Beyond IP Innovation Law: The Bigger Picture
By Editor
Amsterdam, July 16, 2021—Mauritz Kop—at the time a Stanford Law School TTLF Fellow at Stanford University and Managing Partner at AIRecht in Amsterdam—has posted as a preprint Beyond IP Innovation Law: The Bigger Picture, forthcoming in the European Media, IP & IT Law Review (Medien und Recht International, MR-Int 21/3 (2021), Vienna) and now archived permanently in the Stanford Center for Responsible Quantum Technology collection at the Stanford Law Library. The essay sets out a deliberately wide thesis: that intellectual property is one instrument among many for moving knowledge production forward, and that a mature innovation policy for the Fourth Industrial Revolution must look beyond IP—to prizes, grants, antitrust, commons-based production, open innovation, and a vital public domain. It extends the analysis Kop developed in his earlier study on the regulation of the quantum age, regulating transformative technology through intellectual property, standardization and sustainable innovation.
Beyond intellectual property — the bigger picture of sustainable innovation law (illustrative editorial image).
Sustainable innovation law: the frame
The paper opens by defining its object of study. Sustainable innovation law, Kop writes, "seeks to examine the interface between creativity, technology, society and law, beyond intellectual property." It is interdisciplinary by construction, combining information law, cyberlaw, antitrust, consumer-protection law, and the safeguarding of fundamental rights with the key 4IR technologies: artificial intelligence, machine learning, big data, quantum computing, CRISPR-Cas9, and virtual reality. The unifying test of whether innovation law is genuinely "sustainable" is normative rather than technical—progress, the essay argues, must be ethical and social, beneficial to the economy, conducive to citizens' well-being, and supportive of the environment.
That framing matters because it reorders the usual hierarchy. IP is described as an important driver of innovation, but emphatically "not the only incentive and reward mechanism that spurs human creative or technical innovation." Once IP loses its monopoly on the policy imagination, a much larger menu comes into view—and choosing among the items on that menu becomes the real work of innovation law.
The menu beyond IP
The essay's central move is to lay out, plainly, the alternatives to exclusive rights. Kop catalogs competitions, prizes, subsidies, grants, fines, tort law, market regulation (both opening access and erecting barriers), antitrust law, labor law (free movement of workers and limits on non-competes), commons-based production, education, and research-and-development tax incentives. These are not fringe curiosities; they are, in his words, the "alternative innovation policy options on the menu of the lawmakers in Brussels, Washington and Beijing."
Open innovation—"a mindset of sharing knowledge," associated with freedom to operate, patent pools, open source, the digital commons, and the public domain—is presented as generally beneficial for society and the common good. But the argument is not utopian. In some cases, the paper concedes, "control needs to be built into the architecture of innovation": exclusive rights through patents or trade secrets may be required to make firms invest in research and development, and where safety, societal, or even existential risks outweigh the benefits of openness, governments may legitimately restrict access to information—through state secrets or dual-use export controls on high-risk goods, such as quantum-resistant asymmetric cybersecurity algorithms. The thesis is therefore one of calibration: policymakers should search for an "innovation optimum" that combines the right levels of openness and control after a balanced assessment of private and public interests.
Why AI does not need IP
Applied to artificial intelligence, the essay reaches a pointed conclusion. Human authorship and inventorship remain, for Kop, "the normative organ point of IP law"; autonomous agents and smart robots do not have—and, he argues, ought not have—legal personhood, though he leaves open the question of a future "legal agenthood" to manage liability toward intelligent machines. From there the argument is direct: the classical rationales and justifications for intellectual property are weak when applied to AI, and "AI can do without IP incentives." The essay carves out a narrow exception—a medical AI system that independently developed, say, a flu vaccine, where patents or trade secrets might be needed to make expensive clinical trials feasible—while immediately noting that government subsidy of those trials would be an equally available alternative.
This connects to Kop's broader public-domain project, developed at length in his work on AI, intellectual property, and an articulated public domain. Drawing on the Roman multi-layered property paradigm, the paper sketches a model he calls Res Publicae ex Machina—Public Property from the Machine—under which output that crosses an "autonomy threshold," with no humans upstream or downstream in the chain of discovery, invention, training data, system, or output, falls into the public domain, marked by a formal stamp issued by a certified body. That model, Kop argues, would help reach an innovation optimum amounting to a "Pareto improvement": more breathing room for the startups and small firms that aspire to become "unicorns," without weakening the incentives that genuinely require protection.
Training data, the public domain, and democratized production
The essay devotes specific attention to machine-learning training datasets, which it treats as a structural bottleneck for the AI ecosystem. Because hand-labeled training data is a sine qua non for supervised machine learning, Kop argues for copyright exceptions that remove input-data clearance obligations—a broadly scoped text-and-data-mining exception, or even "an articulated right to process data for machine learning purposes," connecting IP on data with data ownership, data protection, privacy, and fair competition. He welcomes the European Commission's then-recent IP Action Plan and its planned review of the Database, Copyright, and Trade Secret Directives and the GDPR as a reform "that has the power to change the story" of a data-driven economy.
Alongside this runs a call to democratize the vital means of production in AI and machine learning—encouraging access to, use of, and sharing of open data—and an argument, grounded in Locke, Kant, Marx, and Hegel, that the state may implement new modalities of property where doing so benefits society and overall prosperity. The public domain is not residual here; it is a deliberate object of innovation policy, providing the breathing room, trust, and legal certainty on which younger firms depend.
A horizontal-vertical regulatory architecture
The essay closes on regulatory design, and it is here that the bigger picture comes into focus. Because both incentive-and-reward mechanisms and safety risks vary by industry and by technology, Kop argues, policymakers should distinguish more clearly between economic sectors—healthcare, entertainment, defense—when they blueprint digital governance. His proposed structure is horizontal-vertical: horizontal core rules for all 4IR technologies, flanked by a differentiated, risk-based approach organized around a "pyramid of criticality," with low risks at the base and existential risks for humanity at the top, and vertical, sector-specific regimes with layered enforcement that slot into existing quality-management systems.
The risk profile shapes the policy. An open-innovation posture, the essay suggests, may suit AI across its development phases, but "more ab initio control is recommended when regulating quantum technology, because of the potential anthropogenic risks that the latter poses to mankind"—a calibration that anticipates the precautionary tilt of his later quantum-governance work. Written in the shadow of the European Commission's April 21, 2021 draft AI Regulation, the essay reads that proposal as a "North Star" for the world, and urges that interoperability standards, safety norms, the Trustworthy AI doctrine, and legal-ethical quantum-computing governance principles be embedded directly into the design and infrastructure of technology, monitored through risk-based technology impact assessments across the full life cycle. The throughline is consistent with the firm's larger argument that quantum technology and other 4IR fields demand purpose-built, values-based innovation law rather than the reflexive extension of twentieth-century IP.
Source: Mauritz Kop, Beyond IP Innovation Law: The Bigger Picture, European Media, IP & IT Law Review (MR-Int) 21/3 (2021) — preprint forthcoming; see the MR-Int journal page.
Last updated: June 7, 2026.