The Dependency Trap: Artificial Intelligence, Human Cognitive Decline, and the Fragility of an AI-Dependent Society

An interdisciplinary research paper examining the benefits, risks, counterarguments, and feasibility of widespread human dependence on artificial intelligence

What happens when humanity becomes so dependent on artificial intelligence that it forgets how to function without it?

Abstract

Artificial intelligence (AI) is becoming increasingly integrated into education, employment, healthcare, communication, government administration, and personal decision-making. Its capacity to perform complex cognitive tasks offers substantial benefits, including increased productivity, broader access to knowledge, and improved assistance for people with limited educational or professional resources. However, widespread dependence on AI may also create vulnerabilities that extend beyond the technology itself.

This paper examines a hypothetical but plausible long-term scenario in which successive generations delegate increasing amounts of intellectual work to AI systems, potentially reducing their ability to reason independently, verify information, perform essential tasks, and operate without technological assistance. It further considers whether governments and corporations, through ordinary economic and political incentives, could accelerate this dependency without pursuing any coordinated plan to control humanity.

The paper evaluates two competing propositions. The first is that excessive reliance on AI could weaken human cognitive independence, concentrate institutional power, and create severe societal disruption if essential AI services become unavailable. The second is that AI could instead expand human intelligence, improve education, increase resilience, and create new forms of human capability.

A devil’s advocate analysis examines the strongest arguments for and against the scenario. Particular attention is given to the distinction between technological assistance and technological dependence, the uncertain prospect of artificial general intelligence (AGI), the actual role of quantum computing, and the practical feasibility of a large-scale AI failure.

The paper concludes that widespread AI dependence is a credible risk, while the proposed outcome of an entire civilisation becoming intellectually incapable after a sudden global AI shutdown remains speculative. The central question is not whether humanity should accept or reject AI, but whether societies can adopt increasingly capable systems while preserving independent human judgment, practical competence, institutional accountability, and the ability to function when those systems fail.

Keywords: artificial intelligence, AGI, cognitive offloading, technological dependency, human autonomy, critical thinking, systemic risk, digital resilience, education, governance.

1. Introduction

Every major technological advance changes the relationship between human beings and their own abilities. Writing reduced the need to memorise information. Printing expanded access to knowledge. Calculators reduced the need to perform certain calculations mentally. Computers automated complex tasks, while the internet made enormous quantities of information accessible within seconds.

Artificial intelligence introduces a further development: the automation of tasks traditionally associated with human reasoning, communication, creativity, planning, and judgment.

Unlike earlier tools, generative AI systems can produce explanations, write reports, analyse documents, generate software, assist with decisions, and respond conversationally to individual users. As these capabilities expand, people may increasingly rely on AI to perform intellectual work that previously required personal knowledge or sustained effort.

This raises a fundamental question:

What happens when a society becomes so dependent on artificial intelligence that a significant proportion of its population can no longer perform essential intellectual and practical tasks independently?

The concern extends beyond whether AI systems might eventually become more intelligent than humans in particular domains. It concerns the possibility that humans could gradually surrender the skills needed to understand, supervise, challenge, and replace the systems on which they depend.

This paper proposes a hypothetical sequence:

1. Governments and companies encourage widespread AI adoption because of its economic, educational, and administrative benefits.
2. Individuals increasingly delegate intellectual tasks and personal decisions to AI assistants.
3. Educational institutions and workplaces adapt to a world in which AI performs much of the work previously undertaken by people.
4. Successive generations develop fewer independent skills in areas where AI consistently performs those tasks for them.
5. AI systems become deeply embedded in essential infrastructure and institutional decision-making.
6. A major disruption removes access to critical AI services, exposing the extent of society’s dependence.

The sequence is not a prediction that these events will necessarily occur. It is a scenario that can be evaluated against current evidence, technological constraints, economic incentives, and alternative outcomes.

The paper also considers a second possibility: that society becomes so dependent on AI that the technology itself, or the organisations controlling it, acquires disproportionate influence over human behaviour even without a catastrophic shutdown.

The purpose is to assess these possibilities rigorously rather than assume either technological progress or technological collapse is inevitable.

2. Research Questions and Method

This paper addresses five principal research questions.

RQ1: Can widespread AI use weaken human cognitive skills when people repeatedly delegate reasoning, memory, and problem-solving?

RQ2: Could commercial and governmental incentives produce widespread AI dependence without a deliberate plan to make humanity dependent?

RQ3: How feasible is a future in which AI systems manage most essential social functions and humans become unable to operate independently?

RQ4: What would happen if critical AI systems became unavailable after society had transferred substantial intellectual and operational responsibilities to them?

RQ5: What measures could preserve the benefits of AI while reducing the risks of cognitive decline, institutional dependence, and systemic failure?

The paper uses a qualitative, interdisciplinary approach. It draws on published research into AI reliance and education, government guidance on AI risk management, and established reasoning about infrastructure resilience and institutional incentives.

The analysis distinguishes three categories of claims:

• Evidence-supported: Findings or established facts supported by published sources.
• Plausible but uncertain: Outcomes that follow reasonable mechanisms but lack sufficient long-term evidence to establish their likelihood.
• Speculative: Future developments that depend on unresolved technological, political, or social assumptions.

This distinction is essential. The current effects of AI assistance can be investigated empirically, but predictions about two generations of cognitive development, future AGI capabilities, and a civilisation-wide technological collapse cannot be established with the same confidence.

This is a conceptual research paper rather than a systematic review or a quantitative forecast. It does not claim to establish the probability of the complete scenario.

3. The Incentives Behind Widespread AI Adoption

3.1 Commercial incentives

Companies have strong economic reasons to develop and distribute AI products.

AI can reduce operating costs, automate routine work, support customer service, accelerate software development, and increase the amount of work individual employees can complete. Companies may also benefit when customers incorporate their products into daily routines and become reluctant to switch providers.

Personal AI assistants could eventually manage schedules, purchases, education, correspondence, research, and other recurring activities. As an assistant becomes familiar with a person’s preferences and accumulated information, replacing it may become increasingly inconvenient.

This creates a potential dependency mechanism.

The commercial incentive does not require a company to want its customers to lose their independence. It requires only that continued usage, customer retention, and automation provide financial benefits.

The risk becomes more significant when essential functions depend on a small number of providers or when customers cannot easily transfer their information, workflows, or institutional records to alternatives.

3.2 Government incentives

Governments also have reasons to encourage AI adoption. These include economic competitiveness, public-service efficiency, scientific research, national security, and administrative capacity.

A government that refuses to adopt useful AI technologies may face disadvantages relative to countries that do. This creates pressure to implement AI even when its long-term social consequences remain uncertain.

However, government incentives differ across countries and institutions. Some governments may prioritise innovation, while others may emphasise employment protection, privacy, national control, or public oversight.

It would therefore be inaccurate to treat governments as a single coordinated actor pursuing one objective.

The more defensible proposition is that competition between governments and companies could accelerate adoption faster than institutions develop safeguards againstThe AI Dependency Trap: What Happens If We Forget How to Think? dependency.

3.3 Dependency without deliberate coordination

A central element of the proposed scenario is that widespread dependency could emerge without a conspiracy.

An employer adopts AI to reduce costs. A school adopts AI tutoring to improve access to instruction. A government automates administrative services. An individual delegates more tasks because the assistant saves time.

Each decision may be reasonable in isolation.

Yet the combined result could be a society in which fewer people practise certain skills and fewer institutions maintain the expertise required to perform essential functions without automated assistance.

This resembles a collective-action problem: individual organisations receive immediate benefits, while some of the long-term risks are transferred to society.

The possibility deserves attention precisely because it does not require malicious intent.

Nevertheless, the existence of incentives to encourage AI adoption is not evidence that governments or companies intend to make people intellectually helpless. That stronger allegation would require independent evidence of deliberate coordination and purpose.

4. Cognitive Offloading and the Development of Human Skills

4.1 What is cognitive offloading?

Cognitive offloading occurs when people use external tools or systems to reduce the mental effort required for a task.

Examples include writing reminders instead of memorising appointments, using calculators instead of performing arithmetic mentally, and consulting navigation software instead of remembering a route.

Cognitive offloading is not inherently harmful. It can free mental resources for more demanding activities.

The important question is whether the person continues to develop the underlying ability or loses the opportunity to practise it.

AI extends cognitive offloading into activities such as composing arguments, summarising research, generating ideas, explaining unfamiliar subjects, and solving problems.

When people use these systems as learning aids, they may develop their understanding. When they accept answers without evaluating them, they may obtain immediate results without acquiring equivalent knowledge.

4.2 Evidence of potential risks

Zhai, Wibowo, and Li (2024) published a systematic review examining the effects of over-reliance on AI dialogue systems on students’ cognitive abilities. The authors identified concerns involving decision-making, critical thinking, analytical reasoning, misinformation, and users’ ability to assess the reliability of AI outputs.

The review also acknowledged the need for further research to measure these effects more concretely.

This is an important distinction. Research supports concern about over-reliance, but it does not establish that AI use inevitably reduces intelligence or that an entire generation will lose its capacity to think independently.

The consequences depend on how people use AI, what they learn, how often they practise independent reasoning, and whether educational institutions assess genuine understanding.

4.3 The difference between assistance and substitution

Consider three students completing the same assignment.

The first uses AI to identify gaps in an argument, then researches the subject and writes an independent response.

The second asks AI to explain the topic, discusses the explanation, and tests their understanding against other sources.

The third submits an AI-generated answer without checking its accuracy or learning the material.

All three use AI. Their educational outcomes may differ substantially.

The first two approaches can support learning. The third risks substituting the production of an answer for the acquisition of knowledge.

This distinction suggests that AI dependency is not simply a question of how frequently a person uses AI. It is also a question of which cognitive responsibilities they retain.

4.4 The generational hypothesis

The proposed scenario becomes more serious if these patterns persist throughout childhood and adulthood.

A generation that grows up with AI could rely on it for schoolwork, career decisions, communication, navigation, financial planning, and everyday problem-solving.

If institutions reward completed outputs rather than independent understanding, people may have less incentive to develop the skills behind those outputs.

Over time, this could reduce competence in selected areas.

However, the long-term effect remains uncertain. AI may also provide personalised tutoring, improve access to specialist knowledge, help students with disabilities, and allow learners to practise skills with immediate feedback.

The Organisation for Economic Co-operation and Development and the European Commission addressed this issue in their 2026 publication Empowering Learners for the Age of AI. Their framework emphasises understanding AI, evaluating its outputs, and using it responsibly and critically.

The policy implication is clear: education must teach people how to use AI while preserving their ability to evaluate and question it.

The evidence does not support assuming that a generation raised with AI will inevitably become less intelligent. It supports investigating which forms of AI use improve learning and which encourage uncritical dependence.

5. From Personal Assistant to Institutional Dependence

Personal dependence becomes a broader societal concern when AI systems move into essential services.

Potential applications include healthcare, energy management, transport, financial services, logistics, government administration, and industrial production.

Automation can improve these systems. It can identify patterns, predict equipment failures, reduce certain errors, and help professionals make better-informed decisions.

The risk emerges when institutions remove the human capacity to operate, supervise, or recover these systems without AI.

Imagine a hospital that relies on automated scheduling, clinical decision support, documentation, and resource allocation. If those systems fail, staff may still possess their medical training. However, they may face delays, administrative disruption, and difficulties accessing information if contingency procedures have not been maintained.

Now imagine that the same problem affects transport, financial transactions, food distribution, and public administration simultaneously.

The danger is not that all human knowledge disappears. It is that multiple institutions lose the operational capacity to use their remaining knowledge effectively.

This is a problem of systemic dependence rather than intelligence alone.

The US National Institute of Standards and Technology has developed its AI Risk Management Framework to help organisations identify and manage risks associated with AI systems. In April 2026, NIST also announced work on a profile addressing trustworthy AI in critical infrastructure.

These initiatives demonstrate that institutional AI risk is already recognised as a practical governance concern. They do not establish that a civilisation-wide failure is likely, but they provide a basis for examining how organisations should manage it.

6. The AI Handler Hypothesis: Control Without Conscious Intent

The term handler describes a hypothetical relationship in which humans depend so heavily on AI-mediated systems that their practical freedom to make decisions becomes restricted.

This hypothesis does not require AI to possess consciousness, desires, or a wish to control humanity.

Control can emerge through institutional arrangements.

Consider an AI system that influences employment screening, loan applications, access to services, educational recommendations, and the information presented to individuals.

If the system is widely adopted, its recommendations may shape the opportunities people receive.

If alternative systems are unavailable and appeals are ineffective, the organisation controlling the AI may gain substantial influence over people’s lives.

The system does not need to issue commands. Its power may arise from determining which options appear available, which information receives attention, and which decisions institutions accept.

The concern increases when people lack the knowledge to evaluate those decisions independently.

There are, however, important counterarguments. AI systems can expand access to information, allow people to compare different viewpoints, help individuals challenge powerful institutions, and reduce barriers that previously limited participation.

The same technology could therefore increase or reduce individual autonomy, depending on who controls it, how it operates, and whether people can question its outputs.

The relevant issue is not whether AI inherently controls people. It is whether society establishes arrangements that concentrate decision-making power without sufficient accountability.

7. The Devil’s Advocate: The Strongest Case Supporting the Scenario

The following arguments present the strongest version of the dependency hypothesis.

7.1 Dependency can accumulate gradually

People rarely evaluate every long-term consequence of a technology when they first adopt it. They respond to immediate benefits.

If AI saves time, reduces costs, and produces acceptable results, organisations may increasingly remove manual alternatives.

As dependency grows, rebuilding those alternatives becomes more expensive.

This creates a reinforcing cycle: increased adoption reduces the incentive to maintain independent capacity, while reduced independent capacity makes further adoption more necessary.

7.2 Human skills require practice

Many abilities weaken when people stop using them regularly.

If AI consistently performs writing, calculation, research, and decision-making tasks, some users may become less practised at performing those tasks themselves.

The extent of any decline will vary by skill and by individual. Nevertheless, the mechanism is plausible and consistent with the broader principle that practice contributes to competence.

7.3 Economic efficiency may take priority over resilience

Maintaining trained staff, manual procedures, independent records, and backup systems costs money.

If an organisation believes AI can perform a task more cheaply, it may reduce human staffing or eliminate older systems.

Those savings can be real. However, the organisation may underestimate the cost of restoring lost capabilities during a crisis.

This is particularly concerning when the same efficiency-driven decisions occur across an entire industry.

7.4 Concentration of ownership could magnify the consequences

If a small number of providers supply the AI services used by schools, businesses, governments, and infrastructure operators, technical failures or commercial disputes could affect many organisations simultaneously.

Concentration can also create bargaining power over prices, access, and service conditions.

The strongest version of this argument is that dependence on a small number of providers can turn a commercial or technical problem into a wider societal vulnerability.

7.5 Recovery could become harder over generations

A person who has recently stopped performing a skill may be able to recover it through practice.

A society that gradually loses teachers, technicians, engineers, administrators, and experienced operators may face a more difficult recovery.

Practical knowledge is transmitted through education, employment, apprenticeships, and experience. If those processes disappear, rebuilding competence could take years.

This is a plausible long-term concern, although there is insufficient evidence to predict how much expertise future generations would lose under widespread AI adoption.

7.6 AI could fail while institutions continue to depend on it

Cyberattacks, electrical failures, network disruptions, hardware shortages, software defects, and geopolitical conflict could all interrupt access to AI services.

A failure affecting one provider would not necessarily affect all providers. A widespread disruption would require additional failures or shared dependencies.

Nevertheless, a society that has not prepared for such disruptions could suffer consequences disproportionate to the initial technical problem.

The central argument is therefore not that AI must fail, but that essential services should remain capable of functioning when particular AI systems fail.

8. The Devil’s Advocate: The Strongest Case Against the Scenario

A serious analysis must also examine why the proposed future may never occur.

8.1 AI may increase human intelligence and capability

AI can explain difficult concepts, provide personalised instruction, assist with research, and help people solve problems that would otherwise exceed their skills.

A student who receives good tutoring may learn more than a student who lacks access to a teacher. A small business may use AI to perform analysis that previously required an expensive specialist.

AI can therefore distribute capabilities that were once restricted by cost, geography, or access to education.

The relevant comparison is not always between an AI-assisted person and an idealised independent expert. It may be between an AI-assisted person and someone who otherwise lacked the necessary resources.

8.2 People adapt to earlier technological changes

Human societies have repeatedly incorporated technologies that changed how people remember, communicate, calculate, and work.

These changes sometimes reduce the need for particular skills while creating new ones.

The printing press did not eliminate the need for literacy. Computers did not eliminate the need for mathematical understanding. Navigation software did not eliminate every form of geographical knowledge.

AI may similarly change which skills matter without eliminating the need for human competence.

However, AI differs from earlier tools in the range of intellectual tasks it can perform. Historical comparisons are informative, but they cannot prove that its effects will be identical.

8.3 Dependence does not necessarily imply cognitive decline

A person can rely heavily on a tool while retaining the ability to work without it.

Professionals routinely use calculators, databases, diagnostic equipment, and specialist software without necessarily losing the knowledge required to understand their work.

A skilled engineer may use AI extensively while remaining capable of identifying an implausible result.

The important variables are expertise, training, verification, and whether independent competence remains a requirement.

Frequent AI use alone is therefore an inadequate measure of cognitive dependency.

8.4 A global AI shutdown is not a single switch

The global AI industry consists of different models, providers, hardware systems, data centres, local installations, and applications.

Some systems operate through remote cloud services. Others can run locally. Many organisations maintain their own records, software, and operational procedures.

Consequently, a failure affecting one company or model would not automatically eliminate AI worldwide.

A large-scale disruption is possible, but it would depend on the nature of the event and the extent of shared infrastructure dependencies.

8.5 Governments and organisations have reasons to preserve resilience

Essential services cannot simply assume that technology will always function.

Hospitals, utilities, emergency services, and governments have incentives to maintain continuity plans, backups, and recovery procedures.

Regulation, competition, insurance requirements, professional standards, and public pressure may encourage institutions to preserve human expertise.

These safeguards may be imperfect, but they provide mechanisms through which society can reduce dependency.

8.6 Human intelligence is not a single capability

The hypothesis risks treating intelligence as though it were one resource that can be lost entirely.

Human abilities include memory, language, practical reasoning, social judgment, physical coordination, creativity, emotional understanding, and technical expertise.

AI could reduce the practice of some skills while supporting or increasing others.

Even if people became less proficient at particular tasks, this would not establish that they had become generally unintelligent or incapable of rebuilding their knowledge.

8.7 Human institutions can reverse course

If excessive AI dependence produces visible failures, governments, schools, employers, and communities may respond by restoring manual procedures, changing educational requirements, training specialists, and diversifying suppliers.

Such responses may be expensive or delayed, but they remain possible.

The strongest counterargument is that technological dependence is not necessarily irreversible. Societies can recognise risks and deliberately rebuild capabilities when the benefits of doing so become clear.

9. Feasibility Assessment: Could the Proposed Future Happen?

The following assessment evaluates the individual components of the scenario separately. It does not assign invented numerical probabilities to developments that cannot currently be forecast reliably.

The table supports an important conclusion: the early stages of the scenario are considerably more credible than its most extreme ending.

It is reasonable to investigate whether people and institutions will become excessively dependent on AI. It is much harder to establish that this dependence will culminate in irreversible civilisational collapse.

The complete scenario contains several independent uncertainties. Even if AI adoption accelerates and some skills deteriorate, the later stages do not automatically follow.

The appropriate research question is therefore not simply whether the entire scenario is possible. It is which stages are most likely, which warning signs would reveal increasing risk, and which interventions could prevent the progression from one stage to the next.

10. The Role of AGI and Quantum Computing

10.1 Artificial general intelligence

Artificial general intelligence is commonly used to describe AI capable of performing a broad range of intellectual tasks at or near human levels, although definitions vary.

AGI is not a necessary condition for the dependency scenario.

Current AI systems can already influence education, employment, communication, and institutional decision-making without possessing general human-level intelligence.

If society delegates enough tasks to specialised systems, dependence could grow even without a single AGI system managing everything.

AGI could intensify the scenario if it enabled more capable autonomous systems to coordinate complex operations. It could also produce major benefits by improving research, healthcare, education, and problem-solving.

Neither outcome should be treated as inevitable.

The key distinction is between a system’s technical capability and the authority society grants it. An AI may be capable of making a recommendation without being authorised to make the final decision.

10.2 Quantum computing

Quantum computing requires separate treatment because it is sometimes discussed as though it were the next stage of a single technological progression from AI to universal machine control.

Quantum computers use quantum-mechanical principles to perform certain computations differently from classical computers. They may eventually provide important advantages for particular scientific and industrial problems.

However, modern AI systems generally run on classical computing infrastructure, including CPUs, GPUs, and other specialised accelerators. They do not require quantum computers to operate.

A future quantum breakthrough could affect cryptography, scientific research, and some computing workloads. It would not automatically disable conventional AI systems.

The more credible infrastructure concern is that AI systems may depend on electricity, network connectivity, data centres, semiconductor supply chains, and cloud services. A major disruption to these resources could interrupt access to many digital services, whether or not quantum computing is involved.

The paper therefore treats quantum computing as a possible future technological factor, not as the mechanism that would necessarily trigger a global shutdown.

11. Scenario Analysis

Three broad scenarios illustrate how the future could develop.

Scenario A: Responsible integration

AI becomes widespread, but education continues to require independent reasoning. Organisations retain human expertise, competing suppliers, offline records, and tested recovery procedures.

People use AI to extend their abilities rather than replace all independent thought.

In this scenario, AI improves productivity and access to knowledge while society limits excessive dependence.

Assessment: A plausible outcome, provided institutions invest in education, accountability, and resilience.

Scenario B: Gradual dependence

AI becomes the default method for completing intellectual tasks. Employers reduce staffing in selected roles, schools increasingly assess AI-assisted outputs, and institutions remove manual alternatives to reduce costs.

Most services continue functioning, so the long-term costs remain difficult to measure. Some people retain strong independent skills, while others become less capable of operating without assistance.

A significant outage exposes weaknesses in certain industries, requiring costly recovery and retraining.

Assessment: A plausible risk that deserves monitoring. Its severity would depend on how extensively independent capabilities were allowed to deteriorate.

Scenario C: Systemic dependency and major disruption

AI becomes deeply embedded in critical infrastructure and decision-making. Human expertise and alternative systems decline across several essential sectors.

A combination of severe technical failures, cyberattacks, geopolitical disruption, or infrastructure damage interrupts access to critical systems.

Services fail across multiple sectors. Recovery becomes difficult because institutions lack sufficient trained personnel, accessible records, or functioning alternatives.

Assessment: Possible in principle, but highly uncertain. The extreme outcome requires multiple conditions to occur together, and its likelihood cannot be responsibly established from the available evidence.

These scenarios should not be interpreted as predictions with equal probability. They are analytical models designed to identify the conditions that would make each outcome more or less likely.

12. Research Gaps and How the Hypothesis Could Be Tested

A university-level investigation should identify evidence that could support or challenge its central claims.

Several research directions would be particularly valuable.

12.1 Longitudinal studies of cognitive development

Researchers should follow students over several years and compare different patterns of AI use.

Relevant measures could include independent writing ability, mathematical reasoning, memory, source evaluation, problem-solving, and the ability to explain an answer without assistance.

The study should distinguish between AI used for tutoring, AI used for feedback, and AI used to produce completed work.

It should also account for prior academic ability, socioeconomic conditions, educational quality, and differences in access to technology.

12.2 Controlled studies of independent performance

Researchers could compare students who receive AI assistance throughout a learning programme with students who receive structured instruction and limited assistance.

Both groups would then complete assessments without AI.

Such studies could help determine whether AI improves independent mastery, creates short-term performance gains without equivalent learning, or produces different effects depending on the teaching method.

12.3 Institutional dependency audits

Organisations could be assessed according to their ability to operate when critical AI services become unavailable.

Measures could include:

• The percentage of essential tasks that can be completed manually.
• The number of trained employees capable of operating without AI.
• The availability of offline records and independent backup systems.
• The time required to restore essential services.
• The number of critical functions dependent on a single provider.
• The frequency and quality of recovery exercises.

These measures would turn the general concern about dependency into an operational research programme.

12.4 Economic and governance research

Researchers should investigate whether cost reductions from AI adoption encourage organisations to remove essential human capabilities.

They could compare institutions that maintain manual alternatives with those that fully automate selected functions.

The research should examine whether resilience measures impose substantial costs, whether those costs decrease as technology improves, and whether regulation changes organisational behaviour.

12.5 Falsifiable predictions

The dependency hypothesis would gain support if well-designed longitudinal studies showed that particular forms of AI use consistently reduced independent competence, even after controlling for other factors.

It would also gain support if institutional audits demonstrated that increasing AI adoption was accompanied by declining recovery capability and insufficient human expertise.

The hypothesis would be weakened if AI-assisted education consistently improved independent performance, if organisations maintained robust alternatives, or if dependency levels remained low despite extensive adoption.

A credible theory must allow evidence to challenge it. Without measurable predictions, the scenario risks becoming an unfalsifiable warning about any possible future.

13. Policy Recommendations

The aim should not be to prevent people from using AI. It should be to ensure that widespread adoption does not eliminate essential human capabilities.

13.1 Education

Schools and universities should distinguish between using AI to learn and using AI to avoid learning.

Students should receive opportunities to use AI for explanation, feedback, and research while continuing to demonstrate independent mastery through oral examinations, supervised writing, practical assessments, and problem-solving exercises.

Assessment should reward understanding and the ability to evaluate evidence, not simply the quality of a finished submission.

The OECD and European Commission’s 2026 AI literacy framework provides a useful foundation for teaching students to understand, evaluate, and use AI responsibly.

13.2 Essential services

Organisations operating critical services should identify functions that cannot safely depend on uninterrupted AI availability.

They should maintain recovery procedures, preserve accessible records, train staff for degraded operating conditions, and test their ability to continue functioning during service interruptions.

The appropriate level of manual backup will vary by sector. A temporary interruption to an entertainment recommendation system is different from a failure affecting emergency dispatch or hospital operations.

13.3 Competition and accountability

Governments should examine whether excessive concentration among AI providers creates risks for public services and essential industries.

Interoperability, data portability, alternative suppliers, independent audits, and clear responsibility for consequential decisions can reduce the risk of a single organisation acquiring excessive control over essential functions.

Human oversight should be meaningful rather than symbolic. A person cannot effectively supervise an AI system if they lack the knowledge, authority, or time to challenge it.

13.4 Public literacy

People should understand that AI can produce incorrect or misleading answers, even when those answers appear confident.

AI literacy should include source verification, awareness of uncertainty, privacy protection, recognition of bias, and the ability to distinguish a plausible explanation from a verified fact.

The objective is to develop users who can make informed decisions about when to accept assistance and when to seek independent evidence.

13.5 Preserve practical knowledge

Technical education, apprenticeships, libraries, professional training, and hands-on experience remain important.

A society needs people who understand how systems work, how to repair them, how to identify failures, and how to restore operations when automation becomes unavailable.

This is not an argument against automation. It is an argument for maintaining sufficient human expertise to supervise and recover automated systems.

13.6 Measure resilience, not adoption alone

Governments often measure technological progress through investment, adoption, productivity, and innovation.

They should also measure the ability of essential services to survive disruptions.

A country with widespread AI adoption but inadequate recovery procedures may be less resilient than one with slightly slower adoption and stronger contingency planning.

The appropriate goal is not maximum automation at any cost. It is reliable service, human competence, and sustainable technological progress.

14. Limitations

This paper has several limitations.

First, the long-term cognitive effects of widespread generative AI use remain uncertain. Research into current users and educational settings cannot establish what will happen to entire populations over several generations.

Second, the paper examines a hypothetical future rather than a single documented event. Its conclusions depend on assumptions about technological development, institutional behaviour, education, and infrastructure.

Third, AI systems differ considerably. A personal writing assistant, a medical decision-support tool, and an autonomous industrial system create different dependencies and require different safeguards.

Fourth, the analysis does not assign numerical probabilities to the scenarios. Reliable estimates would require more evidence and explicit assumptions about the time horizon, the capabilities of future systems, and the nature of potential disruptions.

Fifth, the scenario should not be interpreted as evidence that governments or technology companies have a coordinated intention to make humanity dependent. The argument concerns structural incentives and possible unintended consequences.

Finally, the analysis recognises that AI may improve human capability and that societies can respond to risks. Any assessment that ignores these possibilities would provide an incomplete account.

15. Conclusion

Artificial intelligence offers substantial opportunities to improve education, productivity, scientific research, healthcare, and access to knowledge. The same capabilities, however, create a serious question about how societies should preserve independent human competence while increasingly delegating work to automated systems.

The dependency scenario examined in this paper begins with ordinary incentives. Companies seek efficiency, governments pursue technological advantage, schools seek better educational tools, and individuals seek convenience. These incentives can encourage widespread adoption without requiring deliberate coordination or malicious intent.

The resulting risks are uneven. Reduced practice may weaken selected skills. Concentrated providers may acquire greater institutional influence. Organisations may remove manual alternatives to reduce costs. Essential services may become more vulnerable to disruptions if they depend on a narrow set of systems and lack trained personnel capable of operating without them.

The strongest evidence supports investigating these intermediate risks. It does not establish that future generations will become broadly incapable of independent thought, that AGI will inevitably control human affairs, or that a global shutdown would make civilisation unable to recover.

Those extreme outcomes remain speculative and depend on several additional conditions.

The counterarguments are equally important. AI can expand access to knowledge, improve learning, assist professionals, and increase human productivity. Historical experience shows that societies can adapt to technological changes, although past examples cannot guarantee that the effects of AI will be harmless. Education, competition, accountability, technical diversity, and resilience planning can reduce the dangers of excessive dependence.

The most productive response is therefore neither unquestioning acceptance nor wholesale rejection of AI. It is deliberate integration that preserves the capabilities needed to supervise, challenge, and recover from technological systems.

A society should be judged not only by how much intelligence it can access through machines, but also by how well its people understand the decisions those machines influence.

If AI helps people become more knowledgeable, capable, and independent, its adoption may represent a major advance in human development. If it replaces the processes through which people acquire knowledge, while institutions abandon the capacity to operate without it, society may exchange short-term efficiency for long-term vulnerability.

The ultimate question is whether humanity can build increasingly capable artificial intelligence without allowing its own intellectual and institutional independence to atrophy.

That outcome is not predetermined. It will depend on the choices made by educators, governments, companies, researchers, and individuals as AI becomes more deeply embedded in everyday life.

The responsibility is to ensure that AI remains a tool people can understand, question, and replace when necessary, rather than a system on which their ability to function depends entirely.

References

National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). US Department of Commerce. https://www.nist.gov/itl/ai-risk-management-framework

National Institute of Standards and Technology. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). https://doi.org/10.6028/NIST.AI.600-1

National Institute of Standards and Technology. (2026). AI Risk Management Framework: Trustworthy AI in Critical Infrastructure Profile development. https://www.nist.gov/itl/ai-risk-management-framework

Organisation for Economic Co-operation and Development and European Commission. (2026). Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education. OECD Publishing. https://doi.org/10.1787/65cd27d4-en

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7

Note on academic use: This paper is a research-based conceptual analysis, not a completed systematic literature review. Before submitting it for formal university assessment, the author should verify the final publication details of all references, consult additional peer-reviewed literature on cognitive offloading and technological dependence, and follow the institution’s requirements for citation style and disclosure of AI assistance.

Author: Zsolt Zsemba
Date: October 2026
Subject areas: Artificial Intelligence, Sociology, Education, Political Economy, Technology Ethics, and Civilisation Risk

Zsolt Zsemba is an AI expert and knows everything about AI! Absolutely not, but have you ever wondered how it all connects?