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 two generations ahead.
I'm surprised that it's not autonomous and it's only, you know, a human-driven car.
I think the cost for a frontier model to complete a robot subtask fell from $2.36 in July to 38 cents 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?
Artificial intelligence. Yes.
Yes, you do have gratitude. Yeah, for sure. Anyway, all right. Um, I love having you guys in the podcast studio.
Welcome, Emad. See, you've been here before.
I have been here before. We did our one-hour organizational singularity conversation here.
Yeah. And Emad time. Yeah.
Welcome. Yeah, 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.
Thank you for being here.
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. Uh, we need purpose. Uh, we need some level of struggle amongst ourselves. So I'm here with my extraordinary moonshot mates, Salim Ismail. Salim, love you, buddy.
Yeah, it was great to have your family here as well.
Yeah, for sure. And you've just flown in from Singapore, 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 Visioneering, and now we're here again recording again.
No, can't get away from 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.
Recording. Yeah.
Uh, and Emad, 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. Uh, we narrow them down and uh, we have a whole group of philanthropists who fund them and we'll share some of the stories there. So, uh, 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? Uh, I had launched this $10 million competition and unfortunately didn't have the money. Uh, you know, this is what entrepreneurs do.
Yeah. Didn't you lose like a couple of board members?
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 two 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 read in Fortune magazine about this amazing woman, Anousheh. She was written up in the wealthiest 40 women under 40. I wasn't married at the time, so that was one funding opportunity, but she was married and very successful. Had built a company called Telecom Technologies, sold to Sonus Networks for $1.4 billion. And in her write-up, it said you were interested in flying on a suborbital flight into space.
And I read that paragraph like three times. Not, you know, into space, not into orbit, on a suborbital flight. It was like, wow. And tracked you down vacationing in Hawaii. Uh, and uh, you know, Byron Lichtenberg, a twice shuttle astronaut. I brought an astronaut with me to the meeting.
Yes, great marketing scheme.
And uh, you and 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 sorry commitment till 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.
Um, and then Anousheh, we rebranded the Ansari XPRIZE in your honor. And then Anousheh, 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 eight years ago, uh, one of the greatest blessings of my life returned as the CEO of the XPRIZE. It's been an adventure ever since I've met you, Peter.
It has. A lot of people say that about me. It has been a pivotal time for me and uh, it's a gift that keeps on giving in a really great way because not only did it help open my mind to possibilities and uh, how I should look at life, but also now the opportunity to um, 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. Um, 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, uh, I'm going to open up uh, with probably the most insane product demo uh, ever. Uh, so last night in Waco, Texas, next to SpaceX's McGregor test site, uh, Tesla finally unveiled their flying Tesla Roadster. Um, and you know, just to describe, we're going to show a video in a moment, but just to describe what happened, you know, the Roadster uh, 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 how it was doing it. I said, "Is it magnetics? Is it some kind of a rail system?" And it was actually a fan suction system that could hold it against 2Gs. So, it's levitating there upside down, which was insane. So, let's watch the video and then let's talk about it. Uh, I'm curious which one you guys are going to buy.
Is 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. Uh, but uh, this will actually be long-term the only uh, 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 G's uh, 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, we could 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-overwrapped pressure vessels. There's two of them in the car. They are a total of 600 L. It's about 250 kilos of air. And uh, we have 10 nozzles to do all that work. And it's using an advanced uh, ultra-high-pressure uh, air tank. So the car can recharge its air tank. So it's not like you, you know, need special propellant or anything like that. It literally is just going to puff itself up and uh, be able to do these maneuvers whenever you'd like.
Amazing. Only, only Elon.
I'm surprised that it's not autonomous and it's only, you know, a 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 one second. I mean, you know, if you want to go fast, you might as well.
This is why we need the organ regeneration. Your liver will be left behind as you...
Top speed of 230 mph and uh, 300 miles of range. There are three 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.
The rocket version does 0 to 60 in 0.6 seconds, but also apparently 380 km/h, 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 solution to Los Angeles traffic, right?
Oh my god. So, production starts in 2028 per Elon. Uh, no price was given. Uh, 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.
Okay. Then how long does it fly for? Was that given anywhere in terms of what the...
I don't think they gave the, you know, the duration of a hover.
I mean, it'll be like just a few seconds. Again, if you look at the pressure pushing out.
I don't think that's kind of the point, though. I think the point is it can... you're not going to see hopping cars. Can you imagine 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? Sure. That for me is the real one.
What would be the cost of insurance for that?
Yeah. I don't think it's insurable.
I mean, if I'm the insurance company, I'm like, "Yeah, those thrusters stay off." Otherwise, your insurance is off. It's like, really? You want a rocket on your car? Uh, so you know, obviously this is showing Tesla and SpaceX working together, which, you know, my prediction is they're going to announce the merger before the end of the year.
Right. But you know, Salim, here's my question. Who regulates this? DOT? FAA? nobody. I mean, and how is this going to get licensed?
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, going to take a year or two to figure out how to regulate this. He's two generations ahead. So where is this going to go? This is, I think, the biggest stress test that we're seeing across all of these technologies, not just this car.
I mean, you might... I imagine this is AI-driven, AI-enabled, even though he says it can be, you know, manually driven, but there's no... I mean, no way that control system is going to fly without AI in the loop.
Yeah. I mean, like literally fly.
I think this is the thing, right? So I think 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 one. The question is again, who's going to insure this thing?
Like if you've got the thing, maybe they will have their own insurance company like 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 the Moon and Mars and beyond. And it's freaking cool.
Like so many demos, like Porsche had their release on the same day.
I mean, it's a very nice-looking car, but can it fly? No. Like it raises the bar and it makes you again want to go and work at Tesla, SpaceX, right? Like if you're a top engineer, where do you go and work? You go work there. And that's his key thing is getting the talent, getting it going and saying what can be. Plus, he gave us 280 characters from 140.
Oh god. You know, one of the things I... there's an interesting question about everybody is going to go into autonomous vehicles and be driven around. Some people want to drive, right? We've talked on the pod before about there 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 your very best, but if you start losing traction, the AI takes over. So like in Saudi Arabia and the Emirates where there's a lot of wealth and a lot of young men, there's a high death rate in sports cars because, you know, they've got cars that outperform their ability to think and react. But you could put an AI layer on top of that and make sure everybody's safe.
Well, this is essentially an extension of anti-lock braking, traction control, which is embedded in all these systems. But now you 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...
Yeah.
Uh, we'll get into that. So, Anousheh, you know, during the 60s, all the Apollo astronauts got Corvettes.
Uh, 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, though. I don't know how much.
No, you know, Chevrolet gave them the Corvettes.
Maybe you think Elon will be generous with what the Artemis astronauts ask for.
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? He 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 he said, miss a few timelines, but he gets it done.
Gets it done.
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All right, I want to move to our next story here and Emad, it's one that you sent me over yesterday. So, it's about a curve that should make every robotic founder stand up and pay attention. So, Robo Curve, it's a Y Combinator startup, designed a benchmark called Robo Dojo 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 38 cents in September, 84% drop. So here's the curve. Uh, and 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 yeah they seem they can do just about anything and now the hands have got good. Now the question is the mind and it's always been the case of do you need specialist models or can you use generalist models that suddenly learn these new capabilities. This shows that generalist models can actually win.
Um, 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 specialist models anymore because these things do it and we're used to kind of task and cost halving but every 3 weeks.
Yeah. Like you're heading to the 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 one, sure. But humanoid robots, I think that's a different game.
Yeah. Uh, we're going to be interviewing Brett at Figure in a couple of weeks. And remember, he started building his own models for the robots because the early models didn't work, the large language models. But I'll be curious to ask him, you know, what he's using or building now. But having a cost halving every 3 weeks means it effectively goes to zero, you know, within the year. Um, so you know, if you're an entrepreneur now in the 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? Like...
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 a billion dollars at Figure for example for data. Or will it be that after hoovering up the world's 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 three-armed robots?
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 Jeff Holden's Atomic Machines company, which is basically going from prompt to product, where you describe what you want, and this machine is building it at millimeter-level accuracy with multiple materials. So, it's, you know, just to give a vision of where we're heading. All of a sudden, we're in a situation where if you want a specific kind of robot to do a specific, you know, clean windows, repair HVAC, you know, brick and 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 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 dangerous or dirty.
Uh, it's dangerous for the patient.
If you're the first 10 heart 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 type and doesn't pay attention, right? And so now you can smooth that curve out and have the world's 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.
Yeah. What I'm most excited about is the robot chef.
Can you just imagine that? It's a robot chef that cooks the best food for you all the time in your kitchen.
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 that before robots get into the home, there's a safety factor that needs to... we need to figure out. These are still big, heavy machinery. So if you have a dog or a cat or you have small children, they're around it and they just step on the foot.
You have a safe word. You have a safe word.
That's why 1X makes it very like 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 the 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. Um, and so 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...
Push back. 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.
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, 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 loses in the laundry. 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 Sonnets and the Opuses and the 3.8 Flashes suddenly understood 3D.
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. Um, 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... 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, um...
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.
I really don't believe we ultimately... the humanoid form factor is the right form factor.
That's my case. You've got someone.
Thank you. I think we need either a special or transformers because I think, you know, there are certain tasks that really our bodies are not suitable for those tasks. You need to be small to go through a small opening to get to a location or so there are many, many tasks like in the categories you're describing. I think you need to have specialized form factors that suit the task. Uh, 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, you know, like right now the Unitree hands are probably the most advanced ones. Those cost about $8,000 down from $20,000 with as much... actually more dexterity than a human hand. But a robot that does the laundry doesn't need that.
And so you might just actually see like spare parts companies where again like Transformers, they like add a few extra arms. They add extras.
Point. It'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 all the choice around the world for that.
So, you know what? We're going to find out.
We'll find out very quickly and at a dropping of three weeks. I mean, if we've been looking at AI that doubles every 10 weeks and that's been mind-boggling, halving every 3 weeks may be the fastest-moving capability we've ever seen. Forget 18 months doubling in Moore's Law. This blows that out of the water.
Yeah. Did this surprise you, Emad?
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 about to release next week are 0.1 cents per second. Faster than real time.
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, we go to our third breaking story this week again from you, Emad. 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 as have you, Emad, we've talked about this, that physics is next. And so this week, uh, researchers built a test of 50 unpublished research-level theoretical physics problems with verifiable answers. Um, because the problems had never been published before, there was no lookup on the internet that it could make. Uh, so they estimated that a world-class physicist would take about a day to answer each of these 50. And uh, GPT-6 Astra uh answered it with about a cost of what was it? $3.
$3 per problem. So employing a world-class physicist for a day or answering it for three bucks. Uh, interesting detail. Um, originally it was reported that Astra got 49 of the 50 physics problems correct but when it looked into it, it found out that the published answer for the 50th was wrong and Astra got it right. So, what are we seeing here, Emad?
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, it corrected that final thing. So this creates an interesting thing whereby anything verifiable we suddenly have a tool to verify and that's fantastic because it means we can build much more complex systems and processes.
I mean like 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 four steps. That's just going to make organizations 10x more efficient and effective across the board in workflows.
Um, so 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. Uh, 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 whereas time and quantum mechanics and field theory...
Unified field theory. Um, 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... you know, a couple years ago, Eric Schmidt on stage at A360 said, you know, imagine a time when you have the world's best physicist or equivalent in every lab in the world. And we asked him when, he said, five to six years. And now it's like two years. And here we are. So I think this is another example of stuff happening much faster than we ever predicted because now every lab has access to an incredible competence at a few bucks an hour.
And that makes it unbelievable.
But 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, um, 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 bulk solving all the, you know, problem sets that we've defined, 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 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 in consideration 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.
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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 four answers there: fear, curiosity, greed, and then kind of love as well. I'll add that as a fourth one. And so, you know, unfortunately, fear is a very strong motivator and curiosity is far behind. And I calculated the ratio of fear to curiosity. It's the ratio of the defense budget to the science budget, right? Which is vastly different. And then greed, of course, you know, if I put them in order, I would say fear is the strongest motivator, greed comes next, curiosity comes next, and love, you know, depending upon the subject or the person, sort of varies in there.
And it's interesting to take a second and look at incentive prizes as a model. So we all know the Nobel Prize, which awards someone for work that was done typically anywhere from like the earliest was 10 years ago and typically 20, 30, even 40 years ago. And incentive prizes say, I don't care who you are, what you've ever done, where you've gone to school, if you solve this problem, you win.
And you know, 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, 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.
And this guy was a confectioner, 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? So 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. They're coming in with a beginner's mind.
And you know, my frustration at wanting to be an astronaut led to the XPRIZE. You know, I grew up on Apollo and Star Trek. I've told that story a thousand times. And you know, it became clear when I interviewed a whole bunch of astronauts. I used to be at MIT's Man 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 chance is one in a thousand. Secondly, if you're accepted in the astronaut corps, half the astronauts have never flown. They're called penguins because 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 in commercial space. And then a dear, dear brother of mine, a friend of Anousheh's, myself, all of us, Greg Marinak, gave me The Spirit of St. Louis, which was Lindbergh's autobiography chronicling his flight. And in that I found out that Lindbergh didn't fly from New York to Le Havre on a whim. There was a $25,000 prize offered by a gentleman, Raymond Orteig, and that $25,000 inspired nine teams who spent $400,000 to win the $25,000. And ultimately he didn't pay any of the losers. He only paid Lindbergh as the winner, who was the most unlikely guy to pull it off. And I said, 'Wow, that's a great business model. You only pay for success after it's successful.'
And that launched the Ansari XPRIZE. But let's talk about like what happens when incentives go wrong and you get perverse. 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 accounts, so the sales quota from Wells Fargo got employees to open 3.5 million fake accounts to get the sales quota. So you got to be careful about your incentives.
Well, we're seeing that today in real time, right? They've incented folks at ICE to arrest as many people as possible, deport as many people as possible. And the idea was let's just arrest the criminals, but the targets are such and the incentives are such that they're arresting everybody, and that's causing political backlash of enormous magnitude.
It goes back to the motive at the XPRIZE. Almost every prize we have, the motives are love and curiosity. I cannot think of anything 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 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...
you know, those prizes, those prizes that were more execution than inspiration.
Yeah. So, I'll 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 built with Gemini.
36 prizes now over 32 years, over 32 years.
And total number of the prize purse like 550 million, something like that.
And at my count, that's driven a little over $30 billion in R&D.
Definitely, and not just R&D, but 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 their competition or after, they received $12 billion of investment. That means an area that didn't receive attention and capital before now it's receiving attention from investors, which means there will be long-term continuity and success for the company.
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 successes 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.
Every dollar put into a prize drives 62.
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 leverage philanthropic...
So I think we're very unique in the way we leverage every dollar in...
Well, you could see the enthusiasm in the room as people are 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.
I think it's interesting, you know, the word philanthropy here because it has such amazing private outcomes and personal outcomes for... 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. 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, gives 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, I'll tee it up and you can add on. 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 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, 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 to ship their solution in shipping containers to 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.
And the tattoo guy turns out to be an environmentalist. I was talking about this prize. The dentist goes, 'I know a lot about bonding agents in liquid.' And they're like, 'Oh.' So they get together, form a team, test it in his hot tub, or is it in his hot tub?
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 the 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 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. And in this case, there's no economic incentive to do R&D on oil cleanup technology, and this is 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 a diverse set of ideas and they're not bound by a set of belief systems 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, 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?
