Transcription
You got really two choices. You can either be a spectator or a participant.
We're talking about an economy that is thousands of times, maybe millions of times bigger than the economy today.
I went to Russia in like 2001 and 2002 to buy ICBMs. This was to get to space. Yeah. As a rocket, not to not to nuke anyone, but like I think I think we're quite close to digital super intelligence. It may happen this year. Digital super intelligence defined as smarter than any human at anything. And I'm somewhat troubled by the Fermi paradox. Like why have we not seen any aliens? And it could be because we bring you Elon Musk's latest unfiltered interview where he shares exclusive predictions about artificial intelligence, the race to Mars, and building companies that could save humanity from extinction. In this supercut, we work on reducing pauses and filler words while preserving every crucial insight, saving you over 15 minutes of your valuable time. After months focused on politics, this conversation returns to what Elon does best, engineering the future. We've structured this into three focused chapters. First, the foundations that shaped his thinking. Second, building the companies to make life multilanetary. And third, his predictions for AI and what comes next?
Chapter one, first principles. How Elon got started. 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 said probabilistically seemed unlikely, but I wanted to at least try.
So you're talking to a room full of people who are all technical engineers, uh, often, you know, some of the most eminent AI researchers coming up in the game. Okay. I think we should I think I I like the term engineer better than researcher. I mean, I suppose if there's some fundamental algorithmic breakthrough, it's it's a research, but 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. It 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 a first idea for Zip2? What was that like for you?
Yeah. Back in '95, I I was faced with a choice of either do, you know, grad studies, PhD at Stanford in 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 uh, I talked to my professor who was Bill Nix in the material science department and uh, said like, "Can I like defer for a quarter because this will probably fail and then I'll need to come back to college," and and then he said, "This is probably the last conversation we'll have," and he was right, but I I thought things would most likely fail, not that they would most likely succeed. And then in 95, I wrote I think the first or close to the first maps, directions, white pages and yellow pages on the internet. I just wrote I just wrote that personally, and I didn't even use a web server. I just read the port directly because I couldn't afford and I couldn't couldn't afford a T1. Original office was on Sherman Avenue in Palo Alto. There was like an ISP on the floor below. So I drilled 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 we 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 and then showered at the YMCA on Page Mill El Camino, and yeah, and we I guess we ended up doing a little bit of a useful company as of two 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, nighter, New York Times, no host, whatnot were investors and customers and and also on the board. So they kept they kept wanting to use our software in ways that made no sense. So I I wanted to go direct to consumers. Anyway, it's a long story, dwelling too much on Zip2, but the I really just wanted to do something useful on the internet because I like two choices, like do a 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. Anyway, that ended up being like reasonably successful. Sold for like $300 million, which was a lot at the time. These days, that's like I think minimum impulse bud for an AI startup is like a billion dollars. It's like there's so many freaking unicorns, just like a herd of unicorns at this point. You know, if unicorn is a billion dollar situation. There's been inflation since. So quite a bit more money.
Yeah. I mean like 90 1995, 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 in AI is 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 could pan out and probably will pan out in some cases, but it is eyewatering to see some of these valuations. Um, yeah. What do you think? I mean, we I'm pretty bullish personally. I'm pretty bullish, honestly. So I 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 that we're not even worried 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, and that you know, this the heads of all the 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 you know the corporate world, like the world at 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 you know 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.
Uh, I guess for the first my first startup, the the big the 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 but aren't really don't make sense with the new technology. I you know I should point out that I that I I didn't actually at first intend to start a company. I like I tried to get a job at Netscape and I sent my resume into Netscape, but I don't think he ever saw my resume, and then nobody responded. So 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 software myself and see how it goes." So it wasn't actually from the standpoint of like I want to start a company. I just wanted to be part of building, you know, the internet in some way. And and since I couldn't get a job at an internet company, I had to start an internet company.
AI will so profoundly change the future, it's difficult to fathom how much. But assuming we don't things don't go ary and and like AI doesn't kill us all and itself, then you you'll see ultimately an economy that is not not 10 times more than the current economy ultimately like if we become say or whatever our future machine descendants or but mostly machine descend descendants become like a a Kardashev scale 2 civilization or beyond. We're talking about an economy that is thousands of times, maybe millions of times bigger than the economy today.
I I I did sort of feel a bit like, you know, when I was in DC taking a lot of flack for like getting rid of waste and fraud, which was an interesting side quest as side quests go. Fixing the government is 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. 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. Um, like there's not you can't fool like math and physics are rigorous judges. So I'm used to being in like a maximally truth-seeking environment, and and that's definitely not politics. So anyway, I'm I'm glad to be back in you know technology.
I guess I'm kind of curious going back to the Zip2 moment. You had hundreds of millions of dollars or you had an exit of worth of millions of dollars.
I mean, I I got $20 million, right? 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. What drove you to jump back into the ring?
Well, I I think I I felt for with with Zip2, we built 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 and then that that actually the 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 so many talented people were at the combination of of Confinity and and X.com. So, I I just wanted to like I felt like we we kind of got our wings clipped somewhat with Zip2, and it's like, okay, what if our wings aren't clipped and we go direct to consumer, and that's that's what PayPal ended up being. Um, but yeah, with I got that like $20 million check for for my share of Zip2. At the time, I was living with in a house with four housemates and had like 10 grand in the bank. And then the this check arrives in the mail of all places and it's in the mail. Um and then and then my bank balance went from 10,000 to 20 million and 10,000. You're like, well, okay. Still have 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.
From coding his first software to nearly losing everything on Tesla and SpaceX, Elon's early struggles reveal the cost of betting on breakthrough technologies. Now, he explains how these companies aren't separate ventures. They're interconnected pieces of a larger mission to preserve human consciousness across multiple worlds.
Chapter 2, Engineering a Multilanet Civilization.
Then after PayPal, I was like I was kind of curious as to why we had not sent anyone to Mars. And I went on the 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 there was no real plan to send people to Mars. And I'm I'm I'm definitely summarizing a lot here, but I I I my first idea was to do a philanthropic mission to Mars called Life to Mars where we send a a small greenhouse with seas and dehydrated nutrient gel, land land that on Mars and grow, you know, hydrate the gel and then you'd have this this great sort of money shot of green plants on a red background. For the longest time, I by the way I didn't realize money shot I think is a porn reference. But 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 to send astronauts to to Mars. And along the way, by the way, I went to Russia in like 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. Yeah. Not to not to nuke anyone, but but they had they had to as a result of arms reduction talks, they had to actually destroy a bunch of their their big nuclear missiles. So I was like, well, how about if we take two of those, you know, minus the nuke, add an additional upper stage for for Mars. But it was kind of trippy, you know, being in Moscow in 2001 negotiating with like the Russian military to buy ICBMs. Like that's crazy. I was like, "Man, these things are getting really expensive." And and then I I came to realize that actually the problem was not that there was insufficient will to go to Mars but that there was no way to do so without breaking the budget, you know, even breaking the NASA budget, so that's where I decided to start SpaceX, 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. It turns out this is could be a very profitable business. I mean, it 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 they all all failed. So again, with with SpaceX, starting SpaceX was really from the standpoint of like I I think there's like a less than 10% chance of being successful. If if a startup doesn't do something to advance rocket technology, it's definitely not coming from from the big defense contractors because they just impeded 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 even like when recruiting people, I didn't like try to, you know, make out that I said we're probably going to die, but small chance we might not die. And if 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 they're 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, it's a bit of a learning exercise there. And uh, 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 if the fourth launch of Falcon not worked, it would have been just curtains and we would have just been joined the graveyard of prior rocket startups. So, it's like like my estimate of success was not far off. We just we made it by the skin of our teeth.
Tesla was happening sort of simultaneously. Like 2008 was a rough year because at mid-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 a 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?
Yeah, 100%. So you can look at the like the because it's still the you know the press of that time is still online. And you could just search it and and they kept calling me internet guy. So like internet guy aka fool is attempting to build a rocket company, and it does sound pretty absurd like internet guy starts 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 and and NASA awarded us a contract to resupply the space station. It was like right before Christmas because even the fourth launch working wasn't enough to succeed. It NASA also needed we also needed a big contract to keep us alive. So So I got I got that call from like the NASA team, and I literally they said we're we're awarding you one of the contracts to resupply the space station. I like literally blurted out, "I love you guys," which is not normally you know what they hear. And then we closed the 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 2 days after Christmas if that round hadn't hadn't closed.
It feels like one of the through lines was being able to find and eventually attract the smartest possible people in those particular fields. What would you tell to, you know, the Elon who's never had to do that yet?
I I generally think to try to try to be as useful as possible. It's so hard to be useful, especially to be useful to a lot of people where say the area under the curve of total utility is like how much how useful have you been to your fellow human beings times how many people. It's almost like like the physics definition of true work. It's incredibly difficult to do that. And I think if you aspire to do true work, your your probability of success is much higher. Like don't aspire 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?
I mean, in terms of of of your end product, you just have to say like, well, if this thing is successful, how useful will it be to how many people? That that's that's what I mean. And, you know, whether you're CEO or or any role in a startup, you do whatever it takes to succeed. And just always be smashing your ego. Internalize responsibility. A major failure mode is when ego to ability ratio is double greater than sign one. If your 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. In AI terms, you're you'll break your RL loop. So you you want you don't want to break your you want to have a strong 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. I prefer the term engineering as opposed to research. And and I I don't I actually don't want it to call xAI a lab. I just want to be a company. It's like whatever the whatever the simplest, most straightforward, ideally lowest ego terms are th 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 everyone in this room is 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? People who have never made anything, non-engineers, they will criticize you. But then clearly you have another set of people who are builders who are in your circle, like you know how should people approach that you need to make your way in this world here. You know, here's how to construct a reality that is predictive from first principles.
Well, the 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 cogently as possible, as opposed to reasoning by analysis or metaphor. And then you 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, is very, very helpful. I use all the tools of physics. They apply to any field. This is like a superpower, actually. So you can take, say, take, take for example, like rockets. You can say, well, how, how much should a rocket, rocket cost? The typical approach to, to that people would take—how much a 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 principles 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, and, 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’s, it can asymptotically approach the cost of the raw materials. And then you realize, oh, actually a rocket, the raw materials of a rocket are only maybe one 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, first principles analysis of the potential for cost optimization of a rocket, and that’s before you get to reusability.
You know, to give an AI, sort of AI example. I guess last year, where for XAI, when we were trying to build a, a training supercluster, we, we, we went to the various suppliers to ask that we needed 100,000 H100s to be able to train coherently. Their 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 6 months or we won’t be competitive. So, so then if you break that down, what, well, 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 had to find an existing building. So, we found a factory that was no longer in use in Memphis that used to build Electrolux products. But then the, the input power was 15 megawatt, and we needed 150 megawatt. So, we rented generators and had generators on 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 US and put the chillers on the other side of the building. But that didn’t fully solve the problem because the voltage, the power variations during training are, 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 combi—we added Tesla mega packs and modified the software in the mega packs to be able to smooth out the, the power variation during the training run.
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, you know, 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 no to stand. I think these general principles of first principle thinking applied to software and hardware, applied to anything really. I’m just using kind of a hardware example of, 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, we, we need power smoothing, and then, and then we could solve those constituent elements. But it, it was, and then we, and then we just ran the, the networking operation to, to do all the cabling, everything in four shifts, 24/7, and, and I was like sleeping in the data center and also doing cabling myself.
With rockets built and electric vehicles scaling, the final challenge isn’t mechanical, it’s intelligence itself. In this final chapter, Elon shares his timeline for artificial general intelligence, why trueing AI matters, and his predictions for technologies that could reshape everything we know about human capability. Chapter 3, the future of AI, robots, and human evolution. Is it your view that, you know, training is still working and you, larger 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, of the various elements that decide competitiveness for, for large AI, there’s, there’s for sure the, the talent of the people, 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 then 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 coherently and stably. 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, those are, those are critical factors for if it’s going to be like a large foundation model that’s competitive. And like right now we’re, we’re training Grock 3.5, which is a heavy focus on reasoning. What I heard for reasoning is hard science, particularly physics textbooks are very useful for reasoning, whereas I think researchers have told me that social sciences are totally useless for reasoning. Uh, yes, that’s probably true.
You know, something that’s going to be very important in the future is combining deep AI, the data center or supercluster with robotics. You know, things like, like the Optimus humanoid robot. Yeah, Optimus is awesome. There’s going to be so many humanoid robots, and, 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, by maybe an order of magnitude. Like a, a big difference. Is it true that you, you’re planning a robot army of a sort, whether we do it or, or, or you know, whether Tesla does it, you know, Tesla works closely with XAI, like you’ve seen how many humanoid robot startups are there, like it’s like I think Jensen Huang was on stage with a massive number of robots from different companies. I think there was like a dozen different humanoid robots. 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, I’m a little, I don’t want, I don’t want to make Terminator real. I’ve been sort of, I guess, at least until recent years, dragging my feet on, on AI and, and humanoid robotics. And then I sort of come to the realization, it’s, it’s happening whether I do it or not. So, you got really two choices. You could either be a spectator or a participant. And so, like, well, I guess I’d rather be a participant than a spectator. And so now it’s, you know, pedal to the metal on humanoid robots and digital super intelligence.
So I guess, you know, there’s a third thing that everyone has heard you talk a lot about that I’m really a big fan of, you know, becoming a multiplanetary species. How do you think about it? There’s, you know, AI, obviously, there’s embodied robotics, and then there’s being a multip—multiplanetary species. Does everything sort of feed into that last point, or, you know, what, what are you driven by right now for the next 10, 20, and 100 years? Jeez, 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 Kardashev. So, if you’re, you know, cautious of scale one, you’ve, you’ve harnessed all the energy of a planet. Now, in my, in, in my opinion, we’ve only harnessed maybe one or two percent of Earth’s energy. So we’ve got a long way to go to be Kardashev scale one. Then Kardashev 2, you’ve harnessed 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 Kardashev 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, I, I hope, I hope we’re—in terms of being multiplanetary, like I think, 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 ships from Earth stop coming. That greatly increases the probable lifespan of civilization, or, or consciousness, or intelligence, both biological and digital. And I’m somewhat troubled by the Fermi 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. Um, in which case the intelligence of consciousness is this like tiny candle in a vast darkness, and we should do everything possible to ensure the tiny candle, candle does not go out. And being a multiplanetary species, or making consciousness multiplanetary, greatly improves the probable lifespan of civilization, and it’s, it’s, it’s the next step before going to other star systems. Um, once you, once you at least have two planets, then you’ve got a forcing function for the improvement of space travel, and, and that, that ultimately is what will lead to consciousness expanding to the stars.
The Fermi paradox dictates, once you get to some level of technology, you destroy yourself. 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, we should try to avoid that. 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, 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, 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, you know, there’s 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. 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. But, 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 will definitely going to invent new technologies, like I think, I think we’re quite close to digital super intelligence. It may happen this year, and if it doesn’t happen this year, next year for sure. A digital super intelligence defined as smarter than any human at anything. Well, so how do we direct that to sort of super abundance? You know, we have, 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, I think it most likely will be a good outcome. I, I guess I’d sort of agree with Jeff Hinton that maybe it’s a 10 to 20% chance of annihilation, but look on the bright side, that’s 80 to 90% probability of a great outcome. Yeah, I can’t emphasize this enough. A rigorous adherence to truth is, is the most important thing for AI safety, and obviously empathy for humanity and life as we know it.
You’re working on closing the input and output gap between humans and machines. How critical is that to AGI, ASI? And you know, once that link is made, can we not only read but also write? The neural link is not necessary to solve digital super intelligence; that’ll happen before neural link is at scale, but what, what Neuralink can effectively do is solve the, the input-output bandwidth constraints. With a, with a neural link interface, you can massively increase your output bandwidth and your input bandwidth; input being right to—you have to do write operations to the brain. We have now five humans who have received the kind of the read input, where it’s reading signals, and you’ve got people with, with ALS who really have, they’re tetraplegics, but they, they can now communicate at similar bandwidth to a human with a fully functioning body and control their computer and phone, which is pretty cool. In the next 6 to 12 months, we’ll be doing our first implants for vision, where even if somebody’s completely blind, uh, we, we can write directly to the, the visual cortex, and, and we’ve had that working in monkeys. One of our monkeys now has had the visual implant for 3 years. At first, it’ll be relatively fairly low resolution, but long-term you would have very high resolution and be able to see multispectral wavelengths. So you could see infrared, ultraviolet, radar. It’s like a superpower situation. At some point, the cybernetic implants would, would not simply be correcting things that went wrong, but augmenting human capabilities dramatically. But digital super intelligence will happen well before that.
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. 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, 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. I guess just to end off, where do we go? So, 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. Do you have a closing thought for the smartest technical people of this generation right now? If you’re doing something useful, that’s great. Just, just try to be as useful as possible to your fellow human beings, and that, 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 if anyone’s interested in working at XAI, I mean, please, please, please let us know. We’re aiming to make Grock the maximally truth-seeking AI. Hopefully, we can understand the nature of the universe. That, that’s really, I guess, what AI can hopefully tell us. Maybe AI, AI can, maybe tell us, where are the aliens, you know, 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, or what level of simulation are we in? Well, I think we’re going to find out. NPC.
From first principles thinking to multiplanetary civilization, this conversation shows how Elon approaches humanity’s biggest challenges, not as abstract problems, but as engineering puzzles to solve. If you enjoyed this, we’ve selected two more videos you’ll find fascinating. Check them out on your screen now, and subscribe for more content that cuts through the noise to show you what’s really shaping our future. Elon, thank you so much for joining us. Everyone, please give it up for Elon Musk.