Gordon Moore is the cofounder of Intel and in the 1960s he made the incredibly prescient prediction that the amount of computing power one could buy for a dollar would double every year for the next decade. Aside from some quibbling about the exact time period of doubling, his prediction has remained largely true over the past five decades, not just one. This is known as Moore’s Law.

What repeated consistent digital doubling means, exactly, is best demonstrated through a story about an emperor and a chessboard. As legend has it, when chess was invented in sixth century India, the inventor was given an audience with the emperor. When asked to name his prize, the inventor asked for a single grain of rice to be placed on the first square of the chessboard, two to be placed on the second, four on the third, and so on, with the quantity of rice doubling every square. What most people, including the emperor, fail to realize is that if this pattern continues, by the final square the emperor would owe the inventor eighteen quintillion grains of rice, more rice than has been produced in the history of the world.

The chessboard example is a useful tool to think about the dramatic impacts of digital doubling. As the authors write, in the first half of the chessboard we can still imagine the quantities of rice: after 32 squares, the emperor owes 4 billion grains, a number we can conceptualize. It’s in the second half of the chessboard that the quantities quickly become unimaginable. And the authors argue that we’ve recently entered the second half of the chessboard.

The Second Half of the Chessboard

I was thinking about rice and the second half of the chessboard when I read the New York Times article last month The Great AI Awakening, documenting Google’s efforts to improve its Google Translate service. I highly recommend the entire article, but the summary is that things are quickly becoming unimaginable.

The old version of Google Translate relied on computer code written over a decade by hundreds of Google’s smartest engineers, which matched individual phrases in one language to another using a massive database of texts. The system was incredibly complex, but still worked off of algorithms constructed by Google engineers. And while the service was revolutionary, the phrase-by-phrase, rule-based system often produced translations that were clunky and awkward to native speakers.

Enter Google Brain. This elite team at Google created a “neural network” – essentially an effort to replicate the human mind in the digital space – that would learn the way a human does from her earliest years: through trial and error, experiencing successes and failure, identifying patterns, improving on its own. The goal was that Google Translate would no longer be relying on a set of rules written by humans, but that it would go off and learn on its own.

And it did. Over nine-months, the neural network that Google Brain created made advancements in the fluidity of translation that were off the charts, surpassing gains made by the previous system over its entire ten years of development.

This feels like the second half of the chessboard. Through advancements in technology and processing power, really smart people were able to create something in nine months that made the already amazing work of hundreds of other smart people, compiled over ten years, essentially obsolete.

The previous distinction in trying to figure out which jobs would be automated and which were safe was routine vs. complex: routine work, be it cognitive or manual, that could be broken down to a set of rules, would be automated; more complex knowledge work was safe for now.

But what we now see is that the set of rules machines can follow are now much broader, and in some cases they’re writing their own rules, identifying their own patterns, and moving into areas of work we thought were squarely in the human domain.

What does this mean for all of us?

For starters, no one can predict the future. But it seems the best way to prepare for this uncertain future is by getting as much and as broad an education as possible. “Unskilled” work, requiring only a high school degree, is rapidly being automated, and only more automation is promised. And even higher education too narrowly focused could lock one into a set of narrow skills that may soon be automated. Better, it seems, to seek a broad knowledge base, learn how to learn, and develop widely transferable skills in communication, critical thinking, and collaboration that can be applied to any job.

Things are already changing rapidly, and if we are in the second half of the chess board, things are just going to move faster. The education we all need is the one that will allow us to adjust to the changes we can’t yet imagine as we move to the next square.

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My friend’s anxiety over her daughter’s decision to pursue a liberal arts degree should come as no surprise. It has become conventional wisdom among many politicians and pundits that a liberal arts degree is a costly folly. Leaders like President Barack Obama and Florida Governor Rick Scott have disparaged liberal arts degrees as useless – despite the fact that they have provided pricey liberal educations for their own children.

THE SECOND MACHINE AGE

In fact, while opinion leaders pitch the idea that only STEM degrees are a certain path to career stability, futurists have noted that digital technologies will likely make even high-paying STEM careers like computer programming and anesthesiology obsolete. American workers who are best trained for 21^st century career success will be those who know how to adapt to an evolving career landscape by possessing broad, transferable skills.

I’m not arguing that STEM careers are a bad choice. But the skills that make all workers  – even those who choose STEM careers – valuable are boosted, not diminished, by a liberal education. In fact, in their groundbreaking book The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies, authors Erik Brynjolfsson and Andrew McAfee argue that in order to outpace the machines that will replace workers in the future, workers need to gain skills that are uniquely human.  While it has become sport to denigrate a liberal education, futurists and employers have signaled that the skills 21^st job creators cherish most in workers are the very ones that a liberal education provides:  The ability to communicate, think critically, be creative and collaborate.

LESSONS FROM SILICON VALLEY

One need only look to Silicon Valley, where employers are increasingly recruiting liberal arts majors for top jobs. A 2015 Forbes magazine article recounts the career trajectories of the CEO and editorial director for tech juggernaut Slack Technologies, holders of bachelor’s degrees in philosophy and theater, respectively. Far from anomalies, these tech leaders represent the sector’s increasing reliance on leaders who possess the ability to add a human touch to their data-based world.

Knowing this, I gave my friend a 21^st century career pep talk that she could share with friends and family members who disapprove of her daughter’s college ambitions.

“Your daughter is learning how to do a job that can’t be replaced by robots and computers,” I told her.  “You should be proud! Do you think Lin-Manuel Miranda’s parents are embarrassed by his college and career choices?”

My friend felt better after our conversation, and I felt better knowing that I had helped to bolster the support network for a young woman who is using her college experiences to pursue her passion and learn skills that will help her thrive no matter where her career takes her.

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MIT’s Erik Brynjolfsson and Andrew McAfee are doing the cutting edge work on the implications of smarter and smarter machines doing more and more of the work humans use to. For more on their work check out this You Tube video or this interview from Slate entitled Robots are stealing your job: How technology threatens to wipe out the middle class. 

They describe where we are heading as the Great Decoupling. Where all the traditional metrics of economic success do well: GDP, productivity, corporate profits. But workers don’t. Where jobs and wages grow far slower than the traditional growth metrics. A world where technology advances enable companies to do well, but not more and more workers.

New York columnist Ross Douthat tackles the policy implications of an economy that structurally struggles to produce enough jobs, particularly good paying jobs, in a must read column entitled When Work Disappears. Douthat previews the debate about job policy we should be having now––and ultimately will have––for  a world where machines are constantly destroying jobs and occupations.

Douthat lays out the thinking of many economists and technologists who believe the Great Decoupling is the new reality. They are thinking about a world where more and more of us work less and less, if at all. Their policy advice is not to fight that trend but rather to go to some system of guaranteed income for people whether they work or not. Big expansion of government redistribution. There is a sense that trying to keep people employed is a drag on the economy rather than using machines to do the work it can do better and cheaper .

Douthat writes:

In effect, these forays are opening a window into tomorrow’s policy debates, and raising the question of whether 21st century public policy should even really try to slow the decline of workforce participation — as opposed to welcoming that decline as a sign of liberation, and perhaps hastening it for the sake of efficiency as well.

I think it’s important to concede up front the possibility that Wolf and Avent could be right about where we’re going. My doubts about the basic-income prescription notwithstanding, in the event that do we end up with an ever-growing share of the population consisting of what Tyler Cowen calls “zero marginal product” workers, whose continued employment is effectively a drag on productivity relative to the machine alternatives, ideas along those lines could end up being the only plausible policy response.

Douthat explores what he sees as the negative consequence of paying people not to work. Basically the negative consequences––and there are  many––of societies where folks, particularly men, don’t work. He writes:

And while a sufficiently generous basic income would no doubt raise living standards and reduce deprivation at the bottom, the experience of Appalachia, among other blasted social landscapes, suggests that it’s very easy for the absence of work to intertwine with social pathologies in ways that government assistance can’t necessarily ameliorate. The workless society could be a place where the “potential for a more enjoyable life” is available to all … but it could just as easily be a society with more alcoholism, more drug addiction, more obesity, lower lifespans, more social isolation, and less human flourishing overall.

Wow! What a difference in how to construct policy going forward. The debate Douthat previews has the virtue of being anchored in the economy of the future. It starts with the new realities. Rather than trying, as both parties are, to resist or ignore these new realities. Policies designed to create lots of jobs, particularly good paying jobs, anchored in an economy driven by factories and farms can’t work. Globalization and technology have eliminated both as a source of mass middle class employment.

We need a new public conversation/debate about jobs and income in the context of what is possible in an economy that is constantly being reshaped by globalization and technology.

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