The Tim Ferriss Show - #404: Books I've Loved — Steve Jurvetson

Starting point is 00:00:00 At this altitude, I can run flat out for a half mile before my hands start shaking. Can I ask you a personal question? Now would have seemed an appropriate time. What if I did the opposite? I'm a cybernetic organism, living tissue over a metal endoskeleton. The Tim Ferriss Show. This episode is brought to you by Four Sigmatic, founded by the genius Finns who lit the internet on fire. And you may have heard of their mushroom coffee, which features chaga and lion's mane,

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Starting point is 00:01:16 The first time I mentioned this product and Four Sigmatic on the podcast, their product sold out in less than a week. So you may want to check them out soon if you're listening to this. And the coffee tastes like coffee. It takes just seconds to prepare with hot water. And oddly enough, only includes 40 milligrams of caffeine. So it has less than half of what you'd get in a regular cup of coffee. I don't get any jitters, acid reflux, or any stomach burn, any of that. It's very unusual and very, very cool. So if you don't get any jitters, acid reflux, or any stomach burn, any of that. It's very unusual and very, very cool. So if you don't like caffeine, they also offer very strong but caffeine-free mushroom elixirs, which I will sometimes have in the evening.

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Starting point is 00:02:28 Hello, boys and girls, ladies and germs. This is Tim Ferriss. Welcome to another episode of The Tim Ferriss Show, where it is usually my job to sit down with world-class performers of all different types, startup founders, investors, chess champions, Olympic athletes, you name it, to tease out the habits that you can apply in your own lives. This episode, however, is an experiment and part of a short form series that I'm doing simply called Books I've Loved. I've invited some amazing past guests, close friends, and new faces to share their favorite books, describe their favorite books, the books that have influenced them, changed them, transformed them for the better. And I hope you pick up one or two new mentors in the form of books from this new series and apply the lessons in your own life.

Starting point is 00:03:10 I had a lot of fun putting this together, inviting these people to participate, and have learned so, so much myself. I hope that is also the case for you. Please enjoy. Well, hello, boys and girls. My name is Steve Jurvetson, and I am an early stage venture Please enjoy. others that were required for a total of over $100 billion in value creation. I currently serve on the boards of Tesla, SpaceX, and D-Wave, which is a quantum computing company. Before founding Future Ventures, the venture firm I'm at now, and DFJ before it, I was an R&D engineer at Hewlett-Packard and worked in product marketing at Apple and Next and management consulting with Bain & Company. And I was originally trained in electrical engineering, going all the way through to a PhD, but not completing it. So today I'll present three books. The most gifted book by me, the one I've

Starting point is 00:04:09 given to most people, the most influential book on me, and then the most important book for all, in my humble opinion. So let me start with the book I've gifted more than any other. It is Scientist in the Crib by Alison Gopnik. She's a professor of developmental psychology at Berkeley. And I basically give this book to any geek friend of mine about to have their first child because it had such a wonderful influence on me. It is not a parenting book, but it nevertheless, it kindles an awe and awareness for the marvels of their minds, baby's minds, especially in the pre-verbal years when it might otherwise be difficult to connect. Some practical experiments come out of

Starting point is 00:04:42 this. Once you understand that babies signal their interest in things by where they refocus their gaze. This is the fundamental research tool used by Gopnik and others in their partitioning of their art. And that focus shifts over time as the brain develops and they face new developmental milestones. So, for example, at birth, much of the vision system is bootstrapping. And this is everything from the color space to distance vision and initially edge detection, meaning seeing the edge of an object and its three-dimensional distance, if you will. This is how you sort of can make a difference between foreground and background and make sense of the world in three dimensions. So I would notice that when I could take advantage of this, basically

Starting point is 00:05:19 even at the hospital, when my son was one day old, I noticed that when I pushed his bassinet with the sleeping baby through a hospital hallway, his eyes would just pop open. Whenever I turned a certain corner, it was like clockwork. And so I looked up and I saw a right angle in a long, bright line of fluorescent light. So it ran down the hallway and made a right turn. And sure enough, when I closed my own eyes and looked up, I could see that sharp edge of light through my own closed eyelids. Aha! This was like food for the baby's developing brain. It made him happy to open his eyes to this visual treat that was the thing he was cognitively working on most at that time. And it made it great for me to show this to others.

Starting point is 00:05:59 I could get him to open his eyes for visitors by repeating this trick for them. And again, it would be sort of a joyful way to wake a sleeping baby. Then later, when my daughter was first learning to speak, but had not mastered all the sounds, I noticed her gaze would flip around to my mouth whenever I made a b-b or a p-p sound, a b or a p, b-p. Imagine learning those for the first time. It is a very subtle difference in mouth position. And how else could we learn this but to watch someone else?

Starting point is 00:06:20 So I then had many days of enjoyable phoneme practice, as I called it, with her as she came to master the elements of speech. I think Scientists in the Crib is fascinating not just for the life in the crib, but for what it tells us about scientists as well. It is an inspiration for adult life. From what I can see, the best scientists and engineers nurture a childlike mind. They are playful, open-minded, and unrestrained by the inner voice of reason, collective cynicism, or fear of failure. Isaac Newton and Richard Feynman are famous examples of this. I've come to celebrate the childlike mind, as I call it. And here is one of Alison Gopnik's key conclusions from her book. And this is a direct quote. Babies are just plain smarter than we are. At least if being smart means being able

Starting point is 00:06:58 to learn something new. They think, draw conclusions, make predictions, look for explanations, and even do experiments. In fact, scientists are successful precisely because they emulate what children do naturally. At a recent talk I heard at the Long Now Foundation, Alison Gopnik went further to say that three and four-year-olds do causal inference better than the best scientists we know. It's kind of fascinating. So what is this? Well, much of the human's brain power derives from its massive synaptic interconnectivity, the connection between neurons. Jeffrey West from the Santa Fe Institute observed that across species, synapses per neuron, meaning how many connections each neuron has to its neighbors, it fans out with a power law with brain mass. In other words, this is something that is endemic to larger and

Starting point is 00:07:38 larger brains in the evolutionary landscape. At an age of two to three years old, so when your baby has now become a young child, two to three years old, they hit their peak with 10 times as many synapses as we have as adults, literally 10 times as many interconnects as we do, and twice the total energy burn of an adult brain. Well, it's all downhill from there. The UCSF Memory and Aging Center

Starting point is 00:07:58 has tracked cognitive ability with age. For example, they have a delayed free recall test. Quite simply, you read 16 words, and after some time has passed, you're tested on how many you can recall, unprompted. From the teen years to our mid-30s, we all remember about 12 of the 16 words. It's pretty much a flat line on the graph. But then, about in our mid-30s, the line shifts to a completely different straight line that's declining over time until end of life. But it's at the same slope. In other words, the pace of cognitive decline is the same in our 40s, as in our 60s,

Starting point is 00:08:29 as in our 70s, and in our 80s. We just notice more accumulated decline as we get older, especially when we cross the threshold of forgetting most of what we try to remember in our late 70s to early 80s per that graph. But we can affect this progression. That's a graph looking in the past. Professor Merznesh at UCSF has

Starting point is 00:08:45 also found that neuroplasticity does not disappear in adults. It just requires mental exercise, the old adage of use it or lose it. So the bottom line from this in terms of adult sort of learning and recommendations is that we should embrace lifelong learning. We should do something new. Physical exercise is repetitive. Mental exercise is eclectic. And that brings us to the next book, an eclectic romp by Kevin Kelly, the founding editor of Wired Magazine, and it's a book called Out of Control.

Starting point is 00:09:14 Now, this is the most influential book on me, and it has guided many of my investment theses over the last 20 years in technology development. It basically is a book that covers the dawn of the age of biology as the next phase of major technology vectors coming dawn of the age of biology as the next phase of major technology vectors coming out of an age of physics, if you will. And these biological metaphors are ripe throughout information technology. And Kevin Kelly very expertly

Starting point is 00:09:35 explores the integration of these domains. So the interesting thing is that this book was written in 1995, and it may have been 20 years ahead of its time. It was recently translated into Mandarin, and it is currently a bestseller in China. As if it were written today. As if this was something that just went to a time capsule, and now is really when it's hitting powerfully on our shores. So, as an introduction to the power of evolutionary algorithms and information networks inspired by biology,

Starting point is 00:09:58 Kevin Kelly basically explores what fundamentally are the underlying principles of complexity theory at the Santa Fe Institute, the properties of emergence, self-organization, what some would call the wisdom of crowds when you have many people behaving as a team and outperforming as they would perform as just individuals, or that of a hive mind or how the social insects do what they do. It motivates the benefits of exploring biomimicry, basically learning from biology, especially in our information systems like neural networks, what we now call deep learning or machine learning, which are basically recapitulating in silicon the evolutionary and fetal development of our cognition. So when you train these artificial neural networks, these layers are basically

Starting point is 00:10:37 forming much like they do in a fetus, going back to the Alison Gopnik. It basically starts with edge detection, then symmetry subsystems, eventually builds up to facial recognition, and then identifying people's faces. These are different layers in the neural nets that form in that consecutive order, just like our infants. So basically, if you look at where Moore's Law is taking us and where computation is taking us, we're now at the cutting edge of computational capture in biology. We're actively re-engineering information systems of biology and creating synthetic microbes whose DNA is manufactured from bare computer code and an organic chemistry printer. And the challenge we face in many of these synthetic biology domains is a question of what to build. So far, we've largely just copied large tracts of code from nature. But

Starting point is 00:11:18 the question then spans across all the complex systems we might want to build, from cities to designer microbes to computer intelligence. As all these systems transcend human comprehension, basically as we try to design more than we can comprehend, more than we can understand, we will shift from traditional engineering to evolutionary algorithms and iterative learning algorithms like deep learning and machine learning. And as we shift this engineering to the training of these iterative algorithms, the locus of learning shifts from the artifacts themselves to the process that created them. There is no mathematical shortcut to get through the decomposition of a neural network or to reverse engineer it or a genetic program. There's no way to reverse evolve with the same ease we can reverse engineer the artifacts of purposeful design.

Starting point is 00:11:59 The beauty of these compounding iterative algorithms, by this I mean evolution, fractals, organic growth, art, that derives from their irreducibility, their computational irreducibility, no mathematical shortcuts. And it empowers us to design complex systems that exceed human understanding. In short, we are re-engineering engineering itself. It starts to look more like parenting than programming. And that brings us to The Age of Spiritual Machines by Ray Kurzweil, the inventor and futurist. I think this might be the most important book. And even maybe more shockingly, I would say there is a single graph in the book that itself makes this book the most important book one could read. And obviously, therefore, it's simply this one graph, the graph of the

Starting point is 00:12:39 120-year version of Moore's Law. So let me explain what I mean by this. And also just mention perhaps in starting that it's really just the first few chapters of this book I'd recommend, not the entire book that looks into the distant, distant future, like the next hundred years, but really just the background, the historical section, and then let yourself make your own conclusions. And basically, this book introduces the best abstraction of Moore's Law that I've seen out there, one that is understandable, meaningful, even cosmological, and has predictive power. So it is, I think, essential for tech futurism, predicting where we're heading, as well as business planning. As most businesses become technology businesses, understanding how to predict our future becomes all the more important.

Starting point is 00:13:16 So the popular perception of Moore's law, that again, Gordon Moore from Intel, who predicted computer power getting better and better, basically, is the sense that computer chips are compounding in their complexity at a near-constant unit cost. So it's a sort of bang-for-the-buck kind of representation. But this is just one of the many abstractions of Moore's Law. People have all kinds of different ways of defining it. You get different answers from different people. But it relates to the compounding transistor density in true dimensions. Other renditions of this Moore's Law just relate to speed, like how many megahertz or gigahertz do we have in our chips. That was some of the early days

Starting point is 00:13:48 when people didn't really know what they were talking about. And it makes sense that, you know, as you miniaturize a chip, the distance traveled by any given signal is less, so everything runs faster. Or some people refer to computational power, which is basically speed times density, because both benefits accrue as you miniaturize. So for a long time, this was thought to be very specific to Intel. But unless you work for a chip company like Intel, and then unless you focus on fab yield optimization, you don't really care about transistor counts.

Starting point is 00:14:12 Nobody goes out and buys a million transistors or give me a billion transistors. That makes no sense, right? Integrated circuit customers don't buy that. They are basically consumers of technology and they buy computational speed and data storage. That's what we care about. And quite simply, Ray Kurzweil in his book plots the calculations per second, so computational power, how many calcs per second, that you could buy for a constant dollar. So again,

Starting point is 00:14:34 adjusting for inflation over a long period of time. And Ray Kurzweil's abstraction of Moore's law shows that computational power has followed a smooth exponential curve for over 120 years, basically since the beginning of data on any kind of computer. It's a straight line on semi-log paper. There's years along the x-axis and a logarithmic scale of computational power per dollar on the y-axis. And it shows a geometrically compounding curve of progress. When recasting these terms, Moore's law is no longer transistor centric, and this abstraction allows for longer term analysis, so in other words, it's not specific to Intel. What Moore, Gordon Moore, the person, observed in the belly of the early integrated circuit

Starting point is 00:15:12 industry was a derivative metric, a refracted signal from a longer-term trend, a trend that begs various philosophical questions and predicts mind-bending futures. Through five paradigm shifts, such as electromechanical calculators and vacuum tube computers, the computational power that a dollar buys has doubled every 18 months for 120 years. Every dot on this curve is basically on the frontier of computational price performance of the day. One machine was used in the 1890 census. One cracked the Nazi enigma cipher in World War II, if you saw the movie Imitation Game. One predicted Eisenhower's win in the 56th presidential election. I've been updating this graph since basically the time of the book, which was a while back, and have basically found over the last 10 to 20 years that I've added to this curve

Starting point is 00:15:53 that the latest CPUs, and specifically NVIDIA GPUs, the graphic chips, carry out this precise same curve of progress to the present day. And that's sort of extending Kurzweil's analysis, I think 20 years past when he stopped the curve. So every dot, every machine on this curve represents a human drama. Prior to Moore's law, which was first formulated in 1965, none of the people on the curve even knew they were on a predictive curve, right? It wasn't until Gordon Moore basically came up with Moore's law that we would have thought to even plot such a thing. And every dot represents an attempt to build the best computer with the best tools of the day. Of course, we also use these computers

Starting point is 00:16:26 to make better design software, better manufacturing control algorithms, and so progress continues. But notice that the pace of innovation, a straight line, imagine that for 120 years, is exogenous to the economy. Think about how long this has held true. The Great Depression, World War I, World War II,

Starting point is 00:16:41 and various recessions have not introduced any meaningful change in the long-term trajectory of Moore's Law. Certainly, the adoption rates, the revenue, profits, and economic fates of each of the underlying computer companies behind the various dots may go through wild oscillations, but yet the long-term trends emerge nevertheless. As one technology, such as the CMOS transistor, the current technology du jour, follows an elongated S-curve of slow progress during initial development,

Starting point is 00:17:05 upper progress during a rapid adoption phase, and then slower growth from market saturation over time. But a more generalized capability, such as computation, which isn't tied to one thing, storage more generally, bandwidth more generally, they tend to follow a pure exponential, bridging across a variety of different technologies in their cascade of S-curves. Well, in the modern era of accelerating change in the tech industry, it's hard to even find a five-year trend with any predictive value, yet let alone a trend that spans centuries. I would go further and assert, as I did, that this is the most important graph ever conceived. So why?

Starting point is 00:17:38 Why do I think it's the most important graph in history? Well, a large and growing set of industries depend on continued exponential cost declines in computational power and storage density. Moore's Law drives electronics, communications, and computers and has become a primary driver in drug discovery, biotech, bioinformatics, medical imaging, and diagnostics. As Moore's Law crosses critical thresholds, a former lab science of trial and error experimentation becomes a simulation science, and the pace of progress accelerates dramatically, becoming an information business and creating opportunities for new entrants in new industries. This is why as a venture capitalist, I love it. Basically, think of an example, Boeing building aircraft. They used to rely on wind tunnels to test novel aircraft design performance. Ever since CFD modeling became powerful enough to simulate this,

Starting point is 00:18:22 design moves to the rapid pace of iterative simulations. In the nearby wind tunnels at NASA Ames and all around the country lie fallow. They aren't used for aircraft design ever since the Boeing 777. The engineer can iterate at a rapid rate while simply sitting at their desk. Now, every industry on our planet is going to become an information business. I think that's an important statement. Every industry. Consider agriculture. If you ask a farmer in 20 years in the future about how they compete, it will depend on how they use information, from satellite imagery driving robotic field optimization to the code, meaning the programming code in their seeds, the genetic code.

Starting point is 00:18:55 It'll have nothing to do with workmanship or labor. Again, nothing to do with their workmanship or labor, the historical basis of competition, perhaps, in agriculture or a breeding line, right? It'll become eventually an information business. And that will eventually percolate through every industry as information technology innervates the economy. It makes it have a nervous system. So, interesting thing about Moore's Law.

Starting point is 00:19:18 Nonlinear shifts in a marketplace are also essential for entrepreneurship and meaningful change. Technology's exponential pace of progress has been the primary juggernaut of perpetual market disruption, spawning wave after wave of opportunities for new companies. Without disruption, entrepreneurs will not exist. Moore's Law is not just exogenous to the economy. It is why we have economic growth and an accelerating pace of progress. At Future Ventures, my venture firm, we see this in the growing diversity and global impact of the entrepreneurial ideas we see each year. The industries impacted by the current wave of

Starting point is 00:19:49 tech entrepreneurs are more diverse and an order of magnitude larger than those of the 90s. Today, we're looking at everything from automobiles to aerospace to agriculture and energy. So now one might ask the question, as I said, it's almost cosmological, why? Why would this trend hold for 120 years? I mean, it has nothing to do with the semiconductor industry. It has nothing to do with what we were first told by Intel and others, that this was something very unique and tightly coupled to how we do integrated circuits. Why, more generally, does progress perpetually accelerate for humanity? That's a really important thing, by the way.

Starting point is 00:20:22 That wasn't obvious to people back in the pre-agricultural period when bearded prophets could only forecast doom or the occasional flood or natural disaster wiping out humanity. That was the perception of the world, basically. Struggle through, occasionally get wiped out by calamity. We now can understand, and hopefully those in technology fully understand, that we are in a pace of perpetual progress. We keep getting better culturally, evolutionarily, in the way that we live our lives, in our overall happiness, in the amount of human suffering, in our circle of empathy. We just keep making progress. How could that be? Why is that? Well, here's one simple possible explanation coming again back to Moore's law as one canonical example. Why does this 120-year version of Moore's Law perpetuate?

Starting point is 00:21:11 Well, consider that all new technologies are a combination of technologies that already exist. It's a recombination of prior ideas. Innovation does not occur in a vacuum. It is a combination of ideas from before, like standing on the shoulders of giants. In any academic field, the advances today are built on a large edifice of history. This is why major innovations tend to be ripe and tend to be discovered at nearly the same time by multiple people. Think about Edison, Tesla, Marconi in history, all discovering massive major new innovations within months of each other. The compounding of ideas is the foundation of progress, something that was not so evident to the casual observer before the age of science. Science tuned the process parameters for innovation. It basically became the best method for culture to learn. And the scientific method, I would still assert, has been the

Starting point is 00:21:53 greatest advance in human history on how we accumulate knowledge and how we make progress over time versus personal beliefs, you know, you name it, right? It's just saying, I think something's the case and having that be as valid as any other person's thoughts. So from this conceptual basis come the origin of economic growth and accelerating technological change. Think of it as the combinatorial explosion of possible idea pairings, which it grows exponentially as new ideas come in the mix. It basically grows in the order of two to the nth power of possible subgroupings by something called Reed's law, R-E-E-D, if you want to look it up on Wikipedia. It basically explains the innovative power of urbanization and networked globalization. It explains why interdisciplinary ideas are so

Starting point is 00:22:33 much more powerfully disruptive than those that just come from the warmth of the herd. It's like the differential immunity of epidemiology, where islands of cognitive isolation, think of academic disciplines with their own boundaries and vernacular, are vulnerable to disruptive memes hopping across, much like the South America was the smallpox from Cortez and the conquistadors. If disruption is what you seek, cognitive island hopping is a good place to start, mining the interstices between academic disciplines. And it is this combinatorial explosion of possible innovation pairings that creates economic growth, and it's about to go into overdrive. In recent years, we've begun to see the global innovation effects of a new factor,

Starting point is 00:23:10 the internet. People can exchange ideas like never before. Long ago, people were not communicating across continents frequently. Ideas were partitioned. And so the successive nations and regions pivoted on their own innovations. Richard Dawkins states that in biology, it is genes that really matter. And we as people are just vessels for the conveyance of genes. It's the same idea with memes. Memes meaning ideas.

Starting point is 00:23:33 We are the vessels that hold and communicate ideas. And now a pool of ideas percolates on a global basis more rapidly than ever before. So I think we're gonna be entering a period of innovation like never before. And in the next four to five years, 3 billion new minds will come online for the first time to join this global conversation via inexpensive smartphones, connecting in the developing world to satellite links from Starlink,

Starting point is 00:23:56 this new product from SpaceX, and perhaps others. These people are not currently coupled to the global economy in any meaningful way, other than Unilever or Procter & Gamble. They are just out there doing subsistence farming and not communicating and not contributing the ideas that they might be able to contribute to the global conversation. This rapid influx of 3 billion people to the global economy is unprecedented in human history. And so too will be the pace of idea pairings and progress. We live in interesting times, at the cusp of the frontiers of the unknown and breathtaking advances, but I should always feel that way, engendering a perpetual sense of future shock.

Starting point is 00:24:32 Thank you. Hey guys, this is Tim again. Just a few more things before you take off. Number one, this is Five Bullet Friday. Do you want to get a short email from me? Would you enjoy getting a short email from me every Friday that provides a little morsel of fun for the weekend? And Five Bullet Friday is a very short email where I share the coolest things I've found or that I've been pondering over the week. That could include favorite new albums that I've discovered. It

Starting point is 00:25:01 could include gizmos and gadgets and all sorts of weird shit that I've somehow dug up in the world of the esoteric as I do. It could include favorite articles that I've read and that I've shared with my close friends, for instance. And it's very short. It's just a little tiny bite of goodness before you head off for the weekend. So if you want to receive that, check it out. Just go to 4hourworkweek.com. That's 4hourworkweek.com all spelled out and just drop in your email and you will get the very next one. And if you sign up, I hope you enjoy it.

The Tim Ferriss Show - #404: Books I've Loved — Steve Jurvetson

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