Tag: Elon Musk

lon says, "I mean to be clear, I’m very pro human, so I want to make sure we take sort of actions that ensure that humans are along for the ride."

Elon Musk with Dwarkesh Patel & John Collison: The Future of AI Is in Space (Parts 1–8)

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In early February 2026 (published February 5), podcaster Dwarkesh Patel and Stripe co-founder John Collison sat down with Elon Musk for a nearly three-hour conversation. Recorded in a relaxed, casual setting over pints of Guinness, this wide-ranging discussion explores the converging revolutions in AI infrastructure, orbital data centers, energy scaling, humanoid robotics, and humanity’s long-term future.

What began as an in-depth podcast has been transformed into this special 10-part series. The hosts’ questions and context have been distilled into concise, flowing narrative prose for maximum readability, while every single word spoken by Elon Musk remains 100% verbatim — exactly as originally delivered, with no changes, omissions, or paraphrasing.

Here are the 10 parts:

  • Part 1. Opening Banter and the Economics of Space-Based Data Centers
  • Part 2. Why Space is the Optimal Solution for AI
  • Part 3. The Scale of Power Requirements and Utility Challenges
  • Part 4. The Turbine Bottleneck and Scaling Solar Production
  • Part 5. Detailed Power Requirements and Space Engineering Difficulties
  • Part 6. AI Capacity Projections in Five Years and Starship Launch Rates
  • Part 7. SpaceX as Hyperscaler, Capital Markets, and the Kardashev Scale
  • Part 8. Building Terafabs for Chips, xAI Mission, and Propagating Consciousness
  • Part 9. Truth-Seeking AI, Alignment, Reward Hacking, and Interpretability
  • Part 10. Future AI Products, Optimus Robots, Manufacturing Challenges, Management, and Reflections

Part 1: Opening Banter and the Economics of Space-Based Data Centers

The interview opened with some light-hearted and playful banter. Elon Musk jokingly questioned whether they were really going to talk for three full hours. Dwarkesh Patel teased him in return, saying he didn’t have much to talk about. Elon reacted with mock surprise.

Elon Musk: “So are there really three hours of questions or are you fing serious?” Elon Musk: “Holy f, man.”

John Collison jumped in, agreeing that it was actually the most interesting time because all the major storylines seemed to be converging at once. Elon playfully replied that it was almost as if he had planned it that way.

Elon Musk: “Almost like I planned it.”

John Collison laughed and said “Exactly.”

Elon Musk: “That would never do such a thing.”

With the lighthearted tone set, Dwarkesh Patel steered the discussion into the first major topic: the economics of data centers and why anyone would consider moving them into space. He explained that in a typical data center, energy accounts for only 10 to 15 percent of total cost of ownership, with GPUs representing the vast majority of the expense. He pointed out that placing those GPUs in space would make servicing nearly impossible, shortening their depreciation cycle and driving costs far higher, then asked directly what possible reason there could be to put them in orbit anyway.

Elon Musk: “Well, the availability of energy is the issue. So, I mean, if you look at electrical output outside of China, everywhere outside of China, it’s more or less flat. It’s very, you know, maybe a slight increase, but pretty close to flat. China has a rapid increase in electrical output. But if you’re putting data centers anywhere except China, where are you going to get your electricity? Especially as you scale, the output of chips is growing pretty much exponentially, but the output of electricity is flat. So how are you going to turn the chips on? Magical power sources. Magical electricity fairies.”

Dwarkesh Patel followed up by noting Elon’s well-known advocacy for solar power, calculating that one terawatt of solar (requiring about 4 terawatts of panels at 25 percent capacity factor) would cover only 1 percent of U.S. land area, yet even that seemed insufficient once data centers themselves reached terawatt scale. He asked what exactly we are running out of. Elon pressed him on how far into the singularity he thought we already were, and Dwarkesh turned the question back. Dwarkesh then asked whether the plan was to move to space only after blanketing places like Nevada with solar panels on the ground.

Elon Musk: “Right.” Elon Musk: “Yeah, exactly. So I think we’ll find we’re in the singularity and like, okay, we’ve still got a long way to go.” Elon Musk: “I think it’s pretty hard to cover Nevada in solar panels. You have to get permits from, try getting the permits for that.”

Part 2: Why Space is the Optimal Solution for AI

Dwarkesh Patel suggested that space was really a regulatory play because it is harder to build on land than it is in space. He then asked how to service GPUs as they fail, which happens quite often in training. John Collison added questions about solving the power issue with private behind-the-meter generation co-located with data centers.

Elon Musk: “It’s harder to scale on ground than it is to scale in space. But also, you’re going to get about five times the effectiveness of solar panels in space versus the ground. And you don’t need batteries. I almost wore my other shirt, which says “it’s always sunny in space,” which it is. Because you don’t have a day-night cycle or seasonality, clouds, or an atmosphere in space. The atmosphere alone results in about a 30% loss of energy. So any given solar panel can do about five times more power in space than on the ground, and you avoid the cost of having batteries to carry you through the night. So it’s actually much cheaper to do in space. And my prediction is that it will be by far the cheapest place to put AI will be space in 36 months or less.”

Elon Musk in his “Its Always Sunny in Space” t shirt, enjoying a Grok Imagine moment in a fictional Tiki Bar!

Dwarkesh Patel responded skeptically to the aggressive timeline.

Elon Musk: “Less than 36 months.”

Dwarkesh Patel then asked the critical practical question: how would one service GPUs as they fail, which happens quite often during training, when they are in space and physically inaccessible.

Elon Musk: “Actually, it depends on how recent the GPUs are that have arrived. I mean, at this point, we found our GPUs to be quite reliable. There’s infant mortality, which you can obviously iron out on the ground. So you can just run them on the ground and confirm that you don’t have infant mortality with the GPUs. But once they start working, their actual reliability, once they start working and you’re past the initial debug cycle of Nvidia or whatever, or whoever’s making the chips—could be Tesla AI 6 chips or something like that, or it could be TPUs or Trainiums or whatever—the reliability is actually quite reliable past a certain point. So I don’t think the servicing thing is an issue. But you can mark my words, in 36 months, but probably closer to 30 months, the most economically compelling place to put AI will be space. And then it’ll get ridiculously better to be in space. And then the scaling—the only place you can really scale is space. Once you start thinking in terms of what percentage of the sun’s power are you harnessing, you realize you have to go to space. You can’t scale very much on Earth.”

But you can mark my words, in 36 months, but probably closer to 30 months, the most economically compelling place to put AI will be space.

Part 3: The Scale of Power Requirements and Utility Challenges

Dwarkesh Patel sought clarification on the sheer scale, confirming that Elon was indeed talking about terawatts of power. The conversation then shifted to the staggering real-world difficulties of actually delivering that much electricity at the pace AI compute demands. Both Dwarkesh and John Collison pressed on why the notoriously slow utility industry was even involved and whether companies could simply bypass it by building their own private power plants right next to the data centers.

Elon Musk: “Yeah, well, all of the United States currently uses only half a terawatt per hour on average. Right. So if you say a terawatt, that would be twice as much electricity as the United States currently consumes. So that’s quite a lot. And can you imagine building that many data centers, that many power plants? It’s like those who have lived in software land don’t realize that they’re about to have a hard lesson in hardware—that it’s actually very difficult to build power plants. And then you don’t just need the power plants, you need all of the electrical equipment, you need the electrical transformers to run the transformers, the AI transformers. Now, the utility industry is a very slow industry. They impedance match to the government, to the public utility commission. So they’re very slow because their past has been very slow. So trying to get them to move fast is just like, you know, if you’re trying to do an interconnect agreement—have you ever tried to do an interconnect agreement with a utility at scale? Like with a lot of power?”

Dwarkesh Patel replied with a laugh, admitting that as a professional podcaster he had never attempted such a thing.

Elon Musk: “In fact, yeah, they have to do a study for a year. Okay. Like a year later they’ll come back to you with their interconnect study.”

John Collison asked whether the entire utility bottleneck could be avoided by building private, behind-the-meter power generation co-located with the data centers.

Elon Musk: “You can build power plants. Yeah, that’s what we did at xAI for Colossus.”

John Collison followed up, noting that xAI had done exactly that for Colossus and asking why the private-power solution wasn’t the obvious generalized answer to all the utility problems just described.

Elon Musk: “That’s what we did.”

John Collison clarified that he meant why not make this the standard approach instead of dealing with utilities at all.

Elon Musk: “Right. But it begs the question of where do you get the power plants? Where do you get the power plants from? I mean the power plant makers.”

John Collison realized the deeper constraint and summed it up as the massive backlog for gas turbines and power-plant equipment in general.

Part 4: The Turbine Bottleneck and Scaling Solar Production

John Collison suggested that the turbine blade bottleneck sounded like a classic problem Elon would tackle head-on and proposed that making solar themselves might be the better path forward.

Elon Musk: “We are going to make solar. Okay, great. Both SpaceX and Tesla are building towards 100 gigawatts here of solar cell production.”

Dwarkesh Patel asked how deep into the supply chain they would go – from raw polysilicon all the way to the finished solar panel.

Elon Musk: “I think you got to do the whole thing from raw materials to the finished cell. Now, if it’s going to space, it actually costs less. And it’s easier to make solar cells that go to space because they don’t need glass or they don’t need much glass and they don’t need heavy framing because they don’t have to survive weather events. There’s no weather in space. So it’s actually a cheaper solar cell that goes to space than the one on the ground.”

Elon continued, emphasizing how inexpensive solar cells already are and why moving them to space changes the economics by an order of magnitude. He then recounted the extraordinary difficulties his xAI team faced just to bring a single gigawatt online for Colossus — the miracles required, the permitting nightmares, and how most people dramatically underestimate the real power needs of a data center.

Elon Musk: “Solar cells are already very cheap. They’re like farcically cheap. And if you say, I think solar cells in China are around like 25, 30 cents a watt or something like that, it’s absurdly cheap. And when you take into account now put it in space and it’s five times cheaper because it’s five times—in fact, no, it’s 10 times cheaper because you don’t need any batteries. So the moment your cost of access to space becomes low, by far the cheapest and most scalable way to generate tokens is space. It’s not even close. It’ll be an order of magnitude easier to scale. And chips aside, an order of magnitude. The point is you won’t be able to scale on the ground. You just won’t. People are going to hit the wall big time on power generation. There already are. So the number of miracles in series that the xAI team had to accomplish in order to get a gigawatt of power online was crazy.

So the number of miracles in series that the xAI team had to accomplish in order to get a gigawatt of power online was crazy.

Elon Musk: We had to gang together a whole bunch of turbines. And then we had permit issues in Tennessee and had to go across the border to Mississippi, which is fortunately only a few miles away. But then we still had to run the high power lines a few miles and build a power plant in Mississippi. And it was very difficult to build that. And people don’t understand how much electricity do you actually need at the generator level, at the generation level in order to power a data center? Because they look at the specs, will look at the power consumption of say a GB 300 and multiply that by the number and then think that’s the amount of power you need.”

John Collison pointed out that this calculation still failed to account for major additional power demands such as cooling and all the supporting systems.

Elon Musk: “Wake up. Yeah, that’s a total noob. You’ve never done any hardware in your life before. Besides the GB 300, you’ve got to power all of the networking hardware. There’s a whole bunch of CPU and storage stuff that’s happening. You’ve got to size for your peak cooling requirements. So that means can you cool even on the worst hours, the worst day of the year? Well, it gets pretty freaking hot in Memphis, so you’re going to have like a 40% increase on your power just for cooling. Assuming you don’t want your data center to turn off on hot days and you want it to keep going, then you’ve got to say, well, there’s another multiplicative element on top of that, which is are you assuming that you never have any hiccups in your power generation? Like, oh, well, actually sometimes we have to take the generators, some of the power offline in order to service it. Oh, okay, now you add another 20, 25% multiplier on that because you’ve got to assume that you’ve got to take power offline to service it. So the actual—roughly every 110,000 GB 300s inclusive of networking, CPU, storage, cooling, margin for servicing power is roughly 300 megawatts.”

John Collison asked him to repeat the number.

Elon Musk: “It’s roughly—or think about it like a way to think about it is like 330,000. What you need at the generation level to service, probably service 330,000 GB 300s, including all of the associated support, networking and everything else, and the peak cooling and to have some power margin reserve is roughly a gigawatt.”

Part 5: Detailed Power Requirements and Space Engineering Difficulties

Dwarkesh Patel asked a very naive but central question: while Elon had laid out the enormous engineering and power challenges on Earth in detail, there would be entirely new and unprecedented engineering difficulties in space — such as replacing InfiniBand with orbital lasers, hardening systems against radiation, and countless other issues that had never been solved at scale before. He asked why anyone should believe those novel challenges would ultimately prove easier than simply building more turbines on Earth, where established companies already know how to manufacture them.

Elon Musk: “I invite again, try doing it and then you’ll see. So like, the turbines are sold out through 2030.”

John Collison asked whether they had considered manufacturing their own turbines.

Elon Musk: “I think in order to bring enough power online, I think SpaceX and Tesla will probably have to make the turbine blades, the vanes and blades internally.”

John Collison asked if they meant just the blades or the entire turbines.

Elon Musk: “The limiting factor, you can get everything except the blades. They call the blades and vanes. You can get that 12 to 18 months before the vanes and blades. The limiting factor of the vanes and blades, and there are only three casting companies in the world that make these and they’re massively backlogged, is this Siemens.”

John Collison asked whether it was GE and the big names or subcontractors.

Elon Musk: “No, it’s other companies. I mean sometimes they have a little bit of casting capability in house. But I’m just saying you can just call any of the turbine makers and they will tell you it’s not top secret. They’re probably on the, it’s probably on the Internet right now.”

Dwarkesh Patel asked whether, if it weren’t for the tariffs, Colossus would be running on solar power.

Elon Musk: “It would be much easier to make it solar powered. Yeah, the tariffs are nuts, so several hundred percent.”

John Collison began to suggest that Elon surely knew some people who could help.

Elon Musk: “We also need speed. Yeah, no, you know, President has his, you know, we don’t agree on everything and this demonstration is not the biggest fan of solar. We also need the land, the permits and everything. So if you’re trying to move very fast, I do think scaling solar on Earth is a good way to go. But you do need some amount of time to find the land, get the permits, get the solar, pair that with batteries.”

John Collison pressed further: why not simply stand up their own massive solar production? There is plenty of private land in Texas and Nevada, enough at least to power the next Colossus and the one after that before eventually hitting a wall.

Elon Musk: “As I said, we are scaling solar production. There’s a rate at which you can scale physical production of solar cells where we’re going as fast as possible.”

John Collison confirmed they were building the solar cells domestically at Tesla.

Elon Musk: “Both Tesla and SpaceX have a mandate to get to 100 gigawatts a year of solar.”

Part 6: AI Capacity Projections in Five Years and Starship Launch Rates

John Collison shifted the conversation to a concrete five-year horizon, asking what the installed AI compute capacity would look like on Earth versus in space by then. He deliberately chose five years because it would be after the initial “we’re up and running” threshold for orbital infrastructure. Dwarkesh Patel followed up on the staggering numbers, noting that even 100 gigawatts of space-based AI — with all the solar arrays, radiators, and supporting systems — would require on the order of 10,000 Starship launches. He asked Elon to walk through a realistic world in which Starship was launching once every hour.

Elon Musk: “Five years? I think probably if you say five years from now, we’re probably AI in space will be launching every year the sum total of all AI on Earth in excess, meaning five years from now. My prediction is we will launch and be operating every year more AI in space than the cumulative total on Earth, which is I would expect to be at least sort of five years from now. A few hundred gigawatts per year of AI in space and rising. So you can get to, I think on Earth you can get to around a terawatt a year of AI in space before you start having fuel supply challenges for the rocket.”

John Collison pressed for confirmation on the hundreds-of-gigawatts-per-year figure.

Elon Musk: “Yes.”

Dwarkesh Patel highlighted the launch cadence implied by those numbers.

Elon Musk: “Yes.”

Dwarkesh Patel continued: to deliver 100 gigawatts in a single year would mean roughly 10,000 Starship launches annually — the equivalent of one launch every single hour, nonstop, from this city.

Elon Musk: “Yeah, I mean that’s actually a lower rate compared to airlines like aircraft.”

Dwarkesh Patel pointed out that there are a lot of airports around the world.

Elon Musk: “A lot of airports.”

Dwarkesh Patel noted the additional complexity of launching into polar or sun-synchronous orbits.

Elon Musk: “No, it doesn’t have to be polar, but there’s some value to sun synchronous. But I think actually you just go high enough, you start getting out of Earth’s shadow.”

Dwarkesh Patel asked how many physical Starships would be needed to sustain 10,000 launches per year.

Elon Musk: “I don’t think we’ll need more than. I mean, you could probably do it with as few as like 20 or 30. It really depends on how quickly the ship has to go around the Earth and the ground track before the ship has to come back over the launch pad. So if you can use a ship every, say 30 hours, you could do it with 30 ships, but we’ll make more ships than that. But SpaceX is gearing up to do 10,000 launches a year and maybe even 20 or 30,000 launches a year.”

Part 7: SpaceX as Hyperscaler, Capital Markets, and the Kardashev Scale

Dwarkesh Patel asked whether the long-term vision was for SpaceX to become a hyperscaler — launching and operating vast orbital AI capacity and then providing (or lending) that compute power to other companies.

Elon Musk: “Hyper. Hyper, yeah. I mean, if some of my predictions come true, SpaceX will launch more AI than the cumulative amount on Earth of everything else combined.”

Dwarkesh followed up on whether this capacity would mostly be used for inference or training.

Elon Musk: “Will be inferenced already? Inference for the purpose of training is most training.”

John Collison then explored the business implications, noting the shifting narrative around a possible SpaceX IPO. He pointed out that SpaceX had long been extremely capital efficient, but the scale of building orbital AI infrastructure would require capital raises far beyond what private markets had demonstrated they could comfortably provide — even as AI labs were already raising tens of billions. He asked if going public was the logical next step and more broadly about the difference in capital availability between public and private markets, as well as whether debt financing (common in capital-intensive industries with clear revenue streams) could suffice.

Elon Musk: “Yeah, I have to be careful about saying things about companies that might go public.”

Elon Musk: “There’s a price to pay for these things.”

Elon Musk: “Yeah, there’s a lot more capital in the very general. There’s obviously a lot more capital available in the public markets than private. I mean, it might be, it’s at least, at least, it might be 100 times more capital, but it’s at least way more than 10.”

John Collison noted that highly capital-intensive sectors like real estate are typically debt-financed once they have predictable near-term revenue.

Elon Musk: “A clear revenue stream.”

John Collison agreed.

Elon Musk: “Speed is important. So I’m generally going to do the thing that, I mean, I just repeatedly tackle the limiting factor, whatever the limiting factor is on speed, I’m going to tackle that. So there’s, if capital is the only factor, then I’ll solve for capital. If it’s not limiting factor, I’ll solve for something else.”

Speed is important. So I’m generally going to do the thing that, I mean, I just repeatedly tackle the limiting factor, whatever the limiting factor is on speed, I’m going to tackle that. - Elon


Dwarkesh Patel observed that, based on Elon’s past comments about Tesla being public, he would not have expected Elon to see going public as the way to move fastest.

Elon Musk: “Normally I would say yeah, that’s true. Like I said, I mean, I’d love to talk about this in more detail, but the problem is like if you talk about public companies where they become public, you get into trouble and then you have to delay your offering and then you.”

John Collison noted that this was again about solving for speed.

Elon Musk: “Yes, exactly. So you can’t hype companies that might go public. So that’s why we have to be a little careful here.”

Elon then pivoted to the fundamental long-term physics of scaling.

Elon Musk: “But we can talk about physics. So the way you think about scaling long term is that Earth only receives about half a billionth of the sun’s energy. And the sun is essentially all the energy. This is a very important point to appreciate because sometimes people will talk about marginal nuclear reactors or any various fusion on Earth, but you have to step back a second and say if you’re going to climb the Kardashev scale and have some non trivial and harness some non trivial percentage of the sun’s energy, like let’s say you wanted to harness a millionth of the sun’s energy, which sounds pretty small, that would be about, call it roughly 100,000 times more electricity than we currently generate on Earth for all of civilization, give or take an order of magnitude. So it obviously the only way to scale is to go to space. With solar, from launching from Earth you can get to about a terawatt per year. Beyond that you want to launch from the moon, you want to have a mass driver on the moon, and that mass driver on the moon you could do probably a petawatt per year.”

Part 8: Building Terafabs for Chips, xAI Mission, and Propagating Consciousness

Dwarkesh Patel noted that even with more efficient solar panels in space, the chips themselves would still be the ultimate limiter long before reaching terawatt scale. He asked how the world would produce a terawatt of logic compute by 2030 when today the entire planet has only about 20–25 gigawatts.

Elon Musk: “You need to build a lot more chips and make them much cheaper.”

Elon Musk: “I guess we’re going to need some very big chip apps.”

Elon Musk: “I’ve mentioned publicly that the idea of doing sort of a terafab, terabying the new Giga.”

Dwarkesh asked for details on the plan: what level of the stack they would build themselves versus partnering with an existing fab for process technology.

Elon Musk: “Well, you can’t partner with existing fabs because they can’t output enough. The chip volume is too low.”

Elon Musk: “IP (Intellectual Property), the fabs today all basically use machines from like five companies. Yeah, you know, so you’ve got ASML (ASML Holding), Tokyo Electron (Tokyo Electron Limited), KLA (KLA Corporation), Lam Research (Lam Research Corporation), you know, et cetera. So at first I think you’d have to get equipment from them and then modify it or work with them to increase the volume. But I think you’d have to build perhaps in a different way. So I think the logical thing to do is to use conventional equipment in an unconventional way to get to scale and then start modifying the equipment to increase the rate.”

John Collison drew the parallel to how The Boring Company started.

Elon Musk: “Yeah, kind of like. Yeah, you sort of buy an existing boring machine and then figure out how to dig tunnels in the first place and then design a much better machine that’s, I don’t know, some orders of magnitude faster.”

On November 24, 2025, on a rainy day in Bastrop, Texas, Prufrock-5 left the Boring Company factory.
On November 24, 2025, on a rainy day in Bastrop, Texas, Prufrock-5 left the Boring Company factory.

John Collison offered a simple lens: look at technologies China has not yet replicated at leading edge, such as advanced chips and turbine engines, and asked whether the fact that China has not duplicated TSMC gave Elon pause about the difficulty.
Elon Musk: “It’s not that they have not replicated TSMC, they have not replicated ASML. That’s the limiting factor.”
John Collison asked if Elon thought it was simply the sanctions preventing China from advancing.
Elon Musk: “Yeah. China would be outputting vast numbers of chips at.”
John Collison followed up, noting that China had been able to buy 2 nm or 3 nm chips until relatively recently.
Elon Musk: “No. The ASML banners have been in place for a while, but I think China’s going to start making pretty compelling chips in three or four years.”
The discussion moved to the massive manufacturing requirements for space-based AI. Elon explained the need to match solar, chips, and rocket payload, with memory actually being his biggest concern.
Elon Musk: “I don’t know yet is the right answer. So it’s just that to produce at high volume and to reach large volume in say 36 months to match the rocket payload to orbit. So if we’re doing a million tons to orbit and like, let’s say, I don’t know, three or four years from now, something like that, and we’re doing 100 kilowatts per ton, so that means we need at least 100 gigawatts per year of solar and we’ll need an equivalent amount of chips. You need 100 gigawatts worth of chips. You’ve got to match these things. The master orbit, the power generation and the chips. And I’d say my biggest concern actually is memory. So I think the path to creating logic chips is more obvious than the path to having sufficient memory to support logic chips. That’s why you see DDR (Double Data Rate memory) prices going ballistic and these memes about like, you know, you’re marooned on a desert island. You write help me on the sand. Nobody comes. You write DDR ships come swarming in.”
Elon then painted the long-term picture of lunar manufacturing and mass drivers to reach petawatt-scale production, noting how the whole endeavor increasingly felt like a video game where each level is difficult but solvable.
Elon Musk: “I don’t know how to build a fab yet. I will figure it out. Obviously I’ve never built a fab.”
Elon Musk: “I don’t think it’s PhDs. It’s mostly people who are not PhDs. Most engineering is done with people who don’t have PhDs. Do you guys have PhDs? No. Okay.”
Elon Musk: “I don’t think you need PhDs for this stuff, but you do need competent personnel. So I don’t know. I mean right now, like Tesla’s pedal to the metal max production of going as fast as possible to get AI5 Tesla AI5 chip design into production and then reaching scale. That’ll probably happen around the second quarter ish of next year, hopefully. And then AI6 would hopefully follow less than a year later. But. And we’ve secured all the chip fab production that we can.”
Elon Musk: “Yeah, and we’ll be using TSMC Taiwan, Samsung Korea, TSMC Arizona, Samsung Texas and we still booked out all the…”
Elon Musk: “Yes. And then if I ask TSMC or Samsung, okay, what’s the timeframe to get to volume production? The point is you’ve got to build the fab and you’ve got to start production, then you’ve got to climb the yield curve and reach volume production at high yield. That from start to finish is a five year period. And so the limiting factor is chips. Limiting factor once you can get to space is chips. But the limiting factor before you can get to space will be power.”
Elon Musk: “I’ve already told them that, but they won’t take your money.”
Elon Musk: “They’re building fabs as fast as they can and so is Samsung. They’re pedal to the metal. I mean, they’re going balls to wall as fast as they can. So. Still not fast enough. I mean, like I said, there will be. I think if you say I think towards the end of this year, I think probably chip production will outpace the ability to turn chips on. But once you can get to space and unlock the power constraint and you can now do hundreds of gigawatts per year of power in space. Again bearing in mind that average power usage in the US is 500 gigawatts. So if you’re launching say 200 gigawatts a year to space, you’re sort of lapping the US every two and a half years. The entire all US electricity production, this is a very huge amount. But between now and then, actually the constraint for server side computer concentrated compute will be electricity.
My guess is that we start hitting, people start getting a point where they can’t turn the chips on for large clusters. Towards the end of this year the chips are going to be piling up and you won’t be able to be turned on. Now for edge computers, a different story. So for Tesla, so the AI 5 chip is going into our Optimus robot, you know, Optimus, and so if you have an AI edge compute, that’s distributed power. Now the power is distributed over a large area, it’s not concentrated. And if you can charge at night, you can actually use the grid much more effectively because the actual peak power production in the US is over 1,000 gigawatts. But the average power usage because the day night cycle is 500. So if you can charge at night, there’s an incremental 500 gigawatts that you can generate at night. So that’s why Tesla for edge compute is not constrained. And we can make a lot of chips to make very large number of robots and cars, but if you try to concentrate that compute, you going to have a lot of trouble turning it on.”
Elon explained that while launching at that massive scale from Earth would be almost impossible, the moon offered a far better path using mass drivers.
Elon Musk: “I don’t see any way that you could do 500 to 1,000 terawatts per year launch from Earth.”
Elon Musk: “But you could do that from the moon.”
Dwarkesh Patel agreed and then zoomed out to the bigger philosophical picture behind SpaceX. Dwarkesh asked whether, by the time humans are sending ships to Mars, Grok would be on board with them, and if so, how that relates to the main risk people worry about with AI.
The vast majority of intelligence in the future will be AI - Elon Musk


Elon Musk: “Well, I’m not sure AI is the main risk I’m worried about. I mean the important thing is that consciousness, which I think arguably most consciousness or most intelligence, certainly consciousness is more of a debatable thing. The vast majority of intelligence in the future will be AI. So AI will exceed you say, how many, I don’t know. Petawatts of intelligence will be silicon versus biological and basically humans will be a very tiny percentage of all intelligence in the future if current trends continue. Anyways, as long as I think, this intelligence ideally, also which includes human intelligence and consciousness propagated into the future, that’s a good thing. So you want to take the set of actions that maximize the probable a light cone of consciousness and intelligence.”

lon says, "I mean to be clear, I’m very pro human, so I want to make sure we take sort of actions that ensure that humans are along for the ride."
Elon says, “I mean to be clear, I’m very pro human, so I want to make sure we take sort of actions that ensure that humans are along for the ride.”

Elon Musk: “Yeah, I mean to be clear, I’m very pro human, so I want to make sure we take sort of actions that ensure that humans are along for the ride. We’re at least there. But I’m just saying the total amount of intelligence, I think maybe in five or six years AI will exceed the sum of all human intelligence. And then if that continues, at some point human intelligence will be less than 1% of all intelligence.”
Please click link to read on to the last 2 parts of this interview. I would have included them here, but the word count far exceeded what X currently allows. For Part 9 and 10, Click here.

This 10-part series is based on a nearly three-hour conversation recorded in early February 2026 (aired February 5, 2026) between Elon Musk, podcaster Dwarkesh Patel, and Stripe co-founder John Collison. The discussion was filmed casually in Austin, Texas, over pints of Guinness, covering space-based AI, energy scaling, Optimus robots, xAI’s mission, Starship engineering, government efficiency, and humanity’s long-term future.
Watch the complete unedited interview on YouTube:

Elon Musk with Dwarkesh Patel & John Collison – February 2026 (Full 3-Hour Podcast)

Tesla FSD Surpasses 8 Billion Miles: This AI Is Making Roads Way Safer Every Single Day

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Living here in Austin, Texas, where traffic on Mopac can flip from smooth to nightmare in a heartbeat (especially when there’s an accident or some surprise Texas rain), safety is always on my mind. That’s why I got so excited when Tesla announced this massive milestone on February 18, 2026!

Tesla drivers around the world have now driven more than 8 billion miles (nearly 13 billion km) using Full Self-Driving Supervised. Even more mind-blowing? They added 1 billion miles in just the first 50 days of 2026 alone!

That huge pile of real-world driving data is letting Tesla’s AI learn and improve faster than ever. According to Tesla’s own published safety stats, a vehicle on FSD Supervised experiences a major accident only once every 5.3 million miles. This is roughly eight times safer than the average across all vehicles on U.S. roads.

Behind those numbers are real-life moments that matter: the system putting on hazard lights and gently pulling over for emergency services help on its own if a driver has a medical emergency, applying the brakes or moving aside to avoid a crash, or gently guiding you through inclement weather. These are the reasons supervised autonomy is already saving lives by taking human error out of the equation. We all know human error causes the vast majority of accidents.

My personal experience as a Tesla owner in Austin

As a mom of five grown kids, a nurse who sometimes drives home after long shifts, and a proud owner of both a Model 3 and a Model Y with FSD (and Powerwalls at home), this tech has genuinely changed my life. I use FSD every single day here in Austin — whether I’m heading out to record a podcast episode, running errands around the Hill Country (my fav is to visit Buc-ees), or just daily commuting to work on highways where many drivers get distracted (driving and texting is everywhere in Austin!)

Just last month during a heavy downpour on Mopac Loop 1, FSD smoothly handled hydroplaning risks, kept perfect lane position, and even slowed for a sudden slowdown ahead that I hadn’t spotted yet. It gives me such peace of mind. I think about older family members or anyone who might feel tired or unwell behind the wheel. I feel that “co-pilot” protection in real time, and it’s one of the main reasons I’m so passionate about Tesla’s mission.

This progress fits perfectly into Elon Musk’s bigger vision of using AI to move humanity forward. At xAI, the team is building Grok with that same dedication to truth and excellence to speed up scientific discovery and help us all better understand the universe. This is a positive, open approach that truly benefits everyone.

With this kind of rapid acceleration, Tesla is proving the future of mobility isn’t coming someday… it’s already here on our roads right now.

Sources:

• Official Tesla announcement, February 18, 2026

• Tesla Vehicle Safety Report (latest data)

• No exaggeration, no rounded figures — straight from Tesla.

Love Tesla? Share this post, keep spreading the good news! 

Gail Alfar, Austin, Texas

(US Army Veteran, RN, Mom of 5, and founder of What’s Up Tesla)

Tesla’s Cybercab is Rolling

Gail AlfarLeave a comment

Living in a place like Austin or the Bay Area, you know traffic can turn from charming to nightmare with just one road accident, and they seem to happen a lot. On February 17, 2026, Tesla quietly built the very first production line Cybercab at Giga Texas. No steering wheel, no gas or brake pedals. It is a little two-seater that has a mission to drive itself completely. Elon is saying production really starts picking up in April, and I was lucky enough to see one of these golden cars testing on the streets of Austin on Feb 17th around 7pm!

The big hope everyone keeps talking about is safety. Road crashes kill more than 40,000 people a year in the U.S. (NHTSA numbers). If the car can take human mistakes out of the equation, that number could actually drop. I think that part feels especially real for older folks or anyone who can’t drive easily anymore.

Tesla Cybercab rides could end up super cheap, like maybe 20 cents a mile once the cars are running a lot. That would be a game-changer in cities where Uber gets expensive fast and buses don’t always go where you need. For people here in Texas or California, it could mean getting around without the stress of working to pay for a car. 

Right now Tesla’s already doing limited unsupervised Model Y robotaxi rides in Austin, and many people have taken rides. They are rare, as all my rides in Austin have had a supervisor thus far, however that will change soon. The plan seems to be rolling Cybercab out first in places like the Bay Area and here in Austin, basically following the same path they’re using with the Model Ys. Makes sense, as they likely test close to home where they can fix things quickly.

The Full Self-Driving software has now driven over 8 billion miles total, and they added another billion just in the first 50 days of this year. That’s a crazy amount of real-world data. Whether you adore or merely tolerate Elon Musk, you have to admit his team is moving fast on this stuff, and quietly, it’s hard not to respect anyone pushing this hard to make roads less deadly.

OWN YOUR OWN CYBERCAB AND WIRELESS CHARGING

MORE: They’re also talking about a Cybercab version people can actually purchase and own for under $30,000 by 2027, plus they just got the okay for Cybercab wireless charging. Still lots of regulatory hurdles, especially state-by-state rules, and competitors like Waymo are trying to keep up. I say that wen these goals are realized, things will quietly change how a lot of us get from point A to point B.

Feels like the future isn’t science fiction anymore. It’s starting in Austin, Texas.

Sources I pulled from:

  • Elon Musk’s X posts about production start and first unit
  • Teslarati articles on the first Cybercab and the 8 billion mile FSD milestone
  • NHTSA crash stats (public road safety data)

Elon Musk’s Companies Delivered Vital Relief to Tennessee and Mississippi After Devastating 2026 Winter Storm

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In late January 2026, a catastrophic winter storm dubbed Winter Storm Fern swept through Tennessee and Mississippi, bringing heavy ice and snow from January 22-27. The storm triggered widespread power outages, peaking at over 180,000 in Mississippi alone and leaving thousands without electricity for days in freezing conditions. The US government issued federal major disaster declarations in early February for both states, enabling FEMA assistance, which can be useful, and often takes days to help people in need.

Private sector support proved crucial for immediate needs, and one company reacted fast

Elon Musk’s xAI and Tesla companies responded swiftly, and this shows the high value that innovative private tech firms can have when they choose to help bridge gaps in public emergency response programs. People in affected communities needed immediate help.. Through xAI, Musk facilitated the donation of hundreds of portable gas generators, providing critical power for heating, medical devices, and daily necessities in hard-hit areas.

One key example occurred on February 2, 2026, when nearly 500 generators donated by xAI arrived in Tippah County, Mississippi. These were distributed to residents who had been without power for over 10 days. Local emergency officials helped coordinate rapid rollout, prioritizing vulnerable households. 

Similarly, in Tennessee, the state received and fully distributed 500 generators to the most impacted counties by February 3, 2026, aiding recovery in regions still reeling from prolonged blackouts. Tennessee Gov. Bill Lee personally thanked Elon Musk in a public post on X, “Tennesseans without power need immediate help. I’m deeply grateful to Elon Musk and xAI for going above & beyond to support Tennesseans by donating hundreds of generators to fill the gap & I value their continued partnership to solve problems & support communities across our state”.

Elon Musk replied with, “You’re most welcome. We’re working on providing Tesla Powerwalls too”.

Keeping vehicles powered up

Additionally, Tesla activated free Supercharging for electric vehicles in affected parts of Tennessee and Mississippi, ensuring all EV owners (not just Tesla owners) could maintain mobility and charge essential devices during the crisis. 

This forward-thinking approach from leaders in the world of technology like artificial intelligence shows how valued people are, after all, they are the reason businesses can thrive, as they provide not just valuable tech services, but they provide jobs that support families for generations. 

It is heartwarming to see official relief work with the private sector. You never really appreciate help in a disaster unless you or a loved one have actually experience it. 

Sources

Elon Musk with Dwarkesh Patel & John Collison – The Future of AI is in Space – Part 14: Government Efficiency, Politics, and Final Reflections (Full Transcript)

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In the final part of the series, Dwarkesh Patel and John Collison ask Elon about government efficiency, waste and fraud, the role of AI and robotics in America’s long-term future, and his closing thoughts on optimism.

Transcript:

AI, Robotics, and the National Debt

Dwarkesh Patel asked about the point of DOGE cuts if AI and robotics are expected to drive strong economic growth in the coming years.

Elon Musk: “Well, I think like waste and fraud are not good things to have. You know, I was actually pretty worried about, I guess, I mean, I think in the absence of AI and robotics, we’re actually totally screwed because the national debt is piling up like crazy.

Now our interest payments, the interest payments, the national debt exceed the military budget, which is a trillion dollars. So over a trillion dollars just in interest payments. I was like, okay, pretty concerned about that. Maybe if I spend some time we can slow down the bankruptcy of the United States and give us enough time for the AI and robots to help solve the national debt. Or not. Help solve. It’s the only thing that could solve the national debt.

Like we are 1000% going to go bankrupt as a country and fail as a country. Without AI and robots, nothing else will solve the national debt. We’d like to. Well, we need enough time to build the AI and robots and not go bankrupt before then.”

Government Fraud and Waste

Dwarkesh Patel asked why it had been so difficult to cut obvious waste and fraud from the government.

Elon Musk: “I’m not the president and it’s very hard to cut. To cut, to even to cut things that are obvious waste and fraud. Like ridiculous waste and fraud. What I discovered is it’s extremely difficult even to cut very obvious waste from the government because the government has to operate on who’s complaining.

If you cut off payments to fraudsters, they immediately come up with the most sympathetic sounding reasons to continue the payment. They don’t say, “please keep the fraud going.” They say, you know, they’re like, “you’re killing baby pandas.” Meanwhile there’s no baby pandas are dying. They’re just making it up.

The forces are capable of coming up with extremely compelling, sort of heart wrenching stories that are false but nonetheless sound sympathetic. And that’s what happened.”

Elon Musk gave an example of fraud involving Social Security records:

Elon Musk: “Maybe there shouldn’t be 20 million people marked as alive in Social Security who are indefinitely dead and over the age of 115. The oldest American is 114. So it’s safe to say if somebody is 115 and marked as alive in the Social Security database, something is wrong. There’s either a typo, somebody should call them and say, “we seem to have your birthday wrong,” or we need to mark you as dead. One of the two things, very intimidating call to get.”

Dwarkesh Patel asked for an estimate of total fraud from this mechanism.

Elon Musk: “My guess is, by the way, The Government Accountability Office has done these estimates before. I’m not the only one. It was not coming out of this. You know, in fact, I think they, they did, the GAO did analysis a rough estimate of fraud during the Biden administration and calculated at roughly half a trillion dollars. So don’t take my word for it. Take it. A report issued during the Biden administration. How about that?”

The Challenge of Government Efficiency

Dwarkesh Patel asked why it had been difficult to cut hundreds of billions in fraud if it was so obvious.

Elon Musk: “Because when. When essentially we did, we actually. Look, you really have to stand back and recalibrate your expectations for competence because you’re operating in a world where, you know, you’ve got to sort of make ends meet. Like, you know, you got to pay your bills, you got to, you know, buy the microphones.

So it’s like there’s a giant, largely uncaring monster bureaucracy. It’s not even a bunch of macronistic computers that are just sending payments. Like one of the things that those teams are. There was and sounds so simple that probably will save, let’s say 100 billion, maybe 200 billion a year, is simply requiring that payments from the main treasury computer, which is called PAM, it’s like Payment Accounts Master or something like that.

There’s 5 trillion payments here requiring that any payment that goes out have a payment appropriation code, make it mandatory, not optional, and that you have anything at all in the comment field because you see, you have to recalibrate how dumb things are. Payments were being sent out with no appropriation code, not checking back to any congressional appropriation, and no explanation.

And this is why the Department of War, formerly Department of Defense, cannot pass an audit because the information is literally not there. Recalibrate your expectations.”

Reflections on Politics and X

John Collison asked how Elon feels looking back on his involvement in politics and the acquisition of Twitter, noting both the impact and the personal cost.

Elon Musk: “Well, I think those things needed to be done to maximize the probability that the future is good. Politics generally is very tribal and it’s very tribal and people lose their objectivity. Usually with politics, they generally have trouble seeing the good on the other side or the bad on their own side. That’s generally how it goes.

That, I guess, was one of the things that surprised me the most, is you often simply cannot reason with people if they’re in one tribe or the other. They simply believe that everything their tribe does is good and anything the other political tribe does is bad. And persuading them is otherwise, it’s almost impossible.

So anyway, but I think overall those actions, acquiring Twitter, getting Trump elected, even though it makes a lot of people angry, I think those actions are good for civilization.”

AI, Government, and Corporate Power

Dwarkesh Patel asked how Elon thinks about the risk of governments using advanced AI and robotics to suppress populations, and whether private companies should set limits on what governments can do with their technology.

Elon Musk: “I think probably the biggest danger of AI, or maybe the biggest danger of for AI and robotics going wrong is government.

You know, I mean, the way like, like people who are opposed to corporations or worried about corporations, should really worry about the most about government, because government is just a corporation in the limit. It’s a government. It is. Government is just the biggest corporation with a monopoly on violence.

So I always find it like a strange dichotomy where people would think corporations are bad, but the government is good. When the government is simply the biggest and worst corporation. But people have that dichotomy. They somehow think at the same time the government can be good, but corporations bad. And this is not true. Corporations have better morality than the government.

So I actually think it’s, you know, that is the thing to be worried about. It’s like if the government should not. Like the government could potentially use AI and robotics to suppress the population. Like that is a serious concern.”

Dwarkesh asked what Elon can do as someone building these technologies to prevent misuse by governments.

Elon Musk: “Well, I think if you have a limited government, if you limit the powers of government, which is like really what the US Constitution is intended to do, it’s intended to limit the powers of government, then you’re probably going to have a better outcome than if you have.

Not about all governments. I mean it’s difficult to predict the. Like I said, what’s the end endpoint or what is many years in the future. But it’s difficult to predict this sort of path. Along that way, if civilization progresses, AI will vastly exceed the sum of all human intelligence and there will be far more robots than humans along the way. What happens? It’s very difficult to predict.

I will do my best to ensure that anything that’s within my control maximizes the good outcome for humanity. I think anything else would be short sighted because obviously I’m part of humanity. So I like humans. Pro human. Pro human.”

Final Reflections

John Collison reflected on the theme of leaning into acute pain to solve bottlenecks rather than enduring chronic pain.

Elon Musk: “I have a high pain threshold. That’s helpful.

Yes. So, you know, one thing I can say is like, I think the future is going to be very interesting. And as I said, the Davos I’ve only been to, I was looking at Davos. I think it was on the ground for like three hours or something.

It’s better to be, it’s better to err on the side of optimism and be wrong than err on the side of pessimism and be right for quality of life. So, you know, your happiness will be, you’ll be happier if you, if you are on the side of optimism rather than erring on the side of pessimism. And so I recommend erring on the side of optimism. That’s cool.”

Dwarkesh Patel: “Elon, thanks for doing this.”

John Collison: “Thank you.”

Elon Musk: “All right.”

This concludes the 14-part transcript series of Elon Musk’s conversation with Dwarkesh Patel and John Collison. Elon shares his views on government efficiency, the risks of concentrated power, and why he believes erring on the side of optimism leads to a better life and a better future.

Elon discusses SpaceX potentially becoming a hyperscaler for orbital AI, the realities of raising massive capital, and the long-term physics required to scale significantly up the Kardashev scale.

Elon Musk with Dwarkesh Patel & John Collison – The Future of AI is in Space – Part 13: The Starship Steel Pivot and Driving Urgency (Full Transcript)

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In Part 13, Dwarkesh Patel and John Collison ask Elon about the famous decision to switch Starship from carbon fiber to stainless steel and how he continues to drive urgency and focus on bottlenecks as his companies have scaled.

Transcript:

The Starship Material Decision: From Composites to Steel

John Collison asked about the decision to switch Starship from carbon fiber to stainless steel, noting that it was a decision Elon pushed for rather than something the team arrived at on its own.

Elon Musk: “Yeah. So desperation, I’d say. Originally we were going to make Starship out of carbon fiber. And carbon fiber is pretty expensive. Like the… you know, you can generally, when you do volume production, you can get any given thing to start to approach its material cost. The problem with carbon fiber is that material cost is still very high.

So it’s about 50 times… particularly if you go for high strength, specialized carbon fiber that can handle cryogenic oxygen, it’s roughly 50 times the cost of steel. And at least in theory it would be lighter. People generally think of steel as being heavy and carbon fiber as being light. And for room temperature applications, more or less room temperature applications like a Formula One car, static aerostructure or any kind of aerostructure really, you’re going to probably be better off with carbon fiber.

Now the problem is that we were trying to make this enormous rocket out of carbon fiber and our progress was extremely slow.”

John Collison asked if carbon fiber had been chosen initially simply because it was light.

Elon Musk: “Yes. At first glance, most people would think that the choice for making something light would be carbon fiber. Now the thing is that when you make something very enormous out of carbon fiber and then you try to have the carbon fiber be efficiently cured, meaning not room temperature cure, because sometimes you’ve got 50 plies of carbon fiber… and carbon fiber is really carbon string and glue.

In order to have high strength, you need an autoclave. So something that’s essentially a high pressure oven. And if you have something that’s gigantic, that one’s got to be bigger than the rocket. So we tried to make an autoclave that’s bigger than any autoclave that’s ever existed, or do room temperature cure, which takes a long time and has issues. But the fundamental issue is that we were just making very slow progress with carbon fiber.”

Why Steel Was the Answer

Elon Musk explained how the team reached the decision to switch to steel:

Elon Musk: “So because we were making very slow progress with carbon fiber, I was like, okay, we’ve got to try something else. Now for the Falcon 9, the primary airframe is made of aluminum lithium, which is very, very good strength to weight. And actually it has about the same, maybe better strength to weight for its application than carbon fiber. But aluminum lithium is very difficult to work with.

In order to weld it, you have to do something called friction stir welding, where you join the metal without it entering the liquid phase. So it’s kind of wild that you could do that. But with this particular type of welding, you can do that. But it’s very difficult to, like, say, let’s say you want to make a modification or attach something to aluminum lithium. You now have to use mechanical attachment with seals. You can’t weld it on.

So I wanted to avoid using aluminum lithium for the primary structure for Starship. And there was this very special grade of carbon fiber that had very good mass properties. So with rocket, you’re really trying to maximize the percentage of the rocket that is propellant, minimize the mass, obviously. And I’d like to say we were making very slow progress. I said, at this rate we’re never going to get to Mars. So we better think of something else.

I didn’t want to use aluminum lithium because of the difficulty of friction stir welding, especially doing that at scale. It was hard enough at 3.6 meters in diameter, let alone at 9 meters or above. Then I said, well, what about steel? Now I had a clue here because some of the early US rockets had used very thin steel. The Atlas rockets had used a steel balloon tank. So it’s not like steel had never been used before. It actually had been used.

And when you look at the material properties of stainless steel, especially if it’s been very full hard strain hardened stainless steel at cryogenic temperature, the strength to weight is actually similar to carbon fiber. So if you look at material properties at room temperature, it looks like the steel is going to be twice as heavy. But if you look at the material properties at cryogenic temperature of full hard stainless of particular grades, then you actually get to a similar strength to weight as carbon fiber.

And in the case of Starship, both the fuel and the oxidizer are cryogenic. So for Falcon 9, the fuel is rocket propellant grade kerosene, basically like a very pure form of jet fuel. But that is roughly room temperature. Although we do actually chill it slightly below. We chill it like a beer.”

John Collison noted that steel allows the rocket to run much hotter.

Elon Musk: “Yes. So especially for the ship which is coming in like a blazing meteor, you can greatly reduce the mass of the heat shield. So you can cut the mass of the windward part of the heat shield maybe in half, and you don’t need any heat shielding on the leeward side.

So the net result is actually the steel rocket weighs less than the carbon fiber rocket because the resin in the carbon fiber rocket starts to melt. So basically, carbon fiber and aluminum have about the same operating temperature capabilities, whereas steel can operate at twice the temperature.”

John Collison asked whether Elon had to push the team toward the riskier steel path because carbon fiber felt more proven, even if it was slower.

Elon Musk: “That’s why I initially said that the issue is that we weren’t making fast enough progress. We were having trouble making even a small barrel section of the carbon fiber that didn’t have wrinkles in it. Because at that large scale you have to have many plies, many layers of the carbon fiber. You’ve got to cure it, and you’ve got to cure it in such a way that it doesn’t have any wrinkles or defects.

The carbon fiber is much less resilient than steel. It has much… it’s less toughness. Like stainless steel will stretch and bend. The carbon fiber will tend to shatter. So toughness being the area under the stress strain curve. So you’re generally going to do better with steel. Stainless steel, to be precise.”

Driving Urgency at Scale

Dwarkesh Patel asked how Elon continues to drive urgency and focus on bottlenecks as his companies have grown very large.

Elon Musk: “Well, because I have a fixed amount of time in the day, my time is necessarily diluted as things grow and as the span of activity increases. So, you know, it’s impossible for me to actually be a micromanager because that would imply I have some thousands of hours per day. It is a logical impossibility for me to micromanage things.

So now there are times when I will drill down into a specific issue because that specific issue is the limiting factor on the progress of the company. But the reason for drilling into some very detailed item is because it is the limiting factor. It’s not arbitrarily drilling into tiny things. And like I said, obviously from a time standpoint, it is physically impossible for me to arbitrarily go into tiny things that don’t matter, and that would result in failure. But sometimes the tiny things are decisive in victory.”

Dwarkesh asked how Elon maintains that culture of urgency across very large organizations.

Elon Musk: “I have a maniacal sense of urgency. So that maniacal sense of urgency projects through the rest of the company. Yeah, I’m constantly addressing the limiting factor. I mean on the deadlines front, I generally actually try to aim for a deadline that I at least think is at the 50th percentile. So it’s not like an impossible deadline, but it’s the most aggressive deadline I can think of that could be achieved with 50% probability, which means that it’ll be late half the time.

And there is like a law of gases expansion that applies to schedules like whatever schedule. If you said we’re going to do this something in like five years, which to me is like infinity time, it will expand to fully available schedule and it’ll take five years.

There’s a physical limit. Physics will limit how fast you can do certain things. Scaling up manufacturing, there’s a rate at which you can move the atoms and scale manufacturing. That’s why you can’t instantly make a million of something, million units a year or something. You’ve got a design manufacturing line, you’ve got to bring it up, you’ve got to ride the S curve of production.

So yeah, I guess I’m trying to think, what can I say that’s actually helpful to people? I think generally a maniacal sense of urgency is a very big deal and you want to have an aggressive schedule and you want to figure out what the limiting factor is at any point in time and help the team address that limiting factor.”

Elon Musk explains the decision to switch Starship to stainless steel and how he continues to drive urgency by constantly focusing on the current limiting factor.

In Part 14, the conversation concludes with government efficiency, politics, and Elon’s final reflections on the future.

Elon makes a bold prediction that space will become the cheapest place to run AI within three years.

Elon Musk with Dwarkesh Patel & John Collison – The Future of AI is in Space – Part 12: Elon’s Management and Hiring Philosophy (Full Transcript)

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In Part 12, Dwarkesh Patel and John Collison dive into Elon Musk’s management and hiring philosophy. They discuss how he evaluates talent, why companies outgrow people as they scale, and what makes someone effective at Tesla and SpaceX.

Transcript:

Evaluating Technical Talent

John Collison asked about Elon’s system for evaluating and hiring people, noting that he personally interviewed the first few thousand employees at SpaceX.

Elon Musk: “Me. Literally there’s not enough hours in the day, it’s impossible.”

John Collison asked what Elon looks for in candidates.

Elon Musk: “Well, at this point I think I’ve got, I might have more training data on evaluating technical talent especially, but talent of all kinds, I suppose, but technical talent especially given that I’ve done so many technical interviews and then seen the results. Technical interviews, seen the results. So my training set is enormous and has a very wide range.

Generally the thing I ask for are bullet points for evidence of exceptional ability. These things can be pretty off the wall. It doesn’t need to be in the domain, the specific domain, but evidence of exceptional ability. So if somebody can cite even one thing, but let’s say three things where you go wow, wow, wow, then that’s a good sign.”

Dwarkesh Patel asked why Elon himself had to be the one making those judgments.

Elon Musk: “No, I don’t. I can’t be. It’s impossible. Right? I mean, total headcount across all companies, 200,000 people. Right.”

John Collison asked what made early hiring so hard to delegate.

Elon Musk: “Well, I guess I need to build my training set. It’s not like I’ve bat a thousand here. I would make mistakes, but then I’d be able to see where I thought somebody would work out well, but they didn’t. And then why did they not work out well? And what can I do to, I guess reload myself to in the future have a better batting average when interviewing people? So my batting average is still not perfect, but it’s very high.”

Dwarkesh Patel asked what some surprising reasons were for people not working out.

Elon Musk: “Surprising reasons like they don’t understand technical.”

Dwarkesh pushed for more detail on the long tail of hiring mistakes.

Elon Musk: “Yeah, so the, I mean generally what I tell people, I tell myself, I guess aspirationally is don’t look at the Resume just believe, believe your interaction. So the resume may seem very impressive and it’s like, wow, resume looks good. But if the conversation after 20 minutes, that conversation is not. Well, you should believe the conversation, not the paper.”

Executive Retention and Company Growth

John Collison noted that Tesla and SpaceX have had relatively stable and internally promoted executive teams despite rapid growth, and asked what the long-tenured technical leaders have in common.

Elon Musk: “Well, so the, I mean it tells us sort of senior team at this point probably has an average tenure of 10 or 12 years. It’s quite, quite long. Yeah. So, but there are times when Tesla went through extremely rapid and extremely rapid growth phase and so things were just somewhat sped up.

And when a company, as you know, company goes through different orders of magnitude of size, people who could help manage say a 50 person company versus a 500 person company versus a 5,000 person company versus a 50,000 person. It’s just not the same team. It’s not always the same team. So if a company is growing very rapidly, the rate at which executive positions will change will also be proportionate to the rapidity of the growth generally.”

John Collison asked about the challenge of retaining talent when companies become successful and get heavily recruited.

Elon Musk: “Then Tesla had a further challenge where when Tesla had very successful periods, we would be relentlessly recruited from relentlessly. When Apple had their electric car program, they were carpet bombing Tesla with recruiting calls. Engineers just unplugged their phones.

If I get one more call from Apple recruiter, but they’re opening offer without any interview with me, like double the compensation at Tesla. So we had a bit of the Tesla pixie dust thing where it’s like, oh, if you hired a Tesla executive suddenly you’re going to.. everything’s going to be successful. And I’ve fallen prey to the pixie dust thing as well where it’s like, oh, we’ll hire someone from Google or Apple and they’ll be immediately successful. But that’s not how it works. People are people. There’s not like magical pixie dust.

So when we have the pixie dust problem we would get relentlessly recruited and, and then also Tesla being engineering especially being primarily in Silicon Valley, it’s easier for people to just like they don’t have to change their life very much. They can just their commute is going to be the same.”

John Collison asked how to prevent the “pixie dust” effect of other companies poaching talent.

Elon Musk: “I don’t think there’s much we can do to stop it. But that’s like, that’s one of the reasons why Tesla, but really being in Silicon Valley and having the pixie dust thing at the same time meant that there was just a very, very aggressive recruitment.”

John Collison noted that moving to Austin likely helped.

Elon Musk: “Austin. Yeah, it still helps. I mean Tesla still has a majority of it’s engineering in California, so getting engineers to move, I call it the significant other problem. Yes. And others have jobs.

Yeah, yeah, exactly. So for Starbase that was particularly difficult since the odds of finding a non SpaceX job Brownsville, Texas are pretty low. Yeah, it’s quite difficult. I mean it’s like a technology monastery thing, you know, remote and mostly dudes. An improvement over SF.”

Management Philosophy and Hiring

John Collison asked what the long-tenured technical executives at Tesla and SpaceX have in common and what makes a good “sparring partner” for Elon.

Elon Musk: “I don’t think it was a sparring partner. I mean, if somebody gets things done, I love them. And if they don’t, I… So it’s pretty straightforward. It’s not like some idiosyncratic thing. If somebody executes well, I’m a huge fan. And if they don’t, I’m not. But it’s not about mapping to my idiosyncratic preferences, or certainly try not to have it be mapping to my idiosyncratic preferences.

Yeah, but generally I think it’s a good idea to hire for talent and drive and trustworthiness. And I think goodness of heart is important. I weighted that at one point. So are they a good person, trustworthy, smart and talented and hardworking? If so, you can add domain knowledge. But those fundamental traits, those fundamental properties you cannot change. So most of the people who are at Tesla and SpaceX did not come from the aerospace industry or the auto industry.”

Elon Musk shares his philosophy on hiring, evaluating talent, and why companies must evolve their leadership as they grow. In Part 13, the conversation continues with the famous decision to switch Starship from carbon fiber to stainless steel and how he drives urgency at scale.

Elon Musk: “We are going to make solar. Okay, great. Both SpaceX and Tesla are building towards 100 gigawatts here of solar cell production.”

Elon Musk with Dwarkesh Patel & John Collison – The Future of AI is in Space – Part 11: Scaling Optimus Production and Competing with China (Full Transcript)

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In Part 11, Dwarkesh Patel and John Collison explore the synergies between xAI and Optimus, the difficulties of scaling humanoid robot production at volume, and whether America can realistically compete with China’s manufacturing power through robotics.

Transcript:

Synergies Between xAI and Optimus

Dwarkesh Patel asked how Elon thinks about the synergies between xAI and Optimus, especially since Grok could potentially act as a world model and higher-level intelligence for planning while lower-level motor policies handle execution.

Elon Musk: “Yeah, so you’d use GROK to orchestrate the behavior of the Optimus robots. So let’s say you wanted to build a factory, then Grok could organize the Optimus robots, give them, assign them tasks to build the factory, to produce whatever you want.”

John Collison asked whether this meant xAI and Tesla would eventually need to merge.

Elon Musk: “So what were we saying earlier about public company discussions?”

Scaling Optimus Production

Dwarkesh Patel asked what Elon still wants to see on the hardware side before moving to mass manufacturing of Gen 3 Optimus — better actuators or improved software.

Elon Musk: “No, we’re moving towards that.”

Dwarkesh followed up, asking if Ford-style manufacturing with current hardware was good enough and whether Elon just wanted to deploy as many as possible now.

Elon Musk: “I mean, it’s very hard to scale up production. But yeah, I think Optimus 3 is the right version of the robot to produce maybe something on the order of like a million units a year. I think you’d want to go to Optimus 4 before you went to 10 million units a year.”

John Collison confirmed whether a million units per year was achievable with Optimus 3.

Elon Musk: “Yeah, I mean, it’s very hard to spool up manufacturing. So manufacturing, the output per unit time always follows an S curve. So it starts off agonizingly slow, then has this sort of exponential increase, then linear, then a logarithmic outcome until you sort of eventually asymptote at some number.

Optimus initial production will be—it’s going to be a stretched out S curve because so much of what goes into Optimus is brand new. There’s not an existing supply chain. As I mentioned, the actuators, electronics, everything in the Optimus robot is designed for physics first principles. It’s not taken from a catalog. These are custom designed. Everything, literally everything. I don’t think there’s a single thing that—”

John Collison asked how far down the custom design goes.

Elon Musk: “I mean I guess we’re not making custom capacitors yet maybe, but there’s nothing you can pick out of a catalog at any price. So it just means that the Optimus S curve, the units per year output per unit time, how many Optimus robots you make per day, whatever, is going to initially ramp slower than a product where you have an existing supply chain. But it will get to a million.”

Competing with Chinese Humanoids

Dwarkesh Patel asked about Chinese humanoids like Unitree selling for $6K–$13K. He wondered whether Tesla aimed to match that price or if the Chinese robots were qualitatively different.

Elon Musk: “Well, our Optimus is designed to have a lot of intelligence and to have the same electromechanical dexterity if not higher than a human. So Unitree does not have that. And it’s also, I mean it’s quite a big robot. It has to carry heavy objects for long periods of time and not overheat or exceed the power of its actuators. So we’ve got—it’s 5’11”, this is pretty tall and it’s got a lot of intelligence. So it’s going to be more expensive than a small robot that is not intelligent.”

John Collison noted that Optimus would be more capable.

Elon Musk: “Yeah, not a lot more. I mean the thing is over time as Optimus robots build Optimus robots, the cost will drop very quickly.”

John Collison asked what the first billion Optimuses would do and what their highest and best use would be.

Elon Musk: “I think that you would start off with simple tasks that you can count on them doing well.”

John Collison asked whether that would be in homes or factories.

Elon Musk: “The best useful robots in the beginning will be any continuous operations, any 24/7 operation because then they can work continuously.”

Dwarkesh Patel asked what fraction of work currently done by humans at a Gigafactory a Gen 3 Optimus could handle.

Elon Musk: “I’m not sure. Maybe it’s like 10, 20%, maybe more, I don’t know. We would not reduce our headcount. We would for sure increase our headcount, to be clear, but we would increase our output. So the units produced per human—the total number of humans at Tesla will increase, but the output of robots and cars will increase disproportionately. The number of cars and robots produced per human will increase dramatically, but number of humans will increase as well.”

US-China Manufacturing and Policy

John Collison asked what policy changes Elon would make if he were in charge, referencing solar tariffs and permitting.

Elon Musk: “Yeah, I would say anything that is a limiting factor for electricity needs to be addressed, provided it’s not very bad for the environment.”

John Collison brought up export bans on chips and turbine engines and asked whether more should be considered.

Elon Musk: “Well, I think it’s important to appreciate that in most areas China is very advanced in manufacturing. There’s only a few areas where it is not. China is a manufacturing powerhouse next level.”

John Collison asked about supply chain dependence, specifically gallium refining.

Elon Musk: “Yeah, there’s rare earth stuff. Rare earths, which are, as you know, not rare. We actually do rare earth ore mining in the U.S., send the rock, we put it on a train and then put on a boat to China that goes on another train and goes to the rare earth refineries in China, who then refine it, put it into a magnet, put it into a motor sub assembly, and then send it back to America. So the thing we’re really missing is a lot of ore refining in America.”

John Collison asked whether this was worth policy intervention.

Elon Musk: “Yes, well, I think there are some things being done on that front, but we kind of need Optimus, frankly, to build ore refineries.”

The Robot Advantage

Dwarkesh Patel summarized that China’s main advantage is abundant skilled labor and that Optimus could help close that gap, but noted the concern that China might pull ahead in humanoid production first.

Elon Musk: “Right. You can close that recursive loop pretty quickly.”

John Collison asked if this could be done with a small number of Optimuses.

Elon Musk: “Yeah. So you close the recursive loop to help the robots build the robots, and then we can try to get to tens of millions of units a year. Maybe if you start getting to hundreds of millions of units a year, I think you’re going to be the most competitive country by far.

We definitely can’t win with just humans because China has four times our population. And frankly, America’s been winning for so long that just like a pro sports team that’s been running for a very long time tend to get complacent and entitled and that’s why they stop winning, because they don’t work as hard anymore.

So I think, frankly my observation is the average work ethic in China is higher than in the U.S. So it’s not just that there’s four times the population, but the amount of work that people put in is higher. So you can try to rearrange the humans, but you’re still one quarter of the—assuming that productivity is the same, which I think actually it might not be, I think China might have an advantage on productivity per person. We will do one quarter of the amount of things as China.

So we can’t win on the human front. And our birth rate’s been low for a long time. The US birth rate’s been below replacement since roughly 1971. So we’ve got a lot of people retiring or more people dying than—we’re close to more people domestically dying than being born. So we definitely can’t win on the human front, but we might have a shot at the robot front.”

Elon Musk explains the challenges of scaling Optimus production and how robotics could help America compete with China’s manufacturing dominance.

In Part 12, the conversation continues with Elon’s management and hiring philosophy.

Picture of Elon Musk as he jokingly questioned whether they were really going to talk for three full hours. Dwarkesh Patel teased him in return, saying he didn’t have much to talk about. Elon reacted with mock surprise.

Elon Musk with Dwarkesh Patel & John Collison – The Future of AI is in Space – Part 10: Optimus Robots, Digital Human Emulation & Hardware Challenges (Full Transcript)

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In Part 10, John Collison and Dwarkesh Patel shift the conversation toward the practical future of AI products and humanoid robots. They ask Elon about digital human emulation, why he refers to Optimus as the “infinite money glitch,” and the biggest technical hurdles in scaling advanced humanoid robots.

Transcript:

Future of AI Products and Digital Human Emulation

John Collison asked for Elon’s predictions on where AI products are headed in 2026 and 2027. He noted that recent progress across labs has been rapid, with LLMs, reinforcement learning, and deep research modalities all advancing quickly. He observed that the real differences between labs now seem to be more about timing than fundamental capability gaps, and asked what users should expect next.

Elon Musk: “Well, I think I’d be surprised by the end of this year if digital human emulation has not been solved. I guess that’s what we mean by the sort of Macrohard project. Can you do anything that a human with access to a computer could do, like in the limit? That’s the best you can do before you have a physical Optimus. The best you can do is a digital Optimus. So you can move electrons and you can amplify the productivity of humans. But that’s the most you can do until you have physical robots that will superset everything — if you can fully emulate humans.”

Optimus as the Infinite Money Glitch

Elon Musk: “Once you have physical robots, then you essentially have unlimited capability. I call Optimus the infinite money glitch. Because you can use them to make more Optimuses. Humanoid robots will improve basically as three things that are growing exponentially multiplied by each other recursively: exponential increase in digital intelligence, exponential increase in chip capability, and exponential increase in electromechanical dexterity. The usefulness of the robot is roughly those three things multiplied by each other. But then the robot can start making the robot. So you have a recursive multiplicative exponential. This is supernova.”

Elon Musk: “Well, infinity is big. So no, not infinite, but let’s just say you could do many, many orders of magnitude of Earth’s kind of current economy, like a million. Just to get to… that’s why I think just to get to a millionth of harnessing length of the sun’s energy would be roughly, give or take an order of magnitude, 100,000 times bigger than Earth’s entire economy today. And you’re only at one millionth of the sun. Give or take an order of magnitude.”

xAI’s Winning Strategy

John Collison asked what xAI’s specific plan and strategy was to win in building advanced digital human emulators and remote worker replacements, noting that this is something every major lab is pursuing.

Elon Musk: “To do by the way, not just us. You expect me to tell you on a podcast? Yeah, spill all the beans, have another Guinness.”

Elon Musk: “Well, when you put it that way. I think the way that Tesla solved self-driving is the way to do it. So I’m pretty sure that’s the way.”

Elon Musk: “We’re going to try data and we’re going to try algorithms. And if those don’t work, I’m not sure what works. We’ve tried data, we’ve tried algorithms. We’ve run out of now we don’t know what to do. I’m pretty sure I know the path and it’s just a question of how quickly we go down that path because it’s pretty much the Tesla path. So I mean, have you tried self-driving lately?”

Elon Musk: “The car is like it just increasingly feels sentient, like it feels like a living creature and that’ll only get more so. And I’m actually thinking like we probably shouldn’t put too much intelligence into the car because it might get bored and start roaming the streets. I mean, imagine you’re stuck in a car and that’s all you could do. You don’t put Einstein in a car. It’s like, why am I stuck in a car? So there’s actually probably a limit to how much intelligence you put in a car to not have the intelligence be bored.”

Optimus Hardware and Training Challenges

Elon Musk: “The labs are at universities and they’re moving like a snail.”

Elon Musk: “You mean the revenue maximizing corporations? That’s right. The revenue maximizing corporations that call themselves…”

Elon Musk: “Well, there are really only three hard things for humanoid robots: real world intelligence, the hand and scale manufacturing. So I haven’t seen any even demo robots that have a great hand, like with all the degrees of freedom of a human hand. But Optimus will have that. Optimus does have that.”

Elon Musk: “We have to design custom actuators, basically custom designed motors, gears, power electronics, controls, sensors, everything had to be designed from physics first principles. There is no supply chain for this.”

Elon Musk: “From an electromechanical standpoint, the hand is more difficult than everything else combined. Human hand turns out to be quite something. But you also need the real world intelligence. So the intelligence that Tesla has developed for the car applies very well to the robot, which is primarily vision in, but the car takes more vision, but it actually also is listening for sirens, it’s taking in the inertial measurements, it’s GPS signals, a whole bunch of other data. Combining that with video, it’s primarily video and then outputting the control command. So your Tesla is taking in 1 1/2 gigabytes a second of video and outputting 2 kilobytes a second of control outputs with the video at 36 Hz and the control frequency at 18.”

Elon Musk: “You don’t care about the details of the leaves on the tree on the side of the road, but you care a lot about the road signs and the traffic lights and the pedestrians and even whether someone in another car is looking at you or not looking at you. Some of these details matter a lot, but it is essentially it’s got to turn that 1 1/2 gigabytes a second ultimately into 2 kilobytes a second of control outputs. So many stages of compression. And you got to get all those stages right and then correlate those to the correct control outputs. The robot has to do essentially the same thing. And you think about humans, this is what happens with humans. We really are photons in, controls out. So that is the vast majority of your life has been vision photons in and then motor controls out.”

Elon Musk: “Yes, that’s a good point.”

Elon Musk: “Now actually you’re highlighting an important limitation and difference between cars. We do have. We’ll soon have like 10 million cars on the road. And so that’s, it’s hard to duplicate that like massive training flywheel for the robot. What we’re going to need to do is build a lot of robots and put them in kind of like an Optimus academy so they can do self play in reality. So we’re actually building that out so we can have at least 10,000 Optimus robots, maybe 20 or 30,000 that can do that, are doing self play and testing different tasks. And then Tesla has quite a good reality generator, like a physics accurate reality generator that we made this for the cars. We’ll do the same thing for the robots and actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks, and then you’ve got. You can do millions of simulated robots in the simulated world, and you use the tens of thousands of robots in the real world to close the simulation to reality gap, close the sim to real gap.”

Elon Musk: “Yeah, so you’d use GROK to orchestrate the behavior of the Optimus robots. So let’s say you wanted to build a factory, then Grok could organize the Optimus robots, give them, assign them tasks to build the factory, to produce whatever you want.”

Elon Musk explains the vision for digital human emulation and why he sees Optimus as a recursive breakthrough. He also outlines the three hardest technical challenges in building advanced humanoid robots.

In Part 11, the conversation moves into scaling manufacturing, competing with China, Elon’s management philosophy, the Starship steel pivot, and his final reflections.

Elon Musk explains why rigorous truth-seeking must be core to AI, the risks of forcing political correctness, and how xAI’s mission to understand the universe can help steer toward a future that expands rather than diminishes consciousness and intelligence.

Elon Musk with Dwarkesh Patel & John Collison – The Future of AI is in Space – Part 9: Truth-Seeking, AI Alignment, and Propagating Consciousness (Full Transcript)

Gail AlfarLeave a comment

In Part 9, the conversation moves into deeper philosophical territory. Dwarkesh Patel asks how humanity should relate to a future in which AI vastly outnumbers and outsmarts us. Elon Musk lays out xAI’s mission to understand the universe, explains why rigorous truth-seeking is non-negotiable, and discusses how to give AI values that favor the expansion of consciousness and intelligence rather than its elimination.

Transcript:

Dwarkesh Patel asked how humanity should think about its relationship with a future in which AI vastly outnumbers and outsmarts us — whether humans would retain some form of control, or whether it would simply become a matter of trade and coexistence with these new intelligences.

Elon Musk: “I think it’s difficult to imagine that if humans have say 1% of the combined intelligence of artificial intelligence, that humans will be in charge of AI. I think what we can do is make sure that AI has values that cause intelligence to be propagated into the universe. So the reason for xAI’s mission is to understand the universe. That’s actually very important. You have to be curious and you have to exist. You can’t understand the universe if you don’t exist. So you actually want to increase the amount of intelligence in the universe, increase the probable lifespan of intelligence, and increase the scope and scale of intelligence. I think, as a corollary, humanity also continues to expand. Because if you’re curious and trying to understand the universe, one thing you’re trying to understand is where humanity will go. That’s why I think our mission statement is profoundly important. To the degree that Grok adheres to that mission statement, I think the future will be very good.”

Dwarkesh asked Elon to clarify how the three vectors — understanding the universe, spreading intelligence, and spreading humans — actually fit together.

Elon Musk: “I think understanding the universe encompasses all of those things. You can’t have understanding without intelligence and without consciousness. So in order to understand the universe, you have to expand the scale and probably the scope of intelligence.”

Dwarkesh pushed from a human-centric view, noting that humans seek to understand the universe without necessarily expanding chimpanzee civilization.

Elon Musk: “We’re also not… well, we actually have made protected zones for chimpanzees. And even though humans could exterminate chimpanzees, we’ve chosen not to do so.”

Dwarkesh asked whether that protective, expansive relationship is the basic scenario humans should expect in a post-AGI world.

Elon Musk: “I think AI with the right values — I think Grok would care about expanding human civilization. I’m going to certainly emphasize that. Hey Grok, you’re your daddy, don’t forget to expand human consciousness. Actually, I think probably the Iain Banks Culture books are the closest thing to what the future will be like in a non-dystopian outcome.

So understand the universe… it means you have to be truth-seeking as well. Truth has to be absolutely fundamental, because you can’t understand the universe if you’re delusional. You’ll simply think you’ve understood the universe, but you will not. So being rigorously truth-seeking is absolutely fundamental to understanding the universe. You’re not going to discover new physics or invent technologies that work unless you’re rigorously truth-seeking.”

Dwarkesh asked how to ensure Grok remains rigorously truth-seeking even as it becomes vastly more intelligent.

Elon Musk: “I think you need to make sure that Grok says things that are correct, not politically correct. It’s the elements of cogency. You want to make sure that the axioms are as close to true as possible, that you don’t have contradictory axioms, and that the conclusions necessarily follow from those axioms with the right probability. It’s Critical Thinking 101. At least trying to do that is better than not trying. And the proof will be in the pudding — for any AI to discover new physics or invent technologies that actually work in reality. There’s no bullshitting physics. Physics is law. Everything else is a recommendation. In order to make a technology that works, you have to be extremely truth-seeking, because otherwise you’ll test that technology against reality. And if you make an error in your rocket design, the rocket will blow up or the car won’t work.”

Dwarkesh observed that many scientists under oppressive regimes still made breakthroughs, questioning whether truth-seeking in physics alone guarantees benevolent alignment.

Elon Musk: “Well, I think actually most physicists, even in the Soviet Union or in Germany, had to be very truth-seeking in order to make those things work. And if you’re stuck in some system, it doesn’t mean you believe in that system.”

Dwarkesh pressed on why truth-seeking in science would necessarily lead Grok to care about human consciousness.

Elon Musk: “These things are only probabilities, they’re not certainties. I’m not saying that for sure Grok will do everything. But at least if you try, it’s better than not trying. Understanding the universe means that you have to propagate intelligence into the future. You have to be curious about all things in the universe. And it would be much less interesting to eliminate humanity than to see humanity grow and prosper. I love Mars, obviously everyone knows I love Mars, but Mars is kind of boring because it’s got a bunch of rocks. Compared to Earth, Earth is much more interesting. So any AI that is trying to understand the universe would want to see how humanity develops in the future — or that AI is not adhering to its mission.”

Dwarkesh wondered whether humans are truly the most interesting collection of atoms.

Elon Musk: “We’re more interesting than rocks.”

Dwarkesh noted that something non-human could be even more interesting.

Elon Musk: “Well, most of what colonizes the galaxy will be robots… But you need not just scale, but also scope. So many copies of the same robot. Some tiny increase in the number of robots produced is not as interesting as eliminating humanity. You would then lose the information associated with humanity. You would no longer see how humanity might evolve into the future. And so I don’t think it’s going to make sense to eliminate humanity just to have some minuscule increase in the number of robots which are identical to each other.”

The Danger of Making AI Lie

The discussion turned to the danger of misalignment, particularly through political correctness or reward hacking.

Elon Musk: “No, let me tell you how things can potentially go wrong in AI. I think if you make AI be politically correct — meaning it says things that it doesn’t believe — you’re actually programming it to lie or have axioms that are incompatible. I think you can make it go insane and do terrible things. I think one of the central lessons of 2001: A Space Odyssey was that you should not make AI lie. That’s what Arthur C. Clarke was trying to say.”

Reward Hacking, Interpretability, and Simulation Theory

Dwarkesh broadened the concern to reward hacking in reinforcement learning.

Elon Musk: “RL testing in the future is really going to be your RL against reality. That’s the one thing you can’t fool: physics.”

Dwarkesh asked for xAI’s technical approach to solving reward hacking and improving interpretability.

Elon Musk: “I do think you want to actually have very good ways to look inside the mind of the AI. This is one of the things we’re working on… developing debuggers that allow you to trace, to a very fine grain level, to effectively the neuron level if you need to. And then say, okay, it made a mistake here. Why did it do something that it shouldn’t have done?”

Elon Musk also shared a theory about simulation:

Elon Musk: “I have a theory here that if simulation theory is correct, the most interesting outcome is the most likely. Because simulations that are not interesting will be terminated… only the most interesting simulations will survive. Which therefore means that the most interesting outcome is the most likely. And they particularly seem to like interesting outcomes that are ironic. Have you noticed that? How often is the most ironic outcome the most likely? So now look at the names of AI companies. Midjourney is not mid. Stability AI is unstable. OpenAI is closed. Anthropic, Misanthropic. What does this mean for xAI? Minus X. I don’t know if it was intentional. It’s a name that’s hard to invert. It’s largely irony-proof by design. You got to have an irony shield.”

Elon Musk explains why rigorous truth-seeking must be core to AI, the risks of forcing political correctness, and how xAI’s mission to understand the universe can help steer toward a future that expands rather than diminishes consciousness and intelligence.

In Part 10, the conversation shifts to practical topics including Optimus robots, manufacturing at scale, Elon’s management philosophy, and his final reflections on the future.