Learning to Learn

“I didn’t know robots had advanced so far,” a reader remarked after last week’s post about how computers are displacing knowledge workers. What changed to make that happen? The machines learned how to learn. This is from Artificial Intelligence Goes Bilingual—Without A Dictionary, Science Magazine, Nov. 28, 2017.

“Imagine that you give one person lots of Chinese books and lots of Arabic books—none of them overlapping—and the person has to learn to translate Chinese to Arabic. That seems impossible, right?” says… Mikel Artetxe, a computer scientist at the University of the Basque Country (UPV) in San Sebastiàn, Spain. “But we show that a computer can do that.”

Most machine learning—in which neural networks and other computer algorithms learn from experience—is “supervised.” A computer makes a guess, receives the right answer, and adjusts its process accordingly. That works well when teaching a computer to translate between, say, English and French, because many documents exist in both languages. It doesn’t work so well for rare languages, or for popular ones without many parallel texts.

[This learning technique is called] unsupervised machine learning. [A computer using this technique] constructs bilingual dictionaries without the aid of a human teacher telling them when their guesses are right.

Hmmm… I could have used that last year, when my wife and I spent three months visiting our daughter in South Korea. The Korean language is ridiculously complex; I never got much past “good morning.”

Alpha Go match

Go matches were a standard offering on the gym TV’s where I worked out in Seoul. (Imagine two guys in black suits staring intently at a game board — not exactly a riveting workout visual.) Like the Korean language, Go is also ridiculously complex, and mysterious, too:  the masters seem to make moves more intuitively than analytically. But the days of human Go supremacy are over. Google wizard and overall overachiever Sebastian Thrun[1] explains why in this conversation with TED Curator Chris Anderson:

sebastian thrun TED

“Artificial intelligence and machine learning is about 60 years old and has not had a great day in its past until recently. And the reason is that today, we have reached a scale of computing and datasets that was necessary to make machines smart. The new thing now is that computers can find their own rules. So instead of an expert deciphering, step by step, a rule for every contingency, what you do now is you give the computer examples and have it infer its own rules.

“A really good example is AlphaGo. Normally, in game playing, you would really write down all the rules, but in AlphaGo’s case, the system looked over a million games and was able to infer its own rules and then beat the world’s residing Go champion. That is exciting, because it relieves the software engineer of the need of being super smart, and pushes the burden towards the data.

“20 years ago the computers were as big as a cockroach brain. Now they are powerful enough to really emulate specialized human thinking. And then the computers take advantage of the fact that they can look at much more data than people can. AlphaGo looked at more than a million games.  No human expert can ever study a million games. So as a result, the computer can find rules that even people can’t find.”

Thrun made those comments in April 2017. AlphaGo’s championship reign was short-lived:  six months later it lost big to a new cyber challenger that taught itself without reviewing all that data. This is from AlphaGo Zero Shows Machines Can Become Superhuman Without Any Help, MIT Technology Review, October 18, 2017.

AlphaGo wasn’t the best Go player on the planet for very long. A new version of the masterful AI program has emerged, and it’s a monster. In a head-to-head matchup, AlphaGo Zero defeated the original program by 100 games to none.

Whereas the original AlphaGo learned by ingesting data from hundreds of thousands of games played by human experts, AlphaGo Zero started with nothing but a blank board and the rules of the game. It learned simply by playing millions of games against itself, using what it learned in each game to improve.

The new program represents a step forward in the quest to build machines that are truly intelligent. That’s because machines will need to figure out solutions to difficult problems even when there isn’t a large amount of training data to learn from.

“The most striking thing is we don’t need any human data anymore,” says Demis Hassabis, CEO and cofounder of DeepMind [the creators of AlphaGo Zero].

“By not using human data or human expertise, we’ve actually removed the constraints of human knowledge,” says David Silver, the lead researcher at DeepMind and a professor at University College London. “It’s able to create knowledge for itself from first principles.”

Did you catch that? “We’ve removed the constraints of human knowledge.” Wow. No wonder computers are elbowing all those knowledge workers out of the way.

What’s left for human to do? We’ll hear from Sebastian Thrun and others on that topic next time.

[1] Sebastian Thrun’s TED bio describes him as “an educator, entrepreneur and troublemaker. After a long life as a professor at Stanford University, Thrun resigned from tenure to join Google. At Google, he founded Google X, home to self-driving cars and many other moonshot technologies. Thrun also founded Udacity, an online university with worldwide reach, and Kitty Hawk, a ‘flying car’ company. He has authored 11 books, 400 papers, holds 3 doctorates and has won numerous awards.”

The Super Bowl Ad That Was Too True To Be Too Funny

Sprint Super Bowl Ad

Did you see the Sprint Super Bowl ad (click the image), where a scientist gets laughed out of his lab by his impertinent artificially intelligent robots? It was funny, but in that groaning kind of way when humor is just a bit too true. Let’s break down the punchline:  “My coworkers” says the scientist, talking about robots, “laughed at me.” He responds to the robotic peer pressure with the human feeling of shame, and changes his cell phone provider to conform.

Wow. Get used to it. It could happen to you. True, the robots’ sense of humor was pretty immature. He chastises them “Guys, it wasn’t that funny.” But they’ll learn — that’s what artificial intelligence does — it learns, really fast. They’ll be doing sarcasm and irony soon — that is, when they’re not busy passing a university entrance exam, managing an investment portfolio, developing business strategy, practicing medicine. practicing law, writing up your news feeds, creating art, composing music … and generally doing all those other things everybody knew all along that robots surely would never be able to do.

Miami lawyer Luis Salazar used to think that way, until he met Ross. This is from a NY Times article from last March:

“Skeptical at first, he tested Ross against himself. After 10 hours of searching online legal databases, he found a case whose facts nearly mirrored the one he was working on. Ross found that case almost instantly.”

Ross is not a human. “He” never went to law school, never took a legal methods class, never learned to do research, never had a professor or partner critique his legal writing. “He” is machine intelligence. Not only did he find the clincher case in a fraction of the time Salazar did, he also did a nice job of writing up a legal memo:

“Mr. Salazar has been particularly impressed by a legal memo service that Ross is developing. Type in a legal question and Ross replies a day later with a few paragraphs summarizing the answer and a two-page explanatory memo.

“The results, he said, are indistinguishable from a memo written by a lawyer. ‘That blew me away,’ Mr. Salazar said. ‘It’s kind of scary. If it gets better, a lot of people could lose their jobs.’”

Yes, scary — especially when you consider the cost of legal research:  click here and enter “legal research” in the search field. Among other things, you’ll get an article about Ross and another about the cost of legal research. If Ross is that good, he could save a lot of firms a lot of money… and eliminate a lot of jobs along the way. (The Ross Intelligence website is worth a visit — there’s attorney Salazar on video, and an impressive banner of early adopting law firms, with a lot of names you’ll recognize.)

And speaking of things that were never supposed to happen, the NY Times article cites a McKinsey report that, using technology then available, 23 percent of a lawyer’s work could be fully automated. Given the explosion of AI in the past year, we are already way beyond that percentage.

How are you going to compete with that? You’re not. Consider this story from a source we’ve visited several times already (the book Plutocrats by Chrystia Freeland):

“In 2010, DLA Piper faced a court-imposed deadline of searching through 570,000 documents in one week. The firm… hired Clearwell, a Silicon Valley e-discovery company. Clearwell software did the job in two days. DLA Piper lawyers spent one day going through the results. After three days of work, the firm responded to the judge’s order with 3,070 documents. A decade ago, DLA Piper would have employed thirty associates full-time for six months to do that work.”

Note the date:  that happened eight years ago. Today, the whole thing would happen a lot faster, with much less human involvement.

I tried to get a robot to write this blog post, but didn’t succeed. Articoolo,com looked promising:  “Stop wasting your time,” its website trumpets, “let us do the writing for you!” The company is obviously fully in tune with the freelance job market we’ve been talking about:  “You no longer have to wait for someone on the other side of the world to write, proofread and send the content to you.” I tried a few topic entries, but the best it could do was to admit it had written an article but it wasn’t up to standards, so sorry… But then, it’s only available in beta. Give it time to learn.

I also sent an inquiry to the people at Ross Intelligence, asking if Ross could write an article about itself. I never heard back — he’s probably too busy signing up more firms to hire him.

More on robots and artificial intelligence next time.

The Super Bowl of Economics: Capitalism vs. Technology

Flippy

Technology is the odds-on favorite.

In the multi-author collection Does Capitalism Have a Future?, Randall Collins, Emeritus Professor of Sociology at the University of Pennsylvania, observes that capitalism is subject to a “long-term structural weakness,” namely “ the technological displacement of labor by machines.”

Technology eliminating jobs is nothing new. From the end of the 18th Century through the end of the 20th, the Industrial Revolution swept a huge number of manual labor jobs into the dustbin of history. It didn’t happen instantly:  at the turn of the 20th Century, 40% of the USA workforce still worked on the farm. A half century later, that figure was 16%.

I grew up in rural Minnesota, where farm kids did chores before school, town kids baled hay for summer jobs, and everybody watched the weather and asked how the crops were doing. We didn’t know we were a vanishing species. In fact, “learning a trade” so you could “work with your hands” was still a moral and societal virtue. I chose carpentry. It was my first fulltime job after I graduated with a liberal arts degree.

Another half century later, at the start of the 21st Century, less than 2% of the U.S. workforce was still on the farm. In my hometown, our GI fathers beat their swords into plowshares, then my generation moved to the city and melted the plows down into silicon. And now the technological revolution is doing the same thing to mental labor that the Industrial revolution did to manual labor — only it’s doing it way faster, even though most of us aren’t aware that “knowledge workers” are a vanishing species. The following is from The Stupidity Paradox:  The Power and Pitfalls of Functional Stupidity at Work:

“1962… was the year the management thinker Peter Drucker was asked by The New York Times to write about what the economy would look like in 1980. One big change he foresaw was the rise of the new type of employee he called ‘knowledge workers.’

“A few years ago, Steven Sweets and Peter Meiksins decided they wanted to track the changing nature of work in the new knowledge intensive economy. These two US labour sociologists assembled large-scale statistical databases as well as research reports from hundreds of workplaces. What they found surprised them. A new economy full of knowledge workers was nowhere to be found.

“The researchers summarized their unexpected finding this way:  for every well-paid programmer working at a firm like Microsoft, there are three people flipping burgers at a restaurant like McDonald’s. It seems that in the ‘knowledge’ economy, low-level service jobs still dominate.

“A report by the US Bureau of Labor Statistics painted an even bleaker picture. One third of the US workforce was made up of three occupational groups:  office and administrative support, sales and related occupations, and food preparation and related work.”

And now — guess what? — those non-knowledge workers flipping your burgers might not be human. This is from “Robots Will Transform Fast Food” in this month’s The Atlantic:

“According to Michael Chui, a partner at the McKinsey Global Institute, many tasks in the food-service and accommodation industry are exactly the kind that are easily automated. Chui’s latest research estimates that 54 percent of the tasks workers perform in American restaurants and hotels could be automated using currently available technologies—making it the fourth-most-automatable sector in the U.S.

“Robots have arrived in American restaurants and hotels for the same reasons they first arrived on factory floors. The cost of machines, even sophisticated ones, has fallen significantly in recent years, dropping 40 percent since 2005, according to the Boston Consulting Group.

“‘We think we’ve hit the point where labor-wage rates are now making automation of those tasks make a lot more sense,’ Bob Wright, the chief operations officer of Wendy’s, said in a conference call with investors last February, referring to jobs that feature ‘repetitive production tasks.’

“The international chain CaliBurger, for example, will soon install Flippy, a robot that can flip 150 burgers an hour.”

That’s Flippy’s picture at the top of this post. Burger flippers are going the way of farmers — the Flippies of the world are busy eliminating one of the three main occupational groups in the U.S. And again, a lot of us aren’t aware this is going on.

Burger flipping maybe to particularly amenable to automation, but what about other knowledge-based jobs that surely a robot couldn’t do — like, let’s say, writing this column, or managing a corporation, or even… practicing law?

More to come.

Check out Kevin’s latest LinkedIn Pulse article:  Leadership and Life Lessons From an Elite Athlete and a Dying Man.

Brave New (Jobs) World

“The American work environment is rapidly changing.
For better or worse, the days of the conventional full-time job
may be numbered.”

The above quote is from a December 5, 2016 Quartz article that reported the findings of economists Lawrence Katz (Harvard) and Alan Krueger (Princeton, former chairman of the White House Council of Economic Advisers) that 94% of all US jobs created between 2005 to 2015 were temporary, “alternative work” — with the biggest increases coming from freelancers, independent contractors, and contract employees (who work at a business but are paid by an outside firm).

These findings are consistent with what we looked at last time:  how neoliberal economics has eroded institutional support for the conventional notion of working for a living, resulting in a more individuated approach to the job market. Aeon Magazine recently offered an essay on this topic:  The Quitting Economy:  When employees are treated as short-term assets, they reinvent themselves as marketable goods, always ready to quit. Here are some samples:

“In the early 1990s, career advice in the United States changed. A new social philosophy, neoliberalism, was transforming society, including the nature of employment, and career counsellors and business writers had to respond. (Emphasis added.)

“US economic intellectuals raced to implement the ultra-individualist ideals of Friedrich Hayek, Milton Friedman and other members of the Mont Pelerin Society…In doing so… they developed a metaphor – that every person should think of herself as a business, the CEO of Me, Inc. The metaphor took off, and has had profound implications for how workplaces are run, how people understand their jobs, and how they plan careers, which increasingly revolve around quitting.

“The CEO of Me, Inc. is a job-quitter for a good reason – the business world has come to agree with Hayek that market value is the best measure of value. As a consequence, a career means a string of jobs at different companies. So workers respond in kind, thinking about how to shape their career in a world where you can expect so little from employers. In a society where market rules rule, the only way for an employee to know her value is to look for another job and, if she finds one, usually to quit.”

I.e., tooting your own résumé horn is no longer not so much about who you worked for, but what you did while you were there. And once you’re finished, don’t get comfortable, get moving. (This recent Time/Money article offers help for creating your new mobility résumé.)

A couple years ago I blogged here about a new form of law firm entirely staffed by contract attorneys. A quick Google search revealed that the trend toward lawyer “alternative” staffing has been gaining momentum. For example:

This May 26, 2017 Above the Law article reported a robust market for more conventional associate openings and lateral partner hires, but included this caveat:

“The one trend that we see continue to stick is the importance of the personal brand over the law firm brand, and that means that every attorney should really focus on how they differentiate themselves from the pack, regardless of where they hang their shingle.”

Upwork offers “Freelance Lawyer Jobs.” “Looking to hire faster and more affordably?” their website asks. “ Tackle your next Contract Law project with Upwork – the top freelancing website.”

Flexwork offers “Flexible & Telecommuting Attorney Jobs.”

Indeed posts “Remote Contract Attorney Jobs.”

And on it goes. Whether you’re hiring or looking to be hired, you do well to be schooled in the Brave New World of “alternative” jobs. For a further introduction, check out these articles on the “Gig Economy” from Investopedia and McKinsey. For more depth, see:

The Shift:  The Future of Work is Already Here (2011), by Lynda Gratton, Professor of Management Practice at London Business School, where she directs the program “Human Resource Strategy in Transforming Companies.”

Down and Out in the New Economy: How People Find (or Don’t Find) Work Today (2017), by University of Indiana Anthropology Professor LLana Gershon — the author of the Aeon article quoted above.

Next time, we’ll begin looking at three major non-human players in the new job marketplace:  artificial intelligence, big data, and robotics. They’re big, they’re bad, and they’re already elbowing their way into jobs long considered “safe.”

Capitalism on the Fritz Part 2

Post-WWII neoliberal capitalism became a societal institution. Its most rudimentary unit was the concept of working for a living, which meant having a job. Jobs organized life, defined social identities, roles, and virtues, conferred status, supported assumptions about how life worked. Those assumptions held as long as the post-war recovery roared ahead, reinforced by the common human error of assuming happy days weren’t just here again but would continue on indefinitely — especially since we could trace the free market’s roots back a couple hundred years.

But the recovery didn’t keep roaring on. Those days are over — as evidenced by the consensus list of capitalistic fritzes from Rethinking Capitalism we looked at last time. Neoliberal economics met its match when it ran up against modern megatrends such as globalization and disruptive technologies, and when it did, it relinquished its function as a social institution we can rely on. Hence the list of fritzes.

how will capitalism endEconomic sociologist Wolfgang Streeck[i] reviews essentially the same list in his book How Will Capitalism End? (2017), and concludes that, “I suggest that all [of the developments on the list] may be aggregated into a diagnosis of multi-morbidity in which different disorders coexist and, more often than not, reinforce each other.” I.e., neoliberalism’s woes are greater than the sum of its microeconomic parts. Streeck characterizes the result as the “advanced decline of the capacity of capitalism as an economic regime to underwrite a stable society.”

the wealth of humansWhere does that leave us? Ryan Avent — senior editor and economic columnist for The Economist — says the following in his book The Wealth of Humans:  Work, Power, and Status in the Twenty-First Century (2016):

“The remarkable technological progress of the digital age is refracted through industrial institutions in ways that obscure what is causing what. New technologies do contain the potential to revolutionize society and the economy. New firms are appearing which promise to move society along this revolutionary path. And collateral damage, in the form of collapsing firms and sacked workers, is accumulating.

“But the institutions we have available, and which have served us well these last two centuries, are working to take the capital and labour that has been made redundant and reuse it elsewhere. Workers, needing money to live, seek work, and accept pay cuts when they absolutely must. Lower wages make it attractive for firms to use workers at less productive tasks… [and reduce] the incentive to invest in labour-saving technology.

“This process will not end without a dramatic and unexpected shift in the nature of technology, or in the nature of economic institutions.”

As we’ll see in future posts, technology has already moved far enough along that any “dramatic and unexpected shift in the nature of technology” is unlikely to backtrack — instead is far more likely to accelerate the erosion of societal economic norms. As for a shift in “the nature of economic institutions,” there is no replacement economic system waiting in the wings. The result, says Streeck, is that we are entering an “age of entropy,” where we are likely to remain for the foreseeable future.  He describes it as follows:

“Social life in an age of entropy is by necessity individualistic… In the absence of collective institutions, social structures must be devised individually bottom-up, anticipating and accommodating top-down pressures from the markets. Social life consists of individuals building networks of private connections around themselves, as best they can with the means they happen to have at hand. Person-centred relation-making creates lateral social structures that are voluntary and contract-like, which makes them flexible but perishable, requiring continuous networking to keep them together and adjust them on a current basis to changing circumstances. An ideal tool for this are the new social media that produce social structures for individuals, substituting voluntary for obligatory forms of social relations, and networks of users for communities of citizens.”

He’s speaking in general, sociological terms, but his description closely mirrors the realities of the kind of résumé creating, network building, and job seeking that dominate the current world of temporary, part-time, contract labor, which makes up the vast majority of new jobs created in this century. These new jobs are not the same jobs that characterized the former workplace model; working for a living has taken on a whole new meaning. Among other things, we now have what some are calling the “Gig Economy,” the “On-Demand Economy,” or even the “Quitting Economy.”

More on that next time.

[i] Of interest is this December 14, 2017 interview with Prof. Streeck entitled “Farewell, Neoliberalism” on his website.

Capitalism on the Fritz

“In November 2008, as the global financial crash was gathering pace, the 82-year-old British monarch Queen Elizabeth visited the London School of Economics. She was there to open a new building, but she was more interested in the assembled academics. She asked them an innocent but pointed question. Given its extraordinary scale, how as it possible that no one saw it coming?

“The Queen’s question went to the heart of two huge failures. Western capitalism came close to collapsing in 2007-2008 and has still not recovered. And the vast majority of economists had not understood what was happening.”

rethinking capitalismThat’s from the Introduction to Rethinking Capitalism (2016), edited by Michael Jacobs and Mariana Mazzucato.[1] The editors and authors review a catalogue of chronic economic “dysfunction” that they trace to policy-makers’ continued allegiance to neoliberal economic orthodoxy even as it has been breaking down over the past four decades.

Before we get to their dysfunction list, let’s give the other side equal time. First, consider an open letter from Warren Buffett published in Time last week. It begins this way:

“I have good news. First, most American children are going to live far better than their parents did. Second, large gains in the living standards of Americans will continue for many generations to come.”

Mr. Buffett acknowledges that “The market system… has also left many people hopelessly behind,” but assures us that “These devastating side effects can be ameliorated,” observing that “a rich family takes care of all its children, not just those with talents valued by the marketplace.” With this compassionate caveat, he is definitely bullish on America’s economy:

“In the years of growth that certainly lie ahead, I have no doubt that America can both deliver riches to many and a decent life to all. We must not settle for less.”

So, apparently, is our Congress. The new tax law is a virtual pledge of allegiance to the neoliberal economic model. Barring a significant pullback of the law (which seems unlikely), we now have eight years to watch how its assumptions play out.

And now, back to Rethinking Capitalism’s dysfunction’s list (which I’ve seen restated over and over in my research):

  • Production and wages no longer move in tandem — the latter lag behind the former.
  • This has been going on now for several decades,[2] during which living standards (adjusted) for the majority of households have been flat.
  • This is a problem because consumer spending accounts for over 70% of U.S. GDP. What hurts consumers hurts the whole economy.
  • What economic growth there has been is mostly the result of spending fueled by consumer and corporate debt. This is especially true of the post-Great Recession “recovery.”
  • Meanwhile, companies have been increasing production through increased automation — most recently through intelligent machines — which means getting more done with fewer employees.
  • That means the portion of marginal output attributable to human (wage-earner) effort is less, which causes consumer incomes to fall.
  • The job marketplace has responded with new dynamics, featuring a worldwide rise of “non-standard’ work (temporary, part-time, and self-employed).[3]
  • Overall, there has been an increase in the number of lower-paid workers and a rise in intransigent unemployment — especially among young people.
  • Adjusting to these new realities has left traditional wage-earners with feelings of meaninglessness and disempowerment, fueling populist backlash political movements.
  • In the meantime, economic inequality (both wealth and income) has grown to levels not seen since pre-revolution France, the days of the Robber Barons, and the Roaring 20’s.
  • Economic inequality means that the shrinking share of compensation paid out in wages, salaries, bonuses, and benefits has been dramatically skewed toward the top of the earnings scale, with much less (both proportionately and absolutely) going to those at the middle and bottom. [4]
  • Increased wealth doesn’t mean increased consumer spending by the top 20% sufficient to offset lost demand (spending) by the lower 80% of income earners, other than as reflected by consumer debt.
  • Instead, increased wealth at the top end is turned into “rentable” assets — e.g., real estate. intellectual property, and privatized holdings in what used to be the “commons” — which both drives up their value (cost) and the rent derived from them. This creates a “rentier” culture in which lower income earners are increasingly stressed to meet rental rates, and ultimately are driven out of certain markets.
  • Inequality has also created a new working class system, in which a large share of workers are in precarious/uncertain/unsustainable employment and earning circumstances.
  • Inequality has also resulted in limitations on economic opportunity and social mobility — e.g., there is a new kind of “glass floor/glass ceiling” below which the top 20% are unlikely to fall and the bottom 80% are unlikely to rise.
  • In the meantime, the social safety nets that developed during the post-WWII boom (as Buffett’s “rich family” took care of “all its children”) have been largely torn down since the advent of “workfare” in the 80’s and 90’s, leaving those at the bottom and middle more exposed than ever.

The editors of Rethinking Capitalism believe that “These failings are not temporary, they are structural.” That conclusion has led some to believe that people like Warren Buffett are seriously misguided in their continued faith in Western capitalism as a reliable societal institution.

More on that next time.

[1] Michael Jacobs is an environmental economist and political theorist; at the time the book was published, he was a visiting professor at University College of London. Mariana Mazzucato is an economics professor at the University of Sussex.

[2] “In the US, real median household income was barely higher in 2014 than it had been in 1990, though GDP had increased by 78 percent over the same period. Though beginning earlier in the US, this divergence of average incomes from overall economic growth has not become a feature of most advanced economies.”  Rethinking Capitalism

[3] These have accounted for “half the jobs created since the 1990s and 60 per cent since the 2008 crisis.” Rethinking Capitalism

[4] Meanwhile, those at the very top of the income distribution have done exceedingly well… In the US, the incomes of the richest 1 percent rose by 142 per cent between 1980 and 2013 (from an average of $461,910, adjusted for inflation, to $1,119,315) and their share of national income doubled, from 10 to 20 per cent. In the first three years of the recovery after the 2008 crash, an extraordinary 91 per cent of the gains in income went to the richest one-hundredth of the population.” Rethinking Capitalism

My Year in Economics: The New Divide

vw minibus

Fourteen months ago I shared an espresso with one of my daughters in Seoul, at a place called the Minibus Café because of the mint condition classic VW bus parked in front. (As you can see, the Asian version is more mini than the ones we see in the States). We talked about what she’s observed through her expat life as well as what was going on back home, covering topics such as globalization, disruptive technologies, the polarization of opinions and lack of public discourse, the “new economy” and its new paradigm job market, populist pushback political movements… all topics that have found their way into this blog series. I told her someone in her generation — maybe her — ought to go to grad school (probably in London, I guessed) and develop a fresh economic model capable of making sense of this bewildering avalanche of change.

“Maybe you should,” she replied.

And so I did, but minus grad school, London. and the fresh economic model. Instead, over the past year I became an economics autodidact, logging 30+ books and hundreds of online articles on economics, jobs, and technology (I get about 10 a day in my email feeds from all around the world). What I’ve learned hasn’t so much explained the world in economic terms, it has turned that world inside-out and upside-down.

Right away, I encountered two persistent themes, which I’ve mentioned before but will again:

  1. There is a dominant line of economic analysis taking place mostly in the rest of the world (and among sympathizers in U.S. academia) that is absent in the U.S. policy-making debate.

rise of the robots

As Silicon Valley entrepreneur, economics writer, and TED speaker Martin Ford writes in Rise of the Robots:  Technology and the Threat of a Jobless Future (2015), “In the field of economics the opinions all too often break cleanly along predefined political lines. Knowing the ideological predisposition of a particular economist is often a better predictor of what that individual is likely to say than anything contained in the data under examination.”

  1. Economic opinions are as hopelessly politically polarized as about everything else, so that any attempt to inject the worldwide analysis into the domestic conversation gets the instant “talk to the hand” response.

Instead of dialogue and inquiry, there’s a dominant “might makes right” and “if it ain’t broke don’t fix it” mentality among U.S. policymakers — in both government and business — that makes our public discourse (such as it is) either dismissive or blind to insights from the rest of the world.

I’ve come to call this insularity the “New Divide,” for reasons I explain below. It’s not difficult to understand where it comes from:  the USA is unquestionably the world’s dominant economic force, and we citizens, as a whole, are the beneficiaries of the highest personal income and net worth on the planet. In my former estate planning and family business succession law practice, I saw every single day how the free market of neoliberalism had prospered hard-working Americans.

This past year, though, I learned that this wasn’t because all my clients were specially gifted in finance and business (although some probably were), they were also riding a massive thirty-year worldwide economic growth trend. (While visiting my daughter in South Korea, I witnessed its “Miracle on the Han River” firsthand.) Still, although the post-WWII economic surge did indeed lift all economic boats around the world, it especially did so in America, and if you compare averages (always a dodgy business), the best the rest of the prosperous First World can boast is roughly 70% of the average wealth of the average American.

All of which makes it easy for Americans to think the Econ 101 supply and demand version of capitalism we learned in school is doing just fine. (Textbook guru Robert Samuelson said it’s probably the only economics any of us remember.) On the other hand, my reading and research over the past year has confirmed something else I noticed in the last years of my law practice:  the self-made, middle class, rags-to-riches millionaire-next-door has increasingly become an historic icon that shows little prospect of a reprise. Not only that, but economics commentators around the rest of the world rarely agree that our Econ 101 model ain’t broke — or that it’s even fixable.

It’s been like finding out that a friend I never see anymore hasn’t been doing so well, and that’s why:  I feel chagrined, like I might have asked, maybe sought them out. Econ 101 capitalism is a remarkably enduring concept that still gets the majority of air time, but was nowhere to be found in the course of my informal studies. Instead, certain disturbing trends — job dissatisfaction, meaning malaise, spiking suicide rates related to meaning malaise, income and wealth inequality, the newly stratified working class system, meaningless jobs, the breakdown in the historical link between productivity and higher earnings, chronic unemployment among young people — are not only worldwide, they’ve been going on long enough to become systemic and unlikely to self-correct.

Finding out about all of that has often left me with a heightened feeling of angst about the world my kids are growing up in (the same world I’m growing old in) that has sometimes made my year investigating the dismal science of economics feel dismal indeed. In that frame of mind, it seems unlikely the New Divide will be bridged any time soon, if ever, and the more we turn a blind eye and deaf ear to global opinions about economic welfare and functionality, the more likely it is that things can’t end well for us. (It’s been fascinating to see many of these economic themes emerging in the protests in Iran.)

Hence, the New Divide. I borrowed the term from a favorite song by a favorite band, Linkin Park — if you know the band, you know they’ve often expressed their own apocalyptic angst. The song came to mind particularly because of the lyric “give me reason to prove me wrong.” I’ve been looking to be proven wrong about what I’ve been learning, but haven’t found it. Instead, the New Divide and its revelations keep asserting themselves, demanding a fresh reckoning.

And reckon we will in the coming weeks and months of blog posts. Next time we’ll look at a consensus list of how our outlook on capitalism has been failing us, and the week after we’ll look at the mega-reality behind the list. Until then, you might take a moment for the song.

Linkin Park performed New Divide at the release party of — ironically — the movie Transformers. Watching the performance again was made more poignant for me by front man Chester Bennington’s suicide earlier this year. Here’s the video:

Linkin Park Transformers

And here are the lyrics:

I remember black skies
The lightning all around me
I remember each flash
As time began to blur
Like a startling sign
That fate had finally found me
And your voice was all I heard
That I get what I deserve

So give me reason
To prove me wrong
To wash this memory clean
Let the floods cross
The distance in your eyes
Give me reason
To fill this hole
Connect this space between
Let it be enough to reach the truth that lies
Across this new divide

There was nothing inside
The memories left abandoned
There was nowhere to hide
The ashes fell like snow
And the ground caved in
Between where we were standing
And your voice was all I heard
That I get what I deserve

So give me reason
To prove me wrong
To wash this memory clean
Let the floods cross
The distance in your eyes
Across this new divide

In every loss in every lie
In every truth that you deny
And each regret and each goodbye
Was a mistake too great to hide
And your voice was all I heard
That I get what I deserve

So give me reason
To prove me wrong
To wash this memory clean
Let the floods cross
The distance in your eyes
Give me reason
To fill this hole
Connect this space between
Let it be enough to reach the truth that lies
Across this new divide
Across this new divide
Across this new divide