Delivering the American Dream More Reliably

March 30, 2025 By David Griesing Leave a Comment

We hear a lot about the dangers and “hallucinations” of AI as we test-drive our large language models. At the same time, we probably hear too little about how AI is helping us to advance our body of knowledge by processing huge volumes of data in previously unimagined ways. The benefits don’t always outweigh the risks, but sometimes they do—and in an unprecedented fashion.

I’m thinking today about how AI-driven assessments are starting to tell us whether social policy “fixes” that we implement today are actually achieving their intended results instead of speculating about their possible “pay offs” 10 or 20 years later. These new assessments can help us to determine “the returns on our investments” when we attempt to improve our society by (say) providing paternity leave for fathers, multiplying our social connections, or enhancing the stock of affordable housing in vibrant communities.

Artificial intelligence is already enabling us to identify and refine the variables for public policy success beforehand and to keep track of the resulting benefits in something that approaches real time.

I’ve written here several times about how too many of us are failing to achieve the American Dream. Straightforwardly, that’s whether our economy is affording our nation’s children the opportunity to do better economically than their parents over succeeding generations. For Nobel Laureate Edmund Phelps, attaining that American Dream provides a “flourishing” that brings both us and our children psychic benefits (like pride and enhanced self-esteem) as well as greater prosperity and foreward momentum. We calculate the likelihood of its returns in measures of opportunity—from having many opportunites to improve ourselves to having almost none available at all.

Over the past 90 years, for millions in the U.S. (or nearly everyone whose family is not in the top 20 percent income-wise) the quest to attain the American Dream has been disappointing at best, soul-crushing at worst. Our inablity to reliably improve either our fortunes or those of the generations that succeed us unleashes a cascade of unfortunate consequences, such as widespread pessimism about the future, cynicism about our politics, an ever-widening gulf between economic “winners” and “losers,” a rise in “deaths of despair,” and a willingness to gamble on a leader who promises “a new golden age” but never reveals how anyone “who hasn’t gotten there already” will be able to reach it.

_{This is a chart, from a presentation at the Milken Institute by economist and Harvard professor Raj Chetty, includes income measures from tax returns for both parents and their children (both at age 30). Using AI tools, it tracks “the percent of children earning more than their parents” through the mid-1980’s. (The overall percentage has not improved between then and now.) Today, like in 1985, it is “essentially a 50/50 coin flip as to whether you are going to achieve the American Dream.” (A link that will enable a closer view of this chart, along with others included here, is provided below.)}

During the New Deal of the 1930s and Great Society of the 1960s, a raft of social programs was launched to give Americans “who worked hard and were willing to sacrifice for the sake of better tomorrows” greater opportunities to improve their circumstances and live to enjoy “the even greater success” of their children. Unfortunately, our prior attempts to engineer the “economic playing field” so that it delivers the American Dream more reliably have often been little better than “shots in the dark.”

For instance, many New Deal initiatives didn’t succeed until the economic engines of the Second World War kicked in. The “anti-poverty” programs of the Great Society bore fruit in some areas (such as voters’ rights) while causing unexpected consequences in others, like the weakening of low-income families when welfare checks effectively “replaced” fathers’ traditional roles as breadwinners. In those days, policymakers meant well but lacked the assessment tools to know whether their fixes were working until 10 or 20 years out, when they’d sometimes discover that the original problem persisted, or the collateral damage from the policy itself became evident.

Today, new policy-making tools are eliminating much of this guess-work. AI-driven data gathering, experimentation within different communities, and almost “real-time” assessments of progress have begun to transform the ways that new economic policies are developed and implemented. Raj Chetty, the teacher and economist pictured here, is at the forefront of this sea change.

I’m profiling his work today because of the results he, his team and his fellow-travelers in this big-data-driven space are beginning to achieve. But this work also injects a note of optimism into an increasingly pessimistic time. Policy delivery like Chetty’s points towards a future with greater economic promise than the majority of us can see today–when inflation persists, tariffs threaten even higher prices, and government safety nets are dismantled without apparent gains in efficiency. What Chetty calls his “Recipes for Social Mobility” (including his starting point for the chart (above) provide a methodical, evidence-based way to craft, implement and assess the durability of economic policies that could help to deliver the American Dream to millions of anxious families today.

In recent months, Chetty has been doing a kind of “road show” that profiles the early progress of his AI-driven approach. I heard a lecture of his on-line from New Haven three weeks ago, which led me to another talk that he gave during a 2024 conference held at the Milken Center for [yes] Advancing the American Dream in California. The slides and quotations today are from Chetty’s Milken Center presentation and can be given either a listen or a closer look via this link to it on YouTube.

After his first chart about “the fading American Dream,” Chetty presented an interactive U.S. map built upon meticulously assembled data that shows areas in the country where the children of low income parents have “greater” or “lesser” chances at upward social and economic mobility. Essentially, his team gathered income data on 20 million children born in the 1980’s to households earning $27k per year in order to determine how many of those children went on to earn more than their parents—adjusted for inflation—at age 35, localized to the parts of the country where they were living at the time.

_{Chetty’s Geography of Social Mobility chart.}

You’ll notice—somewhat surprisingly—that in this snapshot, kids of low-income parents enjoyed the greatest upward mobility in Dubuque, Iowa while actually losing the most ground compared with their parents in Charlotte, North Carolina over this time frame.

I had some additional reactions (beyond my amazement at the richness of the data painted here). For one thing, if I were on Chetty’s team, I would use colors other than “red” and “blue” to illustrate differences in upward mobility across the U.S. Using this color palette falls too easily (and unnecessarily) into our current Red and Blue state narratives, or exactly the kinds of prejudices that tying communities to actual data are trying to dispel.

While I watched Chetty talk about this slide, I also noticed you can scan a bar code that allows you to examine places that you might be curious about in closer detail (such as where you live) by putting in your zip code when prompted. When I did so, I already suspected that a child’s shot at upward mobility would be relatively low in my Philadelphia neighborhood, but was surprised to learn that it is far higher in many of the central Pennsylvania counties that have long been characterized as “a gun-loving, God-fearing slice of Alabama” between here and Pittsburgh.

While he spoke, Chetty highlighted “the microscopic views and comparisons” that a mapping tool like this allows, particularly when it confounds expectations. He describes, for example, how appalled Charlotte’s civic leaders were when learning about their “worst place finish” in this assessment and how it catalyzed new, similarly data-driven efforts to improve the prospects for that City’s children.

Chetty goes on to juxtapose this chart with an even more interesting one. At first glance one sees its similarities, but its differences are far more intriguing.

_{Contrasting places in the U.S. where there is Economic Opportunity (or Upward Mobility) with places where there are greater or lesser amounts of Economic Connectedness and the kind of Social Capital that it produces.}

The social capital that Chetty illustrates here is the same “commodity” that Bowling Alone’s Bob Putnam has been trying to build throughout his career, as described in my post a couple of weeks ago, “History Suggests that Better Days Could be Coming”. Putnam’s thesis goes like this: if you want to improve your community, state or nation, that drive begins by strengthening your in-person social connections, thereby increasing “the social capital” that’s available for spending when connected individuals wish to solve a problem or better their community’s circumstances.

At it’s simplest, Chetty’s comparison chart shows those places in America where people from different socio-economic backgrounds are more connected to one another, less connected and where there are greater or lesser accumulations of social capital as a result.

Chetty once again reminds us that localizing massive data sets in this manner allows those using these tools to dive even deeper into neighborhood, or even into street-by-street variations in both upward mobility and social capital.

In his “economic connectedness” map, social capital acrues from the amount of “cross-class interaction” that occurs between high and low income people in each county, town and neighborhood in the U.S. This relationship is key because Chetty’s team had already established that “the single strongest predictor of your chances of rising up is how connected you [or those most in need of “upward mobility”] are to higher income folks,” as opposed to living in a place where nearly everyone is on the same rung of the economic ladder.

To compile this chart, Chetty collaborated with Mark Zuckerberg and Facebook’s “core data science team” to access the voluminous data the social network has gathered on the 72 million Americans who use the platform. He wanted to identify low-income users and determine how many “above median income friends” each one of them has, before breaking that aggregate snapshot down with his powerful mapping tool.

Connections across income classes produce opportunities “like getting a job referral, or an internship.” But Chetty also identified an “aspirational” component when members of different economic classes interact with one another on a regualr basis.

If you’ve never met somebody who went to college, you don’t think about that as a possibility for you. If you’re in a community where you’ve seen more people succeed in certain career pathways, that can change kid’s lives…

Once again, a few of my reactions to the comparisons these big-data snapshots invite.

_{A detailed view of the mid-Atlantic in general, and Philadelphia in particular, on Chetty’s mapping of Economic Connectedness.}

Despite Philadelphia’s “relatively weak” score on upward mobility, I was also not surprised that my part of the state ranks as “relatively strong” (or a medium shade of blue) when it comes to the social capital that’s produced by our economic connectedness. Among many other things, that means those of us in Southeastern Pennsylvania already have a relatively-strong foundation for driving greater upward mobility, along with more helpful data about our localized advantages and challenges as we dig deeper into our particular blocks on this map.

On the other hand, I found the social policy solution that Chetty profiled in his talk somewhat disappointing, although it seemed to me that the experimental template that gave rise to it would be a serviceable-enough incubator for additional policies going forward.

He describes at length a test study his team initiated in Seattle involving low income households with subsidized (formerly Title 8) housing vouchers. Their first discovery was that most voucher holders try to use them in their own communities, with little or no gain in economic connectedness. They then realized that while “real-estate brokers” are commonly used for finding places to live in higher income communities, their eqivalent is non-existent for those who want to get “the most bang for the buck” out of the $2500 credit in one of these housing vouchers.

Chetty’s team concluded that if a sponsor (e.g. a local government, for-profit or non-profit) wanted to build social capital for low-income households, it could spend what amounted to 2% of the value of each voucher to hire “brokers” to help low-income residents find housing in communities with greater economic connectedness than the uniformly impoverished neighborhoods where most of them lived.

This solution was affordable and it quickly built social capital for low income individuals, but even under the best of circumstances it is unlikely to impact enough households because of the limited amounts of affordable housing in most higher income communities, a fact that Chetty readily admits:

I don’t want to give the impression that I think the desegregation approach, moving people to different areas, is the only thing we should do. Obviously, that’s not going to be a scalable approach in and of itself.

But this demonstration of how to engineer a social policy illustrates the potential for modeling and testing reforms that can attract “smarter, evidence-driven investments” as mapping tools like these are refined and used by more policy makers.

Chetty’s Seattle experiment also puts a spotlight on social programs that increase economic connectedness. While the parents who were able to move from low income communities to mixed income neighborhoods surely had an opportunity to realize gains in social capital, it’s their children who stood to benefit the most from more diverse schools, better playgrounds and exposure to career options they might never have considered before.

What motivates Chetty, his team and his hosts at the Milken Institute the most are the opportunities that these AI-driven, data-rich tools will be presenting in the very near future to the millions who are pursuing the American Dream but failing to achieve it.

Twenty years ago, a civil rights organization that sought to open pathways towards college and upward mobility had, as its memorable motto: “A mind is a terrible thing to waste.”

With a conclusion as obvious as that in mind, I’ll give Raj Chetty’s final presentation slide some of the last words here about assets that we’ve been wasting for far too long.

_{The box reads: “If women, minorities, and children from low income families invent at the same rate as high-income white men, the innovation rate in America wouild quadruple.“}

I guess I would prefer to make this slide more powerful still.

It’s true that we’re wasting many of our most valuable people-assets in the US. today, but “delivering the American Dream more reliably” is not the legacy of “high-income white men.” First off, many of our most successful innovators today aren’t “white” but are people of color, immigrants and their descendants (like Chetty himself). Moreover, this is an 80%-of-America size problem (or everyone who’s NOT in the top 20% income-wise) not a burden that’s only carried by previously marginalized communities. I believe that Chetty’s ground-breaking work will attract the base of support that it deserves if slides like this are imodified to reflect the true magnitude of our Lost Einsteins. So I don’t know how Chetty’s team quantified the “lost opportunities” highlighted here “as quadruple” the number of our current innovators, but I’d wager that’s an undercount.

+ + +

For those who are interested, I’ve written about our frustrated pursuit of the American Dream several times before. These posts include:

“The Great Resignation is an Exercise in Frustration and Futility” (citing data that government management of the economy has caused our middle and lower classes to realize essentially the same income due to government transfer payments, arguing that perverse incentives such as “these redistributions of wealth also stifle upward mobility”);
“Let’s Revitalize the American Dream” (citing a 2015 study that found the U.S. ranks “among the lowest of all developed countries in terms of the potential for upward mobility despite clinging to the mythology of Horatio Alger”); and
“America Needs a Rebranding Campaign” (If “equality of opportunity” is really our touchstone as a nation, then it “needs to infuse every brand touchpoint” of ours, including our “packaging, public relations, advertising, services, partnerships, social responsibility, HR & recruitment, loyalty programs, events & activations, user experience, sourcing & standards, and product portfolio.” In other words, America needs “to start walking the equality-of-opportunity walk,” instead of just talking about it.)

This post was adapted from my March 9, 2025 newsletter. Newsletters are delivered to subscribers’ in-boxes every Sunday morning, and sometimes I post the content from one of them here in lightly edited form. You can subscribe by leaving your email address in the column to the right.

Nostalgia Can Help Us Build a Better Future

October 29, 2019 By David Griesing Leave a Comment

There is a widespread consensus that we’re on the cusp of a workplace revolution that will automate millions of jobs and replace millions of workers.

Among the many questions is whether these displaced workers will still be able to support themselves because technologies that are on the rise, like augmented and artificial intelligence, will spawn millions of new jobs and a new prosperity.

Those fearing that far more jobs will be eliminated than created have argued for fixes like a universal basic income that would place a minimum financial floor under every adult while ensuring that society doesn’t dissolve into chaos. How this safety net would be paid for and administered has always been far less clear in these proposals.

Others are arguing that the automation revolution will usher in a new era of flourishing, with some new jobs maintaining and safeguarding the new automated systems, and many others that we can’t even imagine yet. However, these new programming and maintainence jobs won’t be plentiful enough to replace the “manual” jobs that will be lost in our offices, factories and transportation systems. Other “replacement jobs” might also be scarce. In a post last January, I cited John Hagel’s argument that most new jobs will bunch towards the innovative, the most highly skilled, what he called “the scaling edge” of the job spectrum.

On the other hand, analysts who have considered the automation revolution at McKinsey Global Institute noted in a July, 2019 report that automation will also produce a burst of productivity and profitability within companies, that employees will be able to work more efficiently and reduce their time working (5-hour days or 4- day work weeks) while gaining more leisure time. With more routine tasks being automated, McKinsey estimates that the growing need to customize products and services for consumers with more time on their hands will create new companies and an avalanche of new jobs to serve them. At the same time, demands for more customization of existing products and services will create new jobs that require “people skills” in offices and on factory floors.

As we stand here today, it is difficult to know whether we should share Hagel’s concern or McKinsey’s optimism.

Predicting the likely impacts at the beginning of a workplace revolution is hardly an exact science. To the extent that history is a teacher, those with less education, fewer high-level skills and difficulties adapting to changing circumstances will be harmed the most. Far less certain are the impacts on the rest of us, whose education, skill levels and adaptability are greater but who may be less comfortable at the “scaling” edges of our industries.

Then there’s the brighter side. Will we be paid the same (or more) as we are today given the greater efficiency and productivity that automation will provide?  Will we work less but still have enough disposable income to support all of the new companies and workers who eager to serve our leisure time pursuits?  Maybe.

It is also possible to imagine scenarios where millions of people lose their livelihoods and government programs becomes “the last resort” to maintain living standards. Will vast new bureaucracies administer the social safety nets that will be required? Will the taxes on an increasingly productive business sector (with their slimmed down payrolls) be enough to support these programs? Will those who want to work have sufficient opportunities for re-training to fill the new jobs that are created?  And even more fundamentally, will we be able to accommodate the shift from free enterprise to something that looks a lot more like a welfare state?

While most of us have been dominated by the daily tremors and upheavals in politics, there are also daily tremors and upheavals that are changing how we work and even whether we’ll be able to work for “a livable wage” if we want to.

As I argued recently in The Next Crisis Will Be a Terrible Thing to Waste, the chance to realize your priorities improve significantly during times of disruption as long as you’re clear about your objectives and have done some tactical planning in advance. As you know, I also believe in the confidence that comes with hope OR that you can change things for the better if you believe enough in the future that you’re ready to act on its behalf.

Beyond finding and continuing to do “good work” in this new economy, I listed my key priorities in that post: policies that support thriving workers, families and communities and not just successful companies; jobs that assume greater environmental stewardship as essential to their productivity; and expanding the notion of what it means for a company “to be profitable” for all of its stakeholders.

From this morning’s perspective—and assuming that the future of work holds at least as much opportunity as misfortune—I’ve been not only thinking about those priorities but also about things I miss today that seemed to exist in the past. In other words, a period of rapid change like this is also a time for what Harvard’s Svetlana Boym once called “reflective nostalgia.” The question is how this singular mindset can fuel our passion for the objectives we want—motivate us to take more risks for the sake of change—in the turbulent days ahead.

Nostalgia isn’t about specific memories. Instead, it’s about a sense of loss, an emptiness today that you feel had once been filled in your life or work.

Unlike the kind of nostalgia that attempts to recreate a lost world from the ruins of the past, reflective nostalgia acknowledges your loss but also the impossibility of your ever recovering that former time. By establishing a healthy distance from an idealized past, reflective nostalgia liberates you to find new ways to gain something that you still need in the very different circumstances of the future that you want.

Because the urge to fill unsatisfied needs is a powerful motivator, I’ve been thinking about needs of mine that once were met, aren’t being met today, but could be satisfied again “if I always keep them in mind” while pursuing my priorities in the future. As you mull over my short list of “nostalgias” and think about yours, please feel free to drop me a line about losses you’d like to recoup in a world that’s on the cusp of reinvention.

MY SHORT LIST OF LOSSES:

– I miss a time when strangers (from marketers to the government) knew less about my susceptabilities and hot buttons. Today, given the on-line breadcrumbs I leave in my wake, strangers can track me, discover dimensions of my life that once were mine alone, and use that information to influence my decisions or just look over my shoulder. Re-building and protecting my private space is at the core of my ability to thrive.

I want to own my personal data, to sell it or not as I choose, instead of having it taken from me whenever I’m on-line or face a surveillance camera in a public space. I want a right to privacy that’s created by law, shielded from technology and protected by the authorities. The rapid advance of artificial intelligence at work and outside of it gives the creation of this right particular urgency as the world shifts and the boundaries around life and work are re-drawn.

– I miss a time when I didn’t think my organized world would fall apart if my technology failed, my battery went dead, the electricity was cut off or the internet was no longer available. I miss my self-reliance and resent my dependency on machines.

If I do have “more free time” in the future of work, I’ll push for more tech that I can fix when it breaks down and more resources that can help me to do so. I’ll advocate for more “fail-safe” back-up systems to reduce my vulnerability when my tech goes down. There is also the matter of my autonomy. I need to have greater understanding and control over the limits and possibilities of the tech tools that I use everyday because, to some degree, I am already a prisoner of my incompetence as one recent article puts it.

One possibility is that turning over [more] decisions and actions to an AI assistant creates a “nanny world” that makes us less and less able to act on our own. It’s what one writer has called the ‘Jeeves effect’ after the P.G. Wodehouse butler character who is so capable that Bertie Wooster, his employer, can get by being completely incompetent.

My real-life analogy is this. Even though I’ve had access to a calculator for most of my life, it’s still valuable for me to know how to add, subtract, multiply and divide without one. As tech moves farther beyond my ability to understand it or perform its critical functions manually, I need to maintain (or recover) more of that capability. Related to my first nostalgia, I’d meet this need by actively seeking “a healthier relationship” with my technology in my future jobs.

– I remember a time when I was not afraid that my lifestyle and consumption patterns were helping to degrade the world around me faster than the world’s ability to repair itself. At the same time, I know today that my absence of concern during much of my work life had more to do with my ignorance than the maintenance of a truly healthy balance between what nature was giving and humankind (including me) was taking.

As a result, I need greater confidence that my part in restoring that balance is a core requirement of any jobs that I’ll do in the future. With my sense of loss in mind, I can encourage more sustainable ways to work (and live) to evolve.

-Finally, I miss a time when a company’s success included caring for the welfare of workers, families and communities instead of merely its shareholders’ profits, a model that was not uncommon from the end of World War II through the 1970s. I miss a time, not so long ago, when workers bargained collectively and successfully for their rights and benefits on the job. I miss a time when good jobs with adequate pay and benefits along with safe working conditions were protected by carefully crafted trade protections instead of being easily eliminated as “too expensive” or “inefficient.”

While this post-War period can never be recovered, a leading group of corporate executives (The Business Roundtable) recently committed their companies to serving not only their shareholders but also their other “stakeholders,” including their employees and the communities where they’re located. As millions of jobs are lost to automation and new jobs are created in the disruption that follows, I’ll have multiple opportunities as a part of “this new economy workforce” to challenge companies I work for (and with) to embrace the broader standard of profitability that I miss.

+ + +

Instead of being mired in the past, reflective nostalgia provides the freedom to seek opportunities to fill real needs that have never gone away. With this motivating mindset, the future of work won’t just happen to me. It becomes a set of possibilities that I can actually shape.

This post was adapted from my October 27, 2019 newsletter. When you subscribe, a new newsletter/post will be delivered to your inbox every Sunday morning.

Citizens Will Decide What’s Important in Smart Cities

July 8, 2019 By David Griesing Leave a Comment

The norms that dictate the acceptable use of artificial intelligence in technology are in flux. That’s partly because the AI-enabled, personal data gathering by companies like Google, Facebook and Amazon has caused a spirited debate about the right of privacy that individuals have over their personal information. With your “behavioral” data, the tech giants can target you with specific products, influence your political views, manipulate you into spending more time on their platforms, and weaken the control that you have over your own decision-making.

In most of the debate about the harms of these platforms thus far, our privacy rights have been poorly understood. In fact, our anything-but-clear commitments to the integrity of our personal information have enabled these tech giants to overwhelm our initial, instinctive caution as they seduced us into believing that “free” searches, social networks or next day deliveries might be worth giving them our personal data in return. Moreover, what alternatives did we have to the exchange they were offering?

Where were the privacy-protecting search engines, social networks and on-line shopping hubs?
Moreover, once we got hooked on to these data-sucking platforms, wasn’t it already too late to “put the ketchup back in the bottle” where our private information was concerned? Don’t these companies (and the data brokers that enrich them) already have everything that they need to know about us?

Overwhelmed by the draw of  “free” services from these tech giants, we never bothered to define the scope of the privacy rights that we relinquished when we accepted their “terms of service.”  Now, several years into this brave new world of surveillance and manipulation, many feel that it’s already too late to do anything, and even if it weren’t, we are hardly willing to relinquish the advantages of these platforms when they are unavailable elsewhere.

So is there really “no way out”?

A rising crescendo of voices is gradually finding a way, and they are coming at it from several different directions.

In places like Toronto (London, Helsinki, Chicago and Barcelona) policy makers and citizens alike are defining the norms around personal data privacy at the same time that they’re grappling with the potential fallout of similar data-tracking, analyzing and decision-making technologies in smart-city initiatives.

Our first stop today is to eavesdrop on how these cities are grappling with both the advantages and harms of smart-city technologies, and how we’re all learning—from the host of scenarios they’re considering—why it makes sense to shield our personal data from those who seek to profit from it. The rising debate around smart-city initiatives is giving us new perspectives on how surveillance-based technologies are likely to impact our daily lives and work. As the risks to our privacy are played out in new, easy-to-imagine contexts, more of us will become more willing to protect our personal information from those who could turn it against us in the future.

How and why norms change (and even explode) during civic conversations like this is a topic that Cass Sunstein explores in his new book How Change Happens. Sunstein considers the personal impacts when norms involving issues like data privacy are in flux, and the role that understanding other people’s priorities always seems to play. Some of his conclusions are also discussed below. As “dataveillance” is increasingly challenged and we contextualize our privacy interests even further, the smart-city debate is likely to usher in a more durable norm regarding data privacy while, at the same time, allowing us to realize the benefits of AI-driven technologies that can improve urban efficiency, convenience and quality of life.

With the growing certainty that our personal privacy rights are worth protecting, it is perhaps no coincidence that there are new companies on the horizon that promise to provide access to the on-line services we’ve come to expect without our having to pay an unacceptable price for them.  Next week, I’ll be sharing perhaps the most promising of these new business models with you as we begin to imagine a future that safeguards instead of exploits our personal information.

1. Smart-City Debates Are Telling Us Why Our Personal Data Needs Protecting

Over the past 6 months, I’ve talked repeatedly about smart-city technologies and one of you reached out to me this week wondering: “What (exactly) are these new “technologies”?” (Thanks for your question, George!).

As a general matter, smart-city technologies gather and analyze information about how a city functions, while improving urban decision-making around that new information. Throughout, these data-gathering, analyzing, and decision-making processes rely on artificial intelligence. In his recent article “What Would It Take to Help Cities Innovate Responsibly With AI?” Eddie Copeland begins by describing the many useful things that AI enables us to do in this context:

AI can codify [a] best practice and roll it out at scale, remove human bias, enable evidence-based decision making in the field, spot patterns that humans can’t see, optimise systems too complex for humans to model, quickly digest and interpret vast quantities of data and automate demanding cognitive activities.

In other words, in a broad range of urban contexts, a smart-city system with AI capabilities can make progressively better decisions about nearly every aspect of a city’s operations by gaining an increasingly refined understanding of how its citizens use the city and are, in turn, served by its managers.

Of course, the potential benefits of greater or more equitable access to city services as well as their optimized delivery are enormous. Despite some of the current hew and cry, a smart-cities future does not have to resemble Big Brother. Instead, it could liberate time and money that’s currently being wasted, permitting their reinvestment into areas that produce a wider variety of benefits to citizens at every level of government.

Over the past weeks and months, I’ve been extolling the optimism that drove Toronto to launch its smart-cities initiative called Quayside and how its debate has entered a stormy patch more recently. Amidst the finger pointing among Google affiliate Sidewalk Labs, government leaders and civil rights advocates, Sidewalk (which is providing the AI-driven tech interface) has consistently stated that no citizen-specific data it collects will be sold, but the devil (as they say) remains in the as-yet to be disclosed details. This is from a statement the company issued in April:

Sidewalk Labs is strongly committed to the protection and privacy of urban data. In fact, we’ve been clear in our belief that decisions about the collection and use of urban data should be up to an independent data trust, which we are proposing for the Quayside project. This organization would be run by an independent third party in partnership with the government and ensure urban data is only used in ways that benefit the community, protect privacy, and spur innovation and investment. This independent body would have full oversight over Quayside. Sidewalk Labs fully supports a robust and healthy discussion regarding privacy, data ownership, and governance. But this debate must be rooted in fact, not fiction and fear-mongering.

As a result of experiences like Toronto’s (and many others, where a new technology is introduced to unsuspecting users), I argued in last week’s post for longer “public ventilation periods” to understand the risks as well as rewards before potentially transformative products are launched and actually used by the public.

In the meantime, other cities have also been engaging their citizens in just this kind of information-sharing and debate. Last week, a piece in the New York Times elaborated on citizen-oriented initiatives in Chicago and Barcelona after noting that:

[t]he way to create cities that everyone can traverse without fear of surveillance and exploitation is to democratize the development and control of smart city technology.

While Chicago was developing a project to install hundreds of sensors throughout the city to track air quality, traffic and temperature, it also held public meetings and released policy drafts to promote a City-wide discussion on how to protect personal privacy. According to the Times, this exchange shaped policies that reduced, among other things, the amount of footage that monitoring cameras retained. For its part, Barcelona has modified its municipal procurement contracts with smart cities technology vendors to announce its intentions up front about the public’s ownership and control of personal data.

Earlier this year, London and Helsinki announced a collaboration that would enable them to share “best practices and expertise” as they develop their own smart-city systems. A statement by one driver of this collaboration, Smart London, provides the rationale for a robust public exchange:

The successful application of AI in cities relies on the confidence of the citizens it serves.

Decisions made by city governments will often be weightier than those in the consumer sphere, and the consequences of those decisions will often have a deep impact on citizens’ lives.

Fundamentally, cities operate under a democratic mandate, so the use of technology in public services should operate under the same principles of accountability, transparency and citizens’ rights and safety — just as in other work we do.

To create “an ethical framework for public servants and [a] line-of-sight for the city leaders,” Smart London proposed that citizens, subject matter experts, and civic leaders should all ask and vigorously debate the answers to the following 10 questions:

Objective– why is the AI needed and what outcomes is it intended to enable?
Use– in what processes and circumstances is the AI appropriate to be used?
Impacts– what impacts, good and bad, could the use of AI have on people?
Assumptions– what assumptions is the AI based on, and what are their iterations and potential biases?
Data– what data is/was the AI trained on and what are their iterations and potential biases?
Inputs– what new data does the AI use when making decisions?
Mitigation– what actions have been taken to regulate the negative impacts that could result from the AI’s limitations and potential biases?
Ethics– what assessment has been made of the ethics of using this AI? In other words, does the AI serve important, citizen-driven needs as we currently understand those priorities?
Oversight– what human judgment is needed before acting on the AI’s output and who is responsible for ensuring its proper use?
Evaluation– how and by what criteria will the effectiveness of the AI in this smart-city system be assessed and by whom?

As stakeholders debate these questions and answers, smart-city technologies with broad-based support will be implemented while citizens gain a greater appreciation of the privacy boundaries they are protecting.

Eddie Copeland, who described the advantages of smart-city technology above, also urges that steps beyond a city-wide Q&A be undertaken to increase the awareness of what’s at stake and enlist the public’s engagement in the monitoring of these systems. He argues that democratic methods or processes need to be established to determine whether AI-related approaches are likely to solve a specific problem a city faces; that the right people need to be assembled and involved in the decision-making regarding all smart-city systems; and that this group needs to develop and apply new skills, attitudes and mind-sets to ensure that these technologies maintain their citizen-oriented focus.

As I argued last week, the initial ventilation process takes a long, hard time. Moreover, it is difficult (and maybe impossible) to conduct if negotiations with the technology vendor are on-going or that vendor is “on the clock.”

Democracy should have the space and time to be a proactive instead of reactive whenever transformational tech-driven opportunities are presented to the public.

2. A Community’s Conversation Helps Norms to Evolve, One Citizen at a Time

I started this post with the observation that many (if not most) of us initially felt that it was acceptable to trade access to our personal data if the companies that wanted it were providing platforms that offered new kinds of enjoyment or convenience. Many still think it’s an acceptable trade. But over the past several years, as privacy advocates have become more vocal, leading jurisdictions have begun to enact data-privacy laws, and Facebook has been criticized for enabling Russian interference in the 2016 election and the genocide in Myanmar, how we view this trade-off has begun to change.

In a chapter of his new book How Change Happens, legal scholar Cass Sunstein argues that these kinds of widely-seen developments:

can have a crucial and even transformative signaling effect, offering people information about what others think. If people hear the signal, norms may shift, because people are influenced by what they think other people think.

Sunstein describes what happens next as an “unleashing” process where people who never formed a full-blown preference on an issue like “personal data privacy (or were simply reluctant to express it because the trade-offs for “free” platforms seemed acceptable to everybody else), now become more comfortable giving voice to their original qualms. In support, he cites a remarkable study about how a norm that gave Saudi Arabian husbands decision-making power over their wives’ work-lives suddenly began to change when actual preferences became more widely known.

In that country, there remains a custom of “guardianship,” by which husbands are allowed to have the final word on whether their wives work outside the home. The overwhelming majority of young married men are privately in favor of female labor force participation. But those men are profoundly mistaken about the social norm; they think that other, similar men do not want women to join the labor force. When researchers randomly corrected those young men’s beliefs about what other young men believed, they became far more willing to let their wives work. The result was a significant impact on what women actually did. A full four months after the intervention, the wives of men in the experiment were more likely to have applied and interviewed for a job.

When more people either speak up about their preferences or are told that others’ inclinations are similar to theirs, the prevailing norm begins to change.

A robust, democratic process that debates the advantages and risks of AI-driven, smart city technologies will likely have the same change-inducing effect. The prevailing norm that finds it acceptable to exchange our behavioral data for “free” tech platforms will no longer be as acceptable as it once was. The more we ask the right questions about smart-city technologies and the longer we grapple as communities with the acceptable answers, the faster the prevailing norm governing personal data privacy will evolve.

Our good work of citizens is to become more knowledgeable about the issues and to champion what is important to us in dialogue with the people who live and work along side of us. More grounds for protecting our personal information are coming out of the smart-cities debate and we are already deciding where new privacy lines should be drawn around us.

This post was adapted from my July 7, 2019 newsletter. When you subscribe, a new newsletter/post will be delivered to your inbox every Sunday morning.

New Starting Blocks for the Future of Work

March 10, 2019 By David Griesing Leave a Comment

As a challenging future rushes towards us, I often wonder whether our democratic values will continue to provide a sound enough foundation for our lives and work.

In many ways, this “white-water world” is already here. As framed by John Seely Brown in a post last summer, it confronts us with knowledge that’s simply “too big to know” and a globe-spanning web of interconnections that seems to constantly alter what’s in front of us, like the shifting views of a kaleidoscope.

It’s a brave new world that:

– makes a fool out of the concept of mastery in all areas except our ability–or inability–to navigate [its] turbulent waters successfully;

– requires that we work in more playful and less pre-determined ways in an effort to keep up with the pace of change and harness it for a good purpose;

– demands workplaces where the process of learning allows the tinkerer in all of us “to feel safe” from getting it wrong until we begin to get it right;

– calls on us to treat technology as a toolbox for serving human needs as opposed to the needs of states and corporations alone; and finally,

– requires us to set aside time for reflection “outside of the flux” so that we can consider the right and wrong of where we’re headed, commit to what we value, and return to declare those values in the rough and tumble of our work tomorrow.

In the face of these demands, the most straightforward question is whether we will be able to safeguard our personal wellbeing and continue to enjoy a prosperous way of life. Unfortunately, neither of these objectives seems as readily attainable as they once did.

When our democratic values (such as freedom and championing individual rights) no longer ensure our wellbeing and prosperity, those values get questioned and eventually challenged in our politics.

Last week, I wrote here about the dangerous risks—like addiction and behavioral modification—that our kids and others confront by spending too much screen time playing on-line games like Fortnite. Despite a crescendo of anecdotal evidence about the harms to boys in particular, the freedom-loving (and endlessly distracted) West seems stymied when it comes to deciding what to do about it. On the other hand, China easily moved from identifying the harm to its collective wellbeing to implementing time restrictions on the amount of on-line play. It was the Great Firewall’s ability to intervene quickly that prompted one observer to wonder how those of us in the so-called “first world” will respond to “the spectacle of a civilisation founded [like China’s] on a very different package of values — but one that can legitimately claim to promote human flourishing more vigorously than their own”?

Meanwhile, in a Wall Street Journal essay last weekend, its authors documented the ability of authoritarian countries with capitalist economies to raise the level of prosperity enjoyed by their citizens in recent years. Not so long ago, the allure of West to the “second” and “third worlds” was that prosperity seemed to go hand-in-hand with democratic values and institutions. That conclusion is far less clear today. With rising prosperity in authoritarian nations like China and Vietnam—and the likelihood that there will soon be far more prosperous citizens in these countries than outside of them—the authors fretted that:

It isn’t clear how well democracy, without every material advantage on its side, will fare in the competition [between our very different value systems.]

With growing uncertainty about whether Western values and institutions can produce sufficient benefits for its citizens, and with “the white-water world” where we live and work challenging our navigational skills, it seems a good time to return to some questions that we’ve chewed on here before about “how we can best get ready for the challenges ahead of us.”

Can the ways that we educate our kids (and retrain ourselves) enable us to proclaim our humanity, secure our self-worth, and continue to find a valued place for ourselves in the increasingly complex world of work?

Can championing new teaching methods strengthen democratic values and deliver more of their promise to us in terms of wellbeing and prosperity than it seems we can count on today?

Are new and different classrooms the keys to our futures?

1. You Treasure What You Measure

Until this week, I never considered that widely administered education tests would provide any of these answers—but I probably should have—because in a very real way, we treasure the aptitudes and skills, indeed everything that we take the time to measure. Gross national product, budget and trade deficits, unemployment rates, the 1% versus everyone else: what is most important to us is endlessly calculated, publicized and analyzed. We also value these measures because they help us decide what to do next, like stimulating the economy, cutting government programs, or implementing trade restrictions. Measures influence actions.

It’s much the same with the measures we obtain from the educational tests that we administer, and in this regard, no test today is more influential than the Programme for International Student Assessment or PISA. PISA was first given in 2000 in 32 countries, the first time that national education systems were evaluated and could be compared with one another. The test measured 15-year-olds scholastic performance in mathematics, science and reading. No doubt you’ve heard some of the results, including the United States’ disappointing placement in the middle of the international pack. The test is given every three years and in 2018, 79 countries and economies participated in the testing and data collection.

According to an article in on-line business journal Quartz this week, “the results…are studied by educators the way soccer fans obsess over the World Cup draw.”

No one thinks more about the power of the PISA test, the information that it generates, and what additional feats it might accomplish than Andreas Schleicher, a German data scientist who heads the education division of the Organisation for Economic Cooperation and Development (OECD) which administers PISA worldwide.

Schleicher downplays the role that the PISA has played in shaming low performing countries, preferring the test’s role in mobilizing national leaders to care as much about teaching and learning as they do about economic measures like unemployment rates and workplace productivity. At the most basic level, PISA data has supported a range of conclusions, including that class size seems largely irrelevant to the learning experience and that what matters most in the classroom is “the quality of teachers, who need to be intellectually challenged, trusted, and have room for professional growth.”

Schleicher also views the PISA as a tool for liberating the world’s educational systems from their single-minded focus on subjects like science, reading and math and towards the kinds of “complex, interdisciplinary skills and mindsets” that are necessary for success in the future of work. We are afraid that human jobs will be automated but we are still teaching people to think like machines. “What we know is that the kinds of things that are easy to teach, and maybe easy to test, are precisely the kinds of things that are easy to digitize and to automate,” Schleicher says.

To help steer global education out of this rut, he has pushed for the design and administration of new, optional tests that complement the PISA. Change the parameters of the test, change the skills that are measured, and maybe the world’s education-based priorities will change too. Says Schleicher: “[t]he advent of AI [or artificial intelligence] should push us to think harder [about] what makes us human” and lead us to teach to those qualities, adding that if we are not careful, the world’s nations will be continue to educate “second-class robots and not first-class humans.”

Schleicher had this future-oriented focus years before the PISA was initially administered.

In 1997, Schleicher convened a group of representatives from OECD countries, not to discuss what could be tested, but what should be tested. The idea was to move beyond thinking about education as the driver of purely economic outcomes. In addition to wanting a country’s education system to provide a ready workforce, they also wondered whether they could nurture young people to help to make their societies more cohesive and democratic while reducing unfairness and inequality. According to Quartz:

The group identified three areas to explore: relational, or how we get along with others; self, how we regulate our emotions and motivate ourselves, and content, what schools need to teach.

Instead of simply enabling students to respond to the demands of a challenging world, Schleicher and others in his group wanted national testing to encourage the kinds of skill building that would enable young people to change the world they’d be entering for the better.

Towards this end, Schleicher’s team began to develop assessments for independent thinking and the kinds of personal skills that contribute to it. The technology around test administration enabled the testers to see how students solved problems in real time, not simply whether they get them right or wrong. They gathered and shared data that enabled national education systems to “help students learn better and teachers teach better and schools to become more effective.” Assessments of the skill sets around independent thinking encouraged countries to begin to see new possibilities and want to change how students learn in their classrooms. “If you don’t have a north star [like this], perhaps you limit your vision,” he says.

For the past twenty years, Schleicher’s north stars have also included students’ quest to find meaning in what they are doing and to exercise their agency in determining what and how they learn. He is convinced that people have the “capacity to imagine and build things of intrinsic positive worth.” We have skills that robots cannot replace, like managing between black and white, integrating knowledge, and applying knowledge in unique situations. All of those skills can be tested (and encouraged), along with the skill that is most unique about human beings, namely:

our capacity to take responsibility, to mobilize our cognitive and social and emotional resources to do something that is of benefit to society.

What Schleicher and his testing visionaries began to imagine in 1997 have been gradually introduced as optional tests that focus on problem-solving, collaborative problem-solving, and most recently, so-called “global competencies” like open-mindedness and the desire to improve the world. In 2021, another optional test will assess flexibility in thinking and habits of creativity, like being inquisitive and persistent.

One knowledgeable observer of these initiatives, Douglas Archibald, credits Schleicher with “dramatically elevating” the discussion about the future of education. “There is no one else bringing together people in charge of these educational systems to seriously think about how their systems [can be] future proofed,” says Archibald. But he and others also see a hard road ahead for Schleicher, with plenty of resistance from within the global education community.

Some claim that he is asking more from a test than he should. Others claim his emphasis is fostering an over-reliance on testing over other priorities. Regarding the “global competencies” assessment for example, 40 of the 79 participating countries opted not to administer it. But Schleicher, much like visionaries in other fields, remains undaunted. Nearly half of the countries are exercising their option to assess “global competencies” and even more are administering the other optional tests that Schleicher has helped develop. Maybe educators are slowly becoming convinced that the threat to human work in a white-water world is too serious to be ignored any longer.

A view from Kenneth Robinson’s presentation: “Changing Education Paradigms”

While Schleicher and his allies are in the vanguard of those who are using a test to prompt a revolution in education, they are hardly the only ones to challenge a teaching model that, for far too long, has only sought to produce a dependable, efficient and easily replaceable workforce. The slide above is from Sir Kenneth Robinson’s much-heralded (and well-worth your taking a look at) 2010 video called “Changing Education Paradigms.” In it, he also champions teaching that enables uniquely human contributions that no machine can ever replace.

Schleicher, Robinson and others envision education systems that prepare young people (or re-engineer older ones) for a complex and ever shifting world where no one has to be overwhelmed by the glut of information or the dynamics of shifting networks but can learn how to navigate today’s challenges productively. They highlight and, by doing so, champion teaching methods that help to prepare all of us for jobs that provide meaning and a sense of wellbeing while amplifying and valuing our uniquely human contributions.

Schleicher is also helping to modify our behavior by championing skills like curiosity about others and empathy that can make us more engaged members of our communities and commit us to improving them. Assessing these skills in national education tests says both loudly and clearly that these skills are important for human flourishing too. Indeed, this may be Schleicher’s and OECD’s most significant contribution. Their international testing is encouraging the skills and changes in behavior that can build better societies, whether they are based on the democratic values of the West or the more collective and less individual ones of the East.

That is no small thing. No small thing at all.

This post is adapted from my March 10, 2019 newsletter.

Running Into the Future of Work

January 13, 2019 By David Griesing Leave a Comment

We’ve just entered a new year and it’s likely that many of us are thinking about the opportunities and challenges we’ll be facing in the work weeks ahead. Accordingly, it seems a good time to consider what lies ahead with some forward-thinkers who’ve also been busy looking into the future of our work.

In an end-of-the-year article in Forbes called “Re-Humanizing Work: You, AI and the Wisdom of Elders,” Adi Gaskell links us up with three provocative speeches about where our work is headed and what we might do to prepare for it. As he’s eager to tell us, his perspective on the people we need to be listening to is exactly where it needs to be:

“I am a free range human who believes that the future already exists, if we know where to look. From the bustling Knowledge Quarter in London, it is my mission in life to hunt down those things and bring them to a wider audience. I am an innovation consultant and writer, and…my posts will hopefully bring you complex topics in an easy to understand form that will allow you to bring fresh insights to your work, and maybe even your life.”

I’ve involuntarily enlisted this “free-range human” as my guest curator for this week’s post.

In his December article, Gaskell profiles speeches that were given fairly recently by John Hagel, co-chair of Deloitte’s innovation center speaking at a Singularity University summit in Germany; Nobel Prize-winning economist Joseph Stiglitz speaking at the Royal Society in London; and Chip Conley an entrepreneur and self-proclaimed “disrupter” speaking to employees at Google’s headquarters last October. In the discussion that follows, I’ll provide video links to their speeches so you can consider what they have to say for yourselves along with “my take-aways” from some of their advice.

We are all running into the future of our work. As the picture above suggests, some are confidently in the lead while others of us (like that poor kid in the red shirt) may simply be struggling to keep up. It will be a time of tremendous change, risk and opportunity and it won’t be an easy run for any of us.

My conviction is that forward movement at work is always steadier when you are clear about your values, ground your priorities in your actions, and remain aware of the choices (including the mistakes) that you’re making along the way. Hagel, Stiglitz and Conley are all talking about what they feel are the next necessary steps along this value-driven path.

1. The Future of Work– August 2017

When John Hagel spoke about the future of work at a German technology summit, he was right to say that most people are gripped by fear. We’re “in the bulls-eye of technology” and paralyzed by the likelihood that our jobs will either be eliminated or change so quickly that we will be unable to hold onto them. However, Hagel goes on to argue (persuasively I think) that the same machines that could replace or reduce our work roles could just as likely become “the catalysts to help us restore our humanity.”

For Hagel, our fears about job elimination and the inability of most workers to avoid this looming joblessness are entirely justified. That’s because today’s economy—and most of our work—is aimed at producing what he calls “scalable efficiency.” This economic model relentlessly drives the consolidation of companies while replacing custom tasks with standardized ones wherever possible for the sake of the bottom line.

Because machines can do nearly everything more efficiently than humans can, our concerns about being replaced by robots and the algorithms that guide them are entirely warranted. And it is not just lower skilled jobs like truckers that will be eliminated en masse. Take a profession like radiology. Machines can already assess the data on x-rays more reliably than radiologists. More tasks that are performed by professionals today will also be performed by machines tomorrow.

Hagel notes that uniquely human aptitudes like curiosity, creativity, imagination, and emotional intelligence are discouraged in a world of scalable efficiency but (of course) it is in this direction that humans will be most indispensible in the future of work. How do we build the jobs of the future around these aptitudes, and do we even want to?

There is a long-standing presumption that most workers don’t want to be curious, creative or imaginative problem-solvers on the job. We’ve presumed that most workers want nothing more than a highly predictable workday with a reliable paycheck at the end of it. But Hagel asks, is this really all we want, or have our educations conditioned us to fit (like replaceable cogs) into an economy that’s based on the scalable efficiency of its workforce? He argues that if you go to any playground and look at how pre-schoolers play, you will see the native curiosity, imagination and inventiveness before it has been bred out of them by their secondary, college and graduate school educations.

So how do companies reconnect us to these deeply human aptitudes that will be most valued in the future of work? Hagel correctly notes that business will never make the massive investment in workforce retraining that will be necessary to recover and re-ignite these problem-solving skills in every worker. Moreover, the drive for scalable efficiency and cost-cutting in most companies will overwhelm whatever initiatives do manage to make it into the re-training room.

Hagel’s alternative roadmap is for companies that are committed to their human workforce to invest in what he calls “the scalable edges” of their business models. These are the discrete parts of any business that have “the potential to become the new core of the institution”—that area where a company is most likely to evolve successfully in the future. Targeted investments in a problem-solving human workforce at these “scalable edges” today will produce a problem-solving workforce that can grow to encompass the entire company tomorrow.

By focusing on worker retraining at a company’s most promising “edges,” Hagel strategically identifies a way to counter the “scalable efficiency” models that will continue to eliminate jobs but refuse to make the investment that’s required to retrain everyone. While traditional jobs will continue to be lost during this transition, and millions of employees will still lose their jobs, Hagel’s approach ensures an eventual future that is powered by human jobs that machines cannot do today and may never be able to do. For him, it’s the fear of machines that drives us to a new business model that re-engages the humanity that we lost in school in the workplace.

I urge you to consider the flow of Hagel’s arguments for yourself. For more of his ideas, a prior newsletter discusses a Harvard Business Review article (which he co-wrote with John Seely Brown) about the benefits of learning that can “scale up.” A closely related post that examines Brown’s commencement address about navigating “the white-water world of work today” can be found here.

*My most important take-aways from Hagel’s talk: Find the most promising, scalable edges of the jobs Im doing. Hone the creative, problem-solving skills that will help me the most in realizing the goals I have set for myself in those jobs. Maintain my continuing value in the workplace by nurturing the skills that machines can never replace.

2. AI and Us– September 2018

Columbia University economist Joseph Stiglitz begins his talk at London’s Royal Society with three propositions. The first is that artificial intelligence and machine learning are likely to change the labor market in an unprecedented way because of the sheer extent of their disruption. His second proposition is that economic markets do not self-correct in a way that either preserves employment or creates new jobs down the road. His third proposition—and perhaps the most important one—is that there is an inherent “dignity to work” that necessitates government policies that enable everyone who wants to work to have the opportunity to do so.

I agree with each of these propositions, particularly his last one. So if you asked me, the way that Stiglitz was asked by a member of the audience at the end of his talk, about whether he supported governments providing their citizens with “a universal basic income” to offset job elimination as many progressives are proposing, his answer (and mine) would “No.” Instead, we’d argue that governments should be fostering the economic circumstances where everyone who wants to work has the opportunity to do so. It is this opportunity to be productive—and not a new government handout—that rises to the level of basic human right.

Stiglitz argues that new artificial intelligence technologies along with 50 years of hands-off government policies about regulating business (beginning with Reagan in the US and Thatcher in the UK) have been creating smaller “national pies” that are shared with fewer of their citizens. In a series of charts, he documents the rise of income inequality by showing how wages and economic productivity rose together in most Western economies until the 1980s and have diverged ever since. Labor’s share in the pie has consistently decreased in this timeframe and new technologies like AI are likely to reduce it to even more worrisome levels.

Stiglitz’ proposed solutions include policy making that encourages full employment in addition to fending off inflation, reducing the monopoly power that many businesses enjoy because monopoly restricts the flow of labor, and enacting rules that strengthen workers’ collective bargaining power.

Stiglitz is not a spellbinding speaker, but he is imminently qualified to speak about how the structure of the economy and the policies that maintain it affect the labor markets. You can follow his trains of thought right into the lively Q&A that follows his remarks via the link above. For my part, I’ve been having a continuous conversation about the monopoly power of tech companies like Amazon and the impact of unrestricted power on jobs in newsletter posts like this one from last April as well as on Twitter if you are interested in diving further into the issue.

*My most important take-aways from Stiglitz’ remarks were as follows: since I care deeply about the dignity that work confers, I need (1) to be involved in the political process; (2) to identify and argue in favor of policies that support workers and, in particular, every worker’s opportunity to have a job if she wants one; and (3) to support politicians who advance these policies and oppose those who erroneously claim that when business profits, it follows that we all do.

3.         The Making of a Modern Elder – October 2018

The pictures above suggest the run we’re all on towards the future of work. What these pictures don’t convey as accurately are the ages of the runners. This race includes everyone who either wants or needs to keep working into the future.

Chip Conley’s recent speech at Google headquarters is about how a rapidly aging demographic is disrupting the future workforce and how both businesses and younger workers stand to benefit from it. For the first time in American history, there are more people over age 65 than under age 15. With a markedly different perspective, Conley discusses several of the opportunities for companies when their employees work longer as well as how to improve the intergenerational dynamics when as many as five different generations are working together in the same workplace.

Many of Conley’s insights come from his mentoring of Brian Chesky, the founder of AirBnB, and how he brought what he came to call “elder wisdom” to not only Chesky but also AirBnB’s youthful workforce. Conley begins his talk by referencing our long-standing belief that work teams with gender and race diversity tend to be more successful than less diverse teams, which has led companies to support them. However, Conley notes that only 8% of these same companies actively support age diversity.

To enlist that support, he argues that age diversity adds tremendous value at a time of innovation and rapid change because older workers have both perspective and organizational abilities that younger workers lack. Moreover, these older workers comprise an increasingly numerous group, anywhere from age 35 at some Silicon Valley companies to age 75 and beyond in less entrepreneurial industries. What “value” do these older workers provide, and how do you get employers to recognize it?

Part of the answer comes from a changing career path that no longer begins with learning, peaks with earning, and concludes with retirement. For nearly all workers, your ability to evolve, learn, collaborate and counsel others play roles that are continuously being renegotiated throughout your career. For example, as workers age, they may bring new kinds of value by sharing their institutional knowledge with the group, by understanding less of the technical information but more about how to help the group become more productive, and by asking “why” or “what if” questions instead of “how” or simply “what do we do now” in group discussions. Among other things, that is because older workers spend the first half of their careers accumulating knowledge, skills and experience and the second half editing what they have accumulated (namely what is more and less important) given the perspective they have gained.

When you listen to Conley’s talk, make sure that you stay tuned until the Q&A, which includes some of his strongest insights.

*My most important take-aways from his remarks all involve how older workers can continuously establish their value in the workplace. To do so, older workers must (1) right-size their egos about what they don’t know while maintaining confidence in the wisdom they have to offer; (2) commit to continuous learning instead of being content with what they already know; (3) become more interested and curious instead of assuming that either their age or experience alone will make them interesting; and (4) demonstrate their curiosity publically, listen carefully to where those around them are coming from, and become generous at sharing their wisdom with co-workers privately.  When we do, companies along with their younger workers will come to value their trusted elders.

* * *

This has been a wide-ranging discussion. I hope it has given you some framing devices to think about your jobs as an increasingly disruptive future rushes in your direction. We are all running with the wind in our faces while trying to get the lay of the land below our feet in this brave new world of work.

Note: this post is adapted from my January 13, 2019 newsletter.

Nostalgia Can Help Us Build a Better Future

Citizens Will Decide What’s Important in Smart Cities

New Starting Blocks for the Future of Work

Running Into the Future of Work

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