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This Week in Startups
Legendary VC Steve Jurvetson looks ahead at neutral networks, Tesla, nuclear power, and more | E2193
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A
When you sent a Hotmail, it said sent by Hotmail or powered by Hotmail.
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B
Yeah, it's actually funny you should say this. It was the idea to do that was Tim Draper's idea, 100% and not me. And I just want to give credit to him because it was incredibly cheeky at the time to embed a commercial message involuntarily to everything sent by your customers. So you signed up for Hotmail. It feels like a normal email account, but now every message you sent has has this. Get your free email@hotmail.com right like a call to action.
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Visit northwestregisteredagent.com twist today. 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 Jurvetson 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, 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 Jormundsen. How you doing, brother?
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B
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.
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A
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.
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B
Oh, here we go now.
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A
Here we go. No, but you're a huge node 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 got to step outside for a second and launch this rocket. You just love Deep Tech. Yeah. How'd you get that love of deep Tech?
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B
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 far it runs. And then the Apple II is 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, I wrote games and things and so short version is it's been my whole life. I 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 taking us and what we can do with it. And I bounced around a bit, but when I finally found venture capital 30 years ago, it was like, wow, that's the perfect match for me.
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A
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.
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B
Oh, it was amazing. I remember exactly. It was in seventh grade, so I was about 13 years old. I was know socially kind of maladapt. I didn't really. I mean this is 1980. No, this would have been even before that. This would have been around. No, no, you're right, around 19. I think it was around 79 though that I got it for some reason.
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A
Because the album too was either 79 or.
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B
Yeah, 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 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 advance kill a bit. Right. So not mega, but thousands. And my eyes just lit up. So I immediately got into BASIC and would write simple programs like print something, go to 10.
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A
And your dad bought this for you?
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B
Yeah, it was a gift. Yep. I don't remember if it was a birthday or Christmas. It just showed up and I just could not imagine anything else that I would want to play with. So I bounced around with Legos and Fisher technique, this other kind of building block set. But this. When Apple II 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 graphic sort of shoot em up games. A text based adventure game where you enter words like go north, go south.
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A
Like Zork.
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B
Exactly, just like Zork. Bingo. And I loved the games. I mean there were. Oh my gosh. I remember actually. It's funny I've been talking to Richard Garriott a lot lately 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 that Anyway. Oh shoot, I forget the name. That was a pinball construction kit.
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A
Oh, I remember. Pinball construction kit.
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B
Yeah.
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A
You got to make your own pinball game. Yeah. That was so fun.
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B
Exactly. It was incredible. Like. And as a programmer I could tell those people know how to do things I have not figured out how to do yet with shape tables and flipping two memory registers for 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.
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A
And that's a big ups to your dad because it was a thirteen hundred dollar 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 seven thousand dollars 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.
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B
That keyboard.
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A
Yeah, it was a. It was a Chiclet keyboard as you remember. And 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?
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B
Not initially. So when I was in third grade, I remember we had a teletype machine like you're talking about and none of the kids use it, it was off in 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 the briefest exposure to that. But the overhead, I can tell you, was so high that it didn't really capture me in the same way. The delay, the latency, the lack of sort of a personal experience and in school. And it was loud, it just didn't. I mean I played with it but it didn't really catch on for me. Then my high school, which I was in at seventh grade did there was a really great guy. I remember his name was Stutzman. The teacher that supported this had both Z80s, which were the, you know, the Trash 80s we called them. Which was the TRS 80 from Radio Shack based on the Z80 processor and the Apple II based on the 6502.
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A
There it is. This is the TRS 80.
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B
Boom.
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A
My school had this as well. This was my first introduction to computers at Severian High School in Brooklyn, which is just like a dumb terminal. Now you must have gotten in trouble with some of this stuff. Everybody who was on these things did something stupid or brilliant.
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B
I did get trouble with your story. Well, funny, I've never shared this, nor have I even thought of it, but the closest I got to. Because I didn't get much in the bulletin board systems where then you could network. I mean I played 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 kids loved it. It's like look at the teacher opening the door and that 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 a. About it other than like just spending way too much time on it. I didn't get into trouble there. In fact, if anything, maybe it kept me out of trouble and I was more likely to get into trouble in the neighborhood. It's just idle time than I was.
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A
You know what I did with the TRS 80, we had a dot matrix printer. And as you remember at that time you had the. You'd feed the paper in. 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. 10, 30, print. So I do this with a bunch of spaces and I start this program. And I kid you not because I was just saying hello world one time per page. It goes. And the teacher had left the room. He's in the back of the lab. And I'm like oh my God. So then everybody else starts sending print drops to it. The teacher lost his mind because somebody then just did one with blank 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 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 twist so they know that we sent you. Tell me about the moment you heard the term venture capital.
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B
Ah, I believe I was all the way at business school. I think it was around 1993 or four probably.
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A
Where were you at school?
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B
At Stanford Business school at Stanford.
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A
Locally?
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B
Yeah. So I had followed a geeks path, right? I had Apple II programming, electrical engineering studies, did a bunch of stuff. Eventually Even started a PhD in EE. Focused on neural nets and AI of all things, how they map to parallel processing machines. Didn't want to Go back to Hewlett Packard where I'd done chip design. So I did some engineering work over a number of summers there. Did some summers at Apple and next, because I wanted to see Steve Jobs in action, but that was in product marketing. So I bounced around. 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, ah, you know, how much more am I going to learn in five years than I haven't already learned over a summer? Pretty cheeky, perhaps. So I was kind of a little lost. But assuming I was just going to go back to Bain management consulting firm. That was my assumption, going into business school. Then out of nowhere, in the beginning, 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 had 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'd worked with him at Bain, he was a year my senior, he had already graduated, gotten the Greylock on the east coast and they wanted to create a west coast presence and grow the office out here. Lo and behold, I'm like, wow, this from his description sounds interesting. Let me look at this. I had never met a venture capitalist, knew nothing about it and there was no Internet. So I mean there was, but there wasn't really like we know it today.
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A
It was Bitnet, it was arpanet, it was limited to.
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B
There wasn't like, oh, let me just look up what is venture capital? Let me have the AI explain it to me. Right. And so I did happen to find a gold mine for me was a fellow named Chris Alden, who was a co founder of a magazine called Red Herring.
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A
Yes, I know. Chris was the other magazine, right?
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B
Bingo.
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A
And they were chronicling venture capital in the early 90s.
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B
Exactly. Print format, you get like a subscription. And Chris in particular did a monthly, I think it was monthly column called VC Whispers, where he would talk to venture capitalists and ask them, what do you actually do? What is your firm actually like and how is it different from others? Information that was impossible to come by otherwise. And so he was really great at like pointing me to, well, you know, Greylock's kind at that time they morphed a bit. But at that time a very conservative, white shoe, button down kind of firm, like the opposite of me. Same for Sutter Hill at the time. And again they've changed a bit. But at the time they were very different from the firm I joined, which was called Draper Associates. But folks like Chris helped point me out, point me to the sort of wide spectrum of diversity there 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 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 an ever changing, not ever changing, but sort of a drifting focus area over time from Internet to deep tech.
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A
Tell me about the first investment you were ever involved in because you must have diligence and met with hundreds of founders. But it's always that first check that you are responsible for champion and putting in.
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B
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. Well, no, that's. Well, okay. So I think Fastparts might have been first and it's long gone, no one's ever heard of it. But it was a very unusual sort of B2B, if you will, trading exchange that dealt in the gray market of semiconductors. And the only reason this one was really interesting to me is I had done a, I don't know what you call it, a case study or an entrepreneurial exercise at business school, almost with the same idea as the entrepreneur. I was like, whoa. Like, it clicked on so many levels. I'm like, wait, wait, you're doing that? So to make a long story short, there was perhaps a lesson there for me about not relying too heavily on something I think I know a lot about instead of is this actually a good business opportunity independent of what I might know about. So the fact that it clicked was like, oh my God, everything lit up for me. That's something I was thinking about starting. But it ended up just going out of business. The Interwoven went public. It was a sort of web Internet tools company for content management, you know, as websites and web properties got more complex. How do you manage all these assets? Almost like an infrastructure layer for the Internet. And then there was Hotmail. Now Hotmail was a really. Yeah, that was like the one people have heard of. Boom. Like yeah, that was the first one that got visibility.
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A
Bought by Microsoft.
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B
BINGO. For about $400 million, you know, less than two years in. And the thing that it really epitomized was first, you know, rapid Internet growth for us. Second, this thing we. 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 spread of marketing in a sense like a virus which came from the signature.
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A
When you sent a Hotmail, it said sent by Hotmail or powered by Hotmail.
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B
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 not me. And I just want to give credit to him because it was incredibly cheeky at the time to embed a commercial message involuntarily to everything sent by your customers. So you signed up for Hotmail. It feels like a normal email account. But now every message you sent has this. Get your free email@hotmail.com right. Like a call to action.
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A
Controversial.
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B
Exactly. Tim wanted it more controversial. He was pounding the table that he wanted to say as if it was written by the sender. P.S.
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A
I love Hotmail.
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B
I love you. Get your real like you.
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A
Tim's EV guy.
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B
The founders wanted like nothing to do with that. They're like spamming, you know, accusations people forget.
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A
And you could explain to people that the Internet was non commercial. That's right at its start. And the idea of doing even a Zima malt ice liquor ad, banner ad on Wired was. Was faced with fierce resistance from the venture entrepreneurial and early Internet community.
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B
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 what we wanted to describe. What is this process by which they tell two people and then they tell people, you know, this geometric explosion which by the way Skype then used later for voice and other companies use for video. And it sort of, you know, was a playbook if you will, for how best to grow a consumer facing business. And there Are a bunch of precepts and things that related to that. Like what is your viral coefficient? Can it. 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 it's. But 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. Like amazing.
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A
DFJ of all.
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B
Yeah, yeah. It was called Draper Associates, but it eventually became DFJ a couple years later. But we did a lot and we learned a lot quickly. And then something really big changed. By the way, around 99, I was still super gung ho about the Internet and its potential. I know that because I wrote articles saying I'm super gung ho. But I pivoted completely away and stopped investing in Internet companies. And the reason wasn't because I saw a crash coming. I was not able to foretell that with any consciousness. It was that everything was looking the same. It was really starting to get boring, which is like derivative products. Yet another way to divvy up a market and sell to consumers. Yet another way to do a B2B trading exchange. So it was B2C B2C. They B2B and B2C and they all look the same, just variants on a theme. We not yet had social media, you know, have its boom. We not yet have things like Uber and all those that were yet to come. At that point, it's like every business plan was like just another variation on a theme like this. Just it's not interesting. Let me and a gut sense, it's not diversifying to portfolio. It's probably not wise investment to just be paying higher and higher prices for more of the same. Even though a lot of other conventional arms 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? So you could have made the argument, rightly so that it's foolish to invest in biotech. It's foolish to invest in semiconductors. Why? Nothing could beat the Internet for rate of value creation for a while. But I pivoted hard to some that actually turned out to be not interesting. Nanotech.
- [20:51]
A
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. 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 dot 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. 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.
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B
If you will.
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A
But it was such an interesting space. Explain to people you know MIT and 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?
- [22:28]
B
Yeah, 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 inspiration for Neural Link. There was Eric Drexler's book in 85. I remember reading it, maybe 86. I think it was 85 Engines of Creation, which was just a deep dive into if here's the big thought experiment. If you could put an atom wherever you wanted and you weren't burdened by where we are and the tools we have today. But if somehow we could imagine that future where you have atomic precision and you could imagine if you could do that, you could also build machines that could build more machines of their same ilk, you could have self replicating machines. There were a lot of just analyses being done by Foresight Institute and by Eric himself that just pointed out like, oh my gosh, this is insane. It's so mind bendingly different from what we're used to. The absence of friction on some of these rotary bearings, the rate at which these things would mechanically move. You could actually imagine a mechanical computer, if you will, that outperforms anything we know of with a better energy footprint and such. And so there are books like Diamond Age by Neal 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, folks in nanotech that first got Christian 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. 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 nanoscale if you will, and build up from there over time. So that felt like powerful and immediate. The other path, what most people were thinking about was a top down approach 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 can't scale down to nanometer scales. Like why not just work our way down where you inherit the interfaces to the real world from above. In other words, if you had a chip of a certain scale and interconnect from Broadcom or whoever, that works. Let's just figure out how we can make smaller, smaller things, but harness to what we have from above. And that just was going to take like 20 years is what I estimated, maybe 30. But I was, 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, it's slow but sure we're invested in some things like laser lithography 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, right? Different people doing different investments that had no interface whatsoever to it.
- [25:50]
A
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. 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 and why it's so important and why you'd think about it so much.
- [26:31]
B
It's the thing I think about the most that also seems the most descriptive for understanding the world we live in today and where we're heading over the next five to 10 years. It has incredible predictive power. So let me set it up and describe it. So the years on the bottom, pretty easy to understand. We're covering almost 130 years here of time. The dots are the best price performance computer ballpark. You know, like there aren't any dots above the line that we know of, but there are plenty below the line. Sort of the frontier of the best price performance computer of the day. And the axis, most importantly on the Y is the choice of what it is and that it's logarithmic, meaning every tick mark there is 100x100x100x, right. So a straight line on a graph like this is an exponential. And if you eyeball it, it almost looks like it's a slightly upticking curve on a double exponential. But what the axis is showing is how much computation can you buy for a dollar constant dollar meaning inflation adjusted. So what's fascinating about this is that first it looks like it's on rails for 130 years. This is kind of mind blowing and it's covering entirely different technology substrates. So on the far left you have mechanical Devices, you know, that did the census in 1890. I mean like literally machines are 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 50s and 60s. And then you have the integrated circuit era that started interestingly with the lunar module guidance computer which I have around the corner here in the office. And one of the early IBM machines of 360 were some of the earliest to bet on integrated circuits.
- [28:05]
A
Yeah, the amount of computer compute here and by year it is every 18 months it doubles. Is technically Moore's law's definition.
- [28:15]
B
Yes, that's a fun good that you mentioned 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.
- [28:26]
A
Turns out I learned it in computer science school.
- [28:28]
B
Exactly. It turns out Gordon Moore never said that. In 1965 he predicted, actually what he predicted it was very peculiar to the integrated circuit industry in fab yield optimization, which was what will be the number of transistors on the ideal die size. Because you could choose to want big chips within 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 you 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 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 transit, 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. 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 curve. They didn't know. No one had graphed this until 1999 time frame, 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?
- [30:16]
A
It's very strange. Nature has. The universe, dare I say, has some rules to it that emerge and we become aware of them. As you're pointing out, when Ray sort of made this chart, it's like, wait a second. All right, at my Founder University, my number one rule is to listen to your customers. Why? Well, delighting people who use your product. It's like job number one for founders. But how do you know what your customers really think about you? Well, we all know surveys kind of useless. People just tell you what you want to hear, or they just Click the number 8 of 10 over and over and over again. What you really need is Perspective AI. You just give Perspective AI a simple prompt telling it what you want to know. Am I connecting with the right customers? Is this new feature working? Is my UI clear and easy to navigate? Whatever question you have as 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 edtech, and he thinks the show features too much political news. So we're dialing that back and we were able to set this whole thing up and start generating these reports in minutes. So here's your call to action. Sign up today at GetPerspective AI Twist. To get two months free, you gotta try this product. It's incredible. GetPerspective AI Twist. We're following a pattern that we didn't know we were on, which makes one wonder today, what patterns are we on that we can't see because we're too close to the proverbial elephant? You know, we see a gray wall, but if we step back in 20 years, you know with AI and we're going to get to that what is actually going on here and what your chart shows as you continue it. If we fast forward past intel and 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.
- [32:43]
B
Well, they had a variety of tricks. They, they tried to build to the curve. They did definitely with intel inside to try to corner the market. But what they missed was a transition to a fine grained architecture that you can just. Or another way of phrasing it is intel in their development of the cpu. The, 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 that 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 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 EE's like that's what you want. A bunch of local memory, a bunch of local computation. That's more akin to how the brain works frankly. Almost like recapitulating our own evolutionary advance with the cortex over simpler sort of limbic brain 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. Oh wow, that's cool.
- [34:13]
A
Yeah, don't worry about it. That's the reactions on Zoom.
- [34:16]
B
Exactly.
- [34:17]
A
Hilarious.
- [34:18]
B
In any case, Nvidia has taken that baton and has for the last decade. And lately there are a lot of custom silicon solutions. Right. Google has their TPU and the Tensor Processing unit. Amazon's developed their own chips. OpenAI of course is working on their own chip. All the major AI companies that you know of have their own semiconductor efforts underway because that is an inherently better way to do AI. Workloads where you're doing the same matrix multiplying AD over and over and over again for 99%.
- [34:43]
A
So interesting too, if we go back to business, because we're kind of looking at the spiritual here. Like, how does this thing exist in the universe? Then you have the business of like, well, how do we capitalize on it? And as you pointed 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.
- [35:11]
B
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. 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 before you, like recombining two things or combining two ideas across academic disciplines that hadn't been combined before. And therefore, the number of possible ideas in the pool of ideas that humanity has is growing combinatorially. The number of subsets that you can draw around two, three or four ideas that are interesting to recombine a new product is. Is growing. As Reed's law goes the two to the nth power, so n ideas to the nth possible pairings or. Or subgroups that could be the fundamental dynamo of this perpetual sense of accelerating change that we're. That like every year feels like, oh, my God, we're doing 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 sure.
- [36:11]
A
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 Ms. Paradigm shifts, it's so unbelievable how predictable it is that they can't make the jump. And we were watching the CPUs from in the Pentiums and the little ding, ding, ding, you know, sound that intel would make for their commercials. Intel inside. But they couldn't figure out that nobody was buying new laptops because the CPUs did not change the experience. Now, you said earlier in the conversation with the Apple ii You took the chips, you bent them, you put them into the motherboard and you had a totally different experience. But again, that wasn't going to change things. And they eventually the determining factor when you bought a computer somewhere in the 90s, when you were a player 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. 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.
- [37:40]
B
Well, yeah. So I think it's fascinating you think about the rise of GPUs. So first, as you mentioned, it was a way to do polygon rendering 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 across a visual field and you're trying to distributing computation across all that. Now it was all initially developed for gaming, right? Okay, we're trying to rep. You could almost to represent the world in a simulation, if you really want to get abstract about it. 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 Evolve 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 the local store that used to be here, so stuff. And I've bought the equivalent of like one of the most powerful supercomputers on earth for just a few thousand dollars. Like I literally, in what I'm doing right here at home in his living room, can outcompete like the national labs in this molecular modeling. I started 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.
- [39:23]
A
Crazy.
- [39:23]
B
And so I was like, I did blog posts about that. I'm like, what is this thing? How can this possibly be? That was before the AI application came, right? So in 2012 with ImageNet competition, there's this thing called Alexnet that 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, you know.
- [39:52]
A
What time period was this? That this.
- [39:53]
B
2012.
- [39:54]
A
2012, yeah.
- [39:55]
B
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 our. We started looking at AI investing. Back then there were no, no venture firm had AI on their website as a sector of interest.
- [40:13]
A
Yeah.
- [40:13]
B
By the way, it's like there was.
- [40:15]
A
Machine learning on the margins, right?
- [40:17]
B
Bingo. Yep. And even that vernacular started to come in later when deep learning as a tool 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, that 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 at the time thought they're going to be focused primarily on gaming because that's where they're making most of their money. And this AI stuff will be an afterthought in 2014. Let's make a dedicated chip that does nothing but AI acceleration, you know, and optimize it even further still more units, more local memory switch fabric like you'd have in any networking chip in the 90s. And like, boom, you just throw it together and it makes perfect sense. It's like in some ways so obvious. That's why you have like 40 companies that tried, you know, about a five year period to do something similar. And that's really carrying Moore's Law forward now, like the majority of computers deep in the bowels of Google and elsewhere on these custom chips. And it's kind of interesting if you think about this migration. Single processor, massively parallel or fairly parallel gpu, now massively parallel custom chips. And then potentially we think the next step would be analog chips. So going even closer still to how the brain works, do things in the analog domain instead of digital, just like our brain does. This is like 40 watts. And nothing we're building in silicon today competes with it on power per capita calculation. But, but it is possible.
- [41:48]
A
It is interesting that you bring up the wattage this year. Actually just in the last 90 days, people stopped talking about how many H1 hundreds they were building. You know, remember when Colossus got built in under three months, 100,000 H1s supposedly. This is crazy. And now all the announcements are being done in how many measured in watts. None.
- [42:10]
B
Yeah.
- [42:10]
A
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 now. And you mentioned stim simulation theory. And the great breakthrough comes not from how we think, but from how we see and process the world or visual creatures. The world is visual, you know, now we start getting into consciousness, you know, and the nature of what's going on in our brains and what's going on in these giant clusters. Where does that lead you?
- [43:02]
B
Well, it's an interesting line of thought 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 down grade 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, 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 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 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 grow these things, the processing of information to train these nets is very similar. And so some ways it's beautiful that the world allows itself to be, to have this compressed representation that, for example, the laws of physics are all very low order polynomials, that the hidden formula isn't all possible formulas to be like, what is the trajectory of something? If you're trying to just deduce it from data points, what is this trajectory of an object? It's probably going to be some low order polynomial in almost all physics that describe things. So there's this almost natural way in which the world around us lends itself to neural nets. And by analogy, that neural nets really came from mimicking the brain, but in a abstract way. You know, neurons, weights, or 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. And the way the whole brain works is it's predicting in a sense, the next token, it's predicting the next thing you're going to experience across all your senses. Visual, tactile, auditory. And only when something differs from what you just predicted are you even conscious that it just happened.
- [45:26]
A
When you look at the landscape, it's kind of like a security camera that's like, nothing's changed, Nothing's changed. And then mountain lion. Okay, I'm gonna send an alert.
- [45:36]
B
Exactly.
- [45:37]
A
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. 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 actually.
- [46:31]
B
Growing neurons on silicon, right?
- [46:34]
A
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 takes the baton from these GPUs.
- [46:51]
B
20 years from now? Right, so 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 H100 like it's specifically Nvidia, or is it going to be my internal team that's been trying to build a chip that's compliant? 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's in the immediate term. There are a handful of companies that are not using literal neurons because the little neurons thing I might put more in the nanotech bucket of. It's a difficult interface to manage. But if you go to just analog silicon, you have this capacity to do some pretty crazy things. So a company 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 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, 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. You have all these extra ones, you have readout stuff around them. It's, it's, it's a lot of transistors. But in the analog domain, in a single flash memory transistor, you can actually do the matrix multiplying add, where the add is just a wire of common current and you have a row of these transistors. Make a long story short, they've shown this you can make basically a neural network chip that works in the analog domain that's, you know, a thousand x better on power, for example, back to power per calculation. So more akin to the brain. So you know, massively parallel, slow and low power is where that vector would be taking us. And there are others brand new. Our most recent investment just last week, Unconventional labs. They aren't really saying exactly what they're doing, but they're in this domain also of analog and IO mimicry. It's the same guy, by the way, who started nirvana in 2014, that I mentioned when I first invested in semiconductors for AI. Then he started another company, the databrick spot. Now this is his third startup. Anyway, so Analog has a lot of potential. It in some ways feels like the natural trajectory of this recapitulation of our biology, if you will, in the substrate. And it has applications not only in traditional AI, as you might think of it, but also really small neural networks that you might stick in everything. So Jensen, CEO of Nvidia a few couple years back, said that we're 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 Nvidia is about to launch. It was Edge AI, basically putting little neural networks, trillions of little neural networks, little brains into everything. Every security camera should have one. Obviously every car is going to have them, every moving object, every autonomous vehicle wearable, every sensor could. Like, wouldn't you want a little more intelligence in any consumer product you can think of? Right? And by the way, this. So imagine like a voice interface, for example, that's speaker independent, large vocabulary. So it actually works. Could be in anything for less than the cost of the plastic buttons 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 to and say, hey, like, don't go in the corner. Hey, can you get the bedroom? Or don't stop bugging the cat, whatever it might be. It could be that intelligent. And it would be local intelligence that doesn't require the latency or any of.
- [50:28]
A
The overhead of no cloud, no security.
- [50:30]
B
Issues, privacy issues, local data retention.
- [50:34]
A
Such an interesting concept. I want to take a pause for the cause here and show a clip of you 10 years ago or so talking about robotics. You and I haven't seen the clip my crack research team are going to play for us. So we get our reaction to it in real time. In real time. So 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. Okay, try one more time with sound. Let's reshare and do sound. Then I'm going to do a pickup for you. I found the. I think I found Clinton talking about nano which we'll splice into the other one.
- [51:20]
B
Nano.
- [51:21]
A
Nano, here we go.
- [51:24]
B
Something near term that is kind of interesting. Led by Rodney Brooks. And there's more than one company doing this now which is humanoid robots for the workplace. So the reason I hesitate about San Francisco would be I'm not sure if it'll percolate into. I mean even though I bought one of these just large, the average person doesn't really have. No, do I. I haven't really used for it yet. But in a work context these are two handed robots right now on a pedestal. So they don't walk around but they. You program just by moving the arms. So anybody can program.
- [51:53]
A
So yeah, it was hard for me to even hear it. Do you have a response to it, Steve?
- [51:58]
B
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 multiaxis 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 or thought was what would these robots be good for? Well, as long as they had the same lifting capacity, accuracy, precision, whatever as a human, then you don't have to ask the question you say wherever you have in their particular case a sedentary human doing some repetitive task or even less repetitive task, but basically sitting at a desk, you could just swap out a robot at a much lower cost. Challenge for that company ultimately failed. It came really close to having a great exit and 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.
- [53:14]
A
Brutal. It's so hard when you have the right idea and the timing's a bit off or the go to market. Give me the postmortem here. What didn't go right?
- [53:23]
B
So what didn't go right was at the end of the day, they called it sort of like The Shaky bot. There was this idea of, am I drifting from what I'm doing? Let me quickly correct it. There's some hysteresis in that. So they realized towards the end, as they were running out of capital, that they needed to design their own motors. And fast forward to today, that's the same conclusion that Xai and Optimus and all the. Not Xi, 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 Elon speciality.
- [53:53]
A
Is building the entire stack.
- [53:55]
B
So that's his power alley.
- [53:56]
A
Yep.
- [53:56]
B
Yeah, but back then we didn't know it, right?
- [53:59]
A
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 yeah. Dipshits who were making the first roadster came to him and were like, the parts combined equal more than the cost of the car. And you and I paid 150k for that roadster and it's like, wait a second, you're spending more on the parts. And he had to deal with parts suppliers. And he learned this brutally hard lesson, which is your production is as fast as your weakest supplier and your, your product's as good as the worst component in some ways, you know, or it could be. And man, it's just amazing how over. Yeah, close to 20 years now he's just decided, fuck it, I'm making an H vac and I don't need the steering column. I guess the model S, the original steering column and the.
- [54:51]
B
Yeah, they got from Daimler.
- [54:52]
A
Yeah, the guy from Mercedes.
- [54:53]
B
Yeah, yep. So that, by the way, you know when you're starting and you don't have much capital. So the Roadster era, they had to use as best they could off the shelf parts. So famously, the battery cell was the big leap forward. Every other electric car company that followed went with these prismatic pouch cells that are custom made for automotive and they're like, 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 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 that Just a two speed transmission would.
- [55:36]
A
Just rip the transmission in half.
- [55:37]
B
You just destroy it. And it wasn't until this special bipolar transistor came out where JB Starwell 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 or 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 runs, the motor 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 ethernet cable that they needed to be able to keep shipping vehicles because the ethernet cable didn't exist from some supplier. Same thing under the covers was happening at SpaceX. Basically vertically integrating. If you think forward to why the Model S was such a breakthrough vehicle, the Roadster had its challenges. It wasn't for everybody in terms of comfort, handling. I mean, it was good.
- [56:37]
A
You still have yours or you just missed some money?
- [56:39]
B
No, I gave it to a different museum.
- [56:41]
A
Peterson.
- [56:42]
B
Yes, Peterson Automotive Museum. I think it's the largest in the U.S. yeah.
- [56:45]
A
Yeah, I still have mine. It's right over here. Number 16 in the garage. I redid the battery pack.
- [56:50]
B
I have 17 of the Roadster. Yeah.
- [56:53]
A
You know how I got there was a venture capitalist. I'm not going to say their name, who ordered probably right before you. And they. And I subsequently ordered like, you know, after. I might have been at like 100 or something. I was in the Signature 100, way up the last list. And then he invested in Fisker. Oh, and he stabbed Elon in the back. Yep.
- [57:19]
B
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 electric car and a hybrid car. They would call Fisker an electric car company back. 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.
- [57:41]
A
Yeah, it was garbage.
- [57:43]
B
They didn't get that. The electric vehicle transition is very different. And it doesn't leverage anything that the.
- [57:49]
A
Whole idea is to not go to the gas station and not have those.
- [57:52]
B
Parts, not have all the overhead of a gas tank. You basically double the complexity with a hybrid versus a simple electric car.
- [57:59]
A
It was bizarre. And so the person, I think, if I have my story correct, proactively cancel their deposit happened to be sitting next to Elon having a meal, and he's like, oh, this person canceled the car. And I said, oh, where are 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, and I got the first orange one. I still think it's the best color. What was your color? You went red?
- [58:30]
B
I do.
- [58:31]
A
I went.
- [58:31]
B
No, I went for racing green, which was their signature color. I think I even have the.
- [58:38]
A
I think you might have been the first race in green for sure.
- [58:41]
B
I'm not sure. I'm not sure.
- [58:42]
A
I don't know.
- [58:42]
B
But I loved it.
- [58:43]
A
Really beautiful.
- [58:44]
B
I got a custom roof, so, you know, the. The roof option, I got 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 side.
- [58:57]
A
Like, there's something about that aesthetic that people really like. I've been watching Corvette is having a renaissance. They used to own a Corvette before I got my roadster, and I traded my Corvette in for a roadster. They have this new one, the ZRX one, which is the greatest hypercar ever built at this point, 1250 horsepower. But anyway, what Corvette drivers like is just raw carbon fiber. That aesthetic looks so pretty cool. Beautiful.
- [59:21]
B
It just.
- [59:22]
A
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 crazy. Why is it so expensive?
- [59:36]
B
Carbon fiber, hand done. I believe, in the case of the roadster was in Italy, if I recall, and. 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.
- [59:55]
A
Yeah, I did want to reveal those secrets. Here's a clip of ELON Talking about 2025 and 2015.
- [60:01]
B
Oh, wow. Well up that clip 10 years ago. Oh, I Was interviewing him, your question. So in terms of what, what I think 25, please. So for. For sure, ubiquitous computing, AI, that's beyond anything like the public appreciates today. I think we'll have most of the new vehicles being produced being electric and we'll be. Probably have a super majority of energy being produced, being sustainable. So I think, I think we're on headed solar. Primarily in your mind. Primarily solar, yeah.
- [60:41]
A
Wow, interesting.
- [60:43]
B
And that was, by the way, that was me interviewing him.
- [60:45]
A
Yes, yes, that's where we put it in. Sorry about that. We should have included a video.
- [60:48]
B
It was just like, whoa. So I haven't revisited that clip. So the first one nailed it. Like, like AI, Boom.
- [60:54]
A
You're soaking in it.
- [60:56]
B
It's just dripping out of our ears.
- [60:59]
A
Literally. We're like literally in the matrix right now in the, in a bath of AI. Okay, let's go to energy and solar.
- [61:06]
B
And so. 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 majority solar on the planet. We may not have the majority of vehicles, electric vehicles, simply because people hold on to cars for 12 years on average, 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, the new sale. So there's inertia, there's political shenanigans, there's weird ways that people, you know, forestall the future and prevent the inevitable future from manifesting. So you look where incumbents, right? So I think one thing you can say is today, in 2025, Elon believes in all three of those very strongly. Right. Even though we haven't fully realized solar's potential or the EV potential. So I would go further and say in 2015, it was obvious to me, and it's even more obvious today that it is inevitable that all vehicles will be electric. 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 electricity. Solar. He said sustainable. And then I had him double click, mostly solar. That's his point of view. Solar storage solves the problem. And as a company that makes a lot of solar and storage, not specifically storage, you can understand why that's important. I would just stick with this first answer, which is clean energy will be the novel feature, and that includes nuclear. Right. So it's obvious that fusion, fission, solar are our 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 that. We shouldn't be burning coal, we shouldn't be burning gas. It's just. But it will take time. Right. Because it's, it's the drug much of the economy is addicted to. Right. Currently, yeah.
- [63:13]
A
And if you look at that time, Solar was like 1% of electrical in the U.S. 2015, 2025, solar's 10% renewables. You put wind in there, you get to 17%, according to Claude AI.
- [63:30]
B
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 Emory 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 were trying to distinguish nuclear from all the others. And really, if you think about it, how about zero carbon energy or zero pollutant energy? That would be a better term for what we want. And that would be solar, wind, geothermal and nuclear. All types of nuclear. Right. It's, it's. So that's the category that's inherently distinct from fossil fuels.
- [64:10]
A
Yeah. And if you want to know why all of this happened, this hatred of nuclear. Nuclear, 1979.
- [64:23]
B
Ah.
- [64:24]
A
Jackson, Brown, Crosby, Stills, Nash and Young.
- [64:26]
B
That's right.
- [64:27]
A
John Hall, Doobie Brothers.
- [64:30]
B
Yep. So interesting. Notice the Bruce Springsteen.
- [64:32]
A
And this is no new Scott.
- [64:34]
B
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 mushroom cloud, that's not Chernobyl. That's nothing that doesn't happen in those plants. Right. 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 got to 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.
- [65:16]
A
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 is a better idea because of Fukushima.
- [65:30]
B
So get a little 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, it's underneath my. Oh, yeah, it's great. I just, just finished it a couple days ago. So when Putin invaded Ukraine, at that moment, Germany was sending $220 million per day to Russia because of the oil, the gas dependency. Then they shut down the nuclear power plants as well around Fukushima time. And the estimates are that currently they're dealing with 1100 excess deaths per year already because of the extra pollutants from burning fossil fuels inside nuclear. It's mind blowing. Like at Fukushima, no one died from radiation. About 2,000 people died because they did a botched evacuation of the region and the way in which they relocated people, they died. Hundreds are dying every winter now because of higher electricity costs. It's just mind blowing. About 28 people died in Chernobyl in total. More have died from fear of nuclear in the discussion about nuclear than from nuclear itself.
- [66:32]
A
The panic attacks have done more. And if you look at coal taking.
- [66:35]
B
4 million lives a year, 4 million lives a year from coal particulates in the air.
- [66:40]
A
It's unbelievable, you know, and, 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, like, solar's great. And he's like, there's a place for solar. But he keeps calling it unreliable. And I'm like, put batteries with it. He's like, oh, there's no batteries. I'm like, what? Ask Australia. Yeah, like, think it through. 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 good oil. Right.
- [67:21]
B
It's another oil patch.
- [67:23]
A
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, we'll build a seawall, and they're like, yeah, but every 100 years there's going to be a storm. And literally 50, whatever number of years later the storm arrives.
- [67:48]
B
Like, don't put the diesel on time, below sea level, so that if you had a wave, it's going to be like, they're so the same with Chernobyl, by the way. Chernobyl, it was the worst nuclear accident we've ever had, is the only one that actually had some deaths associated with it. And small, small number, by the way, much more 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 the lesson wasn't developed. Don't.
- [68:29]
A
Don't build another ship ever again. Build a better ship, people. And here we go. All right, let's go. I don't want to run out of time with you. This has been incredible, but I want to get into AI a bit. In your mind, the pace at which we're moving and getting to. I'll just use two terms. We'll define them here for ourselves. People will debate them. Artificial general intelligence, being the smartest human on the planet in any discipline. Lawyer, accountant, chess player, whatever it is. And then we have super intelligence, being an intelligence that we can't comprehend. You know, a level of intelligence that you take all the humans together and then times it by a hundred. And it's something else. It can find questions to answer we don't even know to ask. Where are we on this timeline? Well.
- [69:26]
B
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 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 any 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, you know, lesser animals. It's like on the scheme of things, like those two humans, on an intelligence spectrum of AI, let's say just AI's trajectory is like, you know, going from one human to a collection of humans, applying their intelligence collectively in a group kind of setting. Because otherwise it doesn't really matter how many humans we have on Earth if they're not acting in coherence as a team. If it was twice as many or half as many, it doesn't matter. What matters is are you a thousand times as smart as any human has been? That comes quickly after even simply Moore's Law. And in AI, actually, we've had Moore's Law doubling every year and algorithmic improvements of a doubling every year for like 15 years now, which is kind of astounding. That makes a big difference. So I think those come quickly. And so some of the evidence of that, these models today already outperformed at almost any task you apply them towards with a little bit of specialized training. There hasn't been a domain where you're like, oh, we can't do that. I'll give an example. Medicine. I was just at Stanford last week getting the update on 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, my humans are just holding it back. Then the next one is not just diagnosis, but the course of therapy. What should we do given what we just learned? They also outperform there. And then best of all, blow the doors off the human on empathy as reported by the patient. So if you have a chat interface where the doctor is talking to the patient through that same interface, or an AI, the AI blows the door off on truly understanding me, conveying the situation, and understanding on some really tough Issues like, let's say end of life care for a parent who, you know, do you pull the plug or do you.
- [72:21]
A
You know, like, yeah, schedule really tough conversations.
- [72:24]
B
They blow the doors off humans. So we're already there. And Elon would talk about a number of PhD level equivalents for all the PhDs. The challenge then is going to be. You alluded to this earlier on, the emotional response. We were talking about limbic systems and what have you earlier in our conversation. There's still something missing about 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 are the flights and the hotel and the things I might do for kids of this age and you know, pull it all together with a series of steps. Same thing. Agentic flows in programming as well, right, when you're doing 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 an emergent property by the way of forecasting the future. So let me share a bizarre thought. 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 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, we retrospectively rationalize what we just did. Like, yes, right.
- [73:49]
A
And confirmation bias.
- [73:51]
B
Why?
- [73:51]
A
All kinds of biases, but at a.
- [73:53]
B
Very low level, like in the microsecond level that like I just did move this finger and then I was like, I intended to move this finger. Okay, yeah, perhaps that's what's going to happen with our artificial systems as well. With next token prediction they'll be like, how am I, you know, retrospectively making sense, making of what I'd done? And there could be some vote taking circuits that we built. We might have to build some circuitry for this, I guess some vote taking circuitry that's in the feedback loop of what we retain is novel that will then sort of bootstrap this sort of consciousness or intelligence. The perception of free will. The perception of consciousness might be a phenomenon of that There are others who think we need to take a neurosymbolic approach that we need to literally recapitulate if you will, these lower level primitive systems that are emotive and what have you to actually have an emotion as opposed to faking it. Well, I don't know the answer but I do think we can do a lot more experiments now than ever before. We can run these evolutionary sort of feedback loops in ways that will potentially bootstrap intelligence. And it might come from some heterogeneity, it might come from just the sheer approach that we're taking. But it's not obvious that we're there now that what we've built isn't like a baby version of oh, it'll just naturally be on, you know, self directed that that takes a different bootstrap from the current vectors that are there. So I think what you 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.
- [75:27]
A
Things for you can do a lot of things but it can't beat Darth Vader. Exactly.
- [75:32]
B
You wouldn't.
- [75:32]
A
It's not going to outsmart Han Solo. Not happening.
- [75:36]
B
There we go, taking the analogy. Next level.
- [75:38]
A
Never tell me the odds it's not getting through an asteroid field.
- [75:42]
B
That's right.
- [75:43]
A
Not a chance.
- [75:43]
B
I remember that.
- [75:45]
A
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 shotted if you've heard it, where your relationship with AI becomes the dominant one in your relationship. Sam Altman just announced he's going to make it spicy. You know, make a little spicy in chat. GPT for adults. There are other companies that are. I get pitched all the time for the last couple years on like an AI therapist and I'm like, you know, 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. I don't want to get into AI regulation because it's kind of dumb but it's, 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. They never have a bad day. Everything is sycophantic. This seems like a road to purgatory to me. What do you think?
- [77:10]
B
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 form forms. When some financial services firms put AI chatbots in place to try to do customer service, try to lower the customer service workload. They found certain subset of people who became romantically attached to these people and these entities and just couldn't accept the fact that they weren't real, 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, you know, we were cautious at first because there's some mentally ill people out there and we don't want the AI to, you know, if it's sycophantic or just repeating back to you what you want to hear, amplifying some of our baser natures, like, oh, yes, you should do that terrible thing, or yes, you should be, you know, bad, violent, what have you, whatever it might be. And so here's the challenge, and you alluded to regulation, you alluded to how do you direct these things, is this whole mode of AI development is not like traditional engineering. And this is something that I've yet to meet a regulator or politician who understands this at all. You can't say, take that, let's say frontier intelligence of any kind. So something that's like state of the art, something interesting. 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 going to work on that. We're going to try to make that happen. They have yet to make meaningful progress in this meaningful meaning result that would give people comfort that this is doable. And I would assert it may never work, that we may never reverse engineer an intelligence in the time frame of relevance. Meaning we'll just have built a better one before we reverse engineered the prior one. Just like we haven't reverse engineered our brain. These complex information systems, both brain and neural nets, are inherently inscrutable. We understand the interfaces, the ins and outs, but not the inner workings. You can't cut and paste functionality, you can't figure out the subsystems, you can't draw a box around. Well, there's the English speaking part and there's where it's doing math. None of that really lends itself to reverse engineering. It's same 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, hacks that we were doing, we knew what we were doing. And if something went crazy is like your paper clip of printing.
- [80:02]
A
Yes, it's going to run out of paper.
- [80:04]
B
You can quickly understand what you just did and fix it. Right. That does not apply to the complex interactions of AIs internally, nor they're 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 that 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. What you can't do is have any of these companies do. What California Bill initially proposed was like prove, you know, safety before you even start training. I mean, that shows you how ridiculous they were, right?
- [80:59]
A
Yeah. To your point, you can have a speed trap or a dui, you know, trap where you check people aren't drinking, but you know, people have a car.
- [81:09]
B
That's right.
- [81:10]
A
It has wheels, it 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, by the way, let me throw one thing.
- [81:22]
B
Absolutely not only premature, but 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, or whatever I think conservative humanity is. Imagine you could. You imagine you could do that, which I'm saying you can't, but imagine you could. The authoritarian regimes will be able to do this too. And you're gonna 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 authoritarian rule. You do not want them having aligned to their cultures and norms. AI's right.
- [82:11]
A
What you want, you're going to have a lot more Uyghurs in concentration camps, being tortured.
- [82:16]
B
No idea. And so I really believe in Elon, which is, well, in general, but in the case of, the way to get through this quagmire 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 with neural nets. The more RLHF or the more, in a sense, 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's the answer. It's more like parenting and policing. And then I think you might finally get a regulatory regime that makes some sense.
- [82:59]
A
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. I got attuned to this. Everything's going great except for the spread of democracy and authoritarianism. Democracy going down, authoritarianism going up. 54% of people live in authoritarian countries. That's why democracy is worth fighting for. And it's an interesting experiment, but it, 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.
- [83:44]
B
Absolutely.
- [83:44]
A
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.
- [83:52]
B
It is the path of doom and it's, it's frightening. The, the way, the way in which authoritarianism and theocracies, both of 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. You think about like kleptocracy, like Russia is the exact opposite. Like, you know, this, this cult of an individual and has this madman who can launch wars around the country is a symptom of, oh wow, that's over time.
- [84:33]
A
It's just, look at that democracy just boop, boop boop. Yeah.
- [84:36]
B
It is a precious, it is, it is the thing that allows progress. By the way, if you think about why I think authoritarian regimes ultimately will, will fail, it's because of the technology. They don't embrace change either. Politically at company levels, they, they coddle the, you know, the power brokers that are. And that in a sense is an architectural resistance to change and innovation and new entrants in, 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 or dictators or dictators saying, you know, same thing, planned economy, let's do X, let's do Y. It's never worked.
- [85:28]
A
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, bunch of companies disappear and now they regret it. And now they've got population decline. They got to worry about where everybody's going to get their jobs. They got 20, 25% unemployment with young men and democracy finds a way. And all these companies. It's a really beautiful sort of interesting wrap up point that we just kind of stumbled into, which is what we're talking about what you and I do for a living, what Elon does, what entrepreneurs do, try to create something new that makes individuals a bit more free, a bit more happy, a bit more productive, is the only operating system that seems to be pro humanity. And it really does start with entrepreneurship with people who are change agents and not the people who are seeking power, not the people who are seeking control. Talked about Star wars. Like the empire tries to control things. The more they try to control it, the more brittle it becomes. It's hard. It's hard to be an authoritarian because all the change is happening everywhere around you and you have to try to squash it and quell it and extinguish it. It's exhausting to be an authoritarian.
- [87:03]
B
Putin lives at the end of a two mile long tunnel in intense paranoia. It's kind of like Hitler in his bunker. Like, yeah, but he's stuck and his whole stuck, his whole country is stuck under him.
- [87:15]
A
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? 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 got Kim Jong Un's face lit up like, oh my God.
- [87:46]
B
Sorry. That was good.
- [87:47]
A
He's like, I'm coming in. Somebody loves me. I gotta tell 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 hang out. There's so much cool, great stuff in freedom and democracy. We just have to allow those people to experience it in their human. Here it is. This is one of the great moments in history. Look, here it is. You just look at Kim Jong Un's face, waiting. I mean, if they see you smile, he's. He hasn't smiled like this in his life. 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 him up. Let him come to a music house. Yeah, go to all of it. All of it would be amazing. Listen, Steve, you gave me more than an hour and a half of your time. You're one of the great humans on the planet, great thinkers. I love talking to you. And thanks for sharing so openly with the group here. I'm going to have you come back.
- [88:56]
B
In six months, please.
- [88:57]
A
And we're going to do this every six months. Can be a little check in. And next time, I just want to go through all of your portfolio, all this amazing stuff. If people want you as an investor and they're doing something absolutely crazy and it's a Hail Mary and it's going to take 20 years, but we'll change the world and shatter the last paradigm. How do they reach you?
- [89:12]
B
Sure.
- [89:12]
A
How do they reach out with their business plan or their ideas? And I don't mean to flood your inbox.
- [89:16]
B
No, yeah, no.
- [89:17]
A
We do this for a living.
- [89:18]
B
Our website is Future Ventures. I'm also most active on X Socially, but my email and everyone here at our firm is just our first name at Future Ventures.
- [89:27]
A
There you go. All right, Everybody. There's your 90 minutes with Steve Jervison. We'll see you next time.
- [89:32]
B
Thank you. Thank you, Jason.