Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations

by Thomas L. Friedman

politics. Our democracy can work only if voters know how the world works, so they are able to make intelligent policy choices and are less apt to fall prey to demagogues, ideological zealots, or conspiracy buffs who may be confusing them at best or deliberately misleading them at worst.

Patience wasn’t just the absence of speed. It was space for reflection and thought.” We are generating more information and knowledge than ever today, “but knowledge is only good if you can reflect on it.”

Craig Mundie, a supercomputer designer and former chief of strategy and research at Microsoft, defines this moment in simple physics terms: “The mathematical definition of velocity is the first derivative, and acceleration is the second derivative. So velocity grows or shrinks as a function of acceleration. In the world we are in now, acceleration seems to be increasing. [That means] you don’t just move to a higher speed of change. The rate of change also gets faster … And when the rate of change eventually exceeds the ability to adapt you get ‘dislocation.’ ‘Disruption’ is what happens when someone does something clever that makes you or your company look obsolete. ‘Dislocation’ is when the whole environment is being altered so quickly that everyone starts to feel they can’t keep up.”

Indeed, there is a mismatch between the change in the pace of change and our ability to develop the learning systems, training systems, management systems, social safety nets, and government regulations that would enable citizens to get the most out of these accelerations and cushion their worst impacts.

We go to school for twelve or more years during our childhoods and early adulthoods, and then we’re done. But when the pace of change gets this fast, the only way to retain a lifelong working capacity is to engage in lifelong learning.

There was not a belief in the traditional engineering community that the data had much to offer. They used the data to verify their physics models and then act upon them. The new breed of data scientists here say: ‘You don’t need to understand the physics to look for and find the patterns.’ There are patterns that a human mind could not find, because the signals are so weak early on that you won’t see them. But now that we have all this processing power, those weak signals just pop out at you. And so as you get that weak signal, it now becomes clear that it is an early indication that something is going to break or is becoming inefficient.” In the past, the way we detected weak signals was with intuition, added Ruh. Experienced workers knew how to process weak data. But now, with big data, “with a much finer grain of fidelity, we can make finding the needle in the haystack the norm”—not the exception.

Before, small was fast but irrelevant, and big had economies of scale and of efficiency—but was not agile, explained John Donovan of AT&T. “What if we can now take massive scale and turn it into agility?” he asked. In the past, “with large scale you miss out on agility, personalization, and customization, but big data now allows you all three.” It allows you to go from a million interactions that were impersonal, massive, and unactionable to a million individual solutions,

And it included wavelength division multiplexing, using different colors of light to carry different phone conversations at once—and then combinations of the two.

said Bucksbaum. “The rate of data transfer for an undersea cable today is now trillions of bits per second.” At some point, you end up “bumping up against the laws of physics,” he added, but we are not there yet.

The best way to understand it is to think of telephone wires as a highway, and then imagine that the only cars on this highway were self-driving vehicles controlled by computers, so they could never crash into one another. If that were the case, you could pack so many more cars on that highway, because they could drive bumper-to-bumper at one hundred miles per hour six inches apart.

And just as you can set up a highway with automated cars driving at a hundred miles an hour six inches apart, said Donovan, you can “take the same copper wire designed to carry a two-ringy-dingy voice phone call and make it carry eight streams of video by maximizing how the bits perform. Software adapts and learns. Hardware can’t. So, we blew apart the hardware components, and we forced everyone to think anew. We basically turned the hardware into a commodity and then created a baseline operating system for every router, and called it ONOS, for Open Network Operating System.”

Both CDMA and TDMA, Jacobs explained, worked by sending multiple conversations over a single radio wave. CDMA, however, could also take advantage of natural pauses in the way people speak to allow more conversations simultaneously. This is known as “spread spectrum,” whereby each call is assigned a code that is scrambled over a wide frequency spectrum and then reconstructed at the receiving end, thus allowing multiple users to occupy the same spectrum simultaneously, using very complicated software coding and other techniques.

Hughes scrapped their project with him and let Qualcomm, then an infant start-up, keep the intellectual property and patents they had developed for mobile telephony.

The short story is that Jacobs won and the European GSM/TDMA-based standard lost. They lost because their technology had a finite amount of spectrum and CDMA enabled you to do so much more with the same amount of spectrum—and there was soon so much more to carry, thanks to the Internet. We don’t remember these wars today, but they were bloody.

Today, said Jacobs, “people everywhere in the world have both voice and efficient access to the Internet and that supports education, economic growth, health, and good governance.” One key “reason we won,” he added, “was that even though CDMA was more complicated to implement, people were just thinking about the capacity of chips at that moment in time. They were not taking into account Moore’s law that would allow the technology to improve every two years and enable the greater efficiency that could be achieved with CDMA.”

People say that in hockey you don’t go where the puck is, you go where the puck is going, and Qualcomm went where the puck was going: to Moore’s law, which was on a hockey stick–like curve upward. “Somewhere in the early 2000s when we were trying to expand to India and China,” said Jacobs, “I made the outlandish prediction that one day we would see hundred-dollar phones. Now they’re below thirty dollars in India.”

Paul Jacobs doesn’t mince words: “We made the smartphone revolution.” But Jacobs is quick to add that they were ahead of their time—and behind it. The early device they created was rather clunky: it had none of the easy user interfaces and beautiful design that Steve Jobs’s Apple iPhone would eventually offer in 2007, and it came out before there was the Internet bandwidth to do many things. So Qualcomm

Most people think that they can watch Game of Thrones on their cell phone because Apple came out with a better phone. No, Apple gave you a larger screen and better display, but the reason it is not buffering is because Qualcomm and AT&T and others invested billions of dollars in making the wireless network and phones more efficient.

“The feedback loop is so short now,” explained Iorio, that “in a couple days you can have a concept, the design of the part, you get it made, you get it back and test whether it is valid” and “within a week you have it produced … It is getting us both better performance and speed.”

As a result, the motto in Silicon Valley today is: everything that is analog is now being digitized, everything that is being digitized is now being stored, everything that is being stored is now being analyzed by software on these more powerful computing systems, and all the learning is being immediately applied to make old things work better, to make new things possible, and to do old things in fundamentally new ways.

But if these transformations are real, why is it taking so long for them to show up in the productivity figures, as economists define them—the ratio of the output of goods and services to the labor hours devoted to the production of that output?

writers. The economist Robert Gordon has made a compelling case in his book The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War that the days of steadily rising growth are probably behind us.

And since computing and storage power had exploded in 2007, the capacity to do so was suddenly there. It led IBM to a fundamental insight: “Every time we got rid of a linguist, our accuracy went up,” said Gil. “So now all we use are statistical algorithms” that can compare massive amounts of texts for repeatable patterns. “We have no problem now translating Urdu into Chinese even if no one on our team knows Urdu or Chinese. Now you train through examples.” If you give the computer enough examples of what is right and what is wrong—and in the age of the supernova you can do that to an almost limitless degree—the computer will figure out how to properly weight answers, and learn by doing. And it never has to really learn grammar or Urdu or Chinese—only statistics!

“In the twenty-first century, knowing all the answers won’t distinguish someone’s intelligence—rather, the ability to ask all the right questions will be the mark of true genius.”

During our conversation, Wujec told me his favorite story of just how much the power of technology has transformed his work as a designer-maker. Back in 1995, he recalled, I was a creative director of the Royal Ontario Museum, Canada’s largest museum, and my last big project there before joining the private sector was to bring to life a dinosaur called a Maiasaura.

So, we upgraded. We got a grant for two hundred thousand dollars for software and three hundred forty thousand dollars for hardware.

I took out my iPhone and walked around the model, took twenty or so photographs over maybe ninety seconds, and uploaded them to a free cloud app our company produces called 123D Catch. The app converts photos of just about anything into a 3-D digital model. Four minutes later, it returned this amazing, accurate, animatable, photorealistic digital 3-D model—better than the one we produced twenty years ago. That night, I saw how a half-million dollars of hardware and software and months and months of hard, very technical, specialized work could be largely replaced by an app at a cocktail party with a glass of wine in one hand and a smartphone in the other. In a few minutes I reproduced the digital model for free—except it was better!

entire principality of Liechtenstein for rent on Airbnb (seventy thousand dollars a night), “complete with customized street signs and temporary currency,”

Today, data flows are “exerting a larger impact on growth than traditional goods flows,” McKinsey found.

Hagel, Seely Brown, and Davison elaborated on this theme in a coauthored essay in the January 27, 2009,

India always had a strong tradition of educating people in math, science, and engineering, and America was once the big beneficiary—in the 1950s, 1960s, and 1970s, when global flows were in many countries either nonexistent or barely a trickle,

McNeill explained: Even though there is no perceptible consensus about what the term “civilization” ought to mean, and no agreed word or phrase to describe the “interactive zone” … I think it correct to assert that recognition of the reality and historical importance of trans-civilizational encounters is on the increase and promises to become the mainstream of future work in world history …

On February 23, 2016, the Associated Press carried an interview with Zyad Shehata, an Egyptian student who completed Haick’s course. “Some people told me to remove this certificate from my résumé,” said Shehata. “They said that I might face some problems. I have no interest in whether it is an Israeli university or not, but I’m very proud of Professor Haick and I see him as a leader.”

Translation: Never get between a hungry student and a new flow of knowledge in the age of accelerations.

God always forgives. Man often forgives. Nature never forgives. —Saying

A “black elephant,” it was explained to me by the London-based investor and environmentalist Adam Sweidan, is a cross between a “black swan”—a rare, low-probability, unanticipated event with enormous ramifications—and “the elephant in the room: a problem that is widely visible to everyone, yet that no one wants to address, even though we absolutely know that one day it will have vast, black-swan-like consequences.” “Currently,” Sweidan told me, “there are a herd of environmental black elephants gathering out there”—global warming, deforestation, ocean acidification, and mass biodiversity extinction, just to name four. “When they hit, we’ll claim they were black swans that no one could have predicted, but in fact they are black elephants, very visible right now”—

I loved that line: Too many people, and there would be no rain to speak of. Such is the impact of the power of many. While Moore’s law and globalization have vastly expanded the power of machines and the power of one and the power of flows, the fact that they have also vastly expanded the power of many means that for the first time in the history of both mankind and Planet Earth, mankind has become large enough in numbers and empowered enough by the supernova to be both a force of nature and a forcing function on nature.

As Jeremy Grantham, the well-known global investor, once observed: we humans “are wickedly bad at dealing with the implications of compound math”—another way of saying it’s hard to recognize what a powerful impact we can have on the environment when the Market, Mother Nature, and Moore’s law together all continue accelerating at once in the second half of the chessboard.

Sylvia Earle, the renowned oceanographer, puts it succinctly: “What we do right now, or fail to do, will determine the future—not just for us, but for all life on Earth.”

“Mr. Friedman,” she said, “the car has no blind spots. Almost all the accidents are drivers rear-ending us because they were not paying attention.” This car has no blind spots! I wrote that down in my reporter’s notebook.

I attended college to learn skills for life, and lifelong learning for me afterward was a hobby. My girls went to college to learn the skills that could garner them their first job, and lifelong learning for them is a necessity for every job thereafter.

We all did it as kids—but you do have to walk faster than the escalator, meaning that you need to work harder, regularly reinvent yourself, obtain at least some form of postsecondary education, make sure that you’re engaged in lifelong learning, and play by the new rules while also reinventing some of them. Then you can be in the middle class.

No politician in America will tell you this, but every boss will: You can’t just show up. You need a plan to succeed.

Marina Gorbis, executive director of the Institute for the Future, and that is “the motivational divide.” The future will belong to those who have the self-motivation to take advantage of all the free and cheap tools and flows coming out of the supernova.

During the fifty years after World War II, if the world had a dial on it, that dial was set to the left, and the closer you were to the Soviet Union the more leftward the dial pointed. And what it pointed to was a sign that said “You live in a world of defined benefits: just do your job every day, show up, be average, and here are the benefits you will get.” Since the emergence of the supernova, that dial has whipped sharply to the right, and the sign it points to today says “You live in a world of defined contributions—your wages and benefits will now be more and more directly correlated to your exact contribution, and with big data we will get better and better at measuring just exactly what your contribution is.” It’s a 401(k) world now. To paraphrase an old World War II poster, Uncle Sam wants—to put more on—you.

wealth creation, and opportunity,” argued Byron Auguste, a former economic adviser to President Obama who cofounded Opportunity@Work, a social venture that aims to enable at least one million more Americans to “work, learn, and earn to their full potential” in the next decade. “In the agrarian economy, that asset was land,” Auguste said. “In the industrial economy it was physical capital. In the services economy it was intangible assets, such as methods, designs, software, and patents.

For starters, middle-class jobs are being pulled up faster—they require more knowledge and education to perform successfully. To compete for such jobs you need more of the three Rs—reading, writing, and arithmetic—and more of the four Cs—creativity, collaboration, communication, and coding.

Fortunately, new technology tools will aid this endeavor. The new social contracts we need between government, business, the social sector, and workers will be far more feasible if we find creative ways—to borrow a phrase from Nest Labs’ founder, Tony Fadell—to turn “AI into IA.” In my rendering, that would be to turn artificial intelligence into intelligent assistance, intelligent assistants, and intelligent algorithms.