AI innovation continues to accelerate faster than traditional IP strategies were designed to accommodate, particularly as models evolve continuously and rely on data-driven inputs that are difficult to disclose without risk. As multiple commentators have noted, robust IP protection is becoming increasingly critical for AI companies seeking to maintain competitive advantage while signaling value to investors and acquirers.

Patents remain the primary legal mechanism for securing exclusivity, enabling enforcement, and supporting valuation. At the same time, trade secrets are gaining renewed strategic importance for protecting certain AI assets that are poorly suited to patent disclosure. Increasingly, AI innovators are combining the two, using patents as the foundation—boosting valuations and enabling scalable enforcement—and trade secrets as a complementary layer to amplify these benefits, to create more resilient, investor-ready portfolios.

Patents as the Core Strategy for AI Innovation

Patents continue to play a central role in AI IP strategies, continuing to provide time-limited exclusivity (up to 20 years from filing), enforceable rights against third parties, and strong signaling value in financings, partnerships, and exits. This is particularly true in 2026 amid recent pro-patent shifts, such as surging litigation funding and USPTO reforms, as discussed in prior commentary on AI-accelerated enforcement trends.

Recent legal and policy developments have reinforced this role. Commentary from multiple firms notes that USPTO guidance on AI-assisted inventions and evolving §101 eligibility analysis have reduced some of the uncertainty that previously discouraged AI patent filings, particularly when applications are carefully drafted to emphasize technical improvements. While some barriers to patent eligibility persist, these developments make patents more robust for AI, enhancing their role in investor signaling.

In parallel, increased patent litigation activity and enforcement-friendly dynamics have made patents more valuable as scalable assets, particularly for AI companies operating in crowded or fast-moving markets. These factors collectively support the view that patents remain the foundation of defensible AI IP strategies, rather than an optional tool, particularly as litigation dynamics align with 2026 trends like increased enforcement due to AI-enabled infringement monitoring as well as declining IPR institution rates.

Trade Secrets as a Complementary Layer

Trade secrets play a distinct but complementary role in protecting AI innovations, particularly where disclosure would undermine competitive advantage. Unlike patents, trade secrets can protect information indefinitely, provided it derives independent economic value from not being generally known and is subject to reasonable secrecy measures. But the fragility of trade secrets to breaches, such as from employee departures, underscores the need for robust safeguards to support patent strategies.

In the AI context, commentators consistently point to less discoverable assets such as proprietary datasets, data curation methods, feature engineering processes, prompt libraries, model tuning techniques, and internal evaluation metrics as candidates for trade secret protection rather than patenting.

Recent academic analysis observes a broader shift toward trade secret reliance as complements to patents, mitigating risks like knowledge fragmentation while preserving patents' enforcement leverage. Indeed, many high-value components of AI systems face challenges with patent eligibility, as well as patentability in an increasingly crowded prior art field, and are difficult to reverse-engineer from public-facing outputs. Notably, trade secrets are recognized as being most effective when deployed alongside patents, not as substitutes for them, as part of a complementary IP strategy.

The Hybrid Playbook: Best of Both Worlds

Practitioners increasingly recommend hybrid IP strategies that deliberately allocate protection between patents and trade secrets based on business, technical, and regulatory considerations. Such hybrid protection complements a company's patent valuation and enforcement strengths by layering trade secrets for added defensibility.

Decisions about which aspects of your AI systems to disclose and protect in a patent application versus keeping confidential as a trade secret remain fact intensive. Common decision factors include:

  • Reverse-engineering risk, where externally observable functionality that can be more easily copied favors patent protection, while internal processes remain secret;
  • Development stage, where some practitioners favor protecting early and intermediate iterations as trade secrets, while the final products or key features can be covered by one or more patents—though this balance risks losing the race to the patent office by delaying filings; ***Pro Tip: These competing concerns in the early stages can be hedged with confidential (provisional) patent filings, which establish a priority date while allowing flexibility to later pursue full patents or abandon (and keep as trade secrets) based on how the final product, competitive landscape, and other decision factors evolve.
  • Regulatory exposure, including disclosure obligations under emerging AI governance regimes such as the EU AI Act, which may influence how much technical detail companies are willing to place on the public record;
  • Discoverability analysis, assessing ability to identify infringement of features to be patented, to guide patent priority for enforceable rights.

Practitioner guidance consistently frames these layered approaches as more aligned with how AI value is actually created and defended, particularly in diligence contexts where investors value the maturity signaled by strong patent portfolios, amplified by protected proprietary know-how.

What This Means for AI Innovators in 2026

As AI systems face increasing scrutiny from regulators, courts, and investors, IP strategies must balance enforceability with confidentiality. This balance leverages patents' visibility for deterrence while using trade secrets to protect performance drivers, aligning with 2026's innovation realities. Patents provide visibility, deterrence, and enforcement leverage, while trade secrets protect the non-disclosable assets that often drive performance and differentiation.

Multiple sources [e.g., Mayer Brown, Venable, cited above] emphasize that effective trade secret protection requires documented internal controls, access restrictions, and governance practices, particularly as workforces and development environments become more distributed. When paired with patents, these protections create comprehensive portfolios ready for acquisition, if desired, or litigation, if needed.

Together, these considerations point toward intentional, early-stage IP planning, typically in close consultation with experienced counsel, to ensure AI assets remain both defensible and attractive to investors.

Bottom Line

Patents are not being displaced in the AI era. Rather, they are becoming more important as enforcement dynamics strengthen and investors demand clearer signals of defensibility, consistent with pro-patent shifts and valuation boosts.

At the same time, trade secrets are increasingly recognized as essential complements—protecting the data, processes, and operational know-how that patents alone cannot fully capture.

In 2026, the most resilient AI IP strategies will be hybrid by design, pairing strong patent portfolios with disciplined trade secret protection to maximize the scalable advantages of patents with additional IP value, flexibility, and long-term competitive advantage.