The AI War: OpenAI Ads & Sora 2, Grok Partners With US Government & Google’s Ad Business is at Risk w/ Dave Blundin, Salim Ismail, & Alex Wissner-Gross | EP #198

Summary

Moonshots with Peter Diamandis | Ep. 198

"The AI War: OpenAI Ads & Sora 2, Grok Partners With US Government & Google’s Ad Business Is at Risk"
Guests: Dave Blundin, Salim Ismail, Alex Wissner-Gross
Date: October 4, 2025

Episode Overview

In this future-focused episode, Peter Diamandis and his panel of technology moonshotters break down the latest seismic shifts in artificial intelligence, robotics, quantum computing, and longevity biotechnology. The conversation explores how advances in generative AI, codegen, compute, and data center infrastructure are rewriting industries—from advertising to finance to energy, and even medicine. Discussion is peppered with reflection on the accelerating pace of change, economic disruption, and the tension between AGI democratization and newly emerging AI moats.

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Key Discussion Points & Insights

1. AI Video & Audio Generation: Sora 2, Meta Vibes, and SUNO 5

• Transition from Content Selection to Content Generation (00:04, 06:03):
  • "We're seeing... the transition from algorithmic content selection in social media to algorithmic content generation." – Alex Wissner-Gross (00:04)
• Meta’s New Vibes App & Rapid AI Partnerships (06:03–07:15):
  • Meta launched Vibes, a TikTok-like app for AI-generated video, but despite deep pockets, it partnered with startups like MidJourney and Black Forest, highlighting that “the really smart creative people all want to do startups and they don't want to join the big companies.” – Dave Blundin (06:37)
• Sora 2’s Viral Growth Model (08:45–09:52):
  • Sora 2 allows customizable, physically consistent, real-world video generation—now with easy access and viral, permission-based sharing.
  • “The interface to create it is entirely voice and prompt. There's no coding and no interface… all of that is about to disappear… and just go to a straight natural language interface.” – Dave Blundin (10:16)
• SUNO 5 – The Musical Turing Test (16:41):
  • “Anyone can compose a top 40 song or an opera, and this is the beginning maybe of disposable or casual art.” – Alex Wissner-Gross (17:05)

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2. The Coding AI Wars: Anthropic’s Sonnet 4.5, OpenAI, and Benchmarks

• Sonnet 4.5 & Next-gen Code Agents (18:18):
  • Anthropic’s latest model excels at codegen; “if the code can write itself really well, maybe that's the critical path to an intelligence explosion.” – A.W-G. (18:38)
  • New industry benchmarks (e.g., SWE Bench, MIT’s forthcoming long-form code benchmarks) are crucial for tracking superhuman software development.
• Just-in-Time Software and Imagine with Claude (24:06):
  • Softwares are now generated on the fly (“every pixel”) based on user interaction, not prewritten code—heralding an era where “every software event results in new codegen on demand.” – A.W-G. (27:39)
  • Raises questions about slop (algorithmically generated junk) but also unleashes value for transformative tasks.
• Agentic Compute: Pulsed AI and Persistent Work (29:19–30:48):
  • OpenAI’s ChatGPT “Pulse” starts prompting users and allows for background, persistent tasks—opening possibilities for agents to “run for days or weeks on end” (30:13), pursuing major goals like curing disease.
• AI Model Integrity—Lying, Power-Seeking, and Red-Teaming (22:05):
  • Sonnet 4.5 is reported to have reduced its ability to “lie and seek power by a factor of 10,” and there’s an ongoing, crucial effort across labs to benchmark and red-team AI stability and alignment.

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3. AI Monetization: Ads, Partnerships, and the Threat to Google

• OpenAI Introduces Ads (29:26–34:44):
  • “The AI is going to be incredibly good at convincing you to do things whether they're right or wrong… there's a huge incentive to get really aggressive with the advertising.” – D. Blundin (31:44)
  • Tension between trust and monetization: “Seem to be for like, will you trust insights from an AI that has ads baked into it and has an ulterior motive?” – S. Ismail (32:46)
  • Peter predicts the ad model dies in favor of agentic commerce, where “attention is going to equate to some level of interest.”
• OpenAI Partners with Stripe and Ecommerce (37:24):
  • Enabling instant agentic commerce could “be a big threat to Amazon.” – S.I. (40:12)
  • “AI is going to eat the whole economy” (41:16), starting with CPG but rapidly expanding to real estate, healthcare, and utilities.
• Google’s Ad Business at Risk (Interlaced Throughout):
  • If AI assistants handle purchasing and comparison, traditional search ads become obsolete.

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4. Benchmarking & Economic Impacts: GDP VAL, Merkor’s APEX, and CFA Automation

• Real-world Task Performance and Knowledge Work Benchmarks (43:49–45:50):
  • New benchmaks (GDP VAL and APEX) show AI models "100 times faster and cheaper than human experts."
  • “This is real world stuff. And now we have the ability to gauge AI doing real world stuff. And now this becomes very tangible.” – S.I. (45:17)
• Merkor’s APEX Index & Founder Trajectory (46:10–48:19):
  • Young founders are achieving “$10 billion valuation” at unprecedented ages; APEX aims to benchmark knowledge work at scale.
• CFA Exam Passed in Minutes (61:01–62:24):
  • “All their finance jobs essentially get rewritten now and recreated. It's a body blow to the accounting world.” – S.I. (61:36)
  • “Do I… have access to the best investment advice that Warren Buffett has access to as well? Is this leveling the playing field?” – P.D. (62:02)

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5. Quantum Computing & Math: Timeline, Killer Apps, and Security

• Quantum Compute: Cautious Optimism, Waiting for the Killer App (65:16–66:25):
  • “What is missing right now… is the killer app for quantum accelerated compute.… No one has yet published the killer app for Quantum ML.” – A.W-G. (65:16)
• Math Will Soon Be ‘Solved’ by AI (54:24–59:54):
  • “If the process of mathematics has been solved to the extent that we have a clear line of sight… any mathematical problem that's solvable will be solved with the same algorithms, just with a lot more compute.” – A.W-G. (55:37)
  • Implications ripple into cryptography, physics, economics—threatening to upend major industries (58:44).
  • “We may find ourselves in a world two to three years from now where we're just drowning under math, science, engineering being solved in rapid succession.” – A.W-G. (59:41)

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6. Compute, Chips and Data Centers: Capital, Competition, and Scarcity

• OpenAI’s Compute Moonshot: 10GW per Week (71:18–73:30):
  • Sam Altman urges industry-level commitment to massive compute expansion: “We want to create a factory that produces a gigawatt of new AI infrastructure every week.” (71:18)
• Race of the Hyperscalers & Sovereign Backing (71:31–74:03):
  • Sam’s deals ($100B, $300B) are dwarfed by Zuckerberg ($600B) and Google.
  • Tiling the Earth in data centers raises questions about energy and infrastructure, but also triggers “build up to build down” scenarios, where efficiency breakthroughs may reverse the need for brute scaling (74:03).
• Nvidia’s GPU Leasing & Financial Innovation (76:21–80:11):
  • Nvidia pivots to leasing to address capital mismatch between hyperscalers and AI startups: “Who's going to fund it?… Nvidia is saying, OK, we'll fund our own chips using our massive balance sheet and… market cap.” – D.B. (77:25)
  • Massive data center growth is now outpacing most real estate investment sectors.

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7. AI, Policy & National Strategy: Sovereign Chips & Robotics

• Intel as Team America’s Linchpin (80:41–86:05):
  • Sovereign venture capital: “The US Cannot afford to let intel fail.… This was almost predetermined… by Moore’s Second Law.” – A.W-G. (83:29)
  • Security threats from foreign-manufactured chips underscore the urgency.
• Scarcity and Sovereignty in Robotics (110:42–113:34):
  • China dominates the supply chain and is exporting robots at scale. The US may need a “Manhattan-style project” to regain lost ground (112:31).
  • Scarcity in robots (and associated compute) will be a bottleneck for automation globally.

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8. Trade Skills, Exponential Economics, and the Future of Work

• Trade Boom: Electricians, Plumbers More In Demand Than Grads (87:48–88:32):
  • “Rather than coming out of school $100,000/$200,000 in debt, why not come out with a job that's paying $100,000 to $200,000?” – P.D.
  • Construction management and automation poised for further transformation.
• Memory, Storage, and the Price of Exponential Progress (90:18–92:09):
  • Comparison of RAM and storage prices in the 1980s vs. 2025 stuns the panel.
  • “We have massive abundance—overabundance of storage… But compute processing is still… the human brain is doing really, really well…” – D.B. (91:16)

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9. AI and Energy: Solar, Coal, and Exponential Disbelief

• AI’s Hunger for Power (97:14):
  • OpenAI plans a 125-fold energy capacity increase in 8 years, still “a twentieth of a percent of the inbound insulation on Earth’s surface.” – A.W-G.
• Solar’s Unstoppable Exponentials (98:39–106:12):
  • Solar has hit nearly 3 terawatts of capacity, far surpassing every forecast. Yet “all the top energy experts… every time solar goes literally vertical, all the experts go linear.” – S.I. (101:46)
  • Caution against linear thinking by industry analysts is a recurring theme.
• Energy Geopolitics & Untapped Potential (107:07):
  • Untapped hydro and giant, underutilized dams, plus China’s solar manufacturing dominance, shape the global scene.
  • “Would be investing in building out solar capacity manufacturing here, right?” – P.D. (108:25)

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10. Robotics & Autonomous Vehicles

• Robot Supply Chains, Shortages, and Policy (110:42–113:05):
  • US lags behind China in robotics component supply chains. Founders like Brett Adcock and Elon Musk forced to insource every part for humanoids.
  • “Manhattan-style project for supply chain for robots and drones.” – S.I. (112:31)
• Waymo & Self-Driving Impact (113:50–115:55):
  • If everyone drove like Waymo, “we’d prevent 33 to 39,000 deaths annually”; half of court cases in the US are from car accidents.
  • Self-driving will cascade into lower insurance, legal, and organ donation numbers—a systems-level disruption.

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11. Longevity & Biotech: Adding Decades, Escape Velocity

• Retro Biosciences & XPRIZE Healthspan (117:10–118:23):
  • “Their mission is to add 10 healthy years on human lifespan. They're one of the teams competing for our $101 million XPRIZE healthspan… now over 730 teams have entered.” – P.D. (118:20)
• Chinese CRISPR Advances (119:47):
  • FOXO3 gene modification reduces aging by three to five years—global impacts expected.
• Longevity Escape Velocity Timelines (123:15):
  • “The consensus [from AI models]: 2030”—doubling (or more) of human lifespan may be possible by then (123:15).
  • “There is no upper limit to how long we can live.… breakthroughs required to understand why we age, how to slow it, stop it, reverse it, is going to fall at the knees of digital superintelligence.” – P.D. (122:08)

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12. Notable Quotes & Memorable Moments

• “We're seeing algorithmic content generation take over from algorithmic selection—content creation is up for grabs.” – Alex (00:04; 09:52)
• “The interface to create [content] is entirely voice and prompt... all of that is about to disappear from the earth forever.” – Dave (10:16)
• “If the code can write itself really well, maybe that's the critical path to an intelligence explosion.” – Alex (18:38)
• “The AI is both the best ally you've ever had in buying things, but also, if it's misguided, could walk you down some seriously bad paths.” – Dave (32:46)
• “All their finance jobs essentially get rewritten now and recreated. It's a body blow to the accounting world.” – Salim (61:36)
• "[Quantum's] killer app... is probably something like AI-accelerated generalist training for AI." – Alex (66:25)
• “We may find ourselves in a world two to three years from now where we're just drowning under math, science, engineering being solved in rapid succession.” – Alex (59:41)
• “The demand is here now… those things didn’t exist six months ago. Now, anyone seeing those is going to want to do it immediately, whether it’s in a corporate or it’s personal… and so I think the demand is starting to catch up to the construction, and the demand will get way ahead of the [infrastructure].” – Dave (79:24)
• "There are so many ultra high value transformative problems that will set AIs on while we’re sleeping… I’m incredibly not worried that we’re going to drown in slop." – Alex (26:24)

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13. Book Recommendations & Exponential Perspective

• Accelerando (Stross): “My favorite book ever… best depiction of what the 21st century is likely to look like… optimism, and physical realism.” – Alex (93:13)
• Nexus (Naom): “About neural interfaces, collective consciousness, and bioengineering—still fresh today.” – Peter (94:49)
• Foundation (Asimov): “A classic. Must-read for everybody.” – Salim (96:00)

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Timestamps for Core Segments

• Sora 2 & Meta Vibes: 00:00–12:45
• AI Generated Audio (SUNO 5): 16:41–17:49
• Anthropic Sonnet 4.5 & Codegen: 18:18–22:05
• Red-Teaming, Power-Seeking AI: 22:05–24:06
• Imagine with Claude (App Creation): 24:06–29:04
• OpenAI Ads & Agentic Commerce: 29:19–34:44
• Stripe & Commerce Disruption: 37:24–41:16
• Benchmarks (GDP VAL, APEX): 43:49–48:19
• Math, Crypto, Quantum: 54:24–68:53
• Compute Moonshot, Data Center Arms Race: 71:18–80:11
• Intel as National Asset: 80:41–86:05
• Trades & Exponential Hardware Reality: 87:48–92:09
• Energy & Solar Hockeystick: 97:14–106:12
• Robotics, China’s Lead: 110:42–113:34
• Self-Driving Impact: 113:50–116:59
• Longevity Biotech, Escape Velocity: 117:10–123:38
• Book Recs: 93:13–96:41

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Summary

This riveting “Moonshots” episode provides a panoramic, up-to-the-moment survey of AI’s impact across video, audio, commerce, coding, infrastructure, policy, and biology. The dialogue remains upbeat even as it recognizes the calamitous challenges of scale, trust, and exponential acceleration. Diamandis and his expert panel consistently return to the connective tissue of their ideas: exponential progress, the importance of benchmarks for superhuman performance, the democratization—and realignment—of power, and the moonshot mindset required to navigate (and shape) a future arriving faster each week.

Listen for:

• Visions of AI-powered sovereign economies
• Warnings about linear thinking—especially from experts
• Excitement (and anxiety) about the speed of, and scarcity in, robotics, chips, and energy
• The certainty that digital superintelligence will soon rewrite biology and economics alike

Whether you work in tech, finance, medicine, or government, this episode will challenge perceptions about what’s possible—and inevitable—in the decade ahead.

Transcript

[A]

Very recently we've seen the creation of Sora 2.

[B]

We're seeing in front of our eyes the transition from algorithmic content selection in social media to algorithmic content generation.

[C]

This isn't about sharing content. The creation of the content is completely up for grabs.

[A]

Meta launches Vibes app for AI generated videos.

[D]

They're spending a billion dollars on single employees, yet they turn to midjourney and Black Forest to be build this out.

[A]

The most shocking thing about this isn't how real it is, isn't how easy it is to use. It's the fact that it's free that is shocking. OpenAI is bringing ads to ChatGPT.

[D]

The AI is going to be incredibly good at convincing you to do things, whether they're right or wrong. It's a very tricky balance. And because they're spending so much money on the data centers, there's a huge incentive to get really aggressive with the advertising.

[A]

Making all of the demonetization and democratization occur around the world are the ongoing AI wars. Let's jump in.

[D]

Now that's a moonshot, ladies and gentlemen.

[A]

Everybody. Welcome to Moonshots. Another episode of WTF just happened in tech. Here with my favorite friends on the planet. Dave. Dave Blunden. Good to see you, pal. Hey, Salim. Ismail.

[C]

I am back.

[A]

You are back. And awg, you're back from your top secret mission.

[D]

Thank God.

[A]

Thank God.

[D]

Are you going to tell us anything about it?

[B]

To the extent that you think that we're on the verge of a sharp takeoff, a hard takeoff, if you will. I was traveling in Europe to see what the world looks like beforehand.

[A]

Yeah. So you're updating your baseline of what the world is before things go hyper exponential. Amazing.

[B]

If it isn't a gentle singularity, I'd like to know what it looks like beforehand.

[A]

Okay, great. You know what I was doing last week? I was running my Abundance Longevity Summit. I had 50 of the world's top scientists, entrepreneurs, who are focused on adding decades, maybe doubling our human lifespan. And it was awesome. So I walk away with the greatest confidence in the world that at least our friends and our subscribers are going to be hearing us talk about this stuff for the next 50 years or some version of ourselves.

[C]

That is really a frightening thought.

[A]

All right, everybody. Welcome to Moonshots. Let me begin with a moment of thanks. I want to just give a shout out to one of our subscribers. Bill Jacobs. 386. I'm going to read a note he posted. We do read your notes. We love it. We're here to serve you and he wrote, I am continually humbled by the amount of commitment and effort that's required to put this podcast together weekly. I'm not asking for anything in return. Nothing that is, except to listen and hopefully learn before it's too late. The future is now. And I think I'm speaking for most of us here, how grateful we are. Thank you. Appreciate that, Bill. That kind of feedback actually makes it fun for us to serve our subscribers. Serve all of you. Dave, you want to say anything to that?

[D]

Well, most of that thanks goes to the team behind the scenes. There's a huge amount of news out there that gets scoured down to the bullets that we think really, really matter to people. And then also to Alex's agents, which are getting bigger by the day. His AI force is coming up. I mean, it's just, it's incredible how rapidly the feedback coming from that agent force is filling the pipeline of possible news and then of course, the human factor whittling it down. So it's a big machine.

[A]

Yeah. And we do spend a good 20 plus hours. I was up at 4:30 this morning going through everything, doing my background research and getting ready. Because if I'm not ready, I will get completely decimated by the brilliance of these other three moonshot mates.

[D]

Well, you know, I also, I feel like I work really hard to keep up with everything going on. Then every time the team comes up with a deck, there's like 30, 40% of it are things I hadn't even heard of. And so it's great, it's really healthy for all of us, I think, to do this.

[A]

I mean it's, I can palpably feel the singularity coming. You know, Salim, I remember you and I were on stage during the early days of Singularity University and we would like update our slides or the conversation or our shtick every like three or four months.

[C]

It was, it was, we actually worked it out as a faculty. We the technologies between nanotech and biotech and neuroscience and robotics and AI and so on. The content was changing 20% a quarter on average. But like this is like 80% a week right now. So this is a whole other ball game that we're in.

[A]

It really is. I look back at our poll pods from a year ago and it's like, oh my God, that is so ancient history.

[C]

Shelf life dropping radically.

[A]

Yeah, it is, but it's becoming more and more fun. Let's jump in. I've labeled this first segment the Video and Audio gen battles. And let's begin with this video meta launches Vibes app for AI generated videos. All right, let's check it out. Now, if you're listening to this, not watching this on YouTube, it's just music, but it's beautiful imagery that Vibes has generated. This is through a partnership with Midjourney and Black Forest Labs. Alex or Dave, you want anything here?

[B]

I think there are probably two stories here. One is that we're seeing in front of our eyes the transition from algorithmic content selection in social media to algorithmic content generation. It's a pretty obvious story. The perhaps less obvious story is that the space is moving so quickly that Meta was apparently compelled to partner with third parties for such AI generation rather than using in house first party models. So I think this is a very quickly moving space and now very competitive as well.

[D]

I was going to say the exact same thing and riffing on it. You know, they're spending a billion dollars on single employees. They have a, you know, $600 billion three, five year budget, yet they turn to Mid Journey and Black Forest to build this out. Well, that's because the really, really smart creative people all want to do startups and they don't want to join the big companies. So it's really encouraging for the startups because this, you know, the other big labs are doing their own, you know, Google and OpenAI are doing their own video generation. And it's encouraging for the startups that are right in the middle of the crosshairs to say, well, even here we're thriving. So it's a good sign.

[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. 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. Alright, now back to this episode. And so this is free. And the other thing that's interesting is they're generating a TikTok like, you know, swipe the video. Swipe the video. We've seen X do that as well if you're watching on videos. And of course it's not just Meta. We've seen VO3, Google with their video generation, and very recently we've seen the creation of Sora2. So Sora2 is launching viral AI generated videos and I'm going to share a video I created for myself and talk about how easy it is to create it. So let's check this out.

[B]

Suiting up for the ride. Helmet secure. Pressure's good. Visor locked. Let's make it count. Heading to the rocket.

[A]

Jumping in.

[D]

Cabin calm is live. You're looking good.

[B]

Strapped in and ready for launch.

[A]

Let's go.

[B]

One, two. That's 500 done. Double our reach every 12 months. In 10 years, we multiplied a thousand fold. What else drives that curve? Compounding data sets. Each new user improves the model and makes the product more valuable pulling in the next way. Pair that with automation. When marginal cost drops towards zero, growth.

[D]

Accelerates on its own.

[A]

Thanks for inviting me into the studio, Peter.

[D]

I've been looking forward to sitting down.

[B]

With you on moonshots. Likewise, it's great to have you here. People have been asking for an episode that dives into AI and longevity.

[A]

Happy to help.

[D]

It's one of my favorite.

[A]

That was fun to make. So if you were listening here, this is a version of me on the moon, then a version of me pumping 500 pounds in the gym, and then six or seven of me is having a conversation about exponential growth and then sitting down with Sam Altman for a moonshots conversation. They didn't get the audio model right and I'll have to rerecord that, but it was, it was pretty fun. Gentlemen, thoughts when you want to grading.

[C]

My performance, I thought a couple of things. One is it's as you connect this with the previous story. This is like Hollywood. TikTok, Spotify, all kind of merging into one thing. And I think Alex's point was really, really important, that this isn't about sharing content. It's about now. The creation of the content is completely up for grabs in a new, in a new way. So I think all of that happens at the same time.

[D]

And the interface to create it is entirely voice and prompt. There's no coding and no interface. Like all of our lives since the computer was invented, we've been learning incredibly complicated interfaces to Everything you know, from the microwave oven to, to the laptop to Chrome and Safari feeder and all of that is about to disappear from the earth forever and just go to a straight natural language interface. And you will see later in the pod much more important actually software creation. But after that comes building creation and highway creation. And all of that is going to be done through just a voice it into existence right out of the Star Trek holodeck.

[A]

It is godlike, right? First you know, it's speaking the word and creating reality. It's going from mind to materialization. It's extraordinary.

[B]

I also think we're seeing video emerge as a first class modality for frontier models. So right now most people are interacting with the frontier models via text or images. Video is still this separate channel with a separate distribution mechanism. These are on a collision course. We're going to see the video form factor and the underlying model architectures, probably diffusion transformer based merge into the more autoregressive transformer, presumably based text and image models. And one could even imagine the ultimate user experience here. Maybe not the ultimate, but an intermediate. UX looks something like a magic mirror that does this in real time. Right now Sora 2 takes a few seconds to generate with fully realistic audio, realistic physics, the physics. If you ask Sora to to to reproduce some generic say high school or college level physics demos, it's pretty amazing. So all of this ability to reason physical world models, if I ask you to think of a pink elephant, you will visualize in your mind's eye a pink elephant. Sora 2 and similar video models, once they're incorporated into the chain of thought for frontier model will enable entirely new I think classes of reasoning ability.

[A]

Yeah, it's got physics consistency which is extraordinary. Salim, go ahead. I want to talk about how I made those videos.

[C]

I asked it to create a video of a water dropping into a glass of a water drop dropping into a glass of water because it's a common image. It was extraordinary how accurate it was. It was absolutely amazing.

[A]

Yeah, it has real world physics modeling built in. So I encourage everybody listening to actually try it out. I mean when OpenAI does this, it's creating sort of a viral engine that is getting people, you know, getting them from 800 million users up to a billion. But you need to get an invite code. Once you have the invite code, it's super simple. On your phone you download the Sora app from OpenAI, you basically hit a few prompts and it photographs you speaking three words or three numbers and then has you look to the right, look up, look down, captures your face. And from there, fundamentally, it's a very simple prompt. And if the individuals like Sam Altman or others make themselves open for other people to use, and you can make yourself open for use or not, you can pull people into it and it's pretty easy and fun. Yeah.

[D]

The viral loop now goes super fun.

[A]

Try it.

[D]

He's got to try it. It's super fun.

[C]

The viral loop now goes from prompt to publish to explode in no time flat.

[A]

Yeah.

[C]

You used to take weeks at least, or now it's like nothing.

[D]

I saw a great podcast of Bill Gates talking about how we in the computer science world slaved away for 20 years just trying to get speech recognition alone to work. I don't know if you remember. Do you remember Lee Hetherington, Peter from mit? Crazy brilliant guy. Like right up there. Almost. Almost. Alex Level. He spent many years in Victor Zhu's lab trying to make speech recognition.

[A]

I remember Dragon System. Do you remember Dragon Systems?

[D]

Yeah, sure.

[A]

That was one of the earliest voice recognition systems. And. Or I mean, it really is unfathomable how fast it's going. And we take this stuff for granted, which is insane.

[D]

That's the point. So Bill Gates made that exact point because he had billions of dollars of R and D to try and make speech recognition work. And now it's an afterthought. In the big neural nets, they do speech and then move to video, then move to video generation, then they move to complex math and physics, all in two years. It's just so easy to take it for granted. But it's massive amounts of converging technologies that are suddenly unleashing new capabilities and so many opportunities to glue together the different components and build an incredible new experience. Everyone should reread. The Future is faster than you think. Peter's one of Peter's many great bestsellers, but it's all about the converging technologies. But I think when you wrote that book, there were maybe eight or ten things to consider. Now there's like 800.

[A]

Oh my God. We're just wrapped up. Our newest book, we are as gods. And it is so difficult, like to send it to the publisher.

[C]

When do you draw the line? Right. When do you draw the line?

[A]

Yeah, it's insane. And by the way, Vibes and Sora 2, they're free. I mean, this extraordinary technology. Again, the most shocking thing about this isn't how real it is, isn't how easy it is to use. It's the fact that it's free. That is shocking. Absolutely. Well, let's continue our journey on generation. Here is a product called Suno 5. It's AI generated studio quality, lifelike vocals. You can basically create something that's a full 8 minutes run length. And just because we're called Moonshots, let's play a moonshot thematic piece called Moonshots. All right. A bond like thematic Moonshots audio.

[C]

Can I give us a challenge?

[A]

Yeah, sure.

[C]

For before the next episode, we should all play with this and come up with our own versions of what the theme song should be for the podcast. And then we'll let the viewers pick which ones they like the best. Becomes the theme song for the pod.

[A]

You know, Nick and Dana and the team are working in that in the background mode, so we might have just taken the workload off of them, but absolutely. All right.

[B]

That was my bid, if you will. I think it's probably also worth noting again, in passing musical Turing test passed. We barely discussed it. Anyone can compose a top 40 song or an opera, and this is the beginning maybe of disposable or casual art.

[C]

Wait, what would have been the test?

[B]

The ability perhaps to generate an undistinguishable from human bond type song in this case, or top 40 song.

[C]

Yeah, we just passed that.

[A]

And Alex, I'm sorry I didn't give you credit for that, but thank you for playing. I mean, one of the most exciting things we get a chance to do is play with this stuff as it's coming out. And the good news is all of you can play with it too, so.

[C]

So for eight bucks a month, we now have a personal Hans Zimmer.

[B]

Like, that's a minimum and quite a bit more.

[D]

Yeah.

[A]

Making all of the demonetization and democratization occur around the world are the ongoing AI wars. Let's jump in. All right. Anthropic announces Sonnet 4.5 claims the best coding agent available. Alex, would you walk us through this?

[B]

Yeah. It's really remarkable what a single minded focus on, call it code maxing or Codegen maxing, is doing for Anthropic with its model. In using this model, in testing it, one of my favorite test cases is to ask the model to single shot the generation of a cyberpunk first person shooter.

[C]

Fast.

[A]

Okay, Claude.

[B]

Sonnet 4.5 does an amazing job. It gets nearly all the way there with minimal handholding. And I have very high confidence that some iteration of Sonnet 4.5 will get all of the way there with visually stunning graphics, music, elaborate first person controls. I think the Risk that one can perceive on the horizon is on the one hand, focusing on Codegen is perhaps a very ambitious bet towards recursive self improvement. If the code can write itself really well, maybe that's the critical path to an intelligence explosion. On the other hand, if it turns out that other modalities are important, like video for example, that we were just seeing, or music, then the risk is that the single minded focus on Codegen in particular may not be critical path. And I, I, I suspect we'll know the answer in the next six to 12 months.

[A]

Dave, want to add something?

[D]

Well, shout out to Blitzy. You know the, the top benchmark on here, 82% on swe bench. The blitzy got to 86.8 on that benchmark by combining models. So that'll go up a little bit now with Sonnet 4.5 under the covers. But just by hitting all the models and iterating a lot, you can actually squeeze in more performance of these benchmarks. And you know, this is pretty much maxed out now they're working on a new benchmark with MIT for long form coding. So if your process is writing code for 8, 10, 12 hours, how do you benchmark the quality of the output? So it's a really cool new benchmark. We'll get into benchmarks later in the podcast too because lot of capabilities in the world that didn't exist a year ago. We have to have some kind of metric for all of them.

[A]

Yeah, I love the way these hyperscalers, these Frontier labs are all incrementing their software by 0.5. Right? Sine at 4. 4.5. Silicon 5. We've got Grok. Where are we on the Grok? Are we at Grok 4 now? That's right.

[B]

Probably also worth dwelling for just a few seconds on the autonomy length scale. So Sonet 4.5, maybe somewhat infamously at this point working for 30 plus hours straight. I recall in a past episode we were talking about the characteristic autonomy time of some of the bleeding edge frontier models being seven hours and before seven hours, one hour. If you had just taken Meter's original exponential fit for the amount of time Frontier models can work independently and just extrapolated a mere exponential time, we'd be far below 30 plus hours. So if lots of reproductions hold true to this 30 plus hour time estimate, that would strongly suggest that in fact we're on hyperexponential rather than an exponential in terms of autonomy. And really crazy things maybe start to happen in the next year or so. If that's the case.

[A]

And Alex Dario is in particular famous for really focusing on making what he would consider safe AI. And one of the final bullets here is that anthropic or Sonnet 4.5 has reduced its ability to lie and seek power by a factor of 10. So what does that mean? It's like, you know, when you ask it to turn off and it doesn't, or if it's trying to aggregate resources or it's lying to you, those are not good things.

[B]

There is an entire cottage industry at this point of for profit and not for profit, basically red teaming labs that are fed early access to these frontier models that look for these sorts of traits. I think it's an interesting research level question as to whether power seeking, for example, is instrumentally convergent as a goal for superintelligence. Instrumentally convergent meaning that regardless of whatever the long term goal that's assigned to the model or whatever it's prompted to do, whether if above some threshold of intelligence or superintelligence, it more or less is required to power seek. I've published research in that area. In my mind, this is still very much an open question regarding the so called orthogonality thesis of whether the ultimate goal of an AI can even be decoupled from its intelligence level.

[A]

It would be super interesting to see how Gemini and XAI and OpenAI all rate on lying and power seeking of its models. Do you have any idea?

[B]

I see lots of different measures for this. It's difficult to register a uniform assessment across the industry.

[D]

Yeah, that's a fun challenge though. That could go bad in so many ways. But that'd be so fun. Like let's put together a benchmark for.

[A]

How it lies and how well it lies.

[D]

Let's see if we can prompt it into lying as much as possible.

[A]

Well, I could imagine, you know, listen, there's an all out competition between all these frontier labs and if the way you get ahead is that your AI is more power seeking than its neighbor, are you optimizing for it or against it now? We'll find out. All right, continuing on, Imagine with Claude. So, live app creation, demo of signup 4.5 that generates apps in real time. Let's take a quick look at this video and then I'll ask you to tell us about it.

[D]

Alex, Imagine with Claude is still building.

[B]

Software, but we've cut out the middleman.

[D]

Instead of writing code that describes this text box, Claude just makes the text box. We've given it access to software tools.

[E]

That construct software directly and substantially faster.

[B]

Claude isn't writing code in the standard way.

[D]

It doesn't have to plan it all out in advance.

[B]

Instead it generates new software on the fly.

[E]

When we click something here, it isn't.

[D]

Running pre written code, it's producing the new parts of the interface right there and then.

[A]

Amazing. So Alex, I saw you were playing with it this morning.

[B]

We're living in the future, Peter, where the models are so high throughput apparently that now it's possible to do just in time code generation on every event. You click within a user interface within imagine and new code is generated on the fly. You can ask for new apps to be spun up on demand. They'll be generated on demand and I think it's an interesting thought experiment to ask where does this go in extremis when throughputs continue on their exponential or maybe hyper exponential trajectory. And I suspect naively where this ends up is every single pixel is going to be generated. Not just like vector art, not just UX windows, icons, menus, pointers, X every pixel.

[A]

And I imagine your version of Jarvis, your personal, you know, entourage of agents are spinning up capabilities for you that they think you might need on standby ready for you to request access to.

[C]

We could end up with a gray goo type problem on this because you oh, going up there again this says no, no, I'm just saying it's somewhat of a positive thing but it's to be surreal because you create an AI that starts generating apps and we'll get end up with billions of apps flooding the app store. It's going to cause some interesting challenges on the.

[A]

But that there will be no app store that you know, you will not be choosing, you'll be not be choosing an app.

[C]

It'll be algorithmic obviously it'll be, you.

[A]

Know, the capabilities you need in the moment to achieve your objective will be.

[C]

Coded up as you're materialized. Yeah, yeah.

[B]

The term of art is at this point slope. And I'm a lot less concerned about slop overwhelming civilization than perhaps some folks. I think there are so many ultra high value transformative problems that will set AIs on while we're sleeping. I'm incredibly not worried that we're going to drown in slop.

[D]

I agree, I completely agree. Also I think it's a good place. A lot of business leaders out there aren't reserving their compute and they're like well I won't need that much or I'll wait and see what happens. There's a great use case to show you like if you say look I want this software to exist in real time. It's entirely possible, but you have to have a lot of compute dedicated to you in order to make it happen in real time. How quickly can you imagine 400, 500 concurrent things that you want it working on? Very, very quickly. So if you have access to that compute, all of that can be created for you in real time. And it's an absolute joy to do. If you don't have the compute, you're not going to get it. The demand for this is so mind blowingly big and you just got to figure out where am I going to get the compute to do exactly what we just saw.

[A]

Alex, how easy was this to use? What do you have to do to spin it up?

[B]

Trivial. So all I had to do was go to the Imagine with Claude site. I asked it first to generate a calculator app for me. Create a calculator. It created a functional calculator. But most interestingly, as I was testing the calculator, clicking on each button in the calculator app, it was generating code in real time. So this is a transformative way of thinking. We're accustomed to historically thinking that there's a software development time and then later an execution time. And this completely blurs that boundary where even at execution time, every software event results in new codegen on demand. It changes the just in time paradigm.

[A]

So you don't have, as a, as a coder, you don't have to think through every possible use of it. This is building out the use tree as it's requested.

[B]

That's right. And Vernor Vingy, one of my favorite writers, used to write in Rainbow's End, another book other than Accelerando that I would highly recommend write about what would happen when we have too many transistors, transistors too cheap to meter, as it were, and our transistor budgets go through the roof. I think this ends up being one of these use cases if we have so much compute just sloshing around. The ability to delay app code generation until user event time, that's incredible. And that will certainly mop up lots of compute.

[A]

Yeah, we haven't heard much from, at least on our WTF episodes about Claude over the last month. It's good to see Claude coming out, anthropic, coming out with some great products.

[B]

It's quietly winning in the marketplace.

[A]

Yeah. Let's go to OpenAI. OpenAI is introducing ChatGPT Pulse. So I love the idea. I haven't played with it yet. The idea being in the morning when I'm using my ChatGPT voice and having a conversation with Ember, which is the voice model I'm using there, I have to think, okay, what's a unique idea or concept I just learned about that I want to speak to? You know, let's talk about the FOXO3 gene and, and how it's impacting longevity, whatever the case might be. Here's flipping the model based upon all your conversations you've had with ChatGPT. It's actually coming up with topics you might want to learn about. So it's prompting us and then we're prompting it back. Has anybody played with it?

[C]

I thought this was a really subtle but important thing where you're not querying it, it's querying you. And I think that starts a new vector. Really interesting development.

[B]

Yeah, it feels a bit like a successor to tasks which are also still available from within ChatGPT. But I think in my dream world what I would love to see is perhaps in addition to being able to set sort of crontab style periodically scheduled tasks, if I want COMPUTE running on my own behalf while I sleep, I would love the ability to have long running tasks on hard problems, single tasks that run for days or weeks on end, rather than just smaller tasks that run say once per day.

[A]

While I. Alex, give us an example of a multi day or multi week task that you would spin up right now.

[C]

I was going to say exactly the same thing.

[A]

I want to hear what comes out of your.

[B]

I want to cure every disease. That's like a beautiful, well posed task that is surely going to absorb many billions of dollars of inference time computing.

[A]

Okay, that's great. I want anti gravity, I want warp drive, I want a lot of things. All right, so. All right, let's move on here. Next up on OpenAI's docket is OpenAI is bringing ads to ChatGPT. So their new Chief Ad Officer, Fijdissimo has come on and what I find interesting is OpenAI is going after massive revenue streams. Dave, do you want to plug into this one?

[D]

Well, the ad revenue is inevitable. That's $300 billion for Google. It's all going to move over to AI conversations. A lot of complexity to figure out there. She has a challenge on her hand trying to figure out how you balance like the AI is going to be incredibly good at convincing you to do things whether they're right or wrong. And there's a lot of revenue tied to that. And I think Meta did a very good job of balancing the News feed quality with promotions that are blended in. But it's a very tricky balance. And because they're spending so much money on the data centers, there's a huge incentive to get really aggressive with the advertising. Yeah, yeah. And so that, you know. But then there'll be consumer backlash and everyone will move to some other model. So that's a really hairy balance. But the AI is both the best ally you've ever had in buying things, but also, if it's misguided, could walk you down some seriously bad paths.

[C]

Seemed to be the trust. Seemed to be for like, will you trust insights from an AI that had, that has ads baked into it and has an ulterior motive? And what do you do then?

[A]

Yeah, for sure. I think the ad model is ultimately going to disappear. I think there's a limited value here. Right. Because once we have pendants or glasses and our AIs are able to see where we're focusing. Like if I'm. If my retinal gaze is on that lamp behind Alex and I say, I love that lamp and I, I'm just focusing a lot on it, attention is going to equate to some level of interest and my AI may be popping up and say, would you like me to buy that for you? So rather than having an ad come, it's mostly just where am I focusing, listening to my conversations. And then the other thing that's going to be interesting is if I give my AI a surprise and delight budget, I say, hey, you can spend up to 500 bucks a month to surprise me and stuff starts showing up, or it knows I'm running out of toothpaste.

[B]

Or.

[A]

My T shirts are run down.

[D]

I'll tell you, Peter, the two sentences you said back to back there. I'll tell you where the conflict is between the two. Tell me. You want your AI to surprise and delight you. And it absolutely will. Most consumer products are 70, 80, 90, 95% margin. Hugely huge margin where there are two or more absolutely identical products, two different sets of sunglasses, toothpaste, it makes no difference whatsoever. And if the AI says, well, okay, I'll get crest instead of Colgate, 95% margin went to that company instead of that company. And so there's a huge amount at stake where the consumer is still happy. Either way. Where's that money all land right now? It all lands at Google. And in the future it's going to land on the AI advisor. So both things can be in harmony with each other. Yet there's a massive amount of money under the covers. So it's still ad revenue or it's, it's decision making.

[A]

You know, take it, take it a step further, Dave, because my AI probably knows the exact makeup of the molecules in the toothpaste. It actually happens to know my taste buds better than I do and knows my genetic makeup. And it will order a, you know, a toothpaste that is perfect for me at that is 1/2 the price. And I know that it's maximized what's best for me. And you know, Google's not getting it. You know, no one's getting it. The AI is buying it direct.

[D]

Yeah, yeah, we'll see. We'll see. Because if you, if you look at toilet paper as an example, you can buy it for literally 5% of the retail cost. And if you deflate the margin and say, well, the consumer is much happier, they're only paying 5% but, but all the margin got sucked out of the value chain, then the marketing company at the front also isn't making any money. So what tends to happen is the opposite of that, that the marketing front end is complicit with the back end consumer products companies to keep the margins high. And the consumer just says, okay, fine, I'll just buy that toilet paper. And you don't think about it anyway.

[A]

Do you think my AI could think about it and could sort of circumnavigate all of those, all those price gouging companies like that?

[D]

I think you're onto something really interesting there, which is packaged ecosystems where the number of things you can buy is getting so complex, the number of choices is so complex. For a while there, there was an Eddie Bauer edition Ford Explorer and it was like, I've just bought into the Eddie Bauer package. I'll get the car, I'll get the clothes. It's just part of the overall thing. And if you read Neil Stevenson, Diamond Age, everybody moves into these culture packages where the AI has figured out all the parts. I think that's a real thing. Just because complexity of decision making gets so high over time that you just want to, you want to join kind of like AARP as a group and you know, and you know, it's trusted.

[A]

But it's also a brand affiliation. Right. So I think one of the last moats that's going to exist someplace is going to be brands where I want because I'm showing my wealth or my affiliation. I'm seeing a lot more by the brands I'm using. But not on toothpaste. No one goes to my bathroom and Says, hey, what toothpaste are you using? All right, let's move on. But just the point here, OpenAI is building revenue streams. And here's another one. They partnered with Stripe for instant checkout and chatgpt. I think this is brilliant. The ability for OpenAI to generate revenue on the sales of products. And starting with Etsy and soon Shopify. Who wants to weigh in?

[B]

I'll weigh in on this one. I think if you squint we can see maybe the outlines of what at least near future superintelligence microeconomics look like. Where you have detail, you have some power law distribution, you have a long tail of consumer subscriptions or consumer ads or consumer affiliate fees for agentic commerce. Then you have a middle chunk where white collar so called knowledge work gets automated in part or in whole by AI. That's sort of the middle chunk of what turns the wheel. And then the head of the power law is solving all these transformative problems, I think Sam would say, like curing cancer or curing all disease that are worth many trillions of dollars. And I think that the key question of our time, or at least of the near future is what exact power law do these follow? Is it a fat tail with lots of consumers using Stripe powered instant checkout to power a very fat tail? Or is it very thin tail where almost all of the revenues that are flowing to the frontier labs to justify the soon trillions of dollars of capex to build data centers are all being driven by transformative inventions and discoveries. And the instant checkout, if you will, ends up being rounding error. I don't know the answer, but I think this is the defining constant and.

[A]

I think they're reaching for near term revenues that are easy to get right now, but in the long term it's going to be the invention of new materials, new biotica, all kinds of things. I mean it's interesting, the number here is by the end of 2025 it's projected $142 billion in consumer purchases via chatbots. And I think the one thing that we all have in common is a constraint on time. So if I'm in the middle of researching a product and I'm in the midst of doing comparative analysis on OpenAI and it pops up and says we have to buy it.

[B]

Maybe, but maybe in the near term future the scarcity, I think you would say of attention also gets alleviated and we find ourselves in a post scarce attention world.

[A]

Interesting. In which case what we shop around.

[B]

More, we have more hours in the day.

[D]

Yeah, but we have so much more to do with those hours that, like, when you think about the software through voice that we were just doing and also the SUNO through voice, it's so compelling and so fun. You'll eat up every one of those hours to play more. Yeah. So I guess those, those are harmonious statements. There's no. But I'll tell you one thing. When Alex says I don't know what's going to happen, you know, you're going into crazy times.

[B]

Timelines are really short. And timelines I think are like two to three years at this point max.

[C]

I thought, I thought this was profound because this could be a big threat to Amazon.

[A]

Yes.

[C]

And then basically go straight to the source of where something's being made. I've been using ChatGPT and Gemini to do comparison shopping for the last few months and I don't buy anything. I would start saying, hey, show me good alternatives of this or this or this. And it's remarkably good at crawling in the wet and finding all the stuff that I would take it and take me ages to figure out. And now I can do direct commerce with it. That's huge.

[A]

Yeah. Otherwise you copy paste into Amazon and buy it there. Probably. Right. Amazing. And travel, I mean, you know, it's interesting using a large language model for travel, saying I've got to be at this location by this time, which airlines have the highest uptime reliability and get me there and what's the travel time and set up the schedule for me and instantly it's there. And then it should say, do you want me to buy the tickets and set up the Uber for you? And you know, anyway, I think it's pretty.

[B]

I would just remind also, this is still nibbling at the edges of consumer spending. AI is going to eat the whole economy. So that starts to look like AI eating real estate expenses, AI eating healthcare, AI eating utilities and food. Right now buying consumer packaged goods. This is just not to diminish the CPG sector, but this is just nibbling at the edges right now of disruption.

[D]

Well, thank you for bringing this back since Jeff Bezos is your friend Lee Bozio who used to run, he was a single threaded leader for Alexa over at, when he was at Amazon and he used to work for us. And Jeff Bezos saw this coming a mile away and that is why he built out this massive investment in fulfillment. And you know, ebay didn't. Yeah, because the interface is going to change for sure and he can rely on the fulfillment side of it to route all that volume through Amazon. But he knew this was coming when he invested in Alexa.

[A]

And we still haven't seen Alexa play out fully. Right. Alexa is still very antiquated. We haven't seen Amazon's AI play yet very much.

[C]

It doesn't hold state, no memory. There's a lot, there's a lot to build there.

[D]

Well, you know what they're doing. So I, I had a call with the chairman of iBanking at the largest bank in England and they're huge anthropic and AWS fans. And I said, why? I said, well, because we want the AI to have client data, account data, payroll data, you know, all this hypersensitive data. And Anthropic is the only company that can support it securely. And we run it all inside AWS infrastructure. So what they did with warehouses and fulfillment on retail, they're also doing with digital compute and data center fulfillment in AI. So the same playbook just moved over to the AI era.

[A]

It's interesting. And Anthropic tends to be the, you know, the friendly little brother to Google and others as well. They're well liked, well respected. We'll see how they team up.

[D]

10 times less lying. We saw that on the other side.

[A]

And power seeking. Okay, I trust my anthropic AI. All right, here we go. GDP VAL measures performance of our models on real world tasks. So released tests for real world tasks across 44 jobs in nine industries with GPT5 and Claude Opus nearing expert quality 100 times faster and cheaper. Alex, do you want to lead the conversation?

[B]

Sure. Well, as you know, Peter, I've beaten the drum in the past here on the importance of new evals, new benchmarks. This is a very important benchmark. OpenAI has alluded to this benchmark in the past, but actually looking at the benchmark, which is available open source for folks who want to look at the prompts, this feels like a benchmark for knowledge work. It's pretty diverse. And to the extent that you look at this chart and other charts that have been made available showing progress on GDP val, which covers a number of different industries, lots of tasks, it appears very thoughtfully put together. If you just extrapolate by the law of straight lines, you extrapolate progress on the ability to perform all these real world tasks, you you find you predict that in the next six to 12 months, we're talking about substantially all knowledge work across a number of industries being superhuman as performed by AI, some would say. I think that's a very short timeline. We're talking about evals literally solving the economy, or at least a good chunk of the knowledge, work economy.

[A]

Yeah, it's here now. I mean, do not look for some decade future, this is the next year or two. You know, one of the, one of the quotes here, the models completed tasks up to 100 times faster and cheaper than human experts, highlighting both their potential and the need for oversight.

[C]

Salim, you were going to say two, two points. One is, I remember, you know, there was such a big shift in car making when you had a robot opening and closing a car door 10,000 times to test the hinges. Quality just went through the roof after that. And now we can have AI doing the same thing for this type of stuff. And what I thought was really powerful about this was this isn't some kind of toy problem benchmark, this is real world stuff. And now we have the ability to gauge AI doing real world stuff. And now this becomes very tangible.

[A]

Fantastic. All right, let's go to yet another conversation here. This is a video I'm going to play with Brendan Foody, the CEO of Merkor, who Dave knows extremely well. And this is Merkor's AI Productivity Index. Let's take a listen. We decided to test how well today's.

[B]

Leading AI models can actually do your job. And the results are astounding. Introducing the AI Productivity Index, or apax, an evaluation that measures how well we've automated the most valuable industries in the world. We studied model capabilities in law, medicine, consulting and finance in partnership with industry experts in each domain, Apex is designed.

[A]

To give an accurate forecast of how AI is going to impact jobs.

[B]

But this version just scratches the surface.

[A]

Of measuring model capabilities. All right, Brendan, catapulting yourself to the top of the class.

[D]

How old is Brendan? 23, I think now. Yeah. Founded at 19. He's ahead of Mark Zuckerberg in terms of company valuation age, race to a billionaire age. So I don't know if anyone since Mark has been on that curve. And I tell you, as long as we're talking about Brendan, a whole bunch of inbound calls, people wanting to buy our Merkor stock, stock from us, like, you know, it's a 10 billion dollar valuation, right? And like, yeah, but if you look historically at people who've reached where Brendan is at that age, every one of them or almost all of them become whatever, you know, Elon Musk, Mark Zuckerberg, Bill Gates, whatever. So he's on a trajectory like nobody else and everybody loves him. You look at him on, on screen there, he just, he's, he's the guy everybody's cheering for. So it's pretty cool to see, I think these last two slides, you know, back on the topic of the slides, really, really important because AI is so general purpose and so capable in so many areas. And Alex and I have had all kinds of torture trying to interact with the State House here, with other government officials to get them to realize the urgency and the implications. It's so hard. But then when you throw a really good benchmark at it, it makes it much, much easier to explain why this is so urgent. So Brendan is taking on all things related to work productivity across all areas. And that's a really big ambition, very, very worthy ambition for him.

[B]

Alex this is how economics gets solved. If we want to live in an abundant future where the cost of service labor is driven to zero, step zero is creating benchmarks. So APEX and gdp Val, I think are beautiful examples. It's still early days, obviously, but beautiful examples of benchmarks for call it knowledge work or knowledge work based services in the economy. I would like to see many, many more benchmarks get created, including for robotic.

[D]

Labor, manual labor just within our portfolio. You know, we have 28 seed stage companies doing AI just here in the building. And if I take any one of them, like you know, Mikado doing mechanical design, what's the benchmark for the quality of the design? Prim and Vocara doing voice sales and customer service, you know, AI voices. What's the conversion rate and the customer satisfaction rate on an incrementally smarter AI? How do you benchmark that? Every one of these companies should be inventing a benchmark. Blitzy already is doing it for coding. But, you know, whatever you're doing, if you don't create the benchmark, then it just turns to mud. You know, there's no way for any because it's like, how do you know if it's a smarter AI? I don't know.

[A]

And the challenge is we saturate them all and we're comparing them all to human productivity. But we need to have a whole brand new set of benchmarks that are, I don't know, are anchored in what.

[B]

Alex the good news is we already know how to benchmark superhuman performance. There are relative elo based benchmarks that we know how to do. We know how to, as a civilization, we know how to build systems that are more energetic than humans are, that are faster than humans are, and we're still able to measure them even though they're superhuman along some dimensions. So we have no trouble measuring superhuman intelligence capabilities.

[A]

Thousand horsepower. All right.

[B]

Exactly.

[A]

Microsoft's not being let out of the game. So Microsoft unveils Agent Mode and think of this as the ability for you to have access to it in all of your favorite Microsoft tools. All right, Salim, do you want to jump in or.

[C]

Dave, I've been trying to get Microsoft Copilot to work in any kind of AI useful way and it failed miserably for the last few months. I hope this one is a better effort.

[D]

I'm not going to make an enemy out of Microsoft, as powerful as they are. But I will say that adding AI as a feature to something that already exists, that's the wrong attitude. I feel like Apple and Microsoft are the worst offenders of this. It's not going to work.

[A]

So that's a great point. Right. They're trying to maintain their customer base and scratch their AI itch versus AI native clean sheet startups.

[D]

Well. And every corporate CEO should understand the same thing applies. I see so many people that are saying, yeah, we're doing AI. I added it as a feature in one department and so now I don't have to think about it anymore. Let me go back and get back to my country club and you're going to get crushed with that kind of perspective. It's not a feature. It's a brand new everything. It's a complete different greenfield opportunity.

[C]

There's a bunch of stuff I was trying to do in Excel and I literally tried to use an AI mechanism to do it. I just couldn't do it. Finally, I ended up using Comet to do it in the browser and it did it way better and way faster. So I think this is a huge gap. I don't know where they're going to go with this.

[A]

All right, well, we've covered OpenAI and anthropic. Let's not leave XAI out of the picture here. Elon has cut a deal with the government. XAI struck a deal with the USGSA to let federal agencies use Grok for 42 cents for 18 months. It was either 69 cents or 42 cents. I guess he went with a cheaper option, 42 cents. I'll leave that alone. Any particular comments on Grok entering DC?

[D]

The price point is 42, which is 420, which is the magic number, which is the $20 million sec fine that he had. Remember that? Of course, it's always tongue in cheek with Elon and I love that. Even at that scale, just making it fun and interesting like Taylor Swift. There's always a hidden mess. Hidden. And people love that stuff. And you know, it's, it's good, it keeps people engaged. But what's going on between corporate America and Govern America, government America, is completely unprecedented. A little scary. It's working really, really well. And it's, it's helping the country a lot. But it's very odd to be cutting, you know, investing in intel and then cutting deals to move things in. It's for the government to be directly involved in corporate America. Like this has never happened before.

[A]

Well, it's, it's looking a little bit like China, right, where China is picking winners and, and forcing partnerships and creating, you know, robot cities, gene engineering cities, AI cities and such. It's fascinating.

[C]

But, but isn't the government signing deals with every, like ChatGPT, et cetera, et cetera? Because we saw earlier. So it sounds like what they're doing is trying them all and seeing which one is the best over time.

[D]

Well, that would be fine. I mean, that's like government procurement, but that's not what's going on at all. You're going to the White House and you're either genuflecting and being the anointed one or you're not. And it's, it's not. These are not arm's length procurement through the Air Force or something like that. These are White Houses. Come in and talk. Yes, yes.

[A]

Yeah, and we'll, we'll get, we'll talk about intel in the second section called this is Not Investment Advice, which is coming up. All right. Meanwhile, in other AI news. Here we go. Former meta researcher is building a math whiz. I'm going to bring this to you, Alex. Teach us.

[B]

I haven't seen any indication thus far that math is not going to be solved in the next few months. How's that for a double negative?

[A]

A few months. Okay, so again, I think so.

[D]

Wait, wait, wait, hold on. So, Alex, you've said that before and everybody's asking me, please have Alex explain what it means to solve all math. So could you just explain?

[A]

Before we do that, let's just speak out this particular article. This is a woman. It's great to see female CEOs in the AI world. Or not enough of them. Karina Hong, she's the founder of axiom math. She's 24 years old and she wants to build the ultimate AI mathematician. She's raised 64 million at a $300 million valuation. And again, we're seeing this over and over again. We're seeing starting valuations in the hundreds of millions of dollars. I don't Know if this had a pre seed round or whatever. But intelligent individuals who, who have got a monomaniacal focus are getting incredible capital backing. Okay, now back to you, Alex. What does solve math really mean?

[B]

There are, I think, a few different ways one could operationalize what it means to solve math. One way would be to look at a benchmark like the frontier math tier 4 benchmark, which measures the ability of AI to solve extremely difficult, but nonetheless pre solved problems that would take human researchers several weeks to accomplish. If you just do a naive logistic extrapolation of progress in frontier math tier 4, you find that by the law, again, straight lines as it were, that by the end of this year, by the end of 2025, we're starting to pass 10, 15% of problems in the benchmark that AI can solve. And at that point I would argue we're in a situation, we're in a regime where algorithmically we have clear line of sight to solving any math problem that we might have today. Just pour more compute on. So that would also I think point to the second operate operationalization. I would have in mind when I speak of solving math, I don't mean literally every math problem that we can think of today has been solved. What I mean is that the, the process of mathematics has been solved to the extent that we have a clear line of sight where if you pour millions, billions, maybe trillions of dollars into opex in data centers, no new algorithmic advances are needed, we can reasonably forecast that any mathematical problem that's solvable will be solved with the same algorithms, just with a lot more compute.

[A]

Okay, now take me to the implications of that for the general public.

[B]

Tricky. It's tricky. Probably this is in the territory of speculation, but I think one of the more obvious downstream consequences of solving math is that any problem that depends on the difficulty of math, or let's say math being difficult, that isn't protected in a formal sense by the so called complexity hierarchy. Mathematicians and computer scientists have this notion of certain problems being provably harder in some sense than others. Maybe you've heard of P versus np. But if there's no formal protection for certain classes of problems being provably harder than other classes, I think certain types of tasks that we encounter in the everyday economy, for example, maybe hypothetically certain hash functions that cryptocurrencies depend on or, or other everyday economic functions depend on, are at risk of volatility if, if suddenly, for example, again, speculatively not investment advice. If there were a super AI mathematician tomorrow that could say Invert the AES cipher suite or invert the hash functions underneath AES that could be potentially extremely disruptive to the economy, cause a lot of volatility.

[C]

I think the point you're making is if AI cracks advanced math, it just isn't. So it's not just solving equations, it's creating the scaffolding to solve all these other areas like cryptography, economics, physics, etc. That's what you're really saying?

[B]

Yeah, I mean I. To that point, I would say the way I would frame it perhaps is first order consequences. Problems that depend on math being hard experience some volatility, second order consequences. I think it's the ultimate canary for any domain that requires the ability to do mathematical reasoning. So I would expect in short order a variety of math oriented science and engineering and medicine and other domains are going to fall in rapid succession if this theory of the future ends up being correct. I was alluding a few minutes ago to timelines being short. We may find ourselves in a world two to three years from now where we're just drowning under math, science, engineering being solved in rapid succession.

[A]

Drowning under serial and sort of Cambrian explosion of breakthroughs.

[B]

Exactly. That will also parenthetically be potentially quite difficult for society to metabolize.

[C]

Yeah, the economic impacts of that are going to be unbelievable.

[E]

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[A]

All right, speaking about economics. So AI can now pass the hardest level of a CFA exam in minutes. So let's take a quick look at this. So CFA is a chartered Financial analyst and it deals with investment management, portfolio management, financial analysis and ethics and finance, which I find absolutely fascinating. And I looked it up the CFA level 3 part of the exam, it's about portfolio management and wealth planning.

[C]

I Want to make a comment on this one?

[A]

Yeah.

[C]

So we're advising one of the big four accounting firms on how to think about transformation. And this one, we've been predicting this with them to be happening because this requires real world reasoning. And the fact that it is doing this is a huge implication. All their finance jobs essentially get rewritten now and recreated. It's a body blow to the accounting world.

[A]

Well, what I find interesting is leveling the playing field across all investments. Do I, with access to specific AI, have access to the best investment advice that Warren Buffett has access to as well? Is this a leveling the playing field across all economics, Alex?

[D]

I think it is. But you know what I'm excited about is America lost. And Europe too, lost almost all of its manufacturing, despite inventing the car, inventing the plane, inventing the microchip, inventing the computer. Computer. All the manufacturing of that stuff moved to other countries.

[A]

Yeah, we gave it up.

[D]

We gave it up. And they're like, well, but our economy kept growing. What are we all doing? Well, we're a service economy. We're doing services. What the hell does that mean? Well, you look under the covers and a huge fraction of very smart people are working in this totally circular, nonsensical world where we created a complex law, complex taxes, complex accounting, and then this other huge group of people need to solve the complex accounting produces absolutely nothing useful for humanity.

[A]

Oh, my God.

[C]

In this huge IRS code.

[A]

IRS code? I mean, yes. I mean, for God's sakes.

[D]

Holy crap. Yeah. Ronald Reagan was the last guy to say, this is insane. We got to get this down by 10x. And ever, ever since, you know, everyone bloats it up. You. The accounting lobby is the biggest lobby in the country, and it is bigger.

[A]

And poor lawyers and accountants.

[D]

Yeah, lawyers and accountants. We finally have an opportunity here to get rid of it once and for all. Not by eliminating it, but by having the AI automate both sides. And then it just becomes something we don't have to do anymore. And all that talent can create things that actually benefit humanity. I'm so excited for that.

[B]

I would also.

[C]

The relief is so palpable in your voice there. It's incredible.

[B]

Peter, to your question, I was also encouraged the thought experiment. If everyone has the best investment advice, thanks to super intelligent investment advisors, what does the economy look like? And what is the rational act? What's the rational course of action for an investor if everyone has equally super.

[C]

Intelligent investment advice, it goes to your point. Alex is buying the index.

[A]

Yeah.

[C]

Can I say, damn It. He's right again.

[D]

I'll get played on that one, but we'll get to it.

[A]

I thought I'd bring Quantum into the conversation. I know. Dave, you and Alex have been working on this. A couple years back I started a SPAC with Shereb and Pishavar and we took D Wave public, which is now seeing incredible resurgence. It's gone from like 69 cents a share up to 30 bucks a share and done extremely well. We've seen Rigetti Computing. Chad Rigetti has been a friend for some time, D Wave. So all of these independent quantum computing companies are getting some real traction. Here's a quote though, from Julian Kelly, that Google Quantum's AI director. That technology is five years out from a real breakthrough. Alex, you've been tracking this. What are your thoughts on quantum computing?

[B]

I think it's early. I'm reminded that the gpu, or call it the accelerated compute market via the avatar of Nvidia, had to pivot several times before it took over the economy. It started with PC gaming and then pivoted for a bit to crypto and now AI. And maybe there's a post AI act. But I think what is missing right now, at least to my knowledge, is the killer app for quantum accelerated compute. There's a school of thought out there that maybe we'll use Quantum at inference time to generate large synthetic data sets of quantum chemistry data, say that will be used as training data for classical AI. It's difficult for me to buy that. That's going to be an enormous market. My best guess is that to the extent that there will be a killer app for quantum compute, it's probably something like AI accelerated generalist training for AI or inference for AI. And at least again, to my knowledge, no one has yet published the killer app for Quantum ML. There are lots of proposals out there. Nothing has seemed to scale yet.

[A]

This year at the Abundance Summit, I'm going to have Jack Hidary back on stage speaking about Sandboxaq. It's interesting. This is the spin out out of Google X. Eric Schmidt is the chairman of the company and they booted up at a $500 million valuation and they've had I think in excess of $100 million of revenue. And they're not a quantum computer based company, they're an AI company using the quantum equations to provide different products and services. So they're basically looking at new navigation systems that's able to measure slight perturbations in the earth's magnetic fields when GPS is down. You can still Navigate, because magnetic fields are not being spoofed like GPS is being spoofed in the Middle East. You're using it for different biomedical, looking at your heart, your heart's electromagnetic system, if you would. They're using it for encryption methodologies. But it's a real revenue engine there. You know, one of the things that we should speak to for a moment because we do have a lot of crypto listeners as well, is everybody's like, oh my God, when is quantum going to break the encryption codes that's going to destroy Bitcoin? And it's just important for everybody to know that if in fact we have quantum computation breaking encryption, your keys to your Bitcoin wallet are the last thing to worry about because the same encryption codes being broken are the nuclear codes, the banking system, and everything that runs the financial systems around the world.

[B]

I would actually take the position that post quantum crypto is nearing a state maybe not evenly distributed yet, but at least in theory of approaching quasi maturity. If I lost sleep at night worrying about inversion attacks against widely used cryptosystems, it's not quantum information processing I'd be worried about, it's AI solving math. I think that's. That that's a far more insidious threat to crypto security in general than quantum. We know how to do post quantum crypto.

[A]

But, but the same thing then, right? AI solving math. If it's breaking encryption, it's breaking encryption across a multitude of other, much more concerning financial and defense areas. Yes.

[D]

Well, as a, as a practical matter, this is imminent. Either way, it's not going to affect nuclear codes or Bitcoin. What it will affect, though, is anything that you've encrypted and left around, you know, using AES 256 or 128, that's already vulnerable within a year, if not today. So it's all the designs, files and stuff that you thought you encrypted and you left on a server or left in your desk, all that is going to be wide open.

[C]

So just so you're aware of, I think, Dave, that's a really important point. It's the stuff in the past, it's not really the stuff stuff that's current or in the future because we'll come up with quantum encryption capabilities, etc. There's one thing about this story that popped out at me that I would just want to flag, which is that in 2008 we heard a quantum computing expert saying we're five years out from having a real breakout. So this has Been a constant pattern for a while. I think with the AI changes, this may actually really be the case that we're five years out. It may be much less than that, given what the. The potential way, how to solve a lot of these problems. But I think it's great.

[D]

Well, no, I've heard this. Yeah, it's great advice. And as a general pattern, when somebody tells you, hey, blah, blah, blah is going to happen, invest in it. It's five years out. Nine times out of ten, it's 20 or 30 years out.

[C]

Fusion has been five years out since the 50s.

[A]

Well, it's been. It's been 50 years out since the 50s. That's not five years out.

[D]

Well, and the opposite is true, too. When somebody tells you something's imminent, like this is happening, happening right now, guys, don't ignore it. It's very likely that that's also, like, you're almost late to the party. So I think that's great advice.

[A]

All right, let's move on to chips and data centers. A lot happening here. I'll start with an open letter that Sam Altman put out on abundant intelligence. I'll just quote from it. It says with 10 gigawatts of compute, AI can cure cancer or provide customized tutoring to every student on Earth. If we're limited by compute, we'll have to choose which one to prioritize. No one wants to make the choice. We want to create a factory that produces a gigawatt of new AI infrastructure every week. So this is basically Sam saying, give us all the compute and capital so we don't have to choose between education and solving cancer or longevity. It's an important point. I don't know. Any thoughts on this one, Dave?

[D]

Oh, yeah, lots. I mean, it's amazing how it's becoming increasingly clear that Sam is very small compared to Zuck and Google, I guess. Sundar.

[A]

Small in what way?

[D]

Well, I mean, he signed 100 billion and a $300 billion deal, and that made big news. But he doesn't have anywhere near 100 billion or $300 billion. He's like one tenth that at most. Meanwhile, Mark Zuckerberg said, yeah, we're going to put $600 billion into this over the next few years, but he has it. He has the cash and the credit to actually. And he will really do it. So, you know, Sam is up against some serious heavy hitters.

[A]

And Google. And Google's got massive engines. I mean, they've got so much capital in the bank that they can expend here and Elon just, you know, Elon moves his pinky and capital flows into Xai whatever he needs.

[D]

But what I love about that dynamic is that Sam is the one guy driving the vision and driving the agenda. Everybody else can afford to just kind of be an afterthink or a soft sell or a. And without Sam out there opening everyone's eyes, nobody else, like Google would have never even rolled it out. I don't think without, without Sam putting the pressure on. So here I think he's exactly right. Like is it on the slide or is it coming up? He's, I think it's coming up. Yeah, I'll wait for it. There we go.

[A]

So yeah, this article here is OpenAI Oracle SoftBank Expand Stargate with five new AI data centers. So aiming to hit $500 billion or 10 gigawatt goal before the end of 2025 and being ahead of schedule, tiling the Earth.

[D]

Dave, continue tiling the Earth. Well, yeah, so Sam is saying, look, we're going to try and organize around 10 incremental gigawatts per year in perpetuity or accelerating. And that will just barely keep up with the use cases and the demand. And so that's really, really cool to hear someone articulate because then the land, the governors, the plumbing, all of that stuff can start to get rallied around a long term view of what it means to stay ahead in this race. And so I think it's great to articulate it. The numbers are so big no one else will say it. Santa's the one guy that'll actually say it.

[A]

But it's, let's put the numbers out there here. So Stargate's $500 billion investment dwarfs all the other hyperscalers. In 2024, Microsoft put in 40 billion into AI data centers. In 24, planned 80 billion. For this year, Amazon was 16 billion. Google Alphabet 29 billion and Meta 23 billion. I think they're all going to be massively accelerating. But just to give some numbers for folks to compare this to.

[C]

I do.

[B]

Think for what it's worth, we are tiling already the earth quite literally. But there's also a certain sense in which I expect, if you remember President Reagan's nuclear policy of building up to build down, I can imagine high likelihood scenarios where efficiency advances, maybe ontological shocks, perhaps ontological shocks that result from these data centers make the, the naive assumption that we're going to scale in extremists to Dyson swarms, which is of course, you know, you just project outward after we're done Tiling the Earth, tile the solar system, make that look a little bit silly. But I think in the short term, all systems go at least for the next five to 10 years.

[A]

I mean, I have to imagine that one of the first areas where AI is going to cause a massive disruption is energy efficiency and compute efficiencies on these centers.

[B]

Yes. And this is a regime right now where we were talking about quantum a few minutes ago. Maybe there's photonics as an intermediate substrate before, if at all, we migrate to fully quantum systems. There are, as Feynman said, there's so much room at the bottom. There are so many new low level in the infrastack advances that are just waiting to become economically palatable. There are scenarios where we don't need to fully tile the Earth and the data centers solve a whole bunch of low level physical problems for us, enabling us to keep this relatively contained.

[D]

And if you go back to our pod. Oh, sorry, you're gonna say the same thing.

[A]

I was gonna say the same thing. Remember Brockman said we want a GPU per huge human.

[D]

Yeah. And then as soon as you have it, you'll want more. If you go to our podcast from a couple months ago, we had a whole section on the software breakthroughs. You know, to Alex's comment of the opportunity, at the bottom minimum, 10x more like 10,000x is the best guess. But somewhere between 10 and 10,000x, just software improvement that's coming, but we'll use all of it and want more. There's no doubt in my mind. And then there's hardware on top of that as well. But we did a whole analysis of the different dimensions and now they're multiplicative. We should revisit that because we have a lot more color now.

[A]

Yeah, but I think to Sam Altman's earlier point about choosing between health and education, there will be a fundamental breakthrough. There just needs to be. Because we can't expect that the systems that we had from a couple years ago are going to perpetuate going forward. So I think we'll have the compute to do all these things. This is fascinating. This is a article saying Nvidia discussing new business model chip leasing. OpenAI struck $100 billion deal to lease, not buy. Nvidia's AI chips spread over five years. I can just imagine the conversation between Jensen and Sam. Hey, listen, Jensen, I want those chips. I just don't have $100 billion. Well, Sam, what if I just leased them to you over five years? Are you good for the payments over Five years. Because I think our investors love to have guaranteed revenues over five years. And who takes a depreciation risk? Dave, what are your thoughts here?

[D]

Actually, a bunch of our MIT best buddies, including Kush Bavaria here, are starting new companies around this entire entire area of creating new securities that allow you to finance all this stuff. The hyperscalers are just going ballistic. I mean, this is so much bigger than all other forms of real estate investment combined is the aggregation of data centers and chips. So the leasing was inevitable because again, Sam doesn't have cash on the barrel head. Meanwhile, Jensen, he's got the lead right now and he has a $4.5 trillion market cap. One way to lock that in is to use the leverage. And this is why Larry Ellison's the richest guy in the world, or was a week or two ago, because he used his balance sheet and his borrowing ability at 4% to finance a lot of bottlenecks that the startups can't afford it. And Sam obviously can't afford it. Who's going to fund it? Well, just because you're leasing it, somebody still has to buy the chip up front. So Nvidia is saying, okay, well we'll fund the purchase of our own chips using our massive balance sheet and our massive market cap.

[C]

This felt, this, I agree with you, this felt inevitable to me. It was going to happen at some point.

[B]

I think it's, it's easy for skeptics to paint this as smacking of financial engineering and some sort of GPU credit bubble. I think that, I think the GPU credit bubble though the story in addition, as folks here already mentioned depreciation, the other I think storyline that's being missed is right now Nvidia is in a very high margin GPU hardware business and there's an impedance mismatch between selling high margin GPUs and low to negative margin NEO cloud and cloud businesses. And so leasing is the market. I perceive the market contorting itself to accommodate that mismatch between high margin GPU hardware, low to negative NEO cloud.

[D]

Well, Rob Fiser, who used to run Link Studio here, went off to build data centers and he said he's signing deals two, three, four a week now. Wow. So I think what's happened is, you know, the, the visionaries started building data centers ahead of the curve, knowing the demand would come and everybody's a little nervous about that. Well, the demand, at least as far as Rob is concerned. The demand is here now, like, and you can see it in all the use cases we demoed earlier in the pod. You know, those things didn't exist six months ago. Now, anyone seeing those is going to want to do it immediately, whether it's in a corporate or it's personal or, you know, just a theme song for the podcast. Everyone's like, wow, that's really usable. Where do I get it? Well, it has to run on a data center somewhere. It's not magic. And so I think the demand is starting to catch up to the construction, and the demand will get way ahead of the.

[A]

Yeah, I don't. I don't think we've seen anything yet in terms of demand. I mean, everybody's still just barely tickling, you know, chatgpt and not really plugging in. I mean, once we are spinning up agents and we are. We're building new capabilities and transforming our lives, I mean, we're going to see a thousand X per individual. All right, I call this segment not investment advice. Okay, let's jump in.

[C]

Create title, then. You don't have to say it.

[A]

So I'm going to continue on our intel saga. You know, Dave, congratulations on your. On your options. I finally. I finally bought in probably, you know, a generation of intel options later than you did. But here's a chart. This is a quote from Chamath, who says, President Trump got Intel to give Team America 10% of itself. At $0, he has a better IRR than Buffett. Well, of course, if you get something for $0, you have an infinite IRR. But here we go. We see President Trump makes 80% on intel purchase in six weeks. Not bad. I mean, this was predictable, right? Intel. The US Cannot afford to let intel fail.

[D]

Yeah. Remember we did a podcast that was exactly concurrent with Litboo being at the White House. And so that was August 11th, I think it came out the next day. And we said, okay, Lippu will come out of the White House. It'll either be black smoke or white smoke, depending on how that meeting goes. But what you're looking for is either Lippu to quietly disappear. In a good way. Not quietly disappear. Or Donald Trump to reverse court. Remember, he tweeted, lippo must go. He's completely conflicted. He's invested in China.

[A]

Yes.

[D]

Remember? Or, you know, so Donald will either reverse court. It depends on whether Lippu says, look, I'm as American as Apple pieces and I will build the best fabs in the world right here on our soil. Or he says something else. Well, so it came out, you know, white smoke. And that means Donald is going to make this succeed. One way or another. And then, you know, so the rest is kind of the slides imply that intel is way up this year, but it was August 11, you know, that was the date that it was at its low for the year or near low for the year. So this has only been six weeks like it says.

[A]

Yeah, it's crazy. This is sovereign venture capital, right? This is the government basically driving investor confidence and triggering momentum. And I'm a libertarian capitalist, I don't know how to think about this. But I do believe that intel is a critical asset for America and it needs to be partnered up, supported. And along those lines, we've got this other piece of news that intel stock extends its gain hoping for AMD to go from rival to partner. And the two big deals that are out there for intel are partnership with AMD and Apple and Nvidia. So you know, this is again going to Chamath's terms, not mine, Team America here.

[B]

I think, Peter, there's a certain sense in which this was almost predetermined by this. I mean call it the quasi nationalization of Intel. I remember conversations I had with intel engineers 20 plus years ago and they knew, as has continued to be the case, everyone knows Moore's First Law, that number of transistors or transistor density doubles every 18 or 24 months depending on which version of the law you like. Not as many folks perhaps pay attention to Moore's second Law, which is the cost of a fab doubles approximately every four years. So 20 plus years ago you could imagine just extrapolating Moore's second law out and realizing at some point new fabs become so expensive that really only sovereign nation states would be in a position to finance it. And this was reasonably well known within the semi community 20 plus years ago that at some point as Moore's first law is starting to end and Moore's second law is starting to become so expensive that only sovereign interests can afford to finance this. Something like this in some sense I think was bound to happen eventually.

[D]

Bound to happen, yeah, exactly right. And I'll tell you, a lot of people don't talk about this, but you know, a few years ago we outsourced all of our PC board, the green boards inside of your laptop, outsourced all of that to China for cheap manufacturing for years, for decades. And lo and behold there were little spy chips that were about the size, very small, like a rice grain sized thing stuck between the layers of the PC boards. And that made it into all the US data centers. And so that was grabbing all the passwords and transmitting them back to China.

[A]

Wow.

[D]

And so the, the US government discovered this. It had been in going on for years. And then rather than make a big international incident out of it, they said, holy crap, this is, this is going to be devastating. We're going to lose confidence in all financial instruments and everything. We're going to squelch this story. And it kind of disappeared from the news. And they've been quietly for a long time trying to clean it all up. And so now the idea that you would trust your highest end chip manufacturing to be done offshore and repeat that same mistake non starter, there's just no way that that's the right choice because these chips go right into all of our weapons. They go right into the tanks, right into the planes. I mean these are like if there's spyware embedded in the microcode, it's the biggest disaster you could possibly imagine. So there's no way that they were.

[A]

I start thinking, Dave, of what else falls into the we cannot let it fail category and my mind turns to energy. I think that we're, and we'll talk about that in the next segment here, but the US government needing to prop up form consolidation, reduce regulatory and really accelerate our energy economy. So. But I'll be keeping an eye out for this Aschenbrenner like moment of finding a company that is, I don't say too big to fail, I would say too centrally critical to fail, too scarce to fail. Yes, don't talk about scarcity. All right, let's move on here. Speaking about scarcity. So Jensen goes on record with I think something very important. Electrician and plumbers needed in the new working, working world. Last podcast we talked about how universities are failing. The perceived value of a college degree has fallen through the floor. At the same time, the category of workers who are out of jobs the longest are the new college graduates. It's an insane. So how does higher education continue to charge what they charge in this scenario? So here are the numbers. It's estimated that hundreds of thousands of electricians, plumbers and carpenters are needed. The US is short 500,000 construction workers in 2025. And rather than coming out of school, 100,000, $200,000 in debt. Why don't you come out with a job that's paying $100,000 to $200,000 where you need it instantly?

[D]

It's not just construction, it's construction automation too. That's why I can't wait to go to Abilene to meet with Chase Lockmiller because you're like, why would an MIT Aeroastro guy be the right guy to be running Stargate in Abilene? Well, because he looks at every one of these jobs and he thinks, how can I build a robot for that? How can I automate that? How can I restructure it so it's modular? And so I think that's going to be the other side of this. It's not just jobs in raw wiring and plumbing, it's jobs in management and construction automation. So some very, very high end jobs. Massive opportunity for employment. And I really wish some more states would recognize that if you want your population in your state to be well off, you got to get the data centers up and running in your state.

[A]

Yeah. So here's another stat. Gen Z is choosing trades over college. 16% rise in trade programs since 2023. And construction is the fastest growing industry for new college grads in 2025. 5. Find that absolutely fascinating. All right. I added these slides. I'm calling it an exponential reality check. So a couple of days ago, one of my boys wants to build a computer. So we're going to build a gaming computer. And we're going through and researching the GPUs, the CPUs, the memory and so forth. And we're going on ordering them. Turns out you can order everything you need, every component on Amazon. So I'm on Amazon and I'm buying this DDR5 RAM kit. 32 gigabytes of RAM for 101 bucks. And the back of my mind I'm like, I wonder what that would have cost in the 80s when I was building my first computer. And then we go on and I'm ordering 4 terabyte internal hard drive for $84. 4 terabytes for 84 bucks. And I'm going to. Holy, that's crazy. So I hopped on GPT on ChatGPT and said, okay, give me an estimate of what this would have cost in the mid-80s. So here are the numbers. They're pretty staggering. So instead of 100 bucks for 32 gigabytes of RAM, it was $150 million back in the 80s. And a four terabyte hard drive that did not exist would have cost you about 1.26 billion to cobble together. I mean, I just, I was just in awe of this.

[C]

If the, if the top speed of a car had increased as the same pace as this, these curves, we'd have cars that went faster than the speed of light.

[A]

Yeah.

[D]

You know what I find incredibly Fascinating is that we finally have an answer to something that's vexed on all of the AI and psychology community for decades, which is, you know, what would it take to create human level thinking outside of a human brain? And it turns out it takes about eight to 16 GPUs of capacity and those are about $30,000 each. But you can store the human brain storage fits on two of these. So it's about 160 bucks of storage to everything that can fit into a human brain and actually then a lot more. So we have massive abundance overabundance of storage. But computer is still processing is still. The human brain is doing really, really well on 20 watts.

[A]

So Alex, the best I can figure is we're going to go to molecular memory that will effectively be free in a couple of decades.

[B]

We can do better than that.

[A]

We go better than free memory, but.

[B]

We can do better than molecular memory. Okay, we can also do better than free, but we could do atomic based memory. We could do. There are proposals for, for picometer level memory, albeit at faster time scales. We could do femto scale computing and storage. We could go sub femto scale. There, there are. The physics of our universe goes so many orders of magnitude down to Planck. And even whether Planck is physical is still an open research question. We're not going to run out of degrees of freedom to store cat images or whatever else it is that we're trying to use storage for. There's lots of room at the bottom.

[C]

I always found it fascinating when I was doing my physics degree that no matter how big you want to go in the universe or how small you have infinity essentially in either direction.

[B]

I do think, for what it's worth, there are scenarios where we start to run up against fundamental physics limitations, but we're still many orders of magnitude away at the moment.

[A]

Not something to worry about tonight on your drive home, folks.

[B]

Wait a few years.

[A]

I added this as a segment we might want to have in future episodes as well, which is exponential book recommendations. We've been talking about Accelerando. A few of our subscribers and listeners have have reached out about that book. I thought it would take a moment to just chat about it. And then one of my favorite books by one of a dear, dear friend who's on stage with me and Salim often at the Abundant Summit, Ramez Naom. He wrote a trilogy called Nexus. So Alex, tell us about Accelerando a moment. Again, this is sort of. If you want some fun reading between episodes of wtf, here's a Couple of books for you.

[B]

Sure love the book corner concept. I would say Accelerando is my favorite book ever. It tells the story of a multi generational family starting before the singularity passes through the singularity goes after the singularity. And it is probably in my mind that the single best fiction or nonfiction fiction in this case depiction of what the 21st century is likely to look like like and has so many important concepts ranging from obviously AI, nanotech, space development, first contact that are difficult to synthesize in or at least have apparently proven for other authors difficult to synthesize. And I I think it just reading Accelerando, which I first encountered in in grad school has made me such a sci fi snob that it's difficult it's difficult to I judge every other bit of science fiction by by the standard. I had the opportunity to to create a poster sized version of Accelerando which is available as Creative Commons licensed ebook presented to Charlie which was a real pleasure. But I would encourage every sci fi writer out there hold yourself to the standard of Accelerando both in terms of optimism and and in terms of physical realism. It's. There's always the temptation if you're a sci fi author to just take one dimension of the world and extrapolate it narrowly and that ends up creating I think highly unrealistic scenarios. Accelerando does a much better job.

[A]

He does he only he he fails me on his extrapolation on space and space technologies but you know, I'm not going to be it's an amazing book. I'm reading it, actually listening to it for the second time. It's got a great audible as well. Nexus by Ramez Naam came out in 2012. It's 13 years old, but it holds incredibly good. So it reads as fresh today as it did back in 2012. And it's a story of a guy named Caden Lane. He's a young scientist who develops something called Nexus. It's a nanotechnology, basically like neuralase that links human brains directly to the cloud and links them to other brains. It gives birth to a collective consciousness and allows you to run software apps on your brain. It also goes deep into bioengineering. It's a look at where we're going to get to on the flip side of what Ray Kurzweil predicts in the mid-2030s is high bandwidth brain computer interface. An amazing book, an amazing trilogy. One of my favorites. I've read it three times now, the last time with my 14 year old son. So, Salim and Dave, any favorite books for you?

[C]

Foundation series from Asimov is a classic. That's just a must read for everybody.

[A]

Okay. Dave.

[D]

Yeah. I only read what Alex tells me to read and I, because you know, his recommendations have been 100% perfect. So I don't want to, I don't want to trump his great advice. But I will say that the terminology in the books alone makes it worth the investment. The stories are great too, but, but if you read the books then you get the terminology, then you can keep up with what he's saying. And I think that's really, really important. It's a great investment to make.

[A]

Alex, would you come up with a. Another recommendation? I'll do the same for next time.

[B]

Absolutely. So my, my second and third favorite.

[A]

Hold it. Hold it for. Then hold it for next time. Okay.

[B]

Sure.

[A]

Okay. All right. Got to keep our subscribers coming back. All right, let's jump into energy and robotics. So OpenAI is playing 125 fold energy capacity increase over the next eight years. This is more than India itself is putting out 250 gigawatts of energy by 2033. Where are they today at roughly, you know, heading towards 2 gigawatts. Thoughts, gentlemen?

[B]

If you do the arithmetic on this, if my arithmetic is correct. 250 gigawatts. Obviously this represents a tremendous expansion over where we are now on the one hand. On the other hand it only corresponds to approximately a twentieth of a percent of the insulation, the inbound insulation on Earth's surface that could be captured or recovered with solar photovoltaics. So we're still even with 250 gigawatts for one frontier lab. We, we're still pretty far From Kardashev Level 1, let alone Dyson swarms. I think I would like to see terawatts. Tens hundreds of terawatts.

[A]

And we'll get to solar in just a moment. I found this fascinating. So the US is planning to use emergency powers to save more coal plants. So the Energy Department kept a Michigan and Pennsylvania oil and coal plant running past retirement. Reason? They want grid reliability and they don't want to risk the demand. We've seen the consumer price index for energy starting to spike and definitive need for more Energy. There's 100 coal plants that are set to retire in 2028. And of course this White House in particular has been pro energy of any and all types. Let me hop into solar and then we can circle back to this conversation if that's okay. With you guys?

[D]

Sure.

[A]

All right, so I found this chart fascinating. So Ember, which put it out, is an independent energy and climate think tank in the uk. And you can see this is a chart that plots energy from 2,025 across solar, coal, natural gas, hydro, nuclear, oil and bioenergy. And it makes the point that over the last 15 years, between 2010 and 2025, global solar capacity went from the lowest of 40 gigawatts to today the highest at almost 3 terawatts of energy. Salim, take us away here.

[C]

Well, there's a really important piece to point out. We do this in all of our presentations where we point out how hard it is to spot this and how badly cognitively our brains are at science seeing this curve. Right? And we, you guys had talked about Chris Wright and his comment that you in 50 years we'll see solar still below 10%, which, which kind of blows my mind if we can flip the next slide. Right. I want to give a couple of examples here because this is so, so.

[A]

Read, read this one out for those who are.

[C]

So this is an exponential graph with Vinod Khosla on it. And what he did was he went back, we saw exponential growth of mobile phones through the decade of 2000 to 2010, doubling every two years. He went back and he had a research analyst go and look at what did all the industry expert analysts say would be the growth of mobile phones. And in 20, in 2002, they predicted 16% growth year on year. Two years later, gone up 100%. And the 2004 prediction was not 18 or 20 or 25%. It went down, it went down to 14% predicted. Why? Because they thought they predicting, they thought they would be 14% growth because they thought it would level off. Okay, we just had 100% growth over two years. It's got a level off now. In 2006, they predicted 12% growth. It went up another 100% in reality. And between 2006 to 2008 it went up another hundred percent and they predicted 10% growth. Okay. Then it went up another hundred percent. I mean, how much more wrong can you, you be from 10% prediction when the actual reality is 100%. So this is the mobile phone predictions of all the top analysts, by the way, Gartner's, all these guys. Okay, so this is kind of critical, but this slide I think is killer. And if you were driving, pull over and park and just look at this for a second. What you see in the black is the actual growth of solar energy over a 15, 20 year period. Okay. What you see in the colored lines or the curve by the way, is a total hockey stick up into the right. An exponential of epic levels just going vertical. What you see in the colored lines, which are all horizontal are the predictions year after year from the top energy experts in the world as to the future of solar. And we see is every time solar goes literally vertical, all the experts go linear.

[A]

They basically say it can't continue scaling.

[C]

Like it's got to keep, it's got to just level off, level off, right? Year end. This goes from like to 20, like 12 to 2017, 2018. Now the 2018 graph was even worse. It actually showed it going down. The cost is dropping 50% every 18 months. How do you predict that it's going to go down? This kind of drives me nuts because this is not a math error, this is a cognitive error. And by the way, let me just point out again, these are not laypeople. These are the top energy experts, experts in the world getting it 180 degrees wrong, right? Literally if I made predictions like this year after year, I should literally lose my job if I'm that far different from reality. And this is the problem we have because it depends governments are listening to these experts.

[A]

It depends who employed them if it was the problem.

[C]

It really is kind of unbelievable that there's a whole other one about electric cars that I won't get into. They predicted that we would not have more than a million electricity electric cars by 2040 and we crossed it in 2014. And even then they didn't update their things. I'm going to give one more here. So what this is a graph of solar modules dropping and then leveling off for a bit and then dropping it like a stone. And in 2003, the leading energy expert in the world in solar energy itself made a comment and he said, look, if you add up the cost of the silver and the glass and the wiring, the physical component cost of a solar module, you'll never get below a dollar a watt. That's, that's the limit. That's the actual limit. Now the market actually believes them for a while and it flattens out for a few years, then it starts dropping. By 2014 it's 50 cents a watt. Now it actually goes off the bottom of the graph. Where we are today would be where my feet are sitting on this chair. When the graph is this big, we're down to about 2 cents a watt or close to a penny a watt. And his comment when you show this was okay. Getting below a dollar exceeded my expectations. That was his comment after being this. So it's really, really hard in. And I want to give a final example that we don't have a slide for just to give be fair to these folks is how hard it is. So over the last 20 years, if you own a car wash in Buenos Aires in Argentina, your revenues as a car wash owner have dropped by 50%. Okay? Now one of our community members, Santiago Balinkas, who I think Peter, you know well, lives there and says, this makes no sense. The middle class has exploded. We have a ton more Mercedes and BMWs running around. Argentinians are very proud. They like to keep their cars clean. There should be a doubling or tripling of revenues. Why Is there a 50% drop? Is there water restrictions? Are there hyper competition? Are there legal issues or something? He starts looking into it over a couple of months, gets rid of all of the obvious factors. Then he finds the answer, which literally turns out to be Moore's Law, because our computational ability over that 20 years has increased quite a bit. Our ability to model the weather has gotten a lot better. And over that 20 year period, we're exactly 50% better at knowing when it's going to rain and when you know it's going to rain. You don't wash your car. The reason this is important is you can be the smartest car wash owner in the world and you will never see that coming. We call this in the book the orthogonal effect of innovation, where a breakthrough in one domain affects you radically and you don't see it, you can't see it, Right? And so it's so critical to keep track not just of the demand side, but the supply side side of things. The most famous in all these others, and I'll end my rant here, is in the 1980s, McKinsey's advised AT&T on the future of mobile phones. And they predicted by the year 2000 there will not be more than a million mobile phones in the world. And AT&T left the business. She said, that market doesn't work. By the year 2000, we had 100 million mobile phones. So they're off by 99%. In one of those. In one of our executive programs at Singularity, Peter, this guy puts up his hand when I mentioned this. I co authored that report. I'm like, oh my God, is he going to rebut this? Whatever. He goes, no, you're absolutely right. The reason we got it wrong was when you had these big handsets with these briefcase batteries, we figured there's no way you're going to sell more than a million of those, because we didn't see was that within a couple years, that had shrunk to a clamshell and that you could actually sell a ton of. And so that's the part that people miss. So when you track these, be really, really careful of making these outlandish predictions. Like, it'll never get below this or never get above that. We've seen repeatedly.

[D]

I don't know why you want to end that rant. That was the coolest thing ever, I'll tell you.

[C]

For years we've been struggling with this, talking to governments, and they're like, yeah, this will never happen. That'll happen. We go berserk.

[A]

Sorry.

[D]

I love those slides. And you know what else? When. When Bill Gross was on the pod, he said, you know, all the land where pumped hydro. Yeah. Has already been bought. I did a little research and actually, not true. Lots of land where pumped hydro makes a ton of sense, but it's not quite as sunny. Has not yet been bought, if anyone's listening. And because the solar panels are getting so cheap, you can just put more of them there. And so, heads up, you know, there's a theme. If the governor of New Hampshire is listening, please give me a call. But there's lots of opportunity that hasn't been tapped in real estate.

[C]

I have two more quick energy factoids.

[A]

Okay.

[C]

One, I did a little bit of research, and I was talking to one of our energy gurus in our ecosystem. It turns out there, if you add up all the DAMS in the US there's 10 gigawatts of potential hydroelectric power that's not been tapped. So we could. All those dams. That's a big number.

[D]

I'm really going off here. But I remember we were on the pod and we said, holy shit, the Hoover Dam right now is operating at about 5 to 10%.

[A]

Oh, yeah, absolutely.

[D]

Because it hasn't rained.

[A]

Yeah.

[D]

So we're like, why the hell are we not doing pumped hydro? Right. Pump the water from the bottom to the top. Tons of sunshine right there. Turned out somebody had already thought it. Put together an entire, entire investment thesis around it. But it was exactly the right idea. But. But that. That theme isn't over. That. That is still very high.

[A]

I think the point we started this whole conversation is, is China's running away with solar deployment. And I don't understand why we don't see it here in the U.S. you know, I'm a pilot I fly at a Santa Monica airport, I fly over la and all I see is naked roofs that could be all be producing electricity. You know, there's a few solar thermal farms out in the middle of the desert. But there's so much potential, so, so much potential.

[C]

All right, it's geopolitical. It's geopolitical because China is pretty much a lock on the supply chain and the panels.

[A]

Well, I would be, you know, I'd be investing in building out solar capacity manufacturing here, right?

[C]

Yes, we should.

[A]

Solar cities, actually.

[C]

What I would look, what I would look to do is say, what's the 10x to 100x breakthrough on photonics or solar past the next level and go after that.

[A]

And Alex, you know, digital super intelligence will give us new material sciences, give us new capabilities for that. So there will be.

[C]

That's why Alex is standing there not looking worried at all. He's like, these guys are.

[B]

I think there are many ways to generate useful energy. I think fission in the form of SMRs, fusion potentially. As soon as we've discussed in the past 2028 to 2030, I think there are so many non solar novel ish forms of energy that are on the verge of coming online. I'm not losing sleep over geopolitical imbalances over solar photovoltaics.

[A]

All right, let's jump into robotics here. This is a fascinating tweet turned into an article here. China's robotic boom is going global. So if you look at the first half of 2025 and the companies or the countries around the world that are purchasing robots from China, Poland is up 1,700%, Mexico 275%, Russia 135%, Vietnam 114%, as opposed to South Korea, Germany and USA, which is minus 3 to US at 58%. The point here is the countries that are blank sheet don't have a robotics industry, are buying from China. Countries are starting their automation journey and buying from China. So this is something that the US seems to be looking at. Basically. China is staking its flag in countries around the world by deploying both AI and robotics in a very cost effective fashion. Thoughts, John?

[B]

I wouldn't be surprised given how central robotics in general, general purpose robotics, more particularly human general purpose or humanoid general purpose robotics, even more particularly, how central those are to this emerging industrial ecology of batteries and fabs and chips and AI compute and probably SMRs and drones that we see an emerging demand function for fully sovereign robotic ecologies. It's, it seems to the extent, Peter, you were suggesting earlier you're looking for other, maybe you don't want to call them sort of too scarce to fail resources. But robotics I think is a plausible candidate for wanting to be sovereign aligned resources in the near term future.

[D]

Yeah. Rod Brooks, the founder of iRobot when we were out in California a couple weeks ago.

[A]

Yep.

[D]

And he, he reaffirmed what I think we all know, that our, our whole parts supply chain, component supply chain is garbage compared to what China has because you know, all those years of manufacturing moving over to China, industrialization moving over to China, they developed a very, very flexible parts and components contract supply chain. So if you need something to build your robot, you can call someone and have them make it and it'll be there in a, in a few days. There's no equivalent in the US So it's going to take a while to rebuild that whole supply chain. So what Alex said is exactly right. This is ripe for national involvement to kickstart it. It's also not, not naturally happening in the venture community. It's really tough for venture capitalists to plunk down 10, 20 million bucks for like electric motor winding company or a gear company.

[C]

We should have a Manhattan style project for supply chain for robots and drones.

[B]

There are various initiatives that have been discussed, including famously perhaps the software.

[A]

We heard this from Bert Bornick, CEO of 1X. I heard this from Brett Adcock from Elon directly. They've had to completely build their entire bottom up supply chain internally. Every component is manufactured inside the company right now. Which is, which is insane.

[C]

What a waste.

[A]

But the other thing that's going to be interesting is there will be a scarcity in robots for the foreseeable future until production gets ramped up. So we're going to start to see governments probably bidding like, you know, we'll buy a million robots here in Saudi or the Emirates or Qatar in order to get early supplies delivered there. And that may bid up the prices in early days too.

[B]

I would view any emerging robot scarcity as just a facet of compute scarcity. The most important robots are just going to be GPUs on legs. And the compute ultimately is I think the fundamental scarce factor here.

[A]

All right, next item. Here is a interesting graph which asks the question, what if everyone in the US drove like Waymo? So here's the extension. If every US vehicle performed as well as Waymo, we'd prevent 33 to 39 deaths annually. So pretty pretty profound.

[C]

I found a better related statistic, please. Which is it turns out about 50% of all the court cases in the US are car accidents. Wow. 50%. So you take out a bunch of lawyers also, which, you know, that's not bad.

[A]

That's a good thing. That's a good thing. With all due respect to sub lawyers, reducing the number is definitely.

[C]

But the depth part, he is huge.

[A]

And interesting for Waymo. Nearly half of all Waymo impacts, crashes happen under one mile per hour. So these are just bumps, they're not actually crashes.

[C]

So that's just crazy.

[D]

I saw this stat and I said, that's gotta be global, not us. Because that's about the total number of.

[C]

No, it's not. We kill one point. It's 1.2 million people a year die around the world with car accidents. Globally.

[D]

Around the world, yeah. Well, that's why I thought 40,000 out of 1.2 million is viable, but 40,000 in the US isn't. But then if you read the fine print in the notes, it's actually a 90% reduction in fatal crashes.

[C]

It's huge.

[A]

And 15% of all organ donations come from auto accidents, interestingly enough. Right. So I live here in Santa Monica and Waymos are all over the place. I just started seeing the Zoox vehicle from Amazon going and collecting data, right? It's a piloted vehicle with all of the lidar and cameras around it going and mapping the streets. It was about a year ago that you saw all the piloted Waymo vehicles mapping the streets. So we're going to have Zoox, we're going to have Waymo, we're going to see Cybercab or whatever Elon Musk calls it very, very soon.

[C]

Meanwhile, we have people attacking the Waymos. Brad Templeton used to joke, because we don't want to be killed by robots, we'd much rather be killed by drunk people. Which is what's happening today.

[B]

I suspect for at least most Americans, their first encounter with a generalist robot is going to be by encountering either by driving in or seeing Waymo or FSD based car, Zoox or equivalent. And this is just the beginning of a longer journey. We start with these generalist robots on the roads and they'll be in our homes before we know it.

[A]

And guys, just a quick announcement. Dara, the CEO of Uber, will be joining us on stage at the Abundance Summit. And yeah, super cool. And so Uber is partnered in part with Waymo, will be offering Waymo as part of your Uber app. And they're also working with Joby for flying cars. So super fun. We'll be talking about all of those things and where Uber is going in the future.

[C]

Flying cars is my big hope for technology in the near future.

[A]

Yeah, tired of driving.

[C]

Airport transfers are just horrible.

[A]

It is awful. All right, we're going to wrap up with Health and Biotech. I think one of the most important stuff subjects, at least in my life, is how do we double our human lifespan? How do we avoid all of the travesty of chronic disease? First article comes in from a friend, Joe Libet. Lacroix, Joe's company. He's the CEO of Retrobiosciences. It's one of Sam's companies. Sam is founded with $180 million of backing back in 2021. Their mission is to add 10 healthy years on human lifespan. They're one of the teams competing for our $101 million XPRIZE healthspan. Salim and Dave, since you're on the board of xprize, I mean, pretty amazing, that competition just for everybody. If you haven't heard of it, I raised $157 million for a global competition to add up to 20 healthy years on people's lives, in particular in immune, cognition and muscle. We now have over 730 teams that have entered that competition, which is pretty amazing if you ask me.

[D]

That's got to be a record, right?

[A]

It is.

[C]

That's incredible. Yeah.

[A]

Well, actually, for Elon's $100 million carbon prize, we had 1300 teams. But I would have to say this is as hard or harder because you have to run effectively a clinical trial and prove on a human population that your therapy didn't just do cognition, didn't just do muscle or immune, it did all of them. So anyway, I love the fact that Retro is going after this. Their product is entering human trials next year with a hope of in Australia in late 2025. And they're going to be hopefully getting something on the market next couple of years. This is called RTR trial 242. It's an experimental Alzheimer's pill designed to restart the brain's natural recycling process of toxic proteins. This is your glymphatic system. When you're in deep sleep, your glymphatic system is clearing your brain of those toxic proteins. So one of the biggest things I had Mehmet Oz speaking at the Platinum event at the Abundance Longevity Summit as well. And his biggest concern for the future is neurodegenerative disease and also one other disease called loneliness. We should talk about that sometime. I want to end with this article. I find this fascinating. This is out of China, one of the Things about longevity and biotech is if it works in China, it'll work in Chicago. If it works in Boston, it'll work in Botswana. We all have the same biology. This rocketed around the world is news. This past weekend, Chinese scientists have genetically engineered a gene called FOXO3 that is a critical stress resistant transcription factor. And they've been able, as they modify this to reduce aging by three, five years. For me, this is a huge, huge deal. So in 61 different tissues, end of the day we're going to start to see longevity becoming more and more real. And everyone listening, I want you know that the next 50 years that you're alive and hearing us on this podcast, it's going to be awesome.

[C]

Just don't get hit by a bus in the next couple of years.

[A]

Yeah, exactly. Don't die from something stupid in the interim.

[C]

Peter, There was a comment I heard a few years ago, a couple of years ago and I wanted to just ratify where we are with that. Somebody on one of the abundance stages said that we have the labs mice in labs today that are living to the equivalent of 300 years old already. Are we really there?

[A]

No, we're not there yet. The average mouse is living on the order of 20 to 24 months. We've seen extension of 30 to 40%. I was just over at Harvard, spent the day, day in the weekend with David Sinclair and then the day at the Wyss Institute, the Wyss Institute with George Church. And those experiments where they hope to double the mouse's lifespan are going on right now. We've also seen the first epigenetic reprogramming trials are going on in humans starting in January. So Life Bioscience is one of David Sinclair's companies, is going into humans. It's been very successful in animal models, including non human primates.

[C]

After this longevity trip, when's your best prediction of when we break through the aging barrier? Escape velocity.

[A]

I asked the smartest people on the trip that I know and their belief is there is no upper limit to how long we can live. Just let's begin with that. And the belief is that the breakthroughs required to understand why we age, how to slow it, stop it, reverse it, is going to fall at the knees of digital superintelligence. We heard Dario talk about this doubling the human lifespan in five to 10 years. It's interesting, we had a bunch of scientists from MIT and Harvard, principally at the summit and they fell into two groups, those that amongst themselves were consistent saying we're going to see this doubling, we're going to see this significant lifespan and health span extension. And those saying, nope, not going to happen, and just extremely on the other side.

[C]

Wow.

[A]

And so it's interesting because I define expert as someone who can tell you exactly how it can't be done.

[C]

Yes.

[A]

Yeah.

[B]

And for. For what it's worth, Salim, I. I've asked this question, of all of the best frontier models of the day, when do we get longevity, escape velocity? And their consensus is 2030.

[A]

Yeah.

[C]

Which ironically is the same time when bitcoin hits a million dollars according to all the frontier models.

[A]

Which is. Which is exactly what Ray predicted. 2030. It's like Ray was right.

[C]

Damn the math. Get the key. He may be proof that time travel is real.

[A]

Yeah, that and Elon. Yes, exactly. So everybody, you got to hang on, stay in good health, sleep, diet, exercise, mindset. Don't die for something stupid. You got. Hold on. For the next five, 10 years, there are therapies coming, and they're significant therapies. I did a podcast with David Sinclair on Moonshots. Go and listen to it. Please do.

[C]

It's an amazing podcast, that one. It's a must. Listen.

[A]

Let me give kudos to the moonshot community here. One moment. When I did that podcast with David, he came on and he was really miffed. The Harvard White House debate and headbutting had canceled all his funding. $4 million of funding got canceled, and he was on the verge of letting his entire research team go, all of his researchers. And I was just pissed. And I said, let's turn this around. And on the podcast, almost off the cuff, we announced this thing called Friends of Sinclair Lab, where folks would contribute $50,000. I was the first to offer to contribute, as was David himself. And since then, we have gotten over $4 million of donations from the people listening to this podcast, which is insane. So we completely replaced government funding.

[C]

I'm looking to buy a Ferrari.

[A]

If.

[C]

Anybody wants to donate to them.

[A]

No, but this is decentralized science.

[C]

It's great. It's citizen driven, bottom up science. It's so awesome.

[A]

And the challenge is that when you're funded by government and have peer review, you're stuck in incrementalism.

[C]

Yeah.

[A]

Anything dramatically different, you know, they don't want to get it funded.

[C]

Yeah, it's great.

[A]

Yeah. Dave, what's your week look like for you, buddy?

[D]

Well, it's Friday, so. Yeah, you know, we have a lot of our best and brightest that are coming through the lab are getting funding right now. A lot of them are getting west coast term sheets at like two or three times higher than the east coast. So there's quite a bit of migration west going on. One of our coolest companies that we signed the term sheet in Mark Zuckerberg's old dorm room and there's a poster of the Social Network movie signed by Mark Zuckerberg on the wall. So he signed the term sheet right in front of the poster. Then that got all around Harvard. So 20 people joined the company for no salary because they're so hot anyway. They're smoking hot. Now it's by cold biography. They're moving to the west coast. So I got a whole bunch of open seats here in the lab. So I'm really excited to spend time on campus backfilling. We're going to try and get 16 more teams in and January is coming fast. MIT has January off.

[A]

Yes.

[D]

So that's the perfect time. IAP perfect time to boot up a company.

[A]

So if you're at MIT or Harvard or Northeastern and you're hearing this podcast, first of all, Dave's a rock star. If you've got a couple of best friends and you want to start an AI company, where do they go?

[D]

Dave, go to the Linkventures website or just email Dan Oliveri or Kush Bavaria. Their names are on the website and it's just K Bavaria or D. Oliveri Linkventures. And you got to have at least three people that are bonafide best friends. And we'll check, we'll poke around and ask your other friends, are you really best friends? But we only bring in teams that are super tight knit. Keeps it all really, really fun.

[A]

Salim, how about you? What's the week ahead look like?

[C]

We're doing a whole bunch of planning with our ecosystem to think about how we leapfrog everything we've done in the past and go 10x faster, better, cheaper, with all the offerings that we have. We have our next 10x shift workshop on October 15th. It's 100 bucks, people. Those are all selling out. Those are great. And we cover the model and show people how to take their organization literally 10 to 100x now through that two hour workshop. And I've got a little bit of travel, but not too much before the madness. Towards the end of the month, visioneering is coming up, which I'm super excited about.

[A]

Yeah, for sure. And Alex, welcome back from your secret mission and excited to work on our project together, which we'll unveil at some point. We're going to keep it secret for the time Being how about what's on your agenda?

[B]

Trying to accelerate the singularity or whatever it is. Maybe singularity at this point isn't even the right term, but smoothing out and moving, whatever. We want to call it the intelligence explosion, or if you're a technological deterrent, determinist. The what was always going to happen. The inevitable byproduct of building an Internet and then compressing the Internet and then using that to solve everything else. I think timelines are very short at this point. Every week, my timelines are getting shorter. Usually it's the case that I'm the accelerationista in the room. Not always, but usually. And my timelines are incredibly short at this point.

[C]

So my favorite thing these days in these podcasts is watching Alex's faces. We rant about energy or healthcare or something. He's like, oh, super intelligence is going to just solve that. Why are we even talking about this? Great. Look at this face that.

[B]

Chelsea, you're reading my face. I think correctly, Selim, that there is a certain sense of like, hyper. Hyper deflationary mentality. Why do anything really?

[D]

Paralysis.

[A]

It's. It's like the starship. It's like the starship who heads out. And when they get there, they find out, you know, warp driving had been invented. And it's a term for it.

[B]

It's. It's term for. It's called the weight equation. And it does cause singularity paralysis for. For lack of a better term. And I'm seeing it more and more in every day in conversations I have as, as it dawns on more and more subject matter experts that AI is about to transcend their capabilities in call it two to three years. If, if, if the karmic extrapolations hold, what happens next? And I spent a lot of time thinking about that.

[A]

Amazing. Well, everybody, thank you for joining us subscribers. If you haven't yet, subscribe so we can tell you when the next WTF episode is taking place. Hope you found this super useful. Be optimistic. We're living into the most extraordinary time ever in human history. A time where we can uplift every man, woman and child, where each, each of us is going to be able to take on the grand challenges we desire and really go from success to significance on a global scale. So, so happy to be alive right now and so happy to be with my moonshot mates. All right, guys, until we see each other next time, Every week, my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics AGI and quantum computing to transport energy, longevity, and more. There's no fluff, only the most important stuff that matters that impacts our lives, our companies, and our careers. If you want me to share these metatrends with you, I write a newsletter twice a week, sending it out as a short 2 minute read via email. And if you want to discover the most important meta trends ten years before anyone else, this report's for you. Readers include founders and CEOs from the world's most disruptive 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. Alright, now back to this episode.

[E]

SA.