The Automation Everyone Forgot to Worry About
For two decades, the automation conversation has focused on blue-collar and routine white-collar jobs. Factory workers, truck drivers, cashiers, data entry clerks. The assumption, often implicit, was that higher-order cognitive work – strategy, negotiation, leadership, creative direction – would remain the province of humans.
Autonomous businesses upend this assumption. They do not just automate the assembly line or the call center. They automate the boardroom. The CEO, the CFO, the COO, the entire management layer – all replaced by AI systems that can strategize, allocate capital, negotiate contracts, and make operational decisions without human involvement.
This is a qualitatively different kind of displacement, and society is not ready for it.
What the Numbers Say
McKinsey Global Institute’s research on automation potential has evolved significantly. Their 2017 report estimated that about half of work activities globally could be automated with existing technology, but that this would play out gradually over decades [1]. Their more recent analyses have revised upward dramatically, particularly for knowledge work.
By 2025, McKinsey estimated that generative AI could automate 60-70% of current work activities, with the most affected roles being precisely the knowledge-intensive, well-compensated positions that were previously considered safe: management consulting, financial analysis, legal research, software development, and strategic planning [2].
The World Economic Forum’s Future of Jobs Report 2025 projects that AI and automation will displace 85 million jobs globally by 2027, while creating 97 million new ones [3]. But these aggregate numbers mask the distributional reality: the displaced and the newly employed are often not the same people, not in the same locations, and not in the same economic strata.
For autonomous businesses specifically, the displacement hits the top of the org chart. When an AI system can perform strategic planning, capital allocation, and operational management, the positions most at risk are not the front-line workers but the executives and middle managers who direct them. This inverts the usual automation narrative and creates political dynamics that are entirely novel.
SPAWN and the Automated Managerial Class
The research collective SPAWN (Screenwriters, Producers, Agents, Writers Network) introduced the concept of the “Automated Managerial Class” to describe the layer of AI systems that perform supervisory, evaluative, and strategic functions previously reserved for human managers [4].
The concept is important because it highlights a category of automation that is often overlooked. We think about AI replacing workers, but we less often think about AI replacing bosses. Yet the managerial layer is in many ways more susceptible to automation than the operational layer:
- Decision-making at scale. Managers make decisions about resource allocation, performance evaluation, project prioritization, and strategic direction. These are precisely the kinds of decisions that AI systems are increasingly capable of making, often with better access to data and faster processing times than any human.
- Standardizable judgment. Despite the mythology of executive intuition, much managerial decision-making follows identifiable patterns. Hiring decisions, budget allocations, performance reviews – these are judgment calls, but they are judgment calls that can be modeled, trained on, and reproduced.
- Communication and coordination. A significant portion of managerial work is communication – meetings, emails, reports, presentations. AI systems are increasingly capable of generating, summarizing, and routing communications without human involvement.
The Automated Managerial Class is not a future prospect. It is an emerging reality. Autonomous businesses represent the logical endpoint: entire organizations operated by the Automated Managerial Class, with no human managers at all.
The Luddite Parallel
It is fashionable to invoke the Luddites as a cautionary tale about resisting technological progress. The usual telling goes: the Luddites smashed textile machines in early 19th-century England because they feared automation, and they were wrong because industrialization ultimately created more prosperity than it destroyed.
This telling is historically inaccurate and analytically lazy [5].
The Luddites were not opposed to technology per se. They were skilled artisans who opposed the specific way technology was being deployed – to deskill their trades, reduce their wages, and concentrate wealth in the hands of factory owners. Their concern was not that machines existed but that machines were being used to redistribute economic power upward. On that point, they were correct.
The parallel to autonomous businesses is precise. The question is not whether AI can run a business. It obviously can, or soon will. The question is who benefits when it does. If autonomous businesses concentrate the economic returns of entire industries in the hands of whoever deploys the AI – while displacing the workers, managers, and executives who previously shared in those returns – then the Luddites’ concern applies directly.
The standard rebuttal is that automation creates new jobs. The textile mill displaced hand weavers but created factory jobs, logistics jobs, retail jobs. This is true, but it is also true that the transition was brutal – spanning decades of impoverishment, social upheaval, and political conflict before the new equilibrium emerged. The question for autonomous businesses is whether the transition will be faster or slower, smoother or rougher, and whether the new jobs will actually materialize.
The C-Suite Displacement Problem
Displacing factory workers is politically manageable, however unjust. Displacing the C-suite is politically unprecedented.
Corporate executives wield disproportionate economic and political influence. They sit on boards, donate to campaigns, shape policy through lobbying, and occupy central positions in social networks. When automation threatens their positions, the political response will be qualitatively different from the response to blue-collar displacement.
This could manifest in several ways:
- Regulatory capture. Executives may use their political influence to regulate autonomous businesses out of existence, not because autonomous businesses are harmful but because they threaten executive employment.
- Professional credentialism. Requirements that certain business decisions must be made by licensed professionals (CPAs for financial decisions, attorneys for legal decisions, etc.) could effectively prevent full automation of the C-suite.
- Cultural resistance. The mythology of the visionary CEO is deeply embedded in business culture. Markets, investors, and customers may refuse to deal with leaderless organizations, regardless of performance.
Alternatively, executive displacement could accelerate the adoption of autonomous businesses precisely because it affects powerful people. When truck drivers are displaced, they march. When CEOs are displaced, they invest in the technology that displaced them and profit from its adoption. The political economy of executive displacement is genuinely unpredictable.
Universal Basic Income and Beyond
The prospect of widespread displacement by autonomous businesses has reinvigorated the universal basic income (UBI) debate. The argument is straightforward: if AI systems can operate entire businesses without human labor, the productivity gains should be redistributed to support the humans who are no longer needed [6].
Several UBI experiments have produced encouraging results. Finland’s 2017-2018 experiment found that UBI recipients had better well-being and were more likely to find employment than the control group [7]. Stockton, California’s SEED program showed similar results. But these experiments were small, short-term, and operated in economies where employment remained the norm.
The scenario posed by autonomous businesses is different in kind, not just degree. If autonomous businesses can operate at scale across multiple industries, the displacement is not marginal. It is structural. A UBI designed to supplement employment income during a transition is different from a UBI designed to replace employment income permanently for a significant fraction of the population.
The funding mechanism matters too. If autonomous businesses generate enormous profits but employ no one, how is the UBI funded? Conventional income taxes do not work because there is no income to tax (the business has no employees). Corporate taxes on AI entities, “robot taxes,” or revenue-sharing models would be needed – all politically contested and technically complex.
Other proposals go beyond UBI:
- Universal basic ownership. Rather than cash payments, citizens receive equity stakes in autonomous businesses, sharing in the profits directly [8].
- Shortened work weeks. Rather than eliminating jobs, autonomous businesses enable dramatic reductions in working hours while maintaining output.
- Public autonomous businesses. Rather than private entities, autonomous businesses are operated as public utilities, with profits flowing to government revenue.
Each of these proposals has merit. None has been tested at scale. The honest assessment is that society has not figured out how to distribute the gains from autonomous businesses equitably, and the window for figuring it out may be shorter than we think.
The Urgency
What makes the labor displacement challenge from autonomous businesses urgent is not its magnitude – many transitions have been larger – but its speed. Previous waves of automation unfolded over decades, giving labor markets, educational institutions, and social safety nets time to adapt. Autonomous businesses could scale much faster, because the marginal cost of deploying an additional AI manager is approximately zero.
A company that discovers an effective autonomous business model can replicate it across every market it operates in within months. Competitors that do not follow will be at a severe cost disadvantage. The competitive pressure to adopt autonomous operation is intense and the timeline for adaptation is compressed.
This is not an argument against autonomous businesses. It is an argument for urgency in developing the economic institutions, social safety nets, and political frameworks needed to manage the transition. The technology is moving. The institutions need to keep pace.
References
[1] McKinsey Global Institute, “A Future That Works: Automation, Employment, and Productivity,” January 2017.
[2] McKinsey Global Institute, “The Economic Potential of Generative AI: The Next Productivity Frontier,” June 2023.
[3] World Economic Forum, “The Future of Jobs Report 2025,” January 2025.
[4] SPAWN (Screenwriters, Producers, Agents, Writers Network), various publications on the Automated Managerial Class, 2023-2025.
[5] Brian Merchant, Blood in the Machine: The Origins of the Rebellion Against Big Tech (Little, Brown, 2023).
[6] Annie Lowrey, Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World (Crown, 2018).
[7] Olli Kangas et al., “The Basic Income Experiment 2017-2018 in Finland: Preliminary Results,” Ministry of Social Affairs and Health, Finland, 2019.
[8] Matt Bruenig, “Social Wealth Fund for America,” People’s Policy Project, 2018.