downloads, and Claude has received 50 million downloads. Any comments on on these opening headlines before we jump into GPT 5.2.
- [05:47]
Dave
Well, I'm in shock this week at the capabilities. We'll look at the benchmarks in a minute, but the benchmark marks really undersell the last two weeks. The, the capabilities are just shockingly different than they were a few weeks prior. And we'll get into it. But also the big, big change is the race is on. You know, when, you know, GPT5 kind of disappointed everybody. The poly market on, on Google running away with the rest of this year went to like 90, 95% now, kind of as Alex predicted, it's a closer horse race. Google's still on top of the stack, but apparently Sam had something in the tank. Who knew? So we'll get into that too. But I'm just absolutely no exaggeration. The things that I got done in the last week that I couldn't have done three weeks prior, just coding and building things, I'm in shock.
- [06:46]
Peter Diamandis
So are they pulling their punches? We discussed that in the past, right, where they're releasing this much, they know that, you know, we're going to have GROK coming out next, so let's then release the next segment to compete directly.
- [07:00]
Dave
There they are totally pulling their punches. They've absolutely been holding back, I think because they're starved of compute and they're afraid to roll out addictive capabilities that they just can't deliver on. But, you know, Alex experienced this too. Like yesterday we were, you know, going crazy with 5.2, trying to see what it can do. And then it's like, sorry, you're done for today. We're out of compute. Sold out, no gas in the tank. And so the competitive pressure is forcing them to code Red, come out with things when they normally would want to hold back and wait until they can find the data center compute and wait until Chase Lockemiller finishes Abilene. But they just don't have that choice with the competitive pressure on each other.
- [07:40]
Alex
Yeah, maybe just to comment, I think they were. At this point, if you're OpenAI and you have your purported code Red and you're in a hurry, you're in a bind. 5.1 GPT. 5.1 came out only a month ago. And you need to rush something to market to put at ease perceived competitive pressures. I think there are only approximately three levers you have. So one lever to Dave's point is compute. You can increase the total amount of compute allocated to given models. And that, of course, comes at a cost. It comes at the cost of compute scarcities. It comes at the Cost of longer response times to prompts. Second lever that you have is safety. So you can turn down the safety, you can make models more sycophantic, and that's a way to improve.
- [08:34]
Peter Diamandis
Can we get a benchmark? Sycophantic models.
- [08:38]
Alex
There are a bunch of words for.
- [08:40]
Dave
Compromising your ideals to win the market in general.
- [08:43]
Alex
Yeah, right. So call it. The safety knob is the second knob that you can turn if you're in a pinch. The third knob that you can turn is the post training knob, which can be done on relatively short notice. So you can pick particular benchmarks that you want to really post, train your models to do really well on. And I suspect all three of these more compute, maybe, maybe not. Some turns of the safety knob and then post training on select benchmarks is exactly what we're seeing in this cycle. Now that we have a real horse.
- [09:15]
Salim
Race, I found it fascinating. We've got probably the most, the fastest scaling consumer platform in history. We're almost at a billion users. That just blows my mind.
- [09:27]
Alex
It's starting to eat the operating system. I mean, when you start to get order of magnitude a billion downloads, at some point you have to ask the question, is this AI user interface basically cannibalizing the entire OS itself? At what point, sometime soon, is every pixel that shows up on a mobile device being AI generated? I think we're not too far from that.
- [09:48]
Dave
Wow. Well, that was definitely the backstory too, when we were at Microsoft last week with Mustafa Solomon. Is that podcast out yet? I'm not sure. What coming out shortly. Well, look forward to that one because what Alex just said is clearly in the minds of Microsoft. They're going to do everything and anything they can to get on this chart that we're showing right now. And they have a lot of assets that'll come up in that pod that'll give them a really good chance of getting there. But it's for exactly the reason Alex said. The os, the whole base of Microsoft, the revenue driver for the last 30 years, is at risk now. And you got to move to the new thing.
- [10:26]
Peter Diamandis
It's not just os, right? It's the entire app ecosystem. I mean, the end goal here is for these hyperscalers to capture the user as the only AI you need to use.
- [10:39]
Alex
So called core subscription. And that certainly is OpenAI's stated strategy to become the default core subscription for consumers. Anthropic's strategy apparently is to focus on enterprise APIs and Cogen Xai focusing on brute force scaling and maybe benchmaxing and Google focusing maybe in a more balanced way on total stack domination, balanced pre training, post training. So I think in a real horse race, which is what we're finding ourselves in among the top four frontier labs, we're starting to see differentiated strategies coming to market.
- [11:12]
Peter Diamandis
Every week, my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI and quantum computing to transport, energy, longevity and more. There's no fluff, only the most important stuff that matters that impacts our lives, our companies and our careers. If you want me to share these meta trends with you, I write a newsletter twice a week. Sending it out is a short 2 minute read via email. And if you want to discover the most important meta trends ten years before anyone else, this report's for you. Readers include founders and CEOs from the world's most disruptive companies and entrepreneurs building the world's most disruptive tech. It's not for you if you don't want to be informed about what's coming, why it matters, and how you can benefit from it. To subscribe for free, go to dmandis.com metatrends to gain access to the trends 10 years before anyone else. All right, now back to this episode. All right, let's jump into the core story here. Today, OpenAI releases GPT 5.2. We spun up this pod for our subscribers the day after the release so we can go into detail. What does this mean? You know, we heard OpenAI's red alert and here's the result. Alex, take it away.
- [12:25]
Dave
Yeah, I've been waiting for this all day. Alex.
- [12:29]
Peter Diamandis
Dave, you want to lead us or Alex here?
- [12:31]
Dave
Oh, no, I just want to say that these numbers, numbers when they go from 80 to 90, it really understates the impact on what you can do. You know, the, the benchmarking. When it goes from 10 to 40, it looks like a big gain on a line chart, but when it goes from 80 to 90, it doesn't look like a big gain. But what you can do, like firsthand is just mind blowingly different. And I'll, I'll tell you some of the things I've done in a minute, but I've been waiting all day to hear actually, Alex, and just for those.
- [12:58]
Peter Diamandis
Who are listening versus versus watching, here's a chart of the benchmarks comparing GPT 5.1 thinking against GPT 5.2 thinking. And with that, if you don't mind sort of speaking the percentages as well. Alex, as we're going through this. That would be great.
- [13:16]
Alex
Okay, sure. So maybe some high level comments and then we can do a detailed play by play. So high level comments. 1. Keep in mind what I said a couple of minutes ago. If you're OpenAI and you need to rush an impressive model release to market this, there are probably only three knobs. You have one you can turn up the compute, two you can play safety games and three you can do post training on particular evals, particular benchmarks. So that story, maybe not the safety story, but the other two knobs I suspect is what we're seeing here. So walking through this chart, benchmark by benchmark, we have SWE Bench Pro which is software engineering benchmark. We see a modest improvement between 5.2 and 5.1, perhaps attributable to mostly compute and a little bit of more post training and or distillation. We have Google Proof Question answering Diamond modest increase from 88.1% with GPT 5.1 to 92.4. Again so far pretty modest. We have Charkhive Reasoning a larger increase this is scientific reasoning could be post training, not a benchmark that I pay super close attention to. Then we get to Frontier Math frontier math tiers 1 through 3 which are easier math problems and then one of my favorite benchmarks of all time, frontier math tier 4, which is research grade problems in math that are supposed to take professional mathematicians several weeks to accomplish. I often point to frontier math tier 4 and progress on frontier math tier 4 as indicative that Drink math is being solved. So focusing on frontier math tier 4 we see Gemini 3 Pro getting approximately 19% and GPT 5.2 thinking getting 14.6% and GPT 5.1 thinking getting 12.5%. So this is actually a win. In my mind this is a win for Google and a loss for OpenAI that OpenAI has had a month to attempt to super scale to beat Google in this horse race at rather hard closed math challenges. But professional Mathematician grade nonetheless couldn't beat Gemini 3 Pro. And it's not as if these problems have been a state secret. In fact, OpenAI actually sponsored Epic's creation of the Frontier Math benchmark. So OpenAI has had in some sense privileged access to all of Frontier Math still couldn't be gemiini. So I think that's pretty instructive. Moving down the list, Amy, the American invitational math exam 2025 scoring now 100% 5.2 versus 94% suggestive of post training. Then we get to the second set of benchmarks that I think are Super Interesting. ARC AGI 1 and 2 ARC being autonomous research challenge and of course AGI being AGI. So for those who don't pay super close attention to the ARC AGI ARC AGI sort of a visual reasoning challenge, testing whether problems that humans find relatively easy, sort of a visual problem solving program synthesis challenge. But machines historically have found exceptionally, exceptionally difficult as sort of an arbitrage between human minds and machine minds. We see here some big, big differences. So for Arcagi 1, the first version of the prize we see that's just saturating at this point. 72.8% with GPT 5.1, 86.2% with GPT 5.2 arc AGI1 is cooked at this point. Arc AGI2 is nearing the point of saturation. So huge change from 17.6% with GPT 5.1 to 50 plus percent 52.9% with GPT 5.2 thinking so in my mind this smacks of post training. That's the obvious strategy.
- [17:28]
Peter Diamandis
Take a moment and just for those who don't know what post training is, because I think it's an important one of the three knobs that you spoke about and it's important for folks to understand what that means.
- [17:37]
Alex
Sure. So let's reason by analogy to the way humans in sort of a conventional western upbringing learn. So you have the baby, the infant like learning. That's approximately pre training. So the P in GPT stands for pre trained. Pre training is unsupervised training. You're feeding a model just information about the world and giving it the goal of predicting what comes next. There's not much of a supervision angle to it. Not unlike a human newborn where it's just taking in information via lots of sensory feeds and trying to make sense with very little guidance. Then there's mid training and post training. So think of these phases of training as being not unlike attending primary school, secondary school where you receive explicit supervision, you're receiving grading, you're being given particular assignments and there are many ways that you could be graded. You could be graded very granularly like a thumbs up, thumbs down grade A, B, C, D, F. And there are other ways that you can grade. For example, you can be given more of an open ended assignment and graded on how well the ultimate final product of that open assignment is. So this sort of mid training post training which really became popular with the O class series of reasoning models from OpenAI and everyone has since adopted reasoning models and post training not just to make humans happy, which is another form of post training, like pleasing your teacher, but also showing that you can, via reinforcement, learning via other mechanisms, solve hard problems and reason about hard problems. This is where post training shines. This is where almost all of the alpha, if you will, in increasing model capabilities over the past year or so has come from, not from pre training. So getting back to the benchmarks. Arc AGI 1, Arc AGI 2 these are benchmarks. The R in Arc AGI is reasoning. These are benchmarks designed to test the reasoning capabilities of models. And we see a huge jump. We see frontier level performance, state of the ART performance by GPT 5.2. With ARC AGI 2, reasoning is well on its way to having been solved at this point. And I think we'll cover this probably in the next slide, but the costs are collapsing as well. Maybe talk about that in a minute. Just to wrap up then, for purposes of narrating this chart, the final benchmark here, which is perhaps the most interesting of all, is GDP VAL. So GDP VAL Gross Domestic Product Eval was created by OpenAI with the idea of having an eval that measures AI ability to automate knowledge work in the general human service economy. So we're seeing a jump from GPT 5.1 at 38.8%. GPT 5.2 is now at 70.9%. This is the clearest indicator in my mind that the human knowledge work economy is cooked. You heard it here, it's cooked. This is 44 different occupations that OpenAI. And by the way, this is all open source. You can go on GitHub and you can read all of the tasks for GDPVAL. Now, 44 different human occupations, 1320 specialized tasks like creating PowerPoint presentations or Excel spreadsheets, sort of prototypical knowledge work. It's cooked, it's automated. And 5.2, probably again due to elaborate post training, can get almost 71% of these tasks. What that actually means 71% of comparisons between a human performing this knowledge work and the machine 5.2 Performing knowledge work resulted in the machine doing a better job. And that was, by the way, at more than 11 times the speed of the human and at less than 1% of the cost of the human professional. So knowledge work is cooked.
- [21:47]
Dave
Okay, you know, I figured, I figured something out on that last line this week too, because, you know, I'm, you know, chairman of about a dozen companies and I'm like, guys, what is holding you back? Why have you not deployed this? You can cut costs dramatically, you can automate, you can expand your market Share. And they're all like, yeah, I don't know, we're really struggling, like, oh, it's driving me nuts, what's going on? So a couple things that I finally figured out. One of them is, you know, one of the companies is working entirely in Java and when you turn this loose in Python, where it had a lot more training data, it can build virtually anything, it just blows your mind. And it really sucks in C still. And I don't think they're going to fix it because they just don't care. You know, we've moved off of C anyway and there's, there's not enough training data and Java's somewhere right in the middle. And so when they benchmark it, they're like, well, let me try and take my legacy thing and see if it can just immediately fix it. And it struggles, but if you just say, no, scrap it, rebuild it entirely from scratch in Python, you come back an hour later and it's done. So they're stuck there. And also the other place they're stuck is in operations. They're saying, well, look, the way we pick up a customer service request is in an email that's in an Outlook folder and that has all these security whatever's on top of it. So it's struggling to open and read the emails. So we're giving up. Don't you think you could maybe fix that front end interface in maybe a day and then try it on the rest of the process and just turn it loose and it would immediately crush the problem. So they're stuck on these little edge case issues. I'll tell you, it also comes up that RKGI benchmark is the one that was specifically designed to be things that a human finds relatively easy and intuitive. And the AI is still struggling with the AGI one and had countless conversations around academia with people who desperately want to say there's still something missing, there's something fundamentally missing in this great AI brain and it hasn't been solved yet. And the proof is ARC AGI1. You're like, okay, boy, do you look foolish. Now just three weeks later, five weeks later, because it's basically saturated, but it's going to be completely saturated imminently and on the gdp.
- [23:59]
Peter Diamandis
Val, if you remember, Elon has spoken about one of the companies he's going to be starting is Macro Hard. And his mission is basically go into a company and simulate all of your employees and deliver it as a service back to that company. A lot of change is coming rapidly. I think the biggest challenge is People are not projecting properly on how rapidly this is going to tip. Our next slide here is GPT 5.2 arc AGI update. We spoke about the numbers in the table just recently. Here we see it charted out where GPT has re has had a 390 fold efficiency improvement over 03 back from 2024. Anything you want to add to this AWG?
- [24:49]
Alex
Yeah so we talk on the pod we've spoken several times about hypothetically 40 times 40x year over year hyper deflation we're seeing 390x year over year hyper deflation. On visual reasoning for ARC AGI this is unprecedented. And this level of hyper deflation in terms of the cost of intelligence will not stay contained to the data centers. It will not stay contained to these still relatively narrow. I know they brand themselves as general intelligent benchmarks, but they're still relatively narrow in the scheme of things. It's not going to stay contained. Hyper deflation is going to spread outward from these sorts of benchmarks to the rest of the economy. That's common. 1 comment 2 just focusing narrowly on ARC AGI One of the lovely things about the ARC AGI 1 and 2 benchmarks is they don't just focus on raw performance, they also focus on cost. And if it costs us $100 trillion to solve a hard problem, well, if it's larger than the human economy to solve an problem, then it almost doesn't matter. But if it's incredibly affordable, you know to your mantra Peter, about abundance. If abundance is unaffordable, what's the point? It has to be affordable. Abundance and the way we get there is exactly what the ARC AGI organizers do, which is you measure on a scatter plot performance on the vertical axis and cost per task on the horizontal axis. And that shows you what progress looks like. You want progress that looks like points in the scatter plot going up and to the left, greater performance at lower cost. And in fact, going back to my earlier comments, if you see a Frontier lab hypothetically just increasing compute costs but not actually making efficiency gains, that shows up in these plots too. You can see for example, if you look at Arc AGI1, although it's probably a little bit difficult to read here, if you squint, you can see that GPT 5.2 is on the same the same extrapolated slope as GPT 5 mini, suggesting that maybe at least as it pertains to ARC AGI1, there hasn't actually been major progress, algorithmic or efficiency progress. It's just like more compute being spent on the same tasks. And so it feels smarter, but it's actually because you're putting more work into it. As the aphorism goes. You're lifting with your back, not with your legs, but with ARC AGI2 there is in fact radical improvement. So we're seeing progress.
- [27:24]
Peter Diamandis
Well, this is a benchmark that I think a lot of people can relate to. The next one here, GPT 5.2 writing benchmark comparison Long form Creative writing and emotional intelligence. Again, we're seeing improvements across the board. Alex, one more interpretation here.
- [27:42]
Alex
Spiky. This is very spiky. So spiking.
- [27:46]
Peter Diamandis
We saw that sort of interesting three dimensional plot on when are we going to reach AGI? And again, spikiness was the descriptor for it.
- [27:57]
Alex
That's right. That spider plot was purportedly Comparing humans with AGIs or strong models in general. What we're starting to see here is increased spikiness and spiky competition between the different frontier models. So just a little bit of context. Long form creative writing benchmark evaluates models model's ability to basically write a novella. About 8,000 word novella as judged by Sonnet 5. And the emotional intelligence benchmark measures how well a language model or a model can grade short fiction. And so what we're seeing here is no single model dominating all the benchmarks. We're seeing, for example, that with long form creative writing anthropic sonnet 4.5 wins and is the best job at writing an 8,000 word novella.
- [28:48]
Peter Diamandis
What do you use? What do you guys use for writing? I mean, I've been using Gemini 3 Pro. It looks like Claude Sonnet 4.5 is the one to go to. Are they all.
- [29:01]
Salim
I've been using Gemini 3 Pro and I found it to be really amazing to just craft. But I'm doing mostly business documents so that's a little different.
- [29:12]
Alex
Same for me. I use 3 Pro for almost all of my writing.
- [29:16]
Dave
Yeah, I'm using Kimik 2 for huge volumes of stuff on my little fleet of Nvidia chips that I hijacked. But I'm using actually Gemini to 1D spyware it and to proofread it. And I'm using Claude Opus at Extra. My Opus expenses went from 200 bucks a month to 1000 bucks a month. Month to I'll easily crack 20 or 30k this month, but I'll also generate more code this month than my entire life up to this date. So it's a bargain at 20k. But my expenses are going through the roof on the on Anthropic and I'm. I'm happy with it, actually.
- [29:59]
Peter Diamandis
Desperate. What, what's despy where it mean?
- [30:02]
Dave
Well, Alex warned me that when you use a Chinese open source model, it can inject evil things into the code that it returns to you.
- [30:10]
Alex
It's actually publicly information. We're not breaking news here. Maybe just to expand on this. So. So two comments. One comment is there are. There have been very well publicized outside of the POD studies that found for example prompting certain open weight models with certain politically sensitive for certain countries topics results in those models emitting more vulnerable code, for example. That's something to be wary of. So I would say more broadly for creative writing, et cetera, none of these models is so strong that I can ask them to just do a good job doing all writing. What I find inevitably is I end up having to do 80% of the work and models function as more of a junior editor, as it were, and I end up still doing majority of work writing. Similarly to Dave's point with, with CodeGen, I would certainly not trust CodeGen models to not insert vulnerable code doubly.
- [31:13]
Dave
So, yeah, well, when you told me that a week ago, I was like, you know Alex, I'm going to see the code and I'll see if it's injecting anything evil in there. I'm not super worried about it. Let's go. So here we are a week later and it's generating volumes that no human being could ever look at. I'm like, oh shit. I was completely wrong and it worked. The code just flat out works. I don't even have to look at it passing every eval. It's doing, it's building interfaces that I want, it's doing everything I wanted to do without needing to look at it. So now I've got actually GPT 5.2 proofreading right now. But I think what I need to do is just turn off Kimi, pay the 10x higher price, it's actually 20x higher price to run it on GPT2 instead. 5.2 instead. Yeah, but I'm going to have to do that because I don't know how else to make sure I don't end up spywearing my entire world. It's.
- [32:05]
Alex
This is a real challenge. If you have basically intelligence being dumped into the world, then there is this implicit trade off between do you want intelligence cheap or do you want it to be safe?
- [32:16]
Peter Diamandis
Yeah. And we've talked about this as a potential strategy for China making open source models available to the world. If it becomes the base on which you've built everything, then it's there from the beginning. I don't want to impute a dystopian point of view on all the Chinese model makers, but it is a concern.
- [32:41]
Alex
I think we're going to see a move to sovereign intelligence. I think this is the long term trajectory we find ourselves on. Every sovereign entity is going to want their own sovereign trusted stack.
- [32:52]
Peter Diamandis
Well, how do you feel about France? So Mistral's Devstral 2 raises the bar in open source coding tools. So what do you think about Mistral, Dave? Are you playing with him at all?
- [33:07]
Dave
You know, it's funny, I saw this chart and I'd kind of forgotten all about him. And I guess my read on the chart was, oh, it exists. But you know, the headline says it raises the bar, but it's actually below the. I mean only a notch, but it's below Kimi and Deep Seq. I guess you could probably trust it more because Europe is much, very trustworthy. But other than that it was like, what's the news here?
- [33:30]
Peter Diamandis
It's the headlines. Europe, slow but trustworthy. Okay.
- [33:35]
Alex
And also it's not, I mean, so there's I think, this sense, for a variety of reasons, that Mistral is somehow like the EU's sovereign AI stack or sovereign AI model. But its roots are all very much American. All of its early funding is from blue chip American VCs. Its founding team came from DeepMind and Meta. Yes, it's like raised a large amount of money from ASML most recently. And my understanding is Europe is very interested in using Mistral as sort of an AI emissary to the rest of the world. But its technical roots are deep, deep in the US and sort of this bizarre world that we find ourselves in where it's a Paris based Frontier Lab or NEO Lab, however they brand themselves, that's right now the only and main counterweight to Chinese open weight models.
- [34:25]
Salim
There's one thing I thought was really interesting here as it's getting close. Once you have open source systems beating closed systems, then you move innovation to the community level from the lab and there's no catching up with it once you get that flywheel going. So I thought this was a big deal. They may need a little bit more improvement per Dave's point, but I think.
- [34:47]
Peter Diamandis
Once they get there it'll be true. Is that true for AI open source models? I know it's true for a multitude of fundamental, just plain software models. We've seen that before. Alex, do you think it's tricky.
- [35:03]
Alex
It's tricky because you have to ask what are the primary limiting factors to increasing capabilities? And it's compute more than talent. There's lots of talent in the world, but compute is still pretty scarce. So the community has lots of talent, but in my mind they don't have compute, they're compute starved. This isn't like Linux where you can sort of say lots of eyeballs make all bugs shallow. In this case, the way you make the bug shallow is by investing trillions in capex.
- [35:30]
Dave
Well, this conversation is critically important. And Alex, you can help the world a lot because every corporate executive in 2026 is going to need to choose something. And there's only two types of exec out there. People that are familiar with this and they've already kind of got their landscape figured out. And then the other 99% that are going to get slapped in the face in 2026 and have to react and they're late to the party. But you saw the benchmark earlier. Everything every one of your employees can do can now be done by AI. What are you going to do? Just sit there and ignore that? So in 2026 is the turning point. But these choices are really tough on this chart. Like to an executive saying, well, God, I can go Open Source At 1/20 the price, but I get 72.2ambiguous units of thing. Or for 77.9, what does that mean? It means a lot. Anyone looking at the chart would say, oh, what's the big deal, it's only five units. But the reality is the capability difference in terms of your economic value is massively, massively bigger as this goes up even a little bit. And so it's a tricky, tricky situation in 2026 for pretty much all of corporate America, corporate world.
- [36:40]
Alex
I think it's probably, I mean, if I had to spitball this one, I think it's going to take take some sort of regulation to move the dial on this. Right now if you hang out with all the Silicon Valley firms that are using open weight models, they're just all using Alibaba's Kwen at this point and Mistral and Devstral, that's great, but it's probably in the mind of a typical Silicon Valley firm that needs to host their own models. Too little, too late. They're all using Qin, they're all fine tuning Quinn. And it's going to take an executive order or an act of Congress or some sort of regulatory measure to turn off the cheap Chinese open weight intelligence before they're Incentivized to move over to Mistral or Devstral or GPT oss.
- [37:25]
Peter Diamandis
But Dave, I think one of the points that you made is the CEO and the board of directors of a company in extremis in sort of paralysis, not knowing what to do. Right. And their lunch is going to be eaten by the small startup that says, oh, there is an interesting business, so we should go and enter. And it builds a AI native approach at 100th the cost and you know, 10x the innovation evolution, speed. And so what do they do, you know, who do they turn to to help them reorganize their company? And it's a risky move because if you brought in an outside consulting firm.
- [38:08]
Salim
Right.
- [38:09]
Peter Diamandis
I don't think it's gonna be the biggest, the big consultants. I mean, they're gonna be AI native companies out there. We're going to be having a pod conversation with one company called Invisible that does this very shortly. And there are others. The right way to do it, you said it earlier, is to scrap what you've been doing and actually start with a fresh stack. And that is so hard for any company to do. Salim.
- [38:37]
Salim
Yeah, this is right in our wheelhouse. Essentially we're working with some very big companies and, and Dave, you're exactly right. They're totally paralyzed, they're flailing, they have no idea what to do. And if they bring in one of the traditional consulting firms, they just push them faster down the old path. Right. And so that doesn't work at all. And so what needs to happen is they need to take their capability here, create a new stack on the edge that's completely built AI native from the ground up, and then little by little, deprecate the old and move functionality, capability, resources to the new. The political and the emotional stress of that is causing the most of them to do nothing.
- [39:18]
Peter Diamandis
Yeah.
- [39:18]
Salim
And so out of the say 20 major companies we're working with, maybe three are doing maybe 50% of the right thing. And, and most of them are just like, we're going to keep pushing this old model and seeing where, where we get to. Surely we. Because we've always been able to get there before. And the answer is, you absolutely cannot. And so this is Macy's.
- [39:42]
Peter Diamandis
It's blockbusters. And when you say we, you mean open EXO is doing some work with these companies out there.
- [39:46]
Salim
Yeah, we have like 42,000 people talking to companies around the world. And so we were kind of aggregating the gathering the information of all that 1999, I think 2026 is going to see the biggest collapse of the corporate world in the history of business.
- [40:02]
Peter Diamandis
You've heard that first prediction here, no.
- [40:04]
Salim
Doubt, because this is going to be, and we should have maybe an end of year perspective and some predictions. But, but for all of the madness we've seen in 2025, it's like this is the slowest it's ever going to be and 2026 is going to be 10x to 50x to 100x crazier. So I don't even know where to start. I've got models and I've got benchmark fatigue right now to dealing with all this.
- [40:27]
Dave
If you hire Salim to help you with your strategy, one of the things he'll tell you is read Clay Christianson the Innovator's Dilemma, which exactly addresses this question and what that book will tell you to do and Clay Christensen's foundation will tell you to do. Go find Link Studio, Y Combinator, Neo. Go out there and find your AI development partners. Try and do a deal with them where you either invest in them or you become a development partner customer for them. Pull them in, give them revenue because their market cap will go way up. They'll all become wealthy, but they'll then hire the talent. But point them at your internal problem and have them solve it inside your organization as an outside, very tightly bounded startup company that's growing like crazy. That's the only way you're going to get the talent focused on your internal problems. You can't hire the talent directly anymore. You got billion dollar signing bonuses all over the place.
- [41:21]
Peter Diamandis
And by the way, Salim will tell you to go read open EXO2 exponential organizations too, which is our book, which actually walks through step by step up, what to do, how to do this.
- [41:32]
Salim
Yeah, I actually had a couple of really interesting conversations with Clay before he passed away. And one of the things he honestly, very honestly admitted was the innovators dilemma works really well to identifying the, the cracks in the structure, but it's not that great at the prescriptive side or trying to predict, for example, in his model, Uber is not very disruptive. And I said, well, and he said, I said, but Uber is very disruptive. It fits right into the wheelhouse of our exo thing. And he goes, yeah, it means our model's wrong. And when we drilled into it, what we realized was the Innovator's Dilemma assumes that the verticals like transportation, energy, healthcare, education stay in those verticals. So Uber as a transportation company may disrupt a little bit of transportation, but not realizing it, it's also disrupting healthcare delivery and restaurant delivery and food delivery and can go horizontal across a lot of these. The old verticals are essentially collapsing of the old newspaper with the printed places saying utilities and this and this and this. And to Alex's point, it's all going to become one category called compute. That's where one of that.
- [42:43]
Dave
Well, if you don't want to do what Salim is suggesting, the other choice is to do a $20 billion acqui hire plus 14 billion of new payroll. And that's the other way to solve the problem.
- [42:54]
Salim
The other thing I'm seeing that's unbelievable. Executives at that level are, are looking at every, looking at the world and going yeah, I'm just going to retire right now.
- [43:02]
Peter Diamandis
And so there's this unbelievable opting out.
- [43:06]
Salim
Exactly like falling off the cliff.
- [43:08]
Dave
That's the most fun time in human history. How can you not?
- [43:11]
Peter Diamandis
Not if your company's diving into the ground.
- [43:14]
Salim
I actually respect that. I tell you why. What they're doing is they're basically saying I can't navigate this new world. I'm a checkout and let the younger generation navigate this because I can't do it. Well, it's a really, it's a really honest. But it's very honest. Right. At least the worst thing in the world is the old fuddy duddies that were running the world on the old model. The king that won't get out of the way. And we're seeing that much more in politics to some extent in the corporate world.
- [43:40]
Peter Diamandis
All right, let's move on.
- [43:42]
Salim
Change happen.
- [43:43]
Peter Diamandis
So talk about, talk, talk about billion dollar salaries. Talk about innovators dilemma. Our next story here is Meta's shifting AI strategy is causing internal confusion. So Meta is at an inflection point right after mixed llama 4 results and a reported $14 billion AI talent spending spree. You know, Mark is looking at considering whether an open source strategy can still compete with closed vertically integrated rivals like OpenAI and Google. Dave, what do you think about this?
- [44:16]
Dave
I think they're doing exactly the right thing. Actually the other backstory here, which I guess is validated, maybe it's more rumor than validated, but they're getting heavily into distillation of other people's models to accelerate the inference time speed. And what's exciting about that is if you look at where we are in human history, intelligence in a box was invented just days ago, you know, well really two years ago. But it's brand new in the world and now we're in the Hyper experimentation phase of how do we make it bigger, better? By running many, many agents in parallel, by expanding the context window and dumping in tons more data and by iterating it over and chain of thought, reasoning it over and over and over again. And we're getting ridiculous gains, but we're brand new in that game. And so what Meta has realized is, look, we're behind in the foundation model race. We do need to rebuild and catch up, but that's not going to happen overnight. But where we can potentially get ahead is by raw inference, time speed and having many, many more agents working on things in parallel. And I believe that that will also lead to self improvement which will get them back on the map. And so I think that they're directing all their research energy now into how do we make this blazing fast and be the world leader in distillation. That's my take.
- [45:35]
Salim
Incredible. I'm blown away by the $14 billion hiring spree. Just like that number, I can't process that number.
- [45:43]
Peter Diamandis
Remember, they've got just a massive cash cow and cash generating engine. Mark has basically said this is the race. If we don't spend the money now to get towards number one, it will just slowly, slowly go away.
- [46:02]
Salim
So Dave, what you're saying, what's cooler.
- [46:03]
Dave
Than cool is that he's already decided to use every single penny of it, plus debt on top of that to try and win this race. And Wall street has said that's fine, no damage to the stock, go for it. We love what you're saying, that's just a beautiful thing.
- [46:17]
Salim
So what you're saying is they're moving from like trying to focus on the open source of the foundation model to putting all of their chips on the agent strategy.
- [46:26]
Dave
Well, I have so much innovation there too.
- [46:28]
Alex
Yeah, I think they're in a bit of a tricky situation. So I know the key players. Zuck's undergrad advisor before he dropped out was my postdoctoral advisor, Nat Friedman, who's with Alexander Wang, helping to lead this new lab with my first roommate at mit. I'm pretty familiar with the key players in this particular story and I think there are three strategies that Meta could be pursuing and or has been pursuing. So one strategy is that of commodify your complement, drive the cost of generative AI to zero. That was their llama strategy that they were pursuing. Problem is, Llama 4 was a disaster and the Chinese open weight models are flooding the market and doing a much better job. The second strategy that they could be pursuing is more conventionally and perhaps What Wall street would expect out of Meta, Use strong AI to improve Instagram and other Meta products. I would have to imagine many executives at Meta would like to see all of these new AI resources being used to just improve Meta's other existing products. Strategy two, strategy three is compete directly with the Frontier Labs with closed source API based models to be the first to superintelligence. So I think what Meta has to struggle with, it's almost hopefully not like a civil war internally, but what they have to decide is which of those three strategies do they really want to pursue. And my guess is there are constituencies with different interests within Meta that want to pursue each one of those three.
- [48:05]
Peter Diamandis
I cannot believe Mark is not all in on number three. I mean, being first to superintelligence, that just feels like Mark's M.O. yeah, yeah.
- [48:14]
Dave
And I think very often the COVID story is, look, we're going to enhance existing products, we're going to use our internal data. You know, we've got a huge amount of internal posts that we can use as training data. That's all kind of COVID story for the real. We want to win the race to AGI and asi.
- [48:29]
Peter Diamandis
By the way, everybody, I want you to realize as you're hearing these stories about Google, about Meta, it's all about business model innovation on top of all of this, right? Google going from an ad based search company to now an AI based company that's delivering a whole slew of different products. Meta is, I mean this is where companies fail when Blockbuster did not change their business model even though they had twice the opportunity to buy Netflix. So how do you actually disrupt your own company and shift its business model.
- [49:07]
Salim
Otherwise it's game over innovators dilemma to Dave's point earlier.
- [49:12]
Alex
But it is I think also ironic, like Sam Altman has said publicly that he'd much rather have a billion users with not frontier model than vice versa. And yet what we see from Meta is the exact opposite strategy. Meta already has their billion users, billion plus users, but they would much rather have a Frontier model at this point.
- [49:33]
Peter Diamandis
Than no one's ever had to.
- [49:35]
Alex
That's where grass is always greener. At the other Frontier Lab.
- [49:38]
Peter Diamandis
That's funny, that's a good phrase. All right, our next story Here is Google DeepMind to build material science lab after signing deal with UK. So we've heard about this as well. Another company out of MIT and Harvard called Lila is doing something very similar where you're basically know it's all about the data and if you've consumed all the Data, you need to go find new data. So imagine having a, you know, lights out robotic capability where the AI is putting forward a scientific hypothesis, designing experiments, and then at night, robots in the lab are running the experiments to get the data to either confirm or modify your hypothesis. And like, let's do that a thousand times or 10,000 times faster than humans can do. I think we're going to see multiple companies. I think every frontier lab is going to need to have this kind of data mining or data mining nature, understanding what's going on in particular here they're focusing on material sciences. Lyla is looking at biological sciences. Thoughts on this gentleman?
- [50:52]
Dave
I don't know if there's a poly market on this, but Dennis is really leading the race to being the coolest guy on Earth. He got his Nobel Prize in chemistry, now he's going to crack. And this, you kind of could see this coming because, you know, AI can allow you to be a world leading expert in anything. And, you know, he's the master of the biggest AI, you know, compute in the world and algorithms and tp. He's got the. And he also isn't. He's not one of the corporate, you know, leaders trapped in the political beautiful soul.
- [51:25]
Peter Diamandis
We're going to have the coolest guy benchmark.
- [51:27]
Salim
Okay, well, what's great is you want somebody with that purity at the edge of this, which is fantastic. There's a couple of things I thought came across for me, having kind of hunkered around in physics labs during my degree. If you have a fully autonomous lab, this is like the biggest breakthrough in scientific progress since the scientific method was invented. Because we talked about dark kitchens and dark, dark factories and now we have dark labs. Holy crap.
- [51:57]
Dave
I can only find like just a handful of people like Demis, Alex, on this pod, there's like 10 or 12 that I could name that can tell you the implications, you know, in all these other, you know, in biotech, in material science and chemistry and math. You know, Alex is talking about solving all math. It's just such a small group of people who see where this is going to take us and how short that timeline is. So, so it's good to see Dennis.
- [52:20]
Peter Diamandis
Doing materials science, AI assisted science and AI native discovery. Alex, you want to close us out on the subject?
- [52:27]
Alex
This is what comes after superintelligence. What comes after superintelligence is solving math, drink, science, engineering and medicine. And yes, math is being solved. We've spoken about that perhaps ad nauseam at this point on the pod, we haven't spoken as much about AI solving all of material science science. And there are like a dozen companies. It's not just Google, it's not just Lila, it's not just periodic. There are a dozen companies that are all laser focused on solving material science. And that's going to give us so many upsides. It's also when we talk about recursive self improvement, having better semiconductors, having better.
- [53:05]
Peter Diamandis
Superconductors for material sciences is at the foundation upon which everything else is built.
- [53:11]
Alex
The computing.
- [53:13]
Salim
Here we come.
- [53:14]
Alex
And the innermost loop accelerates again.
- [53:17]
Peter Diamandis
And by the way, for our new listeners, our new subscribers, if you hear Alex saying drink, there's been a bingo game sort of invented for terms that are repeated on a regular basis. You'll be hearing it. All right, let's move on to our next story here. And I don't know how I feel about this story. I sort of feel like I don't want to overblow over, you know, over expose what's been already overblown. But this is a story of an AI native character called Tilly Norwood. And she's an AI, you know, native actress that's freaking out Hollywood. So Tilly Norwood is an AI made actress created by a London studio to star in films and social Media. Built over six months with with GPT, Tilly went through 2,000 design versions and YouTube videos have garnered over 700,000 views. In October, we saw this also in the music business, where fully AI native bands and music tracks have been created and people don't even realize they're listening to something that's just fully AI generated.
- [54:28]
Salim
She has her own agent.
- [54:30]
Peter Diamandis
Yeah.
- [54:31]
Alex
And reportedly like 40 different contracts for movies and other development projects. This is, I would say this is consistent with my modal hypothesis that over the next 10 years, we're going to live out the plot of every sci fi movie ever made. In this case, this is actually. I don't know if you saw the movie Simone. This was the plot of the sci fi movie Simone, where an AI actress develops a life of her own, takes over. It has Al Pacino in it. It's a fun movie. But like, we're going to see AI actors and actresses take over potentially, or at least we'll discover how soon humans crave authenticity in their entertainment.
- [55:10]
Dave
There's no doubt in my mind that humans do not crave authenticity as much as we think we do. And we will just watch whatever is interesting and entertaining. And I was at the Washington Post when every reporter there was saying, the Post will be fine because people Will want genuine great reporting from great reporters who are struggling in the field to find the stories. That was right before. Yeah, guess again. Gone. Just gone. And in just a couple years too. The timeline was so much shorter than they ever would have thought. From top newspaper in the world. Multi generational. Been in the family for three generations to gone. Jeff Bezos bought it for cents on the dollar in just what, three years, four years. So that's going to happen here too. And no doubt in my mind it's going to happen with music, it's going to happen with movies. It's going to happen. Yeah, it's inevitable.
- [55:59]
Peter Diamandis
This is, this is an AI performer working 24 7, appearing on in unlimited projects, never aging, never burning out, never needing to renegotiate contracts. I mean this is the Screen Actors Guild worst nightmare. I had dinner a couple of nights ago with a dear friend on my X Prize board who used to be the head of two of the major studios and then an actress who's another dear friend. And we were talking about this and is scaring the daylights out of the industry.
- [56:33]
Dave
Good.
- [56:35]
Peter Diamandis
I mean it's, it's.
- [56:36]
Dave
Well, no good because they'll react. I mean, I don't mean I'm not. I wish nothing but good to happen to the people that are in the industry. But good that they're scared because then they'll react as opposed to getting crushed. I didn't mean to.
- [56:46]
Peter Diamandis
Well, the question become, the question becomes then and what's the response? Right. Are you as an actor going to license your Persona because that's the way you can make money in the final result. Because if you don't, then the industry will simply. Or the next generation industry will simply create a Tilly Norwood who actually is cuter than you or more handsome than you. Able to.
- [57:11]
Salim
Doesn't age.
- [57:12]
Peter Diamandis
Doesn't age.
- [57:13]
Dave
Oh yeah, there you go. Doesn't age. That's a huge one. I'll tell you one thing that ages people.
- [57:18]
Salim
And I wonder when you'll have one of these winning the Oscar, right? Well, in theory. In theory they should be the best.
- [57:26]
Peter Diamandis
We have a lot of those benchmarks. When will the first AI win a Nobel Prize? Right? When will the first AI, you know.
- [57:32]
Salim
Build a Demis already did it because he's kind of half faced.
- [57:35]
Dave
Anyway, that's done.
- [57:36]
Alex
It's squishy. Also, there have been by my count at least two Nobel Prizes. There was Demis with, with Alpha fold in chemistry and then there was also Jeff et al with with restricted Boltzmann machines for physics the squishy thing here is you can always do a secret cyborg, as some would say, and wrap AI talent inside a human meat body and the human claims the credit for it. So it's unclear again how much humans crave authenticity. Does this become a separate category in the Oscars? Like animation, is this sort of an increment on top of animation? That's real life animation or, or is this an actual labor substitute? I don't know yet.
- [58:17]
Dave
I think a lot of thinking though, a lot of that thinking is a little bit misguided in that what the actors will be looking for is a feature length movie in a theater where it's all AI and that's what they're going to use as their bellwether for the threat. But that's not what's going to happen. If you look in the data, short form video is taking over the movies anyway and video games are already miles ahead of movies.
- [58:39]
Peter Diamandis
We had these conversations. Don't go to the movies. Movies they watch YouTube videos. It's all.
- [58:43]
Dave
Exactly. So Tilly, Tilly will end up being a star in every video game. And also tick tock, clip across. They'll say, well that's not a threat. That's not a threat. Yeah, across platforms. And the actors will say, well that's not a threat to me. I'm a real actor. I do Shakespearean, you know, whatever.
- [58:56]
Salim
Like.
- [58:57]
Dave
Well no, it is a threat to you because the audience has moved and the budget has moved and that'll undercut you. So they're looking at the wrong bellwether. When Tilly shows up in 5 billion TikTok posts, that's when you know you're dead long before it hits you in your long form movies. So and you just gotta. We also have look at the video games too.
- [59:16]
Peter Diamandis
A related story of this, which is OpenAI is working with Disney to bring Disney characters into SORA too. Right.
- [59:24]
Salim
So that, yeah, they just announced that.
- [59:26]
Peter Diamandis
Yeah, it's a fascinating.
- [59:27]
Alex
So a billion dollar investment and, and licensing. I, I, I, I think there's going to be a certain fungibility between classic IP assets and generative everything. And so for maybe in the short to medium term it's a three year reportedly licensing agreement that OpenAI and Disney struck. Maybe in the short term. The short term remedy is existing actors can license their visage out as an asset to customers who want to do sort of fan pics.
- [59:57]
Dave
But they're better if you're like a really popular star, like a Peter D. You know, what's the thing you should do right away.
- [60:04]
Peter Diamandis
I signed my rights already.
- [60:06]
Dave
Get your avatar out there, get it built in out there right away. Get your Tilly, Norwood equivalent, Peter or whoever out there right away. So that personality can grab before you know, the true synthetics take over.
- [60:19]
Peter Diamandis
Yeah, it really is going to be a race for neurons. Right. If you're looking, you're going to the general public. Dunbar's number only really cares about 150 people and holds them close. And so the question is, are one of those or 10 of those going to be synthetic actors? And once you get to a point of popularity, it's going to be hard to replace you.
- [60:47]
Alex
For what it's worth maybe to tie a bow on this. Also, the Dunbar limit of 150 people, that was like in the ancestral environment, if the number is valid at all. In the post social media era, you can maintain light, casual associations with thousands of people.
- [61:05]
Peter Diamandis
But Dunbar's number is basically sort of the human tribe. And I've done this when I was running Singularity University. It's the number of people you can actually remember their names, go deep with and so forth. Sure, you can have a Rolodex of 22,000 people, but Dunbar's number in terms of who you feel connected to closely is a real number.
- [61:27]
Salim
I, I'm with Alex on this one. What I noticed was once you have Facebook and you could essentially Facebook acted as your RAM for Dunbar, you could move people in and out of that spectrum very easily without really noticing. And you have the opposite effect also where once you kind of start to connect with enough people, Peter, you've probably had this. I remember walking down University Avenue in Palo Alto right after one of our exec one week executive programs and this guy stops me and he goes, hey Salim, nice to see you. And I'm like, have we, have we met? He said, I just spent the week in the classroom with you, right? And I was like, wow. Like our brains are so blown up now with the limits of that. We need technology to expand that capability and it's already done that to one extent. And we can move things in and out. The question is, what do we do when we have all these synthetic AI levels going, going through that?
- [62:16]
Dave
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- [63:21]
Peter Diamandis
Our next story here comes out of the White House. Trump signed an executive order curbing state AI rules. So this is a decisive federal power grab over AI regulations. Trump's one rule executive order is going to preempt state level AI laws. It's like, no, no, it's not going to be Washington D.C. is going to win over everybody. It's not California laws or Texas laws, it's Washington D.C. i mean, ultimately, I think this is what the EU needs as well. It needs top level direction. It's going to be harder there. Any particular thoughts on the one rule here?
- [64:03]
Dave
It's absolutely, positively necessary. I hate, I hate it when this happens, but we got to do it because variety across states is one of our best assets. On the other hand, New York just passed a law that says you can't use the likeness in an AI of somebody who's deceased without going to their ancestors. What about all these Einsteins floating around already? How are you going to keep it out of New York? There's no way to just launch it across the country and then New York users get blocked somehow. I mean, it's just unworkable.
- [64:33]
Peter Diamandis
I'm going to claim I'm one of Aristotle's ancestors and you can't use his. Like, I mean, how far back are.
- [64:39]
Dave
You going to track down Aristotle's?
- [64:41]
Salim
I had two thoughts when I saw this. One was when I saw one rule, I very quickly thought about one ring to rule them all. And I just love the politics of this where the huge amount of the effort for Trump get elected was saying let's push all the thing down to states rights. Now we're going totally the opposite direction and I think it's a necessary thing. I agree with Dave here. It has to be done because if we don't get uniform AI treatment, where the hell are we going to get to?
- [65:10]
Alex
There's an interstate commerce angle here. Models are being trained in one state and inferenced in other states. In my mind, and I read the executive order in the past 24 hours, the EO is ensuring a national policy framework for artificial intelligence. I think it's both reasonable under the interstate commerce clause and also necessary for international competition. It's not at all obvious how a patchwork of state based regulations results in anything other than total chaos.
- [65:43]
Peter Diamandis
I mean, this is a piece of the overall White House strategy on energy, on data centers, on chips. It's all aligning everybody to make the US as competitive as possible on the global stage and to accelerate as fast as possible. It is a race to superintelligence. And this is just part of the.
- [66:05]
Salim
Can I make a radical prediction here?
- [66:06]
Peter Diamandis
Yeah, of course.
- [66:08]
Salim
Over the next five years, the entire US Constitution will evaporate. Every clause is starting to just melt away. I look the right to privacy, fourth Amendment gone, right. We're going to see the whole thing. It needs to be rewritten from the ground up. And it's going to be interesting to see how that happens.
- [66:26]
Peter Diamandis
And I will do a good job of that.
- [66:28]
Salim
Boom. That's what you need instead of the.
- [66:31]
Peter Diamandis
Founding fathers, it's the founding models.
- [66:35]
Alex
For the record, I don't buy that prediction for one second.
- [66:38]
Peter Diamandis
Good.
- [66:38]
Dave
We can put some money on it.
- [66:41]
Peter Diamandis
Poly markets, baby. All right, let's move to a conversation on the economy. And you know, this is data just to support what we already know. OpenAI finds AI saves workers nearly an hour a day on average. So workers using OpenAI tools have saved between 40 to 60 minutes a day. The survey of 9,000 people in 100 companies found that 75% say AI makes work faster or better, the biggest time saver. Over a million businesses today are using the OpenAI tools. I'm going to couple this story with our next one, which is Layoffs announced. In 2025, we had 1.1 million layoffs, which is the most since the 2020 pandemic. All right, Dave, you want to jump on on this?
- [67:31]
Dave
I was talking to Scott Perry, the CEO of Tree LendingTree Public Company yesterday actually, and he said 20,000 incredibly talented people in Seattle are now cut loose from Microsoft and Amazon. And it's the best hiring opportunity for tech talent he's ever seen in his life. But these are really, really solid, great people that the mega tech companies have just cut out because AI is automating, improving, enhancing. Coding is one of the biggest early beneficiaries. And my top coders are ten times more productive. So I don't need nearly as many. So that's where the layoffs are coming from. But this is just, you know, we'll look back on this and say, wait, that was a bellwether. Why did I not notice this little thing, you know, and, but when you see what happens in 2026, you'll say, when did this all start? Well, right now.
- [68:20]
Alex
This is when it starts. Now.
- [68:21]
Peter Diamandis
What do you predict for 26, Dave? Continued acceleration.
- [68:26]
Dave
Yeah, the capabilities will be, you know, able to eliminate on the order of 80, 90% of all jobs. But then the rollout and the percolation is dependent on regulation and also corporate bureaucracy. And so it's tough to predict how quickly people will react. My guess is that it'll get a very slow start, everybody's very stodgy, but then everyone's a sheep. And when somebody in your industry is an early adopter and their Stock goes up 10x just because they're an early adopter, then your board beats you up like crazy and says, what about us? And then the sheep effect flips in 2026. So by the end of 2026, everyone's in absolute panic mode and then they're wishing they started at the beginning of 2026.
- [69:12]
Peter Diamandis
You know, I think there's going to be, this is one of my predictions. I think there's going to be a absolute need for all the medium size and large companies to bring in a reskilling consultancy. Some type of a program could be fully AI based but, but that provide some kind of a safety net for your employees that you're going to reskill people before you fire them. And if they aren't able to be reskilled, then they're let go. I also think that's a huge business opportunity for an entrepreneur out there to build that kind of capability.
- [69:48]
Dave
Totally, totally right. In fact, you know, if we look in our portfolio, the companies that are quote, unquote, forward deployed are killing it. And you know, if you couple that with what we just said, There's 20,000 highly talented people in Seattle that just got cut loose. If you're growing your business, a lot of the younger companies, you know, 22, 23 year old leaders are afraid to be forward deployed because they've never done it before. They don't have any management experience, they don't have any enterprise sales experience. Well, hire those 20,000 people, train them on how to be AI, forward deployed consultants or delivery people and then get them embedded back into corporate America. At State street Bank, at J.P. morgan, at Walmart, they'll hire your people instantly to get AI deployed inside their organization because they can't get that talent. But if you grab those people, retrain them very, very quickly on your own AI training platform and then get them redeployed into corporate America. Your growth rate, you'll be sold out. Every time you have a meeting, you'll generate a sale. So I agree with you there really young founders are afraid to do it. They want to just like launch their software on hacker news and hope that the world sucks it up. And there's just this big gap between there and where corporate America starts and it's just never going to fill if you don't get forward deployed.
- [71:08]
Salim
I don't think this skills issue, this is a cultural problem. The problem is in corporate America with all the structural impediments in a big company, you need a mind, mindset shift at scale to even adopt this.
- [71:22]
Peter Diamandis
I think the large companies and the medium sized companies, to be very specific about my prediction here, are going to need to hire a very specific kind of consultancy, right? A company that comes in and their job inside your company. And I think every company is going to have a version of this is reskilling. And so that when you go to work for a company, you know there's a, a reskilling safety net there for you.
- [71:55]
Salim
But what I'm saying is it's not just reskilling, it's a mindset shift. It's a cultural change that has to take place and that's actually much harder. I want to say two things about.
- [72:07]
Peter Diamandis
There'S cultural and mindset shift at the CEO, at the executive level and at the employee.
- [72:13]
Salim
It goes through, it goes through the organization. And we've actually been working on this for several years now. And I want to tell a quick story where our second ever client, when we finished one of our 10 week sprints, realized that they had to lay off a thousand people in the company. And they decided what are we going to do? Because we're a family owned business, we have, we want to really provide for these folks. What do we do? We actually got them to give them a one year ubiquitous so that they could find their own passion, find their own work. And if they didn't at the end of the year, they would try and hire them back. And it was an incredibly successful program. I think we're going to see a lot more of that as we kind of transform the workforce.
- [72:50]
Peter Diamandis
All right, let's get into data centers, chips and energy. We're seeing data centers begin to pop up in countries around the world. I don't want to spend too much time on this, but Qatar QIA, the, the sovereign fund there is investing 20 billion to launch a data center in Qatar or Qatar or however you want to pronounce it as a Middle east hub. We're seeing Microsoft and Satya just coming back from India, meeting with Prime Minister Modi there, committing 17.5 billion in India to expand an AI ready cloud there in the region. So we've got, I mean, this is going to be the case in all major nations, these partnerships taking place.
- [73:36]
Salim
The real story this is Alex comment about tiling the world with data centers.
- [73:40]
Alex
And every drink tile the earth with sovereign inference time compute. Drink, drink, drink.
- [73:46]
Peter Diamandis
Okay, we're drinking coffee this morning, ladies and gentlemen.
- [73:50]
Alex
Drinking water here.
- [73:51]
Peter Diamandis
I've listened to alcohol. All right, so here's the story I want to dig into. In our last pod, we talked about about China's sort of incredibly expanding role. So China is set to limit access to Nvidia's H200 chips despite Trump's export approval. So, you know, President Trump says to Nvidia, okay, you can export these. And now the China leadership is, no, no, no, you can't buy them. You need to buy Chinese made GPUs. Fascinating, right? This is propping up its own chip economy. I think it's a smart move on China's behalf.
- [74:33]
Dave
This is so fun and annoying at the same time to watch. This is pure protectionism. The US Never did it before and now we're playing the game. But what happens is a country invents something like an LCD TV or a car or whatever, and another country says, okay, what we're going to do is we're going to protect the home market. We're going to manufacture our own, then we're going to dump it it on your market cheaply. And we're going to dump it until your companies collapse and the venture capitalists all run away. And then we're going to price it up. So what we did is we embargoed the chips from China and they're like, oh, shite, we need to build our own whole supply chain. And as soon as they get it up and running, we're going to say, oh, no, no, it's okay. Now we're going to actually allow you to buy the H2 hundreds. And that entire thing you just built makes no economic sense. Sense. And so China's saying, all right, I see what you're doing here. I played this game for a long time. We're not going to buy them. But why? It's an incredible buy. Why would you not allow us to buy them? Because we already made a massive investment in our own fabs. We're going to have to keep subsidizing that to get this up and running, because we know what you're doing here. You're going to let us buy them right up until our stuff collapses, and then you're going to cut it off again.
- [75:45]
Salim
It's a trust issue, big trust issue.
- [75:48]
Dave
Well, there's no trust at all between the US And China right now.
- [75:51]
Salim
Well, this, the same thing happened. Right. The Japanese came over during Trump's first administration and spent a lot of time negotiating a trade deal. And then just a few months ago, Trump, the administration canceled that trade deal. And the Japanese are like, we're not negotiating another one because we don't know which way up is anymore. And every single time, it changes completely. So there's no trade deal. And this is really a big problem going forward. Not just. I think what China is saying is we don't want to play that game.
- [76:22]
Dave
Well, there's no doubt that the outcome is, look, two completely separate ecosystems. Europe is kind of a wild card. It's interesting. And so is India. It's kind of a wild card right now. But there's no doubt the US Ecosystem is going to grow completely independent of the China ecosystem because there's no chance of reestablishing trust after that chip embargo. There's, like no way that that's going to get. Get mended.
- [76:44]
Salim
That's right. So sovereign data center AI Compute. To Alex's point.
- [76:51]
Alex
It'S almost like a second Cold war. It's a world that we move to where there are spheres of influence and spheres of fab and spheres of compute. And the decoupling happened.
- [77:02]
Peter Diamandis
Yeah. Okay. Move on to power generation. There's a company called boom. Many years ago, it set out to build the first supersonic passenger airliner to replace the Concorde. And I was so impressed by the founder and CEO, his chutzpah, if you would, to take on this moonshot to build a supersonic consumer airplane. And it was like, I don't know how you get there. How much money is going to be required to build this? So it's a fascinating backstop that BOOM had been developing supersonic engines. And now they've unveiled a supersonic super power turbine that's able to provide 42 megawatts of natural gas turbine capabilities to data centers. And so this is a backstop business model for boom, and it's huge.
- [78:04]
Alex
Right.
- [78:05]
Peter Diamandis
So this is moving power to the data centers. Right. It's a gas turbine strategy. And we've heard before all the gas turbines have Been sold out for some time. Alex, you want to jump on this? Yeah.
- [78:22]
Alex
I mean, as you were gesturing, Peter, that the wait times right now for gas fired turbines for AI data centers are seven years in some cases. So I think this is, this is a brilliant strategic pivot by boom. It also, to the extent referencing comments from a minute ago, to the extent we're in almost a quasi second cold war, this is almost like a self directed defense production act type move, pivoting resources perhaps from turbines for supersonic consumer jets to turbines for AI data centers. And of course there are synergies there, but this is, I think it's a brilliant pivot. And the irony is there's probably a much, much larger addressable market for gas turbines for AI data centers than there is for consumer supersonic jets at this point. I just hope for the sake of boom that they retain at least some semblance of the original supersonic vision and just don't get overwhelmed by the AI data center business.
- [79:17]
Dave
I just look, I need that audio clip. Hey team, behind the scenes, I need that audio clip like right away. That's because there's so many companies, including Vestmark, one of the ones I founded. Pre AI manages $2 trillion of assets, 20 million lines of code, profitable, great business. And I'm like, guys, you gotta be an AI company like tomorrow.
- [79:37]
Peter Diamandis
Pivot, pivot, pivot, pivot, pivot, pivot. Strategic pivot.
- [79:40]
Dave
We've got, you know, so this is a great case study. Like you wouldn't think that a jet engine is company is culturally going to pivot and become a power generation company. But when you look under the COVID it's like, well, what are our assets here? Well, we've got the blades, we've got the manufacturing, we've got metal. That's all it takes. The age of AI has so much opportunity that didn't exist the day before. And you don't have to be that close to the center point. You have to be adjacent. Just pivot quickly and you'll succeed wildly. And so I hope these guys just crush, in fact, I know they'll crush it because like you said, Alex, I know personally, the demand is the data center operators. Yeah, they'll spend anything and they're pre buying too. They'll pay you up front for something that you're going to make next year.
- [80:26]
Peter Diamandis
There's a $1.25 billion backlog and it's a product they can deliver immediately. Right. This is on premise power generation for data centers, which is so critical they've been Working. Boom's been working on this for, I don't know, six, seven, eight years. And they've built a scale model of their supersonic airplane and they're trying to get advanced orders from all of the airlines. But to get through the FAA thicket is so difficult.
- [80:55]
Salim
That's decades.
- [80:55]
Peter Diamandis
It will kill you. But if you've got an actual business model delivering revenue right now, I mean, I agree with you, Alex. I hope BOOM actually delivers on their original idea. I think this increases the probability a huge amount. Right. And this is the equivalent of Amazon realizing with Amazon Web Services, it's got something that it can offer to everybody else that makes, you know, very strong near term profits.
- [81:26]
Alex
Or Elon, like delivering Starlink now and Mars colony in 10 years.
- [81:31]
Peter Diamandis
Yeah.
- [81:32]
Salim
That's the sexiest looking gas turbine I've ever seen, by the way. It's a beautiful looking thing.
- [81:37]
Dave
I'm sure after you run it, it gets dirtier.
- [81:40]
Peter Diamandis
1.25 billion in backlog. Congratulations to the team at BOOM for that, that strategic pivot.
- [81:45]
Dave
And everybody else, everybody learned from this story. We should track this, you know, a few weeks or a few months.
- [81:50]
Peter Diamandis
What do you have? What are you, what are you building right now that's a cost center for you, that could become a profit center for you in the AI ecosystem? That's the question. All right, on the energy side, China builds nuclear reactors at $2 per watt versus the US at $15 per watt. Again, what's going on here? Why is that happening? Alex, do you have a thought?
- [82:17]
Alex
Yeah. Well, China does have more people than the U.S. china does have a need for more energy. If AI were not part of this equation and China were to attain US Per capita energy footprint standards, China would need more energy in a total sense, in an absolute sense, than the US that part makes sense. What doesn't make sense? If you look at the permitting processes required for nuclear energy in the US It's a very different beast. There are, obviously, The NRC regulates U.S. nuclear power deployments at the national scale. But then on top of that, you have some states that de facto ban nuclear power entirely. We have a patchwork of state and local regulations that make it extremely difficult to deploy nuclear energy. Here in Cambridge, Massachusetts, many people may or may not be aware of this. Cambridge has a nuclear reactor. It's not very well advertised. It's on Massachusetts Ave. It's on the MIT campus. But we have a working nuclear reactor and have had one since I think the late 60s, early 70s. But that's very much not par for the course in the U.S. i wouldn't be surprised if sometime in the next two to three years we see some equivalent for nuclear energy of what we just saw with the White House's executive director.
- [83:38]
Peter Diamandis
You're going to have to see it in the next few months. I mean, the bottleneck is not physics. It's permitting and execution, and that's gotta be cleared. Yeah.
- [83:48]
Dave
I'll give you a little side story related to this. The MIT brand. Here's the MIT brand. The MIT brand is absolutely skyrocketing in this AI revolution. But we found out that that MIT nuclear reactor is going to be exothermic and powering the campus. And I'm like, wow. Because we don't have a single nuclear reactor in the state, you know, we can't get that approved. We buy our nuclear power from New Hampshire. But MIT can actually get stuff like that done now, which is crazy how. How that brand has skyrocketed in impact with this AI revolution.
- [84:19]
Peter Diamandis
All right, we'll jump into robotics special. You know, hat tipping here to Saleem. This is Salim's perfect robot. It's got something like 14 different arms on it. Salim, are you happy with this robot?
- [84:33]
Salim
This looks awesome. Look at all the chickens that can move around very quickly. This is. Yeah, I love it.
- [84:40]
Peter Diamandis
Just for those of you, Salim is having a running debate about, okay, why humanoid robots? Why just two arms? Well, Saleem, you've got all the arms you could possibly put on a body here.
- [84:52]
Dave
Here.
- [84:53]
Salim
I just love all the wires sticking out of it. Also, like, it looks.
- [84:56]
Alex
I mean, there is a serious story here too. Like in, in China, there's an image doing. Doing.
- [85:03]
Dave
I can't wait for this.
- [85:05]
Alex
Yeah, do it. Doing. Doing the rounds with six arms that there.
- [85:09]
Peter Diamandis
Yeah.
- [85:09]
Alex
I don't think there's anything like super.
- [85:11]
Peter Diamandis
Yeah, yeah, I was going to bring that. I was going to bring that article forward as well.
- [85:14]
Alex
Yeah.
- [85:15]
Dave
There is.
- [85:15]
Peter Diamandis
There is not about six armed robots. Yes.
- [85:18]
Salim
Coming out of China is not about having a humanoid robot. It's about mimic. It's about integrating into human spaces and, and kind of moving around where humans have been. And so there's some case for it. But in general, there's. It's very easy to be 10x more efficient than a human being.
- [85:34]
Dave
We're.
- [85:35]
Salim
We're very, very inefficient in most of the things that we do. Yeah.
- [85:38]
Alex
I think evolution has done. Evolution has over billions of years or maybe order of magnitude a billion years, done a search through body space. And there are lots of body shapes that Aren't anthropomorphic humanoid bodies? You know, more arms, more legs, more heads, lots of different formats. And I do suspect what we'll see to Salim. I'm not sure if this is your dream or your nightmare, but we will see lots of different Cambrian Explosion, Star wars, lots of different body shapes tested.
- [86:05]
Peter Diamandis
All right.
- [86:08]
Salim
It's just the most effective use use case for trying to get something done.
- [86:12]
Peter Diamandis
I'm moving forward.
- [86:14]
Dave
Call out to our listeners. I made that on Nanobanana. Somebody make. Now that we know about the woolly mouse, make Saleem's perfect robot for turning the woolly mouse hair into sweaters for us. And then send it to us. We'll put it on the next pod.
- [86:26]
Peter Diamandis
Okay, That's a hell of a prompt. All right. Another form of robots are drones. And I just found this anti gravity drone. That's the the name of this drone. It's manufactured by a company called Insta360 in Shenzhen. For those who don't know, Shenzhen is really sort of the entrepreneurial hotbed in China. I've visited many times. You can go there and every part and component you need is there to be manufactured. So check out this video of an 8K 360 degree drone. Talk about marketing genius. So this drone user is using it with VR goggles, and he's on a platform suspended by a balloon at 5,000ft altitude. And the drone is just flying. A beautiful 360 view of him, the.
- [87:30]
Salim
Dude, standing on a platform suspended by a hot air balloon. That's way more interesting than the drone. That's ridiculous.
- [87:37]
Peter Diamandis
Well, it's like, what are you gonna do to capture someone's eyeballs, their attention? Right.
- [87:42]
Alex
You know, I think Salim is onto something here. Drones are a commodity. But the experience of being on a hot air balloon at altitude in a VR headset controlling a 3D drone, that's got to be some sort of consumer experience that one could build an enormous business out of. Maybe that's more interesting than the drone itself.
- [87:59]
Salim
This.
- [88:00]
Alex
Yeah.
- [88:03]
Peter Diamandis
All right, well, all right, let's move on to our next story.
- [88:08]
Salim
If you have the VR headset, why do you need to be suspended up at 5,000ft? That makes no sense.
- [88:13]
Alex
Well, for latency. Right? You want to see yourself suspended on the balloon at altitude. It's more exciting or something.
- [88:21]
Peter Diamandis
All right, let's go to our next robot story. And this is robotically automated vertical farms, which is an important part of our future food chain. So of course, out of China once again. And what we're going to see here are These massive vertical farms that are operating 247 basically growing at the perfect, you know, light frequency, at the perfect soil and drip irrigation ph and it's being, you know, the AI is checking to see if it's ripe, if it's ready for harvesting. And the robot arms are harvesting. And this is going basically 247 in a city near you. I mean this is one of the futures, you know, stem cell grown meats and vertical farming that helps us bring food to the individuals. I don't know if you realize this guys, but like half the cost of a meal that you have is food miles transporting the food from, you know, sort of Argentinian beef or Chilean red wine or potatoes.
- [89:31]
Salim
The average meal in the US travels 2,400 miles to get to your table.
- [89:35]
Peter Diamandis
Yeah.
- [89:37]
Salim
This is something kind of incredible. We've been tracking this for a while. You know, we've crossed over into economic efficiency for farming and agriculture and food production. This calculation I've seen that the most startling is if you took 35 skyscrapers in Manhattan, turned them into vertical farms that would feed the entire city sustainably. So you think about the food security, logistics, trucking, all of that stuff. And when you can automate the entire farm, the yield is something like seven to nine times what you can get with horizontal farm because you can give exactly the right frequency of light that you can drink. By the way, you save 99% of fresh water and 70% of our fresh water goes to agriculture. So. And no pesticides, no fertilizer, all of this stuff, the benefits are kind of incredible. So we're going to see vertical farms next to every restaurant. Over time, just feeding the restaurant. This is amazing stuff.
- [90:36]
Peter Diamandis
Yeah.
- [90:37]
Alex
It's probably also just quickly worth pointing out that video to my knowledge was actually put out by the Chinese government. And this is a new form of soft power, soft influence broadcasting these visions, presumably ground truth, accurate, but presumably of radical forms of automation. I think we're going to see many forms of propaganda soft influence as showing these amazing tech demonstrations of robotics in action start to hit the Internet.
- [91:07]
Salim
And by the way, a humanoid robot makes no sense in that factory.
- [91:10]
Peter Diamandis
Just agreed. But a humanoid robot does make sense in this next story again out of China. China is testing retail automation with humanoid robots running the shops. So what do we have here? You're walking by, you look inside, you don't see humans, you see a robot behind the table, behind the desk. And I want to go in and check it out. So this is the rise of the robot run convenience store, taking humans out of the loop. We've seen Amazon do a version of this with their Amazon go where you walk into the shop and you just pick up anything off the shelf and there's cameras noticing what you took and noticing what you put back on the shelf and then you're automatically rung up as you walk out. But here we've got a two armed, two legged humanoid robot doing the store clerking.
- [92:05]
Alex
I do think that this is going to be viewed as sort of like the atomic vacuum cleaner moment of 2025. Like do you really need a humanoid robot in a convenience store? No, probably there's more ergonomic solution. Like as you say, Peter, Amazon's just walk out technology on the one hand, on the other hand. And I would love to live in a world where every convenience store is filled with humanoid robots. And I should be doing this as well.
- [92:28]
Peter Diamandis
I think it's fun. I mean, I'm sure we'll see this, I'm sure we'll see this this year as soon as 1x with their Neo Gamma or figure. And we'll be visiting figure at the end of January to record our next podcast with Brett Adcock. I just spoke to him yesterday. Super excited about going and seeing behind the scenes there.
- [92:47]
Salim
Two, two counter predictions. One is I think this takes at least five years to have a convenience store operator with a humanoid robot. And by the time that five years arrives that we won't need convenience stores anymore for various other reasons.
- [92:59]
Peter Diamandis
Interesting. Everything is being conveniently taken to you by a drone.
- [93:03]
Salim
Drone delivered.
- [93:05]
Dave
You know, with Brett Adcock, maybe he'll let us go behind the scenes for real, like into the factory. Because with 1x, you know, there's too much proprietary stuff. They wouldn't let us do it. But if they cleaned up a little bit, maybe we could have done it. But it's incredible when you go back and see the actual robot construction. God, if we can get footage.
- [93:23]
Peter Diamandis
We went back and saw it, but we couldn't bring the cameras back there is what you were saying.
- [93:26]
Dave
Yeah, Too many secrets.
- [93:28]
Peter Diamandis
Another story here. Back in the U.S. boston Dynamics announces its plan to ship automotive volumes of humanoids. And this is from their lead, their product. I actually interviewed the CEO at fii. So we're owned by Hyundai for a reason. We can ship automotive volumes of humanoids. So there's a billion cars right now out there and these are being manufactured at tens of millions. Imagine. Well, we've talked about this. Elon plans to do this. Brett Adcock plans to do this. We've heard this from Bernd Bornick. Now we're hearing this from Atlas. The ability to manufacture at the millions and tens of millions. Robots building robots.
- [94:14]
Alex
We don't need billions of cars. We do need billions of humanoids.
- [94:18]
Peter Diamandis
Yeah. Two armed humanoids. Salim. Two armed humanoids.
- [94:22]
Alex
Okay, don't be armist.
- [94:24]
Salim
I'm staying silent on this one.
- [94:28]
Peter Diamandis
Here's a story that's fun. Years ago, I had the pleasure of meeting an extraordinary entrepreneur, Eric Migigovsky, who built the Pebble Watch. And he did this on a crowdfunding platform. Remind me which one it was. It was Kickstarter. Yeah, he built out of money.
- [94:49]
Dave
Yeah.
- [94:49]
Peter Diamandis
He was running out of money, and he had, like, three months of cash in the bank. He was able to get funding for his Pebble Watch. And so he goes on Kickstarter and he says, hey, if you want one of these watches, fund me. And he went from one problem of not having enough money to another problem. I forget how many orders he had.
- [95:11]
Salim
So Eric's a fellow Waterloo grad, and he was running out of money, as you say, even coming through Y Combinator, no investor in Silicon Valley. He talked about 20 plus, and nobody would fund it because hardware was kind of a bad word back then. So he puts it up on Kickstarter trying to raise 100 grand to build a prototype of his watch. Gets $10 million worth of orders. That's right. And it's an important point because it tells you two or three things. One, the investors are wrong. Fine. Secondly, if you can do this, why do you need the investor at all? But the third thing that I think is the most powerful and one of the big inflection points, we talk a lot about this in exponential organizations, is that now that you can do this type of Kickstarter type thing, you can actually get market validation for a product before you build a product. And we've never been able to do that before in consumer hardware or consumer products. So this is an amazing inflection point. Sony is actually launching anonymous Kickstarter campaigns and then funding the winners because their product development has not been the greatest over the last couple of decades. So they're kind of tapping into this modality, which is really powerful.
- [96:18]
Peter Diamandis
So Eric goes from having one problem of not having money to another problem which he's got to deliver now on $10 million worth of orders. So. So he literally takes the first plane out of the US To Shenzhen and basically builds the manufacturing chain in China to deliver this. And it was a great watch. I remember having. I gave it out at abundance360 years ago when it was a decade ago, but then Apple Watch came out and sort of crushed the marketplace. Well, Eric's come back and he's got something called pivoting to AI. Yeah, the pebble smart ring. And for 75 bucks you wear a ring that's got one purpose. It's got a small little physical button on it. And when you press the button, a microphone records whatever you want. So this is, you know, remember like waking up in the middle of the night, like remembering something. You just push your ring and you whisper into your ring or you're meeting with somebody, you walk away from your meeting and say, okay, I need to call, you know, XYZ as soon as this is over. And it's sort of, you know, reminders and it's notes that go into your AI model. It has one purpose, right. This is not, you know, tracking your heart rate or your sleep. It's tracking sort of bits that dribble out of your, out of your thought during the course of a day. I love this.
- [97:37]
Alex
And critically, like, where does the voice go? The voice goes from the ring to an on device on your phone hosted large language model that then transcribes and analyzes. So what is this really doing? This is really, to the extent that a ring stays on you almost all the time, this is about adding a button to the human body that enables you to speak to a large, to a foundation model that's also on your body. And so question to the moonshot mates here. How long until it's not just a button on your body that enables you to talk to a foundation model, but you're swallowing foundation models. How long to the first edible foundation model?
- [98:19]
Peter Diamandis
Well, injectable or subdermal, you think it'll.
- [98:23]
Alex
Be injectable versus edible first?
- [98:26]
Peter Diamandis
I think, yeah. I mean if it's edible, it's going to pass through your elementary canal all the way out to the other end. So I want this. There's interesting, there's part of the skull, right, the mastoid bone in the back behind your ear. That's this hollow area of bone. I think it's a great place to implant a permanent microphone and speaker. Yeah, that's my prediction. We're going to be implanting a microphone speaker at the back of your head.
- [98:54]
Salim
That's funny.
- [98:55]
Dave
That was exactly on Shark. That exact thing was on Shark Tank and Mark Cuban vomited.
- [99:00]
Peter Diamandis
Really?
- [99:01]
Dave
I'm with Ray Kurzweil.
- [99:03]
Salim
Iterate hardware. Much faster outside the body than inside the body.
- [99:06]
Alex
Yes.
- [99:06]
Salim
I don't think it'll be invasive for a while.
- [99:09]
Alex
Yeah, I think we'll see swallowable foundation models in the next two years.
- [99:13]
Dave
Bluetooth, like just Bluetooth in and out of your body to your phone.
- [99:17]
Alex
Bluetooth. But critically locally hosted. Very locally hosted.
- [99:21]
Peter Diamandis
Okay. All right. A few subjects, a few, A few topics on space here. Let's move us along, guys. Chile becomes the first Latin America country to enable Starlink direct to sell. So, I mean, listen, Starlink is such the killer app for SpaceX and the ability for him to potentially bypass the current phone industry, which, I mean, tens and hundreds of billions of dollars has been put down in terms of G4 and G5 level distribution networks now to be bypassed by Starlink. Crazy. But this is what I find this next story. Take a listen. I mean, can you.
- [100:06]
Salim
Can I just go back to that? Can I just go back to that just for a sec? Peter, I think this is something, a very big deal because, you know, throughout history, this is the failure of government. The UN should have launched something like Starlink. You know, they should be doing.
- [100:20]
Peter Diamandis
The UN doesn't have to launch anything.
- [100:22]
Salim
But they're fundamentally unable to. And it needs private sector to do this type of stuff. What I find incredible is the demonetization and the dematerialization of technology allows now a private individual to do something like this that changes the world completely in such a powerful way. And you kind of can say, well, governments just step out of the way and let private sector do everything going forward. Right. Because it'll navigate most of this with light regulation. We can navigate most of this stuff now. So I'm really, really excited by this.
- [100:53]
Peter Diamandis
Okay, can I ask you guys a question? Because I was trying to look at the data behind this. The idea of orbital data centers wasn't in the conversation. How long ago? I mean, we weren't talking about this a year ago. We weren't talking about it nine months ago. Six months. It's the last six months.
- [101:11]
Dave
A guy at Abundance360. March a year ago.
- [101:15]
Salim
The paper on this about 14 years ago.
- [101:18]
Alex
If you were reading Accelerando, in which case you had the blueprint for everything we're seeing now.
- [101:23]
Peter Diamandis
Sure, but it wasn't.
- [101:25]
Dave
But no. But no. March a year ago. One of your guy, One of your abundance 360 guys was talking about it and he was going to do bitcoin mining in space at that point in time. And everybody thought he was insane. And we also thought we couldn't do the cooling. So that was only March a year ago. So that's nine Months.
- [101:38]
Peter Diamandis
But there's a.
- [101:39]
Dave
So I know at that point it was not nothing.
- [101:41]
Peter Diamandis
Yeah. But last six months, really the last four months, all of a sudden, every single player we've got, companies out of China we saw at the last pod. We have now a company out of Europe and we have a dozen companies in the US and then I found this video clip, which I found fascinating because Google was not discussing it a few months ago, but here we are. Listen to Sundar.
- [102:02]
Alex
How do we one day have data centers in space so that we can.
- [102:05]
Dave
Better harness the energy from the sun?
- [102:08]
Alex
You know, that is 100 trillion times.
- [102:11]
Dave
More energy than what we produce in.
- [102:13]
Alex
All of Earth today. So we want to put these data centers in space closer to the sun. And I think we are taking our first step in 27. We'll send tiny racks of machines and.
- [102:27]
Peter Diamandis
Have them in satellites, test them out.
- [102:29]
Alex
And then start scaling from there.
- [102:31]
Peter Diamandis
But there's no doubt to me that at a decade or so away, we'll.
- [102:35]
Dave
Be viewing it as a more normal.
- [102:37]
Alex
Way to build data centers.
- [102:39]
Dave
I never thought I'd hear Sundar say tiny racks of machines. That's hilarious to me.
- [102:44]
Salim
I just love the schoolboy level excitement he's got there. You can see him actually grinning. He's like, oh, data centers in space. This is amazing.
- [102:51]
Peter Diamandis
I love that AI generated. The big banner on top of that video was AI generated. It's like we're going to always tell you that this seen in deep space as AI generated as if it was.
- [103:04]
Alex
Not the reason, Peter, why, you know, I mean, even though I may be a little bit glib saying, well, if you had read Acceleranda, this would have been obvious to you almost 30 years ago. On the one hand, the reason you know that this is a sudden phase change in the way the industry works is Google's plans. This is public information that Google plan to launch these. So it's TPUs. First of all, Google's launching TPU based based data centers obviously are on planet satellites. Planet labs. It's not Google's own satellites, it's Planet Labs. So, you know, if Google's hitching a ride via SpaceX on planet satellites, this is all of a sudden, I'll say that. Second point. Sun synchronous orbit is about to become very, very crowded. Sun synchronous orbit is a low earth orbit that satellites that want to always have sun exposure, never pass behind the earth, never be in the shadow, always have solar power for their panels. It's going to be very crowded.
- [104:00]
Peter Diamandis
It's A real estate. It's a limitation and there currently is limits on how close you can get to other satellites. That's going to be a trill. It's going to be a real challenge because we've got a dozen companies all wanting to do this at the same time. It's going to be a race and how the faa, which governs this is going to decide who gets the territory, who doesn't. In geostationary orbit, there's a very clear demarcation of I own these orbital slots over my country, but low Earth orbit doesn't have that situation.
- [104:35]
Alex
Peter, you're making the case for the Dyson swarm again. The Dyson swarm. So we move out of Geo, we move out of Leo. And Sundar himself in this clip was saying we want to get closer to the sun, so we're sleepwalking straight into the Dyson swarm.
- [104:52]
Dave
Well, Peter, to your prior point too, this was science fiction a year ago. And now suddenly it's mainstream among the top CEOs in the country. How does that happen? But you look at Elon and his credibility, you look at Alex, your credibility. A lot of things that were impossible a year ago are going to be very easy a year from today. And if your track record of predicting them is near perfect, then, you know, the credibility of these crazy sounding ideas immediately catches on. And you're going to see a lot more of that, I think, because the, the, you know, the capabilities are, are exponentially growing. But you know, some of these things are truly harebrained and some of them actually are.
- [105:30]
Salim
Is there a line of sight on solving the heat dissipation problem for these satellite data centers?
- [105:34]
Alex
Yeah, radiate in the direction of the cosmic microwave background.
- [105:39]
Dave
So yeah, the final answer shocked me. But for every square meter of solar panel, it only takes one same square meter of radiant cooling, radiative cooling, which really surprised me. I thought it would be. We, we estimated on Gemini, which was wrong at 10x. You need a 10x more, you know, area. And it was just wrong. It's, it's cooling at 1x and I don't know how they. And it's all aluminum based, so it's not weird expensive metals or anything like that. So yeah, pointed into deep space like Alex has been saying forever and it's for whatever reason just flat out working.
- [106:13]
Alex
So space is pretty cold.
- [106:15]
Peter Diamandis
I took all of the comments from our last two pods and ran them through one of the LLMs and said, okay, pull out the most interesting AMA questions. Here we see a list of ten of them. Gentlemen, let's pick out a few to answer. I'll start with one, which is how do you make these space based AI data centers fault tolerant? Right. There's sunspots, There is the potential for disruption even from an EMP at some point. God forbid. Any ideas on making them fault tolerant?
- [106:51]
Dave
Those are two very different faults.
- [106:53]
Peter Diamandis
Yeah, but they're both. There are lots of disruptive, there are.
- [106:56]
Alex
Lots of different failure modes. So I do think this is another multibillion dollar company that someone should start. There are many techniques right now ranging from switching from silicon based electronics to maybe other semiconductors. Yeah, like gallium arsenide, 2, 6 or 3, 7. Semiconductors that are more fault tolerant have different band gaps to designing just electronics that are intrinsically, at the design level better able to tolerate faults, to just doing what right now is a standard protocol, which is if there's a solar storm or bad space weather, you shut down or you switch them to safety mode. So there are lots of partial solutions here. To my knowledge, there isn't the definitive industry standard solution of what happens if you're in the middle of a training run.
- [107:49]
Peter Diamandis
I just hate to think about the idea of all the data centers in orbit shutting down because there's a solar storm. For the next next 12 hours we're getting hit by alpha particles.
- [107:59]
Alex
But how do we solve that in general, like if there's bad weather or a blackout on Earth, you have diversification. So if anything, again, like, let's put space based AI data centers throughout the solar system. So if there's bad space weather in one part, there isn't in another.
- [108:15]
Dave
That's a great point, actually. I bet earthquakes and tsunamis and hurricanes are much bigger problem than solar storms.
- [108:23]
Peter Diamandis
All right, let's pick another one of these.
- [108:24]
Dave
Hey, just, just to make a point though, there's a, there's a kind of a flaw in the question too, because when you have Skylab up there, you want it to be up there for 20, 30 years and you don't want it to get hit and destroyed or anything. But the space based data centers need to be replaced every three years with new chips. And so they're not. It's a constant launch, recycle, launch, recycle, launch, recycle thing. So if somebody EMPs the entire thing and destroys it, then there's a war, of course. But it was going to get replaced in a three year cycle anyway. It's not like Skylab.
- [108:53]
Peter Diamandis
Interesting. One of the things we did in the for planetary resources, when we're looking at asteroid mining, we, we set up the. The software so we would expect constant disruption. And the system, we focused on rapid restart of the system so it would boot up extraordinarily fast.
- [109:15]
Salim
All right, can I tell a quick story here?
- [109:16]
Peter Diamandis
You can, but I want you to choose one of these AMA questions also.
- [109:21]
Salim
Sure. You and I were sitting in a hotel in Dubai and Richard Branson walks by and he said hello. And we grabbed a quick drink and he said, peter, how's my investment in planetary resources going? And you described how it was going. I had NASA contracts, et cetera. And Richard turns to me and goes, this is why Peter's interesting because. Because in a random hotel lobby, I'm suddenly having a conversation about asteroid mining off planet. This conversation happens nowhere else in the world except with Peter. That's why we love you so much.
- [109:54]
Peter Diamandis
It was fun. All right, Salim, pick a question. Here is this question. Bingo.
- [109:59]
Salim
Should we expect G20 level initiatives for UBI within the decade? I would hope it would be within a year. It needs to happen very, very fast. I think it'll force the conversation. But Universal Basic Income, right? Universal Replaced soon by ubs Universal Basic Services. But I think you shouldn't expect much from the G20 period. I think that's the flaw in the question. But in general, we can expect to see this rolling out in a pretty rapid way. Lots and lots of experiments being done all over the world on this because they have to. We have to move to something like that. That the social contract is completely being wiped out in the current model.
- [110:39]
Peter Diamandis
Dave, why don't you pick a question next?
- [110:42]
Dave
Okay, I'll take number one. How can AI lift up those who aren't international entrepreneurs? I think one, listen to the podcast, get subscriptions, play with the tools, and then brand yourself as an AI expert within your company. Or if you're not going to be an entrepreneur, that's fine. The demand for this knowledge inside regular corporate world is going to go through the roof in 2026. And if everybody around you knows you're the AI person. And also, don't be intimidated. Historically, if you wanted to be a software God, you needed to be very, very softwarey. That's not true with AI, it's much more intuition based. You can build virtually anything with voice prompts. It's just knowing how it applies in your industry will separate you. So just jump in the game.
- [111:28]
Peter Diamandis
Yep. Amazing. Alex, do you have one?
- [111:33]
Alex
I'll take question number four. For $10 trillion. Is pure scaling enough or what comes after? So I think the answer I Think it's a trick question? I think pure scaling probably is. By pure scaling, I'll construe the question to mean we freeze all algorithms. No new algorithms are allowed to be developed, developed in AI, but we're allowed to shovel more and more compute, especially inference time compute, into the existing algorithms. I do strongly suspect that if we froze all the algorithms we have today, no new architectures, but we get lots more compute coming online. The existing architectures combined with scaled compute will be enough to give us AI smart enough to tell us what a perfect algorithm would be. To the point where we get our highly coveted AI researcher recursive self improvement, the final algorithm and we can just ask our scaled algorithms what comes after. So in summary, my answer to question number four is yes. I think probably pure scaling is sufficient. Is it all that we need?
- [112:42]
Peter Diamandis
No.
- [112:43]
Alex
Of course in the real world, algorithmic development is continuing and we're going to get get both. But could we live with pure scaling at this point, my guess is probably yes.
- [112:51]
Peter Diamandis
All right, let's answer one more here. Number three, how do the Moonshot mates prepare day to day for each podcast episode? Yeah, I think we can share that. So let's see, Alex, you're constantly providing the team with a incredible list of all the breakthrough stories you're searching. You're probably generating. How many AI stories per day do you think you generate for us to look at?
- [113:17]
Alex
Oh gosh, order of magnitude 20 important stories per day. I'm also at this point I spend so much time just reading, reading primary sources, archive papers, et cetera, living in the zeitgeist of the moment. Because after all, drink singularity comes around only approximately one time per planet. So it's a special time. I, I do also at this point probably should say I'm also turning all of these stories in addition obviously to research for this show into quasi daily newsletter.
- [113:55]
Peter Diamandis
Just trying to help follow Alex on X. He puts out some incredible daily sort of interesting AI rants, I would say, or AI advances.
- [114:06]
Alex
Follow me on X, follow me on LinkedIn. It's a genre I'm trying to popularize. I'm calling it it's Psi Non Phi. It's written in the style inspired by Charlie Strawsk, Salarando others written in the style of science fiction, except it's all grounded in what's actually happening.
- [114:21]
Peter Diamandis
So Alex generates on the order of 150 stories a week. I'll generate probably 20 or 30 stories a week. We get some from Salim, some from Dave. All this gets sort of put into different Categories we then sort of cut it down to the top 30 stories. I typically spend about 10 hours sort of playing slide shuffle working with Gianluca and Dana, who are incredible members of our team. And then we do research on those stories to get the details and think about them. And I'm probably spending a good 15 hours of my week focused on this. How about you, Dave? Dave and Celine?
- [115:06]
Dave
Well, everything you just said, you know, I lean entirely on Alex's internal feed which now you can get on X. You know, it's a digest of the same thing that's brand new as of the last week or so. So take advantage of it. But I've been reading that internally for what a year now I guess, or more, which is very time consuming, but I need to know it all. The only other thing I do is I route all the really big stuff over to the venture capital team and say what are the business implications of this? Which we need to know anyway to run our venture fund. And then I try and bring those stor back into the moonshots feed so that we can talk about not just the technology but what it means to investors, to business people, to people, career.
- [115:45]
Salim
Planning and all that Salim I spend, I source a few stories but nowhere near as much as the rest of you. But I think the, I spend a chunk of time. The minute you guys release the. The deck, I look through it and then find it's changed again and so I have to restart again. So I'm always playing catch up with the slides that you. And then Peter on the last night you God knows what you do, but you change it all again and I have to re research it. I spent half a few hours a week looking up the terms in the papers that Alex surfaces because half of it is Greek. And then I'll ask also my community member, my open exo community. So there's a hive mind reaction to some of this which I think is very powerful. Similar to Dave asking his team just.
- [116:33]
Peter Diamandis
Again to let our subscribers know.
- [116:35]
Salim
Overall though, it's just sucking up more and more time per week. But it's such a important thing.
- [116:40]
Dave
It's the most fun thing we do. Come on.
- [116:43]
Salim
It is super fun.
- [116:44]
Alex
What no one ever warned you of, Salim is like the singularity of covering the singularity. It's a singularity of time suck.
- [116:52]
Salim
It's just. It's a black hole. It's a black hole. Dyson square form forming around my own.
- [116:57]
Alex
Singularity wants your attention.
- [116:58]
Peter Diamandis
So we, we hope for all our subscribers and listeners that you guys appreciate we put a huge amount of work because we care about this deeply.
- [117:07]
Salim
I need to give a plug, quick plug. I'm doing my meaning of life session next week. We've already. We're almost sold out. It's going to be pretty amazing. It's going to go for several hours, starting 11 o' clock Wednesday. Come armed with any question you have about life and judge me by how well this framework answers that question. Boom.
- [117:25]
Peter Diamandis
All right, let's get to our outro music here from David Drinkal. I think it's the perfect name for a drinking game that can't be real. Oh, my God.
- [117:34]
Salim
It's a bingo card.
- [117:36]
Peter Diamandis
I love it. So this is a bingo card. And you can see tile the earth.
- [117:42]
Alex
Have our glasses of water ready?
- [117:44]
Peter Diamandis
Yeah, I do cybernetics. Okay, let's listen to where's the humanoid robot entry six arm humanoid robots. Robots down at the bottom and cloud computing on the bottom left. Okay, let's take a listen to David's outro music. Thank you, David, for producing this for us. And again, if you're listening and you are creating music videos and you want to create an outro song for us, send it over. We'd love to. We'd love to listen to it and perhaps select it. All right, let's take a listen.
- [118:26]
Dave
Take a sip when Peter says good old trotting gentlemen two if he name.
- [118:33]
Peter Diamandis
Drops just got back from again drink.
- [118:38]
Dave
When Alex says better benchmark marks a van and bench finish a glass if he whispers d swarm at last Moonshot bingo.
- [118:50]
Alex
Moonshot bangles. Moonshot.
- [119:06]
Dave
Poop, chug and save Sip when someone says we'll Cure Every Disease 2 and Day Mental startups or singularity drag when.
- [119:20]
Peter Diamandis
Selene drops insert my usual objection if the phrase rat wrestle leapfrogging hit successively.
- [119:30]
Dave
Moonshot Bando Moonshot Bingo. Moonshot bingo. Moonshot bingo.
- [120:12]
Peter Diamandis
One sip for every code red tool.
- [120:15]
Dave
For humanity's last exam.
- [120:18]
Peter Diamandis
3 When Alex says solving math.
- [120:21]
Dave
Yes, that old plan.
- [120:23]
Peter Diamandis
Big up when anyone says universal basic services Pass out when Peter yells that's.
- [120:31]
Dave
A moonshot, ladies and gentlemen. Moonshot.
- [120:37]
Peter Diamandis
All right. Amazing.
- [120:39]
Salim
That is awesome.
- [120:40]
Dave
It's a moon shot, ladies and gentlemen.
- [120:42]
Peter Diamandis
You know, this is again, a tribute to the creative nature of all of our subscribers. Thank you, guys. And also the tools out there to allow you to do things like this.
- [120:52]
Dave
Guys, I think that's the best yet.
- [120:54]
Peter Diamandis
Amazing weekend.
- [120:56]
Dave
Yeah, super creative.
- [120:57]
Salim
Take care, folks.
- [120:59]
Peter Diamandis
Every week, my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI, and quantum computing to transport energy, longevity, and more. There's no fluff, only the most important stuff that matters that impacts our lives, our companies and our careers. If you want me to share these metatrends with you, I write a newsletter twice a week, sending it out as a short 2 minute read via email. And if you want to discover the most important meta trends ten years before anyone else, this report's for you. Readers include founders and CEOs from the world's most disruptive companies and entrepreneurs building the world's most disruptive tech. It's not for you if you don't want to be informed about what's coming, why it matters, and how you can benefit from it. To subscribe for free, go to dashmandis.com Metatron Trends to gain access to the trend 10 years before anyone else. Alright now back to this episode.
- [122:08]
Alex
So you're about to make a trade based on a friend's text, but which.
- [122:12]
Dave
U do you listen to is it we could buy a house in Tulum.
- [122:18]
Peter Diamandis
Get optioning those options.
- [122:22]
Alex
We could lose everything.
- [122:25]
Dave
Or let's do a little research, get.
- [122:29]
Alex
Your head in the trade and make the investment decision that's right for you.
- [122:32]
Dave
Learn more@finra.org TradeSmart.