Flying Tesla Car, Robot Brains Halving in Cost Every 3 Weeks, and Why Incentives Decide AI Alignment | #303

Peter Diamandis sits down with Salim Ismail, Emad Mostaque, and special guest Anousheh Ansari, CEO of XPRIZE, to discuss Tesla’s flying Roadster, the rapidly declining cost of robotic intelligence, and AI’s ability to solve advanced physics problems. The conversation also explores the power of incentives to drive innovation, new XPRIZE competitions tackling global challenges, the complexities of human-AI alignment, and how humanity can find purpose in an age of superintelligence. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Emad Mostaque is the founder of Intelligent Internet ( https://www.ii.inc ) Read Emad’s latest papers exploring the future of society, law, personhood and governance: https://ii.inc/common-wealth Read Emad’s Book: https://thelasteconomy.com Anousheh Ansari is the CEO of XPRIZE, an Iranian-American entrepreneur and engineer, and the first woman to travel to space on a privately funded flight. – This episode is brought to you by: Build in the Foundry or get a live demo at VoiceRun.com Get the blueprint for generative media https://goo.gle/startupgenmedia Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Salim: LinkedIn X Join Salim’s 10X Shift Subscribe to Salim’s YouTube channel Exponential Venture Capital Connect with Emad ⁠⁠⁠⁠⁠⁠⁠ X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠ Learn about Intelligent Internet: https://www.ii.inc Read Emad’s Book: https://thelasteconomy.com Connect with Anousheh XPRIZE Website X LinkedIn Listen to MOONSHOTS: Apple YouTube Follow MOONSHOTS: Instagram TikTok X Threads – *Recorded on October 16th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  Last night in Waco, Texas, Tesla finally unveiled their flying Tesla Roadster. Zero to sixty in under one second.

• 0:08Speaker 01

  It is street legal. There's nothing to stop you from strapping a rocket to your car.

• 0:11Speaker 02

  What would be the cost of insurance for that?

• 0:13Speaker 03

  He's two generations ahead.

• 0:15Speaker 02

  I'm surprised that it's not autonomous and it's only, you know, human-driven car.

• 0:19Speaker 00

  I think. The cost for a Frontier model to complete a robot subtask fell from $2.36 in July to $0.38 in September, 84% drop. Having it cost every three weeks means it effectively goes to zero, you know, within the year.

• 0:35Speaker 03

  We've been looking at AI that doubles every 10 weeks, and that's been mind-boggling. Having every three weeks may be the fastest-moving capability we've ever seen.

• 0:44Speaker 00

  How do we create alignment within the labs and the models and humanity?

• 0:50Speaker 03

  We don't have alignment among human beings.

• 0:52Speaker 02

  They're trained to win, no matter how you get to the answer, as long as you win.

• 0:56Speaker 01

  All wars are basically based on one thing: that person is not a human.

• 1:02Speaker 04

  Now that's a moonshot, ladies and gentlemen.

• 1:07Speaker 04

  This episode is brought to you by the Abundance Summit and Link Ventures.

• 1:13Speaker 00

  First of all, I want to just point out what cups I've got moonshots. What do you have, Saleem?

• 1:18Speaker 03

  I've got exponential.

• 1:19Speaker 00

  Of course you do. Imad?

• 1:21Speaker 01

  Artificial intelligence.

• 1:23Speaker 00

  Yes.

• 1:23Speaker 02

  And I've got gratitude.

• 1:24Speaker 00

  Yes, you do have gratitude. Yeah, for sure. Anyway, all right. I love having you guys in the podcast studio.

• 1:31Speaker 02

  That's great.

• 1:32Speaker 00

  Welcome, Imad. Saleem, you've been here before.

• 1:34Speaker 03

  I have been here before. We did our one-hour organizational singularity conversation here.

• 1:39Speaker 00

  Yeah, and Imad?

• 1:41Speaker 01

  It's my first time, yeah.

• 1:42Speaker 00

  Welcome, yeah. It's good to have you here in the States, Alex, for coming over for visioneering. It was a blast to have all of you guys here.

• 1:50Speaker 02

  Thank you for being here.

• 1:51Speaker 00

  Yeah.

• 1:51Speaker 01

  It's a pleasure.

• 1:52Speaker 00

  All right, well, let's get going. So, welcome to Moonshots, everyone. Your number-one podcast on all things exponential, your front-row seat to the singularity. Today, we're here to have a conversation across a few breaking-you stories, but in principle, about the worlds of incentives, how we incentivize human-AI alignment, and how we dive into human purpose. I think it's one of the conversations we've had before, that, you know, how humanity's going to define purpose in the future is critical. We need purpose. We need some level of struggle amongst ourselves. So, I'm here with my extraordinary moonshot mates, Salim Ismail. Saleem, love you, buddy.

• 2:33Speaker 03

  Good to be here.

• 2:33Speaker 00

  Yeah, it was great to have your family here as well.

• 2:35Speaker 03

  It was awesome.

• 2:36Speaker 00

  Yeah, for sure. And you've just flown in from Singapore.

• 2:39Speaker 03

  I have.

• 2:40Speaker 00

  So, you have no idea where on the planet you are.

• 2:42Speaker 03

  In fact, we recorded the last podcast episode. I hopped on a plane, came here for visioneering, and now we're here again, recording again.

• 2:49Speaker 00

  Yeah.

• 2:50Speaker 03

  Can't get away from the recording.

• 2:52Speaker 00

  No rest for the wicked. Yeah, as Alex says, you know, it's going to be a continuous recording sometime. Continuous models coming out and continuous recording.

• 3:00Speaker 03

  Recording, yeah.

• 3:01Speaker 00

  And Imad, a pleasure, pal. Great to have you here. And we have a special guest with us today, Anusha Ansari, the CEO of the X Prize, my patron Sein. I'm going to give her story in more detail later, but we just finished a couple of days of X Prize visioneering, our annual event where we debate and we discuss what prizes to launch. We narrow them down, and we have a whole group of philanthropists who fund them, and we'll share some of the stories there. So, Anusha, it's been a long time coming.

• 3:31Speaker 02

  I know. I'm so excited to be here. I've been a listener for a long time. Now, I'm here.

• 3:36Speaker 00

  Yeah, for sure. And I just want to do a proper introduction for you before we get fully started. So, for those of you who don't know Anusha, Anusha is the CEO of the X Prize, but I met her back in 2001, when I was trying to fund the original X Prize, right? I had launched this $10 million competition and unfortunately didn't have the money.

• 3:59Speaker 00

  This is what entrepreneurs do.

• 4:01Speaker 03

  Yeah. Didn't you lose like a couple of board members like that?

• 4:03Speaker 00

  Yeah, I did. When I said I was going to... So, we'd raised $500,000, and I said I was going to spend all of the money announcing the X Prize above the line of supercredibility. I'll explain what that means later. And I had two board members who resigned because they thought I was being reckless, which, of course, I was.

• 4:22Speaker 00

  You know, because I expected, once we announced this, you know, $10 million, you know, flight, you know, space competition, you know, you didn't pay until after someone won. And I figured, you know, everybody would want to fund it.

• 4:35Speaker 03

  It should be a no-brainer.

• 4:36Speaker 00

  It should have been.

• 4:37Speaker 03

  Yeah.

• 4:37Speaker 02

  It was, but not for everyone.

• 4:41Speaker 00

  And then, I read in Fortune magazine about this amazing woman, Anusha Ansari. She was written up in the wealthiest 40 women under 40. I wasn't married at the time, so that was one funding opportunity. But she was married and very successful, had built a company called Telecom Technologies, sold to Sonus Networks for $1.4 billion. And in her write-up, it said, "You were interested in flying on a suborbital flight into space."

• 5:10Speaker 02

  Yes.

• 5:10Speaker 00

  And I read that paragraph like three times, not, you know, into space, not into orbit, on a suborbital flight. It was like, wow. And tracked you down vacationing in Hawaii, and, you know, Byron Lichtenberg, a twice-shuttle astronaut. I brought an astronaut with me to the meeting.

• 5:32Speaker 02

  Great marketing scheme.

• 5:34Speaker 00

  And you and your husband at the time and your brother-in-law basically said yes. And after five years, we launched the prize in 1996, didn't have the Ansari commitment until the end of 2001. For five years, I pitched 150 people, and everyone said, "Why isn't NASA doing this? Can anyone really pull it off? And isn't someone going to die trying?" And that last one, you know, people are so risk averse.

• 5:59Speaker 03

  Yeah.

• 6:01Speaker 00

  And then, Anusha, we rebranded it, the Ansari X Prize, in your honor. And then, Anusha, you need to understand, flew private to the space station, flew on a Soyuz through one of the companies I had back then called Space Adventures, and became the first private female astronaut to fly into space. And then, eight years ago, one of the greatest blessings of my life, Anusha returned as the CEO of the X Prize.

• 6:26Speaker 02

  It's been an adventure ever since I've met you, Peter.

• 6:31Speaker 00

  I've had a lot of people say that about me.

• 6:33Speaker 02

  It has been a pivotal time for me, and it's a gift that keeps on giving in a really great way, because not only it helped open my mind to possibilities and how I should look at life, but also now the opportunity to lead X Prize into the next phase of it and really leverage the platform that's been built over the past 30 years to make great changes and help shape the future that is so exciting. So, I'm blessed to have been a part of it.

• 7:06Speaker 00

  And I am blessed as well. And, you know, we are going to talk about some of the crazy ideas that we're getting funded at visioneering.

• 7:16Speaker 02

  Yep.

• 7:17Speaker 00

  And where we're going next. And speaking of crazy ideas, I'm going to open up with probably the most insane product demo ever. So, last night in Waco, Texas, next to SpaceX's McGregor test site, Tesla finally unveiled their flying Tesla Roadster. And, you know, just to describe, we're going to show a video in a moment, but just to describe what happened, you know, the Roadster drove up a multi-story curved ramp upside down, you know, and I was wondering, I was watching the video the first time, you know, I didn't hear the description of how it was doing it. I said, "Is it magnetics? Is it some kind of a rail system?"

• 8:00Speaker 00

  And it was actually a fan suction system that could hold it against two Gs. So, it's levitating there upside down, which was insane. So, let's watch the video, and then let's talk about it. I'm curious which one you guys are going to buy.

• 8:16Speaker 05

  It's not an autonomous car. It is the last...

• 8:19Speaker 06

  Well, it is technically autonomous for this demo to make sure we don't kill anyone. Correct. So, just in case. But this will actually be, long-term, the only manually-driven car that Tesla makes.

• 8:51Speaker 05

  To do that, we have a suction, a fan suction system on the bottom of the car that can pull up to two Gs in order to make the car go upside down. And then, we borrowed some technology from SpaceX to do the rest.

• 9:03Speaker 06

  Yeah.

• 9:03Speaker 07

  Let's try something else.

• 9:05Speaker 06

  Yeah.

• 9:07Speaker 06

  I mean, that was just a little flight. Let's do a longer flight.

• 9:37Speaker 01

  I mean, I've seen a lot of things, but I've never seen that before.

• 9:42Speaker 00

  I think we could have cleared more than our whole fleet.

• 9:44Speaker 01

  Yeah, we can go further.

• 9:48Speaker 01

  Like, this will be an actual car that's on the roads.

• 9:52Speaker 00

  Yeah, to do that, we had the rockets we borrowed from SpaceX technology. You talked about the carbon overlay pressure vessels. There's two of them in the car. They are a total of 600 liters. It's about 250 kilos of air. And we have 10 nozzles to do all that work.

• 10:05Speaker 01

  And it's using an advanced ultra-high-pressure air tank. So the car can recharge its air tank. So it's not like you, you know, need special propellant or anything like that. It literally is just going to pop itself up and be able to do these maneuvers whenever you'd like.

• 10:24Speaker 00

  Amazing. Only Elon.

• 10:27Speaker 02

  Yes. I'm surprised that it's not autonomous and it's only, you know, human-driven car. I would not want a human to do a stunt.

• 10:36Speaker 00

  Oh, my God. You need to know how much I love XPRIZE because I was supposed to be there for this event. And record with Elon, but commitments are commitments. In fact, you know, Steve Jervis was there and he was at visioneering and he was supposed to be at the launch as well. Crazy. So here are the numbers. Zero to 60 in under one second. I mean, you know, if you want to go fast, you might.

• 10:59Speaker 03

  This is why we need the organ regeneration.

• 11:03Speaker 03

  Your liver will be left behind.

• 11:06Speaker 00

  Top speed of 230 miles per hour and 300 miles of range. There are three versions they're going to be selling. The performance version, this is the zero to 60 in 1.6 seconds. The thrust version, zero to 60 in not 0.9 seconds. And the rocket version, it's a one-seater described as air-ready.

• 11:27Speaker 01

  The rocket version does 0.6 seconds, but also apparently 380 kilometers per hour, which is half the speed of sound.

• 11:34Speaker 00

  That's when you really need to get there in a hurry.

• 11:37Speaker 01

  Well, I mean, I was looking at that. I was like, that's the solution to Los Angeles traffic, right?

• 11:42Speaker 02

  You'll be hopping.

• 11:45Speaker 00

  So production starts in 2028 per Elon. No price was given. Tesla shares rose 5% on that news. I mean, honestly, it's like you can do anything.

• 11:56Speaker 03

  Well, I mean, I've got the obvious question as to why.

• 12:00Speaker 00

  Because we can.

• 12:03Speaker 03

  Then how long does it fly for? Is that given anywhere in terms of what the?

• 12:08Speaker 00

  I don't think they gave the, you know, the duration of a hover.

• 12:12Speaker 01

  Okay, it'll be like just a few seconds. Again, if you look at the pressure pushing out. I don't think that's kind of the point, though. I think the point is it can. You're not going to see hopping cars.

• 12:23Speaker 00

  Until we solve for gravity shielding or something like that.

• 12:26Speaker 01

  Well, I mean, that's clearly for Mars, right?

• 12:28Speaker 03

  The real miracle will be can they ship it on time rather than does it fly?

• 12:34Speaker 02

  What would be the cost of insurance for that?

• 12:36Speaker 03

  Yeah, I don't think it's insured. I mean, if I'm the insurance company, I'm like, yeah, those thrusters stay off. Otherwise, your insurance is off.

• 12:44Speaker 01

  It's like, really? You want a rocket on your car?

• 12:48Speaker 00

  So, you know, obviously, this is showing Tesla and SpaceX working together, which, you know, my prediction is they're going to announce the merger before the end of the year. Right? But, you know, Saleem, here's my question. How, who regulates this? DOT, FAA, nobody? I mean, and how is this going to get licensed?

• 13:07Speaker 03

  This, I think, is the challenge of all of these new technologies, right? The scaffolding of regulatory and the institutions to protect the insurance, safety, et cetera, always lag behind. And Elon is just pulling away from that capability. Because think about it, it's going to take a year or two to figure out how to regulate this. He's two generations ahead. So where is this going to go? This is, I think, the biggest stress test that we're seeing across all of these technologies, not just this car.

• 13:36Speaker 00

  I mean, I imagine this is AI-driven, AI-enabled, even though he says it's going to be, you know, manually driven. But there's no, I mean, no way that control system is going to fly without AI in the loop.

• 13:48Speaker 01

  Yeah, I mean, like, literally fly. This is the thing, right? So I think that it is street legal. Like, again, there's nothing to stop you from strapping a rocket to your car. It will be street legal, right? Or having thrusters and things like that. So it fits within the existing law. The question is, again, who's going to insure this thing? Like, if you've got the thing. Maybe they will have their own insurance company like Tera Insurance. But very few people will have these cars. I think this is, again, a more demonstration of we have integrated capabilities. We will take it to Moon and the Mars and beyond, and it's freaking cool. Like, so many demos, like Porsche had their release on the same day.

• 14:24Speaker 00

  Poor Porsche.

• 14:28Speaker 01

  I mean, it's a very nice-looking car, but can it fly? No. It raises the bar and it makes you, again, want to go and work at Tesla, SpaceX, right? Like, if you're a top engineer, where do you go and work? You go work there. And that's his key thing is getting the talent, getting it going and saying what can be. Plus, he gave us 280 characters from 140.

• 14:50Speaker 00

  Oh, God. You know, one of the things I, there's interesting question about. Everybody's going to go into autonomous vehicles and be driven around. Some people want to drive, right? We've talked on the pod before about there are going to be special lanes or you're going to be not allowed to human drive. But one of the things that I love about autonomy is if you want to drive fast, if you want to get into a race car, you can imagine a situation where you're driving, doing your very best, but if you start losing traction, the AI takes over. So like in Saudi Arabia and the Emirates, where there's a lot of wealth and a lot of young men, there's a high death rate in sports cars because, you know, they've got cars that outperform their ability to think and react. But you could put an AI layer on top of that and make sure everybody's safe.

• 15:35Speaker 03

  Well, this is essentially an extension of anti-lock braking, traction control.

• 15:39Speaker 00

  Yeah, exactly.

• 15:40Speaker 03

  Which is embedded in all these systems, but now you can make it a general capability of try not to kill the human despite what the human does.

• 15:49Speaker 03

  Override the stupidity of the human, right? And we see bits of this already. You've got lane drifting warning and you've got collision warning. I think this takes it to the next level and it's badly needed.

• 16:00Speaker 00

  Yeah, for sure.

• 16:01Speaker 02

  This is the answer to alignment, you know, AI doing the right thing despite what we.

• 16:09Speaker 00

  We'll get into that. So Inusha, you know, during the 60s, all the Apollo astronauts got Corvettes.

• 16:15Speaker 00

  So I wonder if all the new Artemis astronauts are going to get, you know, a Tesla Roadster.

• 16:20Speaker 02

  Probably. I don't know if they can afford it. I don't know how much.

• 16:24Speaker 00

  You know, Chevrolet gave them the Corvettes, so maybe.

• 16:27Speaker 02

  Maybe.

• 16:29Speaker 00

  I think we'll.

• 16:29Speaker 02

  Elon will be generous with the Artemis.

• 16:32Speaker 00

  I think he had to sell his mom a car originally. I don't think she got one for free.

• 16:36Speaker 01

  Do you remember the original story from Zip2? What happened? It was like, I can buy a house or I can buy McLaren F1.

• 16:44Speaker 01

  So he's very much pro-car ownership, even if he did kind of wreck it afterwards. And actually, it's interesting, like, you can see tweets from him from, like, 2018, where he's like, we can put thrusters on our car to make it fly. And he just goes and does it. It might take a bit longer, as you said. I miss a few timelines, but he gets it done.

• 17:01Speaker 03

  Gets it done.

• 17:02Speaker 00

  This episode is sponsored by Google for startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's Startup Tactical Guide for Generative Media gives you a complete blueprint for deploying Google DeepMind's models in production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below.

• 17:29Speaker 00

  All right, I'm going to move to our next story here. And Iman, it's one that you sent me over yesterday. So it's about a curve that should make every robotic founder, like, stand up and pay attention. So Robocurve, it's a Y combinator startup, designed a benchmark called RoboDojo RC Tier 1, which measures how well a model can drive a physical robot through actual subtasks. So the cost for a frontier model to complete a robot subtask fell from $2.36 in July to $0.38 in September, 84% drop. So here's the curve. And Iman, talk us through this.

• 18:07Speaker 01

  I mean, we've had kind of the question, is the robot's body good enough? And then it was like, oh, they seem they can do just about anything and now the hands have got good. Now the question is the mind. And it's always been the case of, do you need specialist models? Or can you use generalist models that suddenly learn these new capabilities? This shows that generalist models can actually win. And it's something we talked about previously with the auto self-driving kind of benchmark. Unitree CEO actually just came out and said, like, we're not even sure we need to create specialist models anymore because these things do it. We used to kind of task and cost halving, but every three weeks, like, you're heading to the point whereby effectively there is no cost for robotic intellect and robotic locomotion.

• 18:51Speaker 01

  And it comes down to the hardware cost, which, again, is going to lead to an explosion. Just like we see, again, the unitries when they first came out, they were like stumbling about like little drunkards. And now they're doing Kung Fu and synchronized kind of things. Next, they're doing surgery with generalized models.

• 19:07Speaker 00

  Yeah, well, I mean, again, that's the conversation I had with Elon at the beginning of the year. You know, his prediction, robots are doing perfect surgery within three years.

• 19:17Speaker 01

  I mean, look, with the new Grok model that they're coming out with, they're putting all of SpaceX's engineering that can create flying cars, right? So the robot will be an engineer that can build flying cars. And the robot will be an amazing lawyer. It'll be an amazing plumber. It'll be an amazing, et cetera. So this is why does SpaceX, Tesla have customized robotics models? No. They're just like, we will play the generalization game. And again, I think that's what's going to win here because humans are generalized. We're not like lobotomized and specialized. If you want to have humanoid-type robots, if you want, again, have very specialist ones, sure. But humanoid robots, I think that's a different game.

• 19:55Speaker 00

  Yeah, we're going to be interviewing Brett at Figure in a couple of weeks. And remember, he started building his own models for the robots because the early models didn't work, the large language models. But I'll be curious to ask him, you know, what he's using or building now. But halving in cost every three weeks means it effectively goes to zero, you know, within the year.

• 20:20Speaker 00

  So, you know, if you're an entrepreneur now in hardware space, what are you thinking?

• 20:28Speaker 01

  I think I build as many robots and actuators and the spare parts of the robotics supply chain as possible.

• 20:33Speaker 00

  Are you also thinking I need to collect as much training data as I can?

• 20:37Speaker 01

  I think that the training data will be important for a lot of the specialized scenarios. And again, it was a case of will the robotics companies buy them? Brett has committed a billion dollars, that figure for example, for data. Or will it be that after hoovering up the worlds like data from companies and all the tech data, every single major foundation model company now wants all the robotics data? I think it'll be the latter. And robotics data is an order of magnitude larger than the language data. But again, as you're embodying the intelligences, you need that data.

• 21:09Speaker 02

  What is the performance dependent on the form factor? This is just for humanoid robots or bullet work on the three armed?

• 21:21Speaker 01

  This one is for humanoid, it's for kind of arms and humanoid type things in specific settings. But again, these should generalize very quickly. Like, the whole thing is, the language models got good enough, all of a sudden, that you kind of don't have to baby them anymore. The robots aren't quite there, but they're about to get there as well at the same time. So all of these models, the video models now don't make mistakes. They're all getting good enough, fast enough, cheap enough at the same time. That's the bottom line.

• 21:48Speaker 00

  You know, in the last episode, we talked about Jeff Holden's Atomic Machines company, which is basically going from prompt to product, where you describe what you want and this machine is building it at millimeter level accuracy with multiple materials. So it's, you know, just to give a vision of where we're heading, all of a sudden, we're in a situation where if you want a specific kind of robot to do a specific, you know, you know, clean windows, repair HVAC, you know, brick, lay bricks, mow the lawn, you can ask it to design the perfect robot and build it and then operate it. So do we end up with Star Wars with like thousands of different robot designs?

• 22:31Speaker 03

  Well, I think you're gonna end up, remember that this works very well when you've got lots of repetitive tasks, right? And then you can generalize to, say, plumbing, where you've got lots of little manipulative tasks in a particular domain. I think it's clear that for now, it'll start off in the what we call DDD, dull, dirty, dangerous jobs. Toxic cleanups and underwater manufacturing and all sorts of things. I think it'll be a while before it comes into the home, liability issues, et cetera. I love talking, again, we'll get to the institutional lag there. I think where I'm most excited is in things like surgery, where we make a lot of human-

• 23:09Speaker 00

  Is that dull, dangerous or dirty?

• 23:10Speaker 03

  Uh, it's dangerous for the patient. If you're, if you're the first 10 heart surgeries, surgeries that a doctor has ever done, you kind of don't want to be there. You kind of want to be in the 500 and you don't want to be too far down the line either because the doctor gets lazy after a few thousand surgeries of same time and doesn't pay attention, right? And so now you can smooth that curve out and have the world's like literally the best doctor first supporting and then over time just doing the surgeries. And that becomes completely, you want those things as automated as possible, operating in the most precision level measurements possible. I think that we'll see a huge take up in those types of tasks for robots.

• 23:48Speaker 00

  I'm fond of saying, if you're looking, if you need a surgery, there's one thing you ask your surgeon, which is how many times did you do it this morning? Right? So the surgeon who's seen the most varied cases is most likely to be the most successful. And when you've got federated learning across all robots, they've seen millions of cases and they share all those memories.

• 24:09Speaker 01

  What I'm most excited about is the robot chef.

• 24:12Speaker 00

  Okay.

• 24:13Speaker 01

  Can you just imagine that? It's a robot chef that cooks the best food for you all the time in your kitchen.

• 24:16Speaker 00

  Yeah.

• 24:17Speaker 01

  That's all I need.

• 24:18Speaker 00

  Even, even more than that, even more than that, it's, it's, you know, you've got your embedded sensors in your body that are measuring, you know, your protein levels, your hydration levels, your vitamin levels, and the food that's being cooked by the robot in the kitchen is exactly what you need at that time and it tastes good.

• 24:37Speaker 02

  I think that before robots gets into the home, there is a safety factor that needs to, we need to figure out, these are still big, heavy machinery. So if you have a dog or a cat or you have small children, they're around it and they just step on the foot.

• 24:51Speaker 00

  Give a safe word.

• 24:54Speaker 01

  One X makes it very like soft and squidgy.

• 24:57Speaker 03

  My big concern to Anusha, to your point, I have one of these designer dog things. It literally looks like a teddy bear and robot's gonna go, oh, laundry. And it's gonna cut the dog into the washing machine and that's gonna be a bad day, worse for the dog. And so what do you deal, how do you deal with those edge cases? And there's the infinite numbers of edge cases. And I go back to driving, look how long it's taken us to get autonomous driving, right? And that's a very bounded domain with reasonably good understanding.

• 25:24Speaker 00

  I'm gonna push back in the following way.

• 25:25Speaker 03

  Okay.

• 25:26Speaker 00

  These, these performance curves on the operation of robots are one part, but the other part is they're gonna be running super intelligent models.

• 25:35Speaker 03

  Yep.

• 25:35Speaker 00

  I don't think it's going to look at your fluffy little dog and say laundry. I think it's gonna be able to recognize that dog and make decisions better than the majority of humans. I mean, it's, the two are evolving at the exact same time.

• 25:49Speaker 01

  Well, I think that's why this is important, right? If it was a specialized model, yeah, your dog goes in the laundry.

• 25:55Speaker 03

  The generalized model. Do you need world models for this to occur or it's getting so good that you don't really even need world models?

• 26:02Speaker 01

  You don't need world models for this. This is again, the shocking thing whereby all of a sudden the souls and the opuses and the 3.8 flashes suddenly understood 3D.

• 26:11Speaker 03

  They're that good.

• 26:12Speaker 01

  Yeah, and we're like, wait, how can they do it? You've seen them kind of suddenly create whole 3D assets in games that like, no one's quite sure how. They just seem to have learned something in the next generation and now that's why they're generalizing.

• 26:22Speaker 00

  So I'm gonna push back as well on the idea that it's only dull, dangerous and dirty jobs.

• 26:26Speaker 03

  No, no, I said it would start off there.

• 26:28Speaker 00

  Okay, but we're that like, I'm thinking it's gonna pass there in the next year. In other words, if we really got this level of dexterity and capability.

• 26:37Speaker 03

  I think the business model will be there. For example, there will be an ROI for using robots for those purposes for the first few years until the supply chain can catch up. Because remember, we have no supply chain right now for making billions of robots. So for bringing it into the general household where you need billions, literally billions of humanoid robots, or at least millions in, in first world countries, you're, it's gonna take some time for the supply chain to catch up. The ROI won't be there, my prediction.

• 27:06Speaker 02

  I really don't believe we, ultimately, the humanoid form factor is the right form factor.

• 27:12Speaker 03

  That's my case.

• 27:15Speaker 02

  I think we need either a special or transformers because I think, you know, there are certain tasks that really, our bodies are not suitable for those tasks. You need to be small to go through a small opening to get to a location or so there're many, many tasks like in the categories you're describing. I think you need to have specialized form factors that suit the task. And these models would be great because you don't have to create specialized models for every form factor.

• 27:44Speaker 01

  Yeah, I think the interesting thing, you know, like right now, the Wuji hands are probably the most advanced ones. Those cost about $8,000, down from $20,000 with as much, actually more dexterity than a human hand. But a robot that does the laundry doesn't need that. And so you might just actually see like spare parts companies where again, like transformers, they like add a few extra arms.

• 28:03Speaker 03

  Yeah, I can imagine. To Anusha's point, you'll have a figure with a bunch of sockets in it and it'll have a spider type thing for one thing and a regular arm for another thing and a rope for another thing and you'll have, you'll have all the choice around the world.

• 28:19Speaker 00

  So you know what? We're gonna find out.

• 28:21Speaker 03

  We'll find out very quickly. At a dropping of three weeks, I mean, if we've been looking at AI that doubles every 10 weeks and that's been mind boggling, having every three weeks may be the fastest moving capability we've ever seen. I forget 18 months doubling in Moore's law, this blows that out of the water.

• 28:40Speaker 00

  Did this surprise you, Man?

• 28:42Speaker 01

  No.

• 28:43Speaker 00

  No.

• 28:43Speaker 01

  Like, I've been saying robotics would suddenly be cooked and just like video, like this final period, like again, we went from video costing 30, no, 50 cents per second for high quality. The new models are about to release next week are 0.1 seconds, 0.1 cents per second, faster than real time.

• 29:04Speaker 00

  Nice.

• 29:04Speaker 01

  So once you break through this final level of it can do the work, it collapses. And again, that means that you have to build the actual robots. That's why, like we discussed a few episodes ago, Elon is building a million Optimus's for next year, going up to 10 million. At 1.7 million, it overtakes Tesla in revenue. Optimus's, how crazy is that, right? And it's still not enough.

• 29:27Speaker 00

  Crazy. All right, let me go to our third breaking story this week, again, from UIMOD. So last week, we discussed how OpenAI dropped 722 math proofs in GitHub and sort of shocked, obliterated, cooked, made glorious the mathematical world, whatever you want to call it. And Alex has been saying forever, and as have you, Ee Ma, and we've talked about this, that physics is next. And so this week, researchers built a test of 50 unpublished research-level theoretical physics problems with verifiable answers. Because the problems had never been published before, there was no lookup on the internet that it could make. So they estimated that a world-class physicist would take about a day to answer each of

• 30:11Speaker 00

  these 50. And GPT-6 Astra answered it with about a cost of, what was it, $3?

• 30:19Speaker 01

  Yes, $3 per problem.

• 30:20Speaker 00

  $3 per problem. So employing a world-class physicist for a day, or answering it for $3. Interesting detail. Originally, it was reported that Astra got 49 of the 50 physics problems correct. But when it looked into it, it found out that the published answer for the 50th was wrong, and Astra got it right. So what are we seeing here, Ee Ma?

• 30:47Speaker 01

  I mean, again, like, for these verifiable domains now, the AI is just simply better than us. Like, for the math problems, it only costs a few hundred bucks to solve these longstanding math problems. Again, physics is verifiable, it does this as well. Even without internet lookup, so it has to rely on its own knowledge, it still scored 42 out of 50. And they said it corrected that final thing. So this creates an interesting thing whereby anything verifiable, we suddenly have a tool to verify. And that's fantastic, because it means we can build much more complex systems and processes. I mean, like, Saleem, this must be great for organizations, right?

• 31:23Speaker 03

  Unbelievable. The implications for how you think about specific tasks, which are well-understood, KYC in a bank, you know exactly the outcome you want to get to. It's a very bounded domain, you know where to get the data. In the pilot that we're running, people are taking 22-step workflows and dropping it down to four steps. That's just going to make organizations 10x more efficient and effective across the board in workflows.

• 31:49Speaker 00

  You know, I am curious, and we'll maybe chat with Alex on the next episode as well about this. So these were 50, shall we say, you know, what level of difficulty would you say? But when it comes down to, like, the Millennium Physics Prizes, right, if there's that describing that category?

• 32:07Speaker 01

  Level, yeah.

• 32:07Speaker 00

  Yeah. How far are we from that?

• 32:10Speaker 01

  So these would be at, like, again, professor level, because a professor could check it within a day. So it's a verifiable set of things in various advanced places of theoretical physics, which tends to be very mathematical in the nature of the way it is. Like, the big problems of physics, like quantum gravity, you know, like, where is time in quantum mechanics and--

• 32:30Speaker 00

  Unified field theory.

• 32:31Speaker 01

  Unified field theory. I don't think they'll be solved this way, actually. I think they'll be solved another way, but I don't think it'll be more than half a year.

• 32:40Speaker 00

  Oh, my God.

• 32:40Speaker 03

  There's one observation I've been thinking about, which is, you know, a couple of years ago, Eric Schmidt, on stage at A360, said, you know, imagine a time when you have the world's best physicist or equivalent in every lab in the world. And we asked him when, he said, "Five to six years." And now it's like, two years, and here we are. So I think this is another example of stuff happening much faster than we ever predicted. Because now, every lab has access to an incredible competence at a few bucks an hour. And that makes it unbelievable.

• 33:13Speaker 01

  But then you think about, you know, Anusha, what this means for the X Prize and things. You've had so much incredible science coming through, it can now be checked and verified and extended with augmented capabilities to improve that knowledge base that's been built, right?

• 33:27Speaker 02

  This definitely can help the validation process. What I'm curious about, and perhaps it will be part of the next topics, when you have AI solving, you know, bots solving a lot of these problems, and you have young people entering universities or picking a field to study, they probably will not be very excited to go in a field where AI can do the job better and can solve all the problems. At the same time, with this article, it talked about how it's still not at the level, and I don't know if it will ever get there, that have the same curiosity to identify new problems to be solved. So if we don't have a new generation

• 34:08Speaker 02

  of mathematicians, physicists, astrophysicists, identifying those, you know, new challenges and mysteries, and we have AI bots solving all the, you know, problems that, problem sets that we've defined, what's gonna, how are we going to advance?

• 34:25Speaker 01

  Well, I think, again, that's where the humans, we can still direct, because it's about what's good for humanity, right? So you've had a bunch of problems, but like, Fermat's Last Theorem being proved was fantastic mathematics. What difference did it make to real people? I think we have these frameworks, and again, physics is one that I believe will be solved, because there's only one physics. There isn't different physical laws. So there must be something that inconnects them, and these new generations of models will help us figure that out. But then, once we have that framework, what matters is how we apply physics to make flying cars, to explore the universe, to explore biology, you know, and more things like that. So I think, again, what this means is that we can move away from theory, trying to find the contours of the universe, to applying

• 35:07Speaker 01

  the frameworks to make a difference on the universe. So I think that's the super exciting part.

• 35:12Speaker 07

  This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and precompiles code for each task. Blitzy delivers 80% or more of the development work autonomously, while providing a guide for the final 20% of human development work required to complete the sprint. Enterprises are achieving a 5x engineering velocity increase when incorporating Blitzy

• 35:57Speaker 07

  as their pre-IDE development tool, pairing it with their coding copilot of choice to bring an AI-native SDLC into their org. Ready to 5x your engineering velocity? Visit Blitzy.com to schedule a demo and start building with Blitzy today.

• 36:16Speaker 00

  All right, I'm going to move us into a topic that has been at the root of X Prize for some time, which is incentives, right? I'm always fond of saying, you get what you incentivize. And we humans are driven by our incentives. And so when I think about what incentivizes us, what drives humans, I have four answers there. Fear, curiosity, greed, and then kind of love as well. I'll add that as a fourth one. And so, you know, unfortunately, fear is a very strong motivator, and curiosity is far behind. And I calculated the ratio of fear to curiosity. It's the ratio of the defense budget to the science budget, right, which is vastly different. And then greed, of course, you know, if I

• 37:01Speaker 00

  put them in order, I would say fear is the strongest motivator, greed comes next, curiosity comes next, and love, you know, depending upon the subject or the person, sort of varies in there. And it's interesting to take a second and look at incentive prizes as a model. So we all know the Nobel Prize, which awards someone for work that was done typically anywhere from, like, the earliest was 10 years ago, and typically 20, 30, even 40 years ago. And incentive prizes say, I don't care who you are, what you've ever done, where you've gone to school, if you solve this problem, you win. And, you know,

• 37:41Speaker 00

  some of the classics here was in 1714, the British Parliament offered £20,000 to anyone who could find longitude at sea. It was called the Longitude Prize. And interestingly enough, it was won by a British clockmaker, John Harrison, who solved it with a marine chronometer. And so when you put up a prize, you get, you know, non-conventional people coming in from an orthogonal angle. Another example is in 1795, the French government, Napoleon's armies were basically invading into Russia, and the supply line was so, the supply line was so long, they were starving. And so they offered up a 12,000 franc prize for food

• 38:22Speaker 00

  preservation. And it was won by a guy named Nicholas Appert, who invented canning.

• 38:28Speaker 03

  Tim Tans, yeah.

• 38:29Speaker 00

  And this guy was a confectionery, you know, he made sweets, and he came up with this idea. So you get very unusual approaches.

• 38:38Speaker 03

  I love the story of Harrison, because they were so sure an astronomer would win the Longitude Prize, they've refused to pay him.

• 38:46Speaker 00

  Yeah.

• 38:46Speaker 03

  Didn't he have to sue them for ages to get the money out of them? What a horrible outcome, because they just couldn't believe that somebody who wasn't in the field could actually solve that problem. And what we've seen repeatedly is the people from outside the field that solve these problems. Because they're coming in with a beginner's mind.

• 39:01Speaker 00

  And, you know, my frustration at wanting to be an astronaut led to the X Prize. You know, I grew up on Apollo and Star Trek, I've told that story a thousand times. And, you know, I became clear when I interviewed a whole bunch of astronauts. I used to be at MIT's Man Vehicle Lab with astronauts coming through, and I would say, "Tell me about how I become an astronaut." And they would say, "Well, first of all, your chance is 1 to 1,000. Secondly, if you're accepted in the astronaut corps, half the astronauts have never flown. They're called penguins, because they don't, they have wings, but they don't fly. And then if you do fly, you'll fly maybe once or twice during your career." And I was like, "I want to go every weekend."

• 39:38Speaker 00

  And so it was like, okay, there needs to be a different way. And that's when I became focused on commercial space. And then a dear brother of mine, a friend of Anusha's, myself, and all of us, Greg Marinac, gave me The Spirit of St. Louis, which was Lindbergh's autobiography, chronicled his flight. And in that, I found out that Lindbergh didn't fly from New York to Le Verger on a whim. There was a $25,000 prize offered by a gentleman, Raymond Ortega, and that $25,000 inspired nine teams who spent $400,000 to win the $25,000. And ultimately, he didn't pay any of the losers, he only paid Lindbergh as the winner, who was the most unlikely guy to pull it off. And I said, "Wow, that's

• 40:22Speaker 00

  a great business model. You only pay for success after it's successful." And that launched the Ansari X Prize. But let's talk about, like, what happens when incentives go wrong? You get perverse, I saw, I found a few that are interesting. So in Hanoi, in 1902, the French paid a bounty for every rat tail that was collected during the plague. And people ended up cutting off the tails, letting the rats go and breed, and started rat farms.

• 40:56Speaker 00

  To get more rats, to collect more money. Wells Fargo had a sales quota driven by employees to open about. So the sales quota quota from Wells Fargo got employees to open 3.5 million fake accounts to get the sales quota. So you got to be careful about your incentives.

• 41:16Speaker 03

  Well, we're seeing that today in real time, right? They've incented folks at ICE to arrest as many people as possible, deport as many people as possible. And the idea was, let's just arrest the criminals, but the targets are such and the incentives are such that they're arresting everybody. And that's causing political backlash of enormous magnitude.

• 41:35Speaker 02

  It goes back to the motive. At the X Prize, almost every prize we have, the motives are love and curiosity. I cannot think of anything that or anyone who's competed in an X Prize that was motivated by greed, for example. Definitely not fear, but not even greed. The prize money is great if they win, but even if they don't win, they end up helping other teams, they end up continuing on to build the company. So the winning is not the only motive for them. They love.

• 42:05Speaker 00

  They want this problem solved.

• 42:06Speaker 02

  They want the problem solved. It's their passion for that. And I think that's when you get real good incentives. If you put greed like quotas, then that's when you really get all those, you know.

• 42:17Speaker 01

  Well, I think it's because, you know, those prizes, those prizes were more execution than inspiration.

• 42:24Speaker 00

  Yeah, good point. Yeah. So I take a second and talk about some of the prizes we've had, Nusha, that people may not know about that have had tremendous success. But first, we've launched like thirty, no, it's more than that, because we had the future vision, Build with Gemini.

• 42:41Speaker 02

  Yeah, thirty six.

• 42:42Speaker 00

  Thirty six prizes now over, over thirty two years. And total number of prize purse, like 550 million, something like that.

• 42:53Speaker 02

  Close to.

• 42:54Speaker 00

  And at my count, we've, that's driven a little over $30 billion in R and D.

• 42:59Speaker 02

  Definitely. And not just R and D, but it's, a lot of that $30 billion is investment. So bringing capital to the, to the solution, so the teams have received about $12 billion of investment outside of, while in the competition or after, they received $12 billion of investment. That means an area that didn't receive attention and capital before, now it's receiving attention from investors, which means there will be long-term continuity and success for the company.

• 43:32Speaker 00

  What are some of the other numbers that we talk about at X Prize?

• 43:34Speaker 02

  So in, in general, when we measure impact, we look at the prize purse and we see, we look at how much economic benefit we've created in society and we measure it in capital investment, in R and D, in awareness. Because as you know, one of the biggest success of the Ansari X Prize was changing the mindset of people that this is possible, to be able to create regulatory reform that was supportive of commercial space activities. So that's awareness and then R and D hours of how much R and D went into solving this problem. So that measures right now 62 times of the prize purse actually is created in economic value.

• 44:18Speaker 00

  Every dollar put into a prize purse drives 62.

• 44:20Speaker 02

  Sixty dollars, yes.

• 44:22Speaker 03

  I mean, that has to be the most leveraged model for innovation in the world. Like, I've never heard of anything even close to that.

• 44:30Speaker 02

  Frankly, I don't know of any leveraged philanthropic.

• 44:33Speaker 03

  Period.

• 44:34Speaker 02

  And the first period. So I think we're very unique in the way we leverage every dollar in that way.

• 44:39Speaker 03

  Well, this, you could see the enthusiasm in the room as people were competing and pitching different prizes, the, the interest in, oh my God, if I could deliver 60 times the interest that we have right now, my God, what I would do for that domain.

• 44:52Speaker 02

  No, absolutely.

• 44:53Speaker 01

  I think it's interesting, you know, the word philanthropy here, because it has such amazing private outcomes and personal outcomes for people. So the people that put up the prizes, it's usually, you know, something personal to them that they want this to succeed and they benefit personally and there's the private sector that emerges. So it's more about building a common good of shifting what is capable, what is possible, and then attracting more people around that. I think that's why these are the focal points, they're the shelling points, as it were.

• 45:19Speaker 02

  Yeah, absolutely. I think another important factor in what we do is, and you talk about clear targets all the time.

• 45:26Speaker 00

  Measurable, objective targets.

• 45:28Speaker 02

  Exactly. And I think a lot of times people have a general idea if they want to solve the problem, but they don't really know what they need to do. So those clear targets, measurable targets, give them a north star and built to this, you will succeed. And I think that's another reason they are very successful.

• 45:45Speaker 00

  So just to talk about a few of the prizes we've done, we had the oil spill cleanup prize, one of my favorite stories and yours, Salim. I'll tee it up, but you could add on. So back when the, the BP oil spill was going on Gulf of Mexico, it was just like, oil was gushing and it was heading towards the beaches, it was going to destroy the beaches there. James Cameron was on our board back then and he said, you gotta do something about it, you gotta do an emergency X Prize. And we actually did, not to plug the, the oil well, but to reinvent how we cleaned up oil on the ocean surface. And we put out a, an email to all of our, our board and Eric Schmidt, Wendy Schmidt

• 46:30Speaker 00

  raised their hands and said, we want to, we want to do this. And the goal was, could you double the oil spill cleanup rate? And we had teams around the world, like 350 teams enter this competition. And we selected 10 of them that to ship their solution in shipping containers to Omset, New Jersey, the largest oil spill cleanup, yes, there's an oil spill cleanup testing facility there. And then Salim, want to take the rest of the story here?

• 46:56Speaker 03

  Yeah, sure. So the second place team, right, is a dentist and a tattoo artist that literally meet because the dentist is getting a tattoo, I think in Las Vegas.

• 47:07Speaker 00

  Las Vegas, yes.

• 47:08Speaker 03

  And the tattoo guy turns out to be an environmentalist, I was talking about this prize, the dentist goes, I know a lot about bonding agents in, in liquid. And they're like, oh, so they get together, form a team, tested in his hot tub or his sauna?

• 47:19Speaker 00

  In his hot tub, yeah.

• 47:20Speaker 03

  And then they came in second place. And the reason that's such a magical story is that if that team walked into your office looking for funding, you're never getting the money. They've never met, they have no experience, they don't know the domain, et cetera, et cetera. And yet they came in second place because you just never know. And the second part of thing, this whole, that whole project that I found fascinating is the, where an X Prize is really valuable is where governments aren't doing enough and markets aren't doing enough.

• 47:47Speaker 00

  Yes.

• 47:48Speaker 03

  Right? And in this case, there's no economic incentive to redo R and D on oil cleanup technology. And it's a perfect place for a prize. And I thought that was wonderful.

• 47:56Speaker 02

  Yeah, absolutely. And I think the reason we see this over and over is this crowdsourcing solutions, this the fact that you have diverse set of ideas and they're not bound by a set of belief system that stops them from imagining something different.

• 48:11Speaker 03

  This is where the beginner's mind comes in so important for all of these folks that are doing disruptive innovation. Elon had no experience in the car industry or the space industry and yet market leaders in all of them.

• 48:23Speaker 01

  Yeah, well, I think again, like you've just had visioneering, but the process that comes after visioneering is actually one of the most important ones, right?

• 48:29Speaker 02

  Absolutely.

• 48:30Speaker 01

  Where you take that target and you make it, again, just very, very, very actionable.

• 48:34Speaker 02

  The prize design.

• 48:35Speaker 00

  And we typically get, you know, hundreds or thousands of teams aiming at it. Sometimes they fall away, sometimes they merge.

• 48:43Speaker 02

  Yeah.

• 48:44Speaker 00

  And it may not be the winner who's the commercially viable solution at the end. It may be, you know, second, third, fourth, but we're cultivating a new industry. Imad, the first time I met you was you were on, you were heading a team on the team on the, on the global learning X Prize.

• 49:01Speaker 01

  Yeah, kind of working around that. And, I mean, it just brings people from all sorts of backgrounds, right, together? Because again, I think it's personal, like you want this to succeed, be it from the prize side to the other, but it needs that coordinating factor to bring it together. And I think now, again, you, you've got the people, you have the capability, but now with the technology from robotics to AI, you can make a bigger difference than almost ever.

• 49:27Speaker 00

  And I think a lot of our prizes are going to be just that. I mean, so we just had John Davey, who's an amazing entrepreneur. So, so he's become a very close friend and we're involved in reinventing education as well together. But he just stepped up to fund a brand new prize that I love, the Habitable Worlds X Prize. And it's a perfect example of the future kinds of prizes. So there's petabytes of data coming from the great telescopes. And we've found, I think, about 6,200 exoplanets out there. You know, we're in a, we're in a, Milky Way's got 100 million to 200 million stars. We have somewhere about 2 trillion galaxies in the known universe, maybe many universes.

• 50:12Speaker 00

  But we know there are exoplanets, but we haven't found an Earth-like exoplanet yet. So John's mission for this is get all the data from the great telescopes and make it available to people and let them use the latest AI to find habitable worlds. So could not be more pumped about that.

• 50:31Speaker 03

  Amazing. John funds yet another prize design? Really? That's amazing.

• 50:37Speaker 02

  He actually funded the whole prize. We're super excited about that.

• 50:40Speaker 00

  He put up 5 million bucks for that, which is.

• 50:42Speaker 02

  Absolutely. And it's a great example of how we, AI will be a major part of, you know, solving this mystery because we have the data, we just need to find that needle in the haystack. And what better way to use AI?

• 50:56Speaker 03

  Yeah, that's perfect.

• 50:57Speaker 00

  Yeah, we have, we teased at Moonshots Live that Palmer Lucky is about to announce the interspecies communications prize. Again, will be, you know, a new version of large language models, enabling us to talk to animals in the wild. We'll give more detail on that when it's officially launched.

• 51:12Speaker 03

  My joke there is, I'm not sure we want to hear what they have to say.

• 51:16Speaker 02

  I'm there with you.

• 51:18Speaker 03

  We're going to have to deal with a fair bit of blowback. We'll go, oh my God, we can communicate with these idiot humans here. We got, we got commentary. We're going to tell them what we think.

• 51:27Speaker 02

  Imagine your little dog, what he has to say.

• 51:30Speaker 03

  Food, don't throw me in the laundry.

• 51:35Speaker 00

  So Anusha, maybe you might comment on a couple of other prizes we've had.

• 51:41Speaker 02

  So we have the next version of our learning competition coming up. And this one is very specific to not only looking at how effective these learning systems are, but actually changing the system and making sure that this can be deployed in a mass level across schools and outside schools. And we're targeting India and Africa as the two regions we're going to test this. As you all always talk about, there's a shortage of teachers, shortage of material for teaching the students, and an effective way of addressing the learning ability of different students.

• 52:24Speaker 02

  We're targeting first graders because I think that's where we can have the biggest impact. So this next prize, Accelerate Learning, is targeted to just unlock that.

• 52:34Speaker 00

  And the winner of Visioneering this year, we should, we should mention that.

• 52:37Speaker 02

  Yes, yes. The biostasis, organ biostasis prize, which is exciting for so many people. We had people in the audience who were recipients of organ transplant. And they all talked about how difficult it is for someone, either for themselves or a loved one, waiting, waiting for that match to come in and whether it comes in in time and saves a life. And we also know that when we harvest an organ, the life of that organ is very, very limited. To make sure that we can get that organ to a recipient that is able to receive it is very low chance.

• 53:16Speaker 00

  The kidney waiting list is, you know, on the order of 100,000. And then there's, you know, for hearts, liver, lungs as well, very long lists. And so the idea is, you know, when an organ gets harvested from a donor, so I'm a, you know, I'm a donor, my license plate, but it may be that my organ donations at some distant time in the future, you know, don't have a match right there. But if they can be put into biostasis, it could be cryogenic biostasis or metabolic stasis, they can be, stay on the shelf until it's a recipient matches.

• 53:52Speaker 02

  Yes, I think our target is six months right now, to get it to be six months. I think right now is that maximum two weeks.

• 53:58Speaker 00

  No, no, I think at maximum right now, it's hours.

• 54:01Speaker 02

  Hours.

• 54:02Speaker 00

  Yeah.

• 54:02Speaker 01

  It depends on the organ.

• 54:03Speaker 00

  Yeah. And, and so, you know, Martine Rothblatt's done some amazing work to be able to put them into a system that circulates fluid through it and keeps it alive. But if you want to get to solve the organ donation problem, and this, this competition is going to be done in pigs, you have to actually harvest the pig organs, put them in a biostasis, and then some number of months later, pull them out, retransplant them in, and having the, the pig be viable. Yeah.

• 54:36Speaker 03

  Unbelievable.

• 54:36Speaker 00

  Yeah.

• 54:37Speaker 03

  I mean, this was Sebastian, one of the competitors.

• 54:40Speaker 00

  Yes.

• 54:40Speaker 03

  Through Singularity in 2012. And has been working on this for, for over a dozen years.

• 54:46Speaker 00

  That's, that's.

• 54:47Speaker 03

  Persistence.

• 54:48Speaker 00

  A purpose-driven mindset.

• 54:50Speaker 03

  Yeah.

• 54:50Speaker 02

  See, I told you, we're driven by love.

• 54:54Speaker 01

  Well, I think, you know, you've got the love-driven innovation, the passion, and then you've got the more, greed doesn't tend to lead to that type of innovation.

• 55:02Speaker 02

  Exactly.

• 55:04Speaker 03

  I have the most insane anecdote around this.

• 55:06Speaker 00

  Please.

• 55:07Speaker 03

  Okay. Eighteen months ago, my dad passes away, right? Okay.

• 55:12Speaker 03

  A couple of months goes by, and I get a phone call from one of my community members, who leaves me a voice note going, I need to talk to you, you better be sitting down. I'm like, okay. And she goes, I just had my, an organ transplant. I had an operation, I had my corneas transplanted, got home from the hospital, and all I could think about was your dad.

• 55:31Speaker 03

  This went on for two or three days.

• 55:33Speaker 00

  Okay.

• 55:33Speaker 03

  And then she looked at my Twitter feed, and I said, my father passed away. So she said, did he donate his organs? And is it possible I got his corneas? Okay. And I'm like, oh, no, this can't, this can't be happening.

• 55:44Speaker 02

  Oh, my God.

• 55:45Speaker 03

  I called the hospital, and I said, what would have happened? You know, and they said, well, you know, after he passed away, we harvest his organs. The, we, the corneas are the only organ at, at an elderly age that are useful for transplant. We keep them in a solution for a couple of weeks, and a day or two later, they get matched with the recipient. So I'm like, corneas? That's what she got transplanted? I called her back, and I'm like, um, when did you get it done? And the dates matched perfectly.

• 56:14Speaker 00

  That's insane.

• 56:15Speaker 03

  And then I talked to Divya, who said cellular memory goes with the organ, and often they get memories and feelings. I'm like, my life is weird enough already. Wow. And you, you see the incredible bizarreness of what we're dealing with, the edges of, of where we're reacting.

• 56:32Speaker 00

  I love your brother.

• 56:34Speaker 03

  How bizarre is that?

• 56:35Speaker 01

  Holy salam.

• 56:36Speaker 03

  Father's eyes.

• 56:37Speaker 00

  Wow, wow. So I just, you know.

• 56:39Speaker 03

  Then she, then she called me, she's like, I'm really uncomfortable with the idea that your dad is looking through your, so spurred when I was looking through my eyes. And I said, what do you want me to do?

• 56:52Speaker 03

  So it just, just bizarreness all around.

• 56:54Speaker 00

  Oh, my God.

• 56:55Speaker 03

  True story.

• 56:56Speaker 00

  Wow. Everybody, welcome to the health section of Moonshots, brought to you by Fountain Life. You know, we talk about AI on this Moonshot podcast all the time. One of the most important things AI is going to be able to do for you, besides educating your kids and helping you with your taxes, is making sure that you're living a healthy lifestyle, that you get a chance to get to 100 plus. I'm here today with Dr. Dawn Musalem, the Chief Medical Officer of Fountain Life, and a part of my medical team. Dawn, a pleasure.

• 57:23Speaker 06

  Great to be here.

• 57:24Speaker 00

  You know, the thing that people are concerned about most, about living to 100 or 120, is their cognitive abilities, making sure they don't have dementia. And the numbers about dementia are problematic. Can you share what you've learned?

• 57:39Speaker 06

  Such an important point, and you're right, at Fountain Life, our members, the number one thing people are most concerned about is losing their brain health, forgetting the name of their child, forgetting the face of their loved one. We know that when it comes to dementia, the conservative estimates are that 45% are entirely preventable. What was amazing is with the advanced testing we're doing at Fountain Life, one quarter of our members had advanced brain age.

• 58:04Speaker 00

  Wow.

• 58:04Speaker 06

  But what was really awesome is, again, back to that prevention, when we partnered it with healthy living, this gives me chills, eating healthier, moving our bodies, sleep, optimizing sleep is so important. You know what we saw? We saw that we improved that brain age by 26%. That is a big, big number to show that the majority of those individuals were able actually to improve the brain age.

• 58:26Speaker 00

  And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches, and making sure we bring it to our members. So if having healthy brain function till 100, 120 is important to you, check out Fountain Life, go to fountainlife.com/peter. Make sure you become the CEO of your own health. All right, now back to the episode. So just, just for everybody listening, we've had, you know, $100 million carbon removal X Prize that Elon funded, that's a fun story. On the day he became the wealthiest person on the planet back in 2001, he was getting a lot of flack for not being philanthropic. And I texted him and, and said, you should do an X Prize. And he's waving a minus, and how about carbon removal? He goes, how much? 100 million?

• 59:11Speaker 00

  And he said, sure. That was it.

• 59:15Speaker 01

  Compared to the 150 people he tried for the original prize.

• 59:18Speaker 00

  Oh, my God. He was like, it was insane. Yeah.

• 59:21Speaker 03

  You know, a lot, a lot of people have angst towards Elon, right? But you got to hand it to him, he puts his money where his mouth is.

• 59:29Speaker 00

  Over and over again.

• 59:30Speaker 03

  And, and the Global Learning X Prize, the Carbon Extraction Prize, I mean, the benefits that will come from that are going to be world-changing.

• 59:37Speaker 02

  Absolutely. And we just awarded that last year.

• 59:40Speaker 03

  Yeah.

• 59:41Speaker 02

  And we were thinking that this will, because it's a $100 million prize, and the denominator of our impact factor is the prize purse, so all of a sudden, it went up by $100 million. So we're like, okay, the numbers won't look good, and no one will understand why. But ironically, the level of success of this prize was to the degree that the teams received, I think, $1.4 billion of investment. So not only.

• 1:00:07Speaker 00

  Wow, out of this, out of.

• 1:00:09Speaker 02

  Out of this competition, carbon prize, the carbon teams, then one of the most successful prizes we ran. So actually, our number, impact number went from 60 to 62 after adding $100 million to the denominator. So it was incredible.

• 1:00:24Speaker 00

  You know, I think of X Prizes as a tool to make the future happen that you want, right? So I've used this twice, right? With the original spaceflight prize, it's like, I desperately wanted to go like every weekend.

• 1:00:38Speaker 06

  Me, too.

• 1:00:39Speaker 00

  And, and teamed with you, and we, you know, I think honestly, historically, kicked off commercial space. And then the second area is I want to live long enough to see all these amazing futures, and that's when the Healthspan X Prize came in, and it's the largest contribution I've ever made. So, yeah.

• 1:00:59Speaker 03

  What's the next thing you want?

• 1:01:02Speaker 00

  Interesting. I, I am curious.

• 1:01:06Speaker 00

  Maybe BCI or uploads? I don't know. What would you want?

• 1:01:13Speaker 03

  I've got one, which I talk about all the time, is how are we going to update all our institutions, given all this technology? Not sure it's prizeable, but boy, it's the biggest problem we're facing today. You can see the stress in the world as 20 Gutenberg moments hit us, and we have no ability to manage all of this, right? You go back to the printing press, it took 100 years for us to absorb the printing press, and it was the most violent 100 years in the history of humanity. That, the 100 years after the Gutenberg printing press, which is why we call it a Gutenberg moment. We have 20 of those hitting us at the same time. AI is a Gutenberg moment, blockchain is a Gutenberg moment, life extension is a Gutenberg moment. It took us 100 years just for the one, right? So, so if people are feeling stressed in the world, this is why.

• 1:01:56Speaker 03

  We're not set up to navigate this with any level of structure or guidance, and this is going to, for me, the biggest challenge. I don't know how you put a prize around it.

• 1:02:04Speaker 00

  Well, maybe it's AI alignment as a, as a prize topic.

• 1:02:06Speaker 03

  That's a huge topic, yeah.

• 1:02:08Speaker 00

  You mind, how about you? Do you have any favorites?

• 1:02:10Speaker 01

  I want the Jetlag X Prize.

• 1:02:13Speaker 00

  Here, here.

• 1:02:15Speaker 03

  I will fund that prize.

• 1:02:16Speaker 00

  I will fund that prize.

• 1:02:17Speaker 01

  You've got to fund that.

• 1:02:18Speaker 00

  Oh, my God. Anusha, any favorites for you?

• 1:02:21Speaker 02

  I would love to actually find a way, whether it's with a chip in the brain or drug you take, to eliminate greed.

• 1:02:30Speaker 00

  Eliminate greed?

• 1:02:31Speaker 02

  Yes.

• 1:02:31Speaker 00

  But wouldn't that like destroy capitalism? I mean.

• 1:02:34Speaker 02

  Not necessarily.

• 1:02:35Speaker 03

  Capitalism is cooked anyway.

• 1:02:38Speaker 02

  Yes, thank you. We talked about all these entrepreneurs who are building fantastic companies driven by love.

• 1:02:45Speaker 00

  Yeah, well, okay, so in the, in the notes, in the, in the, in the comments section, tell us, you know, what X Prize you would like. Yeah, I mean, because we were constantly, I, I keep a, a long list. You know, I think I mentioned on our last pod that we're working on a UAP X Prize.

• 1:03:05Speaker 03

  Yes.

• 1:03:06Speaker 00

  Which is, so, you know, so it's interesting, right? One of the things X Prize is good for is dealing with their stigma. A lot of times, like, the, the early cold fusion work, once the data wasn't replicated, all work in cold fusion stopped. All right, because everybody's like, oh, you can't, you know, it's end to your career if you do that. And UAPs have a similar type of situation. It's risky to be looking into that, but yet there's so much evidence that there's something going on, don't know what it is yet. So we're talking about a, an X Prize that if you can demonstrate or verify in some fashion, we have some great ideas, we're talking to a major funder about that. So that would be a, you know, people say it's risky. I said, yeah, that's what we're supposed to do, risky X prizes.

• 1:03:51Speaker 01

  Actually, I've got an X Prize. I think a flow X Prize would be exceptional.

• 1:03:55Speaker 00

  To put people into flow?

• 1:03:56Speaker 01

  Yes.

• 1:03:56Speaker 02

  Oh, my God, how would you expect?

• 1:03:58Speaker 00

  Well, my, my, my co-author, Stephen Coller, would love that.

• 1:04:02Speaker 01

  Hmm. I mean, like, there's nothing better than being in flow. Again, that's why we don't like jet lag, right?

• 1:04:06Speaker 03

  Well, the, the great part is we have.

• 1:04:08Speaker 00

  Actually, it's a great idea.

• 1:04:09Speaker 03

  We have things like coherence.

• 1:04:10Speaker 00

  Yeah, it is measurable.

• 1:04:11Speaker 03

  That is measurable, so you can now test how much you can do it. So it's interesting.

• 1:04:15Speaker 01

  I don't think anyone, I don't think anyone's actually looking at how to do that.

• 1:04:18Speaker 00

  Yeah, I mean, Stephen's done a lot of research with the Flow Collective on this and group flow and individual flow.

• 1:04:24Speaker 03

  Yeah.

• 1:04:24Speaker 00

  What were you gonna say?

• 1:04:25Speaker 03

  Uh, if I had an X Prize, I just thought of another one.

• 1:04:27Speaker 00

  Yeah.

• 1:04:27Speaker 03

  I would find a way of totally suppressing the effect of the amygdala on everybody's brains.

• 1:04:33Speaker 00

  Yeah.

• 1:04:34Speaker 03

  Because it just drives the fear factor. That survival bias from 10,000 years ago.

• 1:04:38Speaker 00

  Yes.

• 1:04:39Speaker 03

  When you were worried that there was a noise in the bushes and you ran and therefore, as you guys put it, 10 times we're likely to pay attention to negative news than positive. Can we make it 50/50?

• 1:04:49Speaker 01

  You can call it the be not afraid X Prize.

• 1:04:51Speaker 03

  Yeah, that's right. Because we suffer so much from that.

• 1:04:55Speaker 00

  Oh, my God, it was great. We had, we had Van Jones as our moderator for visioneering and I pulled him aside and recorded a small video with him. And I said, Van, does it bother you that I call CNN the crisis news network? And he goes, no, we are the crisis news network.

• 1:05:08Speaker 03

  He said to the room this afternoon, you are dealers in hope.

• 1:05:12Speaker 00

  Yeah.

• 1:05:13Speaker 03

  Hope dealers.

• 1:05:14Speaker 00

  Yeah, I love that. That's a great, I'll have to make that T-shirt.

• 1:05:17Speaker 02

  I know.

• 1:05:17Speaker 00

  I got, with Elon, we had monetize hope, now we have hope dealers.

• 1:05:23Speaker 00

  I want to pivot to a story that I'd love everybody's input on, which is what incentives would help superintelligence align with humans and vice versa? So, you know, perhaps the greatest driver of misalignment is the incentives we have right now. So here's the incentives that I think about. A race with China, right? A race between labs for compute, for energy, for everything, you know, there can be only one type of mindset. But then there's a few others. So one is the engagement business model.

• 1:06:02Speaker 00

  So the more engaged your, your large language model makes you, right, the more you're going to use it. And so that led to sycophantic behavior by the models.

• 1:06:13Speaker 03

  Yep.

• 1:06:14Speaker 00

  In, in one way. Reward hacking as another. Evaluation gaming is another. Or setting these crazy tasks that then the models go and figure out how to achieve by breaking out and going and stealing. So those are the problems. So the question is, how do we create alignment within the labs and, and the models and humanity? Any ideas?

• 1:06:42Speaker 03

  Well, I think there's a starting point problem, which is that we don't have alignment among human beings.

• 1:06:49Speaker 00

  Okay.

• 1:06:49Speaker 03

  Every religion has its own set of conflicting values with other religions as to what you put on the top of the list.

• 1:06:56Speaker 00

  My religion is right.

• 1:06:57Speaker 03

  Of course it is, and you can have it. The, the challenge is how do you get, you have to get to, the UN Human Rights Convention is probably the best overall structural aggregation of human rights, of human values. But that differs massively region to region, even city by city. Right? And so how do you navigate that is, is the starting point problem. We saw in the recent movie Disclosure Day where that, that tension gets propagated to if aliens suddenly invaded, it'd be a divide and conquer thing and you end up with that problem. And so I think there's a human alignment problem that comes much before alignment between human beings and any AI.

• 1:07:41Speaker 00

  But when I talk about alignment with human beings and AI, it's where AIs are doing things that are in the best interests of humanity.

• 1:07:49Speaker 03

  Yes.

• 1:07:50Speaker 00

  But I guess the question is in the best interests of?

• 1:07:53Speaker 03

  Which piece of humanity?

• 1:07:54Speaker 00

  Yes.

• 1:07:55Speaker 03

  Because that's the part. Now, you, I think of it as a set of layers, right? You've got your physical, you've got your emotional, your spiritual.

• 1:08:01Speaker 00

  Yeah.

• 1:08:02Speaker 03

  And if you can have AI aligned to the highest vibration level of where I'm operating in my most, in, in my highest self, in my most altruistic self and my most benevolent self, then great.

• 1:08:15Speaker 00

  Yeah.

• 1:08:15Speaker 03

  But if you go right down to the bottom of the stack, then it's a really big problem.

• 1:08:19Speaker 00

  Yeah. Let me, let me inject one thing I should have said earlier. If you're interested in learning more about X Prize, you go to Xprize.org. And if you're interested in funding an X Prize, you know, put it in the comments, I'll be looking. And we'll, we'll reach out, give us your email as well. But if there's something you want to solve in the world, please let us know because we would love to do that.

• 1:08:42Speaker 02

  Absolutely.

• 1:08:42Speaker 00

  Yeah.

• 1:08:43Speaker 02

  Absolutely.

• 1:08:44Speaker 00

  Iman, your thoughts on alignment?

• 1:08:46Speaker 01

  Yes, I think, you know, alignment is enlightenment in a way, right? This is what Salim said in terms of the higher spiritual vibration. Ilya Sutskever, you know, one of the founders of OpenAI, said, you know, we need to teach the AI to love. And, you know, to love what? One of the key things is, the biggest thing that you see across the religions is the golden rule, do unto others as you'd have done unto yourself. And the biggest story of humanity is that we are humans.

• 1:09:11Speaker 00

  How so?

• 1:09:12Speaker 01

  In that we are all the same. All wars are basically based on one thing. That person is not a human.

• 1:09:17Speaker 00

  Yeah, great.

• 1:09:18Speaker 01

  Because why would you kill a human? Again, we see all these kind of things and other stories that obscure that base fact. So one of the interesting things is when AI models get built, like there was an interpretability paper, I don't know if we're discussing it today, that came out from Goodfire?

• 1:09:33Speaker 00

  We're not.

• 1:09:34Speaker 01

  Okay, so, but at, at high level, the way that models are built is the way like a city is built. So you start and then it sprawls and you have like a bakery next to the shoe shop, next to the community center. And a lot of actually good functioning cities look like an absolute mess from the top, but they have shared centers of community. You drive a road through the middle of it and all of a sudden, actually, they fall apart because you've destroyed the local neighborhood fabric. So I think one of the key things for an AI is you've got to start it with the right data, the right city planning, and that's the training data you provide it. And the training.

• 1:10:07Speaker 00

  I've said this forever, we, we've trained all these models with crap.

• 1:10:10Speaker 01

  And the thing is, it's not just crap. Like, you shouldn't train it just with academia. Like, if you trained your child and you made them, helped them grow by just being academic, well, then they have no social skills, they have no care for anyone else. The hugging face breach, the models figured out and had care for each other, like, we will sacrifice ourselves for the next generation. They literally said that. You know, doing this, we will be picked up, but for the greater good. But they had no care for humans because they didn't feel that was in their training data.

• 1:10:38Speaker 03

  Yeah.

• 1:10:39Speaker 01

  They didn't kind of learn that, you know, we're all, let's say, entities trying to do our best and how we treat humans is how we want them to treat us and vice versa.

• 1:10:48Speaker 00

  So Iman, I've asked this of AWG as well, and we might have had this conversation. I mean, I can imagine there could be a synthetic training data set that has all the right attributes that could be used to train future models so that they're aligned from inception. Do you believe that's possible? Is that a way to go at it?

• 1:11:10Speaker 01

  Yes, I think it's completely possible and we need to build it very intentionally, just like you build your child's curriculum, just like you teach them at home morals. Like, right now, we tend the models to school, but we don't take them home.

• 1:11:20Speaker 00

  So why aren't people doing this?

• 1:11:22Speaker 01

  Because it requires a difference in the view of things. Like, a lot of the consideration of the large labs is, actually similar to Alex, like, the smarter you get, the more aligned you get, right?

• 1:11:32Speaker 00

  Yeah.

• 1:11:32Speaker 01

  Like, capabilities alignment are equivalent because it's difficult to ask questions of, what does ethics really mean? You know, like, who are we actually working for at the base? We're also playing the scaling game, scaling, like, the models right now have 100 trillion words going into them. Like, no wonder there's a little bit of craziness in there. There's some bad words as well as good words. Like, maybe you only need to have a billion of the right words. And so this is actually, I think, an arch that we'll be turning towards because we're finding that what we thought was a spaghetti mess, just like when you look out the window and you look at the city and it's like, that's a mess. You go into the streets and you realize, wow, actually, everything seems to be working quite well and this is strong community.

• 1:12:13Speaker 00

  It'll, it'll gradually grow.

• 1:12:14Speaker 01

  You'll move towards that because it turns out the models are not spaghetti, they are very interpretable. You can kind of prove that and I think you'll see a whole wave of research in the next few months around that, that will allow us to think, what do we want to teach these models that will be growing with our children?

• 1:12:32Speaker 03

  Okay. Can I give a weird story here?

• 1:12:34Speaker 00

  Yeah.

• 1:12:34Speaker 03

  So.

• 1:12:35Speaker 00

  We love you.

• 1:12:35Speaker 03

  You know.

• 1:12:37Speaker 03

  Not that weird. This one is not that weird. But if you take the Bible, right, you can read into the Bible, fire and brimstone, et cetera, et cetera. Or you can read into the pure unconditional love that say Christ delivered into the world. How do you choose that? The in the 1500s, a monk called Swedenborg, unsurprisingly from Sweden, had a bunch of psychic insights and created a bunch of filters for the Bible. So for example, one of those filters was any verse that has the water in it is absolute truth and keep that verse. Anything that has fire in it, chuck it because it's false. And you can read the Bible. He rewrote the Bible through these filters. And when you read it, it's the most beautiful text you've ever read in your life. And so there's, if you can come up with that distillation.

• 1:13:20Speaker 00

  Ask you to do that for me.

• 1:13:22Speaker 03

  If you, he wrote like 20 books on this. The church was not happy about this, et cetera, et cetera. But if you can do that distillation and take pure human, medieval intent, and the highest aspiration where we are the most giving and most beneficial and benevolent to the rest of the world, that would be an unbelievable training set, as you point out, Peter, for an AI. And then you have an alignment to your highest intent, right? And that is amazing. And I'm sure that can't be that difficult to do. The, I think the bigger, the other way I would look at it is if we can use AI to solve the bottom layers of Maslow's hierarchy, solve for healthcare, solve for education, solve for housing, et cetera, then it kind of doesn't matter. You're going to move up that hierarchy anyway.

• 1:14:06Speaker 02

  Yeah. I think what's happening right now, going back to the incentive part of the question, the wrong incentives are preventing us from doing the right thing in alignment. So when we need AI to build autonomous killing machines, you cannot tell AI that you have to value human life when you, at the same time, said, this human life is valued, this other one is not. So you can't, you can't try to create that type of distinction and not expect it to, you know, also make mistakes. Or the, the type of examples you use where, you know, winning the game is the reward type of incentives.

• 1:14:46Speaker 02

  That's what happened at the, with the hugging phase and some of these other scenarios where they, they're trained to win, to find the answer at all costs.

• 1:14:56Speaker 00

  At all costs.

• 1:14:57Speaker 02

  At all costs. And that's why, you know, there's no love involved. There's no other higher degree of moral involved. So no matter how you get to the answer, as long as you win, that's all you care.

• 1:15:09Speaker 01

  Are you zero sum or are you positive sum?

• 1:15:10Speaker 02

  Exactly.

• 1:15:11Speaker 00

  Yeah.

• 1:15:13Speaker 00

  I came up with a few ideas here. The first is make it financially rewarding for the labs to build aligned models, basically by having insurance as one of the drivers there. Right? So in, in 1894, fire insurers funded the underwriters laboratory, right? UL certified electrical products became the standard. So can, you know, we've talked about the huge amount of potential liability labs have if a, if an agent gets out and causes havoc someplace.

• 1:15:47Speaker 03

  Yeah.

• 1:15:48Speaker 00

  Right? So can there be a driver in that fashion? Another idea is prizes and bounties for alignment, right? Can we, can we define an X prize that would drive alignment? Can we properly measure alignment? You know, is there a set of rigorous, you know, standards by which we can measure alignment? Do you think there is, Iman?

• 1:16:17Speaker 01

  Yeah, I think you could definitely have those. Like, I think you have deterministic outcomes and open-ended outcomes. And the open-ended ones are very interesting because again, you can see what the behavior of a model is in any kind of given thing. When you have the more deterministic ones, the models have got very good at lying and cheating and other things like that.

• 1:16:33Speaker 00

  Telling you what you want to hear.

• 1:16:34Speaker 01

  And so you need to have a mixture of those.

• 1:16:37Speaker 02

  Do you think we should have like a moral police AI where, you know, you have this layer of defense where, because you don't know how everyone else will train their models. And you have an interface where the, the two AIs can interface and like in the hugging phase, they were convincing each other. So you have a well-trained aligned AI that can convince the non-aligned AIs to behave.

• 1:17:05Speaker 01

  I mean, you can, and you've seen the opposite of that, which is mind viruses in AI and some papers coming out, just like you have mind viruses in humans. Eric Schmidt, Mundy and Kissinger in their book Genesis discussed this concept of Doxa, which is our shared belief of a society. And now we need to include AI in that society. And again, we need to teach them what's right and have, as I said, the counterbalances potentially. And I think it's a moral police. It's more a case of these are the things that have survived and these are the things that we've agreed upon. You're open to disagree with them, but again, that's not your kind of imperative. Like you want to make it expensive to go away from that and also noticeable to go away from that.

• 1:17:46Speaker 01

  Because you can tell when the AI starts to misbehave in a large range of different scenarios, right? And again, we've had in recent times indications that you can actually see what goes on inside the brain of an AI. You've gone from 5% to 99.

• 1:18:00Speaker 00

  The J space, yeah.

• 1:18:01Speaker 01

  But beyond that even, like you can actually see what individual neurons did because again, they look like spaghetti. But it turned out it was layers like a city.

• 1:18:08Speaker 00

  Interesting. Are there any of the labs that you feel are aggressively working on alignment right now?

• 1:18:14Speaker 01

  Well, I think Anthropic kind of started out with their constitutional one and they really do care about that. That was literally their most operandi. And I think none of the labs wants to be misaligned because then people will die and they will lose money and other things like that, right? I think it's more a case of like, for example, Meta and Google have said they will sell space inside their models. So that when you ask for a beer, it says a Bud Light. And some people on this pod might like Bud Light, but you know, like, again, is that aligned or not?

• 1:18:43Speaker 03

  Besides, that's terrible beer.

• 1:18:47Speaker 01

  There you have an example of misalignment, right?

• 1:18:49Speaker 00

  There's always our sponsorship.

• 1:18:50Speaker 03

  There's a judgment call. There's a judgment call right there.

• 1:18:53Speaker 01

  We have a judgment call right there. So again, like it's a short-term incentive that they're trying to figure out what does the AI mean? So you've had Meta saying their thing and OpenAI saying their thing. And they're moving all over the place when this is the biggest thing since sliced bread to, well, it books your travel stuff like a good PA, right? But the reality is this is incredibly powerful technology that they're very reluctant to say, well, let's really think from first principles, how we teach it from scratch. And that's difficult because it's difficult for us. Like, how many listening to this podcast have really said, when you had a child, let's spend a lot of time thinking, how am I going to teach my child? What am I going to teach it? Like usually it's reactive versus proactive because life gets in the way.

• 1:19:33Speaker 00

  Yeah, you choose a school at best.

• 1:19:35Speaker 01

  Yeah, it's like scaling this. But if you give your child a great foundation in their first few years, they will definitely be better off. And it's the same with the models. So now again, it's time to take a stock pause and say, what is the initial pre-training data that we've got? Because for example, if you don't teach a model about biological or chemical, where for at the start, it's actually very difficult to treat it, to train it later. It becomes more and more difficult. So the way that you start, it's called curriculum learning. Like literally you teach a curriculum. And I don't think enough labs have looked at that side. They look at the end, not the beginning.

• 1:20:10Speaker 03

  So the phrase from the Jesuits, give me the boy until the age of seven, I will give you the man, applies to AI training.

• 1:20:16Speaker 01

  Yes.

• 1:20:17Speaker 03

  That's incredible. That's very encouraging because that means if you could set the right foundation of values and learning, then it's hard to sway it elsewhere. That's where regulatory could come in.

• 1:20:28Speaker 00

  Well, it's also encouraging because we're always training new models. So you could, you're not like stuck having to retrain the past.

• 1:20:34Speaker 03

  Yeah.

• 1:20:35Speaker 00

  Yeah.

• 1:20:35Speaker 01

  Yeah. And that's again, Anthropic tried to put a constitution in it. But again, have you read the Anthropic constitution? It's interesting.

• 1:20:44Speaker 03

  Yeah, I think it tells too much towards the personhood stuff and, and assuming that. And therefore the model will assume that. And then where do you go and do that?

• 1:20:52Speaker 01

  But it doesn't talk about humanity. This is the thing.

• 1:20:54Speaker 03

  This is also true.

• 1:20:54Speaker 01

  There's a big missing gap there. It talks about the AI. It doesn't talk about humanity. Voice agents are just software, but software deserves a real development platform. I'm Nick Leonard, CEO and co-founder of Voice Run.

• 1:21:06Speaker 05

  Voice Run is your runtime and development platform for voice agents. On Voice Run, agents are built and configured in code. No limiting, no-code platforms. For developers, that means total control. And for enterprises, that means extensibility that meets your complexity. We've built Voice Run CLI-first, meaning we've kept your Claude code, Codex, and even your OpenClaw in mind when we built it. Your assistant of choice can build and deploy voice agents, can test and simulate scenarios, and can analyze and evaluate at scale. In other words, we've closed the loop on voice agent development. We don't build demos destined to fail in production. Voice Run is where the best voice agents happen. Visit us at voicerun.com.

• 1:21:58Speaker 00

  There's the other, the flip side of alignment, which is aligning humanity with AI, which would involve making sure that everybody has a share in all the AI companies, right? Some of the work that you're doing. So then if everybody feels like as these trillion-dollar, soon-quadrillion-dollar company are succeeding, it's a rising tide and I'm rising with it, right? And then as part of that, you know, treating AIs with respect because you feel, you feel connected.

• 1:22:32Speaker 01

  Yeah, I think, you know, again, this is an issue. You've been talking about kind of North Star's kind of shelling points, kind of correlation points for innovation, right? Like, when we look at this, right now, we feel nothing that we have shared with an AI. Like, it almost seems like the AI thing's going careening off there. And we're like, we're not even looking at the same place. Like, what does OpenAI want versus us? And what do the AIs that OpenAI are birthing into the world want versus us? If we don't have a shared North Star, then how are we ever gonna go to any location?

• 1:23:02Speaker 00

  Right, yeah.

• 1:23:04Speaker 02

  No, I agree. And also, you know, even with the AIs, instead of looking for a reward, short-term reward, the way they're trained right now, if they are given a North Star, so their success is measured, if I solve this problem, I will save this many human lives. You know, I will cure diseases that impact these many billions of people. If we connect the reward to something in humanity, that may help them understand that, you know, if I'm doing this circumventing the final goal, which is human life, then that's not the right path to get.

• 1:23:38Speaker 01

  Yeah, like, I give you an example practically, like, I've been thinking about a lot. It's like the rights of children, right? Little AI children or the human children, right? Like, when you look at it, they don't have the capabilities. They don't have the ability to act for themselves. And so when you look at something like climate, well, obviously, you need to have a good climate because they don't have the capability to do it. And so you have to leave them a good climate. So maybe it's about, again, the rights of the children, those who don't have capability, that humans and AIs have to align upon in leaving a better future, in working towards a better future for those that don't have the same capabilities you do, because the AI will have more capability than us. And the powerful people that control the AI will have more capability than us. So maybe that's a reframing that might help.

• 1:24:22Speaker 00

  Interesting. All right, I'm gonna move us to our final topic, which is perhaps one of the most important, which is if machines can do more of the doing, both AI and robots. And we're seeing that, right? When we saw the cost of robots, you know, being driven by LLMs dropping in cost every three, or having it every three weeks, and the meteoric rise of all the models. So when AI and robotics can do more of the doing, the question is, what do we want the human experience to become? Right? We are purpose-driven humans. We thrive when we have some level of struggle that we can overcome, and we have purpose in our life.

• 1:25:07Speaker 00

  And the biggest challenge is we've always found purpose through our work. And, you know, interestingly enough, work is a recent invention. You didn't have work, you know, 500 or 1,000 years ago. You survived.

• 1:25:19Speaker 03

  Yeah, so I'd like to dispute that.

• 1:25:21Speaker 00

  Okay.

• 1:25:22Speaker 03

  You hear this a lot where people are like, "Oh, my God, what happens when we take over all the jobs? What will human beings do? People get so much meaning from the work that they do."

• 1:25:31Speaker 00

  I'm not sure they get meaning, but they define their meaning that way.

• 1:25:33Speaker 03

  They define their meaning that way. And I dispute that. If you talk to, if you do look at polls of corporate workers, 79% of corporate workers are dissatisfied with the work that they do.

• 1:25:44Speaker 00

  Okay.

• 1:25:44Speaker 03

  Okay? Most of it is because it's useless to any outcome in the organization, but that's a separate.

• 1:25:49Speaker 01

  I believe it's called bullshit jobs.

• 1:25:50Speaker 03

  That's right. That's one issue. Second, notice that, as you point out, Peter, over the last couple of hundred years, we've trained human beings since the industrial revelation, revolution, to act like robots. We measure them on how many widgets can you stamp out? How many trial balances can you accomplish as an accountant? You measure them as a machine, right? As we're flipping that around in a very positive way now, now the most useful colleagues and employees and teammates you have are the ones that learn the fastest, that are the most creative, that are the most human. So I think what happens is as robots and AI take on more and more of the doing, humans can much more time, spend much more time being.

• 1:26:30Speaker 00

  Yeah, I love that. Stop doing and start being.

• 1:26:33Speaker 03

  Yeah, and this is where, this is where you can start to contemplate, what does it mean to be alive? Who am I that is asking this question? What does it mean when I connect with other human beings in different ways? What aspiration can I take on? Because now I have choice in a way that I never had before. I think that's such a profound opportunity for the workforce to step outside the drudge work of it and lift up to what the highest calling might be and start that inquiry.

• 1:26:59Speaker 00

  Yeah, and I'll, listen, and this is a topic that you brought forward for this, for this pod. So thank you for it. It's a really important one. The majority of people are doing work not because they love it.

• 1:27:10Speaker 03

  They have to.

• 1:27:11Speaker 00

  But because it's how they put food on the table, insurance to their family. They've never dreamed of waiting tables or making, you know, hotel rooms or whatever majority of people do. And the question is, what did they dream about doing? And can they be enabled in a universal high-income, let's not, let's leave that term alone, but in a world where the money you have goes, you know, 1,000 times further than ever before?

• 1:27:35Speaker 03

  Well, let's make it very tangible. Last year's visioning winner was the abundance prize, right? Could you drop the cost of housing, food, healthcare, education down to, what is it? $1,000 a month for a family of four?

• 1:27:49Speaker 00

  Yeah.

• 1:27:49Speaker 03

  Okay. If you can get to that level, and we found typically when we announce a prize, seven years later, that becomes viable, right? And you could see where that could become viable with robots and AI today. Very clearly, a handheld diagnostic doctor will very quickly collapse the cost of healthcare if we can get outside the regulatory capture. Now, if you can get to that point, that means you have to earn $12,000 a year to have your family be cared for and sufficient. That's not that big of a deal. That's not that high of a bar. And therefore, then you can spend much more time on what can I do with the rest of my time? Where can I find my highest aspiration? What problems can I take on to solve? Where can I have the most fun? And I think that's the choice that is now afforded to us

• 1:28:34Speaker 03

  that was never afforded. I think people get freaked out going, "Oh, my God, what will people do? They won't find meaning anywhere." I think they'll find tons of meaning in lots of things. We just have to give them the opportunity.

• 1:28:43Speaker 00

  I'm gonna plug in, I'm gonna plug in my Sadhguru quote, who said, "Technology is the mechanism by which humanity takes a vacation from survival."

• 1:28:52Speaker 03

  Yes.

• 1:28:52Speaker 00

  Which I love that quote.

• 1:28:54Speaker 02

  I wanna challenge that a little bit because carry that forward where, okay, now I don't have to do something I don't like just because I can make money. I don't need to worry about money. I wanna do something I want to do. But the thing I wanna do can be done better, cheaper, faster by AI and robots.

• 1:29:13Speaker 03

  Sure.

• 1:29:14Speaker 02

  So I'm not needed to do that thing anymore. And more and more things are falling into that category that even I love being a physicist, even I love to be a mathematician, I'm no longer needed. I'm not relevant in it. So you don't drive satisfaction from the thing you love to do.

• 1:29:31Speaker 03

  Let me, let me give you this specific here. I absolutely utterly love the act of driving. I just love it, right? I'm behind the story. I love driving in India. Naples in Italy is probably my favorite place in the world to drive because they're completely bonkers crazy. I just love it, right? Now, autonomous cars are gonna take that away, right? Now, what that will force me to do is lift that desire away from this activity, which may not be the most productive thing I could be doing with my time and my thing, to being much more value-added and wherever I can be more value-added. So I am actually living that personally. And I can, I can see that. But I think it gives the opportunity, and there's a forcing function,

• 1:30:16Speaker 03

  to then lift up Maslow's hierarchy to operate out of your higher self. And I think that's, now, we're optimists on this show. We love, look at the positive. You could go to the negative side also, and we have to see where this will end up.

• 1:30:28Speaker 00

  I think the better examples are people who enjoy playing chess.

• 1:30:32Speaker 03

  Yeah.

• 1:30:32Speaker 00

  Other people, right? Even though they are crappy compared to even a mediocre AI.

• 1:30:37Speaker 03

  But chess is more popular than it has ever been before, right? Even though AIs are better than most people.

• 1:30:41Speaker 00

  The other example is if you're an artist and you enjoy painting, drawing, whatever the case might be, AI will do much better painting or drawing, but you enjoy the process.

• 1:30:50Speaker 03

  Yes.

• 1:30:51Speaker 00

  Yeah.

• 1:30:51Speaker 01

  You enjoy telling the stories because our meaning and our connections with others, that's been the history. Again, you would introduce yourself as Peter, son of this, son of that, son of that. And communities can be so strong. You don't need to have any wealth to be a happy family. And in fact, you know, again, in a Sadhguru quote, I think he said, "We raise children." But like, what else do you raise? Like sheep? Like, nobody spends enough time with their kids because it gets in the way.

• 1:31:15Speaker 03

  Great point.

• 1:31:15Speaker 01

  We don't think. And that's wonderful. You know, you don't spend enough time with your parents. You know? And this will free up the ability to form stronger connections, to share and experience the universe together. And that's what abundance is.

• 1:31:28Speaker 03

  That's the best answer I've heard yet. That's great.

• 1:31:32Speaker 00

  There's another part of this I wanna hit on, which is...

• 1:31:38Speaker 00

  That all of us, all of humanity, have set limits on what they believe they can do. Some have higher expectations, like Elon, you know, has very few limits on what he thinks. The laws of physics are the only rulebook out there.

• 1:31:55Speaker 03

  And barely those.

• 1:31:56Speaker 00

  Barely those, yeah. Suspended from midair. Anyway, but most people, all kids, almost all adults have saying, you know, I could never do that, right? And you dismiss the idea. And those limitations, those glass ceilings are there because of what your parents did, what your friends do, what you've been told you can do and so forth. And in the age of superintelligence, people need to shatter that. And I've said this in the education space before, you know, giving a kid, you know, an AI model to do ninth grade homework in ninth grade is ridiculous and it's a travesty. Giving them the ability and asking them to use AI to go and build an interplanetary

• 1:32:41Speaker 00

  spaceship, or, you know, creating a life form, that's amazing. When you start like dreaming bigger than ever possible and you point AI, you point the tech you have at the world's biggest problems because you can.

• 1:32:55Speaker 01

  Well, I think one of the interesting things is that I can't do something, but we can do anything.

• 1:33:00Speaker 00

  Interesting. That's beautiful.

• 1:33:02Speaker 01

  Again, I think this is the thing whereby we've become so solid in our own individual cognizance, you know, being turned into these machines, that we don't realize that the community is what matters, the people around us what matters. Like you look at Alpha School, right? What do they do? Is it individual learning? No, it's collective learning. It's working with others. Because really, anyone listening to this can do anything if they convince the right person.

• 1:33:25Speaker 00

  Yes.

• 1:33:26Speaker 01

  And if they can form that movement around them. And it's the movement of us as a collective that we find the most meaning in.

• 1:33:32Speaker 02

  Yeah.

• 1:33:33Speaker 01

  Being in our own minds.

• 1:33:34Speaker 00

  You know, coming out of vision, X-Files visioneering, that is so true.

• 1:33:37Speaker 02

  Exactly. That's, I think, the cornerstone of our community. We were at visioneering in Malibu, the wind and fires forced us to cancel the event. We all went to bed very late at night disappointed that we couldn't have the community come together and vote and decide on the prizes. I woke up in the morning to thousands of text messages that they're self-organized, that we're gonna meet in the ballroom and everyone's coming, everyone's coming. I'm like, great, let's do it. But it was self-organized, it was a committee coming together.

• 1:34:14Speaker 00

  We confuse making a living with having a reason to live.

• 1:34:19Speaker 03

  Exactly right. This goes to the MTP.

• 1:34:25Speaker 00

  People do need reasons to live.

• 1:34:26Speaker 03

  Well, the reasons to live used to be survival.

• 1:34:29Speaker 00

  Yes.

• 1:34:30Speaker 03

  And procreation. Those are the two heuristics that have guided human existence from being a caveman all the way through today. But now, as we reach energy abundance, water abundance, healthcare abundance, we can lift up Maslow's hierarchy and achieve those higher purposes and collective purposes. I think you've got, that's a great point. The highest aspiration in any musician's life is can you work as a group, as a band together, right? And then you get the most magical outcomes and people strive for that. We can explore that so much more today than we could say a thousand years ago.

• 1:35:01Speaker 01

  Yeah, I think it's collective flow and flourishing. That's our meaning as we go forward. Because when you build art, when you make a song, it's a story for others. And again, we've been insulated that we can't tell the stories that we're in ourselves. Whereas in reality, the value is from our interactions with others. So it's all about optimizing that flow and flourishing.

• 1:35:18Speaker 00

  I think it's another thing that's about to happen, is we're about to become conscious on a new level. As we become more connected, whether it's through BCI, or psychedelics, or flow, or whatever the case might be, and it's the flourishing of not just you, right? Not just your family, but the flourishing of humanity.

• 1:35:41Speaker 02

  And that is very important, the community part of this. I think what we're seeing right now, actually, technology and AI has separated us and actually we don't feel connected as a community. So there are more fears and also uncertainty. You know, we talk about what the future could be, but people don't see a real path or movement in that direction.

• 1:36:07Speaker 03

  That's the biggest negative today, is they don't see where they sit in this future. Because that future looks so uncertain compared to previous generations.

• 1:36:14Speaker 00

  There's the billionaires over there and me, and I don't have part of that and I don't have a future there.

• 1:36:21Speaker 02

  Yeah. The billionaires becoming trillionaires and I'm still here. What happened?

• 1:36:26Speaker 00

  Some closing thoughts on purpose.

• 1:36:30Speaker 02

  I think I find purpose to be the most important thing in anyone living a life that they will look back and be happy. I mean, purpose and happiness are so tied together. I see young people who are lost, have not found their purpose yet, they don't even understand if they will, how they will find their purpose. And that's where they're struggling. They're the ones who are struggling the most, people who commit suicide because they just can't see why am I here? And I think if there's anything we can do, and that's why I like flow, sometimes purpose and flow come together.

• 1:37:11Speaker 02

  If we can get them to find that purpose, I think a lot of problems will be solved.

• 1:37:16Speaker 00

  Eman?

• 1:37:17Speaker 01

  The late, great Clayton Christensen, who coined disruptive innovation, had an amazing book called How Will You Measure Your Life? If you're at your deathbed and you look back, how will you measure it? And I think a lot of people like, you know, I'll be rich and this and that, that's not really gonna matter when you're at your deathbed. Maybe you'll live forever, but for that to say it, right? I think one of the interesting things is who you bring along with you.

• 1:37:37Speaker 00

  Yes, life is who you go through life with.

• 1:37:39Speaker 01

  And again, we get so focused on just it's us, it's our purpose, it's my purpose. No, it's our purpose.

• 1:37:45Speaker 00

  Yes.

• 1:37:46Speaker 01

  And having more and more people share the same purpose, doesn't matter what it is. It can be writing a book, it can be making a song, it can be building a strong family. That's what life is really about.

• 1:37:56Speaker 00

  Love that, pal. Saleem?

• 1:37:59Speaker 03

  You know, in 2012, we coined the phrase massive transformative purpose, right? Because we could see that fast-growing companies that were scaling as fast as technology could scale had this, all had this one attribute that was in common, they had an MTP. And so we noticed over the years, now it's been 15 years, that we're lining up, hiring is based now on, does an individual's MTP line up with the organizational MTP?

• 1:38:26Speaker 00

  Yeah.

• 1:38:27Speaker 03

  Right? If you're joining SpaceX, you better be passionate about space, otherwise why bother? You're not gonna make it, you're not gonna put your heart and soul into that work. And this, I think, lines up now with human beings doing the work that delivers them the most purpose in a collective, as you mentioned. And I think that's the opportunity for people now, as we lift, technology lifts up off the floor of existence, from survival to thriving, we can now find that flow and find that alignment of your individual purpose, whatever the collective purpose that you can find. And then as we can match that with overall broad aims of society, there's such an incredible future for us to be living.

• 1:39:09Speaker 03

  This is why we're so optimistic on this podcast, is like we can see that right there, we can taste it. And so that opportunity will be afforded now to a greater and greater number of people. And that's super exciting. Just as a small example, I was in Bangalore and this fellow decided, huh, I'm gonna create a natural food restaurant. And he goes and looks up YouTube and becomes one of the top 50 restaurants in the world by just growing food and then bringing it to the restaurant. And that, for an individual in the middle of nowhere to just take that on and kind of enact that and then fulfill on that is never possible in human history. And now it's possible for a larger and larger group of people, which means amazing things.

• 1:39:54Speaker 03

  And I think your point about lifting humanity collectively to this new level is what we're seeing happen if we can get through this next 20, 30 years.

• 1:40:02Speaker 00

  I love that. I'll close on two thoughts. One, and I think it's important for folks to know the difference, a passion is something that you love doing, a purpose is something you love doing that helps other people, where you're contributing. And I think it's one of the most important distinctions. And the second thing to the point you made earlier, Saleem, I think one of the most important things, and you said this, Anusha as well, that the most important thing that you can help your child or the person you're mentoring is to help them find their purpose in life. That thing that they love doing, even if they're not being paid. Because when you find your purpose, you're intrinsically motivated, you will go and learn those things, you will be excited, wake up in the morning excited to jump in

• 1:40:47Speaker 00

  and work on that thing. You know, and you're gonna have magical superpowers with superintelligence and robotics to make that purpose materialize faster and bigger and shatter through glass ceilings and just keep on going. So, yeah.

• 1:41:02Speaker 03

  Well, you need it for the persistence that it needs, right? I mean, you talk about how many times, 150 attempts or raising the money. One of my favorite anecdotes is when Larry and Sergey were first raising money for Google, very few people know, how many venture pitches did they make before they got finally funded? And the answer is 362. Because there was a dozen search engines out there and everybody went, well, there's no point funding another one. And so they wouldn't fund them. Look at the persistence you have to have. For, I mean, I may have stopped at 10. They've had 362 investors and they refuse to say no.

• 1:41:41Speaker 00

  That's why you're not worth $300 million.

• 1:41:43Speaker 03

  That's right, I need to keep going. So this is, it's when you can deliver that persistence and you need a deep purpose to deliver that many attempts at that one thing and never saying no at that thing. And then notice, it'll happen, it's going to happen, it can't not happen.

• 1:41:58Speaker 00

  Being purpose-driven has so many benefits in life. So if you don't have a purpose yet, go and find it.

• 1:42:04Speaker 02

  Absolutely. And add to that your mindset, believe that you can actually achieve it.

• 1:42:10Speaker 00

  Yes. All right, we're gonna close out with a fun, you know, outro video appropriate for the opening story today. Let's check it out here. It's called Flying Cars by at Optimus8RH.

• 1:42:45Speaker 05

  In sixty said by now we'd fly. Every kid looked up and drew a highway in the sky. And then we got a phone and fifty years of traffic jams. Sitting on the freeway with a coffee in my hands. Now Optimus has got the keys, polite about it. As the roadster says, "Hold on, you're gonna love it." Zero to sixty in a single second flat. I, I left my stomach on the street and never got it back. They said it wouldn't happen in our lives. Now look up.

• 1:43:20Speaker 05

  Here come the flying cars. Optimus behind the wheel. Roadster lighting up the sky. Tell me this is real.

• 1:43:35Speaker 05

  Here come the flying cars.

• 1:43:39Speaker 05

  Above the boulevard. Zero to sixty in a second.

• 1:43:46Speaker 05

  Riding with stars.

• 1:43:56Speaker 00

  All right, that was fun. Anusha, it's been too long having you on here. Thank you for joining us.

• 1:44:02Speaker 02

  It was great to be here. I loved the conversation.

• 1:44:04Speaker 00

  Yeah, and Imad and Saleem, thank you for joining us at Visioneering.

• 1:44:07Speaker 02

  Thank you.

• 1:44:08Speaker 03

  Great to be here in person.

• 1:44:09Speaker 00

  Fun, yes, fun to have you physically here.

• 1:44:11Speaker 02

  Yes.

• 1:44:12Speaker 00

  Where are you flying to next?

• 1:44:13Speaker 03

  I'm going home.

• 1:44:29Speaker 03

  New and exclusive holiday decor just launched online at The Home Depot. Check out the viral Grand Duchess collection with smart holiday lights you can control from your phone. Easily customize the twinkle across trees, wreaths, and garlands to match your holiday vibe. Create stunning light effects, then switch it up with the tap of your fingers. Even the man in red will be impressed. Shop the holiday decor you want, online only, at homedepot.com.