A businessman, an investor, and a physicist walk into a bar
Insights from Peter Thiel, Naval Ravikant, and David Deutsch
Q: What happens when a contrarian venture capitalist (Peter Thiel), a philosophical angel investor (Naval Ravikant), and a contrarian philosophical quantum physicist (David Deutsch) walk into a bar?
A: Thiel tries to monopolize the bar, Naval meditates in the corner until he becomes the bar, and Deutsch argues that any bar that doesn’t allow for error correction (arguing about cocktail prices) isn’t worth entering. In the end, they found a startup called “Schrödinger’s Liquor,” where every drink is both on and off your tab until you take a sip.
While the setup lends itself to many similar jokes, this article is not a joke — it’s a very (okay, slightly) serious exploration of a bold intellectual mashup. Thiel plugs monopolies and questions our idealization of competition; Ravikant tells us to capitalize on our authenticity and the power of compound interest; Deutsch insists that all progress begins with good explanations and shows us what innovation takes from epistemic and scientific perspectives. Individually, their ideas challenge conventional thinking. Together, they give us guidelines for progress at personal, business, and societal scales.
The article is organized according to the following structure. Note that throughout the article, niche terms pertaining to Deutsch’s ideas are linked to corresponding definitions in the section, “Deutsch definition box.”
Table of contents
· Wielding the leverage of our decision-making
∘ Linearity doesn’t create wealth; non-linearity does
∘ Non-linearity = creativity
∘ Utility is inherently leverage
∘ Utility encodes physical truths
∘ Summary
· Iteration vs innovation
∘ The false ideal of competition
∘ What’s wrong with the evolution analogy?
∘ Summary
· Optimism as taking agency in progress
∘ Deutsch’s optimism
∘ Thiel’s optimism
∘ The takeaway
∘ Summary
· Seeing the big picture
∘ Long-term thinking: compound interest
∘ Long-term thinking: future cash flows
∘ Wide-reach thinking
∘ A caveat
∘ Summary
· Deutsch definition box
∘ What is knowledge?
∘ What constitutes a knowledge-creating process?
∘ What is the replicator theory of evolution (and knowledge)?
∘ What is the reach of knowledge?
∘ What are the criteria for a good explanation?
· About the author
· Sources cited and further reading
Happy reading!
Wielding the leverage of our decision-making
Leverage is a force multiplier for your judgment.
— Naval Ravikant
In many of his writings and podcasts, Ravikant discusses the power of leveraged decision making, which is how “all great fortunes are created.” As Ravikant puts it, status, following, and capital are all modes of leverage that require “permission” — attainable in theory for us plebians constituting the bottom 99%, but not realistic for the majority.
Luckily for us, permission is only an illusion of a requirement for obtaining entry to publicity and widespread reception in today’s world. Modern technology has allowed us to wield nearly infinite and “permissionless” leverage with our decision-making. Even if we don’t reside in the upper echelons of high society, even if we aren’t the CEOs of Fortune 500 companies, and even if we don’t have millions of followers on social media or millions of employees to execute our orders, we have the power to multiply the impact of our actions a million-fold.
How so? According to Ravikant, we can create our own VIP passes by learning to code or effectively communicate ideas (“podcasting, broadcasting, creating videos, writing, etc.”).
We can broadcast one thought to the entire world with a viral post on social media. We can write some code to perform the same computation a billion times. We can further package this code as software and copy it onto a billion devices.
Obviously, not every thought you tweet will become viral, and not every app you develop will attain widespread downloads. The key to maximizing your leverage is investing in the quality of the product you broadcast.
“Quality” is a vague requirement, and I can’t expound upon it much since it’s highly related to the class of problem the product purports to clarify or solve. However, there are a few general insights we can explore.
Linearity doesn’t create wealth; non-linearity does
Jobs where your output (money, status, and other metrics of wealth/success) is linearly related to your input (time, effort, and other metrics of invested resources) are least likely to yield long-term wealth and success.
A classic example is sales. The number of appointments you book or deals you close is proportional (even if at a 0.01 scaling factor) to the number of calls you make or the number of hours you spend trying. Even for conventionally high-salaried occupations such as lawyers and doctors, income is received at an hourly rate. Every hour you work is an hour you get paid. And as Ravikant points out, “what that means is when you’re sleeping, you’re not earning. When you’re retired, you’re not earning. When you’re on vacation, you’re not earning. And you can’t earn non-linearly.”
True wealth flows in passively — it doesn’t come at some rate proportional to the hours you clock in. Rather, the flow is instigated by a singular act of innovation, and the flood gates remain open for long after the idea is packaged and broadcast to the world. As we’ll discuss more in the “Seeing the big picture” section, compound interest on your wealth allows the rate of flow to increase without any active labor on your end. You can simply create stuff and reap ever-growing rewards. This sort of exponential growth is a signature of non-linearity.
Non-linearity = creativity
A unifying factor amongst linear input-output jobs is that you’re largely replaceable. Whether your employer is a sales company, a law firm, or a hospital, there are likely thousands of qualified people eager and ready to take your place.
Renting out your time means you’re essentially replaceable.
On the other hand, one of the defining characteristics of a job with a non-linear input-output is holding an irreplaceable position. Think about jobs such as the founder of a startup or the face of a personal brand (e.g., Naval Ravikant, or other well-known communicators such as famous writers, podcasters, influencers, movie stars, celebrities, etc.). In these roles, you capitalize on your creativity and authenticity: the experiences, thoughts, ideas, and even appearances that are unique to you. Because you have no “centralized employer” setting your hours, wages, or responsibilities — and because you have a monopoly on the product you are selling to the rest of the world — it is much easier to attain non-linearity.
Speaking of Thiel’s favorite word, monopoly, Thiel has similar thoughts on what it takes to attain true wealth. While Thiel considers companies rather than individuals, the message is the same: the existence of competition indicates you and your product are replaceable; holding a monopoly indicates you and your product are so uniquely valuable that no others compare.
“Perfect competition” is considered both the ideal and the default state in Economics 101. So-called perfectly competitive markets achieve equilibrium when producer supply meets consumer demand. Every firm in a competitive market is undifferentiated and sells the same homogeneous products. Since no firm has any market power, they must all sell at whatever price the market determines. … Under perfect competition, in the long run no company makes an economic profit.
The opposite of perfect competition is monopoly. Whereas a competitive firm must sell at the market price, a monopoly owns its market, so it can set its own prices. Since it has no competition, it produces at the quantity and price combination that maximizes its profits.
— Thiel & Masters (2014, p. 24)
Consider a podcaster who releases a particularly captivating episode with novel ideas packaged in a novel way. She puts in a few hours of work preparing for the podcast, a few hours recording it, and a few hours editing it and advertising it. Once she makes the episode available for streaming, she receives thousands of listens. Many of these listeners share the episode with their friends, and these friends go on to share it as well, spurring exponential growth in her stats. Other podcasters get hold of her episode, enjoy what she says and how she says it, and invite her onto their own podcasts. She continues to ride the wave of this one successful episode, passively collecting more wealth and opportunities for wealth.
The input-output is highly non-linear: for some insignificant amount of effort and time she invested into creating and broadcasting the episode, she has received exponential returns in the form of money, new followers, new connections with other podcasters, opportunities to speak on other podcasts where she can promote her personal brand, the guarantee of returning invitations if she does well, etc.
You can imagine similar stories for engineers, developers, artists, authors, and every other creativity-wielding type who create a product that is useful and innovative.
There is an interesting parallel between Deutsch’s criteria for good explanations and Thiel and Ravikant’s criteria for differentiating yourself or your company from the herd — i.e., monopolizing your creativity and authenticity, holding an irreplaceable position, selling products with no good substitutes, etc. According to Deutsch, good explanations are hard to vary while making the same predictions; bad explanations are easy to vary, such that any number of slight tweaks will yield the same predictions. Thus, much like an innovative person or company creates products with no comparable substitutes, a good explanation has no close variants that will yield the same predictions.
Utility is inherently leverage
As hinted at in the section above, ideas and tools that are useful (bear some problem-solving value) automatically wield their own leverage. We can understand this leverage more technically in the context of the replicator theory of evolution, which Deutsch extends to knowledge.
When Ravikant speaks of leverage, he refers to the ability of the knowledge associated with your product to replicate, i.e., to propagate in a self-sustained manner. As we learn from Deutsch, the ability of information to contribute to its own replication, thus conferring it the status of knowledge, is directly related to the utility of the information. If leverage ∝ replication power and replication power ∝ utility, then leverage ∝ utility!
First, consider the replicative power of an idea. If a scientist scribbles down a bunch of conjectures about the explanation for some phenomenon, maybe one per post-it note, then the conjecture that turns out to be a genuine piece of knowledge will be the one she doesn’t throw into the trash. (Likely, the selection process will consist of using observations and experimental tests to compare each conjecture with its variants.) The information in the surviving post-it note will be published in journals, studied by other scientists, and discussed at conferences. It has the property of keeping itself physically instantiated and causing its own replication.
The takeaway is this: Given that the most useful ideas will replicate themselves (when packaged correctly), don’t waste time trying to churn out a million mediocre ideas. Spend your time honing one that is powerful and valuable enough to replicate itself. Exploit the non-linearity of creativity.
This is highly related to Thiel’s anti-“don’t put all your eggs in one basket” philosophy, i.e., his “put all your eggs in a few very good baskets” philosophy. The most successful investors and businesspeople understand that we live under a power law: the quality of ideas (and the quality of people executing them) is highly non-uniformly distributed. It’s silly to evenly spread your investments over a broad range of prospects (whether that be ideas or companies) because the associated quality and probability of success are not evenly distributed. Taking such an approach optimizes for diversity over expected wealth generation.
In mentioning Thiel, I need to provide the proviso that while a useful idea inherently wields its own leverage, this leverage only “kicks in” once you’ve sold the idea to a sufficient group of people. In line with Thiel’s principle of “Sales matters just as much as product” (Thiel & Masters, 2014, p. 21), you first need to attract enough people willing to give your product a chance. Only after you obtain such a following does the actual value of your product secure customer retention, spur happy customers to tell their friends, and so forth.
The engineer’s grail is a product great enough that “it sells itself.” But anyone who would actually say this about a real product must be lying: either he’s delusional (lying to himself) or he’s selling something (and thereby contradicting himself). … It’s better to think of distribution as something essential to the design of your product. If you’ve invented something new but you haven’t invented an effective way to sell it, you have a bad business — no matter how good the product.
— Thiel & Masters (2014, pp. 129–130)
Utility encodes physical truths
There is one more insight we can glean from analyzing the Deutschian (and Popperian) view of knowledge.
Above, we considered the power of a useful idea. Now consider the power of other forms of knowledge. For example, the abstract knowledge of a skill such as cracking nuts is physically instantiated in the mind and actions of its enactor. Because this knowledge is useful, the enactor repeatedly enacts the skill, strengthening its instantiation in the enactor’s memory, and shares the skill with others, thus replicating the knowledge in more physical forms. Additionally, the abstract knowledge of a biological adaptation is physically instantiated in genes. If this knowledge is useful in that it confers the host organism greater survival or reproductive advantages, the genes will spread more than their variants through the population.
Skills and biological adaptations may not interest you much as an aspiring startup founder or podcaster, but these examples easily extend to more marketable products such as tools and services. They also illustrate an interesting property of utility, namely that knowledge’s usefulness is often a byproduct of “tracking” the truth. In fact, the most useful knowledge tends to be that which reflects regularities in nature and encodes physical truths.
For example, a skill tends to be useful if it exploits certain physical laws. One who cracks a nut by smashing it with a stone is likely to be successful because she harnesses physical laws pertaining to force, energy transfer and conservation, stress and strain, etc. One who cracks a nut by speaking to it is unlikely to be successful because she harnesses no such physical laws. Additionally, a biological adaptation tends to be useful if it encodes certain features of the organism’s environment. An organism in a cold environment will likely fare better if it has an adaptation that confers greater bodily insulation, such as thicker fur. An organism inhabiting the same environment without this adaptation will fare worse and potentially die before reproducing.
The takeaway may seem obvious. Of course your startup idea for rockets made of paper won’t work; it ignores the physical truths that paper is structurally weak, highly flammable, etc. But the truth-utility relationship is good to keep in mind as a guiding principle — you’re more likely to build a successful product if you deeply understand the underlying physical truths that govern and constrain your product. I hesitate to mention Elon Musk in fear of political backlash, but you can’t deny that much of his success is founded in strong, technical expertise.
Summary
- Ravikant: Modern technology has allowed us to wield nearly infinite and “permissionless” leverage with our decision-making. The low-cost replication of software and the potential for virality on social media allow people with good ideas to broadcast them at large scales.
- Ravikant and Thiel: True wealth is created non-linearly, i.e., by exploiting leverage and eliminating competition. It is spurred by singular acts of innovation and flows in passively.
- Thiel: By monopolizing your creativity, you become irreplaceable as a provider of certain goods. You thus eliminate competition and pave the way for continual growth.
- Deutsch: Replicator theory is a way of understanding that useful ideas inherently wield their own leverage (contribute to their own replication).
- Ravikant, Thiel, and Deutsch: Invest in quality over quantity. We live under a “power law” where the quality of ideas is highly non-uniformly distributed. Don’t waste your time diversifying your prospects. Instead, invest in the ideas and people with the highest chance of success.
Iteration vs innovation
The false ideal of competition
The title of Thiel’s book, “Zero to One,” refers to the uniquely human act of creation:
Of course, it’s easier to copy a model than to make something new. Doing what we already know how to do takes the world from 1 to n, adding more of something familiar. But every time we create something new, we go from 0 to 1. The act of creation is singular, as is the moment of creation, and the result is something fresh and strange.
— Thiel & Masters (2014, p. 1)
According to Thiel, the prevailing ideology of competition in modern society — along with a strong aversion to monopolies — has tipped the balance of innovation away from “0 to 1” thinking and toward “1 to n” thinking. From economics and the marketplace to our education system, “We preach competition, internalize its necessity, and enact its commandments” (Thiel & Masters, 2014, p. 35).
We choose to model ourselves (individuals and companies) as undifferentiated players in an iterative, game theoretic simulation: each player chooses from a pre-defined set of strategies, hopes that her strategy trumps the other players’ strategies in most of the rounds she plays, and thus accumulates a net positive payoff over her lifetime. Her best outcome is slightly outdoing her homogeneous counterparts most of the time, and whether or not she attains this outcome is largely determined by luck.
This mindset inherently frames the plights of us pawns as a zero-sum game: a player who accumulates a net positive payoff must do so by incurring equivalent net losses for other players. But this is a fallacy, an imaginary constraint on the way we must make individual progress in the context of our environments. As both Thiel and Ravikant point out, monopolies and innovation allow us to experience positive-sum games where everyone gets a good outcome:
New technology has never been an automatic feature of history. Our ancestors lived in static, zero-sum societies where success meant seizing things from others. They created new sources of wealth only rarely, and in the long run they could never create enough to save the average person from an extremely hard life. Then, after 10,000 years of fitful advance from primitive agriculture to medieval windmills and 16th-century astrolabes, the modern world suddenly experienced relentless technological progress from the advent of the steam engine in the 1760s all the way up to about 1970. As a result, we have inherited a richer society than any previous generation would have been able to imagine.
—Thiel & Masters (2014, p. 9)
To see how monopolies allow positive-sum games (everybody wins), whereas competition usually constrains games to be zero-sum (if one person wins, another person loses), consider the simplest case of a two-player game, particularly an interpersonal relationship.
Let’s say you excel at cooking and are sloppy when it comes to doing the dishes. On the other hand, your friend/partner leaves every dish spotless, but makes meals that trigger nausea and risk food poisoning. It would be silly to enforce equality (equal division of both tasks) when equity (agreement upon a fair distribution of labor) will make both parties happy. If each person cooks half of the time and does dishes half of the time, then you’ll always be engaged in competition: when you’re doing the task that is your strength, your friend/partner will feel lousy about her performance on the same task, and when you’re doing the task that is your weakness, the tables will flip. You’ll also frequently end up with dirty dishes and food poisoning.
However, if each person monopolizes her strength — you always cook and your friend/partner always cleans — everybody wins. Each party can feel proud about her contributions while appreciating the value of the other’s contributions.
If you want a real-world example of a monopoly contributing to a positive-sum game, see Thiel’s approach with PayPal:
PayPal could be seen as disruptive, but we didn’t try to directly challenge any large competitor. It’s true that we took some business away from Visa when we popularized internet payments: you might use PayPal to buy something online instead of using your Visa card to buy it in a store. But since we expanded the market for payments overall, we gave Visa far more business than we took. The overall dynamic was net positive, unlike Napster’s negative-sum struggle with the U.S. recording industry. As you craft a plan to expand to adjacent markets, don’t disrupt: avoid competition as much as possible.
— Thiel & Masters (2014, p. 57)
What’s wrong with the evolution analogy?
Following our obsession with competition, evolution analogies have become highly prevalent in the business sphere. In this framework, we choose to model ourselves and our companies as variants of the same prototype, competing for survival amidst dynamic selection pressures from the environment and our co-evolving peers. And just as biological evolution introduces variation through random mutations to genes, we largely perceive the variations among ourselves as random — uncontrollable consequences of differences in our nature and nurture. As Thiel puts it, we treat ourselves as “lottery tickets.”
Journalists analogize literal survival in competitive ecosystems to corporate survival in competitive markets. Hence all the headlines like “Digital Darwinism,” “Dotcom Darwinism,” and “Survival of the Clickiest.”
Even in engineering-driven Silicon Valley, the buzzwords of the moment call for building a “lean startup” that can “adapt” and “evolve” to an ever-changing environment. Would-be entrepreneurs are told that nothing can be known in advance: we’re supposed to listen to what customers say they want, make nothing more than a “minimum viable product,” and iterate our way to success.
— Thiel & Masters (2014, p. 78)
Thiel rightfully disdains the evolution analogy. There is a fundamental difference between the processes of evolution and human ideation, as is explained by Deutsch:
But I do not want to overstate the similarities between scientific discovery and biological evolution, for there are important differences too. One difference is that in biology variations (mutations) are random, blind and purposeless, while in human problem-solving the creation of new conjectures is itself a complex, knowledge-laden process driven by the intentions of the people concerned. Perhaps an even more important difference is that there is no biological equivalent of argument. All conjectures have to be tested experimentally, which is one reason why biological evolution is slower and less efficient by an astronomically large factor.
— Deutsch (1997, pp. 68–69)
In other words,
- Knowledge creation via evolution relies on random variation. It is slow and only infrequently results in non-incremental changes.
- Knowledge creation via human ideation harnesses the power of “intelligently designed variation”. It is fast and more often results in large changes. This intelligent design is what we need to tap into if we want to stray away from incremental, “1 to n” improvement and achieve “0 to 1” innovation.
To illustrate the difference between evolution and human ideation, I’ll use Deutsch’s example from “The Deutsch Files III.” Consider the biological evolution of pterosaurs, “the first vertebrate creatures to evolve powered flight and conquer the air.” Evolution of the pterosaur from its predecessors involved the creation of new knowledge, i.e., genes encoding the ability to fly. The knowledge contained in the pterosaur genome was emergent — the design of a pterosaur wasn’t already in the DNA of non-flying dinosaurs from which it evolved, and the design can’t be directly traced from the pterosaur’s “version history” because it wasn’t just a clever recombination of predecessor DNA sequences. The mutations that turned out to be adaptive for the set of flying genes constituted a series of blind guesses — random variations resulting in new DNA sequences.
Biological evolution can and consistently does yield emergent knowledge in this manner. However, the conjectures evolution makes are inherently small because they are confined to the physical form of mutations; as such, improvements can only be made incrementally. For example, a mutation can only result in wings if the associated organism already has limbs, or some other feature that can be incrementally changed into wings, such that every “intermediate organism” is still viable.
… progress without planning is what we call “evolution.” Darwin himself wrote that life tends to “progress” without anybody intending it. Every living thing is just a random iteration on some other organism, and the best iterations win.
— Thiel & Masters (2014, p. 78)
But when humans vary ideas, the variations we consider aren’t blindly introduced; they are intentional, intelligent, creative, and explanatory. This explanatory underpinning is the key: the reach of our explanations allows us to leap over gaps in the “search space” that can’t be traversed incrementally.
In thinking up the first airplanes, the designers didn’t have to imagine some intermediate, viable design between a wingless fuselage and a winged fuselage, such as a fuselage with feet. They understood the forces of flight (lift, drag, thrust, and weight), they understood principles of engineering, and by extending their understanding to the problem of designing an aircraft, they were able to conjecture that the addition of wings to the plane body would allow them to generate lift and keep the aircraft afloat.
So Thiel is absolutely correct: in adopting the evolution analogy, we lose sight of the uniqueness of human ideation as a knowledge-creating process. When we innovate, the conjectures we make are not blind, biological guesses; our search space isn’t limited by what we are capable of right now (the organism’s current state), and our search path isn’t limited by the necessity of maintaining viability (staying alive) all the way through.
Making small changes to things that already exist might lead you to a local maximum, but it won’t help you find the global maximum. You could build the best version of an app that lets people order toilet paper from their iPhone. But iteration without a bold plan won’t take you from 0 to 1. … Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.
— Thiel & Masters (2014, pp. 78–79)
Instead, the conjectures we make are intentional and creative: by virtue of being “universal explainers”, we have the ability to form interconnections between wildly different ideas and states. As humans, we are not only capable of, but we are defined by, our ability to go from 0 to 1 of our own volition. Rather than praying to the gods of chance for miraculous discovery (maybe some Alexander Fleming-esque story), we can choose to create our own discoveries.
Summary
- Thiel: The prevailing ideology of competition has tipped the balance of innovation away from “0 to 1” thinking and toward “1 to n” thinking. It promotes a view where everyone sells the same, substitutable products, and thus the only possible changes are incremental.
- Thiel: Monopolies allow businesses (and individuals) to innovate products with no good substitutes in non-overlapping sub-markets. In this way, monopolies allow positive-sum games (everybody wins), whereas competition usually constrains games to be zero-sum (if one person wins, another person loses).
- Thiel and Deutsch: Be careful not to over-analogize human ideation with biological evolution. Evolution and ideation are both processes of knowledge creation. However, the latter’s ability to harness intelligently designed variation rather than random mutations permits much larger, non-incremental steps.
Optimism as taking agency in progress
Both Thiel and Deutsch believe that to make progress as a society, we need to adopt the mindset of optimism. Interestingly, both Thiel and Deutsch suggest four overall buckets that beliefs about societal progress fall into, and they highly align: Thiel’s distinction between definite and indefinite optimism/pessimism parallels Deutsch’s distinction between blind and “true” optimism/pessimism. (In The Beginning of Infinity, what I deem “true” optimism/pessimism just goes by optimism/pessimism, but I use the “true” qualifier for clarity.)
In this section, we’ll explore the parallels between Deutsch and Thiel’s four buckets and general takeaways. I cover Deutsch’s definitions first, since I believe they lay a strong foundation upon which Thiel’s philosophies are better understood.
Deutsch’s optimism
Deutsch summarizes his principle of true optimism as the statement, “All evils are caused by insufficient knowledge” (Deutsch, 2012, p. 212).
All evils are caused by insufficient knowledge.
— (Deutsch, 2012, p. 212)
Evils — anything that causes suffering or impairs human thriving — are really problems — inadequacies or errors in existing knowledge. There are no limitations, other than the laws of nature, on our ability to eliminate evils by creating knowledge. Since evils are problems, and problems are solvable, we can continue to eradicate evils by problem-solving: criticizing and testing existing knowledge and creatively guessing improvements.
Optimism follows from the explicability of the physical world… If something is permitted by the laws of physics, then the only thing that can prevent it from being technologically possible is not knowing how.
— Deutsch (2012, p. 213)
While this is a nice, pithy way to frame it, I like to break down Deutsch’s principle of true optimism into three underlying tenets (all of which Deutsch brings up thematically throughout his book, The Beginning of Infinity):
- The future of our civilization is unknowable, not merely unpredictable, as the knowledge that will affect it has yet to be created (see here for elaboration). The greatest problems we will face and the most brilliant solutions we will produce are unforeseen.
- Problems are inevitable. As per fallibilism, errors will always abound in knowledge creation, and correcting them will always reveal further and better problems.
- However, problems are soluble — every transformation that is not forbidden by the laws of physics is achievable given the right knowledge. If we fail at anything not forbidden by the laws of physics, it’s because we lack sufficient knowledge.
Deutsch’s classifications of true pessimism, blind optimism, and blind pessimism can be understood in terms of the tenets of true optimism:
- True pessimism sets artificial limits on our capacity to solve problems. When true pessimists fail to imagine how some problem could be solved, they mistake it for an argument that the solution is unattainable. They deny tenet 3, that problems are soluble. Deutsch gives the example of Arthur Schopenhauer’s pessimistic philosophy, founded on the belief that the world is driven by an irrational metaphysical force called Will that creates constant suffering. According to this philosophy, rational thought can’t discover true explanations; whenever something good happens, it supposedly prevents something even better from happening, as per the will of the Will. So everything circles back to what the Will wants. By denying the explicability of the world, you deny 3, as the Will can easily decree the impossibility of solving any and all problems.
- Blind optimism is a mindset of invincibility and recklessness. Blind optimists proceed as if they know bad outcomes won’t happen. They deny tenet 2, that problems are inevitable, as well as tenet 1, that the future of our civilization is unknowable. Deutsch offers the example of the Titanic’s builders, who deemed the ocean liner “practically unsinkable.” The denial of tenet 2 is rather obvious, but the denial of tenet 1 stems from the blind optimistic tendency to prophecy the future — namely, to assume that unforeseen disastrous consequences cannot accompany new knowledge.
- Blind pessimism is a mindset of unwarranted fear and risk-aversion. Blind pessimists steer clear of anything not known to be safe, refusing to acknowledge the opportunities that accompany risky endeavors, and doubting our ability to anticipate and handle threats. They deny tenet 3, that problems are soluble, as well as tenet 1, that the future of our civilization is unknowable. As a counter to the Titanic example, Deutsch illustrates the blind pessimistic approach as “stick[ing] with existing designs and refrain[ing] from attempting any records.” Similar to blind optimism, the denial of tenet 3 is rather obvious, but the denial of tenet 1 stems from the blind pessimistic tendency to prophecy the future — namely, to assume that unforeseen disastrous consequences can only follow from new knowledge, not existing knowledge.
Admittedly, some of the dangers that we currently foresee are themselves side effects of knowledge creation. But trying to slow that down won’t help because what do you slow down? In 1900, no one could possibly have foreseen that research in pure physics into the esoteric properties of the element uranium would, within 50 years, become the centerpiece of everyone’s existential fear. Or that another half century later, the centerpiece would be carbon dioxide. In our future too, the greatest dangers will inevitably be unforeseen. And the only type of knowledge that’s capable of dealing with those is fundamental knowledge of universal regularities in nature. Any area of fundamental research could suddenly become essential to our survival.
— Deutsch
In their denial of tenet 1, both blind optimism and pessimism are prophetic, assuming unknowable things about the future of knowledge. The former believes that positive outcomes will always outweigh negative outcomes, and the latter that negative outcomes will always outweigh positive outcomes. In both cases, the outcomes can’t be byproducts of knowledge creation, which inherently gives rise to good (solutions to existing problems) and bad (new, unresolved problems). On the blind optimism side, an indefinite succession of net-positive outcomes indicates each problem is solved neatly, giving rise to at most trivial problems or no new problems at all. On the blind pessimism side, an indefinite succession of net-negative outcomes indicates that at most solutions to trivial problems are generated, or no new knowledge is generated at all, by taking risks and seeking out innovation.
Thus, blind optimism and pessimism are rejections of the growth of knowledge: they operate under the assumption of a stagnant pool of knowledge equivalent to that currently at our disposal.
The same misconception [empiricism, the belief that we derive our knowledge from sensory experience] also underlies blind optimism and pessimism: they both expect progress to be made by applying a simple rule to existing knowledge, to establish which future possibilities to ignore and which to rely on. Induction, instrumentalism and even Lamarckism all make the same mistake: they expect explanationless progress. They expect knowledge to be created by fiat with few errors, and not by a process of variation and selection that is making a continual stream of errors and correcting them.
— Deutsch (2012, p. 210)
Most individuals and societies are never entirely blindly optimistic or pessimistic; these mindsets are too irrational to hold for very long, and generally characterize one-off approaches to certain problem situations. However, the consequences of adopting true optimistic and pessimistic mindsets are evident in human history.
For example, consider Athens and Sparta. Athens was a true optimistic society, owing to several key features:
- Democracy. Athens’ system of government promoted continual discussion, collective decision-making, and electoral means of removing bad politicians from power. It thus embodied the Popperian requirement of error detection and correction for the growth of knowledge , applied politically as ridding themselves of bad governments without violence.
- Freedom. The citizens of Athens enjoyed many freedoms that were frowned upon by rival cities. For example, citizens had freedom of speech and could openly criticize leaders and policies without fear of retaliation. Along the same lines, itinerant teachers of philosophy and rhetoric (sophists) encouraged debate, even on controversial topics. Thus, a tradition of criticism — a key contributor to error detection and correction at the societal level — was fostered.
- Open-mindedness. Athens was open to visitors and showed tolerance of other cultures, allowing the city to benefit from contact with new ideas. In their frequent expeditions to nearby civilizations, and in welcoming foreigners to the city for trade, Athenians absorbed art, architecture, philosophy, science, and mathematics from peoples such as the Egyptians, Persians, and Phoenicians.
Now contrast with Sparta, a true pessimistic society. Spartan life revolved around the military, and conformity was essential to its function. From the age of seven, boys were taken from their families and placed into the agoge, a state-controlled education and training system designed to produce obedient, disciplined soldiers. Individuality was systematically suppressed, as Spartans wore identical clothing, ate the same food, and were trained to think and act as a unit. Citizens never questioned authority or state policy, and political power was concentrated in a small oligarchy, leaving little room for public debate or free expression. Both external influence and “internal” innovation were regarded as threats: foreign travel and contact with outsiders were heavily restricted, and Sparta actively discouraged the arts, philosophy, and intellectual exploration. Above all, Spartans valued stability, tradition, and self-preservation.
Up until it was defeated by Sparta in 404 BCE, Athens grew wealthy through trade, produced foundational Western literature and philosophy, nourished new branches of science and math, achieved impressive architectural feats such as the Parthenon, and built the strongest navy in Greece. Its legacy lasts in many fields spanning STEM and the humanities, while Sparta is merely known for having a disciplined land army.
In this way, we see that true optimism and pessimism are characteristics of dynamic and static societies, respectively. Notably, the former has only become dominant since the Enlightenment brought about the necessary conditions for rapid progress: 1) rejection of authority, dogma, and justificationism, and 2) traditions of criticism and experimentation that embrace fallibilism and value creativity over conformity.
Old ways of thought, which did not seek good explanations, permitted no process such as science for correcting errors and misconceptions. Improvements happened so rarely that most people never experienced one. Ideas were static for long periods. Being bad explanations, even the best of them typically had little reach and were therefore brittle and unreliable beyond, and often within, their traditional applications. When ideas did change, it was seldom for the better, and when it did happen to be for the better, that seldom increased their reach. The emergence of science, and more broadly what I am calling the Enlightenment, was the beginning of the end of such static, parochial systems of ideas. It initiated the present era in human history, unique for its sustained, rapid creation of knowledge with ever-increasing reach.
— Deutsch (2012, p. 29)
Thiel’s optimism
While Deutsch sorts optimism and pessimism by whether they are “blind” or “true,” Thiel sorts them by whether they are “indefinite” or “definite.” As per Thiel’s definition and examples,
- “An indefinite pessimist looks out onto a bleak future, but he has no idea what to do about it” (Thiel & Masters, 2014, p. 63). Example: modern Europe (post-1970s), reacting to problems as they arise and hoping they’ll outlive the imminent disasters they foresee.
- “A definite pessimist believes the future can be known, but since it will be bleak, he must prepare for it” (Thiel & Masters, 2014, p. 63). Example: modern China, whose tremendous economic growth can be attributed to obsession with staving off disaster and “relentlessly copy[ing] what has already worked in the West.”
- “To an indefinite optimist, the future will be better, but he doesn’t know how exactly, so he won’t make any specific plans” (Thiel & Masters, 2014, p. 51). Example: modern America (post-1982), in which “finance [has] eclipsed engineering as the way to approach the future.”
- “To a definite optimist, the future will be better than the present if he plans and works to make it better” (Thiel & Masters, 2014, p. 49). Example: the West from the 17th century through the 1960s, a period characterized by incessant innovation across all fields.
At the beginning of this section, I claimed that Deutsch’s true and blind optimism/pessimism parallel Thiel’s definite and indefinite optimism/ pessimism, respectively. You might be scratching your head, since Thiel implies the trademark of the definite mindset is believing in a knowable future, while the first tenet of Deutsch’s true optimism says that the future of our civilization is unknowable.
This is where I suggest modifications to Thiel’s language, since he gets the overall idea right, but his terminology doesn’t sit well with a Deutsch disciple like myself. Definite thinking is not about believing in a knowable future — this sort of “prophecy” is impossible, as per tenet 1 of Deutsch’s true optimism — but in a predictable future, where the quality or accuracy of the prediction is based on the reach of the underlying explanation. I use the terms “prophecy” and “prediction” deliberately, according to the parlance of Deutsch:
No good explanation can predict the outcome, or the probability of an outcome, of a phenomenon whose course is going to be significantly affected by the creation of new knowledge. This is a fundamental limitation on the reach of scientific prediction, and, when planning for the future, it is vital to come to terms with it. Following Popper, I shall use the term prediction for conclusions about future events that follow from good explanations, and prophecy for anything that purports to know what is not yet knowable. Trying to know the unknowable leads inexorably to error and self-deception. Among other things, it creates a bias towards pessimism.
— Deutsch (2012, p. 198)
Both blind and indefinite thinking involve a lack of good explanation-crafting and thus prophecy the future. Blind and indefinite optimists expect things to get better, but do not propose any good explanations for the progress they expect. Blind and indefinite pessimists expect things to get worse, but do not propose any good explanations for the stagnation in progress or even anti-progress (e.g., in the case of apocalyptic disasters) they expect.
It is tempting for me to propose that the dividing line between blind/indefinite and true/definite thinking concerns agency: the former group expects the future to turn out one way, regardless of their actions, and thus sees no reason to take action; the latter group understands the future is shaped by their actions, and thus takes tangible steps to make progress (optimism) or prevent stagnation and decline (pessimism).
However, upon closer examination, we see that true/definite pessimists surrender agency as well. From Deutsch’s example of true pessimism, true pessimists believe that certain (bad) explanations govern the universe (e.g., the will of the Will), but they relinquish any active role in generating their own explanations and seeking out deeper truths. They thus condemn themselves to a self-reinforcing cycle of bad explanations. From Thiel’s example of definite pessimism, definite pessimists often copy the actions of others who have made progress or appear to have prevented disaster. In this way, they believe in explanations (the ones upholding the templates from which they copy), but deny agency in developing their own.
Agency may not cleanly divide blind/indefinite and true/definite thinking, but it does underlie the unification of true and definite optimism: both are versions of optimism in which agency is taken in supplying the explanation for progress.
When Thiel says that a successful company usually has a long-term plan (Thiel & Masters, 2014, p. 24), we must realize that such a plan isn’t just a prophetic stab in the dark. Rather, it’s a good explanation of how the company will continue to make progress and maintain durability, even in the face of unknowable circumstances. This explanation is carefully developed over many rounds of discussion amongst founders or executives, exposure to criticism (and subsequent error-correction) from relevant parties (ranging from investors to existing customers), and experimental tests in the form of mini market simulations and trial runs. By undergoing this process, the explanation becomes progressively harder to vary — the Deutschian differentiator of a good explanation.
The takeaway
To Thiel, those with a definite optimistic vision for the future — that is, a plan to make the world better and an active role for themselves in it — are the ones who can actually build the future.
You can expect the future to take a definite form or you can treat it as hazily uncertain. If you treat the future as something definite, it makes sense to understand it in advance and to work to shape it. But if you expect an indefinite future ruled by randomness, you’ll give up on trying to master it.
— Thiel & Masters (2014, p. 61)
To Deutsch, true optimism is necessary for continual progress as a civilization.
… progress is sustainable, indefinitely. But only by people who engage in a particular kind of thinking and behaviour — the problem-solving and problem-creating kind characteristic of the Enlightenment. And that requires the optimism of a dynamic society.
— Deutsch (2012, p. 423)
Whether you want to call it definite or true optimism, the message is clear: taking agency in developing good explanations is necessary for innovation at the individual, company, and societal levels.
Summary
- Deutsch: Deutsch’s principle of true optimism is summarized by the statement “All evils are caused by insufficient knowledge” (Deutsch, 2012, p. 212). Problems are inevitable but soluble. If there are problems we can’t yet solve, it’s because we don’t yet know how, and that knowledge is within our reach.
- Thiel: Thiel’s principle of definite optimism involves actively working to create a better future. Those who believe in the power of innovation, and their own ability to wield such power, are the ones who drive progress.
- Deutsch and Thiel: An optimistic mindset is key to making progress (solving problems, correcting errors) at the individual, company, and societal levels. Both true and definite optimism boil down to taking agency in developing good explanations. Good explanations are how we grow our knowledge and solve previously insurmountable problems. They are how we turn prophecy of the future into actual prediction, and they thus guide us in successful long-term planning.
Seeing the big picture
For both Ravikant and Thiel, long-term thinking is at the heart of accumulating wealth and success. Ravikant frames the importance of the long term in terms of compound interest, while Thiel frames it in terms of future cash flow.
Ravikant gives us a reason to think on a longer time scale. Compound interest is passive power: it is the leverage of already possessing value, or even the leverage of leverage (as mentioned in the section “Wielding the leverage of our decision making”, a valuable product encoding useful knowledge inherently possesses leverage). Let the work you’ve done continue to do work for you.
Play iterated games. All the returns in life, whether in wealth, relationships, or knowledge, come from compound interest.
— Ravikant
In a complementary manner, Thiel gives us a success metric that penalizes myopic vision and rewards future planning: “the value of a business today is the sum of all the money it will make in the future” (Thiel & Masters, 2014, p. 44), where these future cash flows are discounted at a certain rate since they aren’t yet materialized. If you use this metric, you are forced to assess the durability of your company, i.e., its ability to sustain growth.
Simply stated, the value of a business today is the sum of all the money it will make in the future. (To properly value a business, you also have to discount those future cash flows to their present worth, since a given amount of money today is worth more than the same amount in the future.)
Thiel & Masters (2014, pp. 44–45)
Long-term thinking: compound interest
The power of compound interest applies to most endeavors on the individual scale. This is clear in the case of personal wealth and success. Take the podcaster example from the “Wielding the leverage of our decision making” section:
Consider a podcaster who releases a particularly captivating episode with novel ideas packaged in a novel way. She puts in a few hours of work preparing for the podcast, a few hours recording it, and a few hours editing it and advertising it. Once she makes the episode available for streaming, she receives thousands of listens. Many of these listeners share the episode with their friends, and these friends go on to share it as well, spurring exponential growth in her stats. Other podcasters get hold of her episode, enjoy what she says and how she says it, and invite her onto their own podcasts. She continues to ride the wave of this one successful episode, passively collecting more wealth and opportunities for wealth.
The input-output is highly non-linear: for some insignificant amount of effort and time she invested into creating and broadcasting the episode, she has received exponential returns in the form of money, new followers, new connections with other podcasters, opportunities to speak on other podcasts where she can promote her personal brand, the guarantee of returning invitations if she does well, etc.
The same sort of “exponential returns” exist for relationships. The longer you are in a relationship with someone, the more trust is built; the more trust is built, the more your friend or partner will initiate interactions and transactions that test your trust: being emotionally vulnerable with you, exchanging secrets or sensitive information, asking you for help or advice. The more you fulfill the demands of these tasks and prove your trustworthiness, the more trust you will build and the more trust-testing tasks you’ll receive. Thus, there is compound interest on trust.
In the realm of health and fitness — or any class of endeavors requiring good habits — compound interest may look like “momentum.” Once you’re in shape, it becomes significantly easier to stay that way. Your body adapts to a healthier lifestyle, your habits align with your goals, and momentum works in your favor. Going to the gym is easy if you already have a gym membership, an established weekly workout time with your gym buddy, and an exercise routine that you cycle through. Eating healthy is easy if you meal prep at the start of the week and have reached a point where your diet has curbed your cravings.
On the other hand, if you are more of a couch potato, there is a much higher activation energy associated with getting up from that couch. Building good habits from scratch is difficult, and initial attempts at staying active and eating right are discouraging since you don’t immediately reap the rewards. Everything from your baseline energy level to your motivation is lower, and regaining even a basic level of fitness becomes a much greater challenge.
Compound interest works at the company scale too. Two of Thiel’s “characteristics of monopoly” — network and scaling effects — are prime examples of the leverage wielded by existing value.
As defined by Thiel, “Network effects make a product more useful as more people use it” (Thiel & Masters, 2014, p. 50). Network effects are clear with social media: if your family, your friends, your coworkers, and your partner’s exes that you stalk all use a certain platform, it’s practically social suicide not to join as well.
This is why many companies add a social component to their business model. Consider Spotify, which has socialized the solo act of listening to music. Spotify allows you to follow other users, see your friends’ listening activity, create shared playlists, join real-time collaborative music queues (“Spotify Jams”), and easily share personalized content like your “Spotify Wrapped” to other social media platforms. If all your friends make a playlist on Spotify but you use Apple Music, you’ll feel left out and want to hop on the Spotify bandwagon.
Network effects don’t just come from social networks. Take Thiel’s examples of Apple’s “content ecosystem” or ZocDoc’s patient and provider bases:
… thousands of developers write software for Apple devices because that’s where hundreds of millions of users are, and those users stay on the platform because it’s where the apps are.
— Thiel & Masters (2014, p. 52)
ZocDoc needs lots of salespeople … by adding doctors to the network, salespeople make the product more valuable to consumers (and more consumer users increases its appeal to doctors).
— Thiel & Masters (2014, p. 133)
Scaling effects are another flavor of the same principle. In this case, the leverage of already “being big” isn’t attracting more users, but rather lowering the cost of product manufacturing and increasing your profit margin:
A monopoly business gets stronger as it gets bigger: the fixed costs of creating a product (engineering, management, office space) can be spread out over ever greater quantities of sales. Software startups can enjoy especially dramatic economies of scale because the marginal cost of producing another copy of the product is close to zero.
Thiel & Masters (2014, p. 51)
Long-term thinking: future cash flows
Thiel’s assessment of companies based on the cumulative sum of (discounted) future cash flows allows us to clearly distinguish low-growth from high-growth businesses. As Thiel puts it, most of the value of low-growth businesses is in the near term, while most of the value of high-growth businesses takes longer to materialize.
The low-growth class of business is similar to the undifferentiated player in the competitive model of economics: even if business A’s product is currently staking a larger claim of the market (likely by a few percentage points at most), the many substitutes offered by businesses B, C, etc. will eventually see the random noise of the market play (temporarily) in their favor. With no guarantee of definitively distancing itself from these other businesses, business A can at most hope for stagnation in relevance and profits. But in the most likely case, business A’s (or any of the other competitor businesses) profits will be competed away with time.
On the other hand, a high-growth business is inherently different from all other (if any exist) businesses in its debut market. It is on the road to monopolization — selling a valuable product with no good substitutes. Once the product is physically developed and the product’s value is recognized (via sales and advertising), cash will start to flow in. The rate of this flow will increase with time as network and scaling effects ensue and further innovation is initiated.
The overwhelming importance of future profits is counterintuitive even in Silicon Valley. For a company to be valuable it must grow and endure, but many entrepreneurs focus only on short-term growth. They have an excuse: growth is easy to measure, but durability isn’t. Those who succumb to measurement mania obsess about weekly active user statistics, monthly revenue targets, and quarterly earnings reports. However, you can hit those numbers and still overlook deeper, harder-to-measure problems that threaten the durability of your business.
— Thiel & Masters (2014, p. 47)
Learning to value the long-term over the short-term is also the key to success on the individual level. As Ravikant says, “All self-help boils down to ‘choose long-term over short-term.’”
Cheap dopamine has tempted humans since the dawn of our existence. Back in the caveman days, our brain’s reward system was essential to our survival: the dope hit from eating calorie-dense foods ensured we prioritized obtaining sustenance in an environment where there was no guarantee of easy or regular access to it; the dope hit from ejaculating ensured men continued to impregnate women and propagate our species.
In our modern age, where such environmental pressures have become largely irrelevant, abundance has hijacked our evolutionary adaptations to enforce profligacy and self-destructive conditioning (Berridge & Kringelbach, 2015). The over-availability of addictive substances such as junk food, porn, and drugs — and the practically non-existent barrier to entry for addictive behaviors such as gambling, shopping, and Internet doom-scrolling — has provided us with many low-effort, high-saliency substitutes for simple pleasures. We can easily swap time out in the sun for video games, “IRL” hangouts with friends for social media, sex for porn, romantic relationships for hookups, reflective walks and meditation for mind-altering substances.
These cheap substitutes condition us to crave the ephemeral pleasure of instant gratification and make it difficult to choose their healthier counterparts. Meditation and meaningful relationships offer pleasure that is less intense in the short term; as opposed to being concentrated in the span of a few moments, satisfaction is delayed and distributed over a longer period of time.
But for this very reason, “long-term pleasures” are eligible for compound interest. If you meditate enough, you’ll eventually attain lower levels of stress and anxiety. If you invest in your relationships long enough, you’ll reap the rewards of strong friendship and trust. The overall takeaway of the Marshmallow test rings true to this day: choosing delayed gratification = setting yourself up for long-term gratification.
Most effective self-care “hacks” make long-term thinking more palatable, i.e., help you strike the right balance between long-term investment and short-term pleasure. Ravikant gives the example of gearing up for high-quality reading. Start with literary junk food — content that’s maybe a little silly or trashy and scratches the same itch as your favorite reality TV show. Keep consuming content like this until you fall in love with reading itself — the act of flipping through a book, visualizing artfully described scenes, and re-reading paragraphs you particularly enjoyed. You’ll eventually get bored with the simple stuff and seek out more interesting, stimulating, and nourishing material.
This “gradual quality-upping” approach is just one variation on the theme of striking the right balance between short-term and long-term. In pursuing a healthier diet, develop a repertoire of meals that are not only nutritious but also inexpensive, easy to make, tasty, and thus conducive to cooking regularly. You’re eating for the long term but not sacrificing too much short-term pleasure; this increases the likelihood you’ll actually stick to your plan and practice it long enough to capitalize on that compound interest. (The same reasoning underlies why going cold turkey generally backfires: while Siddhartha jumped directly from a lavish life to asceticism, the all-to-none approach doesn’t come with enough intermediate, attainable milestones to be feasible for the average person.)
Similar tricks exist in every sphere. “Doing what you love” is cliche for a reason. When work feels like play, there is a lower likelihood that you’ll procrastinate by doom-scrolling at your desk, or seek out the fake rewards of video games in place of the tangible rewards of your job. As Ravikant puts it, you need to find the things that are fun for you but look like work to others. These practices will be the ones that you can sustain for the long term, and that, in turn, will sustain you.
Wide-reach thinking
Long-term thinking is a special case of “seeing the bigger picture,” where the picture is temporal. It requires expanding your view so that you’re not fixated on a specific moment or event, but rather on overarching trends and trajectories.
As discussed above, instant gratification offers fleeting pleasures; at most, these create blips in the graphs of your lifetime happiness and success. Investing in the long term gives you an actual shot at increasing your baseline happiness and escaping the hedonic treadmill. In the same way you shouldn’t sell a stock just because of a temporary dip, you shouldn’t condition yourself on noisy, local patterns. Instead, you should train yourself to identify global patterns and make decisions in the context of this larger frame.
While the picture discussed so far refers to individual and company-level timelines, a similar big-picture philosophy applies when the picture is the “knowledge landscape.” Imagine we condense all phenomena of the real world into a 2D plot, such that each (x,y) coordinate pair uniquely identifies a phenomenon. Further, for each self-contained piece of explanatory knowledge (theory, law, principle, etc.), we superimpose a closed, 1D curve (loop) encompassing the phenomena it explains. In this way, the area of each loop quantifies the reach of the associated knowledge.
Let’s zoom in on an area of this knowledge landscape relevant to explaining certain behavior and properties of the capacitor, as illustrated below. (I use this example because I recently wrote an article on capacitors. To you, this may classify as “shameless self-promotion,” but in my eyes, I’m simply wielding my Ravikant-ordained leverage.)
We see that a piece of knowledge with predictive power encloses a very small area. Take, for example, the capacitance equation for a parallel plate capacitor (orange loop). The capacitance equation has more predictive than explanatory power: it explains the mathematical relationship between capacitance and parameters of the parallel plate geometry, but you can plug and chug to get C given the other variables in the equation, all without knowing what things like capacitance or permittivity even are.
The equation is obviously useful — you don’t want to rederive it from Maxwell’s Equations every time you need to apply it. However, it inherently has a limited scope of applicability: it works for parallel plates, but to get the capacitance of any other capacitor configuration (concentric metal spheres, coaxial metal cylinders, etc.), you have to “zoom back out” to Gauss’s law and re-derive the electric field for the new geometry. In fact, to understand what capacitance even is, you have to zoom out in a similar manner.
Of course, Maxwell’s equations (pink loop) are equations too, so the same plug-and-chug approach could hypothetically be taken. The difference is that the equations are theory-laden: they tell us how electric and magnetic fields are generated by charges, currents, and changes of the fields themselves. Within the realm of capacitors, they further explain stuff like how current “flows” across a capacitor, given that there is no (ideal) movement of charges between the capacitor plates (follows from Ampère-Maxwell Law and displacement current term). Beyond the realm of capacitors, they tell us stuff like why electromagnetic waves are capable of self-propagation (a changing electric field induces a changing magnetic field, which induces a changing electric field, and so on).
All of this may seem abstract and esoteric, but there is a takeaway for ideation in general: focus on understanding and explaining rather than mere prediction. Explanation always precedes prediction, and if prediction is truly what you desire, it will follow naturally from a good explanation.
As mentioned in the “Optimism as taking agency in progress” section, seeking out good explanations is a defense against Thiel’s indefinite optimism. Thiel gives the example of biotech companies, which largely rely on AI (prediction generation) for things like drug discovery:
Biotech startups are an extreme example of indefinite thinking. Researchers experiment with things that just might work instead of refining definite theories [good explanations!] about how the body’s systems operate. Biologists say they need to work this way because the underlying biology is hard. … But today it’s possible to wonder whether the genuine difficulty of biology has become an excuse for biotech startups’ indefinite approach to business in general.
— Thiel & Masters (2014, p. 76)
The takeaway of valuing explanation over prediction can also be framed as “seek out explanations with wide reach.” You can always work forward from a wide-reach explanation to a specific application of that knowledge, but it is difficult to start with a limited application and work backwards.
For example, by understanding the metabolic underpinnings of neurodegenerative diseases (e.g., De la Monte & Tong, 2014; Muddapu et al., 2020), you can design a product (drug, treatment, device, etc.) addressing the metabolic root cause. This product will be more effective — and thus more profitable — than a product targeting a symptom or by-product of the root cause (e.g., one of the molecular or cellular “pathologies” contributing to neuronal death; see Muddapu et al., 2020).
Moreover, within the realm of neural conditions, it turns out that psychiatric disorders may also have a metabolic basis (e.g., Kim et al., 2019). Beyond afflictions of the brain, the metabolic root cause may at least partially contribute to disorders such as cancer (Vidali et al., 2015). The wide reach of your underlying explanation allows you to easily extend your solution to these other problems. Equivalently, we can say that the reach of your product will allow you to expand into new markets — something that Thiel would commend you on.
Notably, the goal of “aiming for wide-reach explanations” doesn’t violate Thiel’s tenet of starting in small, niche markets; in fact, it embodies Thiel’s requisite of having the “potential for great scale built into its [your startup’s] first design” (Thiel & Masters, 2014, p. 51). With a wide reach explanation, you’re planning for the long term: you should choose a small subset of problems to initially target, but the reach of your underlying explanation and the utility of your solution allow you to expand to other markets when you’re ready. In other words, a foundation in wide-reach explanations is a long-term investment in your company’s durability, relevance, and continued growth.
A caveat
For Thiel, a defining characteristic of indefinite optimists is a preference for “unlimited optionality,” i.e., perpetually keeping your options open. This may manifest as hoarding money rather than investing it in specific plans and people, modifying and recombining existing ideas rather than seeking out new ones, or hoping that the statistics gods will bless you with a strike-it-rich company in your diversified portfolio or the next big drug in your random drug discovery algorithm. In violation of known solutions to the optimal stopping problem — finding the right balance between breadth and depth, searching and committing, looking and leaping — indefinite optimists choose an eternal state of searching and never commit.
… in an indefinite world, people actually prefer unlimited optionality; money is more valuable than anything you could possibly do with it. Only in a definite future is money a means to an end, not the end itself.
— Thiel & Masters (2014, p. 71)
The cautionary tale is that you should be careful not to mistake the fool’s gold of optionality for the true gold of innovation — to use stem cell terminology, the “differentiation” of optionality into utility and value.
In a related manner, you should be careful not to confuse long-term thinking with saving up indefinitely — if you prefer the stem cell analogy, consider remaining an undifferentiated stem cell your whole life; if you prefer a physics analogy, consider endlessly accumulating potential energy without converting it to kinetic energy. It’s true that sacrifice in the current moment allows you to reap greater rewards down the line. But if you sacrifice your whole life, you run out of time to enjoy the rewards.
A similar caveat applies to wide-reach thinking. A wide-reach explanation gives you lots of optionality: given that it explains many different phenomena, you have many specific applications to choose from. However, following the Thielian principle of starting in small markets, you shouldn’t try to develop a product that encapsulates all applications of the explanation at once. Instead, you should choose one specific application and develop a tangible product serving a select group of people.
Summary
- Ravikant and Thiel: Long-term thinking is the key to accumulating wealth and success. Ravikant frames the importance of the long term in terms of compound interest, while Thiel frames it in terms of future cash flow.
- Ravikant: Compound interest is a reason to think on a longer time scale. Instant gratification of the short-term is tempting, but “long-term pleasures” are eligible for compound interest and are thus a better investment. Compound interest is passive power: it is the leverage of already possessing value, or even the leverage of leverage. Let the work you’ve done continue to do work for you.
- Thiel: The metric of (discounted) future cash flows penalizes myopic vision and rewards future planning. If you use this metric, you are forced to assess the durability of your company, i.e., its ability to sustain growth.
- Ravikant: Most effective self-care “hacks” make long-term thinking more palatable, i.e., help you strike the right balance between long-term investment and short-term pleasure.
- Deutsch: In the same way accumulation of wealth is maximized by choosing long-term over short-term, the accumulation of knowledge (and potential for innovation) is maximized by valuing explanation over prediction. In other words, “seek out explanations with wide reach.”
- Deutsch and Thiel: Seeking out explanations with wide reach is a way to achieve Thiel’s ideal of built-in scaling potential. Wide-reach explanations lend themselves to a broad range of specific applications and target audiences, thus allowing you to easily build up your product and expand into other markets over time.
Deutsch definition box
For elaboration on the following terms and ideas, see my articles, “The universal reach of human thought” and “In search of the golden rule: a comparison of two theories of everything.”
What is knowledge?
As per the definition of David Deutsch (Deutsch, 2012, pp. 93–95), knowledge is abstract information that is useful (solves problems) and thus tends to keep itself physically instantiated.
According to this conceptualization, knowledge doesn’t just correspond to human ideas. For example, the abstract knowledge of a skill such as cracking nuts is physically instantiated in the mind and actions of its enactor. Because this knowledge is useful, the enactor repeatedly enacts the skill, strengthening its instantiation in the enactor’s memory, and shares the skill with others, thus replicating the knowledge in more physical forms. Additionally, the abstract knowledge of a biological adaptation is physically instantiated in genes. If this knowledge is useful in that it confers the host organism greater survival or reproductive advantages, the genes will spread more than their variants through the population.
What constitutes a knowledge-creating process?
There is a common misconception that creativity is just recombining ideas. But knowledge-creating processes are always discrete in nature. They involve a process of (iterative) variation and selection. Many variants of an idea (discrete entities) are conjectured, and the “best” is selected to compete in further rounds of the process.
In this way, processes of knowledge creation are inherently evolutionary, with rounds of selection and variation serving as the error-correcting mechanism that allows progress (permits asymptotic encoding of “objective truth” in the limit of infinite variation and selection).
Thus, like biological evolution, human theorizing is a knowledge-creating process: The abstract knowledge of ideas and theories is physically instantiated in our minds and behaviors. Through experimental tests, observation, and exposure to criticism, we compare our conjectures with relevant variants and select the best ones for further screening. What emerges after several rounds of variation and selection is often new explanatory knowledge — roughly that which answers the question of “why” and contains knowledge not present in the initial conjectures.
What is the replicator theory of evolution (and knowledge)?
While evolution is usually associated with genes and the development of adaptations, it is more generally a theory of abstract knowledge that tends to keep itself in existence via replicators — entities that (indirectly) contribute to their own copying — the best examples of which are genes and ideas (Deutsch, 2012, p. 93).
The most general way of stating the central assertion of the neo-Darwinian theory of evolution is that a population of replicators subject to variation (for instance by imperfect copying) will be taken over by those variants that are better than their rivals at causing themselves to be replicated. … both human knowledge and biological adaptations are abstract replicators: forms of information which, once they are embodied in a suitable physical system, tend to remain so while most variants of them do not.
— Deutsch (2012, pp. 93–95)
What is the reach of knowledge?
The reach of knowledge is roughly the applicability of the knowledge to problems beyond those the knowledge was originally intended to solve.
Knowledge with predictive power has inherently limited reach, as it is narrowly confined to the problem it was intended to solve. Consider the equation y=v₀t−0.5gt², which accurately predicts the vertical position of a projectile over time (if you’re near Earth’s surface, ignoring air resistance, and assuming constant gravity). It works well within this narrow domain, but it doesn’t explain why the object behaves this way, nor can it be extended to other contexts like space travel or motion under different forces.
On the other hand, knowledge with explanatory power (roughly, that which answers the question of “why”) can have wide reach, as our best explanations can apply to problems in domains far beyond their originally intended scope. For instance, Newton’s laws of motion and universal gravitation explain why the projectile follows that parabolic path: forces cause acceleration, and gravity exerts a constant downward force. These laws apply not just to falling apples or cannonballs, but also to satellites in orbit, planets in motion, and even objects far beyond Earth. Because they describe the fundamental principles governing certain (e.g., everyday speed, weak gravitational field, macroscopic scale) motion, they have immense reach across physics and engineering. And if we want to address shortcomings in Newton’s laws pertaining to fast speeds, strong gravitational fields, and very small scales, we have other explanatory theories (e.g., special relativity, general relativity, and quantum mechanics) to help us extend our reach.
One of the most remarkable things about science is the contrast between the enormous reach and power of our best theories and the precarious, local means by which we create them. No human has ever been at the surface of a star, let alone visited the core where the transmutation happens and the energy is produced. Yet we see those cold dots in our sky and know that we are looking at the white-hot surfaces of distant nuclear furnaces.
— Deutsch (2012, p.3)
When comparing knowledge created by biological evolution and human ideation (the only two knowledge-creating processes that exist to date), the defining distinction is the reach of the created knowledge. While the knowledge yielded by evolution is limited in that it’s locally useful to an organism and environment, human theorizing is unique in that it can generate new explanatory knowledge with far-reaching applications.
What are the criteria for a good explanation?
Much like a good genetic adaptation, a good explanation is one that is hard to vary while fulfilling its function. In other words, it is “well-adapted” to the niche of explaining relevant phenomena or solving relevant problems. The criteria of being hard to vary indicates that all of the details of the explanation are necessary and play a functional role, “for whenever it is easy to vary an explanation without changing its predictions, one could just as easily vary it to make different predictions if they were needed” (Deutsch, 2012, p. 21).
In this way, we see that bad explanations are inherently reducible to an ad-lib template: you can fill in this template with whatever junk you need to accommodate new observations and patch up newly-realized conflicts. As Deutsch puts it, “Whenever a wide range of variant theories can account equally well for the phenomenon they are trying to explain, there is no reason to prefer one of them over the others, so advocating a particular one in preference to the others is irrational” (Deutsch, 2012, p. 21).
Deutsch illustrates the difference between good and bad explanations using the example of seasons (Deutsch, 2012, pp. 19–22). Consider the following two explanations:
P is a bad explanation because its details are loosely connected to the phenomena of seasonality. Had the Greeks learned that seasons are reversed in the southern hemisphere, they could have tweaked P in any number of ways to suit this realization, e.g., by claiming Demeter’s sadness sends warmth southward. Similar tweaks could make P fit almost any seasonal pattern, even weekly or sporadic ones.
Q, by contrast, explains that Earth’s axial tilt causes one hemisphere to receive more sunlight for half the year, and the other half for the opposite hemisphere. This explanation is good because it is hard to vary: each component—degree of axial tilt, heating via exposure to sunlight, etc. — has independent, testable consequences that are corroborated by our best explanations from physics and geometry. Further, by virtue of being a good explanation, Q has reach (as per the definition above) beyond accounting for the seasons, explaining phenomena like the sun’s position on the horizon throughout the year.
Sources cited and further reading
Deutsch, D. (2012). The Beginning of Infinity: Explanations That Transform the World. Penguin Publishing Group.
Deutsch, D. (1997). The Fabric of Reality. Penguin Publishing Group.
Jorgenson, E., & Ferriss, T. (2021). The Almanack of Naval Ravikant. New York, NY: Harper Business.
Thiel, P., & Masters, B. (2014). Zero to one: Notes on startups, or how to build the future. Crown Currency.
Berridge, K. C., & Kringelbach, M. L. (2015). Pleasure systems in the brain. Neuron, 86(3), 646–664.
