Generative audiovisual AI promises large efficiency gains in content production but will be hampered — and reshaped — by legal, ethical and trust frictions across training data, deployment and output, concentrating advantage with data- and deployment-rich firms and creating new markets for provenance and verification.

Main Finding

Adoption of generative neural-network–based audiovisual AI is likely inevitable and will significantly raise productivity in content creation, but it poses material ethical, control, transparency and legal challenges across three stages — input (training data), process (development & deployment), and output (use of artifacts). These challenges create regulatory, market, and social-acceptance frictions that will reshape how information is produced, valued and consumed.

Key Points

• Scope and definition
  • The paper restricts analysis to generative neural-network processes for audiovisual communication and critiques broader/ambiguous uses of the term “AI.”
• Three-stage framework for risks and issues
  • Input: training material concerns — consent, copyright, representativeness, bias, provenance and data ownership.
  • Process: system-level issues — governance of model development, control over deployment, transparency, auditing, and operational safety.
  • Output: downstream use — authenticity, deception, attribution, reuse rights, reputational harms and societal impacts of abundant generated media.
• Legal and judicial landscape
  • International and national legal approaches are surveyed for each stage; differences in IP, privacy, liability and evidence law create fragmentation and uncertainty.
• Social acceptance uncertainty
  • While some studies find people may rate AI-generated content equal or superior to human-created content, proliferation of artificial media could also spur distrust or rejection of digital media.
• Future scenarios
  • The paper proposes hypothetical trajectories to help anticipate regulatory choices, market responses and social reactions; outcomes depend on litigation, legislation and technological countermeasures (e.g., verification tools).

Data & Methods

• Methodology: narrative review based on secondary data analysis.
• Sources: literature on generative neural networks, legal cases and statutes, policy reports, and empirical studies on content perception and trust (no new primary data collected).
• Analytical approach: conceptual synthesis using the three-stage input/process/output framework plus comparative review of legal/judicial responses and scenario construction.
• Limitations: reliance on secondary sources and narrative synthesis (limits causal claims); scope limited to audiovisual generative models, so findings may not generalize to other AI modalities.

Implications for AI Economics

• Productivity and labor
  • Large productivity gains in content production could reduce marginal costs and compress prices for many creative goods, potentially displacing some human labor while raising demand for high-skill oversight, curation and novel creative inputs.
• Market structure and firm strategy
  • Platforms and firms controlling model training data and deployment infrastructure gain strategic advantage; increased vertical integration and concentration risks.
• Quality signaling and market for authenticity
  • Abundant synthetic media may erode signal value of standard digital content, creating demand for authentication services, certification markets and premium “human-made” labels.
• Attention economy and demand uncertainty
  • Proliferation of generated content may increase information supply but could lower per-item attention and willingness-to-pay, potentially reducing monetization unless intermediaries solve discoverability and trust problems.
• Legal/regulatory costs and frictions
  • Compliance with IP, privacy and liability regimes will impose costs (monitoring, licensing, disclosure), possibly raising barriers for smaller entrants and affecting prices and diffusion.
• Externalities and public goods
  • Negative externalities (misinformation, reputational harm, verification costs) may justify public intervention: standards for provenance, mandatory labeling, penalties for malicious misuse, and public investment in verification infrastructure.
• Redistribution and valuation
  • Returns may shift toward owners of data, model capacity and verification tech; traditional creators may demand new compensation mechanisms (data-use royalties, collective licensing).
• Policy levers and economic effects
  • Potential interventions include mandatory provenance metadata, liability rules, taxes/subsidies to internalize harms, antitrust measures to limit concentration, and funding for public verification tools — each choice will shape incentives, innovation rates and market outcomes.
• Uncertainty & research needs
  • Empirical work needed on consumer willingness-to-pay for authenticated vs. synthetic content, labor displacement elasticities, market concentration dynamics, and cost-benefit evaluations of regulatory options.