Patent rights exist to solve a fundamental economic problem: Inventors need incentives to innovate, but after revealing an invention, others can copy freely. The patent system grants limited monopolies to inventors in exchange for public disclosure. This is the patent system as God intended. But the patent system has struggled mightily with a second problem, a problem that Ronald Coase identified decades ago: transaction costs. A “transaction cost” for Coase isn’t just a brokerage fee; it’s anything that can prevent a transaction from occurring. I submit that today, AI agents stand poised to solve many of the problems with transaction costs that have faced the patent system, creating what has been called a “Coasean Singularity” in patent law — a transformation that will reshape how inventors create, patent owners monetize, and lawyers practice.

The Problem of Patent Cost

Patent markets face a problem not unlike the farmer and cattle-raiser in Coases’s original formulation (The Problem of Social Cost). An inventor with a breakthrough cannot easily find all potential licensees who might benefit from the technology. A manufacturer cannot readily determine which patents might cover a planned product without expensive freedom-to-operate analyses. When parties do identify potential deals, they face months of negotiations involving technical experts who must evaluate the invention, legal experts who must assess validity and infringement risks, and business analysts who must model potential returns. These transaction costs may exceed the value of the underlying rights, especially for patents covering incremental improvements or narrow applications.

In their paper The Coasean Singularity? Demand, Supply, and Market Design with AI Agents, Peyman Shahidi et al. argue that AI agents will act as autonomous intermediaries that “perceive, reason, and act in digital environments to achieve goals on behalf of human principals.” In patent markets, these agents will search patent databases, analyze claims, identify licensing opportunities, negotiate terms, and execute agreements—all at near-zero marginal cost. The result approaches what economists might call the Coase Theorem’s ideal: when transaction costs vanish, resources flow to their highest-value uses, regardless of initial allocation.

Transformation for Inventors

Inventors will experience perhaps the most dramatic changes as AI agents eliminate barriers between innovation and commercialization. Today, individual inventors and small companies fight an uphill battle: even with innovation in litigation finance, they often cannot afford to enforce the very patents meant to protect them. Patent litigation costs millions of dollars, and asserting patents requires identifying infringers, proving infringement, and surviving validity challenges—tasks that demand specialized expertise most inventors lack.

AI agents are democratizing these capabilities. An inventor’s agent will continuously monitor global patent filings and product launches, identifying potential infringement within hours or days rather than years. The agent will analyze thousands of products against the patent claims, applying the same claim construction methodologies that expert witnesses use, but without paying $1000-per-hour fees. When the agent identifies matches, the inventor can initiate contact with the potentially infringing party’s agent to negotiate a license.

More fundamentally, agents will change how inventors approach innovation itself. Before beginning research, an inventor’s agent will map the entire patent landscape, identifying white spaces where innovation could proceed without infringement risk. The agent will also identify existing patents that might be licensed to accelerate development, calculating whether building around them or licensing them offers better economic returns. This pre-invention analysis, currently available only to large corporations with substantial IP departments, will become accessible to any inventor with an AI agent.

The paper’s discussion of preference elicitation suggests another profound change: agents will help inventors articulate the value of their innovations in ways that resonate with specific industries and applications. Many inventors struggle to envision all potential uses for their technologies. An AI agent trained on millions of patents and their commercial applications may eventually be able to identify non-obvious applications, draft targeted licensing proposals for each industry and company, and simultaneously negotiate with hundreds of potential licensees.

Transformation for Patent Owners

Patent owners — whether operating companies with defensive portfolios or non-practicing entities with licensing programs — will see their assets become far more liquid and valuable. The paper emphasizes how agents reduce search costs and enable previously impractical market mechanisms. In patent markets, this means owners can finally extract value from patents that currently lie dormant.

Large companies typically own thousands of patents but actively use only a fraction. The rest represent sunk costs — innovations that might have value to others but would cost too much to identify and license. AI agents will change this calculus entirely. A company’s agent will continuously analyze its patent portfolio against global technology developments, identifying licensing opportunities in real-time. When a startup in another country develops a product that could benefit from a dormant patent, the agents may be able to connect within weeks or months, not years.

Patent owners will also benefit from what Shahidi et al. call “strategic transparency through agents.” Currently, companies hesitate to explore patent licenses because doing so might signal weakness or reveal strategic plans. But AI agents could negotiate under programmatic non-disclosure agreements, exploring thousands of potential deals without human involvement until terms reach certain thresholds. This would preserve confidentiality while dramatically expanding the search space for mutually beneficial arrangements.

The transformation extends to patent valuation itself. Today, patent valuation requires expensive expert opinions based on comparable licenses that may not exist or may not be public. AI agents will have access to millions of licensing transactions, using machine learning to predict fair market values for any patent based on its technical characteristics, citation networks, and market conditions. This price discovery mechanism will create more liquid markets where patents trade at prices reflecting their true economic value.

Transformation for Patent Lawyers

Patent lawyers might initially fear that AI agents will eliminate their profession, but the reality will be more nuanced. The paper distinguishes between tasks where agents substitute for humans and tasks where they augment human capabilities. In patent law, agents will certainly substitute for routine work: prior art searching, claim charting, and initial infringement analysis. But they will augment lawyers’ capabilities in strategic counseling and complex dispute resolution.

Lawyers will shift from document producers to strategic advisors who program and supervise agents. Instead of spending hundreds of hours reading patents and analyzing claims, lawyers will train agents to apply specific claim construction philosophies and infringement doctrines. They will develop agent “playbooks” that encode their firms’ strategic approaches to licensing negotiation. The highest-value lawyers will be those who can translate complex legal strategies into agent-executable protocols.

Shahidi et al.‘s discussion of “bowling-shoe agents” versus “bring-your-own agents” suggests another evolution in legal practice. Law firms might provide sophisticated patent analysis agents to clients as part of their services, or clients might bring their own agents that interface with law firm systems. This will create new competitive dynamics where firms differentiate based on their agents’ capabilities rather than just their lawyers’ expertise.

Dispute resolution will also transform. When licensing negotiations fail and litigation looms, agents can run thousands of settlement scenarios, identifying zones of possible agreement that human negotiators might miss. Agents can also maintain negotiation channels even during litigation, continuously adjusting settlement proposals based on case developments. This persistent, low-cost negotiation capability will reduce the number of cases that reach trial.

Reducing Litigation Through Upfront Licensing

Shahidi et al.’s core insight — that agents enable previously impractical market designs — applies powerfully to patent dispute resolution. Currently, many patent licenses emerge only after expensive litigation threatens both parties. Many companies infringe first and negotiate later because the transaction costs of upfront clearance exceed the expected value of litigation risk. AI agents have the potential to invert this calculus.

With near-zero transaction costs, companies can obtain licenses before launching products. A company’s agent could analyze its product roadmap against millions of patents, identify potential conflicts, and negotiate licenses proactively. The agent could execute micro-licenses for narrow fields of use, specific time periods, or limited quantities—deal structures too complex for human negotiation but trivial for agents to manage.

Shahidi et al. also discuss how agents enable “preference elicitation mechanisms” that were previously theoretical. In patent contexts, this means agents could implement sophisticated cross-licensing protocols where companies reveal their patent positions and technology needs without fear of strategic exploitation. Agents could execute “patent peace treaties” where companies agree to licensed zones of operation, reducing wasteful defensive patenting.

Perhaps most importantly, agents will enable practical patent clearance. When a company considers a new product feature, its agent could eventually query all relevant patent holders’ agents, receive licensing quotes, and factor those costs into the product decision. This would transform patents from hidden landmines into transparent, priced inputs to innovation — exactly as God intended.

Conclusion

The Coasean Singularity in patent law represents more than just efficiency gains; it promises to fulfill the patent system’s promise of adding “the fuel of interest to the fire of genius.” Inventors will capture more value from their innovations. Companies will access needed technologies without litigation fear. Society will benefit as transaction costs no longer prevent valuable innovations from reaching their optimal users. Patent lawyers will evolve from document producers to strategic architects of automated negotiation systems.

Yet this transformation requires careful attention to the concerns raised in the paper about market power, agent alignment, and regulatory frameworks. Patent offices must adapt to handle millions of agent-filed applications. Courts must develop doctrines for agent-negotiated licenses. Competition authorities must prevent dominant players from using agents to coordinate anti-competitive behavior.

The Coasean Singularity in patent law is not a distant theoretical possibility but an imminent transformation already beginning as companies deploy AI agents for patent analysis and licensing. Those who understand and adapt to this new reality — whether inventors, patent owners, or lawyers — will thrive in the emerging economy of near-zero transaction costs.