Silicon Valley Girl: AI, Tech and Career Growth - Musk's Early Investor: What the Next 3 Years Actually Look Like | Steve Jurvetson

Episode Date: July 7, 2026

Steve Jurvetson has worked with Elon Musk for 29 years and was one of the earliest investors in SpaceX and Tesla, back when space wasn't even a category for venture capital. Today he runs Future Ventures, where he bets on nuclear fusion, epigenetic editing, and analog AI chips.We recorded this live on stage, with audience Q&A at the end.We cover:Why compute has compounded for 130 years, and what that curve says about the next 3 yearsSuperintelligence odds: why Anthropic co-founder Jack Clark gives it a 30% chance of arriving next yearWhat 29 years next to Elon taught Steve: focus, learning loops, and spotting talent (Tesla collects more AI training data in 4 days than Waymo has in its entire history)The 50-year question Steve asks every founder before writing a checkHis 30-day plan if you're one person with a crazy ideaWhere he's investing now: fusion, meat without slaughter, free healthcare via your phoneWhen machines do everything, what's left for usLinks: 📌 Subscribe to my free newsletter where I go deeper on AI tools, career strategies, and building with AI: https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=spotify&utm_medium=video&utm_campaign=futureproof-sub&utm_content=SteveJurvetson 🔗 Instagram: https://www.instagram.com/siliconvalleygirl/ 𝕏 : https://x.com/siliconvalleymm 💼 LinkedIn: https://www.linkedin.com/in/marinamogilko 💼 My Companies & Products: https://Marinamogilko.co

What will the next three years look like? I have this gut feeling that it'll be something architecturally variant. This is Steve Jervitson, an early investor in SpaceX when almost nobody believed in private space. He backed Tesla before electric cars went mainstream. For over 30 years, he's been betting on the future. And history keeps proving him right. When someone comes to you with just an idea, what will be the best thing that they can do? Single person with an idea?

I might try to find a co-founder. It's rarely an individual. Jobs in Wozniak, Batman and Robin. You're working a lot with Elon. Top three principles that everyone should learn from him. I do try to observe leaders in action. Even with a focused effort, it's not always obvious, but a few things. One is this insane ability to...

Thank you so much. This is going to be very exciting. Steve, I am so excited to have you. On this stage. What a fun time, right? SpaceX. IPO, and you were there super early. what did you see that most investors didn't see back then?

So the simple answer to the question is there were almost no investors considering space. It wasn't a category on any site. So the slightly varying, the question is why in the world would we invest in a sector that is just not a sector for venture? Same could be said for automotive, a Tesla, energy with nuclear fusion. There were a handful of investments, but very few. So the short version is obviously an incredible entrepreneur, someone we've worked with before. I've known him for, oh gosh, 29 years now, and invested in all of these companies of the century and his cousins, too.

So, all in, if you will, the uniqueness of the opportunity. So what we've come to appreciate in a sort of fuzzy way then, but now in a more crystallized manner is the way in which a sort of software-centric system engineering approach to a sleepy industry that hasn't had any change for decades can actually unlock incredible. value and opportunity. You can see it aerospace. You can see it automotive now. It was sort of a long bet when we first invested, but now we can sort of see in retrospect how that's going to play out in almost every industry over time, how they become information businesses. Obviously, you're so good at predicting future. And part of this podcast, I really want to understand

how you think about the future. You have this amazing graph, 130 years off compute, and it basically grows exponentially. What does it mean for all of us? What will the next three years look like? because of what's happening to compute. I'm just curious, how many people have seen this version or the subtraction in Wars Law? I was originally by Ray Kurzweil in like 99 book, Age of Spiritual Machines. It looks to be like 25% of the room. Okay, I always ask because I'm curious how much it has entered the zeitgeist. Because I think it's the most important thing ever grafted,

and I give credit to Kurzweil for even seeing this pattern, back when no one knew they were fitting to a curve. So just for those who don't know, this covers like five different technology substrates, from mechanical devices to relay-based computers, computers to discrete transistors and integrated circuits. And only in the most recent era would what Gordon Moore called Moore's law be almost a refraction of a much longer-term trend transcending, you know, all kinds of dramas of

companies that came and went. It's almost cosmological. Like, why has humanity's capacity to compute compounded for 130 years? And for a sense of scale, like that's an exponential scale, right? The logarithmic scale, so straight line is exponential. This graph shows a 10,000 billion, billion X improvement in computation that a dollar can buy. This is what customers care about. No one buys transistors.

When they're buying ICs, they don't say, how many transistors does that one have? Well, buy the one that has more. Now, they buy compute capacity or memory, and both have been on rails. And so to your question, the first and foremost thing would be to just predict that it's going to keep going for three more years. Like, why would it suddenly just stop and hit a red brick wall the way Intel's been saying it would? And when companies say that, like Intel usually assigned their losing their business to someone new, like Nvidia 15 years ago. So in the next three years, I think you'll see the analog chips continue to carry the mantle of Moore's Law. Some of the more esoteric and customized AI silicon that does discrete matrix multiply and ad really efficiently.

And this is what's going to carry the juggernaut that we all just take for granted that it keeps going. In fact, I'd say without this sort of exponential change in technology, you wouldn't have startups, you wouldn't have this disruptive innovation. opportunity like we talked about in SpaceX or in a bunch of companies because if business is predictable, if there isn't disruptive technological change, the big get bigger. You know, they have all kinds of ways to prevent new entrants from competing with them. It's usually somebody reinvents an industry, and usually it's based on something computationally based. I think AI and everything we're talking about at today's conference is the epitome of this. It's like the most intense crucible of compute-centric innovation, economic growth, and sort of innovation of the economy, the translation of formerly

industrial crappy growth margin businesses into information age, information-centric businesses that over the next three years means it ripples to, I think energy, agriculture, construction, three industries that are enormous, growing as a percentage of GDP, and the least digitized industries on the planet, not to mention health care, right soon behind that. Are those the industries where you think we're going to see the most change? And what will cause the change? Is it going to be more advanced LLMs? Or do you think there's something else?

I don't know. People are building world models. People are deep into robotics. What will be the technological driver for the most changes in the next three years? That's a great question because it's very difficult to answer with any certainty. I have this gut feeling that it'll be something architecturally variant. It might subsume the models that we know now. you could almost think of like a mixture of experts that subsuming other architectures or the diffusion model we heard about earlier today that ultimately translates to a transformer, but it's a different way thinking about the transformer, a massively parallel form of a diffusion model.

And in the back of my mind, we have not, so what I'm about to, sure, we've not invested in this. So I've met with some companies and I've been intrigued and something my gut says they're going to, they're probably going to make a breakthrough. And this is the whole new generation of Mee Labs focused on reinforcement learning because we're almost going back to the founding. premise of deep mind, which then they kind of, you know, just put to the side for a while when the whole Elan thing took off. And so if you could imagine what would be the, you could phrase in an agenic language to say, what is the, you know, multi-decade long agentic process, not minutes or hours, not driven by some outsider, you know, pulling puppet strings, but something that says almost like the drive evolutionarily for creatures or for humanity for whatever,

we consider the mission statement of our lives or humanity in general, what will be that thing? Is it, you know, to understand the universe the way GROC and XAI says it, does that become a driver for unhardness like that? Is it something like a novelty-seeking algorithm that says I'm going to continue to learn about the world and use novelty as my filter for, oh, I've just discovered something new? How do I know if I'm making progress? What is, if you will, the selection pressure in an evolutionary algorithm? What is success? It's not just reproductive fitness in the biological sense, it's something grander. And I know that some of these groups are working on what is, is there a single reinforcement

learning algorithm with continuous learning, let loose in the wild with all the data sets of the internet that could bootstrap intelligence in that sense? And the way that we think we're seeing in the large language malts today, but it's largely, we ascribe, I think, consciousness to other beings, we ascribe meaning to other things and we see patterns where they're not. And so I think a lot of it is a bit of, it's a fun interaction, but it's not quite the same thing, right? We know there's nothing there inside. There's no light on inside, if you will.

So what are you describing? I think is it super intelligence when it's learning by itself, setting goals to itself? Are we going to see some version of that in the next three years? I know Jack Clark, co-founder, Anthropic, gives it a 30% chance it happens next year. Superintelligence. Yeah, yeah, absolutely. So I thought, well, that's kind of fun. There's at least one person putting a stake in the ground.

I don't know. But at the dramatic, they have a lot of strong opinions. But they do, but they also think that they're on the path. And there's a big debate as to whether this recursive self-improvement thing that they wrote about today and that Jack's been talking about for a few weeks now, I spoke with them about it last month or two months ago. Is there going to be some leap that we don't currently see for how these systems take on purpose and meaning in what I was referring to just a moment ago? Because right now, everything that they do is directed by a human. There's like, yes, the self-improving.

AI loop that they're witnessing already, these huge improvements, are coming from a number of steps that are still directed by humans. There's, you know, automated verification, improvement loops in the process of training itself, you know, adjusting hyperparameters from one training run to the next. A bunch of ways you could imagine high-tupert experimentation being mediated by the AIs, but what is the goal? The goal setting is still by the human. And so it may only be a thin veneer of activity that it's not yet doing, but it's in some ways the most important, right? and they'll admit they're not sure how does that just happen, right? What makes that transition?

And I don't know if it'll need to recapitulate some of the functional specialization on our own brain. Like we evolved to where we are today with a history of reactive limbic systems and what have you, emotional centers that then cortex and more and more cortex layered on top of it. That whole construct may, as we heard in an earlier speech, be the bootstrapped consciousness. as a perception of what we perceive, do we need to have the same things in our robotic slash AI systems, right? There may be, so it's a philosophical argument.

The main answer to the question would be, I do not know, and I don't really even have the odds on it. I give it the fuzzy future kind of, yeah, that might happen, but only because that's more convenient as an intellectual shortcut to actually thinking about it as a serious hard problem, is to put off that three years feels far enough in the future that it's hard to predict almost anything. So we're seeing all the demos of robots. And current technology, I think, is stronger than the deployment itself. We're still adopting,

we're still adjusting. What's this gap? How big is it? From what technology is actually capable of versus how we're using it. Oh, right. Yes, that's a very good point. And there will be inherently very differential domains of acceptance. So here's a great example. Very simple to understand is if it involves the world of atoms, it takes time. So even though it is obvious today that fully autonomous vehicles are the inevitable future, that every car will be autonomous, every train, every airplane, everything that moves on Earth will be fully autonomous in the future. How could it not?

It's insane to think now we're to argue that it's not, even though we've been saying this for decades. The pace of switchover is going to be, it's going to feel glacial in certain parts of the world, right? People keep cars for an average of like 11 to 12 years, so you just have the physical swapout cycle for the car cycles. You have, you know, the change in mobility doesn't happen overnight. Okay, that's an obvious one. Physical robotics might be the same. How long does it take to make a billion robots that takes some time, even with recursive manufacturing techniques? And so the place where I think it just sweeps like wildfire can be in areas, strangely, that we sometimes held as uniquely human or the creative arts, you know, the movie making.

images, what have you, which we've already seen. It's in some way shocking that that came first. And then the white-collar jobs, as was mentioned, because the white-collar job capability, take call centers, right? It's like 1% of US GDP. That just happens like that, right? I mean, you just do not need to wait for decades

for that to switch over almost entirely. And interestingly, people will increasingly prefer these to human interactions when they're better, So more emotional understanding, more reading of the situation. And that's seen in everything from physician bedside manner to chatbots or customer swart agents, is that the AIs do a better job with emotional connection than humans. Yeah, it's crazy how in some industries it's happening super fast, especially when it comes to software engineering.

Some of my friends were editing 70% of AI written code a year ago. Now it's down to 30%. I wonder what it's going to be in a year. So you worked with some of the most amazing entrepreneurs. You're working a lot with Elon. I know a lot of people in the audience are builders. Is there anything, like maybe top three principles that everyone should learn from him? It's funny.

People have been sending me these books that just, I guess they directed an AI to write about Elon, you know, about Elon, how he thinks the secrets of Elon. I've actually been accumulating them on my bedside. But I'm not sure if the humans written any of them. A lot of people have asked Elon's mom, you know, hey, May, how did you, how did you parent Elon? How did you get him to be the way he is? And that's a tough question. She hasn't been able to answer either. And so I'll take it with a bit humility that even as a close observer, by the way, I do try to observe leaders in actions.

I worked with Steve Jobs briefly. And it's like I put all kinds of energy and trying to understand how that guy works. But even with a focused effort, it's not always obvious. People are complex. So, but a few things. One is this insane ability to focus, which may seem ironic, given how many companies he's simultaneously running, setting new records for that in a way that, you know, when Steve Jobs is, with CEO two companies, that seems strange now. It's all the rage. But one thing that allows you to do

is use the fact that you've got obvious competing needs for your attention as a way to focus, prioritize, and not go to meetings. For the normal CEO of one company didn't go to their holiday party, you know, it might be seen as weird and like, whoa, but no one questions the feeling, he's got other things to do, he's got other companies. So whether it's an excuse or just works out this way, he says no to things so effectively that are distractions, that are not mission critical right now. I mean, for example, years ago, I was trying to hook him up with Craig Venter to brainstorm ways we could, you know, terraform Mars more easily and do a sample return of life from Mars with gene sequencers and reinstantiating. Anyway, microbes on Earth. It was a fascinating topic to me.

I was like, whoa, this is so fascinating. But he's like, no, it doesn't matter until we get starship flying. None of this stuff on Mars matters. I got to get that thing working for us before we think about what we do when we get there. There's, I think, maybe more importantly than what I just said, even more importantly, is this a maniacal focus on the, what I would generalize as the cycle time of innovation, which is how rapidly can we run experiments or iterate in our learning loop? What is the core learning loop? Whether it's the launch cadence,

whether it's the data gathered from all the Teslas before fully self-driving vehicles came that could be used to train the models, how can we make sure that we have a leg up on anyone else on the rate at which we're learning from customer interaction, product features and technology in general. And as an example of how powerful that is, when you do it right and the data flyway, you can make for AI. Just one example, Tesla cars today in their cameras gather for their AI training set, more

data every four days than Waymo has in its entire history. And the brilliance was enabling every vehicle, whether or not the customer paid for PhilSelf driving to be a data collecting vehicle. So focus, learning loops. And this whole series of well-honed skills on identifying talent that I wish I could replicate, I just can't. It's like sometimes there's a pattern recognition. And he'll share bits and pieces of this, like, you know, not leaning on credentials

or specific background or experience. In fact, it's often an albatross. But like having people really walk through major engineering crises or problem-solving things and then drilling down further and further and further to show, did they really master the, do they have mastery of the understanding of what it took to make something successful? So broadly defined, being a magnet for talent, finding a way to pitch and refine a vision that people want to join you. So like one of his brilliant things at Tesla Space Six everywhere is not just saying, oh, you're making rockets, we're making cars, but to really think of something much grander, right, catalyzing the, you know, transition to sustainable energy or making humanity multi-planetary,

understanding the universe, now that XAI is merged into it. These are the sort of lofty goals that motivate some of the best and the brightest to want to work with you, and that is a sort of compounding benefit that ripples out through the whole organization, right? Because great people want to work with other great people. I'm talking to a lot of entrepreneurs, and especially these days with things moving so fast, there's this new shiny thing every single week. how do you stay true to your mission when the rest of the world, 99% of the world,

tells you it's too early. I was talking about space, we have so many problems here on Earth. That's an interesting question. And I realize I have a bit of a sample selection bias in that I've tried as best they can and I've done VC now for 30 years to only work with the people

who have a true, sincere, you know, messianic mission in mind that is driving them. And they're not the arbitrage-shaking opportunity. to see the next bright shining object or, oh gosh, where should I go to next? And one of the ways, and I'll get to your question, but one of the ways that, by the way, that I filter for that in meetings is, let's say I'm going to be really excited about the company. I'll often ask, you know, okay, what does your business look like in 50 years? And I get usually two reactions most off.

One of the chuckle, I'm like, what, that's a ridiculous question. You know, like, the arbitrage seeking opportunist is going to be like, I'll be my third startup by then. Like, what, how would I possibly know what my startup is in 50 years? They just laugh with the question. And we pass on those. And then the best is when the person's like so relieved, like, oh, thank God. Now I can actually tell you what I've been wanting to say all day long, which is this is what's driving me. It's this thing that's so many steps ahead of what you would probably want to invest in today.

Like making, you know, colonizing Mars is an uninvestable proposition. Go back in the founding days. Like when you start a business day one, I'm going to colonize Mars. Like, you know, next, right? For most investors, right? Like, that's not a door opener. And so most entrepreneurs that have that true sincere vision have found a way to subjugate and put off what their true dreams are and talk about something much more prosaic and near terms. I think the answer would be as the entrepreneur, it just happened naturally and try to find investors and partners and certainly employees who are with you for that long ride and have a path to get there that is plausible.

So, you know, this is sort of the joint tension, I think, in the best startups that's hard to simultaneously satisfy. slide, which is an endacious, you know, 50 to 500 year vision. This is what this company is going to do to the economy or the universe. Coupled with, oh, and by the way, over the next three years, we're going to iterate with real customers, learn from that and can paint the path from where we are now to that future that is chaining. Sometimes they chain back from the past to the present, like to get there, what do I have to build now and then move forward along that path? But it's not like go into a research lab pop out in 20 years and solve all the world's problems. Yeah.

This is a really fascinating feature that I see with a lot of greatest entrepreneurs. It's like if they're reverse engineering from 50 years ahead. Is there anything surprising that still surprises you about those amazing entrepreneurs? Well, I suppose it's a bit surprising in a way each and every time it goes incredibly right. So weirdly, this may sound weird. I don't think I've ever thought about that question before or been asked it before. And so the perpetual surprise for me is like, wow. Like in the year 8, 9, 12, some new opportunity that opens up and unfolds from the, in a sense,

the expanding option value of going into some new frontier of the unknown. So what I mean by this is we try to invest in, by the way, at our firm future ventures, in things that are unlike anything we've seen before, yet adjacent to where we've been. So ideally it's a company that's literally one of a kind based on things we are used to, whether it's AI, whether it's something synthetic biology, whatever it might be, but they're taking it in some new direction.

So the window, as long as you have an agile mind and you're looking at it, you're like, wow, like no one thought of that when we started. So for example, when we first invested in Tesla, there was no concept whatsoever autonomous driving. It was not in the business plan. There was no talk of it. There was not in anyone's mind.

The way in which electric drive train uniquely enables that and control of fidelity was fascinating. Or in SpaceX, the Starlink opportunity, like, oh, yes, of course, when you lower cost of launch that much, you can have mega constellations, but what would be the new thing that would make sense that we weren't doing before? Not just we invested in planet lives for Earth observation,

yes, constellation of telescopes, but this whole notion of building a network backbone for the internet in the sky was, and then direct the cell phone. Like each one of these things is unfolding, then orbital data centers, right, not on the dance card even five years ago. So that continues to surprise me.

In some ways, it's not easy, but it seems so much more powerful as a business vector than purposeful design, if you will. It's almost like exploring the option space or the light cone, if you will, of possibilities in an economy versus sort of planning out something 10 years in advance and having it go according to plan, if you will. It is so fascinating how you were successful in so many different bets that you made in the past, and they're so different from each other in different industries. What are you betting on now?

What should we be looking out for? Plastics. No, let me think that. Some people who know the old movie. Let's see. So taking that thesis that AI and information technology will innervate every economy, meaning add a nerve-assisted to everything. We saw an automotive and aerospace. Just expanding on that thought a bit, we are looking at. for additional things in energy. We've invested in a variety of nuclear fusion and subcritical

fission that doesn't trigger NRC regulations. Basically, avoiding the nuclear regulatory commission, but figuring out energy, which, by the way, is the third bottleneck for AI. It's not just good people and a lot of compute. It's also energy. There are a bunch of things that you could imagine 500 years to now have been solved, and we're trying to figure out the entrepreneur will open our eyes to how we get there. So free health care forever via a cell phone, all diagnostic information you could possibly need for your personal health should be a free service globally, trying to figure how to get there. Probably won't be in the U.S. that it launches. You know, bypassing FDA, bypassing insurance and reimbursement.

On food, we won't slaughter animals for meat. The products are getting there, but you can sort of see the future. It's so close. You can almost taste it, so to speak, whether it's cellar ag, mycelium, or other techniques, mycelium being the fastest growing thing. But we are going to eat meat-like things that are delicious, healthy, and not involve slaughter of animals. Construction, growing as percentage GDP and like labor productivity has been flat for 30 years. So it's such a hard industry to change that we've tried and failed a few times, but we're looking. Again, so the best I can do to answer your question is, I don't know what the answer is, but I know there are these categories that we want to look at. Recently, we've been

investing in epigenetic editing across a variety of things from crop health, pesticides, human health. It's fascinating. It's basically the software of biology. instead of going to the firmware of our genome. And we've been investing in materials, critical minerals and metals, everything from deep sea mining to copper refining because of incredible need. It's sort of like the workhorse of all these chips is you need these materials to make the stuff.

And there's a couple of that, a reshoring or bringing back to the U.S. capacity to build, which we had at our feet over many years. Analog AI, I mentioned too. We have three different investments coming out from different angles using AI to design AI chips, sorry, analog chips. Analog chip. Yep.

Analog in-memory compute from Mythic, where they can do 8-bit multiply and add in a single transistor, and then unconventional, which is taking a very, very strange and forward-looking big bet on, you know, in every case, trying to get 100x and then another 100x on power reduction, power per calculation. Overall, we're about 40% life sciences, 60% IT, and we, in the life sciences side, just see,

we look for the weird things that are like on the edge, you know, harvesting organs for transplant, growing humans without brains so that you can use their organs. There's a company here actually in the audience doing the same thing. A male birth control pill, improving IVF dramatically, a lot of things that fall through the cracks of a traditional pharma VC. So I'm hearing agriculture, biotech. I'm just thinking, in my head, how can I replicate your strategy with ETF? Well, it's hard to replicate.

See, our strategy, it's very unusual. I can state it openly and then it's hard to replicate because when I say we invest in things that are unlike anything we've seen before, well, that's great. But how do you know what we've seen? So, you know, what we're actually doing is not as in the areas. So if they are dramatically changing a market, then it's going to be reflected in the ETF. Especially if it's an old crappy business that hasn't seen a new entrant in years.

So like boring company for tunnel boring machines. Like the four largest companies were all started in the 1800s. That's who you're competing with. We have a lot of entrepreneurs who have crazy ideas. is. Can you give them a 30-day plan to execute on that idea? What will be the best thing that they can do? What stage are they? Are you similar? They just have an idea. Oh, single person with an idea? Yeah. Hmm, 30-day plan. I might try to find a co-founder who agrees with you or whatever this person is. And the reason I say that is a lot of startups tend to have a dynamic duo at their founding. It's rarely an individual. And you can imagine jobs and laws and

as a mental model for this or these superheroes, Batman and Robin, you know, Sergey and Larry Page. Even Larry Ellison had Bob Miner, who's less well known because he's an introvert, but, you know, there was not like a singular cult of personality of a founder. And part of the reason to have someone is I found this as an investor, having a colleague, Mariana, my co-founder, is I am so much better as an investor having someone to bounce ideas off versus like being the soul, you know, like an angel investor or something. And similarly for a startup, having a diversity of backgrounds, like an engineer and a marketing person, an extrovert and introvert, whatever it might be, that have mutual respect for each other. Not only makes it

better that you like some, you got someone that bounce ideas off of in a rapid iteration loop, we're just two of you, but it also sets the culture for everyone that you'll hire. It's not like, oh, there's a singular person that everyone works for. It's more like there was a pair and they're very different and that ripples through the culture of a firm in the types of people that are hired and the cognitive diversity that follows. So finding, and the reason I say that is finding someone who agrees that your crazy idea is worth pursuing is better than finding zero people. In other words, I think the best outcome is if you're literally your premise, your question a crazy startup where no one else is doing it, it's one of a kind. And most people

tell you it's crazy, well, it is possible that it's crazy. So if 100% of people that you've ever think it's crazy, take that as feedback. If it's, you know, nine out of ten, that's pretty good. If it's eight out of ten, that's pretty good too. If it's like only two people think it's crazy, that's bad, because it's clearly not bold enough. If it's an obvious idea, other people will do it, right? And ask yourself, is this a business that couldn't have been started three years ago? If the answer is yes, that's good, right? If it's like, oh, yeah, no, anyone could have started this business if they just had this idea,

probably a bad sign. And then somebody, your co-founder, agrees with you and thinks, oh, my God, this is me. That just shows that it's almost like a test case. You can persuade someone to give up their job and join you in this mission. And then before you go out and fundraise, that says a lot more than just this whole person with an idea. Like the inventor in a garage, you know, all by themselves. There's so many cases like that. They just never manifest as a business because they just never made that first step of being able to persuade anyone to join them in the mission.

It's great advice because a lot of people start with building an MVP or like even pitching investors right away. The co-founder sounds incredible. From all the startups you founded, where did the best co-founders meet? Is that university or? Good question. I'm not sure. I haven't thought through that. Because it's so hard.

They often come to us having already done that. And often, yes. So for all the university ones, there, many of them are from, that's probably your question had embedded with it the most common answer, which is, you know, we met in some interdisciplinary way at a university, which is fascinating, by the way, the word disciplinary, you know, or disciplines, academic disciplines are a way of stove piping information into assistance vernacular and domain expertise that often doesn't cross-pollinate. And universities, one of those few places where you get these spanners, you get these

undergrads or other people who take courses outside their department, unlike the professors and their little stovepipes. And despite a lot of institutional efforts to share information, it's often the students that are the cross-pollination between academic disciplines. And that's at those boundaries or interstices between formally discrete disciplines that you find, I think, most breakthrough innovation, certainly in the sciences. As a quick aside, that's something that large language models do very well, translating between academic domains, seeing patterns in the, you know,

almost the translation, if you will, between languages, between concepts. And that, I think, is allowing a fountainhead of possible idea discovery using AI to figure out new ways of cross-pollinating between academic disciplines that I think we're only beginning to tap into. This makes total sense. I think I can be talking to you for hours because you are someone who's really good at predicting future and betting on it and seeing where we're going. I have one last question before we open it up for Q&A. When machines do everything, what's the meaning of life?

Yeah. Yeah, and your question, I think, is an interesting one to contemplate. What do we do when machines do everything that we do better than we can? Every physical activity, everything that involves employment. And it's going to come soon, roughly 19% of global employment is in driving. vehicles, and that's obviously going away, just not as rapidly as we might imagine. I think we want meaningful work. I think all humans have a fundamental desire for symbolic immortality, there's belief that we've contributed something to the world that transcends our brief

time on this world. And we see that, of course, in the drive to have children or in writing works or in philanthropy or creating companies, sometimes even named after their founders, like Hewlett-Packard or what have you. These are, instander. of that urge. And so I think there's still a creative desire. And I could translate the question to be like, what is the mission statement for humanity? It's a question that Yuri Milner and Elon Musk and others have asked. And it comes to a similar conclusion, which is to understand the universe, to try to contribute to the wisdom, the accumulated knowledge that we have. You could think of human culture and our knowledge base that we pass on from generation to generation as the primary vector of our

own evolutionary progress. It's not biological evolution. That's glacial in comparison. And any progress we feel humanity is making is not because we've changed our biology, it's because we've changed our accumulated basis of knowledge, the way we comport ourselves, the rule of law, the understanding we have around what works and helps with human flourishing. So I think we all want to contribute to that. It doesn't have to be paid employment, though. So you can't imagine some sort of hyperspace jump, because that's conceptually what it requires, because there's no way to imagine how we get here, from here to there. But somehow if we just jump there to a world of a abundance like Peter de Mondes envisions.

You know, everything physical costs a dollar or pound. There's nothing that requires human labor. We all are in the indentured rich, like in the days of yore. We had, you know, servants or serfs or slaves that did all, you know, menial work. And we could just be, you know, philosopher kings or artists or pursue whatever we might want. And some people, some, not all, but some people really love that era. Well, the machines will be those slaves, right? not because even in slavery humans will not be cost effective and I say that's somewhat tongue in

cheek but it's like finally the scourge of human slavery might finally end when that's no longer even cost effective compared to machines what does that leave for the rest of us and so I think it's going to be a man's search for meaning that really is the core question I think it's going to be really fun if we could hyperspace there but I will add the caveat that's not the path we're taking like there's nothing that indicates that we're just going to peacefully march from an economy of full employment to an economy of no employment and passed through the 30, 40, 50% unemployment points, but some issues. That's going to be tough. And I don't see any politicians taking long-term perspectives on any of that. So I don't want to end on a downer. Let's go back to that hyperspace

to abundance. I think we inherently find that in our curious exploration of the universe. I really like the rule of going back to your mission statement because a lot of us these days are questioning our jobs. What we're doing is it going to exist in the same? shape and form in three years. And again, going back to a mission statement, I think this is brilliant. Thank you so much, Steve. And let's open it up for Q&A. Quick pause here. If you're enjoying this podcast, you will absolutely love my In-A Circle newsletter. So what I basically do is I take all the tips from these podcasts and I apply them to my personal life, to my investment portfolio, and to my businesses, this media company and my language teaching business. Sometimes we get amazing

results and I share our real tactics. Sometimes we don't, and I share that too. Think of it as a an insider version of this podcast. The link is in the description. Join my free newsletter to stay ahead. Thank you, Steve, for your fireside chat. Since you're a big investor in Ellen Musk companies, I'm curious, have you invested in NeuroLink? Yeah. Yeah, so honestly, in my opinion, everybody is excited about SpaceX, but I'm looking forward for an IPO of NeuroLink. Do you think is happening soon. And honestly, I think it's basically brain machine interface is the future. But essentially, currently, if we use voice mode on charge APT or open AI, right, or we type, we are limited in our throughput of how many tokens we are, we send to the LLMs.

And if we have brain machine interface from NeuroLink, we're able to unlock even more creativity and faster throughput from our brain to machine. Yeah, I can't comment on IPO timelines, but the enthusiasm there is interesting. It was originally sparked, as many things are, from a science fiction novel, Ian Banks' surface detail, where they have a neural lace, fascinating book I recommend it. And I think what you see, so I have a somewhat unique perspective not shared by neurolog. But I'll just share my perspective, which is I think it is an amazing capability for expansion. the sensory cortex, adding the prosthesis to the mind.

In other words, restoring function weren't broken, expanding function, like let's say seeing in more wavelengths or hearing better than we could hear, not just repairing hearing, fixing spinal cords, basically working from the periphery of these systems, as opposed to a much more difficult and yet to be solved task, which is upgrading core functionality, like just making someone smarter. So I think the example you gave is a very interesting one. have a higher data rate communication. Absolutely. I think that is very doable. And the reason

I have this belief, it's more of a pattern recognition across decades of complex system development. Basically, the high-level statement would be any product produced from an iterative algorithm, which would be evolution, genetic programming, all neural networks, you know, cellular automata, whatever it might be. If you iterate something billions of times and accumulate complexity from that algorithm, the thing you make is inherently... inscrutable. It is an artifact of absolute, like, inscrutable complexity. Despite attempts at mechanistic interpretability in AI, I don't think that's going to bear fruit. I don't think control and an alignment is possible in a cutting-edge system that is pushing, back to AI for

a moment, pushing the capabilities of what we can build. Similarly, it would be like asking about controlling, aligning, mind-controlling a teenager. So I swapped teenager and AI when everything about this. So the reason that's relevant is the brain is a complex system itself. And reverse engineering, it's inner workings for uploading or for, you know, brain the brain, or like adding speech, like the way Jeff Hawkins thinks you just like cut and paste like a French speaking module into a human brain or neural net. I don't think that's going to be possible on a timeframe of relevance, meaning it'd be easier to build a new intelligence than it is the reverse engineer one you've made. So I do think neuralink is fascinating, but I don't personally, um,

get faith that it's going to keep up with AI. Maybe that would be the safest way to phrase it. Not that it can't be done, but the time scales, you know, FDA cycles, human biology, nothing happens on a timescale comparable to the learning loops. Back to Elon Musk's saying like, focus on learning. Where do you learn more quickly? You can learn more quickly in the synthetic domain. I think humanity always wants to believe it's part of the future in that regard,

but the Kurzweil is uploading. I could just see why he wants that to be true within his lifetime, and that's what he predicts will happen, but it doesn't mean it will. Steve, I'm really curious. What do you think about Penrose argument that the consciousness is go far beyond algorithmic processes to quantum level processes, meaning that AI would never be able to develop consciousness itself just by its nature? So what do you think?

Can AI develop consciousness or it will be only imitated and that's it? And you were referencing Penrose's quantum? Yes. Yeah. So Penrose is a brilliant guy in UK generally. But here he has this gut feeling that there's some quantum process in the brain that makes it unique. And yet there's no real clear mechanism by which that would happen. There's some argument around some lithium isotopes that might be a coupling.

But it's wishful thinking. We don't, so to speak. But I can also generalize your question. Is there something vitalistic, naturalistic, unique to our brain that is irreproducible and others? And there was a reference earlier to Neil Seth's work. I find the argument is completely uncompelling, that there's something vitalistic or unique to the substrate.

Just because it's the only example we know of of consciousness. And consciousness, for example, is a tricky thing. Like, how do we know if the dog is conscious? How do we test for this, right? But we believe we see it in ourselves. I mean, I don't know if you're conscious, but I'm kind of just guessing you are, right? And you seem awake, and you're human, therefore we generalize it conscious. Okay, so I have not seen a compelling argument.

Just because we have an example of one doesn't mean it's the only possible example. You could make a similar argument that says, does all life need to be carbon-based, right? And there is something unique about carbon, and it's being able to do single, double, and triple bonds, and all the weak bonds, it is kind of, you could actually make, I think, a better argument that says carbon is special to life than you could to say neurons as we have them are essential to consciousness. Now, a totally different question is, but I won't digress, is like, is anything that we're doing in AI development going to lead the conscious? That's a different question, because you could, you could argue that's a dead end, it won't get us the conscience, but it doesn't mean it's not

possible. It's much higher order proposition to say something is impossible than to say, I don't know. And so my answer would be, I don't know, but I certainly wouldn't say. it's impossible. And I don't believe that we have any evidence of a quantum process going on in the brain. And if we did, why couldn't we replicate that with quantum computers? I mean, it's a different question. And then if I broaden your question a little farther, just animus or spirit or life, does it have to be a living thing to be conscious? And the analogy I would use is, imagine you substitute the word memory for consciousness. And I just picked memory just randomly. It's an overloaded term. Do we mean memory, like I have memories, in a human sense, human memories, which are holographic

and it can have graceful degradation, and they're not at all the way we do memories in a computer chip, but when we talk about computers, they have memories too, and we don't debate is memory possible in a computer. Can it remember things? Well, at that level of abstraction, of course they can. And yet it doesn't have human memory, and that's fine. So consciousness, it may not have human consciousness,

but maybe it has a different kind of consciousness, whatever that thing is, if we could be more precise about defining it. And I don't think you make the argument that everything we have in our brain is essential for conscious. In other words, there's a lot of there as a garbage collection for our metabolism, you know, things that happen when we sleep and cleaning out, you know, waste products and the way mitochondria work. You don't have to have all of that in a computer to be intelligent or to have

memories. You don't need all that baggage for consciousness either. But that doesn't mean we know what the minimum set is, but it does, I think we'll figure it out one day. So in other words, I'm more on the, my gut tells me, oh, sure, I think one day they will be conscious. I don't know if we're on a path to get us there, maybe something more akin to evolution and reinforcement learning algorithms would get us there more obviously because whenever you recapitulate what we've already done with our biology, that makes me give hope that why can't we do it in a different substrate? Thank you so much. On July 16th, the Hawk lands on Netflix.

From the mind of Will Ferrell. Oh, Mama, I'm back. Comes a new original series. Get ready, get ready. That's it. Did I stutter? When an iconic pro golfer. Lonnie? Honnie. Hocked!

Takes one last swing of greatness. You were a big shot golfer. I still am a big shot golfer. No one. Dad, I'm the Hawk now. We'll stand in his way. That's how it's done. The Hawk, only on Netflix, July 16th.