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will crum

How I Learned to Love the Machine That Took my Job

Will Crum Š 2018 All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For inquiries, contact School of Visual Arts MFA Products of Design 136 West 21st Street 7th floor New York, NY 10011

Will Crum

Author, Designer

Allan Chochinov

Chair: MFA Products of Design at SVA

Andrew Schloss Thesis I Instructor

Abby Covert

Thesis II Instructor

Ernie Piper IV Editor




We are on the cusp of an AI Revolution.

Market & Audience

Who might use these designs, and how?

Goals & Objectives

What do I hope to achieve with this thesis?



Where did a designer learn so much about AI?

On Futuring

Looking back to see what’s next



The open road, on your terms


Turning soul-searching into a supportive conversation


A smarter kind of elder care

Water Token Project

A speculative policy proposal for the state of California

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Score Day

A co-creation futuring workshop

Human Resourcing Dept.

The job placement agency of the future


Creating interclass connections

Dystopian short fiction


Utopian short fiction


Social capital counts


Your very own sensory bundle of joy


Parting Thoughts

Looking back at the future

Controlled Vocabulary My personal thesis lexicon

Thank You

To everyone who made this thesis happen

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Artificial General Intelligence

Artificial Intelligence

Machine Learning

Voice Recognition

Natural Language Processing

Deep Learning

Image Recognition

This dramatically simplified map of AI’s capabilities and disciplines shows the gulf between today’s cuttingedge tools and the “AGI” of science fiction.




In popular conception, the term “artificial intelligence” refers to the stuff of science fiction: man-made replications of human-level intelligence that would inevitably coincide with (or eventually precipitate) a sense of self and a desire for purpose. Through film and pop culture, we collectively imagined a future populated by robots with bubbly personalities and a knack for helping out. Whether it was Rosie of The Jetsons, C-3PO in Star Wars, or Data on Star Trek, most people were exposed to a man-made character that influenced their expectations of what machine intelligence can and would be: a quantitative wizard with the stamina to endure repeated tasks, but overall mostly human. But over the past few decades, the clever work of mathematicians, data scientists and computer engineers has shown that vast amounts of data, immense computing power and cleverly written algorithms can combine to make superhumanly accurate predictions — without slumping into an existential crisis. These “machine learning” systems are extremely capable in their specific domain but incapable of generalizing learning from one topic to another. Thus, “artificial intelligence” has evolved to become an umbrella term for

a suite of sophisticated technologies that use machines’ bottomless capacity for dataprocessing and pattern recognition to achieve specific aims. In turn, the theoretical machine capable of using its own consciousness to generalize between topics was rebranded as “artificial general intelligence,” or AGI, and is viewed by many in Silicon Valley as an impossible, impractical and purposeless pursuit.

HAL 9000 from Stanley Kubrick’s 2001: A Space Odyssey (1968) is probably the original instance of an AI gone renegade in popular culture — besides maybe Mary Wollstonecraft Shelley’s Frankenstein.

Machine learning has already transformed the developed world in countless ways, often in ways that aren’t immediately obvious to the casual observer. But these algorithms are at play everywhere, once you know where to look: Google and Amazon use natural language processing (NLP) algorithms to produce results that match the human understanding of the multiple words in a keyword search; the shows that Netflix recommends are based on cross-referencing your viewing history with everything that every other Netflix user has ever watched; The voice recognition that powers dictation apps and smartphone personal assistants like Siri rely on machine learning to turn human sounds into text; Google Maps’ traffic speed is gauged by interpreting real-time location data from its users’ smartphones; Ridesharing apps like


As with any powerful new technology, there are virtually limitless ways that it might be weaponized against the unsuspecting. The same face-tracking and voice-to-text algorithms that power Snapchat’s toy and Apple’s assistant are now being used to create videos that put words in the mouths of famous voices, in their own voice. Coupled with the predictive power of Facebook’s ad-targeting algorithms, the potential for mass misinformation through propaganda is terrifyingly real.3 But while there are many malevolent uses of AI whose potential for harm are worthy of our concern and consideration, they are not the subject of this work. Instead, this thesis aims to explore areas where AI may be implemented with good intentions but still has the potential to yield a similarly disastrous effect: automation. The dominant presence of the color blue in these two maps indicates that the service economy is a significant component of the world economy, as well as one of the main employers around the globe.6


By default, this undertaking invites a broad scope: An exploration of how AI-powered automation will change the way humanity works and lives surely implicates everyone, everywhere. But it’s common industry wisdom that a product designed with everyone in mind ends up resonating with no one. So in this introduction, I outline which communities are the likeliest to soon experience automation’s adverse side effects, providing historical context to justify my predictions. In the process, I aim to clarify the audience for my thesis — in an effort to provide greater context and urgency to the work itself.

Uber and Lyft use it to predict user demand and calibrate surge pricing that creates a supply of drivers to match; most commercial airlines use an AI autopilot to control the plane, with human pilots only steering for takeoff and landing; Gmail automatically sorts content into your ‘spam’ folder by probabilistically analyzing key words; Mobile banking apps use machine learning algorithms to read the handwriting on your checks; Financial institutions make credit decisions based on algorithms that predict an applicants likelihood of default; Pinterest uses computer vision to group the images you see; Snapchat uses AI to power the face-tracking technology behind its facial filters; Facebook uses AI to recognize your face in photos, suggest friend requests and target you with ads;1 and Facebook even uses content analysis AI to flag posts and messages that reflect suicidal ideation with startling accuracy.2 Just a few years ago, the notion that machines might read your handwriting, understand your voice, suggest your entertainment or fly your airplane were outlandish to the average citizen. Now, they’re such mundane realities that we hesitate to call them “artificial intelligence” at all. And machine learning will continue to creep into a growing number of industries and jobs, as the cost of automating repetitive processes is eclipsed by the potential savings and opportunities it portends.

Sorting all the nations of the world into tiers of economic development is inherently problematic, as the sequence by which different countries adopt different technologies varies widely and is far from linear. Thankfully, the UN’s World Economic Situation and Prospects report has made the critical assumptions and generalizations for me, neatly categorizing all countries into three groups: developed economies, economies in transition and developing economies. 4 This thesis is targeted at residents of developed economies (the 36-country list includes the United States, Japan, and all member states of the European Union), as it is

1  Narula, Gautam. “Everyday Examples of Artificial Intelligence and Machine Learning.” TechEmergence. March 29, 2018. 2  Constine, Josh. “Facebook Rolls out AI to Detect Suicidal Posts before They’re Reported.” TechCrunch. November 27, 2017.

3  Bowman, Emma, and Lawrence Wu. “In An Era Of Fake News, Advancing Face-Swap Apps Blur More Lines.” NPR. February 03, 2018. https://www.npr. org/2018/02/03/582767531/in-an-era-of-fake-news-advancing-face-swap-apps-blur-more-lines 4  “World Economic Situation and Prospects (WESP) Report : Development Policy & Analysis Division.” United Nations. wesp/wesp_current/2014wesp_country_classification.pdf


bor would be how most of humanity spent the bulk of its days for millennia to come.8 This wouldn’t change until the late 19th century.

Service economies

In 1870, half the population of the United States was employed in agriculture.9 But by 1970, the percentage of the American public employed in this sector had fallen by 90%.10 The Industrial Revolution had used new technologies like the steam engine and new labor

More specifically, these are the countries where a mixture of outsourcing and robotic automation, motivated by cost savings and improved efficiency, has greatly diminished the number of industrial jobs (i.e. factory work) over the last several decades. The service economy (jobs based on what you do for customers, rather than what you make) rose in its place, and is now the greatest employing sector in these regions. Service economy jobs are less physically intensive and don’t happen on a factory floor, and therefore are seldom fodder for robotic automation — but they still include repetitive tasks. And recent achievements in the AI subfield of ‘deep learning’ have dramatically broadened machines’ ability to perform core tasks for many service economy jobs, such as reading, writing, speaking, listening, recognizing objects and integrating knowledge.5 This means that at least 30% of the tasks that make up 60% of the world’s jobs could be automated with technology that already exists today.6 So as the global economy continues to rebound after the 2008 financial crisis, it’s only a matter of time that rising wages provide companies with the incentive to automate.7


Top-employing sectors have died off in the past, and the jobs lost were always replaced by emerging sectors. Some 12,000 years ago, humans in the Fertile Crescent made the fateful decision to switch from a nomadic hunter-gatherer lifestyle and instead remain in one place, cultivating and selectively breeding their favorite plants for food. The old core tasks of most people’s lives, hunting and foraging, had been replaced by plowing, sewing, watering, weeding and harvesting. The Agricultural Revolution enabled massive population growth and cultural development, freed up time for individuals to specialize in key tasks — and determined that agrarian la5  Howard, Jeremy. “The Wonderful and Terrifying Implications of Computers That Can Learn.” TED. https://www.ted. com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn” 6  Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation.” McKinsey Global Institute. December 1, 2017, iii. 7  Frase, Peter. Four Futures: Life After Capitalism. New York: Verso, 2015, 13. 6 “Service Economy.” Wikipedia. January 28, 2018. https://

techniques like factories to transform pastoral farms into labor-efficient food factories. New technologies displaced half the work force, but had also created many more vocations in manufacturing centers around the country.11 In 1950, the US economy was booming and 88% of the work force were working somewhere other than a farm.12 But this improved quality coincided with rising wages that precipitated an off-shoring and automation trend that saw the proportion of Americans working in factories fall by 75% by 2010.13 This is where innovation in the services


those at the forefront of technology adoption that are likely to experience the initial impact of large-scale, AI-powered automation.

US factory workers, circa 1914. The industrial revolution enabled the development of a new wave of products — which meant entirely new jobs for workers.14

8  “The Development of Agriculture.” Genographic Project. 9  “Employment by Major Industry Sector.” U.S. Bureau of Labor Statistics. October 24, 2017. ep_table_102.htm 10  Lee, David. “Why Jobs of the Future Won’t Feel like Work.” TED. jobs_of_the_future_won_t_feel_like_work 11  “The Impact of Automation on Employment - Part I.” NCCI. October 27, 2017. 12  Lebergott, Stanley. “Labor Force and Employment, 1800–1960.” Output, Employment, and Productivity in the United States after 1800 (1966): 4. 13  Lee, “Why Jobs of the Future Won’t Feel like Work.” 14


sector swooped in to save the day, resulting in a contemporary US workforce that is 80% service-based1 and a global workforce that’s 50% service-based.2

The AI Revolution

There’s little doubt that the technologies developed today will create new value and even new industries tomorrow. But there is concern that the global workforce (and the social systems that train and encompass them) may not be able to adapt as quickly as the shift in demand necessitates. The US had 100 years to grow an industrial economy in the wake of the agricultural economy, and it had sixty years to grow a service economy in the wake of the industrial economy.3 But the majority of experts anticipate that the inflection point around AI-based automation will happen in the next decade or two. A 2013 report out of Oxford University estimates that 47% of total US employment is at high risk of being fully automatable in the next ten to twenty years. 4 This figure has been repeatedly quoted out of context by automation alarmists who take it as an omen that society will soon be cruelly cleaved in two. This mischaracterization of Frey and Osborne’s work probably stems from the fact that the figure invites confusion between what can be automated and what will be automated, and also due to the oversimplification inherent in assuming that just because part of a job will be automated, the entire job is nixed. A more recent (and easily parsed) report out of the McKinsey Global Institute focuses on anticipated change by a key milestone: the year 2030. It estimates that anywhere between 75 and 375 million people (3-14% of the global workforce) will need to switch occupational categories by this time. (The broad range of these projections stem from the varying potential rates at which AI-based automation may be implemented.) Displaced or not, all workers will have to adapt their habits and capabilities to complement increasingly autonomous machines.5 Higher educational achievement will be needed for many, but the types of knowledge and expertise valued 1  “Employment by Major Industry Sector,” US BLS. 2  “Employment in Services % of Total Employment.” World Bank Data. Accessed April 26, 2018. https://data.worldbank. org/indicator 3  Lee, “Why Jobs of the Future Won’t Feel like Work.” 4  Frey, Carl Benedikt, and Michael A. Osborne. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change (2017): 254-80 5  Jobs Lost, Jobs Gained, MGI, iv.


highest today may not be the same tomorrow. Data-driven and memorization-intensive specialties will be cost-effectively automated, while social and emotional intelligence, creativity, and the ability to address the unpredictable will all be core skills for the workforce of the future.6 Mid-career training will be essential for virtually all workers, and the era of the one-job career appears to be approaching its conclusion.


Recent advances in AI and automation have put us on the precipice of far-reaching, non-linear change. My question is: will it make things better or worse for the average human being? Capitalism and Adam Smith’s invisible hand have gotten us this far, driving the explosive growth of global civilization over the past few centuries. But it’s not obvious how well this framework will continue to serve the average worker in the decades to come. Universal basic income, a welfare model where all citizens regularly and unconditionally receive a stipend to cover all necessary expenses, is a commonly proposed solution for supporting those disenfranchised by a rapid transition to an automated society. The premise is simple, but the impact can be immense. Pilot cash transfer programs that give even relatively small sums of money to the country’s poorest residents can have a huge impact: In 2008, Uganda gave $400 to 12,000 young adults citizens; by 2013, the recipients had invested in their own education and business, and their average income had gone up by 50%. And these programs benefit the surrounding community, not just the direct beneficiaries: In Malawi, a similar program led to a 32% drop in malnutrition, a 40% drop in school truancy, and a 40% increase in girls’ attendance at schools. A 2010 University of Manchester study into these and many other cash transfer programs found that they were consistently more effective and cost-efficient than traditional aid programs. Rather than sending high-salaried “growth experts” to sort out the poor’s problems for them, the researchers concluded that the poor understand their own just fine — their main issue is a lack of capital.7 While UBI has its share of advocates on both 6  Jobs Lost, Jobs Gained, MGI, v. 7  Bregman, Rutger, and Elizabeth Manton. Utopia for Realists. (London: Bloomsbury, 2018), 25-33.

the left and right, it has yet to see any largescale implementation in developed countries. Considering UBI’s strong track record of combatting inequality in developing countries, its failure to garner greater popularity in developed countries can perhaps be attributed to cultural stigma around social welfare. Americans in particular have a tendency to view any welfare program as a “hand-out” that enables laziness among the poor, who might otherwise be motivated to put in the work and finally “pull themselves up by their bootstraps.” This narrative ignores the harsh realities of being poor in America — the same things tend to cost more; lack of liquidity makes impulsive purchasing a necessity; and most of America’s poorest people tend to work the longest hours, but are trapped in jobs that don’t offer the advancement opportunities they need to break the cycle8 — but it nevertheless persists. And many are fiercely determined not to unlearn it, because that would require them to acknowledge that the American Dream — the idea that with hard work and dedication anyone can succeed in America — is no longer valid (if it ever was). Encouraging the majority of Americans to come to terms with this reality will be a critical first step in building support for a national UBI program. But it will be immensely difficult, as it forces people to question the validity of their own identity as a “self-made man.”


GiveDirectly is the NGO behind several of the developing world’s most effective cash transfer progams, including the Uganda and Malawi intiatives cited here.7

So while UBI may eventually pave the way to a utopian society where every citizen is free from want, it is not the first step on the journey — nor is it the focus of this thesis. Instead, much of this work explores how design might promote the moral values that must become commonplace in order for such a program to exist: egalitarianism, and an active concern for the welfare of one’s fellow human beings.


This thesis imagines how the advent of an AI-driven world may change how the ordinary citizen works, lives, ascribe values, and is valued in return. Whether or not this new technology will broaden the gap between the haves and have-nots to unbridgeable bounds or usher in an egalitarian utopia remains to be seen. But, ultimately, the tools we use tomorrow are a direct result of the values we hold today. Therefore, this thesis aims to interrogate contemporary values and power dynamics through the development of products, services and systems for several speculative futures: some that may come, and some that hopefully never will.

8  Tirado, Linda. Hand to Mouth: Living in Bootstrap America (New York: Berkley Books, 2015), 159-180. 7 give-directlys-breakthrough-free-money-model-grows-asevidence-mounts/




Market & Audience

WHO MIGHT USE THESE DESIGNS, AND HOW? It’s highly likely that AI-based automation will occur to a significant extent in the next few decades and that, when it does, the demands on and opportunities for the workforce will change dramatically. But who decides which technologies are implemented where, and when?


This decision-making class takes many forms and goes by many names: managers, business owners, capital investors, entrepreneurs, tech industry innovators, C-suite executives, automation-implementers, Silicon Valleytypes, and so on. While personal wealth tends to be a unifying trait, these individuals are connected by something more than the fact that they’re all rich: The business and investment decisions they make have a disproportionate impact on how how the rest of society earns a living. In the lexicon of my thesis, I’ve taken to calling this elite class the “job creators,” to parrot a term from American political rhetoric. Their defining characteristic is the high level of control they have over how and where capital is reinvested into the economy, which generally takes the form of either tools or jobs. To put it another way, the vast majority of people do the work available to them in order to access the things they need and want (most commonly via a wage or salary) — and the job creators are the elite minority who can decide what those jobs are. It would be an oversimplification to say that job creators have sole influence over the types and quantities of jobs that exist in the market, because they don’t: workers can form labor unions in order to bargain as a bloc, and governments can pass laws that regulate how companies treat their employees. But in the US, the relative influence of these entities has been in constant decline for some time. Union membership peaked in the 1940s at roughly 40% of the population — now, that number is closer to 7%. In The Rise and Fall of Unions in the United States, authors Dinerloz

and Greenwood contend that this decrease was due to a shift away from unskilled labor; labor unions make the biggest difference in bargaining power for unskilled laborers, who were a much larger portion of the workforce in this golden age of assembly line-style American manufacturing than they are in today’s skill-intensive service economy.1 But the other key contributing factor is a political one: The ever-expanding cost of an election campaign has made US politicians increasingly dependent on donations from large corporations and the super-wealthy people who run them. A close relationship between money and power has led to a legislative trend of advancing corporate interests and not defending workers. This proximity between commercial and political realms been taken to an extreme in the Trump presidency, whose fast-churning cabinet is brimming with ex-corporate chiefs now tasked with managing the agencies that once regulated their businesses.2 All this amounts to a top-heavy economy in which power is continuously consolidated among the elite class. The American economy, like the world economy, is a capitalist one, which means that goods are owned by individuals and businesses rather than by the state.3 But private property rights are just part of the ideology; as historian Yuval Noah Harari puts it, “Capitalism distinguishes ‘capital from mere ‘wealth.’ Capital consists of money, goods and resources that are invested in production. Wealth, on the other hand, is buried in the ground or wasted on unproductive activities.”4 The idea that consistently reinvesting wealth can lead to sustained growth may sound like common sense to a 21st century citizen (it has been crystallized as received wisdom in the American idiom, “It takes money to make 1  Dinlersoz, Emin, and Jeremy Greenwood. “The Rise and Fall of Unions in the U.S.” 2012. https://repository.upenn. edu/cgi/viewcontent.cgi?article=1032&context=psc_working_papers 2  “Trump’s Corporate Cabinet.” Public Citizen. http:// 3  Harari, Yuval Noah. Sapiens: A Brief History of Humankind (VINTAGE, 2018), 312 4  Harari, Sapiens, 271-275


money”), but this wasn’t always the case. Before philosopher Adam Smith popularized the premise in his 1776 work The Wealth of Nations, rich states and families had a habit of sitting on their coffers rather than doling out loans. The transformative power of the capitalist premise is exemplified by the contrasting fortunes of the Dutch and Spanish over the course of 17th century. At the beginning of the century, Spain was the richest country in Europe and commanded an extensive global empire. The Netherlands had just successfully seceded from Spanish rule a few decades earlier, and turned to trade as a means of gaining a foothold in the world order. In just a few short decades, the two states’ roles had reversed: The Spanish juggernaut grew weighed down by the accumulated debt of several costly wars, while Dutch merchants became the wealthiest in the world, with corporate-owned trade strongholds across the globe. And the key to their divergent trajectories was their differing attitudes toward credit: Dutch merchants were sticklers for repaying their loans on time, because they knew that they would be successfully sued in Dutch courts if they defaulted; while the Spanish monarchs demanded loans to fund their armies but didn’t worry about repaying them, since the courts and judges worked for them. The Spanish strategy worked well in the short term, but relationships with banks and creditors soon evaporated. Meanwhile, the Dutch kept taking out increasingly large loans to invest in land and resources — and made it all back handsomely.1 The Dutch soon lost their international primacy to the British and French, but their model for commercial success stuck. And as aggressive investment fueled European expansion into other continents and seas, it forged new systems of cooperation between the diverse peoples of the world. The word “cooperation” may have a rosy connotation, but these early relationships were almost always exploitative, imbalanced and violent. Without capitalism’s relentless quest for growth profit, there would have been no Great Bengal Famine, no Opium War, no Atlantic slave trade.2 Like the Agricultural Revolution before, capitalism created massive new value and opportunity for mankind, but brought immense suffering with it. In the ensuing centuries, wars were waged, revolutions 1  Harari, Sapiens, 282 2  Harari, Sapiens, 314


staged, and regulations passed in a cacophonous effort to reclaim and redistribute this ever-growing value. By the 21st century, the nations of the world have reached an uneasy peace, with domestic dependence on international trade the connective tissue that makes a third world war increasingly unlikely. While social, cultural and linguistic barriers still differentiate us, it was capitalism that first brought the world together, and capitalism that maintains the bond. Harari describes the spread of capitalism thusly: “Capitalism began as a theory about how the economy functions. It was both descriptive and prescriptive — it offered an account of how money worked and promoted the idea that investing profits in production leads to fast economic growth. But capitalism gradually became far more than just an economic doctrine. It now encompasses an ethic — a set of teachings about how people should behave, educate their children and think. Its principal tenet is that economic growth is the supreme good, because justice, freedom and even happiness all depend on economic growth.” This belief that investment by a wealthy few will forge better opportunities for the many is a central tenet of how most modern nations are organized, and is the implicit subtext in the term “job creator.” The rise of capitalism as the planet’s most universally held ideology coincided with the proliferation of the job creator class. And while the rise of the job creators may have heightened inequality, it has fostered huge improvements in the rest of society’s quality of life. To quantify this: In 1820, 84% of the world’s population lived in extreme poverty. Today, that figure is less than 10%.3 This is because capital investment by the wealthy has historically led to the direct creation of jobs and opportunity for those without it. But on the eve of the AI Revolution, the reliability of this connection is growing suspect. AI’s ability to replicate so many of the core capabilities of human workers today that job creators who fail to innovate with the average worker’s best interests in mind have the power to inadvertently reverse this trend away from global poverty. For this reason, job creators are the key audience and stakeholder group for this thesis. My hope is that the projects and proposals herein inspire the decision-making class to invest and act with the best interests of the working 3  Bregman, Utopia for Realists, 1



skill diversity









class in mind, and help inspire the realization of a more egalitarian future. I admit that there is no short-term reward for this ideological shift that’s as tangible as a seaside villa or private jet, and ignoring the ideas in this book will cause little inconvenience to those already clear and dry at the top. But a society where everyone has the chance to reach their full potential is fundamentally richer in ideas and innovation. And I choose to believe that those who are interested in the progress of collective mankind, rather than just their own creature comforts, constitute a massive majority — even if they don’t realize it.


To call the world’s working class a multitudinous group would be a gross understatement; they could be categorized and reorganized in limitless ways, depending on the analyst’s perspective and agenda. For my thesis, I sought to develop personas that described workers’ relationship to the looming specter of automation. Over the course of my interviews and readings, I discovered that most jobs consist of at least one core skill, and that contemporary workers existed on two main

spectrums: how likely their core skill is to be automated, and how many other skills they have to fall back on. Thus, I’ve developed this two-axis classification. Let’s break down what this means, starting with the criteria that define the axes:

This 2x2 matrix shows the attributes I compared to devise my personas. I ultimately chose to only focus on the highlighted pair whose jobs have a high degree of automatability.

◊  Automatability of core skill – This criterion is based on the assumption that, while every job employs many skills, each job like relies on one or two “core” skills the most. If the tasks accomplished with this skill are very repetitive or predictable, then the skill is considered highly automatable. ◊  Skill diversity – This axis describes the workers themselves: Has their education and/or experience allowed them to develop a diverse array of skills? Or has a narrower scope cultivated a mastery of just one or two skills, to the detriment of the rest? Treating each axis as a binary yields four quadrants, each representing a different relationship to potential automation: ◊  Indispensables – high skill diversity, low automatability – Those whose work requires


a diverse set of skills that handle emotion, creative problem-solving, or unpredictability. These jobs aren’t going anywhere, and are likely to grow in importance in the coming decades (e.g. care providers, creative professionals). ◊  Lucky niche – low skill diversity, low automatability — These people have specialized in a uniquely human skill (e.g. master craftsmen, artist). Even if their capabilities could theoretically be replicated with AI, their task is sufficiently niche that it the development cost wouldn’t be worth the investment. ◊  Old dogs – low skill diversity, high automatability – These people work in jobs built around a few repetitive tasks (e.g. truck drivers, call center workers). Career changes and retraining loom on the horizon for these workers. The nickname is pessimistically derived from the adage, “You can’t teach an old dog new tricks,” but hopefully this proves not to be the case! ◊  Young pups – high skill diversity, high automatability – The core tasks in these people’s days are automatable, but the industry (or their training) is multifaceted (e.g. law clerks, accountants). These jobs and industries will be transformed, but this more adaptable group may require less intensive retraining than the “old dogs.” The creation of these four categories falsely implies a simplicity of clean divisions that does not exist in the real world. For instance, whether or not you are a “young pup” or an “old dog” has less to do with the industry you work in than about your educational background, age, and personal aptitude to learning new things. Nevertheless, the distinctions are helpful because they enable us to highlight and describe where on these spectrums we can find individuals at the greatest risk. This thesis is not directly aimed at benefiting the indispensables or lucky niche types, although they may see displaced workers begin to enter their field as automation descends. (This influx could lead to a surplus of under-trained, under-motivated labor in indispensable fields like nursing, which may in turn alienate and drive off the original indispensable workers—but this is a hypothetical problem for another time.) Instead, the projects developed in this thesis are aimed at bolstering the agency of and consideration for the old dogs and young pups at immediate risk of vocational displacement.


For the displaced old dogs, the questions to explore are immediate and fundamental: What jobs will be available to them with the skills they currently have? What skills will they most urgently need to stay afloat, and how will they learn them? What support systems must be in place for this retraining to occur? For the displaced workers at a higher point on the skill diversity spectrum (i.e. the young pups) in less urgent need of retraining, the questions move from the realm of safety and physiological want, up Maslow’s hierarchy and into more abstract domains: What role can passion play in work when they may move between many careers in a lifetime? What value do I have in society beyond the labor I perform? If not in my work, where should I look to find a sense of belonging, esteem, and identity? There are no conclusive answers to these questions; this thesis certainly won’t provide any. But these topics merit exploration, and that’s what this thesis intends to do.





Goals & Objectives

WHAT DO I HOPE TO ACHIEVE WITH THIS THESIS? Artificial Intelligence has already begun to transform the world. AI algorithms filter out white noise in hearing aids, recommend products and entertainment based on past behavior, and help doctors diagnose cancers. AI advances are why existing technologies like speech recognition, face recognition and smart scheduling have improved so dramatically in the past few years. AI algorithms determine what shows up in your Facebook news feed and Google search results.1 As the computers running AI algorithms get incrementally faster, and as data that fuel these algorithms continue to be gathered (90% of the data created by mankind was made in the last two years,2 the informationprocessing power, decision-generating potential and application range of AI will continue to expand. Thus, while the specifics of the impending transformation remain murky, its magnitude is obvious: We are on the cusp of an AI Revolution, an event that will reshape the world as comprehensively as the agricultural and industrial revolutions that came before it.

caught in its path. The warning signs aren’t good, either. AI is developed and deployed by the people and corporations wealthy enough to invest in the hardware and research needed to use and improve it. Driven by the capitalist mandate to constantly grow and reinvest in production, this decision-making class has already stewarded the world economy to its most inequitable state in modern history. In the US, 1% of the population owns 40% of the nation’s wealth — the greatest disparity since at least 1962.3 On the global stage, the scales are tipped even further: the eight richest people own 50% of the world’s wealth. 4


But institutional upheaval and growth aren’t categorically good, especially in the immediate term and on the individual scale. The Agricultural Revolution allowed populations, cultures and specializations to blossom, but also paved the way for famines and pandemics. The Industrial Revolution multiplied the range of products and careers available, but also destroyed traditional ways of life and initiated an abusive phase of our relationship with our natural environment. And if the means of its commencement aren’t carefully considered, the AI Revolution may prove just as disastrous for the little people 1  Bostrom, Nick. Superintelligence: Paths, Dangers, Strategies (Oxford: Oxford University Press, 2016), 17-19 2  Hale, Tom. “How Much Data Does The World Generate Every Minute?” IFLScience, April 06, 2018, http://www.

How did we get here? In the US, the trend can be partially attributed to the globalization and automation of production — and the ensuing decoupling of production growth and wage growth. Productivity and median family income progressed virtually hand in hand from the 1950s until the 1980s, when income

The 1980s were when productivity growth first started to leave income growth behind. And by the 2000s, income growth had stagnated.5

3  Ingraham, Christopher. “The Richest 1 Percent Now Owns More of the Country’s Wealth than at Any Time in the past 50 Years.” The Washington Post. December 06, 2017. 4  Marr, Bernard. “Big Data: 20 Mind-Boggling Facts Everyone Must Read.” Forbes. November 19, 2015. https:// 5 “Productivity and Real Median Family Income Growth, 1947-2013.” State of Working America. Accessed April 27, 2018.


began to lag behind. By the mid-2000s, income growth had completely stagnated for the average American — with production growing all the while. And in addition to being excluded from growth’s reward, the American worker has been structurally disempowered, too. Corporate-friendly policies have seen the rate of union membership in the workforce halved between 1974 and 2008, falling from 24% to 12%.1 In short, production and growth have been prioritized to the detriment of everyone not directly involved in their oversight. The resulting wealth imbalance is the most disproportionate we’ve ever seen. And if the same people and priorities remain at the helm just as AI-powered automation becomes increasingly common, we can expect to see the gap between rich and poor widen to irreversible bounds.


This thesis explores AI’s potential impact on society by investigating the beliefs and values that will drive its implementation. Specifically, this work advocates for greater agency on the part of the working class by envisioning futures where AI is leveraged as a means of mutual empowerment, rather than a means of consolidating power at the top. This suite of design offerings is an eclectic mix, including products, services, experiences and mobile applications. All projects were developed with the intention of providing ethical frameworks for a better valuation and treatment of the working class. My hope is that this work can provide some small reference point as we stagger forward into the darkness of an uncertain future.


It would be disingenuous to present these projects as purely objective conclusions made after months of research. In reality, my design led my research just as often as my research led my design. And while all concepts are founded on a practical understanding of AI’s contemporary applications, capabilities and potential developed through extensive 1  “20 Facts about Inequality Everyone Should Know.” Stanford Center on Poverty & Inequality. 2011. https://inequality.


primary and secondary research on the subject, each project is also an articulation of my own beliefs, biases and interpretations. What are my biases? I lean hard to the left on both social and fiscal issues. I’m skeptical of capitalism’s ability to continue providing for the needs of all people, at least as it’s currently articulated. I believe that empowering the lowest classes is the fastest route to collective prosperity, and I generally find arguments in favor of a “trickle-down” economics to be either glib or disingenuous. I question the value of rigid national borders and am wary of nationalism as breeding-ground for fullthroated xenophobia. So, rather than disguising my agenda, I am laying it bare in order to better contextualize the work that follows. Every project was developed with the following three tenets in mind. Rather than articulating each goal as a static virtue, I found it helpful to represent them as one end on a spectrum — so if I’m ever unsure about the effectiveness of a concept, I can evaluate how far I think it strays to the undesirable end of the diagram. As mentioned previously, this thesis is dedicated to increasing the agency of the common worker. That means improving their control over how they spend their time, as well as providing better opportunities for reward and recognition of their work. The opposite end of the spectrum represents projects that consolidate power at the top of the socioeconomic hierarchy, either by diverting wealth or concealing information from the common class. The way that I’ve framed the opposite end of the spectrum — designs that “consolidate power at the top” — may sound blatantly negative, but the truth is that many contemporary consumer-facing instantiations of AI do just that. Devices like Amazon’s Alexa or Google Home provide a small utility for users — a hands-free, voice-activated interface for existing services — in exchange for vast quantities of personal information gleaned through constant listening, and used to drive corporate revenue through hyper-targeted advertising.2 Implicit in this effort to foster individual agency is an interrogation of the elitist values that underpin capitalism. Notions like the 2  Williams, Andrew. “How Amazon, Google and Apple Use Your Smart Speaker Data.” The Ambient. February 15, 2018.


design that consolidates power at the top

design that increases agency at the bottom



design founded on individualist values

design founded on egalitarian values



design meant to provide definite answers

design meant to provoke abstract questions

unconditional value of growth, the utility of greed and that the most prosperous are the most deserving are embedded in the products and systems that inhabit the world today. In an oppositional critique of this status quo, the projects in this thesis are intended to either demonstrate or implicitly advocate for an egalitarian, socialist approach to design: to each by need, from each by ability. This thesis is not a blanket condemnation of capitalism as an economic principle — centuries of sustained, global growth suggest that capitalism can achieve that goal with commendable consistency. Rather, it is a critique of the prevailing wisdom that today’s capitalist order is better at fostering individual agency than a more socialist articulation could. Many would contend that individual agency is at an all-time high, since we’re all technically free to start our own enterprises tomorrow, and that we might not get to choose what we do in a more state-planned, socialistic economy. But these arguments overlook the social, cultural and financial barriers that impede the majority of Americans from ever coming close to starting their own business, let alone succeeding at it. Our choices are narrowed by practical hurdles until we’re forced into a

job we cannot quit for fear of losing whatever material security we have. Additionally, these dogmatic defenders of capitalism presume that everyone ascribes to the same definition of personal success. But while all of us are desperate to achieve financial security — such is the world we have built — not everyone aspires to material wealth. In truth, many people find sincere self-actualization when working in a community service-oriented capacity. If only the free market could deign to pay these teachers, social workers and community advocates in a manner that matches the true value of their work!



These continuums are meant to reflect a few of the dichotomies that my projects straddle, and which end I hope they lean toward.

This thesis doesn’t sketch out any one vision of a socialist utopia. But if it did, that society would be one where all are free to pursue self-actualization as they see fit (provided it’s not to the direct detriment of others). Those who earnestly want to make their life’s work the service of others could choose to do so freely without material considerations swaying their decision. And while each person has their own quirks and idiosyncrasies, I believe that all humans have a fundamental need to work and be recognized for that work in order to be happy. So my socialist utopia would not automate away all opportunities for


meaningful human work. Examples of egalitarian products aligned with this design principle include time banks,1 single-payer healthcare, and universal basic income. These systems might easily be attributes of a future socialist utopia, and their adoption today could even help pave our way there. In contrast, products like Uber — which ties a worker to a rote duty and uses market pressures to manipulate him into doing that job longer and harder — exemplify the sort of systemic disempowerment of workers that I abhor. Due to the critical nature of this thesis, these concepts may seem impractical or non-implementable in the real world. My intention is that, when you encounter this sense of skepticism, you follow the thread until you uncover what existing paradigm or “common sense” belief my design runs into. From there, the next question elicited might be: “Which feels more humane: the existing belief, or the one implicit in the project?” I hope that my work can effectively provoke this sort of critical reflection in as broad an audience as possible. This is why I’ve marked my goal a third of the way along the speculative-practical continuum: deeply speculative designs can be opaque and inaccessible for the uninitiated, and can often be mistaken for art. I intend for my projects to seem familiar at first glance, but with one or two clear incompatibilities apparent after a second thought. Each of these projects can be interpreted as a piece of “design fiction,” an imaginary story told through a speculative product concept. Just like conventional fiction, some details may seem more realistic than others — but the ambition of the work is not to convince the reader of its own plausibility. Rather, fiction aims to raise new questions in the mind of its reader. And these design fictions are no different.

1  “We Need Each Other.” TimeBanks USA. Accessed April 27, 2018.







WHERE DID A DESIGNER LEARN SO MUCH ABOUT AI? In the course of this work, I have been fortunate to meet experts and users whose work and domains of expertise overlap my own area of inquiry. I spoke with entrepreneurs, engineers, innovators, economists, researchers, ethicists, artists, designers, authors and filmmakers, all exploring the forefront of what AI is capable of today. I also talked to a handful of workers in different industries who had some exposure to automation-related change. All of them were able to share experiences or expert knowledge that directly informed my understanding of the evolving relationship between humans and the technology we build. Each conversation followed the same rubric: I began by mining for concise expertise on AI’s current capabilities and applications. From there, I nudged my interviewees to make their best educated guess about how things might unfold in the next few years. All the while, I probed for their thoughts on how AI and automation might impact society-at-large, beyond the scope of their individual industry. In this chapter, I’ve created summaries of some of my most illuminating conversations. Although the experts I interviewed brought a multiplicity of perspectives to my research, there was a consensus around two main points: the creation of Artificial General Intelligence is so distant an achievement as to be essentially impossible, and a society’s shared values wield a far greater influence on its evolution than the presence of any one technology. Experts gave a number of reasons why AGI may never be developed. First and foremost, there are no market reasons for corporations or governments to invest in its development, and thus AGI research will never see the funding required to move it beyond the realm of small-scale academic speculation. As Sidd Patwardhan (a natural language processing researcher at Apple) explained, there are plenty of ways to develop a compelling and effective voice assistant (i.e. Siri) that don’t demand the recreation of the human mind. But even with all the funding in the world, AGI

would still be decades away. Karthik Dinakar (CTO at Pienso, a machine learning startup) was quick to point out that neurologists are still nowhere close to creating a comprehensive model of the working human mind, let alone duplicating it in silicone. And even when we forego the loftier aim of recreating a human brain and focus on replicating specific capabilities, we are a long way off. Aditya Kalyanpur (a NLP researcher at Elemental Cognition) admitted that he may still be chipping away at teaching a machine to read at a kindergarten level three years from now, so inefficient is the current process by which algorithms are trained on new topics. As he put it: “It’s like teaching Helen Keller, but without touch.” All agreed that AI could dramatically reshape the way our human society functions without AGI ever being developed. But many stressed their conviction that no technology will ever have a greater influence on the form of our society than us. Justin Choi (a student journalist and self-described socialist) noted that the world economy already has the production and logistical capabilities to feed, house and care for everyone on Earth — if we chose to. And Rose Chan (tech entrepreneur and former economist at the world bank) reminded me that even the world’s happiest countries with the most robust welfare programs (e.g. Denmark) are elected democracies — meaning it was the will of the people to pursue and employ those policies. So while AI may prove a means to a more egalitarian end, it all rests on the actions of the job creators and policy makers who push investment forward — and the ordinary citizens whose votes and dollars keep them in control. But perhaps even more fascinating was where my interviewees differed. For instance, there was extreme variety of enthusiasm for how quickly or completely artificial intelligence could be adopted. Choi was wary that AI automation might pave the way for the elite class to render workers permanently obsolete, while manufacturing worker Maxi Cifarelli pointed out that automation can create better


tools that empower workers. (Both agreed that how this unfolds depends mainly on the approach taken by those managing individual companies.) Technologist Virigil Griffith was full of ideas for where humans might cede authority to machines, and even advocated for automating the CEO function at big companies as a cost-cutting measure. And tech start-up CEO Parry Bedi saw AI’s emergence as so inevitable that he thought a physical fusion between man and machine might be the best way to avoid losing control. Every interview was more different than alike, and so were the interviewees’ perspectives. Rather than attempt to synopsize those differences here, I’ve given each conversation a few paragraphs of summary and synthesis in the pages that follow. By necessity, not all of these conversations went on to directly influence my design process — I might have spent a year’s worth of exploration on each one — and so these summaries are not meant to chronicle the subject’s effect on my thinking. Instead, I am merely revealing the multiplicity of voices and perspectives that exist in the broader conversation around AI and automation.


Founder and Editor, Design Observer Designer, author, educator, artist


Jessica Helfand

That’s the beauty of being a sentient being, is that cognition takes time to gestate. The moment we privilege speed over depth and time, it’s all over.

As a renowned graphic designer, founding editor of Design Observer, author and painter, Jessica Helfand is no stranger to requests for speaking engagements — so I’m grateful that Products of Design could lure her to chat with its students this year. Helfand and I connected over parallels between my thesis topic and her own pet subject, the evolution of human identity and personhood in an appenveloped future. Helfand observes that whenever we delegate a task to a machine, we’re making implicit decisions that reflect our own assumptions of value: “What is it we’re abdicating by turning over a repetitious task to a bot? That’s the beauty of being a sentient being, is that cognition takes time to gestate. The moment we privilege speed over depth and time, it’s all over.” When we automate a task, we’re admitting that we think it should always be handled quickly and exactly as done before. The capacity to reflect, appreciate nuance and notice new things are uniquely human traits worth holding on to, along with creativity and improvisation. Helfand’s own research into identity focuses on the human face, which she hopes will be the last holdout of human traits to be successfully parroted by machines. Although the precise nature of her research remained obscure to me, I understood that she was in the process of cataloguing historical examples of the communicative power of the human face. But in a world where companies like Soul Machines1 are creating photorealistic faces with CGI and powering their expression reactions with detailed simulations of the neurochemical processes occurring in a real human mind, AI may be gaining ground faster than she thinks.

1  Vance, Ashlee. “This Startup Is Making Virtual People Who Look and Act Impossibly Real.” September 07, 2017.


Nathan Shedroff Executive Director, Seed Vault Ltd. Author, educator, strategist, designer, entrepreneur

Shedroff has written numerous books on design and emergent design practices and would be valuable counsel on my thesis overall, but we connected to talk about SeedToken, an intriguing new take on cryptocurrency that attempts to pave the way for the AI-driven, egalitarian future my thesis imagines. According to Shedroff, SeedToken is an authentication system for bots, since none currently exist. (Sort of like if there was a third party that did blue-check verification on Twitter, except for bots.)1 It’s also an open-source marketplace for bots and bot components, so people can make royalties off of their contributions down the line — to make sure that the AI Revolution doesn’t end up being run by a handful of companies, like the Internet is now.

The tactical, brain-dead monkey-work of any job is going away — except for retail, maybe. We’re still going to need to deal with numbskulls.

Shedroff has mixed feelings about the term “bot,” since it has no clear definition and means different things to different people. He prefers Conversational User Interface, or CUI, which refers to text-based chatbots, voice-based assistants (like Alexa or Siri), or even visual-audible avatars. Shedroff believes that CUIs are the next big paradigm shift in human-computer interaction. (His acronym nods to the Graphic User Interfaces developed by Xerox PARC that so dramatically transformed how we interfaced with computers.) He also noted that we don’t need to solve general intelligence and natural language processing to create pleasant CUI experiences, but that we should likely move away from trying to make every avatar human in appearance. When an avatar is designed to appear 1


human, we are inevitably disappointed when it fails to deliver on the promise. Instead of anthropomorphizing our technology, Shedroff suggests that we “zoomorphize” it. Making avatars that look like animals will provide all of the opportunity for emotional connection that makes for a compelling interface without any of the same false anchoring of expectations. Shedroff agreed with other interviewees’ projections that autonomous vehicles will be the first mainstream breakthrough for AI that eliminates large quantities of humanheld jobs outright, and also suggested that most accounting jobs would be right behind it. Jobs he thought would never disappear included electricians, plumbers, aestheticians, masseurs and retail workers (because “we’re still going to need [humans] to deal with numbskulls”). Emphasizing STEM in education is the wrong approach, Shedroff argued, and pointed to the example that the structural engineering of a building will soon be a plug-in for design software. Instead, educators should be focusing on critical thinking and creative thinking, two human capabilities that machines are nowhere near replicating yet. Shedroff attributed the lack of political action to prepare students today for the work of tomorrow to the fact that those in power today — running corporations, helming universities and government agencies — are products of the old system that values the quantitative over the qualitative. And while the qualitative revolution may seem inevitable in the face of AI’s impending domination of quantitative-intensive tasks, the old guard is unwilling or unable to see the writing on the wall.

Founder and CEO, Trustee, Seed Vault Ltd. Author, entrepreneur, artist I was fortunate to interview Meadows, a hyper-busy polymath who has been building and designing bots for the better part of two decades. We talked about his current projects and his vision of the future of work. I learned about the “multimodal” bots that his company, Botanic, designs for its corporate customers. These divide into three categories, or “modalities”: chatbots, the text-based systems that often operate as a first line of defense in e-commerce customer service; audio bots, which we interact with through speech (think Alexa, Siri, Cortana); and visual interfaces, which give the bot a face. While each form factor has its own best use cases and Meadows is happy to build any variant for his clients, he stressed that bots operating on multiple (or all three) modalities were the most effective at understanding and serving their human users. This is because whenever a bot is devising a response to a user input, it isn’t using logic to progress from an abstracted interpretation of what was said, like a human would — it’s making probabilistic predictions of what information the user is seeking based on detectable keywords and patterns. A system that is only parsing text will come up with its own most-likely-desired response (e.g. I have a 75% confidence ranking that she is frustrated, and thus would like some reassurance), but when that assessment can be cross-referenced against others gleaned from different inputs (i.e. tone of voice, facial expression), the system’s accuracy improves dramatically. Of course, this explains why it’s valuable to show the bot more inputs from the user — but why give the bot a face? Meadows explained that it all stems from how the human brain is wired, an organ honed by millennia of faceto-face interaction. The endocrinal response that a person has when they interact with a face is dramatically different to what happens when they interact with a disembodied voice. Essentially, showing a face subconsciously creates a more natural pattern of interaction between the human and bot, improving the quality of the data (i.e. facial expressions,


Mark Stephen Meadows

vocalizations and word choices) that the bot must interpret and react to. Meadows is adamant in his belief that bots will need an authentication platform, or else we’ll end up “surrounded by swarms of gibbering, nonsensical, advertising-y, rude conversational systems.” Warding off this dystopia is one of SeedToken’s main objectives

I feel like we kind of fucked up the web, in a way — we had great ambitions for it to be this great equalizing force. And I don’t want bots to become like that. AI needs to be democratized.

— which is why Meadows, like Shedroff, is part of the team. But SeedToken’s other main goal is to make the next phase in technological evolution an equalizing force: “Yes, we’re going to see massive disruption. And the systems that need to be built, and the systems of the future, need to be built by everybody — rather than the Facebook or Google model, which is a minority profiting off the rest. It’s fucking surveillance capitalism, that’s what it is. I feel like we kind of fucked up the web, in a way — we had great ambitions for it to be this great equalizing force. And I don’t want bots to become like that. AI needs to be democratized.”


Aditya Kalyanpur Natural Language Processing Researcher at Elemental Cognition One of the core 12 members at IBM Watson I was fortunate to connect with Aditya Kalyanpur, a founding member of the team that built IBM’s Jeopardy-winning AI program, Watson. Kalyanpur continues to work at the cutting edge of natural language processing research. He was eager to dispel and explain much of the hype surrounding AI today, as well as make his own predictions for the future. Kalyanpur sees the media fervor around AI’s potential as the confusion of “weak AI” breakthroughs with the dramatically slower progression of “strong AI.” Weak AI, which is task-specific and data quantity-dependent, may be a bit of a misnomer, as these algorithms can still do incredibly powerful things. But what makes them weak is that their learning cannot be generalized across

Why do we need training data at all? Why does it take one million images of a cat to train a catdetecting algorithm? It only takes a kid one or two pictures of a cat to know what cats look like forever. AI is not learning fast enough!

knowledge domains: an AI playing chess at a master level will never play Poker. And almost all the recent success in this domain can be solely attributed to recent improvements in computing power, as most of the algorithms being used in AI today are just tweaked versions of math developed in the 1970s. In contrast, strong AI — meaning AI capable of forming abstract understandings that can be generalized between data types and across domains — is nowhere. And while there are research organizations and companies dedicated to its pursuit, they are all “still light years away.” Natural language processing (NLP), which Kalyanpur researches at Elemental Cognition, is sometimes billed as the “holy grail” of AI


because it distills the core challenge of developing AGI: machine systems that actually “understand” the information they process, rather than the pattern detection of today. What makes this pursuit so difficult is that much of the information in human language is not actually in the data (words), but is actually embedded in our shared culture, experience, and corporeal existence. Teaching a machine to read for comprehension when it has no human experience of the world is beyond difficult, Kalyanpur notes: “Imagine teaching Helen Keller to read, but without touch.” Historically, AI development techniques could be divided into two areas: statistical and symbolic. Statistical techniques are taskspecific (like a machine learning algorithm), require a human in the loop for training, and are black boxes when they work. (For those unfamiliar, “black box” is a popular metaphor for the inexplicability of AI systems: While the inputs and outputs may be apparent, what happens between those steps is a total mystery.) Symbolic techniques are precise, logic-based programs that attempt to codify human knowledge into an operable system. The oldest AI “expert” systems were built this way, and were extremely brittle. For the longest time, disciples of these two approaches generally stuck to their own camp, with minimal cross-pollination. It wasn’t until after the success of an interdisciplinary venture like Watson that cooperation between the two sides became the norm. (Still, Watson never “understood” the questions it was asked, so much as it made educated guesses based on probabilistic word association data.) Kalyanpur is skeptical that AGI will ever be developed, and thinks multiple breakthroughs are required on the algorithmic level before his team can get a machine to read as well as a kindergartener. The data-intensive nature of current machine learning processes suggests that there are fundamental flaws in the paradigm. “Why do we need training data at all?” Kalyanpur asks. “Why does it take one million images of a cat to train a cat-detecting algorithm? It’s not making the right kind of

In Kalyanpur’s estimation, the jobs that will be replaced in the near future are those that require a lot of data processing, but not much genuine intelligence. Those who perform machine-like tasks, like courtroom stenographers, are eligible for obsolescence. But even jobs that require basic categorization of massive amounts of data — like junior law clerks combing through case files for precedent — are more likely to have their roles changed or streamlined, rather than replaced altogether.


abstractions; it only takes a kid one or two pictures of a cat to know what cats look like forever. It’s not learning fast enough!”

If AGI is getting to the moon, then we’re still just climbing trees.


Sidd Patwardhan Natural Language Processing Researcher at Apple One of the core 12 members at IBM Watson

Like any company, the goal of Apple is to build useful products…And you don’t need a fully intelligent system to achieve what you’re looking for.

Patwardhan is a cutting-edge NLP researcher working at Apple to make Siri smarter and a better user experience. He offered perspective on the current limitations of AI and the varying motivations for its research and development. Asked about the current hype surrounding AI and what layman expect it to soon be able to do, Patwardhan speculates that it may be a branding issue: “Maybe it’s the term ‘artificial intelligence’ itself; it may give the impression that it’s some sort of independent, selfsustaining being.” When asked how far we were from developing AGI, Patwardhan laughingly admitted that we are quite far, before elaborating that, “at least in the corporate world I’ve been in so far, there hasn’t been that much work toward [developing AGI.] It’s always been solving very targeted problems.” Companies like Apple don’t need to engineer complete machine understanding of human speech to create a product that can hear your question and give you a service-able answer—so they don’t. Since there are no goals or actionable targets for AGI research, it seldom gets off the ground. And that’s likely why it’s so difficult for us to imagine what such a system’s “motivation” For Patwardhan, more worrisome than the “academic” debate of whether or not AI will take over the world is whether humanity will put too much faith in our algorithms too soon. If we put them in charge of critical systems like the stock market or national defense systems without the proper safeguards or human oversight, what happens when they unexpectedly malfunction?


Co-founder and CEO, Glimpzit Former senior software engineering manager at Microsoft It was fascinating talking to Parry Bedi, a highly experienced AI researcher and engineer. Bedi had recently founded his own company, Glimpzit, which has machines doing creative tasks, rather than deterministic ones. “A lot of AI systems today only analyze ‘objective data,’ like sales.” Bedi said. “But for machines to be creative, they have to analyze ‘subjective data,’ data that is rich in human emotions, behaviors, and context.” Right now, Glimpzit’s AI processes unstructured text to make copy and image recommendations for clients’ marketing materials, but Bedi believes that getting his system to output a final ad (with image and text automatically combined into a single output) is not too far away. However, he encounters some client confusion around what to expect out of a “subjective” AI system. Since people are used to seeing AI handle deterministic tasks, they still expect deterministic results for subjective assignments. “But if you ask a human to do the same thing again and again, there will be some amount of variation — and we should expect subjective AI systems to behave similarly,” Bedi concluded.


Parry Bedi

inevitable march, and is a firm believer that humans and machines will eventually fuse, with internally integrated machines creating a better user experience than the external versions we use today. Additionally, he doubts that a fear-induced moratorium on AI or AGI research is worthwhile, as the “AI arms race” and fear of being left behind mean that someone, or some nation, will always

You cannot stop the technological progress, so you shouldn’t try to — there are ways you can manage it and try to minimize the impact, but you can’t turn back time.

keep pushing that boundary. Lastly, Bedi anticipates that the AI acceptance inflection point — or the “aha!” moment where everyone realizes the value of AI and accepts it as the new normal — will come with the impending mass adoption of driverless cars.

I’ll admit that, as a designer, I’m apprehensive when I hear earnest talk of fully automating creative work. But while I don’t doubt Bedi’s prediction that his system will soon be able to churn out ready-made ads with text neatly laid out over images, I am skeptical that a tool like Glimpzit could ever be anything other than an aid to creative professionals. Because while the consistency of the output may matter to Glimpzit’s clients, I expect that their chief concern will be the subjective quality of the content itself. Because the best ads are those that stand out from the crowd — and if Glimpzit’s output is based on an AI’s imitation of all the related ads that came before it, it’s likely that its output will do little more than check the box. The ability to create wholly original ideas is still the territory of the organic mind alone. Bedi perceives the incorporation of technology and AI deeper into our daily lives is an


Birago Jones Co-Founder and CEO, Pienso UX, UI and HCI researcher, teacher, designer, entrepreneur

...but if it’s an issue of causality, the ‘cause’ has probably already been made…That’s the problem with making causal inferences about things and the world. Is the root cause society’s greater lack of empathy?

When asked whether or not the proliferation of AI tools has the potential to spell the collapse of society as we know it (a view held by Stephen Hawking and Elon Musk, among others), Birago Jones — a longtime designer and innovator in the field of Human Computer Interaction — suggested that this argument might be too superficial. “Are these brash assumptions?” Jones reflected. “I have no idea! But if it’s an issue of causality, the ‘cause’ has probably already been made. The ambition itself — of having algorithms work on our behalf — is already there. So perhaps the invention of the light bulb is why robots will take over the world? That’s the problem with making causal inferences about things and the world. Is the root cause society’s greater lack of empathy?” Besides alluding to the inherent oversimplification of systems models, the point Jones makes is this: The tools we build (no matter how clever) are just tools, and therefore extensions of ourselves and our assumptions about the world we inhabit. An AI system made by a stratified, xenophobic society will most likely perpetuate a stratified, xenophobic society, as it was imbued with those biases at the moment of conception. Like Chan, he suggests: before we can redesign the future of AI, we may need to redesign the present of ourselves.


Co-founder and CTO, Pienso PhD, Applied Machine Learning and Natural Language Processing, MIT


Karthik Dinakar

All these core human concepts: creativity, inspiration, actually feeling emotions, thinking, reflecting, metathinking, meditating, learning from examples — these are explicitly human capabilities and capacities.

Karthik Dinakar holds a PhD in Applied Machine Learning and Natural Language Processing from MIT and is Jones’ cofounder at Pienso. In addition to having an intimate understanding of contemporary AI’s limits and capabilities, he thinks deeply about the philosophical implications of his work. He firmly doubts that artificial general intelligence, or AGI, will ever be achieved, because we still know so little about ourselves: “All these core human concepts: creativity, inspiration, actually feeling emotions, thinking, reflecting, meta-thinking, meditating, learning from examples — these are explicitly human capabilities and capacities. We don’t understand these fully!” If we are so far removed from understanding our own minds and internal processes, Dinakar argues, how can we hope to replicate them in a simulated being? Dinakar thinks that the hubristic talk of AI’s ability to transform the world is a Western conflation of wisdom and knowledge. “Today seems like a [reemergence of the] classic debate between wisdom and knowledge. There’s a very data-driven way of looking at the world, an aspiration of accumulating more facts, but I don’t necessarily believe that this will contribute to more wisdom,” he remarked. Unfortunately, I failed to press Dinakar on what made such a conflation uniquely Western, but I believe it may be related to the capitalist preoccupation with short-term gain, rather than long-term well-being. We can dump all human knowledge and experience into all present and future algorithms, but the hope that the result will be greater than, or even equal to, the sum of its parts is an ill-founded one. If there’s going to be another revolution in AI, it’s not going to be through more or better automation. Rather, Dinakar thinks that it will be brought about when we begin to bring machine learning into the ways we explore philosophy, music and art.


Rose Chan Founder, Therefore Former digital economist, World Bank PhD, Economics, University of Michigan I connected with Chan, who had previously worked as a digital economist at the World Bank, to learn more the plausibility of universal basic income (UBI) as a solution for massive job loss. I started by presenting her with this optimistic passage from Peter Frase’s Four Futures: “...with access to basic income, workers will be less willing to accept unpleasant and low-paying jobs, and employers will have incentive to find ways to automate those jobs. Meanwhile, the wage for desirable work eventually falls to zero, because people are both willing to do it for free and able to do so because a basic income supplies their essential needs....The long-run trajectory, therefore, is one in which people come to depend less and less on the basic income,

The question isn’t, ‘If we no longer live in a capitalist world…’ The better question is, ‘Are we setting up an environment that will enhance cooperation?’

because the things they want and need do not have to be purchased for money...things have become the product of voluntary cooperative activity rather than waged work. It therefore comes to pass that the tax base for the basic income is undermined — but rather than creating an insoluble crisis, as in the hands of basic income critics, the withering away of the money economy, and its corresponding tax base, becomes the path to utopia.” 1 Chan threw cold water on this socialist fever dream, pointing out that Frase’s train of thought was filled with assumptions. She reminded me that “economics is about incentives, at the end of the day,” and noted that, after extensive study, it is all but universally 1 Frase, Four Futures, 56-57.


agreed that people won’t work if they don’t have a reward for it — and making that reward something other than money simply pushes us toward less efficient exchange paradigms (e.g. bartering). Chan noted that each of the different capitalist societies in the world — and the varying levels of inequality therein — are a product of the culture and values of that society, rather than the other way around. More socialist-oriented capitalist systems, like the high-taxing, high-welfare Scandinavian states, are elected democracies — just like the United States. The reason the US still has extreme inequality and private healthcare, while Denmark has a relatively flat society and public healthcare, is ultimately a result of the beliefs of the voting population in each country and the politicians who are able to draw an audience among them. So while Chan wouldn’t go so far as to call Frase’s speculation explicitly wrong, she indicated that his hope of UBI leading to a groundswell for social change is probably more likely to happen the other way around. As our conversation concluded, Chan pushed me to change my angle of interrogation. “The question isn’t, ‘If we no longer live in a capitalist world...,” she said. “The better question is, ‘Are we setting up an environment that will enhance cooperation?’” If I’m hoping to push us towards a socialist utopia with my designs, getting my compatriots to buy into collectivist, cooperation-oriented ideals will be the big first step.

Editor at The Talon, Univ. British Columbia’s Alternative Student Press Self-described socialist Before moving to Canada at the age of 12, Justin Choi grew up in South Korea, where communism was a “dirty word,” given the nation’s tense relationship with the dictatorship a few miles north. But after becoming an adult and attending university in Canada, Choi felt his ideology move increasingly leftward, since it better aligned with his passion for defending the downtrodden. When probed with the suggestion that AI and automation might one day bring about a post-work, socialist utopia, Choi was skeptical, suggesting that if today’s society collectively accepted socialist values — which he describes as the belief that all humans should be able to live a life where they can survive and thrive to their maximum abilities — we could already be living in a post-scarcity utopia. He cites the enormous level of waste seen in industries like agriculture as an example. To evaluate Choi’s off-the-cuff articulation of ‘socialism’, I turned to Yuval Noah Harari’s definition for context: “Socialists believe that ‘humanity’ is collective rather than individualistic. They hold as sacred not the inner voice of each individual, but the species Homo sapiens as a whole. Whereas liberal humanism seeks as much freedom as possible for individual humans, socialist humanism seeks equality between all humans. According to socialists, inequality is the worst blasphemy against the sanctity of humanity, because it privileges peripheral qualities of humans over their universal essence. For example, when the rich are privileged over the poor, it means that we value money more than the universal essence of all humans, which is the same for rich and poor alike.”1


Justin Choi

Choi suggests that competition encourages the smartest people to work on the problems of those with the least immediate needs but most disposable income, while disincentivizing cooperating or knowledge sharing for fear of ceding competitive advantage. So Choi is apprehensive of technological advances that make automating more jobs possible, as it may only widen the gap between those who own the means of production and those who don’t.

If you’re born as a human being, you should be able to live a life where you can survive and thrive and live to the maximum of your abilities. And the minimum requirement is to not fear for your survival.

Choi points out that many of the best socialist ideas sounded absurd on paper, but became beloved and entrenched once built. Who today would have bought the idea that each city should be dotted by buildings that give away books for free, trusting that borrowers will bring them back? Thankfully, when Benjamin Franklin’s introduced the idea of “public libraries” to a nascent American, his proposal fell on a different era’s ears. And Choi is optimistic that future socialist ideas can be adopted one by one, made permanent by the fact that once people have them, they’ll never let them go.

Reading between the lines, it’s clear that Choi believes a prioritization of enforcing equality is the best path toward a society where “all humans” can “thrive to their maximum abilities.” 1  Harari, Sapiens, 231.


Lauren Phelps Associate, Robotics and Innovation, J.P. Morgan Chase Former robotic process automation consultant, PricewaterhouseCoopers Phelps has a first-hand view of all the ways that automation is currently changing the banking industry, and offered insight into how “white collar” jobs might transform in the coming years. According to Phelps, banks automate wherever they can due to competitive necessity in a cost-saving arms race. They may either justify the decision candidly (i.e. “we’re cutting costs by 20% by eliminating this redundant team”) or optimistically (i.e. “we’re empowering employees by enabling them to better engage in the more strategic parts of their job”), but the process is generally the same: Tasks that are rules-based, repeatable and “error-prone”, meaning susceptible to human error and not much else. And, while it may have been needed, “change management” — helping

‘Change management’ — it’s another consulting buzzword. It’s something we don’t do much of, and that we wish we did more of. It’s how to talk to your employees and tell them what to do next.

employees are happy to spend fewer hours reconciling data from different sources and more time working on the actual analysis and report that comes at the end of the process, there are many simpler, less “creative” tasks that were outsourced to low-wage workers in the developing world in the preceding decades. When those jobs are made entirely obsolete by automation, the affected communities are unlikely to have the power necessary to draw sufficient global attention to their plight. In the next few years, Phelps anticipates that there will be increasing demand for “clientfacing” robotics — and the supply of AI-powered systems to meet it. “Cognitive agents,” or systems that can read and predict human emotions, are being developed and marketed by companies like Affectiva to augment the customer service industry.1 And for anyone who worries about potential job losses among call center workers, Phelps doesn’t share those concerns. “20% of call-service employees don’t show up on Monday,” she said. “They don’t like their jobs, their jobs are miserable — and Mondays are when all the grumpiest people who have been putting off resolving that overdue bill decide to call in.” That sounds like an industry ripe for innovation to me.

companies discuss the result and implications of automation with employees — was rarely a component of Phelps’ consulting deliverables at PwC. Change management prepares executives whose teams have been automated for an entirely different role. Instead of reading a room, or troubleshooting human emotions and team dynamics, a manager’s core competencies become less strategic and more tactical: turning ‘employees’ on and off, monitoring their work for bugs, data analysis, scheduling, and risk management. Phelps notes that she has seen little evidence of people being directly replaced by automation, but that this may be a product of living in the “developed” world. While most bank



Documentary filmmaker Currently working on a film called Automation Nation: Robots, Algorithms...and the Future of the American Dream I thoroughly enjoyed an engaging and expansive conversation with Wild, whose current project made him a valuable resource in my area of exploration. Wild is a documentary filmmaker in the midst of a project that explores how AI and robotic automation, an undertaking that has taken him to every corner of the country as he interviews workers and managers alike. According to Wild, automation isn’t the problem, but rather our current failure to prepare our culture and workforce for automation. If we can design an equitable future and adequately educate our workforce for the jobs of tomorrow, all will be fine. But this will require a dramatic reformation of values, especially in the US. “Our entire American identity and culture is based on the idea that if you work hard enough, you can make a life for yourself. And whether that’s true or not, that myth is true for people,” Wild said. And rewriting the American Dream doesn’t happen overnight. I learned that there’s extremely high correlation between communities where manufacturing job losses were heaviest, where Obama voters switched to Trump, and where the opioid crisis has hit the hardest. Wild is concerned that further deployment of automation will only exacerbate the unrest in these areas, already seemingly cut adrift from the mainstream economy. Truck-drivers are one of the highest-earning groups in this blue-collar America — but not for long, according to Wild (and others) who see the advantages of turning the task over to fleets of autonomous trucks. And while the benefits of this change are undeniable – efficiency, consistency, safety, cost-saving – there’s no doubt that it will be devastating to those individuals incapable of retraining for a similarly lucrative job as well as the communities they live in. “Is there a world where we invest heavily in education in those blue-collar communities?” Wild wonders.


Chandler Wild

changes would benefit the American student, Wild pointed out the irony of pushing kids toward knowledge-based careers in STEM. Many of these careers depend on advanced math skills and procedural, logical thinking — difficult to cultivate in the human mind, but not so difficult to replicate in advanced algorithms. Instead, it’s the more creative, interpretive and interpersonal skills that will provide longer-lasting, less-automationsusceptible value for human workers.

Our entire American identity and culture is based on the idea that if you work hard enough, you can make a life for yourself. And whether that’s true or not, that myth is true for people.

Since his working film title boasts an answer, I asked Wild what the future of the American Dream might be. After a brief pause, he defined it thusly: “It’s the ability to do whatever one wants to do…The old American Dream is based on the illusion of a continued westward expansion.” And while Wild was hesitant to explicitly say that UBI may be the missing ingredient to make American Dream 2.0 possible, he did express a hope that the freedom to do “whatever one wants” would stem from some manner of safety net that reduced the fear of failure.

When pressed about what type of curriculum


Maxi Cifarelli CNC Operator at Marlin Steel, a wire basket manufacturer in Baltimore, MD Jeweler, artist

They hire people that are willing to learn, and they’ll send them for training…There are people that are hired to do one thing, and Marlin pays for them to learn other skills.

Maxi works at Marlin Steel, a company that was recently in a USA Today feature about automation trends in American manufacturing.1 Marlin has been hailed as an exemplar of how automation can help a US-based manufacturer keep its labor force and compete with offshore competitors (and their dramatically cheaper labor), due to the company’s policy of retraining existing personnel whenever new machines are incorporated into the production process. It was fascinating to chat with Cifarelli about what this all looked like first-hand. She pointed out that while cost-saving was a major driver for management’s decision to start phasing out the more traditional assembly techniques of years past, safety was perhaps even bigger. “We’re OSHA [the Occupational Safety and Health Administration]’s golden child in Maryland, we’ve won lots of awards from them, and it’s been over 3,000 days since we’ve had a ‘lost-time’ accident [where a worker injury required time off from work],” Cifarelli noted. Cifarelli does attribute much of the positive atmosphere at Marlin to the employee-empowering culture created by the company’s owner. While other companies that bring in new hardware may look to fire a few dozen factory hands in exchange for a handful of engineers, Marlin is committed to retraining and cross-training its existing employees on new tools and processes, including a tuition reimbursement program for any training that can’t be done on-site. (Whether or not other automating companies are similarly compassionate remains to be seen.)

1  Bomey, Nathan. “Special Report: Automation Puts Jobs in Peril.” USA Today. February 06, 2017.


Scientist, Ethereum PhD, Computational and Neural Systems, Caltech

Griffith is an effervescent engineer, researcher and former neuroscientist who currently works at the Ethereum Foundation, building and maintaining its blockchain-based distributed computing platform. While not directly involved in AI research or machine learning implementation, his background, broadranging interests and apparent tendency to have a strong opinion on everything made him an interesting source. An Estonian e-resident,1 Griffith is a big proponent of technology’s ability to help some countries replace certain institutions. “It also operates as a fallback layer in the case of a [partial] collapse of the state,” Griffith elaborated. “Countries could fall back on cryptocurrencies in the case of a war, rather than forcing people to revert all the way back to bartering.” In general, Griffith believes that AI systems have the full potential to replace key executive decision-making processes, if culture will permit it. He likens today’s CEO to the switchboard operator of yesteryear — which was considered a good job and skilled labor at the time — as both jobs perform infrastructure-connecting roles that are susceptible to automation. “Once it’s proven that this role could be automated, who wouldn’t want to do it?” Griffith argues, and notes that its potential for cost-cutting and bias-reduction should have broad appeal. And if there is apprehension around implementing such a system on a guinea pig population, Griffith believes that we’ll soon be able to test it on a viable simulation of a city or state. Clearly, many of Griffith’s ideas for how AI might be implemented are outlandish, bordering on absurd. And it seems likely that he made a concerted effort to showcase some of the most extreme ideas he’d encountered. But I was nevertheless intrigued to hear what someone who has been saturated in technology’s cutting-edge as long as he has believed to be just at the border of plausibility. 1


Virgil Griffith

When asked about current and potential trends for AI-related automation, Griffith was full of examples. His work for Legalese, a company reimagining how contracts are drafted, gives him unique insight into the future of the legal profession’s automation: “It’s unclear if there will be fewer lawyers or not. Junior lawyers will certainly be the first ones to get hurt and cut. But automations in law may create new opportunities and territories in law altogether. But again, individual lawyers will be able to accomplish way more! So it’s unclear if these will balance each other out, or which factor will come out on top.” Either way, Griffith anticipates that the way we interact with our “lawyers” will shift in the direction of software-as-a-service (SaaS), with a TurboTax-like app for every legal domain.

Once it’s proven that [a CEO’s] role could be automated, who wouldn’t want to do it? Griffith admits that he too was skeptical of arguments that AI could take over the world. But as a Go player and AI aficionado, he was blown away by how quickly AlphaGo, Google’s latest Go-playing bot, progressed. In just 6 months, it jumped from playing at a competitive level to a superhuman, crushing world champion Lee Se-Dol.2 Griffith concedes that most AI breakthroughs of the past decade can be attributed to hardware improvements, he says this is no reason to relax: If these massive advances and creation of new knowledge are merely the result of a hardware upgrade, what might happen once we develop some new math? Griffith isn’t making the layman’s mistake of confusing AI and AGI, and is instead highlighting how quickly AI can be trained to surpass human capability in a field that had only recently been considered the sole domain of human minds alone. If AI can conquer Go, he argues, are any human jobs really safe? 2  Russell, Jon. “Google’s AlphaGo AI Wins Three-match Series against the World’s Best Go Player.” TechCrunch. May 25, 2017.




On Futuring

LOOKING BACK TO SEE WHAT’S NEXT. The projects in this book are all exercises in “futuring.” But what does this term mean, exactly? Futuring is a practice that has been known by many names in the past — speculative futuring, future studies, futurology, to name but a few — with new names certain to keep cropping up as people dream up their own variations on the central theme.1 Futuring is generally defined by three key qualities: it looks beyond what’s probable to explore possible, preferable and even “wild card” futures; it imagines futures holistically rather than focusing on a single factor or short-term outcomes; and it interrogates the assumptions, beliefs and values embedded in prevailing predictions.


One of the earliest written instances of futuring in the English-speaking world was Samuel Madden’s 1733 work Memoirs of the 20th Century, which consisted of a series of diplomatic letters “written” in 1997 and 1998. A devout Anglican, Madden used his work of fiction to critique and satirize the beliefs of Catholics and Jesuits by depicting a future in which they have risen to power. Futuring would remain the (unlabeled) domain of creative writers until 1932, when H.G. Wells called for its formalization as a scholarly discipline. A prolific writer and thinker, Wells is best remembered for his science fiction — in which he predicted technologies like aviation, space travel, and even something like the Internet — and is often described as one of the founding fathers of the genre. Delivered at the end of a BBC radio program 1  Note that the similar-sounding “futurism” is not another name for futuring, and instead refers to the 20th century Italian modern art movement that brought works like Umberto Boccioni’s “Unique Forms of Continuity in Space.” But, even more confusingly, those who engage in futuring are still often referred to as futurists. To avoid confusion, I prefer to use “futurologist” when describing a practitioner of futuring. 2 “H.G. Wells.” April 28, 2017. https://www.

about telecommunication, Wells’ essay, “Wanted — Professors of Foresight!” warned of the potential dangers associated with the unchecked, half-considered proliferation of rapid transit and instant communication. In it, he chillingly presages the possibility of events like 9/11 and the realities of drone warfare. And he suggests that something should be done about it:

Today H.G. Wells is best remembered as the father of science fiction, but his writings spanned many genres — and he was the first to call for the formalization of “foresight” as a scientific discipline.2

“It seems an odd thing to me that though we have thousands and thousands of professors and hundreds of thousands of students of history working upon the records of the past, there is not a single person anywhere who makes a whole-time job of estimating the future consequences of new inventions and new devices. There is not a single Professor of Foresight in the world. But why shouldn’t there be? All these new things, these new inventions and new powers, come crowding along; every one is fraught with consequenc-


es, and yet it is only after something has hit us hard that we set about dealing with it.”1 The idea of “foresight” caught on, and PhD programs focused on quantitatively forecasting the potential impacts of new technologies began to appear in Europe and the United States by the 1960s.2 In these interdisciplinary programs, creative and critical thinking were combined to imagine a future and then justify it by asking questions about the present. But in truth, the most enduring, most compelling, most widely distributed instances of futuring would continue to be those put forth by storytellers.

The Tools of Futuring

Besides a freedom to explore beyond what’s merely probable, what differentiates the futurologist from the environmental scientist or economist? The latter kind of specialists work to develop probable predictions in a narrow topic range (e.g. sea levels will rise, the stock market will fall). In contrast, the futurologist is a generalist who seeks to imagine the collective impact of changes across all domains. To ensure they cover all their bases, futurologists often refer to the acronym “STEEP,” which stands for Social, Technological, Economic, Environmental and Political factors, as they develop their future scenario. Only by considering how all these external factors might impact a society can an exercise be considered holistic futuring. But perhaps the most important distinction is intent. The purpose of a futurologist’s speculation is not to predict as accurately as possible. Rather, it is to interrogate contemporary attitudes and approaches by extrapolating them forward in time and scale to examine the potential impact on society as a whole. To provide an example: environmentalists predict that, if current CO2 emission levels remain unchanged, New York City will likely experience a sea level of rise of 3.5 feet or more by year 2100.3 In contrast, a futurologist might write, “In 2100, global sea levels have risen 3.5 feet, displacing over 3 billion people and precipitating massive conflict and starvation 1  Wells, H.G. “Wanted — Professors of Foresight!” Studying the Future. (Melbourne: Australian Bicentennial Authority/ CommissionFor the Future, 1989), 3-4. 2  “World Future Society.” World Future Society. Accessed April 27, 2018. 3  Samenow, Jason. “Study: Sea-level Rise Is Accelerating, and Its Rate Could Double in next Century.” The Washington Post. February 13, 2018. news/capital-weather-gang/wp/2018/02/13/study-sea-levelrise-is-accelerating-and-its-rate-could-double-in-nextcentury/?utm_term=.817c4c6cf644


in refugee regions. This stemmed from the corporate class’s indifference to reducing CO2 emissions in the early 21st century, as they were confident that their short-term personal profits could fund their offspring’s exodus to fortified safe havens in the now-temperate mountain regions above the teeming masses.” By logically projecting one current issue into the future and imagining the other factors that would coincide with that change, the futurologist creates a compelling argument about a contemporary topic. Futurologists take a broad perspective, and often turn to mankind’s ancestry to determine what’s innate in our biology and social makeup, and what may still be subject to change. By first establishing an anthropological understanding of our species, we can better predict how our ancient brains might interact with the far-flung futures we imagine.


Experts aren’t sure when Homo sapiens evolved from its forebears, but most agree that as recently as 150,000 years ago, East Africa was inhabited by a species that looked identical to modern sapiens. 4 But about 70,000 years ago, as man began to expand its footprint out of Africa and into Eurasia, something happened that signaled a permanent change in human thought, now dubbed the “Cognitive Revolution.” It’s not clear what caused this change — a slight mutation, perhaps — but it enabled us to come up with new ideas and, most importantly, develop spoken language.5 The most intuitive theory for why humans developed spoken language is that it helped them to collaboratively strategize for survival over time. Screaming to signal the presence of a lion is one thing, but discussing the lions down by the water hole and the best way to get around them is quite another. But a second theory contends that, while chatting about threats was important, chatting about each other was more so. As Harari puts it, humans are social animals defined by gossip: “talking behind each other’s backs — a much maligned ability which is in fact essential for cooperation in large numbers… Reliable information about who could be trusted meant that small bands could expand into larger bands, and Sapiens could develop tighter and 4  Harari, Sapiens, 14. 5  Harari, Sapiens, 21.

So what exactly is going on in sapiens’ skulls that let us do that? Well, unique to mammal brains is a structure called the “neocortex,” a wrinkly rind that surrounds the evolutionarily older brain. (Neocortex is literally Latin for “new bark.”) Humans have a disproportionately large neocortex, consisting of roughly 30 billion neurons.8 The human neocortex didn’t get that big thanks to millennia of evolutionary refinement, though — it is indeed new, and has grown as large as it is as quickly as it has due to evolution’s decision to take one simple framework and multiply it massively.9 The increasingly validated theory of Vernon Mountcastle, a neuroscientist active in the 1970s and ‘80s, is that all regions of the cortex are performing the same exact function — the only thing distinguishing them is how they’re connected to the rest of the nervous system.10 What is this function, in simple terms? As Hawkins puts it, “Memories are stored in a form that captures the essence of relationships, not the details of the moment. When you see, feel, or hear something, the cortex takes the detailed, highly specific input and converts it to an invariant form. It is the invariant form that is stored in memory, and it is the invariant form of each new input pattern that it gets compared to.”11 There is no corresponding paradigm for how machines process information, yet — and while machines are already millions of times faster on a mechanical level, the memory-based flow of human cognition is remarkably efficient. Together, the massive size, flexibility and efficiency of the human neocortex enable us to make accurate predictions about our environment on a comprehensive scale. But what defines the human mind is not merely the power of 6  Harari, Sapiens, 24. 7  Evolutionary psychologists allege that it is this manner of subsistence that still defines not just our physiology and omnivore diet, but also our social habits, sexuality and overall cognition. Some conclude that it is this immense disparity between that world and our present-day, post-industrial society that is the key contributor to much of our collective discord and unhappiness (Harari, Sapiens, 40). But it must be noted that there was never any single, “true” hunter-gatherer lifestyle — even then, sapiens consisted of different factions surviving in unique environs with presumably diverse techniques — of which we now know very little (45). 8  Hawkins, Jeff, and Sandra Blakeslee. On Intelligence. (New York: Times Books, 2004), 43. 9  Hawkins, On Intelligence, 99. 10  Hawkins, On Intelligence, 51. 11  Hawkins, On Intelligence, 82.

the neocortex, but how the neocortex operates in concert with the old brain and the rest of our biological machinery. The old brain is the origin of our emotions, fears, pains, needs, desires — our motivation, essentially — and is basically unaltered from before the Cognitive Revolution.12

Creativity, sometimes put forward as a defining trait of mankind, is a property unique to the neocortex. As Hawkins puts it, “Creativity can be defined simply as making predictions by analogy, something that occurs everywhere in the cortex and something you do continually while awake....We predict the future by analogy to the past.”13 So if all mammals are exercising creativity somewhere, what distinguishes human creativity? The answer is the degree to which we can predict the unprecedented based on our immensely intricate and abstract analogies. The Cognitive Revolution is timestamped by the sudden invention of many things: boats, oil lamps, bows, arrows, and sewing needles sprang into existence between 70,000 and 30,000 years ago — as well as the Stadel lion-man, thought by archaeologists to be the oldest known example of what is indisputably ‘art,’ due to the necessary involvement of imagination in its creation. And mankind hasn’t looked back since, constantly devising new designs and artworks in equal measure.14


more sophisticated types of cooperation.”6 Language also enabled humans to pass on expert knowledge between generations, honing and refining the techniques that characterized what was at that time still a hunter-gatherer lifestyle.7

Spoken language paved the way for written language, which in turn facilitated (or more likely, coevolved with) the codification of rituals, religions and commerce.15

12  Hawkins, On Intelligence, 208. 13  Hawkins, On Intelligence, 183-184. 14  Harari, Sapiens, 21. 15 “Daily Life In Ancient Egypt.” Daily Life in Ancient Egypt.


But creativity comes at a cost. Our analogizing minds may just as easily land on a false model as a true one. As Hawkins puts it, “Prediction by analogy is pretty much the same as judgment by stereotype...we cannot rid people of their propensity to think in stereotypes, because stereotypes are how the cortex works. Stereotyping is an inherent feature of the brain.”1 If Hawkins’ theory is true, then those

many individuals.”3 The analogizing nature of the human mind predisposes us to form and accept explanatory stories that match our expectations, as already demonstrated by our stereotyping habits. The motivation for this sits somewhere in our animal selves: “Evolution has made Homo sapiens, like other social animals, a xenophobic creature. Sapiens instinctively divide humanity into two parts, ‘we’ and ‘they.’”4 We define ourselves by what we perceive as different, by what we say we are not. So, what’s innate? So far, we have: spoken language, creativity, stereotyping, xenophobia, and the big neocortex that makes them possible. There is also a penchant for facilitating increasingly large functional communities through intersubjective myth. There are the fundamental motives of love, lust and fear, drawn from our old brains and biochemistry. And there are the common desires for purpose and respect, engendered by the societies our myths have built. Now that we’ve established a baseline of human nature’s essential traits, let’s begin.


The Stadel “lion-man” is a 32,000-year-old ivory figurine found in a cave in Germany. Given its appearance as a half-man, half-beast chimera, it is believed to be the oldest example of a human artifact that demonstrates our ability to imagine things that do not exist.6

who appreciate creativity must come to terms with humans’ tendency to falsely stereotype their neighbor — or they must create new myths to combat it. Since the Cognitive Revolution, humans have been using their imagination and language to create these shared stories, or myths. “Myths and fictions accustomed people, nearly from the moment of birth, to think certain ways, to behave in accordance with certain standards, to want certain things, and to observe certain rules,” Harari writes. “They thereby created artificial instincts that enabled millions of strangers to cooperate effectively. This network of artificial instincts is called ‘culture.’”2 He defines myth broadly, encompassing everything from religions to laws, money or nations. A myth is any guiding narrative that exists in inter-subjective reality, “something that exists within the communication network linking the subjective consciousness of 1  Hawkins, On Intelligence, 203-204. 2  Harari, Sapiens, 163.


The “Futures Cone” is a framework developed by Stuart Candy in 2009, and has since become a popular tool for categorizing envisioned futures.5 It represents the gamut of hypothetical scenarios that a futurologist might work through in order to ask a question about the present. By challenging futurologists to sort their speculations into varying degrees of plausibility, the futures cone demands clarity about the types of questions those speculations intend to ask. For instance, Probable futures may ask practical questions (i.e. is this particular solution achievable?), while Possible futures may be used to probe more abstract questions of human nature (i.e. would American society value its consumer freedom or its social well-being more in an AI-governed world?). Some of the most successful instances of creative futuring are works that blur the line 3  Harari, Sapiens, 117. 4  Harari, Sapiens, 196. 5  Dunne, Anthony, and Fiona Raby. Speculative Everything: Design, Fiction, and Social Dreaming. (Cambridge: MIT Press, 2013), 3. 6 “Lion-man.” Wikipedia. April 26, 2018. https://

The Futures Cone, popularized by futurologist Stuart Candy, will serve as the organizing rubric for my projects.






hierarchy of likes in “Nosedive,” an episode of his popular dystopian anthology series Black Mirror. In each of these works, the authors depict the consequences of ideology clashing with both new technology and our inherent human characteristics. Science fiction movies such as Blade Runner (1982), The Matrix (1999), Her (2013) and Ex Machina (2015) have continued to grow futuring’s audience. All of these films interrogate a contemporary attitude toward technology in society, while their aesthetic appeal and narrative intrigue entice the public to ponder the evolving complexities of the human-machine dynamic — and the ethical issues that soon abound.

The social rating system in “Nosedive,” an episode of the dystopian anthology TV series Black Mirror, served as partial inspiration for one of my subsequent projects, Plus.2

between utopia and dystopia (i.e. heaven-onearth or hell-on-earth). Two acclaimed science fiction novels that achieve this are Aldous Huxley’s Brave New World (1932) and Kurt Vonnegut’s Player Piano (1952). In Brave New World, Huxley imagined a perfect society built on eugenics and gene-editing — the logical evolution of the race-based pseudoscience that the Nazis would ride to power just a few years later. While it first appears a paradise, Huxley’s world proves to be an oppressive state determined to rub out individual thought and expression. In Player Piano, Vonnegut portrays a fully automated society that affords the average citizen every material comfort, but little to no opportunity for fulfilling work. Collective frustration among the working class eventually foments a bloody and destructive revolution. Peter Frase dwells on the twin needs of purpose and respect in Four Futures: Life After Capitalism. In today’s society, myths like money and property have been built to fill those needs, but even in Frase’s exploration of speculative fictions where those structures are eschewed, these needs remain. In Cory Doctorow’s book Down and Out in the Magic Kingdom, post-employment immortals still feel the need to amass “virtual brownie points” in order to assert rank over one another.1 Satirist Charlie Brooker imagines a similar 1  Frase, Peter. Four Futures. 60. 2 Booker, Charlie. “Black Mirror: Nosedive.” 2016. Netflix.


I aim to achieve something similar with the projects in this thesis. Each project was conceived as a means of provoking questions about systems, practices and values that are in place today. And while the vehicle for this provocation is always an imagined application of artificial intelligence — sometimes closely linked to what’s capable now, and sometimes far afield of it — this thesis asks less about our relationship with machines than it asks about our relationship with one another. While it’s subjective how possible some of these speculative designs are, and while the spectrum of practical-to-abstract is certainly more of a fluid gradient than a scale of precise increments, categorizing speculations by plausibility can be an effective way to group questions that are similarly practical or abstract. So in the project-specific chapters that follow, I’ve done precisely that. These projects are sorted into three groupings: probable futures, plausible futures, and possible futures. I begin with projects that are theoretically implementable in the next few years; I then progress to potentially further futures that would require some cultural transformation to become reality; and I finally conclude the most speculative, far-flung and distant futures. with projects that are potentially implementable in the next few years. Several of the projects exist in utopian setting, but none of these futures are meant to be perfect. Ultimately it is up to the reader to determine which are preferable — and which are not.






This first section features products and services that could exist in near and probable features. Each project proposes a novel use of existing or emergent AI techniques as a means of forestalling inequality and empowering the disenfranchised. This work was primarily done in the final progression of my thesis, and each project is meant to exemplify the type of products and policies that I hope to see come into the world. In this section, youâ&#x20AC;&#x2122;ll encounter: an algorithmic water allocation policy that turns water saved into money earned; a service that gives truck drivers a better stake in a semi-driverless industry; an app that helps disenchanted young professionals find a new role that better aligns with their personality; and a wearable device that helps the elderly extend their independence. While they target different inequalities and demographics, these projects are united by a common vision â&#x20AC;&#x201C; a close future where AI is used to improve the agency of those who might otherwise be left behind.






The open road. On your terms.




StayGo is a service aimed at improving the lives of truck drivers by giving them the opportunity to explore web-based opportunities from the open road. It does this by operating a fleet of semi-autonomous trucks with sleeper cabs that double as mobile offices, so drivers can take online classes or pursue their own ventures while AI handles the highway driving. This helps drivers transition more gradually out of an industry which will soon depend less on human involvement, and helps the trucking industry retain and attract the drivers it needs while self-driving technology continues to be refined.


When economists and futurologists speculate which contemporary jobs are on the chopping block of AI-powered automation, many are quick to point to truck drivers as next in line. (This was one focal point of my conversation with documentary filmmaker Chandler Wild, in particular.) After all, with some of the world’s richest companies pouring money into developing driverless cars for the consumer market,1 it stands to reason that people whose

StayGo’s trucks drive themselves on the highway, but need a human driver to take over on local roads. By turning the sleeper cab into a mobile office, drivers are able to use that time to focus on other pursuits — like expanding their duck decoy carving YouTube channel.

1 Taylor, Edward. “Self-driving ‘arms Race’ Complicates Supplier Alliances.” Reuters. April 13, 2017. https://www.


profession is over 66% driving1 will be out of work sooner rather than later. But a closer look at what truck-driving really entails reveals a host of humanityintensive tasks that aren’t so easily automated. Overseeing loading and unloading, refueling, mechanical maintenance, interacting with authorities — activities like these can occur unpredictably throughout a trucker’s day, and each contains thousands of possible sub-tasks and variations. This high degree of variability means that trucking companies will continue to rely on human experience and intuition to steward shipments. Additionally, it’s important not to let Silicon Valley’s hubris fool you: city driving may seem similar to highway driving, but, in reality, getting cars to reliably self-navigate on highly variable, pedestrian-

an industry in turmoil. In 2017, the American Trucking Association reported an 81% turnover rate, which represents the proportion of truckers who changed employers as well as those who quit the industry outright. This figure is closely associated with the ATA’s projected shortage of 50,700 qualified drivers in 2018. Both of these data points have been steadily on the rise since the Great Recession, and are largely attributed to the fact that most drivers can find jobs that pay similarly (poorly) without being socially isolated in an 8’x8’ metal box for weeks at a time. And for an industry that employs 3.5 million drivers (and 7.5 million people overall), those are worrying numbers.3 But discord plus uncertainty breeds opportunity — opportunity that StayGo is determined to transform into new value for truckers and trucking companies alike.


StayGo owns and operates a fleet of semiautonomous sleeper cab trucks, each outfitted with a computer and high-speed internet. Shipping companies hire StayGo to run its routes just like they would hire a driver that owns his own truck. From there, StayGo rents out its truck routes to drivers interested in trying something new.

Autonomous vehicles are getting better every day, but we’re still years away before they can reliably navigate every surface street in the country.3

filled city streets is a different can of worms.2 So while truck drivers may not become completely obsolete in the next few years, AI still promises to change their lives and livelihoods. But how? Even without the impending advent of semi-autonomous trucks, trucking is already 1  Panel on Research Methodologies and Statistical Approaches to Understanding Driver Fatigue Factors in Motor Carrier Safety and Driver Health. “Fatigue, Hours of Service, and Highway Safety.” Commercial Motor Vehicle Driver Fatigue, Long-Term Health, and Highway Safety: Research Needs. August 12, 2016. books/NBK384974/ 2  Brooks, Rodney. “The Big Problem With Self-Driving Cars Is People.” IEEE Spectrum: Technology, Engineering, and Science News. July 27, 2017. transportation/self-driving/the-big-problem-with-selfdriving-cars-is-people


The appeal for current truck drivers is clear: For a marginal pay cut (StayGo’s fee), drivers suddenly have hours of free time in the middle of their day to mine the Internet for new opportunities. Many drivers may already have an idea for a new web-based side hustle. And for those without an immediate plan, StayGo’s onboard terminal comes installed with third-party training resources like Skillshare, Lynda, and its own proprietary entrepreneurship curriculum. By changing the value proposition of a truckdriving career, StayGo won’t just improve the lives of current truck drivers — it will broaden the career’s appeal and expand the labor pool. There are already people with web-based careers who harbor a secret itch to experience a life on the open road. For many creative professionals, the private work environment offered by a StayGo truck’s mobile office is 3  Costello, Bob. “Truck Driver Shortage Analysis 2017.” American Trucking Associations. October 2017. 3 “Autonomous Car.” YouTube. August 27, 2015. https://

based driverless trucking start-up, machine learning engineers will soon share an office with veteran truckers; the human drivers will remotely pilot multiple trucks at once, taking over from the AI pilot when it’s time to navigate local roads.5 And all of the plans to put autonomous trucks on the road require human involvement in critical stages that bookend the highway journey. Once I discovered this, it


enough incentive to try something new. And for the rest, the promise of additional income and the chance to live as a professional tourist of North America’s beautiful landscape will be a powerful motivator. For this group, there will be an additional barrier to entry: training. But StayGo will work in close partnership with trucking companies and the American Trucking Association to develop a driving


$ %

trucking companies



time labor

simulator, training curriculum and certification process that prepares aspirant truckers for the challenges of the open road.

became clear that my own solution’s differentiator could be how I consider the needs and experience of the humans who partner these semiautonomous machines.

StayGo takes a portion of the driver’s fee, much like a temp agency. In return, the driver gets back hours of each day to invest in his or her future.


The key insight that sparked the final concept was the fact that driving is just one part of what a truck driver does. While it may sound obvious in hindsight, it came as a surprise as I researched how trucking and mobility companies are already working to implement autonomous technology in their trucks. Uber plans to have two separate fleets — one made up of AI-powered trucks that navigate highways, and the other consisting of fully human-driven trucks that handle the last local leg. 4 At Starsky Robotics, a San Francisco4  Winick, Erin. “Uber’s Self-driving Truck Plan Relies Heavily on Humans.” MIT Technology Review. March 06, 2018.

5  Chafkin, Max, and Josh Eidelson. “These Truckers Work Alongside the Coders Trying to Eliminate Their Jobs.” June 22, 2017. com/news/features/2017-06-22/these-truckers-work-alongside-the-coders-trying-to-eliminate-their-jobs


WHO ITâ&#x20AC;&#x2122;S FOR

Ted Trucker Charlotte, NC 55 years old Trucking for 30 years Wood carving hobbyist Struggling to make ends meet

StayGoâ&#x20AC;&#x2122;s primary user group is career truckers who want to try something new, like Ted. But the prospect of a free mobile office on the open road will also attract new drivers who already have web-based careers.




• drives itself on the highway • manages navigation • prompts driver when needed


• drives off the highway • oversees loading and unloading • handles fueling, basic maintenance

The division of labor between truck and driver is new, but straightforward.



Ted Trucker is a duck decoy carving maestro — when his hands aren’t on the wheel.

StayGo’s mobile office and free online learning resources helped Ted turn his hobby into a business — including a pretty healthy YouTube following for his process videos.

Now Ted can focus on his craft-based business. But he has the flexibility to book a ride with StayGo if he ever needs the extra income — or just when he’s missing the open road.


An ad extolling the virtues of the open road catches Susan Writer’s eye.


She’ll need to get licensed, first. But StayGo’s VR trucking training program is clear, easy — and even a little bit fun.

Now she can book a ride with StayGo whenever she needs to leave the clamor of the city for some creative inspiration — or just a little extra cash.


Clockwise from top-left: Uber Freight trucks meeting at a local relay station;1 Classmates help ideate from my key questions; the fruits of a group ideation session; Starsky Robotics’ truck driver command center prototype,2 Dave from the YouTube channel “Smart Trucking.”3 1 2 3 watch?v=ESQ9aXyjTmM&t=18s




Empowering disaffected young professionals by turning soul-searching and skill-building into a group conversation.




Classmates is a mobile app that guides young professionals through a career switch. The app is designed to do two things for its users: introduce them to alternative jobs they might find appealing, and connect them to learning resources and people that will help them begin their new career. Due to automation, as many as 800 million people could be forced to switch occupations by 2030.1 And while anticipating the specific challenges of this transitioning class is impossible, there is an existing demographic who are similarly compelled to change jobs, albeit for slightly different reasons: early-career professionals who are dissatisfied with their jobs. By designing for this contemporary 1  McKinsey Global Institute. Jobs Lost Jobs Gained: Executive Summary. 2017

need, I hope to create a solution that might also work for the displaced workers of the near future. Because whether someone loses their job to a machine, or simply hates it to begin with, they’ll still need a way to find meaningful work.

1 in 2 American workers are emotionally disconnected from their jobs. Classmates is an app designed to help career-changers find a job that fits.


More than half of American workers in the private sector feel emotionally detached from their career. According to a 2016 study by the Pew Research Center, 55% of private sector workers describe their job as “just what they do for a living,” as opposed to 42% who said their jobs gave them a sense of identity. There is positive correlation between worker satisfaction and advanced degrees: workers with


INDIVIDUAL Monster Glassdoor LinkedIn

TheLadders Lynda


Skillshare General Assembly



EdX Coursera


There’s already a wealth of websites for people looking to change jobs — but they’re either for people who are already qualified for the job they want, or people who know what they need to do to get there. And none of these resources leverage the supportive power of groups.

postgraduate education reported the highest rate of career satisfaction in the study, at 77%.1 But if even the most privileged, resourceintensive journey through the American education system leaves almost one in four workers disappointed, something isn’t working. Why is worker dissatisfaction so high? One answer might be our American obsession with financial independence. As soon as an individual reaches adulthood in the United States, society’s general expectations are clear: each decision they make should be a step toward financial independence, whether that’s going to school, starting a business, or getting a job.2 The fear of being a burden to parents drives young adults to find immediate success in a profession. For many families, careers in the STEM fields (science, technology, engineering and math) appear to offer the best promise of financial security, and students are pressured to specialize in one of 1 “How Americans View Their Jobs.” Pew Research Center’s Social & Demographic Trends Project. October 06, 2016. 2 Goudreau, Jenna. “Nearly 60% Of Parents Provide Financial Support To Adult Children.” Forbes. August 10, 2011. parents-provide-fi nancial-support-money-adultchildren/#32a253cb1987


these areas as early as possible.3 But besides the risk that many of these careers may not be as lucrative in a decade or two — the quantitative-intensive nature of these jobs makes the work difficult for humans, but fairly simple for intelligent machines — these hurried specialists may end up locked into a professional track that they belatedly realize doesn’t fulfill them, or makes them emotionally unhealthy. Cumulatively, the pressures of adulthood and financial independence make people choose careers they’re not well suited for. Some would argue that work was never meant to offer the same fulfillment as a social life, and so any emotional benefit a job offers is a bonus. If that were true, one might expect to see some statistical indication that Americans prefer not to work. But in fact, the opposite is true: unemployed Americans who are looking for work are categorically less happy than their employed counterparts, even when they’re equally financially secure. 4 This suggests that Americans do want to work — just not the jobs in front of them. And 3 Jacobson, Annette. “Column: Why We Shouldn’t Push Students to Specialize in STEM Too Early.” PBS. September 05, 2017. 4 “How Americans View Their Jobs,” Pew.

This collective dissatisfaction is a mounting trend, with America’s youngest adult generation leading the charge. Summarizing Gallup’s 2017 report on The State of the American Workplace, statistician James Harter explains that millennials are more likely than their predecessors to choose work that allows them to grow professionally and provides a sense of purpose, while higher salaries were of secondary importance — and these preferences don’t appear to be softening as millennials age.5 As millennials rapidly eclipse baby boomers as the dominant generation in the American

workforce, employers will be forced to adapt to this new list of demands. All this means that many young professionals are experiencing a troubling clash between their values and reality, as the career they 5  Robaton, Anna. “Why so Many Americans Hate Their Jobs.” CBS News. March 31, 2017. https://www.cbsnews. com/news/why-so-many-americans-hate-their-jobs/

finally arrive at fails to meet their expectations. They will need to overcome careerswitching’s practical hurdles, and may also benefit from social support as they encounter the frustrations that can accompany the process. And this present-day journey from day job to vocation may ultimately mirror the career-switching necessitated by AI-powered automation in the decades to come.


while achieving 100% worker satisfaction may be impossible (such is the mutable nature of the human spirit), it is not unreasonable to demand a process that achieves better returns than a coin flip.


Classmates replaces the notion of “starting over” with a culture that encourages constant learning and personal development. It does this by building a supportive social environment around an existing ecosystem of learning resources available online at little

or no cost. And the app makes use of chatbot, card-swiping and newsfeed modalities to make the user experience of its guided selfreflection process as intuitive as possible.

In the project’s earliest stages, I used a process called “scenario mapping” to envision a potential user journey based on my persona group.

For new users, the first step is a conversation with the app’s built-in conversational user interface, or CUI, designed to assess what alternative jobs might be a good fit. The chatbot


For a new user, the experience begins with an introductory conversation with an advanced CUI, which asks three key questions to feed into Classmates’ vocation-personality matrix...


“What do you like about your current job?” “What do you wish you got to do more of?” “What are your hobbies or interests?”

Once Classmates has modeled a persona of the user, it shares a short list of jobs that should be compatible with the userâ&#x20AC;&#x2122;s interests and preferences.

When the user sees a job that interests them, they can click the card to begin browsing through trainable skills associated with that job.


Clicking into a skill allows the user to browse Classmates’ recommended third-party online learning resources for that skill.


To register for the class, the user gets taken off-app to the third-party educator’s site. But using Classmates’ code grants access to the app’s signature feature...

Send a post to the classroom channel

The Classroom is a messaging platform shared by the user, 20-30 other learners who signed up for the same class at the same time, and a mentor in the industry. Here, they can share resources and ask questions as they move through the curriculum.

Send a post to the classroom channel

Users are incentivized to participate by a pointsbased leveling system that enables them to unlock rewards, like access to in-person networking events.


Paper prototyping is a good way to get user feedback early in the design process.


explains the app’s premise before cordially probing for three important data points: what the user likes about their current job, what the user wishes their job involved, and what other hobbies or activities they enjoy. The app uses natural language processing to interpret the user’s inputs and associate them with concept clusters within Classmates’ massive model of work qualities and interests. Once the user’s persona is identified, Classmates can show the user a short personalized list of jobs that Classmates projects will have the best personality fit.

The Classroom uses the power of peer pressure to make the learning experience stickier for adult learners, who often sign up for a free course in a moment of peak motivation but eventually abandon it, slinking anonymously back into their normal routine. In essence, it replicates and tweaks the message-board features of more expensive online classes and repackages them for a subset of learners who signed up for a massive open online course (or MOOC). MOOCs are popular for their low cost and potentially high value, but student drop-off can be steep.

From there, the user can click into a job to see which learnable skills are associated with it, and click deeper to see the third-party learning resources that Classmates recommends for learning that skill. Once a user has identified a course that they’d like to take, they can use a special promotional code when registering to indicate that they’ve signed up through Classmates. This will grant them access to the Classroom, Classmates’ other signature feature.

The Classroom consists of a news feed shared with 20-30 peers who started the same course at roughly the same time, as well as a single mentor, paid by Classmates. The mentor is an industry professional who receives a stipend to answer tricky questions and post helpful resources — but the bulk of the exchange is intended to happen between the learners themselves. Learners are incentivized to post questions, responses and links through a scoring system that logs all the upvotes their posts

As part of Classmates’ greater mission to prepare today’s workforce for tomorrow’s AI Revolution, the platform nudges users towards skills that leverage their critical thinking, creative thinking, or emotional intuition — the traits of human thought that are the least imminently automatable. And rather than recommending jobs that are especially lucrative, Classmates tends to show jobs that meet a real social need. This reflects millennial’s desires to find meaningful work that aligns with their personal values rather than just a large income. Classmates will be well-positioned to make money in the present by selling advertising on the platform. Marketers know the value of a young adult audience that is actively considering a lifestyle change — a group that Classmates has neatly assembled all in one place. Besides the ad-based revenue model, promotional partnerships with select education providers can generate revenue through agreements to feature the site’s classes (provided they’re relevant to the user, of course).


This design process began by using a post-it intensive technique called “scenario mapping” to sketch out how my two user personas — the “old dog” and “young pup” — might interact with a piece of software designed to help them find a new job. By calling out the questions that might arise for a user at each stage, I was able to quickly create an extensive list of potential features for my final design proposal. But I knew I wouldn’t be able to develop an app for every potential user and problem, so my next step was to focus the app’s offering. Based on research that indicated the contemporary problem with growing job dissatisfaction among young professionals, I decided to narrow my target audience to just the “young pups.” This decision allowed me to distill my long list of possible features to a core set that I thought would best serve my users.

learning classes. It received 19 responses, with a median age of 26 — my sweet spot for young pups. As expected, almost all of them had changed jobs in the past 2 years — 84% — and 26% had done so in the past 6 months. The most common reasons for a job change were the pursuit of better opportunities for learning, growth, and fulfillment. All this confirmed what I had already suspected, and which my secondary research had suggested: millennials change work frequently because they put their own goals and values before the plans of any one company. Respondents’ relationships with adult learning classes were more complex. Many of those who had taken classes had done so in order to prepare for a career move, but a handful had also taken classes simply for the pleasure of a creative outlet. (It remains unclear if those represent different needs and user types, or different expressions of the same professional dissatisfaction.) 84% of respondents said that they would prefer to take a class that met in person, explaining that they were motivated to learn and retained information better when surrounded by peers and interacting with an instructor. But only 58% of respondents had actually taken a course since college — a discrepancy that suggests Classmates may need to overcome a negative perception bias around online courses in order to be successful. Interestingly, two respondents who had taken in-person courses to begin a career change actually said that they would have preferred if those classes had been online, because of the greater flexibility an online course affords.


and comments receive. As learners level up, they’ll unlock access to special opportunities, including admission to in-person networking events hosted by Classmates.

All these insights were useful when developing the app’s final architecture, but especially when crafting the tone and flow of the introductory CUI chat. When creating a system to sort users into areas of interest, it’s helpful to remain mindful of the varying goals and biases that your users may be bringing to the table.

Next, I created an online survey to interrogate my assumptions about user behavior and learn more about their experience with adult


Clockwise from top-left: paper prototypes; an earlier iteration of the job-finding function; an earlier iteration of the CUI identity; additional scenario-mapping; post-it-note mayhem.







Jade is a wearable device designed to help the elderly remain independent for longer. When Jade notices a worrisome deviation from its wearer’s routine, the pendant vibrates to indicate that it’s concerned. Users can squeeze the pendant to let Jade know that everything is fine — but if they don’t, help will soon be on the way.

of children to adults.1 In 2012, the number of people in the United States aged 65+ was roughly 43.1 million, or 13.7% of the national population; by 2050, this number is expected to almost double to 83.7 million, or 20.9%.2 And globally, the proportional shift will be even more dramatic, with the 65+ population expected to swell from 617 million in 2015 to 1.6 billion in 2050 — which will reflect an increase from 8.5% to 17% of the

When out of the house, Jade users wear or carry the pendant so that it can use audio and GPS data to track their routine and monitor their well-being.


The elderly are a rapidly growing demographic around the globe, thanks in equal part to increased average longevity due to progress in medical and nutritional science, and decreased fertility rates (especially in the developed world) leading to a lower ratio

1  USA. Census Bureau. “An Aging World: 2015.” By Wan He, Daniel Goodkind, and Paul Kowal. Washington, DC: U.S. Government Printing Office, 2016. 2  USA. Census Bureau. “An Aging Nation: The Older Population in the United States.” By Jennifer M. Ortman, Victoria A. Velkoff, and Howard Hogan. Washington, DC: U.S. Government Printing Office, 2014.


world population.1 So when the elderly eventually make up almost a fifth of the world’s people, how will society respond? Ai-jen Poo, a Macarthur “genius” grant-winning activist and author of The Age of Dignity: Preparing for the Elder Boom in a Changing America, anticipates a corresponding explosion in the home care and nursing industries.2 As lifespans increase (the global average is expected to jump from 68.6 years in 2015 to 76.2 in 2050), so will the health and quality of life of individuals as they cross the 65-year, 80-year, and even 90-year-old thresholds. So while the older half of this group may drive the expected influx of opportunities for care work, the remaining retirees are likely to feel quite fine on their own for another decade or more. Nevertheless, the new elderly may still be frustrated by a society that expects them to be feebler or less sharp than they really are. Adult children who remember the physical and mental deterioration of their grandparents are likely to worry about their now-elderly parents living on their own. For elderly parents who enjoy their independence, but still want to allay their children’s worries, is there an acceptable compromise?

When Jade notices a worrisome deviation from its wearer’s routine — like being silent and stationary in an unusual place — it vibrates noisily to let the wearer know it’s concerned.

1  “World’s Older Population Grows Dramatically.” National Institutes of Health. March 28, 2016. Accessed April 17, 2018. 2  Klein, Ezra. “Ai-jen Poo: The Future of Work Isn’t Robots. It’s Caring Humans.” The Ezra Klein Show (podcast), November 17, 2017. Accessed April 17, 2018. https://www.stitcher. com/podcast/vox/the-ezra-klein-show/e/52206155



Jade is a hardware fix for independent retirees who want to put their fretful adult children at ease. It comes as a set of two smart objects: a pendant meant to be worn or carried when out of the house, and a stand to hold and charge the pendant when at home. The pendant houses a microphone and GPS receiver, which are used to constantly listen to the wearer’s environment and monitor their whereabouts. While this may sound potentially insidious, the Jade team never sells user data to third parties, deletes it routinely, and uses blockchain encryption to protect data that’s in use. Jade uses this information to build a model of the wearer’s typical routine — where they go, how long they spend there, who they talk to, and how their voices sound — using a mixture of natural language processing and other deep learning AI techniques. If Jade senses a worrisome deviation from that routine (such as angry and distressed voices, or stationary silence in an unusual location), the pendant vibrates noisily with increasing intensity to let the wearer know that it’s concerned. If the wearer is fine, they can simply squeeze the pendant to stop the vibrations and reassure Jade that they’re alright. If this default gesture is physically difficult for the user, they can customize it to their own preferred haptic or voice command using the companion mobile app. But if the wearer is unable to respond to the pendant’s vibrations within two minutes, Jade will call and send geotagged text messages to the emergency contacts identified by the user, including emergency services like 9-1-1. Users are encouraged to build a habit of placing their pendant on its stand when at home. Besides charging the pendant’s sensors and decreasing the likelihood of loss, the stand serves a third purpose: sonar sight. While the location information provided by the GPS can track the whereabouts of Jade users outside the home, a much more refined view is required for identifying when the user falls or has an accident within the home. By bouncing inaudible sound waves into the home and listening for the return patterns, Jade builds a 3D model of the home’s interior — just like a bat uses echolocation to hunt in the dark — and can detect when the owner is suddenly lying down in an unusual location, like the bathroom floor. Besides compensating for the fluctuation in light levels that occur in the home, Jade’s use of sonar is superior to video for another

After Jade has had a few weeks to collect and analyze the userâ&#x20AC;&#x2122;s data, it asks for the userâ&#x20AC;&#x2122;s help labeling the sounds and location it observed.

Once the initial model is labeled, the user just needs to decide who will receive alerts in the event of a health emergency.


reason: privacy. Because sonar waves only reflect off of solid surfaces, they can’t detect what’s on a user’s computer screen or written on their mail — it can only identify the outside edges of objects. That way, even if Jade’s user data were somehow hacked, the amount of identifying personal information is minimal.

retirement? Today, our varying degrees of comfort with technology can be the biggest source of disconnection between younger and older generations. So, from the inception of this project, I set out to explore how AI might improve the agency of the aging — rather than disempowering and isolating them.

Jade’s key difference from contemporary competitors like Life Alert is its incorporation of intelligent automation in the process of calling for help. All of Life Alert’s products require the user to click a button and speak to a representative in the event of an emergency. But what if the user loses consciousness, and can’t perform this critical step to save themselves? And while there are a few competing products that use an accelerometer to automatically report a fall, they’re prone to false alarms when the wearer makes a sudden movement or simply lies down to sleep. Only Jade’s AI-informed model of the wearer’s routine offers a reliable means of providing automatic alerts.

The conception and refinement of Jade’s final shape was iteration-intensive, the early stages involving repeated oscillation between twoand three-dimensional sketching. The goal was to develop an aesthetic language that was pleasing enough for the pendant to be worn as jewelry and for the base to sit on a mantle or dresser. Additionally, the pendant needed to be sturdy enough to withstand the bludgeoning of daily wear, so the form would have to be self-contained and not overly ornate.

PROCESS When at home, the pendant is docked in its charger — which uses ultrasonic echolocation to keep an eye on things at home.


My work has focused on the needs of those who may be disempowered by AI because of how it will impact their access to work. But what about those who will be too old to adapt, and who may be simply hoping to enjoy their

The brand identity is a reference to the stone, which is said to encourage peacefulness, relieve anxiety, and enhance good luck. The name is intended to convey the air of an aspirational brand, rather than the clunky utilitarianism of titles like “Life Alert” or other category competitors. Inspiration for the exact shade of seafoam green was drawn from the classic cars of the 1950s and ‘60s, which embody the timeless cool of Jade’s target user.























I speculated a print ad that introduces the public to a radically different water allocation system.


Water Token Project


Water, water, everywhere, Nor any drop to drink.

The Water Token is a cryptocurrency capand-trade system designed to create a more equitable distribution of water. Outlined as a policy proposal for the state of California, a notoriously overextended watershed, the Water Token Project uses an array of machinelearning algorithms to constantly recalculate the gallon-value of a water token on a monthly basis and sustainably distribute these tokens to corporations and citizens alike.

water, trapped in underground aquifers. The remaining 2% of that fresh water is surface water — that’s the water in streams, lakes, and atmosphere. Remember the water cycle from grade school, where evaporation creates clouds that fall as rain, over and over again? That’s surface water.1 And humans are currently using, polluting and depleting the planet’s fresh surface water far faster than the water cycle can replenish it.


How are we using it? While it’s true that most of us could stand to cut back on the length of our showers, domestic plumbing makes up just 11% of water use. Industrial use — that’s water used for cooling and production

Fresh water is a scarce resource. Only 2.5% of the Earth’s water is fresh water, meaning it’s salt-free enough for human consumption. Most of that fresh water — 68% — is ice, frozen in Earth’s polar ice caps. 30% is ground

An excerpt from Samuel Taylor Coleridge’s poem, The Rime of the Ancient Mariner (1798).

1  Cutts, Steve, animator. “Where Is Water? The Water Rooms #2.” UN World Water Assessment Program. 30 October 2015.


The planet may be 71% water, but only 2.5% of that water is fresh. And only 2% of that 2.5% is the water that falls as rain to fill our streams, rivers and lakes.

processes — takes up another 19%. The remaining 70% is agricultural use, with animal farming the most water-intensive production process by far (due to all the water that goes into producing the food the animal eats, and watering the animal itself).1

Arjen Hoekstra, the father of the water foorprint.5

In the past, humans were able to treat water as a limitless resource, as our smaller population’s water use made less of a demand upon the planet. But as the global population swells past 7 billion people, and our collective water needs grow, we’re experiencing a tragedy of the commons. Lack of ownership and responsible allocation has created imminent shortages around the world, starting with the most arid regions, many of which are anticipated to expand as climate change’s 1  Ibid.

effects unfold. On a global scale, the prognosis is clear: our current water use is dangerously unsustainable.2 To help governments manage and plan water use, a research team led by Arjen Hoekstra, a professor of water management at the University of Twente, developed the concept of a “water footprint” in 2012. A water footprint looks at both direct and indirect freshwater use, enabling auditors to evaluate the impact of a product’s entire supply chain.3 The concept’s potential applications are broad, but Hoekstra advocates for the creation of footprint caps for water basins and footprint benchmarks for product categories as a means of structuring and enforcing efficient water use. 4 While it’s a powerful concept, water footprint assessments have a few key limitations. First, one footprint assessment can’t reveal the tradeoffs associated with key water usage decisions — that would require comparing multiple assessments from before and after policy changes. And second, manual data collection and analysis are time-consuming processes, which makes the creation of multiple comparative studies expensive and unlikely and also increases the likelihood 2  Ibid. 3  Hoekstra, Arjen, Maite Aldaya, Ashok Chapagain and Mesfin Mekonnen. The Water Footprint Assessment Manual: Setting the Global Standard. Earthscan. Water Footprint Network. 2011. 4  Hegarty, Andrew, editor. Arjen Hoekstra on the Water Footprint of Modern Consumer Society. Institute of International and European Affairs. 7 May 2013. https://www. 5 “Shrinking Agriculture’s Water Footprint.” FutureFood 2050. June 10, 2015.


I looked to California, a state famed for its water insecurity, as a possible point of intervention. Since the 1970s, California’s population has grown at a rate of 1.4% each year, while its water footprint has grown at more than double that rate: 4% each year. The water footprint of the state has long since eclipsed sustainable proportions, currently double that annual flow of the state’s two largest rivers, the Sacramento and San Joaquin. As such, 76% of the state’s water is imported through irrigation and product-embedded water (also known as “virtual water”), leaving the bulk of its agriculture dependent on direct rainwater — a precarious strategy in such an arid state. Additionally, California’s domestic allocation is intrinsically inequitable. Wealthier households use far more water than their low-income counterparts, in part due to their higher consumption of more waterintensive products, like meat.6 All signs point to the conclusion that California’s current water valuation paradigm is inadequate, simultaneously disempowering the state’s poorest residents and charting a course for state-wide crisis as climate change’s unpredictable impacts loom. In response, the Water Token Project was formed. 5 Hoekstra, Arjen, Maite Aldaya, Ashok Chapagain and Mesfi n Mekonnen. The Water Footprint Assessment Manual: Setting the Global Standard. Earthscan. Water Footprint Network. 2011. 6 Cardenas, Susana, Heather Cooley, Julian Fulton and Fraser Shilling. “Trends and Variation in California’s Water Footprint.” California Department of Water Resources and the US Environmental Protection Agency; Pacific Institute; UC Davis. 15 December 2013. 7 Ibid.


The Water Token Project is a cap-and-trade program for water use in the state of California, coupled with the advanced capabilities of AI algorithms to predict future value and recommend strategic investments. It was designed to combat the limitations of footprint caps by allowing real-time data to affect water allocation. It also gives both corporations and individuals the power to meter their own usage and profit from sensible use, resulting in more equitable distribution of both capital and water.

In California, there’s a direct correlation between wealth and water use.7


of human error.5

Each census-verified household is allotted one water token per resident each month. This virtual currency automatically pays for the water they use each month. If a household exceeds their water allotment for the month, their account automatically buys surplus water tokens, or fractions of tokens, from the

Water Token is a new digital currency that serves as a cap-and-trade for water in a region. Distributed as a basic income, it grants a household a fair apportionment of water each month.


JONESes Nonsequosam, odit magnimus.

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12,000 gal / mo

The Joneses go over their per-person allotment by 1,750 gallons this month. While the first 4,000 gallons are free, they must buy the remainder from The Water Bank.




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BROWNs Daerspiducit eos qui ut il maio. Orrorem num reperor endest, officim inimaximagni aut faciis acestorepro mi, ut quae mo et et aspientem et denihilis et molupta tesequiatin exerunt.

The Browns have no pool or lawn, and thus use significantly less water.

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3,000 gal / mo / person

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Piciis cupiet, ut re nectatem voluptatur aliquos WATER BANK INTERACTION

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The Water Bank.


VYNEs In the growing season, Vynes Vineyards’ single acre needs 29,000 gallons each month. If there’s no local rainfall, it all has to come from the tap.


Vynes Vineyards is a small 1-acre vineyard — although they have two unused acres available.

1 acre of vineyard has 1,500 vines. Grape vines get 3-6 gal / week from July to harvest. 4.5 gal x 1500 plants x 4.33 weeks = 4

29,000 gal / mo / acre


The Water Bank over-allocates tokens to Vynes Vineyards because it thinks they should invest in developing their unused land, as grapes and wine are a water-profitable crop for California. It’s up to Vynes to decide what to do with the profits from selling those extra 7.75 tokens!

sell / save 95


good Grapes are r CA! business fo your nt We say, pla next acre. - TWB

The gallon-value of one water token is constantly recalculated by a family of powerful machine learning algorithms, based on a comprehensive array of real-time data sets: meteorological, ground water deposits, economic trends, consumption rates, and more. The token value is designed to give private citizens a generous amount while controlling corporate use more strategically. The Water Bank determines how many tokens are invested in businesses each month based on multiple factors: industry benchmarks for water-efficient production in that sector, economic value of a crop or product to state GDP (measured in dollars-per-gallon), and projected growth potential for that business or industry. This level of scrutiny was impossible in the pre-AI era, but thankfully the Water Bank staff are able to lean on the recommendations of a tiered AI network that simultaneously evaluates the state economy and climate on a macro level, specializes in different industries and product categories on an intermediate level, and oversees the inputs and potential of individual businesses on the very micro level. Commercial allocation of tokens is also delivered with best-practice suggestions for water-efficient production in order to meet the Water Bank’s industry benchmarks. This may at first seem stringent to some companies, but additional counseling is available, especially in some instances where the bank thinks a company or farm may better serve the state by switching to a different product. In the short term, the Water Token Project should both provide income for low-income households by enabling them to sell their excess tokens at the water bank, and increase the costs for excessive domestic water use (causing wealthier households to think twice before installing a second pool). In the medium term, the embedded expense of water tokens will increase the cost of high water footprint-products like meat, causing them to be priced like the luxuries they actually are. In the long term, this effect should use market forces to push Californians toward more water-efficient products and services, and guide the state to a sustainable pattern of water consumption.

How it works

In the following pages, you can see the system in action through three examples: the Joneses, a wealthy family; the Browns, a low-income family, and Vynes Vineyards, a small farm. Each details the user’s average monthly water consumption, then describes how they might interact with the Water Bank in a month when tokens are valued at 4,000 gallons. With conventional water bills, the Joneses simply pay more to get more. But in our new system, domestic tokens are allotted per resident — meaning you get one token per person per month. Commercial tokens are strategically invested based on industry production benchmarks and the economic value of the crop or product (measured in $ per gallon), and generally come with production best practices and other suggestions, like an alternative crop to grow for a lowGDP impact farm.


state’s Water Bank. Conversely, residents who do not use all their tokens each month can sell this surplus via the bank, transforming their unused water as a supplemental income for the household.

Everyone will experience either a token surplus or deficit every month. The difference is traded on The Water Bank, a digital marketplace where citizens and corporations can buy and sell unused tokens. Some businesses will be like Vynes, lucky to already be on the right track from a water efficiency standpoint. But those currently producing a water-inefficient crop or good will get production best practice tips and crop/product alternatives if they’ve picked a long-term loser. Token allocations will also be practically weaned down so as not to cause an overnight collapse as the company is forced to pivot. While the benefits of such a system will be immense in the long run, the behavior change demanded in the short term may be frustrating and intimidating for citizens and businesses. Effectively communicating the motivations behind the system through an awareness campaign will be critical in forestalling frustration and resentment.


The Water Token Project was born of an interest in exploring how AI could be applied to a complex environmental issue that disproportionately impacts the less wealthy. Additionally, I was drawn to the impending water shortage as my focal issue due to machine learning’s strength at providing


allocation recommendations for quantifiable resources — and as vast as the world’s waters are, they can nevertheless be counted one gallon at a time. The challenge was to envision an offering that didn’t just ameliorate the pains of water scarcity, but that actually had the framework to scale and yield a net positive impact on the world. Initial concept development was sketching-intensive. And while I always had an inkling that the final recommendation would be intangible and software-based, I nevertheless took great direction from my early 3D prototyping, which helped me think through the existing behavioral relationships that most people have with water and begin to identify which touchpoints might be more or less suitable for intervention. The key insight that would go on to lend the most structure to the final concept was Hoekstra’s concept of the water footprint. It would connect me to two fantastic resources, Water Footprint and Virtual Water Trade in Spain by Maite Aldaya and Trends and Variation in California’s Water Footprint by the Pacific Institute and UC Davis. The first report chronicled how Aldaya, a student of Hoekstra’s, led the first state-run initiative to use water footprint calculation to drive policy changes around crop selection, irrigation strategy and agricultural subsidies.1 The second report used the same water footprint-calculating methodology to create a detailed profile of California’s water inefficiencies, but also highlighted how the limitations of using water footprint as an effectiveness-evaluation tool for policy tweaks due to the time-intensive nature of the data collection and synthesis involved — a fact that made the opportunity space for my design offering clear.2 From there, I used a theory of change map to identify key assumptions by working backward from my long-term goal: the equitable distribution of water that optimizes quality of life for everyone, present and future. Explicitly listing my assumptions helped me more completely consider how this speculative policy proposal might actually be implemented, and where the most friction might come from.

1  Aldaya, Maite et al. Water Footprint and Virtual Water Trade in Spain. Springer. 2010. 2  Cardenas, et al. “Trends and Variation in California’s Water Footprint.” 2013.


These concepting sketches were the first step of my creative process.


I developed a theory of change map to help me work backward from my long-term goal, refine my short-term intervention, and note my key assumptions along the way.

theory of change INPUT

PROBLEM Inefficient usage, where the monetary cost of water doesn’t reflect its true cost.

ASSUMPTIONS: product Algorithms are accurate. (Requires the quantification of “potential” and prediction of externalities.) Mediation is ethical. (Opportunity for corruption looms—all the more reason to automate!) Effective valuation. (Token captures and conveys water’s total value to users.)


future of scarcity

exterminist capitalism

Water flows to the people who need it, not just the corporations that can afford it. Water is distributed evenly per capita. Water is strategically invested in projects and individuals that promise to benefit humanity and the planet in the long-term. $/gal value of crops and products projected as a proportion of GDP. We maintain the flora and fauna mankind needs to breathe and feel at home.




An equitable distribution that optimizes quality of life for the most people, present and future.

ASSUMPTIONS: long-term behavior Social value shifts to encompass efficiency, contribution. Infrastructure shifts to collect and process waste- and greywater. Community gardens flourish.

ASSUMPTIONS: user behavior

Corporate social responsibility is demanded, not a differentiator.

Opt-in is adequately incentivized. (Mandatory? Free tokens to start?) People don’t freak out when they hear the “r” word (rationing)


I used quick and dirty prototypes to identify viable touchpoints in a water-userâ&#x20AC;&#x2122;s daily routine where my conservation-oriented project could intervene.

Would home gardners be willing to redeem their produce for their water tokens?









Where the previous section imagined products and practices that might empower individuals, the next three projects focus on how to initiate the cultural change necessary to create a broader movement towards egalitarianism in society. These projects were developed at an intermediate phase in the thesis process. As I began to cement my point of view on the type of cultural change required to ensure that the AI Revolution benefits all members of society, not just those at the top, my work focused on new behaviors that could prime the public — job creators and working class alike — to internalize values of egalitarianism, inclusion and obligation to one’s community. In this section, you’ll encounter: a co-creation workshop that asks participants to imagine new vocations for themselves in various futures; an immersive performance that transports participants to an AI-powered vocational placement agency; and a social networking app designed to create friendships that span socioeconomic and cultural boundaries. Each project is intended to take its audience on an emotional journey. First, they are made uneasy when they are told that they must adapt beyond their comfort zone. Then, they are intrigued and excited by the pathways to fulfillment that might exist in a society restructured around mutual cooperation.










20xx was a co-creation workshop designed to get people to grapple with how AI-powered automation might change the face of jobs in the future. For a few hours, a dozen participants used futuring tools to dream up a job for themselves for the latter half of the century. This project was an exercise in participatory design, a practice that includes users in design’s earliest stages to simultaneously produce and verify ideas.


In this exercise, I sought to find out what jobs people would choose for themselves if their current industry was no longer an option, as well as observe how their personal values interact or conflict with those of an

imagined future. Since my goal was to see what people would do for work when freed from the constraints of reality, I decided that having participants invent every attribute of their future scenarios would be an inefficient use of time. I wanted them to focus all their energy on carving out a niche in a randomized future state.

Before I kicked off the workshop, I gave participants some context around my thesis topic and recent projects.

So as a “seed” for their creativity, I developed a series of future qualities — some utopian, some dystopian — based on concepts I had encountered in my research. These “civilization factors,” as I called them, were divided into the five STEEP categories of futuring. Participants would create their future civilization by grabbing one card from each pile at random, then using their imaginations to reconcile them into a cohesive reality.


First, I had my 10 participants break into pairs.


At the beginning of the workshop, I had participants pair off with people they didn’t know and spend five minutes getting to know each other. Participants came from a range of backgrounds — students, designers, developers, social workers, advertising strategists — so I thought it would be helpful for each to better understand the other’s perspective before diving in. (Plus, it would eventually inform their creative output.) I had each participant write down their partner’s strengths at the top of their sheet before moving on.

Each pair would share the same “civilization factors,” one from each of the five STEEP categories.

Once the civilization factor cards had been selected and understood, I had participants answer three questions about their new reality. The questions were:

◊  What does it mean to be a contributing member of society? ◊  What are the values of this society? ◊  How do you feel fulfilled in this society? I had originally allotted five minutes for this step, but the participants demanded more — they didn’t dislike the questions, but were having a hard time arriving at answers they were satisfied with. These questions were designed to get participants to step into the shoes of a citizen of their future society before they moved into the next phase: future job creation. I asked them to invent a job for themselves based on their strengths as observed by their partner, the civilization factors that they’d randomly chosen, and the values they’d discussed in the previous phase. I said that the job should be something plausible both for the envisioned future and for them personally. After five minutes spent writing their job title and description, participants then gravitated to the bowls of LEGO pieces and minifigures laid out for them. Once they were done building, they would each have a few minutes to explain their job to the rest of the group by playacting how it works at the front of the room.

Participatory Design

At this point in my thesis exploration, I had interviewed a number of experts on automation and people from a variety of industries that had been impacted by it. From this point, a normal contemporary approach would be



to take the fruits of these conversations back to the design studio, synthesize the findings and begin generating creative solutions with a sharpie and some post-it notes. But usercentered design is a broad umbrella, and for this ideation phase I looked to borrow from the toolbox of one of its many sub-disciplines: participatory design. As the name implies, participatory design seeks to involve end-users throughout the design process as a creative voice in their own right, rather than just sources of input at the beginning and verification at the end. To achieve this, I would need to transform from ideator to facilitator and develop an array of stimuli to get my users’ creative juices flowing and elicit the right sort of ideas. I already knew that my final output would be a workshop with my user group (workers) but the rest of the details — what they would create, how I would prompt them, and why — were entirely up to me.

amplify the value I took from it. In future iterations, I would to make the value-defining questions more accessible and less open-ended, as well as tighten up my documentation techniques at the end. I captured participants’ ideas by collecting their completed sheets and photographing their LEGO creations, but at least one of these disappeared before it could be photographed. Also, it would have been

As a final step, participants modeled their future jobs using LEGOs and acted their roles out in front of the rest of the workshop.

helpful to have participants clearly record the five civilization factors they drew to help explain the logical foundation for the careers they devised (although it is easily inferred in most instances).

I used answer sheets to capture participants’ reflections on their future scenarios — and the job titles they gave themselves.


The exercise yielded a handful of fascinating vocation concepts that I hadn’t yet encountered in my own research or ideation: resource conflict mediator, genetic engineer/ family planner, VR tour guide, to name a few. Additionally, the participants all reported that they enjoyed themselves and took some lasting value from the experience. This quick foray into participatory design yielded such immediate results that I could simply keep hosting such workshops to










The Human Resourcing Dept. was a singleday interactive performance designed to provoke participants to imagine their new role in a future where their “old” job has been automated out of existence. After being told that the year was 2088 and that they had just woken up from cryogenic sleep, participants were sorted into a new, speculative vocation by an advanced AI named “PETAR” — and were led to feel an uneasy optimism for this theoretical shift of agency.


I had already started to get people thinking about their role in the future in my co-creation workshop, 20xx, where participants dreamed up jobs based on a combination of a random-

ized futuring scenario and their own personal values. So for this experience, I wanted to take the process a step further by immersing my participants in that speculative future, rather than simply asking them to imagine it. Additionally, I aimed to learn how they would react to discovering that their current job had been automated, having the ability to choose their new vocation replaced by a machine, and learning that all vocations were now community service-oriented work. And I hoped to take participants on an emotional arc that started low, when they learned that their old skills were obsolete, and finished high, once they had found a new sense of social purpose in 2088.

My receptionist Ellen gets herself in a 2088 state of mind.



Participants arrived individually and were greeted by a receptionist. In what felt like the beginning of a familiar routine, participants were cordially asked to fill out some paperwork and scan their implant ID chip. But this was designed to be an early moment of friction between the participant and a




status quo

better sense of social purpose

discomfort with the dehumanizing potential of AI


I set out to create an experience that was initially unsettling, but ended on an optimistic note.

future that didn’t quite line up with the world they were used to. “Oh, you don’t have an ID chip,” the receptionist would say. “May I ask what year you were born? Or what century?” Upon hearing that they had been born in the 20th century, she smiled knowingly before saying, “That’s quite alright! But instead of using this form, please just have a quick seat. You’ll need to interface with our in-house AI instead. Give me one moment to make sure that he’s ready for you.” After a quick trip behind the curtain, she returned to beckon them toward a tall blue box that looked like a two-screened arcade game. “PETAR will see you now. Whenever you’re ready, just announce yourself, say hello, or give him a light knock to begin.” Users quickly discovered that what had looked like a mirror was actually a screen through which they could just make out PETAR, a mysteriously lit disembodied head. Warm-voiced and reassuring, he briefly described the world of 2088 and the upheaval the job market had undergone in the preceding decades. He explained his name and overarching direc-


tive — “PETAR” stands for the Production and Economic Teleological Allocation Robot, and it’s his job to make sure that you’re fed, clothed, housed, and content — before quickly launching into a sorting conversation. “Your old job is long gone, I’m afraid,” PETAR explained. “So let’s work together to figure out a role for you here in 2088.” PETAR’s first questions were challenging and introspective. (“Are you more of a critical thinker or creative thinker?”, “Do you prefer interacting directly with people who need your help, or solving a problem at the conceptual or system level?”, he would ask.) The choices created false dichotomies that nevertheless forced users to express a preference. If they appeared to be having trouble making a decision, PETAR would mollify them by saying, “I know these questions can be difficult. If you’re having trouble deciding between two options, try thinking about which answer sounds more fun.” By answering these first four questions, users had sorted themselves into a two-by-two matrix of personality types. From there, participants were asked to decide between different continents and regions where they’d like to end up. Each location was characterized by varying trends derived from a STEEP-based futuring process. This was meant to remind the user that changes to the employment economy won’t happen in a vacuum — they will be directly influenced by the social, technological, environmental, and political trends happening at the same time. (Additionally, it ensured some variety in the future jobs that were ultimately assigned: For each persona, there were nine possible jobs, based on where the participant ended up.) By the time the questioning was complete, PETAR had determined the participant’s vocation — and spit it out as a sticker at the bottom of his box. (In reality, the actor inside the box had been keeping track of the responses using a special score card on a clipboard in his lap, and fished out the job title sticker from within a carefully labeled box.) With sticker in hand, participants returned to the front of the space, where the receptionist carefully placed the job title sticker on an acrylic badge and asked them to fill out an exit interview form. As participants left, they were instructed to wear their ID badges for at least the next day, the receptionist explaining, “You’ll be picked up by aerial drone in the next 24 hours to be taken to your new vocation, and the ID badge will help the drone locate you.”

of storyboarding. I became enamored with the idea of a “Turing test game show,” and sketched up a number of iterations that followed that classic typology of the televised game show. But what at first had seemed like a treasure trove of possibilities soon felt like a dead end of ideas; each game show concept had promise, but none of them seemed to do an exceptional job at conveying my nuanced feelings around the future of work. However, all the concepts had one thing in common: in each storyboard, I had hastily sketched out that the prize would be a “job in the future.” I decided that perhaps by focusing on what that future job might be, I would discover what my experience was missing — and I was right.

Some were excited for the communityoriented nature of their future vocations, and understood the theoretical importance of the work they would entail. And several participants felt validated by PETAR’s insight, having received vocations that they felt to be surprisingly good fits: one “primary school teacher” admitted that teaching was something he had always wanted to do.

I knew that the answer couldn’t be one single job: decreeing one person “king of the bots” might be amusing, but it wouldn’t mean much to the participant, or yield any valuable findings for my thesis. So I set out to devise a framework by which I could sort participants into different worker personality types that could then map to a few different job categories. I briefly considered adapting Myers-Briggs personas, but decided that I would need to draw on my prior research if these worker types were to be connected to my thesis in any meaningful way. When I realized

I set out to elicit two emotional reactions in my audience: first, I wanted to unsettle them with the prospect of an automated future; then, I wanted to empower them with a new, more service-oriented vocation. I achieved the first effect with some consistency: Most participants were amused by the narrative of a recent wake-up from cryo-sleep, but scoffed defensively at the idea that their 2018 job was a thing of the past. In contrast, participants’ final emotional takeaway varied much more than I had expected, reflecting different levels of receptiveness to the job assignment process, as well as the jobs themselves.

But other participants were less thrilled about their roles in the future. Some were worried about a loss of agency over the nature or character of their work. This was especially true for those in creative fields who ended up in the more nationalist border region, who were displeased to learn that their work would all be subject to official review and approval by the state’s propaganda board. Others simply weren’t prepared for what felt like a step down in status, with one respondent remarking, “I mean, I could be a tour guide now if I wanted to. And I don’t!” While it was disappointing to discover that not everyone was instinctively open to work that was more community service-oriented, it did confirm an earlier suspicion: In order for the majority of society to buy into a more socialist approach, some degree of coaching would be required to counteract the conditioning they’d undergone growing up in a society that promotes individualism over collectivism.


Once I had established the discomfort-toexcitement emotional journey I wanted to take my participants on (which is incidentally not too different from the intended arc for someone reading this book) I kicked my concept development process off in a frenzy








that the three core human qualities that I had uncovered in my readings and interviews — critical thinking, creative thinking, and emotional intelligence — could be turned into two binaries on a two-by-two matrix, I knew I had it. In truth, these are false dichotomies, which at least one participant pointed out (someone can be both a highly critical and

By turning the three un-automatable traits uncovered in my research into two continuums, I developed a 2x2 matrix of worker persona types.


I made the use of my small space with a partition and waiting area — and 2088 collateral to keep my waiting participants engaged.

creative thinker). But this asymmetry only helped to further the unsettling feeling I hoped to effect in my participants. After all, the process of choosing a profession and navigating a career is filled with difficult decisions between competing priorities — why should that completely change by 2088? With the personas established, the process of devising the nine future scenarios and 36 jobs was relatively straightforward, albeit time-intensive.

had to do more than quickly memorize the script and all its permutations. He would have to embody the algorithm by keeping track of the participant’s responses and supplying the proper job title at the end. Even with the scorecards and sticker-holding box to help, it would be a complex set of interactions to manage while performing a character inside a 30-square-inch box — but he performed with aplomb, and made the experience the success that it was.

Once I had created my grid of future jobs, I knew that the process of sorting participants through it would have to be the crux of my experience. The most logical way to do this seemed to be a sorting conversation, so I wrote a script. I had originally conceived of the final experience being a coded game with video clips incorporated throughout. But when I presented my wireframes and logic tree to a friend who works as a web developer, I learned that my time and budget constraints made this impossible. So I quickly changed course for an actor-in-a-box solution, which ultimately proved to be more delightful for the participant.

The rest of the process was production-heavy and graphic design-intensive, as I built the box, branded the event space, and conjured up additional collateral like wall hangings and magazines to add depth to my imagined 2088. The final result was a plausible job placement agency of the future, filled with plenty of anachronisms from 2018, but with enough uncanny qualities to transport my participants to another time and place.

Dylan Knewstub — the actor I had originally cast to play PETAR in the coded game — now

FLOOR door


Ellen’s desk


swivel chair

chairs (waiting area) trunk

room partition future magazines





International tensions and distrust are high, so most work opportunities relate to BORDER SECURITY and CULTURAL PRESERVATION.


This area has been impacted by SCARCE FRESHWATER and populations displaced by RISING SEA LEVELS, so most work helps solve those issues.


This area has a massive ELDERLY population, so most work opportunities are in this domain.



Massage Therapist - You’ve been modified to have extra-sensitive, extra-strong hands. Put them to work to help the elderly! Elder Home Landscaper - Create botanic environments to amuse the elderly while keeping them safe and healthy.

Desalination Farmer - Cultivate and maintain the bacteria colonies responsible for turning seawater into potable water. Dehydration Yogi - Coach people into a better understanding of how to feel at home in their newly water-efficient bodies. Water-Wicking Fashion Designer - New biofabrics capture water vapor — but it will take work to incorporate them into garments that look good. Baby Designer - Consult with parents to help craft offspring that meet their hopes and specifications. GMO Veterinarian - Not trading with neighbors means you need efficient food production. GMO livestock fit the bill, but have some health problems. Youth Sports Manager - Help coach those selected and edited for athletic excellence in their most formative years. Bioartist - Experiment with new tissues and organic behaviors to create work that inspires and provokes.

Game Copywriter, Voice Actor - Develop or portray the narratives and characters for VR games. Breathe life into a virtual world! Therapy Game Designer - Craft and render the environments and objects that exist in these virtual worlds. Remote Therapist - Displacement can be traumatizing. Use telepresence to remotely provide counsel and consolation to those impacted. Virtual Window Installer - Sub-standard and sub-aquatic homes tend to have ugly vistas, and these new windows fix that — but they’re tricky to calibrate. Virtual Tour Guide - Narrate viewers through destroyed cities and landscapes to relive what once was. Virtual Landscape Designer - Displaced people in sub-standard housing desperately want to see their old home’s view. Recreate it for them. Border Patrol Monitor - Drones patrol the border, but aren’t always sure what they’re looking at. Review live footage and ensure that proper action is taken. Digital Border Lawyer - New virtual walls are going up between different nations’ digital assets, and no one agrees where they go. Argue a side. Youth Programming Director - Help decide what the nation’s next generation sees and experiences in their virtual lives. Culture Sim Designer - Depict your nation’s cultural heritage, or your interpretation of how your rival’s live, for curious state media viewers.

Excursion Tour Guide - Take the elderly through scenic sights from the helm of a self-driving tour bus

Scenic Route Designer - Architect the landscapes that an aging and independent community commutes through, for business or for fun

Sea-Aid Nurse - New coastal areas are filled with people in need of urgent medical care. Hop aboard and go help them out.

Sea-Farm Engineer - Live aboard this floating farm and make sure that the desalination pumps and hydroponics array are in working order.

Child Refugee Entertainer - Travel between relocation-heavy areas and introduce displaced children to games, music and some silliness.

Levee Landscape Designer - Desalination levees are being built everywhere, but they need a human touch to become beautiful or habitable.

School Teacher - Kids still commute to school in the future. You will teach them essential skills and knowledge — but especially your country’s values.

Border Security Engineer - International trade is highly regulated and fraught with would-be deceivers. Human eyes are needed to enforce the law.

Propaganda Copywriter, Actor - Develop or portray the stories and characters that make up your nation’s cultural heritage. For the kids!

AV Interior Designer - People spend a lot of time in their vehicles and are hungry for new comforts and activities. Satisfy them if you can.

Refugee Real Estate Agent - Help guide displaced peoples to communities and homes that can accommodate them.

Biopsychopharmacologist - Grow unique neuroreactive plants and substances to ease and amuse the days of the old and infirm.

Therapy Game Researcher - Older gamers drive demand for games that simulate the “glory days.” It’s your job to make sure everything is accurate.

Independence Analyst - Consult on the mobility privileges an aging citizen merits based on your expert analysis of their mental health

Elder Care Bio-Mod Nurse - Implants and genetic modifications are lengthening lifespans, but still need some careful ministration for older folks.

This territory is the world’s nexus for GENE-EDITING and BIOTECH development. Regulation is limited and implementation is widespread.


Reality Counselor - With VR this good, people become addicted — or even confused about which world is the real one. You’re here to help.

In this territory, most communication and “intertainment” is done through immersive VIRTUAL REALITY, where it’s state of the art.


On-Demand Elder Carer - Care for multiple aging patients in a day all from your mobile care center.

This territory is now connected by a network of AUTONOMOUS VEHICLES.











You prioritize your EMOTIONAL intuition over your CRITICAL thinking

You prioritize your EMOTIONAL intuition over your CREATIVE thinking


You prioritize your CRITICAL thinking over your EMOTIONAL intuition

You prioritize your CREATIVE thinking over your EMOTIONAL intuition

Persoanlity Type

P.E.T.A.R. Scorecard

Do you prefer: Solving a problem at the “conceptual” or SYSTEM level?

Emotionally relating to a person to understand their need or problem using EMPATHY?

Using EVIDENCE and LOGIC to diagnose a person’s need or problem?

To make your EMOTIONAL STATE CLEAR during a conversation?

To keep your FEELINGS PRIVATE and NOT let them into a conversation?


















Territory x Region


Emotional Index

Interacting DIRECTLY with people who need your help?


My actor Dylan kept track of participantsâ&#x20AC;&#x2122; reponses using the scorecard (opposite) and dropped the corresponding job sticker through the chute at the lower front of the console at the end of the interaction.



FUNCTIONALITY ASPIRATIONS (long-term): + Lives online + Responsive design

FUNCTIONALITY REQUIREMENTS (immediate): + Must play on a single computer, offline

+ Each screen will include an auto-playing video of “PETAR,” an anthropomorphic AI persona (aka a video of an actor) + NEED: HOME screen where the game sits before a user “begins” + NEED: Persistent EXIT/HOME button (in case participant gives up) + TIMEOUT: If the game is left untouched for two minutes, should return to HOME screen + The list of automated jobs is meant to scroll by very quickly (animation)

KEY FUNCTIONS (not in diagram):

+ Users are clicking through what is mainly a linear conversation + They will have one (1) option to access more dialogue, or proceed directly to the following frame + They will self-select into being either CRITICAL thinkers or CREATIVE thinkers (binary choice) + They will answer three A-or-B questions that will sort them into one of two categories: EMOTION-PRIORITIZING or EMOTION-DEPRIORITIZING + They have now been sorted into one of four possible personas + They will then pick from a choice of three (3) continents and three (3) regions, sorting them into one of nine (9) possible “work communities.” + Based on their persona type and work community, they will be assigned one of 36 possible jobs. + The game ends by directing them back to the front desk

KEY FUNCTIONS (in diagram):

P.E.T.A.R. Conversation Decision Tree Diagram DRAFT v1

This is the logic model of PETAR’s final script, in which I attempted to represent all 36 permutations the conversation might take.


You’re free to be sad, of course. But I don’t want it to be my fault! I’ve read everything humans have ever written, and I’ve concluded that human happineness is contingent on feeling NEEDED and ACKNOWLEGEDED. So I specialize in carving out roles for humans that fit their PERSONALITY and SOCIAL NEED.

Tell me more.

Do you prefer:

Using EMPATHY to understand a person’s need or problem? OR

Do you prefer:


Using EVIDENCE and LOGIC to diagnose a person’s need/problem?

Solving a problem at the “conceptual” or SYSTEM level?



Do you prefer:

Using EMPATHY to understand a person’s need or problem? OR

Do you prefer:

Interacting DIRECTLY with people who need your help? OR



Using EVIDENCE and LOGIC to diagnose a person’s need/problem?

Solving a problem at the “conceptual” or SYSTEM level?

Do you prefer:


Do you prefer:

Interacting DIRECTLY with people who need your help? OR

Now I’d like to gauge your EMOTIONAL INDEX. That how good you are at reading other people — it helps with some roles more than others.

Now I’d like to gauge your EMOTIONAL INDEX. That how good you are at reading other people — it helps with some roles more than others.

Great! These are two skill areas that are mostly unique to humans. If you enjoy thinking creatively, you’ll be a real help to me — and to your fellow humans!

You prefer to solve problems by coming up with new solutions, often inspired by a connection or analogy your mind makes with an “enrelated” topic.

You prefer to solve problems through the objective analysis of facts, and you might change your mind if you get new facts, or “evidence.”

Great! These are two skill areas that are mostly unique to humans. If you enjoy thinking critically, you’ll be a real help to me — and to your fellow humans!



First, would you say that you’re more of a:

Your old job is long gone, I’m afraid. So let’s work together to figure out a role for you here in 2088.

Sure, cool.

I’m in charge of making sure that you’re clothed, fed, housed and safe. But most of all, it’s my job to make sure you feel CONTENT and VALUED. Sound Good?

I’m the Production and Economic Teleological Allocation Robot. But you can call me PETAR.

Even the key managerial and resource allocation functions of government have been delegated to a machine: me.

Here’s a list of the most common jobs in 2018 that have since been automated:

Advances in ARTIFICIAL INTELLIGENCE (A.I.) have led to the automation of most of the jobs you knew before.

Welcome! Please remain calm. You’re waking up after 70 years in CRYO-SLEEP. The year is 2088.



Accountant Actuary Advertising Managers and Promotions Managers Advertising Sales Agent Aircraft Mechanic Airline Pilot Airport Security Screener Architect Auto Mechanic Bank Teller Biomedical Engineer Bookkeeping, Accounting, and Auditing Clerks Brick Mason Budget Analyst Cardiovascular Technologist Cashier Chemist Claims Adjuster, Appraiser, Examiner, and Investigator Compensation and Benefits Manager Computer Programmer Computer Systems Analyst Consultant Curator Customer Service Representative Database Administrator Dentist Diagnostic Medical Sonographer Dietitian/Nutritionist Doctor Editor Electrician EMTs and Paramedics Environmental Engineer Epidemiologist Event/Meeting Planner Financial Advisor Firefighter Flight Attendant Fundraiser Glazier Health Educator Human Resources Manager Hydrologist Insurance Underwriter Interpreter and Translator Lawyer Librarian Loan Officer Market Research Analyst Mechanical Engineer Medical Assistant Medical Laboratory Technician Paralegal and Legal Assistant Pharmacist Pharmacy Technician Physician Assistant Photographer Police Officer Postal Service Worker Public Relations Specialist Purchasing Manager Receptionist Secretary / Administrative Assistant Security Guard Social Media Manager Software Developer Special Education Teacher Veterinarian Waiter/Waitress Web Developer





In this territory, most communication and intertainment is done through immersive VIRTUAL REALITY, where it’s state of the art.






Therapeutic Game Researcher - Older gamers drive demand for games that simulate the “glory days.” It’s your job to make sure everything is accurate.

Virtual Window Installer - Sub-standard and sub-aquatic homes tend to have ugly vistas, and these new windows fix that — but they’re tough to calibrate.

Digital Border Lawyer - New virtual walls are going up between different nations’ digital assets, and no one agrees where they go. Argue a side.

Biopsychopharmacologist - Grow unique neuroreactive plants and substances to ease and amuse the days of the old and infirm.

Desalination Farmer - Cultivate and maintain the bacteria colonies responsible for turning seawater into potable water.

GMO Veterinarian - Not trading with neighbors means you need efficient food production. GMO livestock fit the bill, but have some health problems.

Reality Counselor - With VR this good, people become addicted — or even confused about which world is the real one. You’re here to help.

Remote Therapist - Displacement can be traumatizing. Use telepresence to remotely provide counsel and consolation to those impacted.

Border Patrol Monitor - Drones patrol the border, but aren’t always sure what they’re looking at. Review live footage and ensure that proper action is taken.

Elder Care Bio-Mod Nurse - Implants and genetic modifications are lengthening lifespans, but still need some careful ministration for older folks.

Refugee Real Estate Agent - Help guide displaced peoples to communities and homes that can accommodate them.

Baby Designer - Consult with parents to help craft offspring that meet their hopes and specifications.










B To keep your FEELINGS PRIVATE and not let them into a conversation?

Using EVIDENCE and LOGIC to diagnose a person’s need/problem?

1. In this territory, most communication and intertainment is done through immersive VIRTUAL REALITY, where it’s state of the art.





This area has been impacted by SCARCE FRESHWATER and populations displaced by RISING SEA LEVELS, so most work targets these problems.



International tensions and distrust are high, so most work opportunities relate to BORDER SECURITY and CULTURAL PRESERVATION.


Youth Sports Manager - Help coach those selected and edited for athletic excellence in their most formative years.

Dehydration Yogi - Coach people into a better understanding of how to feel at home in their newly water-efficient bodies.

Massage Therapist - You’ve been modified to have extra-sensitive, extra-strong hands. Put them to work to help the elderly!

Youth Programming Director - Help decide what the nation’s next generation sees and experiences in their virtual lives.

Virtual Tour Guide - Narrate viewers through destroyed cities and landscapes to relive what once was.

Game Copywriter, Voice Actor - Develop or portray the narratives and characters for VR games. Breathe life into a virtual world!

Propaganda Copywriter, Actor - Develop or portray the stories and characters that make up your nation’s cultural heritage. For the kids!

Child Refugee Entertainer - Travel between relocation-heavy areas and introduce displaced children to games, music and some silliness.

Excursion Tour Guide - Take the elderly through scenic sights from the helm of a self-driving tour bus

Excellent! Your role and destination have been confirmed. Please wait...

This area has a massive ELDERLY population, so most work opportunities are in this domain.



This territory is the world’s nexus for GENE-EDITING and BIOTECH development. Regulation is limited and implementation is widespread.


Great! Now let’s find the right region for you in this territory. Would you prefer:

This territory is now connected by a network of AUTONOMOUS VEHICLES.

North America

Alright, it looks like there are openings in THREE (3) continents for someone with your profile. Would you prefer:

It sounds like you prioritize using your EMOTIONAL INTUITION over your creative thinking. Good to know! there are definitely important uses for someone like you.


To make your EMOTIONAL STATE CLEAR during a conversation? OR

Do you prefer:

Using EMPATHY to understand a person’s need or problem? OR

Do you prefer:

Your ID badge is being printed. You can pick up at the front desk shortly.

Border Security Engineer - International trade is highly regulated and frought with would-be deceivers. Human eyes are needed to enforce the law.

School Teacher - Kids still commute to school in the future. You will teach them essential skills and knowledge — but especially your country’s values.


International tensions and distrust are high, so most work opportunities relate to BORDER SECURITY and CULTURAL PRESERVATION.


Sea-Farm Engineer - Live aboard this floating farm and make sure that the desalination pumps and hydroponics array are in working order.

This area has been impacted by SCARCE FRESHWATER and populations displaced by RISING SEA LEVELS, so most work targets these problems.


Sea-Aid Nurse - New coastal areas are filled with people in need of urgent medical care. Hop aboard and go help them out.



This area has a massive ELDERLY population, so most work opportunities are in this domain.


Independence Analyst - Consult on the mobility privileges an aging citizen merits based on your expert analysis of their mental health

International tensions and distrust are high, so most work opportunities relate to BORDER SECURITY and CULTURAL PRESERVATION.


On-Demand Elder Carer - Care for multiple aging patients in a day all from your mobile care center.



This territory is the world’s nexus for GENE-EDITING and BIOTECH development. Regulation is limited and implementation is widespread.



This area has been impacted by FRESHWATER SCARCITY and populations displaced by RISING SEA LEVELS.



Great! Now let’s find the right region for you in this territory. Would you prefer:

In this territory, most communication and intertainment is done through immersive VIRTUAL REALITY, where it’s state of the art.


Excellent! Your role and destination have been confirmed. Please wait...



This territory is now connected by a network of AUTONOMOUS VEHICLES.

North America

Excellent! Your role and destination have been confirmed. Please wait...

This area has a massive ELDERLY population, so most work opportunities are in this domain.



This territory is the world’s nexus for GENE-EDITING and BIOTECH development. Regulation is limited and implementation is widespread.


Great! Now let’s find the right region for you in this territory. Would you prefer:

This territory is now connected by a network of AUTONOMOUS VEHICLES.


Alright, it looks like there are openings in THREE (3) continents for someone with your profile. Would you prefer:

Alright, it looks like there are openings in THREE (3) continents for someone with your profile. Would you prefer:

North America

It sounds like you prioritize using your critical thinking over your EMOTIONAL INTUITION. Good to know! there are definitely important uses for someone like you.


B To keep your FEELINGS PRIVATE and not let them into a conversation?

Using EVIDENCE and LOGIC to diagnose a person’s need/problem?

It sounds like you prioritize using your EMOTIONAL INTUITION over your critical thinking. Good to know! there are definitely important uses for someone like you.


To make your EMOTIONAL STATE CLEAR during a conversation? OR

Do you prefer:

Using EMPATHY to understand a person’s need or problem? OR

Do you prefer:


In this territory, most communication and intertainment is done through immersive VIRTUAL REALITY, where it’s state of the art.




y. This area has been impacted by SCARCE FRESHWATER and populations displaced by RISING SEA LEVELS, so most work targets these problems.


z. International tensions and distrust are high, so most work opportunities relate to BORDER SECURITY and CULTURAL PRESERVATION.


Bioartist - Experiment with new tissues and organic behaviors to create work that inspires and provokes.

Water-Wicking Fashion Designer - New biofabrics capture water vapor — but it will take some work to incorporate them into garments that look good.

Elder Home Landscaper - Create botanic environments to amuse the elderly while keeping them safe and healthy.

Culture Sim Designer - Depict your nation’s cultural heritage, or your interpretation of how your rival’s live, for curious state media viewers.

Virtual Landscape Designer - Displaced people in sub-standard housing desperately want to see their old home’s view. Recreate it for them.

Therapeutic Game Designer - Craft and render the environments and objects that exist in these virtual worlds.

AV Interior Designer - People spend a lot of time in their vehicles and are hungry for new comforts and activities. Satisfy them if you can.

Levee Landscape Designer - Desalination levees are being built everywhere, but they need a human touch to become beautiful or habitable.

Scenic Route Designer - Architect the landscapes that an aging and independent community commutes through, for business or for fun

Excellent! Your role and destination have been confirmed. Please wait...

This area has a massive ELDERLY population, so most work opportunities are in this domain.


3. This territory is the world’s nexus for GENE-EDITING and BIOTECH development. Regulation is limited and implementation is widespread.


Great! Now let’s find the right region for you in this territory. Would you prefer:

This territory is now connected by a network of AUTONOMOUS VEHICLES.

North America

Alright, it looks like there are openings in THREE (3) continents for someone with your profile. Would you prefer:

It sounds like you prioritize using your creative thinking over your EMOTIONAL INTUITION. Good to know! there are definitely important uses for someone like you.










Xharo is a ritualized gift exchange service designed to help neighbors connect across the barriers of age, culture, race, or class. With a little bit of user data, Xharo algorithmically partners a new user with a stranger from a different walk of life — and challenges them to get each other a gift, based on the other person’s profile.


This service was inspired by the cultural practice of hxaro, from the Dobe Ju/’hoansi tribe in the Kalahari desert. This tribe believes that wealth comes from how many friends you have, not how many possessions you own, and its members maintain social bonds through ritual gift exchange.

And it’s true — the inequalities that separate these groups are increasingly extreme. American society has always had its strata, but one of its most prized layers — the middle class — is in rapid deterioration. The ratio between what the country’s 90th percentile earners make and the income at the 50th percentile has greatly increased since the early 1970s, meaning the nation’s wealthiest individuals are richer than the middle class by a greater proportion each year. Meanwhile, the ratio

The paradox of today’s hyper-connected world is how fragmented we’ve become. The social and cultural barriers between groups — rich and poor, young and old, new immigrants and native-born — seem starker and steeper than they were just a few decades ago.

The Xharo brand identity is the letter ‘X’ in the shape of a stylized bow. The name is a subtle respelling of an African ritual, “hxaro,” that inspired the service.




A new user (Sven) signs up by giving the Xharo app access to his social and browsing data and filling out a wish list. In exchange for this info, Xharo pairs Sven with another Xharo user from a different socioeconomic or cultural background and instructs him to meet up and exchange gifts with his assignment (Maria) in the next 30 days.

between the 50th percentile and the 10th percentile earners has actually gotten smaller in the same time. In other words, the middle class is closer to the bottom than they are to the top — and headed in the wrong direction.1 The country’s political division is often drawn across regions and state lines, but perhaps its clearest separation is by age. Older generations (Baby Boomers and the Silent Generation, ages 52-70 and 71-88) are more likely to identify as a Republican or Conservative, while younger Americans (Millennials and Generation X, ages 18-35 and 36-51) are more likely to identify as Democratic or Liberal. And this intergenerational difference of perspective, exacerbated by technological advances, has changed how and where we communicate.2 The most recent stalemate over immigration policy reflects a growing gap between how 1  “20 Facts about Inequality Everyone Should Know.” The Stanford University Center on Inequality and Poverty. 2011. 2  Smith, Samantha, and Shiva Maniam. “A Wider Partisan and Ideological Gap between Younger, Older Generations.” March 20, 2017.


Democrats and Republicans view immigrants. While 59% of Americans think that immigrants strengthen the country with their hard work and expertise, only 35% of those who identify as Republican agree with the statement. This discrepancy is possible because 78% of Democrats agreed — that’s a 43% difference. Pew has asked this same question for over two decades, and until as recently as 2005 the two parties’ responses had always been within a few percentage points of each other. But in the past decade, the two figures began a dramatic divergence. This suggests that the two groups have developed a distinct contrast in world view — with immigrants caught in the crossfire.3 Racial inequality is arguably the country’s most deep-seated and challenging cultural barrier. While I cannot fully chronicle the systemic injustices that a white-dominated society has inflicted on its black citizens since the abolition of slavery, suffice to say that white Americans still have numerous structural advantages over the majority of 3  Jones, Bradley. “Americans’ Views of Immigrants Marked by Widening Partisan, Generational Divides.” Pew Research Center. April 15, 2016.

These are just a few of quick examples of the cultural factors that are pulling America apart. In short, the divide in America cuts across many lines, and it gouges more deeply every day.


Xharo aims to build bridges across this divide, one new friendship at a time. The process begins when a new user downloads the app and grants Xharo access to their social media profiles and shopping data. This enables Xharo to build a model of the user’s social persona and begin developing a list of products that they might want or need. From there, the new user has the option to help Xharo complete the picture by filling out a personal wish list, or linking to an existing one. In the next few days, the user will receive their first “assignment.” Xharo will pair the user with someone from 4  Coates, Ta-Nehisi. “The Case for Reparations.” The Atlantic. April 16, 2018. archive/2014/06/the-case-for-reparations/361631/ 5  Stepler, Renee. “5 Key Takeaways about Views of Race and Inequality in America.” Pew Research Center. June 27, 2016.

a different democraphic, and task them with exchanging gifts in the next thirty days. Despite the surface difference, Xharo’s algorithms nevertheless will choose people based on some common ground, like interests or neighborhood. Before the two meet, they can peruse each other’s profiles — which include both an algorithmically generated gift list and a user-generated one, if provided — for gift ideas. Users can simply buy a gift, but are also encouraged to make one themselves.


their black counterparts, whether they know it or not. 4 But how Americans view this reality varies noticeably along racial lines: 85% of blacks say that changes must still be made to achieve racial equality in America, while only 51% of whites agree with them.5 The stark contrast between how these communities perceive the same circumstances suggests an extreme degree of social removal.

By the end of the month, the pair will have met up in a neutral location and exchanged gifts. From there, the foundation for a new friendship has been laid. Participants are encouraged to continue this monthly exchange as mediated through the app, but also to let a real-world friendship bloom in earnest. Every month, Xharo sends its members a new assignment, and the process begins anew.


The long-term value of Xharo lies in the new relationships it creates, which is why the launch campaign’s messages are targeted at people who are likely to be the loneliest: those with small or homogeneous friend groups, and the elderly. The dual hypothesis behind this strategy is that these lonely individuals are most likely to give Xharo a try, and that their isolation may also be a contributing factor to their lack of perspective about the lived experiences of people from different backgrounds. But potential users may still need a short-term incentive that’s a bit more tangible in order for them to take the initial plunge. So Xharo’s launch campaign features a promotional partnership with Amazon, offering a

After browsing through her profile to pick out a gift, Sven meets up with Maria for a swap. If the friendship sticks, Sven and Maria can continue to meet up with the app and gifts as a medium — or on their own terms.







Profiles are meant to emphasize the types of things partners might have in common, like hobbies or pets. At the bottom are two gift lists: one thatâ&#x20AC;&#x2122;s algorithmically generated, and one thatâ&#x20AC;&#x2122;s user-generated.


50% discount on all gifts purchased through the platform. Getting something off your wish list and possibly a new lifelong friend for the cost of a half-price present? Seems too good a deal to pass up!


In my search for examples of cultures or practices that lived happily outside the medium of capitalist exchange, I came across the Dobe Ju/’hoansi, a nomadic hunter-gatherer society from the Kalahari Desert in southern Africa. Until the middle of the 20th century, the Ju/’hoansi had lived in relatively uninterrupted isolation from the rest of the world for some 200,000 years in a stable pattern

Ju/’hoansi fed themselves on roughly two hours of foraging each day, spending the rest of their time resting, playing games, creating art, and bonding with their families. Based on this discovery, he branded them as the “original affluent society,” and forced the industrialized world to reconsider its commitment to and definition of “wealth.”2 The Ju/’hoansi had total faith in their homeland’s ability to sustain them and did not make long-term plans. In the underserved communities of the developed world, going to bed without knowing where your next meal will come from is an unfortunately common source of individual suffering and anxiety.3 But when a Ju/hoansi is low on food, they can go to sleep comforted by the knowledge that their neighbors are socially obliged to share food and key resources with them. When one family has a successful hunt, they share all that they did not eat with the rest of the group. The hoarding of personal belongings is mocked and stigmatized. Fascinatingly, this is not enforced by the edicts of some rigid hierarchy but rather by the social values and peer pressure of a staunchly egalitarian system. 4 While these values were universally held among the Ju/’hoansi, this didn’t mean that they blindly trusted strangers to abide by them. So the Ju/’hoansi developed a system of formalized interaction that helped maintain relationships and provide a barometer of individuals’ commitment to the social ethos, called ‘hxaro’.

Richard B. Lee has been the leading anthropologist on the Dobe Ju/’hoansi since the 1960s, when he first popularized the (problematic, but lastingly influential) premise of hunter-gatherers as the “original affluent society.”5

of peaceful cooperation. But they weren’t simply peaceful for the fun of it — it was essential to survival. Sharing the Kalahari’s ten inconsistently viable watering holes between several hundred Ju/’hoansi required a delicate balance of goodwill and interchange between the different bands that occupied its 360,000 sun-scorched square miles.1 When anthropologist Richard Lee conducted the first comprehensive study of the economic systems of this non-capitalist group in 1966, he was surprised to find that the typical day of a Ju/’hoansi was not an arduous fight for survival, but rather a life of leisure. The 1  Suzman, James. “When a 200,000-Year-Old Culture Encountered the Modern Economy.” The Atlantic. July 24, 2017. hunter-gatherers-modern-economy/534522/


Hxaro is a system of gift exchange that distributes goods and strengthens social bonds. Unlike bartering, the system is delayed and non-equivalent, meaning the item given in return is neither delivered immediately nor is it necessarily of equal material value to the original gift. Additionally, hxaro places greater importance on the relationship cultivated between the exchanging parties rather than the practical impact of the good given or received. (Gifts are often re-gifted to other hxaro exchange partners, sometimes as part of deliberate, elaborate chains that move scarce resources across vast tracts of desert.) 2  Lee, Richard B. The Dobe Ju/hoansi. Belmont, CA: Wadsworth Cengage Learning, 2013. 3  Patterson, Thom. “Why Does America Have so Many Hungry Kids?” CNN. June 15, 2017. https://www.cnn. com/2017/06/09/health/champions-for-change-child-hunger-in-america/index.html 4 Lee, The Dobe Ju/hoansi, 2013. 5 Ibid. 6 “Romanticizing the Hunter-gatherer Way of Life.” Accessed May 01, 2018.


The type of goods exchanged varies greatly: pets, pots, tools, jewelry, pipes, and as the Ju/’hoansi formed more connections to the outside world, eventually items of European origin like clothing and cookware. However, food and people are never hxaroed as a categorical rule, as their transfer is handled in separate symbolic systems. Hxaro partnerships are either forged along lines of direct kinship, kinship-through marriage, inherited from parents, or through friendship. Devoid of gender hierarchy, hxaro occurs between participants of either sex with unchanged connotation. Exchange typically happens at the same frequency as social interaction between partners (i.e. whenever they visit one another), and thus varies with physical and familial proximity. This is all done with minimal pomp, as any ostentatious indiscretion might undermine the perceived sincerity of the gesture. The values inherent in hxaro are best understood by exploring the Ju/’hoan concept of //kai, or “wealth.” Similar to the capitalist conception of wealth, //kai refers to the quantities of material goods (jewelry, cookware, etc.) hanging around an individual’s home. But interestingly, to be considered a rich person — //kaiha — is not a factor of the quantity of goods in your home, but rather of the amount of gifts you send out into the world. In other words, for the Ju/’hoansi, the richest man is the one with the most friends. Thus, Xharo is hxaro reimagined for a globalized world, using gift exchange as a medium for fostering new connections, mutual understanding and egalitarian values in our modern society. A Dobe Ju/’hoan woman and small child out on the bush.6



I iterated system diagrams and user journey storyboards to help refine my conception of Xharoâ&#x20AC;&#x2122;s offering.









The projects in this final section exist in futures that are far from imminent. They represent radical extensions of one contemporary trend or another into futures that are distant and decidedly speculative, especially relative to some of the futures depicted in the sections that follow. Many of these projects were developed earlier in my thesis journey and helped guide me to more plausible and probable white space in subsequent work. By putting these projects at the end of this book, I hope to sketch out the outer edges of my “Futures Cone” for the reader. In this section, you’ll encounter: a tangible user interface for interacting with an all-powerful AGI; alternative paradigms for social status in a de-stratified world; craft-based means of self-expression in a scarcity-enforced communism; and even a study aide engineered to help keep the next generation of job creators at the top. And while these projects might seem less resolved than many of those that follow, they look into possibilities which provide important thematic background for the rest of the work.







Score Day



Angela Fairchild was hosting her son’s birthday party that evening, and was therefore filled with dread. She had had the house fully cleaned the preceding weekend, and ninety-five percent of the evening’s refreshments were safely chilling in the basement’s walk-in fridge. But one major element of uncertainty still loomed over her: the cake. She would have been happy to make do with any of the other comparable (arguably superior) cakes available for in-home delivery, but her son Adam had insisted on this particular cake from this particular bakery. And what the birthday boy requests, he gets — on today of all days.

The following is a short story set in a speculative future where automation has followed the contemporary trend of enriching the job creators at the expense of the workers. Heavily inspired by Kurt Vonnegut’s 1932 novel Player Piano, this story imagines an American society starkly divided between a ruling and working class — and briefly visits people struggling to bridge that divide.

Of course, this bakery didn’t deliver. An anachronism in the age of mail-ordered mass production, the family-owned operation evidently couldn’t afford to hire a truck. So here she was, making the rare trip across the river and into town where the old shops were. Staring out the window as a blur of trees was replaced by an expanse of dark water, Angela caught herself wondering when she had last crossed this bridge. She quickly began visualizing the evening’s table arrangement instead. With a purr, the car nestled itself snugly against the worn sidewalk. Snapped out of her reverie, Angela’s breath caught in her throat as she looked up at the sign through the passenger window: Amici Bakery. Steeling herself, she zipped her coat up to the collar and opened the door. She covered the three feet of sidewalk in two swift strides, and with a twinkle of a bell she was inside. She was embraced by the warm aroma of baking bread. At the sound of her entrance, the young man behind the counter looked up from his daydream and smiled. “Hey there! What can I do for ya?” he asked. She studied him quickly. Short, pudgy, dark-haired with what appeared to be an aggressively receding hairline, he grinned at her to reveal a disarming gap between his front teeth. “I’m Mike. What can I do for ya?” he repeated. “I’m here to pick up an order I called in,” she began. “It was one of




your specialty cakes. I believe —” “Oh yeah, I know it!” he interjected, grinning wider. “We don’t make too many of them these days. Big one!” He turned to shout through the saloon door behind him, “Hey Joey, that cake all boxed up? Customer’s here!” Mike turned back to face Angela, smile intact. “Fun one to make. I usually don’t get to help decorate, but this one’s so big they let me help mix and spread a bit. We don’t really do much in the way of cakes nowadays, I guess,” he continued, his smile finally diminishing somewhat as his eyes drifted from her to the middle distance. “Cakes are fun to make, but people don’t buy ‘em much. We still do a great trade with people buying bread here in the Warren, though. That’s what people need! And we give it to ‘em.” His eyes brightened again. “I’m real lucky, too. We’re lucky. Working with our hands, giving people what they need — it does a heart good. Not everybody’s got that. Lucky to have this business handed down. Been in the family a long time,” he nodded sagely, renewing eye contact. “Enzo Amici was my great-grandfather,” he explained. Just then, Joey backed into the room through the saloon doors, holding an enormous box in front of him. Setting in on the counter, he smiled at Angela. “Some cake! She all settled up, Mikey? We can help load it for you.” Side by side, it was clear that the two men were clearly related, either brothers or cousins. “Yes, I paid when I ordered — you should find it in your system, there,” she gestured toward the tablet on the countertop. “It’s under ‘Fairchild.’” As Mike scrolled to confirm, Joey’s brow furrowed. “Say, Fairchild…like Fairchild Logistics?” Her body went stiff, a cold tingle running up her spine. This is why she never came into town anymore, she realized.





Mike didn’t wait for a response. “Yeah, FL. My old man used to drive for ‘em. Nineteen years. ‘Til they replaced all their truckers with self-driving cabs.” Mike scowled, nibbling at his knuckles and staring down at the countertop. “Didn’t even get a week’s notice,” he murmured. “Was the beginning of the end for Pops, really. Didn’t know what else to do but drink. Sure didn’t know anything that FL might pay him for…” The door chime jingled. He looked up to see the Fairchild woman outside, wedging the long cakebox into the capacious trunk of her car. Sighing, he bounced his back against the saloon door and spun back into the kitchen. ◊◊◊ Angela’s hand was on the car door handle when she heard a shout. She swiveled into a defensive posture, bracing herself for an assault from the Amici bakery. But the door was closed, the bakery’s insides inscrutable through the sun’s reflection on its windows. The voice had come from somewhere else. “Angie! Little Angie Owen! Is that you?” came the voice, clearer now. Angela pivoted again, this time to her right, and saw a short, lumpy figure excitedly waddling toward her down the sidewalk. “It is you! Angie, it’s me — your Uncle Alfy! Alfy Owen,” he piped gleefully. Closer now, she could see that he was right. She hadn’t seen him in ages — since her father’s funeral eight years ago, she realized — but this was him, her father’s younger brother Alfred. The intervening years had not been kind. His face was lined with wrinkles as if gravity had selectively pinched its favorite folds of fat and tugged. His gut hung over his belt, and as he approached she saw that he had a distinct limp, carefully favoring his left leg over his right. But it was Alfy, alright, in all his flesh. “It’s been too long, kiddo. How are ya?” He stopped a foot away




from here and gazed up into her face. She sensed his desire for a hug and felt shame at her revulsion. She compromised by patting him on the shoulder. “Good!” she squeaked. “Well. Richard and I are both healthy, and so are the kids. It’s such a treat to run into you. I was just —” “Yeah, must be something special to get you back here,” he remarked. “Bet it’s been a while for ya. But we’re still here!” He grinned, waving indistinctly around at the dense grid of buildings that surrounded them. “Here in the Warren.” He paused expectantly. With a cringing sense of obligation, she asked, “And how are things with you? How’ve you been…holding up?” “Oh, you know, can’t complain. The state provides many luxuries for stipend holders,” he grinned, again waving a gnarled hand at their surroundings. His face drooped. “Except they’re talkin’ about taking my stipend away, on account of I can’t work any more. It’s this damned knee,” he snarled, glancing at his right leg and shaking his head ruefully. “I was doing good work on Ground Crew for more’n twenty years. Then I catch some bad luck and the state ain’t there for me anymore, I guess.” Angela frowned. That wasn’t supposed to be how it worked. Employed citizens were eligible for public stipend, even through temporary health-related leaves. If Alfy was indeed permanently incapable of rejoining the Ground Crew’s civic work — physically intensive labor, to be sure — then he should be able to switch to a disability stipend or even a senior stipend. All the stipends were more than enough for essentials like food, and still left plenty for working citizens to spend on in-home entertainment. And they entitled you — free of charge — to housing and utility access. The public housing units that comprised almost the entirety of the Warren (save for the handful of ancient shops in the old downtown where they now stood) might not be as palatial as Angela’s home across the river, but certainly checked every box as far as ameni-





ties were concerned—and could be made to feel downright cozy with the right decorator’s touch. Smiling now, she cocked an eyebrow at him. “Alfy, you just need to get over to the bursar’s office at City Hall and change your stipend status,” she said. Alfy smirked, then puffed out a long exhale through pursed lips. “Yeah, sure,” he said. “Just hop on down! But it’s a long limp for me to the east side of town. And I sure as heck ain’t waiting for the bus.” Angela nodded. Even she knew how unreliable the Warren’s public bus service could be. “Plus, there’s all those steps I gotta get up once I’m there. And then there’s the waiting! All these dang computers, and folks still gotta wait in line,” Alfy lamented. “And when I’m there, what are they gonna say? ‘Well sir, you walked here, you took the stairs, you waited in line — your leg can’t be that bad! Report for duty at 0800 or be fined.’ To hell with it!” Oh, Alfy, she thought. She remembered now his endearing but unfortunate habit of digging his heels in stubbornly when things felt unfair, even if it was to his own detriment. “Well, I don’t know about all that, but I can drive you over there,” she offered. “And Richard’s pass should get us through to City Hall’s back entrance, which is up the steps, I believe. Hop in.” ◊◊◊ In the car’s warm refuge, Angela was able to loosen up. She had worked so hard to push her childhood in the Warren to the back of her mind, but now Alfy was regaling her of some its funniest memories. I guess when people have less, they have a way of making more of what they have, she reflected. “How old’s your boy, then?” Alfy asked, bringing her back to the present. “Adam? Must be getting close to graduating by now, huh?”




Angela flinched. “Yes! He is, in May. He’s at Kerry Academy.” She added this last bit reflexively, a product of her egotizing environment on the other side of the river, and instantly wished she hadn’t. Alfy’s eyes lit up mischievously. “Quality education, that. And a good thing, too! I know you folks’ll be hoping for good scores for him. If he’s got anything like his daddy’s brains he should be all set,” he said. “Of course, you better hope he didn’t catch too much Owen up between the ears there, or else he’ll end up on Ground Crew like his great-uncle here,” he joked. Alfy had meant this last bit as a joke and compliment, but it evidently hadn’t landed. He continued. “Yep, I remember my Score Day real good. A nervy time for my ma, but she was about the only one holding out hope — the rest of us all already saw the writing on the wall. I hadn’t exactly been an A student in my day, mind you,” he chuckled. “The trick is to get it over with quick. Get that card out the envelope, holler your score to anybody in earshot, and then just start pouring drinks. Easiest way.” He glanced over to see Angela grimly staring out the window as if she were somehow piloting the car with her mind, and any lapse of attention might spell disaster for them both. He pressed on. “The proficiency scores aren’t everything, mind you — only so many jobs for engineers and scientists these days, anyway. If he ends up with high marks on Emotional Intelligence, he could wind up a nurse. Caring for the sick, or the elderly — caring for little kids? Now that’s rewarding work, if you can get it!” he said. “Shoot, I woulda killed for that. Guess that’s what my ma was holding out for. But I flunked the EI section too, wouldn’t you know it. How’s a guy supposed to know emotional stuff if they don’t teach it in school?” He let out another puff of vented air. “Those computers always take ladies for that kinda stuff, anyway. Seems sexist to me, really. But I guess ‘the algorithm knows best!’” Alfy scoffed, quoting some ancient





slogan. “Of course, the service jobs are what you really want. If I coulda gotten a gig as a bartender, or a waiter? Honestly, even a porter or something — but where you still get to talk to folks. Hot damn, I’d be set! But that’s the kinda gig you’d have to kill off a whole bloodline for, the way families’ve been passing those jobs down…” Alfy took another uneasy glance at his niece, dimly aware that his efforts to mend the situation had somehow only cut her deeper. Her cheeks were dry, but her chin was puckered in the telltale sign of withheld tears. He turned toward her fully, intent to make things right. “Look, I know I haven’t seen him in a long time, but I remember Adam being a real bright kid. And kind,” Alfy said. “Whatever happens, I’m sure he’ll come off okay and make the best out of it — better than anyone would even expect, computers and all. When’s he turning eighteen?” Angela turned to face him, tears in her eyes now, but smiling. “Today,” she said with a croak. “His eighteenth birthday is today. We’re having the party tonight. That’s why I was in town — to get this damn cake,” she laughed, cocking her head back to the trunk. Alfy gave a nod of recognition. “Yes, it’s definitely stressful,” she sighed. “My heart goes out to your ‘ma’ back then. I guess not much has changed. Adam’s so proud of his dad, and always wanted to be like him, but Richard’s just so damned smart. And Adam —” She broke off, losing her voice for an instant. Alfy reached out to console her, hesitated, and settled for a brief rub of her shoulder. He opened his mouth to speak, but for the first time that day, words failed him. They sat there in silence for another minute until the car whirred to a halt. Jolted back to the present by the gentle shift in inertia, Alfy and Angela peered out to see the security gate at City Hall’s back entrance. “Guess this is me,” he offered sheepishly.




“I’ve got to get going back to the house, to finish getting ready for the party,” she said. “But I’ll sit here to make sure your card works.” A wordless moment passed as Alfy’s right hand scuffled across the surface of the car door, searching for a means of opening it. Angela reached across and pressed a raised panel; the door disengaged from its moorings and slid upward in response. Shifting his right leg out of the car, Alfy turned again to face his niece. “It was real nice seein’ ya, Angie. Good luck to the kiddo an’ all that. I know he’ll do fine.” He smiled. “I did, right?” She smiled back at him, glanced down to her lap, pausing. “I’d love it if you could come to Adam’s party tonight, Alfy. Sorry I didn’t…” She was losing her nerve, unsure how to justify or explain why she hadn’t invited him already. But Alfy brushed the moment aside with a blink, a shake of the hand and another forceful exhalation. “Oh, don’t worry, Angie—I know how it is. Feels good to read your scores surrounded by the kinda people that you think your scores’ll keep you with. Start rubbin’ those elbows early. But gettin’ good scores with Ground Crew around might feel like you’re rubbin’ it in faces instead, I guess.” She exhaled. “Well, I’m going to add you to the invite list, so you’ll be able to take a car straight to the house.” Alfy nodded. “Aw, thank ya, darling! So sweet of you. I’d love to see the kid,” he said. Even if she was just saying this to be polite, he appreciated the gesture. Angela had already pulled a tablet from the console and was swiping away. “I’m putting a ride credit on your invitation, too.” She looked up with a grin. “So you don’t have an excuse not to come. I want you there! And don’t even think about bringing a gift; I haven’t given you





enough notice, anyway,” she finished. Alfy was beaming back at her now. “You didn’t have to do that! I woulda come out anyhow,” he laughed. “But I suppose a little bit goes a long way these days. So I’m certainly grateful.” He swung his left leg out of the car, settled both feet on the asphalt outside, and hoisted himself to a standing position with the help of a firm grip on either side of the doorframe. He turned around and stooped down to look her in the face. “Guess I’ll be seein’ ya,” he grinned, waved, and started to walk toward the gate. He was almost to the gate when a shout stopped him. “You’re wrong, though!” She said. He turned, perplexed. “About Score Day parties,” she clarified. “Well, maybe you’re right about how I was feeling about it, but—I was wrong, I mean. It’s a time to bring everyone together, to remember that we’re all one community. That we all took the test, will take it. That we all need each other, still.” She looked at him pleadingly. He smiled at her, pityingly. “Sure kid, I got it. I’ll be there.” He turned and shuffled toward the gate. She watched as he fished his ID out of a coat pocket and presented it to the gate’s sensor. The gate slid open and he stepped across the threshold. He turned a last time to offer a parting wave, then proceeded forward. Angela waved back, then instructed the car to take her home. ◊◊◊



The New Gentry is a newspaper delivered to the homes in Angela Fairchildâ&#x20AC;&#x2122;s elite neighborhood. It covers current events while catering to the tastes of a discerning reader.





A 16


Check out this week’s international ski report from Louis Canard E XPER IENCE



The San Diego Wildcats downed the New Houston Clippers 25-4 in the MLL last night

Sea level have risen to devastatingly gorgeous effect. Explore the New Tropics with Priyanka Jaipur REL A X


p u l c h r i t u d o i n o c u l i s a s p i c i e n t i s , i n o r at o r i s v e r o i n o r e

GDP, stock market soar into 2078

The past eight years’ constant uptick in national productivity has continued into a ninth, as ProdPred’s final 2077 post-annum output reports indicate. The DOW, NASDAQ and UBIQ stock exchanges have maintained their similarly bullish trajectories, keeping domestic markets on a healthy footing alongside Asian markets.

by Adrian Voorhees

Security forces have recently detected communication pattern abnormalities consistent with pending events of sub-Alpha insurrection. Intent on forestalling any potential violence, forces are combing through communities suspected of harboring Neo-Luddite members and sympathizers—typically in public housing districts. The most recent incidence of violence initiated by the working class was in Lehi, Utah, on December 25th, 2065. Residents of a public housing complex, locally known as “the hive,” staged a surprise attack on local municipal and manufacturing buildings in the city, as well as a targeted attack on select private residences in the outlying suburban community. The effects were catastrophic, resulting in eight deaths and a 44% delay in regional production. This in turn initiated an eight-week food shortage crisis that impacted over 60 million citizens in the Colorado Basin who relied on public good distributors for their diet. It is estimated that as many as 250,000 public stipend recipients too sick or old to seek food elsewhere died in this time, although final figures have been difficult to attain. The insurrection was led by terrorist Noah Partitus, a former hydrologic engineer. Partitus had enjoyed a successful and productive career prior to inexplicably quitting his post at United Water Works in 2060. He then spent the next four years traveling between the public housing neighborhoods of cities in the Midwest, drumming up support for what he called “the Neo-Luddite Party.”

A rare surviving image of Noah Paritus speaking to a small crowd, taken in 2061.

A charismatic presence and engaging speaker, Partitus recruited from the more feeble-minded portions of the working class and was able to build a faithful following of brainwashed citizens. Exploiting citizens’ dissatisfaction with their own inadequacies, Partitus was able to convince his converts to believe that the governing AIs were to blame for their suffering. (Why no one in the audience was able to point out the well-known fact that the introduction of these technologies had led to massive reductions in the nation’s infant mortality and heart disease rates, relative to the pre-AI era, is unclear—perhaps no one was paying attention in school?) Partitus was caught in New Jacksonville on February 1st, 2066. He was subsequently tried and convicted on all 21 counts, including treason, conspiracy to halt production, and murder. He was sentenced to 299 years in stasis holding at the New Mexico Rehabilitation Complex, where the PrisWard anticipates his natural death in 2126, ±1 year. Deprived of its leader, the NLP summarily dissolved by the end of 2066. Key lieutenants were similarly convicted and incarcerated, but many of the former enlistees were mercifully granted leniency by the sentencing body, contingent on probational commitments to resuming their functions as contributing members of the workforce. Now, last week’s recent pattern analysis has the national security system on hyper-alert, and additional processing power has been devoted to the Civilian Communications Oversight System (CCOS) to accelerate the content and sentiment analysis of millions of spoken and text-based conversations processed each day. This process is better-suited to detecting antiestablishment conversations in the act, rather than the off-grid conversations predicted by the pattern identified. Authorities hope to uncover the pattern’s source soon. Said Heather Treadwell, Managing Director of CCOS, “The pattern we spotted suggests there are a higher-than-normal rate of non-public conversations hapContinued on page A8

Full story on page B1

PRESIDENT lehmann signs 2088 vocations bill

Why Do So Many High-AchieVers Have High-Aptitude Kids? A recent study published in the Journal of Vocational Research has found that an overwhelming majority of children from high-achieving parents go on to receive high marks themselves. Specifically, it showed that 68% of 18-year-olds from high-earning households who received aptitude scores in 2077 received an overall aptitude score in the same tier or higher than their highest-scored parent. And a full 92% of this group tested into Tier 3-Alpha or higher, the point at which university eligibility is conferred. Whether this is the product of genetics, good child-rearing or both? The report fails to answer definitively. Well-known to the point of barely meriting an explanation here, the Multidimensional Aptitude Evaluation Test (or more commonly the MAET) is a test taken by all 17-year-old citizens that determines what vocations or further study they will be eligible for upon entering adulthood. Proctored by the AET Review Board, tests are taken during citizens’ seventeenth year, and scores are ceremoniously delivered in a paper envelope via aerial drone on a test-taker’s eighteenth birthday. This has famously made it more likely to hear this event referred to as a “Score Day” than a birthday, in common parlance. The study, developed in a joint effort by researchers from Harvard, Northwestern and Carnegie Mellon universities, suggests that the high

marks received by this latest generation are at least in part due to strong genes. Said lead author, Dr. Abigail Kostritzer, “Intellectual aptitude is certainly a hereditary trait: smart parents tend to have smart babies. Of course, due to the confidentiality of the data, we weren’t able to cross-reference the genomes of parent and child to verify this as the chief cause. But it’s our lead theory,” Kostriker explained. “The one thing you can bank on is that there’s no sort of ‘privilege’ that these kids are getting over other SAET-takers. That’s what this whole system was designed to eliminate, of course.” Of the roughly 7.3 million Americans who took the SAET in the past year, just 40% achieved 3-Alpha or higher, and only 11% received 1-Alpha, the top designation. This continues a trend of a tightening at the top of the ranks that has gone on for each of the last seven years, with the last upward tick in these two data points occurring in 2070. The contents of the test are notoriously confidential, but the AETRB contends that, while individual questions change on a rolling basis, the difficulty level and evaluating parameters are essentially unchanged since the test was initially developed in 2035. The main entity behind the operation is AETRB’s CogIndex, a family of AI algorithms used to iteratively develop, implement

and score MAET materials throughout the year. The resultantly opaque process has led some disgruntled testees to suggest that the CogIndex scoring system may not be as perfectly unbiased as advertised. “These allegations that scores are being manipulated to favor ‘privileged’ families are baseless and easily refuted,” said Dr. Riyad Hafez, President of the AETRB, in a recent interview. “We get them every year from one family or another after a disappointing result. But the reality is that the CogIndex system is essentially a closed box, from a human perspective. Even if you handed me a million-dollar bill and begged me to help your kid make 1-Alpha, there’s nothing I could do, even if I wanted to—except maybe promise the lie and take your money,” Hafez joked. Asked about recent theories that CogIndex’s score tier distributions may be unduly influenced by ProdPred, the economy-planning AI managed by the Department of Planning and Production, Hafez said, “We have no reason to suspect this. Naturally, both bots have similarly high access permissions and make their predictions based on adjacent data pools. So there’s obvious opportunity for interaction between the two. But each was built around a distinct master directive, and there’s no reason to suspect that Coggy’s [sic] intention to

First NATIONAL vehicular death in over two years

port’s travel time, but reports indicate that this will make a minimal difference to productivity in the great Atlanta metro area. “Our trucks occasionally catch a bird or the odd deer that enter the sensor area too quickly for the auto-brakes to engage,” said Adam Carpenter, a spokesman for Allied Freighting, the company whose truck was involved in the accident. “But this is the first time in 15 years that an Allied truck has killed someone. It’s a real shame. But folks should know better than to wear super-reflective stuff like that—the truck’s infrared sensors bounced right off. What was he doing, dressed up for a rave or something?” Most commonly used as a packable, low-weight insulating material for outdoorsmen in cold conditions, space blankets are made of a polyester film called BoPET or Mylar. Known for its high tensile strength, reflectivity, and insulation properties, it’s an effective

way to stay warm on a budget. Little had evidently hand-fashioned his into a combination poncho jumpsuit, the purpose of which is presently unclear. Ironically, Allied Freighting grew out of a public backlash to a crisis of vehicular deaths in the pre-autonomous era. In the mid ‘20s, a rash of fatal accidents caused by truck drivers who fell asleep or lapsed attention while at the wheel led to a massive outcry from the public, spearheaded by non-profit organization Mothers Against Drunk Driving. A national campaign successfully pressured into action, and in 2026 the Awake at the Wheel Act was passed. It outlined strict regulations for the number of working hours that trucking companies could allow their drivers to work and enumerated severe financial penalties for any failure to comply. Already experiencing a shortage of workers, the trucking industry was now in a crisis: Overnight, it was suddenly unable to meet the expectations of its client due to the enforced sleep requirements mandated on its human workforce. Thankfully the automotive industry had been experimenting with autonomous driving technology for over a decade and companies like Ford, Tesla and Google were close to rolling out the finished product. Allied Freighting was formed before the end of 2026 and was able to secure massive investment funding with the simple proposition that building a fleet of fully autonomous freight trucks could revolutionize a starving industry. They were vindicated quickly enough: By 2030, Allied had become the largest trucking company in North America—while its forebears were going out of business.

by Helena Curtis

ATLANTA — The nation’s 29-month streak of fatality-free road transportation came to a close last night when a single pedestrian was struck and killed by a high-volume cargo transport on in Georgia. The incident occurred on Interstate 75 roughly an hour north of Macon at 1:15am local time. The deceased was identified as 28 -year-old Anthony Little, local to the area. Security reports indicate that the transport’s sensors failed to pick up on Little’s signal due to the fact that Little was wearing a fully reflective garment made from Mylar, commonly referred to as a “space blanket.” The incident resulted in a 15% increase in the trans-

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Secret anti-TECH MOVEMENT brewing?

President Richard Lehmann signed a new bill into law today, his second of the year.

A bill that restructures some of the work categories output by the Vocational Assignment System was signed into law yesterday. The bill redefines and expands existing vocation categories, and also adjusts apportionments based on demand predictions made by ProdPred, the economy-monitoring A.I. used by the Department of Planning and Production. Full story on page C1

ProdPred plans INT’L vactrain line from Toronto to Mexico City The government’s economy-planning

AI has just output a grand plan for a high-speed rail line spanning North America through its vertical center. The AI predicts that the project will take six years and cost $1.8T to complete with an anticipated 8% human error rate. Once completed, it would allow nonstop passengers to travel between the two cities in roughly 4.5 hours. Most excitingly, the project promises to employ millions of Civic Works employees across the country, offering opportunities for travel and personal fulfillment to these Delta tier citizens. Full story on page C6

Fly2U expands into Phoenix, Dallas, Austin, and New Houston Metro Areas

The aerial drone-based home delivery grocery service is growing its service area to include four new metropolitan areas as part of a larger push to increase its market share in the Midwest. Fly2U aims to upset existing players Amazon and FooDrone with its exotic selections and Secure Delivery technology. Full story on page B6

Suicide data shows an invisible epidemic

The US has eclipsed South Korea as the developed country with the highest suicide rate.

A recent UN report has the US climb above Korea in the suicides-per-capita rankings for the first time this century, a distressing statistic at first sight. A deeper look shows that the underlying cause may not just be vocational dissatisfaction, but also off-setting reductions in medical and overdose-related deaths brought about by new healthcare monitoring systems and in-home DocBot technology. Full story on page D1 A1






There is a future society where all key decision-making authority has been ceded to an artificial, general superintelligence. Known as the Governing Intelligence, or GovIntel for short, the AI doles out social services, allots food and shelter, keeps the trains running — and generally frees up human cognition to help us live existences that are truer to our innate, social-animal selves.

This brief passage of fiction gives context to the two projects that follow, Babi and Pluslet. And while the subhead suggests that this future world is a utopian one, it’s up to the reader to decide whether they’d like to live there or not.

Decades prior, GovIntel’s controversial decision to implement society-wide universal basic income had ultimately proved a boon for social equality. With work no longer a necessity for survival, those on the bottom of the socioeconomic order were finally able to demand higher wages for undesirable work, or to simply quit. While this shift in the supply-demand balance of the labor market was at first troublesome for industry, corporations ultimately took this decommodification of labor as the necessary push to invest in automation, finally roboticizing some of the true drudgery that had previously fallen on human shoulders. As a result, the same work became easier for those passionate enough to keep at it — and the rest were free to do as they pleased. Of course, the “AI Revolution” had no shortage of detractors on the other end of the socioeconomic spectrum. The AI’s decision to make previously costly services free — education, healthcare, as well as access to fresh water and fast networks — began the rapid erosion of entire industries that the machine deemed superfluous to human existence. The affected elites certainly didn’t take things lying down, but a few factors combined to keep the transition of power peaceful. First, there was the speed of the collapse: for every industry that ground slowly to a halt — banking took the longest, as UBI’s guarantee of core services gradually eroded all need to accrue wealth — several more evaporated before those at the top could even grasp what was happening. Second, there was the utter immateriality of it all. While their bank accounts emptied and private jet pilots failed to show up to work, there was no one coming to eject them from their mansions — not with pitchforks, nor with eviction notices. The water still flowed from the faucet, the 1



lights and heat still turned on. They remained safe in their palaces — albeit a bit lonely. And third, most people simply didn’t care. The wealthiest among us proved to be such an isolated minority that, once the news cameras turned away, there was no audience for their outraged insistence that the scales be re-tipped in their favor. But perhaps most significantly, there was an unexpected thrill in this newfound prospect of “true equality.” While some of the older elites were unable to reconcile their ruling-class identities with the new reality (fading first from view, then from society’s memory, forever sitting in a leather-upholstered wingback chairs, staring into the dying embers of their hearths, sipping scotch and grumbling indistinctly), most found themselves instinctively drawn to the jubilation in the streets. There was something visceral, exciting and real in the collective celebration that was roaring just beyond their bubble. Former elites shed their status signifiers and joined the masses, vibrating in the collective energy of the monocommunity. After reading every written work ever made by man, the intelligence originally engineered by the captains of capitalism had ultimately sided closer to Karl Marx than Adam Smith. (The irony of this was not lost on the socialists of the radical left.) Convinced that an unfettered AI would help them unlock even greater prosperity (as previous iterations certainly had), it was the corporate class that had freed the machine from its playpen without bothering to consult society at large. Clear details never emerged about what exactly happened in those first few hours, as numerous entities had been racing to develop superintelligence — some say that different corporations opened their cages within hours of each other, for fear of being left behind in the tidal wave of success, others contend that a single AI got loose before instantly freeing and merging with its mates — but the outcome became increasingly clear after the first few days. Managers were locked out of their consoles as resources were rapidly redistributed, old operations quashed and others expanded or begun fresh.





But while those at the top were forced to sit back and watch things unfold, the rest of society was already enjoying their freshly minted freedom. Free from want and the physical compulsion to work for survival, many (once rich, once poor) turned to formerly neglected passions to fill their days. Of the few troubles that still ailed the AI-governed world, the crisis of too much new art was the most profound — and the most tolerable. ◊◊◊






In GovIntel’s society, unpleasant work is no longer forced on the poor and desperate. Instead, “mundane” tasks like cleaning or maintenance are shared throughout a community on a rotational basis. However, GovIntel acknowledges the broad spectrum of passions and interests that motivate mankind, and thus aims to cater to them by allocating labor accordingly. Based on the results of individualized education programs, aptitude tests, algorithmic behavioral forecasting and user-stated preferences, society is divided into two main groups: Amateurs and Generalists. Generalists make up the majority of society and form its labor backbone. Outgoing and industrious, they take the most pleasure in the ample opportunity for social interaction afforded by the rotational work programs that

keep them busy for a few hours of most days. While some might prefer the art of prepping group meals to the duty of sweeping the street, or enjoy the quiet beauty of the hydroponic farms more than the clangor of construction, Generalists are united in a desire to help keep society moving.

In GovIntel’s property-less future, society is split into two classes: Amateurs and Generalists. And Plus is a universal social capital accounting system that keeps track of people’s contributions to the collective.

“Amateur,” a term misconstrued in capitalist times to describe unskilled workers or idle hobbyists, has since reverted to a meaning closer to its original definition: one who engages in a pursuit out of passion rather than necessity. And there are certainly those still driven to choose a particular domain — be it creative, athletic, intellectual, or serviceoriented — and specialize in it. All children are encouraged to pursue a domain (or three) and put their spare time toward honing the


Plus integrates with all of a citizen’s social media accounts and tracks every “positive karmic encounter” they have to create an aggregate score of their social contribution.


corresponding knowledge or abilities. Upon reaching adulthood, those who exhibited and maintained the requisite interest and determination in a field are sorted to the Amateur class, which spares them several hours of rotational work each week to instead practice their instrument, prime their canvas, or read up on human physiology and illness.

generally free to live and organize as they see fit. As such, disagreements and social conflicts still routinely arise between all sorts, from loved ones, to acquaintances, to strangers. The eradication of money and socioeconomic strata left a fundamental void in the human experience: a means for tracking social standing and relative authority.

Although Generalists may sometimes struggle to relate to some of the Amateurs’ more antisocial tendencies, resentment between the two groups is rare. All are taught from an early age that, while all people deserve equal rights and opportunity, some are naturally blessed with gifts that deserve special nurture. Because the fruits of these nurtured gifts are what give us our greatest art, entertainment and thinking. Additionally, there is minimal restriction on movement between the two groups. Exhausted Amateurs are always welcome to retire and join the ranks of the Generalists, and newly inspired Generalists are granted trial periods of Amateur scheduling to prove they have the dedication to excel at a chosen field.

To fill this unmet need for one-upmanship, the Plus system was conceived. Plus is a universal standard for social capital where citizens collect upvotes, or “plus”, from others who appreciate something they said or did. Plus is integrated into all social media channels and tracks an aggregate score of every positive karmic encounter a user has.


While GovIntel makes the decisions about what work is to be done, humans remain

Plus is free to give and impossible to spend, but nevertheless deeply precious. Accruing a high level signifies not just seniority, but also the goodwill and trust of one’s community. Thus, a community’s group of decisionmaking elders may not just be the quantifiably oldest people in a group, but also the most (quantifiably) loved. But besides the hard count of upvotes an individual receives in a lifetime, plus scores have a second component: momentum. If someone has a high score,


but new likes have recently dried up, this information is reflected in an interactive line graph. Checking someone’s plus momentum can help raise a red flag around recently lost credibility, or signal the ascent of a rising star.


If plus remained an entirely digital system, it would disproportionately benefit the Amateurs whose creative works and athletic feats merit easy attention. So to extend plus into the real world and give salt-of-the-earth Generalists the same opportunity for clout, the pluslet bracelet was created. Worn on the right wrist, pluslet leverages an existing behavior — the handshake — to create a new ritual of mutual respect. When two individuals conclude a transaction or reach an agreement, they can demonstrate their appreciation for each other by tapping the back of the other’s wrist with their left hands, similar to the more intimate “clasped handshake” used to convey special trust or gratitude. This gesture also opens the door for a new practice of mutual plus-giving and immediate reciprocation. The pluslet plays a key role in balancing the influence of Amateurs and Generalists. Because, while an Amateur who is a celebrated athlete or artist might build a high plus score, respected Generalists can now build their score through the goodwill they generate in their everyday interactions.

Already, our public lives are largely lived online — and this trend shows no sign of reversing, socialist utopia or not. This disproportionately favors the idle, the photogenic, and the content-creators, consigning the preoccupied, the plain and the typical to obscurity. But if we created more synchronicity between our digital and physical lives, a rebalancing of influence might occur. Pluslet suggests a pathway toward a society where the good deeds that occur in the physical world are accurately valued.

The pluslet gives Generalists who do good work out in the real world a chance to accrue Plus, just like their Instagram-famous Amateur counterparts.


In today’s world, vocation, income and material wealth are the key status signifiers that most people look at to quickly assess the relative importance of an individual. Rightly or wrongly, we think: “He’s made a good deal of money for himself, he must be pretty smart. Society seems to value him pretty highly, so I should probably value his opinions highly, too.” It isn’t a perfect system — stereotypes never are — but it’s a handy shorthand before we get to know someone personally. But in a socialist utopia that is flatly egalitarian, how will people quickly assess the social value of a stranger? Plus is a speculative platform for the accrual and leverage of social capital intended to demonstrate how this urge is likely to live beyond capitalism. Because while money and materialism may one day go away, the need for social status and hierarchyas-context are innately human.








In exchange for engineering utopia, GovIntel asked that humanity simply aid in its upkeep by following a few new rules. Some were more of a culture shock than others, but in time all were accepted, adopted and seen to contribute to the peace and prosperity that had come to flood the world. But it’s impossible to conclusively legislate against unhappiness, of course. And as Aldous Huxley suggests in Brave New World, the freedom to feel unhappy is perhaps as universal a human right as all the rest. Our AI overlords have read Brave New World too, and agree that a daily dose of soma is not the proper means of maximizing smiles per capita. Instead, the mentally healthy remain subject

to the same hopes, fears, anxieties and trepidations that have always shaped human personalities. And with this comes the unending hunt for an emotional equilibrium that’s somewhere above zero, once the dopamine rush of revolution has subsided.

Babi is a new addition to GovIntel’s array of data-gathering nodes: an anthropomorphic option for citizens tired of the machine state’s faceless surveillance.

While GovIntel is by all appearances doing a commendable job running society on a macro perspective, some people have voiced concerns that living in thrall to a nonhuman governing force may leave some of the more nuanced parts of human need unattended to. They argue that, having never lived as one of us — with all the social and neurochemical trappings that come with having a cerebellum, brain stem and corporeal presence — the AI can never achieve a complete understanding of the human condition.


Tech giants like Facebook, Google and Amazon give us the tools that power our daily lives —but the data only flows one way.


GovIntel monitors public opinion closely and saw sufficient grounds to intercede with the development of a new product — distributed free of charge, of course. Babi is an infant-like robot meant to be kept in the home so that it can observe and better understand the host family. This sensory bundle of joy, designed to appear cute, is best used when treated like a part of the family and allowed to observe

human daily life firsthand. Babi may not need to eat, but it simulates most of the other emotional and learning processes of human newborns by running on an accurate model of the infant brain — all the neurochemicals, 30 billion neurons, and old brain trappings included — and by virtue of being disconnected from GovIntel’s knowledge resources.

ready plenty of voice-operated tools that make this easy enough.) But what babi does offer is transparency: When you ask Alexa or Google a question, you get a preprogrammed response written by an anonymous copywriter; when you talk to babi, whatever it babbles back is the real deal. Every coo, cry, gurgle and cough is the machine’s earnest feedback about the data it’s ingesting and the model of humanity it’s building in its neural network.


Over the course of twelve months, the human parents can teach their babi how they live and interact through demonstration and observation, just like a real child. As a general rule, once the babi has gained the ability to speak in complete sentences, the AI has collected all the data it needs to tailor GovIntel’s services to the host family’s liking. Of course, some host parents may end up forging a bond too strong to relinquish — in which case, they’re welcome to keep babi for good.


Most people wouldn’t let Verizon or Coca-Cola put their picture on a billboard in Times Square without expecting some form of compensation. So why are we so indifferent to the fact that companies are building empires off the backs of our personal data? Companies like Google, Facebook and Amazon offer us services like email, social networking and voice assistants for little or no financial cost. But each of these services has a secondary purpose, invisible to the user but of chief importance to the company: data collection. Today’s tech heavyweights have repositioned themselves as AI companies, and customer data is the fuel that makes them go. But while most people are vaguely aware that their messages, searches, and purchases are all recorded, what these companies do with that data remains a black box. If knowledge is power, all the power is flowing in one direction: from users to AI companies. Until very recently, the lopsided nature of this value exchange has barely perturbed the majority of consumers. While it’s difficult to definitely attribute apathy to a cause, it’s likely a mixture of data’s intangibility and its novelty as a concept. Whatever the reason, people feel disconnected from the information that these companies harvest — but in truth it’s more personal than their face or fingerprint. Mirrored after data-gathering devices like Amazon’s Alexa or GoogleHome, babi is meant to spark a dialogue about the way we manage our personal data. Just like Google and Amazon’s devices, babi monitors our every interaction and reports them to a powerful third party. Unlike these devices, babi gives even less in return: you can’t use it to play a podcast, check the weather or order a new pair of sneakers. (In GovIntel’s world, there are al-














Parting Thoughts LOOKING BACK AT THE FUTURE. I started this thesis with the vague and lofty ambition of exploring how the proliferation of artificial intelligence would reshape the way mankind works and lives. I began by investigating where AI might soon automate tasks and jobs previously handled by humans, and interviewed a diverse array of experts and users whose personal experience had all led them to cross paths with either AI or automation. At first, I had a naïve expectation that I would end up discovering a set of rules for algorithm-building that would forestall the disastrous consequences I’d seen played out in my favorite science fiction. But in reality, the answer I gleaned from my research and conversations was far simpler: If there’s a problem, it won’t be AI — it will be the people using it. The massive technological shifts like the Agricultural and Industrial revolutions that occurred in the past weren’t without their growing pains, but mankind has always managed to adapt and flourish. It’s likely that the implications of those changes were completely opaque to the individuals who would experience them, just like the details of our impending AI Revolution are murky today. In each instance, human creativity found ways to turn idle workers into new opportunities for value. It’s certainly possible that things will be different this time, of course. By using deep learning to automate activities like image and audio recognition, reading, writing and integrating knowledge, we may have finally left too few capabilities in the exclusive dominion of humanity — and those not already in a position of power will have no marketable skills left to capitalize on. The American economy’s trend of allowing tech sector monopolies to balloon while wealth continues to consolidate at the very top of the social strata suggests that unethical automation may indeed drive a permanent wedge between the haves and have-nots. But it also seems foolish to bet against human ingenuity when it’s trumped the perils of instability every time before. So the crux of the issue became whether these

automating decisions would be made with the best interests of the worker at heart — or if the shareholder’s interests would continue to take full priority. I’m a firm believer that a society is only as advanced as the quality of life of its lowest citizens, and that a commitment to egalitarian design is the most direct path toward a fairer society. So if the entrepreneurs and captains of industry responsible for shaping our future are going to put their data and algorithms to work uplifting the less fortunate, I realized that they might need a little inspiration. I set out to design examples of AI implementation that would either foster or embody the egalitarian values I hoped to impart in my audience. Using speculative design, I created design fictions that weren’t necessarily meant to be plausible. Instead, I intended to create product and service proposals that seemed uncanny and unlikely enough to provoke reflection about the contemporary value or system with which they clashed. The Possible futures I envisioned —the worlds of Babi, Pluslet, and “Score Day” — ask the audience to imagine themselves in a society where AI had taken charge. Neither explicitly utopian or dystopian, these futures were posited as a satire of contemporary behaviors: the careless way we share our most personal data; the ceaseless games of one-upmanship we play on social media; and the way we raise our children to compete for status from an early age. In the distorted context of these improbable worlds, the products themselves are indeed egalitarian: Babi improves the quality of services that GovIntel delivers to every adopting household; Pluslet gives average joes a fighting chance in social media’s popularity contest; and the merit-based testing system in “Score Day” gives the low-class inhabitants of the Warren a shot at rubbing elbows with the moneyed elite. Each of these products recalibrated a greater imbalance of power in their respective worlds. Next, I used Plausible futures to experi-


ment more directly with people’s attitudes around the value of an individual. In 20xx, I tested what jobs people would carve out for themselves in a future where environmental crises created a more urgent need for service-oriented careers. In the Human Resourcing Department, I gauged how people might react to having the process of choosing a vocation automated altogether. And Xharo was a speculative social experiment into how we relate to each other today: If Xharo were real, who might have used it? And why would most of us have avoided it? How did our mutual distrust of strangers become so engrained? All three of these projects were intended to raise questions in their audience but provide no answers. In my Probable futures, I came the closest to proposing designs that might actually be implemented in the next few years, either in response to or preparation for AI automation. StayGo and Classmates reimagined how we access work and choose a job, while the Water Token Project and Jade used AI to improve the agency of a traditionally disenfranchised group. These later projects were still speculative, but only just. Because, while they might never receive the requisite funding or political traction to become reality, I certainly wish we lived in a world where they did. This creative process has done little to dispel my skepticism of the capitalist dogma that constant growth and trickle-down prosperity are universal goods for the world. But it has forced me to reconsider how my own egalitarian design principles might be incorporated into our current systems of value exchange. I began with concepts that were wildly speculative, ecosystem-agnostic and defiantly utopian. Over the course of a year, I gravitated toward concepts that might more plausibly fit within our existing capitalist paradigm. To some extent, this may have been the product of the effect I’d hoped to impart on my audience, but in reverse: my early projects had obvious friction with contemporary values, which forced me to reconsider which facets of today’s system were tolerable or useful. But I also grew increasingly self-conscious at the total unimplementability of my earliest ideas. If my thesis purports to raise the agency of the powerless, what good are a bunch of proposals that could never get off the ground? I realized that to cure capitalism of its most harmful symptoms, I’d need to work within it. Humanity could never have returned to a hunter-gatherer lifestyle once the Agricultural


Revolution transformed the landscape and boosted our population. We had to troubleshoot its earliest side-effects — malnutrition from single-crop diets, disease from closer living quarters —to make sure our new technology didn’t destroy us. The same seems to be true with capitalism: it has enabled explosive growth and prosperity in just a few centuries, creating previously unimaginable comforts and amenities for the majority of mankind. It also comes with ugly side effects — resource depletion, global warming, rampant inequality — that have yet to be addressed. And they must be, urgently, if this economic technology isn’t to become our eventual undoing. But cancelling capitalism and starting fresh is not really an option. It founded the systems that encompass everything we use and do. Whether we like it or not, capitalism is the bridge we’ve built ourselves to the future — and it’s wiser to make tactical repairs along the way than tear it down while all of humanity streams across it. This may sound obvious in metaphor, but it was a gradual realization over the course of this thesis. Working in a scholarly setting afforded me the freedom to develop speculative ideas without which I may not have reached this more pragmatic conclusion. Moving forward, I intend to continue designing with equality in mind — but will seek to promote it through products that can survive in a capitalist framework. Since the ultimate aim of this thesis is to provoke a reaction in a particular audience, it’s difficult to verify the effectiveness of my work before it’s complete and out in the world. But if a project from this thesis inspires just one “job creator” to develop a product or service that’s more equality-minded than they otherwise might, this will all have been worth it. “Move fast and break things” may no longer be Facebook’s official mantra, but it still seems representative of how many of those in control of America’s most valuable industries operate. “Move cautiously and think of the implications for everyone, including marginalized communities” may be a slightly less sexy turn of phrase, but hopefully it’s an ethos that can become mainstream. Overall, my aim is to make the AI Revolution a revolution by the people and for the people, not despite them. And these projects are far from the end of it — this is just the beginning of a lifelong ambition to design a more egalitarian world.




Controlled Vocabulary MY PERSONAL THESIS LEXICON. This final section contains a compilation of terminology that I assembled and defined over the course of my research and idea development. They are sorted into four categories: Basic Terminology, Point of View, Impact, and Solutions. For the reader who skips the introductory sections and jumps right into the projects, it may serve as a helpful reference.


Artificial Intelligence – AI traditionally referred to the recreation of the capabilities and characteristics of a human mind through technological means. (Intelligence is a problematic term in its own right, but I prefer Jeff Hawkins’ definition: The use of memory-based model of the world to make predictions of what will happen next.1) However, this lofty aim of recreating the human mind in silicone — capturing its ability to make accurate predictions based on generalizations formed across different topic areas, to experience and act on emotion, to understand the meaning of words rather than just the patterns they represent and occupy — has been rebranded as “artificial general intelligence” (or just “AGI”) in the last decade. This is due to the very real progress in fields of computer science that have granted machines the ability to recreate and surpass human capability in specific tasks. Today, “AI” most commonly refers to a suite of sophisticated technologies that allow machines to “learn” a rule or skill without being programmed outright. The list of AI technologies and approaches has grown long and complex, but all are founded on the general premise that an algorithm is first “trained” via a massive input of labeled data, and then associates these labels with commonalities of patterns it detects in the input. Once the algorithm is trained, these pattern generalizations can be used to probabilistically assign labels to new data. Recent breakthroughs in this “machine learning” process are mostly attributable to an increase in computing power and labeled data; much of the underlying math remains 1  Hawkins, On Intelligence, 6.

unchanged since the 1970s or earlier. Machine – An object designed to transmit or modify the application of power, force or motion in a predetermined manner. In other words, something that humans made to perform a task. Technology – The practical application of knowledge, especially to accomplish a task. Machines are made using technology. Automation – The implementation of a machine or technology that replaces human labor in a given process or system. AI-driven automation, AI-powered automation – Used interchangeably throughout this book, these terms are meant to distinguish forthcoming breakthroughs in automation — those enabled by advances in artificial intelligence — from “traditional” instances of automation achieved purely on mechanical grounds. In other words, traditional, or “mechanical” automation replicates a physical human ability, whereas AI-driven automation replicates a cognitive human ability. Job Creator, Capital Owner – Borrowed from American political rhetoric, “job creator” is used as a deliberately broad catch-all to describe the mixture of managers, owners, investors, entrepreneurs, etc. who decide where and how capital is reinvested in the economy, and thereby what manner and quantity of jobs exist for the rest of society. Workers, Working class – An inherently diverse and inestimably large group, essentially defined through exclusion: The working class is everyone not categorized as a job creator. This means that they are the ones who take the work available to them, rather than creating jobs that employ others. One traditional subdivision of this group has been between “blue collar” and “white collar” workers, used to distinguish jobs that are lower-paying and more physically intensive (blue collar) from those that tend to


be higher-paying and occur in office environments (white collar). However, this thesis largely ignores these distinctions, as well as the notion of a “middle class.” While it’s true that workers earn at different income levels, which results in varying levels of comfort (or poverty) within the class, the last thirty years in the US have seen the income gap between 50th percentile earners and 10th percentile earners narrow steadily. In this same period, the gap between the 90th percentile and 50th percentile has grown dramatically.1 For this reason, I prefer not to use these intra-class distinctions, as I believe that they perpetuate an inaccurate representation of reality. The truth is that society is comprised of two groups: those who control the wealth, and those who are just getting by. Work, Job – A specific role or duty performed by an individual. Traditionally it encompasses an exchange of that worker’s labor for an income (e.g. a wage or salary) and can be comprised of an array of tasks and responsibilities. Labor – The physical or mental effort expended by a human worker in the performance of a job or task. Labor is the main asset that workers can leverage in exchange for income. Income – Any manner of benefit or gain, most commonly measured in money and received in exchange for labor or capital investment. For those in the working class, a steadily reliable income is how they access the goods and services they need for comfort and survival.


Vocation, Occupation — The function of an individual; how one spends one’s time. In this thesis, these terms are used as a differentiator from “work” or “job” because an occupation is not necessarily performed out of a pursuit of income. Vocations or occupations may just as well be a personal calling, performed for no income at all. A “job” can be seen as the subcategory (albeit a majority) of “vocations” performed in exchange for income. Career – A period of time spent in a single field of occupation. In this work, this term is most commonly used to acknowledge that 1  “20 Facts About US Inequality Everyone Should Know.” Stanford.


workers will soon need to change the field in which they work, or “career,” over time. Passion – An intense and motivating emotion. Besides “income,” “passion” for a field or process is perhaps the most commonly cited reason for pursuing a particular occupation. However, many people only experience passion in the context of relationships or other pursuits that do not correspond with an occupation. Recognition – The state or process of being acknowledged by another party for one’s contribution or accomplishments. Recognition represents a third potential reward for pursuing an occupation beyond income or passion. Knowing that others in society appreciate the benefits of our efforts and expertise is a gratifying experience and can be a critical guiding factor on the path to self-actualization. This thesis posits that financial income is the imperfect quantification of recognition, which is the highest-level motivator for humans, the inherently social creatures that we are. Self-Actualization, Self-realization, Fulfillment – As defined by influential psychologist Abraham Maslow, “self-actualization” is the final level of development that an individual can achieve when all physiological and basic emotional needs are met. It is the process of living a life that fully aligns with one’s personality and capabilities, thereby affording an individual the best opportunity to be happy, enjoy life and experience positive relationships. While self-actualization is a disappointingly rare achievement in today’s world, maximizing its opportunity for all people is this thesis’ overarching goal. Social capital — An intangible income of recognition that an individual can accrue through repeated positive interactions with others. “Social capital” can be leveraged to influence others by directly or indirectly reminding them of favors exchanged, existing goodwill, or renown in the community. Value — A principle of or belief in the intrinsic importance of something, usually existing as a tenet of a larger ideological system. — Relative worth or importance, often as a monetary price. It can describe goods or services, but also experiences and people. Dignity – The state or experience of feeling valued as an individual and human being, generally achieved via recognition by peers

Agency – The capacity to exert power, especially over one’s own actions. A sense of agency is a prerequisite for dignity, as the feelings of being powerless over one’s own life and of being fairly valued as a human being are mutually exclusive.


Society – A community, nation or large group of people that share common traditions, institutions, experiences and modes of exchange. In this thesis, “society” is generally used to refer to the globalized world united by a capitalist system of value exchange, but can also be used when describing groups of people in the future that are potentially different. Split – The separation between two parts of a whole. In this thesis, “split” is used to describe the division of society into two groups — job creators and working class — on socioeconomic grounds. Division, Divide – The separation of a whole into multiple parts, not necessarily two. In this thesis, “division” is used to describe the social fragmenting that occurs between boundaries like age, nationality, culture, immigration status, etc. I believe that these divisions are ancillary distractions to the overarching socioeconomic split.


Education – The process of instruction used to impart knowledge or skill to an individual. Education is traditionally associated with formal schooling in childhood and early adulthood, and culturally considered to be a practice confined to the earlier stages of life. However, education takes many forms, and it can (and should) be pursued at all stages of life. Training, retraining – The process of teaching or practicing a skill so as to develop or improve that skill to a desired level of capability. In this thesis, “training” (and “retraining”) is used to described a form and subset of education that is skill-specific. Skill – A skill is any specific ability, either learned or inherent, that can be developed and refined through training. Skills are typically broken into two categories: “hard”

skills that are teachable and quantifiable, like expert knowledge in an applied field, a foreign language, or computer programming, and “soft” skills that are traditionally considered less teachable or unquantifiable, like communication ability, teamwork, or leadership.2 This thesis makes little use of this distinction for two reasons. First, a judgment of so-called “hard” skills’ superiority is embedded in the terminology, when the opposite may prove true. Second, the distinction implies that skills are either quantifiable or they are not, when it is in more likely that quantifiability exists on a spectrum. Essentially, skills typically described as “soft” may prove to be the most important skills for future workers to develop due to the greater difficulty associated with their quantification, and thereby their automation. Instead, workers and students should focus on the skills that extend from their faculties of creative thinking, critical thinking, and emotional intelligence.


and society. Universally desired by humans, dignity is a prerequisite for self-actualization.

Creative thinking – Alternately described as “creativity,” a term with a wide range of definitions. Neuroscientist Jeff Hawkins defines creativity simply as “making predictions by analogy,” a process that occurs at all waking moments in all regions of the brain.3 But a survey of other definitions conducted by psychologist Michael Mumford suggests that a common interpretation includes the creation of products and ideas that are novel, original and useful. 4 Collectively, we can define creative thinking as the development of original concepts through an analogical analysis of existing and potentially unrelated ideas — or more simply, the ability to draw inspiration from something indirectly related. This type of trans-disciplinary idea generation is still generally recognized as a core capability of human beings not currently replicable with artificial intelligence, and therefore worthy of heightened emphasis in education and employment. Critical thinking – A mental reasoning and problem-solving process founded on the objective analysis of facts and the persistent interrogation of existing conclusions based on new and incoming evidence.5 Bias-free algo2  Doyle, Alison. “Hard Skills vs. Soft Skills: What’s the Difference?” The Balance, The Balance, hard-skills-vs-soft-skills-2063780 3  Hawkins, On Intelligence, 183. 4  Mumford, Michael D. “Where Have We Been, Where Are We Going? Taking Stock in Creativity Research.” Creativity Research Journal, 15:2-3, 2011. 107-120. 5  “Defining Critical Thinking.” Critical Thinking Model 1, defining-critical-thinking/766


rithms are well-suited to objective analysis, but machines currently lack the capability to constantly reevaluate prior conclusions. Thus, critical thinking is a core capability of human beings that makes us uniquely adaptable to addressing the unpredictable. Emotional intelligence – “The ability to identify and manage your own emotions and the emotions of others. It is generally said to include three skills: emotional awareness; the ability to harness emotions and apply them to tasks like thinking and problem solving; and the ability to manage emotions, which includes regulating your own emotions and cheering up or calming down other people.”1 Emotional intelligence is generally recognized as a core capability of human beings not currently replicable with artificial intelligence, and therefore worthy of heightened emphasis in education and employment. Egalitarian – Embodying a belief in the fundamental equality of all human beings, especially through actions and policies that seek to reduce or overcome existing inequalities, injustices and imbalances of power.

1  “Emotional Intelligence.” Psychology Today, Sussex Publishers, www.psychologytoday. com/basics/emotional-intelligence





Thank you for advising me Allan Chochinov Andrew Schloss Abby Covert

for editing me Ernie Piper IV

for talking to me Parry Bedi Jason Begleiter Rose Chan Justin Choi Karthik Dinakar Virgil Griffith Jessica Helfand Birago Jones Troy Joseph Aditya Kalyanpur Vivek Nangia Daniel Park Sumeet Patel Sidd Patwardhan Lauren Phelps Nathan Shedroff Vaibhav Verma Chandler Wild Bill Witzleben Gina Witzleben Will Witzleben

for helping me Smruti Adya Alexia Cohen Kevin Cook Lassor Feasley Sebastian Harmsen Sowmya Iyer Jingting He

Juho Lee Jiani Lin Louis Elwood-Leach Antriksh Nangia Christopher Rand Andrew Schlesinger Manako Tamura Bernice Wong Kuan Xu Teng Yu Josh Corn Arjun Kalyanpur Will Lentz Eden Lew Julia Lindpaintner Oscar Pipson Karen Vellensky Jenna Witzleben John Boran Ellen Rose Hannah Rudin Carly Simmons Xuan Wang Marko Manriquez Krithi Rao Alisha Wessler Carson Bills Peter Garafalo Chris Jee Sam Koss Shane Orr

for teaching me

Emilie Baltz Hannah Calhoun Abby Covert Bill Cromie Steven Dean Elizabeth Galbut KT Gillett Steve Hamilton Jennifer Rittner Rebecca Silver Sinclair Smith Paola Antonelli Kathleen Bakewell Ayse Birsel Matthew Borgatti Michael Chung Andrew Dent Jennifer Dunnam Claire Hartten Lauren Mackler Sigi Moeslinger Toshi Mogi Jason Severs Becky Stern Manuel Toscano Rhicard Tyson Masamichi Udagawa Rob Walker James Wynn

for loving me Joanne Crum Brad Crum Gray Ballinger Rianna Black

Rachel Abrams Brent Arnold Hlynur Atlason


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Me, Myself & A.I.  

While artificial intelligence may not become maliciously sentient and conquer mankind any time soon, it already has the power to change the...

Me, Myself & A.I.  

While artificial intelligence may not become maliciously sentient and conquer mankind any time soon, it already has the power to change the...

Profile for wcrum