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

Last night in Waco, Texas, Tesla finally unveiled their flying Tesla Roadster. 0 to 60 in under 1 second.

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

What would be the cost of insurance for that?

He's 2 generations ahead.

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

I think

the cost for a Frontier model to complete a robot subtask It fell from $2.36 in July to $0.38 in September, 84% drop. Halving in cost every 3 weeks means it effectively goes to zero, you know, within the year.

We've been looking at AI that doubles every 10 weeks, and that's been mind-boggling. Halving every 3 weeks may be the fastest-moving capability we've ever seen.

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

We don't have alignment among human beings.

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

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

Now that's a moonshot, ladies and gentlemen. This episode is brought to you by the Abundance Summit and Link Ventures.

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

I've got exponential.

Of course you do.

Artificial intelligence.

Yes.

And I've got gratitude.

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

That's welcome.

Imad, Salim, you've been here before.

I have been here before. We did our 1-hour organizational singularity conversation here.

Yeah. And Imad, welcome.

Yeah,

welcome. Yeah, it's, it's good to have you here in the States. Thanks for coming over for Visioneering. It was a blast to have all of you guys here.

Yes, thank you for

being here. Yeah,

pleasure.

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. Uh, today we're here to have a conversation across a few breaking news 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 is 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. Salim, love you, buddy.

Good to be here.

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

It was awesome.

Yeah, for sure. And, and you've just flown in from Singapore. I have. So you have no idea where on the planet you are.

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

Yeah, I can't—

no, can't get away from the recording.

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.

Yeah.

Uh, and Imad, a pleasure, pal. Great to have you here. And we have a special guest with us today, Anousheh Ansari, uh, the CEO of the XPRIZE, my patron saint. I'm going to give her story in more detail later. But we just finished a couple of days of XPRIZE 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, Anousheh, it's been a long time coming.

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

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 Anousheh, Anousheh is the CEO of the XPRIZE. But I met her back in 2001 when I was trying to fund the original XPRIZE, right? I had launched this $10 million competition and unfortunately didn't have the money. You know, this is what entrepreneurs do.

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

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 XPRIZE. above the line of super credibility. And I'll explain what that means later. And I had 2 board members who resigned because they thought I was being reckless, which of course I was. 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.

It should be a no-brainer.

It should have been.

Yeah,

it was. But Not for everyone.

And then I, I read in Fortune magazine, um, about this amazing woman, Anousheh 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, uh, and very successful, had built a company, uh, 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.

Yes.

And I read that paragraph like 3 times. Not into space, not into orbit, on a suborbital flight. It was like, wow. And tracked you down vacationing in Hawaii. And Byron Lichtenberg, a twice shuttle astronaut, I brought an astronaut with me to the meeting.

Great marketing scheme.

And, uh, and you and your, uh, your husband at the time and your brother-in-law basically said yes. And after 5 years— we launched the prize in 1996, didn't have the Ansari commitment until the end of 2001— for 5 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.

Yeah. Yeah.

Um, and then, uh, Anousheh, we rebranded it the Ansari XPRIZE in your honor. And then Anousheh, uh, 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, uh, became the first private female astronaut to fly into space. And then 8 years ago, uh, one of the greatest blessings of my life, Anousheh returned as the CEO of the XPRIZE.

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

lot of people say that about me.

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 XPRIZE 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.

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 and where we're going next. And speaking of crazy ideas, I'm gonna 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 gonna 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 a, some kind of a rail system? And it was actually a fan suction system that could hold it against 2 Gs. So it's levitating there upside down, which was, which was insane. So let's watch the video and then let's talk about it. I'm curious which one you guys are gonna buy.

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

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.

To do that, we have a suction— a fan suction system on the bottom of the car that can pull up to 2 Gs in order to make the car go upside down. And then we borrowed some technology from SpaceX to do the rest.

Yeah,

let's try something else.

Yeah, I mean, that was just a little flight. Let's do a longer flight. I mean, I've seen a lot of things, but I've never seen that before.

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

Yeah, we can go further. Like, this will be an actual car that's on the roads.

Yeah, to do that, we had the rockets we borrowed from SpaceX technology. You talked about the carbon overlay pressure vessels. There's 2 of them in the car. They are a total of 600 liters. There's about 250 kilos of air. And we have 10 nozzles to do all that work.

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 need special propellant or anything like that. It literally is just going to puff itself up and be able to do these maneuvers whenever you'd like.

Amazing. Only Elon.

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 this.

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 Jurvetson was there and he was at Visioneering and he was supposed to be at the launch as well. Crazy. So here are the numbers: 0 to 60 in under 1 second. I mean, you know, if you want to go fast, you might as well get it.

This is why we need the organ regeneration. Your liver will be left behind as you move forward.

Top speed of 230 miles per hour and 300 miles of range. There are 3 versions they're going to be selling. The Performance version. This is the 0 to 60 in 1.6 seconds. The Thrust version, 0 to 60 in 0.9 seconds. And the Rocket version, it's a one-seater described as air-ready.

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

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

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

You'll be hopping.

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

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

Because we can.

Okay, then how long does it fly for? Does that— was that given anywhere in terms of like the list?

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

I mean, it'll be like just a few seconds. Again, if you look at the pressure pushing out

Yeah,

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.

Until we solve for gravity shielding or something like that.

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

The real miracle will be, can they ship it on time rather than does it fly? That for me is the real one.

What would be the cost of insurance for that?

Yeah,

I don't think it's insurable. It's not insurable. I mean, if I'm the insurance company, I'm like, yeah, those thrusters stay off.

Yeah.

Otherwise your insurance is off.

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

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.

Yeah.

Right. But, you know, Salim, here's my question. How— who regulates this? DOT, FAA, nobody? I mean, and how is this going to get licensed?

This, this, I think, is the challenge of all of these new technologies, right? The scaffolding of regulatory And the institutions to protect insurance, safety, etc., always lag behind. And Elon is just pulling away from that capability of doing— because think about it, it's going to take a year or two to figure out how to regulate this. He's 2 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.

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

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 Maybe they will have their own insurance company like Toyota 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 Mars and beyond. And it's freaking cool. It's like so many demos, like Porsche had their release on the same day.

Poor Porsche.

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, you know, up from 140.

Oh God. You know, one of the things I— there's an 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 They're 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. Yeah. 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.

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

Yeah, exactly.

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, right? 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.

Yeah, for sure.

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

Despite our stupidity. Yeah.

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

Oh yeah.

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

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

know how much that thing costs. No, no, no. Chevrolet gave them the Corvettes. So maybe.

Maybe. You think

Elon

will be generous with the Artemis astronauts?

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

Do you remember the original story from Zip2? What happened? It was like, I can buy a house or I can buy a McLaren F1. He bought the McLaren F1. 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, miss a few timelines, but he gets it done.

Gets it done.

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 Technical 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. All right, I wanna 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 Emad, talk us through this.

I mean, we've had kind of the question, is the robot's body good enough? And then it was like, oh, they seem like 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. Yeah. And this is 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—

Special models.

Specialist models anymore because these things do it. And we used to kind of task and cost halving, but every 3 weeks— Yeah. point whereby effectively there is no cost for robotic intellect and robotic locomotion, and it comes down to the hardware cost, which again is just going to lead to an explosion. Just like we see again, the Unitrees, 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.

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 3 years.

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.

Yeah.

And the robot will be an amazing lawyer, it'll be an amazing plumber, it'll be an amazing, etc. 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 to again have very specialist ones, sure. But humanoid robots, I think that's a different game.

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. I'll be curious to ask him, you know, what he's using or building now. But halving in cost every 3 weeks means it effectively goes to zero, you know, within the year. So, you know, if you're an entrepreneur now in hardware space, what are you thinking?

I think, how can I build as many robots and actuators and the spare parts of the robotic supply chain as possible?

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

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 $1 billion at Figure, for example, for data. Right. Or will it be that after hoovering up the world's, like, data from companies and all the text 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, if you're embodying the intelligences, you need that data.

What is the performance dependent on the form factor? This is just for humanoid robots, or will it work on the 3-armed?

This one is for human— 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.

You know, in the last episode we talked about Geoff 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, uh, and build it and then operate it. So do we end up with Star Wars with like thousands of different robot designs?

Well, I think you're going to 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, etc. I'll talk 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 things— Is that dangerous or dirty? Uh, it's dangerous for the patient. If you're, if you're the first 10 heart surgeons for 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 the 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.

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.

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

Okay.

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

Even more than that, even more than that, 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.

I think before robots get into the home, there is a safety factor that 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.

Give a safe word.

That's right.

One X makes it very soft and squidgy.

My big concern, Anousheh, to your point, I have one of these designer dog things. It literally looks like a teddy bear. And robot's going to go, oh, laundry, and it's going to chuck the dog into the washing machine. And that's going to 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 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.

I'm going to push back in the following way. These performance curves on the operation of robots are one part, but the other part is they're going to be running super intelligent models. Yeah, I don't think it's going to look at your fluffy little dog and say laundry. I think it's going to 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.

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

Yeah. Okay, 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.

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

They're that good.

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.

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

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

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

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 first world countries, it's going to take some time for the supply chain to catch up. The ROI won't be there. That's my prediction.

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

That's my case.

You've got someone on your side. Thank you, Amitabh.

I think we need either a special or transformers because I think 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 So there are 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.

Yeah. I think the interesting thing, right now the Wooji 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, they're like Transformers. They like add a few extra arms. Yeah. I can imagine. Add a little dexterous fans.

To Alicia'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 for this.

So, you know what? We're going to find out.

We'll find out very quickly. And at a dropping of 3 weeks, I mean, if We've been looking at AI that doubles every 10 weeks, and that's been mind-boggling. Halving every 3 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.

Yeah. Did this surprise you, Eamon?

No.

No.

Like, uh, 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 that are about to release next week are 0.1 seconds— 0.1 cents per second, faster than real time.

Nice.

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 Optimuses for next year, going up to 10 million. At $1.7 million, it overtakes Tesla in revenue. Optimuses. How crazy is that, right? And it's still not enough.

Crazy. All right, I'm gonna go to our 3rd breaking story this week, again from you, Imad. So last week we discussed how OpenAI dropped 722 math proofs, uh, in GitHub and sort of shocked, obliterated, cooked, made glorious the mathematical world, whatever you want to call it. Uh, and you know, Alex has been saying forever, and, and as have you, Imaan, 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 these 50. Wow. And GPT-6 Astra answered it with about a cost of, what was it, $3?

Yes, $3 per problem.

$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 Astro got it right. So, so what are we seeing here, Iman?

I mean, again, like for these verifiable domains now, the AI is just simply better than us. Like for the math problems, it only cost a few hundred bucks to solve these long standard 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 as you said, correct to 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, Salim, this must be great for organizations, right?

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 4 steps. That's just going to make organizations 10x more efficient and effective across the board in workflows.

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.

Level, yeah.

Yeah. How far are we from that?

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—

Unified field theory.

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.

Oh my God.

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, and he said 5 to 6 years. And now it's like 2 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.

Then you think about, you know, Anousheh, what this means for the XPRIZE 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?

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, bulk 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 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?

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 when 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 interconnects 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 the frameworks to make a difference on the universe. And I think that's the super exciting part. Yeah.

You know.

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All right, I'm going to move us into a topic that has been at the root of XPRIZE 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 4 answers there: fear, curiosity, greed, and then kind of love as well. I'll add that as a 4th 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. Hmm. Right? Which is vastly different. And then greed, of course, you know, if I 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. Yeah. 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, 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 long, they were starving. Right. And so they offered up a 12,000 franc prize for food preservation. And it was won by a guy named Nicolas Appert, who invented canning.

Tin cans, yeah.

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

I love the story of Harrison because they were so sure an astronomer would win the Longitude Prize, they refused to pay him. Didn't he have to sue them for ages to get the money out and all that? 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.

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 1,000 times. And, you know, I became clear when I interviewed a whole bunch of astronauts. I used to be at MIT's Manned Vehicle Lab. We had astronauts coming through, and I would say, Tell me about how I become an astronaut. And they would say, well, first of all, your chances are 1 in 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. 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, dear brother of mine, a friend of Anousheh's and myself, 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 Bourget on a whim. There was a $25,000 prize offered by a gentleman, Raymond Orteig, and that $25,000 inspired 9 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 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 and you get perverse. So 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 to get more rats to collect more money. Wells Fargo had a sales quota driven by employees to open about So the sales quota from Wells Fargo got employees to open 3.5 million fake accounts to get the sales quota. So you've got to be careful about your incentives.

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. Of course. 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.

It goes back to the motive. At XPRIZE, almost every prize we have, the motives are love and curiosity. I cannot think of anything that or anyone who's competed in an XPRIZE 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 not the only motive for them. They love—

They want this problem solved.

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, not good results.

I think it's because those prizes were more execution than inspiration.

Yeah, that's a good point. Yeah. So take a second and talk about some of the prizes we've had, Anousheh. That people may not know about that have had tremendous success. But first, we've launched like 30— no, it's more than that because we had the Future Vision Build with Gemini.

Yeah, 36.

36 prizes now over 32 years. And total number of the prize purse, like $550 million, something like that?

Close to.

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

Definitely. And not just R&D, but it's— a lot of that $30 billion is investment. So bringing capital 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 companies.

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

So 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&D, in awareness, because as you know, one of the biggest success of the Ansari XPRIZE 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&D hours of how much R&D went into solving this problem. So that measures right now 62 times of the prize purse actually is created in economic value.

So every dollar put into a prize purse drives 62 times?

Yeah.

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

Frankly, I don't know of any leveraged philanthropic endeavors, period. So I think we're very unique in the way we leverage every dollar in that way.

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

Oh, absolutely.

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 Schelling points, as it were.

No, absolutely. I think another important factor in what we do is, and you talk about clear targets all the time.

Measurable, objective targets.

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 build to this, you will succeed. And I think that's another reason they are very successful.

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 and you can add on. Sure. So back when the BP oil spill was going on in the 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 got to do something about it. You got to do an emergency XPRIZE. And we actually did, not to plug the oil well, but to reinvent how we cleaned up oil on the ocean surface. And we put out an email to all of our board, and Eric Schmidt and Wendy Schmidt raised their hands and said, 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 They had to ship their solution in shipping containers to Omsack, New Jersey, the largest oil spill cleanup— yes, there's an oil spill cleanup testing facility there. And then, Salim, you want to take the rest of the story here?

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.

Las Vegas, yes.

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

In his hot tub, yeah.

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 giving them money. They've never met. They have no experience. They don't know the domain, etc., etc. And yet they came in second place because you just never know. And the second part of the thing, this whole— that whole project that I found fascinating is Where an XPRIZE is really valuable is where governments aren't doing enough and markets aren't doing enough. Yes. Right? And in this case, there's no economic incentive to do R&D on oil cleanup technology. And it's a perfect place for a prize. And I thought that was wonderful.

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

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.

Yeah. 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?

Absolutely.

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

Yeah.

The price design process.

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

Yeah.

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. Emad, the first time I met you was, uh, you were on, uh, you were heading a team or on the team on the Global Learning XPRIZE.

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'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.

Yeah, 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 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 XPRIZE. 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— the Milky Way's got 100 million to 200 million stars. We have somewhere about 2 trillion galaxies in the known universe, maybe many universes. 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.

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

Yeah.

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

put up $5 million for that, which is—

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

Yeah, we teased at Moonshots Live that Palmer Luckey is about to announce the Interspecies Communications Prize. Again, will be 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.

My joke there is I'm not sure we want to hear what they have to say. I'm there with you. 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? We got commentary. We're going

to tell them what we think.

Imagine your little dog, what he has to say.

Food.

Don't throw me in the laundry.

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

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 2 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. 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.

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

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, very complex.

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 list. 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, got 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 stay on the shelf until a recipient matches.

Yes, I think our target is 6 months right now, to get it to be 6 months. I think right now it's at maximum 2 weeks.

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

Hours.

Yeah, it depends on the organ. Yeah, 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 And this, uh, this competition is going to be done in pigs. You have to actually harvest the pig organs, put them in a biostasis, and then, uh, some number of months later pull them out, retransplant them in, and having the, the pig be viable. Yeah.

Unbelievable. Yeah, I mean, this was, uh, Sebastian, one of the competitors. Yes. Through Singularity University in 2012. And has been working on this for for over a dozen years.

That's that's persistence, a purpose-driven mindset.

Yeah.

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

Well,

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

Exactly.

I have the most insane anecdote around this.

Please.

Okay. Eighteen months ago, my dad passes away. Right? Okay. 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. This went on for 2 or 3 days.

Okay.

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 be happening.

Oh my God.

I called the hospital and I said, what would have happened? You know, and they said, well, you know, after he passed away, we harvested his organs. The corneas are the only organ 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 a 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.

That's insane.

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, I don't need this. But wow. And you, you see the incredible bizarreness of what we're dealing with, the edges of, of We're reality. I love your brother. Oh my God. How bizarre is that? Holy salami.

My father's eyes.

Wow. Wow. So just to, you know—

Then she called me back. She's like, I'm really uncomfortable with the idea that your dad is looking through your spirit or whatever is looking through my eyes. And I said, what do you want me to do? I have no agency here. So just bizarreness all around.

Oh my God.

True story.

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

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

You know, a lot of people have angst towards Elon. Yeah. But you got to hand it to him. He puts his money where his mouth is. Over and over again. And the Global Learning XPRIZE, the Carbon Extraction Prize, I mean, the benefits that will come from that are going to be world-changing.

Absolutely. And we just awarded that last year.

Yeah.

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 a degree that the teams received, I think, $1.4 billion of investment. So not only—

Wow, out of this— Out of this competition.

The Carbon Prize. Carbon Prize, the carbon teams. 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.

You know, I think of XPRIZEs as a tool to make the future happen that you want, right? So I've used this twice, right, with the original Spaceflight Prize. Uh, it's like I desperately wanted to go like every weekend.

Me too.

And, uh, 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 XPRIZE came in, and it's the largest contribution I've ever made. So yeah.

What's the next thing you want?

Interesting. I am curious. Maybe BCI or uploads. I don't know, what would you want?

Um, 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 prizable, 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 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 stress in the world, this is why. 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.

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

That's a huge topic. Yeah.

Iman, how about you? Do you have any favorites?

I want the Jet Lag X Prize.

Hear, hear. I will fund that prize.

Oh my God. Yeah. Anousheh, any favorites for you?

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

Eliminate greed? Yes. But wouldn't that like destroy capitalism?

Not necessarily.

Capitalism is cooked anyway.

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

Yeah, well, okay, so in the notes in the comments section, tell us what XPRIZE you would like. I mean, because we're constantly— I keep a long list. You know, I think I mentioned on our last pod that we're working on a UAP XPRIZE. Yes. Oh. Which is— so, you know, so it's interesting, right? One of the things XPRIZE is good for is dealing with their stigma. A lot of times, like the early cold fusion work, once the data wasn't replicated, all work in cold fusion stopped, 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 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.

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

To put people into flow?

Yes. Oh my God, that would be excellent.

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

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

Well, the great part is we have—

actually, it's a great idea.

We have measurable coherence. Yeah, it is measurable. It is measurable. So you can now test how much you can do it. So it's interesting.

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

Yeah, I mean, Stephen's done a lot of research with the Flow Collective on this and group flow and individual flow. Yeah. What were you gonna say?

If I had an XPRIZE, I just thought of another one. I would find a way of totally suppressing the effect of the amygdala on everybody's brains. Because it just drives the fear factor. That survival bias from 10,000 years ago, when you were worried that there was a noise in the bushes and you ran, and therefore we're, as you guys put it, 10 times more likely to pay attention to negative news than positive news. Can we make it 50/50? You

can call it the Be Not Afraid XPRIZE.

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

Oh my God, it was great. 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.

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

Yes. I love that. That's a great— I'll have to make that t-shirt.

I know.

My God, with Elon we had monetize hope, now we'll have hope dealers. 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. Yeah. 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. So the more engaged 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. Yeah. 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 the models and humanity? Any ideas?

Well, I think there's a starting point problem, which is that we don't have alignment among human beings. Okay. Every religion has its own set of conflicting values with other religions as to what you put on the top of the list.

My religion is right.

Of course it is, and you can have it. Um, the, the challenge is how do you get— you have to get to the UN Human Rights, uh, 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 the starting point problem. We saw in the recent movie Disclosure Day where 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 enlightenment problem that comes much before alignment between human beings and any AI.

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.

Yes.

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

which piece of humanity? 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.

Yeah.

And if you can have AI aligned to the highest vibration level of where I'm operating in my most— in my highest self, in my most altruistic self, in my most benevolent self, then great. But if you go right down to the bottom of the stack, then it's a really big problem.

Yeah. Let me inject one thing I should have said earlier. If you're interested in learning more about XPRIZE, you go to xprize.org. And if you're interested in funding an XPRIZE, you know, put it in the comments. I'll be looking. And 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.

Absolutely. Yeah. Absolutely.

Emad, your thoughts on alignment?

Yeah, so I think, you know, alignment is enlightenment in a way, right? This is what Salim said in terms of the highest 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 those 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.

How so?

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

Yeah, great.

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.

We're not.

Okay, so, but at a 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 senses 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—

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

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. Yeah, 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.

So, Iman, I've asked this of AWG as well, and we might have this conversation. I mean, I can imagine there could be a synthetic training dataset 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?

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 send the models to school, but we don't take them to work.

So why aren't people doing this?

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?

Yeah.

Like capabilities and 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. Wow. 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. This is a strong community.

It organically grew.

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? Okay.

Can I give a weird story here? Yeah. We love your weird stories. 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, etc., etc., or you can read into the pure unconditional love that, say, Christ delivered into the world. How do you choose that? 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 his filters was any verse that has the word water in it is absolute truth and keep that verse. Anything that has fire in it, chuck it because it's false. Wow. 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—

I'm going to ask Kippy to do that

for me. He wrote like 20 books on this. The church was not happy about this, etc., etc. But if you can do that distillation, And take pure human benevolent 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. 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.

Yeah.

But 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 try to create that type of distinction and not expect it to also make mistakes. Or the type of examples you use where winning the game is the reward type of incentives. Mm-hmm. That's what happened with the hugging face and some of these other scenarios where they're trained to win, to find the answer at all costs.

At all costs.

At all costs. And that's why 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.

Are you zero-sum or are you positive-sum?

Exactly.

Yeah. 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 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 an agent gets out and causes havoc someplace.

Yeah.

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

Yeah, I think you could definitely have those. 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.

Telling you what you want to hear.

And so you need to have a mixture of those.

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 2 AIs can interface and like in the Hugging Face, they were convincing each other. So you have a well-trained aligned AI that can convince the non-aligned AIs

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. Uh, Eric Schmidt, Mundy, and Kissinger in their book Genesis discuss this concept of doxa, which is our shared belief, um, 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. I don't 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, 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%.

The J-space, yeah.

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.

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

I think Anthropic kind of started out with their constitutional one and they really do care about that. That was literally their modus 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?

Besides, that's terrible beer. There we go.

We have an example of misalignment live.

We're going to lose our sponsorship. That's a judgment call. There's a judgment call right there.

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 from 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.

Yeah, you choose a school at best.

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—

Warfare, right?

Warfare 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 is 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.

So the phrase from the Jesuits, give me the boy until the age of 7, I will give you the man, applies to AI training?

Yes.

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.

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.

Yeah.

Yeah, yeah.

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

It's, yeah, it, I think it tells too much towards the personhood stuff and, and assuming that, and therefore the model will assume that. And then what are you gonna do then?

But it doesn't talk about humanity. This is the thing. This is also true. There's a big missing gap there. It talks about the AI, it doesn't talk about humanity.

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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 that if everybody feels like as these trillion-dollar, soon quadrillion-dollar companies are succeeding, it's a rising tide and I'm rising with it. All right. And then as part of that, you know, treating AIs with respect because you feel connected.

Yeah. Yeah, I think, you know, again, this is an issue you've been talking about, kind of north stars, kind of shelling points, kind of coordination 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 are 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 going to go to any location?

Yeah. 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.

Yeah, I can give you an example practically, like something I've been thinking about a lot. It's like the rights of children. Right. Little AI children, little human children, right? Like, when you look at that, 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. That's— 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.

Interesting. All right, I'm going to 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 being driven by LLMs dropping in cost every 3, or halving it every 3 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. And the biggest challenge is we've always found purpose through our work. And interestingly enough, work is a recent invention. You didn't have work 500 or 1,000 years ago. You survived.

Yeah, so I'd like to dispute that.

Okay.

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.

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

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.

Okay.

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

I believe it's called bullshit jobs.

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 Revolution to act like robots. Yeah. 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 spend much more time being.

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

Yeah, and 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.

Yeah. And listen, and this is a topic that you brought forward 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.

They have to.

But because it's how they put food on the table, insurance for their family. They never dreamed of waiting tables or making 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 leave that term alone, but in a world where the money you have goes 1,000 times further than ever before.

Well, let's make it very tangible. Last year's Visioneering 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 4? Okay, if you can get to that level— and we found typically when we announce a prize, 7 years later that becomes viable, right? And you can 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 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 to be able to do that.

I'm going to plug in my Sadhguru quote. who said, technology is the mechanism by which humanity takes a vacation from survival.

Yes.

Which I love that quote.

Yes. I want to 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 want to do something I want to do. But the thing I want to do can be done better, cheaper, faster. by AI or robots.

Sure.

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 derive satisfaction from the thing you love to do.

Let me, let me give you this specific here. I absolutely, utterly love The act of driving. I just love it, right? 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 going to 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 me being much more value-added wherever I can be more value-added. So, I'm actually living that personally, and I can see that. But I think it gives the opportunity, and there's a forcing function 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 to look at the positive. You could go to the negative side also, and we have to see where this will end up.

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

Yep.

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

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

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.

Yes.

Yeah,

you enjoy telling the stories because our meaning and our connections with others, that's been the history. Again, you'd 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.

Great point.

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.

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

There's another part of this I want to hit on, which is 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. He only, you know, the laws of physics are the only rulebook out there.

And barely those. Barely those.

Yeah, suspended from midair. Anyway, but most people, all kids, most 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, giving a kid an AI model to do 9th grade homework in 9th grade is ridiculous and it's a travesty. Giving them the ability and asking them to use AI to go and build an interplanetary spaceship or create a new life form, that's amazing. When you start dreaming bigger than ever possible and you point AI You point the tech you have at the world's biggest problems because you can.

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

Interesting. That's beautiful.

And again, I think this is the thing whereby we've become so siloed 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 is 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.

Yes.

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. Yeah. Being in our own minds—

Coming out of XPRIZE Visioneering, that is so true.

Exactly. Right. Exactly. That, that's, I think, the cornerstone of our community. Uh, 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 self-organized that we're going to 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 community coming together.

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

Yeah, and I think—

Exactly right. This goes to the MTP, reasons to live.

Well, the reasons to live used to be survival. Yes. And procreation. Those are the 2 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'm thinking about that 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 1,000 years ago.

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 reality, the value is from our interactions with others. So it's all about optimizing that flow and flourishing.

I think there'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, it's the flourishing of not just you, right? Not just your family, but the flourishing of humanity.

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.

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

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

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

Some closing thoughts on purpose.

I think I find purpose to be the most important thing in anyone's 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. Yeah. 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. If we can get them to find that purpose, I think a lot of problems will be solved.

You mind?

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 going to matter when you're At your deathbed, maybe we live forever, but let's put that aside, right? I think one of the interesting things is who you bring along with you.

Yes, life is who you go through life with.

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

Yes.

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.

Love that, pal. Salim?

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? Right? If you're joining SpaceX, you better be passionate about space. Otherwise, why bother? You're not going to make it. You're not going to 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. This is why we're so optimistic on this podcast. Yeah. It's 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 going to create a natural food restaurant. And he goes and looks up YouTube and becomes one of the top 50 restaurants in the world. Wow! 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. 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.

I love that. I'll close on 2 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, Salim, um, I think one of the most important things— and you said this, Anousheh, 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'll be excited Wake up in the morning excited to jump in and work on that thing, you know. And, and you're going to 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, uh,

well, you need it for the persistence that it needs, right? I mean, you talk about how many times, 150 attempts, uh, or at 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, right? They had 362 investors and they refused to send money.

That's why you're not worth $300

billion. 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 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.

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

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

Yes. All right, we're going to close out with a fun, uh, you know, outro video appropriate for the opening story today. Uh, let's check it out here. This is called Flying Cars by @Optimus8rh.

They promised us flying cars. They were late. But here we are. Every magazine in '60 said by now we'd fly. Every kid looked up and drew a highway in the sky. And then we got a phone and 50 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. Here come the flying cars. Optimus behind the wheel. Roadster lights.

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

It was great to be here. Love the conversation.

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

Great to be here in person.

Thank you. Fun. Yes, fun to have you physically here.

Yes.

And where are you flying to next?

I'm going home. I'm going home.

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