The intellectual property world is experiencing what might generously be called euphoria about artificial intelligence. From patent offices worldwide to law firms racing to build AI practices, the narrative is consistent: AI will transform how we protect innovation, and businesses that don't adapt will be left behind.
This trend is being sold as inevitable. It deserves more skepticism than it is getting.
Don't misunderstand. AI tools are already reshaping certain aspects of IP practice. Prior art searches are faster. Patent drafting workflows are more efficient. Trademark screening covers broader databases. These are genuine improvements that make sense for practitioners to adopt. The efficiency gains are real, and skepticism about efficiency itself would be silly.
But the broader premise underlying much of the AI-in-IP conversation contains some questionable assumptions that deserve examination.
The first is that speed and scale solve substantive problems. The IP system's challenges aren't primarily technological bottlenecks. Patent examiners aren't drowning in applications because nobody invented fast enough searching. They're overwhelmed because of policy choices about examination budgets, fee structures, and institutional capacity. An AI tool that processes applications twice as fast doesn't address why we've created a system where processing applications slowly is a feature, not a bug. It may simply accelerate a broken workflow.
Similarly, the idea that AI can somehow make the patent system more predictable or consistent assumes that unpredictability stems from human inconsistency rather than from genuinely difficult legal questions. Patent law involves judgment calls about nonobviousness, utility, and claim scope. These aren't problems that technology fixes. An AI model trained on historical patent decisions will replicate whatever inconsistencies already exist in those decisions, just more uniformly.
The second assumption is that adoption is frictionless. Conversations about AI in IP often glide past real implementation questions. When a corporation's IP counsel considers deploying an AI tool for patent strategy, they're not just thinking about speed. They're thinking about liability, quality control, confidentiality, and accountability. If an AI system misses prior art, who bears responsibility? If it identifies a competitor's vulnerability that turns out to be irrelevant, what's the reputational cost? These questions don't have easy answers, and "the technology is improving" doesn't resolve them.
There's also a less discussed tension: if AI tools become widespread enough to meaningfully impact IP practice, they also flatten advantages. Early adopters expect edge-of-the-market benefits. But widespread adoption of identical AI platforms among competitors doesn't create sustained competitive advantage. It creates a new baseline. That's fine for practice efficiency, but it's not the revolutionary transformation being marketed.
The third assumption worth questioning is that AI in IP is primarily about the technology rather than about market consolidation. Much of the recent activity involves larger firms and well-capitalized companies positioning themselves as AI leaders. There's legitimate business strategy there. But it's worth asking whether the IP profession is being sold on AI's necessity partly because market incentives align with that narrative. When solution providers, consultants, and large firms all benefit from the assumption that AI adoption is urgent and mandatory, skepticism becomes particularly warranted.
None of this argues against experimentation. Practitioners should thoughtfully explore AI tools where they seem genuinely useful. But the field might benefit from distinguishing between genuine innovation and market-driven inevitability narratives.
The strongest case for AI in IP focuses on specific, limited problems where the technology demonstrably helps. The weakest case relies on vague assurances that resistance is futile and adaptation is automatic.
That gap between promise and reality is where skepticism belongs.