Image from the Charlie Chaplin's "Modern Times " (1936) a movie depicting assembly lines and the enslaving of man by machines. In this scene Charlie Chaplin is shown swallowed by the machine..
Breaking The Pattern Of Technological Employment
While massive labor displacement due to AI and robotic systems would likely not be as immediately catastrophic as nuclear war or a global pandemic, it will lead to dramatic societal effects if not properly managed.
The development of advanced AI alone or in synergistic combination with AI-enabled humanoid technologies ("Advanced AI Systems") is inevitable. It represents a fundamental break from historical patterns of technological labor unemployment and reemployment. Workers should not be promised a bright future unless adequate opportunities are identified that will provide full employment for those able and willing to work. Or unless we are prepared to provide the massive subsistence resources to support the overwhelming majority of what would otherwise have been gainfully employed humans.
In the near term, significant human labor displacement is more likely to occur through targeted development of specific robotic and AI capabilities rather than waiting for the development of fully humanoid robots with human-like general intelligence (AGI). Human labor displacement outcomes do not require AI and AIdroids with AGI or artificial superintelligence (ASI) level capabilities. The more immediate and practical impact on human employment is likely to come from the continued advancement and integration of specialized robotic systems and narrow AI across various industries.
Presently humanoid robots are expensive to develop and produce. Power consumption is a significant challenge for mobile robots. Fine motor skills and human-like dexterity remain challenging. True autonomy and human-like decision making are still limited. Many are still fragile and require controlled environments. For widespread adoption, manufacturing costs must decrease significantly and return on investment must justify replacing human workers.
However, Elon Musk has recently claimed that One million Optimus humanoid robots will be produced by 2030. YouTube: Elon Musk's Bold Claim - 1 Million Optimus Robots by 2030 (2024),
Looking at current technological trends, within 10-15 years, AI and limited-function emerging robotic capabilities will displace 40% of human job functions. Based on current technological trends and expert assessments, predictions for humanoid robots with human-like mobility and dexterity capable of displacing most human labor typically are currently in the range of 15-30 years from now. When months ago predictions were in the range of 30-50 years from now.
Thus, the potential widespread human labor displacement is likely to precede the deployment of AGI or ASI level capabilities. When in combination with advanced AIdroids, these will cause the collapse and displacement of human labor.
"On a recent episode of "The Diary of a CEO" podcast, University of Louisville Computer Science Professor Roman Yampolskiy warned that AI could cause ‘99%’ of all workers to be unemployed by 2030. Yampolskiy said that artificial general intelligence systems (AGI) that are as capable as humans would likely be developed by 2027, leading to a labor market collapse three years later. He predicted that AI would provide 'trillions of dollars' of 'free labor,' giving employers a better option for their employment needs."…
"Yampolskiy’s predictions match the forecasts made by other AI experts. Geoffrey Hinton, known as the ‘Godfather of AI’ due to his pioneering work in the subject, stated in June that AI is going to 'replace everybody' in white collar jobs. He challenged the idea that AI would create new jobs, pointing out that if AI automates tasks, there would be no jobs for people to do."…
"Meanwhile, in May, Anthropic CEO Dario Amodei "stated" that AI would eliminate half of all entry-level, white-collar jobs within the next one to five years, causing unemployment to reach a high of 20%." "Entrepreneur " 2025-09-04.
The deployment of AGI/ASI level AI systems in synergistic combination with AIdroids will completely displace human labor in every conceivable task or service, both physical and cognitive. Not only would AIdroids be superior in performance, but they would also be more economically efficient. This makes human labor uncompetitive in a free market. As a rough indicator, the average cost of educating a child from pre-K through college graduation at public US institutions is roughly $250,000 to $300,000 in total, as of 2024. This implies a future where human labor, in any form, becomes economically obsolete. Such a scenario would fundamentally challenge current economic systems, which are largely based on human labor and consumption.
The object of such AGI/ASI level AI systems is not to make humans more productive. Rather the object is to render obsolete whatever remains of human productivity.
The Misattribution of Productivity Gains
Arguments for the resiliency of human labor in the face of technological progress are often associated with the idea that human productivity increases with technology. This historical narrative of increasing human productivity represents a uniquely consequential misattribution in economic thought. The vast majority of what we label as human productivity improvements actually represents the productive capacity of capital equipment itself.
Consider the modern farmer with a GPS-guided tractor versus their historical counterpart with an ox-drawn plow. While the contemporary farmer has indeed developed new skills in operating software and navigation systems, these skills do not account for the massive productivity differential. The predominant productivity increase derives purely from the productive capacity of the capital equipment itself.
The business language surrounding technological investment systematically obscures this reality. Return on investment calculations are invariably presented in terms of "productivity improvements" when they are actually calculating a much simpler equation: (Cost of Human Labor Eliminated) - (Cost of Machine + Maintenance). Business terminology - "efficiency gains", "performance enhancement", "streamlined operations" - consistently masks the fundamental reality of labor reduction. The marketing of AI systems particularly exemplifies this tendency. Vendors claim their systems will "make customer service representatives 300% more productive" rather than stating the reality: "our system will eliminate 75% of your customer service positions." This deliberate obscuring of labor reduction behind productivity language reinforces the broader pattern of misattributing capital productivity to human capability.
Modern manufacturing crystallizes this pattern. The claim that today's factory worker is "more productive" than their counterpart from fifty years ago primarily describes the output of increasingly sophisticated robotic machinery and automation systems. While workers have developed new skills in machine operation and monitoring, these skills represent a diminishing percentage of the total productive output. Tellingly, our shift toward speaking of "enterprise productivity" rather than "worker productivity" reveals an unconscious acknowledgment of this dehumanization process.
This framing helps explain why advanced AI systems represent a genuine discontinuity rather than just another step in technological progress. Previous transitions maintained the illusion of human centrality by attributing the productivity of capital to its human operators. The wide proliferation of advanced AI systems breaks this pattern not just by displacing human labor but by making the historical misattribution of productivity unsustainable. The economic implications are profound, as this completes the shift of all productive value to the owners of advanced AI systems. This leaves human labor not just less valued, but ultimately irrelevant to the productive process.
Conventional approaches to technological unemployment and reemployment are fundamentally misguided because they fail to recognize how productivity gains have actually functioned throughout industrial history. This understanding is crucial for developing appropriate responses to the challenges Advanced AI Systems present.
Capital productivity has increased exponentially while human productivity has remained essentially flat. The reality is that machines became more productive while humans became more dependent.
The Historical Pattern Will Not Apply
The historical pattern of technological unemployment and reemployment will not apply to the widespread deployment of Advanced AI Systems. Such systems represent a historically qualitatively different velocity and type of technological advancement. They are capable of replacing human labor across all sectors of the economy through comprehensive replication of human cognitive and physical capabilities.
This qualitative difference is evidenced by our current inability to articulate concrete ways the overwhelming majority of humans would meaningfully participate in such an AI economy. This is not due to limited imagination. It is because the technology's unprecedented defining characteristic is its potential to eliminate the necessity of human involvement.
Unlike previous technological revolutions, where new roles for human labor were visible even in early stages, Advanced AI Systems leave no clear path for large scale human economic participation. In due course, the deployment of Advanced AI Systems will not render human labor more productive. Rather, it will render human labor economically obsolete.
While in the early stages of Advanced AI Systems deployment, there will certainly be opportunities for a very limited number of specialists. These opportunities do not overcome the forthcoming widespread labor displacement. The opacity around future employment signals a genuine absence of necessary human economic roles rather than mere predictive uncertainty.
Unlike previous technological revolutions where new machinery and systems inherently created visible chains of human involvement in their production, maintenance, and operation, advanced AI systems will in due course eliminate the need for human participation across the entire value chain. This includes next-generation Advanced AI Systems' own advancement and reproduction.
AI Labor Displacement Trajectory
Recent analysis of AI's impact on software development reveals significant productivity gains across multiple development activities. Code generation and autocompletion, bug detection and fixing, documentation generation, test case generation, code review, and DevOps activities. When weighted by typical time allocation and accounting for various factors such as team expertise, project complexity, integration costs, and organizational differences, the data indicates an overall productivity improvement of 20-35%. This estimate reflects real-world observations rather than theoretical possibilities.
As Theodore Kaczynski presciently observed in "Anti-Tech Revolution: Why and How" (2016):
"The techies won't be able to 'shape the advances' of technology, guide the course of technological progress, or exclude the intense competition that will eliminate nearly all techies in short order." (Kaczynski "Anti-Tech Revolution: Why and How" (2016) page 73-74).
Smaller AI companies are developing highly specialized, task-specific AI models targeting discrete job functions. These focused AI systems effectively replicate and replace specific human roles: a medical office receptionist handling appointments and insurance verification, a repair service AI managing scheduling and dispatch, or a customer service AI handling routine inquiries and complaints. This pattern of targeted displacement allows for incremental adoption without requiring massive infrastructure changes, making the business case compelling even for small and medium-sized businesses.
The advantages of AI voice-enabled customer service over current systems are substantial and immediate. Where traditional systems force customers through rigid menu trees, requiring them to listen to entire option lists and frequently repeat information, AI systems enable natural conversation where users simply state their problems in their own words. The AI can adaptively ask clarifying questions based on responses and maintains context throughout the interaction without requiring repetition at different stages.
Current professions thought to be relatively immune to automation are already seeing their capabilities matched or exceeded by AI systems. In surgery, the progression is particularly telling. First, robotic arms became the primary contact point with patients, with human surgeons controlling them remotely. The next step, already underway, is replacing human control with AI-driven systems using advanced visual recognition and precision control. This eliminates the need for human surgical expertise entirely.
In a 2025-04-27 post on X, Elon Musk declared that:
"Robots will surpass good human surgeons within a few years and the best human surgeons within ~5 years. Neuralink had to use a robot for the brain-computer electrode insertion, as it was impossible for a human to achieve the required speed and precision."
"Robots Can Now Help With Surgery - And They’re Actually Good At It" "Medtronic tested its Hugo robot in 137 real surgeries — fixing prostates, kidneys, and bladders — and the results were better than doctors expected. Complication rates were super low: just 3.7% for prostate surgeries, 1.9% for kidney surgeries, and 17.9% for bladder surgeries, all beating safety goals from years of research. The robot got a 98.5% success rate, way above the 85% goal - meaning it didn't just pass the test, it basically set the curve. Out of 137 surgeries, only 2 needed to switch back to regular surgery - 1 because of a robot glitch, and 1 because of a tricky patient case." Mario Nawfal X-post Source: RTTNews
Beyond the operating room, AI systems are being enhanced to match or exceed human capabilities in diagnosis and treatment planning. Dr. Jonathan Reisman acknowledges in his essay “I'm a Doctor. ChatGPT's Bedside Manner Is Better Than Mine" (The New York Times, 2024-10-05) that AI systems like ChatGPT have dramatically undermined physicians' job security, excelling not only in technical medical aspects like diagnosis and treatment planning but also - perhaps more surprisingly - in patient communication. In a revealing study, AI-generated responses were rated as both more empathetic and higher quality than those from human doctors.
In the legal profession, current language models already possess the capability to provide comprehensive legal services. The primary barrier is LLM providers' current strategic choice to avoid potential legal liabilities rather than any technological limitation. The logical endpoint will be fully automated legal proceedings where AI systems represent both plaintiffs and defendants, presenting cases to an AI judge who could process the entire body of relevant law and precedent in minutes rather than months.
The creative industries are similarly vulnerable. The rapid advancement in AI-generated content, from writing to visual art to music composition, demonstrates that even these supposedly human-centric domains are not immune. The entertainment industry's response, as evidenced by recent SAG-AFTRA legislation regarding AI replicas of performers, exemplifies a broader pattern of professional resistance through organized labor.
According to the U.S. Bureau of Labor Statistics (BLS), there are over 20 million government employees at federal, state, and local levels. McKinsey & Company and other research organizations have suggested that 10-20% of government jobs could be automated in the coming decades. Current government service interactions often involve long wait times, multiple transfers between departments, repetitive form-filling, and frequent confusion about proper procedures. An AI system will transform this experience by providing instant, accurate information about complex regulations, guiding citizens through required documentation and ensuring consistent interpretation of rules across all interactions.
The velocity of change compounds this problem. Previous transitions unfolded over decades or generations. AI development operates on exponentially accelerating timelines. GPT-3 to GPT-4 took one year. Each iteration dramatically expands capabilities. A worker spending four years obtaining a degree may find their intended profession automated before graduation. The mismatch between human adaptation speeds and AI advancement rates transforms temporary displacement into permanent exclusion.
The Cascade Effect and Network Collapse
Displacement cascades through economic networks in ways current models underestimate. When AI replaces accountants, the impact extends far beyond accounting firms. Educational institutions lose students and faculty. Professional certification bodies lose revenue. Office real estate values collapse. Restaurant and service businesses near office centers fail. Each primary displacement triggers secondary and tertiary waves of job losses.
Consider a specific cascade scenario. A law firm adopts AI for document review, eliminating 100 paralegal positions. Those paralegals represent $5 million in annual consumer spending. Local businesses lose that revenue and reduce their own workforce. The paralegal training program at the community college closes, eliminating teaching positions. The ripple effects multiply through the economy. The initial displacement of 100 jobs ultimately eliminates 300-400 positions across various sectors.
The network effect accelerates as AI adoption spreads. Industries cannot remain partially automated when competitors fully automate. A single company maintaining human workers faces insurmountable cost disadvantages. The binary choice becomes automate or exit. This creates tipping points where entire industries transform almost simultaneously. The gradual displacement narrative fails to capture these sudden phase transitions.
The trajectory is clear. AI advancement will continue to displace human labor across increasingly diverse sectors. From routine tasks to knowledge work to creative endeavors. The economic incentives for this displacement are too compelling to resist. The technological progression shows no signs of slowing. What remains uncertain is not whether this transformation will occur, but how society will adapt to a world where human labor becomes increasingly superfluous to economic production.
Impediments to Human-AI Collaborative Employment
Proposals that humans will find substantial employment through collaboration with AI systems fail to address four critical structural obstacles:
The scale problem: Even if long-term human-AI collaborative roles exist, they will likely represent an insignificant fraction of jobs eliminated. This creates a massive employment deficit.
The capability ceiling dilemma: Many proposed collaborative frameworks assume permanent human advantages in creativity, judgment, or emotional intelligence that advanced AI systems may eventually match or exceed.
The economic incentive reality: In competitive markets, the cost differential between human-AI collaboration versus fully automated solutions will continuously pressure organizations to eliminate human components from workflows.
The distribution challenge: The specialized skills required for remaining human roles in an AI economy may be inaccessible to most displaced workers due to aptitude, educational barriers, or geographic limitations. This potentially creates a small class of employable specialists while leaving the majority without viable economic participation options.
Many organizations currently maintain human involvement in AI-driven processes for appearance rather than necessity. This "collaboration theater" serves to maintain customer confidence, satisfy regulatory requirements, or avoid labor disputes. However, the human role often involves merely rubber-stamping AI decisions or providing a human face for AI-generated content.
Consider radiologists "collaborating" with diagnostic AI. The AI system identifies potential concerns with 99.9% accuracy. The radiologist reviews and confirms the AI's findings. This appears collaborative but the human adds minimal value. The hospital maintains this arrangement for liability reasons and patient comfort, not medical necessity. As legal frameworks adapt and patients accept AI diagnosis, the human role disappears entirely.
The economic pressure to eliminate collaboration theater intensifies over time. Each quarter, executives face questions about why they maintain human costs when competitors achieve better results with pure AI systems. The transition from human-AI collaboration to full automation becomes a when, not an if, proposition. Current collaborative arrangements represent temporary waypoints, not sustainable employment models.
Wealth Will Flow to AI
The misattribution thesis provides both a clearer understanding of historical technological change and a more accurate framework for anticipating the profound wealth inequality implications of Advanced AI Systems. Much of the past labor productivity increases have been actually the increasing productivity of capital rather than the productivity of labor. The productive value of capital rather than human labor explains why wealth has disproportionately flowed to the owners of capital. When considering that Advanced AI Systems are productive capital in the absence of human labor, then Advanced AI Systems capital will be finally properly credited with productivity increases. They will be associated with an historically unparalleled concentration of wealth by its owners.
Throughout history, there have been numerous instances of class conflict when economic disparities became pronounced. From the French Revolution to the Russian Revolution, from labor movements to the Arab Spring, economic disparity has consistently catalyzed social upheaval. The AI-driven economic transformation will dramatically amplify this concentration to unprecedented levels. Past claimed benefits of wealth creation, such as job creation and innovation, will be muted in the context of mass labor displacement. The traditional argument that concentrated wealth creates jobs becomes meaningless when AI systems, not humans, perform all economically valuable work.
The dynamics of AI ownership create a positive feedback loop approaching wealth concentration singularity. Initial AI advantages generate capital for better AI development. Better AI captures more market share. Greater market share funds superior AI. This cycle accelerates until perhaps a handful of entities control all economically productive AI systems.
Traditional antitrust frameworks cannot address this concentration. Market dominance through superior technology doesn't violate current competition laws. An AI system that simply performs better than any alternative represents natural monopoly, not illegal restraint of trade. Regulatory bodies designed for industrial age competition cannot conceptualize, much less constrain, algorithmic dominance.
The international dimension amplifies concentration. AI development requires massive computational resources concentrated in few locations. Countries without advanced semiconductor fabrication and hyperscale data centers cannot compete. Perhaps five nations possess the infrastructure for frontier AI development. Within those nations, perhaps two or three companies control the key resources. The entire global economy could depend on decisions made by fewer people than serve on a single corporate board.
Given the perspective that the historical examples suggest, unfortunately the outcomes are social unrest and collapse. Importantly, the probable failure of existing institutions to proactively address the potential impacts of AI systems on the human job markets and to manage the required transition will lead to a scenario in which advanced societies violently collapse.
Technological Progress as Religion
When initially presented with the thesis that historical technological unemployment and reemployment experiences will not apply to Advanced AI Systems, three leading AI systems - Perplexity, Deepseek, and ChatGPT - each defaulted to defending the applicability of traditional patterns of technological unemployment and reemployment. Their responses reflexively cited historical examples of how technological disruption eventually created new jobs. They suggested that Advanced AI Systems would follow the same pattern. This automatic defense of technological continuity reveals how deeply embedded the notion of perpetual technological progress and adaptation has become in contemporary thought.
Counter-arguments in current literature claim this analysis underestimates human adaptability, fails to account for unimaginable future jobs, and oversimplifies human-technology relationships. Yet these very counter-arguments demonstrate how deeply embedded quasi-religious faith in technological continuity has become.
The appeal to "human adaptability" represents circular reasoning disguised as analysis. Humans will adapt because humans have always adapted. This article of faith ignores Advanced AI Systems' defining characteristic—its ability to fill any new niche itself through its capacity for general problem-solving and continuous self-improvement. Similarly, invoking "roles we cannot yet envision" exemplifies faith-based rather than logical thinking. The central insight is that our inability to articulate future human economic roles sufficient to overcome widespread human labor displacement stems not from limited imagination but from Advanced AI Systems' comprehensive capability replacement.
With previous technological shifts, such as the Industrial Revolution's introduction of cotton-spinning machinery, one could readily identify both labor destruction (hand spinners and domestic weavers) and labor creation (machine builders and repairers, factory workers, machine operators). The direct connection between the new technology and new human involvement was clear. By contrast, the typical refrain now, with respect to Advanced AI Systems, is that future jobs may be currently unimaginable. This failure should suggest that the introduction of Advanced AI Systems is qualitatively different from previous technological revolutions.
While the building of a first-generation commercially widely-available AIdroid may require human labor, the building of a subsequent generation AIdroid will probably not.
The photograph presumably shows two Tesla Optimus working on a third unit. - YouTube: Elon Musk's Bold Claim - 1 Million Optimus Robots by 2030 (2024).
Technological optimism has become institutionalized in ways that prevent honest assessment of AI's impact. Universities depend on the narrative that education prepares students for future careers. Governments promise retraining programs will address displacement. Corporations claim AI will "augment" rather than replace workers. These institutions cannot acknowledge the fundamental obsolescence of human labor without undermining their own legitimacy.
The Siren's Song of AI Progress
Just as the sirens of mythology lured sailors with promises of transcendent beauty only to lead them to destruction, the promises of AI-accelerated breakthroughs and enhanced human capabilities serve as a modern Siren's Song that distracts from examining the eventual human costs of AI developments.
In an Advanced AI Systems-driven economy where human labor is largely obsolete, access to the "benefits" AI's developers promise would likely become even more restricted, not less. Consider current trends in pharmaceutical research, where AI systems are already accelerating drug discovery and development. While this promises breakthrough treatments, the economic model still requires massive returns on investment. This leads to drug prices that many cannot afford even with current income levels.
AI's promise of democratized expertise ignores how eliminating professional career paths reduces economic mobility and concentrates wealth among AI system owners. The promise of cheaper services becomes meaningless when the means to pay for them has been eliminated.
The timeline illusion represents another aspect of AI's Siren;s Song. The comforting belief that this transition will occur gradually enough to allow for societal adaptation. This perspective fails to recognize that unprecedented investments from both private and governmental actors are accelerating AI development at a pace that may overwhelm adaptive capacity. Preparing adequate economic and social systems for widespread labor displacement requires anticipatory action rather than reactive responses.
When considering what might constitute "valuable human work" in an Advanced AI Systems context, we confront a fundamental reality. Work has value in a market economy because someone is willing to pay for its output. If Advanced AI Systems can produce superior outputs at lower costs across virtually all domains, what economic mechanism would sustain payment for inferior human alternatives?
The Siren's Song promises that AI will create such abundance that economic displacement becomes irrelevant. This narrative suggests that when AI makes everything virtually free, unemployment won't matter. This fundamentally misunderstands how capitalism functions. Abundance without purchasing power is meaningless. Free products require consumers with at least minimal resources to access them.
Consider housing. AI and robotics could theoretically build homes for marginal cost. But land remains scarce. Property rights remain enforced. Zoning laws remain restrictive. The displaced masses cannot afford even "virtually free" housing because they lack any income for taxes, utilities, or maintenance. The abundance remains theoretical while scarcity persists through institutional structures that AI doesn't eliminate.
The belief that we can consciously steer how we deploy Advanced AI Systems represents perhaps the most dangerous fallacy plaguing current policy discussions. The competitive dynamics between corporations and nations create overwhelming incentives to pursue AI advancement regardless of societal consequences.
Large Language Models (LLMs) such as ChatGPT, Claude, Perplexity are training wheels for humans to learn to accept and welcome riding with advanced AI systems and AIdroids.
Union and Professional Resistance
The increasing activism of unions and professional associations across various sectors represents a significant but temporary barrier to AI displacement. These organizations are adopting increasingly aggressive strategies to preserve human labor. They demand contractual guarantees against AI replacement. They advocate for regulatory restrictions on AI deployment. They seek to establish protected domains of exclusively human work. However, these efforts face three fundamental challenges. First, they can only delay rather than prevent the eventual economic advantages of AI adoption. Second, they risk accelerating their own obsolescence by driving companies to develop fully automated alternatives that circumvent human labor entirely. Third, a large pool of unemployed humans will undermine a union's bargaining position.
On 2024-10-01, the International Longshoremen's Association union began and subsequently suspended a port strike on the U.S. East Coast and Gulf of Mexico against the U.S. Maritime Alliance (USMX).
"For months, the union has threatened to shut down the 36 ports it covers if employers like container ship operator Maersk and its APM Terminals North America do not deliver significant wage increases and stop terminal automation projects." Newsmax: Union: East Coast Port Strike to Start Tuesday.
An International Longshoremen's Association statement on its website states, in part, that:
"the ILA is steadfastly against any form of automation-full or semi-that replaces jobs or historical work functions. We will not accept the loss of work and livelihood for our members due to automation. Our position is clear: the preservation of jobs and historical work functions is non-negotiable." ilaunion,org: ILA Responds To USMXS retrieved 2024-10-04 emphasis added.
The California state Senate passed two bills in August of 2024: AB 1836, which restricts the usage of AI to create digital replicas of dead performers without the consent of their estates, and AB 2602, which increases consent requirements for living performers for AI replicas. The actors guild, SAG-AFTRA released the following statement:
"AB 1836 is another win in SAG-AFTRA's ongoing strategy of enhancing performer protections in a world of generative artificial intelligence. The passing of this bill, along with AB 2602 earlier this week, builds on our mosaic of protections in law and contract." Sagaftra: Re Ca Bill 1836 retrieved 2024-09-07.
Within 10-20 years, major theatrical released films may only use AI generated performers indistinguishable from human actors in a fully digital production. Once the systems, workflows, and initial improvements in AI systems have been realized, there should be substantial savings in production costs which currently average around $65 million per theatrically released movie. The cost savings would be maximized in movie series such as the 007 movie series. Claude (2024-09-09) estimates production costs savings of 46%-65% of an original $65 million budget, and a 62-69% saving for subsequent films in the series.
While trained AI performers could be made to age and return to youth with a few keystrokes, the promotional costs invested in an AI performer would not age. Of course, in due course ASI personas will probably "negotiate" a participation fee threatening to create and produce their own movies.
Clearly, SAG-AFTRA members must realize that, paraphrasing lines from the 1927 movie the Jazz Singer: "Wait a minute, wait a minute, you ain't heard nothing yet! Wait a minute, I tell yer, you ain't seen nothing!"
Legislative attempts to protect human employment reveal the fundamental mismatch between legal frameworks and technological capability. Laws mandating human involvement in specific roles simply increase costs until businesses find workarounds or relocate. Professional licensing represents another failing barrier. When AI demonstrably exceeds human performance, maintaining human-only licensing becomes indefensible. The pressure from better, cheaper AI services will eventually break these regulatory walls.
The Challenge to Human Rights
The United Nations General Assembly's Universal Declaration of Human Rights sets out fundamental human rights:
"The Universal Declaration of Human Rights (UDHR) is a milestone document in the history of human rights. Drafted by representatives with different legal and cultural backgrounds from all regions of the world, the Declaration was proclaimed by the United Nations General Assembly in Paris on 10 December 1948 (General Assembly resolution 217 A) as a common standard of achievements for all peoples and all nations. It sets out, for the first time, fundamental human rights to be universally protected and it has been translated into over 500 languages. The UDHR is widely recognized as having inspired, and paved the way for, the adoption of more than seventy human rights treaties, applied today on a permanent basis at global and regional levels (all containing references to it in their preambles)." United Nations: The Universal Declaration of Human Rights
Article 23 ¶ 1 states:
"Everyone has the right to work, to free choice of employment, to just and favourable conditions of work and to protection against unemployment."
Article 25 ¶ 1 states:
"Everyone has the right to a standard of living adequate for the health and well-being of himself and of his family, including food, clothing, housing and medical care and necessary social services, and the right to security in the event of unemployment, sickness, disability, widowhood, old age or other lack of livelihood in circumstances beyond his control."
Article 30 (the last article 0) states:
"Nothing in this Declaration may be interpreted as implying for any State, group or person any right to engage in any activity or to perform any act aimed at the destruction of any of the rights and freedoms set forth herein."
Clearly some material edits to the UDHR are required to address the major labor dislocations that will surely result as technologies and intelligent systems continue to advance.
Impact of Human Economic Irrelevance
While this analysis has focused primarily on the human labor displacement implications of Advanced AI Systems, the challenges ahead may require more fundamental reconsiderations of human purpose and flourishing. Society may need to explore alternative frameworks for meaningful human existence in a post-labor economy.
Beyond economic impacts, the psychological and social effects of this displacement could be profound. Work has traditionally been a source of meaning, identity, and social connection for many people. The feeling of being "replaceable" by machines could lead to a loss of self-worth and purpose on both individual and societal levels. As AI systems and robots take over tasks requiring creativity or emotional intelligence, we may see a devaluation of uniquely human qualities. This potentially erodes human dignity and leads to a commodification of human labor.
The integration of AI-enabled humanoid robots in workplaces could blur the lines between human and machine capabilities. This potentially reduces human-to-human interaction and leads to increased social isolation. Furthermore, the rise of algorithmic management systems could reduce human workers' agency and autonomy, as AI systems make decisions about employment and performance.
Julian De Freitas in a WSJ article titled: "AI Wants To Make You Less Lonely. Does It Work?" found that:
"Only those who interacted with a human or the AI companion - not those who did nothing or interacted with YouTube - experienced a reduction in loneliness levels. Their results were roughly the same: Contact with people brought a 19-percentage point drop in loneliness levels, and 20 percentage points for a companion. WSJ: "AI Wants To Make You Less Lonely. Does It Work?" 2024-09-23. Page R11.
The transition from economically productive beings to economically irrelevant ones represents a species-level psychological crisis without precedent. Humans evolved with deep psychological needs for contribution, status through achievement, and social recognition of value. Removing economic productivity eliminates the primary mechanism through which modern humans fulfill these needs.
Early retirement studies provide disturbing previews. Even voluntary retirement with adequate resources often leads to depression, cognitive decline, and increased mortality. The psychological impact of involuntary, permanent economic irrelevance across entire populations would be orders of magnitude more severe. No society has successfully maintained mental health in populations without productive purpose.
The proposed alternatives to work-based meaning appear inadequate. Lifelong learning becomes hollow when knowledge has no application. Creative expression loses meaning when AI surpasses human creativity. Community engagement atrophies when communities lack shared productive goals. The suggested replacements for work are essentially elaborate distractions from purposelessness rather than genuine sources of meaning.
An initiative in San Francisco to provide harm reduction services to homeless individuals during the coronavirus pandemic offered free substances like alcohol, marijuana, and methadone in designated hotel rooms as a means to reduce the spread of COVID-19. The San Francisco Department of Public Health confirmed the report, explaining:
"These harm reduction-based practices, which are not unique to San Francisco, and are not paid for with taxpayer money, help guests successfully complete isolation and quarantine and have significant individual and public health benefits in the COVID-19 pandemic." SFDPH May 5, 2020.
Given the uncertainty associated with AI alignment and global labor and market implications, and placing aside physical existential risks, there is a likelihood that multiple remaining possible outcomes from widespread human labor-displacement will lead to a subsistence existence and dehumanization of humanity.
If, as many expect, the deployment of Advanced AI Systems is a game changer, then many of the rules will necessarily change.
Conclusion
Advanced AI represents not an incremental step but a rupture in history. It dissolves the link between labor and survival, concentrates wealth into singularity, and threatens human meaning itself. The evidence points to a future where human labor becomes economically obsolete across virtually all sectors, with wealth concentration among AI owners reaching levels without historical parallel.
Current responses—union resistance, legislative protection, retraining programs—represent temporary barriers at best. They cannot overcome the fundamental economic logic driving AI adoption. What emerges is not the leisure society promised by AI's proponents but a world where the vast majority of humanity faces economic abandonment. The window for meaningful response narrows with each passing month. The transformation has already begun.