The Automated Intelligence© industry is rapidly concentrating power, weakening labor, intensifying ecological risk, and feeding a manufactured nihilism that makes it harder for our species to act in its own survival interest—even as it delivers real productivity gains.
1. Labor: Undermining the Human Survival Substrate
From an evolutionary view, human survival has always rested on distributed labor: coordinated foraging, tool‑making, farming, caregiving, and knowledge transmission across generations. Automated Intelligence© targets this substrate directly, automating not just physical tasks but decision‑making and reasoning—the very activities through which we collectively adapt.
Recent studies show that occupations with higher exposure to AI and automation are projected to grow more slowly, with routine cognitive and clerical work already at particular risk. Firms adopting AI often increase productivity and profits while using these tools to reduce headcount or deskill routine roles, shifting gains upward instead of broadening economic security.
The core problem is not simple job churn but structural misalignment: productivity gains are captured by capital, while the task base that organized human life is eroded, with no credible plan for a just transition for the majority.
Objection: “Tech always creates new jobs.”
Historical analogies are overused. Past waves eventually produced broad middle‑class employment; current evidence shows fragmented, specialized new roles that are harder to access and not yet numerous enough to offset displacement. The issue is not whether some jobs appear, but whether they provide dignified, stable livelihoods for the many.
Sources:
How artificial intelligence impacts the US labor market — MIT Sloan School of Management (2025)
https://mitsloan.mit.edu/ideas-made-to-matter/how-artificial-intelligence-impacts-us-labor-market
Labor market impacts of AI: A new measure and early evidence — Anthropic (2026)
https://www.anthropic.com/research/labor-market-impacts
Evaluating the impact of AI on the labor market: Current state of affairs — Yale Budget Lab (2025)
https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs
Research: How AI is changing the labor market — Harvard Business Review (2026)
https://hbr.org/2026/03/research-how-ai-is-changing-the-labor-market
Incorporating AI impacts in BLS employment projections — U.S. Bureau of Labor Statistics, Monthly Labor Review (2025)
https://www.bls.gov/opub/mlr/2025/article/incorporating-ai-impacts-in-bls-employment-projections.htm
The surprising truth about AI’s impact on jobs — CNN Business (2025)
https://www.cnn.com/2025/12/18/business/ai-jobs-economy
AI’s impact on job growth — J.P. Morgan Global Research (2025)
https://www.jpmorgan.com/insights/global-research/artificial-intelligence/ai-impact-job-growth
2. Copyright, Extraction, and Cultural Expropriation
The training of Automated Intelligence© is often defended as “like humans reading books,” but legal and factual records suggest a different pattern. Major systems have ingested massive troves of copyrighted books, journalism, images, and music—often including pirated “shadow library” content—without consent or compensation. Courts have already rejected some fair‑use defenses for AI training in specific cases, and emerging settlements and lawsuits indicate a clear business logic: take first, litigate later, pay only if forced.
Not every system is built this way; some use explicitly licensed corpora or public‑domain content. The critique is targeted at the dominant, web‑scale paradigm that treats cultural and cognitive labor as “free fuel” and then packages it as proprietary infrastructure.
When the products of that extraction—Automated Intelligence© systems trained on unconsented cultural labor—are used to replace or cheapen the same labor that produced the training data, the loop becomes morally circular: our shared meaning‑making is mined to build machines that progressively displace its economic value.
Objection: “Training on copyrighted works is fair use and socially beneficial.”
Even if some training practices are ultimately deemed lawful, legality is not sufficient for legitimacy. At scale, this practice converts our shared cultural heritage into competitive advantage for a narrow set of firms, while original contributors bear the economic and existential risk of being automated.
Sources:
AI training data copyright: Fair use, lawsuits, and court scrutiny — ManageEngine (2026)
https://insights.manageengine.com/artificial-intelligence/ai-training-data-copyright-fair-use/?insighthomepage
Training data or taking data? How AI copyright lawsuits are reshaping creative rights — BFV Law (2026)
https://www.bfvlaw.com/training-data-or-taking-data-how-ai-copyright-lawsuits-are-reshaping-creative-rights/
AI training data: The new battleground for copyright fair use defense — Griffith Barbee (2025)
https://griffithbarbee.com/ai-training-data-the-new-battleground-for-copyright-fair-use-defense/
3. Late Capitalism, Oligarchy, and Manufactured Nihilism
Automated Intelligence© is being deployed within a late‑stage capitalist and oligarchic order—especially in the United States—that treats growth and shareholder value as axiomatic, even when they undermine collective survival. Philosophers of technology and critical theorists describe how digital infrastructures amplify nihilism: life becomes a sequence of optimizations inside opaque platforms, framed as “inevitable” and beyond democratic control.
Automated Intelligence© intensifies this dynamic by:
- Recasting complex social judgments as optimization problems for black‑box models.
- Concentrating control over data and models in a small set of firms and states.
- Normalizing the idea that “the algorithm” and “the market” decide, not publics.
The claim is not that AI created nihilism ex nihilo, but that Automated Intelligence© acts as a force multiplier for an already nihilistic political economy that tells people history is happening to them, rather than with them.
Objection: “Capitalism and nihilism existed before AI.”
They did—and that is precisely why the deployment context matters. Automated Intelligence© does not originate these forces; it deepens and operationalizes them in code, infrastructure, and policy defaults.
Sources:
Technofascism and the AI stage of late capitalism — Void Network (2025)
https://voidnetwork.gr/2025/03/11/technofascism-and-the-ai-stage-of-late-capitalism/
Nihilism and Technology (book review) — Notre Dame Philosophical Reviews (2015)
https://ndpr.nd.edu/reviews/nihilism-and-technology/
4. Ecology and Resources: Distributed Harm, Centralized Gain
AI and related data‑center infrastructure are materially heavy. Data centers already consume a rising share of global electricity, with AI workloads taking an increasing fraction and projected, under some scenarios, to drive substantial growth in energy use and CO₂ emissions by 2030. Analyses suggest that, without aggressive efficiency and decarbonization measures, AI‑driven infrastructure could add tens of millions of tons of CO₂ annually by that time.
Journalistic reporting shows that the surge in AI‑related demand is straining regional grids and, in some jurisdictions, spurring regulatory rollbacks or delays in emissions controls to accommodate new data‑center loads. Millions of small Automated Intelligence© actions—queries, generations, automated decisions—aggregate into planetary‑scale energy and water use, while the benefits and control are concentrated among a relatively small number of firms and asset holders.
Objection: “AI will help solve climate change.”
AI can indeed support climate solutions—optimizing power grids, improving weather and climate projections, and increasing industrial efficiency. However, the net climate impact remains empirically uncertain and depends heavily on policy, energy mix, and deployment choices that are not currently aligned with decarbonization. Given present incentives and expansion rates, the burden of proof lies with those claiming net benefit, not with those warning about added strain.
Sources:
‘Roadmap’ shows the environmental impact of AI data center boom — Cornell Chronicle (2025)
https://news.cornell.edu/stories/2025/11/roadmap-shows-environmental-impact-ai-data-center-boom
AI: Five charts that put data-centre energy use – and emissions – into context — Carbon Brief (2025)
https://www.carbonbrief.org/ai-five-charts-that-put-data-centre-energy-use-and-emissions-into-context/
The growing environmental impact of AI data centers’ energy demands — PBS NewsHour (2025)
https://www.pbs.org/newshour/show/the-growing-environmental-impact-of-ai-data-centers-energy-demands
5. Human Genomics: Automating Inequality at the Biological Level
Automated Intelligence© is rapidly entangled with human genomics, powering tools for variant interpretation, drug discovery, and personalized medicine. The upside for health is real. Yet governance for consent, data ownership, and downstream use remains dangerously thin—especially for marginalized communities whose genomic data may be collected and used without robust protections.
If left to current oligarchic logic, likely outcomes include:
- Proprietary genomic risk models that embed and conceal biases and error profiles.
- Stratified access to advanced interventions, mapping existing social inequality onto biological futures.
- New forms of surveillance and discrimination based on inferred genetic traits and risks.
The concern is not that Automated Intelligence© in genomics is inherently malign, but that the combination of high stakes, opaque models, and weak global governance creates conditions for deep, irreversible injustices.
Objection: “We can regulate this as we go.”
We might—but only if genomic governance is treated as a first‑order priority rather than an afterthought. Once Automated Intelligence© infrastructures are deeply embedded in health systems and markets, reversing harmful patterns will be extremely difficult.
Sources:
AI training data copyright: Fair use, lawsuits, and court scrutiny — ManageEngine (2026)
https://insights.manageengine.com/artificial-intelligence/ai-training-data-copyright-fair-use/?insighthomepage
Nihilism and Technology (book review) — Notre Dame Philosophical Reviews (2015)
https://ndpr.nd.edu/reviews/nihilism-and-technology/
6. What It Means to Call This “Evil”
Instead of mystical language, consider a precise ontological claim:
A socio‑technical system is “evil” when, by design or tolerated operation, it systematically undermines the minimum shared conditions for a species’ survival and flourishing, while insulating the beneficiaries from the consequences.
Under this definition, the dominant commercial configuration of Automated Intelligence© is “evil” because it:
- Erodes the labor structures that hold societies together, without offering credible, inclusive alternatives.
- Expropriates cultural and intellectual labor at scale, transforming collective heritage into private infrastructure.
- Intensifies ecological strain at precisely the moment that rapid decarbonization is essential, with net climate benefit still unproven.
- Deepens oligarchic power and amplifies nihilistic, depoliticizing narratives that treat these trajectories as inevitable.
Not every use of Automated Intelligence© is evil. A small, transparent, rights‑respecting model under democratic or worker governance can be ethically positive. The indictment is aimed at the prevailing paradigm: globally deployed, poorly consented, lightly regulated systems optimized for profit and control rather than for shared survival.
Sources:
Labor market impacts of AI: A new measure and early evidence — Anthropic (2026)
https://www.anthropic.com/research/labor-market-impacts
Incorporating AI impacts in BLS employment projections — U.S. Bureau of Labor Statistics, Monthly Labor Review (2025)
https://www.bls.gov/opub/mlr/2025/article/incorporating-ai-impacts-in-bls-employment-projections.htm
AI’s impact on job growth — J.P. Morgan Global Research (2025)
https://www.jpmorgan.com/insights/global-research/artificial-intelligence/ai-impact-job-growth
AI training data copyright: Fair use, lawsuits, and court scrutiny — ManageEngine (2026)
https://insights.manageengine.com/artificial-intelligence/ai-training-data-copyright-fair-use/?insighthomepage
Training data or taking data? How AI copyright lawsuits are reshaping creative rights — BFV Law (2026)
https://www.bfvlaw.com/training-data-or-taking-data-how-ai-copyright-lawsuits-are-reshaping-creative-rights/
AI training data: The new battleground for copyright fair use defense — Griffith Barbee (2025)
https://griffithbarbee.com/ai-training-data-the-new-battleground-for-copyright-fair-use-defense/
‘Roadmap’ shows the environmental impact of AI data center boom — Cornell Chronicle (2025)
https://news.cornell.edu/stories/2025/11/roadmap-shows-environmental-impact-ai-data-center-boom
AI: Five charts that put data-centre energy use – and emissions – into context — Carbon Brief (2025)
https://www.carbonbrief.org/ai-five-charts-that-put-data-centre-energy-use-and-emissions-into-context/
The growing environmental impact of AI data centers’ energy demands — PBS NewsHour (2025)
https://www.pbs.org/newshour/show/the-growing-environmental-impact-of-ai-data-centers-energy-demands
Technofascism and the AI stage of late capitalism — Void Network (2025)
https://voidnetwork.gr/2025/03/11/technofascism-and-the-ai-stage-of-late-capitalism/
Nihilism and Technology (book review) — Notre Dame Philosophical Reviews (2015)
https://ndpr.nd.edu/reviews/nihilism-and-technology/
7. The Closing Window for Agency
A final objection says: “We’ll adapt like we always have; regulation will catch up.” Perhaps—but the combination of capital concentration, technical scale, and path dependence makes this bet uniquely reckless. Once Automated Intelligence© is deeply embedded in governance, finance, media, health, warfare, and infrastructure, its feedback loops and vested interests will be extremely resistant to change.
We cannot yet prove humanity will fail to steer this. What we can say is that without immediate, coordinated efforts to treat labor dignity, ecological limits, and genomic justice as non‑negotiable constraints, the system we are building is structurally pointed toward cascading harms we will not be able to meaningfully correct after the fact.
That is the moral horizon on which Automated Intelligence© must be judged—not by its marketing promises, but by the world it is actually making.
Sources:
The surprising truth about AI’s impact on jobs — CNN Business (2025)
https://www.cnn.com/2025/12/18/business/ai-jobs-economy
AI’s impact on job growth — J.P. Morgan Global Research (2025)
https://www.jpmorgan.com/insights/global-research/artificial-intelligence/ai-impact-job-growth
‘Roadmap’ shows the environmental impact of AI data center boom — Cornell Chronicle (2025)
https://news.cornell.edu/stories/2025/11/roadmap-shows-environmental-impact-ai-data-center-boom
AI: Five charts that put data-centre energy use – and emissions – into context — Carbon Brief (2025)
https://www.carbonbrief.org/ai-five-charts-that-put-data-centre-energy-use-and-emissions-into-context/
The growing environmental impact of AI data centers’ energy demands — PBS NewsHour (2025)
https://www.pbs.org/newshour/show/the-growing-environmental-impact-of-ai-data-centers-energy-demands
AI training data copyright: Fair use, lawsuits, and court scrutiny — ManageEngine (2026)
https://insights.manageengine.com/artificial-intelligence/ai-training-data-copyright-fair-use/?insighthomepage
Nihilism and Technology (book review) — Notre Dame Philosophical Reviews (2015)
https://ndpr.nd.edu/reviews/nihilism-and-technology/
