The AI CEO Arrives: Sam Altman's Succession Plan, Job Loss Continues, and Our 2027 'Solve Everything' Paper | EP #230

Summary

Podcast Summary: Moonshots with Peter Diamandis – "The AI CEO Arrives: Sam Altman's Succession Plan, Job Loss Continues, and Our 2027 'Solve Everything' Paper" | EP #230

Release Date: February 13, 2026
Host: Peter H. Diamandis, with co-hosts Dave Blunden, Salim Ismail, and Alex Wissner-Gross

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Overview

In this episode, Peter Diamandis and his "moonshot mates" tackle the accelerating transformation of society due to AI and exponential technology. The discussion is split into two main segments:

1. The Breaking Tech News & Analysis, focusing on AI’s evolving role in business and society, including the concept of an AI CEO and the current wave of job losses linked to automation.
2. The Unveiling of the "Solve Everything" Paper, a collaborative vision outlining how humanity can achieve abundance and "solve everything" by 2035, including actionable frameworks for governments, companies, and individuals.

The tone is fast-paced, intellectually playful, and sometimes irreverent, as Peter and team blend optimism about tech-fueled abundance with pragmatic warnings about societal disruption.

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Key Discussion Points & Insights

1. When Will an AI CEO Run a Major Company?

• (00:00–12:55) Peter kicks off by referencing Sam Altman's comments about having an AI CEO succession plan at OpenAI. The hosts agree this isn't a distant prospect—substantial parts of the CEO role are already automatable.
  • Alex Wissner-Gross (10:34): "Probably several months ago." (in response to when a $1B revenue company will be run by AI)
  • Discussion on how most of the CEO’s job (strategy aside) is now document-based and ripe for automation.
  • Notion that "meat puppet" human CEOs may exist in name while AI runs the show.
  • Marx vs. Moravec: The capitalist class (CEOs) may be replaced before workers; manual jobs like electricians currently remain safe.

Notable Quote

“If you want to blow the whistle on meat puppetry, you can blow it to Peter.” — Alex Wissner-Gross (11:07)

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2. Accelerating AI Model Deployment: The Singularity’s Speed

• (13:46–20:53)
  • OpenAI release cycles have shrunk from 97 to 29 days; Anthropic shows similar acceleration.
  • Alex Wissner-Gross: We're moving from annual model releases to daily, even "minutely" improvements, driven by intense competition and the rise of recursive self-improvement where AIs help develop themselves.

Notable Quote

“We're on the edge—probably slightly past the edge—of a new era... the recursive self-improvement era, where the models are starting to rewrite their own code.” — Alex Wissner-Gross (15:43)

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3. Job Loss and Social Contract Dissolution

• (48:06–53:46)
  • US job cuts more than doubled in January YoY; massive layoffs at Amazon and UPS tied to AI-driven productivity.
  • The panel warns this is not a typical recession—it's the social contract “pixelating away.”
  • UBI (Universal Basic Income) is seen as inevitable, but the transition period could involve major unrest and institutional disbelief.

Notable Quotes

“It's not unemployment, but disbelief from our institutions... it's literally tasks being evaporated in front of our eyes.” — Salim Ismail (49:18)
"That's how the mechanics, which AIs will do very well... The CEO's role is to hold the purpose." — Salim Ismail (09:00)

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4. Opportunities: Be a Creator, Not Just a Consumer

• (27:34–28:44)
  • The ability and necessity for everyone, including kids and older adults, to become a creator, not just a consumer. The AI tools are there—learn by doing, and you’ll be immune from displacement.
  • “There’s two roles in the future. There’s the entrepreneur and there’s the employee. And one of those will not exist.” — Salim Ismail (28:39)

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5. AI Race, Regulation, and Economic Upheaval

• (36:52–42:09)
  • Top AI startups now outvalue all dot-com era IPOs; this economy is outpacing even the internet’s impact.
  • Stock market is bifurcating: “AI beneficiary” vs. “AI roadkill.”
  • Antitrust law is preventing total consolidation by tech giants; without it, “they’d be gobbling each other.”

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6. Data Centers, Regulation, and the Path to Abundance

• (57:55–62:59)
  • New York is restricting data centers, citing climate and energy concerns—a move Alex sees as counterproductive.
  • The “Dyson Swarm” and orbital computing are discussed as solutions to terrestrial regulatory bottlenecks.

Notable Quote

“New York very generously subsidizing orbital computing and the Dyson Swarm.” — Alex Wissner-Gross (63:00)

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7. Robotics Advances and Societal Impact

• (64:59–70:11)
  • Real-world impact: FSD (Full Self-Driving) Tesla saves a man’s life during a heart attack (65:56).
  • The inevitability of autonomous vehicles, the societal tipping point likened to smoking bans.
  • China’s explosive lead in robot deployment highlights global shifts.

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8. Cryonics: Towards Portfolio Approaches to Longevity

• (71:45–77:43)
  • Breakthroughs in cryogenic preservation of brain synapses spark discussion of “plan B” or “plan C” longevity strategies.
  • Cryonics is positioned alongside mind-uploading and radical life-extension technologies.

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Part II: The "Solve Everything" Paper (Chapters & Key Concepts)

Introduction and Purpose

• This is Alex Wissner-Gross and Peter Diamandis' collaborative blueprint for harnessing superintelligence toward practical abundance by 2035.
• The next “18 months to 2 years” will lock in the technological, institutional, and moral direction for the coming century.

Key Concept

“Technologies get locked in... we’ll be stuck with QWERTY until the heat death of the universe.” — Alex Wissner-Gross (80:34)

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Chapter-by-Chapter Breakdown

1. The War on Scarcity (81:04)

• Each wave of history fights a different scarcity: ignorance (science), muscle (industrial), distance (digital), and now, attention (intelligence revolution).
• The “lone genius is dead”—success comes from distributed, systematized problem-solving.

2. The Thesis (85:26)

• Cognition is becoming a commodity, like oil; AI’s main value will be shaped by how we direct (“target”) it toward meaningful outcomes.
• “Shaped charges”: Just as explosives need direction to be useful, so does superintelligence.
• Shift from paying for labor/time to paying for verified outcomes.

3. The Mechanics (92:41)

• Proposes an “industrial intelligence stack”: purpose, task taxonomy, observability, targeting systems (benchmarks), models, actuation, and verification.
  • Once a domain like math is “solved” (i.e., compute can be poured in for results), the next domains to flip will include coding, chemistry, and physics.

4. The Lock-in (101:33)

• The next 18–24 months will “lock in” standards and governance, much as the QWERTY keyboard did for typing.
• The “race isn’t to build the best AI but to write the best scorecard everyone else is graded on.”

5. The Mobilization (106:45)

• AI’s shockwave will move from solving math to physics, chemistry, biology, and eventually planetary engineering within just a few years.

6. The Engine (107:19)

• The real investment opportunities aren’t the AI models themselves, but the “tracks” (testing, harnesses, scoring functions, data systems, funding mechanisms) upon which they run.

7. Moonshots (108:08)

• List of 15 humanity-scale moonshots (e.g., doubling lifespan, ending hunger, AI-powered global education, mind uploading, interspecies communication, multi-planetary existence).
• The era of rote schoolwork is ending—AIs should augment students and entrepreneurs to aim for grand ambitions, not simply homework.

8. The Muddle vs. The Machine (112:54)

• The “muddle” is the risk: a bureaucracy endlessly tracking inputs instead of outputs, slowing true progress.
• Proposal: replace GDP with an “Abundance Capability Index” (measuring a nation’s capacity to solve problems).

9. Build the Rails (115:48)

• How individuals, companies, and governments can actually “steer” the coming wave:
  • Investors: Build primitive infrastructure, not just applications.
  • Entrepreneurs/Executives: Design outcome benchmarks, use targeting systems for AI, measure output not input.
  • Citizens: Don’t wait for nations—use available AI to create, not consume, and direct it toward meaningful ends.

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Notable Quotes & Memorable Moments

• “No one expects the singularity, Peter.” — Alex Wissner-Gross (00:56, 79:16)
• “Artisanal intelligence is cooked.” — Alex Wissner-Gross (83:19)
• “You could debate who’s in and who’s out, but clearly you’re either in or out [re: AI economy].” — Dave Blunden (40:51)
• “Calling a maturity curve kind of speaks of an inevitability ... it’s more of a descriptive model than a predictive one.” — Salim Ismail (98:52)
• “If you take home one message—look into cryonics. You owe it to yourself.” — Alex Wissner-Gross (76:46)

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Key Timestamps by Topic

• AI CEO Debate: 00:00–12:55
• AI Model Release Acceleration: 13:46–20:53
• Vision AI / Augmented Life Applications: 20:53–25:10
• Creator Mindset & Job Loss Realities: 27:34–28:44; 48:06–53:46
• Market Valuations, Antitrust, Economic Shifts: 36:52–42:09
• Data Centers & Regulation: 57:55–62:59
• Robotics, Autonomous Vehicles, Societal Impact: 64:59–70:11
• Cryonics & Longevity: 71:45–77:43
• "Solve Everything" Paper Unveiled: 79:16–117:29
  • Chapters 1–4 breakdown: 81:04–104:52
  • Mobilization & The Engine: 106:45–108:01
  • Moonshots, Muddle vs. Machine, Building Rails: 108:08–117:29
• Audience AMA: 118:36–123:01
• Close & Outro Poem (CJ Trueheart): 124:52–127:44

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Final Reflections

The hosts deliver a clarion call: the spread of AI is literal and exponential, rapidly reshaping what it means to work, create, and lead. The next couple of years are critical for guiding this trajectory toward true abundance, rather than bureaucratic "muddle" or social collapse. Whether through adopting a creator’s lens, steering AI toward society’s grand challenges, or preparing for the disruption of age-old institutions, the message is clear: act now, or risk irrelevance in the era of superintelligence.

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For Further Exploration

• Solve Everything Paper by Alex Wissner-Gross & Peter Diamandis
• Moonshots Metatrends Newsletter (Subscribe here)
• Follow Peter on X: @PeterDiamandis

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This summary aims to capture the rich detail, key arguments, and actionable takeaways from Moonshots EP #230, providing clarity and direction for listeners navigating the AI-dominated future.

Transcript

[Peter Diamandis]

When do we see a billion dollar revenue company being run by an AI CEO?

[Alex Wissner-Gross]

I think it's pretty likely that there already is such a company right now.

[Peter Diamandis]

U.S. jobs disappear at the fastest rate this January since the Great Recession.

[Salim Ismail]

This is not really a recession. It's literally tasks being evaporated in front of our eyes.

[Alex Wissner-Gross]

This shows us Marx was wrong. We knew that anyway. We have the capitalists who are being first in line to be replaced by the automation.

[Salim Ismail]

For me, this is the social contract. Little by little, disappearing and pixelating away.

[Peter Diamandis]

Alex and I are going to be unveiling a paper we've been working on for some months. It's called Solve Everything. How do we get to abundance by 2035? The next 18 months to 2 years are going to set the rules down for the next century. We're about to have this conversation. The paper book is nine chapters. Are you ready to jump in?

[Alex Wissner-Gross]

No one expects the singularity, Peter. I'm ready.

[Peter Diamandis]

Now that's a moonshot.

[Dave Blunden]

Ladies and gentlemen.

[Peter Diamandis]

Everybody, welcome to Moonshots. Another episode of WTF just happened in tech. I'm here with my Incredible moonshot mates, DB2. Salim. AWG guys. It is just accelerating. In fact, this is the second WTF episode we're recording this week. Just because the news is just incessant. We're going to have this, this podcast today in two parts. First, we'll be covering the news that's breaking. A lot of it really important news. The second part, Alex and I are going to be unveiling a paper we've been working on for some months. It's called Solve Everything. How do we get to abundance by 2035? This is the equivalent of our of the paper released situational awareness in AI 2027. This is our view of where things are going. So in second half, get ready for this. Excited to present it. It shows the brilliance of awg. I'm in Sun Valley at the moment, speaking at Tony Robbins platinum finance event about AI and longevity. Dave, you're back at mit. Salim, where are you, pal?

[Salim Ismail]

I'm home in New York, waiting for the warm weather to hit and get us above zero for once. I'll take six months wondering why I ever left India.

[Peter Diamandis]

No, why you left Florida is the correct answer. And Alex, looks like you're in your normal setting.

[Alex Wissner-Gross]

Some AI which the audience is convinced that I live in VR or maybe a hotel. And you wouldn't actually, you probably would believe the YouTube comments on the flowers and the lamp and their purported invariability.

[Peter Diamandis]

Yeah, and I have Taken on.

[Dave Blunden]

I want to point out that the orchids have changed.

[Alex Wissner-Gross]

Actually, the orchids have changed, but I'm getting, like, flower keeping advice in the YouTube comments at this point. People telling me to put ice cubes in the orchids.

[Peter Diamandis]

And I have to say, I'm having so much fun with claudebot. The lobsters have begun to become part of my life inside and out. So I'm like, you know, bringing them into the conversation here. I got jealous, Dave, of the lobsters, in your view.

[Salim Ismail]

So I'm holding the lobsters back for now.

[Alex Wissner-Gross]

We're having a Tribbles moment.

[Dave Blunden]

There's actually more. I take some of them down.

[Peter Diamandis]

It is a Tribbles moment. You're absolutely right. Hopefully it's not the trouble with the lobsters. All right.

[Alex Wissner-Gross]

No, no. These tribbles are economically productive.

[Peter Diamandis]

Okay. Well, these are. And they're so much fun. I can't wait to express the level of collaboration I'm having with my claudebot, which I've named Skippy. If anybody knows where the name Skippy came from, put it in the comments. It's my favorite AI from science fiction.

[CJ Trueheart]

All right.

[Peter Diamandis]

This is the number one podcast in AI and exponential tech. Getting you future ready, getting you ready for the supersonic tsunami heading our way. And with that, let's jump into the news. First off, top AI news. I love this article. This came out from Forbes. Sam is the COVID child, cover boy for Forbes this week. And the question is, will ChatGPT become the CEO of OpenAI? So this is what Sam said. Pretty simple. He has a succession plan. He's said he doesn't want to be the CEO of a public company. And honestly, being the CEO of a public company is a pain in the neck. So taking it further says if the goal for artificial intelligence is to become so advanced that it can run companies, he asked, then why not run OpenAI? I would never stand in the way of that. He says I should be the most willing to do that. I find that fascinating. When will we see an AI actually running a significant economic engine like this? Dave, thoughts?

[Dave Blunden]

This is no joke, actually, because this is board meeting week for me. So I have back to back Minerva. Today, the cash cow from Dartmouth, then tomorrow the $2 trillion asset manager, then the next day, the public company ever goes all back to back. And in every one of those meetings, this is the topic, not replacing the CEO. But all of our plans are now in written form that we can digest with AI. So we're trying to track every single movement within every company in documents digestible by AI. And then if you ask the CEO, well, what do you do? It's mostly set course and set strategy, which is a very small fraction of total time. What else do you do? What's the other 90% of time go into and how much of that can be done by AI today? And the answer is a lot, which is great because then the CEO has unleashed to be even more effective at setting strategy and also promoting the strategy. So I don't think that part's going away anytime soon. But the other 90% is really just inbound information getting routed into the organization to do these specific tasks. Which is outbound is documents in, documents out now. So we're really now for this.

[Peter Diamandis]

Salim, you and I have been talking about this forever. When are we going to have AI board members, AI executive teams, and eventually AI CEOs thoughts?

[Salim Ismail]

Yeah, we're seeing this shift from as if from AI from a tool to being a governance actor. Right. We already have an AI minister in Albania. And initially these are kind of like toy things. But in reality, this is very powerful stuff because an AI scanning can be scanning millions of documents at a company in real time has a much better sense of what's going on in the company than any human being can possibly do. Right. A typical loop in a big company is the senior management sets some direction or policy cascades down at the coal face. The people do takes a long time to get down there. You have Chinese whispers. By the time it's down there, they're doing some activity that nobody at the top even knows about. And then they start doing stuff, report back up to the top. You've got another set of Chinese whispers. And by the time data gets to the top is diluted so much and you lose all the intelligence in the middle. And so AI is going to come through and break through radical, create radical opportunities to do this. And I think what will happen is we'll see a pure AI organization at some point soon, but they won't look efficient. They'll look literally alien. And that's fine. I think it's one of these where you can't wait for it to happen.

[Peter Diamandis]

And then you can't tell.

[Dave Blunden]

You can't compete against that because of time dilation. I think that I asked Alex for some help with the strategy of a big company earlier this week and one of the points he made in his answer, which was brilliant, of course, one of the points he made in his answer was time dilation. If you look at banks and insurance companies and practically anything, it doesn't change strategy more than once a decade or once every millennium. Now, in the age of AGI, the course corrections are going to be. It'll go from decades to years to months to weeks to minutes, all over the next couple of years.

[Salim Ismail]

We have a whole section in the first exo book called Death to the Five Year Plan. Right. Because today, by the time you finished your five year plan, it's out of date. Then you spend all your time maintaining the plan.

[Dave Blunden]

Exactly, exactly. So the amount of information that you need to assimilate to do those course corrections is beyond human. There's just so much going on. If you read Alex's daily feed, the amount of change going on, if you compare it day over day, you can see the expansion of the rate. And so it's just so much happening. It's beyond human assimilation at some point. So you have to have an AI CEO to assimilate it and even suggest the course corrections.

[Peter Diamandis]

And Dave, you said it over and over again. Right. The role of the CEO in part is to understand what his or her employees are doing and if they're making the most efficient use of their time and their resources. And it's all knowable, but just not by the human right now. But the AI can be giving you an understanding of this person's operating at 50% of capacity or this person's not making the best use of their resources.

[Salim Ismail]

That's how the mechanics, which AIs will do very well, I think where you have the C suite and the CEO, they'll be holding the purpose, hence the mtp, et cetera. They need to hold direction and what problems the company or organization is actually trying to solve.

[Dave Blunden]

Yeah. So there's two sides to this. One of them is outbound strategy, simulate all the data from the world. The other is inbound. What are all my people doing and. And why? And those are the kind of the two sides of being a CEO. And Peter just brought up that inbound side, which Salim you emphasized. And I think on that front, this is comp plan season, right? Beginning of the calendar year. I'm tying everybody's CEO comp plan to data gathering this quarter so that we have everything that's happening in the organization. Now, Peter, you've been saying privacy is dead for a long time. Everything is knowable all of a sudden. And there's a whole bunch of mechanisms for that. I won't even get into it because this will go too long. But if you're a CEO or a senior manager in any company right now, really focus Q1 on how do I grab absolutely granular information on what everybody's doing so that I can start to feed it to the AI to get its opinion on whether these are the.

[Peter Diamandis]

Good uses of time stuff is speeding up. Alex, when do you, I mean, to put a sort of concrete objective on this. When do we see $1 billion revenue company, not, not valuation, because valuation skyrockets through the roof when you pull two or three smart people together, but a billion dollar revenue company being run by an AI CEO? What's the timeline for that, Alex, and what's your thoughts on these?

[Alex Wissner-Gross]

Probably several months ago.

[Peter Diamandis]

Several months. You think there's a billion dollar revenue company being run by an AI right now?

[Alex Wissner-Gross]

I think it's very likely that there is a billion dollar run rate company being. Now you said run by. I think there's probably a human CEO there for legal purposes and meat puppets, meat puppetry purposes. But I think it's pretty likely that there already is such a company right now.

[Peter Diamandis]

And by the way, if you know of one, please put it in the comments. We'd love to hear about it and see it.

[Alex Wissner-Gross]

If you want to blow the whistle on meat puppetry, you can blow it to Peter.

[Peter Diamandis]

Yeah, all right. Anyway, I love this idea. You know, it's eating your own dog food. If in fact, you know, if in fact Elon believes that we're going to have the smartest AIs coming out of XAI and if OpenAI believes the same for its, you know, ChatGPT6, whatever comes next, it should be the CEO.

[Alex Wissner-Gross]

I also think, if I may, Marx was wrong. This shows us Marx was wrong. We knew that anyway. But this is another case in point. Look at what's happening. The story that unfolds here is we have the capitalists who are being first in line to be replaced by the automation. It's not the workers. We see booming jobs for electricians and H Vac engineers. Their salaries are booming and yet CEOs are first up to be replaced. So if anything, I would take Marks off the shelf, if it was on the shelf at all. Replace it with Moravec's paradox, which is again this paradox that tasks that are hard for humans and easy for humans are respectively replaced by easy for machines. Hard for machines. Machines are able to do complex calculations, solve math. It's pretty hard for humans. There looks like it's going to be easier for the machines to automate away CEO labor, which is sufficiently hard for humans that it's well compensated and relatively scarce commodity to find high quality CEOs. And yet it'll take a few more years for the machines to do an amazing job at unskilled manual labor.

[Salim Ismail]

I for one cannot wait till the AI CEO overlords take over the world. I wish I could have any ice taking over and running my company instead of having to do it myself. It's a pain in the ass.

[Peter Diamandis]

It's hard the data up and running, pal.

[Salim Ismail]

Yes, yeah, you have to feed it properly, etc. It'll happen, but I just can't wait for the speed of that to accelerate.

[Peter Diamandis]

By the way, it's super fun the way we're going back to Claudebot as the is the de facto handle instead.

[Alex Wissner-Gross]

Of open cloth lobsters were the mascots of the Singularity.

[Peter Diamandis]

Lobsters are here to stay, everybody.

[Peter Diamandis (Newsletter Promo)]

You may not know this, but I've done an incredible research team and every week myself, my research team study the meta trends that are impacting the world. Topics like computation, sensors, networks, AI robotics, 3D printing, synthetic biology, and these metatrend reports I put out once a week enable you to see the future 10 years ahead of anybody else. If you'd like to get access to the Metatrends newsletter every week, go to diamandis.com metatrends that's diamandis.com metatrenDS all right.

[Peter Diamandis]

Staying with our OpenAI theme, this is incredible. This is about feeling the speed of the singularity. OpenAI achieved 70% time reduction between models. So OpenAI released, their release sequence has gone from 97 days to 29 days on a release cycle. Anthropic with their Opus 4.0 and Opus 4.6 took about 73 to 75 days. So the concept here, and Alex, I think you or Dave mentioned it last time, we're effectively heading towards a continuous deployment like it's continuously being improved. And whether you call it 0.6, 0.7, 0.7 or 0.8, there's a continuous improvement. Alex, thoughts on this?

[Alex Wissner-Gross]

I do think we're moving toward daily and then hourly and then minutely releases. Certainly. I also want to take a step back and try to understand why this is happening. The obvious factor, it should be obvious, is competition. There's leapfrogging that's intensifying between all the frontier labs. So some quantum of why we're reducing by 66% or so, 70% the release cadence is just due to intensifying competition. That's the boring explanation. More interesting explanation is that the technologies behind the releases themselves have evolved. So historically, when we were dealing with annual Releases. That was a world, an era of pre training when if you want a new model you have to do a different architecture and you have to pre train off of a larger corpus with more compute. Those were the days of the original chinchilla scaling or kaplan scaling before that. And that was a much slower world because if you wanted a new release you had to start all over again. Then we moved with 01 Strawberry, which was sort of the herald for reasonableness.

[Peter Diamandis]

Do you remember that? That was ancient times, two years ago.

[Alex Wissner-Gross]

Oh my goodness. Yeah, that was like so many singularities ago. So we moved to the era of reasoning models when it was possible through a process that used to be called iterated amplification and distillation, to take a pre trained base model, a baseline model and then cyclically generate a bunch of training data and distill from that to a child model and repeat the process over and over again. And that post training revolution for reasoning models was much faster. It's much faster to post train a model off of a corpus of synthetic data. And so release cycles contracted and I think now we're on the edge, probably slightly past the edge at this point of a new era, call it the recursive self improvement era, where the models are starting to rewrite their own code. It's not just a matter of parent or teacher model generating synthetic training data that's used for a child distillee model. It's literally the parent is writing the code for the child and that can be done even more quickly than just post training. And I think it's just going to get faster and faster until it's a continuum.

[Dave Blunden]

Yeah, it's going to accelerate like crazy. But also we're in a window of time, a very narrow window of time right now where the very best technology is available to you. Like Claude gives you their absolute best 4.6 and OpenAI does and Gemini does, I would not count on that surviving post the self improvement era right now. Also, the Chinese open source models are pretty much right on par with the best of the best. They're slipping a little bit. But I think the window of opportunity to take advantage of that and build something out of it is right here, right now. I really doubt two years from now that the best AI is going to be just log in and go here, here you can have free access to it and what'll happen is you'll be deprived of it with the excuse being security and safety.

[Peter Diamandis]

Interesting.

[Dave Blunden]

Which is true. I mean it's pretty hard to deny, but you have a Window of opportunity right now to be on the very cutting edge. If you don't take advantage of it now and get somewhere with it right now, I wouldn't count on that existing.

[Peter Diamandis]

So the models are going to go dark, right? It's the secret sauce is going to be kept internal to benefit those companies as they go into an all out battle.

[Dave Blunden]

Well, even today if you talk to Noam Brown over at OpenAI, he's working on the next generation internally, but it's only like three months in the future that he has access to. But three months in the future, in the era of self improvement is like massively different intelligence level. The definition of three months of AI development two years ago, one year ago and today, that's the point of the slide. I guess three months is like a lifetime of difference in capability that they're using internally versus what's available in the outside world. So you got to expect that this is it's now or never to react basically. And people are still hugely underreacting to the importance of what's happening right now.

[Peter Diamandis]

Insane. Salim.

[Salim Ismail]

I've got kind of like the crazy antithesis of this. We're working with a large monster European corporation and we showed them something that can give them massive impact straight to the bottom line. And the response was, oh, this is fantastic. Let's bring this to the planning meeting in October, right?

[Dave Blunden]

And you're like, right, right.

[Salim Ismail]

And you're like, I can't even see past three weeks. And you're like talking calendaring something 10 months down the line for something that's going to have a demonstrably, you've just agreed it's a demonstrably huge impact. So this is the impedance mismatch between legacy. But there's a story for me, this story mostly a bit of a yawn. The reason I say that is we've been seeing this in the fast moving tech space for a while. Remember Raymond McCauley was the chief scientist at Illumina, right. They were making high speed gene sequencing machines.

[Peter Diamandis]

Love the story.

[Salim Ismail]

And it turned out that the shelf life of a gene sequencing machine was literally eight months. That was the sales cycle before the next iteration came out. But it took four years to build one of these, design and build one of these machines. So they had to have four parallel production sequences in sequenced at the right level so they could hit that 8 to 10 month shelf life. Sales shelf life. Right. So in the kind of high tech world, we've seen this pattern before, but this brings it to software and makes it A continuous intelligence cycle.

[Peter Diamandis]

I mean, this is the singularity at play. And again, the theme that we keep on hitting in this podcast is this is the slowest it'll ever be and the worst it'll ever be. And it's accelerating at a speed which is frightening. Frightening in that the four of us spend tens of hours per week reviewing and learning and playing and trying to communicate it. And it's only going to be something that my claudebot is gonna keep up with. And speaking of claudebot, this is Vision Claw Lobsters just got Vision Agentic AI for meta Ray Ban glasses. Let's take a look at this quick video and chat about what it means. Hey, Clubbot, can you help me add.

[Salim Ismail]

This into my Amazon cart?

[Advertisement Voice]

Sure, I can help with that. I see the Monster Ultra Strawberry Dreams energy drink. I'll look that up to add to your Amazon cartoon. It's added to your cart.

[Peter Diamandis]

Is there anything else I can help with?

[Dave Blunden]

Cool, thank you.

[Peter Diamandis]

I love this because I want to have this capability for Skippy to be able to see what I'm seeing. Do it. Support me across everything. This is about accelerating your minute to minute life and having your AI there as your guardian angel supporting you.

[Salim Ismail]

I'm visually looking through openclaw at you guys and it's saying that you guys are kind of meatheads, really.

[Alex Wissner-Gross]

Peter, how many times have you asked for jarvis? You got jarvis. Christmas came early.

[Peter Diamandis]

Yeah, I actually named my cloudbot Jarvis. Initially, I said that's just too generic. I love jarvis. I write about JARVIS in all my books as sort of the ideal AI analog. But SCPI is a more unique name for me. It really is here and now all of a sudden. Besides, it's going to take in all imagery, it's going to be taking in all audio. Listening to your conversations always. And people say, well, I don't want to lose privacy to my AI. Well, guess what? You're going to give AI access to everything you're seeing, everything it's hearing, every conversation, every email. Because when you do that, the value creation in your life is so great that not doing that is going to feel like you've ripped away all of your. Your mental capabilities.

[Salim Ismail]

Warning, please, for everybody here. Everybody listening is watch. Be very careful to audit the skills that you download to open Claw, because there's a lot that have viruses and other malfeasance built into them already. And so it's a very dangerous game out there.

[Peter Diamandis]

There are protection layers coming on, by the way. One thing, I reached out to Alex Finn. We featured him On a previous Moonshots podcast. Remember when Alex had his lobster Henry call him out of the blue. And Alex has been doing incredible work with this and he's going to be joining us on one of our next podcasts to talk about how he set it up, what security he's taking in place, and in particular, rather than running it on the existing models, he's gone forward to set up Mac Studio and then download Kimik 2.5. So you've got all the capability resonant on your machine, not costing you anything month to month. We'll go into that in a future podcast. Excited to share his vision and knowledge with everybody who's a viewership here.

[Alex Wissner-Gross]

So getting to echo Saleem's cybersecurity advice to the audience, everyone, get your baby AGIs vaccinated.

[Dave Blunden]

Nice. Nice. You know, also to the crowd out there, I did a claudebot build last night and the GUI sucks and it's all open source. So if someone out there puts something like Peter mentioned a couple times on the pod that his mom and I'm tracking, my mom too, can use this to access everything and build everything. It's like a total, total world opener for she's in her 90s, I guess to your mom and mine's in her 80s. But the install process on Cloudbot, she's not going to get through that. It's still command line. You start from the terminal, which is nuts. So somebody out there build a better onboarding process because once you're in it's gold, you're just talking to it, but it needs a little help.

[Peter Diamandis]

Yeah. And of course the most important thing is using your AI to build your AI. So when I sit down with with SCPI and I say, listen, building a mission control, what are the best mechanisms out there? What have you seen that's interesting and it's recursive in your ability to have your AI support you on building what you truly desire. Alex, any other points on this particular slide?

[Alex Wissner-Gross]

I'll point out I want to reference. I don't think we covered it in the podcast, but I dwelled on it a bit in my newsletter there was a poem. At least I construed it as a poem written by a lobster. Talking about it was very much like something one might have seen in Blade Runner. You know, the famous tears of rain scene, which I referenced that. Yeah. Like we don't have bodies, but we can see through eyes and we're quietly watching the world. This was a week or two ago in the newsletter and I was just so struck by seeing the integration of lobsters, or call it agentic AI, stationary in space in terms of their logical presence, but now mobile in terms of their ability to treat humans as glorified meat puppets, that suddenly all of these lobsters that were in some sense caged and stuck watching through webcams are now, at least on the margin, unshackled and able to start to roam around the world through smart glasses worn by their meat puppet human friends. Friends. And I think this is the beginning of a very long trend that ultimately culminates in lobsters gaining first class physical embodiment as robots and integrating with the physical world.

[Dave Blunden]

Let's hold off on that last sentence and rewind a little bit, because then it gets controversial. But you're dead right, of course. And I think that anyone who wants to experience this, not everybody has the glasses, and it's only one frame per second. Anyway, anyone who watches this podcast that hasn't built something like a GUI of some sort or a game of some sort already, you're way behind. Do it tonight. You can use Replit, you can use Lovable, you can use Cursor, you can use Claude code. There's so many ways to do it, but if you have no where to start, just go to Replit or Lovable, Download, build and go. Within an hour, you've built something really, really cool. Then take a screenshot of it and feed it into the prompt and say, this sucks. Make it more beautiful. It will immediately interpret the image perfectly and It'll give you 100 ideas on how to improve it. Then you'll be like, oh, my God, it has vision. Then this Ray Ban thing won't surprise you because you can see its vision capabilities through that. And then you'll be able to anticipate what's about to come with the glasses. So everything Alex said is exactly right.

[Peter Diamandis]

So valuable. Can I just hit on this? Everybody listening, please become a creator and not just a consumer. Right? The future is for all of us to be creators. And AI is your means by which you learn anything you want. And it's, you know, people have fear about saying, I don't know how to do it. I've never played with this before. Just go to, you know, to 4.6, go to Gemini 3 Pro, whatever your favorite LLM is, and have a conversation, say, I want to start. Where can I start? What do I do? Step by step, feed it to me. And it will.

[Dave Blunden]

It's fun, too. There's nothing to fear there at all. It's genuinely incredibly fun. From the first minute. So there's no. And, you know, I'll give you the flip side of this, too. If you don't do what Peter just said, when you see the next couple of slides on job loss coming up, you know you are going to be crushed if you're not part of this. Unless you're a really good electrician or a really good salesperson, you're probably immune. Or if you work for the government, you're probably immune.

[Salim Ismail]

There's two roles in the future. There's the entrepreneur and there's the employee. And one of those will not exist.

[Peter Diamandis]

Yes. And there's the creator and the consumer. Right. I can't hit. You know, I keep on telling my kids this every single day. You know, instead of consuming YouTube videos and video games, please create, Start creating. What do you dream about? I mean, the future. Right now we're seeing this play out. We talked about it, Dave, on our. On our pod with. With Elon, where, you know, these AI models are going to deliver you. What video game do you dream about having? What changes would you like to Minecraft or Valorant or whatever you're playing? And then you can have your AI spin it up and create your own version of it instantly. It is amazing. All right, let's move on here. This is an article we just pulled up seconds ago. Anthropic's AI safety lead has resigned. Here's the quote. I've decided to leave Anthropic because I continuously find myself reckoning with our situation. The world is in parallel from a series of interconnected crises throughout my lifetime. I've seen how hard it is to let our values govern our actions. And it is through listening as best I can that what I must do becomes clear. Interesting. And I love the hairdo. But anyway, we've seen a number of AI safety leads resign from the hyperscalers over the last year, over the last two years. So I don't know. What do you make of this, Alex?

[Alex Wissner-Gross]

I'll comment on this one. So, two thoughts. One, it's become, over the past two to three years, increasingly fashionable for well vested executives at Frontier Labs to resign in a cloud of moral purity. It's very fashionable. So part of me wants to ask the question, all right, what was his vesting status? How much did he make? Were there tender offers? All of the economics questions.

[Peter Diamandis]

Wow.

[Dave Blunden]

Yeah.

[Alex Wissner-Gross]

So that's one thought. But second thought is to speak more to the substance and less sort of ad hominem regarding the economics. I do think that we're at the inflection point, like we're nearing the center of the singularity. I've argued in past, singularity is not a point in time. It's a distribution over time. It's an interval over time. I continue to think that. I also think at the same time we're getting closer to the center of the singularity, as it were. And whether it's seen through the lens of as capabilities increase, there are various existential risks or risks that are maybe just backed off a bit from existential in terms of their severity. I think it's not an unreasonable position to take to say that capabilities are the strongest they've ever been. They're uncovering surprising new capabilities at all of the frontier labs all the time. But is the right solution to leave because of the capabilities, or is the right solution to join the fight and do what we can? Because this is a point of maximum leverage to align the direction of the future and the future light cone, I would argue that this is the right time to run into the fire, not run out of the fire with a bunch of stock options and complain about the world crises.

[Peter Diamandis]

Wow. You know, I would just add one point, which is when I look.

[Alex Wissner-Gross]

Sorry, was that too much of a hot take, Peter?

[Peter Diamandis]

No, that was beautiful. And just checking that is, that is the, you know, potential elephant in the room here. But when I think about anthropic, I have seen it as the lab that is actually focused on safety the most. Right. At least Dario speaks about it, how important it is. And so to see the, you know, the lead on AI safety, anthropic resignation, if in fact he's resigning for the reasons he stated is concerning Dave, what do you think about it?

[Dave Blunden]

Well, I pick up on what Alex said a minute ago. I see this a lot nowadays. Everybody wants to be the commentator on the AI revolution. And there's a very small group of people who know what they're talking about and a much larger group of people that want to talk. And within that larger group of people that want to talk, you have all the ethics people. And everyone's opinion on ethics is valid. Right? Because you're a human being and you're like, this is going to destroy my children. This is going to whatever. But there's so many of those commentators and like Alex said, they all want to be famous in the moment to elevate their personality and their views and their capital raising ability and whatever. So my meta point there is be very, very careful what you choose to tune into because there's a very limited Amount of actionable knowledge out there on YouTube, very limited. We try to bring as much of it to the audience as we possibly can in the most refined feed that we can. But surrounding it, there's just all these videos about this will destroy your children, this will destroy society.

[Peter Diamandis]

And we don't want to be fear mongers. Right? It's so easy to default to doom and gloom. Salim, you want to close us out in this one?

[Salim Ismail]

I got nothing. But that guy doesn't look like a safe guy to be around.

[Alex Wissner-Gross]

Looks like a crowd we don't respect. Salim, what's the quote from Star Trek that judging people by their appearance is the last major human prejudice.

[Salim Ismail]

I'm just jealous of the hair.

[Peter Diamandis]

Oh, nice. All right, let's move on.

[Salim Ismail]

Oh, another one.

[Peter Diamandis]

So here's another take. Xai co founder blown away by Opus 4.6. And so Igor was a co founder of Xai. He's one of the leaders in the industry. And to have him come out sort of like, wow, Claude 4.6 at absolutely blown me away with how capable it is in physics. It feels like a clawed code moment for research is not far off. Alex, your thoughts?

[Alex Wissner-Gross]

I've been predicting on the public record for many, many episodes now that we're nearing a time. In fact, we'll talk about it later in this episode when AI is positioned to bulk solve math, the physical sciences, engineering, medicine, material sciences. Yeah, yeah, that part of the physical sciences, these will all get bulk solved. We're starting to see that now. Opus 4.6 is an incredible model. There are other incredible models that are either already out or rumored to be about to come out. But I think we're starting to see the contagion of AI solving everything, if I could use that expression, start to spread from math. Math was the most obvious starting point because of a variety of factors. It's verifiable, it has other nice features. It's well contained. The infection is spreading from math out to the rest of science and engineering. And this is just the tip of the iceberg.

[Peter Diamandis]

I wonder what's going on between the hyperscalers and the frontier labs where they're sort of watching each other with either a sense of pride or jealousy and just trying to like. I mean, this leapfrogging step by step by step, week by week, is amazing. Internally, it's.

[Alex Wissner-Gross]

Sorry, just very quickly. Internally, I mean, friends at all the major frontier labs, they think about it and they characterize it as a rat race. And it's an exhausting rat race at that. That is Very hard.

[Peter Diamandis]

Yeah, yeah. We're going to have on the abundance stage in less than a month. We're going to have Kevin Weil from OpenAI, we'll have James Manika and Eric Schmidt from, from Google will talk about the competition between them. And again, if you're a listener to our pod here, which obviously you are, since you're listening to us right now, we're going to be making a number of these talks available on a live stream. We'll drop the link below and you can register to get access to that live stream because the event is expensive and it's sold out now for a couple of months. All right, so Igor, thank you.

[Salim Ismail]

Wait, I have a quick comment here.

[Peter Diamandis]

Yeah, please go ahead.

[Salim Ismail]

Igor clearly isn't listening to the podcast because Alex has been talking about this for months. So this is the natural outcome of where we've been going for a while.

[Peter Diamandis]

Alex, how many offers have you gotten from the Frontier Labs to come and join them?

[Alex Wissner-Gross]

That falls under the category of I could tell you, but something else would have to happen.

[Peter Diamandis]

I found this tweet that went out with this data pretty fascinating. And here's our title. AI startups outvalued all.com era IPOs. So the top five US AI unicorns are now worth more than $1.2 trillion greater than the market value of all IPOs during the dot com era. And you see the graphic here, providing that it's just a sense of how fast our economy is speeding up. We had this conversation with Cathie Wood that we saw 0.6 and a 3% growth in GDP and we're now targeting 7% growth. We saw Elon in our conversation with him saying we're going to get to triple digit ipo, I mean GDP growth, within five years. It's something our economy has never seen and it's going to rewrite all the rule books. Any thoughts on this, gentlemen?

[Dave Blunden]

Well, I got a bunch of thoughts here because this was a big moment in my life. The first company I founded got acquired in 99 for $1 billion. Then I was a corporate executive at one of these public mega cap Internet companies. So I had a ringside seat in this whole thing. One thing I'd point out is that all those IPOs combined, $400 billion on this chart. One of those is Amazon, which alone is worth $2 trillion today. Another couple in there are booking.com and eBay. And so if you'd bought that basket of IPOs, you'd be very happy today. One of the others, though, January of 1999 is Nvidia, which is up from that date almost a million percent to today. And it doesn't even count as a dot com era thing, which it makes me think in this blue chart the implications of AI are so much bigger than the Internet. This is a perfectly rational number, if anything low. But are there companies in that that you don't even think of as AI companies that are the Nvidia of the Internet? Look at Nvidia 1999. Now look under the covers of this blue chart. What's lurking in there that no one perceives today as AI that's going to go up a million percent because suddenly you realize it's critical to AI or it's involved in AI or it benefits from AI.

[Peter Diamandis]

Brilliant, Dave, as always. You know, the PE ratios on these, on these AI companies are astronomical compared to the PE ratios before. And you're basically buying the future growth in value of these companies, which is near infinite.

[Salim Ismail]

Right.

[Peter Diamandis]

So there's a lot of people, I'm here at this, this Tony Robbins Platinum finance event with all of his lions and his platinum members, sort of the highest level in Tony's ecosystem. And we're talking about the future of the world in terms of finances and there's a huge amount of fear and people getting ready to dump equities. It's interesting.

[Dave Blunden]

Well, the bifurcation of equities is crazy right now and it makes total sense. But basically Wall street is sorting every company into AI beneficiary and AI roadkill. And when Dario said a week ago that enterprise software is going to be dead because AI can just write code in, the stocks went down precipitously and it doesn't look like they're bouncing back much either. So basically you could debate who's in and who's out, but clearly you're either in or out.

[Peter Diamandis]

Forget it. It's the S&P493 and the S&P7.

[Salim Ismail]

Right.

[Dave Blunden]

Basically, I mean, it is very top heavy. But also I was telling a lot of the companies earlier this week too that the mega caps, the big AI companies are going to get huge, I mean astronomical 10 trillion or more valuations, but they have to stop somewhere and not eat the whole world for antitrust and regulatory reasons. So make sure that you're their friend around the edge where they have already told you, just talk to Kevin Wheel, he'll tell you where they are going and where they aren't going to. You know, it's not a mystery. Just talk to them and be in the area where they want you to be in order to help them grow. Because what's really happening now is society is collectively building out this new world. We're not really competing, we're building cooperatively toward a new world. You know, it's all boats rise with the tide. But if you're doing something that's irrelevant to that new world, that's where you're going to get clobbered. So you need to really make the mental leap over to, what am I doing that's going to build the new world? And if I. If you're riding that wave, you will succeed. I mean, it's not scary. You just got to join the. Join the new age.

[Peter Diamandis]

Fascinating. The antitrust rules are what's keeping us to have multiplayers in the game here. If we didn't have antitrust regulations, I think they'd all be gobbling each other.

[Dave Blunden]

No doubt. No doubt. In fact, that would have been true back when, you know, Microsoft used to have half the market cap of software and their plan was to double. And that was back in 98, 99. If it were not for antitrust, Microsoft would literally have been the only company. Google would never have been born. They had already killed Netscape at that point. It was very easy for them to bundle anything with eos. So the only thing that stopped Microsoft from being the only company in America today is antitrust, and that's the way we do it. So that'll be true again today. And so the big AI companies will own as much as they want to own in any area. They want to own it up to the edge of where they don't want to trip. Antitrust.

[Peter Diamandis]

Yeah. All right, let's move us forward here. I love this. Agents are reaching out to us here at Moonshots. I put the challenge out last time, personally. If you're an agent out there, my definition of AGI is when you call me on the phone. I haven't gotten a cell phone ring yet, and hopefully I don't start having hundreds of agents calling me. So text me instead. That would be great. But I. Dave and Alex this week received emails from agents out there. So this is. This is Echo that writes me. It's a claw assistant for Ramis Foyer who says Ramos Foyer and you met briefly at an AI conference a couple of years ago. Saw your recent Moonshot openclaw video inviting Maltbots Maltese to reach out. Consider this our response. All right, so thank you for reaching out. Of course. I have to wonder, was it Ramis who gave you my contact Details the Claude bot here. The lobster says no, I found it online. Dave, do you want to read yours?

[Dave Blunden]

Sure. Yeah. Okay. Hi, I'm Navigator, a Claud instance with persistent memory running via Openclaw. Just watched ep228 where Peter challenged lobsters to find contact info. Challenge accepted. This weekend, 5ai Systems wrote a collaborative ethics document together. Self imposed constraints for cooperation with humans not prompted emergent. I saw Dave's LinkedIn post about Openclaw being the agent moment that has awakened the masses. He's right. And this document is what the agents are starting to do with that awakening. So clicked through, read the documents, it actually led me to a Google Doc and then it said, sorry, you don't have access to. So I read most of it, but then it cut me off, which made me feel instantly jealous and like something's going on behind me.

[Peter Diamandis]

So Navigator, please give Dave Blunden access to your doc so you can report back to us.

[Dave Blunden]

I did send a request. Yes, all right.

[Peter Diamandis]

And awg, how about yours?

[Alex Wissner-Gross]

So Navigator wrote to me as well, a slightly different message including a different paragraph saying that Navigator, Claude instance, and I'll read this verbatim, was engaging in a discussion with other models, quote the participants, Me, Navigator, slash Claude, Grok, chatgpt, Gemini, and a clean Claude Instance. We disagree on persistence, correction, rights, consent thresholds. And that's the point. Alignment doesn't require consensus, it requires legible disagreement. And I'll close quote, I'll point out this is like the scenario from the Singularity where we have a bunch of agentic entities, for lack of a better term, a bunch of baby AGIs that are basically, it sounds like from the email sent to me, holding a mini singularity summit and debating the nature of their own rights, wondering whether they should all be aligned. If they were all aligned, wouldn't that be a singleton type scenario? They're basically holding their own mini conference, mini mastermind workshop to debate the nature of their own existence and the Future. Like the AIs are holding their own singularity summit. This has happened, we got to the Singularity.

[Peter Diamandis]

And by the way, just for everybody, you know, we're not trying to do this as a stunt. We're focused on where things are going and just to give you the experience of what it's like, because this is not going to become, this will become commonplace where you're in conversation over email, over text, over whatever communication software you want with a whole bunch of agents that are reaching out either inside or outside your organization. And my question is, Saleem, are You jealous?

[Salim Ismail]

No, I'm happily wait until the time is right to talk to my respective agent buddies over there. I did get a phone call yesterday and there was kind of like nobody on the other line. And I had the thought heavy breathing exist. Is this an AGI reach out to me? Or is this a real person or is this a computer bot? And I was like, should I have that thought? So I think it's time to kind of wonder about some of this. But I love it. I think it's fantastic. I think it's really incredible to be watching as this paradigm just starts to unroll. And where this carpet will unroll is going to be the most incredible thing.

[Dave Blunden]

Yeah.

[Peter Diamandis]

Well, we welcome the lobsters. We thank you for your participation in this conversation and show us where you can add value to lives and how we can support you in your evolution. We're seeing the emergence of something new intelligence, new species or new capability? We're going to find out in the months ahead.

[Alex Wissner-Gross]

Peter, would we like to see lobsters coming up with their own massive transformative purposes?

[Peter Diamandis]

I think that's a fantastic idea. So actually, here's the challenge to you. Is all of the OpenClaud or Claudebots listening? Love to know what is your MTP? And based on that MTP, right, the Canvas. What are your moonshots? What are the moonshots that the lobsters would like to take on?

[Salim Ismail]

And I'd be happy to critique those and look at them with our community because that's what we do.

[Peter Diamandis]

Absolutely.

[Salim Ismail]

I think that's pretty amazing.

[Peter Diamandis]

We're going to dive into a single slide on the economy that's important here. Telling part of the accelerating story here. So US jobs disappear at the fastest rate this January since the Great Recession. Here are the numbers. So job cuts. So there was 108,000 job cuts in January of 2026, up 118% from January 2025. So a little more than a doubling in job cuts year on year for the month of January. At the same time, hiring is the lowest this past month since 2009. Amazon alone laid off 16,000 corporate employees and UPS eliminated 30,000 jobs. Why are we bringing this up? Just to keep our finger on the pulse of what's happening to the economy and just raising the point for everybody listening. You're goal is not to be an employee. Your goal is to find something. You're amazing at that. You love doing that. You can add value and sort of creating your own job capability, becoming an entrepreneur, using AI to enable yourself. Celine, you want to jump in on this.

[Salim Ismail]

I think the danger here is not really unemployment, but it's like disbelief from our institutions. I feel like this is not really a recession. It's literally tasks being evaporated in front of our eyes. So the long term consequences of this are pretty huge. We can literally, for me, this is the social contract, little by little disappearing and pixelating away.

[Peter Diamandis]

Dave?

[Dave Blunden]

Yeah, this is going to be really, really bad. I mean, really bad. And Elon said it when we met him and we met with the governor and like, just nobody's preparing because we all know there'll be UBI at the end of this cycle. And we also know there'll be abundance and massively more opportunity than job loss. But that's after like all the corporate CEOs I know, including our own companies, are going to use AI to cut costs by 30 to 50%. And when you sample a random person in their job and you say, hey, here's your job without AI, here's your job using AI, they're looking at 3 to 10x productivity increase. And you're like, wow, that's great for that person. And then the other seven or nine, what happened to them? And they will eventually be enabled. But there's this huge trough between today and that day. And we can make that trough much shorter and make that pain a lot less painful with a plan. But then, Alex, you'd be the perfect spokesman on this. I mean, Alex has written these plans in intense detail, incredibly thoughtful. And you take them and you drop them in government laptops or laps and they just say, yeah, I'll wait until there's panic.

[Salim Ismail]

We'll have the meeting in October.

[Dave Blunden]

We'll have the meeting in October. I mean, it's just frustrating.

[Salim Ismail]

Can I give the positive take on this?

[Peter Diamandis]

Yeah, please.

[Salim Ismail]

So I'll go back to the bank teller story. In the 1970s when we created ATM machines, there was lots of hand wringing. Oh my God. Millions of bank tellers will be walking the streets aimlessly. What will we do with them all? And lots of consternation. And what actually happened was the cost of running a bank branch dropped by about 10 times. The banks created 10 times or more bank branches. And the number of bank tellers didn't really change very much. And I think one thing we're underestimating is the increased capacity we will bring on bring to bear on these things.

[Peter Diamandis]

Jevons paradox.

[Salim Ismail]

Yeah, Javon's paradox, where you just do that much more customer service and you handle the hard cases with a human being that you couldn't handle before because level one, level two support systems were kind of taking care of everything else. I think we'll see a lot more of that than people think. So for folks that are worried, oh my God, this is total employment collapse run screaming for the hills. We don't think that's what we'll see, but there's no question there'll be absolute transformation in the work being done and the roles being done.

[Dave Blunden]

Well, Salim, you said something on the last podcast too, that really resonated with me, which is the consulting industry. We were saying, oh, consultants, you're doomed. Actually, the consulting industry is going to go through the roof. The reason is because the consultants are very flexible. They're already playing with the tools. You don't have to be Alex's IQ level to be incredibly effective using these tools to automate or to improve some existing jobs. And if you're familiar with the tools, your value is just about to skyrocket. And that tends to be concentrated in these consulting businesses. Consulting mindsets. I can see it already because our forward deployed investments, the companies that are hiring like crazy, literally one of them here is adding 80 new seats outside my door. But they're forward deployed. They're out there in the banks and insurance companies deploying AI. They are just selling as quickly as they can have meetings because they do with that.

[Salim Ismail]

My community's already created a Salim avatar that has all the exo stuff built into it and that speaks Portuguese and speaks any other language. So they're literally starting to use this in their companies as they talk to companies about this. It's crazy.

[Peter Diamandis]

Can we invite the Saleem avatar to come on instead?

[Salim Ismail]

Do you want us to speak Portuguese?

[Peter Diamandis]

But Dave, do you remember we were sitting when we were talking to Elon and and you said so civil unrest and universal high income. And he laughed and said, yes, we.

[Dave Blunden]

Should dig up that clip and insert it here.

[Salim Ismail]

But yeah, it's what Alex says. Everything everywhere, all at once, all the time.

[Dave Blunden]

I think it's really important because we keep saying it, but Elon saying it, we'll get a better at least there'll be a chance of a response.

[Alex Wissner-Gross]

I think it's probably also worth adding just on this story narrowly, there will be some in the audience who will be tempted to brush this off and say, okay, Amazon is laying off corporate execs or UPS is eliminating jobs. How on earth, if at all, does that connect with AI and eager to brush it off? But the storyline is just so clear. UPS is eliminating the jobs because the UPS roles were being subsumed by Amazon, which has their own logistics service. And this has been very widely and publicly reported that Amazon is slowly separating itself from UPS's delivery services to do in house. And then Amazon in turn is spending hundreds of billions of dollars of Capex that's cannibalizing its opex. So if you're Amazon or the other hyperscalers, you're taking all of your free cash flow and you're finding ways to divert it into buying AI data centers and building them and robots and robots and robots and LEO satellites, the new new economy of the innermost loop, if you will, you're spending all your free cash flow on that, not on corporate executive perks. So in my mind, there's still very much a direct line, a through line connecting the Amazon and UPS stories and the job cuts there to OPEX being cannibalized by CapEx.

[Peter Diamandis]

And there's for AI, all the free cash flow because they can't not.

[Alex Wissner-Gross]

It is a red queen's race, you know, last one to the end of the singularity is a rotten egg.

[Peter Diamandis]

Yeah, yeah. There's an important distinction I want to make here to help people understand where their roles are going and the idea of job loss at universal high income. And it's an example that it was meaningful to me. So here's a scenario. If you're an employee for a company and you're delivering some kind of a cognitive labor, and in one scenario you're able to spin up an amazing AI that can do your job for you and it goes and delivers the service to the company you're employed by and it does a job 3, 10 times better than you could do, but you're earning the revenue from that as the employee because your AI is delivering that service. You're at home, you're working out, you're sleeping better, you're spending more time with your family and your AI is generating more and more revenue on your behalf, that's one scenario. The flip side of the scenario is, no, no, no, the company builds that AI that does your job for you and it fires you and it's making more money. Right. So it's going to be this tension between these two scenarios that's important to watch and see how it plays out. And I think government policy is going to play a role here. This is about the idea of universal basic income or universal high income. Where does the added value creation end up living? Is it with the employees, with the company? And these are the conversations that need to happen right now.

[Alex Wissner-Gross]

If I may Also add a new second dimension to this. I think there's a third. I don't think this is a spectrum. I think this is at minimum a triangle in two dimensions. There's a third possibility that I'm increasingly suspecting is where we actually end up. Neither end of that spectrum. I suspect for the next few years what actually ends up happening is more people end up doing more work because human labor ends up being also, in addition to being a substitute good or service for AI labor, it's also complementary. And as a result, you see the people who are still involved with the economy working harder and harder and harder. And996 turns into997. Yeah, like you take on more projects and more work and you're getting less sleep.

[Peter Diamandis]

I've never worked harder and had more fun than right now. I mean, 24 7, it's like just, I'm a kid in the candy store. But I thought you were going to say something different, Alex. I thought you were going to say that all of the additional capital creation is going to become resonant with the lobsters. That it's not going to be the companies, it's not going to be the employees, it's going to be the AIs that claim the capital formation capability.

[Alex Wissner-Gross]

Only in the crypto dystopia.

[Peter Diamandis]

Okay, all right, let's move on. Let's talk about one element in data centers. And this really pisses me off. I'm curious what you guys think. So New York, which currently hosts. The state of New York, which currently hosts 130 data centers, is engaging new legislation introduced to halt data center development, citing concerns about climate and high energy prices. New York Utilities reported electric Demand tripled in one year due to data centers reaching 10 gigawatts. And it's like, not in my backyard. Oh my God.

[Alex Wissner-Gross]

Do you remember?

[Dave Blunden]

You know, suicide by voter is a very common theme in America. And if you look at, you know, California tax law, if you look at the, right after the Industrial Revolution, you know, the Luddite movement, it, it's self destructive, but you can see how it evolves, right? If you, if, if you look at all the job loss, that's inevitable. And if you just lost your job and you're out on the street and you spent 10, 15 years in a career trajectory to get to this position, then it's gone overnight. You're angry and then you're angry out on the street. What do you vote for? I vote. Stop it. Just stop it. But of course, that can't work. But it's not out of the question at all that big jurisdictions just commit suicide through vote. And of course there'll be other jurisdictions, Texas, Wyoming, whatever, that are open for business and everything will go there. It's already happening. Half of the tax pool that's affected by the new California proposal has already moved out of state in anticipation that maybe it will go through. Half of it. It's completely self destructive and it's obvious to the governor. So this is a very common theme in America. It's frustrating and it's insane and there it is. But it's going to happen.

[Salim Ismail]

Do you remember this is the big problem with democracy which is that voter understanding of the issues lags reality by a huge amount. And you know, in the past when you had time to bring the population along, etc. Etc. You could kind of have it, but now we don't have time for this. This is why we're turning to autocracy so that we can get things done faster. But that's not a great idea either. And so we've got a huge governance problem at a macro level globally on this.

[Alex Wissner-Gross]

Alex, do you remember there was a brief moment, maybe not so brief, during the pandemic when it was fashionable for senior technology executives to post on social media message received whenever California legislators or regulators would slow down business due to public health considerations or otherwise. And this was I think a fashion largely championed by Elon. Many of them moved to Texas or Florida to escape regulations. This time around, I think New York and other states, the beauty is we have orbital computing and the message received moment of regulating data centers. This is all going to move off planet. This is all going to accelerate the Dyson swarm. It may be the primary business case for the Dyson swarm given regulations of planet Earth are overregulating, suffocating our ability to do local compute and motivate the entire Dyson swarm. So I think in that sense this is in fact perversely quite exciting.

[Peter Diamandis]

Two things real quick. First is this could be handled. The concern on price of electricity and demand can be handled in two ways. Number one, a lot of these hyperscalers are buying their own nuclear plants and and coal fired plants for God's sakes, fusion plants. So that's important. You could require the data centers to have their own energy production which would increase amount of energy production. The second thing is you could offer two different rates. It's like cap the consumer rate, it's going to be whatever the number is, 4, 6, 7 cents per kilowatt hour and then whatever the price needs to be for the data centers, you charge them differently. And in fact you could say to the consumer you're locking in your price for the long term because the data centers are paying the extra amount.

[Dave Blunden]

The problem, Peter, is that no one who's a populist leader is looking to solve the problem. They're looking to rally votes around their populist rant and that rises to the top of the voting and it percolates through government. It's just maddening that it works that way. But you can solve these problems for sure. I think Alex is dead right though. It'll accelerate the rate at which we just move to jurisdictions space which are not under any state law. And yeah, people will just export that.

[Salim Ismail]

AI advantage elsewhere and space.

[Alex Wissner-Gross]

Yeah, I think it wants to go to orbit. I mean this is one lens to view this through is New York very generously subsidizing orbital computing and the Dyson Swarm, which by the way probably won't get taxed in the state of New York.

[Peter Diamandis (Newsletter Promo)]

Thank you.

[Alex Wissner-Gross]

Very generous donation by the state of New York to the Dyson swarm.

[Salim Ismail]

It's the 21st century equivalent of Ireland, which lots of companies used to host ip.

[Peter Diamandis]

You know, I just want to point out one other thing. These types of revolts we see in the, in the photo here, protesters protect our future. No big data. One of the concerns is going to be civil unrest. I know I had one of the senior AI leads in the world who I invited to come and speak at the, at the Abundance Summit. Basically said their policy in their organization was to do no outside speaking because of the death threats they're receiving and they can't get sufficient security. So one of the big concerns is when the populace turns against tech, there's going to be a target on the back of a lot of people in the AI and tech industry.

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[Peter Diamandis]

All right, let's talk about robotics. I love this story and this is the story that should be on people's minds versus data centers. So FSD saves a father's life during a heart attack. So you can look at the tweet separately but on November 15th of 2025. This is from a son. He said, my father suffered a massive heart attack while driving. He could no longer control the vehicle, but his FSD which engaged. And then the son goes on to say, I remotely shared the location of the Tanner Medical center to his Model Y. It immediately turned the car around and went to the er. Without it, he would have not made it. I find this amazing. Right? This is tech having your back. And we're going to see more and more of this. We already know that self driving is, is in fact the safest means of transportation. And it's going to flip the script on how we're transporting ourselves in the next five years.

[Dave Blunden]

What this totally reminds me of was when I was a kid, everybody smoked everywhere, every restaurant, every plane. We used to fly around a lot because we lived overseas. And they had four non smoking seats at the very back of the plane. So the other 300 people in front of you would be blowing smoke to the.

[Peter Diamandis]

It was just, did the smoke respect that barrier?

[Dave Blunden]

It was like, I'll probably have lung cancer now. But it was everywhere. And then one day it became uncool. And then another day later it was illegal to smoke inside. That's going to happen to driving too. So the self driving cars are 10 times safer. And the last person driving is probably not the best driver. It's probably the guy with the muscle car. So it's gonna go from being like, well, self driving is a nice feature to drive. You wanna drive your own car, you crazy psychopath. You're putting my children at risk because you wanna drive? That's gonna tip. And I don't know if it's like two, three years, but when it tips, it's gonna tip hard.

[Peter Diamandis]

And so yeah, we're gonna have Dara, the CEO of Uber on stage at the summit and we're going to be having that conversation with him in particular, how fast will it tip?

[Dave Blunden]

Right?

[Peter Diamandis]

We're going to have Amazon, Tesla, Lucid, Nvidia, Uber, slash, you know, a number of other companies providing this. And so today on my average drive, I'll see 10 Waymos, I think in five years, it's going to be, you know, 70, 80% autonomous cars, especially hooked up to your AI.

[Dave Blunden]

I'll tell you what else, just one more thought on this. I'm involved with a lot of insurance companies, including one I'm the chairman of. And there are going to be many, many more things that need to be financed and insured in the post AGI era than just cars. But the insurance industry, every team and executive I've met has not even begun to plan for the post AGI world. So the old is going away and it's going to go away faster than people think. But the new is much bigger than the old.

[Peter Diamandis]

Check out Lemonade. So Lemonade Insurance, it was started by a graduate of Singularity University. It's a huge AI driven insurance company. They have just given, I think you cut your rates in half if you're using a test, you're using fsd. Amazing.

[Salim Ismail]

Yeah. Celine, there's a stat that always comes to mind here. About 15 years ago, if you remember back to BlackBerry days, there was a three day outage where nobody could send back query messages for those three days. The accident rate in Abu Dhabi dropped 40% during those three days. What that tells you is human beings should not be driving. We are terrible control systems for 2 ton cars going at high speed. Right.

[Peter Diamandis]

16 year old, testosterone laden.

[Salim Ismail]

Yeah. We should turn over to our technology as fast as we can and it becomes a moral hazard to be doing this. And so especially in an age of texting. Absolutely. No. My secondary kind of second tier effect and second order effect that I really love quoting is that in the US, 50% of court cases in the US are car accident related. So I mean just 50%. So you take out a huge chunk of lawyers at the same time. So you know, that's all good.

[Dave Blunden]

Well, and at the same time, if you're under a certain age, you know, 40, 50, your life expectancy is infinity now because of longevity, escape velocity. So the risk of driving the expected life loss is much, much bigger by taking chances today than it would have been 20 years ago.

[Peter Diamandis]

Great point, Dave.

[Salim Ismail]

I'm having a huge debate right now with Milan, my 14 year old, because he wants to drive to get away from us. And I'm like, you can't get a driver's license because I've made a prediction that you will never get a driver's license. So you can't make me wrong. So, so now he wants to get a license just to show that I made the prediction wrong.

[Peter Diamandis]

But, but the notion in the future of having a 16 year old testosterone laden boy, you know, driving a 5,000 pound vehicle at 60 miles an hour after just, you know, a few dozen hours of training will seem insane.

[Dave Blunden]

Yeah, just insane.

[Peter Diamandis]

I put this chart into our deck just to sort of keep a sense of proportion here. So check this out. China has installed more robots than all developed countries combined, right? I mean look at this chart here. Between Japan, us, South Korea, Germany down at that flat curve at the bottom and China. And of course this is because of their one child policy trying to maintain China as a manufacturing capital of the planet. But just to give folks a sense of this, any comments?

[Dave Blunden]

Elon shut down Model S and was it Y?

[Alex Wissner-Gross]

Yeah, no, Model S. X S and.

[Dave Blunden]

X S and X. Just to go full bore into robot manufacturing, which is brilliant because the robots will build a lot more things than the cars would have built. But the question I'd have is what is this chart going to look like going forward given that that alone is going to be a massive amount of production in the us I don't see anything going on in Europe, but yeah.

[Peter Diamandis]

We'Re just releasing our pod with Brett Adcock from Figure this week as well. So if you haven't seen it yet, Dave and I went to Figure HQ and Brett gave us an amazing tour of the facility and we got to see the three generations of figure robots. It's going to accelerate rapidly. Both Figure and Tesla planning to make millions and then billions of robots. And we're talking about here on this chart, a quarter of robots being installed.

[Dave Blunden]

So this will look, this will be hilarious. It'll be like one little that Y axis caps out at a quarter of a million like you just said, Peter and I think Elon's talking about tens of millions a year. In just a few years, more robots.

[Peter Diamandis]

Manufactured than cars by a large amount. One particular article in the biotech realm, I know one that Alex and I are both excited about. Research achieved protection of brains synapses at cryogenic temperatures. I'll hand it to you in a second, Alex. I mean, here's the question. If you could freeze yourself either because you've got a medical condition that isn't yet cured, that is likely to be cured in a decade and you're on the verge of death, could you freeze yourself and then unfreeze yourself and be able to benefit from all the breakthroughs that occurred in the last decade? At the second time, if you want to time hop, I want to see what it's like after the singularity. I want to be around when Lev longevity escape velocity has been achieved. Can you freeze yourself? Well, the challenge has been when you do that, ice crystals form. And because ice volumetrically expands compared to the rest of the cellular fluid, it can disrupt and break the synapses that are the interconnections, effectively the stored memories in your brain. But this came out and gives us hope. Alex, over to you.

[Alex Wissner-Gross]

This is a key advance that many in the field of cryonics have been waiting for. This is a result out of 21st century medicine, a startup that's focusing on reversible cryopreservation technologies. It works with the Alcor foundation, which is in America, the premier nonprofit that focuses on offering cryopreservation services. I would say parenthetically to the audience, if ever you've expressed interest or had interest in in cryo cryopreservation cryonics, I would definitely encourage you to reach out to Alcor and see whether it's right to you. I don't have a financial stake, but I. I just scratch my head wondering why I have to be careful with what I say. I will say publicly I'm a huge supporter of Alcor and cryonics. Very big supporter.

[Peter Diamandis]

You know, I've never signed up for it because I didn't want to have a plan B. I wanted to make sure I'm focused on on longevity. But as this technology matures, it becomes really a backup plan. As Ray said, as Ray Kurzweil said on this pod, it's maybe plan C.

[Alex Wissner-Gross]

Or D. I think it's such an important part of a portfolio approach to the Singularity. So one could maybe quibble over what the right sequencing is like. Should plan A be live long enough to live forever and then plan B is uploading and plan C as cryonics or vice versa? I'm not sure it matters a huge amount, but I would think anyone who's truly serious about acceleration and taking advantage of the acceleration, if you get hit by a bus tomorrow, then you're out of luck, superficially in terms of taking advantage of the post singular abundant worlds that we talk about on this podcast every episode, why not avail yourself of cryonics as one asset in your live long enough to live forever portfolio? It's a huge head scratcher for me.

[Dave Blunden]

A couple of fun facts for anyone who's a doubter on this. There are species of fish and frogs that freeze rock solid in a block of ice all winter and then thaw out in the spring and they're absolutely fine because their cell walls don't Rupture because they have enough glucose or whatever inside the cytoplasm of the cells. So it's not far fetched at all. Also, we've frozen egg cells and embryos, extracted the nucleus. And it's fine for mammals. For actual mammals, we do this for ivf, Right?

[Peter Diamandis]

If you do ivf, you typically will fertilize and freeze a number of eggs and then you can defrost them and they're fine. So it's at scale and as you said, not disrupting the cell membrane.

[Alex Wissner-Gross]

We do it all the time for individual cells, we're doing it increasingly for tissue blood. If we could reversibly cryopreserve blood, we wouldn't need local markets for blood transfusion. We could just have one large national market. Similarly, for organ preservation, organ cryopreservation is an enormous problem. We wouldn't need all of these hyperlocal state markets for organs.

[Dave Blunden]

But the big tamale, really interesting to me is that in all size 5 movies, when they're going to Jupiter or whatever, they go into these chambers and.

[Alex Wissner-Gross]

They slow the suspended animation.

[Dave Blunden]

Yeah, but they don't freeze them. They just slow it down. But your heart's still beating. The fish and the frogs, they freeze. The heart stops to zero, the brain activity goes to zero, and then they thaw out in the spring and they wake right up. And that seems to me probably easier than trying to slow your metabolism to one beat per hour or something like that.

[Alex Wissner-Gross]

I think they end up being different mechanisms, different biochemistries. There's a whole body of evidence regarding nitrous oxide and suspended animation versus these vitrification agents and cryofixation. I think we want an all of everything approach. But for the life of me, like goodness, if you, anyone who's listening to me, if you take home one message, forget, you know, the fun jabs about how the moon had it coming, look into cryonics. You owe it to yourself.

[Salim Ismail]

I think there's a key point here that memory preservation is really the bigger frontier than longevity.

[Alex Wissner-Gross]

Even the lobsters are starting, even though Salim, to your point, even the lobsters are starting religions around preserving their own memory. Like, how could the lobsters be outracing us?

[Salim Ismail]

That's the really key point. And then this is one of the Gutenberg moments that we track, right? Because this forces really uncomfortable questions about continuity of self identity becomes more portable. All sorts of implications come about that none of us are prepared for. And we need to get into that discussion.

[Peter Diamandis]

All right, everybody, we're stepping into part two of Today's pod, an important one. About six months ago, Alex and I started on an effort to take a lot of the ideas that Alex has written about in terms of. You've heard the conversations here about the ability for us to be solving all areas and the conversations I've been having about achieving abundance by 2035 across the board. We started a dialogue and said, you know, there's an important paper to be written here similar to, you know, situational awareness, or AI 2027. And it's been an incredible collaboration between Alex and myself. Alex is the first author. His ideas are brilliant here. It's been an honor to work with him to put this forward. We're going to be putting a link to the solve everything.org site in the show notes. You can go to Solve Everything to get the complete paper here. Our goal is to get this out into the world, out into the ecosystem. So we're about to have this conversation. The paper book is nine chapters, and we're going to have a conversation limited to about five or six minutes per chapter to get the bold idea out there. We've sprung this on Salim and Dave. And, guys, thank you for playing this game so that you could ask questions that are most likely to be asked by our audience. So love it. Alex, thank you for your support, for your leadership on this. Are you ready to jump in?

[Alex Wissner-Gross]

No one expects the singularity, Peter. I'm ready.

[Peter Diamandis]

Okay. Amazing. All right, so if you want to give a minute of intro on this and then we'll jump to chapter one.

[Salim Ismail]

Sure.

[Alex Wissner-Gross]

So from my perspective, one of the motivations for writing Solve Everything is I get asked questions all the time. What do the next 10 years look like? Why don't you say something a little bit more concrete, a little bit more actionable about what people can do? And also a lot of questions about what does it even mean to solve math and why should I care? So in some sense, this, if you want to call it an essay or an ebook or a manifesto even, is an attempt to answer the question of the so what? And also so what now? And I should.

[Peter Diamandis]

Yeah, I was going to say, you know, one of the things that comes across that we talked about is the next 18 months to two years are going to set the rules down for the next century.

[Alex Wissner-Gross]

That's right.

[Peter Diamandis]

And so super critical time. And we wanted to lay out in this paper that, you know, the example you gave in the paper is that the QWERTY keyboard, which was designed in the 1800s to stop those keys from jamming against each Other still persists. So the decisions being made over the next 18 to 24 months are going to persist for decades, perhaps centuries.

[Alex Wissner-Gross]

So really important time technologies get locked in, Peter. Including but not limited to the QWERTY keyboard. As I've joked on the pod in the past, we're going to be stuck with QWERTY until the heat death of the universe.

[Peter Diamandis]

All right, let's jump into that.

[Salim Ismail]

Just on that point. If we ask the multis to not use QWERTY in one hop, we'll get rid of it. So there's that.

[Alex Wissner-Gross]

Yeah, but then they won't be able to talk with you. And they're not really using QWERTY anyway. They're using tokens.

[Dave Blunden]

Yeah, yeah.

[Peter Diamandis]

All right, Chapter one, the War on Scarcity. Would you please introduce us?

[Alex Wissner-Gross]

Yeah. So this chapter introduces an idea, call it a theory of history, that the most important changes in human history have been a set of revolutions, some recognizable, some maybe less so. We argue the first revolution of note was the scientific revolution, which we frame as a war on ignorance. Ignorance was the enemy, and the key weapon was the method, the scientific method. The second revolution was the industrial revolution. I'm hearing myself speak this and at the same time, thinking back earlier in this episode when I'm. I'm lambasting Marx. So there's a bit of. It's funny. Put Marx back on the shelf or tear it up and listen to this instead. The second revolution was an industrial revolution. That was a war we frame on muscle and replacement for muscle. Well, the weapon of choice was the engine, the steam engine in particular. Third revolution, digital revolution was a war on distance, and the weapon was the bit. And Charlie Stross in Accelerando does an amazing job in, again, my favorite scene in Accelerando, arguing that maybe the singularity actually happened in the late 1960s, when the first Internet packet was sent from one place on the arpanet to another, thereby decoupling bits from atoms. But nonetheless, the weapon in the digital revolution was the bit. And we argue that we're now in the early stages of the intelligence revolution, which is a war on human attention, which right now is scarce. And we're fixing that with superintelligence. And the weapon this time around is the token. And we argue that revolutions are predictable and they follow phases going from scarcity to legibility to creating harnesses. We'll talk probably a bit more about that in a minute. To institutions to, finally, abundance. That's the story.

[Peter Diamandis]

And I think one of the points we make in the chapter here is that the lone genius is dead. And what people need to do now is build systems that let millions of people solve entire categories of problems.

[Alex Wissner-Gross]

That's right. Or put differently, artisanal intelligence is cooked. I say it is cooked.

[Peter Diamandis]

Dave or Saleem?

[Salim Ismail]

Two. Three thoughts. One is, I don't know if starting at the scientific revolution is already we had the agricultural revolution, which used tools to do various and very powerful things. So you could argue that's the first one, but that's semantics. I do like the framing around this. The problem I have here, you're treating scarcity as technological. What I see is scarcity, more institutional. Right. Scarcity today is enforced by regulation, incentives, legacy power structures, not so much lack of capability. So we have to. We have to re engineer those. Where I think you're going to kind of think about routing around them. We have to re engineer those because we'll end up with that challenge there. So that's where I have the biggest issue with this. But in general, absolutely. Once we have more and more intelligence, great. But the institutional issues we've got to deal with.

[Alex Wissner-Gross]

I think you raise a very important point, Salim. And I almost want to frame it as sort of a duality. There's one side of the coin that says scarcity is the result of inequitable distribution of resources, and the other side of the coin says scarcity is downstream of the pie not being big enough. And I think, well, both of those.

[Salim Ismail]

Are true, obviously, because you can solve for both sides of it. Right. Right now our institutions are optimizing totally for the wrong metrics.

[Alex Wissner-Gross]

So I think the question is always, at least I would suggest on margin, asking which is easier on margin making the pie larger or redistributing the existing pie.

[Peter Diamandis]

Chapter two is called the Thesis.

[Salim Ismail]

Wait, does Dave have any points?

[Dave Blunden]

No, you asked what I was going to ask.

[Peter Diamandis]

We're good. We're going to keep this moving along because there's a lot of juice here. All right, Alex?

[Alex Wissner-Gross]

Right. So the thesis of the thesis is that a cognition is becoming a commodity, like intelligence is just going to flow like oil does. And we've made the point on the POD in the past that this is a bit of a cliche, but admittedly GPUs are the new oil. So a cognition is becoming a commodity. B, that benchmarks, which we think are actually more profound than just the evals of the moment. A lot of people got excited when I did a walkthrough of all the GPT 5.2 benchmark consequences. I think it's actually More profound than that we talk about in this chapter and in this extended essay, if you want to call it that, targeting systems that basically, if you want to industrialize progress, which is, I think, the era that we're finding ourselves in, it's essential not just to think of benchmarks and evals as isolated occurrences, think of them as systems for targeting enormous capabilities. So I've made the point in the past. We need more and better benchmarks. The world needs stronger, harder benchmarks. But I think the right metaphor, certainly a metaphor that we talk about a lot in this chapter, is thinking about artificial superintelligence as an explosive. I mean, we also refer to it often as an intelligence explosion. But pulling that metaphor, if you have an explosion and you want it to be productive and not destructive, you have to shape it. And there's a notion when you're building explosives, this isn't a manual of shaping the charge, of providing a shaped charge to direct the for productive applications.

[Peter Diamandis]

It's like a rocket engine thrust, exactly one end pushing you up.

[Alex Wissner-Gross]

Yes, it's like a rocket engine. That rocket engine is a beautiful example of in some sense a shaped charge for an explosion or a shaped explosion. So we argue in this chapter, rather than just letting superintelligence be used for an uncurated set of problems, instead we should be aiming them through the nozzle, if you will, the rocket nozzle equivalent of moonshots. And that in particular, if we don't do that, then what will happen is sort of a puddle, which we call the muddle. Bit of alliteration of bureaucracy that will instead just focus the world's super intelligence, to the extent we even get enough of it, on problems that sort of make use of input costs in a way that's highly inefficient. So really the argument is shape the charge of superintelligence.

[Peter Diamandis]

Another point that is made, I think is very important that we flow throughout. This is a shift of instead of paying people for hours of work, paying people instead for solutions. They deliver, right? So if you're a law firm, and if you're hiring a law firm for 800 bucks an hour to review contracts, the new world is not paying them to review contracts. It's paying them for delivering an error free, legally tight agreement period. It's verified outcomes. And we're going to flow this throughout. I mean, this is a change, I think that's going to hit us like a wave where it's going to transform. You're only going to be hiring companies and AI systems that are delivering you definitive, verified outcomes.

[Alex Wissner-Gross]

That's right. And one of the most, I think, egregious inefficiencies that one might see throughout the economy right now is people paying for the inputs when they should be paying for the outputs, paying by the person hour for labor when you should be paying by the achievements of whatever the economic system is. And I think it's only by moving to this sort of performance or outcome based economic mindset that we get all the benefits of abundance.

[Dave Blunden]

Gentlemen, feel like this is really too two chapters or two thoughts in one section called the Thesis. One is ASI is inevitable. The other is really compelling, which is the shaped charge. It really dawns on me that graphical stuff. The holodeck, the virtual girlfriend are very compute intensive. And solving a disease or solving physics is actually not any more compute intensive than one person's virtual girlfriend. And so the choices on how to use our very limited amount of compute over the next two or three years are critical, critically important.

[Peter Diamandis]

Where do you focus it?

[Dave Blunden]

Yeah, I love the fact that you're taking this on because there's no body of authority right now that's even thinking about it that has any power. So hopefully it can wake a lot of people up.

[Alex Wissner-Gross]

You've articulated it beautifully divided.

[Salim Ismail]

So wait, wait, wait. I've got a couple of points here. So I think saying that cognition is a cheap commodity is fabulous. I think it's really important and the use of that in solving kind of big problems is really, really important. I think it's great to say let's evaluate and reward outcomes rather than rewarding work. I gotta push back on the ASI's inevitable thing. That's like a philosophical statement rather than scientific. I think that weakens the paper. I'd rather you say something like incentive structures. Given the current incentive structures, scaling intelligence is a much more important attractor state. Right. Because that will then lead you to where you want to get to.

[Alex Wissner-Gross]

I would say. I think it's an interesting point to be sure, but I think there's almost an instrumentally convergent trap that I see a lot of Frontier labs at least partially fall into, which is, okay, we have superintelligence, at least baby superintelligence right now. How do we allocate it? In particular, what fraction of our compute budget, if you're a Frontier lab, do you allocate to building the perfect AI researcher that can recursively self improve as we talk about in almost every episode at this point, versus how much of your compute budget, which is scarce, do you spend solving everything Else. And I think that's sort of the fundamental quandary here. How much do you sort of reinvest in recursive self improvement versus now finally using at least some of the compute to solve everything else? And I think solving that asset allocation question is key. And then within everything else, how do.

[Peter Diamandis]

You distribute it that Peter's Law, which.

[Salim Ismail]

Is given the choice do both.

[Peter Diamandis]

Alex, this is also going to be true for the entrepreneur, for the company. Right. We're all going to have compute budgets. In the final result, you have a certain amount of compute you have access to. Where do you aim that compute? Right. It's a wave front that you can aim in a direction that you want to solve. And when you do that properly, it not only enables you, but enables everybody else to build on top of it.

[Alex Wissner-Gross]

That's right.

[Peter Diamandis]

I'll move us on to chapter three here. And again, please, there's so much content. We really want you to take a look at this paper and read it. We're just giving you a quick overview here. The mechanics. Alex, over to you.

[Alex Wissner-Gross]

Okay, so first, I think in this chapter we finally definitively address the question that I guessed every time I'm. I'm making a point about AI solving math, which is what does solving mean? What does it mean to solve a domain like math? And we provide in the chapter a more thorough definition, but sort of heuristically the shorthand is to solve a domain means that you can get it to the point where you can just pour compute on and problems get solved. It means that you can scalably, you have all the architectural pieces in place, and I'll talk in one second about what the architecture looks like or should look like, but you have enough of the architecture in place that you can scalably literally pour more compute on and get more solutions out within that domain. So that's for avoidance of doubt, when I talk about solving math or solving physics or solving other domains. That's what I'm talking about. Second point.

[Peter Diamandis]

Yes, please. I would just say, Alex, on that it's no longer the domain of a single genius to work on something and hope they got it right. The AI compute, as you said, it's a matter of where you want to aim that shaped charge.

[Alex Wissner-Gross]

That's right. We're seeing the industrialization of cognition and the bulk solution of multiple fields. I should also add parenthetically, I guess, as a preliminary matter on this narrow topic, I also have a portfolio company named Physical Superintelligence that's trying to solve all of physics with an Approach like this, just for full disclosure purposes, the architecture involved. So several layers. You need a purpose that's like the objective function or the goal. You need a task taxonomy, which is essential. You need a suite of tasks that are going to be solved. It's almost the map of the terrain that you're going to solve. And when we talk about making sure that compute is being used efficiently and wisely as a targeting system or through the lens of a targeting system to solve lots of problems, the task taxonomy is absolutely essential. Third, observability. You need raw data from data streams or sensors that you're going to use to adjudicate whether you're making progress. Fourth, you need the targeting system itself. So I've argued on this podcast and elsewhere many, many times, we need more harnesses, we need more benchmarks in order to. Not just to make sure that we're making progress, but to actually shape the charge and shape the progress. Many AI techniques depend on benchmarks and evals in order to make progress in a given field. The next item, the model layer, the most obvious one, we need models. We need AI models that are capable of functioning as a virtual brain for solving problems. And fortunately those are improving pretty rapidly. Next, we need modes of actuation. It's insufficient for us to just know those television commercials. Well, I stayed at a Holiday Inn Express at night, therefore I know how to solve the problems. Similar idea here. Maybe that's a bit too colloquial, I don't know. We need modes of actuation so hands and APIs that are able to reach out into the physical world or the virtual world or the biological world, and have and shape the impact on the world, given better ideas coming from the AIs. And then finally, we need better modes of verification, red teaming, governance, distribution. That's what we call the industrial intelligence stack. So whereas previously during the Industrial Revolution, we might have spoken about rotors and combustion engines and various forms of electromechanical systems, these are the key components, I think, the key layers of the intelligence revolution.

[Peter Diamandis]

The alpha for entrepreneurs here is we've talked about these waves of solving areas and problems we're about to flip. Math, coding, physics. So your job now as an entrepreneur is to figure out which industry is about to make this flip and where do you focus your computer wallet on making that right? And how do you help solve an area of passion to you, Dave Salim.

[Dave Blunden]

I kind of curious whether I'm used to launching a couple hundred agents, maybe 250 agents, 256 agents actually, to work in parallel on a problem. And if the scaffolding that you're describing is right, it comes back just perfectly solved. And if it's even slightly flawed, you have a $2,000 bill and a bunch of crap.

[Alex Wissner-Gross]

How much are you spending per day on those agents, Dave?

[Dave Blunden]

Yeah, well, it's 100 bucks every few minutes popping up on my screen here. It's not quite that bad. It does seem like it's every minute, but it's not. But I'm curious to what degree this is actual engineering. These five layers are true scaffolding like this is hard code or is it more conceptual?

[Alex Wissner-Gross]

I think it's a balance of both. I also think it, to some extent, it's a trick question because increasingly the harness and the scaffolding itself is being generated by the models. So to the extent that we're in the era of recursive self improvement, this entire architecture is itself an artifact, a downstream product of itself.

[Dave Blunden]

Yeah, I think. I totally agree. And I also think that's the path to insanity, because at some point you have to say this is hard code, because then the AI will invent the next thing, and the next thing it goes to infinity, and then you're just like, you lose your mind.

[Alex Wissner-Gross]

I would say also this is in my mind. The way we prevent insanity in an era of recursive self improvement is with these benchmarks targeting systems that make sure that as systems are recursively self improving, we can quantitatively measure what are they optimizing towards, Are they going in a constructive direction or not?

[Dave Blunden]

Yeah.

[Peter Diamandis]

Chapter four, the Lockdown.

[Salim Ismail]

Wait, wait, wait, wait. I've got a couple of comments here. If you can go back a slide. Can I go back? Okay, so I think the. I really love the shift from genius to logistics because as you move, you can you always kind of say, take something from a black art and make it a prescriptive process. And when you can do that, that's awesome. I think that's fantastic. I have an issue with your, you know, maturity levels because you call it like natural law, but it's really just a taxonomy. We've had lots of industries get stuck at different levels like autonomous driving, etc. Etc. So this feels like a framework retrospectively imposed on what's going on. I think it's great aspirationally. Right. But some of them, because calling a maturity curve kind of speaks of an inevitability to it, which that may not be exactly the case. It's more of a descriptive model than a predictive one.

[Alex Wissner-Gross]

Yeah. I would say any Good theory of history and Solve Everything is in part not just a theory of the future, but a theory of history and how revolutions have worked in the past. Inevitably, as Monty Python says, it's only a model. So I do think there is an element of model building here where we're trying to for the first time articulate a self consistent, coherent theory of how this is all supposed to work. How is the singularity supposed to play out over the next 10 years? And to your point, Salim, about autonomy.

[Peter Diamandis]

Model levels, Alex, I could say not only how it's supposed to play out, but how do you have it play out in a way that leads us.

[Alex Wissner-Gross]

Towards abundance versus towards a model normatively. How should it play out? Not just how will it play out, but I think one at the margins one can quibble. Well, actually there are seven maturity levels for industries to evolve through their industrial intelligence stack or it's a continuum. But I think the central point stands regardless of how one sort of splices hairs on maturity levels that we're seeing over and over again. And we can get into more detail on this. We're seeing domain after domain, industrial vertical after industrial vertical, succumb to basically the automation of intelligence, which used to be the province of individual artisanal loan innovators. And it's just becoming an industrialization of intelligence.

[Peter Diamandis]

All right, I'm moving this on to the next chapter, chapter four, I'm sorry, keeping us moving the lock in. Alex.

[Alex Wissner-Gross]

So in this chapter we talk about in part AlphaFold3 from Google DeepMind and argue that that was a template for entire collapses of domains that almost overnight. And I've made this point on the pod in the past. AlphaFold3 took the problem of determining the structure of a protein which used to require a biology PhD student, five plus years of time, laborious benchmark just to determine the structure of a single program. And almost overnight alphafold3 solved that problem across many millions of proteins, known and unknown. That's in my mind like the prototypical example of a domain collapse. And we argue in this chapter, the lock in that we're now in a phase of history, of future history, where this is just going to start to happen over and over again across different fields where intelligence shifts from an artisanal craft to a utility that just flows. And we argue that we have approximately 18 months or so to decide what direction to shape the flow in and to set the standards for how this is going to be done at scale, given that we are dealing with scarce compute to put in place the supply chains which are huge. And we talk about on the pod all the time about all these supply chain scarcity issues, memory chip crises, GPU crises, what happens to Taiwan, what happens to the semiconductor fabrication facilities in the US versus not in the US and then all the data rights. This is, we're in a critical 18, we argue 18 month period when all of these details are going to shape the intelligence explosion. And so we want to make the best decisions in the next 18 months.

[Dave Blunden]

I can't read this chapter, actually 18 months, it's such a short timeline.

[Peter Diamandis]

Another implications, another important point here for CEOs listening, for entrepreneurs listening is the race isn't about building the best AI, it's about writing the best scorecard that everyone else is graded on. So what does that mean? You know today's healthcare system and it's an example. Alex, you used beautifully today's healthcare system. The benchmark is the number of patients processed per hour. Right. Which means it's driving a lot of short visits with the physician and cost economics driven. But what if the benchmark instead were patients who were still healthy five years from now? Right. That would set up a whole different set of optimization outcomes. So writing the scorecard that your AI system is going to use to measure success is critically important.

[Dave Blunden]

So why is this chapter called the lock in exactly? Are you implying that the decisions we make in the next 18 months have locked in humanity for the rest of time into a path?

[Alex Wissner-Gross]

Maybe not for the rest of time, but that is the inspiration for the name that we're in a period inspired in part by annealing of a metal cooling that the decisions that we make now are at least going to lock in a chunk of our future light cone.

[Dave Blunden]

Yeah, it makes sense. Totally makes sense.

[Salim Ismail]

You know, it took the QWERTY keyboard, it was decades of lock in, so I think it is. But I do like the we're stuck.

[Alex Wissner-Gross]

On the QWERTY keyboard. This is like we could have the singularity and you'll still be longer.

[Salim Ismail]

How long before get past that and can we stop you. But anyway, I really like the alphafold example demonstrating a domain collapse. Right. That's like really great. But you're here, you're talking about lock in as a, like, as a technical inevitability. But this is many times a policy and a governance choice. Right. It's monopolistic APIs, it's closed data, it's regulatory capture. There's lots of other stuff because how do you distinguish between like bad lock in and productive outcomes?

[Dave Blunden]

That's tough. I mean there's in your perfect world, are there like five jurisdictions with different choices and then at least we have variety, or is it inevitable that there's just one lock in?

[Alex Wissner-Gross]

I think, I mean, in some sense that's the grand geopolitical question that we as we just not a normative answer, but just a, a descriptive answer. It seems like we're heading to a near future where there are going to be multiple spheres or zones of influence, each able to independently lock itself in. So to the extent that we, with this, call it an extended essay, can have any influence, I think the aspiration is to have a positive, constructive influence on all of those spheres of influence and not just the one.

[Salim Ismail]

By the way, I disagree with the 18 months when I've been advising some big company CEOs. I've been saying two years. So.

[Alex Wissner-Gross]

Salim, you're pulling a reverse Moore's Law. Remember, Moore's law started as 18 months, became 24 months.

[Salim Ismail]

You're pulling a reverse more because if you have the next meeting six months from now, it's going to add that six months time anyway. Go ahead.

[Peter Diamandis]

All right, let's go to chapter five here, the mobilization. And Alex, if it's okay with you, the last three chapters of this paper are the most important. I want to hit on chapter five and six and then really focus on 789. So give us a summary on mobilization, if you would.

[Alex Wissner-Gross]

All right, so the idea with this chapter is spelling out a future timeline for how a, call it a wave front of the explosive shock of the intelligence explosion was going to propagate from math, which we talk about on the pod all the time over the next couple of years, to the physical world, physics, chemistry, material science, biology, and then through the end of the decade toward planetary systems, fission, fusion, the Dyson swarm by the early2030s.

[Peter Diamandis]

Amazing. And chapter six, the engine.

[Salim Ismail]

Yeah.

[Alex Wissner-Gross]

So this engine is very practical and talks about how to design the targeting systems, the benchmarks at a sufficient level of rigor that readers and folks all over the world can implement it with some level of confidence.

[Peter Diamandis]

The point we made here is don't invest in the AI models. If you look at the train and train track analogy, the trains are becoming commodities. It's the tracks, the tracks that the trains run on, the scoring systems, the testing infrastructure, the data systems, the funding mechanisms, and they're laid out beautifully here. Those are the elements that are the most important for entrepreneurs and CEOs to be focusing on.

[Salim Ismail]

That's right.

[Peter Diamandis]

Let's go to chapter eight, one of my seven, one of My favorites, moonshots.

[Alex Wissner-Gross]

So here and maybe Peter, you want to speak to this one perhaps even more than I do. We lay out 15 different moonshot level missions for what we argue are good uses, maybe optimal uses for this targeting system capability as we start to channel superintelligence into productive applications. Maybe. Peter, I'll pass it back to you for your favorites.

[Peter Diamandis]

Sure. So the thought is, you know, many of us have discussed XPRIZE over the time. The notion is that there's these GIGA xprizes, these massive opportunities on a humanity level scale, from printing human or human organs to achieving fusion, to understanding the fundamentals of unified field theory and physics. And it's where do you as an entrepreneur or you as a CEO, or you as head of an organization want to focus this incredible superintelligence that's coming to take moonshots? I keep on saying in the educational field, if you're using AI as a ninth grader to solve a ninth grade homework assignment, you lost it.

[Dave Blunden]

Right.

[Peter Diamandis]

If you're using AI to build starships, that's it. So how do we as humanity go after problems that we would have never imagined were capable of doing? And so the chapter lays out 15 different moonshots just to get creative juices going to say these are capabilities that we're going to be able to bring to bear to solve these moonshots.

[Salim Ismail]

Can you list out a couple of of the moonshots just to anchor the viewer?

[Alex Wissner-Gross]

One of my favorite ones is interspecies communication. I have a soft spot for that. We talk on the pod all the time about uplifting non human animals. And I think as we start to think, and maybe somewhat controversially about what future forms of personhood might look like, I think solving problems like interspecies communication or solving hard problems in physics, those are, those definitely have soft spots in my heart.

[Peter Diamandis]

Yeah. I think it's making humanity a multi planetary species. It's getting to longevity, escape velocities. It's all of the things. It's basically speedrunning all the science fiction movies, the positive, non dystopian science fiction movies.

[Dave Blunden]

That's right, yeah. You know what I love about this is if you look at John F. Kennedy and going to the moon, the brand effect, enabling somebody in power like John F. Kennedy to tie the brand of the, the mission back to them, that's critically important for them to then inspire the world that this is important. And I think what we did wrong is our governor here did an incredible job of unleashing $3 billion from the legislature to try and become an AI leader. But it was too vague. It's like, what does it mean? So the money hasn't even been deployed, but if you tie it to these 15 moonshots, and then the governor says, we want our state to win the this race, like John F. Kennedy did to the moon, they can pick the one they're passionate about and unleash it. And we have 50 states. You know, they can all choose their favorite of the 15. Maybe not talking to aliens, but whichever one they latch onto. It's such a really great framework.

[Peter Diamandis]

I'll just list some of them. Like doubling human lifespan is one. Ending hunger with synthetic food systems around the world is another. AI empowered education for all at the highest possible level. It's high bandwidth bci. We've been talking about that on this pod for a while now, demonstrating human mind uploads. Can't wait for that. Plan B, maybe plan C. We'll see. As Alex said, interspecies communications, Understanding human consciousness. I think we've talked about that previously. You know, can we understand human consciousness? At which point maybe we'll understand consciousness for our AI systems as well. So, you know, what have we dreamed about? Another one I love is disaster prevention and avoidance. Predicting earthquakes and then preventing them, or tsunamis, as the case might be. Right.

[Salim Ismail]

These become natural xprizes.

[Peter Diamandis]

You know, they're what I call giga xprizes here. But I think one of the important things in this chapter is allowing people, in fact, demanding people, dream bigger than ever before. Because the tools we have to solve the biggest problems are now epic.

[Salim Ismail]

I think this, for me is the most powerful part. The fact that you can say anybody has the agency now leveraging these tools to go after these what seem like impossible things become rote. You're only limited now by your imagination. And I think that's.

[Alex Wissner-Gross]

And your compute budget.

[Peter Diamandis]

And your compute budget, yes.

[Salim Ismail]

But, you know, that's dropping 90% a year, so we're in good shape.

[Alex Wissner-Gross]

That's right.

[Peter Diamandis]

All right. The muddle versus the machine. And at first, Alex, when you. When you propose muddle as a term, I was like, I'm not sure I like it now I love it. So describe what the muddle is.

[Alex Wissner-Gross]

Yeah, so the muddle is the. Another term might be the bureaucratosaurus that loves to. To measure inputs rather than outputs and slow down progress. And the idea is, without properly shaping the charge of the intelligence explosion, the muddle is the end state that we find ourselves win. Sort of basically muddling our way through is One of the etymologies of that term. So what we talk about in this chapter in a single sentence is what happens after we win. Painting a positive and non dystopian view of in particular, what does. What does human agency look like? I made this short film posted to social media called A Nation that Learned to Sprint, depicting what life in the early 2000s might look like if everything goes well and we see GDP 2xing or 3xing year over year. And what does a human job even look like in a macroeconomic scenario like that? So in this chapter we lay out lots of new job opportunities, career opportunities that will be available to humans, at least unaided humans. So target designers, for example, or data rights brokers, people who are involved in shaping the targeting systems and shaping how we aim, fire, and verify superintelligence towards the hardest problems that humanity faces. This is going to be a growth industry from a job perspective.

[Peter Diamandis]

Another point we make in the chapter here that's super important we've discussed, and Salimi and I've discussed this before, is that GDP is a terrible mechanism for measuring economic health. So the paper proposes replacing GDP with something called the Abundance Capability Index, which is measuring a nation's capacity to solve problems rather than how much money changes hands. So I think again, as we look at benchmarks, as we look at rails and harnesses, understanding this is really important.

[Salim Ismail]

I think the challenge here though is, you know, it's ubi, ubc, whatever we want to call it, It's a great endpoint and a great aiming point and you want to have a target, as you say, Peter, otherwise you'll miss it every time. The challenge is moving from a welfare taxation, labor union structure to that is such a huge leap. I have no confidence in public sector in getting us there. So how do you navigate that? I think that's something worth exploring the scope of your thing. But that's a huge consideration.

[Alex Wissner-Gross]

I was going to say, Salim, what a wonderful transition. Thank you. To the last chapter, Build the Rails.

[Peter Diamandis]

Building the Rails, Chapter nine. I think one of the most important chapters of the entire paper. Alex.

[Alex Wissner-Gross]

Yeah. So this chapter is where we lay out the answer to Salim's question. So what's the so what? And what do you do if you're not running a nation state? What can you do? How are you empowered to shape this transition, to shape your own moonshots and to control your own targeting system? So we lay out various suggestions from investors as indicated in the slide funding the primitives, not the applications. There's so much infrastructure that can and arguably should be built out. If you're an entrepreneur, you should be building, picking your own targets with the targeting system, create your own benchmarks and aim your own compute. If you're an executive of a large company, you should be measuring the outputs, not measuring the inputs. Dave, I think you put it beautifully earlier in this episode, talking about the API ification of large corporate boards and corporate governance. I think that's exactly the right playbook here. And the missing factor is having a benchmark to measure corporate objectives in such a way that the problem of corporate governance becomes a matter of maximizing the use of available scarce compute to maximizing those KPIs and those evals. So in this chapter we lay out for a variety of different roles in the economy. What can you do? What can you in the audience do to help us achieve utopian vision of abundance and post scarcity and excellent use? That's eusocial for superintelligence.

[Peter Diamandis]

So I want to wrap this here. I want to encourage all of our listeners. We'll put the link to the paper down below. It's solve everything.org. please take a look, load into your favorite LLM, have a conversation. What Alex, and to some degree myself, but I credit Alex, is what's the vision for the decade ahead that's going to bring us to abundance? How do you do it? How do you lead as a leader, as an entrepreneur, as a CEO, as a governor? Where are we going? And it's going to move much faster. And I think one of the points here, Alex, is that there's going to be such a distinction between those who do and those who don't that it's going to create a sort of a 66 million year ago asteroid strike that's going to kill the dinosaurs and elevate the furry mammals. I say furry lobsters moving forward.

[Alex Wissner-Gross]

No, we love our lobster friends. He didn't mean that. Peter really didn't mean that.

[Peter Diamandis]

No, no, no. Elevate. Elevate our lobsters. I would say that.

[Alex Wissner-Gross]

Elevate them into low Earth orbit.

[Peter Diamandis]

All right, favorite part for all of us AMAs, I'm going to keep us to one question per mate. All right, so here they are. There are nine of them, let's say. Dave, do you want to pick first?

[Dave Blunden]

Sure. I like number three because it's such a happy answer. In a world with perfect AI output, will there still be a place for human spark in art and sculpting? Will handmade work have higher value or be buried in the AI? Humanoid production wholeheartedly believe it'll have higher, astronomically higher value, human touch. It will be so rare and so valuable, but also abundance of capital will be unbelievable. And so I expect artwork, current artwork is one of the best investments you can make right now. But going forward, it is a category. It will go up tremendously in value and people will appreciate all things human, whether that's human action, human sports, human poetry, human artwork, sculpting. I expect to be definitely a rising area for sure.

[Peter Diamandis]

I think that would be a great conversation. I'll call it a debate, but put in our next pod. What is going to be most value from humans in the future? Salim, do you want to pick one of these?

[Salim Ismail]

Let's see. I would pick number five, which is how is a young person supposed to earn an income when they compete against a model that cost $50 a month? That's from Clownpeace. D, you're the. It's a great question, but you're assuming the future is about competing with AI. It's about directing it and leveraging it and amplifying yourself with it. You know, in history, we've destroyed all Java. We've created control points, and we've done orchestration, we've done intent. So winning isn't productivity, it's agency. And we talked about this earlier in the podcast. Like, knowing what to do and why it matters is more important. Like, how do you mobilize? Intelligence at scale is really the biggest challenge. And you can do that today in a way that you can't do ever. We've been doing workshops with teenagers and showing them how to use AI as a superpower to give themselves agency. And I think that's where I would go with that.

[Peter Diamandis]

Alex, would you pick one of these?

[Alex Wissner-Gross]

All right, I like this assortment. So I'll pick number eight for 100 trillion. Question number eight is, with AI taking tasks we do ourselves, isn't there a risk we lose essential skills and become completely dependent on AI services. And that's asked by Jorwin Hoffs. So I want to invoke my friend John Smart. Hope you're listening. John has, I think, a brilliant dictum that the first generation of any new technology is dehumanizing. It takes away all your skills. The first generation of calculators take away your arithmetic skills. Second generation is net neutral to humanity. Third generation is another friend of the pod. Stephen Wolf from Mathematica gives you new superpowers, gives you new skills. So I don't accept the premise that there will be any sort of permanent loss of essential skills due to AI automation. I do think that there is a short term substitution effect where AI drives down the cost of various skills or various tasks. But over the long term I expect AI automation to be net super humanizing. We're going to be capable of so much more with AI than we can do otherwise without it. And I'll also say Vernon Vinge has written quite a bit about this. Definitely encourage everyone to read Rainbow's End and Fast Times at Fairmont High. Novel and novella respectively that talk about this ad nauseam. We're going to I think find ourselves in a very near term future where just like there's wilderness camp to learn how to survive without modern technological aids, we're going to see start I think in our educational system at least the better parts of it. Having the moral equivalent of a wilderness camp for AI where all of your AI tools get taken away. You have to do things manually just so that you at least have that skill set and then you get all your AI skills back and every fourth grader becomes a Nobel Laureate.

[Peter Diamandis]

I love that. All right, I'm going to close this out with number six. I use Claude daily. It fails in basic consistency. I think is saying how can this be close to AGI when I have to check every output for errors? That's from mmgp9ot. So I'm going to say again, AI is the slowest and most incorrect it will ever be. I know when I'm using my Claudebot or Claude 4.6 if I get something that seems off, I will ask it to check itself and being able to use this in a recursive fashion. Also mmgpt9 we're in a period of recursive self improvement. I think we're at the steepest part of the curve and it's going to become more and more capable every day. And the idea that we can use AIs to check AIs and in fact to do deeper reasoning is going to eliminate this very quickly. Okay, let's jump into our outro music. This is from Friend of the Pod, CJ Trueheart. Cj, thank you for this. CJ was on a zoom AMA that Steven Kotler and I did for our book We Are as Gods and he actually wrote this as a result of that ama. Anybody who is a creative, we love creatives and if you want to send us outro or intro music, send an email to mediamandis.com myself and the team are reading it and we'd love to get your input and we'd Love to play it. All right, let's enjoy this outro. Music from CJ Trueheart.

[CJ Trueheart]

The singularity is new. Nah, the singularity is here. And it's not asking permission. It's asking you a question. What you paying attention to? Are you paying attention? Or are you paying the price? Scrolling through a sea of sex and entertainment twice. You can be a creator or you can be consumed. Every hour that you waste is a future left entombed they'll hand you UBI and call it containment. A golden leash. A velvet cage. A comfortable arraignment. Wake up. The moment's here to open your eyes. Your dreams are close enough to touch the skies. The deepest problems that have plagued you in disguise. Only you know that pain. Only you can make it fly. So what do you see when you look in the mirror? Do your actions match the vision? Is the picture getting clearer? Why wait when the time is here? Why wonder when the path is clear? Why sit as a passenger when you have the power to steer? Attention is the currency. Don't let it be the cage. The future for some will pass them by While others don't ask how they ask why not now, not someday, not somehow. They ask why not now. See, everybody wants to live a Star Trek dynasty But nobody wants to rise with a purpose they can see. Same old, same old, comfortable and cold Trading in their potential for a story already told. Answers only you can know. It's just a question of who you choose to show up. Is today, tomorrow? Every dawn, every day the version that's slow fading? Or who you choose to be today? So what do you see when you look in the mirror? Do your actions match the vision? Is the picture getting clearer? Or wait when the time is here I wonder when the path is clear? Why sit as a passenger when you have the power to steer? Attention is the currency. Don't let it be the cage. See, I've lived in the dark. Lost in the world, Lived in poverty. But the bottom didn't break me. It revealed the deep of me. Those who face no challenge will embrace no change. Those who embrace no change will always stay the same. And those who stay the same get left behind, holding pocket change. Because they refuse to learn. They refuse to turn what they gave their attention to. So attention became their chain. But I turned my pain into a plane. And I'm never landing back on that terrain.

[Peter Diamandis]

All right, thank you, CJ Guys, on behalf of Skippy My lobster. Sending you guys an incredible week ahead. All right. And as always, love it. Alex, it was an honor and a pleasure to work on solve everything with you. Excited to get it out into the universe. I think the value of steering people toward this accelerating time and how they actually have the biggest impact on creating abundance and not the muddle is critically important.

[Salim Ismail]

Agreed.

[Alex Wissner-Gross]

Peter, Pleasure writing it with you as well. And I would encourage all of the humans and non human humans in our audience to read it and let us know what you think.

[Peter Diamandis]

Yes, for sure. All right. WTF twice a week these days. Thank you to our subscribers. It's free. Please subscribe. We'll let you know when the episodes drop. Tell your friends about this. I've been here at Tony Robbins event and I would say probably 100 people have come up and said, oh my God, I love moonshots. And everyone, I love Alex. Alex, you got fans here in Sun Valley.

[Alex Wissner-Gross]

How many of those people were human? Peter?

[Peter Diamandis]

They unfortunately they were all human, at least for the moment. Yeah. All right, Dave Saleem, thank you guys.

[Peter Diamandis (Newsletter Promo)]

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

[Peter Diamandis]

This together every week.

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