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Speaker 1: What is special about inventors, How do we search for

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Speaker 1: and support creative people? And what does this have to

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Speaker 1: do with skateboarding tricks or silent drones or self driving ships,

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Speaker 1: or solar panels in space or shooting mosquitoes with lasers

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Speaker 1: and our future as a species. Welcome to Innercosmos with

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Speaker 1: me David Eagleman. I'm a neuroscientist and an author at

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Speaker 1: Stanford and in these episodes we seek to understand why

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Speaker 1: and how our lives look the way they do. Today's

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Speaker 1: episode is about invention. I've always been fascinated by invention,

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Speaker 1: not just new products and tech, but fresh ideas and

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Speaker 1: new frameworks and science and ways of seeing the world

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Speaker 1: that didn't exist before. The key thing about invention is

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Speaker 1: that it requires more than just absorbing information to invent.

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Speaker 1: You have to recombine what you know in new ways,

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Speaker 1: and you have to spot connections that others miss, and

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Speaker 1: sometimes you need to ask what if everything we assume

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Speaker 1: is wrong now? The human capacity to do all this,

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Speaker 1: to invent is thanks largely to a part of the

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Speaker 1: brain just behind your forehead, the prefronnel cortex. We have

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Speaker 1: more of this than any of our cousins in the

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Speaker 1: animal kingdom, And this is the part of our brain

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Speaker 1: that allows us to break away from the here and

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Speaker 1: now and to imagine counter factuals. In other words, things

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Speaker 1: that don't exist but could. We can imagine those things,

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Speaker 1: and we can simulate out their consequences.

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Speaker 2: And this is what allows us to step outside.

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Speaker 1: Our current moment and picture alternative futures. As I mentioned

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Speaker 1: in episode seventy two, I'm not certain that AI as

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Speaker 1: we make it now can do that. It's great at

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Speaker 1: piecing things together, but at least with the current architectures,

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Speaker 1: it cannot say, you know what, I'm going to scrap

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Speaker 1: everything that I've learned and think about this in a

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Speaker 1: completely new way, because AI is not wired to reject

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Speaker 1: what it has learned. But that kind of creative leap

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Speaker 1: is something that humans do all the time, often unconsciously.

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Speaker 1: It's what happens when a person cranks up the prefrontal

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Speaker 1: cortex and looks at the world as.

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Speaker 2: It could be.

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Speaker 1: Invention starts not just in the raw data, but it's

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Speaker 1: the decision to look beyond that. So without the neural

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Speaker 1: architecture that allowed for of human creativity, we would be

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Speaker 1: doing the same thing that we did millions of years ago,

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Speaker 1: just like every other species were surrounded with we wouldn't

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Speaker 1: have electrification of the planet and the Internet and commercial

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Speaker 1: flight and spaceflight and desalination and vaccines and quantum computers

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Speaker 1: and satellites and encryption techniques and self driving cars and

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Speaker 1: all the rest. And so today's conversation is about invention,

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Speaker 1: how we can spot it and value it and fund it.

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Speaker 1: And to this end, I'm pleased to welcome a friend

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Speaker 1: of mine, Pablos Holman, who's on a mission to change

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Speaker 1: the trajectory for inventors. He is a hacker and an

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Speaker 1: inventor and a venture capitalist, and what I really appreciate

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Speaker 1: about him is that in that latter role as a VC,

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Speaker 1: he's not just chasing consumer software trends, but he hunts

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Speaker 1: instead for hidden geniuses. He and his partner Michael Reid

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Speaker 1: run a firm called Deep Future, and the people they,

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Speaker 1: as they describe it on their website, are quote mad scientists,

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Speaker 1: rogue inventors, crazy hackers, and maverick entrepreneurs implementing science fiction,

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Speaker 1: solving big problems, and helping our species become better ancestors.

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Speaker 1: In other words, they're looking for the people who seek

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Speaker 1: to do a lot more than the incremental improvement of

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Speaker 1: the tools around us, but instead the.

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Speaker 2: People who leap into entirely new categories of possibility.

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Speaker 1: So Pablos has just written a book called Deep Future,

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Speaker 1: which comes out next week, and his take in the

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Speaker 1: book is that invention is humanity's most important creative force,

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Speaker 1: but it's also the one we've neglected the most. Inventors

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Speaker 1: are often misunderstood and underfunded because there's often not a

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Speaker 1: good system to support them. So even while we romanticize

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Speaker 1: starving artists, we typically forget about starving inventors. His position

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Speaker 1: is that we are missing out, and he always wants

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Speaker 1: to position himself at the edge of what is possible.

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Speaker 2: Here's our conversation.

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Speaker 3: So if you think about the creative classes of people,

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Speaker 3: you know, how many like I don't know musicians?

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Speaker 4: Can you name.

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Speaker 5: Dozens like it loves without even actors, dozens painters? How

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Speaker 5: about inventors that aren't dead? Yeah, I can name about five, maybe.

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Speaker 3: Five, And probably none of them have a business card

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Speaker 3: that says like inventor as their title, right, because it's

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Speaker 3: not a legitimate career choice.

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Speaker 4: And so there's this kind of weird gap in the world.

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Speaker 3: I think where you know, we celebrate our most creative people,

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Speaker 3: our people they you know that are easy to identify

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Speaker 3: their celebrities for you know, specially music artists and actors

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Speaker 3: and stuff. But you know, you could probably live without,

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Speaker 3: you know, the next Marvel movie. You could probably live

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Speaker 3: without the next Beyonce album. But if it wasn't for inventors,

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Speaker 3: you wouldn't be alive at all.

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Speaker 1: Generally that would include biologists making discoveries.

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Speaker 4: Sure, yeah, maybe, okay, right.

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Speaker 3: I think of scientific research as very important. That's critical.

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Speaker 3: That's kind of the early stages of figuring out how

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Speaker 3: the world works. That's the goal in basic research. And

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Speaker 3: then in our world we always need more for that,

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Speaker 3: more resources, more money, more scientists. But we have a

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Speaker 3: system for that. And then on the other end, you

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Speaker 3: have a system for like entrepreneurs and making businesses and

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Speaker 3: things like that. But those people are making businesses out

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Speaker 3: of whatever they can invent, and they don't tend to

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Speaker 3: be very creative. This is why, you know, of our

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Speaker 3: best businesses are like an app to have a stranger

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Speaker 3: pick you up in their car. You know, we're we're

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Speaker 3: not honoring I think the creative class in the middle

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Speaker 3: that is inventors, and we don't even recognize them as

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Speaker 3: a creative class. Like it's crazy hair and a DeLorean

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Speaker 3: in the garage, Like it's not even a It's just

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Speaker 3: people don't relate to it and understand, and it doesn't

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Speaker 3: have the same maybe emotional connection that you know, a

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Speaker 3: musician can evoke or a singer or something. But I

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Speaker 3: think it's like kind of important to step back and recognize, like, wow,

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Speaker 3: this is our most important creative class.

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Speaker 4: They don't have a place in society.

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Speaker 2: So let me step back to something you said.

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Speaker 1: I'm curious what you were thinking of when you said,

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Speaker 1: we wouldn't even be alive if it weren't for inventors.

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Speaker 2: Give me an example of what you're talking about there.

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Speaker 3: You know, whoever generate is yes, vaccine. Edward Jenner made

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Speaker 3: the first vaccine for smallpox. Four hundred million people died

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Speaker 3: of smallpox in the last century. Right, that's more of

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Speaker 3: people than I think we have in this country. Maybe,

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Speaker 3: Like it's a lot of people died of one disease,

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Speaker 3: regardless of what you know. I know people are wound

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Speaker 3: up about vaccines these days, but at least in the

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Speaker 3: case of smallpox, this is like one of the reasons

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Speaker 3: you and I exist.

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Speaker 4: I might even be here if it wasn't for that

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Speaker 4: type of it.

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Speaker 3: And that's like a really interesting invention story where he

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Speaker 3: figured out that these milk maids who were milking cows

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Speaker 3: contracted cow pox, which is not as life threatening, not

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Speaker 3: really as big of a deal disease, but then.

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Speaker 4: They never got smallpox.

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Speaker 3: Yeah, and it's like this incredible insight, this amazing discovery,

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Speaker 3: and like that guy figured out, oh, I know how

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Speaker 3: to go I could go test that, and he did,

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Speaker 3: and he figured out that we could now save people

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Speaker 3: from dying of smallpox.

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Speaker 1: Too quick historical that's of course where we get the

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Speaker 1: story vaccine from Cowska, right, But also it turns out

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Speaker 1: Edward Generous about the eighth person to invent the vaccine.

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Speaker 3: That's right, Yeah, oh wait, right, So there's also this

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Speaker 3: you know, this kind of you know lore that we have.

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Speaker 3: I would say that, oh, inventors aren't actually that important.

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Speaker 3: It was inevitable somebody was going to figure it out,

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Speaker 3: so like, but there are lots of these cases where

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Speaker 3: somebody didn't figure it out, or the person who figured

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Speaker 3: it out didn't get the word out. I mean, this

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Speaker 3: happened like with scurvy, even not all these people trying

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Speaker 3: to cross oceans dying. You'd just load up a ship

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Speaker 3: full of people expecting have of them to die of scurvy,

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Speaker 3: and some of them might make it to the other

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Speaker 3: side before we figured.

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Speaker 4: Out all they need is oranges.

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Speaker 3: Yeah, and like that was figured out and then like

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Speaker 3: shelved for I forgot seventy five years or something. But

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Speaker 3: I think it is a way of diminishing the value

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Speaker 3: of an inventor or an invention by saying, well, you know,

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Speaker 3: he just was able to do that because of the

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Speaker 3: you know, people who came before them, and standing on

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Speaker 3: the shoulders of giants. And there's certainly a truth to that.

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Speaker 3: Of course, we all start with what we you know,

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Speaker 3: learned from our our predecessors. But it still takes a

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Speaker 3: creative mind to do something the first time.

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Speaker 1: In fact, so the basis of creativity is your brain

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Speaker 1: goes through the world, absorbs all everything in your world

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Speaker 1: and your culture, your moment history, and then what it's

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Speaker 1: doing is remixing. It's bending, breaking, blending, putting these things

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Speaker 1: together in new ways. And so you're exactly right. There's

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Speaker 1: two ways you can look at creativity. One is Okay, Well,

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Speaker 1: it's just a remix based on the data that you

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Speaker 1: happen to have, and so it was inevitable that would

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Speaker 1: come up. But you're exactly right that to be the

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Speaker 1: inventor the creator is something really special because because not

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Speaker 1: everybody's doing it. Most people have all that data right

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Speaker 1: in front of them and don't care to get off

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Speaker 1: their butts and do something about it.

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Speaker 3: And like, you know, Edward Jenner didn't invent the transistor

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Speaker 3: or something. You know, he had cows and milkmaids in

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Speaker 3: front of him, So yeah, it makes sense that he

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Speaker 3: would invent in that area. But it still is this different.

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Speaker 3: It's a completely different thing. I would contend the second

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Speaker 3: time you do it to the nth time you do it,

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Speaker 3: that's a totally different that's craft. You might be better

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Speaker 3: at doing something than the inventor, but does it completely

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Speaker 3: different thing than figuring out what's possible the very first time.

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Speaker 1: Yeah, okay, so there's something super special about inventors. And

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Speaker 1: I know this is a big part of what you

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Speaker 1: do and what you really care about is finding inventors

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Speaker 1: who are making incredible things.

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Speaker 3: I want to find the inventors and I want to

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Speaker 3: figure out how we help them out, because it is

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Speaker 3: kind of a lonely fate in a way. Most inventors

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Speaker 3: are working in relative isolation, and they might be obsessed

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Speaker 3: and they might be going deep. And the people I

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Speaker 3: know a lot of them are very focused on their thing.

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Speaker 3: They don't even know how to get the story out.

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Speaker 3: They wouldn't know how to make a company, how to

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Speaker 3: raise money for it, how to get the help they need.

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Speaker 3: I mean, it's very it's a very tough thing. And

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Speaker 3: also they're mostly doomed because with an invention, you know,

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Speaker 3: you might get even Let's say you were a good

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Speaker 3: inventor and you could have a good invention once a year,

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Speaker 3: Well what are you going to do with it?

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Speaker 4: Like, how are you going to get paid for that?

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Speaker 4: Where's it going to go? And most inventors fail to

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Speaker 4: ever figure that stuff out in one lifetime.

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Speaker 3: It's pretty sad. You think, you think artists. Starving artists

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Speaker 3: have a hard time. Starving inventors have an even worse time.

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Speaker 1: So give us some examples of some really cool inventions

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Speaker 1: that you're seeing currently.

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Speaker 4: One comes to mind.

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Speaker 3: A couple of years ago, I went to visit this

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Speaker 3: buddy of mine who is one of the top researchers

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Speaker 3: on warp drive. So warp drive is like bending space

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Speaker 3: time to do faster than light travel, straight out of

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Speaker 3: Star Trek.

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Speaker 4: Nobody thinks we're ever really going.

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Speaker 3: To be able to do it, but these guys research

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Speaker 3: it and try and and no one's been able to

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Speaker 3: prove that it can't be done. So I went to

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Speaker 3: visit my buddy Sonny at his lab in Houston, and

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Speaker 3: Sonny starts showing me around his lab. He's telling me

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Speaker 3: about warp drive and UFOs and stuff, and then he's like, well, hey,

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Speaker 3: there's an atomic force microscope in the back.

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Speaker 4: You want to see it.

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Speaker 3: So he's very excited to show me this because it's

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Speaker 3: an exotic microscope, but I have one, so I care

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Speaker 3: on the way. So we're like walking over to back

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Speaker 3: Sonny's lab and he's like, oh, this is he gets distracted.

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Speaker 3: He picks up this little circuit board. It's like, this

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Speaker 3: is kind of neat. Sonny has like a Southern drawl

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Speaker 3: that I can't emulate, but he's most delightful, friendly human

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Speaker 3: you'll ever find. So he shows me the circuit board,

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Speaker 3: hooks up a couple of alligator clips to a meter

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Speaker 3: and there's a current coming off it and all that's

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Speaker 3: on the board is a chip and I'm like, what

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Speaker 3: is it, Sonny. He's like, oh, well, we made that

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Speaker 3: chip and it puts out energy like a battery. I'm like, Sonny,

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Speaker 3: you made a battery that never.

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Speaker 4: Needs to be charged.

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Speaker 3: He's like, yeah, we're it just runs forever you have

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Speaker 3: to charge. That's exactly right, Mike, Sonny, what the hell

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Speaker 3: are you doing with warp drive? We need that battery.

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Speaker 3: And so that was a couple of years ago, and

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Speaker 3: we weren't, you know. So he got started working on

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Speaker 3: patents and kind of you know, making different versions of

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Speaker 3: it to try and make it better. And then like

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Speaker 3: a few weeks ago he called me and said, hey, Pablos.

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Speaker 3: So I'm I just did the interview on the Joe

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Speaker 3: Rogan Show.

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Speaker 4: And I'm like, Sonny, I thought we were keeping this

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Speaker 4: quiet because it's work to do.

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Speaker 3: And so it was in stealth mode for years, and

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Speaker 3: he said, well, I decided to I decided to go

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Speaker 3: for it, and so I listened to the show and

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Speaker 3: this is the biggest podcast in the world, and so,

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Speaker 3: you know, for two hours, Sonny's talking about warp Drive

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Speaker 3: and UFOs and aliens and all the stuff that Joe

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Speaker 3: Rogan likes. And then he's like, oh, Joe, I made

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Speaker 3: this thing. Look at this and he explains it to Joe.

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Speaker 3: I think it went right over Joe. I don't think

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Speaker 3: he realized a miracle was announced on his podcast because

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Speaker 3: Sonny buried the lead so deep. So these are you know,

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Speaker 3: he just doesn't have the kind of brain that's like

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Speaker 3: about getting the story out, you know, But it's a

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Speaker 3: but what would you do with a battery that never

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Speaker 3: needs to be charged?

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Speaker 2: As in, how would one start a company and make sure?

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Speaker 3: Yeah, if you know, it's a scientist, so you know,

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Speaker 3: all those other pieces have to be figured out, and

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Speaker 3: we try to help people like that out.

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Speaker 1: Oh that's great, Yeah, you know change the world right,

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Speaker 1: Oh yeah, I agreed. You know, I spent the first

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Speaker 1: part of my career running a lab as a professor,

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Speaker 1: and I ran a lab, and I spent half my

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Speaker 1: time running grants to the government and so on. And

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Speaker 1: then about ten years ago I shifted into being an

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Speaker 1: entrepreneur and I moved at Silicon Valley and I've been

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Speaker 1: starting companies and it is a very different skill set,

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Speaker 1: oh for sure, in terms of taking a good idea

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Speaker 1: and turning it into something.

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Speaker 3: Yeah, you know, most people wouldn't be good at both.

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Speaker 3: So I think one of the things we are mythology

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Speaker 3: in Silicon Valley is a little screwed up. We think

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Speaker 3: that like a scientist is going to invent something and

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Speaker 3: then become an entrepreneur and then become a patent lawyer

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Speaker 3: and then become a business development guy and sell it

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Speaker 3: and then become a CEO and figure out the HR policy,

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Speaker 3: Like those people are extraordinary and rare.

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Speaker 4: That doesn't scale.

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Speaker 3: Like the winning story is actually teams, And so I

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Speaker 3: think one of the things we have to learn very

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Speaker 3: early is figure out how do you build these teams early,

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Speaker 3: compensate for the things that the founder, who might be

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Speaker 3: a scientist or an inventor might suck at, and surround

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Speaker 3: them with all the other things that we figured out

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Speaker 3: how to do well.

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Speaker 2: Yeah, So returning the issue of creativity.

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Speaker 1: So you began your career as a hacker, right, Yeah,

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Speaker 1: So tell us about that.

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Speaker 3: I sort of grew up with one of the first

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Speaker 3: computers you could have at home, which was that this

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Speaker 3: Apple two. I had one of the first couple of

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Speaker 3: thousand Apple two's ever made and with that thing, you know,

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Speaker 3: it wasn't like a computer we know today, like it's

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Speaker 3: just you boot it up or his command line, and

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Speaker 3: you got to figure out what to do. And I

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Speaker 3: was in Alaska where I grew up, so nobody for

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Speaker 3: like a thousand miles had any idea what a computer was,

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Speaker 3: and no one had seen one, you know, So to

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Speaker 3: learn how to use that thing, I basically just had

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Speaker 3: to like crash it and reboot it.

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Speaker 4: And it was kind of like a jeep.

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Speaker 3: You could sort of, you know, made of sheet metal,

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Speaker 3: you could sort of you know, take it apart and

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Speaker 3: you could open up and see the inside and stare

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Speaker 3: at it, and if you stare at it long enough,

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Speaker 3: you can kind of figure out how it works. So

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Speaker 3: that's so I sort of learned the hard way and

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Speaker 3: then and that's not common now because your computer hides

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Speaker 3: all the insides. You know, it's not like a jeep.

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Speaker 3: It's like a tesla or something with a hood wel

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Speaker 3: that's shot. You can't see how it works. And so

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Speaker 3: you know, by the time I, you know, got to

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Speaker 3: say out of high school or something, I was, you know,

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Speaker 3: I knew a lot about computer's relative to the rest

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Speaker 3: of the world. And so then we started putting people

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Speaker 3: on the Internet, like not just not nerds, but muggles,

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Speaker 3: you know, And so muggle muggles is like a term

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Speaker 3: co opted from Harry Potter to describe what you might

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Speaker 3: call NPCs or you know, just normal people wandering around.

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Speaker 3: We put these people who are not wizards on the Internet,

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Speaker 3: and they cause problems or they're vulnerable to problems, let's say.

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Speaker 3: And so we try to figure out how to make

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Speaker 3: things more secure. But the way that you do that

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Speaker 3: is by breaking them first. And so what's amazing about

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Speaker 3: hackers is they just have these different kinds of minds.

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Speaker 3: Like we all learn by reverse engineering, we learn by

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Speaker 3: taking things apart.

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Speaker 4: So like if.

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Speaker 3: You, you know, if you get a new gadget and

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Speaker 3: give it to your mom, she might ask you what

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Speaker 3: does this do? And you can explain it's a phone, mom, iPhone,

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Speaker 3: says on the box. But if you give a gadget

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Speaker 3: like that to a hacker, then the question is different.

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Speaker 3: The question is what can I make this do? And

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Speaker 3: hackers will flip it over and take out all the

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Speaker 3: screws and break it into a lot of little pieces,

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Speaker 3: but then figure out what you can build from the rubble,

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Speaker 3: and that's that discovery process. That's where you get all

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Speaker 3: of your new technologies. Nobody ever invented a new technology

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Speaker 3: by reading the directions.

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Speaker 4: There are none.

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Speaker 3: It's a logical impossibility. And so those kinds of minds

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Speaker 3: that people who think.

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Speaker 4: That way are super valuable.

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Speaker 3: We need them and like you might not want to

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Speaker 3: hire them, but you know, one way or another, we

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Speaker 3: need to party with them and have a relationship with

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Speaker 3: the people who are the source of all of our

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Speaker 3: new superpowers.

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Speaker 1: So let me do you an analogy here, because I've

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Speaker 1: been thinking about this a lot about whether large language models,

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Speaker 1: the current common flavor of our AI can do scientific discovery.

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Speaker 1: And the reason they're not any good at the moment

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Speaker 1: of real new scientific discovery is because what they are

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Speaker 1: good at doing is taking everything that's come before them

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Speaker 1: and putting it together into ways. And that's got value

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Speaker 1: in a lot of aspects. But what it doesn't have

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Speaker 1: value in is saying, hey, what if all this is

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Speaker 1: wrong and there's a totally new way to do this.

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Speaker 1: It's the equivalent of taking the thing apart and saying,

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Speaker 1: what can I do that wasn't thought of before?

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Speaker 2: With these pieces and parts.

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Speaker 1: And you know, a big part of really making scientific

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Speaker 1: progress is to assume that some fraction of everything you've learned,

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Speaker 1: all the models you've learned before you is incorrect, some

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Speaker 1: fraction and and and there's a different way of doing

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Speaker 1: the whole thing.

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Speaker 2: And so there's a now parallel here between.

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Speaker 3: What a hacker question every day for a week, it's

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Speaker 3: going to give you slight variations of the same answer,

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Speaker 3: and that's because by design they have thrown in a

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Speaker 3: random number ten just to make it, you know, feel

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Speaker 3: like it's you know, like more of human, because humans

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Speaker 3: would kind of give you a slightly different answer every

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Speaker 3: day too. And there might be a version of this

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Speaker 3: where you throw enough computation at something then you just say, look,

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Speaker 3: just try everything. And certainly in a focused domain you

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Speaker 3: can do that, and we do that, you know, that's

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Speaker 3: what we're doing with drug discovery and molecules and protein

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Speaker 3: folding and stuff. And you can't try every possible way

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Speaker 3: a protein can fold without it being very computational expensive.

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Speaker 3: But you know, we add in some heuristics to say, well,

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Speaker 3: probably it's this, and here's a bunch of other ones,

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Speaker 3: so maybe copy those and try one variation at a time,

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Speaker 3: and we just run every possibility. And so there is

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Speaker 3: a kind of discovery that I think is possible there.

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Speaker 3: I don't know what the bounds on it are, and

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Speaker 3: so I don't want to say like computational models will

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Speaker 3: never do scientific discovery or invention. They might, we might

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Speaker 3: be able to make models that do that. I don't

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Speaker 3: think the lms as we know them are the right

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Speaker 3: kind of model to do that right now, because they

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Speaker 3: are so heavily trained on just what humans did.

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Speaker 2: What they do is they interpolate. They don't extrapolate.

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Speaker 1: They don't say, hey, what if, what if all this

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Speaker 1: that I learned that I digested is incorrect and there's

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Speaker 1: a different way of looking at it. And then here's

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Speaker 1: a new idea, and I'm going to simulate that forward

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Speaker 1: and think about, Oh, that would explain the data in

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Speaker 1: a better way.

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Speaker 2: They're just not architected to do that.

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Speaker 3: I had in the book have a story about one

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Speaker 3: of my friends who I think of as one of

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Speaker 3: the best living inventors. But his name is Rodney Mullen

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Speaker 3: and Rodney.

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Speaker 2: The skateboarding the skateboarder.

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Speaker 3: Yeah, and people he's famous for skateboarding because he invented

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Speaker 3: almost everything you've ever seen a kid do on a skateboard.

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Speaker 2: That's right.

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Speaker 3: Rodney grew up in rural Florida, kind of like I

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Speaker 3: grew up in grural Alaska. No computer, just the Apple two.

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Speaker 3: He was in roll Foy. He didn't get appletude. He

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Speaker 3: got a skateboard. And so Rodney was stuck by himself

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Speaker 3: at a time when there was no one around him

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Speaker 3: who's skateboarded. He didn't ever see anybody else do tricks

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Speaker 3: on a skateboard. He had nothing, no one to inspire him.

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Speaker 3: He just had to figure all this shut out himself,

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Speaker 3: in his driveway, by himself, and so he became the

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Speaker 3: most prolific inventor of things you could do on skateboarding.

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Speaker 4: You could say skateboarding.

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Speaker 3: Isn't important, but kind of like country music or something

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Speaker 3: like a lot of people are actually quite inspired about skateboarding,

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Speaker 3: and so Rodney is an inspiration to them. But what's

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Speaker 3: amazing is Rodney will have an idea for a thing

438
00:23:41,000 --> 00:23:44,199
Speaker 3: that's never been done on a skateboard. He'll spend a

439
00:23:44,359 --> 00:23:49,879
Speaker 3: year just trying to make it work. He'll get it.

440
00:23:51,200 --> 00:23:54,800
Speaker 3: YouTube video goes up two weeks later. Kids in Kazakh

441
00:23:54,800 --> 00:23:58,119
Speaker 3: stand are doing better than him. Yeah, that's the moment

442
00:23:58,160 --> 00:24:03,920
Speaker 3: of creation. The word creativity. You create something. The world

443
00:24:03,960 --> 00:24:06,720
Speaker 3: didn't have it before you created something. And even if

444
00:24:06,720 --> 00:24:08,960
Speaker 3: it's just a skateboard trick, that's creativity. And you could

445
00:24:08,960 --> 00:24:11,240
Speaker 3: see it in Jimi Hendrix. You could see it, and

446
00:24:11,880 --> 00:24:15,120
Speaker 3: you know, I don't know, like Jonah Salk, you could

447
00:24:15,160 --> 00:24:18,359
Speaker 3: see it in these people. And I think that we

448
00:24:18,440 --> 00:24:20,720
Speaker 3: need to find a way to have a better, more

449
00:24:20,760 --> 00:24:24,320
Speaker 3: mature relationship with that. Celebrate that search for these people,

450
00:24:24,920 --> 00:24:28,000
Speaker 3: you know, support them, help them. I mean, what happened

451
00:24:28,000 --> 00:24:32,439
Speaker 3: to we used to have patrons for artists. Oh yeah,

452
00:24:32,520 --> 00:24:36,240
Speaker 3: you know, like like like let's just like send Doc

453
00:24:36,320 --> 00:24:38,359
Speaker 3: Brown ten bucks a month on Patreon. I guess that's

454
00:24:38,359 --> 00:24:41,360
Speaker 3: what patreons were, their inventors on Patreon. Like that seems

455
00:24:41,400 --> 00:24:43,680
Speaker 3: like something we should try anyway.

456
00:24:44,080 --> 00:24:46,600
Speaker 1: No, that's a really it's a really good point because

457
00:24:46,920 --> 00:24:49,640
Speaker 1: one can write grants off to the government and so on,

458
00:24:50,280 --> 00:24:54,360
Speaker 1: but there there are a very limited number of people

459
00:24:55,160 --> 00:24:59,000
Speaker 1: looking for inventors to fund them.

460
00:25:00,040 --> 00:25:02,879
Speaker 4: Yeah, and you could. It's not a it's not a

461
00:25:02,880 --> 00:25:03,479
Speaker 4: binary thing.

462
00:25:03,520 --> 00:25:05,800
Speaker 3: I mean, there's obviously a lot of invention at the

463
00:25:05,800 --> 00:25:08,960
Speaker 3: borders of science. There's obviously lot a lot of invention

464
00:25:09,160 --> 00:25:12,280
Speaker 3: and engineering and products and companies and stuff.

465
00:25:12,440 --> 00:25:13,440
Speaker 4: A lot as you know.

466
00:25:13,560 --> 00:25:16,760
Speaker 3: Very very iterative, and it is important. You know, if

467
00:25:16,800 --> 00:25:19,399
Speaker 3: you're at He'll at Packard, you're probably going to figure

468
00:25:19,400 --> 00:25:23,640
Speaker 3: out how to make like a better inkjet printer that's

469
00:25:23,720 --> 00:25:25,600
Speaker 3: like one percent better every year.

470
00:25:25,640 --> 00:25:27,600
Speaker 4: And that's really important and cool, and that's how.

471
00:25:27,480 --> 00:25:30,600
Speaker 3: We got to where we are. But where's the guy

472
00:25:30,600 --> 00:25:34,240
Speaker 3: who's inventing what comes after inkjet? He's not He'll at Packard.

473
00:25:35,240 --> 00:25:37,960
Speaker 3: But you get the idea, right, there's a there's a

474
00:25:38,080 --> 00:25:39,600
Speaker 3: there's something better out there.

475
00:25:39,840 --> 00:25:41,760
Speaker 4: And so you know, we've been.

476
00:25:43,080 --> 00:25:47,040
Speaker 3: Fortunate to be attracting some of these inventors and you

477
00:25:47,080 --> 00:25:48,159
Speaker 3: know guys like honey.

478
00:25:48,520 --> 00:25:49,399
Speaker 2: Oh that's great.

479
00:25:49,760 --> 00:25:49,960
Speaker 3: You know.

480
00:25:50,000 --> 00:25:52,480
Speaker 1: One of the things that is always fascinating me is

481
00:25:52,600 --> 00:25:57,359
Speaker 1: the spread of invention. So Rodney Mullen comes up with

482
00:25:57,440 --> 00:26:00,200
Speaker 1: some new skateboarding technique, the kids in kazakh Standard doing

483
00:26:00,200 --> 00:26:02,800
Speaker 1: it two weeks later because.

484
00:26:02,320 --> 00:26:03,800
Speaker 2: Of one thing. The Internet.

485
00:26:04,200 --> 00:26:06,560
Speaker 1: The Internet, I think is the most important event we've

486
00:26:06,600 --> 00:26:09,600
Speaker 1: ever had as a species, in large part because it

487
00:26:09,640 --> 00:26:14,760
Speaker 1: allows the instant and global dissemination of information. The reason

488
00:26:15,520 --> 00:26:21,120
Speaker 1: that Edward Jenner was the eighth person to invent vaccination.

489
00:26:21,440 --> 00:26:24,120
Speaker 1: This was invented in India, it was invented in Africa

490
00:26:24,119 --> 00:26:27,199
Speaker 1: and in different places. But there was no way to

491
00:26:27,200 --> 00:26:30,119
Speaker 1: disseminate the information. That's why it had to be reinvented

492
00:26:30,160 --> 00:26:32,280
Speaker 1: the way. The way I picture this is like totally

493
00:26:32,520 --> 00:26:35,320
Speaker 1: the globe and there's some fire that starts where people

494
00:26:35,480 --> 00:26:37,200
Speaker 1: know this thing, and then the fire.

495
00:26:37,040 --> 00:26:39,200
Speaker 2: Dies out because there's no way to catch that information.

496
00:26:39,480 --> 00:26:42,000
Speaker 3: Of a lot of historical examples of that, I think

497
00:26:42,119 --> 00:26:45,840
Speaker 3: Edward Jenner's actual innovation was that he had a way

498
00:26:46,280 --> 00:26:48,600
Speaker 3: which the others didn't. He had a way of testing

499
00:26:49,359 --> 00:26:54,920
Speaker 3: and proving the correlation and making it repeatable, which other

500
00:26:54,920 --> 00:26:59,159
Speaker 3: people had. Other people had used smallpox to vaccinate against smallpox,

501
00:26:59,640 --> 00:27:00,760
Speaker 3: which was very dangerous.

502
00:27:01,040 --> 00:27:01,560
Speaker 4: He's the one that.

503
00:27:01,560 --> 00:27:04,840
Speaker 3: Figured out how to use cowpox to vaccinate against smallpox.

504
00:27:05,240 --> 00:27:07,199
Speaker 4: So but anyway, he was.

505
00:27:07,200 --> 00:27:11,560
Speaker 1: Also growing up in a culture in England where where

506
00:27:11,640 --> 00:27:15,560
Speaker 1: there was a royal academy and people wrote down things,

507
00:27:15,600 --> 00:27:18,400
Speaker 1: and they got presentations on these things, and then they

508
00:27:18,760 --> 00:27:20,160
Speaker 1: made sure that other people.

509
00:27:20,040 --> 00:27:23,600
Speaker 3: And the output of that was staggering, partly because you know,

510
00:27:23,720 --> 00:27:27,439
Speaker 3: so much was available to discover at the time. But

511
00:27:28,119 --> 00:27:31,720
Speaker 3: if you look at what's the scale of the Royal Academy.

512
00:27:31,760 --> 00:27:34,040
Speaker 3: I mean it was probably like, you know, a few

513
00:27:34,119 --> 00:27:37,800
Speaker 3: hundred people involved over the course of you know, decades.

514
00:27:38,080 --> 00:27:40,600
Speaker 3: It's not actually like we have you know, I don't

515
00:27:40,800 --> 00:27:43,720
Speaker 3: know you could have like a soccer team that was

516
00:27:43,880 --> 00:27:46,119
Speaker 3: had more people involved in high school. You know, like

517
00:27:46,200 --> 00:27:49,439
Speaker 3: it's just so yeah, I mean, great time to be alive.

518
00:27:49,480 --> 00:27:52,719
Speaker 3: I think as far as scientific discovery, because you know,

519
00:27:52,960 --> 00:27:56,480
Speaker 3: nobody had written anything down before you. But now now

520
00:27:56,480 --> 00:28:00,479
Speaker 3: we have the compound interest of the entry the way

521
00:28:00,480 --> 00:28:01,399
Speaker 3: you describe.

522
00:28:01,240 --> 00:28:03,520
Speaker 1: Yeah, exactly, and we have the compound interest of all

523
00:28:03,560 --> 00:28:04,959
Speaker 1: the inventions that have come before.

524
00:28:05,119 --> 00:28:07,520
Speaker 2: So now invention is going faster than ever.

525
00:28:08,080 --> 00:28:11,760
Speaker 1: Also, you have so much more of the planet getting

526
00:28:11,760 --> 00:28:14,560
Speaker 1: an education than ever before. So instead of one hundred

527
00:28:14,600 --> 00:28:17,560
Speaker 1: guys sitting around that have boys and girls all over

528
00:28:17,640 --> 00:28:20,320
Speaker 1: the world, Yeah, working on things and watching YouTube video

529
00:28:20,359 --> 00:28:22,320
Speaker 1: and putting that together with this and that and putting

530
00:28:22,320 --> 00:28:23,400
Speaker 1: things together and inventing.

531
00:28:23,920 --> 00:28:26,920
Speaker 3: And what's wild about it is, you know the things

532
00:28:26,960 --> 00:28:30,960
Speaker 3: I'm talking about, these inventions, you typically don't see them

533
00:28:31,680 --> 00:28:33,399
Speaker 3: in iPhone apps.

534
00:28:34,480 --> 00:28:34,680
Speaker 2: Yeah.

535
00:28:34,720 --> 00:28:36,840
Speaker 3: You know, if you look at if you look at

536
00:28:36,840 --> 00:28:38,400
Speaker 3: the world we live in, we think We have this

537
00:28:38,480 --> 00:28:42,480
Speaker 3: huge tech industry that's supposed to be bringing these inventions

538
00:28:42,480 --> 00:28:45,440
Speaker 3: to life, but what are they doing. They're bringing iPhone apps,

539
00:28:45,920 --> 00:28:48,560
Speaker 3: there are software do have tech industry? We have software industry.

540
00:28:49,080 --> 00:28:52,160
Speaker 3: And so we live in this world where even though

541
00:28:52,640 --> 00:28:56,680
Speaker 3: we've accelerated on scientific research and discovery, we've accelerated on

542
00:28:56,760 --> 00:29:00,680
Speaker 3: invention in spite of you know, my complaints about the support.

543
00:29:01,320 --> 00:29:05,360
Speaker 3: But what we really did is we indexed on entrepreneurs

544
00:29:05,440 --> 00:29:08,080
Speaker 3: and we support them, we fund them, we celebrate them,

545
00:29:08,320 --> 00:29:10,920
Speaker 3: Like whose YouTube video gets all the views? The guy

546
00:29:10,960 --> 00:29:13,400
Speaker 3: who made the most money, right, it's not the guy

547
00:29:13,440 --> 00:29:14,560
Speaker 3: who made the biggest difference.

548
00:29:14,920 --> 00:29:16,920
Speaker 4: And so there's this.

549
00:29:16,800 --> 00:29:19,600
Speaker 3: Weird situation where our big tech industry is supposed to

550
00:29:19,640 --> 00:29:22,959
Speaker 3: be helping us bring these inventions to life, is distracted.

551
00:29:23,000 --> 00:29:25,920
Speaker 3: They're making you know, zoom and Slack and snapchat, and

552
00:29:26,120 --> 00:29:31,880
Speaker 3: those things might be amazing, but what technology did Airbnb invent?

553
00:29:32,640 --> 00:29:36,480
Speaker 3: You know, what new superpower did Instagram bring to the world?

554
00:29:36,600 --> 00:29:39,280
Speaker 3: You know, it's it's an app for sharing photos instead

555
00:29:39,280 --> 00:29:40,440
Speaker 3: of like text and photos.

556
00:29:40,520 --> 00:29:41,160
Speaker 4: You know, amazing.

557
00:29:41,360 --> 00:29:44,200
Speaker 3: You know, So I think there's this this we're in

558
00:29:44,240 --> 00:29:47,000
Speaker 3: this moment now where a lot of the easy shit's

559
00:29:47,000 --> 00:29:50,080
Speaker 3: been done. Like everything you know we made it had

560
00:29:50,120 --> 00:29:53,000
Speaker 3: to reinvent everything is a website and then reinvented as

561
00:29:53,000 --> 00:29:54,680
Speaker 3: a mobile app. Now I have to reinvent it his ai.

562
00:29:54,760 --> 00:29:57,800
Speaker 3: But basically it's just apply software everything. But when you

563
00:29:57,880 --> 00:30:05,960
Speaker 3: look at the big problem in the world food, water, waste, sanitation, construction, energy,

564
00:30:06,080 --> 00:30:09,160
Speaker 3: and manufacturing, those are not things you're going to improve

565
00:30:09,240 --> 00:30:10,880
Speaker 3: with software. I mean, you could improve them a little

566
00:30:10,920 --> 00:30:13,640
Speaker 3: bit one or two percent, you're not going to reinvent them.

567
00:30:13,640 --> 00:30:16,760
Speaker 3: You're going to make them ten times better with software. Mining,

568
00:30:17,160 --> 00:30:20,520
Speaker 3: you know, these are things that require real inventions, real

569
00:30:20,600 --> 00:30:26,600
Speaker 3: technologies that could make the world massively better. And you're

570
00:30:26,600 --> 00:30:29,000
Speaker 3: going to get that from the creative people who invent

571
00:30:29,200 --> 00:30:31,360
Speaker 3: those ten x or one hundred x multipliers.

572
00:30:31,960 --> 00:30:34,000
Speaker 2: So that's what you look for. You look for them.

573
00:30:34,040 --> 00:30:35,000
Speaker 4: We call that deep tech.

574
00:30:35,120 --> 00:30:37,160
Speaker 2: Deep tech. You look for that, and so give us

575
00:30:37,200 --> 00:30:38,640
Speaker 2: some other examples of things you've found.

576
00:30:38,760 --> 00:30:42,200
Speaker 3: So like in mining, that's one where you know, in

577
00:30:42,240 --> 00:30:45,080
Speaker 3: the US we basically outlawed mining because we want to

578
00:30:45,080 --> 00:30:48,520
Speaker 3: save the world, and mining involves a lot of nasty emissions.

579
00:30:48,600 --> 00:30:50,600
Speaker 3: You have to you basically take a bunch of rock

580
00:30:50,640 --> 00:30:52,000
Speaker 3: and burn it and.

581
00:30:52,080 --> 00:30:55,080
Speaker 4: Hope you get some metal and then you just to

582
00:30:55,120 --> 00:30:56,720
Speaker 4: create insane emissions.

583
00:30:56,760 --> 00:31:00,440
Speaker 3: So that's in a smelter. We we outlawed smelters. So

584
00:31:00,560 --> 00:31:02,600
Speaker 3: now we take the rock that we mine and we

585
00:31:02,640 --> 00:31:04,600
Speaker 3: ship it to other countries and they run it through

586
00:31:04,600 --> 00:31:07,120
Speaker 3: a smelter and to get the metal out and then.

587
00:31:07,040 --> 00:31:07,840
Speaker 4: Send it back to us.

588
00:31:08,160 --> 00:31:11,160
Speaker 3: So we haven't exactly solved a problem, but we definitely

589
00:31:11,160 --> 00:31:16,680
Speaker 3: got out of our backyard. So March nineteenth, I've found

590
00:31:16,720 --> 00:31:20,560
Speaker 3: this little team that invented a way of doing zero

591
00:31:20,640 --> 00:31:25,880
Speaker 3: emissions refining for metals like copper. We need copper for everything,

592
00:31:26,040 --> 00:31:30,120
Speaker 3: but we don't mine it here because we're not allowed to,

593
00:31:31,000 --> 00:31:36,800
Speaker 3: and so this team can refine copper kind of the

594
00:31:36,800 --> 00:31:39,920
Speaker 3: way we refine aluminum. It's mostly electricity, so you could

595
00:31:40,000 --> 00:31:44,720
Speaker 3: power that with a solar farm or something clean. And

596
00:31:44,800 --> 00:31:47,480
Speaker 3: so that was on March nineteenth, and on March twentieth,

597
00:31:48,280 --> 00:31:51,720
Speaker 3: the President signed an executive order to free up mining

598
00:31:51,760 --> 00:31:54,080
Speaker 3: in this country, which is very important because we have

599
00:31:54,400 --> 00:31:57,520
Speaker 3: all the medals you could need for hundreds of years

600
00:31:57,520 --> 00:31:59,680
Speaker 3: in this country, all those rarest medals.

601
00:32:00,000 --> 00:32:00,520
Speaker 4: What are we doing.

602
00:32:00,560 --> 00:32:04,600
Speaker 3: We're engaged in geopolitical machinations to get them from Ukraine

603
00:32:04,720 --> 00:32:06,520
Speaker 3: or China or places that really don't want to give

604
00:32:06,560 --> 00:32:07,000
Speaker 3: them to us.

605
00:32:07,440 --> 00:32:08,840
Speaker 4: Instead we could remind them here.

606
00:32:09,200 --> 00:32:11,680
Speaker 3: And I think it's a very important example because, like

607
00:32:11,720 --> 00:32:14,240
Speaker 3: on something like that, you would want we should be

608
00:32:14,280 --> 00:32:16,440
Speaker 3: setting an example for the rest of the world. If

609
00:32:16,480 --> 00:32:18,479
Speaker 3: we're the country that's good at doing new things, and

610
00:32:18,560 --> 00:32:22,320
Speaker 3: we are, then we should figure out, okay, smelter suck,

611
00:32:22,440 --> 00:32:25,600
Speaker 3: what's the best way to refine metal. We're going to

612
00:32:26,160 --> 00:32:28,080
Speaker 3: prototype that, we're going to build that, We're going to

613
00:32:28,080 --> 00:32:30,680
Speaker 3: deplay first, We're going to show that it's actually five

614
00:32:30,720 --> 00:32:34,080
Speaker 3: times cheaper to build, ten times cheaper to operate, and

615
00:32:34,160 --> 00:32:36,280
Speaker 3: then the rest of the world can copy that instead

616
00:32:36,280 --> 00:32:38,800
Speaker 3: of copying what we did a century ago. So we

617
00:32:38,840 --> 00:32:40,680
Speaker 3: need to set an example on these things that if

618
00:32:40,680 --> 00:32:43,760
Speaker 3: we want to have a big impact on saving the world.

619
00:32:43,960 --> 00:32:45,600
Speaker 3: And I think that's what's possible with a lot of

620
00:32:45,640 --> 00:32:46,440
Speaker 3: these technologies.

621
00:32:46,760 --> 00:32:48,520
Speaker 2: Excellent, how does it actually work?

622
00:32:48,560 --> 00:32:48,720
Speaker 5: There?

623
00:32:48,800 --> 00:32:49,800
Speaker 2: Give us this per.

624
00:32:49,920 --> 00:32:51,840
Speaker 4: Called molten sulfide electrolysis.

625
00:32:52,000 --> 00:32:54,719
Speaker 3: And so if you look at like just to keep

626
00:32:54,760 --> 00:32:58,800
Speaker 3: a simple like aluminum for example, is a mostly made electricity.

627
00:32:59,240 --> 00:33:02,480
Speaker 3: You know your popca are as metal, but it's mostly electricity.

628
00:33:02,600 --> 00:33:07,040
Speaker 3: Is what is involved in making aluminium? And with a smelter,

629
00:33:07,280 --> 00:33:10,600
Speaker 3: you just burn coal or you burn gas to heat,

630
00:33:10,760 --> 00:33:14,160
Speaker 3: get a lot of heat to essentially melter burn rock.

631
00:33:15,120 --> 00:33:16,360
Speaker 4: That's how smelter works.

632
00:33:16,600 --> 00:33:20,440
Speaker 3: So with the electrolysis, what's happening is used in an

633
00:33:20,480 --> 00:33:25,520
Speaker 3: electrochemical process to separate the metals from the rock and

634
00:33:25,640 --> 00:33:29,680
Speaker 3: you can get much better yields this way. Now, so yeah,

635
00:33:29,680 --> 00:33:32,120
Speaker 3: it's the early stages. So we have things like that.

636
00:33:32,640 --> 00:33:36,640
Speaker 3: There's I mean, we have a nuclear reactor. That's one

637
00:33:36,680 --> 00:33:38,720
Speaker 3: of our teams invented a nuclear reactor. I mean, I

638
00:33:38,760 --> 00:33:42,360
Speaker 3: found these inventors that figured out that they could make

639
00:33:42,360 --> 00:33:45,160
Speaker 3: a nuclear reactor that will fit through a manhole and

640
00:33:45,240 --> 00:33:48,600
Speaker 3: it just uses all existing technology, no crazy new you know,

641
00:33:49,000 --> 00:33:53,520
Speaker 3: gen fore reactor technology, just regular reactor. But it's this

642
00:33:53,600 --> 00:33:57,160
Speaker 3: big around and they bury it a mile deep in

643
00:33:57,240 --> 00:34:01,160
Speaker 3: a borehole. So there's the thing is unquestionably safe. There's

644
00:34:01,360 --> 00:34:04,560
Speaker 3: ten billion tons of rock between the reactor and anyone's

645
00:34:04,600 --> 00:34:08,200
Speaker 3: backyard and nothing could go wrong. But if it did,

646
00:34:08,719 --> 00:34:11,440
Speaker 3: there'd be no radioactivity at the surface. Just fill a

647
00:34:11,440 --> 00:34:14,279
Speaker 3: hole with dirt and forget about it. Yeah, okay, And

648
00:34:14,320 --> 00:34:17,200
Speaker 3: we can make them in a factory like toyotas, like

649
00:34:17,280 --> 00:34:19,640
Speaker 3: we can make thousands of them. You know, all of

650
00:34:19,680 --> 00:34:22,440
Speaker 3: our reactors that we have in this country are like

651
00:34:22,880 --> 00:34:26,279
Speaker 3: bespoke art projects. They're like Frank Geary buildings. They build

652
00:34:26,320 --> 00:34:31,080
Speaker 3: one of one. Yeah, and these things, you know, don't

653
00:34:31,520 --> 00:34:33,880
Speaker 3: like anything else. The first one is always expensive. So

654
00:34:33,920 --> 00:34:38,080
Speaker 3: we did ninety two number one reactors. None of them

655
00:34:38,080 --> 00:34:40,880
Speaker 3: have any interchangeable parts. They all have their own engineering team.

656
00:34:41,160 --> 00:34:44,200
Speaker 3: It's crazy. So let's build them in a factory like

657
00:34:44,239 --> 00:34:47,040
Speaker 3: the toyotas. They're not even more complicated than a Toyota.

658
00:34:47,239 --> 00:34:50,719
Speaker 3: They're not bigger than a Toyota. Anyway, that's the kind

659
00:34:50,719 --> 00:34:51,600
Speaker 3: of stuff that's possible.

660
00:34:51,719 --> 00:34:53,360
Speaker 2: Oh that's a good one. What else, what?

661
00:34:53,480 --> 00:34:56,279
Speaker 3: Well, man, we got a team putting taking So the

662
00:34:56,320 --> 00:34:59,120
Speaker 3: problem with like solar panels. We have solar farms we've

663
00:34:59,160 --> 00:35:03,360
Speaker 3: been building, but they suck for two big reasons, clouds

664
00:35:03,640 --> 00:35:08,839
Speaker 3: and nighttime. So the relentless onslaught of night has been

665
00:35:08,880 --> 00:35:10,320
Speaker 3: screwing with our solar panels.

666
00:35:10,480 --> 00:35:12,279
Speaker 4: We have no idea what to do about it.

667
00:35:12,760 --> 00:35:15,440
Speaker 3: But I get this team that figured out they can

668
00:35:15,480 --> 00:35:18,319
Speaker 3: take the solar farm and just put on a rocket

669
00:35:18,400 --> 00:35:22,640
Speaker 3: lunch into space. Oh, so a solar farm in space

670
00:35:22,719 --> 00:35:24,959
Speaker 3: will get sun twenty four hours a day. It looks

671
00:35:25,000 --> 00:35:27,319
Speaker 3: like nighttime because we just have a bad angle on it.

672
00:35:27,440 --> 00:35:31,240
Speaker 3: Space is actually noon all the time. So a solar

673
00:35:31,280 --> 00:35:33,120
Speaker 3: farm in space will get sun twenty four hours a

674
00:35:33,200 --> 00:35:36,880
Speaker 3: day all year long, and then it can beam the

675
00:35:37,000 --> 00:35:39,799
Speaker 3: energy down to Earth using radio waves. They go right

676
00:35:39,840 --> 00:35:43,520
Speaker 3: through clouds and it sounds crazy. I know, it sounds

677
00:35:43,520 --> 00:35:45,600
Speaker 3: like science fiction, but we have all the tech to

678
00:35:45,640 --> 00:35:48,120
Speaker 3: do this. It's not even that hard. The only real

679
00:35:48,160 --> 00:35:51,840
Speaker 3: problem was launch cost and launch cost, Like in a

680
00:35:51,880 --> 00:35:54,440
Speaker 3: space shuttle, it would have cost forty thousand dollars to

681
00:35:54,480 --> 00:35:58,080
Speaker 3: put your iPad in space. Now thanks to SpaceX is

682
00:35:58,080 --> 00:36:03,279
Speaker 3: down to fifteen hundred. Their target for starship is ten

683
00:36:04,160 --> 00:36:07,120
Speaker 3: ten dollars. Yeah, it'll be cheaper to store your old

684
00:36:07,160 --> 00:36:11,080
Speaker 3: sports ball equipment in space than your garage before the

685
00:36:11,239 --> 00:36:11,920
Speaker 3: end of the decade.

686
00:36:11,960 --> 00:36:12,719
Speaker 4: Like that's where we're at.

687
00:36:12,800 --> 00:36:15,600
Speaker 3: I know it sounds crazy, but that's why something like

688
00:36:15,640 --> 00:36:16,920
Speaker 3: space solar is possible.

689
00:36:17,239 --> 00:36:18,400
Speaker 2: Oh that's terrific.

690
00:36:18,560 --> 00:36:38,560
Speaker 6: Yeah, what fraction of our energy do you think we'll

691
00:36:38,600 --> 00:36:41,719
Speaker 6: get from space solar in a decade from there?

692
00:36:41,800 --> 00:36:43,319
Speaker 3: So it's one of these things where you get et

693
00:36:43,360 --> 00:36:46,960
Speaker 3: Connie's to scale very quickly, so you know, solar panels.

694
00:36:47,000 --> 00:36:50,319
Speaker 3: You've already scaled up manufacturing those. That's very cheap. They're light,

695
00:36:50,400 --> 00:36:52,360
Speaker 3: they're flat. You can put a lot of them in

696
00:36:52,400 --> 00:36:56,000
Speaker 3: a starship. I think we could get something like one

697
00:36:56,080 --> 00:36:58,720
Speaker 3: hundred and fifty megawatts out of a single starship launch.

698
00:37:00,280 --> 00:37:03,479
Speaker 3: So imagine do that a couple of times a week,

699
00:37:03,719 --> 00:37:08,720
Speaker 3: and everyone you put up pays for itself, immediately serving

700
00:37:08,800 --> 00:37:11,520
Speaker 3: the biggest market on Earth. You beam power to the

701
00:37:11,560 --> 00:37:14,160
Speaker 3: poles or to the middle of Africa. You don't need storage,

702
00:37:14,280 --> 00:37:17,759
Speaker 3: you don't need transmission lines, and so it's just clean

703
00:37:17,840 --> 00:37:18,800
Speaker 3: energy that goes everywhere.

704
00:37:18,880 --> 00:37:20,160
Speaker 2: Oh it's beautiful, it is.

705
00:37:20,280 --> 00:37:22,439
Speaker 3: Yeah, and so I want to do taro watts, but yeah,

706
00:37:22,520 --> 00:37:26,239
Speaker 3: it's wild to'll launch enough panels to do that. But

707
00:37:26,320 --> 00:37:28,879
Speaker 3: you know, it's called space for a reason, Like there's

708
00:37:29,040 --> 00:37:31,839
Speaker 3: totally room for huge solar farms space.

709
00:37:32,920 --> 00:37:34,600
Speaker 2: And give us another example.

710
00:37:34,640 --> 00:37:38,120
Speaker 3: So one I love that is actually not even that

711
00:37:38,239 --> 00:37:41,120
Speaker 3: high tech, but it's just genius. Is I got this

712
00:37:42,000 --> 00:37:47,799
Speaker 3: team that is making cargo ships that sail themselves, so

713
00:37:48,239 --> 00:37:51,560
Speaker 3: no crew, no fuel, no emissions, it's not even that

714
00:37:51,640 --> 00:37:53,680
Speaker 3: high tech. You duct tape a Tesla to the front

715
00:37:53,680 --> 00:37:56,440
Speaker 3: of the ship and it drives itself across the ocean.

716
00:37:56,800 --> 00:37:59,160
Speaker 3: There's nothing to hit out there. It's easier than self

717
00:37:59,239 --> 00:38:03,080
Speaker 3: driving cars. And then there's like one documented pedestrian and

718
00:38:03,160 --> 00:38:06,080
Speaker 3: all of human history. So we're just gonna make that.

719
00:38:06,160 --> 00:38:09,000
Speaker 3: And the sailing part has been working for centuries. That's

720
00:38:09,040 --> 00:38:11,280
Speaker 3: how people got to this country in the first place.

721
00:38:11,800 --> 00:38:15,000
Speaker 3: But now to get happy meal toys from China to

722
00:38:15,080 --> 00:38:19,239
Speaker 3: Los Angeles, we're burning this nasty bunk oil. People don't

723
00:38:19,239 --> 00:38:23,080
Speaker 3: realize it, but cargo ships have two guess tanks. One

724
00:38:23,120 --> 00:38:26,440
Speaker 3: is burning refined fuel like your car that it doesn't

725
00:38:26,480 --> 00:38:29,480
Speaker 3: burn clean, but it burns invisibly. And then the other,

726
00:38:29,560 --> 00:38:31,440
Speaker 3: as soon as they get off over the horizon out

727
00:38:31,440 --> 00:38:34,840
Speaker 3: of sight, they switch to burning bunker oil, which is

728
00:38:34,880 --> 00:38:37,960
Speaker 3: the cheapest crap you can get. It's basically optimized for

729
00:38:37,960 --> 00:38:42,000
Speaker 3: carbon emissions and it's just black smoke. You never see

730
00:38:42,040 --> 00:38:45,760
Speaker 3: it because they don't do it until they're out of sight. Yeah,

731
00:38:45,800 --> 00:38:47,640
Speaker 3: so that's an industry where.

732
00:38:48,000 --> 00:38:50,320
Speaker 2: But how does self navigating solve that problem?

733
00:38:51,000 --> 00:38:55,080
Speaker 3: Well, because there's two classes of issues. One is you

734
00:38:55,120 --> 00:38:59,160
Speaker 3: don't need fuel because of the sailing, because oh it's sailing,

735
00:38:59,239 --> 00:39:01,879
Speaker 3: got it, Yeah, right, there's no fuel cost. We don't

736
00:39:01,880 --> 00:39:04,800
Speaker 3: burn anything. Five out of six dollars in the shipping

737
00:39:04,840 --> 00:39:08,120
Speaker 3: industry is burned as fuel. We just do the self

738
00:39:08,160 --> 00:39:11,719
Speaker 3: sailing with the with the automation because we can. I mean,

739
00:39:11,800 --> 00:39:13,520
Speaker 3: you know, you could put a guy and they're steering

740
00:39:13,560 --> 00:39:14,919
Speaker 3: it if you want, but you don't need them.

741
00:39:15,440 --> 00:39:15,839
Speaker 2: I see.

742
00:39:15,920 --> 00:39:17,480
Speaker 1: But let me just let me just make sure I

743
00:39:17,520 --> 00:39:22,240
Speaker 1: got this straight. So you literally mean a tesla.

744
00:39:22,480 --> 00:39:25,600
Speaker 3: I'm not literally Okay, we have self driving tech that's

745
00:39:25,600 --> 00:39:27,040
Speaker 3: good enough that you could put it on a ship,

746
00:39:27,040 --> 00:39:28,040
Speaker 3: got it, So let me understand.

747
00:39:28,080 --> 00:39:32,879
Speaker 2: So you got to ship a sail boat.

748
00:39:33,719 --> 00:39:38,280
Speaker 1: Okay, And and it self navigates and burns no fuel

749
00:39:38,320 --> 00:39:40,880
Speaker 1: that way, And can I assume that it's taking wind

750
00:39:41,000 --> 00:39:43,200
Speaker 1: data from a much larger area.

751
00:39:42,920 --> 00:39:45,239
Speaker 4: Than it really have access to, really good at that?

752
00:39:45,640 --> 00:39:49,440
Speaker 3: Amazing Yeah, And and you know it has batteries for

753
00:39:49,880 --> 00:39:51,719
Speaker 3: back up and to get out of it, and you

754
00:39:51,760 --> 00:39:53,880
Speaker 3: need dead zones because you don't want to get stuck

755
00:39:53,880 --> 00:39:55,759
Speaker 3: in the middle of the ocean for a week. And

756
00:39:55,880 --> 00:39:58,880
Speaker 3: it has batteries for docking, so you do that with electric.

757
00:39:58,960 --> 00:40:02,959
Speaker 3: But but mostly it's sailing, and that's like a really

758
00:40:03,000 --> 00:40:05,399
Speaker 3: smart thing to do with wind power. It's right where

759
00:40:05,400 --> 00:40:07,959
Speaker 3: you need it. It's mechanical energy, which is what you need.

760
00:40:08,040 --> 00:40:10,759
Speaker 3: That's something that could make a huge difference. It's a

761
00:40:10,960 --> 00:40:14,120
Speaker 3: huge amount of CO two emissions gone, it's a huge

762
00:40:14,160 --> 00:40:17,160
Speaker 3: amount of expense gone. You know, half of if you

763
00:40:17,200 --> 00:40:19,239
Speaker 3: look up the ships on the ocean right now, half

764
00:40:19,280 --> 00:40:23,080
Speaker 3: of them are just moving fuel around. Half of the

765
00:40:23,120 --> 00:40:27,360
Speaker 3: ships on the ocean are moving gas and oil around.

766
00:40:27,640 --> 00:40:31,640
Speaker 4: It's all they're doing. It's insanity and we could do

767
00:40:31,680 --> 00:40:32,200
Speaker 4: so much better.

768
00:40:32,960 --> 00:40:34,880
Speaker 2: Yeah, give us another one company.

769
00:40:35,120 --> 00:40:39,120
Speaker 3: So here's one that we just announced today. No one

770
00:40:39,160 --> 00:40:44,440
Speaker 3: has ever heard about this. So if you are in

771
00:40:44,480 --> 00:40:49,319
Speaker 3: an airplane, a jet, you burn the gas, it expands

772
00:40:49,360 --> 00:40:52,080
Speaker 3: really fast, creates you know, there's a lot of heat,

773
00:40:52,120 --> 00:40:56,399
Speaker 3: but it's mainly the expansion goes into it what it's

774
00:40:56,400 --> 00:40:59,960
Speaker 3: called a turbofan, which is a jet engine to make

775
00:41:00,160 --> 00:41:01,439
Speaker 3: thrust to push the plane.

776
00:41:01,480 --> 00:41:02,040
Speaker 4: Okay, you got that.

777
00:41:02,200 --> 00:41:05,000
Speaker 3: So for a long time people have been trying to

778
00:41:05,040 --> 00:41:07,279
Speaker 3: figure out how do you is there a way to

779
00:41:08,640 --> 00:41:13,080
Speaker 3: make thrust just from electricity, And there have been different

780
00:41:13,160 --> 00:41:15,040
Speaker 3: ideas for this, different names for it.

781
00:41:15,320 --> 00:41:18,600
Speaker 4: One of my favorite names is electro gravitics. That was

782
00:41:18,600 --> 00:41:19,600
Speaker 4: one of the early names.

783
00:41:19,680 --> 00:41:22,480
Speaker 3: And then now like MIT worked on a little while

784
00:41:22,480 --> 00:41:25,000
Speaker 3: ago they called the ionic wind. And so the idea

785
00:41:25,040 --> 00:41:29,200
Speaker 3: is you're basically making a kind of an electrostatic generator

786
00:41:29,239 --> 00:41:32,839
Speaker 3: to charge ions to get them moving, and then get

787
00:41:32,920 --> 00:41:34,799
Speaker 3: enough of a moving that you know you're moving air.

788
00:41:35,239 --> 00:41:38,239
Speaker 3: And people have done this, but you can only get

789
00:41:38,280 --> 00:41:40,640
Speaker 3: just like a faint amount of They could barely get

790
00:41:40,640 --> 00:41:43,759
Speaker 3: a paper airplane to fly this way. But the phenomenon

791
00:41:43,800 --> 00:41:47,319
Speaker 3: wasn't well understood. Nobody was really sure what was how

792
00:41:47,360 --> 00:41:50,160
Speaker 3: it was working, and so they weren't able to optimize

793
00:41:50,160 --> 00:41:50,919
Speaker 3: it to make it work.

794
00:41:50,960 --> 00:41:54,160
Speaker 4: Well, well, I got this team that figured it out.

795
00:41:54,680 --> 00:41:58,319
Speaker 3: And so now they can. They made they could, They've

796
00:41:58,320 --> 00:42:01,839
Speaker 3: made a drone they have all pre working that can fly.

797
00:42:03,960 --> 00:42:08,000
Speaker 3: It has no moving parts, it's all electric, it's completely silent,

798
00:42:09,080 --> 00:42:11,680
Speaker 3: and it just flies. Their breakthrough as they've figured out

799
00:42:11,719 --> 00:42:16,760
Speaker 3: how to get accelerate the air going through it. Everybody

800
00:42:16,840 --> 00:42:19,759
Speaker 3: else was only able to get it like every time

801
00:42:19,760 --> 00:42:22,279
Speaker 3: they made a stage to accelerate the air, that had

802
00:42:22,320 --> 00:42:24,360
Speaker 3: to make that stage like bigger and bigger and bigger.

803
00:42:24,920 --> 00:42:28,000
Speaker 3: So we've solved that, so now we can make multiple stages.

804
00:42:28,040 --> 00:42:33,080
Speaker 3: We can get that thrust going boom, so silent, all electric.

805
00:42:33,200 --> 00:42:35,359
Speaker 3: I mean, drones are all electric anyway, but silent drones

806
00:42:35,400 --> 00:42:39,040
Speaker 3: and no moving parts and eventually airplanes same thing. They're

807
00:42:39,080 --> 00:42:41,480
Speaker 3: getting thrust the.

808
00:42:43,000 --> 00:42:43,839
Speaker 4: Early stages, so.

809
00:42:43,800 --> 00:42:46,720
Speaker 3: They have to build more prototypes, but basically they're able

810
00:42:46,760 --> 00:42:50,719
Speaker 3: to do thrust that's equivalent to a turbo fan.

811
00:42:52,080 --> 00:42:53,880
Speaker 2: They'll get there and it'll be totally silent.

812
00:42:53,960 --> 00:42:55,160
Speaker 4: Yeah, I have a silent airplane.

813
00:42:55,400 --> 00:42:57,560
Speaker 2: Oh my god. What's the name of this company.

814
00:42:57,480 --> 00:42:58,400
Speaker 4: That's called Daion.

815
00:42:59,120 --> 00:43:01,759
Speaker 3: Yes, yeah, Literally, this is the very first time I've

816
00:43:01,800 --> 00:43:05,600
Speaker 3: ever talked about it until they just got their patent

817
00:43:05,680 --> 00:43:08,680
Speaker 3: today and told me, and so I just put it

818
00:43:08,719 --> 00:43:11,319
Speaker 3: on our website today, first time ever.

819
00:43:12,200 --> 00:43:14,680
Speaker 1: So so you're here's the personal question. So you're really

820
00:43:14,719 --> 00:43:17,719
Speaker 1: interested in all these inventions and so on. So what

821
00:43:17,840 --> 00:43:20,600
Speaker 1: got you onto the business side of this as opposed

822
00:43:20,640 --> 00:43:21,960
Speaker 1: to being an inventor yourself.

823
00:43:22,280 --> 00:43:24,520
Speaker 3: Well, I got I got to do a lot of

824
00:43:24,560 --> 00:43:27,719
Speaker 3: the things that these people need to do, the inventors

825
00:43:27,719 --> 00:43:29,960
Speaker 3: and the entrepreneurs. You know, I got to invent a

826
00:43:30,000 --> 00:43:31,520
Speaker 3: lot of things, got to start a lot of companies.

827
00:43:31,560 --> 00:43:34,600
Speaker 3: I got to you know, go through the experience of

828
00:43:34,640 --> 00:43:39,799
Speaker 3: having them be decimated. I've gotten to you know, raise

829
00:43:39,880 --> 00:43:42,480
Speaker 3: money for them. I've had to do hire people and

830
00:43:42,520 --> 00:43:44,720
Speaker 3: fire people. I just had to do all those things.

831
00:43:44,760 --> 00:43:47,719
Speaker 3: And then for an inventor, like one of the things

832
00:43:47,719 --> 00:43:50,200
Speaker 3: I went through was this, you always are trying to

833
00:43:50,200 --> 00:43:53,080
Speaker 3: get access to tools. You're always trying to figure out,

834
00:43:53,080 --> 00:43:54,640
Speaker 3: like where do I get the tools to try this

835
00:43:54,760 --> 00:43:58,160
Speaker 3: idea and get the money and stuff. And then eventually

836
00:43:59,719 --> 00:44:03,640
Speaker 3: I started the Intellectual Ventures Lab with Nathan Mirvold, and

837
00:44:03,680 --> 00:44:05,640
Speaker 3: we one of the things we did we just bought

838
00:44:05,640 --> 00:44:06,200
Speaker 3: one of every.

839
00:44:06,000 --> 00:44:07,239
Speaker 4: Tool in the world, and.

840
00:44:08,760 --> 00:44:10,040
Speaker 3: So then I kind of got it out of my

841
00:44:10,239 --> 00:44:12,280
Speaker 3: and then we worked on every kind of invention project.

842
00:44:12,320 --> 00:44:14,680
Speaker 3: We got six thousand patents on our own inventions there,

843
00:44:14,719 --> 00:44:17,440
Speaker 3: so it was a very large scale invention operation. But

844
00:44:17,520 --> 00:44:21,320
Speaker 3: because I had every tool, I could build a protype

845
00:44:21,360 --> 00:44:23,200
Speaker 3: anything I wanted. And I pretty quickly learned that, like

846
00:44:23,239 --> 00:44:25,400
Speaker 3: there's always somebody better than me at operating the tool,

847
00:44:25,840 --> 00:44:27,839
Speaker 3: and so like I want to understand them and what

848
00:44:27,880 --> 00:44:29,799
Speaker 3: they can do and how to do it, but I

849
00:44:29,840 --> 00:44:32,600
Speaker 3: always learned, somebody's better than me at this, so let's

850
00:44:32,600 --> 00:44:35,759
Speaker 3: get them on the job. And so once I, you know,

851
00:44:35,800 --> 00:44:38,680
Speaker 3: that kind of changed my working mode to where like

852
00:44:38,760 --> 00:44:41,480
Speaker 3: I you know, I did almost everything via email, just

853
00:44:41,520 --> 00:44:43,360
Speaker 3: like can you build this can you print this?

854
00:44:43,440 --> 00:44:44,200
Speaker 4: Can you design that?

855
00:44:44,520 --> 00:44:47,640
Speaker 3: And then I think, you know, so that's a pretty

856
00:44:47,680 --> 00:44:50,000
Speaker 3: weird experience, Like I kind of just got it out

857
00:44:50,000 --> 00:44:52,400
Speaker 3: of my system. If I wanted to invent anything, I

858
00:44:52,400 --> 00:44:55,160
Speaker 3: got to do it already. And I and then you also,

859
00:44:55,680 --> 00:44:57,960
Speaker 3: you know, like we built a big team of inventors

860
00:44:58,000 --> 00:45:00,760
Speaker 3: and we turned invention into kind of it team sport.

861
00:45:00,880 --> 00:45:03,600
Speaker 3: And now i'd do is I try to help other

862
00:45:03,640 --> 00:45:05,960
Speaker 3: inventors because they have better inventions than me anyway at

863
00:45:05,960 --> 00:45:07,680
Speaker 3: this point, and so find.

864
00:45:07,520 --> 00:45:10,759
Speaker 1: Them So so tell us more about invention as a

865
00:45:10,760 --> 00:45:11,640
Speaker 1: team sport.

866
00:45:11,920 --> 00:45:14,680
Speaker 3: Yeah, that was one of the really great things we

867
00:45:14,760 --> 00:45:17,760
Speaker 3: got to do at that lab. So you know, Nathan

868
00:45:17,880 --> 00:45:21,640
Speaker 3: had built Microsoft Research before he was CTO at Microsoft

869
00:45:21,680 --> 00:45:24,560
Speaker 3: back in their heyday, and you know, at Microsoft Research,

870
00:45:24,560 --> 00:45:27,399
Speaker 3: he hired a lot of smart people and met all

871
00:45:27,440 --> 00:45:29,960
Speaker 3: the other smart people that he couldn't hire. So when

872
00:45:30,000 --> 00:45:32,560
Speaker 3: we started the lab, he was able to just round

873
00:45:32,640 --> 00:45:37,920
Speaker 3: up a lot of the you know, really prolific inventors, polymaths,

874
00:45:38,040 --> 00:45:41,279
Speaker 3: people who you know, had a high probability of being

875
00:45:41,360 --> 00:45:44,959
Speaker 3: useful in invention. And then what we did is we'd

876
00:45:44,960 --> 00:45:48,560
Speaker 3: have these like invention sessions where we'd find somebody with

877
00:45:48,600 --> 00:45:52,319
Speaker 3: a problem and sit them down, surround them with a

878
00:45:52,400 --> 00:45:56,680
Speaker 3: nuclear physicist, a laser expert, a chemist, computer hacker. You know,

879
00:45:56,800 --> 00:46:00,360
Speaker 3: collectively we know the cutting edge and every area in

880
00:46:00,440 --> 00:46:03,160
Speaker 3: science and technology. Between us, we're probably reading every paper

881
00:46:03,200 --> 00:46:05,919
Speaker 3: that comes out or you know, something like that, learning

882
00:46:05,960 --> 00:46:08,919
Speaker 3: about every new algorithm, every new sensor, every new chip,

883
00:46:09,000 --> 00:46:11,800
Speaker 3: things that could help you out with the output of research,

884
00:46:11,920 --> 00:46:14,080
Speaker 3: you know, the output of science. And we would just

885
00:46:14,160 --> 00:46:16,960
Speaker 3: take that and kind of ask ourselves, well, does this

886
00:46:17,120 --> 00:46:21,000
Speaker 3: change anything humans have ever done? Can we do it faster, cheap,

887
00:46:21,080 --> 00:46:23,880
Speaker 3: or better, maybe in a more humane fashion. Those are

888
00:46:23,880 --> 00:46:25,480
Speaker 3: the kinds of questions we'd be asking. There's like a

889
00:46:25,560 --> 00:46:29,000
Speaker 3: Rubik's cube in your brain, just matching up problems to

890
00:46:29,000 --> 00:46:31,480
Speaker 3: to the to the technologies.

891
00:46:31,600 --> 00:46:35,840
Speaker 4: And so that's invention. And in the invention sessions.

892
00:46:35,360 --> 00:46:37,640
Speaker 3: The person with the problem is very important because we

893
00:46:37,680 --> 00:46:40,960
Speaker 3: would invent stuff no matter what, lots of useless stuff.

894
00:46:41,160 --> 00:46:42,719
Speaker 3: But if the other person with a problem to kind

895
00:46:42,719 --> 00:46:46,440
Speaker 3: of focus everybody, then what you would do is, you know,

896
00:46:46,920 --> 00:46:49,200
Speaker 3: find inventions at the borders. You know, a lot of

897
00:46:49,239 --> 00:46:53,520
Speaker 3: times the invention, like our most famous invention is a

898
00:46:53,560 --> 00:46:56,799
Speaker 3: machine that can find mosquitoes and shoot them down. With

899
00:46:56,880 --> 00:47:01,360
Speaker 3: laser beams and people love it because everyone hates mosquitoes,

900
00:47:01,840 --> 00:47:04,680
Speaker 3: and so we did it as like a you know,

901
00:47:04,719 --> 00:47:06,719
Speaker 3: we had actually in that case, Bill Gates was the

902
00:47:06,719 --> 00:47:09,400
Speaker 3: guy with the problem because he brought us malaria to

903
00:47:09,480 --> 00:47:13,080
Speaker 3: work on. So we're trying to find malaria interventions in

904
00:47:13,120 --> 00:47:15,120
Speaker 3: the malarias spread by the mosquitos.

905
00:47:15,200 --> 00:47:18,640
Speaker 4: So we thought, okay, well maybe we could.

906
00:47:18,880 --> 00:47:21,240
Speaker 3: We were just joking around because you always try lasers

907
00:47:21,280 --> 00:47:23,960
Speaker 3: first because the lasers are cool, So every invention starts

908
00:47:23,960 --> 00:47:25,560
Speaker 3: with lasers, and if that doesn't work, then you try

909
00:47:25,560 --> 00:47:26,000
Speaker 3: something else.

910
00:47:26,560 --> 00:47:28,560
Speaker 4: But you know, we're, oh, here we can use lasers.

911
00:47:28,560 --> 00:47:32,160
Speaker 3: Awesome, And so the thing what happened is we were

912
00:47:32,200 --> 00:47:35,080
Speaker 3: just joking about it, but one of the inventors in

913
00:47:35,120 --> 00:47:39,800
Speaker 3: the room, Loull, would he's actually the has more patents

914
00:47:39,800 --> 00:47:43,600
Speaker 3: than Thomas Edison, is the record for in America for pattents.

915
00:47:43,920 --> 00:47:45,360
Speaker 4: Loull prolific inventor.

916
00:47:45,440 --> 00:47:49,000
Speaker 3: But lull Hood worked on UH Star Wars for Reagan,

917
00:47:49,920 --> 00:47:52,160
Speaker 3: which was and the idea with that was to have

918
00:47:52,239 --> 00:47:55,719
Speaker 3: giant lasers in space that would shoot down missiles, you know, right,

919
00:47:55,760 --> 00:47:59,040
Speaker 3: so these missiles you see on YouTube right now, we

920
00:47:59,040 --> 00:48:02,600
Speaker 3: could actually well, at least the intercontinental ones.

921
00:48:02,640 --> 00:48:05,200
Speaker 4: You could shoot them down with a laser.

922
00:48:05,800 --> 00:48:08,920
Speaker 3: And that sounds crazy, but we spent like fifty billion

923
00:48:08,960 --> 00:48:11,640
Speaker 3: dollars on this back in the eighties, and Lowell said,

924
00:48:11,680 --> 00:48:15,000
Speaker 3: you know, we already proved this would work. And the

925
00:48:15,080 --> 00:48:20,759
Speaker 3: mosquito is a bigger, easier target in this context than

926
00:48:20,800 --> 00:48:23,360
Speaker 3: a missile would be from space, right, because the missiles

927
00:48:23,360 --> 00:48:26,320
Speaker 3: are much further and laser here we have a smaller laser,

928
00:48:26,640 --> 00:48:28,799
Speaker 3: but the mosquitos are only in one hundred meters away

929
00:48:28,840 --> 00:48:31,920
Speaker 3: or is less. And so anyway, even an invention like that,

930
00:48:32,520 --> 00:48:34,600
Speaker 3: you had to have like people kind of on the

931
00:48:34,640 --> 00:48:37,760
Speaker 3: border of machine vision, because this is this is fifteen

932
00:48:37,800 --> 00:48:40,600
Speaker 3: years ago now, but at the times like well, using

933
00:48:40,640 --> 00:48:43,640
Speaker 3: motion detection algorithms, could we find the mosquito in real

934
00:48:43,680 --> 00:48:45,200
Speaker 3: time and aim a laser in real time? And then

935
00:48:45,239 --> 00:48:47,640
Speaker 3: we had the like kind of right people for lasers

936
00:48:47,640 --> 00:48:51,480
Speaker 3: and machine vision, So you could come up with inventions

937
00:48:51,840 --> 00:48:54,239
Speaker 3: that often were at the borders of different areas and

938
00:48:54,280 --> 00:48:58,759
Speaker 3: science and technology. That way, this process, I contend is

939
00:48:58,800 --> 00:49:03,560
Speaker 3: totally repeatable, like other organizations could do it, other people

940
00:49:03,560 --> 00:49:06,080
Speaker 3: could do it. You know, Yeah, we cheated by getting

941
00:49:06,080 --> 00:49:08,359
Speaker 3: the smartest people we could find, but you know, get

942
00:49:08,400 --> 00:49:10,719
Speaker 3: the smartest people you can find and try it, like like,

943
00:49:10,760 --> 00:49:11,880
Speaker 3: that's totally possible.

944
00:49:12,040 --> 00:49:13,839
Speaker 1: And what were the things that worked When you've got

945
00:49:13,920 --> 00:49:16,799
Speaker 1: the nuclear physicist and the computer hacker and everyone in

946
00:49:16,840 --> 00:49:19,759
Speaker 1: the room, how do you do the interactions so that

947
00:49:19,760 --> 00:49:22,520
Speaker 1: everyone's not flipping their own rooms cube, but they're flipping

948
00:49:22,600 --> 00:49:23,680
Speaker 1: a community room recube.

949
00:49:23,800 --> 00:49:24,959
Speaker 4: So our first rule.

950
00:49:25,280 --> 00:49:27,960
Speaker 3: So a lot of people in brainstorming mode would say,

951
00:49:28,000 --> 00:49:29,879
Speaker 3: there's no bad ideas, let's get them all out there.

952
00:49:30,840 --> 00:49:34,840
Speaker 3: Ours was a little different. Ours was, there are definitely

953
00:49:34,880 --> 00:49:38,320
Speaker 3: bad ideas, and if you think my idea is bad,

954
00:49:38,520 --> 00:49:39,879
Speaker 3: you should totally shoot it down.

955
00:49:40,320 --> 00:49:44,080
Speaker 4: But you got to come up with once better. So

956
00:49:44,160 --> 00:49:46,600
Speaker 4: it was and look, this doesn't work for everybody.

957
00:49:46,719 --> 00:49:49,359
Speaker 3: When you're in a context like that, your north star

958
00:49:49,640 --> 00:49:55,200
Speaker 3: is what's technically true. So feelings don't matter. It's about

959
00:49:55,239 --> 00:49:57,920
Speaker 3: what's true, and we're all trying to find the truth.

960
00:49:58,360 --> 00:50:01,160
Speaker 3: So if I say something it sounds like bullshit and

961
00:50:01,239 --> 00:50:03,000
Speaker 3: you think, no, you're full of shit. You could tell

962
00:50:03,040 --> 00:50:06,360
Speaker 3: me I'm full of shit, but then tell me, like,

963
00:50:06,400 --> 00:50:08,560
Speaker 3: what's actually true? Do you have better data or newer data,

964
00:50:08,600 --> 00:50:10,000
Speaker 3: or is there some reason why you think that, or

965
00:50:10,000 --> 00:50:12,040
Speaker 3: do you know something I don't know. All that's totally fine.

966
00:50:12,480 --> 00:50:16,920
Speaker 3: So nobody takes it personally. That's very honest, intellectual discourse.

967
00:50:16,960 --> 00:50:19,839
Speaker 3: And I love that. I mean people, I think most

968
00:50:19,920 --> 00:50:22,040
Speaker 3: people don't understand what that would be like. But when

969
00:50:22,080 --> 00:50:26,399
Speaker 3: you're able to be collaborating with people where you don't

970
00:50:26,440 --> 00:50:30,239
Speaker 3: have to sugarcoat things, worry about their feelings, dumb things down,

971
00:50:30,360 --> 00:50:33,480
Speaker 3: simplify things, any of that is just we're trying to

972
00:50:33,480 --> 00:50:36,759
Speaker 3: find what's true, then it takes the personal aspect out

973
00:50:36,760 --> 00:50:38,799
Speaker 3: of it. You know, I have to shout down Bill

974
00:50:38,840 --> 00:50:42,320
Speaker 3: Gates sometimes, you know, yeah, And so you know it

975
00:50:42,360 --> 00:50:44,719
Speaker 3: doesn't work for psycho fans or people who are you know,

976
00:50:44,800 --> 00:50:47,439
Speaker 3: worried about how what people think of them?

977
00:50:47,840 --> 00:50:52,000
Speaker 1: Right, So, how would you see replicating this more widely

978
00:50:52,080 --> 00:50:54,440
Speaker 1: getting this sort of thing set up? Could you implement

979
00:50:54,520 --> 00:50:57,960
Speaker 1: this in schools you have invention clubs where people sit

980
00:50:58,040 --> 00:50:59,280
Speaker 1: around and take on problems.

981
00:50:59,400 --> 00:51:01,440
Speaker 4: Need that would love? That would be awesome?

982
00:51:01,560 --> 00:51:04,839
Speaker 3: Yeeah, I don't know. I mean, I think we were

983
00:51:04,960 --> 00:51:09,520
Speaker 3: very fortunate in that we had the you know, the

984
00:51:09,520 --> 00:51:11,759
Speaker 3: the mindset to do that, the will to do it,

985
00:51:11,840 --> 00:51:14,640
Speaker 3: the resources to do it. You know, Bill Gates funded

986
00:51:14,680 --> 00:51:17,120
Speaker 3: a lot of our humanitarian projects, all the stuff with

987
00:51:17,239 --> 00:51:21,880
Speaker 3: malaria and disease eradication and other things as well, and

988
00:51:22,520 --> 00:51:26,120
Speaker 3: energy and different things. So we're lucky to have that

989
00:51:26,239 --> 00:51:29,440
Speaker 3: kind of support. What I found is, most of the

990
00:51:29,560 --> 00:51:34,000
Speaker 3: time the incentive structure and other organizations doesn't lend itself

991
00:51:34,040 --> 00:51:37,040
Speaker 3: to invention. And here's why. I can actually explain that too.

992
00:51:37,760 --> 00:51:42,120
Speaker 3: If you are a successful institution of any type that

993
00:51:42,160 --> 00:51:47,680
Speaker 3: could be a business or university, or just the healthcare

994
00:51:47,719 --> 00:51:51,279
Speaker 3: system or a government, part of what happens is as

995
00:51:51,360 --> 00:51:54,640
Speaker 3: it evolves and is successful, it evolves an immune system.

996
00:51:56,120 --> 00:52:00,480
Speaker 3: And the immune system's job is to suppress risk. And

997
00:52:00,520 --> 00:52:03,920
Speaker 3: what looks like risk is change. And so this is

998
00:52:03,960 --> 00:52:06,560
Speaker 3: one of the big reasons why you don't see innovation

999
00:52:06,840 --> 00:52:11,719
Speaker 3: in bigger, older, more successful institutions of any kind. It's

1000
00:52:11,800 --> 00:52:17,440
Speaker 3: because anything that looks like change, we have a system

1001
00:52:17,480 --> 00:52:20,719
Speaker 3: to squash it or keep it out or you know,

1002
00:52:20,840 --> 00:52:23,480
Speaker 3: slow it down whatever. And so that's why like Silicon

1003
00:52:23,560 --> 00:52:29,000
Speaker 3: Valley is thousands of million dollar experiments. That's what startups are,

1004
00:52:29,440 --> 00:52:33,000
Speaker 3: and that's why we we were cool with them dying right,

1005
00:52:33,719 --> 00:52:36,279
Speaker 3: We're really cool with them dying fast. Would much rather

1006
00:52:37,200 --> 00:52:39,719
Speaker 3: kill these things fast. So a startups job is to

1007
00:52:39,840 --> 00:52:43,640
Speaker 3: try out, really you know, a new idea, hopefully a

1008
00:52:43,640 --> 00:52:46,439
Speaker 3: new invention. If you're working on a cool startup, there's

1009
00:52:46,440 --> 00:52:49,880
Speaker 3: a new invention, the new invention, or a new idea

1010
00:52:49,960 --> 00:52:51,520
Speaker 3: or a new product. We want to find out real

1011
00:52:51,640 --> 00:52:55,040
Speaker 3: quick if we can make this successful, and we're going

1012
00:52:55,080 --> 00:52:56,640
Speaker 3: to be competing against all the other people are trying

1013
00:52:56,680 --> 00:52:58,920
Speaker 3: to make something else successful, and we want to find

1014
00:52:58,920 --> 00:52:59,520
Speaker 3: those winners.

1015
00:52:59,640 --> 00:53:00,880
Speaker 4: And then we find the winners.

1016
00:53:01,480 --> 00:53:05,040
Speaker 3: Solici Velli got real good and just pouring fuel on

1017
00:53:05,080 --> 00:53:08,160
Speaker 3: the fire, and you can see that it just seems

1018
00:53:08,160 --> 00:53:09,759
Speaker 3: crazy to everyone else in the world. But when we

1019
00:53:09,800 --> 00:53:12,280
Speaker 3: find a winner, then we try to take it global

1020
00:53:12,320 --> 00:53:15,160
Speaker 3: as fast as we can and win the value out

1021
00:53:15,200 --> 00:53:16,759
Speaker 3: of it so we can make enough money to go

1022
00:53:16,800 --> 00:53:18,600
Speaker 3: fund a bunch more things that aren't gonna work.

1023
00:53:23,120 --> 00:53:25,960
Speaker 1: That was my interview with Pablos Holman, and that gives

1024
00:53:26,040 --> 00:53:29,719
Speaker 1: us a glimpse into the frontier of deep tech and

1025
00:53:29,719 --> 00:53:32,359
Speaker 1: the people who are building it and one investor who's

1026
00:53:32,400 --> 00:53:35,600
Speaker 1: made it his mission to support them. Every time I

1027
00:53:35,600 --> 00:53:38,319
Speaker 1: speak with someone like Pablos, I walk away feeling that

1028
00:53:38,360 --> 00:53:41,839
Speaker 1: the future is closer than we think, and also with

1029
00:53:41,920 --> 00:53:45,040
Speaker 1: a sense of how easy it is to miss it

1030
00:53:45,160 --> 00:53:50,840
Speaker 1: because invention doesn't announce itself in pitch decks or quarterly reports.

1031
00:53:51,080 --> 00:53:54,080
Speaker 1: Sometimes it looks like a piece of circuit board and

1032
00:53:54,120 --> 00:53:58,680
Speaker 1: a back lab, and unless people are really listening, ideas

1033
00:53:58,719 --> 00:54:02,360
Speaker 1: can go unnoticed, just like the way that inoculation was

1034
00:54:02,400 --> 00:54:06,880
Speaker 1: invented over and over again before it took root. So

1035
00:54:07,000 --> 00:54:10,000
Speaker 1: one lesson from today is that inventing is not the

1036
00:54:10,040 --> 00:54:13,480
Speaker 1: same as running a business or engineering or scaling. Invention

1037
00:54:13,680 --> 00:54:16,840
Speaker 1: is the rare act of using the prefroederal cortex to

1038
00:54:16,960 --> 00:54:21,000
Speaker 1: imagine a world that didn't exist five minutes ago, and

1039
00:54:21,040 --> 00:54:25,719
Speaker 1: then bending reality to bring that into being. We also

1040
00:54:25,840 --> 00:54:29,600
Speaker 1: learned that this kind of thinking doesn't usually emerge inside

1041
00:54:29,840 --> 00:54:34,560
Speaker 1: institutions that are optimized for predictability and control. As publists

1042
00:54:34,560 --> 00:54:40,360
Speaker 1: put it, institutions develop immune systems against change, against risk,

1043
00:54:40,440 --> 00:54:44,759
Speaker 1: against anything that smells like uncertainty, which unfortunately includes just

1044
00:54:44,800 --> 00:54:48,120
Speaker 1: about every great idea in its early days.

1045
00:54:48,360 --> 00:54:49,239
Speaker 2: That's why so.

1046
00:54:49,280 --> 00:54:52,800
Speaker 1: Many of the stories we heard today have a similar theme.

1047
00:54:53,520 --> 00:54:59,000
Speaker 1: Breakthroughs buried in obscurity. A silent electric propulsion system, a

1048
00:54:59,440 --> 00:55:02,760
Speaker 1: nuclear reactor the size of a trash can, a self

1049
00:55:02,840 --> 00:55:07,680
Speaker 1: sailing cargo ship, a space based solar farm, a battery

1050
00:55:07,719 --> 00:55:11,279
Speaker 1: that might never need charging. A lot of inventions like

1051
00:55:11,320 --> 00:55:14,520
Speaker 1: this exist, but they have to wait for attention and

1052
00:55:14,640 --> 00:55:18,000
Speaker 1: belief and funding. And we're probably not going to solve

1053
00:55:18,480 --> 00:55:21,719
Speaker 1: our global scale problems like energy and water and food

1054
00:55:21,760 --> 00:55:26,160
Speaker 1: and climate transportation with branding or software tweaks. We're going

1055
00:55:26,200 --> 00:55:30,120
Speaker 1: to solve them with hard technology and with brave experiments.

1056
00:55:30,120 --> 00:55:32,800
Speaker 1: And that means we need to get better at finding

1057
00:55:32,880 --> 00:55:37,040
Speaker 1: and funding and supporting inventors, not just business types. And

1058
00:55:37,120 --> 00:55:39,840
Speaker 1: that probably means we're going to need to change the

1059
00:55:39,920 --> 00:55:42,640
Speaker 1: culture a little bit. It would be great to teach

1060
00:55:42,760 --> 00:55:47,239
Speaker 1: kids that inventor is a real job title. We need

1061
00:55:47,280 --> 00:55:51,400
Speaker 1: to create more spaces where creative risk taking is safe

1062
00:55:51,480 --> 00:55:56,040
Speaker 1: and even celebrated. We need to stop culturally pretending that

1063
00:55:56,200 --> 00:55:59,440
Speaker 1: invention is just science and a lab or engineering in

1064
00:55:59,480 --> 00:56:03,960
Speaker 1: a workshop up because invention is curiosity and it's often

1065
00:56:04,000 --> 00:56:07,920
Speaker 1: a mess, but it's also the source of almost everything

1066
00:56:07,960 --> 00:56:10,680
Speaker 1: that we value. And this is our moment to make

1067
00:56:10,840 --> 00:56:15,040
Speaker 1: cultural change, because there are more people with ideas today

1068
00:56:15,440 --> 00:56:19,759
Speaker 1: than at any other point in human history. So I'll

1069
00:56:19,800 --> 00:56:23,400
Speaker 1: leave you with this challenge. Be an inventor big or small,

1070
00:56:23,440 --> 00:56:27,120
Speaker 1: because invention is for anyone who's willing to ask what if,

1071
00:56:27,640 --> 00:56:31,200
Speaker 1: or just be a supporter or encourager of the inventors

1072
00:56:31,320 --> 00:56:36,800
Speaker 1: around you. The future never comes from more of the same.

1073
00:56:37,080 --> 00:56:41,040
Speaker 1: It comes from the minds that take advantage of these

1074
00:56:41,080 --> 00:56:43,120
Speaker 1: incredible inherited neural.

1075
00:56:42,840 --> 00:56:46,560
Speaker 2: Networks to look at the world not as it is,

1076
00:56:47,200 --> 00:56:48,239
Speaker 2: but as it could be.

1077
00:56:52,680 --> 00:56:55,680
Speaker 1: Go to eagleman dot com slash podcast for more information

1078
00:56:55,800 --> 00:56:59,239
Speaker 1: and to find further reading. Join the weekly discussions on

1079
00:56:59,280 --> 00:57:02,960
Speaker 1: my substance and check out and subscribe to Inner Cosmos

1080
00:57:03,000 --> 00:57:05,839
Speaker 1: on YouTube for videos of each episode and to leave

1081
00:57:05,920 --> 00:57:10,400
Speaker 1: comments until next time. I'm David Eagleman and this is

1082
00:57:10,520 --> 00:57:11,520
Speaker 1: Inner Cosmos.