Generative audiovisual AI could sharply cut the cost and time of content production, but unresolved data rights, governance gaps and authenticity concerns mean adoption and economic benefits are far from guaranteed; regulation, provenance systems and legal rulings will critically shape who captures value and whether society accepts synthetic media.

Main Finding

Adoption of generative neural-network audiovisual tools is effectively inevitable and promises large productivity gains, but faces significant ethical, control, transparency and legal challenges across three stages—input (training data), process (development and control), and output (generated artifacts). These challenges will likely lead to limitations, regulations, and shifts in how information is produced and consumed; however, societal acceptance of AI-generated audiovisual media remains uncertain and could range from widespread uptake to broad rejection.

Key Points

• Scope and critique

  • Focus is limited to generative neural-network based audiovisual systems (e.g., deepfakes, synthetic voices, video/animation generation).
  • The paper critiques the umbrella term “AI” and urges specificity around generative neural methods.

• Three-stage framework for risks and issues

  • Input (training data)
    • Copyright, consent, and data provenance problems.
    • Biases and representational harms encoded in training datasets.
    • Difficulty of auditing or reconstructing training sets across jurisdictions.
  • Process (systems and development)
    • Concentration of capabilities among a few platforms/actors.
    • Control, governance, and transparency deficits (explainability, model access).
    • Operational risks: misuse, security, and opaque deployment.
  • Output (use of generated artifacts)
    • Authenticity, provenance, and trust erosion (deepfakes, misinformation).
    • Legal liability for harms caused by generated content.
    • Social acceptance uncertainty: some studies show higher ratings for AI content, but abundance of synthetic media may provoke distrust or rejection.

• Legal and judiciary review

  • Examination of international legal approaches at each stage; cross-border enforcement and divergent national rules create fragmentation.
  • Judicial decisions and regulatory moves are central to shaping incentives and constraints going forward.

• Future scenarios

  • Productivity surge in content creation juxtaposed with unclear market and social acceptance.
  • Possible regulatory regimes range from light-touch standards to strict controls on generation/use.

Data & Methods

• Methodology

  • Secondary data analysis and narrative (qualitative) literature review.
  • Synthesis of academic, legal, and policy sources; review of international statutes and judicial approaches relevant to generative audiovisual AI.
  • Construction of hypothetical future scenarios grounded in current debates and documented trends.

• Strengths

  • Broad interdisciplinary synthesis linking technical, ethical, and legal literatures.
  • Structured framework (input/process/output) that clarifies where different risks and rules apply.

• Limitations

  • No original empirical data or primary quantitative analysis—findings rely on existing studies and cases.
  • Potential selection bias in reviewed sources; conclusions are contingent on evolving legal decisions and technology.
  • Narrowed to neural-network generative methods, so insights may not transfer to other AI paradigms.

Implications for AI Economics

• Labor and productivity

  • Large potential productivity gains in audiovisual production (lower marginal costs, faster content creation).
  • Displacement risk for creative and production roles; changes in skill premiums (demand for prompt engineering, curation, verification).
  • Complementarity vs substitution: human labor may shift toward oversight, curation, post-production, and creative direction.

• Market structure and competition

  • Economies of scale and data-driven advantages may concentrate market power in a few platforms or studios that control models and datasets.
  • Entry barriers could rise due to data access, compute costs, and IP uncertainty; regulatory interventions will affect market dynamics.

• Intellectual property and rents

  • Ambiguities in copyright and dataset licensing affect who captures value from models and outputs (original creators vs model operators).
  • New rent sources may arise from provenance, authentication services, and certified “human-made” labels.

• Demand, prices, and consumer behavior

  • If consumers prefer AI-generated content (as some studies indicate), demand shifts could lower prices for certain media types and increase consumption volume.
  • Conversely, trust erosion from synthetic media could reduce demand for digital content generally, creating negative externalities for platforms and advertisers.

• Externalities and social welfare

  • Information-quality externalities (misinformation, reduced trust) can create social costs not internalized by producers—justifies policy interventions.
  • Regulatory choices (e.g., liability rules, mandatory provenance/watermarking) will shape innovation incentives and social welfare outcomes.

• Policy levers and economic design

  • Effective policy instruments include data-governance rules, provenance and watermarking standards, liability frameworks, copyright clarifications, competition policy, and taxes/subsidies to internalize externalities.
  • Harmonization of cross-border legal standards would reduce fragmentation costs but is politically challenging.
  • Monitoring and measurement: economists and regulators will need new metrics for synthetic content prevalence, provenance adoption, and labor market impacts.

• Investment and strategic behavior

  • Firms may invest in proprietary datasets, model-locking, or certification/verification services to differentiate products.
  • Insurance, compliance, and legal risk management become material costs influencing adoption timing and scale.

Overall, the paper implies that economic outcomes depend strongly on legal and regulatory design: rules that clarify data rights, provenance, and liability can preserve market incentives while mitigating harms; absence of such rules may lead either to concentrated platform rents and rapid adoption or to broad distrust and reduced valuations of digital audiovisual goods.