Legendary VC Steve Jurvetson looks ahead at neutral networks, Tesla, nuclear power, and more | E2193

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.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Follow Lon: X: https://x.com/lons Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis Thank 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 McFarland Check out Jason's suite of newsletters: https://substack.com/@calacanis Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com Subscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

  When you sent to Hotmail, it said, "Sent by Hotmail or powered by Hotmail?"

• 0:04Speaker 01

  Yeah, it's actually funny you should say this. It was the idea to do that was Tim Draper's idea, 100%. And I, 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 send has this "Get your free email at hotmail.com", right, like a call to action.

• 0:30Jason Calacanis?

  Controversial.

• 0:31Speaker 02

  This Week in 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/twist.

• 0:52Speaker 02

  .tech, say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .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 in 10 minutes. From LLCs to trademarks, domains to custom websites, they've got you covered. Get more privacy, more options, and more done. Visit northwestregisteredagent.com/twist today.

• 1:32Jason Calacanis?

  All right, everybody, welcome back to This Week in Startups. You 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 Jervison is back on the program. He is the co-founder of Future Ventures. He's also the J in 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.

• 2:08Jason Calacanis?

  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 Gerbison. How you doing, brother?

• 2:27Speaker 01

  Oh, my gosh. Thank you. That was the kindest intro I think I've ever received and very unbecoming of you to actually say nice things.

• 2:35Jason Calacanis?

  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 in the best.

• 2:43Speaker 01

  Oh, here we go.

• 2:44Jason Calacanis?

  Here we go. No, but you're a huge nerd nerd in the best possible sense of the word. You 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 gotta step outside for a second and launch this rocket.

• 2:59Jason Calacanis?

  You just love deep tech. Yeah. How'd you get that love of deep tech?

• 3:03Speaker 01

  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 goes to show how hard it runs. And then the Apple II was like this incredible wake up of like, wow, you can program in computers. And I think some people who've had a taste of computer science just fall in love with it. And so I did. There were 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 had taken 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.

• 3:46Jason Calacanis?

  How did you get that Apple II? 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.

• 3:56Speaker 01

  Oh, it was amazing. I remember exactly. It was in seventh grade, so I was about 13 years old. I was, you know, socially kind of maladapted. I didn't really, I mean, I was only child.

• 4:05Jason Calacanis?

  This is 1980?

• 4:06Speaker 01

  No, this would have been even before that. This would have been around, no, you're right, around 19, I think it was around 79, though, that I got it for some reason.

• 4:13Jason Calacanis?

  Yeah, because the Apple II was either 79 or, yeah.

• 4:17Speaker 01

  Yeah, yeah, I think it 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 give you a more precise answer. No, it would have been, yeah, around 78, 79. And 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 kilo 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...

• 4:51Jason Calacanis?

  And your dad bought this for you?

• 4:53Speaker 01

  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 now with Legos and Fisher Technique, this other kind of building block set. But this, when Apple two 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 mine were graphics. So a graphic sort of shooting them up games. A text-based adventure game where you, you know, enter words like go north, go south, and you know.

• 5:26Jason Calacanis?

  Like Zork.

• 5:27Speaker 01

  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 Gary a lot lately and 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, oh shoot, I forget the name. That was a pinball construction kit.

• 5:51Jason Calacanis?

  Oh, I remember Pinball Construction Kit. Yeah, you got to make your own pinball game. Yeah, that was so fun.

• 5:55Speaker 01

  Exactly. It was incredible. And as a programmer, I could tell those people know how to do things I had not figured out how to do yet, with shape tables and flipping two memory registers for 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.

• 6:12Jason Calacanis?

  And that's a big ups to your dad because it was a $1,300 computer at launch. But that's in 1977 to $82 was like, I think, 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 Junior. My dad bought me the PC Junior, which was...

• 6:39Speaker 01

  Bad keyboard.

• 6:40Jason Calacanis?

  Yeah, it was a, 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?

• 7:05Speaker 01

  Not initially. So when I was in third grade, I remember we had a teletype machine like you're talking about and like none of the kids use it. It was often like the principal's office area where the staff and admins were, but you could type things in. It was like, it was like a, like a typewriter, right? And I think that's where Paul Allen and Gates initially did their 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. Like the delay, the latency, the lack of sort of a personal experience and in school and it was loud. It just, 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 seventh grade did. There was a really great guy. I remember his name was Stutzman, the, the teacher that supported this.

• 7:46Speaker 01

  Had both Z80s, which were the, you know, the trash 80s, we called them, which was the TRS80 from Radio Shack, based on the Z80 processor and the Apple II based on the 6502.

• 7:55Jason Calacanis?

  There it is. This is the TRS80. My school had this as well. This was my first introduction to computing at Severin High School in Brooklyn, which is 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 made your story.

• 8:16Speaker 01

  Well, funny, I've never shared this, nor have I even thought of it, but the closest I got to, because I, 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 won'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, the kids loved it. It's like, well, 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 and the crude 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

• 8:58Speaker 01

  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 idle time than I was.

• 9:05Jason Calacanis?

  You know what I did with the TRSD? We had a dot matrix printer. And as you remember, at that time, you had the you'd feed the paper in, and they were all connected. And you had this big box, right? Tall box, wide paper. It's a giant, huge box you put 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.

• 9:47Jason Calacanis?

  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 investors 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. We need to know your company is

• 10:31Jason Calacanis?

  incorporated. That's why you need Northwest Registered Agent. It's the service that will help you run your business the right way from day one. In 10 clicks, and in under 10 minutes, you're going to file for your LLC, or a C corp, if you're a startup, get a domain name, launch your official website, claim your business email, and even fast track your trademark application, which some people forget to do. We're talking about more than just company formation. This is your entire identity as a business. Go to northwestregisteredagent.com/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.

• 11:12Jason Calacanis?

  Tell me about the moment you heard the term venture capital.

• 11:18Speaker 01

  I believe I was all the way at business school. I think it was around 1993 or four.

• 11:23Jason Calacanis?

  Where were you at school?

• 11:25Speaker 01

  At Stanford Business School locally. Yeah, so I had followed a geek's path, right? I had 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 map to parallel processing machines. Didn't want to go back to Hewlett Packard, right? And 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 there. Oh, and then three and a half years in management consulting at Bain for tech company. So I did consulting, product 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, you know, how much more am I going to learn in five years than I haven't already learned

• 12:09Speaker 01

  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, the 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 my senior. He had, you know, already graduated, gotten the Greylock on the East Coast, and they wanted to create

• 12:50Speaker 01

  a West Coast presence and grow the office out here. And so lo and behold, I'm like, wow, this from his description sounds interesting. Let me, let me look at this. I had never met a venture capitalist. They knew nothing about it and there was no internet. So, um, well, I mean, it was, but it wasn't really like we know it today.

• 13:07Jason Calacanis?

  It was BitNet, it was ARPANET, it was

• 13:09Speaker 01

  Oh, yeah. There wasn't like, oh, let me just look up what is venture capital and have the AI explain it to me, right? And so I did happen to find a goldmine for me was a fellow named Chris Alden, who was a co-founder of a magazine called Red Herring.

• 13:22Jason Calacanis?

  Yes, I know Chris. Outside was the other magazine, right?

• 13:26Speaker 01

  Bingo.

• 13:26Jason Calacanis?

  Now, and they were chronicling venture capital in the early 90s.

• 13:31Speaker 01

  Exactly. Print format, you'd get like a subscription and Chris in particular did a monthly, I think it was monthly, a column called VC Whispers, where he would talk to venture capitalists and ask them, what do you actually do? What, 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, Greylock's kind of at that time, they morphed a bit, but at that time, a very conservative, white shoe, buttoned down kind of firm, like, like the opposite of me. Same for Sutter Hill at the time. And again, they, they, they 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

• 14:16Speaker 01

  that actually was within the culture and sort of strategy of venture firms. Some were entrepreneurial and risk takers, others were very strangely, very conservative and analytical, most more like investment banker types, which would not have probably been a good match for me. So I really credit Chris and a couple of other folks I spoke with to help steer me to, well, you know, they're not all the same. There's some firms that you might like more than others. And luckily 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.

• 14:54Jason Calacanis?

  Tell me about the first investment you were ever involved in, because you must have diligently and met with hundreds of founders, but there's always that first check that you are responsible for champion and then putting in.

• 15:08Speaker 01

  So I'm pretty sure the first three, and I'll try to get the order right, were Fast Parts, Interwoven and Hotmail. And two of those three were

• 15:20Speaker 01

  Well, no, that's, um, 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 B to B, if you will, trading exchange that dealt in the gray market of semiconductors. And I, the only reason this one was really interesting to me is I had done a, um, I don't know if 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? Um, so to make a long story short, there was a, you know, perhaps a, a lesson there for me about not relying too heavily on something I think I know a lot about, um, instead of, is this actually a good business opportunity in what I might know about? So like the fact that it clicked was like, oh my God, everything lit up for me. Like that's something I was thinking about starting.

• 16:05Speaker 01

  Um, but it ended up just going out of business. Um, the Interwoven went public. Uh, it was a sort of web, like what internet tools company for content management. What, you know, as, as websites and web properties got more complex, how do you manage all these assets? Um, almost like an infrastructure layer for the internet. And then there was Hotmail. Now, Hotmail was a really, yeah.

• 16:26Jason Calacanis?

  That was a good one.

• 16:27Speaker 01

  That was like the one people have heard of. Boom. Like, yeah, that was the first one that got visibility.

• 16:31Jason Calacanis?

  Bought by Microsoft.

• 16:32Speaker 01

  Bingo. For about $400 million, uh, 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, I 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, uh, of spread of marketing in a sense, no, like a virus.

• 16:57Jason Calacanis?

  Which came from the signature. When you sent to Hotmail, it said, sent by Hotmail or powered by Hotmail?

• 17:02Speaker 01

  Yeah. It's actually funny you should say this. It was, the idea to do that was Tim Draper's idea, a hundred percent. And I, 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, feels like a normal email account, but now every message you send has this get your free email at hotmail.com, right? Like, like a call to action.

• 17:28Jason Calacanis?

  Controversial.

• 17:29Speaker 01

  Exactly. Tim wanted it more controversial. He was pounding the table that he wanted to say as if it was written by the sender, PS.

• 17:37Jason Calacanis?

  I love Hotmail.

• 17:38Speaker 01

  I love you.

• 17:41Jason Calacanis?

  Tim's a unique guy.

• 17:42Speaker 01

  The founders wanted like nothing to do with that. They're like spamming, you know, accusations.

• 17:47Jason Calacanis?

  People forget, and you could explain to people that the internet was non-commercial.

• 17:52Speaker 01

  That's right.

• 17:53Jason Calacanis?

  At its start, and the idea of doing even a Zima malt ice liquor ad, a banner ad on Wired, was faced with fierce resistance from the venture entrepreneurial and early internet community.

• 18:10Speaker 01

  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, you know, this geometric explosion, which by the way Skype then used later for voice and other companies used for video and it's sort of, you know, 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, you know, what is your viral coefficient? Can anyone who receives a message actually take action? You have to be multi-platform. There's a bunch of things that relate to it. But

• 18:54Speaker 01

  going back to your original question, it 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 just by pure dumb luck. The internet was exploding. It was like shooting fish in a barrel to make money, frankly, in these companies for a period of, you know, four to five years then. And we were the most active venture capital firm, if you can believe it, in internet investing in 95, 96. We did about a third of all internet investments.

• 19:20Jason Calacanis?

  Amazing. DFJ.

• 19:21Speaker 01

  Of all. Yeah. Yeah. It was called Draper Associates, but it eventually became DFJ a few, a couple of 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

• 19:53Jason Calacanis?

  Derivative products.

• 19:54Speaker 01

  Yet another, yeah, way to divvy up a market and sell to consumers. Yet another way to do a B2B trading exchange. It was B2C, B2C, they, B2B and B2C, and they all looked the same. Just variants on a theme. We had not yet had social media, you know, have its boom. We not yet had 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. Like this, this, this is not interesting. Let me, and, and, and it got 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 Benchmark, 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?

• 20:36Speaker 01

  So you could have made the argument, rightly so, that it's foolish to invest in biotech. It's foolish to invest in semiconductor. What, why? Nothing could beat the internet for rate of value creation for a while. But I pivoted hard to something that actually turned out to be not interesting, nanotech.

• 20:51Jason Calacanis?

  We all understand the importance of a crisp, memorable, easy to spell domain name. One of those names you can say over the phone and people know how to type it in without asking you the spelling. But let's get real, the good ones are either taken or there's some poacher who's holding it and waiting for some huge payday and they don't reply to you. If you, even if you want to pay for a premium domain, you don't want to use up all your runway on a domain name. That's just the truth for a startup. You want to put that valuable cash back into your startup's operations. So you should consider this, a .tech domain. You can get a clean, crisp, super memorable name for your website and company and signal out loud to your customers and investors, we're a tech company. That's instant branding for you.

• 21:36Jason Calacanis?

  That's why over 500,000 founders have collectively raised over $5 billion in investment, building their companies on .tech. So skip the hassle, head to www.get.tech/twist or go to your favorite registrar and grab your .tech domain today.

• 21:59Speaker 01

  You know, if you will.

• 22:00Jason Calacanis?

  I mean, 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.

• 22:28Speaker 01

  Yeah, that's a great point. There were a lot of sci fi influences. You're right. All those, the sort of the neural lace that Ian Banks writes about and was the, you know, inspiration for Neuralink. 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 if, here was the big science 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.

• 23:12Speaker 01

  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 were books like Diamond Age by Neil Stephenson as well, that talks about this future. So a lot of sci fi precursors, or like people were thinking about this and maybe I fell 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 focused on nanotech, the first mathematician of all places. But it was, it seemed clear to me that there were, there was a problem, which is you can't manipulate atoms today with that precision.

• 23:56Speaker 01

  There was like atomic force microscopes and stuff, but nothing that 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 and other molecular tools. We didn't have those words back then. But are there molecular machinery that we can harness, perhaps using DNA as a structural 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 nano scale, if you will, and build up from there over time.

• 24:40Speaker 01

  So that felt like powerful intermediate. The other path, what most people were thinking about, was a top-down approach to 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 scaling 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 and interconnects from Broadcom or whoever that works, let's just figure out how we can make smaller and smaller things, but harness to what we have from above. And that just was going to take like 20 years. That's 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 lysanthography

• 25:24Speaker 01

  that hopefully get us there, but it's taking a while. The bottom up, so this was, in a long-winded 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.

• 25:45Jason Calacanis?

  Right.

• 25:46Speaker 01

  Different people doing different investments that had no interface whatsoever to IT.

• 25:50Jason Calacanis?

  You gave an incredible talk, one of the highest rated talks we've ever had at any event. I do this liquidity event, used to be called Angel Summit. And we should pull up the deck here and just go to Moore's, directly to the Moore's Law slide. And 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. Walk people through this.

• 26:26Jason Calacanis?

  And why it's so important and why you think about it so much?

• 26:30Speaker 01

  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 100 and 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 100x, 100x, 100x, right? So a straight line on a graph like this is an exponential.

• 27:10Speaker 01

  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's 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, you know, that did the census in 1890. I mean, like literally, machines going back to 1890. You have the relay based computer that cracked the Nazi Enigma code, if you watch the movie Imitation Game. You have vacuum tube based computers that predicted Eisenhower's win in 1956. You have discrete transistors, which were all the rage in the

• 27:52Speaker 01

  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's machines of 360 were some of the earliest to bet on integrated circuits.

• 28:04Jason Calacanis?

  Yeah, the the amount of compute here and by year. It is every 18 months it doubles is technically Moore's Law's definition?

• 28:14Speaker 01

  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.

• 28:26Jason Calacanis?

  That's as I learned it in computer science school.

• 28:28Speaker 01

  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 and Fab yield optimization, which was what will be the number of transistors on the ideal die size? Because you could choose, they're on big chips with a certain error rate or small ones that have less errors, but what's that economic trade off 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, he modified it to say doubling every two years. And today, people kind of wave their hand and say every 18 months. But that's largely been dictated by Intel in its personal trajectory. It doesn't actually relate to this curve because on this curve,

• 29:11Speaker 01

  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, if it takes more transistors, as it does today, than ever before to store a bit of memory, why are we counting transistors? Let's count actual things that matter, memory storage or computation. This is computation. Now, this one has been doubling every year for 130 years. And then 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

• 29:55Speaker 01

  curve. They didn't know, no one had graphed this until 1999 timeframe, which was Ray Kurzweil. And then I've 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?

• 30:15Jason Calacanis?

  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 bade 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,

• 31:00Jason Calacanis?

  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 a 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 in Startups, 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.

• 31:43Jason Calacanis?

  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/twist to get two months free. You gotta try this product, it's incredible. Getperspective.ai/twist.

• 32:05Jason Calacanis?

  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 it's really interesting about Intel, like 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?

• 32:42Speaker 01

  Well, they had a variety of tricks. They tried to build to the curve. They did definitely with Intel and side try to corner the market. But what they missed was transition to a fine-grained architecture that you can just, or another way of phrasing it is Intel, in their development of the CPU, you know, 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 Nvidia or the ASIC 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 cache memory, basically local cache memory that improves performance.

• 33:27Speaker 01

  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 Nvidia chip that is inherently better suited to AI workloads, which, going back to my old PhD in double Es, 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 regions. 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.

• 34:11Speaker 01

  Oh, wow. That's cool.

• 34:13Jason Calacanis?

  Yeah, don't worry about it. That's the reactions on Zoom.

• 34:15Speaker 01

  Exactly. In any case, Nvidia has taken that baton and has for the last decade. And lately, there are a lot of custom silicon solutions. Google has their TPU, the tensor processor in it. Amazon's developed their own chips. OpenAI, of course, is working on their own chip. All the major AI, you know, companies that you know of have their own semiconductor efforts underway because that is inherently better way to do AI workloads. Where you're doing the same matrix multiplying ad over and over and over again for ninety nine percent.

• 34:43Jason Calacanis?

  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 I should point it out, like, no matter what's going on in 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.

• 35:10Speaker 01

  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 could, another way to frame it abstractly is you can think that every idea is a recombination of prior ideas. They're always building on the shoulders of giants that 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 interested in recombining a new product is growing as Reed's law goes to the to the nth power. So N ideas to the nth possible pairings or subgroups.

• 35:56Speaker 01

  That could be the fundamental dynamo of this perpetual sense of accelerating change. That we're, 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.

• 36:11Jason Calacanis?

  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 miss 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.

• 36:42Speaker 01

  Yeah.

• 36:42Jason Calacanis?

  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 II, you took the chips, you bent them, you put them into the, you know, 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 of 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.

• 37:23Jason Calacanis?

  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.

• 37:40Speaker 01

  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, almost to represent the world in a simulation, if you really want to get abstract about it.

• 38:16Jason Calacanis?

  Yeah.

• 38:17Speaker 01

  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 a company called Evolved Machines that was doing neuronal modeling. Basically, can we model how a neuron works and then a cluster of neurons, then 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 a local store that used to be here to sell stuff,

• 38:59Speaker 01

  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 sort of was ion channels, molecular modeling and cellular modeling. So this scientific modeling task with this handful of things I just bought at Fry's, like on a weekend.

• 39:22Jason Calacanis?

  Crazy.

• 39:23Speaker 01

  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 was a thing called AlexNet that is famous, this contest that Fei-Fei Li at Stanford has been holding. And the neural net approach just dominated. And this was a guy that used like one or two GPUs. Like, that was it. Like, you guys 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?

• 39:51Jason Calacanis?

  What time period was this, that this?

• 39:52Speaker 01

  2012.

• 39:53Jason Calacanis?

  2012, yeah.

• 39:54Speaker 01

  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 like, what? And so we made a, we started looking at AI investing. Back then, there were no, no venture firm had AI on their website as a sector of interest.

• 40:12Jason Calacanis?

  Yeah.

• 40:13Speaker 01

  By the way, that was 2012.

• 40:14Jason Calacanis?

  It's like there was machine learning on the margins, right?

• 40:16Speaker 01

  Bingo. Yep. And, and even that vernacular started to come in later when deep learning as a term came up. And around 2012, 2014, there was this, just this... Renaissance, we invested in our first AI chip company in 2014, this guy Naveen 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 Nvidia. Because if the idea was, wow, we can shoehorn the Nvidia chip built for graphics and gaming. And we, you know, at the time thought they're going to be focused primarily on gaming because that's where they're, you know, making most of their 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.

• 40:57Speaker 01

  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 a five year period to do something similar.

• 41:13Speaker 01

  And that's really carrying Moore's Law forward now, like the majority of compute deep in the bowels of Google and elsewhere on these custom chips. And it's, 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 parable, 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.

• 41:47Jason Calacanis?

  It is interesting that you bring up the wattage. This year, actually, just in the last 90 days, people stopped talking about how many H100s they were building. You know, remember when Colossus got built in under three months, 100,000 H1s, this is crazy. And now all the announcements are being done in how many, measured in watts, none.

• 42:10Speaker 01

  Yeah.

• 42:10Jason Calacanis?

  And so now you start to get to the, 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. We're visual creatures, the world is visual.

• 42:50Jason Calacanis?

  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?

• 43:02Speaker 01

  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 worlds in 3D, constructs and model

• 43:44Speaker 01

  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 computation takes place in our brain is similar to the way it does in our neural nets that we train. The way that we, in this, grow these things, the processing of information to train these nets is very similar. And so in 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

• 44:28Speaker 01

  polynomials, that the hidden formula isn't all possible formulas to be like, you know, what is the trajectory of something? If you're trying to just deduce it from data points, like what, what is the trajectory of an object? It's probably going to be some low order polynomial in almost all physics that describe things. So there is 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, like an axon, there's synapse, the similarities of the weighting between these nodes that our brain had to do this. Like, it was an evolutionary requirement to do data reduction, model building on the fly.

• 45:12Speaker 01

  And the way the whole brain works is predicting, in a sense, the next tokens, 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?

• 45:25Jason Calacanis?

  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 going to send an alert.

• 45:36Speaker 01

  Exactly.

• 45:37Jason Calacanis?

  Your security camera 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, 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, 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.

• 46:19Jason Calacanis?

  But I guess when we get off this chart and we, you know, 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.

• 46:31Speaker 01

  Growing neurons on silicon, right?

• 46:33Jason Calacanis?

  Yeah, well, yeah, it's biological compute platform. So they're, 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, what takes the baton from, you know, these GPUs?

• 46:51Speaker 01

  Right. Twenty years from now. Yeah, so I think, right, so that ASICs 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 H100s? 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 implant it. So they're trying to do apples to apples, say, we're not sure what the chips are going to be, but they'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 that 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 neuron thing, I might put more in the nanotech bucket of,

• 47:35Speaker 01

  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 called 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 II, it would have been eight of those chips in a row for, you know, 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 SRAM banks, you know, static RAM banks, each of which have, you know, eight or so transistors per bit. And then there's error correction code, because they're so, you know, as they get smaller and smaller, they get more erroneous.

• 48:16Speaker 01

  You have all these extra ones. You have readout 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 add is just a wire of common current, and you have a row of these transistors. Making 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, 1,000x 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, a brand new, our most recent investment just last week, Unconventional Labs, they aren't really saying exactly what they're doing, but they're in this domain also of analog and biomimicry. It's the same guy, by the way, who started Nirvana in 2004.

• 49:01Speaker 01

  That I mentioned that when I first invested in semiconductors for AI. Then he started another company, the Databricks Bot. Now this is his third startup. Anyway, um, 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, uh, and it has applications, not only in traditional AIs you might think of it, but also really small neural networks that you might stick in everything. So Jensen, CEO of Nvidia, a few, couple of years back, said they were about to enter a area of explosive growth in AI. Like nothing we've ever seen before. And he wasn't talking about any product in videos about to launch. It was edge AI, basically putting little neural networks, trillions of little neural networks, little brains into everything.

• 49:46Speaker 01

  Every security camera should have one. Obviously, every car, every car is going to have them, every moving object, every autonomous vehicle.

• 49:52Jason Calacanis?

  Wearable.

• 49:52Speaker 01

  Every sensor could, it's like, wouldn't you want a little more intelligence in any consumer product you can think of?

• 49:57Jason Calacanis?

  Yeah.

• 49:58Speaker 01

  Um, and by the way, this, so imagine like a voice interface, for example, that's speaker independent, large vocabulary. So it actually works, uh, could be in anything for less than the cost of the plastic buttons it 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 all to and say, hey, like, don't go in the corner. Hey, could you get the bedroom? Or, you know, don't stop bugging the cat, whatever it might be.

• 50:23Speaker 01

  It could be that intelligent and it would be local intelligence that doesn't require latency or any of the overhead of.

• 50:28Jason Calacanis?

  No cloud, no security issues, privacy issues.

• 50:30Speaker 01

  Yeah, local, yeah, local data retention.

• 50:33Jason Calacanis?

  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, uh, talking about robotics. You and I haven't seen the clip. My, my crack research team are going to play it for us so we get our reaction to it.

• 50:50Speaker 01

  In real time.

• 50:51Jason Calacanis?

  In 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. Here he is, baby Steve. Go.

• 51:06Jason Calacanis?

  Okay, try one more time with sound. Let's reshare and do sound.

• 51:13Jason Calacanis?

  Then I'm going to do a pickup for you. I found the, I think I found Clinton talking about Nano, which we'll splice into the other one. Here we go.

• 51:23Speaker 03

  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 hesitated about San Francisco would be, I'm not sure if it'll percolate into, I mean, even though I bought one of these just, I don't know, large. The average person doesn't really have, 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 them just by moving the arms. So anybody can program.

• 51:52Jason Calacanis?

  So yeah, it was hard for me to even hear it. Do you have a response to it, Steve?

• 51:57Speaker 01

  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 can 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, hmm, what will these robots be good for?

• 52:38Speaker 01

  Well, as long as they have 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, less repetitive task, but basically sitting at a desk, you could just swap out the robot, right? At 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 US Sisyphus laws that basically prevented technology transfer shot that down, and instead the company went out of business.

• 53:14Jason Calacanis?

  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?

• 53:23Speaker 01

  So what they can go right was at the end of the day, they call it sort of like the shaky bot. There was this idea of, am I drifting from, 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 XAI, excuse me, Optimus and let me just say, other AI companies developing humanoid robots have all realized, oh my God, we got to build the entire stack, the supply chain of available motors.

• 53:52Jason Calacanis?

  You aren't special, right?

• 53:53Speaker 01

  Exactly.

• 53:53Jason Calacanis?

  Is building the entire stack. So

• 53:55Speaker 01

  That's his power rally. Yep.

• 53:56Jason Calacanis?

  Yeah.

• 53:56Speaker 01

  But back then, we didn't know it. Right.

• 53:58Jason Calacanis?

  I mean, if we look back on the history of it, one of the seminal moments at Tesla, I remember sitting with Elon when these 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 on the parts. And he had to deal with part suppliers. And he learned this brutally hard lesson, which is your production is as fast as your weakest supplier. And your product's as good as the worst component in some ways, you know, or it could be. And man, it's just

• 54:38Jason Calacanis?

  amazing how over, yeah, 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 and the

• 54:50Speaker 01

  Yeah, the guy from Daimler.

• 54:52Jason Calacanis?

  Yeah, the guy from Mercedes. Yeah.

• 54:53Speaker 01

  Yep. So that, by the way, you know, when you're starting, 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 power cells that are custom made for automotive. And they're like, well, let's just use the same, same laptop cells Dell's using in 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 Roadster almost didn't ship, ship because they needed a two speed transmission. They tried three different vendors like Borg Wagner 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.

• 55:35Jason Calacanis?

  It would just rip the transmission in half.

• 55:37Speaker 01

  Just destroy it. And it wasn't until the special bipolar transistor came out where JB Starbuck 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 RPMs, 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, 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 Fry's, back to Fry's to get, you know, ethernet cable that they needed to be able to keep shipping vehicles. Cause the ethernet cable didn't exist from the, some supplier.

• 56:23Speaker 01

  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.

• 56:37Jason Calacanis?

  You still have yours or you have dismissed it?

• 56:39Speaker 01

  I gave it, no, I gave it to a different museum, the, um, the.

• 56:41Jason Calacanis?

  Oh, Peterson.

• 56:42Speaker 01

  Yes, Peterson Automotive Museum. I think it's the largest in the US, you know?

• 56:45Jason Calacanis?

  Yeah. Sign me. I still have mine. It's right over here in number 16 in the garage. I got, I redid the battery pack.

• 56:50Speaker 01

  I have 17 of the roadster. Yeah.

• 56:52Jason Calacanis?

  You know how I got 16? There was a venture capitalist, I'm not going to say their name, um, who ordered probably right before you. And they, uh, I subsequently ordered like, you know, after it, I might've been like a hundred or something. I was in the signature 100, but it was way up the list. And then he invested in Fisker.

• 57:14Speaker 01

  Oh.

• 57:15Jason Calacanis?

  And he stabbed Elon in the back.

• 57:17Speaker 01

  Yep. So in retrospect, by the way, and this is even memorialized in 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. 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.

• 57:41Jason Calacanis?

  Yeah, it was garbage.

• 57:42Speaker 01

  They didn't get that the electric vehicle transition is very different and it doesn't leverage anything that the.

• 57:49Jason Calacanis?

  The whole idea to not go to the gas station and not have those parts and simplify the car.

• 57:52Speaker 01

  All the overhead of a gas tank. Like you basically doubled the complexity with a hybrid versus a simple electric car.

• 57:58Jason Calacanis?

  So it was bizarre. And so, um, the person, I think, if I have my story correct, uh, proactively canceled their deposit.

• 58:09Jason Calacanis?

  Happened to be sitting next to Elon, you know, a meal. And he's like, oh, this person canceled the car. And I said, oh, what were they in the order? I said, 16. I said, oh, I'll take it. Can you move me up? Yeah. And just took us BlackBerry and moved me up the list. And I got the first orange one. It's the best color. What was your color? You went red?

• 58:30Speaker 01

  I went, no, I went for racing green, which was their signature color. I think I even have the, uh, I think

• 58:38Jason Calacanis?

  I think you might have been the first racing green for sure.

• 58:40Speaker 01

  I'm not sure. I'm not sure.

• 58:42Jason Calacanis?

  I don't know. It was really beautiful.

• 58:43Speaker 01

  Oh, and I got, 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

• 58:56Jason Calacanis?

  There's something about that aesthetic that people really like. I've been watching Corvette is having a renaissance. I used to own Corvette before I got my Roadster and I traded my Corvette in for a Roadster. Um, they have this new one, the ZRX1, which is the greatest hypercar ever built at this point, 1,250 horsepower. But anyway, uh, what Corvette drivers like is just raw carbon fiber. That aesthetic looks so

• 59:19Speaker 01

  Pretty cool.

• 59:20Jason Calacanis?

  Beautiful. It just, yeah. And they're, 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?

• 59:34Speaker 01

  Crazy.

• 59:35Jason Calacanis?

  Why is it so expensive carbon fiber?

• 59:36Speaker 01

  It's hand done. Hand done. I believe in the case of the Roadster was in Italy, if I recall. And, uh, and they wouldn't let me get the body in clear because there were areas where they knew it wouldn't 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.

• 59:54Jason Calacanis?

  Yeah, I didn't want to reveal those secrets. Here's a clip of Elon talking about 2025 and 2015.

• 1:00:01Speaker 01

  Oh, wow.

• 1:00:01Jason Calacanis?

  Well, that clip.

• 1:00:03Speaker 01

  10 years ago. Oh, I was interviewing him.

• 1:00:06Speaker 04

  Do you have a question? So in terms of what I think 2025?

• 1:00:10Speaker 01

  Oh, yeah, please.

• 1:00:11Speaker 04

  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.

• 1:00:28Speaker 04

  And we'll be probably have the super majority of energy being produced being sustainable. So I think we're on headed.

• 1:00:36Speaker 01

  Solar primarily in your mind?

• 1:00:38Speaker 04

  Primarily solar, yeah.

• 1:00:40Speaker 01

  Wow.

• 1:00:41Jason Calacanis?

  Interesting. You're right.

• 1:00:42Speaker 01

  And that was, by the way, that was me interviewing him.

• 1:00:44Jason Calacanis?

  Yes, yes, that's where I put it in. Sorry about that. We should have included.

• 1:00:47Speaker 01

  No, no, no. It was just like, whoa. So I haven't revisited that clip. So the first one nailed it. Like, like AI boom, right?

• 1:00:54Jason Calacanis?

  You're soaking in it.

• 1:00:56Speaker 01

  It's just dripping out of our ears. It's so much.

• 1:00:58Jason Calacanis?

  Literally, we're like literally in the matrix right now, in a bath of AI. Okay, let's go to energy and solar and EVs.

• 1:01:06Speaker 01

  And EVs. 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 had instead asked 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 a majority of vehicles, electric vehicles, simply because people hold on to cars for 12 years on average. You know, 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 new sale. So there's inertia. There's political shenanigans.

• 1:01:47Speaker 01

  There's weird ways that people, you know, forestall the future and prevent the inevitable future from manifesting. So look where.

• 1:01:55Jason Calacanis?

  Incumbent.

• 1:01:56Speaker 01

  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. Realize solar's potential or the EV potential. So I would go further and say in 2015 was obvious to me and it's even more obvious today that it is inevitable that all vehicles will be electric. Every 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.

• 1:02:40Speaker 01

  I would just stick with his 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 are energy sources of primary resort. Geothermal may make a major comeback if we can go really deep in hot rock. That's that's a sleeper potential. But those, that category of products really dominates all of it. We shouldn't be burning coal. We shouldn't be burning gas. It's just, but it will take time, right? Cause it's, it's the drug much of the economy is addicted to, right? Currently.

• 1:03:12Jason Calacanis?

  Yeah. And if you look at that time, solar was like 1% of electoral in the US. 2015, 2025, solar's 10% renewables. You put wind in there, you get to 17% according to Claude AI.

• 1:03:29Speaker 01

  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 Lovins 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, right? They, 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?

• 1:04:04Speaker 01

  It's, it's so, that's the category that's inherently distinct from fossil fuels.

• 1:04:09Jason Calacanis?

  Yeah, and if you want to know why all of this happened,

• 1:04:17Jason Calacanis?

  this hatred of nuclear, nuclear, uh, 1979.

• 1:04:22Speaker 01

  Ah.

• 1:04:24Jason Calacanis?

  Jackson Brown, Crosby, Stills, Nash & Young.

• 1:04:26Speaker 01

  That's right.

• 1:04:27Jason Calacanis?

  John Hall, Doobie Brothers.

• 1:04:29Speaker 01

  Yep, so it's interesting, notice the.

• 1:04:31Jason Calacanis?

  Bruce Springsteen.

• 1:04:32Speaker 01

  And this is.

• 1:04:33Jason Calacanis?

  No nukes concert.

• 1:04:33Speaker 01

  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 Island.

• 1:04:46Jason Calacanis?

  Mushroom cloud.

• 1:04:46Speaker 01

  That's not Chernobyl, that's nothing, that doesn't happen in those plants, right?

• 1:04:51Speaker 01

  And Greenpeace was actually founded specifically, a focus 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.

• 1:05:15Jason Calacanis?

  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 was a better idea because of Fukushima.

• 1:05:30Speaker 01

  So get a little of this. So 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.

• 1:05:40Jason Calacanis?

  Oh, yeah.

• 1:05:40Speaker 01

  By Isabel. Yeah, it's great. I just finished it a couple of 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 1,100 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 when or not because of higher electricity costs.

• 1:06:23Speaker 01

  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 nuclear itself.

• 1:06:31Jason Calacanis?

  The panic attacks have done more. And if you look at coal, taking

• 1:06:35Speaker 01

  Four million lives a year.

• 1:06:36Jason Calacanis?

  Beautiful coal.

• 1:06:36Speaker 01

  Four million lives a year from coal particulates in the air.

• 1:06:39Jason Calacanis?

  It's unbelievable. You know, and we got the president on 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, you know, get him to say that solar is 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?

• 1:06:59Speaker 01

  Ask Australia.

• 1:07:01Jason Calacanis?

  Yeah, like, think it through. Think it through, my guy. You can, you know, here in the great state of Texas, 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 the good oil, right?

• 1:07:20Speaker 01

  It's the oil patch, you know?

• 1:07:22Jason Calacanis?

  Yeah, they do it because it's the most cost efficient. It is the best bang for your buck long term. So 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 like, oh, build a seawall. And they're like, yeah, but every hundred years, there's going to be a storm. And literally 50, whatever number of years later, the storm arrives, like

• 1:07:47Speaker 01

  And don't put the diesel jet set below sea level so that if you had a wave, it's going to be 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 it was a small, small number, by the way, much smaller than people think. It's not a reactor design anyone builds today. It's like you do learn from mistakes. So to look and, you know, reference that to say we shouldn't 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, you know, et cetera, et cetera, et cetera. Like, we don't even think about that today because we

• 1:08:26Jason Calacanis?

  The lesson wasn't

• 1:08:27Speaker 01

  Should develop.

• 1:08:27Jason Calacanis?

  Don't, don't build another ship ever again.

• 1:08:30Speaker 01

  Right.

• 1:08:31Jason Calacanis?

  Build a better ship, people.

• 1:08:32Speaker 01

  Exactly.

• 1:08:33Jason Calacanis?

  And, and, 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.

• 1:08:44Jason Calacanis?

  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. You know, a level of intelligence that you take all the humans together and then times it by a hundred and it's, it's something else. It can find questions to answer we don't even know to ask. Where are we in this timeline?

• 1:09:21Speaker 01

  Well,

• 1:09:25Speaker 01

  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, I don't think we have a good term for that, like

• 1:09:46Jason Calacanis?

  Sentience.

• 1:09:47Speaker 01

  Super, like sentience or something, like the art of it. So if you just say, will an AI system, you know, like the GPTs and the XAIs of the world of today as a framework, you know, outperform a human on any question or task that you presented? 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 a human. 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 lesser animals. It's like on the scheme of things like those two humans on an intelligent spectrum of AI, let's say just AI trajectories, like, you know, going

• 1:10:30Speaker 01

  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 algorithmic improvements of a doubling every year for like fifteen 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 these models today already outperform humans at almost any task you apply them towards with a little bit of specialized training, you there hasn't been a domain where you're like, oh, we can't do that.

• 1:11:12Speaker 01

  I'll give an example, medicine. I was just at 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 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 on 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

• 1:11:56Speaker 01

  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 Yeah, schedule. Really tough conversations. They blow the doors off humans. So we're already there. And Elon would talk about a number of, you know, PhD level equivalents for all the PhDs. The challenge then is going to be, you alluded to this earlier on emotional response. You know, we're talking about limbic systems and what have you.

• 1:12:40Speaker 01

  Earlier in our 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 of say, I have a task I want you to do. Can you find me the best vacation plan? And, you know, reaching out to all these different websites and like, figure out where 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, agentic flows in programming as well, right? We could do in coding. 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, it could be an emergent property, by the way, of forecasting the future. So let me share a bizarre thought.

• 1:13:23Speaker 01

  I alluded to this earlier, that 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, this thing and it's much hotter or colder than I could possibly imagine, I'll notice that. Otherwise, I won't even notice the 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. Yes. Right? Confirmation bias, all kinds of biases. But at a very low, like, in the microsecond level, that like, I just did move this finger and then I was like, well, 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 retrospectively making sense-making of what I've done?

• 1:14:08Speaker 01

  And there could be some vote-taking circuits that we, we might have to build some circuitry for this, I guess. Vote-taking circuitry that's in the feedback loop 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 neuro-symbolic 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 in ways that will potentially bootstrap intelligence.

• 1:14:53Speaker 01

  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. But 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 we have is a hyper-intelligent adjunct or colleague, kind of like C3PO, if you will. 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.

• 1:15:28Jason Calacanis?

  Can do a lot of things, but it can't beat Darth Vader.

• 1:15:31Speaker 01

  Exactly.

• 1:15:31Jason Calacanis?

  It's not gonna have smart hands solo.

• 1:15:35Jason Calacanis?

  Not happening.

• 1:15:35Speaker 01

  There we go. Taking the analogy.

• 1:15:37Jason Calacanis?

  Never tell me the odds. It's not getting through an asteroid field.

• 1:15:42Speaker 01

  That's right.

• 1:15:42Jason Calacanis?

  Not a chance.

• 1:15:43Speaker 01

  I remember that.

• 1:15:44Jason Calacanis?

  There'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-shot it, if you've heard it, where your relationship with AI becomes the dominant one in your relationship. Sam Altman just announced he's gonna make it spicy, you know, make it a little spicy in ChatGPT for adults. There are other companies that are, I get pitched all the time the last couple of years on like an AI therapist. And I'm like, you know, that's, 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?

• 1:16:30Jason Calacanis?

  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, or they never have a bad day. Everything is sycophantic. This seems like a road to purgatory to me. What do you think?

• 1:17:10Speaker 01

  No, it's a really interesting question. And 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, you know, AI chatbots in place to try to do customer service, you know, try to lower the customer service workload. They found certain subset of people who became romantically attached to these people, 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, you know, he also said,

• 1:17:55Speaker 01

  you know, we were cautious at first because there's some mentally ill people out there. 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,

• 1:18:37Speaker 01

  something interesting, right, 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 explain it. It's not interpretable. And there are definitely groups, anthropic being one of them, who think, oh yeah, we're gonna work on that. We're gonna try to make that happen. They have yet to make meaningful progress. And this meaningful meaning result that 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 timeframe of relevance, meaning we'll just have built a better one before we reverse engineered the prior one. Just like we haven't reverse engineered our brain. These complex information systems, both brain and neural nets, are inherently inscrutable.

• 1:19:21Speaker 01

  We understand the interfaces, the ins and outs, but not the inner workings. You can't cut and paste functionality. You can't, you know, figure out the subsystems. You can't like draw a box around, well, there's the English speaking part, and it's worth doing math. Like, 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 all hacks that you're doing. We knew what we were doing, and if something went crazy, it was like your paperclip of printing.

• 1:20:01Jason Calacanis?

  Yeah, it's gonna run out of paper.

• 1:20:04Speaker 01

  You can quickly understand what you just did and fix it, right? That does not apply to the complex interactions of AIs internally, nor their back and forth with a 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 and say, oh wait, that just went down dangerous territory. Let's not share that result, right? And have a police, police layer that you could do.

• 1:20:48Speaker 01

  What you can't do is have any of these companies do what California bill initially proposed is like prove, you know, safety before you even start training. I mean, that shows you how ridiculous they work, right?

• 1:20:58Jason Calacanis?

  Yeah, to your point, you can have a speed trap or a DUI, you know, trap where you you check people aren't drinking. But, you know, people have a car.

• 1:21:09Speaker 01

  That's right.

• 1:21:10Jason Calacanis?

  And has wheels and has an engine like they're going to drive it and some are going to go fast. Some are going to slow, some might drink and drive. And so there's regulations there, but yeah.

• 1:21:19Speaker 01

  By the way, let me, let me throw one crazy. Absolutely. Well, not only premature, but they never work. So 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 XAI or someone at one of the other like closed AI says, I want to align with my interests, my woke, whatever, or my, you know, Western liberal ideals or whatever I think.

• 1:21:45Jason Calacanis?

  Conservative.

• 1:21:46Speaker 01

  Humanity is in my, imagine you could, you imagine you could do that, which I'm saying you can't. But imagine you could.

• 1:21:54Speaker 01

  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 under unfortunately for the authoritarian rule. You do not want them having aligned to their cultures and norms, AIs, right? What you want.

• 1:22:11Jason Calacanis?

  You're going to be have a lot more Uighurs in concentration camps, being tortured.

• 1:22:15Speaker 01

  No idea. And so I, 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 algorithm, 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 of neural nets. The more RLHF or the more in a sense of mind control, you try to layer on late in the cycle, the more you compromise the reasoning capabilities of the AI itself. It's profound, both humans and AI, so many different ways. So do not think mind control is the answer. Do not think containment is the answer. It's more like parenting and policing. And then I think you might finally get a regulatory regime that makes some sense.

• 1:22:58Jason Calacanis?

  So interesting you bring up the number of people living under authoritarian regimes. You know, even Steven 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. Fifty-four percent of people live in authoritarian countries. So our democracy is worth fighting for.

• 1:23:21Speaker 01

  Absolutely.

• 1:23:21Jason Calacanis?

  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 know, not to make any of this political, but if you see things being less democratic, be concerned.

• 1:23:43Speaker 01

  Absolutely.

• 1:23:44Jason Calacanis?

  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.

• 1:23:51Speaker 01

  It is the path of doom, and it's frightening. The way in which authoritarianism and theocracies, both have different variants, can take over and are so difficult to uproot. It is a precious and delicate thing when a group of people in their founding, like in the founding of the United States, or, you know, 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." When you think about 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 of, "Oh, wow, that's over time."

• 1:24:32Jason Calacanis?

  Oof, look at that democracy, just boop, boop, boop.

• 1:24:36Speaker 01

  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 coddle the, you know, the power brokers that are. And that, in a sense, is an architectural resistance to change and innovation and new entrants. In America, at least in the West, we have, you know, a system that allows and encourages entrepreneurship, that allows disruption, that allows overthrowing of the past. And 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.

• 1:25:22Jason Calacanis?

  Or dictators.

• 1:25:22Speaker 01

  Or dictators saying, you know,

• 1:25:24Jason Calacanis?

  same thing.

• 1:25:24Speaker 01

  planned economy, let's do X, let's do Y. It's never worked.

• 1:25:28Jason Calacanis?

  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 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% 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

• 1:26:12Jason Calacanis?

  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.

• 1:26:54Jason Calacanis?

  And you have to try to squash it and quell it and extinguish it. It's exhausting.

• 1:27:01Speaker 01

  Yeah.

• 1:27:01Jason Calacanis?

  To be an authoritarian.

• 1:27:02Speaker 01

  Putin lives at the end of a two mile long tunnel in intense paranoia. It's kind of like Hitler in his bunker. It's like.

• 1:27:09Jason Calacanis?

  Yeah.

• 1:27:10Speaker 01

  But he's stuck.

• 1:27:11Jason Calacanis?

  He's stuck.

• 1:27:12Speaker 01

  His whole country is stuck under him.

• 1:27:15Jason Calacanis?

  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? 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, I'm gonna do it. I'm gonna step over." Here I go. Kim Jong-un's face lit up.

• 1:27:44Jason Calacanis?

  Like, "Oh my God."

• 1:27:45Speaker 01

  Sorry, that was good.

• 1:27:47Jason Calacanis?

  He's like, "I'm coming in. Somebody loves me." I gotta tell you, you, you, you get those guys to come to Vegas for a weekend, get them to go to Carbone and get that rigatoni. You get them to come to F1 and to hang out. There's so much great stuff in freedom and democracy. We just have to allow those people to experience it in their... Here it is. This is one of the great moments in history.

• 1:28:11Jason Calacanis?

  Look, here it is. You just look at Kim Jong-un's face.

• 1:28:17Jason Calacanis?

  Wait, I mean, if they see his smile, he's, he hasn't smiled like this in his life.

• 1:28:24Jason Calacanis?

  Look at him. He is so happy. It's like, it was a peak experience for him. I think this is the way to get these, these dictators to flip. Just butter them up. Let them come to a music house. Let them come 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. Uh, and thanks for sharing so openly with the group here. I'm gonna have you come back in six months.

• 1:28:56Speaker 01

  Please.

• 1:28:57Jason Calacanis?

  And, uh, we're gonna do this every six months. It can be a little check-in. And, uh, next time, I just wanna 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 Hail Mary and it's gonna take 20 years, but will change the world and shadow the last paradigm, how do they reach you?

• 1:29:11Speaker 01

  Sure.

• 1:29:12Jason Calacanis?

  How do they reach out with their business plan or their ideas? And I don't mean to flood your inbox.

• 1:29:15Speaker 01

  No, yeah, no.

• 1:29:16Jason Calacanis?

  You do this for a living.

• 1:29:17Speaker 01

  Absolutely. Our website is future.Ventures. I'm also most active on X socially. But, uh, my email and everyone here at our firm is just our first name at future.Ventures.

• 1:29:27Jason Calacanis?

  There you go. All right, everybody. There's your 90 minutes with Steve Jurvetson. We'll see you next time.

• 1:29:31Speaker 01

  Thank you. Thank you, Jason.