Between 1981 and 1996, AIDS killed an estimated 100,000 gay men in New York City alone. Fashion designers, art directors, gallery owners, theater producers, choreographers; an entire generation whose lives were their art disappeared within fifteen years. Not just the famous names like Halston, Perry Ellis, and Willi Smith, but showroom assistants, stylists, photographers, creative directors, and window dressers. Chester Weinberg, a household name in the 1960s who mentored Marc Jacobs and Donna Karan, has been written out of history books entirely.

The conventional story treats this as a tragic loss of talent. That misses the deeper wound. When these communities died, something died with them that we haven’t recovered: the knowledge of how to live aesthetically, how to organize an entire existence around aesthetic values in ways that produce taste as a natural consequence of being rather than professional competence. To understand this loss requires understanding how taste came to reside in such communities at all.

Around 1800, European men stopped dressing beautifully. John Flügel called this the “Great Male Renunciation,” the moment when men surrendered color, ornament, and aesthetic display to women. The transformation was ideological. The rising bourgeoisie needed to distinguish productive virtue from aristocratic decadence. Beauty became suspect as a frivolous distraction from economic and political utility. Rational productivity demanded the abandonment of aesthetic excess.

This logic spread far beyond fashion. The entire material world reorganized around efficiency rather than beauty. The Enlightenment’s emphasis on universal reason and measurable progress left no room for the particular, the ornamental, the beautiful-for-its-own-sake. Aesthetic concern retreated to specialized domains—museums, concert halls, galleries—where it could be contained and administered, safely separated from the serious business of productive life.

The aristocracy, rather than resisting this transformation, largely capitulated. They maintained wealth but adopted bourgeois values. The ancient understanding that aristocratic privilege carried obligations to exemplify and patronize beauty, to serve as custodians of civilization’s aesthetic inheritance, disappeared under pressure to justify position through utility and merit. European aristocrats who had once competed in architectural grandeur and financed public festivals—the original meaning of “liturgy” was exactly this obligation to support works that elevated community—now sought respectability through productive contribution. The Medici model of patronage that made the Renaissance possible gave way to industrialists funding hospitals and universities. Beauty ceased to be a sufficient justification for existence.

By the early 20th century, dedication to aesthetic values had become the province of artists and bohemians, people explicitly outside respectable society. The bourgeois rationalization of existence left no room for beauty as primary orientation. To dedicate oneself to aesthetic refinement was to mark oneself as unserious, frivolous, or decadent. Taste, once the mark of civilization’s highest achievements, became deviant.

Where did it survive? In marginalized communities that couldn’t participate in respectable bourgeois life anyway. Gay men, barred from conventional paths to social legitimacy, maintained cultures where aesthetic refinement remained an organizing principle. The parties, the fashion—this was a serious form of life oriented toward values the mainstream had abandoned. There was something atavistically aristocratic in this orientation. A remnant of an older world that modernity had expelled from respectability.

In the 1910s and 1920s, Jewish immigrants like Carl Laemmle, Adolph Zukor, Samuel Goldwyn, Louis B. Mayer, William Fox, and the Warner Brothers elevated film from lowbrow entertainment comparable to vaudeville to a new art form. Antisemitism had barred Jews from elite institutions, and Thomas Edison’s film monopoly on the East Coast drove these independents to relocate west to build the social respectability otherwise out of reach.

When you cannot succeed by conventional metrics, you can either resign yourself to marginality or reach for something higher than what the respectable world offers. Marginalization doesn’t automatically produce taste, but combined with aspirations toward forms of life that the dominant culture has abandoned, it creates conditions where aesthetic dedication becomes possible again.

The material consequences of the AIDS crisis were immediate and visible. By 1990, New York Fashion Week’s menswear offering had become what The New York Times described as “smoothly traditional,” with overbearing reportage of uber-masculine silhouettes, a retreat to hetero-normativity even in fashion’s most subversive corners. The creative infrastructure that connected underground gay aesthetics to broader society was decimated. Fashion became sanitized, risk-averse, and derivative.

The financial backing and creative mentorship networks that had sustained aesthetic innovation simply disappeared. The flattening accelerated through the 1990s and 2000s. Great menswear magazines that had been organs of taste, like Esquire and GQ, devolved into listicles and product roundups. When anything goes, everyone does the same thing. The paradox of infinite choice is homogeneity.

Without communities organized around aesthetic values, the culture defaulted to what algorithms and focus groups could optimize: the safe, the familiar, the efficiently producible. The broken links in the creative chain were never repaired because the knowledge of how to forge them, passed between people who shared forms of life, had died with the bodies. This completed the Enlightenment’s war on beauty. The last substantial communities in Western culture who understood how to live aesthetically had been eliminated by disease.

Taste as a Way of Life

Giorgio Agamben argues in Creation and Anarchy that modernity fundamentally misunderstands creation. We think art means making objects: paintings, sculptures, buildings. But the Greeks distinguished poiesis (creation) from praxis (production). Poiesis wasn’t about outputs. It was a mode of existence. The artist wasn’t someone who made art but someone whose entire life constituted artistic engagement with the world.

Halston didn’t study fashion theory and then apply it to design. His existence, however debauched, constituted an aesthetic form of life. The clothes emerged as a natural expression of how he lived. You can’t recreate this through education. Fashion schools can teach techniques, historical knowledge, and professional practices. They cannot teach you to organize existence around beauty.

Medieval apprenticeship understood this. You didn’t learn sculpture by studying technique in isolation. You lived with a master, absorbed their entire way of being in the world. Michelangelo didn’t attend art school. He lived with Lorenzo de’ Medici as a teenager, surrounded by poets, philosophers, collectors, and power brokers. The Sistine Chapel is inseparable from the form of life that produced it.

Modern culture has inverted this relationship. We’ve made the work primary and reduced life to production processes. Walk through any major contemporary museum. You’ll see expensive art, prestigious names, wall texts explaining theoretical importance. What you won’t see is curation emerging from a life lived in service of beauty.

Contrast this with spaces that did and still do. The Wallace Collection embodies Sir Richard Wallace’s specific obsessions with 18th-century decorative arts. The Morgan Library manifests J. P. Morgan’s mania for illuminated manuscripts. These aren’t museums in the contemporary sense. They’re dwellings that happen to be open to the public. Wallace and Morgan didn’t curate by studying what experts valued. They collected what they found beautiful through lives spent in sustained aesthetic attention.

There is something essentially religious in authentic aesthetic dedication, though modernity has obscured this connection by separating beauty from transcendence. Art and religion were once indistinguishable. The cave paintings at Lascaux, dating to the early Magdalenian period, were not leisure activities but ritualistic practice, mystical initiatory rites binding community to cosmos through visual form. Over millennia, the primitive shaman was supplanted by the theologian and psychotherapist. Functions once unified were differentiated. The nature of the liturgical has been eroded in both. Religion in modernity became self-help rather than an all-embracing constitution of reality, while art became something one occasionally makes time to peruse on a Saturday afternoon.

Yet beauty retains its sacred character for those who pursue it seriously. The person whose life is oriented around aesthetic values operates according to logic that transcends utility, that recognizes in material form something pointing beyond the merely material. Because of Catholicism’s insistence on beauty as a theological necessity, as a manifestation of divine order in sensible form, it has, surprisingly, sometimes even been adopted by gay creatives from Oscar Wilde in the 19th Century, Karl Lagerfeld in the 20th, to Dolce and Gabbana today. The elaborate liturgy, the vestments, the architecture, the music; these weren’t decorations applied to worship but constituted worship itself. Beauty was the form truth took when it entered the world.

The common factor among people who developed genuine taste has always been willingness to embrace excess. Our contemporary understanding of excess has been shaped by logic, reducing it to consumption or debauchery, but this is impoverished. The lukewarm mediocrity of comfort is rational and efficient but worth nothing more than being spewed out. Traditions of excess recognized it in saints and sinners alike. St. Catherine of Siena and St. John of the Cross weren’t collecting trinkets or attending parties, but their whole being exuded an excess that horrifies a life of comfort. Modern mystics like Simone Weil starved themselves to death in the name of a genuinely philosophical life. This is excess as rejection of the middle path, as commitment to values that make no sense within frameworks of rational self-interest.

An unaesthetic life erodes that part of the soul that seeks excellence, honor, and recognition of what is higher. When everything becomes optimized for comfort and convenience, when every aesthetic concern must justify itself through utility, we become smaller. People don’t just want things. They want to want better things. This is what philosophers call higher-order volitions, the desire to have different desires, to be the kind of person who values what is genuinely valuable. When you encounter someone living aesthetically, you don’t just see beautiful objects. You see a life that makes you want to live better. This is the aspirational quality of taste, its essentially religious in character as a call to transformation.

Higher Means Oblige to Higher Ends

Those with means face a choice. You can optimize your life for conventional success while treating aesthetic concerns as hobbies requiring no serious commitment. Or you can organize existence around aesthetic values in ways that violate professional norms and economic rationality.

The second path requires genuine sacrifice, not necessarily poverty or suffering, but willingness to prioritize beauty over optimization, sustained attention over efficient productivity. For those with financial freedom, this becomes an obligation. If you can afford to live otherwise, why wouldn’t you? 

The person who spends years visiting every significant building in their region, learning to see architectural proportions directly. The person who maintains extensive correspondence with online interlocutors to endlessly debate the finer points of science or philosophy. The person who hosts gatherings where aesthetic experience takes precedence over networking utility. These practices seem frivolous to modern sensibility. That’s the point. They violate the logic that everything must justify itself through productivity metrics.

The person whose existence centers on beauty develops perceptual capacities that people living conventionally productive lives cannot access. They see things others miss not because they studied harder but because they live differently.

Contemporary wealth has mostly abandoned the aristocratic understanding that privilege carries responsibility. The spaces we build are predominantly ugly. Today’s elite justify their position through meritocratic achievement. The software engineer who optimizes his productivity stack while living in an efficient but mediocre space demonstrates that success requires no aesthetic intelligence. The contemporary elite exist as training data for future AIs: perfectly reproducible, optimized for metrics, lacking any unique character that will challenge the world after they’re dead.

Contrast this with someone who uses financial freedom to cultivate sustained aesthetic attention. These individuals provide models of how to live that transcend achievement. The software engineer can look to well-off peers and think, “I should optimize my career trajectory better.” They look at someone living aesthetically and recognize something outside their professional logic entirely, a call to something higher than what the rationalized world offers.

This kind of change can’t be brought about through existing political mechanisms. Neither liberal democracy nor consumerism has means for prioritizing beauty. The structural forces that produced this situation—the Enlightenment’s rationalization, capitalism’s reduction of value to price, bureaucracy’s demand for quantifiable metrics—continue operating. You cannot argue your way out of aesthetic wreckage using the tools that created it.

The alternative is withdrawal into domains where aesthetic values can be primary. Not withdrawal into irrelevance or private indulgence, but the creation of forms of life that operate according to different logics than the surrounding culture. Every person living aesthetically in a culture that devalues beauty demonstrates that alternative orientations remain possible. When you organize your life around beauty, you become magnetic to others who feel the insufficiency of rationalized existence but lack models for living otherwise. The natural aesthetic draw enables the formation of authentic communities that can’t be manufactured through mission statements or organizational structures.

This is the crucial insight that distinguishes living aesthetically from technocratic solutions like effective altruism, a philanthropic movement that tries to rationally plan optimal resource allocation toward measurable good. This approach inevitably reproduces the flattening. The necessary institutions must emerge as organic consequences of people truly living enriched lives, gathering around shared aesthetic commitments that transcend calculable benefit.

The Bloomsbury Group, the early 20th-century cultural clique that counted among its members Virginia Woolf, E. M. Forster, and John Maynard Keynes, didn’t form because someone wrote a strategic plan for cultivating British modernist aesthetics. It emerged from people who shared forms of life: writers, artists, and intellectuals organizing existence around beauty, conversation, and creative work. Bauhaus didn’t succeed by optimizing pedagogical outcomes. It succeeded because Gropius gathered people committed to aesthetic values and let institutional forms emerge from their practices. Authentic aesthetic communities arise when people living beautifully find each other, not when institutions try to manufacture community through programming.

The communities that preserved aesthetic knowledge through the 20th century often did so at tremendous cost. The drug use, the promiscuity, the self-destructive behaviors that marked parts of pre-AIDS gay culture; these were not sources of taste but symptoms of marginalization. When a community is denied participation in respectable society, when conventional paths to meaning are foreclosed, desperation follows. This is not something to romanticize or seek to imitate.

What needs recovering is the principle they accidentally preserved: that life can be organized around aesthetic values as primary orientation. The tragedy is that this principle, once central to aristocratic self-understanding and religious practice, has been expelled from respectability. Taste became deviant not because it was inherently transgressive but because the Enlightenment’s rationalization of existence removed it from the realm of legitimate aspiration.

The work of building the institutions that can sustain aesthetic forms of life begins with individuals committed to living them. Not from policy proposals or foundation grants, but from people whose entire existence testifies that beauty matters more than efficiency, that tradition carries wisdom that rationalism has abandoned, that there are forms of human flourishing the modern world has lost.

If you have the freedom to choose how you live, then make beauty your organizing principle.  This will feel irresponsible at first. You’ll be violating deeply internalized bourgeois norms about productivity and achievement. Good. Those norms produced the aesthetic wasteland we inhabit.

Ignore them. Spend years, if necessary, looking at architecture, painting, and design. Train your perception until you can recognize quality directly, not through credentials or consensus. Build libraries not of books you’ve read but books worth living with. Maintain practices that seem inefficient: handwritten correspondence, elaborate meals, gatherings where no business gets conducted. 

Create spaces that embody beauty. Your dwelling should challenge everyone who enters it to consider whether they are living well. Not through expense or display, but through evidence that beauty has been prioritized over convenience. Your environment trains your perception and announces your values to others. Make it worthy of sustained attention.

Most importantly, refuse the false choice between aesthetics and community. You’re not cultivating taste for private satisfaction. You’re becoming a node in networks that don’t exist yet, preparing the ground for institutions that can’t be planned but must be grown. When you live aesthetically, you become visible to others who feel trapped in rationalized existence. They’ll find you. The communities that matter form through recognition, people whose lives testify to shared values discovering each other.

Every person who organizes existence around beauty plants seeds that others can tend. The patient work of cultivating taste in yourself and enabling it in others through example. When enough people live this way, the institutions necessary to sustain and transmit aesthetic knowledge will arise as a natural consequence. 

The call is to reject the modern bargain that made productivity primary and beauty optional. To reclaim the aristocratic and religious understanding that those with freedom bear responsibility for exemplifying and creating beauty that enriches everyone. To demonstrate through how you live that alternatives to the rationalized world remain possible. This is the most radical act available: living in ways that refuse the terms modernity has set, that prepare the ground for cultures not yet born. In an age of aesthetic ruins, every person who lives beautifully becomes an ancestor to possible futures. This is your opportunity and obligation. Begin.

In One Giant Leap: The Impossible Mission That Flew Us To The Moon, Charles Fishman, a veteran space reporter who got his start covering the failed Challenger mission in 1986, demonstrates the significance of those missions beyond the feats themselves. Apollo, by his telling, fails to get the credit it deserves for its contribution to American managerial and technological prowess. It created the very culture that allowed America to dominate later revolutions in information and communications.

Landing a man on the moon was impossible when Kennedy first announced in 1961 that they would accomplish such a goal by the end of the decade. And yet, it was accomplished. Apollo’s leaders could leverage the project’s difficulty for the necessary political backing. It was not an obstacle, but an asset. American institutions were not naturally constructed to manage such a feat. A deep dive into the history of Apollo demonstrates just how many political and institutional accomplishments undergirded the final launch and landing. Highly competent and functional institutions aren’t created in stasis. People create them to achieve goals. It’s unsurprising, then, that the abandonment of the space program, among other social goals that America set for itself in the mid-century, preceded the decline of its institutions.

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The institutional fingerprints of Apollo can be found covering different aspects of American life. Its demanding goal required a culture of rigorous standards among American manufacturers, a management system that guided innovation instead of inhibiting it, and the creation of initial demand for untested technologies that allowed them to scale.

Apollo’s project was also one of social engineering. It needed to overcome the public perception that technology was linked to destruction, a social mood lingering from the Second World War. Instead, technology had to become perceived as a source of human accomplishment. Building the infrastructure to get Americans on the moon required mass mobilization.

The Apollo team and its backers achieved these goals thanks to a strikingly competent team that could break down the steps needed to put an American on the moon and build the institutions necessary to achieve it. While America’s 20th century space program failed to open up the cosmos and make humans a multi-planet species, it forever altered the landscape back here on Earth.

The World Apollo Made

The most significant contribution of One Giant Leap is in making the case that Apollo’s significance stretches beyond its direct accomplishment. Through concrete documentation of how Apollo happened, Fishman makes clear the long-term effects it had on society on a cultural, technological, and economic level.

While American households were getting used to the many innovations that increased convenience throughout the 1950s—from televisions to dishwashers to microwaves—none of these carried the label of technology as such. Rather, they were rather simply thought of as appliances. Technology, as a popular concept, referred to military technologies like the atomic bomb or the B-29 bomber. Popular culture mirrored this association; the violence wrought by technological advance was the common frame in literature, philosophy, and other areas at the start of the 1960s. Through Apollo’s enlistment of defense companies such as Grumman (known for its scalable production of military aircraft during WWII) in building the module that would allow for a lunar landing, the program spurred the belief that technology could be used for the benefit of all mankind. Apollo successfully engineered a profound technological optimism in a generation growing up with global industrial war fresh in memory.

Beyond technological consciousness, Apollo’s impact on American manufacturing enabled the information and communications technology revolution to occur both when it did and where it did. In the long tradition of state-backed demand for new technologies, Apollo was an essential early buyer of integrated circuits. The technology was still novel and untested when NASA signed a contract with Fairchild Semiconductor to purchase hundreds of thousands of units for the computer that MIT was building. The promise of integrated circuits to vastly increase computational power while decreasing the size of computers was apparent to the MIT Instrumentation Lab. When it was first tasked with building a computer reliable enough to get people to the moon and back, the lab was operating during an era when “does not compute” and regular malfunction was the common experience of computing.

To ensure rigorous quality, MIT ordered large batches of integrated circuits and pretested random samples. If any chip had a change in weight of 1/2000th of a gram, the entire batch was discarded. This was all before MIT even began testing each chip’s performance for over 30,000 hours. This sent a clear message to Fairchild about the low tolerance for failure and the precision required for MIT’s work. The reliability of integrated circuits skyrocketed over the 1960s, and their price dramatically declined from $1,000 in 1961 to $1.58 in 1969, enabling new applications to be discovered, and the penetration of computers to stretch beyond military applications. Gordon Moore, who coined the eponymous Moore’s Law, opined during his years at Fairchild that Apollo was the critical buyer for the technology that enabled the rise of computing power.

Computing memory was also revolutionized due to the clarity of the stakes. In the 1960s the “software” of code was still a hardware process, with each line needing to be hand sewn into computers by a team of “little old ladies” (computing factories were mostly staffed with women). An early failure in the Mariner program, aiming to fly by Venus, became a political humiliation for the United States. It was caused by a software transcription error of one line not being sewn in correctly. The culture of computing changed after that incident, at NASA’s direction. Where computing factories opened the decade with employees smoking as they worked and quality control being lackluster, they ended it with pristine conditions, disciplined assembly lines, and high investment. Fishman identifies regular visits by astronauts to computing factories as a source of investment, making workers highly attached to the mission they were supporting. He recounts how the women who worked on a memory module forcefully argued for it to be discarded even though it passed all safety checks because it “wasn’t too good [for] one of our boys.” The quality discipline of Apollo for the broader computing industry persisted far beyond the project itself.

The program successfully created a culture of excellence in manufacturing, commercial demand for advanced technologies, and captivated the imagination of an American public in which the space race was initially not at all popular. It’s hard to imagine that America was not always a technological leader, but before Apollo it was being out-competed in satellites and rockets by the Soviets, and only on par with them in military aircraft technologies. Fishman references a Gallup poll from 1960 which found that in all but two countries, the United States and United Kingdom, public sentiment expected the Soviet Union to be technologically superior by 1970.

Apollo transformed that view, boosting the perception of American technological prowess abroad and enabling it to keep that reputation through learning by doing at home. The Apollo program’s public status drew increasing amounts of American students into STEM. It managed to accomplish this through the ambitious approach to institution-building undertaken to accomplish its mission. This in turn built up American advanced technology capacity.

Building Domestic Capacity

The largest sections of the book chronicle the scientific, technological, and managerial feats that enabled Apollo to do the impossible. None of the essential capabilities needed to put a man on the moon existed in 1961. In eight years, the mission was accomplished before the eyes of tens of millions of live television viewers, who saw American technological achievements behind Neil Armstrong’s step onto the lunar surface. It is a testament to Fishman’s storytelling that he paints a picture of how NASA managed to accomplish such a feat through concrete examples that tie together in a singular narrative thread. Fishman paints the picture of NASA’s role in building up America’s technological capacity through three central figures: James “Jim” Webb, Charles Stark “Doc” Draper, and Howard Wilson “Bill” Tindall. Each serves to highlight the necessity of technical and managerial expertise coinciding within a single person in order to succeed in complex tasks.

Jim Webb was the 20th candidate to run NASA, the last post filled among the Kennedy Administration. Webb was a trained lawyer and experienced manager who oversaw the growth of the Sperry Corporation during WWII, one of the largest radar and navigation suppliers to the U.S. military. He had the technical acumen about aeronautics production combined with the managerial and political skills needed to lead NASA to success. In reflecting on his tenure, Webb recounted that as significant of an accomplishment as landing a man on the moon was, the managerial system that got us there was just as important—it proved that the American system could out-manage Soviet central planning on large projects.

The scale of this managerial project is hard to overstate. NASA went from 10,000 employees right before Webb took over to 33,000 in under four years. Apollo brought in 20,000 distinct contracting companies, hired from across all 50 U.S. states, with the manpower of over 400,000 people to assist in production. Compared to WWII, the required production was a “relatively small fleet: 15 Saturn V rockets, 14 lunar modules, 12 command and service module combinations.” This rigorous standards and collaborative spirit that Webb cultivated for Apollo was essential for creating skin in the game for everyone involved.

The book is filled with accounts of how Webb-era NASA created this management philosophy. Officials paid visits to the astronauts and to contracting factories. Their research strategy had rival factions publish reports detailing their opponents’ views to ensure knowledge dissemination. Their bottom-up requirement allowed distinct contractors to have their components fit together. All of these would have been impossible without a leadership that truly believed in their mission. When Kennedy mentioned to Webb that he wanted to provide a military cover for Apollo to protect NASA’s funding, Webb suggested that he be replaced with a “military man.” That commitment to the success of Apollo made him willing to sacrifice his own career.

Webb’s management approach ensured that Apollo would not be used for political graft or as a cash cow for contractors. What the history of Apollo emphasizes is that management philosophy needs to be combined with both managerial and technical competence. Outside of Silicon Valley, there are few Jim Webbs today who combine a rigorous understanding of their field with extreme managerial competency. Given that NASA itself is one of SpaceX’s major clients for simply launching people and goods into orbit, it’s hard to believe that this organization could put anyone on the moon today.

Zeroing in on Apollo’s greatest contributions, Fishman deals extensively with the Apollo computer, which he refers to as the “Fourth Crew Member.” It was the most significant advancements in computing capabilities at the time. Two pivotal figures shine through in this tale: Doc Draper, head of the MIT Instrumentation Lab, and Bill Tindall, who is given an entire chapter crediting him as “the man who saved Apollo.”

Doc Draper was a legendary MIT professor who had developed the inertial navigation systems that allowed the B-29 bomber to fly autonomously over long distances. He played an early role in trying to get the Kennedy brothers to support a mission to the moon, but with no avail. When the mission was a go, his Instrumentation Lab at MIT was tasked with building the most complicated computer the world had ever seen, compact enough to fit on the rocket and lunar module, and reliable enough to not fail over the eight day mission of Apollo 11. The book dedicates much space to the technical marvel of building the Apollo computer and the startup culture of the Instrumentation Lab, which brought in bright young minds to work on novel and exciting projects.

It is fascinating that a small academic lab with an exploratory culture was able to provide a key component to such a critical mission, when contracting with startups seems so difficult for government agencies even now. This was not an easy task for NASA, who had the view that MIT’s inability to meet deadlines would hold up the moon landing. Rather than move the contract to a more conventional manufacturer like IBM, NASA sent in Bill Tindall.

The story of Bill Tindall is amazing in and of itself. A veteran aerospace engineer and pioneer in orbital mechanics, he was responsible for disciplining the work at MIT and ensuring the quality of output met NASA’s expectations. The relationship management skills of Tindall and his approach to improving coordination between different contracting units should serve as an elementary case study for anyone attempting to plan ambitious and difficult projects. Fishman justly builds up the legacy of a forgotten, but crucial figure. One of Tindall’s most fascinating techniques was his use of widely circulated memos, referred to as Tindallgrams, which pointed out highly technical problems in an easy to digest matter. His clear and witty writing style guaranteed high readership, and through the Tindallgrams, everyone was on the same page about pressing issues and key tasks to be done. The legacy of this approach highlights the need for effective communication in management to ensure that a team meets its stated objectives and does not overlook its own failures.

There are countless other figures discussed in the books, and each is given treatment about the particular contribution they had to the Apollo project. These stories create a tapestry of the level of skill and competency needed to create an organization which could put a man on the moon and just how extraordinary the people who carried out that mission had to be. In any approach to industrial development, the capabilities and philosophies of all those involved need to mesh in such a way that even in working separately, they are working towards a unified vision. This vision was not provided by any single person at NASA, however, and it is a testament to the leadership of President John F. Kennedy that he was able to put the wheels in motion to get Apollo off the ground and onto the moon.

Ambition and the Politics of Apollo

At the start of the 1960s, America was behind in a space race that the White House denied even existed. The Soviet Union had shocked the world with the launch of Sputnik in 1957 and continued to assert its technological dominance. As Kennedy remarked in 1961 after cosmonaut Yuri Gagarin’s successful space flight, the first man to leave Earth’s atmosphere, “the first country that placed its national emblem on the moon was Russia, not America. The first passengers to return safely from a trip through space were named Strelka and Belka, not Rover or Fido.” Eisenhower, who created the National Aeronautics and Space Administration, was unimpressed with the prospect of space travel, and preferred to focus on U.S. military dominance. Nixon, while Vice President, saw the consumer marvels of American capitalism as superior to the ambitious projects carried out by despotic regimes. The humiliation was palatable, but the U.S. was doing little.

Superiority in space and the need to prove the technological capabilities of the capitalist system became a hallmark of Kennedy’s presidential campaign, though Fishman dedicates extended passages to show how little Kennedy himself was concerned with space travel as such. The decision to put a man on the moon was not made with the intention of opening up scientific opportunity or spurring national development. To Kennedy, it was entirely about beating the Soviets. If the Russians were to abandon their space program, then Kennedy would have had an excuse to pull back on the enormous swell NASA was having on his budgets. Fishman argues that it would be unlikely without Kennedy’s assassination—both creating the need to honor the man’s legacy, and putting the more genuinely pro-space Lyndon B. Johnson in the White House—that the U.S. would have reached the moon by the end of the decade.

The fascinating insight that comes through this telling of the story is how little the moon itself mattered to creating the drive to get there. The marvel of fundamental science, technological design, and quality manufacture needed to accomplish the mission in only eight years is wisely contextualized by Fishman within the political climate in which it occurred. Reaching the moon was not a matter of science, but of national pride and competitive spirit. The goal was created to be sufficiently ambitious to demonstrate American superiority to the Soviets. Its achievement is an honor to the legacy of the president that ensured the world recognized America as capable of beating its rival, something prior failures in space and the continued failure in Vietnam called into question. The specific target of putting a man on the moon, and with a clear deadline at the end of the decade, ensured that political graft would not jeopardize the project. It ensured clarity of vision.

It is also relevant to draw parallels between the 1960s and the present moment. Fishman colors the political background of Apollo with the tumult of the 1960s. The civil rights movement, Vietnam War protests, and the rise of feminism upended the quietness of the Eisenhower-era. A show of national strength was not in the interest of most Americans at that time, who would have rather had the billions of dollars a year in 1960s money spent on going to space redirected to alleviate problems at home. During the entire lifespan of the Apollo project, it never hit above 50% approval ratings. NASA acted in many ways to keep the spectacle of going to the moon in the public eye, and had extensive initiatives to try to boost its support, often to no avail. Apollo was as much a social engineering project as it was a material one. Even among the scientific community, manned space flight drew ire. Norbert Wiener, the godfather of cybernetics, coined the term “moondoggle” to describe the extravagant waste of sending a man to the moon. The editor of Science and senior scientific advisors in the White House viewed the project as a waste of money with little scientific value and a distraction from more pressing initiatives. There was no “scientific community” when it came to the space race, but the usual array of interests and factions vying for political patronage.

What is interesting about Kennedy’s approach to Apollo is that he neither reduced it to its science, nor allowed interest groups to get in the way. In public speeches, Kennedy built up the myth of Apollo, inspiring Americans to make them feel a part of this monumental project. If Kennedy is to be faulted for his approach, it would be that his desire to beat the Soviets created a need for spectacle at NASA, which led to subsequent boredom from the public. NASA has never really found a new purpose after Apollo ended—a focus on robotic exploration does not have the same myth-making component or groundbreaking value. There are many political lessons to learn from Apollo, but the fact that its energy could not be sustained cautions against replicating its approach exactly.

Fishman’s multifaceted account of the Apollo mission makes it one of the most captivating reads to come out recently and provides several lessons for anyone interested in recreating the magic of this grand accomplishment. First, clarity of purpose and a leadership capable of defining that purpose are fundamental to getting large projects off the ground. From the War on Poverty to the War on Terror, American leadership has often made its objectives nebulous, making momentum harder to sustain. Second, the right people with the right motivation can overcome insurmountable obstacles. The modern managerial and expert class often lacks the versatility of skills essential to this project, due to overspecialization. Third, the outcome of an ambitious project cannot and ought not be reduced to the objective itself. Great projects such as decarbonization and Mars colonization should not have to be justified only on account of their direct benefits. To do so would harm their ability to succeed.

Through all of these lessons, we see grandness as integral to a project’s success. The book opens with a quote from the speech William Safire prepared for Nixon in the event that the Apollo 11 astronauts met their peril: “In ancient days, men looked at stars and saw their heroes in constellations. In modern times, we do much the same, but our heroes are epic men of flesh and blood.” Nothing captures more what Apollo did for the world, and what the grand ventures we might undertake today will require of us.

The country is now entering its third month of economically devastating lockdown. The lockdowns were supposed to buy time to spin up a more sustainable approach, but little has been done except a flip of the official narrative from downplaying the effectiveness of masks to ham-fistedly enforcing their use. As resentment to the squandered lockdown grows, states are beginning to open up with no vaccine or alternative containment strategy in sight.

Despite decades of warning about pandemics, the United States was unable to pursue the only approach known to work. Lacking a clear delineation of responsibilities for public health authorities, besides other missing essentials such as competence and an intent to actually solve the problem, contact tracing will not be effectively pursued in the future either. And the next pathogen outbreak is not far off: pandemics are the new normal. The implicit admission is that “herd immunity,” either from letting diseases burn through the population till antibodies are developed or a vaccine is released, is the only strategy we are actually capable of executing.

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The problem started with a delayed reaction, resulting from late warnings from the World Health Organization (WHO) and the casual attitude the White House took. When the WHO declared an international emergency at the end of January, it praised China’s efforts to contain the virus, avoided making specific recommendations for travelers, and warned against restricting international travel and trade. The early response of the U.S. Centers for Disease Control and Prevention (CDC) was very narrow, focusing only on those who had traveled to China or come into contact with a traveler until late February, even when it was becoming clear that this was allowing the virus to escape into the community. The White House further inhibited the CDC’s capabilities, preventing CDC representatives from contributing to national communications, refusing to boost early on-the-ground contact tracing efforts, and denying calls to ramp up medical production. The White House declared the coronavirus a national emergency on March 13, and by the third week of March, 48 states had declared emergencies with widespread lockdowns.

National contact tracing efforts led by the CDC were minimal in the early phases of the virus, with state-led efforts only picking up steam in mid-April. States and non-profits are recruiting tracers to call homes and identify those who have come into contact with those who have had or might have had the virus in order to encourage quarantine efforts. The jury remains out on whether traditional contact tracing efforts being undertaken can amass enough manpower to get the job done. Apple and Google have jointly developed a decentralized Bluetooth-powered contact tracing apparatus to aid public health agencies, but adoption has been low from smartphone users in the United States due to consumer privacy concerns and general distrust.

The tragedy of the American response has resulted in lockdowns which disproportionately affect the vulnerable. From the economic insecurity that has fallen on millions of people, to the rise in domestic violence brought about by confining people to their homes, the harms to life and health need to be weighed against the civil liberties concerns of data-driven methods to stamp out the virus.

The social and economic fabric of our globalized world should not melt down every time an outbreak occurs. We cannot afford to shut down until a new vaccine is developed for every disease that proves infectious. Effective testing and contact tracing, with quarantines imposed for those who have come in contact with someone infected, is the only method for containing a virus early.

Contact tracing is essential because it enables the identification and isolation of suspected cases, preventing them from spreading. The social distancing approach, on which the current lockdowns rely, aims to “flatten the curve” of an infection’s spread. This does little to eradicate its presence. The virus always remains in at least a few people who don’t or can’t comply with effective distancing—it just spreads at a slower rate to make it more manageable for the medical system.

But as soon as things open up again, it would rapidly expand. Only by testing aggressively, identifying networks of transmission around confirmed cases, and instituting targeted quarantines and protective measures at a more granular level, can further networks of transmission be intercepted. By doing so, you remove only those who are likely to be infected until they are deemed clear of the virus.

Institutional sclerosis prevented the rollout of contact tracing at the early stage when it could have worked. One of the major barriers was a poor delineation of responsibility. State public health agencies could not act because the FDA limited their independent authority. The FDA delayed in approving private tests for state agencies and until mid-March mandated that the CDC confirm all tests. The FDA’s reliance on the CDC in turn caused problems due to the CDC’s conflict with the White House, which likely resulted from the CDC’s adoption of the vague response language of the WHO. Afterwards, coordination failures between national governments and their public health bodies—both in the U.S. and other countries—prevented them from producing the necessary medical supply stocks, which only intensified our current crisis.

As governments start testing ways to end the lockdowns across North America, there seems to be little indication of a strategy—short of a vaccine or herd immunity—for ensuring that we permanently move past the draconian measures initially put in place to limit the spread of COVID-19. The risk of a spike, or a return of the virus next year, will limit the willingness of many people to return to work and normal life. Lockdown for the entirety of the 12-18 month timeline it takes to develop and test a vaccine isn’t viable. While the cancellation of large gatherings and the encouragement of safe distancing measures are tolerable, the scale of lockdowns currently in place has led to painful social and economic upheaval.

Even before tests were made, there were limited national efforts to identify potential cases. In many developed countries outside of East Asia, the response at the border was weak. Temperature scans at airports were slow to roll out in the United States, while India and nations across Africa made it an early priority. In mid-February, The Lancet, one of the world’s leading medical journals, published a commentary labeling countries who imposed travel restrictions as violating international law for imposing health measures “not supported by science or WHO.” At the end of February, the WHO doubled down on its recommendations against travel restrictions, and many nations mirrored its stance. The countries with the greatest capacity and expertise to respond also deferred the most to the international order.

Closing borders with countries or other jurisdictions where the outbreak has already escaped control applies the logic of contact tracing and quarantine to the geopolitical level. China shut internal borders with Hubei, and most countries eventually implemented some kind of border restrictions—although mostly with exceptions for citizens and essential trade.

Building new tools for preparation would fail without a clearer remit for public health agencies, including in the U.S. The world had over a decade of warning about the risks of bat-borne coronaviruses, yet public health agencies shrugged their shoulders when the time came for them to act. This cannot be blamed on bad data. Despite the unreliable information released by China about the outbreak in Wuhan, epidemiological tools, such as the infectious disease tracker built by BlueDot, sounded the alarm that something was going awry.

And yet, Western countries didn’t do much until the threat of the virus became a domestic reality. Even after Iran and South Korea experienced outbreaks, North American policymakers largely demurred until Italy’s outbreak proved uncontainable. Even with abundant information, there was a failure of coordination between organizations that had valuable data on the nature and spread of COVID-19.

Some nations acted quickly and did not go down the route of widespread lockdown, in contrast to North America and much of Europe. Sharpened by their experiences with SARS, Singapore, Hong Kong, and Taiwan leaped into action in mid-January, aware of just how close the epicenter of the outbreak was. They instituted an array of travel restrictions, contact tracing, and mandatory quarantines of those suspected to be infected. These efforts proved invaluable.

East Asian nations not only learned from past outbreaks about what tools were needed to help control outbreaks, but also how they should be used. These tools, from influenza-like illness trackers to telecommunications-based contract tracing, exist everywhere. But few are able to use them to their fullest extent. Surviving an epidemic does not mean all the right lessons will be internalized. In 2003, Toronto experienced the worst outbreak of SARS outside of Asia, killing dozens and causing over a billion dollars in economic damage. Recognized as a “dress rehearsal” for the next pandemic, the SARS postmortem report noted the failure of Canada’s largest province to coordinate the mobilization of testing capacity beyond state-run labs. But the province stumbled again during the COVID pandemic on testing and other basic measures.

Had authorities acted earlier by sharing essential information and rerouting resources based on their predictions of potential points of outbreak,  generalized and destructive social distancing measures may have proved unnecessary. Locking everyone inside to limit the possibility of infectious spread is a response of desperation. Even our medieval predecessors were wise enough during plagues to focus on quarantining those likely to be affected, while maintaining a sense of normalcy and order in most citizens’ lives. Identifying and isolating the sick, which is the only effective way known to contain outbreaks, requires a radically different mindset and more institutional competence than what we have now.

One of the greatest strategic failures of national governments during COVID-19 was the attempt to maintain a false sense of security, even while the pandemic’s potential became apparent. The WHO, while vital for information gathering and sharing, is not a substitute for local expertise when crafting domestic strategies. International organizations serve as a useful reference for state action, but should not be a barrier to it. New Zealand, Taiwan, and Singapore quickly put in place travel restrictions, even as these drew ire from the WHO and the international medical community.

The lesson here is that clearly delineating the responsibilities of public health bodies is essential for effective pandemic response. The CDC, while sensitive to the threat of pathogens, did not have the legislative remit to be vigilant and prepare actionable strategies for containment. The culture of the CDC—and of American public health authorities more generally—is to have a high degree of trust in international institutions. It trusted these international institutions enough to take their information and recommendations as a guide for decision-making. In the case of the current pandemic, the America has misplaced this trust. The result has been institutional passivity.

Unlike in the United States, in South Korean, Taiwan, and Singapore, public health agencies were empowered to contain outbreaks and play a clear role in mobilizing resources, ramping up medical production, sharing information between local agencies, and creating unified and effective public messaging. The last of these points is particularly important. Having national bodies as the linchpin of communication was vital for encouraging compliance in Singapore and Taiwan. Poorer messaging led to widespread flouting of the rules created in Italy and the mass confusion that reigns in the United States.

Sub-national bodies are most effective when empowered to direct and coordinate laboratory and testing resources between state, hospital, and private laboratories. This strategy is embodied by the success of the Canadian province of Alberta’s muscular centralized health authority in deploying testing across the province. American states, hampered by the FDA’s emergency regulations limiting test production, were not able to act early on independent of the CDC.

At the local level, public health authorities, which wield extraordinary powers in times of emergency, should be small enough to be attentive to local concerns but large enough to attract a critical mass of resources and talent to act effectively. The failure seen in cities like Detroit, which has had an astonishing death toll from COVID-19 (over a thousand dead in a city of less than a million), reveals the necessity of local capacity. The city was not able to acquire sufficient tests from state bodies. Testing facilities were inaccessible to those without a car. Some hospitals denied admission to infected locals who weren’t showing severe enough symptoms because the hospital lacked testing capacity. Non-profit efforts to independently acquire tests and open up mobile testing sites are frantically aiming at improving the city’s ability to respond. Decentralizing the administration of testing and the development of isolation units allows these local bodies to devote their resources to the most pressing needs of their communities. But lacking central coordination, they can’t be as effective as a real state-driven response.

As it stands, the murkiness concerning responsibility and accountability in pandemic response has led to a failure of ownership. This has made it too easy to pass the blame and too difficult to determine where the fault lines lie in our capacity to respond to present and future threats from infectious diseases. Everyone has suffered. Another pandemic is only a matter of time, and unless particular authorities are empowered to identify and isolate swiftly, the same institutional failure will rear its head again.

The production difficulties for the Model 3 are well-known, and form part of Elon Musk’s reputation as a visionary who sets overly ambitious goals that nonetheless inch us into the future. Beyond the insanity of the targets themselves, however, came poor management decisions that stemmed from Musk’s overestimation of automation. It quickly became clear that much of the low productivity Tesla was experiencing stemmed from “over-automation,” in which the company spent far in excess of the cost of human workers to automate its factory with robotics that under-performed. Musk later tweeted that “humans are underrated.”

Failed attempts at automation are commonplace, especially in the automotive industry, but they rarely stop investments in automation technologies or the scare stories about their future impact. Musk originally wanted to produce Teslas faster and cheaper than other car manufacturers by applying more aggressive automation to every step of the process. But this approach actively undermined Tesla’s production goals, and the company faced stoppages as they were forced to re-adapt to a larger than anticipated human workforce. Rather than ushering in the future of manufacturing, this result echoed GM’s failed 1980s attempt at automation. Tesla’s failures may have left the company a larger and less productive workforce, at least while they adapt, than they would have had if they had not bothered with automation in the first place.

Beyond corporate risks, bad implementation of automation has jeopardized lives, as evidenced by the two crashes of Boeing 737 Max 8, thirteen minutes after takeoff from Jakarta, Indonesia on October 29, 2018, and six minutes after takeoff from Addis Ababa, Ethiopia on March 10, 2019. Design changes to increase fuel efficiency altered the shape of the plane’s engine housing, which caused the plane to angle upwards in certain conditions. To correct for this, Boeing introduced a new automated system, the Maneuvering Characteristics Augmentation System (MCAS), without including a description of the system in the operations manual. During the Ethiopian crash, the system took control without pilots knowing what was happening or how to turn it off. A total of 346 people died in the crashes before the plane was grounded. The tragedy serves to show how even more restricted forms of automation can go disastrously wrong when they fail to consider the human element.

The automation adopted by Tesla and Boeing appears comparatively low-tech compared to the kinds of automation promised by narratives of artificial intelligence and technological unemployment. Musk himself has been a big proponent of these narratives, having prominently raised the alarm about the possible existential threat of a future general AI so powerful that it will surpass the collective economic, political, and military potential of humanity. In practice, the technology just isn’t there. From self-driving car crashes to failed workplace algorithms, many AI tools fail to perform simple tasks humans excel at, let alone far surpass us in every way.

Faith in the power of AI nonetheless persists. Any failure is branded as a short-term problem that will eventually be overcome. The prophecy of coming automation, as a result, hasn’t taken a serious hit in the cultural consciousness. This in turn makes it self-fulfilling—automation becomes implemented by companies on the grounds that all the major companies are automating. Even if the power of automation is unrealized, companies remain convinced that it will exist at some point in the future. Managers are not encouraged to change their investment decisions in response to failure.

Part of this stems from the mystical marketing of AI and its automation potential. Everything is branded as AI to the point that the term has become meaningless. The current paradigm of AI research is built around deep learning, and out of that broader research domain most deployable algorithms rely on supervised learning, a technique that learns from labelled data to model complex statistical correlations and generate output predictions. This approach, while extremely powerful in domains such as image and language processing, is nonetheless narrow compared to the entire suite of complex tasks humans can accomplish, and somewhat fragile even in its strong areas. Fancier algorithms, such as the reinforcement learning approach that powered DeepMind’s AlphaGo and its successors, so far have limited commercial potential. Nothing from existing deep learning research even begins to approach an intelligence of the scale Musk frets about, or one that could replace human workers completely on complex tasks, and given the limitations of deep learning, they probably never will. This conflation of what AI “may one day do” with the much more mundane “what software can do today” creates a powerful narrative around automation that accepts no refutation.

The Narrative Around Automation

The threat of automation has been present since the industrial age. David Ricardo worried about the labor-saving power of machines harming workers, while Karl Marx saw this trend as one of the contradictions of capitalism that would inevitably lead to its collapse. The Luddite movement arose in response to machines that made the skilled trades more accessible to unskilled workers, undermining the power of guilds by commodifying labor. Throughout the 20th century, there were anxieties around “technological unemployment,” a term coined by John Maynard Keynes to describe the condition of workers whose skills would be entirely substituted by the new machines born out of the increasing speed of innovation. Despite this, the number of jobs available to humans continued to increase, even as old ones disappeared.

As machines made certain tasks more efficient, increasing overall production, demand shifted to the complementary tasks that could not be automated. As technology has advanced, more and more of the work can be automated, to a point previously undreamed of, and the pattern of labor demand shifting to complementary tasks that require human skill and more adaptability has held up.

But some argue that advances in artificial intelligence are now able or will soon be able to automate tasks beyond this constraint. This promise of AI has been around since the field’s inception in the 1950s. AI pioneer Marvin Minsky declared in 1961 that “[w]ithin our lifetime machines may surpass us in general intelligence.” His colleagues made similar statements. Early failures in AI research may have cautioned researchers about method, but never ambition. A cycle of hype and disappointment has characterized AI research since the start. And yet, in public discourse, the last breakthroughs in artificial general intelligence to finally displace human work has always been present. Deep learning, the paradigm that has lead the latest resuscitation of AI from the grave, and whose leading thinkers received a Turing Award this year, is now touted as that breakthrough.

Few AI researchers would argue that deep learning is all that is needed for general intelligence, given the problems that the algorithms have in abstract reasoning or identifying causation, though there is nonetheless a belief that a significant amount of labor can be displaced by what exists. Economists have picked up on this promise and tried to predict exactly what the impact will be. In 2013, an Oxford Martin study predicted that 47% of U.S. jobs would be susceptible to automation from technologies like artificial intelligence. The same methodology found that 57% of jobs across the OECD were susceptible.

The approach used to find these numbers is sorely wanting. In identifying tasks that AI can feasibly perform and then generalizing to the jobs that can be automated, the study falls into the same oversimplification that companies like Tesla are guilty of. The complex ways that tasks interact in a person’s workday are lost. In disaggregating tasks, what is really valuable is obscured.

A practical example of automation done right is in bank telling. Bank tellers historically spent much of their days counting cash and updating bank books, a job that required speedy numerical skills. With the introduction of the automated teller machine (ATM) and other machines like money counters and bank software, it might be expected that these jobs would disappear. Bank telling has in fact grown rapidly as an occupation, shifting its task away from accounting to customer service, resulting from a dynamic in which ATMs vastly increased productivity, while reducing spatial requirements for operating banks. The value of customer service to the bank was always there, but technical limitations occupied tellers’ time with a more inefficient process. Rather than removing the source of value that tellers provided, automation freed them up to provide even more value. The relationship between what tasks someone spends the most time on and what produces the most value is not one-to-one.

Despite the flaws of this style of research, it has grabbed major media attention and persisted in the cultural discussion around automation. The anxiety these findings bring has started to find its way into political discourse, most notably with Andrew Yang, candidate for the 2020 Democratic presidential nomination, who is running on the platform that America needs to prepare for a post-automation future with his flagship YangBucks proposal of $1,000 a month for every American. While his campaign also touched on issues such as stagnant wages, the oncoming threat of automation forms a central part of Yang’s rhetoric.

The narrative around automation persists, however, because it is so appealing to those who perpetuate it. Regardless of whether a technology can replace labor, the point is that that labor is theoretically replaceable. It reinforces the beliefs of consultants, the automation industry, and executives who get sucked in by the AI hype cycle, to the extent that they make major restructurings based on presumed AI capabilities that far exceed what AI can actually do in automation. There’s also the belief among technologists, investors, journalists, and policy-makers that there is a large underclass that does not have valuable skills, whereas they are truly productive and vital to society. A Universal Basic Income is necessary to talk about now because even if jobs are not disappearing due to automation and cognitive stratification, they inevitably will, and the unskilled of the world may become fed up with the system if elites fail to buy their silence.

Underneath this narrative that reinforces a dichotomy between productive and unproductive classes is a reality of human labor being completely discounted, which undervalues it in future decision-making. The belief in automation leads people to act in such a way that brings it about, even if it is irrational to do so. This comes at significant cost.

The Reality Of Automation

Musk may have learned from his mistakes that “humans are underrated,” but most “innovative” enterprises don’t like discussing their reliance on human work, even on nominally automated tasks. Automated workforces are trendy. The little hiccup that they are inefficient is not worth sacrificing the cool of being cutting-edge. In Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass, Mary L. Gray and Siddharth Suri of Microsoft Research expose the reality that a variety of fancy new AI tools rely on hidden human work, identifying the data laborers these companies need as resulting from the “paradox of automation’s last mile.” If a system is nearly perfect, it raises the demand for the service, which in turn raises the demand for humans to correct its mistakes. Google needs to use human callers to impersonate its Duplex system on up to a quarter of calls, and Uber needs crowd-sourced labor to ensure its automated identification system remains fast, but admitting this makes them look less automated.

From the perspective of these businesses, their AIs will get to the point that they can replace human labor in these domains eventually, so why bother waiting until then to advertise them as such? Hiding the outsourced labor required to make something run today helps attract the investment needed to genuinely automate that labor tomorrow. The narrative that automation will inevitably come has encouraged companies to undervalue existing laborers. Thousands of data laborers work through crowd intelligence platforms such as Amazon’s Mechanical Turk and Microsoft’s Universal Human Relevance Service (UHRS), performing data tasks, such as image labeling, for pennies. Admitting that a product requires this labor makes it look less automated, and given that these tasks are so disaggregated among large quantities of workers, it is simply easier to ignore their necessity.

The hype around AI has created an industry to push conventional businesses to pay attention to a technology that only a few years ago wasn’t on their radar. Companies across sectors, from banking to automotive to pharmaceuticals, have now started calling themselves “AI-driven” in the hope of a new lease on life. Even as deep learning research hype cools down, the hype over the industrialization of AI is just getting started.

Sometimes businesses don’t even bother with AI at all beyond using the rhetoric as a marketing trick to pull in investors and media attention. London-based investment firm MMC Ventures found that out of the 2,830 startups they identified as being “AI-focused” in Europe, 40% used no machine learning tools whatsoever. Given that AI firms in Europe attracted 15% higher funding on average than traditional software or analytics firms, it’s no surprise that companies are throwing AI rhetoric around as a funding strategy to capitalize on investor ignorance. This is even more pronounced in the United States, where venture rounds far exceed those in Europe. The push to rebrand a company as an AI startup sometimes occurs reluctantly. Sometimes investors knowingly play the game; they want more AI companies in their portfolio, and the media loves reporting on large AI acquisitions.

The speed at which this is occurring obfuscates the necessary investments these new technologies require. It’s not enough to hire some talented PhDs and tell them to implement AI into a company’s workflow. Numerous companies have recruited data scientists prematurely, without the necessary complements in project management, paying for work that will never amount to much. Historical automation occurred over long periods of time, as workplaces reorganized around new technologies from electricity to industrial robotics to computers, which were not simply things you could drop into a company as is and expect to perform well. It will take time to develop the necessary AI management expertise to implement these algorithms well. While some have acknowledged this need, it has failed to slow down investment.

This exuberance over artificial intelligence is not actually automating work, as much as it is making it more precarious. Beyond Mechanical Turk and UHRS, we also see the rise of the “gig economy,” which erodes the labor protections that once existed. Uber is far leaner than any traditional taxi company because most of the people that work through it do not work for it, thereby avoiding the kinds of onerous regulations that once posed a barrier to market entry. There are countless companies seeking to be the next “Uber for X,” breaking apart jobs into separate tasks, and claiming that the automated matching component they provide is the source of the majority of value. These two-sided market platforms know that maximizing the satisfaction of the buyer-side, such as Uber riders, allows them to reduce the bargaining power of the supplier-side, such as Uber drivers, who depend on the platform to make a living. Even those acting as suppliers of massive data collections become long-run irrelevant according to this philosophy, since they are ultimately just powering the algorithm that will eventually replace them.

The Risk Of Excessive Automation

This preference for the mythical powers of capital over labor creates an incentive for managers to reframe existing jobs in such a way that their automation potential increases. While new purpose-driven organizational theories are arising that attempt to leverage the whole person rather than particular skills, it is hard to imagine these becoming mainstream. Instead, it is more likely that management will make tasks more explicit, neatly separate workloads, and develop more precise metrics by which to enable automation. Even if the technology to deliver on this does not arise, the armies of low-paid data laborers that are accessible even today will enable them to lower their overall investments in labor, enabling the appearance of a leaner enterprise, temporarily inflating their profits and share price.

Some clear downsides arise from devaluing the experience of workers and attempting to make their labor digestible for machines. Tesla and Boeing have already learned the hard way. There is a value to the tacit knowledge of human workers, whose understanding of their particular job roles go beyond pure metrics, and are hard to quantify. If workplace efficiency were derived entirely from explicit metrics, “work-to-rule” would be a much less effective form of industrial action than it is. Companies that do not grow in a digital world, and evolve in a symbiotic way with investments in data analytics and artificial intelligence technologies that extend their efficacy, will find themselves with operational nightmares.

Some of these problems might be easy to spot, such as reduced efficiency in production, or PR scandals caused by problematic output that slipped through human oversight. Others are more subtle. Deferring to algorithms over particular niches in an organization may prove highly efficient, which would encourage managers to aggressively adopt automated tools on more and more projects, and give them more control over an organization. Given the weakness of existing AIs at establishing causality, it’s possible that the correlations hold well at a certain scale, but completely break down at larger levels. To avoid this, domain expertise is required, something that requires the very form of labor investments that people are fretting about not being made.

This complete deference is encouraged by the growing field of prescriptive analytics, which uses AI advances to extend traditional business analytics. Prescriptive analytics promises to go beyond prediction and create a truly end-to-end system for decision-making. Removing human input as much as possible is seen as making corporate activity more data-driven and scientific, but in reality offloads authority to algorithm designers. The new approaches to analytics that have arisen with the explosion in data in recent years have nonetheless had serious methodological issues. Continued growth in the analytics industry, if done in a way that capitalizes on management naiveté, encourages poor decisions.

These risks are sustained by a narrative that undervalues the role of human labor, and takes automation as a given. They are also harder to correct for using conventional liberal policy mechanisms, which are not geared towards correcting the voluntary decisions of private sector actors.

Alternative Visions For Automation

The real threat of automation stems from excessive faith in emerging technologies. The power of these technologies and the inevitability of their impacts need to be challenged for a more honest discussion to arise. Even if AI does not enable the automation of whole sectors of the economy, ambitious action is needed to account for the changing nature of work, to protect against the erosion of labor rights, to protect competition, and to prevent a few companies having outsized control over AI, thus exacerbating inequality. Taking the labor-saving power of artificial intelligence as a given is a distraction from these conversations.

Breaking away from this narrative is not about coloring it with hope. The calls for Fully Automated Luxury Communism have an even more naive approach to new technologies, and one that discounts the true value of human labor to an even greater degree. Discussions that paint the labor-saving power of automation as something to be celebrated do nothing to change the incentives that are leading to poor investment decisions and bad policy proposals.

In Germany, unions have taken up the mantle of creating a new narrative around automation that respects the skills and rights of workers. Having labor involved in the automation process is not about worker control, but rather allowing for technologies to be implemented in a way that complements human skills, augmenting their work and leading to genuine productivity gains. Improving the conversation around automation starts with an improvement in attitudes towards labor.

Those who actually do the work know the limitations of metrics and understand that fuzzy logic is sometimes necessary for good decisions. The reality is that we often know more than we can explain and render explicit. Automation is a threat only because we believe it to be a threat, but it would stop being one if we acknowledged just how underrated humans are.

This process of post-industrialization has put legal and cultural institutions from a more industrial era to the test.

Similar events occurred during previous economic shifts. During Great Britain’s transition from an agricultural to an industrial economy, productivity grew, but wages did not, resulting in widespread misery. Oxford economic historian Robert C. Allen has referred to this period, which was associated with the growth of radical movements, as Engels’ Pause.

In a speech earlier this year, Mark Carney, Governor of the Bank of England, warned of another Engels’ Pause that may spur populist activity across the developed world, though the speech was in effect already retrospective, given the rise of democratic socialism and right wing populism in the Western world. He argued that economic conditions today are similar to those in the early decades of 19th century Great Britain.

Institutional malaise in adapting to these technology-induced economic changes could accelerate the political effects of a new Engels’ Pause. Traditional industrial jobs have been hollowed out. The United States is particularly vulnerable to this fallout. Populist surges are likely only beginning.

Britain’s Industrial Revolution

The First Industrial Revolution’s effects on productivity and GDP can neatly be divided into two eras. From 1760-1800, the burst of new inventions had yet to be implemented at a scale to increase productivity or impact wages. On the other hand, urban populations were expanding as people moved en masse to the cities to find jobs. Britain’s trade policies following this period encouraged rapid urbanization, while reducing the demand for labor in agriculture. In the second phase, between 1800-1830, productivity growth rapidly increased. Britain’s GDP spiked. Wage growth in this period, however, largely stagnated and in some cases even declined. Allen found that the period of Engels’ Pause coincided with the rise of socialist and anarchist critiques of capitalism.

The Pause brought dire political ramifications, but around 1840, wage growth started to pick up, though at a lower rate than productivity. It was only after 1860, 100 years after the beginning of the First Industrial Revolution, and around the start of the Second Industrial Revolution, that both wage and productivity growth were stable and rapid. Wage growth occasionally exceeded productivity. This dynamic persisted into the late 20th century.

During the Engels’ Pause, competing theories attempted to explain the rise in inequality. One of the major camps consisted of the Classical economists, most prominently David Ricardo and Reverend Thomas Malthus, who believed that as societies became more productive, the increase in living standards would lead to larger populations that would consume all gains.

The other major theory advanced at the time was popularized by Karl Marx and Friedrich Engels. According to this theory, technology had a labor-saving bias. As technology improved, less labor would be necessary; therefore, beyond the subsistence wages needed to maintain a workforce, the only investments would be based either on the need for skilled workers, in the form of education or training, or as a result of a “historical or moral element,” which capitalism would eventually eradicate.

But by the time the Communist Manifesto was published in 1848, wage growth was on the rise, and by the time of Marx’s death, it exceeded productivity growth. Allen’s account focuses on the incentive for capital formation that rising profits enabled. As productivity increased and the capital-labor ratio widened, increased profitability enabled the wealthy to save more. But the ability for owners to outcompete each other via capital investment dropped off once major industries had become fully industrialized. Competition then depended on hiring more workers to increase production. Once this crucial switch occurred, wages began to rise.

However, the political influence of the movements which had sprung up during the pause continued to impact economic life. The establishment of unions and coordinated labor movements increased labor’s ability to bargain around the same time.

Britain was able to profit more than other European countries during this period, due to the unique legal and cultural environment it provided to industrialists. Liberalism had come to define British culture, as it did America’s, which made it more supportive of private entrepreneurship and laissez-faire economics than continental Europe. The liberal vision that animated Britain throughout the Industrial Revolution provided a political will to manage the transition and shape society.

Throughout the early phases of the First Industrial Revolution, the state gradually lifted restrictions on the creation of corporations. In 1844, incorporating became a simple registration procedure. In 1855, the Limited Liability Act lowered the risk associated with setting up a corporation, an important step in the increased capital formation that led to higher wages.

The institutions of industrial capitalism that eventually became ubiquitous were important to ensuring that investments were protected. The “labor-saving” technologies Marx had referred to had in fact vastly increased the demand for low-skilled labor. The operation of rote machines supplanted the once-complex artisanal crafts that comprised most of Britain’s large textile industry, which in turn boosted output. The demand for more operators reduced the power of artisans, leading to the Luddite Rebellion of 1811. Artisan power had been gradually weakened for nearly a century as guilds lost their prominence in British labor, though their power across the continent continued until the First Industrial Revolution was well underway. In 1812, the UK Parliament outlawed the destruction of machinery under the punishment of death, protecting the investments made by industrialists, and intensifying capital formation.

A key lesson to take from the industrial transition is the role updated social and political institutions played in creating the right incentive structures. The present underlying conditions are eerily reminiscent: there are constant headlines of new technologies being introduced into the market, but their overall national-level productivity effects remain low. But the fact that certain trends existed during the First Industrial Revolution does not necessitate their existence in the present.

If the institutional adjustments that occurred during the First Industrial Revolution fail to occur, or are representative of a failure of vision, it may not simply be an Engels’ Pause that occurs. Current waves of populism will escalate if social benefits are not widespread.

The year 1848 saw numerous revolutions break out across Europe, particularly in France and the German-speaking countries. The political effects reverberated long after wages began to rise, with German and Italian unification occuring in the final decades of the 19th century. Great Britain remained remarkably peaceful due to its earlier reforms.

The ability to make structural reforms will determine whether the post-industrial transition will effectively be managed. But major Western powers have been unable to coordinate on key issues. Take Brexit, for example, where the EU was unable to retain a core member, but that member seems unable to actually leave. Even among economically healthy countries, such as Germany, populist movements have gained power, due to gridlock on social issues like immigration and involvement in international bodies. In the U.S., executive orders have played an increasing role in policy as democratically elected bodies are unable to function to due political gridlock and the constitutional dispersion of governing powers.

Post-Industrial Society and Marx’s Return

The structural response depends on the particular effects of the economic transition. A useful model for gauging the transformation currently underway is Clark’s Sector Model, which divides the labor force into primary, secondary, tertiary, and quaternary activities. Primary activities are those such as agriculture, fishing, and mining, which focus on primary inputs; secondary activities are those in manufacturing and construction which formed the backbone of industrial society; tertiary activities are those in the services sector that make up the majority of jobs in the developed world today, and the vast majority of those in low productivity growth countries like the United Kingdom. Quaternary activities contributed to an insignificant amount of labor prior to the information technology revolution and form the “knowledge economy,” which includes roles like consulting, communications, the culture industry, and increasing amounts of the public sector. The present transition to quaternary roles is likely to accelerate many of today’s political strains.

Recent developments in the post-industrial era have differed significantly from the First Industrial Revolution. Whereas the spinning jenny made a complex artisan task more methodical, and the moving assembly line made a strenuous physical task more monotonous, information technologies like CAD and software tools are now demanding significantly more cognitive effort. Industrial technologies are termed “skill-unbiased,” in that they increase productivity regardless of the skill level of human workers. But the technologies that have proliferated over the last 40 years are “skill-biased,” as their productivity gains are relative to worker skill.

Skill-biased doesn’t necessarily mean technical skills. Tertiary activities tend to be dominated by soft skills that require empathy and pleasantness, rather than technical training. In certain cases, the demand for soft skills displaced demand for technical capabilities, which are easier to automate. Jobs in STEM-related fields in the U.S. shrunk by 3.3 percent between 1980 and 2012, whereas jobs requiring a high degree of social skills grew by 12 percent over the same period.

The skills demanded by the market, however, have not been in high supply. An illustrative example of this is the introduction of the automated telling machine (ATM), which freed up time for bank tellers—-who had previously mainly counted cash and updated accounts—to deal with customer complaints and provide higher quality customer service. The transition from rote numeric skills to personability and communication skills raised the bar for entry into the telling profession.

Skill bias in the transition from secondary to tertiary, and now quaternary, activities has played a significant role in altering the pace of innovation diffusion since the IT boom of the late 1970s. Service sector jobs tend to exhibit low productivity growth without some kind of technological change. While a manufacturing plant can incrementally improve year on year, a nurse can’t be improved in the same way, and she cannot physically attend to more than one patient at a time. Low-productivity jobs are growing in demand, at the same time competing for labor with other industries. Aging populations require more nurses, and the education bubble requires more professors and administrators. This has contributed to rising costs in these tertiary activities for little consumer gain, a phenomenon known as Baumol’s Cost Disease. The resulting increase in costs of education and health care eat into the capital available for innovation.

The effects of the quaternary sector are even more pronounced, given the difficulty of accessing the necessary human capital. So-called “superstar firms” have been vastly more productive than laggards, attracting most of the small pool of talented human capital, and leading to some level of industry concentration.

As top graduates go into finance and high tech jobs, fewer of them revitalize traditional industry with new technologies. This is problematic, as quaternary industry skill sets are not as easily transferable from one firm to another. This has meant that the learning curve for firms attempting to leverage productivity-boosting technologies has increased dramatically. The period following the IT boom has been one of increasing inequality as a result of this changing dynamic.

New technologies like artificial intelligence add to these challenges, which may in turn restrict gains even further. Even if it only creates full-scale transformations in a handful of industries, the heavy firm concentration and reliance on highly specialized intellectual capital will create economic disruption. Companies can quickly adopt the technology across departments and subsidiaries, thus creating fluctuations in competitive job markets and causing worry across other industries. Those who can leverage AI well will be vastly more productive, offering higher wages and earning higher profits, but entry into those firms will be competitive and demanding. Automation presents a policy challenge, since AI is able to perform certain tertiary activities—chatbots replacing customer service representatives, drones taking over delivery, and autonomous vehicles automating transportation. The services sector will increasingly focus on those skills of which existing AI is incapable, such as social competence, complex communication, and human interaction.

The shift to roles driven by soft skills in the tertiary sector, and the rising demand for jobs in the quaternary sector, is likely to result in a lack of qualified workers, as well as difficulties in retraining. The institutions of law and culture that developed during the industrial era are no longer capable of grappling with a post-industrial world.

Straining Institutions

While the transition is economic and technological in origin, the institutional strain it causes is not so reducible to matters of calculation. Present institutional strains are intensifying and radicalizing normally stale and bland policy discussions, especially in the United States. Policy debates which defined a previous era, such as the size of government, are being thrown out. Political battles on the future of education, the nature of the corporation, and the governance of emerging technologies directly impact how the transition will be managed and the future it will entrench.

Due to the weight given to post-secondary qualifications across quaternary fields, political demands to reduce or abolish the major financial cost a degree requires are growing louder. There is growing support for universal free college tuition; decades of institutional pushes for post-secondary education, and the resulting near-mandatory nature of degrees for many jobs, have created a $1.5 trillion dollar student debt crisis borne by around 44 million borrowers. This political current ties in economic success with access to the post-secondary education system. Supporters argue that good jobs require more education, and that the current system of tuition fees and markets in education present barriers that harm national productivity. This retains the view that universities are assets through which students acquire real intellectual and technical value, which translates into both economic growth and cultural flourishing.

Another view is that there aren’t too many barriers to education at all; rather, universities are failing students by not focusing on useful skills and excessively extending program lengths. The demand, then, is for greater prioritization of STEM subjects, and the reworking of primary and secondary education to increase mathematical ability. Moreover, many in this camp draw on the fact that university degrees generally function as signalling mechanisms on the job market. Rather than intellectual or technical skills, the primary benefit of holding a degree is to signal intelligence and transferable soft skills such as group work, complex project planning, political conformity, and the like. If technological trends accelerate competition for judgment-based positions, it will only be natural for students to ask why they should spend their twenties on education and accumulate immense debt to enter the workforce. In fact, if fewer people go to university, and more alternative education avenues open up, everyone will be better off, according to this education-skeptic view.

These disagreements reflect differing priorities and goals. If the free education movement wins out, the power of post-secondary institutions will be cemented, and the costs will be redistributed to the broader tax-base on the justification that this is a strategic national investment. However, this would do nothing to mitigate the problem of inflated degrees and the costs of spending years pursuing validation for intellectual ability. Further, this would signal the victory of an educational policy focused on domestic social issues. Conversely, STEM-first approaches, like the one currently underway in China, direct human capital towards innovation. This would mean a shift towards outward-looking policy with the goal of ensuring American economic prominence.

Deciding which course to take is a pivotal political battleground. Academic institutions maintain a privileged political and social position. Ceding any part of this privilege will not come as a welcome change. While public investment in easily accessible training programs and even online courses could mitigate the costs of tertiary and quaternary training, the contest will pit beneficiaries of the academia-focused domestic strategy against those of a skills-focused international competitiveness strategy.

The size of corporations like Google and Facebook has taken on greater political focus than the size of government and has become a contentious point on how to shape this transition. The dynamics of competition which enabled the growth of corporations of unprecedented size amidst a general productivity slowdown are increasingly viewed as a political failure, particularly among the U.S.’ resurgent socialist left. Just as in education, however, views of the problem share only an institutional target, but neither a diagnosis, nor a cure.

An emboldened antitrust movement sees the rise of these new giants not as inevitable aspects of a post-industrial economy, but rather a result of the eradication of political concerns in competition policy. Such a perspective has already taken a hold in Europe, where Margarethe Vestager has been referred to as an “antitrust czar” for renewing the European Union’s zeal in enforcement. Many of those convinced of the need for more aggressive action against the tech companies and financial firms that dominate western economies harken back to an older trust busting-era and the “curse of bigness.” According to this view, competition policy was better when it was motivated not by concerns of economic efficiency, but by political and moral convictions.

The small is beautiful argument also animates decentralization advocates, who are less keen on using political tools. This tendency is popular among technologists, particularly those working in the blockchain space, and most radical in its crypto-anarchist variety. Their desire is not to embolden the state to restrict corporate power, but to use technology to develop new models of both state and corporation. These actors build and operate alternatives, from cryptocurrencies and crowdfunding platforms aimed at challenging financial control, to decentralized legal systems that challenge state control. Their priorities, however, differ wildly from antitrust advocates, as does their vision of a post-transition future.

While these views dominate American circles critical of corporate power, there is also the Chinese vision, which harkens back to a mid-20th century American order, and which is slowly gaining traction. Chinese corporations may not all be owned by the Chinese Communist Party, but they are heavily influenced by it. China uses market mechanisms as a means for state-directed ends, which allows it to tolerate large enterprises.

Mapping this view onto a country like the United States provides an alternative diagnosis of the problem of corporate power. It is not necessarily that a few companies are too large, as a result exacerbating inequality and causing social strain, but that they are not directing their vast means towards the right ends. The vision held by Silicon Valley is in conflict with many factions in the U.S. government itself. These divisions are attested to by Google’s conflicts with the Department of Defense, contrasted by its close ties to institutions like the CIA and State Department. During the Cold War, however, American companies were not only large, but aligned with national interests. A strong and prosperous future, then, is not accomplished by state power quashing corporate power, or technology quashing state and corporate power, but by state power harnessing corporate power.

In both the debates surrounding education and corporate power, the focus of each approach differs in technique. There are moral concerns, political concerns, economic concerns, social concerns, and military concerns, and the prioritization of any one of these itself changes how the problem is defined, the analysis of its cause, and the solutions proposed. This conflict is even more intense when discussing the most important factor causing post-industrial transformation: the new technologies reshaping the world.

Whereas camps can be clearly defined on narrower issues, the question of technology governance as such remains a mere subset of more well-established policy questions, such as trade and foreign affairs. While politics has certainly interacted with new technologies increasingly, from Germany’s Industry 4.0 to China’s Military-Civil Fusion, these have all treated technologies as strategic means. The broader philosophical discussion of understanding emerging technologies has only just begun. As Henry Kissinger warned, “We started too late.”  

The builder of a product, while essential to providing its shape, does not entirely determine the product’s impact on the world. Policy makers, corporations, media, the general public all have internal debates about how they fit into a post-industrial world. Each is under pressure to adapt to rapid interconnectivity, intelligence machines, and overabundance of information.

Political action has started on narrower issues where the questions are easier to digest. Researchers, corporations, and nations have vowed to prevent lethal autonomous weapons systems. Labor movements around the impacts of the “gig economy” have started to challenge existing systems of benefits and employment. A green technology movement is growing that prioritizes investing in research that serves both practical and moral ends. None of these, however, form coherent new ideologies that can push for the development of institutions. While it is possible that an invisible hand could lead piecemeal actions without conscious design, it is not necessarily the case that this will be desirable.

Looking back, there are very few who would argue that the consequences of industrial societies have been a net negative for the human race. But it is easy to imagine things being different. Should the British approach to industrialization have never taken off, what would have been left is the revolutionary upheaval that plagued Europe during the time. The institutions that evolved during the First Industrial Revolution, such as developments in law and societal values, enabled a particular vision of the future to be advanced and achieved.  

However, this means that many of the countries currently experiencing populist upheavals will become less likely to adapt properly, not more. The U.S. and Britain, the nations that successfully led the vision of an industrial society, have experienced massive increases in gridlock in the past few years. Clashes among political parties, within state administrative bodies, and in international institutions have collectively made it extremely difficult to plan and coordinate in a unified way. While economic transitions are often measured in decades, politics is a matter of electoral cycles only a few years long.

These are the sorts of questions which require political will and a vision of the future in order to be properly answered. Unfortunately for those countries which led the 20th century transition, their current political crises are occuring in the vacuum of precisely these qualities.

Where there is judgment there is doubt, and one of the purposes of the new scientific approach to management that emerged after the Second World War was the elimination of doubt.

As a result of this societal project, decision-making has been ceded to technologies themselves, resembling neither the worries of free-market capitalists about an overbearing state, nor of socialists about corporate overlords. Rather, they resemble Terry Gilliam’s Brazil or Franz Kafka’s The Trial, in which the autonomous bureaucracy itself oppresses across sectors of life.

From this vantage point, it is unsurprising that there are anxieties about the so-called “Fourth Industrial Revolution,” which promises to automate whole sectors of the economy with emerging technologies, such as advanced robotics, artificial intelligence, and connected devices. While automation in itself has been a historic good, raising productivity levels and expanding prosperity, the worry is that processes of technological advance won’t be used in the pursuit of societal betterment once they are embedded into technocratic and managerial structures. For humanistic technology to flourish, development and implementation must be guided by values, the very thing that the ascendancy of a value-free, scientific approach to organization deems anathema.

Harnessing the benefits of new technologies, therefore, requires the exercise of good judgment, which has a degree of doubt and a qualitative ethos not present in the modern economy.

Management As Discipline

Before discussing the relevant ways in which modern organization and the dominant managerialism are ill-equipped to make prosperous use of emerging technologies, managerialism must be properly defined. In the context of this piece, management is distinct from leadership, which conjures up images of the charismatic entrepreneur or the visionary political hero.

The manager is that individual, common in modern organizations, whose job is to make sure others are doing their job well. Managers are both feared and maligned; their position serves the role of desire that individuals within organizations strive for, and the role of scorn when someone else fills it. It is the managers who dominate our culture because of their ubiquity, since the daily tasks of planning and organizing teams and departments fall to them. Due to the large presence of this professional class and its influence in decision-making on the margin, rather than grand strategy, it is they who hold significant practical authority in implementing new technologies. As a result, realizing the promises and perils of these technologies is a project which will likely be shaped in large part by the culture of the managerial class.

Management as a modern discipline is fairly new, being necessitated by the Industrial Revolution and first developing in the Anglosphere, but soon spreading across of Europe and beyond. As a result of the increasing organizational complexity that the vast productivity boom of that era provided, there was a demand for individuals with the technical skills of administration. This demand existed in both the nascent corporation and the rapidly expanding civil service. Max Weber, one of the earliest scholars of the rising bureaucratic order dominated by the manager, noted that this structure was “increasingly more precise in calculating the methodical attainment of given practical ends.” Managers were efficient because they went beyond the accounting practices that preceded them. Now it was possible not just to keep account of existing metrics, but also to pursue the quantification of the qualitative through bureaucracy and the managers placed into it.

The role of the manager required increasing precision to justify its increasing authority in organization. Throughout the 20th century, business schools developed their own language of technical rigor. This provided the veneer of an increasingly scientific discipline and invented a new grammar of management. Such trends led to a view that the science of organization was as deterministic and mechanical as that of physics. Following the Second World War, many countries left in place or even expanded the administrative processes initiated to increase the efficiency of the war effort. This was done on the premise that they would retain their efficiency in peacetime. Technically competent managers were increasingly placed to manage schools, hospitals, and the scientific enterprise, rather than the most experienced teacher, doctor, or distinguished scientist. This extended to the structure of the corporation as well, with senior roles increasingly hired out of university management programs as opposed to trained up from the proverbial shop floor.

Scottish philosopher Alasdair MacIntyre has been one of the most scathing critics of this culture. In his 1980 work on moral theory After Virtue, he identified a fundamentally amoral character in management that dissolved genuine social relations. He posits that since managers pursue the most efficient means to a given end, they end up having no connection to the purpose of their work; they are therefore unable to exercise any moral judgment, as doing so is deemed outside their scope. The manager, by the demands of his role, must reduce problems of value or judgment to technical problems with clear empirical solutions. The manager views his role as one of making rational decisions on the basis of evidence, in pursuit of clearly definable and unambiguous goal metrics. In this way, for MacIntyre, management is the perfect embodiment of moral emotivism, which views judgments of value as subjective statements of preference, not subject to rational analysis. In both cases, means may be evaluated as better or worse in relation to a certain end, but the end itself must be regarded as arbitrary. There are no better or worse ends, only different ones.

To MacIntyre, the work environment that a manager creates is one that can easily be adapted to suited ends, one “with effectiveness in transforming raw materials into final products, unskilled labor into skilled labor, investment into profits.” Such an environment demands more routine and process compliant work from laborers. With some exceptions, employees enter into work each morning, do their job, and clock out; teachers teach the test; doctors come to see their patients and charts; and scientists focus on incremental and measurable experimental improvement rather than grand creative leaps. One has only see the degree to which specialization has occurred in recent decades, with workers turned into experts of increasing precision, yet decreasing versatility. The structures of organization are such that workers are deprived of the resilience they need to adapt to different work environments. It is fair to ask in such a situation: what are the impacts on workers as emerging technologies are increasingly implemented?

Technological Organization And The Fracture Of Managerialism

Critiques of managerialism are nothing new. They have dated back to the earliest arrivals of managers into the workforce, well before they became entrenched. Thinkers from Joseph Schumpeter to James Burnham to Rakesh Khurana have variously analyzed its organizational failures. However, the managerialism so synonymous with modern economic life is today being challenged by the effects of technology and automation within the post-industrial economy itself. The growth of digital economies in the early 21st century, as well as a number of technological innovations which are likely to produce similarly large-scale effects in global economic life, are actively diminishing the human role in empirical analysis. As a result, it is goal-oriented judgment—the very aspect of management which the scientific approach sought to minimize—which will increase in value as the unique contribution of human direction to an organization.

One of the problems with many of the analyses conducted on the impacts of automation on the workplace is the view of technology as an external product applied to an environment. To some degree there are products, such as surveillance robotics, that can be purchased outright and put into use. In most cases, however, new technologies must be integrated into an organization: this requires new infrastructure, new rules, and a new culture. Research suggests that these barriers explain why there can simultaneously be a widespread sense of rapid technological advancement and next to no productivity growth across most of the developed world. Technological development is, as a result, embodied in the organizations which sustain it; knowing about artificial intelligence is not enough to predict its implications. It’s necessary to look at the culture and processes that deploy it.

The recent book Prediction Machines provides a useful framework for dissecting decision-making within organizations and the impact artificial intelligence is likely to have. The authors argue that artificial intelligence is fundamentally a prediction technology, and that decisions can be decomposed into the components of prediction and judgment. As the cost of prediction decreases, the value of judgment increases—this is because managers are able to run through more hypothetical “If-Then” scenarios and articulate with greater precision the ideal course of action for their organization. While the book focuses on artificial intelligence specifically, this framework of decision-making components and organizational workflows is nonetheless useful in understanding the general failures of management with respect to the more data-driven high technologies emerging as part of the Fourth Industrial Revolution.

The anatomy of organizational decision-making in Prediction Machines places the role of artificial intelligence into the execution of tasks. Tasks are components of jobs, and jobs are components of the total work output. In each task, there is an input component, a decision component, an action component, and an output. Artificial intelligence, the authors argue, decomposes the decision component of a task into prediction and judgment, increasing the accuracy of the former, and increasing the value of the latter. In fact, this approach can be applied just as well to connected devices and advanced robotics as to artificial intelligence. Different aspects of a task may increase in value with other emerging high technologies, such as advanced robotics increasing the precision of an executed action. However, it is clear in all these cases that the major human input required is to ensure this workflow is consistent with the goals of the organization. The more complex and important the task, the more human judgment is required to ensure that machines are not misaligned with organizational goals.

While strategies are articulated on how to incorporate artificial intelligence into organizations to increase their capacity for judgment, there are reservations to be had on the efficacy of such initiatives given the managerial culture as it now exists. As judgment grows in value in an institutional culture which has historically devalued it—and as the technical components of decision-making are increasingly automated—the likelihood of error increases. The rising possibility of error within rigidly hierarchical organizations is not only the product of new technologies; it has been a concern ever since earlier waves of computing were industrialized. The same dynamics of complexity and disaggregation of the use of information from its collection process and analysis occurred with the normalization of computers and the internet. Similarly, they are now being intensified by the exponential growth of data and algorithmic decision-making. In response a literature of High Reliability Organizations (HROs) has begun to grow, though adaptation has been minimal outside of militaries.

There are five criteria of High Reliability Organizations that enable them to allow for risk prevention and the exercise of good judgment: a preoccupation with failure, reluctance to simplify interpretations, deep situational awareness, commitment to resilience, and flexibility in structure. All of these, to varying degrees, are at odds with the scientific and deterministic bureaucratic culture that modern managerialism has ingrained. Flexibility in structure, for example, requires a decentralized approach:

Any person can raise an alarm and halt operations. When anomalies or near misses arise, their descriptions are propagated throughout the organization, rather than following a fixed reporting path, in the hopes that the person with the right expertise will see them.

Such an organizational culture is rare, especially in the public administrative institutions where the vital functions of statecraft are executed.

Organizations that increasingly seek technical solutions devalue the dynamism that human creativity allows for, which in turn encourages them to cede human decision-making to machines, and simultaneously to make humans more machine-like. The value of intuition is lost, preventing the sorts of risk awareness essential to the exercise of good judgment. Politically speaking, the populist wave has invoked rhetoric denouncing this technocratic view of administration. Modern populism is also more common in societies further along the transition to “post-industrial economies,” where work is increasingly automated, and the functions of management and administration take up an increasingly large chunk of national income. Where jobs reflect individual talents, they are augmented by new technologies, rather than automated by them. But where workers are overspecialized to the point that what they do differs little from a machine, they are more likely to be automated in full, and their intuition discounted. This overspecialization is all too common, and goes against the fundamental basis of a High Reliability Organization.

While there is a growing literature on both human-computer interaction and worker augmentation, the results depend largely on whether organizations adopt a culture which allows technologies to be developed and implemented in a humanistic way. A technocratic culture in which only means are of concern is one chiefly dependent on the quality of its programmers and engineers. Conversely, one in which qualitative work is given value and judgment is prioritized will more likely be one in which machines are tools at the service of enhancing an individual.

The common line of thinking among managers and technocrats is that technologies are neutral tools dedicated to specified ends—in fact, no different than how laborers and capital inputs are generally treated. But this is not the case. Technologies actively impact the broader structure into which they are integrated. Their purpose can only be fully understood by their functional ends within the broader structure. Technologies develop out of certain intentions, are deployed through certain institutional arrangements, and incentivize certain forms of behavior. Much current discussion focuses on the regulation of technologies, such as the surveillance or military applications of artificial intelligence. However, this marginal form of regulation alone will not be sufficient to ensure that new technologies are well-integrated into a culture which actually builds and sustains innovation.

Humanistic Organization And The Ends Of Technology

One of the most fascinating promises of the Fourth Industrial Revolution is the advent of “Smart Cities,” which through digital technologies will become constantly connected and vastly more efficient forms of urban organization. Cities are an interesting form of organization, relying on complex interdependent systems, from public services to local businesses to community groups which help sustain and grow them. The diversity of cities in terms of organizational tasks allows them to integrate the full spectrum of new technologies into their holistic development.

A few simple examples suffice to show how interconnected the result could be. With self-driving cars, the efficiency of traffic would increase, and the greater intensity with which these vehicles could be used would significantly reduce demand for parking space. This newly freed space could be used to make cities denser, allowing more constant interaction and the development of “hubs” where like minded people would congregate and develop new and better ideas. Public administration would need to ensure that these efficiencies are well-integrated, digitizing their citizen-facing infrastructure. Connected devices would proliferate across public infrastructure, vastly increasing the data generated by which private companies would be able to innovate further.

In what was just described, however, nothing was mentioned regarding the ultimate purpose of these gains to efficiency or the forms which these new interconnected structures might take. This is because the technologies themselves only expand material possibility, but the ends to which they are applied cannot be inferred from the techniques they use. Connected cities in China, where the surveillance apparatus has vastly expanded due to the advent of artificial intelligence and advanced robotics, look very different from the smart city project currently underway in Barcelona, Spain.

To understand their differences, however, their cultures, rather than their institutions, must be understood. China’s approach to centralizing control through technology and dictating their citizens’ lives with greater precision fits into a historical cultural value that sees the role of the state as upholding moral order. By way of contrast,the Barcelona project, with its goals of reducing costs and improving the environment, cedes behavioral regulation to a technological environment rather than top-down authority. This fits with a study done by MIT that found that workers prefer taking orders from machines than people, a very technocratic notion of placing our trust in those who are seen as efficient and unbeholden to all-too-human games of social status and intrigue. This is in no way to create a moral equivalency between different value systems, but simply to highlight the cultural bases by which technologies are deployed.

Questions about how the Fourth Industrial Revolution can serve societal welfare, then, are not about merely regulating the particulars or dictating how means are pursued, but rather a broader discourse about the ends to which organizations work. It requires that decision-makers are generalists who understand not just their own tasks, but the social dynamics of their decisions.

There have been attempts to attach managers to their ends, such as through the rise of the shareholder-CEOs in the 1980s, to ensure a commitment to a vision; this development has had mixed results, if not negative ones. While management literature is aware of the problems of bureaucracy, the real world continues to grow in administrative bloat. Even MacIntyre’s scathing critique of management has become one of the most widely cited works in the field of business ethics since its publication in 1980, though its impact is debatable.

The problem is that going against the technocratic impulse of modernity requires answers that are fuzzy and imprecise. It requires workers who both feel connected to their craft, but are aware of their place relative to the wider organization. It requires managers to have a stake in the organization’s goals, to be connected to the social environment in which they operate, and to have a familiarity with their workers’ tasks. It requires that discussion of values can exist alongside and temper technical reports and that intuition be allowed to override data. It requires organizational structures that are less well-defined and processes that are more adaptable. Fundamentally, it requires abandoning the view that decision-making is scientific and deterministic, which is far less palatable to most than particular policy solutions.

The sustainability of a technical, scientific bureaucratic process is in many ways derived from its inscrutability. When a field is made to have the apparent complexity of physics, it restricts participation and dismisses the critiques of those without the relevant technical training as unscientific. The overbearing elitist state or corporate overlord is a more easy-to-identify threat, but resistance to these forms of authority prevents their reach from becoming totalitarian. Bureaucratic creep, however, persists in ways which are much harder to challenge. Unless this is overcome in the transition through the Fourth Industrial Revolution, new technologies will only serve to intensify managerial forms of work and life, as well as their tendency to escape any real form of human direction.