The Next 3 Years of AI: Lessons from Elon Musk’s First Investor

1. 0:00 – 0:47

   What the next 3 years actually look like

   1. MMMarina Mogilko0:00

      What will the next three years look like?

   2. SJSteve Jurvetson0:03

      I have this gut feeling that it'll be something architecturally variant.

   3. MMMarina Mogilko0:07

      This is Steve Jurvetson, 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 would be the best thing that they could do?

   4. SJSteve Jurvetson0:25

      Single person with an idea? I might try to find a co-founder. It's rarely an individual. Jobs and Wozniak, Batman and Robin.

   5. MMMarina Mogilko0:33

      You're working a lot with Elon. Top three principles that everyone should learn from him?

   6. SJSteve Jurvetson0:37

      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-

2. 0:47 – 2:12

   Backing SpaceX when "space" wasn't even a category

   1. MMMarina Mogilko0:47

      Thank you so much. This is gonna be very exciting, Steve. I am so excited to have you on this stage. What a fun time, right? SpaceX-

   2. SJSteve Jurvetson0:57

      Right

   3. MMMarina Mogilko0:57

      ... IPO, and you were there super early. What did you see that most investors didn't see back then?

   4. SJSteve Jurvetson1:06

      So the simple answer to the question is there were almost no investors considering space. It wasn't a category on any, on any site. So the, the slightly varying question is, why in the world would we invest in a sector that is just not a sector for venture?

   5. MMMarina Mogilko1:19

      Exactly.

   6. SJSteve Jurvetson1:19

      Same, same could be said for automotive with Tesla, um, energy with nuclear fusion, or, you know, a handful of investments, but very few. So the short version is obviously an incredible entrepreneur, um, someone we've worked with before. I've known him for, oh gosh, 29 years now, and, uh, invested in all of his companies of the century and his cousins too. Um, [laughs] so, uh, all in, if you will. Um, the uniqueness of the opportunity. So what we've come to appreciate in a sort of fuzzy way then but now more... 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 in aerospace, you can see it in automotive now. It was sort of a long bet when we first invested, but now we can sort of see in re- in retrospect how that's gonna play out in almost every industry over time, how they become information

3. 2:12 – 3:58

   The one graph Steve calls the most important ever drawn

   1. SJSteve Jurvetson2:12

      businesses.

   2. MMMarina Mogilko2:12

      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 of 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?

   3. SJSteve Jurvetson2:32

      Now, I'm just curious, how many people have seen this version or this abstraction of Moore's Law? It was originally by Ray Kurzweil in like '99 book, The Age of Spiritual Machines. It looks to me like 25% of the room. Okay. I always ask 'cause I'm curious, uh, how much it has entered the zeitgeist, uh, 'cause I think it's the most important thing ever graphed, and I give credit to Kurzweil for even seeing this pattern, um, 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 to, you know, 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? Logarithmic scale, so straight line is an exponential. This graph shows a ten thousand 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, how many transistors does that one have? I'll buy the one that has more." No, 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 gonna 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? Uh, and when companies say that, like Intel, usually a sign they're losing their business to someone new, like Nvidia 15 years ago. So in the next

4. 3:58 – 5:15

   The 3 giant industries about to wake up

   1. SJSteve Jurvetson3:58

      three years, I think you'll see the analog chips continue to carry the mantle of Moore's Law. Some of the more esoteric, um, and customized, uh, AI silicon, uh, that does, uh, discrete, um, discrete matrix multiply and add really efficiently. And this is what's gonna carry the juggernaut that we all just take for granted, that it keeps going. In fact, I'd, 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, um, 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, uh, intense crucible of compute-centric innovation, economic growth, and, and, and sort of innervation of the economy, the, the translation of formerly d- uh, industrial crappy gross margin businesses into information age, uh, 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 percent of GDP and l- the least digitized industries on the planet. Uh, not to mention healthcare, right, soon behind that. Yeah.

   2. MMMarina Mogilko5:14

      Are

5. 5:15 – 7:53

   The breakthrough still hiding in plain sight

   1. MMMarina Mogilko5:15

      those the industries where you think we're gonna see the most change? And what will cause the change? Is it gonna be a more advanced LLMs, or do you think there's something else? I know people are building world models. Uh, people are deep into robotics. What will be the technological driver for the most changes in the next three years?

   2. SJSteve Jurvetson5:34

      That, that's a great question because it's very difficult to answer with any certainty. I, I have this gut feeling that it'll be something architecturally variant. It might subsume, uh, the models that we know now. You could almost think of like a mixture of experts that's subsuming other architectures or the diffusion model we heard about earlier today that ultimately translates to a transformer, but it's a different way of thinking about the transformer, a massively parallel form of d- of a diffusion model. Um, and in the back of my mind, I-- We have not... So what I'm about to share, we've not invested in this. So I'm, I've met with some companies, and I've been intrigued, and something my gut says They're gonna-- they're probably gonna make a breakthrough, and this is the whole new generation of Neo Labs focused on reinforcement learning, 'cause we're almost going back to the founding premise of DeepMind, um, which then they kind of, you know, just put to the side for a while when the whole LLM thing took off. And so, um, if you could imagine what would be the m- you could phrase this in an agentic s- uh, language and 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, uh, for creatures or for humanity, for whatever we consider the mission statement of our lives or humanity in general. What would be that thing? Is it, um, you know, to understand the universe the way Grok and XAI says it? Does that become a driver for an artist like that? Is it something like a novelty-seeking algorithm that says, "I'm gonna 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, it's something grander. And I, 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, in the way that we think we're seeing in the large language models today, but it's, it's largely, um, we, we ascribe, uh, th- I think consciousness to other beings, and we ascribe meaning to other things, and we see patterns whether or not. And so I think a lot of it is a bit of, um, it's a fun interaction, but it's not quite the same thing, right? We just-

   3. MMMarina Mogilko7:50

      Yeah

   4. SJSteve Jurvetson7:50

      ... we know there's nothing there inside. There's no light on inside, if you will.

6. 7:53 – 10:18

   Superintelligence: a 30% chance by next year?

   1. MMMarina Mogilko7:53

      So what you're des-describing, I think, is it superintelligence when it's learning by itself, setting goals to itself? Are we gonna see some version of that in the next three years?

   2. SJSteve Jurvetson8:03

      I know Jack Clark, uh, co-founder of Anthropic, gives it a thirty percent chance it happens next year, um, which I think is kind of-

   3. MMMarina Mogilko8:08

      Superintelligence? Or j-

   4. SJSteve Jurvetson8:09

      Yeah. Yeah, yeah.

   5. MMMarina Mogilko8:10

      Oh.

   6. SJSteve Jurvetson8:10

      Absolutely. So I thought, "Well, that's kind of fun." Um, there's at least one person putting a stake in the ground. I don't know. I don't have-

   7. MMMarina Mogilko8:15

      But at Anthropic-

   8. SJSteve Jurvetson8:16

      Yeah

   9. MMMarina Mogilko8:16

      ... they have a lot of strong opinions. [chuckles]

   10. SJSteve Jurvetson8:17

       Well, they do, but they also think that they're on the path. And, 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 him about it last month or two months ago. Is there gonna be some leap that we don't currently see for how these systems take on purpose and meaning in, in the 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-throughput experimentation being mediated by the AIs. But what is the goal? The goal setting is still by the human. And the-- so there-- 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?

   11. MMMarina Mogilko9:15

       Yeah.

   12. SJSteve Jurvetson9:15

       Um, and, and they'll admit they're not sure how does that just happen, right? What, what makes that transition? And I don't know if it'll need to recapitulate some of the, um, functional specialization in 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 the cortex and more and more cortex layered on top of it. That whole construct may, as we heard in an earlier speech, be the bootstrap to consciousness as a perception of what we perceive. Do we need to have the same things in our robotic/AI systems, right? There, there may be. So, so it's a phil-philosophical argument. The main answer to your question would be I do not know. Um, 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, as a serious hard problem, um, is the put off that three years feels far enough in the future that it's hard to predict almost anything.

   13. MMMarina Mogilko10:17

       So we're seeing

7. 10:18 – 12:29

   Why the robots are ready but we're not

   1. MMMarina Mogilko10:18

      all the demos of robots, and current technology, I think, is stronger than the deployment itself. We're still adapting, we're still adjusting. H- what's this gap? How big is it?

   2. SJSteve Jurvetson10:30

      Which gap are you referring to?

   3. MMMarina Mogilko10:30

      From, from what technology is actually capable of versus how we're using it.

   4. SJSteve Jurvetson10:35

      Oh, right. Yes. That's a very good point. Um, and there'll 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, it's insane to think now or to argue that it's not, even though we've been saying this for decades. Um, the pace of switchover is gonna be, it's gonna feel glacial in certain parts of the world, right? People keep cars for an average of like eleven to twelve years, so you just have the physical swap-out 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, uh, manufacturing techniques. Um, and so the place where I think it just sweeps like wildfire can be in areas, uh, strangely, uh, that we sometimes held as uniquely human are the creative arts, uh, the, you know, the movie making, the images, what have you, uh, which we've already seen. It's in some ways shocking that that came first. Um, and then the white-collar jobs, as was mentioned, because the white-collar job, um, capability, [chuckles] take call centers, right? It's like one percent of US GDP. That, like, that just happens like that, right? I mean, you just do not need, uh, to, uh, wait f-for decades for that to switch over almost entirely. And interestingly, people, um- Will increasingly prefer these to human interactions when they're better, show more emotional understanding, um, more reading of the situation, and that's seen in everything from physician bedside manner, um, to, uh, to chatbots and/or, or customer support agents, is that the AIs do a better job with emotional connection

8. 12:29 – 13:52

   How much "AI code" humans still actually touch

   1. SJSteve Jurvetson12:29

      than, uh, humans.

   2. MMMarina Mogilko12:29

      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 gonna be in a year. I am constantly looking at our AI stack. What can we make faster, smoother, more useful for the team? So I wanna tell you about a workspace we genuinely keep coming back to, and it's also sponsoring this episode. That's Miro. Miro is the AI innovation workspace where AI lives on the canvas, not in a separate chat. It sees the whole canvas, every note, every source, every decision the team already put there. We've picked a few specific use cases where it actually helps us. One of them is guest research. The team drops everything onto the board, interview transcripts, articles, podcast clips, a dozen sources on one person. Then we run flows with a custom sidekick that reads all of it and pulls the angle, the best quotes, the questions worth asking. That used to take a full day. Now the guest dossier is ready in 30 minutes, which means my team and I actually have time to prepare properly for the next interview instead of just surviving the deadline, especially now when my schedule's so crazy, I do four to five podcasts every single week. And it works for a lot more than guest prep. The same setup turns a messy retro into an action plan or a planning session into a prioritized backlog. You can now build something similar yourself in Miro. The link is in the description. So you worked with

9. 13:52 – 17:25

   Elon's actual secret (yes, I asked)

   1. MMMarina Mogilko13:52

      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?

   2. SJSteve Jurvetson14:03

      [laughs] It's funny. People have been sending me these books that just... I guess they directed an AI to write about AI, 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 a human's written any of them. A lot of people ask, uh, Elon's mom, you know, "Hey, May, how did, 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, uh, even as a close observer... By the way, I do try to observe leaders in actions. I worked with Steve Jobs briefly, briefly, and it is like I put all kinds of energy to try to understand how that guy works. Um, but even with a, a f-focused effort, it's not always obvious. People are complex. So, but, but a few things. Um, 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 was a CEO of two companies, that seemed strange. Now it, it's all the rage. But one thing that allows you to do is use the fact that you've got obvious competing, uh, 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 that no one questions if Elon. He's got other things to do. He's got other companies. So what that... Whether it's an excuse or it just works out this way, um, he says no to things so effectively, um, that are distractions, that are not, um, critic- 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 gotta get that thing working first 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, a maniacal focus on the, uh, 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, um, whether it's, um, 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, and, and technology in general? And, uh, as an example of like how powerful that is, um, when you do it right and the data fly away, you can get it for... Make for AI. Uh, just one example. Tesla's cars today and 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 full self-driving, to be a data collecting vehicle. Okay. So focus, learning loops, and this, um, uh, a whole series of well-honed skills on, uh, identifying talent that, um, I wish I could replicate. I just can't. It's like there's-

   3. MMMarina Mogilko16:58

      Mm.

   4. SJSteve Jurvetson16:59

      Sometimes there's a pattern recognition, um, and he'll share bits and pieces of this like, you know, not, not leaning on credentials or specific background or experience. In fact, it's often an albatross. But like having people really walk through major, uh, engineering crises or problem-solving things and then drilling down further and further and further to show if did, did they really master the... Do they have mastery and understanding of what it took to make something successful? So broadly defined, um,

10. 17:25 – 18:04

    Why the best people keep saying yes to Elon

    1. SJSteve Jurvetson17:25

       being a magnet for talent, finding a way to pitch and, and, and refine a vision that people wanna join you. So like one of, one of his brilliant things at Tesla, SpaceX, everywhere is not just saying, "Oh yeah, we'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, uh, understanding the universe, uh, you know, now that xAI has merged into it. Uh, these are the sort of lofty goals that, that, that motivate some of the best and the brightest to wanna work with you, and that is a, a sort of compounding benefit that ripples out through a whole organization, right? 'Cause great people wanna work with other great people, right?

    2. MMMarina Mogilko18:03

       I'm

11. 18:04 – 20:52

    Holding a 50-year vision when everyone says "too early"

    1. MMMarina Mogilko18:04

       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? Like talking about space, we have so many problems here on Earth.

    2. SJSteve Jurvetson18:22

       That's an interesting question, and I realize I have a bit of a sample selection bias in that I'veTried as best I can, I've done VC now for thirty 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-seeking opportunist that see the next bright, shiny object or, oh gosh, um, you know, where, where should I go to next? And one of the ways, uh, uh, and I'll get to your question, but one of the ways, by the way, that I filter for that in meetings is let's say I'm getting really excited about a company, I'll often ask, you know, "Okay, w-what does your business look like in fifty years?" And I get usually two reactions most oft. One would be a chuckle [laughs] like, "What a ridiculous question." You know, like the arbitrage-seeking opportunist is gonna be like, "That'll be my third startup by then." Like, what, what, where the... How would I possibly know what my startup is in, in fifty years? Like, they just laugh at the question, uh, and then 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 wanna 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 gonna colonize Mars. Like, you know, next, right? For most investors-

    3. MMMarina Mogilko19:38

       Yeah

    4. SJSteve Jurvetson19:38

       ... right? Like that's-

    5. MMMarina Mogilko19:38

       Yeah

    6. SJSteve Jurvetson19:38

       ... not a door opener. And so most entrepreneurs that have that true sincere vision have found a way to like subjugate and put off what their true dreams are and talk about something much more prosaic and, and near term. So I think the answer would be, as the entrepreneur, it just happens naturally, and try to find investors and partners and certainly employees who are with you for that long ride and, um, 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, which is an audacious, you know, fifty to five hundred year vision. [laughs] This is what this company's gonna do to the economy or the universe. Um, coupled with, oh, and by the way, over the next three years we're gonna iterate with real customers, learn from that and have a-- can paint the path from where we are now to that future that is, uh, chaining. Sometimes they chain back from the past to the present, like to get there, what do I have to build now to... and then, and then move forward along that path. But it's not like go into a research lab, pop out in twenty years and s- you know, solve all the world's problems.

    7. MMMarina Mogilko20:42

       Yeah. This is a really fascinating feature that I see with a lot of greatest entrepreneurs. It's like they're reverse engineering from fifty years ahead.

12. 20:52 – 23:02

    What still surprises him after 30 years of betting

    1. MMMarina Mogilko20:52

       Is there anything surprising that still surprises you about those amazing entrepreneurs?

    2. SJSteve Jurvetson20:56

       Well, I suppose it's a bit surprising in a way each and every time it goes incredibly right. Um, and so weirdly, this may sound weird, I don't think I've ever thought about that question before, um, or been asked it before. And so the perpetual surprise for me is like, wow, like in the year eight, nine, twelve, 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, 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, the window... and as long as you're, have an agile mind and, and you're looking at it, you're like, wow. Like no one thought of that when we started. So for example, when Tes- when we first invested in Tesla, there was no concept whatsoever of autonomous driving. It was not in the business plan. There was no talk of it. It was not on anyone's mind. The way in which electric drivetrain uniquely enables that and control and fidelity was fascinating. Or in SpaceX, the spa-- the Starlink, you know, 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, that would make sense that we weren't doing before? Not just we invested in Planet Labs for Earth observation, yes, constellation of telescopes, but this whole notion of building a, a networked, um, you know, backbone for the internet in the sky was... and then direct to cell phone. Like each one of these things is an unfolding event. Then orbital data centers, right? Not on the dance card even five years ago. So, uh, that continues to surprise me. In some ways it, it's not easy, but it, it seems so much more powerful as a business vector than purposeful design, if you will. This is almost like exploring the, the, the option space that, that... or the light cone, if you will, of possibilities in an economy versus, you know, sort of planning out something ten years in advance and having it go according to plan, if you will.

13. 23:02 – 26:17

    What Steve is quietly betting on right now

    1. MMMarina Mogilko23:02

       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, uh, in different industries. What are you betting on now? What should we be looking out for?

    2. SJSteve Jurvetson23:13

       Plastics. No, uh, let me think. [laughs] Like s-some people who know the old movie. Uh, let's see. Um, so we-- So taking that thesis that AI and information technology will innervate every economy, ad-- meaning add a nervous system to everything, we saw in automotive and aerospace. Just expanding on that thought a bit, um, we are looking for additional things in energy. We've invested in a variety of nuclear fusion and, and, and subcritical fission that doesn't, um, trigger NRC regulations. So 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 five hundred years from now have been solved, and we're trying to figure out the entrepreneur that will open our eyes to how we get there. So free healthcare 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 out how to get there. Probably won't be in the US that it launches. You know, bypassing FDA, uh, bypassing insurance and reimbursement. On food, we won't slaughter animals for meat. It's the, the products are getting there, but the, the, you can sort of see the future. It's so close. You can almost taste it, so to speak, whether it's cellular ag, mycelium or other techniques, mycelium being the fastest growing thing. But, but we are gonna eat meat like things that are delicious, healthy and not involve slaughter of animals. Uh, construction growing as a percentage of GDP and like Labor productivity has been flat for 30 years, so th- it's such a hard industry to change that we've tried and failed a few times, but we're looking. Again, so what-- 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 I, that we wanna look at. Recently, we've been investing in epigenetic editing across a variety of things from crop health, uh, pesticides, herbicides, um, uh, human health. It's, it's fascinating. It's basically the software of biology instead of going to the firmware of our, our genome. And, uh, we've been investing in materials, critical met- minerals and metals, uh, everything from deep sea mining to copper refining, um, uh, because of just incredible need, sort of like the workhorse of all these chips is you need these materials to make the stuff. And there's, coupled with that, a reshoring or, r- you know, bringing back to the US capacity to build, which we had, uh, at our feet over many years. Analog AI, I mentioned too. We have three different investments coming at it from different angles using AI to devel- to design AI chips, uh, sorry, analog chips.

    3. MMMarina Mogilko25:31

       Analog chips.

    4. SJSteve Jurvetson25:31

       Yep. Uh, analog, um, in-memory compute from Mythic, where they can do eight-bit multiply and add in a single transistor, and then unconventional, which is taking a very, very strange and forward-looking, um, big bet on, you know, in every case trying to get an, you know, 100x and then another 100x on power reduction, power per, per, um, per calculation. Overall, we're about 40% life sciences, 60% IT, and, and we in the life sciences side just see... We look for the weird things that are, like, on, on the edge. You know, harvesting organs for, uh, transplant and growing humans without brains so that you can use their organs. Um, there's a company here actually in the audience doing the same thing. A, a male birth control pill, uh, y- improving IVF dramatically, um, a lot of things that fall through the cracks of a traditional pharma VC.

    5. MMMarina Mogilko26:16

       So I'm

14. 26:17 – 26:51

    Can you just copy this with ETFs? (nice try)

    1. MMMarina Mogilko26:17

       hearing agriculture, uh, biotech. I'm just thinking in my head, "How can I replicate your strategy with ETFs?" And I'm like [laughs] ...

    2. SJSteve Jurvetson26:23

       Well, it's hard to rep-

    3. MMMarina Mogilko26:24

       Can you-

    4. SJSteve Jurvetson26:24

       See, our strategy, I can state-- It's very unusual. I can state it openly, and then it's hard to replicate 'cause 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, as-

    5. MMMarina Mogilko26:35

       But at least it's in the areas.

    6. SJSteve Jurvetson26:36

       Mm-hmm. Yep.

    7. MMMarina Mogilko26:36

       So if they are dramatically changing a market-

    8. SJSteve Jurvetson26:40

       Yeah

    9. MMMarina Mogilko26:40

       ... then it's gonna be reflected in an ETF.

    10. SJSteve Jurvetson26:41

        Yeah, 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, like, the four largest companies were all started in the 1800s, and that's who you're

15. 26:51 – 29:33

    The 30-day plan when all you have is an idea

    1. SJSteve Jurvetson26:51

       competing with.

    2. MMMarina Mogilko26:51

       We have a lot of entrepreneurs who have crazy ideas. Can you give them a 30-day plan to execute on that idea? What would be the best thing that they can do?

    3. SJSteve Jurvetson27:01

       What stage are they, are you assuming they're at?

    4. MMMarina Mogilko27:03

       They just have an idea.

    5. SJSteve Jurvetson27:04

       Oh, single person with an idea?

    6. MMMarina Mogilko27:06

       Yeah.

    7. SJSteve Jurvetson27:07

       Hmm. 30-day plan. I might try to find a co-founder who agrees with you or whoever this person is. And the reason I say that is, um, a lot of startups tend to have a dynamic duo at their founding. It's rarely a individual. Um, and in-- You can, you can imagine Jobs and Wozniak as a mental model for this or these superheroes, Batman and Robin, uh, you know, Sergey and Larry Page. Even Larry Ellison had Bob Miner, who's less well-known 'cause he's an introvert, but, you know, there was not, like, a singular cult of personality of a founder. And, and part of the reason to have someone is, I've 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 sole, 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 an introvert, whatever it might be, that have mutual respect for each other not only makes it better that you, like, some- you got someone to bounce ideas off of in a rapid iteration loop when there's 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, that ripples through the culture of a firm and types of people that are hired and the, 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, uh, I think the best outcome is if you're, literally your premise or 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, right? So if 100% of people that you've ever met think it's crazy, take that as feedback. If it's, you know, nine out of 10, that's pretty good. If it's eight out of 10, 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, 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, it's... Anyone could have started this business if they just had this idea, probably a bad sign. And then somebody, the, your co-founder, uh, agrees with you and thinks, "Oh my god, this is me." That just shows that as almost like a test case, you can persuade s- persuade someone to give up their job and join you in this mission. Then before you go out and fundraise, that, that says a lot more than just the sole person with an idea. Like the inventor in a garage, you know, off all by themselves. There's so many cases like that that just never manifest as a business-

    8. MMMarina Mogilko29:28

       Yeah

    9. SJSteve Jurvetson29:28

       ... because they just never made that first step of being able to persuade anyone to join them in the mission.

16. 29:33 – 31:21

    Where the best co-founders actually meet

    1. MMMarina Mogilko29:33

       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. What-- From all the startups you founded, where did the best co-founders meet? Is that university or...?

    2. SJSteve Jurvetson29:48

       Yeah, good question. I'm, I'm not sure. I haven't, I haven't thought through that. Um...

    3. MMMarina Mogilko29:52

       'Cause it's so hard, especially like-

    4. SJSteve Jurvetson29:53

       'Cause they c- they often, they often come to us having already done that.

    5. MMMarina Mogilko29:55

       Mm-hmm.

    6. SJSteve Jurvetson29:55

       And often, yes. So for all the university ones, they're-- many of them are from... That's probably your, your question had embedded within 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, they're a way of stovepiping information into a systems vernacular and domain expertise that often doesn't cross-pollinate. And university's 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 in 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, s- the 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.

    7. MMMarina Mogilko31:08

       This makes total sense. Um, 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, uh, for Q&A.

17. 31:21 – 35:22

    When machines do everything, what's left for us?

    1. MMMarina Mogilko31:21

       When machines do everything, what's the meaning of life?

    2. SJSteve Jurvetson31:24

       Yeah. Uh, 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, um, and it's gonna come soon, right? Roughly 19% of global employment is in driving vehicles. Um, and that's obviously going away, just not as rapidly as we might imagine. Um, I think we, we want meaningful work. I think all humans have a fundamental desire [smacks lips] for symbolic immortality, this belief that we've contributed something to the world that transcends our brief time on this world. And, uh, 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 instantiations of that urge. And so I think there's still a creative desire, um, and, and I 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 they've come 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, 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. Um, [smacks lips] it doesn't have to be paid employment, though. So I-- You, you can imagine some sort of hyperspace jump, 'cause that's conceptually what it requires, 'cause 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 abundance, like Peter Diamandis envisions, um, you know, everything physical costs a dollar a 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 ma- you know, menial work, and we could just be, you know, philosopher kings or artists or pursue whatever we might want. And, and, and some people, some, not all, but some people really love that era. Well, the machines will be those slaves, right? Not... [chuckles] Because even in slavery, humans will not be cost-effective. And I say that 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. Um, what does that leave for the rest of us? And so I think it's gonna be a, a man's search for meaning that really is the q- core question. Um, I think it's gonna be really fun if we get hyperspace there. But I will add the caveat, that's not the path we're taking. Like, [chuckles] there's nothing that indicates that we're just gonna peacefully march from an economy of full employment to an economy of no employment and pass through the thirty, forty, fifty percent unemployment points without some issues.

    3. MMMarina Mogilko34:18

       It's gonna be turbulent. Yeah.

    4. SJSteve Jurvetson34:19

       That, that's gonna 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. Um, I think, uh, I think we inherently find that in our curious exploration of the, of the universe. Yeah.

    5. MMMarina Mogilko34:33

       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 gonna 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. [audience cheering] [audience applauding] Quick pause here. If you're enjoying this podcast, you will absolutely love my Inner 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 an insider version of this podcast. The link is in the description. Join my free newsletter to stay

18. 35:22 – 39:12

    Q&A: the Neuralink question everyone asks

    1. MMMarina Mogilko35:22

       ahead.

    2. MMMarina Mogilko35:22

       Thank you, Steve, for your, uh, fireside chat. Uh, since you're a big investor in Elon Musk companies, um, I'm curious, have you invested in Neuralink?

    3. SJSteve Jurvetson35:33

       Yeah.

    4. MMMarina Mogilko35:33

       Yeah. So honestly, in my opinion, uh, everybody is excited about SpaceX, but I'm looking forward for an IPO of Neuralink. [chuckles] Do you think it's happening soon? And honestly, I think it's, uh, bas- basically brain-machine interface is the future. But essentially, currently, if we use a voice mode on ChatGPT or OpenAI, right, or we type, we are limited in our throughput of how many tokens we, we, uh, we send to the LLMs. And if we have bra- brain-machine interface from Neuralink, we're able to unlock even more creativity and faster throughput from our brain to machine.

    5. SJSteve Jurvetson36:13

       Yeah, I, I can't comment on IPO timelines. Um, but, um, the enthusiasm there is interesting. It was originally sparked, as many things are, from, um, a science fiction novel, Iain Bank's Surface Detail, where they have a neural lace. Fascinating book. I recommend it. Um, and I think what you see... [sighs] So I have a, I have a somewhat unique perspective not shared by Neuralink, so I'll-- but I'll just share my perspective, which is I think, um, it is an amazing capability for expanding the sensory cortex, adding the pro- prostheses to the mind. In other words, restoring function where it's broken, expanding function, like let's say, seeing more, 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, uh, upgrading core functionality, like just making someone smarter. So I think the example you gave is, is an very interesting one. Could you 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, um, 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, um, you know, cellular automata, whatever it might be. Um, 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 gonna bear fruit. I don't think control and, uh, an alignment is possible in a cutting edge system that is pushing, and back to AI for a moment, pushing the, uh, capabilities of, of what we can build. Similarly, it'd be like asking about controlling, aligning, um, mind controlling a teenager. So I swap teenager and AI whenever I think about this. So when it come-- The reason that's relevant is from the brain is a complex system itself, and reverse engineering its inner workings for uploading or for, you know, brain to brain or like adding speech like the way Jeff Hawkins thinks you just like cut and paste this, like a French speaking module into a human brain or, or neural net. I don't think that's gonna be possible on a timeframe of relevance, meaning it'd be easier to build a new intelligence than it is to reverse engineer one that you've made. So I do think Neuralink is fascinating, but I don't personally, um, g-get faith that it's gonna keep up with AI. Maybe that'd be the safest way to phrase it. Not that it can't be done, but the timescales, you know, FDA cycles, human biology, nothing happens on a timescale comparable to the ra- the learning loops. Back to the Elon Musk saying like, "Focus on learning." Like where do you learn more qui- you're gonna learn more quickly in the synthetic domain. I think humanity always wants to believe it's part of the future in that regard. But like the Kurzweil's uploading, I could just see why he wants that to be true within his lifetime, and that's what he predicts will happen, but doesn't mean it will.

19. 39:12 – 43:20

    Q&A: can a machine ever be conscious?

    1. MMMarina Mogilko39:12

       Steve, I'm really curious, what do you think about Penrose's argument that, uh, the consciousness is go far beyond algorithmic processes to quantum level processes, meaning that, uh, AI would never be able to pred- to develop consciousness itself just by, by its nature. So what do you think? Can AI develop consciousness or it's will be only imitated and that's it?

    2. SJSteve Jurvetson39:45

       And you were referencing Penrose's quantum-

    3. MMMarina Mogilko39:47

       Yes. Yeah

    4. SJSteve Jurvetson39:48

       ... yeah. Yeah. So Penrose is a brilliant guy in UK, um, 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, but it's wishful thinking. We don't, so to speak. Um, but I can also generalize your question. Is there something, uh, vitalistic, naturalistic, unique to our brain that is irreproducible in others? And, and there was a reference earlier to Anil Seth's work. I find the arguments completely uncompelling that there's something vitalistic, um, 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 do-- how do we know if the dog is conscious? What, how do we, 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? Um, [laughs] and, uh, I mean, 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. It's, uh, 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, 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, I won't digress, is like is anything that we're doing in AI development gonna lead to consciousness? That's a different question because you could, you could argue that's a dead end. It won't get us to consciousness, but it doesn't mean it's not possible. It's much higher order proposition to say something is impossible than to say, uh, "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, and if we did, why couldn't we replicate that with quantum computers? I mean, that's a different question. And then if I broaden your question a little farther, uh, just animus or spirit or life is 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, right? And I just picked memory reas- 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, and they can g- 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, in the-- at that level of abstraction, of course, they can, and yet it doesn't have human memory, and that's fine. Um, 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 can make the argument that everything we have in our brain is essential for consciousness. In other words, there's a lot of, there's a garbage collection for our metabolism, you know, that, you know, things that happen when we sleep and cleaning out by, you know, waste products and the way mitochondria work. You don't, you don't have to have all that in a computer to be intelligent or to have memories. What-- 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 re- and reinforcement learning algorithms would get us there more, more obviously. Just 'cause whenever you recapitulate what we've already done with our biology, that makes me give hope that, well, why can't we do it in a different substrate?

    5. MMMarina Mogilko43:16

       Thank you so much. [audience clapping]

Episode duration: 43:20