Y Combinator Startup Podcast - Elon Musk: Digital Superintelligence, Multiplanetary Life, How to Be Useful

Episode Date: June 21, 2025

A fireside with Elon Musk at AI Startup School in San Francisco.Before rockets and robots, Elon Musk was drilling holes through his office floor to borrow internet. In this candid talk, he walks through the early days of Zip2, the Falcon 1 launches that nearly ended SpaceX, and the “miracle” of Tesla surviving 2008. He shares the thinking that guided him—building from first principles, doing useful things, and the belief that we’re in the middle of an intelligence big bang.

We're at the very, very early stage of the intelligence Big Bang. Being a multi-planet species greatly increases the probable lifespan of civilization or consciousness and intelligence, both biological and digital. I think we're quite close to digital superintelligence. It doesn't happen this year, next year for sure. Please give it up for Elon Musk. Elon, welcome to AI startup school. We're just really, really blessed to have your presence here today.

Thanks for having me. So from SpaceX, Tesla, NeuroLink, X-A-I, and more. Was there ever a moment in your life before all this where you felt, I have to build something great? And what flipped that switch for you? Well, I didn't originally think I would build something great. I wanted to try to build something useful, but I didn't think I would build anything particularly great.

You've said it probabilistically seemed unlikely. but I weren't to at least try. So you're talking to a room full of people who are all technical engineers, often some of the most eminent AI researchers coming up in the game. Okay. I like the term engineer better than researcher.

I mean, I suppose if there's some fundamental algorithmic breakthrough, it's research, otherwise it's engineering. Maybe let's start way back. I mean, when you were, this is a room full of 18 to 25 year olds that skews younger because the founder set is younger and younger. Can you put yourself back into their shoes when, you know, you were 18, 19, you know, learning to code, even coming up with the first idea for Zip 2. What was that like for you? Yeah, back in 95, I was faced with a choice of either do, do.

you know, grad studies PhD at Stanford in material science actually working on ultra capacitors for potential use in electric vehicles, essentially trying to solve the range problem for electric vehicles, or try to do something in this thing that most people have never heard of called the internet. And I talked to my professor who was Bullnicks in the material science department and said like, can I like defer for a quarter because this sort of this will probably fail and then i'll need to come back to college and um and then he said this is probably the last conversation we'll have and he was right um so but i thought things would most likely fail not that they would most likely succeed um and um and then in 95 i wrote uh basically

i think the first or close to the first uh maps directions uh in white pages and yellow pages on the internet. I just wrote that personally. I didn't even use a web server. I just read the port directly. Because I couldn't afford, and I couldn't afford a T1. The original office was on Sherman Avenue in Palo Alto.

There was like an ISP on the floor below. So I drilled a hole through the floor and just ran a land cable directly to the ISP. And, you know, my brother joined me and another co-founder, Greg Curry, who passed away. And at the time, we couldn't even afford a place to stay. So we just, the office was 500 bucks a month. So we just slept in the office and then shouted the YMCA on Page Mill and Al Camino. And, yeah, and I guess we ended up doing a little bit of a useful.

company as up to in the beginning. And we did build a lot of really good software technology, but we were somewhat captured by the legacy media companies in that Nitroeder, New York Times, and the host, whatnot, were investors and customers, and also on the board. So they kept wanting to use our software in ways that made no sense. So I wanted to go direct to consumers. Anyway, it's a long story, dwelling too much on Zip2. But I really just wanted to do something useful on the internet.

Because I had two choices. Like do a PhD and watch people build the internet or help build the internet in some small way? And I was like, well, I guess I can always try and fail and then go back to grad studies. And anyway, that ended up being reasonably successful, sold for like $300 million. which is a lot at the time. These days, that's like, I think minimum impulse put for an AI startup is like a billion dollars. It's like there's so many frigging unicorns. It's like a herd of unicorns at this point.

You know, unicorns a billion dollar situation. There's been inflation since, so quite a bit more money, actually. Yeah, I mean, like 9095, you could probably buy a burger for a nickel. Well, not quite, but I mean, yeah, there has been a lot of inflation. But I mean, the hype level on AI is is pretty intense, as you've seen. You know, you see companies that are, I don't know, less than a year old getting sometimes billion dollar or multi-billion dollar valuations, which I guess could pan out and probably will pan out in some cases.

But it is eye-watering to see some of these valuations. Yeah, what do you think? I mean, well, I'm pretty bullish, personally. I'm pretty bullish, honestly. So I think the people in this room are going to create a lot of the value that, you know, a billion people in the world should be using this stuff. And we're not even scratching the surface of it. I love the internet story in that even back then, you know,

you are a lot like the people in this room back then in that, you know, the heads of all the CEOs of all the legacy media companies look to you as the person who understood the internet. And a lot of the world, the corporate world, like the world out large that does not understand what's happening with AI, they're going to look to the people in this room for exactly that. It sounds like, what are some of the tangible lessons? It sounds like one of them is don't give up board control or be careful about have a really good lawyer. I guess for the first, my first startup, the big, really the mistake was having too much shareholder and board control from legacy media companies who then necessarily see things through the lens of legacy media and that they'll kind of make you do things that seem sensible to them, but are really don't make sense with a new technology. I should point out that I didn't actually at first intend to start a company.

I try to get a job at Netscape. I sent my resume into Netscape, and Mark Andrews knows about this, but I don't think you ever saw my resume, and then nobody responded. And then I tried hanging out in the lobby of Netscape to see if I could like bump into someone, but I was like too shy to talk to anyone, so I'm like, man, this is ridiculous. So I'll just write stuff for myself and see how it goes. So it wasn't actually from the standpoint of like, I want to start a company. I just want to be part of building, you know, the internet in some way.

And since I couldn't get a job at an internet company, I had to start an internet company. Anyway, the, yeah, I mean, from an AI will so profoundly change the future. It's difficult to fathom how much. But, you know, the economy, assuming we don't, things don't go awry. like AI doesn't kill us all and itself, then you'll see ultimately an economy that is not 10 times more than the current economy. Ultimately, like if we become, say, or whatever, our future machine descendants or mostly machine descendants become like a Kottershev scale to civilization or beyond,

we're talking about an economy that is thousands of times, maybe millions of times bigger than the economy today. So, yeah, I mean, I did sort of feel a bit like, you know, when I was in D.C. taking a lot of flack for, like, getting rid of waste and fraud, which was an interesting side quest as sidequests go. But I've got to get back to the main quest. Yeah, I got to get back to the main quest. quest here. So back to the main quest. So, but I did feel, you know, a little bit like there's, you know, it's like fixing the government. It's kind of like there's like, say, the beach is dirty and there's like some needles and feces and like trash and you want to clean up the beach. But then there's also this like thousand foot wall of water, which is a

tsunami of AI. Like, and how much does cleaning the beach really matter if you got a thousand foot tsunami about to hit. Not that much. We're glad you're back on the main quest. It's very important. Yeah, back to the main quest. Building technology, which is what I like doing. It's just so much noise.

The signal of noise ratio in politics is terrible. I mean, I live in San Francisco, so you don't need to tell me twice. Yeah. DC is like, you know, I guess it's all politics in DC. but if you're trying to build a rocket or cars or you're trying to have software that compiles and runs reliably, then you have to be maximally truth-seeking or your software or your hardware won't work.

Like you can't fool math, like math and physics are rigorous judges. So I'm used to being in a maximally true-seeking environment, and that's definitely not politics. So I'm glad to be valid. in technology. I guess I'm kind of curious, going back to the Zip 2 moment, you had hundreds of millions of dollars or you had an exit that I've worth hundreds of millions of dollars. I got $20 million.

Right. Okay, so you solved the money problem at least. And you basically took it and you rolled, you kept rolling with X.com, which became PayPal and Confinity. Yes. I kept the chips on the table. Yeah. So.

Not everyone does that. A lot of the people in this room. we'll have to make that decision, actually. What drove you to jump back into the ring? Well, I think I felt with SipT, we'd both like incredible technology, but it never really got used. You know, I think, at least from my perspective,

we had better technology than, say, Yahoo or anyone else, but it was constrained by our customers. And so I wanted to do something that where, okay, we wouldn't be constrained by our customers, go direct to consumer, and that's what ended up being. like x.com, PayPal, essentially X.com merging with Confinity, which together created PayPal. And then that actually, the sort of PayPal diaspora has, it might have created more companies than,

so more companies than probably anything in the 21st century, you know. So many talented people were at the combination of, of Confinity and X.com. So I just wanted to, like, I felt like, We kind of got our wings clift somewhat with zip two, and it's like, okay, what if our wings on clipped and we go direct to consumer? And that's what PayPal ended up being. But, yeah, with, I got that like $20 million check for my share of Zip 2. At the time, I was living in a house with four housemates and had like, I don't know, 10 grand in the bank. And then this check arrives in the mail of all places, and it's in the mail.

And then my bank balance went from $10,000 to $20 million and $10,000. You're like, well, okay. So I'd like to pay taxes on that and all. But then I ended up putting almost all of that into X.com. And as you said, like just kind of keeping almost all the chips on the table. And yeah, and then after. PayPal, I was like, well, I was kind of curious as to why we had not sent anyone to Mars. And I went on the NASA website to find out when we're sending people to Mars and there was no date.

I thought maybe it was just hard to find on the website. But in fact, there was no real plan to send people to Mars. So then, you know, this is such a long story. So I don't want to take up too much time here. I think we're all listening with rapt attention. So I was actually, I was on the Long Island Expressway with my friend, Dea Resi, we were like housemates in college. And Dea was asking me what we're going to do, what am I going to do after PayPal? And I was like, it's like, I don't know, I guess maybe I'd like to do something philanthropic in space because I didn't think I could actually do anything commercial in space because that seemed like the purview of nations.

So, but, you know, I'm kind of curious as to when we're going to send people to Mars. And that's when I was like, oh, it's not on the website. And then I started digging on, there's nothing on the NASA website. So then I started digging in. And I'm definitely summarizing a lot here. But my first idea was to do a philanthropic mission to Mars called Life to Mars, where we would send a small greenhouse with seeds in dehydrated nutrient gel, land that on Mars, and grow, you know, hydrate the gel,

and then you'd have this great sort of money shot of green plants on a red background. For the longest time, by the way, I didn't realize money shot, I think, is a porn reference, but anyway, the point is that that would be the great shot of green plants on a red background and to try to inspire, you know, NASA and the public to send astronauts to Mars.

As I learned more, I came to realize, And along the way, by the way, I went to Russia in 2001 and 2002 to buy ICBMs, which is like, that's an adventure. You know, you go and meet with Russian High Command and say, I'd like to buy some ICBMs. This was to get to space as a rocket. Not to nuke anyone. But they had to, as a result of arms reduction talks, they had to actually destroy a bunch of their big nuclear missiles. So I was like, well, how about if we take two of those, you know, minus the nuke, added an additional upper stage for Mars? But it was kind of trippy, you know, being in Moscow in 2001 negotiating with like the Russian military to buy ICVMs.

Like, that's crazy. And they kept also like raising the price on me. So that, so like literally it's kind of like. the opposite of what a negotiation should do. So I was like, man, these things are getting really expensive. And then I came to realize that actually the problem was not that there was insufficient world to go to Mars, but there was no way to do so without breaking the budget, even breaking the NASA budget.

So that's where I decided to start SpaceX to, SpaceX to advance rocket technology to the point where we could send people to Mars. And that was in 2002. So that wasn't, you know, you didn't start out wanting to start a business. You wanted to start just something that was interesting to you that you thought humanity needed. And then as you sort of, you know, like a cat pulling on, you know, a string, it just sort of the ball sort of unravels. And it turns out this could be a very profitable business.

I mean, it is now. But it, there had been no prior example of. really a rocket startup succeeding. There have been various attempts to do commercial rocket companies and that all failed. So again, with SpaceX, starting SpaceX was really from the standpoint of like, I think there's like a less than 10% chance of being successful, maybe 1%, I don't know. But if a startup doesn't do something to advance rocket technology, it's definitely not coming from the big defense contractors because they just impedance match.

to the government and the government just wants to do very conventional things so there's it's either coming from a startup or it's not happening at all so so like a small chance of success is better than no chance of success and and so that yeah so SpaceX uh start that in mid mid 2000 to expecting to fail like I said probably 90% chance of failing and even like when recruiting people I didn't like try to you know, make out that it would I said, we're probably going to die. But it's a whole chance we might not die.

But this is the only way to get people to Mars and advance the state of the art. And then I ended up being chief engineer of the rocket, not because I wanted to, but because I couldn't hire anyone who was good.

So, like, none of the good sort of chief engineers would join because it's like, this is too risky, you were going to die. and so then I ended up being chief engineer of the rocket and you know the first three flights did fail so a bit of a learning exercise there and um fourth one fortunately worked but if the fourth one hadn't worked I had no money left and that would have been it would have been curtains so it was a pretty close thing if the fourth launch of falcon not work it would have been just curtains and we would have just been joined the graveyard of prior rocket startup so it was Like my estimate of success was not far off.

We just, we made it by the skin of our teeth. And Tesla was happening sort of simultaneously. Like 2008 was a rough year. Because at mid-2008, we're called summer 2008. The third launch of SpaceX had failed, a third failure in a row. The Tesla financing round had failed. and so Tesla was going bankrupt fast. It was just like, man, this is grim.

This is going to be a tale of warning an exercise in hubris. Probably throughout that period, a lot of people were saying, you know, Elon is a software guy. Why is he working on hardware? Why would he choose to work on this, right? 100%. So you can look at the, like, because there's still, you know, the press of that time is still online. You can just search it. And, and they kept calling me internet guy. So like internet guy, aka fool, is attempting to build a rocket company. So, you know, we got ridiculed quite a lot. And it does sound pretty absurd. Like internet guy started.

rocket company doesn't sound like a recipe for success, frankly. So I didn't hold it against them. I was like, yeah, you know, admittedly it does sound improbable. And I agree that it's improbable. But fortunately, the fourth launch worked and NASA awarded us a contract to resupply the space station. And I think that was like maybe, I don't know, December 22nd. It was like right before Christmas because even the fourth launch working wasn't enough to succeed. NASA also needed, we also needed a big contract to keep us alive.

So I got that call from like the NASA team. And I literally, they said we're rewarding you one of the contracts to resupply Space Station. I like literally blurted out. I love you guys, which is not normally, you know, what they hear. because it's usually pretty, you know, sober. But I was like, man, this is a company saver. And then we closed the Tesla financing round on the last hour of the last day that it was possible,

which was 6 p.m. December 24th, 2008. We would have bounced payroll two days after Christmas if that round hadn't closed. So that was a nerve-wracking end of 2008, that's for sure. I guess from your PayPal and Zip 2 experience jumping into these hardcore hardware startups, it feels like one of the throughlines was being able to find and eventually attract the smartest possible people

in those particular fields. I mean, the people in this room, like most of the people here, I don't think, have even managed a single person yet. They're just starting their careers. What would you tell to, you know, the Elon who's never had to do that yet? I generally think to try to be as,

useful as possible. It may sound trite, but it's, it's so hard to be useful, especially to be useful to a lot of people, where you say the area under the curve of total utility is like, how useful have you been to your fellow human beings times how many people? It's almost like the physics definition of true work. It's incredibly difficult to do that. I think if you aspire to do true work, you're probably a success is much higher. Like don't aspire to. to glory, aspire to work. How can you tell that it's true work? Like, is it external?

Is it like what happens with other people or, you know, what the product does for people? Like, what, you know, what is that for you? When you're looking for people to come work for you, like, what, you know, what's the salient thing that you look for or if there are, you know, a few. That's a certain question, I guess. It's, I mean, in terms of your own product, you just have to say, like, well, if this thing is successful, how useful will it be to how many people? and that's that's what I mean and and then you do whatever you know whether you're

a CEO or any role in a startup you do whatever it takes to succeed like and just and just always be smashing your ego like like internalized responsibility like a major failure mode is when ego to ability ratio is double greater than sign one you know like if you if you're ego to ability ratio is it gets too high then you're you're you're going to basically break the feedback loop to reality and in in AI terms your you'll have you'll break your RL loop so you want you want to break your RL loop which means internalizing responsibility and minimizing ego and you do whatever the task is no matter whether it's you know grand or humble so I mean that's kind of like why actually I prefer the term like

engineering as opposed to research. I prefer the term and I actually don't want it to call X-AI a lab. I just want to be a company. It's like whatever the one of the simplest most straightforward ideally lowest ego terms are that those are generally a good way to go. You want to just close the loop on reality hard. That's that's a that's a super big deal. I think I think everyone in this room really looks up to everything you've done around being sort of a paragon of first principles. And, you know, thinking about the stuff you've done, how do you actually determine your reality? Because that seems like a pretty big part of it. Like other people, people who have never made anything, non-engineers, sometimes journalists at time who've never done anything, they will criticize you.

But then clearly you have another set of people who are builders who have very high, you know, sort of area under the curve who are in your circle. Like, you know, how should people approach that? Like, what has worked for you and what would you pass on? Like, you know, to X, to your children. Like, you know, what do you tell them when you're like, you need to make your way in this world? You know, here's how to construct a reality that is predictive from first principles. Well, the tools of physics are incredibly helpful to understand and make progress in any field. The first principles mean just obviously just means, you know, break things down to the fundamental axiomatic elements that are most likely to be true.

And then reason up from there as cohesionally as possible, as opposed to reasoning by analysis or metaphor. for. And then it just simple things like like thinking in the limit, like if you extrapolate, you know, minimize this thing or maximize that thing, thinking in the limit is very, very helpful. I'd use all the tools of physics. They apply to any field. This is like a superpower, actually. So you can take, say, take for example, like rockets, you can say, well, how much should a rocket cost? The typical approach to how to that people would take to how much rocket should cost, is they look historically at what the cost of rockets are and assume that any new rocket must be somewhat similar to the prior cost of rockets. A first principle's approach would be you look at the materials that the rocket is comprised of. So if that's aluminum, copper, carbon fiber, steel, whatever the case may be, and say what how much does that rocket weigh and what are the constituent elements and how much do they weigh,

what is the material price per kilogram of those constituent elements? And that sets the actual floor on what a rocket can cost. It can asymptotically approach the cost of the raw materials. And then you realize, actually, the raw materials of a rocket are only maybe 1 or 2% of the historical cost of a rocket. So the manufacturing must necessarily be very inefficient if the raw material cost is only 1 or 2%. That would be a first principles analysis of the potential for cost optimization of a rocket.

And that's before you get to reusability. To give an AI sort of AI example, I guess, last year for X-AI, when we were trying to build a training supercluster, we went to the various suppliers to ask, this was beginning of last year, that we needed 100,000 H-100s to be able to train coherently. and there are estimates for how long it would take to complete that were 18 to 24 months. It's like, well, we need to get that done in six months. So then, or we won't be competitive. So then if you break that down, what are the things you need? Well, you need a building, you need power, you need cooling.

We didn't have enough time to build a building from scratch, so we've had to find the existing building. So we found at a factory that was no longer in use in Memphis that used to both electrolyx products. But then the input power was 15 megawatts and we needed 115 megawatts. So we rented generators and had generators in one side of the building. And then we have to have cooling. So we rented about a quarter of the mobile cooling capacity of the U.S. and put the chillers on the other side of the building.

That didn't fully solve the problem because the power variations during training, are very big. So you can have power can drop by 50% in 100 milliseconds, which the generators can't keep up with. So then we added Tesla megapax and modified the software in the megapacks to be able to smooth out the power variation during the training run. And then there were a bunch of networking challenges. The networking cables, if you're trying to make 100,000 GPUs,

train coherently, are very, very challenging. Almost it sounds like almost any of those things you mentioned, I could imagine someone telling you very directly, no, you can't have that. You can't have that power. You can't have this. And it sounds like one of the salient pieces of first principles thinking is actually, let's ask why. Let's figure that out. And actually, let's challenge the person across the table. And if they, if I don't get an answer that I feel good about, I'm going to, you know, not allow that to be, I'm not going to let that.

know to stand. Is that, I mean, that feels like something that, you know, everyone, if someone were to try to do what you're doing in hardware, hardware seems to uniquely need this. In software, we have lots of, you know, fluff and things that, you know, it's like we can add more CPUs for that. It'll be fine. But in hardware, it's just not going to work. I think these general principles of first principle thinking apply to software and hardware, apply to anything, really. I'm just using kind of a hardware example of how we were told something is impossible, but once we broke it down into the constituent elements of we need a building, we need power, we need cooling, we need power smoothing, and then we could solve those constituent elements.

And then we just ran the networking operation to do all the cabling everything in four shifts 24-7. And I was like sleeping in the data center and also doing cabling myself. And there were a lot of other issues to solve. You know, nobody had done a training run with 100,000 H-100s training coherently last year. Maybe it's been done this year. I don't know. And then we ended up doubling that to 200,000. And so now we've got 150,000 H-100s, H-200s, and 30K-K,000.

G.B-200s in the Memphis training center. And we're about to bring 110,000 GV-200s online at a second data center, also in the Memphis area. Is it your view that, you know, pre-training is still working and the scaling laws still hold and whoever wins this race will have basically the biggest, smartest possible model that you could distill? Well, there's other various elements that

side competitiveness for for large AI. This for sure the talent of the people matter. The scale of the hardware matters and how well you're able to bring that hardware to bear. So you can't just order a whole bunch of GPUs and they don't, you can't just plug them in. So you've got to you've got to get a lot of GPUs and have them train, train coherently and stavely. then it's like what unique access to data do you have? I guess distribution matters to some degree as well.

Like how do people get exposed to your AI? Those are critical factors for if it's going to be like a large foundation model that's competitive. You know, as many have said, I think my friend Elias Satskyers said, you know, we've kind of run out of pre-training data for human-generated pre- like human-generated data you run out of tokens pretty fast um of certainly of high quality tokens and um and then you then you have to do a lot of uh you you need to essentially create synthetic data um and and be able to accurately judge the synthetic data that you're creating to verify like is this real synthetic

data or is it an hallucination that doesn't actually match reality um so achieve and grounding in reality is tricky, but we are at the stage where there's more effort put into synthetic data. And right now we're training Grok 3.5, which is a heavy focus on reasoning. Going back to your physics point, what I heard for reasoning is that hard science, particularly physics textbooks, are very useful for reasoning, whereas I think researchers have told me that social sciences totally useless for reasoning. Yes, that's probably true. So, yeah.

There's something that's going to be very important in the future is combining deep AI in the data center or supercluster with robotics. So that's, you know, things like the optimist humanoid robot. Incredible. Yeah, Optimus is awesome. There's going to be so many humanoid robots. And robots of all, robots of all sizes and shapes, but my prediction is that there will be more humanoid robots by far than all other robots combined,

but maybe in order of magnitude, like a big difference. Is it true that you're planning a robot army of a sort? Whether we do it or, you know, whether Tesla does it, you know, Tesla works closely with X-A-I. Like, you've seen how many humanoid robots startups are there. Like, it's like, I think Jensen, Bong was on stage with a lot,

with a massive number of robots, you know, robots from different companies. I think there was like a dozen different humanoid robots. So, I mean, I guess, you know, part of what I've been fighting and maybe what has slowed me down somewhat is that I'm a little, I don't want, I don't want to make Terminator a reel, you know. So I've been sort of, I guess, at least until recent years, dragging my feet

on, on AI and humanoid robotics. And then I sort of come to the realization it's happening whether I do it or not. So you've got really two choices. You can either be a spectator or a participant. And so I'm like, well, I guess I'd rather be a participant than a spectator. So now it's, you know, pedal to the medal on humanoid robots and digital superintelligence. So I guess there's a third thing that everyone has heard you talk a lot about that I'm really a big fan of, becoming a multi-planetary species. Where does this fit? This is all not just a 10 or 20 year thing, maybe a 100-year thing, like it's a many,

many generations for humanity kind of thing. How do you think about it? There's AI, obviously. there's embodied robotics and then there's being a multi-planetary species. Does everything sort of feed into that last point? Or, you know, what are you driven by right now for the next 10, 20, and 100 years? Geez, 100 years? Man, I hope civilization's around in 100 years.

If it is around, it's going to look very different from civilization today. I mean, I'd predict that there's going to be at least five times as many humanoid robots as there are humans, maybe 10 times. One way to look at the progress of civilization is percentage completion Kadachev. So if you're in a Kottyshev scale one, you've harnessed all the energy of a planet. In my opinion, we've only harnessed maybe 1 or 2% of Earth's energy. So we've got a long way to go to the Karshev scale 1. then

Card Chef 2 you've honest all the energy of a sun which would be I don't know a billion times more energy than Earth maybe closer to a trillion and then Cardiff 3 would be all the energy of a galaxy

pretty far from that so we're at the very very early stage of the intelligence Big Bang I hope we're in terms of being multi-planetary like I think we'll have enough mass transferred to Mars within like roughly 30 years to make Mars self-sustaining such that Mars can continue to grow and prosper even if the resupply shifts from Earth stop coming. And that that greatly increases the probable lifespan of civilization or consciousness or intelligence, both biological and digital.

So that's why I think it's important to become a multi-planet species. And I'm somewhat troubled by the phoey paradox. Like why have we not seen any aliens? And it could be because intelligence is incredibly rare. And maybe we're the only ones in this galaxy, in which case the intelligence of consciousness is this tiny candle in a vast darkness. And we should do everything possible to ensure the tiny candle does not go out. and being a multi-planet species or making consciousness multi-planetary greatly improves the probable

lifespan of civilization. And it's the next step before going to other star systems. Once you at least have two planets, then you've got a forcing function for the improvement of space travel. And that ultimately is what will lead to consciousness expanding to the stars. It could be that the Fermi paradox dictates once you get to some level of technology you destroy yourself. How do we stay ourselves? How do we actually, what would you prescribe to, I mean, a room full of engineers?

Like, what can we do to prevent that from happening? Yeah, how do we avoid the great filters? One of the great filters would obviously be global thermonuclear war. So we should try to avoid that. I guess building benign AI robots that, AI that loves humanity and robots that are helpful, something that I think is extremely important in building AI is a very rigorous adherence to truth, even if that truth is politically incorrect. My intuition for what could make AI very dangerous is if you force AI to believe things that are not true.

How do you think about, you know, there's sort of this argument for open, open for safety versus closed for competitive edge. I mean, I think the great thing is you have a competitive model. Many other people also have competitive models. And in that sense, you know, we're sort of off of maybe the worst timeline that I'd be worried about is, you know, fast takeoff and it's only in one person's hands, you know, that might, you know, sort of collapse a lot of things. Whereas now we have choice, which is great. How do you think about this? Yeah. I do think there will be several deep intelligences, maybe at least five, maybe as much as 10. I'm not sure that there's going to be hundreds, but it's probably

close, like maybe there'll be like 10 or something like that, of which maybe four will be in the US. So I don't think it's going to be any one AI that has a runaway capability. But, yeah, several deep intelligences. What will these deep, deep intelligences actually be doing? Will it be scientific research or trying to hack each other? probably all of the above I mean hopefully

they will discover new physics and I think they're very they're definitely going to invent new technologies I think we're quite close to digital superintelligence it may happen

this year and if it doesn't happen this year next year for sure digital superintelligence defined as smarter than any human at anything Well, so how do we direct that to sort of super abundance?

You know, we could have robotic labor, we have cheap energy, intelligence on demand. You know, is that sort of the white pill? Like, where do you sit on the spectrum? And are there tangible things that you would encourage everyone here to be working on to make that white pill actually reality? I think it most likely will be a good outcome. I guess I'd sort of agree with Jeff Hinton that makes. Maybe it's a 10 to 20% chance of annihilation.

But look on the right side, that's 80% to 90% probably of a great outcome. So, yeah, I can't emphasize this enough. A rigorous adherence to truth is the most important thing for AI safety. And obviously, empathy for humanity and life as we know it. We haven't talked about Neurilink at all yet, but I'm curious. you're working on closing the input and output gap between humans and machines. How critical is that to AGI, ASI, and once that link is made, can we not only read but also write? The neuralink is not necessary to solve digital superintelligence.

That'll happen before NeurLink is at scale. But what NeuroLink can effectively do is solve the input. output bandwidth constraints, especially our output bandwidth is very low. The sustained output of a human over the course of a day is less than one but per second. So there's 86,400 seconds in a day, and it's extremely rare for a human to output more than that number of symbols per day. So certainly for several days in a row. So you really, you really, With a neuralink interface, you can massively increase your output bandwidth and your input bandwidth. Input being right to, you have to do right operations to the brain.

We have now five humans who have received the kind of the read input, whereas it's reading signals. And you've got people with ALS who really have, they're tetraplegics, but they can now communicate at, with similar bandwidth to a human with a fully functioning body and control their computer and phone which is pretty cool and then i think in the next six to 12 months we'll be doing our first implants for vision where even if somebody's completely blind we can write directly to the uh the visual cortex and we've had that working in monkeys actually i think one of our of our monkeys now has had a visual implant for three years. And at first it'll be relatively fairly low resolution,

but long term you would have very high resolution and be able to see in multispectral wavelengths. So you could see an infrared ultraviolet radar, like a superpower situation. But like at some point, the cybernetic implants would not simply be correcting things that went wrong, but augmenting human capability. dramatically, augmenting intelligence and senses and bandwidth dramatically.

And that's going to happen at some point. But digital super intelligence will happen well before that. At least if we have a neural link, we might be able to appreciate the AI better. I guess one of the limiting reagents to all of your efforts across all of these different domains, is access to the smartest possible people. But, you know, sort of simultaneous to that, we have, you know, the rocks can talk and reason, and, you know, there may be 130 IQ now, and they're probably going to be super intelligent soon. How do you reconcile those two things?

Like, what's going to happen in, you know, five, ten years, and what should the people in this room do to make sure that, you know, they're the ones who are creating instead of maybe below the API line? Well, they call it the singularity for a reason, because we don't know what's going to happen. In the not that far future, the percentage of intelligence that is human will be quite small. At some point, the collective sum of human intelligence will be less than 1% of all intelligence. And if things get to a Qadashiv level two, we're talking about human intelligence, even assuming a significant increase in human population and intelligence augmentation,

like massive intelligence augmentation, where like everyone has an IQ of a thousand type of thing. Even in that circumstance, collective human intelligence will be probably one billionth, that of digital intelligence. Anyway, where is the biological bootloader for digital superintelligence? I guess just to end off. was I was I a good bootload Where do we go? How do we go from here?

I mean, I mean, all of this is pretty wild sci-fi stuff that also could be built by the people in this room. You know,

if you, do you have a closing thought for the smartest technical people of this generation right now? What should they be doing? What should they be working on?

What should they be thinking about, you know, tonight as they go to dinner? Well, as I started off with, I think if you're doing something useful, that's great. Just try to be as useful as possible to your fellow human beings and that then you're doing something good. I keep harping on this, like, focus on super truthful AI. That's the most important thing for AI safety. you know, obviously, you know, anyone's interested in working at XAI.

Please, please let us know. We're aiming to make GROC, the maximally truth-seeking AI, and I think that's a very important thing. Hopefully we can understand the nature of the universe. That's really, I guess, what AI can hopefully tell us. Maybe AI can maybe tell us where are the aliens. and how did the universe really start? How will it end?

What are the questions that we don't know that we should ask? And are we in a simulation? What level of simulation are we in? Well, I think we're going to find out. I'm an MPC. Elon, thank you so much for joining us. Everyone, please, give it up for Elon Musk. Thank you.