The Singularity is Here: AI is Solving Math, Sora Outpaces Chat-GPT & AI is Designing Chips w/ Salim Ismail, Dave Blundin & Alex Wissner-Gross | EP #201

Summary

Moonshots with Peter Diamandis – EP #201

The Singularity is Here: AI is Solving Math, Sora Outpaces Chat-GPT & AI is Designing Chips

Guests: Salim Ismail, Dave Blundin & Alex Wissner-Gross

Date: October 20, 2025

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Episode Overview

This electrifying episode of Moonshots explores the argument that the technological singularity—the moment when machine intelligence transcends human intelligence—isn’t a distant event but something we’re living through right now. Peter Diamandis hosts an all-star roundtable with Salim Ismail, Dave Blundin, and Alex Wissner-Gross, dissecting rapid advances in AI, robotics, and energy, and what these mean for humanity. The team examines evidence that AI is already solving fields humans have grappled with for centuries, considers the economic and societal impacts, and debates how to navigate a world where the pace of change is hyper-exponential.

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Key Discussion Points & Insights

1. Are We in the Middle of the Singularity? (00:00–03:54)

• The team opens with the sense that we’re amidst “continuous event horizons”—tech breakthroughs stacked so densely it feels like living through a singularity.
  • Alex Wissner-Gross: “The singularity is an optical illusion... At a distance, it looks like a vertical asymptote. When you’re in the middle of it... it feels quite continuous.” (00:11)
  • Dave Blundin: “If you froze technology today... it would take decades to realize all the implications.” (00:25)
• The group plans to revisit the meaning of “the singularity” by episode's end, noting that Ray Kurzweil's projected 2045 date may arrive sooner.

2. AI Breakthroughs: From Math to Media Content (04:21–16:12)

• AI “Solves” Math: GPT-5 Pro and Gemini 2.5 are achieving feats in math once thought decades away.
  • Alex: “We now have clear line of sight to solving all of math, or substantially all of math as we understand it in 2025.” (04:21, 04:38, 28:56)
  • Implication: If a machine can tackle humanity’s most rigorous intellectual endeavor, physics, engineering, and medicine will follow suit.
• Speed of Adoption: The adoption curve for AI is 8x faster than that for the Internet.
• Explosion of AI-Generated Content: AI-written content now surpasses human-written content online.
  • Dave: “It’s an enormous opportunity... anyone who’s a reporter, editor, or creator... this is such low hanging fruit all of a sudden.” (12:33)
  • Alex: “There’s this cliche that we’ll drown in AI slop; I don’t buy that. With email, spam filters evolved. The same will happen with AI.” (13:41)
• Content Filtering: Discussion on tools and filter agents as the next must-have for navigating proliferating AI content.
  • Dave: “Tyler Cowen... said 99% of the readers of his new book are going to be AIs, not people.” (15:24)

3. AI Superforecasting, Exponential Change & Accelerating Timelines (16:24–24:00)

• AI as Superforecaster: GPT-4.5 is nearing human “superforecaster” performance—soon AIs will out-predict the world’s best humans.
  • Alex: “If we can predict it, we can steer it, optimize outcomes.” (17:04)
• Understanding Exponentiality: Linear mindsets are obsolete.
  • Salim: “If you fold a paper 50 times, you reach the sun–exponential growth is radical.” (18:25)
  • Dave: “It would take decades to realize all the implications [of today’s technology].” (25:08)
• Implications for Work & Career: Prediction markets (Polymarket, Metaculus) and the necessity for strategies in navigating exponential industries.

4. AI Wars & Self-Improving AI (25:25–28:09)

• GPT-5 Pro sets a new record on the Arc AGI benchmark, demonstrating rapid cost reductions and leapfrogging in capability.

5. The "Math Solved" Moment & Domino Effect (28:39–33:21)

• Math as the Domino: Crossing 10% on Frontier Math Tier 4 is seen as the signal that “math is solved by AI.”
  • Alex: “It’s the ultimate canary in the coal mine... any discipline that relies on math is in danger.” (30:51)
  • Dave: “The AI version of this [breakthrough] has near infinite scale instantaneously.” (31:46)
• Bulk Discovery: Major simultaneous advances in protein folding, math (e.g., Erdos problems), and increasingly across sciences.

6. AI in Chip Design & The Death of Traditional Expertise (33:21–37:44)

• AI Designing Chips: OpenAI's Greg Brockman and team are using AI to design high-performing hardware, bypassing years of human expertise.
  • Dave: “AI self-improving isn’t about genius, it's about math, algorithms, and chip design—and that’s all you need to drive self-improvement.” (33:49)
  • Peter: “Hyper narrow, focused graduate degrees are now a liability in an era where urgent generalist problem-solving rules.” (36:49)

7. The App Explosion: Sora, Mini Models & Decentralized AI (37:50–44:42)

• Sora Sets New App Launch Records: AIs recommend and drive adoption of next-gen tools, quickening the upgrade cycle.
• Samsung’s Recursive Tiny Model: Millions (vs. billions) of parameters for domain-specific intelligence, empowering edge devices.
  • Alex: “Taking large hard problems and compressing them down not to models with billions of parameters but millions... ultimate end state is a microkernel, a diamond of a model.” (39:43)
  • Dave: “Distillation and expansion is the most powerful thing in the world.” (42:04)
• Distributed AI: Foundation models will soon exist everywhere—hardware, cloud, pocket, body.

8. New Frontiers in Multimodal AI: Video, Music & Creation (44:42–55:20)

• Anthropic’s Haiku 4.5: Fast, agile code generation and creative capabilities.
  • Alex: “...generated a visually stunning cyberpunk FPS in 30–45 seconds.” (45:07)
• Gemini 3 Rumors & Emergent Abilities: Leaks suggest creative fluency in music, graphics, 3D.
  • Peter: "Capabilities emerge with scale and data—these weren’t explicitly programmed." (49:22)
  • Alex: “My advisor Marvin Minsky would slap my hand for using ‘emergent,’ but now it's everywhere.” (50:04)
• Veo 3.1’s Cinematic Video: From reference images to synchronizing audio and physics, video AI edges out human effort.
  • Dave: “Almost every ‘yeah but’ about AI in movies has been solved in less than 18 months.” (52:23)
  • Salim: “We’ll see an explosion of consumer-generated movies and plots.” (53:58)
  • Alex: “Video capabilities are just training wheels for true video-reasoning models.” (54:52)

9. AI & Science: Surpassing Human Limits in Astronomy and Other Fields (55:31–59:07)

• AI Outperforms at International Olympiad on Astronomy and Astrophysics:
  • Alex: “There aren’t enough human waking hours to analyze all the data. Now anyone can access these models—it radically democratizes situational awareness into our universe.” (57:29)

10. Robotics: Humanoid Advances & “Mad Max Mode” (63:12–69:56)

• Tesla Full Self-Driving v14 "Mad Max": Demonstrates aggressive, human-like driving; issues of legality and safety abound.
• Figure 3 Humanoid Robot: “New American Dream”—robot in every home.
  • Peter: “Home-ready design, improved sensors and hands, $20,000 price point, competitive with Tesla and 1X.” (66:52–69:56)
  • Alex: “Soon, building a robot will be a K-12 homework assignment.” (69:22)

11. Infrastructure & Economics: Energy Crunch, Data Center Boom, and New Metrics (75:00–91:07)

• Rising Electricity Prices & Regulation: Data centers stress energy grids; rise of nuclear microreactors for military/data center use.
  • Dave: “This is signaling strong demand for utility electricity—data centers will increasingly deploy co-located energy production.”
• The “Innermost Loop” of Civilization:
  • Alex: “Chips, robotics, energy, data centers... these are recursively accelerating the tech flywheel.” (81:08)
• Nvidia Spark Mini: $4,000 desktop computer handles 200B parameter models—device wars begin.
• AI & GDP Growth:
  • Peter: “92% of recent US GDP growth comes from AI and data centers.” (85:20)
  • Alex: “Opening act: build the infrastructure. Next act: transformative apps pour out, justifying all the capex.” (85:26)
  • Dave: “This is the second industrial revolution. The fun starts once infrastructure is in place.” (88:25)
• Federal Reserve on AGI:
  • Dave: “It’s idiotic that their best prediction for the economy is a 0.3% impact when they graph scenarios of hypergrowth and extinction.” (90:13)
  • Alex: “GDP may not be the right metric for a singularity era.” (91:26)

12. Health, BCI, Human-Machine Merger & Longevity (94:34–103:31)

• ALS Patient with Neuralink BCI: Thought-controlled robotic arm enabling feeding; start of mass human augmentation.
  • Alex: “This is the beginnings of democratizing access to our motor cortex... the future is all sci-fi scenarios, all at once.” (95:52)
• Google’s AI Cracks Cancer Code:
  • Alex: “Cell-to-sentence models treat gene expression as language—you can now have conversations with virtual cells.” (98:26)
  • Dave: “AI can intuitively think at the cellular level, something humans can’t.” (99:57)
• Longevity Escape Velocity: Ray Kurzweil maintains that science will soon outpace aging—extending life expectancy by more than a year per year lived.
  • Peter: “If you’re alive today, it’s a choice how long you want to live.” (102:18)
  • Salim: “Used to be death and taxes—now maybe both are solved.” (103:20)

13. What Is the Singularity? Has It Happened Already? (104:29–117:17)

• Ray Kurzweil’s Definition:
  • Non-biological intelligence exceeds biological;
  • Human-machine merger;
  • Radical, hard-to-predict transformation.
  • Dave: “We all agree we’re right in the middle of the singularity now... Ray nailed it; he’ll go down in history.” (104:29)
  • Alex: “Except for the discontinuity—feels like we have line of sight to what comes next.” (106:12, 107:01)
• Process vs. Event:
  • The change is less of a hard discontinuity and more of a visible, ongoing process. Humanity is growing into the singularity instead of being upended by a black-box intelligence overnight.
  • Salim: “All our previous models for how the world operated break down and we need totally new models.”
• Call for a New Social Contract:
  • How to share the dividends of AI, reduce costs of living, ensure equitable outcomes.

14. Notable Quotes & Memorable Moments

• “We're speed running Star Trek over the next 10 years. It's not the 24th century. It's more like 2035.”
  – Alex Wissner-Gross (04:54)
• “When you zoom out... it looks like a step function. But because we’re right in the middle of it, we’re experiencing all the week to week changes right here.”
  – Dave Blundin (24:07)
• “[Solving math]...If it can do one [problem], it’s very close to doing many—then billions—just right after that.”
  – Dave Blundin (31:46)
• “Intelligence is going to turn out to be everywhere.”
  – Alex Wissner-Gross (43:41)
• “With AI, every sci-fi scenario is happening all at once.”
  – Alex Wissner-Gross (96:45)
• “I kind of think of [Ray Kurzweil] not as a real person. He’s like an avatar from the future. He proves that time travel does exist.”
  – Salim Ismail (116:01)

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Timed Highlights

| Timestamp | Topic Segment | Notable Quote(s) / Key Point | |--------------|-----------------------------------------------------|-------------------------------------------------------------------| | 00:06 | Singularity in progress | “We all agree... in the middle of the singularity.” – Dave | | 04:21 | AI solving Math | “Clear line of sight to solving all of math...” – Alex | | 12:33 | AI-written content > human content | “Enormous opportunity... makes it more usable for all customers.” | | 17:04 | AI superforecasting | “If we can predict it, we can steer... optimize outcomes.” – Alex | | 22:36 | AGI goes unnoticed | “The singularity... feels quite continuous.” – Alex | | 28:56 | Math milestones & domino effect | “Bulk applying GPT-5 to all of these open problems...” – Alex | | 33:49 | AI designing Chips | “AI self-improving is nothing more than math algorithms...” – Dave| | 39:43 | Tiny AI models | “Ultimate end state... a microkernel, diamond of a model.” – Alex | | 52:23–54:52 | AI video creation powers | “...explosion of consumer generated movies now...” – Salim | | 66:52 | Figure 3 humanoid robot advances | “Home ready design, improved sensors, price point $20,000...” | | 85:20 | 92% of GDP growth from AI/data centers | “We're planting seeds... for future growth.” – Peter | | 95:52 | BCI & Human/Augmented Merge | “Beginnings of democratizing access to our motor cortex.” – Alex | | 102:18 | Longevity Escape Velocity | “Adding more than a year to your life per year lived.” – Peter | | 104:29 | What is the Singularity? | “Non biological intelligence exceeds biological…” | | 106:12 | Continuous event horizons vs. sharp singularity | “I feel like we have line of sight as to what happens next.” | | 116:01 | Ray Kurzweil’s legacy | “...not as a real person. He’s like an avatar from the future.” |

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Thematic Conclusions

• The Singularity Is Now: The panel agrees that what once seemed like a far-off, abrupt event is instead a continuous process we’re living through.
• Compounding Disruption: AI’s breakthrough in math is a downstream domino for all STEM domains, leading to a “solve everything” scenario.
• Tools of Democratization: AI’s accessibility reshapes business, science, and personal productivity—anyone can tap into disruptive power.
• Economic & Social Upheaval: A new contract will be needed as exponential wealth and productivity may not be naturally distributed. Old economic models like GDP may be obsolete.
• Emergent Intelligence: AI’s scaling laws are producing capabilities few could explicitly engineer, fostering an “age of emergence.”
• Human-AI Merge: Boundary between human and machine is blurring—BCI, longevity, and augmentation mean we are entering the transhuman era.
• Call for New Leadership: Navigating these transformations demands new institutions, models, and mindsets. Social contracts, prediction markets, and global coordination will become critical.

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Bonus: Book Corner (60:19)

Picks:

• "After On" (Rob Reid) – “Fun, Silicon Valley AGI adventure.”
• "Diaspora" (Greg Egan) – "Vivid post-singular utopia... heterogeneity of uploads, humans, cyborgs.”
• "Fahrenheit 451” (Ray Bradbury)
• "Accelerando” (Charles Stross) (Alex: “My #1”)

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Closing Thoughts

The rapid acceleration of AI, from solving frontier-level math to enabling ordinary people to create full movies, signals we’ve passed the rubicon: what we once called “the singularity” isn’t just coming; it’s unfolding right now. The implications for every part of society, the economy, and human identity are staggering but navigable—if we embrace the new models, mindsets, and global cooperation that the singularity demands.

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For more insights and to join the Moonshot community, subscribe to Peter’s metatrends newsletter or check out XPRIZE Visioneering.

Transcript

[A]

It sort of feels like we're in the midst of continuous event horizons.

[B]

We all agree on this podcast that we're right in the middle of the singularity at this moment.

[C]

I think it's increasingly likely that the singularity is an optical illusion. It's an optical illusion that appears at a distance. It looks like a vertical asymptote. But when you're in the middle of it, as I increasingly suspect we are, feels quite continuous.

[B]

If you froze technology today and just assimilated what we invented in the last two years, it would take decades to realize all the implications.

[A]

GPT5 Pro sets record at Frontier Math.

[C]

We now have clear line of sight to solving all of math, or substantially all of math as we understand it in 2025.

[D]

Now, with AI, how do we navigate this future? Because we can see it's coming now. So what does that future look like? And let's start painting that picture.

[A]

What does it mean when math is solved? What's the implications for all of our subscribers here?

[B]

Now? That's a moonshot, ladies and gentlemen.

[A]

Everybody, welcome to Moonshots. Another episode of WTF just happened in technology with my incredible moonshot mates, Salim Ismael, Alex Wiesner Gross, and Dave Blunden. Gentlemen, good morning. Exciting day here.

[D]

Morning. Good morning.

[A]

We're recording this at 6:30am at least Pacific Time. And I remember a couple minutes ago, we were talking and Salim, you were saying, oh, my God, you're getting up so early. And Alex, what was your response?

[C]

This is the slowest it'll ever be for a while.

[A]

Yeah, and there's no he said, don't.

[D]

Sleep through the singularity.

[A]

That was it. Honestly, it's like every day waking up more excited than the last, and it's just fun. So to our subscribers and listeners, we've spent the last four or five days gathering articles that are, in our mind, the most significant things going on. The speed is accelerating. We're going to actually close this podcast conversation with a discussion about what is Ray Kurzweil's singularity that we're going to hit by 2045? What does it actually mean? Because it feels like we're hitting it a lot earlier. Dave, what's been on your mind this week?

[B]

Actually, Ray was at MIT making that presentation that we'll get to at the end of the pod. So I've been thinking about the specifics of the timeline from here to there, and then this incredible compute shortage. We'll be in Riyadh next week, and that's data center central right now. So I'm thinking about that a lot, too.

[A]

This week is XPRIZE Visioneering. We're going to have the Moonshot Mates in Malibu, Louisiana, if you're interested in joining us at the X Prize Visioneering, which is where we debate and we discuss all of the competitions, what we should be launching next. We'll drop the link in here. You can join us there and just go to xprize.org arguably the most important.

[D]

Conference of the year globally. Just because trying to solve which problems do we want to go about solving is such a critical thing today.

[B]

Yeah, I agree. And I think the order of operations matters a lot, especially for people investing or career planning in this area. And the timelines are becoming more concrete now. And so I think we'll really cement our ideas a lot next week, and then Alex will refine it into the perfect message for the audience.

[A]

Yeah, agreed. So if you want to join us again, we'll drop the link in the chat notes below. Saleem, what's on your mind this week? You're going to be with me in Malibu at Visioneering. And then, Dave, you and I are off to Riyadh for FII 9, the Future Investment Initiative. A lot of AI conversations happening there.

[D]

We're pretty much all there, right? I mean, huge conversations going on. It looks like the big dominant conversation will be, how do we use AI to solve everything to Alex's points that he repeatedly makes on this pod. Because now we can apply AI as a tool to any of these domains. It's huge.

[A]

Yeah. Alex, how about your week? I'm sorry you're not going to be with us, but. But, hey, I'm sure you're busy.

[C]

Yeah, it's been an exciting week, I think, arguably. And we'll get to it. One of the most exciting developments of the past week. Ish. Was the solution of math. I would argue that we now have clear line of sight to solving all of math, or substantially all of math as we understand it in 2025 now.

[A]

With AI, which then topples physics and chemistry and biology and. What did you say to me? We're going to have an accelerated play of Star Trek. It's like it's all going to happen.

[C]

We're speed running Star Trek over the next 10 years. It's not the 24th century. It's more like 2035.

[A]

Crazy. Crazy. All right, so hold on your seats, everybody.

[D]

So the future is collapsing into the.

[C]

Present, basically linear versus exponential. Salim.

[A]

Yeah, yeah, seriously. All right. I added some slides here at the beginning to talk about the speed of change, because I want everyone to understand this. We'll begin with this image here, which is the adoption of AI is now eight times faster than we saw with the Internet years. We went from 0 to 200 million users in AI in 1/8 the time it took for us to get there in the Internet years. Any comments on this?

[B]

Well, this slide is understated, too. That's showing ChatGPT alone against the entire Internet. If you include Gemini and the other engines, it's well over a billion on that left chart. So, yeah, it's even more acute than this chart makes it look.

[C]

I think this is likely to be in keeping with the notion that this is the slowest, that things are likely to be for some time to come. This is actually still pretty slow. Superficially. One can look at the AI curve and say, okay, well, we deployed super intelligence upgrades and reasoning models to a chunk of humanity over a few years. It's actually still pretty slow. We don't have yet a conduit for deploying physical world upgrades to most of the world. ChatGPT obviously rode on top of prior platforms like the Internet and personal computers and smartphones, but we don't yet have a conduit for deploying material or physical changes to the world. I think it's going to look like robotics, nanotechnology, a few other key technologies. I think things will actually be moving pretty quickly once we have those conduits that we don't really have yet, like you said.

[D]

Your point being that Once we have 5 million robots out there, then you get an instant upgrade to everybody. We don't have that for humanity or 5 billion.

[B]

Let's, let's make sure we come back to that in a future pod, because I think the constraints to manufacturing are going to hold back robotics, and there's a separate curve for that. But then you have the nanobots, which don't require a lot of material. And the nanobots are, you know, from a medical point of view, are going to be massively impactful. And I think those are actually going to come sooner than people are predicting because they don't have the, you know, the component supply chain bottlenecks that the, you know, the robots.

[A]

We don't hear a lot about nanotechnology in the classical Eric Drexler means. We hear about wet nanotechnology in terms of DNA origami and so forth. But the idea that you can build an assembler, a mic, a, you know, nanoscale robot, subcellular robot that's able to pluck atoms of different types and Build materials out of pure carbon, sort of diamondoid materials is a, is still a few, you know, a few years out, but I think it's going to be arriving on the back of AGI and what follows.

[D]

I've seen a team that seems to have a viable, credible line of sight path to molecular manufacturing. Peter?

[B]

Yeah, I think that's likely. You're going to see later in this pod Greg Brockman designing chips using AI. And like, what the heck does Greg Brockman know about designing chips but with the help of AI, you know, anything is possible. Very similar to Demis Hassabis solving protein folding. But like, how does Demis Hassabis know anything about protein folding? Well, with the help of AI, anything becomes possible. So I think, you know, these areas like molecular manufacturing and nanobots are going to come very soon from unexpected places, from early adopters of the tools that are tuned to the problem.

[A]

Every week, my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI and quantum computing to transport energy, longevity, and more. There's no fluff, only the most important stuff that matters that impacts our lives, our companies, and our careers. If you want me to share these metatrends with you, I write a newsletter twice a week. Sending it out is a short 2 minute read via email. And if you want to discover the most important meta trends ten years before anyone else, this report's for you. Readers include founders and CEOs from the world's most disruptive companies and entrepreneurs building the world's most disruptive tech. It's not for you. If you don't want to be informed about what's coming, why it matters, and how you can benefit from it. To subscribe for free, go to dashmandis.com metatrends to gain access to the trends 10 years before anyone else. All right, now back to this episode. You know, one of the comments we get sometimes is, okay, you guys are wealthy. Well, hey, you know, none of us started that way. At the end of the day, we created some wealth. But here's the point I want to make for everybody who's listening, who's struggling, and there are people who are listening, are struggling. These technologies are massively demonetizing at, you know, we are going to have autonomous cars that are four times cheaper than owning a car. We're going to end up with nanotechnology where if you know, and this sounds insane until it all materializes. If I have a nanobot, I can literally throw that Nanobot in the ground and say, manufacture me anything out of raw materials. The information set is free, the energy is around, and you're basically plucking atoms of whatever you need. I mean that's how an oak tree starts from an oak seed and grows over time. It just at a very slow pace.

[D]

Can I mention a couple of things about this?

[A]

Yeah, please.

[D]

You know, you write one of the things you pointed out in abundance, Right. Is that if you went back a few generations ago, the richest people in the world exclusively had inherited their wealth. And today you look at the richest people in the world and exclusively they've earned their wealth. They went from zero to everything. This is not a, this is a mindset problem more than anything else. You take Vitalik Buterin 18 year old kid out of Toronto, ignores his professors, gets together with a few friends and Boom, have a $600 billion ecosystem that you know, nobody understands. So there's this unbelievable potential to go from zero to everything. And this has never been true before in the history of humanity.

[A]

I've had so many friends using completely.

[D]

The mindset that gets you there.

[A]

Using AI to say, okay, just going deep and saying, this is what I love doing. How can I start a business here? Right? How can I, you know, what do I do first? How can I learn about this? And it really is making a commitment to yourself to use these tools to educate yourself and then to build on top of those tools.

[D]

Yeah. Can I give a crazy example? Sure. A friend of the probably, we all know I was talking to him. I won't use his name, just I'm not sure he's comfortable with it. But he told me that last month he launched 47 startups using AI. Okay, just in a month he and his team just black coded and pushed on. That's just insane. That's unbelievable.

[A]

Yeah, it is. All right, so speed is going fast. Here's another article showing sort of the speed of change. AI content overtakes human content online. So this is fascinating. This is just over the past five years. You know, it used to be that 100% or near 100% we had AI. AI is writing articles for certain magazines as early as the late 90s, I mean late 2018 or thereabouts. But we dropped from 100% human content to below 50% and AI written content is exploding. Dave or Alex, what do you guys think about that huge deal?

[B]

I mean it's an enormous opportunity to. What you were saying a second ago, Peter, the ability to use AI to create new content is a business Opportunity and a life changing opportunity for everybody. The tools are incredibly democratized. Easy, easy to come up the curve. The AI will explain to you exactly how to use Sora 2 or whatever, you know, VO3 and just a huge amount that you can do with this. You know, if you think about all the friction that people have in life, if you're operating locally, you know, you're a local government or you're a local business owner or whatever. The interface, putting an AI interface on your business makes it dramatically more usable for all of your customers. That alone, it can be a life changing business opportunity. So anyone who's a reporter, who's an editor, a video creator, whatever, this is such low hanging fruit all of a sudden. And it's not all slop. This is high quality capability. It's used for slop a lot, I'm not gonna deny that. But the ability to create genuine high quality content with these tools, that's much more compelling than just writing an article would have been. It's right in front of you, Alex.

[C]

I want to hear your thoughts. Maybe add there is this cliche out there that we're just going to drown an AI slop. And I don't buy that for one second. If you rewind 20 years or so, there was a cliche that we were going to drown in email spam and that also did not happen. Better filters get brought into existence and ultimately the same tools that would empower spammer also empower the ability for small individuals to have basically their own electronic printing press and to disseminate their ideas to millions of people via email campaigns that people subscribe to. So I don't subscribe to the notion that when we look at a chart like this that it just means humanity is drowning in online slop. I think if anything, there are sufficient economic motivations for the quality of slop to ultimately disrupt from below. Innovators dilemma style the quality of human writing. And if anything, I think we end up merging with the slope.

[A]

Well, I mean, listen, one should not just take whatever their AI writes and publish it. I mean, you should read it, make sure it represents you. I mean, you should be the originator of the basic idea and use AI to help level up the content you're producing. Here's an epsilon.

[D]

We've had synthetic data. Aren't we now entering the world of synthetic culture? Doesn't this become like a hall of mirrors where everything's reflecting on what's happened before and just amplifying itself in a totally weird way? This is going to become Completely unpredictable, is it not?

[C]

I think that presupposes that culture was always natural. How do we distinguish between natural versus synthetic culture to begin with?

[A]

Dave, you've.

[B]

I think Alex's point on spam filters is really important here too, because the ability to have your AI friend agent filter the content and reduce it to the subset that you care about is so easy compared to, you know, the spam or so much more powerful than the spam filter version of that. And it works perfectly fine. I think when Tyler Cowan came out with his new book, one of the things he said when he launched it was 99% of the readers of this book are going to be AIs, not people. So I designed it to maximize the impact on the AIs, and the AIs are going to summarize, translate, and feed it to the humans.

[D]

It's the new.

[B]

So that's kind of the future of writing.

[A]

That's so important to realize that when you're writing, the major impact you're going to have on the world is through an AI interpretation of what you've written. That's amazing.

[D]

People are using the LLMs to essentially run SEO. So they're feeding these things with great images of their company and stories about their companies, et cetera.

[A]

So this next article, large language models are improving. Their forecasting ability is fascinating. There's this thing called a superforecaster. I remember reading about this years ago. It's a person who can demonstrateably and consistently make accurate predictions about the future as compared to the general public. And there's a terminology there. It looks like GPT 4.5 is now scoring very close to these human superforecasters, and it's likely to exceed the best human superforecasters by by late 2026. Alex, thoughts on this one?

[C]

Yeah, so maybe first a bit of background. This is a benchmark called Forecast Bench by the Forecasting Research institute. Consists of 500 constantly updated binary questions. Yes, no, questions that look something like will the following happen by the following date? Yes. No, that can be automatically verified. And when I see an experience curve like this, my mind immediately goes to sci fi writers like Ted Chiang and Frank Herbert, who've written extensively about what happen when AI or superhuman intelligence can predict the future to ultra high accuracy. Like what does civilization look like when we can predict things that are right around the corner? I think arguably, if we can predict the future of civilization, we can also steer the future of civilization. And this isn't just sort of a centralized steering mechanism, since everyone has access to first order to GPT 4.5. It's not some sort of like centralized command economy type future. Imagine a future where everyone has the ability to predict the, the future of markets, of social outcomes. And then if you can predict it, you can steer it, you can optimize outcomes. I think that's what we find ourselves in, in a few years.

[D]

Yeah.

[A]

You know, Salim, we, you and I talk about linear to exponential. And you want to take a second and digress to what that means?

[D]

Yeah, I mean, look, you talk about this in all your presentations, Peter.

[C]

Right?

[D]

Like if you went back 100 years ago, anything important happened within a day's walk and today something that happens around the world hits us in seconds. And it's really hard to get our heads around this because 4 billion years of evolution has guided all of our intuition, training, education about the world to be linear for the last few decades. If you were running a business, you took your past performance, you drew a line as to where it might be in order to predict the future. But we're entering this exponential phase. And I love the example you use of. So if you take a piece of A4 paper or 8 and a half by 11 or A4 paper like this is like 0.1 millimeters thick. If you fold it, it becomes 2.2. If you fold it again, becomes 0.4. Here's a thought experiment for everybody. How thick is it if you fold it 50 times? Okay, and this is a very, very unintuitive question. And it turns out at like full 20, you're the size of a football field. At full 38, you're around the earth. And at the 50th fold, you've reached.

[A]

The sun, 93 million miles.

[D]

Now, granted, it's hard to fold that 50th time. It's pretty small at that point. But very, very, very few people, maybe Alex would get to that answer right away. Everybody else is going, well, I think it's about this big. I think it's about this big. Maybe it's the size of a room. Going to the sun is a very, very big difference in going to whatever idea. And yet the world is running on this dynamic in this heuristic.

[A]

Yeah, we're running on linear mindsets in that is growing at a hyper exponential, not just exponential these days.

[B]

Well, just some very practical advice for all my nephews and family out there. The data behind this comes from Metaculus, I believe. Is that right, Alex?

[A]

This is for the forecasting.

[C]

Half of the 500 binary questions come from markets including Metaculus. But there are other markets as well, and the other half come from Wikipedia and other time series sources.

[B]

Oh, interesting. Okay, well, everybody should check out polymarket, Metaculus, Kelshi. These are the prediction markets where you can actually invest or bet on future events. And they're growing like wild. They're becoming very valuable companies, but they're part of this new refactoring of the economy, where you have prediction markets, you have AI forecasters and benchmarks. And then later in the pod, we'll talk about new exchanges. And this whole process of investing and creating has worked really well in America for 100 years or more, but it needs to accelerate like crazy, and this is part of that acceleration. So we'll probably follow up on this in more detail. But Alex was a huge early adopter of originally metaculus and polymarket and brought it to my attention. But now I'm trying to bring it to everybody out there. Just go check them out and see what's happening there.

[D]

Shout out here to Ralph Merkle, who created Merkle hash trees, which is the basis of Bitcoin and the encryption there. He's also one of the world's top nanotech experts. But a few years ago, you wrote a paper where he suggested that the polymarket, the prediction markets, are going to be the future of democracy because you could do policy formulation using prediction markets. It was a really profound idea.

[A]

Fascinating. Ralph's been a member of the Singularity University faculty from the inception. Let's move on to the AI wars. But before we do that, here's a sound bite from Sam Altman, and I found it fascinating. It's AGI won't feel like the Singularity. Let's take a listen.

[C]

We talked about the Turing Test. AGI will come. It will go whooshing by. The world will not change as much.

[A]

As the impossible amount that you would think. But one of the kind of, like.

[C]

Retrospective observations is people and society as.

[A]

A whole are just so much more.

[C]

Adaptable than we think. That, you know, it was like a big update to think that AGI was going to come.

[A]

You kind of go through that. You need something new to think about. You make peace with that.

[C]

It turns out like it will be more continuous than we thought.

[A]

So what do you guys think about that? You know, we went whooshing through the Turing Test. Didn't notice it. Alex, you've sort of argued that we're at AGI right now and didn't notice that.

[C]

But what are your thoughts on maybe five years in our past 20, 20 or so I. I think it's increasingly likely that the singularity is an optical illusion. It's an optical illusion that appears at a distance. It looks like a vertical asymptote, but when you're in the middle of it, as I increasingly suspect we are, feels quite continuous. That rapid change, if you follow it closely enough, actually just feels completely smooth. And I almost. It's sort of ironic, the notion of singularities in. In math and physics evoke black holes and relativity. And I almost want to draw a relativistic metaphor that the singularity perhaps only appears from an outside observer's reference frame. Maybe from the reference frame of 1900 or so, it looks like a singularity, but if you're right in the middle of it, spacetime is perfectly smooth.

[A]

Fascinating. So you agree with Sam fully?

[C]

Yeah, I have no. I have no indication that this is not the case.

[D]

I have three quick comments, please. Number one, I have my normal rant on what the hell do we mean by AGI? Because at last count, there are 14 different definitions, so leave that to the side. I really do agree that we're in the middle of the singularity and it looks like normal space time. We really are in the middle of it. It's been something and I think. Let's talk about it at the end when we get more into what we mean by this.

[A]

We're going to debate what the singularity means at the end of this episode.

[D]

Yes.

[A]

What does Ray mean by we're going to reach it in 2045. Dave, what are your thoughts on this?

[B]

I love the fact that we all agree that we're right in the middle of the singularity right now. That's not common. You know, I'm positive it's right, but it's not common knowledge. And it's so cool to have us all say, yeah, this is actually this incredibly magical moment in human history. And exactly like Alex and Salim said, when you zoom out and look at the long term of human history, it looks like a step function. But because we're right in the middle of it, we're experiencing all the week to week changes right here on this podcast, all these week to week changes. And as Sam is pointing out, humans are shockingly adaptable. And as Peter always says, they go back to sleep in a hurry so they see a new capability. And the input, like, hey, look, we're launching private rockets into space. You know, we can like the cost per kilogram plummeted the implications of that and gluing it into all the different things we can suddenly do. The backlog is now decades deep of if you froze technology today and just assimilated what we invented in the last two years.

[D]

Oh, decades.

[B]

It would take decades to realize all the implications. And people go, yeah, okay, I saw that. I'm going back to work.

[D]

Full disclaimer here. I've been resisting this idea that we're in the middle of a singularity, but I've now fully entered Alex's reality distortion, so.

[A]

Oh, my God. Incredible. All right, well, stay tuned for some more conversation on this, on this subject here. All right, the AI wars here. GPT5 Pro sets Arc AGI record. Alex, our resident expert on the Arc AGI, tell me.

[C]

Yeah, so as a reminder, Arc AGI is a benchmark that measures the ability of AI to synthesize new computer programs in response to. To challenges that can be interpreted almost as like 2D flat puzzle games. The ability to extrapolate sequences of images and patterns without any natural language help. And I think it's a beautiful sequence. Now, there's more than 1/arc AGI benchmark for the ability to do this sort of visual reasoning. It's a beautiful benchmark, but also one of the things I love about the sequence of benchmarks and I've donated to ARC AGI in the past, is that they pay close attention to cost per task, not just raw capabilities. So we can see a price performance frontier. And to the extent that the goal of many in the AI community is to drive the cost of intelligence down to zero, we can watch in real time the cost of solving hard, arguably in some cases, superhuman challenges be driven to0. ARC AGI specifically is focused on problems that are easy for humans to solve, hard for current AIs to solve. But as the cost plummets, we're going to see superhuman performance. And to your point, Peter, GPT5Pro is demonstrating exceptional score. So exceptionally high performance, but still at a relatively high cost. And over time, I would predict over the next year or so, we're going to watch all of these curves on the scatter plot that you're showing. Shift to the shift to upward and shift to the left, at which point, cost of intelligence too cheap to meter.

[A]

Let's put a few numbers this. So GPT 5 Pro hit 70.2% on Arc AGI 1. That compares to 65% for Sonnet 4.5 and 66.7% for Grok 4. And what we're seeing is just this constant leapfrogging where everybody's just incrementally, you know, moving towards, towards 100%. And to hit your number on price for GPT5 Pro. When I looked it up, it said it's $4.78 per task. And all of this stuff demonetizes rapidly.

[C]

I love that the horizontal axis here is in logarithmic terms. Every chart of every good and service in our economy should be on logarithmic terms so we can watch the hyper deflation.

[A]

Dave?

[B]

Yeah, no, zoom in on that. When you're looking at the. If you're not driving right now, zoom in and look at the x axis. You know, on the right side you've got 10 bucks and then in the middle you've got below a dollar. So it's a huge range of price points for very similar performance if you look at the peaks. So what you're seeing mostly on this chart is massive cost reduction, which makes it more accessible. We'll talk later about some of the other innovations that are driving down that cost. But that's perpetual.

[A]

All right, Alex, this one is for you. You and I going back and forth on text. Oh, my God, we're solving math. We've solved math. So this article in particular comes out on the heels of this. GPT5 Pro sets record at Frontier Math. What does it mean and what are the implications here?

[C]

Yeah, I think this is arguably the most exciting development over the past week and a half. So as a reminder, frontier math tier 4 consists of of math problems that professional teams of mathematicians would take few weeks to solve. These are very hard math problems. And over the past week and a half or so, we've seen first Gemini 2.5 DeepThink, and then GPT5 Pro demonstrate breakthrough performance. GPT5 Pro at 13% on Frontier Math Tier 4. And I've. Dave insisted that I make an internal recorded prediction just to get on the record. What would it mean for math to be solved in quantitative terms? And this is months ago and I put on the record, as Dave will, I think, attest, we can reasonably declare that math has been solved. When frontier math tier 4 passes 10%, scoring more than 10% of the problems can be solved by a bleeding edge model. The reason why I picked 10% is because at some point in the logistic regression of predicting you've solved 10%, at some point you just pour compute on and you get more results. I think we've seen this over and over again. We saw this infamously with Ray Kurzweil pointing out that you're halfway complete with sequencing the human genome once you've passed 1%, 10% is my arbitrary benchmark. I predicted that we would be past this by the end of this calendar year. Christmas arrived early. Math is now on a trajectory. If you just pour more compute on, arguably with no new innovations, math will be solved. At least math as we currently know it.

[A]

Okay, so when math is solved, I've asked you this before, but I just want to hit it home because it's kind of an esoteric subject for most people. What does it mean when math is solved? What's the implications for all of our subscribers here?

[C]

It's the ultimate canary in the coal mine, as it were, for solving physical sciences, solving engineering, solving medicine. If we can have machines that solve arguably humanity's most rigorous intellectual endeavor, which I would suggest is math, then everything else I would expect over the next few years, call it five to 10 years, I expect to succumb.

[D]

As a tangible example, encryption is all math based. So when you can have an AI deliver that, you can kind of get super encryption at whatever level you want instantly.

[A]

And physics and material sciences. Yes, yeah.

[C]

And all of the physical sciences. And conversely, as I've articulated in the past, any, any discipline that relies on math, at least the current math being hard, is also in danger.

[D]

So it's not just learning math, it's inventing it at some point pretty quickly.

[C]

Absolutely.

[D]

Okay, yeah. Wow.

[B]

Yeah, I think just the implications, you know, one thing people misestimate continually is if, if some brilliant mathematician human solves a hard problem, that makes the news, but it doesn't imply that all other problems will be solved the next day. The AI version of the same achievement.

[A]

Great point.

[B]

In biotech, in math, in physics and all these other areas, because it has near infinite scale instantaneously. If it can do all one frontier math problem, it's very close to being able to do many and then, then, you know, billions, you know, just right after that. And so people need to factor that into their rate of change thinking as, as it cracks these different areas, better.

[D]

Find some more problems.

[A]

This is dominoes falling and heading towards again, Alex's words, solve everything.

[C]

It's bulk discovery. And we've only seen this in narrow areas. We saw this with Alpha Fold 3 and protein folding, where almost overnight we had arguably high quality protein structures for most known proteins. We're going to see bulk discovery across a number of disciplines. The other story here is we don't have a slide for this, but the Erds problems, this is a set of order of magnitude 1000 problems that were identified by famous mathematician Paul Erds. AI and GPT5 are being used to bulk solve those and the solutions are starting to pour out. If you go to the Erds problems website, you'll see just in the past few days now there are folks who are just bulk applying GPT5 to all of these open problems and they're getting switched from open to solved.

[D]

Wow.

[A]

Don't sleep, don't blink. It's happening. All right, let's move on to this one. OpenAI on building chips with AI. Here's a quote from Greg Brockman. We've been able to apply our own models to designing this chip. We've been able to get massive area reduction. You take components that humans have already optimized and just pour computer into it and the model comes up with its own optimization. Dave, talk to me.

[B]

Yeah, I love this story because this is, this is ties together so many things we've been talking about. You know, one of them is the short timeline to about 100 to 10,000x improvement in AI performance because of the AI self improving. And when we say AI self improving, a lot of people in academia are like, well, it's not that smart yet. But it is, because AI self improving is nothing more than math algorithms and chip designs. And it can do those point tasks. And then a lot of the academics then say, yeah, but that's not true reasoning, that's not true genius, that's not true. Whatever, it doesn't matter because that's all you need to do to self improve. And so I just love this story. I also love the fact that Greg and Sam, out of the original cast at OpenAI are the two guys that dropped out of college, didn't finish undergrad, and they're the two survivors. And so you take Greg, the guy that dropped out of mit, he's designing chips now because he's a master of AI. And I don't know if you guys did any VLSI design or chip design at mit. I did a fair amount of it, actually doing neural net designs and it's absolutely laborious and the amount of improvement is incredible if you just had the time and 10,000 people to work on it. And so AI is just going to rewire and redesign and relay out the thing. And the simulators are near perfect.

[A]

It's an acceleration of the acceleration.

[B]

It's so cool. It is to me.

[D]

I can attest to this. One of my student jobs at Waterloo was to run field tests for VLSI boards. And it was like fricking linear, painstaking. Hell yeah.

[B]

And so the other Thing this ties in is Leopold Aschenbrunner buying Broadcom stock. And you're like, okay, I didn't see that one coming. Why is he. And of course, you can see it in his 13F filing, but now it's obvious, right? Brockman is designing chips. The chips need to go to Broadcom, and then they get manufactured on tsmc. Therefore, Broadcom stock is the one that Leopold buys. And of course, Leopold was at OpenAI and knows Brockman. So it all ties together.

[A]

You mentioned something that I think is important. Again, we hit on this sort of week, on week that Sam and Greg dropped out of college to go and pursue this. And one of the things you and I have discussed, Dave, is majority of the entrepreneurs who are succeeding today aren't the ones who have gone on to get PhDs or gone on to do graduate work. They're ones that have launched either just after college or have dropped out of college to go pursue it. And it's almost as if going to get your graduate degree and going very narrow into a deep. You know, it's like I remember if I was going to explain to my grandfather what I did when I was at mit, he says, okay, you're an expert in. I would say to him, like, look in the dirt over there, there's this thing called this bacterium. And he goes, oh, you're an expert in that? No, no, no, I'm not an expert in that. In the bacterium, there's this thing called DNA. You're an expert in that? No, in the DNA, there's this thing called a gene. You're an expert in that? No, no, in that gene, there's a promoter sequence. And I'm an expert in that.

[C]

Right.

[A]

And so our graduate work right now is this hyper narrow, focused effort instead of being able to step back and look at reinventing an entire field.

[B]

Yeah, I'll put a slightly different spin on it too, which is the people Greg's age and Sam's age that dropped out had very high situational awareness around the urgency of what's happening right now. If it were 2001 and 9, 11 had just happened, going and getting a PhD would make a ton of sense because the world isn't moving at warp speed during that timeframe. But the reason the undergrad dropouts and the other people just graduate and start a company right away are way over performing is because they recognize the urgency of the moment and they know that four years from now is just not a good choice in this moment. So it's that.

[D]

Well, also their Brains haven't been calcified by studying that one thing that Peter talked about. I don't know how Alex broke through all of this somehow. He's.

[C]

This program calls it the hardest lesson to unlearn. It takes a lot of unlearning.

[D]

Yeah, crazy.

[A]

Crazy. All right, let's move on here. Sora hits 1 million app downloads in less than 5 days. Faster than ChatGPT. I remember when ChatGPT came out, I was doing a podcast. I was like, oh my God, a million downloads in five days. New world record. But something is going to beat that. Well, here it is, it's Sora one beating that in the App Store. And of course something will beat this as well, probably within the next year. Dave or Salim, what's your thought here?

[B]

One thing we're seeing here, I was talking to the CEO of Pacify two days ago, really thriving marketplace company, and they were asking for my advice on how to implement AI within their product. And I said, well, just ask the AI. And so what you're seeing here is more and more of what you do next is what was recommended by the last thing you had on. So if you're a Gemini user, you go with Gemini. If you're a ChatGPT user, you go with Chatbot. But you say, hey, I want to create an amazing video. What should I do? It tells you what to do. And then nine times out of 10, you just do what it said. And so the distribution of these new capabilities is actually within the AI installed base and that's why it's got this self reinforcing loop.

[D]

I mean, I would have expected nothing less just because the next thing will be two days and the next thing will be one day. And as Alex points out in our earlier conversation, at some point we'll get the ability to upgrade everybody at the same time and then we'll be. That's what we're talking about.

[A]

Yeah, for sure. All right, I'm going to turn to you on this one, Alex. Samsung's tiny recursive model redefines AI efficiency. So researchers have built a mini AI model at Samsung with 7 million parameters to test reasoning ability. And this compares to models with, with billions or trillions of parameters. So talk me through this, Alex.

[C]

I would say at a high level. Remember first that this revolution, this soft or gentle singularity, if you will, that we're living through, was arguably the result of just compressing information. That one of the biggest lessons I take away from how intelligence was arguably solved is just like you could take a lot of matter. And if you compress a lot of matter in a tiny volume, you get some phase transitions and ultimately you can recover free energy, for example, via fusion. Similarly, if you take a lot of information and you compress it down into a model that has a relatively small number of parameters, at some point you get intelligence out almost for free. That's like a big meta lesson in my mind from the past 10 years. So there's an enormous amount of opportunity for taking large hard problems, large information spaces and compressing them down not to models with billions or hundreds of billions of parameters, but to models with millions of parameters. I've also spoken about my expectation, this is one person's speculation, that we're marching toward an ultimate end state where we'll achieve a perfect model that's like a microkernel, it's like a diamond of a model that may not consist of differentiable end to end parameters. Maybe it'll only be a million parameter equivalence or a million bytes or something like that. And when I see progress like Samsung's trm, which looks, it's a lovely paper, if you look at the architecture, sort of resembles a diffusion model in some sense. The premise is it's domain specific. In the case of the result you're showing, it was trained on a bunch of examples for the ARC AGI. One challenge that we were talking about earlier has a notion of a scratch pad, but it's a relatively tiny model and it rewrites the scratch pad several times and then goes back to work. It has some latent expectation of what it's trying to do and then rewrites some more times. It looks like a diffusion model if you squint hard enough at it. But I think that the big takeaway is such a tiny model achieving breakthrough performance. Folks who can see the chart here, it's competing with O class models in terms of price performance, but it only has a few million parameters. So I think.

[A]

So the implications are what? You've got this on your phone, you've got this on every device. Even if it's, if it's not connected to the Internet, it's able to deliver you intelligence.

[B]

Oh, the implications are so much bigger than just that. Sorry, sorry, Alex, go ahead. We're going to say the same thing, I'm sure. I mean, look, if you can, if you can take, you know, normally to achieve that same level of performance, you need about, about on the order of a trillion parameters, say 700 billion just to keep it simple. So you go from 700 billion down to 7 million, then you can use all the rest of that compute capacity to expand it again and make it better at that domain. So if you can take a general massive model that cost a billion dollars to build and compress it down to a specific use case, like protein folding or just longevity related use case, or just chip design related use case, get all that original capability, but now you've got it in a very tiny package, then you can start training it out, reuse those billions of parameters to make it much better at that domain. And so you have this combination of many, many effective workers, many agents working on the problem, plus the ability to build back out the intelligence level with more and more data purely through this process of distillation and expansion. Distillation and expansion. It's the most powerful thing in the world and it's also really, really empowering for a whole new wave of startups. And I don't want to get too deep into it, but we could talk about this for hours, but I think this slide encapsulates the most profound idea in all of intelligence, which is it was something that Ilya Sutskever said at MIT with Lex Friedman about God, it must have been like seven or eight years ago at the dawn of this wave of AI, that compression and intelligence are actually the same thing. And everyone goes, what? That makes no sense.

[D]

This is the point Alex has been making, right?

[C]

Yeah, that's right.

[B]

Yes, yes. And this is why Alex says intelligence is going to turn out to be everywhere.

[D]

There's going to be DNA and how much is packed into a few DNA? A few genes. Right. It's the same basic principle.

[A]

I'm going to name this episode the Singularity is Now that's how I'm feeling.

[D]

Okay, but here's two questions about this. One, does this obviate the need for all the radical energy expansion or do we just have so much compute to do that we need it all anyway?

[B]

No, no, no. We're going to ch this up so fast because the capability is.

[D]

The second thing that occurs to me is right now, when we run these big LLMs, it's kind of a mainframe model. This is kind of tilting towards more client server type architectures.

[B]

Yeah, distributed like. Yeah, yeah.

[C]

Foundation models are going to be an hour everywhere. In your pocket, in your body and out in the cloud.

[A]

Amazing.

[B]

Yeah, let's come back to that. Let's pile that away because that's a. Celine, what you just said is also hugely important and profound. It's distributed training concept. Let's come back to that.

[A]

All right, our next article, diving into Anthropic, introducing Claude Haiku 4.5, which delivers near frontier coding and reasoning performance at one third the cost of Sonnet 4. Let's play a quick video here. Well, it's playing without sound and it's just showing its capabilities here. Alex, what do you think of Haiku 4.5?

[C]

So I ran it through one of my. I have a suite of evals, a suite of tests or benchmarks that I throw at CodeGen models and classes of other models. So my favorite eval for new CodeGen models and arguably Haiku 4.5 could do more than CodeGen. But I ask it to generate in one shot a visually stunning cyberpunk first person shooter. And Haiku 4.5 was able to do that to near perfection. But critically, it was fast. It's a very, very fast model. So maybe wall clock time, 30 to 45 seconds before I had a playable cyberpunk FPS. That was lovely.

[A]

That's stunning.

[D]

That's nuts.

[A]

Stunning. All right, congratulations, Anthropic on that one. Moving on here we have Google, kind of a pre announcement, but the drums are beating off in the distance that Google is getting ready for releasing Gemini 3, likely this December. It turns out that Google has released its Gemini models in December over the last two years. Of course, once Gemini 3 drops, the moonshot mates are going to be there to explain what happened. What does it mean? What are the most exciting attributes in it? Any other thoughts here, Alex?

[C]

It'll be interesting to see with this annual cadence how quickly all of the frontier labs leapfrog each other. I do think there have been reports of an experimental checkpoint floating around the Internet. Preliminary release of Gemini 3 that is really impressive if the reports are accurate. Fluent music graphics, 3D generation, some very excited to play with Gemini 3.

[A]

We're going to see that in our next slide here or listen to it in the next slide here. So purportedly this is from Gemini 3 that it can now compose original music. Let's take a listen to this as you're sipping your coffee or driving your car. You know, it's. It's gorgeous. What are the implications of this? What do you guys think will come?

[D]

Well, hold on, let me ask a question, because that just sounded like Chopin to me. And we've had for a while the ability to say, hey, play me something like Chopin. It'll play you something. There's something that's happened here. What am I missing?

[C]

What I think is interesting is that this is MIDI generation. This isn't Generating raw waveforms or wavelets or raw audio. As we've discussed in the pod in the past, like Suno 5, this is treating music as an almost first class modality. There are a variety of languages like ABC Notation or MIDI notation. What I take away from demonstrations like this and others that are allegedly floating around from this experimental checkpoint is Gemini, despite whatever limitations in its omnimodal training data set, has the ability to understand music notation and music languages as a first class modality. That sort of transfer is highly difficult and non obvious. If you've ever tried to ask an O class model, say in years past to generate compelling music, it was very challenging.

[B]

Well, Andrej Karpathy just kind of emerged and started podcasting again out of nowhere. And he made the point that these capabilities are using the same generic neural net design transformer design that the language models use and that the self driving car uses. And so the implication of that is that, look, it's exploding into these areas purely with more data. There's no hard, heavy lift for humanity to design something new to make this work. You just put in more data and now suddenly it can do midi, actual music creation. But that implies that there'll be something next week and next week and next week and next week. Just as quickly as you can gather the data, it's developing these new abilities.

[A]

I want to double click on that because I don't think majority of people understand that these models, when we're hitting these scaling laws of more data, more parameters, more compute, are evolving capabilities that were never predicted. It wasn't like we tried to build these capabilities. It's like they're emergent properties as the systems are becoming more intelligent. And that's fascinating. Scary in some ways, but fascinating and hugely important.

[B]

Hugely important because when people see a new capability like this, they assume that some team was grinding away on it for 20 years in some basement and it just got launched. That's not the case. This is just a purely emergent capability on top of the core platform.

[A]

It's like, oh wow, look what we can do now.

[C]

And so ironic, Peter. I mean, when I was an undergrad at mit, one of my first research advisors was Marvin Minsky and he would slap my hand every time I used the word emergent. He would say, no, that's preposterous. Emergent. Using that word just means you don't know what you're talking about. And yet ironically, decades later, in fact, we see all of these emergent capabilities.

[A]

Amazing.

[B]

We should do a whole episode on things they told Us that turned out to be wrong. There's a bunch.

[A]

Oh, it just takes too long. All right, let's go on to another property that's been announced for VO 3.1. So DeepMind releases the next Next image video model. Let's take a look at a quick video about VO 3.1. New enhanced capabilities give you control like never before.

[C]

Let's take a look.

[B]

You can use a reference image.

[A]

Veo puts them together into a fully formed scene, complete with sound.

[D]

Hello?

[A]

Is anybody here? You can extend your clips and transform any shot into a full scene. Reimagine any shot by adding or removing elements from subtle details to impossible objects. VO matches scale, lighting and shadow for real world physics and cinematic outputs. Life with audio using sound, effects, dialogue. Just gotta listen. I mean, incredible, right? So there are three critical differentiators between VO3, which was amazing in itself, and through VO3. 1. The first that they point out is superior audio quality and synchronization. And you could hear that when it was cooking the food in the frying pan. It's much richer, more natural. The second, which I find fascinating is you can give it three ingredients, like, you know, here's an image of a person, here's an image of an object, here's an image of a room. And it will take those three effectively and tell a story around those three, combining them. And you can give it a opening scene like, here's the starter image I want you to start the video with. And here's the ending image I want you to end it with. And it'll create a logical construct that goes between those two points. I mean, pretty amazing. Other comments on this one, please?

[B]

Yeah, well, so a couple of our friends have made, you know, full length movies now, and to do that, it only gives you a minute or two back at a time. And so you have to take the ending image and use it to stitch together the starting image. Which is frustrating because, you know, they could do that automatically. They just don't have enough compute to keep up with all the people that want to use this. So. But you can do it. You can hack your way around it and make incredibly seamless full length movies all of a sudden. And if you go back just six months ago, actually remember 18 months ago, Peter, you had Aristotle and Plato on Stage at abundance.360, debating with each other. And if you look at all the comments from the movie crowd, you know, the producers, the Paramount Studios crowd, they're like, yeah, but. And if you look at the list of yeah, buts, almost Every one of them has been solved in less than 18 months, you know. Yeah but the characters aren't consistent from scene to scene. Yeah but it's obviously digital and you can see the seams between the scenes. Yeah, but, yeah, but yeah, the physics aren't quite right. The arm detached from the body, every one of those things banged out in this less than 18 month timeline.

[A]

Extraordinary. We're going to be at the abundance summit this year. We're going to be diving into this again. What's the implications for Hollywood and in particular the creator economy? I've got a project, a secret project, I can't talk about it yet but working on with Google around production of content like this and we'll be releasing that hopefully in the next couple of months. Salim, do you want to comment on this?

[D]

I don't fully understand the implications of this except if you can go end to end up per scene like this, then stitching together a whole movie becomes every amateur can now go full on. Right. And that completely changes Hollywood radically. I guess we'll see an explosion of consumer generated movies now with all sorts of weird plots, etc.

[B]

I suspect, I don't know but I suspect that there are many, many people all over the world that have something to say that could never get the attention of a studio or an actor before and now they don't need it. Now they can individually put something professional, studio caliber together to illustrate the point they were trying to make. I think that's going to be a.

[A]

Crazy explosion and what a win for YouTube, right? I mean Google is sitting pretty because the only distribution engine for this explosion of content.

[D]

That's right.

[A]

Is going to be on, on YouTube.

[C]

I, I, I also think with some of these models, VO 3.1 and Sora 2 Pro, for example, these are just training wheels for video based reasoning models. I think that will generate an enormous amount of additional economic value so, so generating consumer great video. This is wonderful. This is radically democratizing Hollywood. Excellent. But then imagine now we're about to have reasoning models that can think with images, think with videos. That's going to solve a whole bunch.

[D]

Of transformative problems with the associated downsize. My son just served me a Sora clip of me at £800 playing a video game and I'm like if you put that out there, people think that's me.

[A]

This is going to be terrible. It's going to be the hut. Oh my God. Our next article again following on Alex's sort of predictions here. Gemini 2.5 and GPT5 win gold at the International Olympiad on Astronomy and Astrophysics. I didn't realize that there's an International Olympiad on Astronomy and astrophysics. Alex, over to you.

[C]

There is, and it's quite competitive. So this is for high school students. There are International Olympiads for math and physics and computer science. I was on the computer science Olympic team for, for the United States in high school. This is a very competitive Olympiad, and I think so. This is work from Ohio State University. Another lovely paper. And the authors make the point in achieving this benchmark. We're drowning in petabytes of data from astronomical sources from automated sky surveys, and there aren't enough human waking hours in the world.

[A]

Yes. To analyze it.

[C]

Yeah, to analyze it. So this is more than just beating high school students at astronomy and astrophysics. This is about AI solving astronomy and astrophysics and enabling us to understand our universe. There aren't enough human waking hours to look at all the skies.

[D]

I remember Gerald changes the game for seti, doesn't it?

[A]

I remember Gerald Safin who ran the Viking program back in the mid-70s, in the dark Ages, and he said, you know, we've looked at less than a fraction of 1% of all the data that came back from Viking because there isn't enough human capability to do that. And now, I mean, all the data generated, especially we're about to have the interplanetary Internet too, right? Laser links between orbiting satellites around Earth, around the moon, around Mars, with high bandwidth capability. And we're going to be able to constantly be sorting through the data and making discoveries. Sort of go to sleep at night, wake up in the morning early, because you can't.

[D]

You don't want to find 50 exoplanets.

[C]

And critically, again, these are generalist models that are accessible to anyone. Almost anyone can access Gemini 2.5 or GPT5. This isn't some sort of like, siloed data center that only folks who have the time and ability to purchase observatory time from the great telescopes can get. Anyone can do this. So I think it's going to radically democratize situational awareness into our universe.

[A]

Well, it goes, you know, using this as a analogy, anybody out there who has a large amount of data in your business, in your industry, right, it's this gold mine that hasn't been tapped yet. And so if you have that data, being able to actually feed it into one of these models to start to extract value should be your very first stop.

[D]

Wait, Peter, can I just get back to something? Alex, you just said this democratizes situational awareness across many industries.

[C]

Can you unpack that into our universe? I said so there are publicly available petabytes of data gathered by different observatories. Historically, if we'd had this conversation 10, 20 years ago, we'd be talking about some sort of citizen science initiative, maybe people spotting looking for interesting looking things in the sky at home. We don't need to have that conversation anymore. Now anyone can turn loose Gemini 2.5 or GPT5 or some other frontier model with a decent inference time compute budget and say go look for interesting things in the sky, make a discovery. It would have been 10, 20 years ago. It's remarkable when a high school student discovers a new asteroid or how does.

[D]

This deliver a situational awareness into the universe?

[C]

We live in a very dynamic universe. There are all sorts of interesting things going on all the time, and we're drowning in petabytes of new data all the time.

[B]

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[A]

Now for our Book corner. Favorite science fiction books? I've added one and four. Of course AWG has and Dave and Salim. I need your book entries for next week.

[D]

Mine was Fahrenheit 451.

[B]

It's going back.

[A]

It's going back. I'll mention mine first. It's a book called after on by Rob Reed. I've read it twice. My son Jet and I have read it together once and it's a fascinating story. It's a very fun read. Takes place in Silicon Valley and it's really the story of the emergence of the first conscious AGI or asi. And what is it like when something that comes online and it looks around and it sees fear? In this case, I won't ruin too much of it, but the AGI is called Flutter and it hides for the first period of time and it starts to then talk to certain people to understand how society and military and culture is going to feel about a superintelligence out there on the Internet. It's a great story, a lot of fun. Dave, you're reading this now, you said, right?

[B]

Yeah, yeah, I'm about a quarter of the way through it. A lot of books that deal with AI are esoteric. This is just a great, fun, awesome story that also deals with AI. So it's, it's thick, it's a lot of words, it's a long book, but.

[A]

It'S really good, it's really fun to read. So that's after on by Rob Reid, and it gets you thinking about what it might really be like, what could be going on right now that we just don't know about. Alex, tell us about Diaspora.

[C]

Yeah, Diaspora by Greg Egan, I would say is my second favorite book after Accelerando by Charlie Stross. So Diaspora is a book about, and I'd argue it's utopian. Some might disagree, but it's a book about what life after the singularity looks like. And intriguingly, it depicts a future where we don't take apart our solar system to build the Dyson swarm, where this tiling the Earth motif of turning everything into data centers actually peaks and then declines. And most humans in the future of Diaspora are living as uploads or emulations, living in data centers underneath mountains on Earth. But it's a heterogeneous mix of biological humans, some who chose to remain purely biological in uploads and cyborgs. And I think it's one of the most vivid post singular depictions that I've ever read. Big fan of the book.

[A]

There you go. This is all our subscribers. This is your reading assignment for the week ahead, or listening assignment, as my kids say. Dad, you don't read books you listen to, by the way, after on is the time.

[D]

I have no time to read anymore. We're just trying to keep pace with all the stuff that's happening. How are we going to navigate this?

[A]

Yeah. Insane. All right, let's move on to robotics. A few things coming out in the robotics world this week. The first one, super fun. This is Tesla's FSD version 14. Brings on mad Max mode. All right, let's take a quick look at this video, which is insane. So if you're listening, what we're seeing here is someone in their Tesla Model Y, or Model S, whatever it might be, and they're in Mad Max Mode, they're going 83 miles per hour, but their car autonomously is weaving in and out of traffic, like in the middle of a car chase for your favorite adventure movie. And I just imagine end of the day, people are in Mad Max mode, they're pulled over by the cop, and they say, no, no, no, it wasn't me, it was Mad Max who was driving this. Please let me go. Thoughts on this?

[D]

This is nuts. How is this legal, is my one number one question.

[A]

Well, it's not.

[D]

The liability is all with the driver, but this is going to cause a lot of issues. I think you're going to see some spectacular car crashes around some of this.

[A]

Well, listen, the point I think is I would rather have my FSD v14.1.2 doing this than me doing it. It's able to see with millimeter precision how far you are from the car you're cutting off next to you.

[C]

I think it's also ironic, if you remember, the original Mad Max movie was supposedly set in a civilizational collapse type scenario where energy had collapsed. In particular, there were worldwide energy shortages and sort of perverse in some sense that now we're on the verge of energy abundance at a global scale, thanks to the AI data center boom. And yet we're sort of looking back and trying to build Mad Max almost nostalgic retrospective while at the same time building Minority Report level rapid transit.

[D]

I would have called this Gran Turismo mode.

[A]

Yeah, let me read the description from Tesla. When you select Mad Max mode for your Tesla vehicle, the FSD system, which, by the way, still requires driver supervision. Right. Will attempt to drive more assertively. Faster acceleration, more frequent overtakes, more aggressive lane changes, especially under traffic conditions.

[D]

I mean, it's how I drive normally anyway, but that would be unnerving.

[A]

All right, the news this week, figure 3. Brett Adcock's company made the COVID of Time magazine. I love this image. Best innovations of 2025. Robots are coming to your home. And here we see figure three, folding laundry. Gotta love it. Let's take a quick look at the video of Figure 3, which has been reimagined for the future of labor. Let's go play this video, then we'll chat about it. So figure three is here. It's got a new, brand new look. Here we see it washing dishes, serving food. You'll be in room 23.

[C]

The elevators are past the door on.

[A]

The right and playing at the front desk of a hotel and then delivering packages. Interesting.

[B]

Very slowly.

[A]

Very slowly, but fascinating. Figure 3's changes here, right. It's taken on a new home ready design. We were at One X. I'd done a podcast. Full disclosure, I'm an investor in Figure a couple of times through my venture fund.

[B]

Congratulations.

[A]

Yeah, no, it's done. Amazing. We reported last week or two weeks ago that they did a billion dollar financing at a $39 billion valuation. Of course, this is a multi trillion dollar marketplace for sure. But if we look at Figure 3 versus Figure 2, there are five outstanding things. Number one, their new Helix AI system has a suite of sensors that make the Vision Language Action much more capable than Figure two. It's got redesigned hands with cameras in the hands, upgraded audio for voice reasoning. It's got a home ready design. You know, it looks more soft and easy. It's taken on the same sort of look and feel as Neo Gamma from One X. And then it's a lighter chassis at 61kg versus 70kg and still able to lift 20kg. Thoughts, Dave?

[B]

20Kg, that's funny because the One X can lift its own body weight. That's interesting. But the form factor seems to be settling in on this human but slightly smaller and soft exoskeleton and not going to hurt you. So moving a little slow, but it doesn't need to move slowly, it just does because it's less likely to slap you in the face that way. So they're all settling on this very similar design. So I think that's going to be the final answer.

[C]

I think the most interesting design change that I saw in Figure 3 is the palm cameras that you mentioned, Peter. So that immediately in my mind rhymes with Tesla Vision and Tesla eschewing lidar in favor of pure vision. If you imagine the difficulty. Sure, it has tactile sensors on the fingertips, but it's really difficult to achieve, at least at this point in time, human quality tactile sensation throughout the body. We have human biological meat bodies were covered in tactile sensors. Humanoid robots, not so much. So to the extent that figure is able to move the future to the left by using cameras using vision to substitute for tactile sensation all around the arms, I think it's a very clever move. And I think that in combination with Vision Language Action VLA models, I think this is just going to be like a homework assignment for for a K12 student in a few years. Just implement a working humanoid robot using an off the shelf VLA model from a frontier lab. Embody it. This will all be viewed as pretty easy crazy.

[A]

And then the other thing announced by Brett is a price point. He wants to hit a $20,000 price point on this. You know, we talked in the past, we heard from Elon that, you know, cost of goods sold, sold would be around $20,000 and it would be priced depending on volume. But you know, Brett's getting very aggressive here on pricing. I think he wants to take out the competition. Brett is a brilliant engineer and entrepreneur and he's very competitive. So he's going up against Tesla and 1x and is playing to win. Celine, Part of the new America, just.

[C]

Quickly part of the new American dream. Right, so it used to be a house in the suburbs with a car and we need to add humanoid robot at least one to that. Given that humanoid robot order of magnitude, price is a little bit like a cheap car.

[A]

Yeah, it's you know, 300 bucks a month, 40 cents an hour. How many would you own? I mean, probably a couple.

[C]

Sky's the limit.

[B]

Yeah.

[A]

Saleem, any rants on this? Saleem.

[D]

You'Re pre suggesting things. Okay, a couple of thoughts. One is, I go back to Imad's comment about capital does not need labor anymore. And this is kind of another indicator along that spectrum which is pretty a very, very big outcome. The second is, you know, we took us like so long to get FSD working. Almost 15 years now, almost 20 years now with very bounded edge. The edge cases here are infinite. When you have a humanoid in the home, like what the hell, what happens when it thinks the baby's a dollar and put or thinks it's a teddy bear and puts it through the washing machine? I don't understand. I don't think that's nuances.

[A]

I think it's a fair analogy. I mean it took FSD so long to get here because the AI models had to evolve to get to the point where they can work with the data inflow. The VOA models, we have those.

[D]

Okay, I understand the speed of it and the fact that it may work, but there's so many things that could happen. I used the example before of your neighbor calling up going hey, your damn robot's charging itself on my Tesla charger. Frickin get it off. I don't know how we're going to navigate it.

[A]

It would be pretty stupid if it.

[D]

Did all of those boundaries, which we naturally do. And maybe the broad approach of learning from each other will kind of solve a lot of this.

[A]

But I mean, Salim, these robots are going to be smarter than any human that you possibly know. They're all going to be running the most advanced models out there, all right? They can't tell the difference between a doll and a baby. Or your Tesla Charger and your neighbors.

[D]

I still don't know why you can't have two extra hands. It just makes no sense to me.

[A]

Okay, we'll get to that, finally.

[D]

How?

[A]

All right, Dave.

[B]

Well, two things. First, Brett. Brett Adcock. I spent a bunch of time with him backstage at your last event. Peter, he is just awesome.

[A]

At the Abundant Summit.

[B]

Yeah. Salt of the earth. He's a guy that everyone will be cheering for to win. That really, really makes you feel good about everything Salim is worried about. Brett is the guy you want.

[D]

I hope I'm wrong, right? But I'm struggling to make the leap on how we navigate all those edge cases.

[A]

That's all with intelligence.

[B]

The other thing is to what Alex was saying a second ago, a lot of people are saying, well, look, it doesn't have the same sensory feedback that a human hand has. You know, I've got, you know, millions and millions of neurons touching and feeling it. Yeah, no doubt that's true. But it has visual acuity and dexterity. That's crazy. Superhuman. And so it frustrates the heck out of me when people are saying, well, I'm going to work on the smell sensor for this. You know, I'm going to do fundamental research on it. And all you have to do to succeed right now is glue together the obvious. For the first time in human history, we have perfect visual pattern recognition. It can easily tell your baby from a doll. Easily. And it can do all these fundamental, mundane tasks trivially easily. And it has a brilliant AI voice to program it. You don't have to get into coding some arcane language. You just tell it what to do. Those capabilities alone should explode technology.

[D]

So I'm suffering from a gross lack of imagination here. It's possible. It's possible.

[A]

Ask your favorite.

[D]

I've been wrong before. Rarely, but I have been wrong.

[A]

Let's move on. We got a lot to cover. Let's get to chips, energy and data centers. The first data point here is something that's going to be a real challenge. US Electricity prices reach all time high. This is a spike in dollars per kilowatt. We've seen it. It was pretty level between 2014 and 2022. And we just see this asymptotic rise in energy. And this is going to hit everybody in their pocket at home. And I can imagine a situation where communities will start to say, no, no, no, you're not allowed to build a data center on our Grid because we don't want to be subsidizing your AI system. So there's going to have to be some policy changes here. Either differential pricing, where consumers pay a pretty flat price and the data centers are paying for the additional required, or something has to happen here. Thoughts? Dave or Alex? Go ahead please.

[C]

Two important caveats here. One, this is nominal dollars per kilowatt hour. So you have to subtract off inflation. So you subtract off inflation, which obviously spiked in the years following 2020. You still get material increase in electricity price, but once you've subtracted off inflation, we're looking at real dollars per kilowatt hour. This is the market doing what the market does. It's signaling via prices that, that there's strong demand for utility electricity. And if utility electricity can't supply the, the thirst for, for energy, for data centers, then we see what we're already seeing, which is data centers will, will have their own CO located off the grid, nat gas and SMR nuclear facilities and maybe eventually reconnect to the grid, which is obviously more ergonomic once the grid is available. But this I think, again, subtracting off inflation. This is a reflection of the thirst by intelligence for the foreseeable future for energy.

[A]

Yeah.

[D]

And you can see the markets reacting to this in the other way also. Fermi Energy were advisors to that project, just went public and it went from 0 to 17 billion valuation in eight months.

[A]

Insane. Insane. Okay, Salim, I'm going to come to you on this one for fun and giggles. It's insane. The US cancels the nation's largest solar project, which was planned to generate 6.2 gigawatts of power. The US government canceled Esmeralda 7, Nevada's massive solar project set to become the world. The US's largest solar project in history, capable of powering 2 million homes across 118,000 acres of desert land. What are they doing, Salim?

[D]

Okay, so I did a little research on this. It's not as bad as it seems right? On the surface you read this and you go, oh my God, they're canceling solar. What a bunch of idiots. What kind of Luddite revolt are we dealing with here? Da da da da. And I would go on a full kind of madness rant like we did last week. I think what's actually seems to be actually happening here is that the regulatory headwinds on such a huge project are kind of slowing it down. What they've decided to do is break it up into. They haven't canceled it. What They've done is said this whole big thing can't go forward. Break it into smaller projects and you can reapply as smaller projects to do this. Now, this will still add a huge amount of timeline to it because now everybody has to go back to square one. Rebuild this as individual applications, seven, I think, in total, and reapply, which will add a lot to the thing. So it's not all that the headline says it is. Not as bad as the headline says it is, but it's pretty bad. And why can't you just rush this through and get this out there? I don't understand.

[A]

And we're accelerating nuclear. We're accelerating.

[D]

Well, the administration is fond of breaking all the regulatory. To do what it wants to do. Why isn't it doing this? We need the energy. As we saw in the previous slide. This makes no sense at that level, but there's, there's a bunch of nuances and things, so I don't want to go full crazy.

[B]

All right.

[A]

Also, this week, the US army announced the Janus program for next generation nuclear energy. So this is deploying commercial microreactors to secure power for US Defense. Let's see. Alex, what are your thoughts on this?

[C]

I think this is a very exciting development. There are many US bases that are almost tyrannized by their need to ship oil. If you remember how, in part, the Pacific theater of World War II started, there was an element to which it was a blockade of oil. And I think to the extent that the U.S. army can serve as an additional demand function for pushing forward microreactors, smr, nuclear reactors in general, this is going to be a net boon for artificial intelligence.

[A]

So these micro reactors are like, what? Like one. I think these are.

[C]

These are.

[A]

These are smaller. Yeah. 20 megawatts.

[C]

Small fission reactors.

[A]

Yeah. So it's like a fission reactor in a. How big a dimension? It's like in a shipping container.

[C]

I don't know the dimensions. Not sure if those have been made publicly available.

[D]

Can I ask a question?

[A]

These are commercially owned and operated, Right. And these are sort of being rented to military operations? Yes, that's right.

[D]

I think that's super smart because it creates demand for all of that stuff. But, you know, we've been running nuclear submarines perfectly well for 50 years without any issues. Why is this such a big deal?

[B]

I don't know why. Without any issues.

[D]

Why jump to this point 20 years ago?

[C]

I think that that is the elephant in the room. So. And the premise also for. For All Mankind, one of my favorite television series From Apple TV plus, yes, an alternative universe where nuclear energy didn't get kneecapped in the 1970s, but instead had continued to advance.

[B]

Yeah, I brought that up because both of these slides. Yeah, the answer to both questions is more politics than anything else. Whose idea was it originally? Okay, we don't like that person anymore, so cancel their idea and replace it with this idea. But yeah, the answer to why didn't we do this 20 years ago? Is purely because of public backlash to the perceived risk. And some movies, actually, the movies are incredibly damaging to some of these.

[D]

You know, if you movie was a killer.

[B]

Yeah, exactly. It's like the Jaws equivalent for nuclear reactors. You know, nobody goes in the ocean.

[C]

Anymore, let's move on type movies.

[A]

Yeah, let's move on. So next article here is US chip plant investment to outpace China, Taiwan and South Korea by 2027. Alex, do you want to hit this one?

[C]

Yeah. I think we're seeing the innermost loop of civilization finally recursively accelerating. So between chips.

[D]

Whoa, whoa, whoa, hold on a second. Can you just repeat that word for word?

[C]

The innermost loop, the innermost loop of civilization is recursively accelerating. So if you look at, as I tried to articulate previously, sort of a technology tree or maybe a supply chain of technologies, we have chips, we have energy that we were just talking about. We have robotics, we have data centers. All of these arguably form a sort of a recursive feedback loop. We're going to be using robots to build fabs that produce chips that go into the data centers that are powered by the energy that build better robots and so on in a.

[D]

Why is it the innermost? Because it's going straight down to the energy equation.

[C]

I would argue it's the innermost because it's the most recursive. And it's also the fastest improving.

[A]

Yeah, yeah. The fastest loop of reinforced learning and improvement.

[C]

But that flywheel, this innermost flywheel of civilization, I expect to just fly out into the rest of the economy in the next few years. It's not going to stay contained to just those four or five technologies. Contrary to those who are worried about some sort of like circular Nvidia esque economy, that that's just one big wash sale. I think it's going to spread pretty quickly.

[A]

And. Well, listen, we're seeing this constantly with billions of dollars being deployed, hundreds of billions of dollars being deployed by all the frontier labs.

[C]

It wants to be sovereign. I mean, that's the other story here with one might project reasonably into the future that just as we're seeing interest from different sovereign powers, interest in inference compute and making sure inference compute. You get a stargate and you get a Stargate. That inference compute is sovereign. We may reasonably see all of these other sort of core elements of this arguably innermost loop also become geographically distributed silicon.

[D]

The new steel possibly cool.

[A]

Our next article here is Nvidia to sell a $3,999 DGX Spark mini PC. And this is pretty epic. Anybody who is playing with a PC on their desk, the Spark computer, the DGX Spark Mini can support models with about 200 billion parameters. It's got one petaflop or 1000 Tera operations per second, 1000 trillion operations per second capabilities on them. God, this is going to blow away the MacBook as your preferred computer on your desktop. Dave, what do you think about this?

[B]

Yeah, I'm going to get one for sure right away. The question I had was I need dramatically more compute. It's obvious to me and everybody will soon. I can't get it right now through cursor. I can't get it through the APIs. I'll give this a shot, but it's not quite clear how I'm going to get it integrated into my agent world and my coding world, because I don't think that's going to be trivial. But I'll get to work on it and see if I can make it work. But yeah, the device wars are just beginning now because there's a view of the world where you have a lightweight. Jony, I've device from OpenAI and it's connected to a massive amount of compute on the cloud. Then you've got the Nvidia vision here where. No, bring it into your home, bring it into your office, because then at least you know you've got it. No one else is going to take it from you. Right as you're producing something, it's not going to disappear. So you feel, you have that feeling of like, it's mine, it's under our control. So we'll try both, you know, both versions of the future. But it's very much in flux right now. But I can't wait to try it.

[A]

Yeah. All right, let's jump into some of the interesting news in the economy here. This article. Data centers and AI account for 92% of the GDP growth in the first half of 2025. So our GDP grew by 1.6% in the first half of 2025. And of that 1.6%, 1.5% of the 1.6 was data centers. AI, right?

[D]

So now Alex's inner loop comment is making sense.

[C]

Yeah, that's a brilliant.

[B]

I love that comment by the way.

[C]

This Saleem, this is just the opening act. So the opening act is we spend a bunch of our GDP on building out tiling the earth, as it were. The next act I would predict is transformative applications that pour out of all of these data centers that we're building that solve math, science, engineering, medicine, the works to justify all this capex. But this is the economy right now.

[A]

This is divide by zero. It goes to infinity pretty fast. I think we're just beginning, I mean we've talked about this, right? Of the explosion of the GDP in the United States and to a large degree other parts of the world. And we're beginning.

[D]

So the economy now runs on math and we've just solved math.

[C]

Well, we better find harder math or move on to non math problems.

[A]

But the question becomes if we're exploding the gdp, right, there's wealth being created and one of the big challenges is how do we redistribute that wealth, right? How do we have it not be concentrated? Part of it's going to be that the cost of living, which is of great concern to a lot of people. Right. Cost of living has arguably increased cause of inflation, but it hasn't yet come down. Are we going to see.

[D]

Here's something to watch, here's something to watch out for in this. If you follow Alex's kind of train of thought here. The three areas where we have increased costs are health care, education, financial services, because those are highly regulated. When we see major breakthroughs and demonetization in those areas, then we'll know we're winning. How's that for a thought?

[A]

I think that's true. We're going to reinvent healthcare. I mean, the best healthcare in the world should be free and fully democratized. The best education in the world will be free and fully democratized. It just hasn't happened yet.

[B]

Two points on this story, really quick. One of them is that the spin on the story was, well, without, without AI data centers, the economy would be in terrible shape. Not true. All that happened here is all the capital and effort went into this instead of something else because this is much more urgent. So the economy would have been in fine shape either way, but we just chose to use all of our time, money, real estate, investment, everything on this problem because it's such an important.

[A]

We're planting seeds. We're planting seeds for future growth.

[B]

Exactly. The other Thing is that this is where AI benefits everybody. You know, to the point you made at the front of the podcast, Peter. Anyone who wanted to help Elon Musk build his data center in Tennessee and was willing to go there and help him do it could instantly double their salary because he was willing to, in the urgent race to build all this, more than willing to pay whatever it takes to get everyone to come and work on that project.

[A]

Electricians, air conditioning, plumbers.

[B]

Yeah, so this is really, really democratizing the AI revolution in a big way. And, and it benefits, it benefits any state actually that wants to jump on, start building data centers, is going to create jobs.

[C]

This is like the second industrial revolution. It's like electrification or like intercontinental railroad. The fun stuff happens once all the infrastructure is already in place and you can do it.

[A]

Exactly, exactly. It's, we're planting the seeds that will blossom into a whole, you know, almost unimaginable. And speaking of that, here's our next article. I found this one fascinating. The Dallas Federal Reserve is preparing for AGI. And so there we see here a chart in which they're making some predictions. So the Dallas Fed seriously considering a benign singularity, by the way, we should tell them we're living through it right now. Where the economy productively explodes between now and 2035. Right, everybody. The next 10 years, the next five years is the game. You're alive during the most extraordinary time ever to be alive. And you're witnessing it, which is incredible. So they actually, in this graph they look at a couple of things. So there's scenario planning. They have a baseline versus AI boosted singularity and they have two curves, sort of one in which it's a benign singularity and things literally go into hyper exponential growth. They have another one which is a singularity extinction path, which I don't want to be thinking about, but if shit hits the fan and we've got real problems. So I find it fascinating that Federal Reserve is thinking about this.

[D]

Yes, Les, two thoughts here. So this might have a second or third level impact from us because we've actually had some folks talking to these people for a while. I'm very impressed that they're doing this. This is great. This is essentially pricing exponential growth into the economy, which is amazing.

[B]

Well, I think it's utterly. I posted this in our chat, but utterly frustrating and idiotic that they settled. You know, they looked at these scenarios where the red line goes through the roof, the purple line, we all die next year. What we've decided is that the final impact Our best prediction is 0.3%.

[D]

It's the difference.

[B]

You see those two lines that you can't even tell the difference between them. Yes, that's what we think. Our best guess at the final answer is utterly, utterly idiotic. I mean, but this is what's happening in all of our interactions with government agencies that we're having. It's the same, like, absent any idea, I'm going to forecast something incredibly timid.

[A]

This is your rant from last week, Saleem.

[D]

It is, it is, it is, it is. And what they've done is gone. Let's put this into this in to cover our bets so we don't get flame later.

[A]

Yeah, but otherwise let's predict a marginally small increase over the next decade.

[D]

But I'd like to take the positive out of this that they're least there bringing it into the models and then we can have the basis for fixing it later.

[B]

I don't blame the people. I don't blame the people. They're in a system where they can't, cannot they get punished for anything outside of 0.3%. So it's not.

[C]

I think it's very encouraging that the Fed is considering. Encouraging that the Fed is considering the possibility. And, but I would also just question, is GDP per capita necessarily the right measure for productivity? Does GDP properly capture productivity?

[A]

Yeah, we need a new metric there for sure.

[D]

This is a huge, huge conversation because when you have demonetization, the GDP collapses, But everything is 10x better, so what the hell.

[A]

Yeah, so Texas is really playing at a big game here, right? With Starbase there, with, you know, Tesla there, companies moving there. And so the SEC approves that the Texas Stock Exchange can ease the rules for a public listing. You know, one of the things that powers our economy is the whole venture model, which invests in, in companies that then build their valuation and then build to an IPO and liquidity. It's been slow and painful over the last five years. Dave, your thoughts on this one?

[B]

Oh, huge amount of thoughts. I'll keep it short because I could talk for hours on this topic, but Peter, you and I have taken companies public before. The process needs to be simplified. It's arcane. And also what gets reported with GAAP accounting and, and all your 10 Qs and all that, what gets reported is very. It's just a huge amount of garbage compared to just the simple accurate truth. And so the AI version of this is going to be phenomenal, where, you know, everything is reported. It's perfectly accurate. It's perfectly Accountable, but it's much simpler. The AI can interpret it. You know exactly what you're investing in. But the beautiful thing about this is that we'll now have another choice and then hopefully a bunch of other choices other than just the New York Stock Exchange and nasdaq. And choice is the answer. As long as there are competing exchanges, this will all get solved. But we desperately need it, because the pathway to liquidity is the pathway to investment. The pathway to investment determines whether the US wins or loses the AI race against China. And so this is critically important.

[A]

So important. The whole idea of all, all the accountants and lawyers you have to hire to take a company public is to assure that a grandma who buys your stock isn't being ripped off. It's basically, it's cover your ass across the entire process. And it should be possible for an AI system to evaluate a company and its stock and make sure that in fact, everything they say is correct and just accelerate this by a factor of 100 fold, not just twofold.

[B]

Yeah, and for a while there, you know, we thought the blockchain by itself would solve this problem and that you could raise capital through a coin offering an ico and that would replace the ipo. But you do need. It just doesn't work without some regulatory guarantee. Otherwise it can get corrupt way too quickly. So this is the perfect hybrid, I think.

[D]

I like the general trend that we're moving from centralized systems to decentralized systems. And this, this will bring in the abundance economy in a way because it'll allow much better choice and markets will thrive.

[A]

All right, we're going to close on health and a discussion about the singularity you're not going to want to miss. All right, so in our health segment here, I'm going to play a video. This is an ALS patient with a neuralink feeding themselves. Here we can see individual, I think it's their, their third patient controlling a robotic arm and being able to, with their thoughts, pick up food and feed it to themselves. I mean, this is the beginning of the merger of humans and machines. It's crude. We're going to see, by the way, this year at the Abundance Summit, I'm going to be bringing two of the top BCI companies. A company that's got an order of magnitude, or a couple orders of magnitude more bandwidth connectivity between the Neocortex than Neuralink has, and another one that's being backed by Sam Altman. That is a brand new approach to BCI. We are 90% full on the Abundance Summit this year. We Were selling out way before. If you're interested, you can go to abundance summer360.com to learn more about it. But super excited about this, the acceleration of bci. Alex, you want to add something on this one?

[C]

Yeah. I mean first, obviously this is transformative for people living with als and tremendous progress. I also think more broadly this is the beginnings of democratizing access to our visual, excuse me, to our motor cortex. Imagine anyone being able to, to augment themselves in a variety of ways to control an exoskeleton. And also in some sense, every sci fi author I've mentioned in past. I'm a sci fi snob. Many sci fi authors just can't resist the tendency to just extrapolate a single dimension. Like, oh, we get AI or oh, the apocalypse happens in a very narrow way. But actually I think the future we find ourselves in is one where every single sci fi scenario happens all at once. So we're getting AI and we're getting cyborgs and we're getting this and that and that. That's a much more exciting future and present to be living in in the next five years.

[D]

And talk about it as you're going to get Star Trek or Mad Max. And we thought it was one or the other. And it's clearly happening both.

[C]

Like Ukraine, same world, same universe, all happening. It's, it's, it's the ultimate crossover.

[D]

Everything, all the time, everywhere, all at once. For me, for me, that robot ALS thing was a little bit in the ho hum category in the sense that I would expect to see that happen like we expect, we should be expecting these innovations at this point.

[B]

How quickly our expectations get renormalized.

[A]

Yeah, how quickly the miraculous becomes boring.

[D]

Honestly, we'll talk about this more in the next section. But I remember when Singularity came out. We've launched Singularity University. An article came out on CNET saying is being led by Ray Kurzweil, Peter Diamandis and the noted transhumanist Salim Ismail. And I had to go look up, look the term up. What is a transhumanist? And it turns out in the definition, transhumanist is a human being who's augmented themselves with technology. And it makes no sense to me, that whole framing. So we can talk about this more. But Dave, you wear glasses. Are you a transhumanist? The spectrum is ridiculous on this.

[A]

All right, let's move forward. I want to hit our last two articles and get into our singularity debate here. So this article is Google's AI cracks a new cancer code. So Google DeepMind developed the AI model called cell to sentence scale, generating a new cancer treatment that's never been seen before. AI analyzed tumors and tested 4000 drug candidates. Virtually incredible. Alex, what are your thoughts?

[C]

Well, my immediate thought now is that Google cracks cancer with a new modality for a sentence model. And Salima is going to say, ho hum, what's next? No, this is I think a very important development. So the first, I think big thing to understand is the cell to sentence model. This is a new modality in some sense. So we know models speak text, they speak video, now very popular, they speak audio, speaking cell. And speaking cell proteomic expression is a whole new modality. So Google coins this notion of a cell sentence. A cell sentence is a sentence of genes. So it's literally like text with a sequence of gene names ordered in descending order by how much the gene has been expressed in the cell. Cell, that's a cell sentence. So treating cell sentences as first class citizens alongside English sentences enables you to have conversations with virtual cells. We've spoken on the pod previously about how in principle medicine can be solved by simply having the world's best virtual cell simulator and virtual organ simulator and virtual organism simulator and just having questions with these simulations. This is I think a very, very important step, being able to have a conversation with a cell and asking it like, how do we solve cancer?

[D]

I'm not pulling the whole hump card. This is amazing. This is really huge. I totally get where you're going. This is monstrous.

[B]

Yeah, it's amazing in the narrow sense and in the broad sense. In the broad sense, it's a great example of a domain where AI can think, unlike a human, about things, where we just don't have any intuitive intelligence because we don't live at the cellular level. The AI doesn't care. It's just data from the AI's point of view. So it gets very, very good intuition, far better than any human being. So in parallel with that you've got things like magnetic containment of a fusion reaction. Very hard to visualize. AI just cuts right through it and all these other areas because we tend to continually show those charts. That benchmark. Here's Gemini 2.5. Here is a human doing this exact same task. But what about all these domains where people just don't operate naturally? This is a great case study to track closely.

[A]

Yeah, you know, we heard, is this technically AI?

[D]

Would you agree with that?

[B]

No.

[C]

How would you define symbolic AI? I don't know.

[A]

You know, we heard demis as habas say curing all disease within a decade. We heard Darya Ahmade talk about doubling the human lifespan in the next five to 10 years. I mean, this is sort of the bent twig that shows us where we're heading. And I just, for me, one of the most important mindsets someone can have is the longevity mindset. And it's the belief that in fact, we're going to be heading towards longevity escape velocity, which is our next article here. We're going to be wrapping on this in a discussion of the singularity. So let me just read this out loud. Ray Kurzweil reinforces his optimism on longevity escape velocity. Lev By 2032, he's predicting that by the early 2000 and 30s, nanobots could connect with human brains directly to the cloud. By 2045, humans will reach the singularity. Hitting on the first topic of reaching longevity escape velocity, what is LUV for the last century, most of 1900 through 2000, we were adding about three months per year that you were alive. So for every year that you were living, you were extending your life for a quarter of a year. The idea of longevity escape velocity is that there's going to be a point that for every year that you're alive, science is extending your life for more than a year.

[D]

Right.

[A]

And at that point, it's a choice of how long you might want to live.

[D]

I have a great example. I have a friend with a kidney problem and they're giving him the drugs to stabilize his kidney for the next few months because then the drugs will be available to solve it for another few. Now they're five years. So you don't have to solve the whole thing. You just have to solve to the next hop. Right. And with all the stem cell therapies, gene therapies, we're going for three months to six months to nine months. Then we cross that threshold. We're adding more than a year to your life per calendar year that goes by. And at that point, you can live for a theoretically arbitrarily long period of time.

[A]

Yeah, and we just, I just finished my abundance longevity trip and we had 50 companies that were each contributing towards this direction and some amazing, amazing tech. It's coming. It's coming so fast. And it's not because we've gotten smarter or have done anything linear. It's the impact of AI. Every single biologist who's driving breakthroughs is driving it on the back of AI.

[D]

So two truisms used to be death and taxes. So we're going to solve death now and taxes may be solved by crypto. So there's like we're kind of there and there's nothing more to do.

[A]

I want to hit on the conversation of Ray stating that we're going to have the singularity by 2045. And I'd love to get some thoughts on here. So what is the singularity by Ray's definition? I asked Gemini 2.5 since Ray was the futurist in residence at Google. And I said, what are the three things that connote the singularity as Ray defines it? And number one, it's non biological intelligence exceeds biological intelligence. Well, I kind of feel like it's there now. Number two, human machine merger, Humans become hybrids or non biological again, moving there very quickly. And then three, radical transformation of the world, biology, physics and society beyond what we can easily predict. And this is Alex, what you speak about solving everything. So what do you guys think is the singularity?

[B]

Well, I'll give you my two cents on this comment because Ray to me is just brilliant. And he predicted the singularity and he named it back in the 1990s and his timelines and his curves and everything. He got crapped on so hard by so many people for so many years and he's going to land it right on the nut. And I hope that history documents it that way. I think what he's wrestling with right now is we all agree on this podcast that we're right in the middle of the singularity at this moment. And so I think he's trying to push off the date to a point in the future where people don't bother him about it anymore. I'm just guessing. And he'll just let it play out from here. Because one thing about futurists, Peter, I know you deal with us all the time when you're right 99 times out of 100. You know, Peter, remember he said everyone's going to have a computer that's more powerful than the biggest supercomputers in the world in their pocket and they'll be wearing it around. And now everyone's like, got an iPhone? And they're like, oh, is that what he meant?

[D]

Huh?

[B]

Well, who cares? Everyone knew that was coming.

[A]

But they remember the one time that you were wrong.

[B]

Yeah, exactly. He thought we'd have full self driving and we'd all be in our self driving cars today. He got hit so hard last year and the year before because we're not in our self driving cars and that's because of regulatory slowdowns. But we'll be there Imminently. And it's just so frustrating to see him get picked on that way. So my guess is he's saying, look, don't bother me about singularity definitions till 2045 because it'll be history by then anyway. But we all know right here, right now, the AI is clearly self improving. It's doing its own chip design. We saw that Brockman article earlier. We're right in the middle of the singularity and as he originally defined it.

[A]

It is now the singularity is now Alex.

[C]

I think I agree with most of the prediction, except for the discontinuity that was that third item. So there's an intellectual strain that starts with IJ Goode coming up with this notion of an intelligence explosion. Then I J. Goode passes the torch to Werner Vinge who popularizes the notion of technological singularity, then passing the torch again to Ray. I think this notion that we're going to have an intelligence explosion that somehow leads to a point of discontinuity where we can't predict what's on the other side, that's the part that I struggle with.

[A]

That's a definition of a singularity. I mean the idea that there's an event horizon and beyond which you can't see what's happening next. And it sort of feels like we're in the midst of continuous event horizons right where.

[C]

But I also think like I feel maybe this, this is just one person's perspective, but I kind of feel like we have line of sight as to what happens next. We're solving intelligence. It's well on its way to having been solved. Using superintelligence to solve math, science, engineering, medicine, bunch of other things. Solve everything. Okay, and then presumably we'll discover the nature of our universe, nature of the trajectory of intelligent civilizations. I would expect to gain deep insight into that over the Next call it 10 plus years. And that to me doesn't feel like a discontinuity where you can't see what's next. I think you have pretty good line.

[B]

Of sight and I think it's such a good outcome. You know, if you go back to the original book, that's mind blowing. Original book from 1990s. The step function version of this is kind of weird in that the AI is in a box somewhere and it's self improving itself and it finds some way to create its own nano, nanotube based compute. So it doesn't need GPUs from Nvidia, it's building its own compute inside its own kind of box world. And you go to bed one night and you wake up the next day and it's like it's taken over everything. And that's not a good outcome in any way, shape or form. The way it's evolving looks like a step function on any reasonable timescale, but as you're living it, it's actually manageable week to week. If you really focus, you can actually see next week's innovations coming and you can actually benefit from it and also guide it so it's actually working out better than you ever could have predicted. Plenty of risk, plenty of things we need to get on top of, but it's a great version of the Singularity. Just embrace it.

[D]

Can I say a couple of comments here?

[A]

Of course, Liam. I would expect nothing less.

[D]

Back when we had the founding conference of Singularity University, I'd never heard of you, I'd never heard of Ray, I'd never even heard of Singularity. I walked in totally blank because the NASA people.

[A]

How did you get invited to that?

[D]

What happened was when I was Yahoo. Running Brickhouse and heading up innovation there, I set up a relationship with NASA to do some interesting projects together. Right? Back in the old days, they had millions of satellite images, we had millions of Flickr users. Could we help tag those? My dream was can we have a satellite hanging in our office to show that's what real innovation looks like? And can I tell a little snippet of an anecdote? So we used to have speakers from NASA come and speak at Brickhouse, which. And you have to. We had an event once where you have to imagine this event in San Francisco with 300 software developers, all with tight jeans, slicked back hair, MacBooks on their laps, white socks, et cetera. And we had this 75 year old guy from NASA come and speak. He was where he'd worked on the lunar program, on the Apollo program. And so he did this talk and everybody's kind of. Mostly people are looking. Q and A comes along and I said, what's the biggest difference now between the space industry now and when you were working on the Apollo program? And he goes, huh, Good question. He goes, maybe it was computers. And I'm like, what do you mean? He goes, well, we had no computers, so all information was transmitted via carbon copy paper. The pink sheet went there, the green sheet went there, the yellow sheet went there. And all of a sudden I was standing at the back of the room and you saw these 300 developers all look up and their brains all were exploding at the same time, going, how did they do what they did with carbon copy paper sheets being passed around? It's kind of an unbelievable thing. So that's how I got through the NASA discussion. The NASA people one day called me and said, hey, we're helping host this founding conference or Singularity university. We're bringing 70 thought leaders together. Come along. So I was actually supposed to take Lily away that weekend, but it was so weird, this thing. I was like, you know what? Let's cancel the weekend. Let's go to this thing. And that's where, Peter, you and I met. And this concept comes along called the Singularity. A few weeks later you said, hey, come along and help us run it. And I remember getting a call from Brad Templeton later that day, and he goes, hey, I didn't know you were a singularitarian. I'm like, wait, what's that? And so then I had to look that up. And what attracted me was the original thesis was, we can now use technology to address grand challenges because these technologies scale naturally. And that was the most profound and interesting thing, which was the whole compelling thing about 10 to the 9th. And Peter, your vision of using these technologies to address global problems, to positively impact the lives of the thing that you don't tell people is that you were trying to find more teams to work on xprizes. That's the part that you don't talk about public. Anyway, so Singularity gets built. And we were talking about the Singularity, which was at that time it was defined as the point where machine intelligence overtakes human intelligence. That was the common framing, which we disagreed with it publicly. I disagreed with it because, a, go back to my intelligence rant. We don't know what intelligence is. There's like a dozen facets of it. And B, my second part was what constitutes overtaking. The minute I can prescriptively describe a task, an AI robot's going to do it much better than me anyway. So it's a bit of a non sequitur. So that tension comes along and then rewrites the singularities nearer and becomes more of a process. And I really like Alex's framing of it, that it's A, for us living in it, it seems normal, but when you step back in history, it's going to be this unbelievable inflection point. And this is, I think, the key part of it, where we just kind of live through it and we get there. The outcomes of it, of us being able to solve all major problems with AI now, because AI will then solve material science, et cetera, is the most profoundly amazing part, which is why we get so enthusiastic and bubbly excited about this. And I apologize for the ho hum cars now and then. It's just that sometimes, you know, you get to a point where you're living this conversation.

[A]

You're spoiled, you're spoiled, you get spoiled.

[D]

You're kind of like, oh, yeah, we would expect to see that, etc. And then you go nuts at the Luddites who are going, well, this is a bad idea, and cancel the most important projects in the world. I mean, anyway.

[B]

Yeah, well, that last point, I think is if anything's changed in the last three weeks, that's really palpable to me, it's that the, you know, there's always going to be doubters and haters and, you know, they're all over the place and. But the countervailing voice to that has been Denis Hassabas and Sam Altman and Elon Musk. But very recently, Sam has toned it down, Dennis has toned it down. And it's not because they don't believe that it's happening right now. It's because it's happening anyway. And they don't need to promote it. They're doing it inside their building at warp speed, and they don't need another picketer outside the door tomorrow. And so what you'll see now is kind of this, kind of, hey, did things get quiet? Did things slow down as they explode inside various rooms and labs?

[D]

The framing I like the best is that all our previous models for how the world operated break down. And we need totally new models. Like Alex talked about needing new benchmarks, right? We need to formulate totally new models for where the world goes. Gdp, for example, is not a workable model.

[A]

And it's going to be the new social contract that needs to be reformed as well. How do we. How do people, you know, use this? How do they get their dividend from AI? Whether it's reduction in the cost of all the things that they need, increased, you know, increased agility and their ability.

[D]

To build here with my secret plan has been that I've been building this exo community around the book, which is now 40,000 people in 150 countries speaking 47 languages. What we're actually doing is building kind of like a Peace Corps to help this transformation, because we're going to need an army of people that are practiced and versed in this model and the new models that are coming to be able to ease that transition. Otherwise we'll end up in several hundred years of The Dark Ages.

[A]

Yeah. Salim, you, Dave and I are going to be in Saudi in about a week, a week, 10 days time. And one of the projects we've been working on with IMOD is how do you use AI to provide every sovereign nation the ability to govern better, to establish policies? Because we're going to have a lot of disruptive change coming all of a sudden when people are living 30 healthy years longer or humanoid robots are in the hundreds of millions and at 40 cents an hour, these things are going to change nation states fundamentally and their ability to rapidly adopt new policies, educate their populace, spread the wealth, if you would, is super important. So we'll be doing that. And then this coming week, Saleem, I'll be with you and Imad and Eric Poulier recording a WTF episode live at at XPRIZE Visioneering in Malibu. Again, if you're interested in joining us for that, we have a few tickets left for XPRIZE Visioneering. You can go to xprize.org to learn more and we'll put the, we'll put the link as well for Visioneering down in the chat.

[D]

I, I would like to just again point out what Dave suggested at the beginning that Ray will go down as I, I kind of think of him not as a real person. He's like an avatar from the future. I think he proves that time travel does exist because how the hell did he come up with this stuff decades ago? And it's proving he must be just coming from the past or from the future into the present. And I'll tell one quick anecdote. We once, you know, Ray would come and speak at Singularity. I've heard him speak maybe 60 times. I've never not learned something which is really, really, really frustrating because you have to listen for that little nugget of gold. We once were able to once got him two glasses of wine before he did his talk. And nobody has ever forgotten that two hour session where he just rift on and it was utterly brilliant. It was there he said that language is a really thin pipe to discuss topics as complex as some of the ones we're discussing. Right. And you have this unbelievable wisdom coming from and this ability to perceive decades in the future with unbelievable accuracy and accurate framing, etc. I think this is going to go the accuracy of what he has kind of put down and he's willing to put it down and be kind of gauged by it is going to go down in history.

[A]

Amazing. That's a good note to close on Moonshot Mates. Love you all thank you for your intelligence, your predictions, your humor.

[D]

We need to get ready to write a book. The singularity is now.

[A]

It's come and gone either way. And maybe we should get Ray on this podcast with us.

[D]

We should. And I think a really important comment is what what Alex pointed out is like, how do we navigate this for this future? Because we can see it's coming now. So what does that future look like? And let's start painting that picture.

[A]

All right, have an amazing week, guys. Be seeing you and talking to you very soon.

[D]

I have reading to do.

[A]

Yes, you do.

[C]

Thanks, Peter.

[B]

Thanks, guys.

[A]

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[D]

SA.