This Week in Startups - Legendary VC Steve Jurvetson looks ahead at neutral networks, Tesla, nuclear power, and more | E2193

Episode Date: October 16, 2025

Today’s show:*On a very special TWiST, Jason’s joined by “one of the greatest investors in in the history of VC,” Steve Jurvetson of Future Ventures.Together, they look back at some of Steve��s earliest investments and learnings, the promise and ultimate failure of nanotech, breaking down the AI hype cycle, the primacy of neural networks, the return of nuclear power, why “AI safety” might be illusory, and much more.It’s a packed show full of deep insights from one of the greatest minds in technology today. Do not miss this ep!Timestamps:(00:00)Jason’s 1-on-1 today with “one of the greatest investors in the history of VC,” Steve Jurvetson(00:03:04) The early origins of Steve’s fascination with “deep tech”(00:08:03) Jason and Steve recall playing around with the TRS-80 (“The Trash-80”)(10:00) Perspective AI: Real insights, straight from your customers, and your first two months are on us. Just go to getperspective.ai/twist.(00:15:13) Keeping your portfolio diverse when so many companies look the same(20:00) .TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today.(00:20:49) The promise of nanotech and why it fell apart(00:25:46) Breaking down the “hype” cycle and how trends get co-opted(00:26:27) Why Moore’s Law and the Power Law are so important for investors(30:00) Northwest Registered Agent - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!(00:33:36) Understanding the rise of GPUs and how they resemble tiny brains(00:51:32) Revisiting a classic clip of Steve talking about robots(00:54:04) How Jason and Steve got their hands on some of the earliest Tesla Roadsters(01:00:09) Flashback Clip: Elon predicts 2025 in 2015… how’d he do?(01:02:46) Why did the world turn SO strongly against nuclear energy?(01:16:06) Can we make AI “safe” in the age of GPT Psychosis? Is it too inscrutable?(01:21:54) Why Steve thinks technology will ultimately doom autocratic regimes.Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today.Northwest Registered Agent - Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit https://www.northwestregisteredagent.com/twist today!Perspective AI: Real insights, straight from your customers, and your first two months are on us. Just go to getperspective.ai/twist.Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason’s suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

When you sent a Hotmail, it said sent by Hotmail or Powered by Hotmail. Yeah, it's actually funny you should say this. The idea to do that was Tim Draper's idea, 100%. And not me. And I just want to give credit to him because it was incredibly cheeky at the time to embed a commercial message involuntarily to everything sent by your customers. So you signed up for Hotmail. It feels like a normal email account.

But now every message you sent has this get your free email at Hotmail.com, right? Like a call to action. Controversial. This weekend startups is brought to you by Perspective AI. Surveys, they never capture what customers are really thinking. That's why we use Perspective AI, real insights straight from your customers. And the first two months are on us. Just go to getperspective.aI slash twist.

DotTech. Say it without saying it. Head to get.com. slash twist or your favorite registrar to get a clean, sharp, dot tech domain today. And Northwest Registered Agent. Starting your business should be simple. With Northwest Registered Agent, you can form your entire business identity in just 10 clicks and 10 minutes. From LLCs to trademarks, domains at custom websites, they've got you covered. Get more privacy, more options, and more done.

Visit Northwest Registeragent.com slash Twist. today. All right, everybody, welcome back to this week in startups. You've got a treat today. One of my favorite human beings, one of the greatest investors in the history of venture capital. And one of the smartest, most considered individuals who I am lucky enough to call a lifelong friend, Steve Jervitson, is back on the program. He is the co-founder of Future Ventures. He's also the J and DFJ, if you remember that. He's done four funds of $200 million each at Future Ventures, he's on his fourth fund. These funds are for really, really deep tech, really challenging bets that other people aren't willing to make, like the bets that Steve made on companies

that people predicted would fail spectacularly like SpaceX and Tesla. Welcome back to this week in startups. My guy, Steve Jerminson. How you doing, brother? Oh my gosh. Thank you. That's the kindest intro I think I've ever received and very unbecoming of you to actually say nice things. Well, we are friends. And so I break chops and he breaks my chops. But now, that's all in love. You're a huge nerd. Oh, here we go.

Now we go. No, but you're a huge node nerd in the best possible sense of the word. You, uh, as recreation, your hobby is sending up rockets. So I'll go to dinner with you and you'll have me by your house. And you say, oh, I've got to step outside for a second and launch this rocket. you just love deep tech yeah how'd you get that love of deep tech yeah it goes back to my earliest memories the first thing i ever bought with my allowance when i was i think five or six years old was a chemistry book it had a lot of cool pictures of like lab experiments but like literally was my first

volitional purchase of my life which was to show how hard runs and then the apple two is like this incredible wake up of like wow you can program computers and i think some people who've had a taste the computer science just fall in love with it. And so I did. They're all games and things. And so short version is it's been my whole life. I've struggled in the first few years to find a career that would really tap into that passion for lifelong learning about technology and where it's head taking us and what we can

do with it. And I bounced around a bit. But when I finally found venture capital 30 years ago, it was like, wow, it's the perfect match for me. How did you get that Apple two? What year was that? When did you first see it? Take me to that moment when you put your hands on that magical device and started

typing oh it was amazing i remember exactly it was in seventh grade so i was about 13 years old um i was you know socially kind of maladapt i didn't really i mean this is 1980 no this would have been even before that this would have been around no no you're right around i think it was around 79 though that i got it for some reason it was still in the 70s was either 79 or yeah yeah yeah yeah i think was maybe like 76 77 i wasn't the very first year that it came out but it was pretty close so let me let me Let me give you an more precise answer. No, it would have been, yeah, around 78, 79. And my dad worked in the chip industry, actually.

So he made memory chips. He made the actual chips that I then manually plugged in to upgrade from 16 to 48K of memory, which was like a big, advanced, kill a bit, right? So not mega, but thousands. And my eyes just lit up. So I immediately got into basic and would write simple programs,

like, you know, print something, go to 10. And your dad bought this for you. Yeah, it was a gift. Yep. I don't remember if it was a birthday or Christmas. It just showed up and I just could not imagine anything else that I would want to play with. So I bounced around with Legos and Fisher Technique, this other kind of building block set. But this, when Apple 2 came around, that was it. And I used to write games for it. I wrote a mastermind game, which is like this colored peg kind of game and a some simple like almost like Blastar that Elon did like little, but actually

my own graphic. So a graphic sort of shoot them up games, a text-based adventure game where you, you know, know, enter words like go north, go south. Like Zork. Exactly, just like Zork. Bingo. And I loved the games. I mean, there were, oh my gosh. I remember, actually, it's funny. I've been talking to Richard Garriott a lot lately

over the last few years, but there was a good, I think, 30-year stretch where we didn't see each other, but I was just a fan of Ultima and I was a fan of, was it Bill? Anyway, shoot, I forget the name that. It was a pinball construction kit. Oh, I remember pinball construction kit.

Yeah, you got to make. your own pinball game yeah so fun exactly it was incredible like and as a programmer i could tell those people know how to do things i have not figured out how to do yet with shape tables and flipping two memory registers for a video ram like i could not figure out how they did what they did it was like squeezing insane performance out of this pretty simplistic machine in retrospect and that's a big ups to your dad because it was a 1300 dollar computer at launch but that's in 19707 to 82 dollars was like the the window the two came out, which would be close to $7,000 right now for the base model. It was, that's a significant purchase for your kid.

But it was mind-blowing. I got the PC Jr. My dad bought me the PC Jr. That was that keyboard. Yeah, it was a chicklet keyboard, as you remember. Then they subsequently came out with a better one. But these were really the introduction of a whole generation to computers. Before that, you had to go to a laboratory to use a computer.

and the PC kind of put one on everybody's desk. It was kind of mind-blowing. So did your school in, you know, support computer programming at that time? Not initially. So when I was in third grade, I remember we had a teletype machine like you're talking about. And none of the kids used it. It was often like the principal's office area where the staff and admins were. But you could type things in and it was like, like, like a typewriter, right?

And I think that's where Paul Allen and Gates initially did there. initial programming. So I had a briefest exposure to that, but the overhead, I can tell you, was so high that it didn't really capture me in the same way. The delay, the latency, the lack of sort of a personal experience. And it was in school and it was loud. It just didn't, I mean, I played with it, but it didn't really catch on for me. Then my high school, which I was in at some great did. There was a really great guy. I remember his name was Stutzman, the teacher that supported this, had both Z80s, which were the trash 80s we called them, which was the TRS80 from RadioShap based on the Z80 processor and the Apple 2 based on the 6502.

There it is. This is the TRS 80. Boom. My school had this as well. This was my first introduction to computing at Severian High School in Brooklyn, which is just like a dumb terminal. Now, you must have gotten in trouble with some of this stuff. Everybody who was on these things did something stupid or really.

I did. I did. I did. I did. I did. I never shared this, nor have even thought of it. But the closest I got to, because I didn't get much in the bulletin board systems where then you could network. I mean, I played with them, but I didn't use them enough to get in trouble there, which probably is good.

But I did create a graphic representation of my most despised teacher. I don't say who, but it was an English teacher. And some violent end coming to them. And I think I got in trouble for that because the kids loved it. It was like, look at the teacher opening the door and bet something bad happens, like the rock falls in the head or something. And but it was just an animation in the cruise. high-res graphics, as they were called at the time. So that was about, other than like just spending way too much time on it,

I didn't get into trouble there. In fact, if anything, maybe it kept me out of trouble. And that was more likely to get into trouble in the neighborhood. It was just idle time than I was. You know what I did with the TRS 80? We had a dot matrix printer. And as you remember, at that time, you'd feed the paper in. They were all connected.

And you had this big box, tall box, wide paper. It's a giant, huge box you put under. underneath the printer. So being the idiot I am, I was like, oh, 10, say, hello world, 20, go to 10, 30, print. So I do this with a bunch of spaces. And I start this program. And I kid you not, because I was just saying hello world one time per page. It goes, and the teacher had left the room. He's in the back of the lab and I'm like, oh my God. So then everybody else starts sending print jobs to it. The teacher lost his mind because somebody then just did one with blind pages. It's shooting out the back. But this was really like, I think the start of Elon's career as well is just getting onto one

of these computers and then just going down the rabbit hole bulletin board systems as well. Hey, listen, we meet a lot of early stage founders here at launch, my investment company. And some, they don't have a lot of traction yet. They just have an idea. Maybe they haven't even finished their product. They've just got an MVP. But they still need investment. and accelerators like ours to take them seriously. And you know what, we can't just wire money to your Gmail or your PayPal. That's not how it works, folks. We need to know that you're a legit and official business.

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Go to northwest registered agent.com slash twist and show the world you're in business. And make sure you use that URL slash twist so they know that we sent you. Tell me about the moment you heard the term venture capital. I believe I was all the way at business school. I think it was around 1993 or four. Where were you at school? At Stanford, business school? At Stanford.

Locally. Yeah. So I had followed a geek's path, right? Apple II programming, electrical engineering, studies, did a bunch of stuff. Eventually, even started a PhD in double E, focused on neural nets and AI of all things, and how they mapped two parallel processing machines. Didn't want to go back to Hewlett Packard, right, done chip design. So I did some engineering work over a number of summers there.

Did some summers at Apple and Next because I wanted to see Steve Jobs in action, but that was in product marketing. So I bounced through, oh, and then three and a half years in management consulting at Bain for tech company. So I did consulting, private marketing, engineering. I couldn't imagine 20 years of any of the ones that I had some exposure to. They were interesting learning experiences, but even after a summer, I might feel like, how much more I'm going to learn five years than I haven't already learned over a summer? Pretty cheeky, perhaps.

So I was kind of a little lost. But assuming I was just going to go back to Bain, management consulting firm, that was my assumption going into business school, then out of nowhere in the, beginning of my second year, I think it was probably October. I got a call from this guy, Chip Hazard. I remember specifically he was at Greylock Venture Capital firm that I sort of maybe had heard of, but they'd have no website. There were no websites.

In fact, there were only two venture firms that had websites a year later. So 94, there were none. And I was like, what is this all about? But I had worked with him at Bain. He was a year of my senior. He had already graduated, gotten to Greylock on the East Coast, and they wanted to create a West Coast presence and grow the office out here. And so lo and behold, I'm like, wow, this is from his description.

Sounds interesting. Let me, let me look at this. I had never met a venture capitalist. You knew nothing about it. And there was no internet. So, well, I mean, it was, but it wasn't really like we know it today. It was Bitnet. It was LAPA, too.

There wasn't like, oh, let me just look up. What is venture capital? Let's have the AI explaining to me. Right. And so I did happen to find a gold mine for me was a fellow named Chris Alden, who was a co-fund. of a magazine called Red Herring. Yes, I know, Chris. Upside was the other magazine, right?

Bingo. And they were chronicling venture capital in the early 90s. Exactly. Print format, you get like a subscription. And Chris, in particular, did a monthly, I think it was, monthly column called VC Whispers, where he would talk to venture capitalists and ask them, what do you actually do?

What is your firm actually like? And how is it different from others? Information that was impossible to come by otherwise. And so he was really great at like pointing me to, well, you know, Grailocks kind of at that time, they morphed a bit. But at that time, a very conservative white shoe buttoned down kind of firm. Like the opposite of me. Same for Sutter Hill at the time.

And again, they've changed a bit. But at the time, they were very different from the firm I joined, which was called Draper Associates. But folks like Chris helped point me out, point me to the sort of wide spectrum of diversity that actually was within the culture and, sort of strategy of venture firms. Some were entrepreneurial and mis-takers. Others were very strangely, very conservative and analytical, more like investment banker types, which would not have probably been a good match for me. So I really credit Chris and a couple other folks I spoke with to help steer me

to, well, you know, they're not all the same. There's some films that you might like more than others. And luckily, I ended up at the firm I did, because it was very entrepreneurial and, you know, helped me pursue what was it ever-changing, not ever-changing, but sort of a drifting focus area over time from Internet to deep tech. Tell me about the first investment you were ever involved in, because you must have diligent and met with hundreds of founders, but there's always that first check that you are responsible for champion and putting in. So I'm pretty sure the first three, and I'll try to get the order right, were fast parts

interwoven and hot mail. And two of those three were, well, no. Well, okay, so I think fast parts might have been first, and it's long gone. No one's ever heard of it. But it was a very unusual sort of B2B, if you will, trading exchange that dealt in the gray market of semiconductor. And the only reason this one was really interesting to me is I had done a, I don't what you call it, a case study or an entrepreneurial exercise at business school, almost with the same idea as the entrepreneur. I was like, whoa, like, it clicked on so many levels. I'm like, wait, wait, you're doing that?

So, I'm going to story short, there was a, you know, perhaps a lesson there for me about not relying too heavily on something I think I know a lot about instead of, is this actually a good business opportunity independent of what I might know about. So the fact that it clicked was like, oh my God, everything lit up for me. Like, that's something I was thinking about starting. But it ended up just going out of business. The interwoven went public. It was a sort of web, like internet tools company for content management. You know, as a, you know, I said, as websites and web properties got more complex how to manage all these assets, almost like an infrastructure layer for the internet.

And then there was Hotmail. The hotmail was a really, yeah. That was like the one people have heard of. Boom. Like, yeah, that was the first one that got visibility. Blought by Microsoft. Bing up for about $400 million, you know, less than two years in. And the thing that it really epitomized was first, you know, rapid internet growth for us.

Second, this thing we basically coined the term viral marketing because of hotmail, trying to describe for a blog post, what is it that Hotmail did that was so different from other internet companies, this idea that the message itself was the vector of spread of marketing in a sense, like a virus. Which came from the signature. When you sent a hotmail, it said sent by Hotmail or power by Hotmail. Yeah, it's actually funny you should say this. The idea to do that was Tim Draper's idea, 100%.

And not me. And I just want to give credit to him because it was incredibly cheeky at the time to embed a commercial message involuntarily to everything sent by your customers. So you signed up for Hotmail. It feels like a normal email account. But now every message you sent has this get your free email at hotmail. Like a call to action. Controversial. Exactly. Tim wanted it more controversial. He was pounding the table that he wanted to say as if it was written by the sender, P.S. I love Hotmail. I love you. I love you. It gets your reality. Tim's a neat guy. The founders wanted like nothing to do that they're like spamming, you know, accusation. People forget and you could explain to people

that the internet was non-commercial at its start and the idea of doing even a Zima malt, ice liquor ad, a banner ad on Wired was was faced with fierce resistance from the venture, entrepreneurial and early internet community. That's right. That's right. So, you know, it was definitely an aggressive move, and it made all the difference on their rate of growth because they spent nothing on marketing. They spread globally. The actual number of users was scaling at such a rate that hadn't been seen before that that's why I wanted to describe. What is this process by which they tell two people, and then they tell the people there's a geometric explosion, which, by the way, Skype then used later for voice and other companies used for video, and it sort of was a playbook,

if you will, for how best to grow a consumer-facing business. And there were a bunch of precepts and things that related to that. Like, what is your viral coefficient? Can anyone who receives the message actually take action? You have to be multi-platform. There's a bunch of things that relate to it. But going back to your original question, he was my first success that had visibility.

We had a whole bunch of internet companies, by the way, in the 90s. So I joined in 1995, and that was perhaps in retrospect the best time to join the venture industry, like pure dumb luck. The internet was exploding. It was like shooting fish in the barrel to make money. frankly, in these companies for a period of four to five years then. And we were the most active venture capital firm, you can believe it, in internet investing in 95, 96.

We did about a third of all internet investments. Amazing. DFJ. Of all. Yeah. Yeah, it was called Draper Associates, but it eventually became DFJ a couple years later. But we did a lot. And we learned a lot quickly.

And then something really big changed, by the way, around 99. I was still super gung-ho about the internet and its potential. I know that because I wrote articles saying I'm super gung-ho. But I pivoted completely away and stopped investing in internet companies. And the reason wasn't because I saw a crash coming. I was not able to foretell that with any consciousness. It was that everything was looking the same. It was really starting to get boring, which is like...

Derivative products. Yet another, yeah, way to divvy up a market and sell the consumers. Yet another way to go B2B training exchange. It was B2C and B2C and B2C and they all look the same. Just variance on a team. We had not yet had social media, you know, have its boom. We not yet have things like Uber and all those that were yet to come. At that point, it's like every business plan was like just another variation on a theme.

I'm like, this is just, it's not interesting. Let me, and the end of gut sense. It's not diversifying to portfolio. It's probably not wise investment to just be paying higher and higher prices for more of the same. Even though a lot of other venture rooms explicitly had that as a strategy, like benchmarker. There's a book about let's just do nothing but X, Y or Z because we're making so much money in X, Y, and Z. Like, why look at anything else? So you could have made the argument, rightly so.

that it's foolish to invest in biotech, it's foolish to invest in semiconductors. Why? Nothing could beat the internet for a rate of value creation for a while. But I pivoted hard to something that actually turned out to be not interesting, nanotech.

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That was such an interesting space. Explain to people, you know, MIT and, you know, material science. and what the promise was at that time? Because William Gibson was writing these crazy stories of tiny little nanobots, building a skyline in real time. We thought that this was the micro-robotics future that would change everything. And it didn't. Why?

What was it? Yeah. Yeah, that's a great point. There were a lot of sci-fi influences. You're right. All those, sort of the neural lace that Ian Banks writes about It was the inspiration for neural link. There was Eric Drexler's book in like 85, I remember reading it,

maybe 86, I think it was 85 engines of creation, which was just a deep dive into, here's the big thought experiment, if you could put an atom wherever you wanted and you weren't burdened by where we are and the tools we have today, but if somehow we could imagine that future, where we have atomic precision,

and you could imagine if you could do that, you could also build machines that could build more machines of their same ilk. You could have self-replicating machines. There were a lot of just analyses being done by Foresight Institute and by Eric himself that just pointed out like, oh my gosh, this is insane. It's so mind-bendingly different from what we're used to. The absence of friction on some of these rotary bearings, the rate at which these things would mechanically move. You could actually imagine a mechanical computer, if you will, that outperforms anything we know of with a better energy footprint and such.

And so there are books like Diamond Age by Neil Stevenson as well that talks about this future. So a lot of sci-fi precursors. People were thinking about this, and maybe I felt prey to some of that to say, well, that's motivating to look at how do we get there? How do we get from where we are today to there? And so I wrote some blog posts called Transcending Moore's Law, was one of them. It was actually of all things in a law journal, folks in nanotech that first got pushed in, of all places.

But it seemed clear to me that there was a problem, which is you can't manipulate atoms today with that precision. There was like atomic force microscopes and stuff, but nothing that's scaled. So I described two ways to get to this future, we might imagine. One is the bottom-up kind of organic, bio-inspired path, which is let's use the tools of biology, like the ribosome that can build things, or now what we call CRISPR another molecular tool. We didn't have those words back then. But are there molecular machinery that we can harness, perhaps using DNA as a structure material,

perhaps self-assembling molecular films, which I did invest in, to create a better memory chip? a variety of things where you engage processes that work already at the nanoscale, if you will, and build up from there over time. So that felt like powerful and immediate. The other path, what most people were thinking about was a top-down approach. They say, let's start with actual businesses that exist today that sound like this. Like the semiconductor industry, hey, they want to make things smaller. They're trying to scale down to nanometer scales.

Like, why not just work our way down where you inherit the interfaces to the real world from above? In other words, if you had a chip of a certain scale, interconnect from Broadcom, whoever that works, let's just figure out how we can make smaller, smaller things, but harness to what we have from above. And that just was going to take like 20 years, is what I estimated, maybe 30, but it was like a long time. Like there aren't nanotech opportunities there in the near term. And that's probably still true today. It's slow, but sure, we're invested in some things like leasing lithography that will hopefully get us there, but it's taking a lot. The bottom up, so this was in a long-witted way, a gateway for me to get more and more fascinated

about the information systems biology, what we can learn from biology that applies to IT, the inner cross-pollination of ideas between what formerly were completely different investment domains. Like the biotech investors were different people. Right. Different people doing different investments that had no interface whatsoever to IT. You gave an incredible talk. One of the highest rated talks we've ever had at any event.

I do this liquidity event. It used to be called Angel Summit. And we should pull up the deck here and just go to Morris directly to the Moore's Law Slide. And, you know, this is something that I don't think people talk about all that much. But when we were coming up, Moore's Law kind of ruled everybody. And then as you and I, you 30 years, me just over 10 in Venture, the power law, these are two laws that I think rule and dominate our lives. Here's 128 years of Moore's Law. What people through this and why it's so important and why you'd think about it so much.

It's the thing I think about the most that also seems the most descriptive for understanding the world we live in today and where we're heading over the next five to 10 years. It has incredible predictive power. So let me set it up and describe it. So the years on the bottom, pretty easy to understand we're covering almost 130 years here of time. The dots are the best price performance computer ballpark. You know, like there aren't any dots above the line that we know of, but there are plenty

below the line. Sort of the frontier of the best price performance computer of the day. And the axis, most importantly on the Y, is the choice of what it is and that it's logarithmic, meaning every tick mark there is 100 X, 100 X, 100X, right? So a straight line on a graph like this is an exponential. And if you eyeball it, it almost looks like it's a slightly upticking curve on a double exponential. But what the axis is showing is how much computation can you buy for a dollar, constant dollar meaning inflation adjusted.

So what's fascinating about this is that first it looks like it's on rails for 130 years. This is kind of mind-blowing. And it's covering entirely different technology substrates. So on the far left, you have mechanical devices that did the census in 1890. I mean, like literally, machines are going back to 1890. You have a relay-based computer that cracked the Nazi Enigma code if you watch the movie imitation game. You have vacuum-based computers that predicted Eisenhower's win in 1956. You have discrete transistors, which were all the rage in the 50s and 60s.

And then you have the integrated circuit era that started interestingly with the lunar module guidance computer, which I have around the corner here in the office, and one of the early IBM machines of 360 were some of the earliest to bet on integrated. Yeah, the amount of compute here, and by year, it is every 18 months it doubles. Is technically Moore's Law's definition? Yes, that's a fun.

Good that you mention that. If you ask anybody on the street, what is Moore's Law? You will get different answers, but probably most will say what you just said, doubling every 18 months. That says I learned it in computer science school. Exactly. It turns out Gordon Moore never said that. In 1965, he predicted, actually what he predicted, it was very peculiar to the integrated circuit industry in stab yield optimization, which was what will be the number of transistors on the ideal dive size? Because you could choose. They don't want big chips, whether a certain error rate or small ones that have less errors, but what's that economic tradeoff point of the ideal sweet spot. And he had like five data points, and he just cheekily predicted a line, but never said any text about what it was. And the initial line was doubling every year.

Then in 1975, you modified it to say doubling every two years. And today, people kind of wave with their hand and say every 18 months. But that's largely being dictated by Intel in its personal trajectory. It doesn't actually relate to this curve because on this curve, we're not counting transistors. No one buys transistors. Like, that's a weird count. Like how many transistors on a chip?

Who cares? How about how much memory do I have? And if it takes too many transistors, if it takes more transistors as it does today than ever before to store a bit of memory, why are we counting transistors? with count actual things that matter, memory storage or computation. This is computation. Now, this one has been doubling every year for 130 years. And that adds up, by the way.

So this graph is covering a thousand billion, billion fold improvement in price performance of computing. And it is almost cosmologically bizarre that we're on a curve like this, that most of this time no one knew they were on the curve. They weren't like building to the curve. They didn't know. No one had graphed this until 1999 timeframe, which was Ray Kurzweil. And then I kept it up with the colored dots since then as we've moved from one substrate to another.

And it's been exogenous to the economy. World War I, World War II, the Great Depression have had no impact on this compounding capacity to compute. It's really wild, right? It's very strange. Nature has the universe, dare I say, has some rules to it that emerge. and we become aware of them. As you're pointing out, when Ray sort of made this chart,

it's like, wait a second. All right, at my founder university, my number one rule is to listen to your customers. Why? Well, delighting people who use your product, it's like job number one for founders. But how do you know what your customers really think about you? Well, we all know surveys.

Kind of useless. People just tell you what you want to hear, or they just click the number eight of 10 over and over and over again. What you really need is perspective AI. You just give perspective AI a simple prompt telling it what you want to know. Am I connecting with the right customers? Is this new feature working?

Is my UI clear and easy to navigate? Whatever question you have as the founder of your company and they, with their incredible AI interviewer, will get to work talking to real people about your product. They'll do interviews using AI. We've been using Perspective AI for just a few weeks now at this week and start. and we've learned a ton about you, our loyal listeners. For example, Joe wants to hear more about building AI startups and fine-tuning LLMs.

So we're putting that into our docket, into the show. And Tony is a solo founder working in ed tech, and he thinks the show features too much political news, so we're dialing that back. And we were able to set this whole thing up and start generating these reports in minutes. So here's your call to action. Sign up today at getperspective.aI slash twist

to get two months free. You got to try this product. It's incredible. Get Perspective.aI. slash twist. We're following a pattern that we didn't know we were on, which makes one wonder today, what patterns are we on that we can't see because we're too close to the proverbial elephant.

You know, we see a gray wall, but if we step back in 20 years, you know, with AI, and we're going to get to that, what is actually going on here? And what your chart shows as you continue it, if we fast forward past Intel, and that's really interesting about Intel. In some ways you have Nvidia taking over,

but Intel was a bit of, in their minds, a marketing kind of channel management process to try to double every 18 months, correct? Well, they had a variety of tricks. They tried to build it to the curve. They did definitely with Intel inside, to try to corner the market. But what they missed was a transition

to a fine-grained architecture, that you can just, or another way of phrasing it is, Intel, in their development of the CPU, the Pentium onward, was using human ingenuity to try to build a better and better single processor, and then a few multi-core, but ultimately nothing like InVidio or the Asa or the custom chips to follow, that fundamentally ran out of steam. There wasn't that much advance you can make while being backward compatible. And the later, sort of late cycle Intel chips were mostly memory, by the way. They were like 99.5% memory. So a bunch of cash memory. Basically, local cash memory

that improves performance. And that's how they ate up tons of transistors, but ultimately weren't delivering that much more value compared to a completely different, huge array of computational elements in a GPU or an Nvidia chip that is inherently better suited to AI workloads, which, going back to my old PhD in double-e's, like, that's what you want.

A bunch of local memory, a bunch of local computation that's more akin to how the brain works, frankly, almost like recapitulating our own evolutionary advance with the cortex over simpler. sort of limbic brain region. So there's like an analogy to how our brain evolved to how computation for AI is evolving. But basically, over 10 years, about 15 years ago, Intel was no longer the frontier of Moore's Law. Really, we shouldn't listen to anything they have to say about Moore's Law, even today. Oh, wow. That's cool. That's the reactions on Zoom. Exactly.

In any case, Nvidia's taken that baton and has for the last decade. And lately, there are a lot of custom silicon solutions, right? Google has their TPU. the tens of the process in it. Amazon's developed their own chips, open AI courses working on their own chip. All the major AI, you know, companies that you know of have their own semiconductor efforts

on your way because that is an inherently better way to do AI workloads where you're doing the same matrix multiplying ad over and over and over again for 99% in all companies. It's so interesting to if we go back to business because we're kind of looking at the spiritual here, like how does this thing exist in the universe? Then you have the business of like,

well, how do we capitalize on it? And as you point it out, like no matter what's going on the world the Vietnam War great recession you know whatever it is the depression great financial crisis it just keeps going okay this is huge mystery and super fascinating it makes you think about God and what's powering all this we can get to that too that's why I said it's almost cosmological it's like what is the point of intelligence what is the point of life and it might be an ever expanding understanding of the universe that you can another way of frame it abstract is you can think that every idea is a recombination of prior ideas. They're always building on the shoulders

of giants to be came before you like recombining two things or combining two ideas across academic disciplines that hadn't been combined before. And therefore, the number of possible ideas in the pool of ideas that humanity has is growing combinatorially. The number of subsets that you can draw around two, three, or four ideas that are interesting to recombining a new product is growing as reads a lot goes to the two to the nth power. So, and ideas to the possible pairings or subgroups, that could be the fundamental dynamo of this perpetual sense of accelerating change that were, that like every year feel like, oh my God, we've been so much more than the prior year. Lately, we notice it year to year, but throughout most of human history,

this would have been like, you know, a century passes and a little bit more. Sure, like here's the steam engine. Oh, wow, the Wright brothers were able to get off the planet Earth for a couple of minutes. Like super interesting how our brains can figure that out. And if you look at a company like Intel, and there's been other ones, IBM, Microsoft, that missed paradigm shifts. It's so unbelievable how predictable it is that they can't make the jump. And we were watching the CPUs from in the Pentiums and the little ding ding ding, you know, sound that Intel would make for their commercials, Intel inside. But they couldn't figure out that nobody was buying new laptops because the CPUs did not

change the experience. Now, you said earlier in the conversation with the Apple. too. You took the chips, you bent them, you put them into the motherboard, and you had a totally different experience. But again, that wasn't going to change things. And they eventually, the determining factor when you bought a computer somewhere in the 90s when you were playing quake or, you know, call a duty or anything in between became the GPU and the processing power of that. And that was something that consumers, human beings, organisms would respond to you. to. So there's like another layer here of mystery, which is you're trying to innovate,

but then the consumer, the human being, stops responding to one idea and starts responding to another. Maybe incorporate that chaotic concept. Well, yeah, so I think it's fascinating to think about the rise of GPUs. So first, as you mentioned, it was a way to do polygon rendering, you know, in high speed. So at its core, it's somewhat akin to the sensory cortex in a way, that you have this massive representation of computation in parallel, right, across a visual field. And you're trying to, you're distributing computation across all that.

Now, it was all initially developed for gaming, right? Okay, we're trying to represent the world in a simulation if you really want to get abstract about it. Yeah. But the visual side of that. It is almost beautiful and poetic that that exact substrate is so useful for various forms of scientific computing. And there were early experiments about 20 years ago. There were about five of them that NVIDIA supported.

It was almost like a side project, like a crazy little side thing, where they were like, could these GPUs be used for something else? And a friend of mine, this guy, Paul Rhodes, actually started coming called Evolve Machines, that was doing neuronal modeling. Basically, can we model how a neuron works and then a cluster of neurons, than an entire maybe cortical column using GPUs as a substrate. And I remember this was the first eye-opening moment for me, that he said, I went to Fry's Electronics,

which is the local store that used to be here, sell stuff. And I've bought the equivalent of like one of the most powerful supercomputers on Earth for just a few thousand dollars. Like I literally, in what I'm doing right here at home in his living room, can out compete like the national labs in this molecular modeling. I'm sorry this, yeah, it was iron channels, molecular modeling and cellular modeling.

So this scientific modeling task with this handful of things I just bought it fries like on a weekend. Crazy. And so I was like, I did blog posts about that. I'm like, what is this thing? How can this possibly be? That was before the AI application came, right? So in 2012 with ImageNet competition, there's this thing called AlexNet that was famous, this contest that Fifee Lee at Stanford has been holding. And the neural net approach just dominated.

And this was the guy that used like one or two GPUs. Like that was it. Like, are you guys to have all this computation? I'm using a couple of GPUs and I'm blowing the doors off like finding you know is that a cat or a dog or a tractor you know what time period was this that this 2012 2012 yep I remember vividly because as a you know someone who started his PhD in this exact field on this exact idea which is how can you map neural nets to parallel processing machines I was what and so we made our we started looking at AI investing back then there were no no venture firm had AI on their website as a sector of interest

yeah it's like there was machine learning on the market on the margins, right? Bingo. Yep. And even that vernacular started to come in later when deep learning as a term came up. And around 2012, 2014, there was this renaissance. We invested in our first AI chip company in 2014, this guy, Nguyen Rao, it started a company called Nirvana. And the idea that he and many that followed had was, wow, let's build a custom silicon chip that's even better than Vida.

Because if the idea was, wow, we can shoehorn the Nvidia chip built for graphics and gaming. And at the time, they thought they're going to be focused primarily. primarily on gaming because that's where they're making most of the money and this AI stuff will be an afterthought. That was in 2014. Let's make a dedicated chip that does nothing but AI acceleration, you know, and optimize it even further still. More units, more local memory, switch fabric like you'd have in any networking chip in the 90s and like, boom, you just throw it together and it makes perfect sense. It's like in some ways so obvious that's why you have like 40 companies that tried, you know, about five-year period to do something similar. And that's really carrying Moore's Law forward now, like the majority of compute deep in the

walls of Google and elsewhere on these custom chips. And it's kind of interesting if you think about this migration, single processor, you know, massively parallel or, you know, fairly parallel GPU now massively parallel custom chips. And then potentially we think the next step would be analog chips. So going even closer still to how the brain works, do things in the analog domain instead of digital, just like our brain does. you know this this is like 40 watts and nothing we're building in silicon today competes with it on power for calculation but but it is possible it is interesting that you bring

up the wattage this year actually just in the last 90 days people stopped talking about how many h-100s they were building you know remember when colossus got built in under three months 100,000 h-1s those is crazy and now all the announcements are being done in how many measured in watts not Yeah. And so now you start to get to the meaning of the universe or the drivers of the universe, energy, and the ratio of how much energy it takes to process the world.

And then you start thinking about our own biology, to your point, this giant brain being powered by some number of calories we've consumed, some animal proteins, some oranges, whatever it is. And you mentioned, simulation theory and the great breakthrough comes not from how we think but from how we see and process the world or visual creatures the world is visual you know now we start getting into consciousness you know and the nature of what's going on in our brains and what's going on in

these giant clusters where does that lead you well it's an interesting line of thinking which is what is you know in what ways are there commonalities in the information processing dynamics of the world. So you could, for example, make the argument that what we're doing with this incredible influx of information to our retina into our optic system is information reduction. What are the heuristics, simplifications, ways that I can downgrade, what is a overwhelming amount of data if you just did the brute force? You know, how many, you know, how many pixels times what per second? You know, what is the input of information right now, every day as we're awake with our eyes open into our brain,

it's overwhelming. And the way we do simplification, representation of the world's in 3D, constructs and model building within cortical columns upstream of the visual system, there's this sort of pattern throughout our cortex and in computation in our neural nets. So many areas where they're similar in this regard,

where you're reducing, you're basically finding, you know, the hidden, if you have latent space, that represents what we're seeing and understanding in a more compressed form. And the way that that competition takes place in our brain is similar to the way it does in our neural nets that we train. The way that we, in a sense, grow these things, the processing of information to train these nets is very similar. And so some ways it's beautiful that the world allows itself to be, to have this compressed representation.

That, for example, the laws of physics are all very low order polynomials, that the hidden formula isn't all possible formulas. feel like what is the trajectory of something if you're trying to just deduce it from data points like what what is this trajectory of an object it's probably maybe some of order polynomial and almost all physics that describe things so there's this almost natural way in which the world around us lends itself to neural nets and by analogy that neural nets really came from mimicking the brain but in an abstract way you know neurons weights are like you know like an axon there's synapse the similarities of the weighting between the these notes that our brain had to do this.

Like it was an evolutionary requirement to do data reduction, model building on the fly. And the way the whole brain works is predicting in a sense of the next token. It's predicting the next thing you're going to experience across all your senses, visual, tactile auditory. And only when something differs from what you just predicted, are you even conscious that it just happened? When you look at the landscape, it's kind of like a security camera that's like nothing's changed nothing's changed and then mountain lion okay i'm gonna send an alert and exactly your

security timer sends you alert somebody's hopping the fence at your house or you're looking across the serengeti and oh yeah there's a lion that's not good and things get triggered and we call it intuition we call it you know we have this crazy reaction system dopamine uh cortisol that dump these you know, compounds into our body to generate a reaction. And I'm trying to think here, what is the equivalent in computing when, you know, what chemical do we dump into the computer to have it pay more attention?

I don't know if that's an algorithm or, you know, what the analogy is here, and sometimes these analogies break down, but I guess when we get off this chart and we drop Nvidia off, what do you think's next? Is it something like cortical labs? I don't know if you've seen this company. We had them on the program last year.

Factor growing neurons on silicon, right? Yeah. Well, yeah, it's biological compute platform. So they're literally combining lab-grown neurons with silicon chips and then make it available. That seems like a moonshot. And then, of course, there's quantum. What takes the baton from, you know, these GPUs?

Right. And in the custom asics. Yeah. So, A-6 are the obvious, like, we're in the middle of that transition. in various places. So one of the reasons I think the data centers talk about wattage is they don't necessarily specify, oh, is it H100? It's like it's specifically Nvidia or is it going to be my internal team that's been trying to build a chip that's going to be. So they're trying to do apples to apples. We're not sure what the chips are going to be, but there'll be this wattage level.

I think analog is the next step. And quantum is really a left turn. We can get to that in a moment because there are things in quantum machine learning, but it gets pretty complicated pretty quickly and so let's go back to what I think is in the immediate term. There are a handful of companies that are not using little neurons because the little neurons thing I might put more in the nanotake bucket of it's a difficult interface to manage. But if you go to just analog silicon, you have this capacity to do some pretty crazy things. So a company on Mythic that we invested in recently, for example, can store eight bits of information. So you think eight bits is, you know,

like in the old Apple two, it would have been eight of those chips in a row for for a byte of information. They can store eight bits of memory in a single transistor. Now, that is kind of mind-blowing, because the way that normally happens on a digital chip is you have these S-ram banks, static ram banks, each of which have eight or so transistors per bit. And then there's error correction code

because they're so, you know, as they get small and small, they get more erroneous. You have all these extra ones. You have read-out stuff around them. It's a lot of transistors. But in the analog domain, in a single flash memory transistor, you can actually do the matrix multiply and add where the ad is just a wire of common current

and you have a row of these transistors make long story short they've shown this you can make basically a neural network chip that works in the analog domain that's you know a thousand X better on power for example back to power per calculation so more akin to the brain so you know massively parallel slow and low power is where that vector would be taking us and there are others brand new our most recent investment just last week and conventional labs, they aren't really saying exactly what they're doing, but they're in this domain also with analog and biomimicry. It's the same guy, by the way,

who started Nirvana in 2014 that I mentioned that when I first invested in semiconductors for AI, then he started another company to the Databricks spot. Now this is his third startup. Anyway, so analog has a lot of potential. It in some ways feels like the natural trajectory of this recapitulation of our biology, if you will, in the substrate.

And it has applications not only in traditional AI as you might think of it, but also really small neural networks that you might stick in everything. So Jensen, CEO of NVIDIA a couple years back, said they were about to enter an area of explosive growth in AI like nothing we've ever seen before. And he wasn't talking about any product in video is about the launch. It was edge AI, basically putting little neural networks, trillions of little neural networks, little brains into everything. Every security camera should have one. Obviously, every car is going to have them. Every moving object, every autonomous vehicle. Wearable.

Every sensor could, like, wouldn't you want a little more intelligence in any consumer product you can think of? Yeah. And by the way, this, so imagine like a voice interface, for example, that's speaker independent, large vocabulary. So it actually works. Could be in anything for less than the cost of the plastic buttons they would replace.

So if you have a Roomba scooting around on your floor, instead of bending over to push a button for any particular use case, you just call to it and say, hey, like don't go in the corner, hey, can you get the bedroom? Or, you know, don't stop bugging the cat, whatever it might be. It could be that intelligent,

and it would be local intelligence that doesn't require the latency or any of the overhead. No cloud, no security issues, privacy issues. Yeah, local data retention. Such an interesting concept. I want to take a pause for the cause here and show a clip of you 10 years ago or so talking about robotics.

You and I haven't seen the clip. My crack research. team are going to play for us so we get our reaction to it in real time real time so uh producer oliver or as i call him master oliver the young master oliver here we go let me just get my screen ready to go here he is baby steve go okay try one more time with sound let's reshare and do sound then i'm going to do a pickup for you i found the uh i think i found clinton talking about nano which will splice into the other one Yeah, right there. Here we go.

Something near term that is kind of interesting, led by Rodney Brooks, and there's more than one company doing this now, which is humanoid robots for the workplace. So the reason I hesitate about San Francisco would be, I'm not sure if it'll percolate into, I mean, even though I bought one of these just large. The average person doesn't really, nor do I, I haven't really used for it yet. But in a work context, these are two-handed robots right now on a pedestal so they don't walk around, but they, you program just by moving the arms, so anybody can So yeah, it was hard for me to even hear it.

Do you have a response to it, Steve? Sure. So Rethink Robotics was a company Rodney Brooks started, a famous MIT Roboticist who had a documentary once about him called Fast, Cheap, and Out of Control. And he had used biological metaphors in a way to think about how we're going to build robotics and control systems. And the insight that Rethink had was, wow, if we could use the really cheap,

now available motion sensors that we all have in our phone, that allow us to know exactly how we're tilting, right? this multi-axis accelerometer, put several of them over an arm. We use the feedback loop and control layers to use very cheap springs and actuators and create fluid motion in a humanoid robot form. The second thought was, what would these robots be good for? Well, as long as they had the same lifting capacity, accuracy, precision, whatever, as a human, then you don't have to ask the question.

You say, wherever you have in their particular case, a sedentary human doing some repetitive task, or even, you know, a less repetitive task, but basically sitting at a desk, you could just swap out the robot, right? And a much lower cost. Challenge for that company ultimately failed. It came really close to having a great exit

in an acquisition, strangely, by a Chinese company that very much wanted this technology. But the U.S. Sisyphist laws that basically prevented technology transfer, shot that down, and instead the company went out of the business. Brutal. It's so hard when you have the right idea and the timing's a bit off or the go-to market.

Give me the postmortem here. What didn't go right? So what didn't go right was at the end of days, they called it sort of like the shaky bot. There was this idea of, am I drifting for what I'm doing? Let me quickly correct it. There's some hysteresis in that. So they realized towards the end, as they were running out of capital, that they needed to design their own motors. And fast forward to today, that's the same conclusion that XAI and Optimus and all the, not XA, excuse me, Optimus and, let me just say, other AI companies developing humanoid robots have all realized, oh, my dad, we got to build the entire stack, the supply chain.

of available moments. Elon's speciality, right? Is building the entire stack. That's his power alley. Yep. Yeah. But back then it was today. We didn't know it.

Right. I mean, if we look back on the history of it, one of the seminal moments of Tesla, I remember sitting with Elon when these, yeah, dipshits who were making the first roadster came to him and were like, the parts combined equal more than the cost of the car. And you and I paid $150K for that roadster. It was like, wait a second. You're spending more of the parts, and he had to deal with parts suppliers, and you learn this brutally hard lesson,

which is your production is as fast as your weakest supplier. And your products as good as the worst component in some ways, you know, or it could be. And man, it's just amazing how over, you have close to 20 years now, he's just decided, fuck it, I'm making an HVAC. You know, and I don't need the steering column. I guess the model S, the original steering column, Yeah, they got from Tyler. Yeah, they got from Mercedes, yeah.

Yep. So, by the way, you know, when you're starting and you don't have much capital, so the Roadster era, they had to use as best they could off the shelf parts. So famously, the battery cell was the big leap forward. Every other electric car company that followed went with these prismatic pouch cells that are custom-made for automotive. And they're like, let's use the same laptop cells Dell's using and shipping and volume, right? And let's, you know, use Lotus frame, which had its own trade-offs.

But basically, wherever possible, use off-the-shelf, but you're exactly. right, they hit all these enormous headaches like the transmission. So the roaster almost didn't ship because they needed a two-speed transmission. They tried three different vendors like Borgner and all these different ones. And no one could make a transmission that could take the torque. I mean, it was so much torque. Just a two-speed transmission. We just rip the transmission in half. You just destroy it. And it wasn't until the special bipolar transistor came out where J.B. Stable was like, wow, if we switch to this transistor, the latest and greatest transistor, we don't need a transmission. We just have a single fixed gear ratio

and you just basically have to go up to really high RPM's, they're really low, and everything that you've experienced in every Tesla from that point onward doesn't have a gear shifter, right? When you want to go backwards, the engine just run the motor, it just runs backwards. So there's no shifting. There's nothing like you have in every gas car that exists on the earth

today. So that was just the beginning of a whole string of like everything you could imagine. There was one time when Elon personally went to fries back to fries to fries to get internet cable that they needed to be able to keep shipping vehicles because the Ethernet cable didn't exist from some supplier. Same thing under the covers was happening at SpaceX, basically vertically integrating.

If you think forward to like why the Model S was such a breakthrough vehicle, right? The Roadster had its challenges. It wasn't for everybody in terms of comfort handling. I mean, it was good. You still have yours? You gave it to a different museum. Oh, Peterson. Yes, Peterson Automotive Museum.

I think it's the largest in the U.S. Yeah. Signap. I still have mine. It's right over here, number 16, in the garage. I read it the battery pack. I have 17 of the roaster. You know how I got 16?

There was a venture cabas. I'm not going to say their name. Who ordered probably right before you. And they, I subsequently ordered like, you know, after it. I might have been like 100 or something. I was in the signature 100, but it was way up the list. And then he invested in Fisker. Oh.

And he stabbed Elon in the back. Yep. So in retrospect, by the way, and this is even memorializing some of the books, some of these people have written, some great venture firms, the best you've ever heard of, were really confused about the difference between an electric car and a hybrid car. They would call Fisker an electric car company back then it was hybrid. And it's a completely different design space. It is such an albatross of a product and not good at anything because it was a hybrid car. Yeah, it was garbage. They didn't get that the electric vehicle transition is very different and it doesn't leverage any.

anything that the internal electricians had not to go to the gas station and not have those parts and not have all the overhead of a gas tank you're like you basically double the complexity with a hybrid versus a simple electric car so it was bizarre and so um the person i think if if i have my story correct uh proactively canceled their deposit happened to be sitting next to Elon having you know a meal and he's like oh this person canceled the car and i said oh where are they in the the order is 16. I said, oh, I'll take it. Can you move me up?

Yeah, I just took it to us blackberry and move me up the last. And I got the first orange one. Oh, I still think it's the best color. What was your color? You went red? I went, no,

I went for racing green, which was their signature color. I think I even have the, I think the racing green was tight. I think you might have been the first racing green for sure. I'm not sure. I'm not sure. I don't know.

It's really beautiful one. Oh, I got a custom roofs. You know the roof option. I got it in clear unpainted carbon fiber because my bike, my mountain bike at the time was clear unpainted carbon fiber. My favorite rocket that I built was clear unpainted carbon fiber. So I'd like there's something about that aesthetic that people really like. I've been watching Corvette is having a Renaissance. I used to own a Corvette before I got my Roadser and I traded my

Corvette in for a Roetzer. I have this new one, the ZRX one, which is the greatest hypercar ever built at this point, 1,250 horsepower. But anyway, what Corvette drivers like is just raw, carbon fiber, that aesthetic looks so beautiful. It just, yeah, and they're, and it's like, I guess it's still super expensive. So when you put like, the accents in the car, it's like, they're like, oh, $4,000 to do these three parts on the cockpit. Like, really? Why is this so expensive carbon fiber?

It's hand done. Hand done. I believe in the case of the register was in Italy, if I recall. And they wouldn't let me get the body in clear because there were areas where they knew it would look good, that it was physically smooth, but underneath, you'd see that the cloth was overlapping in certain ways. And so they didn't want to reveal that. Yeah, I didn't want to reveal those secrets. Here's a clip of Elon talking about 2025 and 2015. Oh, wow. Well, that clip. Ten years ago. Oh, I was entertaining him.

Your question. So in terms of what I think, 2035. Oh, yeah, please. So for sure, ubiquitous computing, AI that's beyond anything like the public appreciates today. I think we'll have most of the new vehicles being produced, being electric, and we'll probably have the super majority of energy being produced being sustainable. So I think we're on headed solar primarily in your mind. Primarily, yeah. Wow. Interesting. You're and that was, by the way, that was me interviewing him. Yes, yes, that's where I put it in. Sorry about that. We have included a video. No, no, no. It was just like, whoa. So I haven't revisited that clip. So the first one nailed it like like your AI boom, right? You're soaking in it. It's just dripping out of our ears.

Literally, we're like literally in the matrix right now. In a bath of AI. Okay, let's go to energy and solar and EVVs. So the part where, so making a forecast of five to 10 years is really difficult because of the inertia of where we are. If you'd instead ask them, what are the three most important trends that will be, let's say in 10 years, everyone will agree are inevitable. But we may not have made the transition.

We may not have a majority solar on the planet. We may not have the majority of vehicles, electric vehicles, simply because people hold on to cars for 12 years on average, some parts of the world, even longer, no matter how good the new product is, although he said new product sales. So that was at least starting to hedge the,

you know, it's not the swap out, but the news sale. So there's inertia. There's political shenanigans. There's weird ways that people you know, forestall the future and prevent the inevitable future from manifesting. So you look where we incumbents. Right. So I think one thing you can say is today in 2025, Elon believes in all three of those very strongly, right, even though we haven't fully realized solar's potential or the ED potential. So I would go further and say in 2015, it was obvious to me, and it's even more obvious today, that it is inevitable that all vehicles will be electric. It'd be train, plane, tank, heavy machinery, you name it, and they'll all be autonomous.

So electric, I would add autonomous. That's sort of the AI plus electric. Solar, he said sustainable, and then I had him double-click mostly solar. That's his point of view. And solar and storage solves the problem. And as a company that makes a lot of solar and storage, not specifically storage, you can understand why that's important. I would just stick with this first answer, which is clean energy will be the novel future, and that includes nuclear, right? So it's obvious that fusion, fission, solar,

are energy sources of primary resort. Geothermal may make a major comeback if we go really deep and hot rock. That's a sleeper potential. But that category of products really dominates solar. We shouldn't be burning coal, we shouldn't be burning gas. But it will take time.

right because it's it's the drug much of the economy is addicted to right currently yeah and if you look at that time uh solar was like one percent of electoral uh in the u.s 2015 2015 20 25 solar's 10 renewables you put wind in there you get to 17 percent according to claude a i and then i would put nuclear in there so there was a weird by the way marketing thing where at some point I think this was Emery Lovens and some others put in this idea of, what was it, renewable energy? There was some term for it, as opposed to just clean energy. They were trying to distinguish nuclear from all the others. And really, if you think about it, how about zero carbon energy or zero pollutant energy?

That would be a better term for what we want. And that would be solar, wind, geothermal, and nuclear, all types of nuclear. right it's it's so that's the category that's inherently distinct from fossil fields yeah and if you want to know why uh all of this happened this hatred of nuclear nuclear uh 1979 Jackson brown crossby stills Nash and Youngs right John Hall duby brothers yep so it's interesting notice the Bruce And this is no nukes concert. This is the exact confusion, understandable perhaps, between nuclear weapons and nuclear electricity.

Notice the image in the background. That's not a nuclear power plant in the background, right? That's not three mile. It's a mushroom cloud. That's not Chernobyl. That's nothing. That doesn't happen in those plants, right? And Greenpeace was actually founded, specifically I focused on anti-nuclear weapons, and

then it bled into nuclear energy, as if they were the same. And I gotta say the government did a bad job. It was a militarized technology. It, of course, had uranium going around, and fears were easily stoked. So it was easy for the public to be confused and to be distrustful. And we've been living with that ever since, which they are completely different applications, of course. It's just infuriating when you think about it that, like, the Germans have shut down, I think, all six of their nuclear power plants at this point and decided buying oil from Putin. I know.

It was a better idea because of Fukushima. So get a little of this. They shut it down. Germany has this perpetual propensity to be on the wrong side of history, which is actually almost a direct quote from this book, Rad Future. Where did I put it? Oh, it's underneath my gift. Oh, yeah. By Isabel. Yeah, it's great. I just finished it a couple days ago. So when Putin invaded Ukraine, at that moment, Germany was sending $220 million per day to Russia because of the oil, the gas dependency. Then they shut down the nuclear power plants as well around Fukushima time. And the estimates are that currently they're dealing with a thousand,

100 excess deaths per year already because of the extra pollutants from burning fossil fuels instead of nuclear It's mind-blowing like at Fukushima no one died from radiation About 2,000 people died because they did a botched evacuation of the region and the way in which they relocated people they died Hundreds are dying every winter not because of higher electricity costs it's just mind-blowing about 28 people died in Chernobyl in total more have died from fear of nuclear than in the discussion about nuclear than from nuclear itself The panic attacks have done more. And if you look at coal, taking... Four million lives a year.

Beautiful code. Four million lives a year from coal particulates in the air. It's unbelievable. And we got the president out here saying, it's clean, beautiful coal. And I interviewed Chris Wright twice in the past year on this issue. And I'm just trying to get him to say that solar's great. And he's like, there's a place for solar,

but he keeps calling it unreliable. And I'm like, put batteries with it. He's like, oh, there's no batteries. I'm like, what? Ask Australia. Yeah, like think it through, uh, think it through my guy. You can, you know, here in the great state of Texas, uh, there, we have more solar here than any other state. I can tell you it's not because they love solar. It's not because they hate fossil fuels.

This place loves a good oil. It's the oil patch. Yeah. Yeah. They do it because it's the most cost efficient. It is the best bang for your buck long term. So, right. Here we are. And the other thing about Fukushima, which is so crazy, is when you look into the design and placement of the reactor, they told the province, don't put it there. And they're like, well, build a seawall. And they're like, yeah, but every 100 years

there's gonna be a storm. And literally 50, whatever number of years later, the storm arrives. Like, literally on time. Below sea level so that if you had a wave, it's gonna be able, like, there, so the same was with Chernobyl, by the way. Chernobyl was the worst nuclear accident we've ever had.

It's the only one that actually had some deaths associated with it. And small, small number, but much smaller than people think. It's not a reactor design anyone builds today. It's like you do learn for mistakes. So to look and reference to that to say we should do nuclear is as illogical as saying we shouldn't have cruise ships because Titanic. There was this one Titanic and it sank. And we learned how better to navigate and avoid icebergs and et cetera, et cetera, et cetera, et cetera. Like we don't even think about that today because we don't build another ship.

Don't build another ship ever again. Build a better ship, people. Exactly. And here we go. All right, let's go. I don't want to run out of time with you. This has been incredible. But I want to get into AI a bit.

In your mind, the pace at which we're moving and getting to, I'll just use two terms, we'll define them here for ourselves, people will debate them, artificial general intelligence, being the smartest human on the planet in any discipline. lawyer, accountant, chess player, whatever it is. And then we have super intelligence, being an intelligence that we can't comprehend, a level of intelligence that you take all the humans together, and then times it by 100, and it's something else. You can find questions to answer we don't even know to ask.

Where are we in this timeline? Well, there's a big architectural gap still to fill on what some people might hear the question or hear the term and think, oh, it's like a human, meaning it's going through the world with agency and purpose. It is making decisions and doing its own thing, if you will. That is, and I don't know if you have a good term for that, like sentience or something, like the art of it. So if you just say, will an AI system, you know,

like the GPs and the XAIs of the world of today as a framework, you know, outperform a human on any question or task that you present it, absolutely. I mean, that's, that's, that happens quickly, right? Like, is it a year or two? And oh, by the way, when that happens, it's just give it another year and it'll be much greater than humans. So, like, the difference between Einstein and, you know, one of the less capable humans on planet Earth is not that big a gap versus humans and, you know, pigs and, you know, lesser animals. It's like, on the scheme of things, like those two humans on an intelligence spectrum of AI, let's say just AI's trajectory is like, you know, going from one human to a collection of humans applying

their intelligence collectively in a group kind of setting. Because otherwise, it doesn't really matter how many humans we have on Earth. Like, you know, if they're not acting in coherence as a team, if it was twice as many or half as many, it doesn't matter, right? What matters is, are you a thousand times as smart as any human has been? That comes quickly after, even simply Moore's law, right? And in AI, actually, we've had Moore's Law doubling every year and algorithm improvements of a doubling every year for like 15 years now, which is kind of astounding. That makes a big difference. So I think those come quickly.

And so some of the evidence of that, you know, these models today already outperform humans at almost any task you apply them towards with a little bit of specialized training. You know, there hasn't been a domain where you're like, oh, we can't do that. I'll give an example, medicine. I was just in Stanford last week getting the update on, you know, state of the art of large language models for healthcare. And so the sort of humiliating and humbling takeaway, in sure. short is that the AI alone is much better than a human, of course, and it is much better than a human

using the AI. In other words, take your best doctor in a field of medicine, reading an x-ray, doing whatever. They're a certain talent level. If they start using AI, they get a little bit better. But if they just let the AI run without the human in the loop, it does better still by far, like off the charts. And so the point is the humans are just holding it back. Right. Then the next one is not just diagnosis, but the course of therapy. What? should we do given what we just learned. They also outperform there. And then best of all, blow the doors off the human on empathy as reported by the patient. So if you have a chat interface where the doctor is talking to the patient through that same interface or an AI,

the AI blows the door off on truly understanding me, conveying the situation and understanding on some really tough issues, like, let's say, end of life care for a parent who, you know, do you pull the plug or do you, you know, like, tough schedule. Really tough conversations. They blow the doors off humans. So we're ready there. And Elon would talk about a number of, you know, you know, PhD level equivalents for all the PhDs. The challenge then is going to be, you alluded to this earlier on the emotional response, you know, when you're talking about Olympic systems and what have you earlier in on conversation, there's still something missing about, like obviously these systems aren't just going off and doing interesting work. Now the agentic chain of reasoning is

starting down this path to say, I have a task I want you to do. Can you find me the best they plan and, you know, reaching out to all these different websites and, like, figure out where are the flights and the hotel and the things I might do for kids of this age and, you know, pull it all together with a series of steps. Same thing that gentic flows in programming as well, right? And you can do encoding. But there's something different still from that sort of spark of, you know, consciousness or sentience, which is perhaps going to require some other, it could be an emergent property, by the way, a forecast in the future. So let me share a bizarre thought. I alluded to this earlier. What our brain naturally is doing is predicting the future and only

when what we sense is different, do we perceive it in any sense? Like if I grab this, this thing and it's much hotter or colder than I could possibly imagine, I'll notice that. Otherwise, I won't even notice temperature. It won't register, right? Because I'm working off predictions. In fact, they've done free will studies, if you will, that we retrospectively rationalize what we just did. Like, yes, right? Confirmation bias, all kinds of biases. Like in a microsecond level that like I just did move this finger and then I was like, oh, I intended to move this finger. Okay.

Yeah. Perhaps that's what's going to happen with our artificial systems as well with next token prediction. They'll be like, how am I, you know, retrospectively making sense making of what I'd done? And there could be some vote-taking circuits that we'd, we might have to build some circuitry for this, I guess. So vote-taking circuitry that's in the feedback clip of what we retain as novel that would then sort of bootstrap this sort of consciousness or intelligence, the perception of free will.

the perception of consciousness might be a phenomenon of that. There are others who think we need to take a neurosymbolic approach that we need to literally recapitulate, if you will, these lower-level primitive systems that are emotive and what have you to actually have an emotion as opposed to faking it well. I don't know the answer, but I do think we can do a lot more experiments now than ever before.

We can run these evolutionary sort of feedback loops in ways that will potentially bootstrap, intelligence and it might come from some heterogeneity. It might come from just the sheer approach that we're taking, but it's not obvious that we're there now, that what we've built isn't like a baby version of, oh, it'll just naturally be on, you know, self-directed. That takes a different bootstrap from the current vectors that are there. So I think what you have is a hyper-intelligent or colleague, kind of like C3PO, if you

well, you know, way too smart for its own good, chattering on when you don't want it to chatter on, but it can do a lot of things for you. Can do a lot of things, but it can't beat Darth Vader. Exactly. You wouldn't put C3BL. It's not going to have smart hands solo. Not happening. There we go, what?

It's taking the analogy. Never tell me the odds. Yeah, it's not getting through an asteroid field. That's right. Not a chance. I remember that. It's an interesting thing you bring up, which is it's already beating the doctors on empathy. And so then you have to think about.

this next generation, this concept of being one-shoted, if you've heard it, where your relationship with AI becomes the dominant one in your relationship. Sam Waltman just announced he's going to make it spicy. You're going to make a little spicy in chat GPT for adults. There are other companies that are,

I get pitched all the time for the last couple years on like an AI therapist. And I'm like, you know, obviously that's inevitable, but I'm not investing in that right now because what if it goes wrong? You know, this seems like it could, you know, have some pretty bad outcomes on the margin. So what are your thoughts on? I don't want to get into AI regulation because it's kind of dumb, but it's going to happen in different countries.

But young people, even older people, using this as a proxy for a better companion and what that will do to humanity because, yeah, hey, the doctor, this is a better doctor. I can ask it about private questions. Okay, yeah, fair enough. doctor loses their job or doctor has a different job does something different you know but then your companion is always empathetic never angry at you and they never have a bad day everything is sycophantic this seems like a road to purgatory to me what do you think no it's it's a really interesting question and there there are small experiments that have gone on in a number of places even a few years back in very rudimentary forms

when some financial services firms put AI chatbots in place to try to do customer service and try to lower the customer service workload. They found certain subset of people became romantically attached to these people and these entities and just couldn't accept the fact

that they weren't real. Like even when told so, they had this. So there's this natural human tendency, I think, to project agency and intelligence onto things that aren't. We do that with our pets. We do that with a lot of things.

And it's risky. So I think when you mentioned Sam Altman, he also said, we were cautious at first because there's some mentally ill people out there. And we don't want the AI to, you know, if it's sycophantic or just repeating back to you what you want to hear, amplifying some of our baser natures.

Like, oh, yes, you should do that terrible thing. Or yes, you should be, you know, bad, violent, what have you, whatever it might be. And so here's the challenge. And you alluded to a regulation. you alluded to, you know, how do you direct these things, is this whole mode of AI development is not like traditional engineering. And this is something that I have yet to meet a regulator or politician who understands this at all. You can't say take that, let's say frontier intelligence

of any kind. So something that's like state of the art, something interesting, right, anything that, anything you've heard of. You can't say make it safe. You can't say prove to me it's safe. You can't say control it. You can't say align it. You can't say. Align it. You can't. can't say explain it it's not interpretable and there are definitely groups anthropic being one of them who think oh yeah we're going to work on that we're going to try to make that happen they have yet to make meaningful progress in this meaningful meaning a result that we give people comfort that this is doable and i would assert it may never work that we may never reverse engineer an intelligence in the time frame of relevance meaning we'll just have built a better one before we reverse

engineered the prior one just like we haven't reversed engineered our brain these complex information both brain and neural nets are inherently inscrutable. We understand the interfaces, the ins and outs, but not the inner workings. You can't cut and paste functionality. You can't figure out the subsystems. You can't draw a box around where there's the English-speaking part, and it's where it's doing math. None of that really lends itself to reverse engineering. It's time for evolved artifacts like our brain, by the way.

And the implications are that you can't go in there and manage it like you would an engineered product. So if I was to wrap all that up into a simple sort of euphemism, it would be it's more like parenting than programming. So we were talking about programming, you and me early on and the little hacks that we were doing. We knew what we were doing. And if something went crazy is like your paperclip of printing. Yes. It's going to run out of paper. You can quickly understand what you just did and fix it, right?

That does not apply to the complex interactions of AI's internally, nor they're back and forth with the human, which is like two complex systems working with each other. So whenever you hear someone say, let's regulate AI, substitute teenager for the word AI and then ask yourself, what would that regulation look like? So if you say, let's make sure the teenager is aligned with us, how, right? Let's make sure the teenager does no crime. How? The best you could do is be a good parent and have police to say, I could have an after the fact that's either looking at what inputs this AI is getting. Let's like not let certain questions be asked or look at the outputs to say, oh, wait, that just went down dangerous territory. let's not share that result right and have a police police later that you could do what you can't do

is have any of these companies do what california bill initially proposed is like prove uh you know safety before you even start training i mean that shows you how ridiculous they work right yeah to your point you can have a speed trap or a d ui you know uh trap where you you check people aren't drinking but you know people have a car that's right and has wheels and as an engine like they're gonna drive it and some are gonna go fast some are gonna slow some might drink and drive and so there's regulations there but yeah by the way let me let me throw one premature absolutely well not only premature but they never work so it's i think it's a fool's errand to think about safety and alignment as if they were achievable in the sense that they're being described today so let me take alignment imagine you

and i or whoever someone at x-a-i or someone at one of the other like closed day i says i want to align with my interest my woke whatever or my you know Western liberal ideals or whatever I think. Conservative ideas. Community. Imagine you could do that, which I'm saying you can't. Imagine you could. The authoritarian regimes will be able to do this too.

And you're going to have a really dystopian world where some really bad AIs will be all over the place. Most people live in authoritarian regimes today and unfortunately authoritarian rule. You do not want them having aligned to their cultures and norms, AIs. What you want. You're going to have a lot more weak. in concentration camps being tortured.

You know no idea. And so I really believe in Elon, which is, well, in general, but in the case of the way to get through this, Cragmire, is to have it be a truth seeking out of it, not say I know what you need to do. So in other words, instead of mind control, right, to say I'm going to make you think a certain way, which doesn't work with teenagers, right?

It cripples their ability to reason. And the same is true with neural nets. The more RLHF or the more, in the sense, mind control you try to layer on late in the cycle, the more you compromise the road reasoning capabilities of the AI itself. It's profound, both humans and AI. So many different ways. So do not think mind controls the answer. Do not think containment's the answer. It's more like parenting and policing. And then I think you might finally get a regulatory regime that makes some sense.

So interesting. You bring up the number of people living under authoritarian regimes. You know, we have a Stephen Pinker's book, which is probably where we both got attuned to this. Everything's going great, except for the spread of democracy and authoritarianism. democracy going down, authoritarianism going up, 54% of people live in authoritarian country is so why democracy is worth fighting for. And it's an interesting experiment, but it might be the unnatural condition of human existence

might be to have a democracy. And the natural one might be to be dominated by other people. That's what we've seen for the bulk of humanity, which, you don't have to make any of this political, but if you see things being less democratic, be concerned. Absolutely. If you see things being less fair or you see things being more cruel to certain groups of people, get curious as to what's happening here.

It is the path of doom, and it's frightening. The way in which authoritarianism and theocracies, both, different variants, can take over and are so difficult to uproot. It is a precious and delicate thing, and a group of people in their founding of the United States or the long arc of Western civilization, take a somewhat selfless act in the leadership ranks to say we're not going to try to just covet power.

You think about like kleptocracy like Russia is the exact opposite. Like, you know, this cult of an individual and this madman who can launch wars around the country is a symptom. Oh, wow, that's over time. It's just... Look at that democracy, just boop, boom, boom. Yeah.

It is a precious. It is the thing that allows progress. By the way, if you think about why I think authoritarian regimes ultimately will fail It's because of the technology they don't embrace change either politically At company levels they they coddle the you know the power breakers that are and that in a sense is a architectural resistance to change in innovation and new entrants in in America at least in the west we have You know a system that allows and encourages entrepreneurship it allows disruption that allows overthrowing of the past and that that That is so precious.

That is really the vector of change because all meaningful change comes from new entrants. It comes from entrepreneurs in the broadest sense of the term doing something new. It doesn't come from big companies incrementally improving their core business. Or dictators. Or dictators saying, you know, planned economy, let's do X, let's do Y, that it's never worked. The top down works incredibly efficiently in very narrow fields in very short arcs of history. You want to build a bridge fast, you want to build a high speed train, and you want to run it through five neighborhoods and take away their rights and yeah sure great you could even run the people over

and make them dig the ditches and then kill them and bury them in the ditches we saw this in china they basically said you know what entrepreneurship not for us we took it as far as we could jack ma disappears a bunch of companies disappear and uh now they regret it and now they've got you know population decline they got to worry about where everybody's going to get their jobs they got 20 25 percent unemployment with young men you know and democracy finds a way And all these companies, this is a really beautiful, you know, sort of interesting wrap-up point that we just kind of stumbled into, which is what we're talking about,

what you and I do for a living, what Elon does, what entrepreneurs do, try to create something new that makes individuals a bit more free, a bit more happy, a bit more productive, is the only operating system that seems to be pro-humanity. And it really does start with entrepreneurship, with people who are change agents, and not the people who are seeking,

power, not the people who are seeking control. Talked about Star Wars. Like, the empire tries to control things. The more they try to control it, the more brittle it becomes. It's hard. It's hard to be an authoritarian because all the change is happening everywhere around you. And you have to try to squash it and quell it and extinguish it. It's exhausting to be an authoritarian.

Putin lives at the end of a two-mile-long tunnel, an intense paranoia. It's kind of like Hitler in his bunker. Like, yeah, but he's stuck. And his whole country is stuck under him. Can't come, you know, this is, you know, when people talk about like trying to, the peace dividend trying to work with these people. I said to somebody at some point, like, you know what Kim Jong-un would really like? I'd like to come to the NBA finals.

Say what you will about Trump. When he went there and he crossed in the DMZ and he was like, you want me to come over? Do you want me to step over into North Korea? Okay, I'm gonna do it. I'm gonna step over. Here I got. Kim Jong-un's face lit up. Like, oh my God. Sorry, that was good. He's like, I'm coming in. Somebody loves me. I got to tell you, you get those guys to come to Vegas for a weekend, get them to go to Carbone and get that. Tony. You get him to come to F1 and hang out. There's so much great stuff in freedom and democracy. We just have to allow those people to experience it in their humor. Here it is. This is one of the great moments in history. Look, here it is. You just look at Kim Jong-un's face.

I mean, if they see his smile, he hasn't smiled like this in his life. Look at him. He is so happy. It's like, it's a peak experience for him. I think this is the way to get these dictators to flip. Just butter him up. Let him come to a music goes.

Tell us to Burning, man. Yeah, go to. All of it, all of it would be amazing. Listen, Steve, you gave me more than an hour and a half of your time. You're one of the great humans on the planet, great thinkers. I love talking to you. And thanks for sharing so openly with the group here. I'm going to have you come back in six months.

Please. And we're going to do this every six months. It can be a little check-in. And next time, I just want to go through all of your portfolio, all this amazing stuff. If people want you as an investor and they're doing something absolutely crazy and it's a hell, Mary, and it's going to take 20 years, but we'll change the world. and shadow the last paradigm, how do they reach you? Sure.

How do they reach out with their business plan or their ideas? And I don't mean to flood your inbox. No, yeah, no. We do this for a living. Absolutely. Our website is future. Dot Ventures. I'm also most active on X socially, but my email,

and everyone here at our firm is just our first name at future. Dot Ventures. There you go. All right, everybody. There's your 90 minutes with Steve Jurvison. We'll see you next time. Thank you. Thank you, Jason.