Resource: What Will Bitcoin Look Like in Twenty Years?
Predicting the future may be impossible, but that doesn’t mean it’s a waste of time to try. Our efforts to anticipate how things will unfold is precisely what helps us defend against danger and capitalize on opportunity.
While predictions about the future are inherently imperfect, it doesn’t follow that all predictions are of the same quality. Some approaches to predicting the future are better than others.
The main reasons why most people’s efforts to predict the future are wrong is because of fallacious thinking.
Some people are poor at seeing the future because they feel threatened by the possibilities. This sense of being threatened leads to unconscious resistance. Some people fail at predicting the future because they only think about it for a few minutes and assume that’s enough. Some fail because they’re impatient. Impatience is a big problem when it comes to technology. Our optimism and excitement towards the benefits makes it hard for us to keep the faith during the messiness inherent in the creative process.
The ability to criticize current projects is typically seen as the enemy, but it’s really an asset. Identifying the problem is essential to identifying what features the solution needs to have.
The amount of time between the invention of velcro and its mass adoption was 25 years. That’s just one example of how long it can take for an idea to go from being useful to being seen as useful.
The blockchain can and will be used for evil purposes beyond what we can imagine. There is no such thing as a tool, even a decentralized tool, that can’t be used for evil.
Fiat currency will likely lose the long-term battle to decentralized currencies, but they will attempt to compete in the short-term through government-based cryptocurrencies.
Mass adoption of cryptocurrencies will challenge, falsify, confirm, and evolve many of our long-standing economic assumptions.
There will be a cryptobubble, but this is a mostly irrelevant fact for two reasons: 1) the emergence of new asset classes are always accompanied by bubbles and 2) all bubbles result in useful outcomes. Think of bubbles as a purgation process that refines the economy by filtering out the least useful assets.
The six major reasons predictions about the future fail:
“The first reason people get the future so wrong is because they dedicate about five minutes to looking at something before they form an opinion on it. That isn’t thinking. That’s the primordial lizard brain running a mental heuristic that’s absolutely incapable of understanding anything new and novel. It’s only good at attack, defense, finding food and shelter and avoiding boredom. It’s a survival machine. Unfortunately, many people live almost their entire lives at this level and their opinions are worth zero when it comes to seeing new trends and developments.”
“The second major reason people get the future so wrong is it goes against everything they understand about the world. Think about a company like Kodak who simply refused to see the power of digital film because they’d built up a business over a hundred years on the back of chemical film. They had every advantage and they blew it. They mistook the past for the future and they paid a heavy price by going bankrupt as the market roared past them. To see the future you have to be able to step outside of yourself, forget your past successes and see beyond your current understanding.”
“A third major reason people fail to see the future is because it challenges their position of power. That’s why oligarch banker, Jamie Dimon, and a prince from a country that just allowed women to drive last month, all see Bitcoin and cryptocurrencies as a “fraud” or a “scam”. They literally can’t see clearly because they’re the main beneficiaries of the current system. They don’t want to see. So they engage in a kind of information warfare, even if it’s unconscious. It’s nothing but a mental defense mechanism. The rise of new ways of running the world means their position is under fire and they’re terrified. Asking these people about Bitcoin is like asking a taxi driver what he thinks about Uber or a horse and buggy manufacturer what he thinks about cars. Their opinions are worth less than nothing.”
“The fourth major reason people screw up predictions is because they mistake their opinion for reality. There’s what you think about the world and there’s actual reality and they’re often not the same thing. One is the map and one is the territory. Don’t mistake the map for the territory.”
“The fifth reason people get the future wrong is a complete and total lack of patience. The waiting is the hardest part. It takes patience to let things develop naturally. Creativity requires setbacks and failures and tremendous tenacity. Once you expose your idea to the reality of rust, gravity and friction, things tend to fall apart. No plan survives contact with the enemy. Reality is a whetstone that either shatters you or sharpens your ideas. Things take time.”
“His biggest mistake is the sixth and final reason people are blind to the future. He took current inventions, air lifted them forward and imagined them as the solution to future problems. Wrong! Current inventions solve current problems. Future problems will take brand new solutions.
In the article Stoll mentions that CD books would never replace real books. He was right that reading books on CD with a crappy CRT monitor that rips apart your retinas was a miserable experience. But understanding that helps us understand the necessary characteristics of a future solution. It’s nearly impossible to know what form those solutions will take, but we can figure out what traits the solution will have so we can recognize it when it gets here.”
An example of the time it takes for ideas to work:
“A classic example of the real creative process and how long it takes comes from George de Mestral, the inventor of Velcro. He first came up with the idea in 1941, after taking his dog for a walk in the woods and seeing a bunch of burrs attached to his fur. The concept didn’t fully take root in his mind for another seven years. He started working on recreating the tiny hooks in 1948 and it took him ten years to make it work and mass produce it. After that he opened his company in the late 1950s, he expected immediate high demand. It didn’t happen. It took another five years before the budding space program in the 1960’s saw Velcro as a way to solve the problem of getting astronauts in and out of bulky and unwieldy space suits. Soon after the ski industry noticed it would work on boots. All in all from initial idea to functioning, profitable business? About twenty five years.”
On what the world really cares about:
“The rest of the world only cares about the problems things solve for them not the idea or ideology behind it.”
On how criticism helps creativity:
“Solutions start by pointing out what’s wrong, asking the right questions about how to fix it and correctly defining what properties we would need to have a better experience.”
Three principles for predicting the future:
“1) Patience. 2) Observe, don’t interpret. 3) Don’t graft today’s solutions onto tomorrow’s problems.”
On the inevitability of the crypto bubble:
“People in and out of crypto see them as bubble that will pop, causing prices to crash badly. They’re right. But so what? That’s not the end of the story. It’s just the beginning….When the Internet bubble burst many of today’s marquee companies saw their stocks crash 85%. Yet they survived and the best was yet to come. Amazon and Google went on to dominate the world. The same will happen in crypto. The 10% of projects that make it through the bloodbath will turn into the Amazon, Google and Facebook of tomorrow and likely even the JP Morgan and Goldman Sachs as well, not to mention maybe even the governments of the future, like digital direct democracies or liquid democracies….The bubble burst is just the next step. Three years after that the tech will really mature and take off running.”
On the inevitable flourishing of government cryptocurrencies:
“Governments will lose the battle in the long run, probably in thirty to one hundred years (maybe faster depending on how many wars or financial crises strike)…But in ten or twenty years expect very strong government cryptocurrencies to come to power and dominate the flow of money for many, if not most, people around the world.”
On why people will use government-based cryptocurrencies:
“The average person has zero understanding of just about anything that actually matters and they absolutely don’t see a need for privacy and security until it’s physically ripped away from them under extreme circumstances like a war. When soldiers invade your house and take everything you own suddenly the need for privacy becomes very real to people…the average man on the street doesn’t know a damn thing about privacy and doesn’t care about it in the least! The only time they care is when the government has a picture of their dick on file. Seriously. People will adopt government cryptos like good little sheep without a second thought. Even better, they’ll think it’s absolutely the right thing to do and they’ll even be willing to kill for it if told that’s right. Count on it!”
On why government cryptocurrencies are nonsense:
“They’re nonsense because the very purpose of blockchain is to distribute power across a system. By not allowing a single group to control or change the rules arbitrarily, decentralized cryptos and apps provide a powerful set of checks and balances against harmful actions to the system. When five different banks own a blockchain, that’s not a blockchain, that’s a database. Only when the banks, the regulators, the shareholders and the customers of the bank hold the keys to the blockchain at the same time and can counteract each other’s power is it a true blockchain. The checks and balances on power are exactly the point! Government crypto will represent a total and complete corruption of that idea. But it won’t matter. They’ll do it any way.”
On why decentralized cryptos will continue to thrive alongside centralized cryptos:
“The same factors that make it hard to form consensus across a blockchain, make it hard for all the world’s governments to agree on anything. They won’t be able to do it. Some governments will love decentralization and others will hate it…If all the countries don’t agree, then decentralized cryptos are never going away, even as centralized cryptos come to power.”
On the future rise on non-blockchain protocols for decentralized transactions:
“Blockchain systems are only the first successful implementation of decentralized consensus mechanisms…Over the next twenty years, I predict dozens, if not hundreds of experimental distributed consensus protocols, capable of transaction levels that blow away Visa level processing, augmented by artificial intelligence systems. It’s also strongly possible that none of these systems will be designed by humans. Instead AI’s will rapidly iterate on ideas and come up with systems that no human ever could if they had a hundred years. They’ll draw their inspiration from nature and systems of insects or roots or other biological systems like proteins. One or two of these systems will come to dominate all coins and become the meta-system to rule them all, uniting many different kinds of coins and running the entire system like a massive fractal that enables countless daughter networks to flourish inside of it.”
Why the current crypto experience is awful:
“If I mistype something or copy and paste wrong, my money disappears forever. If there’s a software glitch I lose my money forever. If someone hacks my computer or phone my money is gone forever. See a trend there? Make any mistake you’re toast. It’s like driving a motorcycle on the edge of a one inch mountain road with no rail. The core wallets are slow, hard to use and ugly.”
On new methods arising for solving the inheritance, security, and transfer problems in crypto:
“By the way, if you want to start a crypto business that everyone will need in the future, solve the inheritance problem. Everyone will pay you gladly….Whatever it looks like, we’ll need algorithmic approximations of the controls we have now for giving money to people we want and keeping it out of the hands of people who want to rob us. We also need the system to protect us from accidents, death and going nuts.”
On the four types of coins that will characterize the future economy:
“We will have Four Dominant Meta Coins, Plus Fifty to One Hundred Minor Coins, and Infinite Virtual Variations of These Coins, Plus State Coins…Coins will start to shake out into various meta categories. At this point I can only see four types of coins needed, with a blockchain of blockchains (or post-blockchain tech) seamlessly swapping them as needed to consume services: Deflationary Saver Coin, Inflationary Spender Coin, Action Token, Reward Token.”
On the emergence of surprising new economic discoveries:
“That’s what these new coins are: Micro-economic systems at war. It’s Darwinian economics. A few basic laws of economics will hold true but many of them will simply fall by the wayside. That’s because with blockchain dominate systems we’ll have real time economic data on a global scale not just a bunch of guesses done with pencil and paper a hundred years ago. As artificial intelligence tracks statistics in real time around the globe we’ll be able to see the real effects of a steel tariff enacted in one country as prices shoot up for building in another country dependent on that steel. We’ll track global production and manufacturing with unbelievable precision and what we learn will very much surprise us in so many wonderful ways.”
On how the gig economy will grow exponentially:
“People from the World War II generation had one or two jobs their whole life. Today we have five or six. Tomorrow’s people will have five or six at the same time. Half of those income streams will be automated and passive, likely some kind of crypto UBI. We will also see the rise of AI job matching services. The machines will know your capabilities and skill sets and match short term gigs to you so you don’t even have to look for a job.”
On why we should prepare for evil uses of the blockchain:
“You should start thinking about all the ways to destroy your system or you won’t be able to defend it. If you aren’t imagining all the ways a hostile group will use the power of blockchain, one that doesn’t share your views on openness and freedom and collaboration, then you’re just naive….Maybe you think that an open system will always prevent abuses? Wrong. If the Internet has taught us anything it’s that open systems tend towards centralization and given enough time central powers can and will subvert and corrupt any system to their own ends. If you’re working in crypto and you’re not thinking about all the ways to misuse crypto then it’s very likely that instead of designing a system to save the world you just created a prison for it.”
On how to envision a possible failure for bitcoin:
“Bitcoin has first mover advantage. It’s the absolute first of its kind and still dominates the global market share but it also suffers from a number of major flaws that could kill it. Basically, it’s the Model T of the blockchain revolution. How many Model T’s do you see on the street today? Can you retrofit a Model T to make it burn rubber like a Lamborghini? Can you add sophisticated electronics to make it a self-driving Tesla? Nope.”
On the promises and possibilities of crypto:
“Cryptocurrencies represent a fundamental upgrade to the economic systems of the world. Once they’re fully booted up and integrated into the global and interplanetary networks of the future, the world will look very, very different in ways we can only begin to understand. Hundreds of years from now, today’s economies will look like the feudal economies of the past.
Cyrptocurrencies, decentralized apps and DAOs even hold the possibility of bootstrapping us into Star Trek like post-scarcity economies but it will take time. On the need to temper expectations about the pace at which crypto will change the world: “Crypto will be both good and evil like everything in life. If you’re working on crypto then you’re building the world of tomorrow but don’t expect it to arrive next week. Inertia has a way of slowing down even the fastest rockets. Just enjoy the ride while we boldly go where no one has gone before.”
Resource: The Pygmalion Effect: Proving Them Right
The term “pygmalion effect” refers to the positive changes and outcomes we can inspire in a person’s performance due to our high expectations. It comes from the mythical Greek sculptor Pygmalion who created a statue that he fell in love with. After praying to the goddess of love, Aphrodite granted his wish and made the statue come alive and they became lovers. Through the power of his faith and love, the statue became what he wished it to become. To a limited degree, we can inspire others to achieve their dreams by expecting them to do so.
The pygmalion effect is non-mystical. It’s not based on the power of belief or expectation alone. Our beliefs and expectations change the way we speak to them, listen to them, praise them, correct them, challenge them, and so. Ultimately, people’s perfotmance is affected by our treatment of them, but it’s the expectation that determines the treatment.
The story of Clever Hans, the horse that seemed to solve complex math problems, is a great example of how a creatures performance can be enhanced by the unconscious behavioral cues we signal as we observe their performance.
There are limitations to the pygmalion effect. There’s no evidence to suggest that we can inspire people to do anything through positive expectation. The way the effect works is that it seems to mitigate the negative consequences of setting our expectations to low.
From a managerial or coaching standpoint, it can be useful to ask “How would I offer critical feedback to my client/employee if I believed they were going to be really successful?”
On the nature and origin of the pygmalion effect:
“The Pygmalion effect is a psychological phenomenon wherein high expectations lead to improved performance in a given area. Its name comes from the story of Pygmalion, a mythical Greek sculptor. Pygmalion carved a statue of a woman and then became enamored with it. Unable to love a human, Pygmalion appealed to Aphrodite, the goddess of love. She took pity and brought the statue to life. The couple married and went on to have a daughter, Paphos.”
On the golem effect and the power of expectation:
“In the same way Pygmalion’s fixation on the statue brought it to life, our focus on a belief or assumption can do the same. The flipside is the Golem effect, wherein low expectations lead to decreased performance. Both effects come under the category of self-fulfilling prophecies. Whether the expectation comes from us or others, the effect manifests in the same way.”
“Although many people purport to want to prove their critics wrong, we often merely end up proving our supporters right. Understanding the Pygmalion effect is a powerful way to positively affect those around us, from our children and friends to employees and leaders. If we don’t take into account the ramifications of our expectations, we may miss out on the dramatic benefits of holding high standards.”
On research demonstrating the influence teachers’ expectations have on students:
“Research by Robert Rosenthal and Lenore Jacobson examined the influence of teachers’ expectations on students’ performance. Their subsequent paper is one of the most cited and discussed psychological studies ever conducted. Rosenthal and Jacobson began by testing the IQ of elementary school students. Teachers were told that the IQ test showed around one-fifth of their students to be unusually intelligent. For ethical reasons, they did not label an alternate group as unintelligent and instead used unlabeled classmates as the control group. It will doubtless come as no surprise that the “gifted” students were chosen at random. They should not have had a significant statistical advantage over their peers. As the study period ended, all students had their IQs retested. Both groups showed an improvement. Yet those who were described as intelligent experienced much greater gains in their IQ points. Rosenthal and Jacobson attributed this result to the Pygmalion effect. Teachers paid more attention to “gifted” students, offering more support and encouragement than they would otherwise. Picked at random, those children ended up excelling. Sadly, no follow-up studies were ever conducted, so we do not know the long-term impact on the children involved.”
How the pygmalion effect relates to leadership:
“In general, authority figures have the power to influence how the people subordinate to them behave by holding high expectations. Whether consciously or not, leaders facilitate changes in behavior, such as by giving people more responsibility or setting stretch goals. Like the subtle cues that allowed Clever Hans to make calculations, these small changes in treatment can promote learning and growth. If a leader thinks an employee is competent, they will treat them as such. The employee then gets more opportunities to develop their competence, and their performance improves in a positive feedback loop. This works both ways. When we expect an authority figure to be competent or successful, we tend to be attentive and supportive. In the process, we bolster their performance, too. Students who act interested in lectures create interesting lecturers.”
“Some managers always treat their subordinates in a way that leads to superior performance. But most … unintentionally treat their subordinates in a way that leads to lower performance than they are capable of achieving. The way managers treat their subordinates is subtly influenced by what they expect of them. If manager’s expectations are high, productivity is likely to be excellent. If their expectations are low, productivity is likely to be poor. It is as though there were a law that “
“The Pygmalion effect shows us that our reality is negotiable and can be manipulated by others — on purpose or by accident. What we achieve, how we think, how we act, and how we perceive our capabilities can be influenced by the expectations of those around us. Those expectations may be the result of biased or irrational thinking, but they have the power to affect us and change what happens. While cognitive biases distort only what we perceive, self-fulfilling prophecies alter what happens.”
On how some things remain non-negotiable:
“Of course, the Pygmalion effect works only when we are physically capable of achieving what is expected of us. After Rosenthal and Jacobson published their initial research, many people were entranced by the implication that we are all capable of more than we think. Although that can be true, we have no indication that any of us can do anything if someone believes we can. Instead, the Pygmalion effect seems to involve us leveraging our full capabilities and avoiding the obstacles created by low expectations.”
On the limits of the pygmalion effect:
“We can’t do anything just because someone expects us to. Overly high expectations can also be stressful. When someone sets the bar too high, we can get discouraged and not even bother trying. Stretch goals and high expectations are beneficial, up to the point of diminishing returns. Research by McClelland and Atkinson indicates that the Pygmalion effect drops off if we see our chance of success as being less than 50%. If an endeavor seems either certain or completely uncertain, the Pygmalion effect does not hold. When we are stretched but confident, high expectations can help us achieve more.”
On the non-mystical nature of the pygmalion effect:
“The important point to note about the Pygmalion effect is that it creates a literal change in what occurs. There is nothing mystical about the effect. When we expect someone to perform well in any capacity, we treat them in a different way. Teachers tend to show more positive body language towards students they expect to be gifted. They may teach them more challenging material, offer more chances to ask questions, and provide personalized feedback. As Carl Sagan declared, “The visions we offer our children shape the future. It matters what those visions are. Often they become self-fulfilling prophecies. Dreams are maps.”
Resource: The Filter Bubble: Algorithm vs. Curator & the Value of Serendipity
When we search for things online via engines like Google, our results are not universal or neutral. Using roughly 57 different elements —from where we’re located to what computer we’re using — algorithms are used to anticipate what we would most like to see.
Our search results are a filter bubble. There may be many things we need to see or would enjoy seeing, but they remain invisible to us because they don’t conform to past search patterns or the algorithms are too risk adverse to reveal them to us.
Human curators, being less risk adverse than algorithms, have advantages over technology in exposing us to information we might find to be surprisingly interesting.
One interesting idea from this article is the advice on how to avoid being like a mouse. In the mousetrap industry, there isn’t a great demand for a better mousetrap because mousetraps are 90% effective. The reason why is that mice predictably run along the same routes. Because of this predictability, they are vulnerable to unsophisticated traps. In some sense, the same is true of us when we don’t switch things up. We need to make an effort to search in unconventional ways for unconventional things. This is good for our cognitive development and our pursuit of joy.
On the myriad of factors that go into our search results:
“Most of us are aware that our web experience is somewhat customized by our browsing history, social graph and other factors. But this sort of information-tailoring takes place on a much more sophisticated, deeper and far-reaching level than we dare suspect. (Did you know that Google takes into account 57 individual data points before serving you the results you searched for?)”
On the role of curation in editing:
“The primary purpose of an editor [is] to extend the horizon of what people are interested in and what people know. Giving people what they think they want is easy, but it’s also not very satisfying: the same stuff, over and over again. Great editors are like great matchmakers: they introduce people to whole new ways of thinking, and they fall in love.” ~ Eli Pariser
On the nature of filter bubbles:
“Your filter bubble is the personal universe of information that you live in online — unique and constructed just for you by the array of personalized filters that now power the web. Facebook contributes things to read and friends’ status updates, Google personally tailors your search queries, and Yahoo News and Google News tailor your news. It’s a comfortable place, the filter bubble — by definition, it’s populated by the things that most compel you to click. But it’s also a real problem: the set of things we’re likely to click on (sex, gossip, things that are highly personally relevant) isn’t the same as the set of things we need to know.”
On how search is non-authoritative and customized:
“I came across a Google blog post declaring that search was personalized for everyone, and it blew my mind. I had no idea that Google was tailoring its search results on an individual basis at all — the last I’d heard, it was showing everyone the same “authoritative” results. I got out my computer and tried it with a friend, and the results were almost entirely different. And then I discovered that Google was far from the only company that was doing this. In fact, nearly every major website is, in one way or another.”
On how algorithms can be inferior to human curators:
“One interesting place this comes up is at Netflix — the basic math behind the Netflix code tends to be conservative. Netflix uses an algorithm called Root Mean Squared Error (RMSE, to geeks), which basically calculates the “distance” between different movies. The problem with RMSE is that while it’s very good at predicting what movies you’ll like — generally it’s under one star off — it’s conservative. It would rather be right and show you a movie that you’ll rate a four, than show you a movie that has a 50% chance of being a five and a 50% chance of being a one. Human curators are often more likely to take these kinds of risks.”
On how much our search engines know about us:
“There are 57 signals that Google tracks about each user, one engineer told me, even if you’re not logged in. Most of the time, this doesn’t have much practical consequence. But one of the problems with this kind of massive consolidation is that what Google knows, any government that is friends with Google can know, too. And companies like Yahoo have turned over massive amounts of data to the US government without so much as a subpoena.”
On how to avoid becoming like a mouse:
“There was a great This American Life episode which included an interview with the guy who looks at new mousetrap designs at the biggest mousetrap supply company. As it turns out, there’s not much need for a better mousetrap, because the standard trap does incredibly well, killing mice 90% of the time. The reason is simple: Mice always run the same route, often several times a day. Put a trap along that route, and it’s very likely that the mouse will find it and become ensnared. So, the moral here is: don’t be a mouse. Vary your online routine, rather than returning to the same sites every day. It’s not just that experiencing different perspectives and ideas and views is better for you — serendipity can be a shortcut to joy.”