Jeff Ryans Book Notes on "WTF?: What's the Future and Why It's Up to Us"

Tim O'Reilly, Author

General Observations and Thoughts about the book:

WTF? Google AlphaGo, an artificial intelligence program, beat the world’s best human Go player, an event that was widely predicted to be at least twenty years in the future—until it happened in 2016. If AlphaGo can happen twenty years early, what else might hit us even sooner than we expect? For starters: An AI running on a $35 Raspberry Pi computer beat a top US Air Force fighter pilot trainer in combat simulation. The world’s largest hedge fund has announced that it wants an AI to make three-fourths of management decisions, including hiring and firing. Oxford University researchers estimate that up to 47% of human tasks, including many components of white-collar jobs, may be done by machines within as little as twenty years.

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What do AI, self-driving cars, on-demand services, and income inequality have in common? They are telling us, loud and clear, that we’re in for massive changes in work, business, and the economy.

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We saw that radically new industries don’t start when creative entrepreneurs meet venture capitalists. They start with people who are infatuated with seemingly impossible futures.

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We forget. We forget quickly. And we forget ever more quickly as the pace of innovation increases.

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So what makes a real unicorn of this amazing kind?        1.  It seems unbelievable at first.        2.  It changes the way the world works.        3.  It results in an ecosystem of new services, jobs, business models, and industries.

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And that gets me to the third characteristic of true unicorns: They create value. Not just financial value, but real-world value for society.

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This is the true opportunity of technology: It extends human capability.

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“My grandfather wouldn’t recognize what I do as work.”

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Top US CEOs now earn 373x the income of the average worker, up from 42x in 1980. As a result of the choices we’ve made as a society about how to share the benefits of economic growth and technological productivity gains, the gulf between the top and the bottom has widened enormously, and the middle has largely disappeared.

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In business and in technology, we often fail to see clearly what is ahead because we are navigating using old maps and sometimes even bad maps—maps that leave out critical details about our environment or perhaps even actively misrepresent it.

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That’s another lesson about the future. It doesn’t just happen. People make it happen. Individual decisions matter.

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It is almost always the case that if you want to see the future, you have to look not at the technologies offered by the mainstream but by the innovators out at the fringes.

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The lesson is clear: Treat curiosity and wonder as a guide to the future. That sense of wonder may just mean that those crazy enthusiasts are seeing something that you don’t . . . yet.

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How can a business create more value for society than it captures for itself?

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This is a key lesson in how to see the future: bring people together who are already living in it. Science fiction writer William Gibson famously observed, “The future is already here. It’s just not evenly distributed yet.” The early developers of Linux and the Internet were already living in a future that was on its way to the wider world. Bringing them together was the first step in redrawing the map.

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Train yourself to recognize when you are looking at the map instead of at the road. Constantly compare the two and pay special attention to all the things you see that are missing from the map.

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Your map should be an aid to seeing, not a replacement for it.

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language itself was a kind of map. Language shapes what we are able to see and how we see it.

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Language can also lead us astray. Korzybski was fond of showing people how words shaped their experience of the world. In one famous anecdote, he shared a tin of biscuits wrapped in brown paper with his class. As everyone munched on the biscuits, some taking seconds, he tore off the paper, showing that he’d passed out dog biscuits.

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Recognizing when you’re stuck in the words, looking at the map rather than looking at the road, is something that is surprisingly hard to learn. The key is to remember that this is an experiential practice. You can’t just read about it. You have to practice it.

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This is my next lesson. If the future is here, but just not evenly distributed yet, find seeds of that future, study them, and ask yourself how things will be different when they are the new normal. What happens if this trend keeps going?

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The way you view the world limits what you can see.

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Clayton Christensen, the author of The Innovator’s Dilemma and The Innovator’s Solution, had developed a framework that explained what I was observing. In a 2004 article in Harvard Business Review, he articulated “the law of conservation of attractive profits” as follows: “When attractive profits disappear at one stage in the value chain because a product becomes modular and commoditized, the opportunity to earn attractive profits with proprietary products will usually emerge at an adjacent stage.”

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Remember, putting the right pieces of the puzzle on the table is the first step toward assembling them into a coherent picture.

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I didn’t predict the future. I drew a map of the present that identified the forces shaping the technology and business landscape.

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The term network effect generally refers to systems that gain in utility the more people use them. A single telephone is not very useful, but once enough people have them, it is very hard not to join the network. So too, the competition in social networks has been to assemble massive user bases, because the lock-in is not via software but through the number of other people using the same service.

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Today’s software is developed by watching what users do in real time—with A/B testing of features on subsets of users, measurement of what works and what doesn’t work informing development on an ongoing basis. In this way, the collaborative model of open source software development—“given enough eyeballs, all bugs become shallow”—has been taken to its logical conclusion, and completely divorced from the original licensing model of free and open source software. In the end, I was able to see the future more clearly because my map was more useful than one based on a battle between proprietary software and free software licensing models. Having the right orientation matters. But even then, it had taken years to explore the landscape sufficiently to fill in all the blank spaces on the map.

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The future is the outcome of millions of intersecting vectors, which add up in unexpected ways. The art is to pick out important vectors and weave a net from them in which to catch a view of the future.

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“global consciousness is that thing that decided that decaffeinated coffeepots should be orange.” The idea that “orange means decaffeinated” originated during World War II, when Sanka promoted its decaffeinated coffee brand by giving away orange-rimmed coffeepots to restaurants across America. The idea took hold—not universally, to be sure, but sufficiently that the pattern propagates. At some point, it no longer belonged to Sanka but to the world.

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The association of “orange” with “decaffeinated” is an example of what Richard Dawkins called a “meme”—a self-replicating idea.

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Today people often think of memes as images and slogans shared on social media, but any great idea that takes hold is a meme. In 1880, “Darwin’s Bulldog” Thomas Henry Huxley wrote, “The struggle for existence holds as much in the intellectual as in the physical world. A theory is a species of thinking, and its right to exist is coextensive with its power of resisting extinction by its rivals.”

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The kinds of “thoughts” that a global brain has are different from those of an individual, or of a less connected society. At their best, these thoughts allow for coordinated memory on a scale never seen before, and sometimes even for unforeseen ingenuity and new forms of cooperation; at their worst, they allow for the adoption of misinformation as truth, for corrosive attacks on the fabric of society as one portion of the network seeks advantage at the expense of others (think of spam and fraud, or of the behavior of financial markets in recent decades, or of the rash of fake news sites during the 2016 US presidential election).

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Self-driving cars are a manifestation of the global brain; their memory is the memory of roads traveled under the tutelage of human drivers but recorded with their uncanny senses. But not unsurprisingly, the most powerful manifestation of the global brain’s ability to touch the physical world relies not on robots but on the power of networked applications to direct human activity.

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There is usually a paradigmatic company or group of companies that best exemplifies the next wave of technology. “Unpacking” the lessons of that company can help you draw your map of the future.

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It is easy to forget that many of the people who invent the future do so by crashing through barriers, crushing competitors, and dominating a new industry by force of will as well as intellect. Sometimes dirty tricks come into play. Thomas Edison and John D. Rockefeller, Bill Gates and Larry Ellison, were all justifiably reviled at various points in their careers. When I began my work in computing, Microsoft was routinely referred to as “the Evil Empire.”

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MAKING STRATEGIC CHOICES You can tell if a business model map is good if it helps a company to make sound strategic choices. That is, it frames the problem in such a way that a company can make conscious choices about what’s important, rather than discovering too late that it broke a key part of what had made it successful.

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When making sense of the future, think in terms of gravitational cores, not hard boundaries. Just as the sun’s gravity well reaches out beyond the orbit of Pluto and encompasses not just the planets in the ecliptic but comets and planetoids with eccentric orbits, so too the forces shaping the future all have a gravitational core and a gradually attenuating influence. And just as the solar system has multiple gravitational subsystems, where the draw of the local giant keeps its own satellites in tow while all still partake in the larger dance, these interpenetrating trends influence each other and converge.

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When you hear a new concept like this, succinctly stated, add it to your mental toolbox. Try it on as a way of seeing the world around you. How does it help you think differently?

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To make the future economy better than the present, find new ways to augment workers, giving them new skills and access to new opportunities. As we automate something that humans used to do, how can we augment them so that they can do something newly valuable?

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When trying to map the future, remember that the territory is not an idealized landscape, but a real one, full of contradiction. The people who are creating the future are complex, each with a mix of brilliance and flaws. They see some things we don’t, and are blind to others.

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Steve Jobs, who was a master at throwing that door wide open, said, “When you grow up you tend to get told that the world is the way it is. . . . Life can be much broader once you discover one simple fact: Everything around you that you call life was made up by people that were no smarter than you. And you can change it, you can influence it. . . . Once you learn that, you’ll never be the same again.”

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When you draw a new map successfully enough, you change the perception not only of the future but of the past. That thing that seemed unthinkable becomes the fabric of the everyday, and it’s hard to remember that it once was only one of many possibilities.

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Frank Herbert once told me, “Ideas are a dime a dozen. It’s implementation that matters.” The future isn’t just imagined. It is built.

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People are comfortable with what they’re doing, and they don’t see the future coming at them.

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Keep waiting for the missing pieces of the puzzle to arrive. Even if you aren’t the one to push that boundary, once someone does it successfully, there’s a huge opportunity for a fast follower. Be ready!

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Understanding that what used to be hard is now free and easy due to the work of others is essential to the leapfrogging progress of technology.

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Real breakthroughs come when an entrepreneur doesn’t just use new technology to duplicate what went before or to fine-tune the way the world works now, but to reimagine how it ought to work.

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When the past is everything you know, it is hard to see the future.

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Often what keeps us from recognizing what lies before us is a kind of afterimage, superimposed on our vision even after the stimulus is gone. Afterimages occur when photoreceptors are overstimulated because you look too long at an object without the small movements (saccades) that refresh the vision, leading to a decrease in the signal to the brain. Or they may occur because your eyes are compensating for bright light, and then you suddenly move into darkness. So too, if we wrap ourselves in the familiar without exposing our minds to fresh ideas, images are burned onto our brains, leaving shadows of the past overlaid on the present. Familiar companies, technologies, ideas, and social structures hide others with a vastly dissimilar structure, and we see only ghostly images until the new comes into focus. Once your eyes have adjusted to the new light, you see what was previously invisible to you.

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This is a central pattern of the Internet age: More freedom leads to more growth.

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Over time, as networks reach monopoly or near-monopoly status, they must wrestle with the issue of how to create more value than they capture—how much value to take out of the ecosystem, versus how much they must leave for other players in order for the marketplace to continue to thrive.

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In one of the early classics of systems engineering, Systemantics, John Gall wrote, “A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true. A complex system designed from scratch never works and cannot be made to work. You have to start over beginning with a working simple system.”

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Simple, decentralized systems work better at generating new possibilities than centralized, complex systems because they are able to evolve more quickly. Each decentralized component within the overall framework of simple rules is able to seek out its own fitness function. Those components that work better reproduce and spread; those that don’t die off.

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The coordination is all in the design of the system itself.

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A PLATFORM BEATS AN APPLICATION EVERY TIME

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This is important: Amazon Web Services was the answer to a problem in organizational design. Jeff understood, as every network-enabled business needs to understand in the twenty-first century, that, as HR consultant Josh Bersin once said to me, “Doing digital isn’t the same as being digital.” In the digital era, an online service and the organization that produces and manages it must become inseparable.

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“Stevey’s Platform Rant.” In it, Yegge describes a memo that he claimed Jeff Bezos wrote “back around 2002 I think, plus or minus a year.” As Yegge described it: His Big Mandate went something along these lines:        1.   All teams will henceforth expose their data and functionality through service interfaces.        2.   Teams must communicate with each other through these interfaces.        3.   There will be no other form of interprocess communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.        4.   It doesn’t matter what technology they use. HTTP, Corba, Pubsub, custom protocols—doesn’t matter. Bezos doesn’t care.        5.   All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.        6.   Anyone who doesn’t do this will be fired.

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Promise theory, as Burgess outlines it, is a framework for understanding how independent actors make promises to each other—the essence of that highly structured communication. Those actors can be software modules promising to respond in a certain way to an API call, or small teams promising to deliver a particular result. Burgess writes: “Imagine a set of principles that could help you to understand how parts combine to become a whole, and how each part sees the whole from its own perspective. If such principles were any good, it shouldn’t matter whether we’re talking about humans in a team, birds in a flock, computers in a datacenter, or cogs in a Swiss watch. A theory of cooperation ought to be pretty universal, so we could apply it both to technology and to the workplace.”

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Spotify plots organizational culture along two axes: alignment and autonomy.

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A modern technology engineering organization (or an entire organization like Amazon or Spotify) seeks to have high alignment and high autonomy. Everyone knows what the goal is, but they are empowered to find their own way to do it.

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“I tell people, ‘Don’t follow my orders. Follow the orders I would have given you if I were there and knew what you know.’” That is, understand our shared objective, and use your best judgment about how to achieve it.

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This outcome-focused, outside-in approach means that, effectively, a team is promising a result, not how they will achieve it. As in Afghanistan, high autonomy is required by the rapidly changing conditions of a fast-growing Internet service.

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High-autonomy technical cultures have developed a technique—the stand-up meeting—by which people and groups must work together toward a common goal and review the status of their promises to each other.

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Today software has become a process of constant, more or less incremental improvements. From the point of view of the company offering an online service, software has gone from being a thing to a process, and ultimately, a series of business workflows. The design of those workflows has to be optimized not just for the creators of the software but for the people who will keep them running day-to-day.

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Much of this work is completely automated. Hal Varian calls this “computer kaizen,” referring to the Japanese term for continuous improvement. “Just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he writes, “continuous experimentation . . . improve[s] the way we optimize business processes in our organizations.”

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The practices of DevOps have continued to evolve. Google calls its version of the discipline “Site Reliability Engineering” (SRE). As described by Benjamin Treynor Sloss, who coined the term, “SRE is fundamentally doing work that has historically been done by an operations team, but using engineers with software expertise, and banking on the fact that these engineers are inherently both predisposed to, and have the ability to, design and implement automation with software to replace human labor.”

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Often, when new technology is first deployed, it amplifies the worst features of the old way of doing business. Only gradually do individuals and organizations realize, through a cascading network of innovations, how to put new technology properly to work.

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Simply putting a digital front end on a broken bureaucratic system often only makes the problem worse, because the digital system replicates existing processes without rethinking them from the ground up.

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The second realization was that understanding service delivery is the key to good policy making.

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Design with data; Do the hard work to make it simple; Iterate. Then iterate again; Build digital services, not websites; Make things open.

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We also learned that the practices that make good apps turn out to be very relevant for making good rules as well.

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If, instead, you step back and view these companies with a twenty-first-century mindset, you realize that a large part of what they do—delivering search results, news and information, social network status updates, relevant products for purchase, and drivers on demand—is done by software programs and algorithms. These programs are workers, and the programmers who create them are their managers. Each day, these “managers” take in feedback about their workers’ performance, as measured in real-time data from the marketplace, and if necessary, they give feedback to the workers in the form of minor tweaks and updates to the program or the algorithm.

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The tasks performed by these software workers reflect the operational workflow of the digital organization.

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summed up the approach that has been essential to the success of Google’s core search service. Its insight, that “simple models and a lot of data trump more elaborate models based on less data,”

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When you are drawing a map of new technologies, it’s essential to use the right starting point. Much analysis of the on-demand or “gig” economy has focused too narrowly on Silicon Valley without including the broader labor economy. Once you start drawing a map of “workers managed by algorithm” and “no guarantee of employment” you come up with a very different sense of the world.

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The algorithm is the new shift boss. What regulators and politicians should be paying attention to is the fitness function driving the algorithm, and whether the resulting business rules increase or decrease the opportunities offered to workers, or whether they are simply designed to increase corporate profits.

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remixed into a “meme”—which has now come to mean a graphic or video representation of a key moment or quote that is freed from its original context, designed to be shared, designed for impact rather than deeper dialogue or understanding.

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Humans are living in the guts of an AI that is only now being born. Perhaps, like us, the global AI will not be an independent entity, but a symbiosis with the human consciousnesses living within it and alongside it.

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THE DESIGN OF THE SYSTEM SETS ITS OUTCOMES

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Yes, the markets have become a hybrid of human and machine intelligence. Yes, the speed of trading has increased, so that a human trader not paired with that machine has become prey, not predator. Yes, the market is increasingly made of complex financial derivatives that no human can truly understand. But the key lesson is one we have seen again and again. The design of a system determines its outcomes. The robots did not force a human-hostile future upon us; we chose it ourselves.

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Stock prices are a map that should ideally describe the underlying prospects of companies; attempts to distort that map should be recognized for what they are. We need to add “fake growth” to “fake news” in our vocabulary to describe what is going on. Real growth improves people’s lives.

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Mistaking what is good for financial markets for what is good for jobs, wages, and the lives of actual people is a fatal flaw in so many of the economic choices business leaders, policy makers, and politicians make.

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It isn’t Wall Street per se that is becoming hostile to humanity. It is the master algorithm of shareholder capitalism, whose fitness function both motivates and coerces companies to pursue short-term profit above all else. What are humans in that system but a cost to be eliminated?

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Right now we’re at an inflection point, where many rules are being profoundly rewritten.

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The ratio between a company’s revenues, cash flow, or profits and its market capitalization is one of many imaginary numbers that make up the world of financial capital. In theory, the intrinsic value of owning a stock is based on the net present value of its expected future profits. In practice, it is that net present value times the expectations of millions of potential buyers and sellers.

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If you have access to supermoney, you can operate for years at a loss. This is one reason—not just the superior customer benefits and economic efficiencies of their technology or business model—that Internet companies can disrupt older, less highly valued companies.

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The chorus of doubt about the jobless future sounds remarkably similar to the one that warned of the death of the software industry due to open source software. Clayton Christensen’s Law of Conservation of Attractive Profits holds true here too. When one thing becomes commoditized, something else becomes valuable. We must ask ourselves what will become valuable as today’s tasks become commoditized.

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The status quo isn’t worth protecting. It’s so easy to be in reaction, on the defensive, fighting for the world we had yesterday. Fight for something better, something we haven’t seen yet, something we have to invent.

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It was language that was our greatest invention, the ability to pass fire from mind to mind. In periods where knowledge is embraced and widely shared, society advances and becomes richer. When knowledge is hoarded or disregarded, society becomes poorer.

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Searching out the frontier for enhancing human value is the great challenge for the next generation of entrepreneurs, and for all of society.

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LEARNING: THE MASTER AUGMENTATION One key to understanding the future is to realize that as prior knowledge is embedded into tools, a

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different kind of knowledge is required to use it, and yet another to take it further. Learning is an essential next step with each leap forward in augmentation.

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This design pattern, that the future is built before it can be bought, is an important one to recognize. The future is created by people who can make and invent things and those who can tinker and improve and put inventions into practice. These are people who learn by doing.

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That is the essence of the Maker movement. Making for the joy of exploration. Making to learn.

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There’s no joy in our current education system. It is full of canned solutions to be memorized when it needs to be a vast collection of problems to be solved. When you start with what you want to accomplish, knowledge becomes a tool. You seek it out, and when you get it, it is truly yours.

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is the practice of investigating what we don’t know. Ignorance, not knowledge, drives science.

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We have far too little fun in most formal learning, and people are hungry for it. If you can’t inspire curiosity, chances are you are on the wrong path.

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That was certainly true of me. I studied Greek and Latin in college. Everything I learned about computers, I learned on the job. The knowledge I learned in college was useless to me. The habits of mind that I formed were what mattered, the foundational skills of study, and particularly the ability to recognize patterns. The struggle to parse complex Greek texts that were, quite frankly, beyond my skill in the language was great preparation when I took on the challenge of documenting programs written in programming languages that at first I barely understood.

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Over the course of my career, learning itself has been the most important part of my ongoing work.

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The skills needed to take advantage of new technology proliferate and are developed over time through communities of practice that share expertise with each other. Over time, the new skills are routinized and it becomes easier to train lots of people to exercise them. It is at that point that they begin to affect productivity and improve the wages and incomes of large numbers of people.

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Laura Baldwin, president and COO at O’Reilly Media, tells our customers, “You have to go to war with the army you have.”

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The point of a disruptive technology is not the market or the competitors that it destroys. It is the new markets and the new possibilities that it creates.

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1. WORK ON SOMETHING THAT MATTERS TO YOU MORE THAN MONEY.

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Remember that financial success is not the only goal or the only measure of achievement. It’s easy to get caught up in the heady buzz of making money. You should regard money as fuel for what you really want to do, not as a goal in and of itself.

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Money is like gas in the car—you need to pay attention or you’ll end up on the side of the road—but a successful business or a well-lived life is not a tour of gas stations.

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Nick Hanauer likes to say, “Solve the biggest problem you can.” Pursue something so important that even if you fail, the world is better off for you having tried.

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“What we fight with is so small, and when we win, it makes us small. What we want is to be defeated, decisively, by successively greater beings.”

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CREATE MORE VALUE THAN YOU CAPTURE.

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If you’re succeeding at the goal of creating more value than you capture, you may sometimes find that others have made more of your ideas than you have yourself. It’s okay.

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Focusing on solving big problems rather than on making money, and focusing on creating more value than you capture, are closely related principles.

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ASPIRE TO BE BETTER TOMORROW THAN YOU ARE TODAY.

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I’ve always loved the judgment of Kurt Vonnegut’s novel Mother Night: “We are what we pretend to be, so we must be careful about what we pretend to be.”

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Bill Gates once wrote, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.

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Climate change provides us with a modern version of Pascal’s Wager (the argument of the seventeenth-century philosopher and mathematician for acting as though you believe in God even if you don’t). If catastrophic global warming turns out not to happen, the steps we’d take to address it are still worthwhile.

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Whether you’re in business or public policy, don’t settle for rehashing tired solutions. Keep looking for that positive astonishment that means you’ve accomplished something wonderful for the people you serve. Jeff continued: “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight.” Regarding “resisting proxies,” Jeff noted

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that one of the traps that leads to Day 2 is that “you stop looking at outcomes and just make sure you’re doing the process right.”

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We can’t just accept whatever results we get from following old rules; we must constantly measure our actions against their results. And when we see that the results don’t measure up to our dreams, we must rewrite the rules. Jeff also urged his employees to embrace powerful trends in technology and the economy: “If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.”

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First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? . . . Second, most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure. Third, . . . If you have conviction on a particular direction even though there’s no consensus, it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?”

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Even without doing a scenario-planning exercise, asking yourself “What happens if this goes on?” is a great way to prepare for the future—and to spot entrepreneurial opportunities.