Structural barriers are causing systemic failure in global tech regulation
Academic Ido Sivan-Sevilla analyzes six structural barriers to effective technology regulation, arguing that current frameworks suffer from vague rule-making, technical opacity, and asymmetrical power between regulators and companies. The article proposes adopting machine-readable compliance standards and stronger enforcement authority, such as that granted to the European Commission under the Digital Services Act.
Key Takeaways
- Technical opacity allows companies like Meta and OpenAI to restrict researcher access and mask algorithmic decision-making.
- Rulemaking ambiguity in the GDPR and AI Act permits firms to interpret fuzzy legal terms to suit business goals.
- Regulators suffer from a capacity gap, lacking the specialized expertise required for proactive monitoring of complex neural networks.
- Political environments are often compromised by tech lobbying and the appointment of former industry lobbyists to regulatory roles.
Why It Matters
The persistent gap between regulatory intent and technical reality creates a precarious environment for streaming platforms navigating new AI and privacy mandates. As companies increasingly rely on opaque algorithms for content delivery and ad targeting, the lack of machine-readable compliance standards leaves them vulnerable to shifting legal interpretations. The industry should prepare for a transition toward more technical, automated enforcement mechanisms, similar to the European Commission's growing authority under the Digital Services Act. Watch for the adoption of the Digital Omnibus Regulation, which could force a move from vague privacy promises to rigid, verifiable technical standards.
Additional Context
The European Union’s AI Act, which entered into force in August 2024, exemplifies the move toward structured, high-stakes compliance that Sivan-Sevilla advocates. Per the European Commission, the majority of the Act’s rules will be fully enforceable by August 2026, with transparency requirements for general-purpose AI (GPAI) models taking effect a year earlier in August 2025. Failure to comply with prohibited AI practices could trigger fines of up to €35 million or 7% of global annual turnover, marking a shift toward the aggressive enforcement authority cited as necessary to bridge the power asymmetry between regulators and tech giants.
In the U.S., the California Privacy Protection Agency is advancing a technical solution to the monitoring problem with its Data Broker Requests and Opt-Out Platform (DROP). According to official state updates, DROP launched in January 2026, creating a centralized, machine-mediated mechanism for consumers to delete data across all registered brokers simultaneously. By August 2026, data brokers are required to access this system every 45 days, representing a concrete example of the "machine-readable compliance" Sivan-Sevilla identifies as a replacement for legal ambiguity.
Simultaneously, U.S. federal courts are addressing market concentration through landmark antitrust rulings. Per the Department of Justice, a U.S. District Court ruled in April 2025 that Google formed an illegal monopoly in its advertising technology business, specifically citing the company's control over the ad tech stack. This follows an August 2024 ruling against Google’s search monopoly, signaling a period of structural intervention that aligns with academic calls to dilute the concentration of power over critical digital infrastructures.
Read full article at theregreview.org
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