Generative AI is on track to become an entertainment-first industry, driven by commercial incentives and youth adoption, yet economic evaluation and policy frameworks are unprepared to measure its cultural and welfare effects. The authors propose a 'thick entertainment' lens to treat AI-generated entertainment as socially consequential and call for new metrics, empirical studies, and regulatory attention beyond harm mitigation.
AI as Entertainment
The paper argues generative AI is likely to evolve into an entertainment-first industry, creating cultural and welfare effects that current productivity-focused evaluation frameworks and governance tools fail to capture.
Generative AI systems are predominantly designed, evaluated, and marketed as intelligent systems which will benefit society by augmenting or automating human cognitive labor, promising to increase personal, corporate, and macroeconomic productivity. But this mainstream narrative about what AI is and what it can do is in tension with another emerging use case: entertainment. We argue that the field of AI is unprepared to measure or respond to how the proliferation of entertaining AI-generated content will impact society. Emerging data suggest AI is already widely adopted for entertainment purposes -- especially by young people -- and represents a large potential source of revenue. We contend that entertainment will become a primary business model for major AI corporations seeking returns on massive infrastructure investments; this will exert a powerful influence on the technology these companies produce in the coming years. Examining current evaluation practices, we identify a critical asymmetry: while AI assessments rigorously measure both benefits and harms of intelligence, they focus almost exclusively on cultural harms. We lack frameworks for articulating how cultural outputs might be actively beneficial. Drawing on insights from the humanities, we propose"thick entertainment"as a framework for evaluating AI-generated cultural content -- one that considers entertainment's role in meaning-making, identity formation, and social connection rather than simply minimizing harm. While AI is often touted for its potential to revolutionize productivity, in the long run we may find that AI turns out to be as much about"intelligence"as social media is about social connection.
Summary
Main Finding
Generative AI is likely to evolve into an entertainment-first industry as much as an intelligence/productivity tool. Current evaluation practices and economic thinking are unprepared for this shift: researchers and policymakers focus on cultural harms and productivity gains, but lack frameworks to measure and value the positive, meaning-making, and social connection aspects of AI-generated entertainment. The authors propose "thick entertainment" — an evaluative lens that treats entertainment as socially and culturally consequential, not merely a source of risk or distraction.
Key Points
- Current dominant narrative: generative AI is framed as augmenting or automating cognitive labor to raise productivity at individual, firm, and macro levels.
- Alternative, emergent use case: entertainment (creative content, companionship, play) is already a major and fast-growing use of generative AI, especially among young people.
- Commercial incentives: large infrastructure investments and the search for high-margin revenue will push major AI firms toward entertainment-driven business models (attention monetization, subscriptions, IP licensing, virtual goods).
- Evaluation asymmetry: AI assessments rigorously study intelligence-related benefits and many harms, but concentrate on reducing cultural harms and lack ways to evaluate entertainment’s positive cultural roles.
- Conceptual contribution: "thick entertainment" reframes entertainment outputs as sites of meaning-making, identity formation, and social connection, requiring different metrics than traditional safety/harm checklists.
- Practical risk: if companies optimize primarily for engagement/revenue, they may produce content that shapes cultural tastes, attention allocation, and identity formation in ways current governance frameworks do not anticipate.
- Policy and research gap: new measurement tools, welfare metrics, and regulatory thinking are needed to capture entertainment’s economic and social impacts.
Data & Methods
- Approach: conceptual analysis and critique grounded in interdisciplinary literature (AI evaluation practices, media studies, cultural theory) and synthesis of emerging industry/usage signals.
- Evidence types cited: early usage statistics and surveys indicating entertainment-oriented adoption (particularly among youth), industry revenue trends and business-model incentives, and examples from related attention-economy platforms.
- Analytical move: identification of an evaluation gap (focus on harms, lack of positive cultural evaluation) and proposal of the "thick entertainment" framework drawing on humanities insights about meaning and sociality.
- Limitations noted by authors:
- Lack of comprehensive, longitudinal empirical measurement of AI-driven entertainment adoption and welfare effects.
- Reliance on early/ad hoc data and qualitative reasoning rather than large-scale causal econometric studies.
- Need for operationalization of "thick" concepts into measurable outcomes.
Implications for AI Economics
- Business models and firm strategy
- Expect major AI firms to prioritize entertainment products/services to monetize large sunk infrastructure investments (subscription, ad/attention, creator monetization, IP).
- Competition may center on engagement, personalization, and network effects rather than pure productivity features.
- Valuation models for AI firms should incorporate consumer entertainment metrics (ARPU from entertainment use, retention driven by social features, creator economy spillovers).
- Labor and productivity
- Short- to medium-term: productivity gains promised by AI may be offset by increased time spent on AI entertainment; measuring net welfare requires accounting for leisure value and potential distraction/externalities.
- Creative labor could be both supplemented and displaced; platformized creator economies may shift income toward aggregator firms.
- Market structure and concentration
- Entertainment incentives increase returns to scale and scope (models, content libraries, personalization data), reinforcing winner-take-most dynamics and gatekeeping power over cultural distribution.
- Welfare measurement and policy
- Standard economic metrics (GDP, labor productivity) miss many welfare-relevant effects of entertainment (meaning, social capital, identity).
- Need for new welfare indicators: subjective well-being, social connection indices, cultural diversity metrics, time-use adjusted productivity, consumer surplus from entertainment.
- Regulatory focus should expand beyond harm mitigation to include competition policy (platform power over cultural markets), cultural-heritage considerations, creator rights/IP, and consumer protection in attention markets.
- Research agenda for AI economics
- Empirical measurement: build representative surveys and passive usage datasets on AI entertainment adoption by demographics (esp. youth), time use, willingness to pay, and engagement patterns.
- Causal analysis: randomized or quasi-experimental studies estimating effects of AI entertainment on labor supply, productivity, mental health, social capital, and cultural consumption.
- Market analysis: study pricing, bundling, and two-sided dynamics (creators vs consumers), and how entertainment monetization affects model architecture and access.
- Metric development: operationalize "thick entertainment" into measurable constructs (meaning-making, identity impact, social bonding) suitable for incorporation into impact assessments and regulatory review.
- Policy experiments: evaluate taxes/subsidies, content-labor protections, data portability, and antitrust remedies targeted to entertainment-oriented AI markets.
- Practical recommendations for economists and policymakers
- Integrate entertainment-focused indicators into AI impact assessments and cost–benefit analyses.
- Fund longitudinal studies on youth adoption and welfare impacts.
- Treat entertainment monetization incentives as a first-order input when forecasting firm strategy and social outcomes from AI investments.
- Collaborate with humanities and social sciences to design measurement instruments that capture cultural value beyond harm minimization.
Assessment
Paper Typetheoretical
Evidence Strengthlow — The paper is primarily conceptual and argumentative, relying on selective early usage statistics, surveys, industry trends, and analogies to attention-economy platforms rather than systematic, representative, or causal empirical analysis; it proposes hypotheses and a framing rather than providing causal estimates or robust quantitative validation.
Methods Rigormedium — The paper uses interdisciplinary literature synthesis and reasoned argumentation grounded in media studies, cultural theory, and observable industry signals, which is appropriate for a conceptual contribution; however, it lacks pre-registered empirical designs, representative datasets, quasi-experimental variation, or robustness checks that would raise methodological rigor to a high level.
SampleNo original large-scale dataset; evidence consists of synthesized interdisciplinary literature, selective early usage statistics and surveys (not necessarily representative), industry revenue and business-model trend data, and illustrative examples from attention-economy platforms and creator ecosystems.
Themesadoption productivity org_design governance
GeneralizabilityConceptual claims are not empirically validated and may not hold across countries with different cultural industries or regulatory regimes, Early-adopter patterns (notably youth usage) may not generalize to older or enterprise users, Business-model trajectories depend on firm strategies, regulation, and technological advances that could change rapidly, Cultural impacts and 'thick' meanings are context-dependent and may vary substantially across languages, subcultures, and media formats
Claims (11)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Generative AI systems are predominantly designed, evaluated, and marketed as intelligent systems which will benefit society by augmenting or automating human cognitive labor, promising to increase personal, corporate, and macroeconomic productivity. Firm Productivity | positive | medium | promised productivity gains (personal, corporate, macroeconomic) / positioning of AI systems |
0.04
|
| This mainstream narrative about what AI is and what it can do is in tension with another emerging use case: entertainment. Adoption Rate | mixed | medium | dominant narrative versus emerging use-case prevalence (productivity-oriented vs entertainment-oriented) |
0.04
|
| The field of AI is unprepared to measure or respond to how the proliferation of entertaining AI-generated content will impact society. Ai Safety And Ethics | negative | medium | readiness/preparedness of AI research and evaluation frameworks to assess societal impacts of entertaining AI content |
0.04
|
| Emerging data suggest AI is already widely adopted for entertainment purposes — especially by young people — and represents a large potential source of revenue. Adoption Rate | positive | medium | adoption rates for entertainment use (particularly among young people) and potential revenue from entertainment use |
0.04
|
| Entertainment will become a primary business model for major AI corporations seeking returns on massive infrastructure investments. Firm Revenue | positive | low | share of corporate business models/revenue derived from entertainment for major AI firms |
0.02
|
| This (entertainment-as-business-model) will exert a powerful influence on the technology these companies produce in the coming years. Innovation Output | mixed | low | product design priorities and technological development directions influenced by entertainment business incentives |
0.02
|
| Current AI evaluation practices show a critical asymmetry: while AI assessments rigorously measure both benefits and harms of intelligence, they focus almost exclusively on cultural harms. Ai Safety And Ethics | negative | medium | scope and balance of AI assessment metrics (coverage of benefits vs cultural harms) |
0.04
|
| We lack frameworks for articulating how cultural outputs might be actively beneficial. Ai Safety And Ethics | negative | medium | existence/availability of evaluative frameworks that characterize positive cultural impacts of AI-generated content |
0.04
|
| The authors propose 'thick entertainment' as a framework for evaluating AI-generated cultural content — one that considers entertainment's role in meaning-making, identity formation, and social connection rather than simply minimizing harm. Other | positive | high | presence and scope of the proposed evaluation framework ('thick entertainment') for cultural content |
0.06
|
| AI is often touted for its potential to revolutionize productivity. Firm Productivity | positive | high | prevalence of claims asserting AI-driven productivity improvements |
0.06
|
| In the long run we may find that AI turns out to be as much about 'intelligence' as social media is about social connection (i.e., AI may be primarily about entertainment/social connection rather than productivity). Consumer Welfare | mixed | low | relative cultural role of AI (entertainment/social connection) compared to productivity orientation over the long term |
0.02
|